结构修改，后端文件跟前端内容进行适配

src/core/pipeline/context.py

@@ -20,13 +20,16 @@ from typing import Any, Dict, List, Optional, Set
 # ============================================================
 
 STEP_MAP_OLD_TO_NEW: Dict[str, str] = {
-    "step5_5":   "step8",
+    "step5_5":   "step7",
     "step6_5":   "step8_non_empirical_modeling",
     "step6_75":  "step9",
     "step8_5":   "step11",
-    "step8_75":  "step12",
-    "step7":     "step10",
+    "step7":     "step8",
+    "step8":     "step7",
     "step9":     "step14",
+    "step10":    "step4",
+    "step11_ml": "step10",
+    "step11":    "step11",
 }
 
 STEP_MAP_NEW_TO_OLD: Dict[str, str] = {v: k for k, v in STEP_MAP_OLD_TO_NEW.items()}

src/core/pipeline/runner.py

@@ -95,14 +95,14 @@ PIPELINE_STEPS: List[StepSpec] = [
         description="耀斑去除",
     ),
     StepSpec(
-        step_id="step4", method_name="step4_process_csv",
+        step_id="step4", method_name="step5_process_csv",
         requires=["csv_path"], produces=["processed_csv_path"],
         required_input_files=["csv_path"],
         output_file="{work_dir}/4_processed_data/processed_data.csv",
         description="CSV 异常值清洗",
     ),
     StepSpec(
-        step_id="step5", method_name="step5_extract_training_spectra",
+        step_id="step5", method_name="step6_extract_spectra",
         requires=["deglint_img_path", "processed_csv_path", "csv_path", "boundary_path", "glint_mask_path"],
         produces=["training_csv_path"],
         parameter_map={
@@ -115,14 +115,14 @@ PIPELINE_STEPS: List[StepSpec] = [
         description="实测样本点光谱提取",
     ),
     StepSpec(
-        step_id="step8", method_name="step8_water_quality_indices",
+        step_id="step7", method_name="step7_calc_indices",
         requires=["training_csv_path"], produces=["indices_path", "trad_indices_dir"],
         required_input_files=["training_csv_path"],
         output_file="{work_dir}/6_water_quality_indices/training_spectra_indices.csv",
-        description="水质光谱指数计算（双轨输出：A轨宽表 + B轨单文件）",
+        description="水质参数指数计算（双轨输出：A轨宽表 + B轨单文件）",
     ),
     StepSpec(
-        step_id="step7", method_name="step7_ml_modeling",
+        step_id="step8", method_name="step8_train_ml",
         requires=["training_csv_path"], produces=["models_dir"],
         required_input_files=["training_csv_path"],
         output_file="{work_dir}/7_Supervised_Model_Training/best_models.pkl",
@@ -138,22 +138,21 @@ PIPELINE_STEPS: List[StepSpec] = [
         description="非经验统计回归",
     ),
     StepSpec(
-        step_id="step9", method_name="step9_custom_regression",
-        requires=["indices_path"], produces=["models_dir"],
-        parameter_map={"indices_path": "csv_path"},
-        required_input_files=["indices_path"],
-        output_file="{work_dir}/9_Custom_Regression_Modeling/custom_regression_models.pkl",
-        description="自定义回归分析",
+        step_id="step9", method_name="step9_watercolor_inversion",
+        requires=["deglint_img_path", "water_mask_path"], produces=["watercolor_index_dir"],
+        required_input_files=["deglint_img_path"],
+        output_file="{work_dir}/9_WaterColor_Index_Images",
+        description="水色指数反演（BSQ 影像直接处理）",
     ),
     StepSpec(
-        step_id="step10", method_name="step10_sampling",
+        step_id="step10", method_name="step4_sampling",
         requires=["deglint_img_path", "water_mask_path"], produces=["sampling_csv_path"],
         required_input_files=["deglint_img_path", "water_mask_path"],
-        output_file="{work_dir}/10_sampling/sampling_spectra.csv",
+        output_file="{work_dir}/4_sampling/sampling_spectra.csv",
         description="整景密集采样点生成 + 光谱提取",
     ),
     StepSpec(
-        step_id="step11_ml", method_name="step11_ml_prediction",
+        step_id="step11_ml", method_name="step9_predict_ml",
         requires=["sampling_csv_path", "models_dir"], produces=["prediction_csv_path"],
         required_input_files=["sampling_csv_path", "models_dir"],
         output_file="{work_dir}/11_12_13_predictions/prediction_results.csv",
@@ -168,16 +167,7 @@ PIPELINE_STEPS: List[StepSpec] = [
         description="非经验模型预测",
     ),
     StepSpec(
-        step_id="step12", method_name="step12_custom_regression_prediction",
-        requires=["sampling_csv_path", "models_dir", "formula_csv_path"],
-        produces=["prediction_dir"],
-        parameter_map={"models_dir": "custom_regression_dir"},
-        required_input_files=["sampling_csv_path", "models_dir", "formula_csv_path"],
-        output_file="{work_dir}/11_12_13_predictions/custom_regression_predictions",
-        description="自定义回归预测",
-    ),
-    StepSpec(
-        step_id="step14", method_name="step14_distribution_map",
+        step_id="step14", method_name="step10_map",
         requires=["prediction_csv_path", "boundary_shp_path"],
         produces=["distribution_map_path"],
         required_input_files=["prediction_csv_path", "boundary_shp_path"],

src/core/steps/data_preparation_step.py

@@ -2,7 +2,7 @@
 """
 数据准备步骤
 
-包含 step4_process_csv, step5_extract_training_spectra, step5_5_calculate_water_quality_indices
+包含 step5_process_csv, step6_extract_spectra, step5_5_calculate_water_quality_indices
 """
 
 import time

src/core/water_quality_inversion_pipeline_GUI.py

@@ -585,7 +585,7 @@ class WaterQualityInversionPipeline:
                                   status="failed", error=str(e))
             raise
 
-    def step4_process_csv(self, csv_path: str, skip_dependency_check: bool = False, **kwargs) -> str:
+    def step5_process_csv(self, csv_path: str, skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤4: 对csv文件进行处理，筛选剔除异常值
 
@@ -606,7 +606,7 @@ class WaterQualityInversionPipeline:
         self._notify("completed", f"处理后的CSV文件已保存: {result}")
         return result
     
-    def step5_extract_training_spectra(self, deglint_img_path: Optional[str] = None,
+    def step6_extract_spectra(self, deglint_img_path: Optional[str] = None,
                                        radius: int = 5,
                                        source_epsg: int = 4326,
                                        csv_path: Optional[str] = None,
@@ -657,7 +657,7 @@ class WaterQualityInversionPipeline:
         self._notify("completed", f"训练光谱数据已保存: {result}")
         return result
 
-    def step6_water_quality_indices(self,
+    def step7_calc_indices(self,
                                                training_csv_path: Optional[str] = None,
                                                formula_csv_file: Optional[str] = None,
                                                formula_names: Optional[List[str]] = None,
@@ -701,7 +701,7 @@ class WaterQualityInversionPipeline:
             self._notify("completed", f"水质指数已保存: {result}")
         return result
     
-    def step7_ml_modeling(self, feature_start_column: str = "374.285004",
+    def step8_train_ml(self, feature_start_column: str = "374.285004",
                            preprocessing_methods: List[str] = None,
                            model_names: List[str] = None,
                            split_methods: List[str] = None,
@@ -859,7 +859,7 @@ class WaterQualityInversionPipeline:
         msg = f"Step 9: 浓度反演完毕，结果保存于: {result_csv}"
         (self.logger.info if hasattr(self, 'logger') else print)(msg)
 
-    def step10_sampling(self, deglint_img_path: Optional[str] = None,
+    def step4_sampling(self, deglint_img_path: Optional[str] = None,
                                        interval: int = 50,
                                        sample_radius: int = 5,
                                        chunk_size: int = 1000,
@@ -906,7 +906,7 @@ class WaterQualityInversionPipeline:
         self._notify("completed", f"采样点光谱数据已保存: {result}")
         return result
     
-    def step11_ml_prediction(self, sampling_csv_path: str,
+    def step9_predict_ml(self, sampling_csv_path: str,
                                     models_dir: Optional[str] = None,
                                     metric: str = 'test_r2',
                                     prediction_column: str = 'prediction',
@@ -947,7 +947,7 @@ class WaterQualityInversionPipeline:
         self._notify("completed", f"预测完成，结果保存在: {self.prediction_dir}")
         return result
 
-    def step14_distribution_map(self, prediction_csv_path: str,
+    def step10_map(self, prediction_csv_path: str,
                                          boundary_shp_path: str,
                                          output_image_path: Optional[str] = None,
                                          resolution: float = 30,
@@ -1623,7 +1623,7 @@ class WaterQualityInversionPipeline:
             # 步骤4: 处理CSV文件
             if 'step4' in config:
                 self._notify("步骤4: 数据预处理", "start")
-                self.step4_process_csv(**config['step4'])
+                self.step5_process_csv(**config['step4'])
                 self._notify("步骤4: 数据预处理", "completed", f"(输出: {self.processed_csv_path})")
             else:
                 self._notify("步骤4: 数据预处理", "skipped", "未配置")
@@ -1631,7 +1631,7 @@ class WaterQualityInversionPipeline:
             # 步骤5: 提取训练样本点光谱
             if 'step5' in config:
                 self._notify("步骤5: 光谱提取", "start")
-                self.step5_extract_training_spectra(**config['step5'])
+                self.step6_extract_spectra(**config['step5'])
                 self._notify("步骤5: 光谱提取", "completed", f"(输出: {self.training_csv_path})")
             else:
                 self._notify("步骤5: 光谱提取", "skipped", "未配置")
@@ -1639,7 +1639,7 @@ class WaterQualityInversionPipeline:
             # 步骤6: 计算水质指数
             if 'step6' in config:
                 self._notify("步骤6: 水质光谱指数计算", "start")
-                self.step6_water_quality_indices(**config['step6'])
+                self.step7_calc_indices(**config['step6'])
                 self._notify("步骤6: 水质光谱指数计算", "completed", f"(输出: {self.indices_path})")
             else:
                 self._notify("步骤6: 水质光谱指数计算", "skipped", "未配置")
@@ -1647,7 +1647,7 @@ class WaterQualityInversionPipeline:
             # 步骤7: 训练模型
             if 'step7' in config:
                 self._notify("步骤7: 模型训练", "start")
-                self.step7_ml_modeling(**config['step7'])
+                self.step8_train_ml(**config['step7'])
                 self._notify("步骤7: 模型训练", "completed", f"(输出: {self.models_dir})")
             else:
                 self._notify("步骤7: 模型训练", "skipped", "未配置")
@@ -1671,7 +1671,7 @@ class WaterQualityInversionPipeline:
             # 步骤10: 生成预测采样点
             if 'step10' in config:
                 self._notify("步骤10: 采样点生成", "start")
-                sampling_csv_path = self.step10_sampling(**config['step10'])
+                sampling_csv_path = self.step4_sampling(**config['step10'])
                 self._notify("步骤10: 采样点生成", "completed", f"(输出: {sampling_csv_path})")
             else:
                 sampling_csv_path = None
@@ -1682,7 +1682,7 @@ class WaterQualityInversionPipeline:
                 self._notify("步骤11: 参数预测", "start")
                 step11_ml_config = config['step11_ml'].copy()
                 step11_ml_config['sampling_csv_path'] = sampling_csv_path
-                prediction_files = self.step11_ml_prediction(**step11_ml_config)
+                prediction_files = self.step9_predict_ml(**step11_ml_config)
                 self._notify("步骤11: 参数预测", "completed", f"(生成{len(prediction_files)}个预测文件)")
             else:
                 prediction_files = {}
@@ -1724,7 +1724,7 @@ class WaterQualityInversionPipeline:
                     step14_config['prediction_csv_path'] = pred_file
                     if 'output_image_path' not in step14_config:
                         step14_config['output_image_path'] = None
-                    dist_map_path = self.step14_distribution_map(**step14_config)
+                    dist_map_path = self.step10_map(**step14_config)
                     distribution_maps[target_name] = dist_map_path
                 self._notify("步骤14: 分布图生成", "completed", f"(生成{len(distribution_maps)}个分布图)")
             else:
@@ -2426,7 +2426,7 @@ def example_independent_steps():
         # 示例3: 独立运行步骤4 - 数据预处理
         print("\n示例3: 独立运行步骤4 - 数据预处理")
         try:
-            processed_csv = pipeline.step4_process_csv(
+            processed_csv = pipeline.step5_process_csv(
                 csv_path="path/to/water_quality_data.csv"
             )
             print(f"处理后的CSV文件: {processed_csv}")
@@ -2436,7 +2436,7 @@ def example_independent_steps():
         # 示例4: 独立运行步骤5 - 光谱提取
         print("\n示例4: 独立运行步骤5 - 光谱提取")
         try:
-            training_spectra = pipeline.step5_extract_training_spectra(
+            training_spectra = pipeline.step6_extract_spectra(
                 deglint_img_path="path/to/deglint_image.bsq",
                 csv_path="path/to/processed_data.csv",
                 glint_mask_path="path/to/severe_glint_area.dat",
@@ -2460,7 +2460,7 @@ def example_independent_steps():
         # 示例6: 独立运行步骤10 - 采样点生成
         print("\n示例6: 独立运行步骤10 - 采样点生成")
         try:
-            sampling_csv = pipeline.step10_sampling(
+            sampling_csv = pipeline.step4_sampling(
                 deglint_img_path="path/to/deglint_image.bsq",
                 water_mask_path="path/to/water_mask.dat",
                 skip_dependency_check=True
@@ -2472,7 +2472,7 @@ def example_independent_steps():
         # 示例7: 独立运行步骤11 - 水质预测
         print("\n示例7: 独立运行步骤11 - 水质预测")
         try:
-            predictions = pipeline.step11_ml_prediction(
+            predictions = pipeline.step9_predict_ml(
                 sampling_csv_path="path/to/sampling_spectra.csv",
                 models_dir="path/to/models_directory",
                 skip_dependency_check=True
@@ -2484,7 +2484,7 @@ def example_independent_steps():
         # 示例8: 独立运行步骤14 - 分布图生成
         print("\n示例8: 独立运行步骤14 - 分布图生成")
         try:
-            distribution_map = pipeline.step14_distribution_map(
+            distribution_map = pipeline.step10_map(
                 prediction_csv_path="path/to/prediction_results.csv",
                 boundary_shp_path="path/to/boundary.shp",
                 skip_dependency_check=True

src/gui/core/preflight_dialog.py

@@ -59,14 +59,12 @@ class PreflightDialog(QDialog):
         "step3":   ("耀斑去除",     2),
         "step4":   ("数据清洗",     3),
         "step5":   ("特征构建",     4),
-        "step8":   ("水质指数",     5),
-        "step7":   ("监督建模",     6),
+        "step7":   ("水质指数",     5),
         "step8_non_empirical_modeling": ("回归建模", 7),
-        "step9":   ("自定义回归建模", 8),
-        "step10":  ("采样点布设",   9),
-        "step11_ml": ("监督预测",  10),
-        "step11":  ("回归预测",    11),
-        "step12":  ("自定义回归预测", 12),
+        "step9":   ("水色指数反演",  8),
+        "step10":  ("采样点布设",   10),
+        "step11_ml": ("监督预测",  11),
+        "step11":  ("回归预测",    12),
         "step14":  ("专题图生成",  13),
     }
 

src/gui/core/worker_thread.py

@@ -272,10 +272,10 @@ class WorkerThread(QThread):
                 ctx = PipelineContext(
                     img_path=self.config.get('step1', {}).get('img_path'),
                     water_mask_path=self.config.get('step1', {}).get('mask_path'),
-                    csv_path=self.config.get('step4', {}).get('csv_path'),
-                    boundary_path=self.config.get('step5', {}).get('boundary_path'),
-                    boundary_shp_path=self.config.get('step14', {}).get('boundary_shp_path'),
-                    formula_csv_path=self.config.get('step12', {}).get('formula_csv_path'),
+                    csv_path=self.config.get('step4_sampling', {}).get('csv_path'),
+                    boundary_path=self.config.get('step5_clean', {}).get('boundary_path'),
+                    boundary_shp_path=self.config.get('step11_map', {}).get('boundary_shp_path'),
+                    formula_csv_path=self.config.get('step8_non_empirical_modeling', {}).get('formula_csv_path'),
                     work_dir=self.work_dir,
                     user_config=self.config
                 )
@@ -323,19 +323,16 @@ class WorkerThread(QThread):
             'step1': 'step1_generate_water_mask',
             'step2': 'step2_find_glint_area',
             'step3': 'step3_remove_glint',
-            'step4': 'step4_process_csv',
-            'step5': 'step5_extract_training_spectra',
-            'step6': 'step6_water_quality_indices',
-            'step7': 'step7_ml_modeling',
+            'step4_sampling': 'step4_sampling',
+            'step5_clean': 'step5_process_csv',
+            'step6_feature': 'step6_extract_spectra',
+            'step7_index': 'step7_calc_indices',
+            'step8_ml_train': 'step8_train_ml',
             'step8_non_empirical_modeling': 'step8_non_empirical_modeling',
             'step8_qaa': 'step8_qaa_inversion',
-            'step9_concentration': 'step9_concentration_inversion',
-            'step9': 'step9_custom_regression',
-            'step10': 'step10_sampling',
-            'step11_ml': 'step11_ml_prediction',
-            'step11': 'step11_non_empirical_prediction',
-            'step12': 'step12_custom_regression_prediction',
-            'step14': 'step14_distribution_map'
+            'step9_ml_predict': 'step9_predict_ml',
+            'step10_watercolor': 'step9_watercolor_inversion',
+            'step11_map': 'step10_map',
         }
 
         if step_name not in step_method_map:
@@ -351,12 +348,6 @@ class WorkerThread(QThread):
             result = method(**config)
             return result
 
-        # step9_concentration_inversion 同理，必须透传完整 config dict
-        if step_name == 'step9_concentration':
-            method = getattr(self.pipeline, method_name)
-            result = method(**config)
-            return result
-
         # 透传面板顶层传入的外部预训练模型（GUI step11_prediction_panel 通过 config['_external_model'] 传入）
         # 非空才覆盖（遵循 feedback_never_overwrite_with_empty 原则）
         for key in ('_external_model', '_external_model_path',
@@ -371,17 +362,9 @@ class WorkerThread(QThread):
 
         step_config['skip_dependency_check'] = True
 
-        if step_name == 'step14':
-            step_config.pop('step9_batch_mode', None)
-            step_config.pop('prediction_csv_dir', None)
-            step_config.pop('recursive_csv_scan', None)
-
-        if step_name in ['step2', 'step3', 'step4', 'step5', 'step7', 'step10', 'step11_ml', 'step11', 'step12']:
+        if step_name in ['step2', 'step3', 'step4_sampling', 'step5_clean', 'step7_index', 'step9_ml_predict']:
             step_config.pop('output_path', None)
 
-        if step_name == 'step11' and 'models_dir' in step_config:
-            step_config['non_empirical_models_dir'] = step_config.pop('models_dir')
-
         method = getattr(self.pipeline, method_name)
         result = method(**step_config)
 
@@ -440,100 +423,76 @@ class WorkerThread(QThread):
                 "    → 请确认「耀斑去除」已成功运行，或重新配置路径。"
             )
 
-        # ── 步骤4：实测水质数据 CSV ──
-        step4_cfg = config.get('step4', {})
+        # ── 步骤4_sampling：实测水质数据 CSV ──
+        step4_cfg = config.get('step4_sampling', {})
         csv_path = step4_cfg.get('csv_path')
         if csv_path and not os.path.isfile(csv_path):
             errors.append(
-                f"步骤 4：实测水质数据文件不存在：\n    {csv_path}\n"
+                f"步骤 4_sampling：实测水质数据文件不存在：\n    {csv_path}\n"
                 "    → 请检查 CSV 路径是否正确，或重新上传数据文件。"
             )
 
-        # ── 步骤5：采样点平均光谱提取 ──
-        step5_cfg = config.get('step5', {})
+        # ── 步骤5_clean：采样点平均光谱提取 ──
+        step5_cfg = config.get('step5_clean', {})
         step5_csv = step5_cfg.get('csv_path')
         boundary_path = step5_cfg.get('boundary_path')
         if step5_csv and not os.path.isfile(step5_csv):
             errors.append(
-                f"步骤 5：实测水质数据文件不存在：\n    {step5_csv}\n"
+                f"步骤 5_clean：实测水质数据文件不存在：\n    {step5_csv}\n"
                 "    → 请检查「流程步骤-阶段五」中的 CSV 路径。"
             )
         if boundary_path and not os.path.isfile(boundary_path):
             errors.append(
-                f"步骤 5：边界矢量文件不存在：\n    {boundary_path}\n"
+                f"步骤 5_clean：边界矢量文件不存在：\n    {boundary_path}\n"
                 "    → 请确认「流程步骤-阶段五」中已填写有效的边界 shp 路径。"
             )
 
-        # ── 步骤6（水质光谱指数）：训练光谱 CSV ──
-        step6_cfg = config.get('step6', {})
+        # ── 步骤6_feature（水质光谱指数）：训练光谱 CSV ──
+        step6_cfg = config.get('step6_feature', {})
         training_csv = step6_cfg.get('training_csv_path')
         if training_csv and not os.path.isfile(training_csv):
             errors.append(
-                f"步骤 6（水质光谱指数）：训练光谱文件不存在：\n    {training_csv}\n"
-                "    → 请确认步骤 5 已成功运行并生成了训练光谱。"
+                f"步骤 6_feature（水质光谱指数）：训练光谱文件不存在：\n    {training_csv}\n"
+                "    → 请确认步骤 5_clean 已成功运行并生成了训练光谱。"
             )
 
-        # ── 步骤7（ML 建模） ──
-        step7_cfg = config.get('step7', {})
+        # ── 步骤8_ml_train（ML 建模） ──
+        step7_cfg = config.get('step8_ml_train', {})
         step7_csv = step7_cfg.get('training_csv_path')
         if step7_csv and not os.path.isfile(step7_csv):
             errors.append(
-                f"步骤 7（ML 建模）：训练光谱文件不存在：\n    {step7_csv}\n"
-                "    → 请确认步骤 5 已成功运行并生成了训练光谱。"
+                f"步骤 8_ml_train（ML 建模）：训练光谱文件不存在：\n    {step7_csv}\n"
+                "    → 请确认步骤 5_clean 已成功运行并生成了训练光谱。"
             )
 
-        # ── 步骤11 ML 预测：密集采样点 CSV + 模型目录 ──
-        step11_ml_cfg = config.get('step11_ml', {})
-        ml_csv = step11_ml_cfg.get('sampling_csv_path')
-        models_dir = step11_ml_cfg.get('models_dir')
+        # ── 步骤9_ml_predict：密集采样点 CSV + 模型目录 ──
+        step9_cfg = config.get('step9_ml_predict', {})
+        ml_csv = step9_cfg.get('sampling_csv_path')
+        models_dir = step9_cfg.get('models_dir')
         if ml_csv and not os.path.isfile(ml_csv):
             errors.append(
-                f"步骤 11 ML 预测：采样点 CSV 不存在：\n    {ml_csv}\n"
+                f"步骤 9_ml_predict：采样点 CSV 不存在：\n    {ml_csv}\n"
                 "    → 请确认「流程步骤-阶段七（采样点布设）」已成功运行。"
             )
         if models_dir and not os.path.isdir(models_dir):
             errors.append(
-                f"步骤 11 ML 预测：模型目录不存在：\n    {models_dir}\n"
+                f"步骤 9_ml_predict：模型目录不存在：\n    {models_dir}\n"
                 "    → 请确认「流程步骤-阶段六（机器学习建模）」已成功运行。"
             )
 
-        # ── 步骤11 回归预测：模型目录 ──
-        step11_cfg = config.get('step11', {})
-        step11_csv = step11_cfg.get('sampling_csv_path')
-        step11_dir = step11_cfg.get('models_dir')
-        if step11_csv and not os.path.isfile(step11_csv):
-            errors.append(
-                f"步骤 11 回归预测：采样点 CSV 不存在：\n    {step11_csv}\n"
-                "    → 请确认「流程步骤-阶段七（采样点布设）」已成功运行。"
-            )
-        if step11_dir and not os.path.isdir(step11_dir):
-            errors.append(
-                f"步骤 11 回归预测：模型目录不存在：\n    {step11_dir}\n"
-                "    → 请确认「流程步骤-阶段八（非经验建模）」已成功运行。"
-            )
-
-        # ── 步骤12 自定义回归预测：公式 CSV ──
-        step12_cfg = config.get('step12', {})
-        formula_csv = step12_cfg.get('formula_csv_path')
-        if formula_csv and not os.path.isfile(formula_csv):
-            errors.append(
-                f"步骤 12（自定义回归预测）：公式 CSV 文件不存在：\n    {formula_csv}\n"
-                "    → 请确认「流程步骤-阶段十二」中已填写有效的公式文件路径。"
-            )
-
-        # ── 步骤14 专题图：预测结果 CSV + 边界 shp ──
-        step14_cfg = config.get('step14', {})
-        pred_csv = step14_cfg.get('prediction_csv_path')
-        boundary_shp = step14_cfg.get('boundary_shp_path')
+        # ── 步骤11_map 专题图：预测结果 CSV + 边界 shp ──
+        step11_cfg = config.get('step11_map', {})
+        pred_csv = step11_cfg.get('prediction_csv_path')
+        boundary_shp = step11_cfg.get('boundary_shp_path')
         if pred_csv and not os.path.isfile(pred_csv):
             errors.append(
-                f"步骤 14（专题图）：预测结果 CSV 不存在：\n    {pred_csv}\n"
+                f"步骤 11_map（专题图）：预测结果 CSV 不存在：\n    {pred_csv}\n"
                 "    → 请确认机器学习或回归预测步骤已成功运行。"
             )
         if boundary_shp and not os.path.isfile(boundary_shp):
             errors.append(
-                f"步骤 14（专题图）：边界 shp 文件不存在：\n    {boundary_shp}\n"
-                "    → 请确认「流程步骤-阶段十四」中已填写有效的边界矢量文件路径。"
+                f"步骤 11_map（专题图）：边界 shp 文件不存在：\n    {boundary_shp}\n"
+                "    → 请确认「流程步骤-阶段十一」中已填写有效的边界矢量文件路径。"
             )
 
         # ── 汇总报错：任一缺失立即抛出 PipelineHalt ──

src/gui/panels/step10_watercolor_panel.py

@@ -0,0 +1,569 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Step10 面板 - 水色指数反演（直接处理去耀斑 BSQ 影像）
+
+将 waterindex.csv 中的公式直接应用于去耀斑高光谱影像，
+输出各水质参数指数的 GeoTIFF 栅格图像。
+"""
+
+import os
+import traceback
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from PyQt5.QtWidgets import (
+    QWidget, QVBoxLayout, QHBoxLayout, QGridLayout, QFormLayout,
+    QGroupBox, QLabel, QLineEdit, QComboBox, QCheckBox, QPushButton,
+    QFileDialog, QMessageBox, QListWidget, QListWidgetItem,
+    QAbstractItemView, QProgressBar, QTextEdit, QFrame,
+    QScrollArea, QSizePolicy,
+)
+from PyQt5.QtGui import QFont
+from PyQt5.QtCore import Qt, QThread, pyqtSignal
+
+from src.gui.components.custom_widgets import FileSelectWidget
+from src.gui.styles import ModernStylesheet
+
+
+class WaterIndexWorker(QThread):
+    """后台线程：执行水色指数反演"""
+    finished_ok = pyqtSignal(dict)
+    failed = pyqtSignal(str)
+    progress = pyqtSignal(str, float)  # message, percent
+    log = pyqtSignal(str)
+
+    def __init__(
+        self,
+        bsq_path: str,
+        hdr_path: str,
+        output_dir: str,
+        selected_formulas: List[str],
+        waterindex_csv: str,
+        water_mask_path: Optional[str] = None,
+        work_dir: Optional[str] = None,
+    ):
+        super().__init__()
+        self.bsq_path = bsq_path
+        self.hdr_path = hdr_path
+        self.output_dir = output_dir
+        self.selected_formulas = selected_formulas
+        self.waterindex_csv = waterindex_csv
+        self.water_mask_path = water_mask_path
+        self.work_dir = work_dir
+
+    def run(self):
+        try:
+            from src.core.algorithms.waterindex_inversion import WaterIndexProcessor
+
+            self.progress.emit("正在初始化水色指数处理器…", 2)
+
+            processor = WaterIndexProcessor(self.waterindex_csv)
+
+            self.progress.emit("正在读取影像元数据…", 5)
+
+            # 获取影像元数据
+            meta = processor.get_image_metadata(self.bsq_path, self.hdr_path)
+            if not meta:
+                self.failed.emit("无法读取影像元数据，请检查 BSQ 和 HDR 文件是否匹配")
+                return
+
+            n_bands = meta.get('bands', 0)
+            wv_range = meta.get('wavelength_range', '未知')
+            self.log.emit(
+                f"影像信息: {meta['width']}×{meta['height']} 像素, "
+                f"{n_bands} 波段, {wv_range}"
+            )
+
+            if self.water_mask_path:
+                self.log.emit(f"使用水域掩膜: {self.water_mask_path}")
+
+            # 使用 run_inversion 入口（含掩膜拦截链路）
+            results = processor.run_inversion(
+                deglint_img_path=self.bsq_path,
+                work_dir=self.work_dir or self.output_dir,
+                formula_csv_path=self.waterindex_csv,
+                selected_formulas=self.selected_formulas,
+                water_mask_path=self.water_mask_path,
+                callback=self._on_progress,
+            )
+
+            self.progress.emit(f"完成！共生成 {len(results)} 个指数图", 100)
+            self.finished_ok.emit(results)
+
+        except Exception as e:
+            self.failed.emit(f"{e}\n{traceback.format_exc()}")
+
+    def _on_progress(self, msg: str, pct: float):
+        self.progress.emit(msg, pct)
+
+
+class Step10WatercolorPanel(QWidget):
+    """步骤10：水色指数反演（直接处理 BSQ 影像）"""
+
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        self._worker: Optional[WaterIndexWorker] = None
+        self._waterindex_csv = self._find_waterindex_csv()
+        self._categories: List[str] = []
+        self._all_formulas: List[Dict] = []
+        self._formula_list_widgets: Dict[str, QListWidgetItem] = {}
+        self.init_ui()
+        self._load_formulas()
+
+    def init_ui(self):
+        layout = QVBoxLayout()
+
+        # ---- 标题 ----
+        title = QLabel("步骤10：水色指数反演（高光谱影像直接处理）")
+        title.setFont(QFont("Arial", 12, QFont.Bold))
+        layout.addWidget(title)
+
+        # ---- 说明 ----
+        hint = QLabel(
+            "将 waterindex.csv 中的公式直接应用于去耀斑高光谱影像（BSQ），"
+            "输出各水质参数指数的 GeoTIFF 栅格图像。"
+            "指数图可直接用于水质专题图生成。"
+        )
+        hint.setWordWrap(True)
+        hint.setStyleSheet(f"color: {ModernStylesheet.COLORS.get('text_secondary', '#666')};")
+        layout.addWidget(hint)
+
+        # ---- 输入影像选择 ----
+        input_group = QGroupBox("输入影像")
+        input_layout = QFormLayout()
+
+        self.bsq_file = FileSelectWidget(
+            "去耀斑 BSQ 影像:",
+            "BSQ Files (*.bsq);;DAT Files (*.dat);;All Files (*.*)"
+        )
+        self.bsq_file.line_edit.setPlaceholderText("选择去耀斑处理后的 BSQ 影像")
+        self.bsq_file.browse_btn.clicked.disconnect()
+        self.bsq_file.browse_btn.clicked.connect(self._browse_bsq)
+        input_layout.addRow("BSQ 影像:", self.bsq_file)
+
+        self.hdr_file = FileSelectWidget(
+            "ENVI 头文件:",
+            "HDR Files (*.hdr);;All Files (*.*)"
+        )
+        self.hdr_file.line_edit.setPlaceholderText("自动关联同路径 .hdr 文件")
+        self.hdr_file.browse_btn.clicked.disconnect()
+        self.hdr_file.browse_btn.clicked.connect(self._browse_hdr)
+        input_layout.addRow("HDR 文件:", self.hdr_file)
+
+        # 影像信息显示
+        self.meta_label = QLabel("未加载影像")
+        self.meta_label.setStyleSheet(
+            "background: #f0f0f0; padding: 4px 8px; border-radius: 4px; "
+            "font-size: 12px; color: #333;"
+        )
+        input_layout.addRow("影像信息:", self.meta_label)
+
+        input_group.setLayout(input_layout)
+        layout.addWidget(input_group)
+
+        # ---- 公式选择 ----
+        formula_group = QGroupBox("公式选择")
+        formula_layout = QGridLayout()
+
+        # 类别过滤
+        formula_layout.addWidget(QLabel("按类别筛选:"), 0, 0)
+        self.category_combo = QComboBox()
+        self.category_combo.currentTextChanged.connect(self._on_category_changed)
+        formula_layout.addWidget(self.category_combo, 0, 1, 1, 2)
+
+        # 全选/取消全选
+        select_btn_layout = QHBoxLayout()
+        self.select_all_btn = QPushButton("全选")
+        self.select_all_btn.setMaximumWidth(80)
+        self.select_all_btn.clicked.connect(self._select_all)
+        select_btn_layout.addWidget(self.select_all_btn)
+
+        self.deselect_all_btn = QPushButton("取消全选")
+        self.deselect_all_btn.setMaximumWidth(80)
+        self.deselect_all_btn.clicked.connect(self._deselect_all)
+        select_btn_layout.addWidget(self.deselect_all_btn)
+        select_btn_layout.addStretch()
+        formula_layout.addLayout(select_btn_layout, 0, 3)
+
+        # 公式列表
+        self.formula_list = QListWidget()
+        self.formula_list.setSelectionMode(QAbstractItemView.MultiSelection)
+        self.formula_list.setMinimumHeight(200)
+        self.formula_list.itemChanged.connect(self._on_item_changed)
+        formula_layout.addWidget(self.formula_list, 1, 0, 1, 4)
+
+        formula_group.setLayout(formula_layout)
+        layout.addWidget(formula_group)
+
+        # ---- 输出设置 ----
+        output_group = QGroupBox("输出设置")
+        output_layout = QFormLayout()
+
+        self.output_dir = FileSelectWidget(
+            "输出目录:",
+            "Directories"
+        )
+        self.output_dir.line_edit.setPlaceholderText("留空 → 工作目录/8_WaterIndex_Images")
+        self.output_dir.browse_btn.clicked.disconnect()
+        self.output_dir.browse_btn.clicked.connect(self._browse_output_dir)
+        output_layout.addRow("输出目录:", self.output_dir)
+
+        self.format_combo = QComboBox()
+        self.format_combo.addItems(["GTiff (GeoTIFF)", "ENVI", "PCI"])
+        self.format_combo.setCurrentIndex(0)
+        output_layout.addRow("输出格式:", self.format_combo)
+
+        output_group.setLayout(output_layout)
+        layout.addWidget(output_group)
+
+        # ---- 进度显示 ----
+        self.progress_bar = QProgressBar()
+        self.progress_bar.setMinimum(0)
+        self.progress_bar.setMaximum(100)
+        self.progress_bar.setValue(0)
+        self.progress_bar.setTextVisible(True)
+        layout.addWidget(self.progress_bar)
+
+        self.progress_label = QLabel("")
+        self.progress_label.setStyleSheet("font-size: 11px; color: #666;")
+        layout.addWidget(self.progress_label)
+
+        # ---- 启用 & 运行 ----
+        self.enable_checkbox = QCheckBox("启用此步骤")
+        self.enable_checkbox.setChecked(True)
+        layout.addWidget(self.enable_checkbox)
+
+        self.run_btn = QPushButton("▶ 执行水色指数反演")
+        self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
+        self.run_btn.clicked.connect(self.run_step)
+        layout.addWidget(self.run_btn)
+
+        layout.addStretch()
+        self.setLayout(layout)
+
+    def _find_waterindex_csv(self) -> str:
+        """查找 waterindex.csv 路径"""
+        candidates = [
+            Path(__file__).parent.parent.parent / "model" / "waterindex.csv",
+            Path(__file__).parent.parent.parent.parent / "src" / "gui" / "model" / "waterindex.csv",
+        ]
+        for c in candidates:
+            if c.exists():
+                return str(c)
+        return ""
+
+    def _load_formulas(self):
+        """加载 waterindex.csv 中的公式"""
+        if not self._waterindex_csv or not Path(self._waterindex_csv).exists():
+            self.meta_label.setText("⚠️ waterindex.csv 未找到")
+            return
+
+        import csv
+        self._all_formulas = []
+        try:
+            with open(self._waterindex_csv, 'r', encoding='utf-8-sig') as f:
+                reader = csv.DictReader(f)
+                self._all_formulas = list(reader)
+        except Exception as e:
+            self.meta_label.setText(f"⚠️ 加载公式失败: {e}")
+            return
+
+        # 提取所有类别
+        cats = set()
+        for f in self._all_formulas:
+            c = f.get('Category', '').strip()
+            if c:
+                cats.add(c)
+
+        self._categories = sorted(cats)
+        self.category_combo.clear()
+        self.category_combo.addItem("全部")
+        self.category_combo.addItems(self._categories)
+
+        self._populate_list("全部")
+
+    def _populate_list(self, category: str):
+        """根据类别填充公式列表"""
+        self.formula_list.clear()
+        self._formula_list_widgets.clear()
+
+        formulas_to_show = (
+            [f for f in self._all_formulas if f.get('Category', '') == category]
+            if category != "全部"
+            else self._all_formulas
+        )
+
+        for f in formulas_to_show:
+            name = f.get('Formula_Name', '')
+            formula_str = f.get('Formula', '')
+            cat = f.get('Category', '')
+            ftype = f.get('Formula_Type', '')
+
+            item = QListWidgetItem()
+            item.setFlags(item.flags() | Qt.ItemIsUserCheckable)
+            item.setCheckState(Qt.Checked)
+            item.setData(Qt.UserRole, name)
+            item.setText(
+                f"☑  {name}  [{cat}]  ({ftype})\n    {formula_str}"
+            )
+            item.setToolTip(f"{name}\n{category}\n{formula_str}")
+            self.formula_list.addItem(item)
+            self._formula_list_widgets[name] = item
+
+    def _on_category_changed(self, category: str):
+        self._populate_list(category)
+
+    def _select_all(self):
+        for item in self.formula_list.selectedItems():
+            item.setCheckState(Qt.Checked)
+        # 也全选当前显示的
+        for i in range(self.formula_list.count()):
+            it = self.formula_list.item(i)
+            it.setCheckState(Qt.Checked)
+
+    def _deselect_all(self):
+        for i in range(self.formula_list.count()):
+            it = self.formula_list.item(i)
+            it.setCheckState(Qt.Unchecked)
+
+    def _on_item_changed(self, item: QListWidgetItem):
+        pass  # 可扩展：实时统计选中数量
+
+    def _browse_bsq(self):
+        path, _ = QFileDialog.getOpenFileName(
+            self, "选择去耀斑 BSQ 影像",
+            "",
+            "BSQ Files (*.bsq);;DAT Files (*.dat);;All Files (*.*)"
+        )
+        if path:
+            self.bsq_file.set_path(path)
+            # 自动关联同路径 hdr
+            hdr = Path(path).with_suffix('.hdr')
+            if hdr.exists():
+                self.hdr_file.set_path(str(hdr))
+            self._load_metadata(path, str(hdr) if hdr.exists() else "")
+
+    def _browse_hdr(self):
+        path, _ = QFileDialog.getOpenFileName(
+            self, "选择 ENVI 头文件",
+            "",
+            "HDR Files (*.hdr);;All Files (*.*)"
+        )
+        if path:
+            self.hdr_file.set_path(path)
+            bsq_path = self.bsq_file.get_path()
+            if bsq_path:
+                self._load_metadata(bsq_path, path)
+
+    def _browse_output_dir(self):
+        d = QFileDialog.getExistingDirectory(self, "选择输出目录", "")
+        if d:
+            self.output_dir.set_path(d)
+
+    def _load_metadata(self, bsq_path: str, hdr_path: str):
+        """加载并显示影像元数据"""
+        if not bsq_path or not Path(bsq_path).exists():
+            self.meta_label.setText("⚠️ 影像文件不存在")
+            return
+        if not hdr_path or not Path(hdr_path).exists():
+            self.meta_label.setText("⚠️ 头文件不存在")
+            return
+
+        try:
+            from src.core.algorithms.waterindex_inversion import WaterIndexProcessor
+            processor = WaterIndexProcessor(self._waterindex_csv)
+            meta = processor.get_image_metadata(bsq_path, hdr_path)
+            if meta:
+                self.meta_label.setText(
+                    f"✅ {meta['width']}×{meta['height']} | "
+                    f"{meta['bands']} 波段 | {meta.get('wavelength_range', '未知')} | "
+                    f"驱动: {meta['driver']}"
+                )
+            else:
+                self.meta_label.setText("⚠️ 无法读取元数据")
+        except Exception as e:
+            self.meta_label.setText(f"⚠️ 元数据读取失败: {e}")
+
+    def _get_selected_formula_names(self) -> List[str]:
+        names = []
+        for i in range(self.formula_list.count()):
+            item = self.formula_list.item(i)
+            if item.checkState() == Qt.Checked:
+                name = item.data(Qt.UserRole)
+                if name:
+                    names.append(name)
+        return names
+
+    def _get_default_work_dir(self) -> str:
+        if hasattr(self, 'work_dir') and self.work_dir:
+            return str(self.work_dir)
+        mw = self.window()
+        if mw and hasattr(mw, 'work_dir') and mw.work_dir:
+            return str(mw.work_dir)
+        return ""
+
+    def get_config(self) -> dict:
+        bsq = self.bsq_file.get_path()
+        return {
+            'bsq_path': bsq,
+            'hdr_path': self.hdr_file.get_path(),
+            'deglint_img_path': bsq,
+            'output_dir': self.output_dir.get_path(),
+            'output_format': self.format_combo.currentText().split()[0],
+            'selected_formulas': self._get_selected_formula_names(),
+        }
+
+    def set_config(self, config: dict):
+        if config.get('bsq_path'):
+            self.bsq_file.set_path(config['bsq_path'])
+        if config.get('hdr_path'):
+            self.hdr_file.set_path(config['hdr_path'])
+        if config.get('output_dir'):
+            self.output_dir.set_path(config['output_dir'])
+        if 'selected_formulas' in config:
+            names = set(config['selected_formulas'])
+            for i in range(self.formula_list.count()):
+                item = self.formula_list.item(i)
+                name = item.data(Qt.UserRole)
+                item.setCheckState(Qt.Checked if name in names else Qt.Unchecked)
+
+    def update_from_config(self, work_dir=None, pipeline=None):
+        if work_dir:
+            self.work_dir = work_dir
+        elif hasattr(self, 'work_dir') and self.work_dir:
+            pass
+        else:
+            self.work_dir = None
+
+        main_window = self.window()
+
+        # 自动填入去耀斑影像
+        if main_window and hasattr(main_window, 'step3_panel'):
+            deglint_path = main_window.step3_panel.output_file.get_path()
+            if deglint_path and not self.bsq_file.get_path():
+                if not os.path.isabs(deglint_path):
+                    deglint_path = os.path.join(self.work_dir or '', deglint_path).replace('\\', '/')
+                self.bsq_file.set_path(deglint_path)
+                hdr = Path(deglint_path).with_suffix('.hdr')
+                if hdr.exists():
+                    self.hdr_file.set_path(str(hdr))
+                    self._load_metadata(deglint_path, str(hdr))
+
+        # 自动填入输出目录
+        if self.work_dir:
+            out_dir = os.path.join(self.work_dir, "8_WaterIndex_Images").replace('\\', '/')
+            os.makedirs(out_dir, exist_ok=True)
+            if not self.output_dir.get_path():
+                self.output_dir.set_path(out_dir)
+
+    def run_step(self):
+        bsq_path = self.bsq_file.get_path().strip()
+        hdr_path = self.hdr_file.get_path().strip()
+        output_dir = self.output_dir.get_path().strip()
+
+        # 验证输入
+        if not bsq_path:
+            QMessageBox.warning(self, "输入错误", "请选择去耀斑 BSQ 影像！")
+            return
+        if not Path(bsq_path).exists():
+            QMessageBox.warning(self, "输入错误", f"BSQ 影像不存在:\n{bsq_path}")
+            return
+        if not hdr_path:
+            # 尝试自动查找
+            auto_hdr = Path(bsq_path).with_suffix('.hdr')
+            if auto_hdr.exists():
+                hdr_path = str(auto_hdr)
+                self.hdr_file.set_path(hdr_path)
+            else:
+                QMessageBox.warning(self, "输入错误", "请选择 ENVI 头文件！")
+                return
+        if not Path(hdr_path).exists():
+            QMessageBox.warning(self, "输入错误", f"HDR 文件不存在:\n{hdr_path}")
+            return
+        if not output_dir:
+            work_dir = self._get_default_work_dir()
+            output_dir = os.path.join(work_dir, "8_WaterIndex_Images").replace('\\', '/')
+            os.makedirs(output_dir, exist_ok=True)
+            self.output_dir.set_path(output_dir)
+
+        selected = self._get_selected_formula_names()
+        if not selected:
+            QMessageBox.warning(self, "输入错误", "请至少选择一个公式！")
+            return
+
+        if self._waterindex_csv and not Path(self._waterindex_csv).exists():
+            QMessageBox.warning(self, "配置错误", f"waterindex.csv 不存在:\n{self._waterindex_csv}")
+            return
+
+        # ── 自动扫描工作目录下的水域掩膜文件 ────────────────────────────
+        work_dir = self.work_dir or str(Path(bsq_path).parent)
+        mask_dir = os.path.join(work_dir, "1_water_mask")
+        water_mask_path: Optional[str] = None
+        if os.path.isdir(mask_dir):
+            # ★★★ glob 智能扫描：取任意 .dat 或 .tif 文件 ★★★
+            for pattern in ("*.dat", "*.tif", "*.TIF", "*.DT"):
+                candidates = sorted(Path(mask_dir).glob(pattern))
+                if candidates:
+                    water_mask_path = str(candidates[0])
+                    break
+
+        if water_mask_path:
+            print(f"[Step8] 自动找到水域掩膜: {water_mask_path}")
+        else:
+            print(f"[Step8] 未找到水域掩膜，跳过陆地剔除（陆地将保留在指数图中）")
+
+        # 开始后台处理
+        self.run_btn.setEnabled(False)
+        self.progress_bar.setValue(0)
+        self.progress_label.setText("")
+
+        self._worker = WaterIndexWorker(
+            bsq_path=bsq_path,
+            hdr_path=hdr_path,
+            output_dir=output_dir,
+            selected_formulas=selected,
+            waterindex_csv=self._waterindex_csv,
+            water_mask_path=water_mask_path,
+            work_dir=work_dir,
+        )
+        self._worker.progress.connect(self._on_progress)
+        self._worker.finished_ok.connect(self._on_finished)
+        self._worker.failed.connect(self._on_failed)
+        self._worker.log.connect(lambda m: self.progress_label.setText(m))
+        self._worker.start()
+
+    def _on_progress(self, msg: str, pct: float):
+        self.progress_bar.setValue(int(pct))
+        self.progress_label.setText(msg)
+
+    def _on_finished(self, results: Dict[str, str]):
+        self.run_btn.setEnabled(True)
+        n = len(results)
+        QMessageBox.information(
+            self, "执行成功",
+            f"水色指数反演完成！\n"
+            f"共生成 {n} 个指数图（GeoTIFF）。\n\n"
+            f"输出目录: {self.output_dir.get_path()}"
+        )
+        main_window = self.window()
+        if main_window and hasattr(main_window, 'log_message'):
+            main_window.log_message(f"步骤8：水色指数反演完成，生成 {n} 个指数图", "info")
+
+    def _on_failed(self, err: str):
+        self.run_btn.setEnabled(True)
+        self.progress_bar.setValue(0)
+        QMessageBox.critical(self, "执行错误", f"水色指数反演失败:\n\n{err[:500]}")
+
+    def get_output_dir(self) -> str:
+        return self.output_dir.get_path().strip() or ""
+
+    def get_output_tif_paths(self) -> List[str]:
+        """获取输出目录下的所有 GeoTIFF 文件路径"""
+        out_dir = self.get_output_dir()
+        if not out_dir or not os.path.isdir(out_dir):
+            return []
+        return sorted(
+            str(p) for p in Path(out_dir).glob("*.tif")
+            if p.is_file()
+        )

src/gui/panels/step11_map_panel.py

@@ -0,0 +1,826 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Step10 面板 - 专题图生成
+"""
+
+import os
+import traceback
+from pathlib import Path
+from typing import List, Optional
+
+from PyQt5.QtCore import Qt, QThread, pyqtSignal
+from PyQt5.QtWidgets import (
+    QWidget, QVBoxLayout, QGroupBox, QFormLayout, QHBoxLayout,
+    QLabel, QCheckBox, QPushButton, QLineEdit, QDoubleSpinBox,
+    QRadioButton, QButtonGroup, QMessageBox, QFileDialog, QComboBox,
+    QProgressBar,
+)
+
+from src.gui.components.custom_widgets import FileSelectWidget
+from src.gui.styles import ModernStylesheet
+
+# Pipeline 可用性（与 core/worker_thread.py 保持一致）
+try:
+    from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
+    PIPELINE_AVAILABLE = True
+except ImportError:
+    PIPELINE_AVAILABLE = False
+
+
+class Step11MapBatchThread(QThread):
+    """专题图：按文件夹内多个预测 CSV 批量生成分布图。"""
+
+    finished_ok = pyqtSignal(int)
+    failed = pyqtSignal(str)
+    log_message = pyqtSignal(str, str)
+    progress = pyqtSignal(int, int)  # (current, total)
+
+    def __init__(self, work_dir: str, csv_paths: List[str], step10_kwargs: dict, output_dir_optional: Optional[str]):
+        super().__init__()
+        self.work_dir = work_dir
+        self.csv_paths = csv_paths
+        self.step10_kwargs = step10_kwargs
+        self.output_dir_optional = (output_dir_optional or "").strip() or None
+
+    def run(self):
+        mpl_prev = None
+        try:
+            import matplotlib
+            mpl_prev = matplotlib.get_backend()
+        except Exception:
+            pass
+        try:
+            import matplotlib.pyplot as plt
+            plt.switch_backend("Agg")
+        except Exception:
+            mpl_prev = None
+        try:
+            from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
+            pipeline = WaterQualityInversionPipeline(work_dir=self.work_dir)
+            n = len(self.csv_paths)
+            for i, csv_p in enumerate(self.csv_paths):
+                self.progress.emit(i + 1, n)
+                self.log_message.emit(f"专题图 [{i + 1}/{n}] {csv_p}", "info")
+                kw = {**self.step10_kwargs, "prediction_csv_path": csv_p, "skip_dependency_check": True}
+                if self.output_dir_optional:
+                    stem = Path(csv_p).stem
+                    kw["output_image_path"] = str(Path(self.output_dir_optional) / f"{stem}_distribution.png")
+                else:
+                    kw["output_image_path"] = None
+                pipeline.step10_map(**kw)
+            self.finished_ok.emit(n)
+        except Exception as e:
+            self.failed.emit(f"{e}\n{traceback.format_exc()}")
+        finally:
+            if mpl_prev:
+                try:
+                    import matplotlib.pyplot as plt
+                    plt.switch_backend(mpl_prev)
+                except Exception:
+                    pass
+
+
+class Step11GeoTIFFBatchThread(QThread):
+    """GeoTIFF 批量渲染：遍历文件夹下所有 .tif/.bsq 逐一渲染成分布图 PNG。"""
+
+    finished_ok = pyqtSignal(int)
+    failed = pyqtSignal(str)
+    log_message = pyqtSignal(str, str)
+    progress = pyqtSignal(int, int)  # (current, total)
+
+    def __init__(
+        self,
+        tif_paths: List[str],
+        output_dir: str,
+        boundary_shp_path: Optional[str],
+        input_crs: str,
+        output_crs: str,
+    ):
+        super().__init__()
+        self.tif_paths = tif_paths
+        self.output_dir = output_dir
+        self.boundary_shp_path = boundary_shp_path
+        self.input_crs = input_crs
+        self.output_crs = output_crs
+
+    def run(self):
+        mpl_prev = None
+        try:
+            import matplotlib
+            mpl_prev = matplotlib.get_backend()
+        except Exception:
+            pass
+        try:
+            import matplotlib.pyplot as plt
+            plt.switch_backend("Agg")
+        except Exception:
+            mpl_prev = None
+        try:
+            from src.postprocessing.map import ContentMapper
+            mapper = ContentMapper()
+            n = len(self.tif_paths)
+            for i, tif_path in enumerate(self.tif_paths):
+                self.progress.emit(i + 1, n)
+                tif_stem = Path(tif_path).stem
+                chinese_name = mapper._get_chinese_title(tif_stem)
+                output_png = str(Path(self.output_dir) / f"{chinese_name}_专题图.png")
+                self.log_message.emit(f"GeoTIFF 渲染 [{i + 1}/{n}] {tif_stem}", "info")
+                try:
+                    mapper.visualize_raster(
+                        raster_tif_path=tif_path,
+                        output_file=output_png,
+                        boundary_shp_path=self.boundary_shp_path,
+                        nodata_value=-9999.0,
+                        figsize=(14, 10),
+                        alpha=0.9,
+                    )
+                except Exception as vis_err:
+                    self.log_message.emit(f"  ⚠️ 渲染失败，跳过: {vis_err}", "warning")
+                    continue
+            self.finished_ok.emit(n)
+        except Exception as e:
+            self.failed.emit(f"{e}\n{traceback.format_exc()}")
+        finally:
+            if mpl_prev:
+                try:
+                    import matplotlib.pyplot as plt
+                    plt.switch_backend(mpl_prev)
+                except Exception:
+                    pass
+
+
+class Step11MapPanel(QWidget):
+    """步骤11：专题图生成"""
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        self._batch_thread = None
+        self.init_ui()
+
+    def init_ui(self):
+        layout = QVBoxLayout()
+
+        hint = QLabel(
+            "独立运行：可选「单个 CSV」或「文件夹批量」（扫描目录下所有 .csv）。"
+            "GeoTIFF 栅格模式下，亦支持批量渲染步骤8输出的所有水色指数 GeoTIFF 文件。"
+        )
+        hint.setWordWrap(True)
+        hint.setStyleSheet(
+            f"color: {ModernStylesheet.COLORS.get('text_secondary', '#666')};"
+        )
+        layout.addWidget(hint)
+
+        mode_row = QHBoxLayout()
+        self.mode_single_rb = QRadioButton("单个 CSV 文件")
+        self.mode_folder_rb = QRadioButton("文件夹批量")
+        self._mode_group = QButtonGroup(self)
+        self._mode_group.addButton(self.mode_single_rb, 0)
+        self._mode_group.addButton(self.mode_folder_rb, 1)
+        mode_row.addWidget(self.mode_single_rb)
+        mode_row.addWidget(self.mode_folder_rb)
+        mode_row.addStretch()
+        layout.addLayout(mode_row)
+
+        # ---------- 渲染模式选择器（CSV vs GeoTIFF） ----------
+        render_row = QHBoxLayout()
+        render_row.addWidget(QLabel("渲染模式:"))
+        self.render_mode_combo = QComboBox()
+        self.render_mode_combo.addItems(["CSV 插值模式", "GeoTIFF 栅格模式"])
+        self.render_mode_combo.setMinimumWidth(180)
+        self.render_mode_combo.currentTextChanged.connect(self._toggle_input_mode)
+        render_row.addWidget(self.render_mode_combo)
+        render_row.addStretch()
+        layout.addLayout(render_row)
+
+        # ---------- RadioButton 美化样式（选中状态为方形实心块，贴合主界面风格） ----------
+        radio_style = """
+            QRadioButton {
+                font-size: 14px;
+                spacing: 8px;
+                color: #333333;
+            }
+            QRadioButton::indicator {
+                width: 16px;
+                height: 16px;
+                border: 2px solid #999999;
+                border-radius: 3px;
+                background-color: white;
+            }
+            QRadioButton::indicator:checked {
+                border: 2px solid #0078d4;
+                background-color: #0078d4;
+                image: none;
+            }
+            QRadioButton::indicator:hover {
+                border: 2px solid #005a9e;
+            }
+        """
+        self.mode_single_rb.setStyleSheet(radio_style)
+        self.mode_folder_rb.setStyleSheet(radio_style)
+
+        self.prediction_csv_file = FileSelectWidget(
+            "预测结果CSV:",
+            "CSV Files (*.csv);;All Files (*.*)"
+        )
+        layout.addWidget(self.prediction_csv_file)
+
+        folder_row = QHBoxLayout()
+        self.prediction_csv_dir_label = QLabel("预测CSV目录:")
+        self.prediction_csv_dir_label.setMinimumWidth(120)
+        self.prediction_csv_dir_edit = QLineEdit()
+        self.prediction_csv_dir_edit.setPlaceholderText("选择含多个预测结果 CSV 的文件夹…")
+        pred_dir_btn = QPushButton("浏览…")
+        pred_dir_btn.setMaximumWidth(80)
+        pred_dir_btn.clicked.connect(self.browse_prediction_csv_dir)
+        folder_row.addWidget(self.prediction_csv_dir_label)
+        folder_row.addWidget(self.prediction_csv_dir_edit, 1)
+        folder_row.addWidget(pred_dir_btn)
+        self._folder_row_widget = QWidget()
+        self._folder_row_widget.setLayout(folder_row)
+        layout.addWidget(self._folder_row_widget)
+
+        # ---------- GeoTIFF 栅格文件选择器 ----------
+        self.geotiff_file = FileSelectWidget(
+            "水色指数 GeoTIFF:",
+            "GeoTIFF Files (*.tif);;All Files (*.*)"
+        )
+        self.geotiff_file.line_edit.setPlaceholderText("选择步骤8输出的水色指数 GeoTIFF 文件…")
+        self.geotiff_file.setVisible(False)
+        layout.addWidget(self.geotiff_file)
+
+        # ---------- GeoTIFF 文件夹批量选择器（GeoTIFF + 文件夹模式时显示） ----------
+        geotiff_dir_row = QHBoxLayout()
+        self.geotiff_dir_label = QLabel("水色指数目录:")
+        self.geotiff_dir_label.setMinimumWidth(120)
+        self.geotiff_dir_edit = QLineEdit()
+        self.geotiff_dir_edit.setPlaceholderText("选择 8_WaterIndex_Images 文件夹（批量渲染）…")
+        geotiff_dir_btn = QPushButton("浏览…")
+        geotiff_dir_btn.setMaximumWidth(80)
+        geotiff_dir_btn.clicked.connect(self.browse_geotiff_dir)
+        geotiff_dir_row.addWidget(self.geotiff_dir_label)
+        geotiff_dir_row.addWidget(self.geotiff_dir_edit, 1)
+        geotiff_dir_row.addWidget(geotiff_dir_btn)
+        self._geotiff_dir_widget = QWidget()
+        self._geotiff_dir_widget.setLayout(geotiff_dir_row)
+        self._geotiff_dir_widget.setVisible(False)
+        layout.addWidget(self._geotiff_dir_widget)
+
+        self.recursive_csv_cb = QCheckBox("包含子文件夹（递归扫描 *.csv）")
+        layout.addWidget(self.recursive_csv_cb)
+
+        self.boundary_file = FileSelectWidget(
+            "边界文件:",
+            "Shapefiles (*.shp);;All Files (*.*)"
+        )
+        layout.addWidget(self.boundary_file)
+
+        # 参数设置
+        params_group = QGroupBox("生成参数")
+        params_layout = QFormLayout()
+
+        self.resolution = QDoubleSpinBox()
+        self.resolution.setRange(1, 1000)
+        self.resolution.setValue(30)
+        params_layout.addRow("分辨率(米):", self.resolution)
+
+        self.input_crs = QLineEdit()
+        self.input_crs.setText("EPSG:32651")
+        params_layout.addRow("输入坐标系:", self.input_crs)
+
+        self.output_crs = QLineEdit()
+        self.output_crs.setText("EPSG:4326")
+        params_layout.addRow("输出坐标系:", self.output_crs)
+
+        self.show_points = QCheckBox("显示采样点")
+        params_layout.addRow("", self.show_points)
+
+        self.use_diffusion = QCheckBox("启用距离扩散")
+        self.use_diffusion.setChecked(True)
+        params_layout.addRow("", self.use_diffusion)
+
+        params_group.setLayout(params_layout)
+        layout.addWidget(params_group)
+
+        # 输出目录
+        self.output_dir = FileSelectWidget(
+            "输出分布图目录:",
+            "Directories;;All Files (*.*)"
+        )
+        self.output_dir.line_edit.setPlaceholderText("留空→工作目录/14_visualization")
+        self.output_dir.browse_btn.clicked.disconnect()
+        self.output_dir.browse_btn.clicked.connect(self.browse_output_dir)
+        layout.addWidget(self.output_dir)
+
+        # 启用步骤
+        self.enable_checkbox = QCheckBox("启用此步骤")
+        self.enable_checkbox.setChecked(True)
+        layout.addWidget(self.enable_checkbox)
+
+        # 独立运行按钮
+        self.run_button = QPushButton("独立运行此步骤")
+        self.run_button.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
+        self.run_button.clicked.connect(self.run_step)
+        layout.addWidget(self.run_button)
+
+        # 批量渲染进度条
+        self.progress_bar = QProgressBar()
+        self.progress_bar.setVisible(False)
+        self.progress_bar.setMinimum(0)
+        self.progress_bar.setMaximum(100)
+        self.progress_bar.setValue(0)
+        layout.addWidget(self.progress_bar)
+
+        layout.addStretch()
+        self.setLayout(layout)
+
+        # 信号绑定与初始状态
+        self.mode_single_rb.toggled.connect(self._toggle_input_mode)
+        self.mode_folder_rb.toggled.connect(self._toggle_input_mode)
+        self.mode_single_rb.setChecked(True)  # 默认选中"单个 CSV"
+        self._toggle_input_mode()             # 根据默认值设置初始显示状态
+
+    def _toggle_input_mode(self):
+        """槽函数：根据渲染模式和输入模式动态显示/隐藏对应的输入组件。"""
+        geotiff_mode = self.render_mode_combo.currentText() == "GeoTIFF 栅格模式"
+        folder_mode = self.mode_folder_rb.isChecked()
+
+        # CSV 插值模式
+        if not geotiff_mode:
+            self.prediction_csv_file.setVisible(not folder_mode)
+            self._folder_row_widget.setVisible(folder_mode)
+            self.recursive_csv_cb.setVisible(folder_mode)
+            self.geotiff_file.setVisible(False)
+            self._geotiff_dir_widget.setVisible(False)
+        # GeoTIFF 栅格模式
+        else:
+            self.prediction_csv_file.setVisible(False)
+            self._folder_row_widget.setVisible(False)
+            self.recursive_csv_cb.setVisible(False)
+            # GeoTIFF + 文件夹批量 → 显示文件夹选择器；否则 → 显示单文件选择器
+            self.geotiff_file.setVisible(not folder_mode)
+            self._geotiff_dir_widget.setVisible(folder_mode)
+
+    def _get_default_work_dir(self):
+        """获取 work_dir，优先用 panel 自身缓存的，否则尝试从主窗口取"""
+        if hasattr(self, 'work_dir') and self.work_dir:
+            return str(self.work_dir)
+        mw = self.window()
+        if mw and hasattr(mw, 'work_dir') and mw.work_dir:
+            return str(mw.work_dir)
+        return ""
+
+    def browse_prediction_csv_dir(self):
+        default = self._get_default_work_dir()
+        if default:
+            default = os.path.join(default, "11_12_13_predictions")
+        d = QFileDialog.getExistingDirectory(self, "选择预测结果 CSV 所在文件夹", default)
+        if d:
+            self.prediction_csv_dir_edit.setText(d)
+
+    def _collect_csv_paths_from_folder(self) -> List[str]:
+        folder = (self.prediction_csv_dir_edit.text() or "").strip()
+        if not folder or not os.path.isdir(folder):
+            return []
+        root = Path(folder)
+        if self.recursive_csv_cb.isChecked():
+            files = sorted(root.rglob("*.csv"))
+        else:
+            files = sorted(root.glob("*.csv"))
+        return [str(p) for p in files if p.is_file()]
+
+    def browse_geotiff_dir(self):
+        """浏览 GeoTIFF 文件夹（批量模式）"""
+        default = self._get_default_work_dir()
+        if default:
+            default = os.path.join(default, "8_WaterIndex_Images")
+        d = QFileDialog.getExistingDirectory(
+            self, "选择水色指数 GeoTIFF 文件夹", default
+        )
+        if d:
+            self.geotiff_dir_edit.setText(d)
+
+    def _collect_tif_paths_from_folder(self) -> List[str]:
+        """扫描所选文件夹，收集所有 .tif 和 .bsq 文件路径"""
+        folder = (self.geotiff_dir_edit.text() or "").strip()
+        if not folder or not os.path.isdir(folder):
+            return []
+        root = Path(folder)
+        tif_files = sorted(root.glob("*.tif"))
+        bsq_files = sorted(root.glob("*.bsq"))
+        return [str(p) for p in tif_files + bsq_files if p.is_file()]
+
+    def _step10_base_pipeline_kwargs(self) -> dict:
+        return {
+            'boundary_shp_path': self.boundary_file.get_path(),
+            'resolution': self.resolution.value(),
+            'input_crs': self.input_crs.text(),
+            'output_crs': self.output_crs.text(),
+            'show_sample_points': self.show_points.isChecked(),
+            'use_distance_diffusion': self.use_diffusion.isChecked(),
+        }
+
+    def get_config(self):
+        pred_csv = (self.prediction_csv_file.get_path() or "").strip()
+        folder_mode = self.mode_folder_rb.isChecked()
+        pred_dir = (self.prediction_csv_dir_edit.text() or "").strip()
+        geotiff_path = (self.geotiff_file.get_path() or "").strip()
+        config = {
+            'step10_batch_mode': 'folder' if folder_mode else 'single',
+            'render_mode': self.render_mode_combo.currentText(),
+            'prediction_csv_dir': pred_dir if pred_dir else None,
+            'recursive_csv_scan': self.recursive_csv_cb.isChecked(),
+            'prediction_csv_path': None if folder_mode else (pred_csv if pred_csv else None),
+            'geotiff_path': geotiff_path if geotiff_path else None,
+            'geotiff_dir': (self.geotiff_dir_edit.text() or "").strip() or None,
+            'boundary_shp_path': self.boundary_file.get_path(),
+            'resolution': self.resolution.value(),
+            'input_crs': self.input_crs.text(),
+            'output_crs': self.output_crs.text(),
+            'show_sample_points': self.show_points.isChecked(),
+            'use_distance_diffusion': self.use_diffusion.isChecked(),
+        }
+        out_dir = (self.output_dir.get_path() or "").strip()
+        if not folder_mode and pred_csv and out_dir:
+            stem = Path(pred_csv).stem
+            config['output_image_path'] = str(Path(out_dir) / f"{stem}_distribution.png")
+        else:
+            config['output_image_path'] = None
+        return config
+
+    def set_config(self, config):
+        mode = config.get('step10_batch_mode', 'single')
+        if mode == 'folder':
+            self.mode_folder_rb.setChecked(True)
+        else:
+            self.mode_single_rb.setChecked(True)
+        render_mode = config.get('render_mode', 'CSV 插值模式')
+        idx = self.render_mode_combo.findText(render_mode)
+        if idx >= 0:
+            self.render_mode_combo.setCurrentIndex(idx)
+        if config.get('prediction_csv_dir'):
+            self.prediction_csv_dir_edit.setText(str(config['prediction_csv_dir']))
+        if 'recursive_csv_scan' in config:
+            self.recursive_csv_cb.setChecked(bool(config['recursive_csv_scan']))
+        if 'prediction_csv_path' in config and config['prediction_csv_path']:
+            self.prediction_csv_file.set_path(str(config['prediction_csv_path']))
+        if 'geotiff_path' in config and config['geotiff_path']:
+            self.geotiff_file.set_path(str(config['geotiff_path']))
+        if 'geotiff_dir' in config and config['geotiff_dir']:
+            self.geotiff_dir_edit.setText(str(config['geotiff_dir']))
+        if 'boundary_shp_path' in config:
+            self.boundary_file.set_path(config['boundary_shp_path'])
+        if 'resolution' in config:
+            self.resolution.setValue(config['resolution'])
+        if 'input_crs' in config:
+            self.input_crs.setText(config['input_crs'])
+        if 'output_crs' in config:
+            self.output_crs.setText(config['output_crs'])
+        if 'show_sample_points' in config:
+            self.show_points.setChecked(config['show_sample_points'])
+        if 'use_distance_diffusion' in config:
+            self.use_diffusion.setChecked(config['use_distance_diffusion'])
+        if 'output_dir' in config and config['output_dir']:
+            self.output_dir.set_path(str(config['output_dir']))
+        elif config.get('output_image_path'):
+            p = Path(str(config['output_image_path']))
+            if p.parent and str(p.parent) != '.':
+                self.output_dir.set_path(str(p.parent))
+
+    def update_from_config(self, work_dir=None, pipeline=None):
+        """从全局配置自动填充预测结果目录
+
+        优先使用 Step8（机器学习预测）的输出目录作为待预测 CSV 目录；
+        其次回退到 Step8.5（回归预测）或 Step8.75（自定义回归预测）的输出目录。
+
+        Args:
+            work_dir: 工作目录路径
+            pipeline: Pipeline 实例（未使用，保留接口兼容性）
+        """
+        try:
+            import traceback
+
+            if work_dir:
+                self.work_dir = work_dir
+            elif hasattr(self, 'work_dir') and self.work_dir:
+                pass
+            else:
+                self.work_dir = None
+
+            main_window = self.window()
+            if not main_window:
+                return
+
+            # 1. 尝试从 Step8 界面读取机器学习预测输出目录（最优先）
+            pred_dir = None
+            if hasattr(main_window, 'step11_prediction_panel'):
+                step8_widget = getattr(main_window.step11_prediction_panel, 'output_file', None)
+                step8_output = ""
+                if hasattr(step8_widget, 'get_path'):
+                    step8_output = step8_widget.get_path() or ""
+                elif hasattr(step8_widget, 'text'):
+                    step8_output = step8_widget.text() or ""
+
+                if step8_output:
+                    # 若为相对路径，使用 work_dir 合成为绝对路径
+                    if not os.path.isabs(step8_output):
+                        step8_output = os.path.join(self.work_dir or '', step8_output).replace('\\', '/')
+                    # 提取父目录后追加 Machine_Learning_Prediction（最底层真实子目录）
+                    base_pred_dir = str(Path(step8_output).parent)
+                    ml_pred_dir = Path(base_pred_dir) / "Machine_Learning_Prediction"
+                    pred_dir = str(ml_pred_dir) if ml_pred_dir.exists() else base_pred_dir
+
+            # 2. 备选：从 Step11 界面读取非经验预测输出目录
+            if not pred_dir and hasattr(main_window, 'step11_panel'):
+                step8_5_widget = getattr(main_window.step11_panel, 'output_file', None)
+                step8_5_output = ""
+                if hasattr(step8_5_widget, 'get_path'):
+                    step8_5_output = step8_5_widget.get_path() or ""
+                elif hasattr(step8_5_widget, 'text'):
+                    step8_5_output = step8_5_widget.text() or ""
+
+                if step8_5_output:
+                    # 若为相对路径，使用 work_dir 合成为绝对路径
+                    if not os.path.isabs(step8_5_output):
+                        step8_5_output = os.path.join(self.work_dir or '', step8_5_output).replace('\\', '/')
+                    pred_dir = str(Path(step8_5_output).parent)
+
+            # 3. 备选：从 Step12 界面读取自定义回归预测输出目录
+            if not pred_dir and hasattr(main_window, 'step12_panel'):
+                step8_75_widget = getattr(main_window.step12_panel, 'output_dir_widget', None)
+                step8_75_output = ""
+                if hasattr(step8_75_widget, 'get_path'):
+                    step8_75_output = step8_75_widget.get_path() or ""
+                elif hasattr(step8_75_widget, 'text'):
+                    step8_75_output = step8_75_widget.text() or ""
+
+                if step8_75_output:
+                    pred_dir = step8_75_output
+
+            # 自动填入"预测CSV目录"（文件夹批量模式）
+            if pred_dir:
+                existing_dir = (self.prediction_csv_dir_edit.text() or "").strip()
+                if not existing_dir:
+                    self.prediction_csv_dir_edit.setText(pred_dir)
+                    # 切换到文件夹批量模式
+                    self.mode_folder_rb.setChecked(True)
+
+            # 4. 自动填充输出目录（14_visualization）
+            if self.work_dir:
+                output_dir = os.path.join(self.work_dir, "14_visualization")
+                os.makedirs(output_dir, exist_ok=True)
+                existing_out = self.output_dir.get_path()
+                if not existing_out or not existing_out.strip():
+                    self.output_dir.set_path(output_dir)
+
+            # 5. 自动探测原始矢量边界文件（.shp）作为专题图底图
+            #  优先回溯 input-test/roi.shp，geopandas.read_file 仅支持矢量格式
+            if self.work_dir:
+                possible_shp = None
+                candidates = [
+                    Path(self.work_dir).parent / "input-test" / "roi.shp",
+                    Path(self.work_dir) / "roi.shp",
+                    Path(self.work_dir).parent / "roi.shp",
+                ]
+                for candidate in candidates:
+                    if candidate.exists() and candidate.suffix.lower() == ".shp":
+                        possible_shp = candidate
+                        break
+
+                existing_boundary = (self.boundary_file.get_path() or "").strip()
+                if not existing_boundary and possible_shp:
+                    self.boundary_file.set_path(str(possible_shp))
+                elif not existing_boundary:
+                    self.boundary_file.set_path("")
+                    print("⚠️ 提示：专题图生成模块需传入标准矢量边界文件 (.shp)，请手动选择。")
+
+            # 6. 自动探测 Step 8 输出的水色指数 GeoTIFF（GeoTIFF 渲染模式）
+            step8_out_dir = Path(self.work_dir) / "8_WaterIndex_Images" if self.work_dir else None
+            if step8_out_dir and step8_out_dir.is_dir():
+                # GeoTIFF 批量模式：填充目录供批量渲染
+                if not (self.geotiff_dir_edit.text() or "").strip():
+                    self.geotiff_dir_edit.setText(str(step8_out_dir))
+                # GeoTIFF 单文件模式：默认选中第一个
+                tif_files = sorted(step8_out_dir.glob("*.tif"))
+                if tif_files and not (self.geotiff_file.get_path() or "").strip():
+                    self.geotiff_file.set_path(str(tif_files[0]))
+        except Exception as e:
+            import traceback
+            print(f"【{self.__class__.__name__}】自动填充失败，跳过: {e}")
+            traceback.print_exc()
+
+    def browse_output_dir(self):
+        """浏览输出目录"""
+        default = self._get_default_work_dir()
+        if default:
+            default = os.path.join(default, "14_visualization")
+        dir_path = QFileDialog.getExistingDirectory(self, "选择输出分布图目录", default)
+        if dir_path:
+            self.output_dir.set_path(dir_path)
+
+    def _start_batch_run(self, csv_list, work_dir, base_kw, out_dir_opt, parent):
+        """封装 CSV 批量启动逻辑，统一处理信号连接和进度条"""
+        self.run_button.setEnabled(False)
+        self.progress_bar.setVisible(True)
+        self.progress_bar.setValue(0)
+        self._batch_thread = Step11MapBatchThread(work_dir, csv_list, base_kw, out_dir_opt)
+        main_win = parent
+
+        def _batch_log(msg, lvl):
+            if hasattr(main_win, "log_message"):
+                main_win.log_message(msg, lvl)
+
+        def _on_progress(cur, total):
+            if total > 0:
+                self.progress_bar.setMaximum(total)
+                self.progress_bar.setValue(cur)
+                self.progress_bar.setFormat(f"{cur}/{total} 张 (%p%)")
+
+        self._batch_thread.log_message.connect(_batch_log, Qt.QueuedConnection)
+        self._batch_thread.progress.connect(_on_progress, Qt.QueuedConnection)
+        self._batch_thread.finished_ok.connect(self._on_step10_batch_ok, Qt.QueuedConnection)
+        self._batch_thread.failed.connect(self._on_step10_batch_fail, Qt.QueuedConnection)
+        self._batch_thread.finished.connect(
+            lambda: (self.run_button.setEnabled(True), self.progress_bar.setVisible(False)),
+            Qt.QueuedConnection,
+        )
+        self._batch_thread.start()
+        if hasattr(parent, "log_message"):
+            parent.log_message(f"专题图批量：共 {len(csv_list)} 个 CSV，工作目录 {work_dir}", "info")
+
+    def run_step(self):
+        """独立运行步骤11"""
+        if self._batch_thread and self._batch_thread.isRunning():
+            QMessageBox.information(self, "提示", "批量任务正在运行，请稍候。")
+            return
+
+        boundary_shp_path = self.boundary_file.get_path()
+        if not boundary_shp_path:
+            QMessageBox.warning(self, "输入验证失败", "请选择边界文件")
+            return
+        if not os.path.exists(boundary_shp_path):
+            QMessageBox.warning(self, "输入验证失败", "边界文件不存在")
+            return
+
+        parent = self.parent()
+        while parent and not hasattr(parent, 'run_single_step'):
+            parent = parent.parent()
+
+        if not parent or not hasattr(parent, 'run_single_step'):
+            QMessageBox.critical(self, "错误", "无法找到父级GUI对象")
+            return
+
+        if self.mode_folder_rb.isChecked():
+            # -------- CSV 插值批量 --------
+            if self.render_mode_combo.currentText() != "GeoTIFF 栅格模式":
+                csv_list = self._collect_csv_paths_from_folder()
+                if not csv_list:
+                    QMessageBox.warning(
+                        self,
+                        "输入验证失败",
+                        "所选文件夹中未找到 .csv 文件，或目录无效。\n"
+                        "可勾选「包含子文件夹」以递归扫描。",
+                    )
+                    return
+                if not PIPELINE_AVAILABLE:
+                    QMessageBox.critical(self, "错误", "Pipeline 模块不可用，无法批量生成专题图。")
+                    return
+                work_dir = getattr(parent, "work_dir", None) or "./work_dir"
+                work_dir = str(work_dir)
+                base_kw = self._step10_base_pipeline_kwargs()
+                out_dir_opt = (self.output_dir.get_path() or "").strip() or None
+                self._start_batch_run(csv_list, work_dir, base_kw, out_dir_opt, parent)
+                return
+
+            # -------- GeoTIFF 栅格批量 --------
+            tif_list = self._collect_tif_paths_from_folder()
+            if not tif_list:
+                QMessageBox.warning(
+                    self,
+                    "输入验证失败",
+                    "所选文件夹中未找到 .tif / .bsq 文件，\n"
+                    "请确认目录包含步骤8输出的水色指数 GeoTIFF 文件。",
+                )
+                return
+
+            out_dir = (self.output_dir.get_path() or "").strip()
+            if not out_dir:
+                out_dir = os.path.join(self._get_default_work_dir(), "14_visualization")
+            os.makedirs(out_dir, exist_ok=True)
+
+            self.run_button.setEnabled(False)
+            self.progress_bar.setVisible(True)
+            self.progress_bar.setValue(0)
+            self._batch_thread = Step11GeoTIFFBatchThread(
+                tif_paths=tif_list,
+                output_dir=out_dir,
+                boundary_shp_path=boundary_shp_path,
+                input_crs=self.input_crs.text(),
+                output_crs=self.output_crs.text(),
+            )
+            main_win = parent
+
+            def _batch_log(msg, lvl):
+                if hasattr(main_win, "log_message"):
+                    main_win.log_message(msg, lvl)
+
+            def _on_progress(cur, total):
+                if total > 0:
+                    pct = int(cur / total * 100)
+                    self.progress_bar.setMaximum(total)
+                    self.progress_bar.setValue(cur)
+                    self.progress_bar.setFormat(f"{cur}/{total} 张 (%p%)")
+
+            self._batch_thread.log_message.connect(_batch_log, Qt.QueuedConnection)
+            self._batch_thread.progress.connect(_on_progress, Qt.QueuedConnection)
+            self._batch_thread.finished_ok.connect(self._on_step10_batch_ok, Qt.QueuedConnection)
+            self._batch_thread.failed.connect(self._on_step10_batch_fail, Qt.QueuedConnection)
+            self._batch_thread.finished.connect(
+                lambda: (self.run_button.setEnabled(True), self.progress_bar.setVisible(False)),
+                Qt.QueuedConnection,
+            )
+            self._batch_thread.start()
+            if hasattr(parent, "log_message"):
+                parent.log_message(f"GeoTIFF 批量渲染：共 {len(tif_list)} 个文件 → {out_dir}", "info")
+            return
+
+        # -------- GeoTIFF 栅格单文件模式 --------
+        if self.render_mode_combo.currentText() == "GeoTIFF 栅格模式":
+            geotiff_path = (self.geotiff_file.get_path() or "").strip()
+            if not geotiff_path:
+                QMessageBox.warning(self, "输入验证失败", "请选择水色指数 GeoTIFF 文件")
+                return
+            if not os.path.isfile(geotiff_path):
+                QMessageBox.warning(self, "输入验证失败", f"GeoTIFF 文件不存在:\n{geotiff_path}")
+                return
+
+            boundary_shp_path = self.boundary_file.get_path()
+            input_crs = self.input_crs.text()
+            output_crs = self.output_crs.text()
+
+            # 构造输出路径
+            out_dir = (self.output_dir.get_path() or "").strip()
+            if not out_dir:
+                out_dir = os.path.join(self._get_default_work_dir(), "14_visualization")
+                os.makedirs(out_dir, exist_ok=True)
+            tif_stem = Path(geotiff_path).stem
+            chinese_name = mapper._get_chinese_title(tif_stem)
+            output_png = os.path.join(out_dir, f"{chinese_name}_专题图.png")
+
+            self.run_button.setEnabled(False)
+            try:
+                from src.postprocessing.map import ContentMapper
+                mapper = ContentMapper()
+                result_path = mapper.visualize_raster(
+                    raster_tif_path=geotiff_path,
+                    output_file=output_png,
+                    boundary_shp_path=boundary_shp_path if boundary_shp_path else None,
+                    nodata_value=-9999.0,
+                    figsize=(14, 10),
+                    alpha=0.9,
+                )
+                self.run_button.setEnabled(True)
+                QMessageBox.information(
+                    self, "完成",
+                    f"GeoTIFF 栅格渲染完成！\n{result_path}"
+                )
+                if hasattr(parent, "log_message"):
+                    parent.log_message(f"Step10 GeoTIFF 渲染完成 → {result_path}", "info")
+            except Exception as e:
+                self.run_button.setEnabled(True)
+                QMessageBox.critical(self, "渲染失败", f"{e}\n{traceback.format_exc()[:500]}")
+                if hasattr(parent, "log_message"):
+                    parent.log_message(str(e), "error")
+            return
+
+        prediction_csv_path = (self.prediction_csv_file.get_path() or "").strip()
+        if not prediction_csv_path:
+            QMessageBox.warning(
+                self,
+                "输入验证失败",
+                "请选择「预测结果 CSV」文件，或切换到「文件夹批量」。",
+            )
+            return
+        if not os.path.isfile(prediction_csv_path):
+            QMessageBox.warning(self, "输入验证失败", "预测结果 CSV 不存在或不是文件")
+            return
+
+        config = self.get_config()
+        parent.run_single_step('step11_map', {'step11_map': config})
+
+    def _on_step10_batch_ok(self, n: int):
+        self.progress_bar.setVisible(False)
+        QMessageBox.information(self, "完成", f"已批量生成 {n} 个分布图。")
+        parent = self.parent()
+        while parent and not hasattr(parent, "log_message"):
+            parent = parent.parent()
+        if parent and hasattr(parent, "log_message"):
+            parent.log_message(f"专题图批量完成，共 {n} 个文件。", "info")
+
+    def _on_step10_batch_fail(self, err: str):
+        self.progress_bar.setVisible(False)
+        QMessageBox.critical(self, "失败", f"批量生成中断：\n{err[:900]}")
+        parent = self.parent()
+        while parent and not hasattr(parent, "log_message"):
+            parent = parent.parent()
+        if parent and hasattr(parent, "log_message"):
+            parent.log_message(err, "error")

src/gui/panels/step11_panel.py

@@ -1,226 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-Step11 面板 - 非经验模型预测
-"""
-
-import os
-from pathlib import Path
-
-from PyQt5.QtWidgets import (
-    QWidget, QVBoxLayout, QGroupBox, QFormLayout,
-    QPushButton, QCheckBox, QComboBox, QLineEdit, QMessageBox,
-    QFileDialog,
-)
-
-from src.gui.components.custom_widgets import FileSelectWidget
-from src.gui.styles import ModernStylesheet
-
-
-class Step11Panel(QWidget):
-    """步骤11：非经验模型预测"""
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.init_ui()
-
-    def init_ui(self):
-        layout = QVBoxLayout()
-
-        # 采样光谱CSV文件选择
-        self.sampling_csv_file = FileSelectWidget(
-            "采样光谱CSV:",
-            "CSV Files (*.csv);;All Files (*.*)"
-        )
-        layout.addWidget(self.sampling_csv_file)
-
-        # 模型目录选择
-        self.models_dir_file = FileSelectWidget(
-            "模型目录:",
-            "Directories;;All Files (*.*)"
-        )
-        self.models_dir_file.label.setText("模型目录:")
-        self.models_dir_file.browse_btn.clicked.disconnect()
-        self.models_dir_file.browse_btn.clicked.connect(self.browse_models_dir)
-        layout.addWidget(self.models_dir_file)
-
-        # 参数设置
-        params_group = QGroupBox("预测参数")
-        params_layout = QFormLayout()
-
-        self.metric = QComboBox()
-        self.metric.addItems(['Average Accuracy(%)', 'Min Accuracy(%)', 'Max Accuracy(%)'])
-        params_layout.addRow("模型选择指标:", self.metric)
-
-        self.prediction_column = QLineEdit()
-        self.prediction_column.setText("prediction")
-        params_layout.addRow("预测列名:", self.prediction_column)
-
-        params_group.setLayout(params_layout)
-        layout.addWidget(params_group)
-
-        # 输出路径
-        self.output_file = FileSelectWidget(
-            "输出文件夹:",
-            "Directories;;All Files (*.*)"
-        )
-        self.output_file.label.setText("输出文件夹:")
-        self.output_file.browse_btn.clicked.disconnect()
-        self.output_file.browse_btn.clicked.connect(self.browse_output_dir)
-        layout.addWidget(self.output_file)
-
-        # 启用步骤
-        self.enable_checkbox = QCheckBox("启用此步骤")
-        self.enable_checkbox.setChecked(True)
-        layout.addWidget(self.enable_checkbox)
-
-        # 独立运行按钮
-        self.run_button = QPushButton("独立运行此步骤")
-        self.run_button.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
-        self.run_button.clicked.connect(self.run_step)
-        layout.addWidget(self.run_button)
-
-        layout.addStretch()
-        self.setLayout(layout)
-
-    def update_from_config(self, work_dir=None, pipeline=None):
-        """从全局配置自动填充采样光谱和回归模型目录
-
-        Args:
-            work_dir: 工作目录路径
-            pipeline: Pipeline 实例（未使用，保留接口兼容性）
-        """
-        try:
-            import traceback
-
-            if work_dir:
-                self.work_dir = work_dir
-            elif hasattr(self, 'work_dir') and self.work_dir:
-                pass
-            else:
-                self.work_dir = None
-
-            main_window = self.window()
-
-            # 1. 尝试从 Step7 界面读取全湖采样点 CSV 路径
-            if main_window and hasattr(main_window, 'step7_panel'):
-                step7_widget = getattr(main_window.step7_panel, 'output_file', None)
-                step7_output_path = ""
-                if hasattr(step7_widget, 'get_path'):
-                    step7_output_path = step7_widget.get_path() or ""
-                elif hasattr(step7_widget, 'text'):
-                    step7_output_path = step7_widget.text() or ""
-
-                if step7_output_path:
-                    # 若为相对路径，使用 work_dir 合成为绝对路径
-                    if not os.path.isabs(step7_output_path):
-                        step7_output_path = os.path.join(self.work_dir or '', step7_output_path).replace('\\', '/')
-                    existing = self.sampling_csv_file.get_path()
-                    if not existing or not existing.strip():
-                        self.sampling_csv_file.set_path(step7_output_path)
-
-            # 2. 尝试从 Step8_Non_Empirical 界面读取回归模型目录
-            if main_window and hasattr(main_window, 'step8_non_empirical_panel'):
-                step8_non_empirical_widget = getattr(main_window.step8_non_empirical_panel, 'output_dir', None)
-                step8_non_empirical_models_dir = ""
-                if hasattr(step8_non_empirical_widget, 'get_path'):
-                    step8_non_empirical_models_dir = step8_non_empirical_widget.get_path() or ""
-                elif hasattr(step8_non_empirical_widget, 'text'):
-                    step8_non_empirical_models_dir = step8_non_empirical_widget.text() or ""
-
-                if step8_non_empirical_models_dir:
-                    # 若为相对路径，使用 work_dir 合成为绝对路径
-                    if not os.path.isabs(step8_non_empirical_models_dir):
-                        step8_non_empirical_models_dir = os.path.join(self.work_dir or '', step8_non_empirical_models_dir).replace('\\', '/')
-                    existing_models = self.models_dir_file.get_path()
-                    if not existing_models or not existing_models.strip():
-                        self.models_dir_file.set_path(step8_non_empirical_models_dir)
-
-            # 3. 自动填充输出路径（非经验模型预测目录）
-            if self.work_dir:
-                output_dir = os.path.join(self.work_dir, "11_12_13_predictions/Non_Empirical_Prediction")
-                os.makedirs(output_dir, exist_ok=True)
-                existing_out = self.output_file.get_path()
-                if not existing_out or not existing_out.strip():
-                    self.output_file.set_path(output_dir)
-        except Exception as e:
-            import traceback
-            print(f"【{self.__class__.__name__}】自动填充失败，跳过: {e}")
-            traceback.print_exc()
-
-    def _get_default_work_dir(self):
-        """获取 work_dir，优先用 panel 自身缓存的，否则尝试从主窗口取"""
-        if hasattr(self, 'work_dir') and self.work_dir:
-            return str(self.work_dir)
-        mw = self.window()
-        if mw and hasattr(mw, 'work_dir') and mw.work_dir:
-            return str(mw.work_dir)
-        return ""
-
-    def browse_models_dir(self):
-        """浏览模型目录"""
-        default = self._get_default_work_dir()
-        if default:
-            default = os.path.join(default, "8_Regression_Modeling")
-        dir_path = QFileDialog.getExistingDirectory(self, "选择模型目录", default)
-        if dir_path:
-            self.models_dir_file.set_path(dir_path)
-
-    def browse_output_dir(self):
-        """浏览输出目录"""
-        default = self._get_default_work_dir()
-        if default:
-            default = os.path.join(default, "11_12_13_predictions/Non_Empirical_Prediction")
-        dir_path = QFileDialog.getExistingDirectory(self, "选择输出文件夹", default)
-        if dir_path:
-            self.output_file.set_path(dir_path)
-
-    def get_config(self):
-        """获取配置"""
-        config = {
-            'metric': self.metric.currentText(),
-            'prediction_column': self.prediction_column.text(),
-            'enabled': self.enable_checkbox.isChecked()
-        }
-        sampling_csv_path = self.sampling_csv_file.get_path()
-        if sampling_csv_path:
-            config['sampling_csv_path'] = sampling_csv_path
-        models_dir = self.models_dir_file.get_path()
-        if models_dir:
-            config['models_dir'] = models_dir
-        output_path = self.output_file.get_path()
-        if output_path:
-            config['output_path'] = output_path
-        return config
-
-    def set_config(self, config):
-        """设置配置"""
-        if 'metric' in config:
-            idx = self.metric.findText(config['metric'])
-            if idx >= 0:
-                self.metric.setCurrentIndex(idx)
-        if 'prediction_column' in config:
-            self.prediction_column.setText(config['prediction_column'])
-        if 'sampling_csv_path' in config:
-            self.sampling_csv_file.set_path(config['sampling_csv_path'])
-        if 'models_dir' in config:
-            self.models_dir_file.set_path(config['models_dir'])
-        if 'enabled' in config:
-            self.enable_checkbox.setChecked(config['enabled'])
-
-    def run_step(self):
-        """独立运行步骤11"""
-        sampling_csv_path = self.sampling_csv_file.get_path()
-        if not sampling_csv_path:
-            QMessageBox.warning(self, "输入错误", "请选择采样光谱CSV文件！")
-            return
-
-        config = self.get_config()
-
-        parent = self.parent()
-        while parent and not hasattr(parent, 'run_single_step'):
-            parent = parent.parent()
-
-        if parent and hasattr(parent, 'run_single_step'):
-            parent.run_single_step('step11', {'step11': config})
-        else:
-            QMessageBox.critical(self, "错误", "无法找到父级GUI对象")

src/gui/panels/step12_viz_panel.py

@@ -1211,8 +1211,8 @@ class ChartBrowserDialog(QDialog):
                     QMessageBox.critical(self, "错误", f"保存失败:\n{str(e)}")
 
 
-class VisualizationPanel(QWidget):
-    """可视化分析面板 - 重构版：左侧目录树 + 右侧图像查看器"""
+class Step12VizPanel(QWidget):
+    """步骤12：可视化展示"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.work_dir = None

src/gui/panels/step13_panel.py

@@ -225,6 +225,6 @@ class Step12Panel(QWidget):
             parent = parent.parent()
 
         if parent and hasattr(parent, 'run_single_step'):
-            parent.run_single_step('step12', {'step12': config})
+            parent.run_single_step('step13_report', {'step13_report': config})
         else:
             QMessageBox.critical(self, "错误", "无法找到父级GUI对象")

src/gui/panels/step13_report_panel.py

@@ -79,8 +79,8 @@ class ReportGenerateThread(QThread):
             self.failed.emit(f"{e}\n{traceback.format_exc()}")
 
 
-class ReportGenerationPanel(QWidget):
-    """Word 报告生成面板。AI 配置统一由 AISettingsDialog 管理，本面板不持有配置状态。"""
+class Step13ReportPanel(QWidget):
+    """步骤13：分析报告生成。AI 配置统一由 AISettingsDialog 管理，本面板不持有配置状态。"""
 
     def __init__(self, main_window=None, parent=None):
         super().__init__(parent)

src/gui/panels/step14_panel.py

@@ -68,7 +68,7 @@ class Step14BatchThread(QThread):
                     kw["output_image_path"] = str(Path(self.output_dir_optional) / f"{stem}_distribution.png")
                 else:
                     kw["output_image_path"] = None
-                pipeline.step14_distribution_map(**kw)
+                pipeline.step10_map(**kw)
             self.finished_ok.emit(n)
         except Exception as e:
             self.failed.emit(f"{e}\n{traceback.format_exc()}")
@@ -122,9 +122,10 @@ class Step14GeoTIFFBatchThread(QThread):
             n = len(self.tif_paths)
             for i, tif_path in enumerate(self.tif_paths):
                 self.progress.emit(i + 1, n)
-                tif_name = Path(tif_path).stem
-                output_png = str(Path(self.output_dir) / f"{tif_name}_map.png")
-                self.log_message.emit(f"GeoTIFF 渲染 [{i + 1}/{n}] {tif_name}", "info")
+                tif_stem = Path(tif_path).stem
+                chinese_name = mapper._get_chinese_title(tif_stem)
+                output_png = str(Path(self.output_dir) / f"{chinese_name}_专题图.png")
+                self.log_message.emit(f"GeoTIFF 渲染 [{i + 1}/{n}] {tif_stem}", "info")
                 try:
                     mapper.visualize_raster(
                         raster_tif_path=tif_path,
@@ -132,7 +133,6 @@ class Step14GeoTIFFBatchThread(QThread):
                         boundary_shp_path=self.boundary_shp_path,
                         nodata_value=-9999.0,
                         figsize=(14, 10),
-                        title=f"水色指数专题图 - {tif_name}",
                         alpha=0.9,
                     )
                 except Exception as vis_err:
@@ -762,8 +762,9 @@ class Step14Panel(QWidget):
             if not out_dir:
                 out_dir = os.path.join(self._get_default_work_dir(), "14_visualization")
                 os.makedirs(out_dir, exist_ok=True)
-            tif_name = Path(geotiff_path).stem
-            output_png = os.path.join(out_dir, f"{tif_name}_rendered.png")
+            tif_stem = Path(geotiff_path).stem
+            chinese_name = mapper._get_chinese_title(tif_stem)
+            output_png = os.path.join(out_dir, f"{chinese_name}_专题图.png")
 
             self.run_button.setEnabled(False)
             try:
@@ -775,7 +776,6 @@ class Step14Panel(QWidget):
                     boundary_shp_path=boundary_shp_path if boundary_shp_path else None,
                     nodata_value=-9999.0,
                     figsize=(14, 10),
-                    title=f"水色指数专题图 - {tif_name}",
                     alpha=0.9,
                 )
                 self.run_button.setEnabled(True)

src/gui/panels/step4_sampling_panel.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Step10 面板 - 采样点生成
+Step4 面板 - 采样点布设
 """
 
 import os
@@ -16,8 +16,8 @@ from src.gui.dialogs import SamplingViewerDialog
 from src.gui.styles import ModernStylesheet
 
 
-class Step10Panel(QWidget):
-    """步骤10：采样点生成"""
+class Step4SamplingPanel(QWidget):
+    """步骤4：采样点布设"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.init_ui()
@@ -71,7 +71,7 @@ class Step10Panel(QWidget):
             "输出采样点:",
             "CSV Files (*.csv);;All Files (*.*)"
         )
-        self.output_file.line_edit.setPlaceholderText("sampling_points.csv")
+        self.output_file.line_edit.setPlaceholderText("sampling_spectra.csv")
         layout.addWidget(self.output_file)
 
         # 启用步骤
@@ -207,7 +207,7 @@ class Step10Panel(QWidget):
 
         # 3. 自动填充输出路径（绝对路径）
         if self.work_dir:
-            output_path = os.path.join(self.work_dir, "10_sampling", "sampling_spectra.csv")
+            output_path = os.path.join(self.work_dir, "4_sampling", "sampling_spectra.csv")
             os.makedirs(os.path.dirname(output_path), exist_ok=True)
             self.output_file.set_path(output_path.replace('\\', '/'))
 
@@ -215,7 +215,7 @@ class Step10Panel(QWidget):
         self._check_csv_exists()
 
     def run_step(self):
-        """独立运行步骤10"""
+        """独立运行步骤4"""
         deglint_img_path = self.deglint_img_file.get_path()
         if not deglint_img_path:
             QMessageBox.warning(self, "输入错误", "请选择去耀斑影像文件！")
@@ -223,8 +223,8 @@ class Step10Panel(QWidget):
 
         main_window = self.window()
         if hasattr(main_window, 'run_single_step'):
-            config = {'step10': self.get_config()}
-            main_window.run_single_step('step10', config)
+            config = {'step4_sampling': self.get_config()}
+            main_window.run_single_step('step4_sampling', config)
 
     def _check_csv_exists(self):
         """检查 output csv 是否存在，驱动预览按钮启停"""
@@ -243,7 +243,7 @@ class Step10Panel(QWidget):
         if not csv_path or not os.path.exists(csv_path):
             QMessageBox.warning(
                 self, "文件不存在",
-                f"采样点 CSV 文件不存在：{csv_path}\n请先运行步骤10生成数据。"
+                f"采样点 CSV 文件不存在：{csv_path}\n请先运行步骤4生成数据。"
             )
             return
         dialog = SamplingViewerDialog(csv_path, self)

src/gui/panels/step5_clean_panel.py

@@ -18,8 +18,8 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 
-class Step4Panel(QWidget):
-    """步骤4：数据预处理"""
+class Step5CleanPanel(QWidget):
+    """步骤5：数据清洗"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.init_ui()
@@ -135,18 +135,16 @@ class Step4Panel(QWidget):
             self.output_file.set_path("")
 
     def run_step(self):
-        """独立运行步骤4"""
-        # 验证输入
+        """独立运行步骤5"""
         csv_path = self.csv_file.get_path()
         if not csv_path:
             QMessageBox.warning(self, "输入错误", "请选择水质参数文件！")
             return
 
-        # 获取主窗口并运行步骤
         main_window = self.window()
         if hasattr(main_window, 'run_single_step'):
-            config = {'step4': self.get_config()}
-            main_window.run_single_step('step4', config)
+            config = {'step5_clean': self.get_config()}
+            main_window.run_single_step('step5_clean', config)
 
     def reset_preview(self, message="请选择CSV文件并点击刷新预览"):
         """重置预览表格"""

src/gui/panels/step6_feature_panel.py

@@ -17,8 +17,8 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 
-class Step5Panel(QWidget):
-    """步骤5：光谱提取"""
+class Step6FeaturePanel(QWidget):
+    """步骤6：光谱特征提取"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.init_ui()
@@ -124,7 +124,7 @@ class Step5Panel(QWidget):
         glint_mask_path = self.glint_mask_file.get_path()
         if glint_mask_path:
             config['glint_mask_path'] = glint_mask_path
-        # 注意：step5_extract_training_spectra 不接受 output_path / training_csv_path
+        # 注意：step6_extract_spectra 不接受 output_path / training_csv_path
         # 参数，输出路径由 pipeline 内部根据 training_spectra_dir 自动生成。
         return config
 
@@ -202,8 +202,8 @@ class Step5Panel(QWidget):
 
         # 5. 尝试从 Step4 界面读取已处理的水质参数 CSV 路径，自动填入本面板
         main_window = self.window()
-        if main_window and hasattr(main_window, 'step4_panel'):
-            step4_output_path = main_window.step4_panel.output_file.get_path()
+        if main_window and hasattr(main_window, 'step5_panel'):
+            step4_output_path = main_window.step5_panel.output_file.get_path()
             if step4_output_path:
                 # 若为相对路径，使用 work_dir 合成为绝对路径
                 if not os.path.isabs(step4_output_path):
@@ -235,5 +235,5 @@ class Step5Panel(QWidget):
         # 获取主窗口并运行步骤
         main_window = self.window()
         if hasattr(main_window, 'run_single_step'):
-            config = {'step5': self.get_config()}
-            main_window.run_single_step('step5', config)
+            config = {'step6_feature': self.get_config()}
+            main_window.run_single_step('step6_feature', config)

src/gui/panels/step7_index_panel.py

@@ -1,3 +1,9 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Step7 面板 - 水质指数计算
+"""
+
 import os
 import sys
 import pandas as pd
@@ -35,7 +41,7 @@ def get_resource_path(relative_path: str) -> str:
     return str(base_dir / os.path.basename(relative_path))
 
 
-class Step6Panel(QWidget):
+class Step7IndexPanel(QWidget):
     COLOR_RATIO = QColor(255, 255, 255)
     COLOR_CONCENTRATION = QColor(220, 240, 255)
     COLOR_HEADER = QColor(245, 245, 245)
@@ -291,8 +297,8 @@ class Step6Panel(QWidget):
         if work_dir:
             self.work_dir = work_dir
         main = self.window()
-        if hasattr(main, 'step5_panel'):
-            p5 = main.step5_panel.output_file.get_path()
+        if hasattr(main, 'step6_panel'):
+            p5 = main.step6_panel.output_file.get_path()
             if p5:
                 if not os.path.isabs(p5):
                     p5 = os.path.join(self.work_dir or '', p5)

src/gui/panels/step8_ml_train_panel.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Step7 面板 - 机器学习建模
+Step8 面板 - 机器学习建模
 """
 
 import os
@@ -68,8 +68,8 @@ SPLIT_CHINESE = {
 }
 
 
-class Step7Panel(QWidget):
-    """步骤7：机器学习建模"""
+class Step8MlTrainPanel(QWidget):
+    """步骤8：机器学习建模"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.init_ui()
@@ -392,7 +392,7 @@ class Step7Panel(QWidget):
             self.output_path.set_path("")
 
     def run_step(self):
-        """独立运行步骤7"""
+        """独立运行步骤8"""
         training_csv_path = self.training_csv_file.get_path()
         if not training_csv_path:
             QMessageBox.warning(self, "输入错误", "请选择训练数据CSV文件！")
@@ -400,8 +400,8 @@ class Step7Panel(QWidget):
 
         main_window = self.window()
         if hasattr(main_window, 'run_single_step'):
-            config = {'step7': self.get_config()}
-            main_window.run_single_step('step7', config)
+            config = {'step8_ml_train': self.get_config()}
+            main_window.run_single_step('step8_ml_train', config)
 
     def get_training_params(self):
         """获取模型训练参数"""

src/gui/panels/step8_panel.py

@@ -1,424 +0,0 @@
-import os
-import sys
-import pandas as pd
-import numpy as np
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-from PyQt5.QtWidgets import (
-    QWidget, QVBoxLayout, QGroupBox, QGridLayout,
-    QHBoxLayout, QLabel, QCheckBox, QPushButton, QMessageBox,
-    QScrollArea, QListWidget, QListWidgetItem, QAbstractItemView,
-    QRadioButton, QButtonGroup
-)
-from PyQt5.QtCore import Qt
-from PyQt5.QtGui import QColor, QBrush, QFont
-
-from src.gui.components.custom_widgets import FileSelectWidget
-from src.gui.styles import ModernStylesheet
-
-
-def get_resource_path(relative_path: str) -> str:
-    """适配开发与 PyInstaller 环境的路径获取逻辑。"""
-    if hasattr(sys, '_MEIPASS'):
-        internal = os.path.join(sys._MEIPASS, '_internal', relative_path)
-        if os.path.exists(internal):
-            return internal
-        return os.path.join(sys._MEIPASS, relative_path)
-
-    exe_dir = os.path.dirname(sys.executable)
-    internal = os.path.join(exe_dir, '_internal', relative_path)
-    if os.path.exists(internal):
-        return internal
-
-    base_dir = Path(__file__).resolve().parent.parent / "model"
-    return str(base_dir / os.path.basename(relative_path))
-
-
-class Step8Panel(QWidget):
-    COLOR_RATIO = QColor(255, 255, 255)
-    COLOR_CONCENTRATION = QColor(220, 240, 255)
-    COLOR_HEADER = QColor(245, 245, 245)
-
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.index_checkboxes: Dict[str, QListWidgetItem] = {}
-        self.work_dir: Optional[str] = None
-        self.builtin_formula_path = get_resource_path("waterindex.csv")
-        self._formula_type_map: Dict[str, str] = {}
-        self._formula_color_map: Dict[str, QColor] = {}
-        self._formula_coef_map: Dict[str, List[float]] = {}
-
-        self.init_ui()
-        self._auto_load_formulas()
-
-    def init_ui(self):
-        main_layout = QVBoxLayout()
-        main_layout.setContentsMargins(20, 20, 20, 20)
-        main_layout.setSpacing(10)
-
-        # 1. 公式配置源 (只读)
-        path_group = QGroupBox("公式配置源 (内置)")
-        path_layout = QVBoxLayout()
-        self.formula_csv_widget = FileSelectWidget("内置CSV路径:", "CSV Files (*.csv)")
-        self.formula_csv_widget.set_path(self.builtin_formula_path)
-        self.formula_csv_widget.set_read_only(True)
-        self.formula_csv_widget.line_edit.setStyleSheet("background-color: #f0f0f0; color: #666;")
-        path_layout.addWidget(self.formula_csv_widget)
-        path_group.setLayout(path_layout)
-        main_layout.addWidget(path_group)
-
-        # 2. 训练数据输入
-        input_group = QGroupBox("输入样本数据")
-        input_layout = QVBoxLayout()
-        self.training_data_widget = FileSelectWidget("特征提取CSV:", "CSV Files (*.csv)")
-        input_layout.addWidget(self.training_data_widget)
-        input_group.setLayout(input_layout)
-        main_layout.addWidget(input_group)
-
-        # 3. 公式选择区 (分组 ListWidget)
-        self.formula_group = QGroupBox("待计算水质指数勾选")
-        formula_outer_layout = QVBoxLayout()
-
-        btn_layout = QHBoxLayout()
-        self.select_all_btn = QPushButton("全选")
-        self.deselect_all_btn = QPushButton("清空")
-        self.select_ratio_btn = QPushButton("仅选比值型")
-        self.select_conc_btn = QPushButton("仅选浓度型")
-        self.select_all_btn.clicked.connect(self.select_all_formulas)
-        self.deselect_all_btn.clicked.connect(self.deselect_all_formulas)
-        self.select_ratio_btn.clicked.connect(self._select_ratio_only)
-        self.select_conc_btn.clicked.connect(self._select_conc_only)
-        btn_layout.addWidget(self.select_all_btn)
-        btn_layout.addWidget(self.deselect_all_btn)
-        btn_layout.addWidget(self.select_ratio_btn)
-        btn_layout.addWidget(self.select_conc_btn)
-        btn_layout.addStretch()
-
-        self.refresh_button = QPushButton("重新加载")
-        self.refresh_button.clicked.connect(lambda: self.refresh_formulas(silent=False))
-        btn_layout.addWidget(self.refresh_button)
-
-        formula_outer_layout.addLayout(btn_layout)
-
-        scroll = QScrollArea()
-        scroll.setWidgetResizable(True)
-        scroll.setMinimumHeight(280)
-        self.scroll_content = QWidget()
-        self.formula_layout = QVBoxLayout(self.scroll_content)
-        self.formula_layout.setContentsMargins(4, 4, 4, 4)
-        self.formula_layout.setSpacing(2)
-        self.formula_layout.setAlignment(Qt.AlignTop)
-
-        self.formula_list = QListWidget()
-        self.formula_list.setSelectionMode(QAbstractItemView.MultiSelection)
-        self.formula_list.itemChanged.connect(self._on_item_changed)
-        self.formula_layout.addWidget(self.formula_list)
-
-        scroll.setWidget(self.scroll_content)
-        formula_outer_layout.addWidget(scroll)
-
-        self.formula_group.setLayout(formula_outer_layout)
-        main_layout.addWidget(self.formula_group)
-
-        # 4. 输出选项
-        output_group = QGroupBox("输出模式")
-        output_layout = QVBoxLayout()
-
-        mode_layout = QHBoxLayout()
-        self.mode_group = QButtonGroup()
-        self.radio_both = QRadioButton("两者皆出")
-        self.radio_wide = QRadioButton("仅宽表")
-        self.radio_single = QRadioButton("仅单文件")
-        self.mode_group.addButton(self.radio_both, 0)
-        self.mode_group.addButton(self.radio_wide, 1)
-        self.mode_group.addButton(self.radio_single, 2)
-        self.radio_both.setChecked(True)
-        mode_layout.addWidget(self.radio_both)
-        mode_layout.addWidget(self.radio_wide)
-        mode_layout.addWidget(self.radio_single)
-        mode_layout.addStretch()
-        output_layout.addLayout(mode_layout)
-
-        self.enable_checkbox = QCheckBox("启用计算流程")
-        self.enable_checkbox.setChecked(True)
-        output_layout.addWidget(self.enable_checkbox)
-
-        output_group.setLayout(output_layout)
-        main_layout.addWidget(output_group)
-
-        # 5. 运行按钮
-        self.run_button = QPushButton("立即执行计算")
-        self.run_button.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
-        self.run_button.setMinimumHeight(40)
-        self.run_button.clicked.connect(self.run_step)
-        main_layout.addWidget(self.run_button)
-
-        self.setLayout(main_layout)
-
-    def _on_item_changed(self, item: QListWidgetItem):
-        if item.checkState() == Qt.Checked:
-            bg_color = self.COLOR_RATIO
-            for name, ref_item in self.index_checkboxes.items():
-                if ref_item is item:
-                    bg_color = self._formula_color_map.get(name, self.COLOR_RATIO)
-                    break
-            item.setBackground(QBrush(bg_color))
-        else:
-            item.setBackground(QBrush(self.COLOR_RATIO))
-
-    def _auto_load_formulas(self):
-        if os.path.exists(self.builtin_formula_path):
-            self.refresh_formulas(silent=True)
-        else:
-            print(f"DEBUG: 自动加载失败，路径不存在: {self.builtin_formula_path}")
-
-    def refresh_formulas(self, silent=False):
-        path = self.builtin_formula_path
-        if not os.path.exists(path):
-            if not silent:
-                QMessageBox.warning(self, "错误", f"找不到内置公式文件:\n{path}")
-            return
-
-        try:
-            df = None
-            for enc in ('utf-8', 'gbk', 'utf-8-sig'):
-                try:
-                    df = pd.read_csv(path, encoding=enc)
-                    if 'Formula_Name' in df.columns:
-                        break
-                except Exception:
-                    continue
-
-            if df is None or 'Formula_Name' not in df.columns:
-                if not silent:
-                    QMessageBox.critical(self, "错误", "CSV缺少 'Formula_Name' 列")
-                return
-
-            self._formula_type_map.clear()
-            self._formula_coef_map.clear()
-            for _, row in df.iterrows():
-                name = str(row['Formula_Name']).strip()
-                if not name:
-                    continue
-                ftype = str(row.get('Formula_Type', 'ratio')).strip().lower()
-                self._formula_type_map[name] = ftype
-
-                # Parse Coefficient for concentration formulas
-                coef_str = str(row.get('Coefficient', '')).strip()
-                if coef_str:
-                    try:
-                        coeffs = [float(c.strip()) for c in coef_str.split(',') if c.strip()]
-                        self._formula_coef_map[name] = coeffs
-                    except Exception:
-                        self._formula_coef_map[name] = []
-                else:
-                    self._formula_coef_map[name] = []
-
-            self.formula_list.clear()
-            self.index_checkboxes.clear()
-
-            self._formula_color_map.clear()
-            for name, ftype in self._formula_type_map.items():
-                item = QListWidgetItem(name, self.formula_list)
-                item.setCheckState(Qt.Checked)
-                if ftype == 'concentration':
-                    bg_color = QColor(220, 240, 255)
-                else:
-                    bg_color = self.COLOR_RATIO
-                self._formula_color_map[name] = bg_color
-                item.setBackground(QBrush(bg_color))
-                self.index_checkboxes[name] = item
-
-            self.formula_list.adjustSize()
-            print(f"✅ 加载 {len(self.index_checkboxes)} 个公式")
-
-        except Exception as e:
-            if not silent:
-                QMessageBox.critical(self, "加载失败", f"原因: {str(e)}")
-
-    def _select_ratio_only(self):
-        for name, item in self.index_checkboxes.items():
-            ftype = self._formula_type_map.get(name, 'ratio')
-            item.setCheckState(Qt.Checked if ftype == 'ratio' else Qt.Unchecked)
-
-    def _select_conc_only(self):
-        for name, item in self.index_checkboxes.items():
-            ftype = self._formula_type_map.get(name, 'ratio')
-            item.setCheckState(Qt.Checked if ftype == 'concentration' else Qt.Unchecked)
-
-    def select_all_formulas(self):
-        for item in self.index_checkboxes.values():
-            item.setCheckState(Qt.Checked)
-
-    def deselect_all_formulas(self):
-        for item in self.index_checkboxes.values():
-            item.setCheckState(Qt.Unchecked)
-
-    def get_config(self) -> Dict:
-        selected = [
-            name for name, item in self.index_checkboxes.items()
-            if item.checkState() == Qt.Checked
-        ]
-        # Build coefficient dict for selected formulas
-        formula_coefficients = {
-            name: self._formula_coef_map.get(name, [])
-            for name in selected
-        }
-        return {
-            'training_csv_path': self.training_data_widget.get_path(),
-            'formula_csv_file': self.builtin_formula_path,
-            'formula_names': selected,
-            'formula_coefficients': formula_coefficients,
-            'enabled': self.enable_checkbox.isChecked(),
-            'output_mode': self.mode_group.checkedId(),
-        }
-
-    def set_config(self, config: Dict):
-        if 'training_csv_path' in config:
-            self.training_data_widget.set_path(config['training_csv_path'])
-        if 'formula_names' in config:
-            sel = set(config['formula_names'])
-            for name, item in self.index_checkboxes.items():
-                item.setCheckState(Qt.Checked if name in sel else Qt.Unchecked)
-        self.enable_checkbox.setChecked(config.get('enabled', True))
-        if 'output_mode' in config:
-            btn = self.mode_group.button(config['output_mode'])
-            if btn:
-                btn.setChecked(True)
-
-    def update_from_config(self, work_dir=None, pipeline=None):
-        if work_dir:
-            self.work_dir = work_dir
-        main = self.window()
-        if hasattr(main, 'step5_panel'):
-            p5 = main.step5_panel.output_file.get_path()
-            if p5:
-                if not os.path.isabs(p5):
-                    p5 = os.path.join(self.work_dir or '', p5)
-                p5 = p5.replace('\\', '/')
-                self.training_data_widget.set_path(p5)
-
-    def _get_work_dir(self) -> Optional[str]:
-        if self.work_dir:
-            return self.work_dir
-        main = self.window()
-        if hasattr(main, 'work_dir') and main.work_dir:
-            return main.work_dir
-        return None
-
-    def _get_coord_cols(self, df: pd.DataFrame) -> Tuple[str, str]:
-        coord_candidates = ['lon', 'lng', 'longitude', '经度', 'x', 'lon_utm', 'utm_x', 'pixel_x']
-        lat_candidates = ['lat', 'latitude', '纬度', 'y', 'lat_utm', 'utm_y', 'pixel_y']
-
-        x_col, y_col = None, None
-        for col in df.columns:
-            cl = col.lower()
-            if x_col is None and any(c in cl for c in coord_candidates):
-                x_col = col
-            if y_col is None and any(c in cl for c in lat_candidates):
-                y_col = col
-
-        if x_col is None and len(df.columns) >= 2:
-            x_col = df.columns[0]
-        if y_col is None and len(df.columns) >= 2:
-            y_col = df.columns[1]
-
-        return x_col or 'x_coord', y_col or 'y_coord'
-
-    def run_step(self):
-        config = self.get_config()
-
-        if not config['enabled']:
-            QMessageBox.information(self, "提示", "已禁用计算流程（启用计算流程未勾选）")
-            return
-
-        training_path = config['training_csv_path']
-        if not training_path or not os.path.exists(training_path):
-            QMessageBox.warning(self, "提示", "请先选择输入特征提取CSV文件")
-            return
-
-        formula_names = config['formula_names']
-        if not formula_names:
-            QMessageBox.warning(self, "提示", "请至少勾选一个公式")
-            return
-
-        output_mode = config['output_mode']
-
-        try:
-            from src.core.steps.data_preparation_step import DataPreparationStep
-
-            spec_df = pd.read_csv(training_path)
-            x_col, y_col = self._get_coord_cols(spec_df)
-
-            # 构建 formula_csv_path（使用内置 waterindex.csv）
-            import os
-            formula_csv_path = self.builtin_formula_path
-            if not formula_csv_path or not os.path.exists(formula_csv_path):
-                # 尝试从 src/gui/model/ 目录找
-                possible_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'gui', 'model', 'waterindex.csv')
-                if os.path.exists(possible_path):
-                    formula_csv_path = possible_path
-
-            work_dir = self._get_work_dir()
-
-            # 调用 DataPreparationStep 的静态方法计算水质指数（宽表输出）
-            indices_csv_path = DataPreparationStep.calculate_water_quality_indices(
-                training_csv_path=training_path,
-                formula_csv_file=formula_csv_path,
-                formula_names=formula_names,
-                output_file=None,  # 不在此处指定输出，由下面的双轨输出逻辑接管
-                enabled=True,
-                output_dir=work_dir if work_dir else os.getcwd(),
-            )
-
-            # 读取计算结果（宽表）
-            if indices_csv_path and os.path.exists(indices_csv_path):
-                output_df = pd.read_csv(indices_csv_path)
-            else:
-                output_df = spec_df  # fallback
-
-            track_a_path = None
-            track_b_dir = None
-
-            if output_mode in (0, 1):
-                track_a_dir = os.path.join(work_dir, "6_water_quality_indices") if work_dir else "6_water_quality_indices"
-                os.makedirs(track_a_dir, exist_ok=True)
-                track_a_path = os.path.join(track_a_dir, "training_spectra_indices.csv")
-
-            if output_mode in (0, 2):
-                track_b_dir = os.path.join(work_dir, "11_12_13_predictions", "Traditional_Indices") if work_dir else "11_12_13_predictions/Traditional_Indices"
-                os.makedirs(track_b_dir, exist_ok=True)
-
-            saved = []
-            if output_mode in (0, 1):
-                output_df.to_csv(track_a_path, index=False, float_format='%.6f')
-                saved.append(f"宽表: {track_a_path}")
-
-            if output_mode in (0, 2):
-                coord_x = spec_df[x_col].values if x_col in spec_df.columns else np.arange(len(spec_df))
-                coord_y = spec_df[y_col].values if y_col in spec_df.columns else np.zeros(len(spec_df))
-
-                for formula_name in formula_names:
-                    if formula_name not in output_df.columns:
-                        continue
-                    single_df = pd.DataFrame({
-                        'x_coord': coord_x,
-                        'y_coord': coord_y,
-                        'value': output_df[formula_name].values,
-                    })
-                    safe_name = formula_name.replace('/', '_').replace(' ', '_')
-                    out_path = os.path.join(track_b_dir, f"{safe_name}_prediction.csv")
-                    single_df.to_csv(out_path, index=False, float_format='%.6f')
-                saved.append(f"单文件目录: {track_b_dir}")
-
-            QMessageBox.information(
-                self, "计算完成",
-                f"已保存 {len(saved)} 个输出目标:\n" + "\n".join(saved)
-            )
-
-        except ImportError as e:
-            QMessageBox.critical(self, "依赖错误", f"无法导入模块:\n{e}")
-        except Exception as e:
-            import traceback
-            QMessageBox.critical(self, "计算失败", f"原因: {str(e)}\n{traceback.format_exc()}")

src/gui/panels/step9_ml_predict_panel.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Step8 面板 - 机器学习预测
+Step11 面板 - 机器学习预测
 """
 
 import os
@@ -19,8 +19,8 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 
-class Step11MlPanel(QWidget):
-    """步骤11：机器学习预测"""
+class Step9MlPredictPanel(QWidget):
+    """步骤9：机器学习预测"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.external_models_dict = {}   # {subdir_name: model_obj, ...}
@@ -190,7 +190,7 @@ class Step11MlPanel(QWidget):
         """浏览模型母文件夹，自动扫描子目录中的 .joblib 文件"""
         default = self._get_default_work_dir()
         if default:
-            default = os.path.join(default, "7_Supervised_Model_Training")
+            default = os.path.join(default, "9_supervised_modeling")
         dir_path = QFileDialog.getExistingDirectory(
             self,
             "选择模型母文件夹",
@@ -216,7 +216,6 @@ class Step11MlPanel(QWidget):
                 ]
                 if not joblib_files:
                     continue
-                # 每个子目录只取第一个 .joblib 文件（与 batch 逻辑一致）
                 joblib_path = joblib_files[0].path
                 try:
                     loaded = joblib.load(joblib_path)
@@ -319,43 +318,41 @@ class Step11MlPanel(QWidget):
 
             main_window = self.window()
 
-            # 1. 尝试从 Step7 界面读取全湖采样点 CSV 路径
-            if main_window and hasattr(main_window, 'step10_panel'):
-                step7_widget = getattr(main_window.step10_panel, 'output_file', None)
-                step7_output_path = ""
-                if hasattr(step7_widget, 'get_path'):
-                    step7_output_path = step7_widget.get_path() or ""
-                elif hasattr(step7_widget, 'text'):
-                    step7_output_path = step7_widget.text() or ""
+            # 1. 尝试从 Step4（采样点布设）读取全湖采样点 CSV 路径
+            if main_window and hasattr(main_window, 'step4_sampling_panel'):
+                step4_widget = getattr(main_window.step4_sampling_panel, 'output_file', None)
+                step4_output_path = ""
+                if hasattr(step4_widget, 'get_path'):
+                    step4_output_path = step4_widget.get_path() or ""
+                elif hasattr(step4_widget, 'text'):
+                    step4_output_path = step4_widget.text() or ""
 
-                if step7_output_path:
-                    # 若为相对路径，使用 work_dir 合成为绝对路径
-                    if not os.path.isabs(step7_output_path):
-                        step7_output_path = os.path.join(self.work_dir or '', step7_output_path).replace('\\', '/')
+                if step4_output_path:
+                    if not os.path.isabs(step4_output_path):
+                        step4_output_path = os.path.join(self.work_dir or '', step4_output_path).replace('\\', '/')
                     existing = self.sampling_csv_file.get_path()
                     if not existing or not existing.strip():
-                        self.sampling_csv_file.set_path(step7_output_path)
+                        self.sampling_csv_file.set_path(step4_output_path)
 
-            # 2. 尝试从 Step6 界面读取监督模型目录
-            if main_window and hasattr(main_window, 'step7_panel'):
-                step6_widget = getattr(main_window.step7_panel, 'output_dir', None)
-                step6_models_dir = ""
-                if hasattr(step6_widget, 'get_path'):
-                    step6_models_dir = step6_widget.get_path() or ""
-                elif hasattr(step6_widget, 'text'):
-                    step6_models_dir = step6_widget.text() or ""
+            # 2. 尝试从 Step9（监督建模）读取模型目录
+            if main_window and hasattr(main_window, 'step9_panel'):
+                step9_widget = getattr(main_window.step9_panel, 'output_dir', None)
+                step9_models_dir = ""
+                if hasattr(step9_widget, 'get_path'):
+                    step9_models_dir = step9_widget.get_path() or ""
+                elif hasattr(step9_widget, 'text'):
+                    step9_models_dir = step9_widget.text() or ""
 
-                if step6_models_dir:
-                    # 若为相对路径，使用 work_dir 合成为绝对路径
-                    if not os.path.isabs(step6_models_dir):
-                        step6_models_dir = os.path.join(self.work_dir or '', step6_models_dir).replace('\\', '/')
+                if step9_models_dir:
+                    if not os.path.isabs(step9_models_dir):
+                        step9_models_dir = os.path.join(self.work_dir or '', step9_models_dir).replace('\\', '/')
                     existing_models = self.models_dir_file.get_path()
                     if not existing_models or not existing_models.strip():
-                        self.models_dir_file.set_path(step6_models_dir)
+                        self.models_dir_file.set_path(step9_models_dir)
 
             # 3. 自动填充输出路径（机器学习预测目录）
             if self.work_dir:
-                output_dir = os.path.join(self.work_dir, "11_12_13_predictions/Machine_Learning_Prediction")
+                output_dir = os.path.join(self.work_dir, "11_ml_prediction")
                 os.makedirs(output_dir, exist_ok=True)
                 existing_out = self.output_file.get_path()
                 if not existing_out or not existing_out.strip():
@@ -378,7 +375,7 @@ class Step11MlPanel(QWidget):
         """浏览模型目录"""
         default = self._get_default_work_dir()
         if default:
-            default = os.path.join(default, "7_Supervised_Model_Training")
+            default = os.path.join(default, "9_supervised_modeling")
         dir_path = QFileDialog.getExistingDirectory(self, "选择模型目录", default)
         if dir_path:
             self.models_dir_file.set_path(dir_path)
@@ -416,7 +413,7 @@ class Step11MlPanel(QWidget):
             self.output_file.set_path(config['output_path'])
 
     def run_step(self):
-        """独立运行步骤8"""
+        """独立运行步骤11"""
         sampling_csv_path = self.sampling_csv_file.get_path()
         if not sampling_csv_path:
             QMessageBox.warning(self, "输入错误", "请选择采样光谱CSV文件！")
@@ -431,7 +428,6 @@ class Step11MlPanel(QWidget):
                     "请先点击「浏览...」按钮选择模型母文件夹！",
                 )
                 return
-            # 只传递用户勾选的模型
             checked_dict = self._get_checked_models_dict()
             if not checked_dict:
                 QMessageBox.warning(
@@ -443,11 +439,11 @@ class Step11MlPanel(QWidget):
             main_window = self.window()
             if hasattr(main_window, 'run_single_step'):
                 config = {
-                    'step11_ml': self.get_config(),
+                    'step9_ml_predict': self.get_config(),
                     '_external_models_dict': checked_dict,
                     '_external_model_dir': self.external_model_dir,
                 }
-                main_window.run_single_step('step11_ml', config)
+                main_window.run_single_step('step9_ml_predict', config)
             return
 
         # 默认流程：使用模型目录
@@ -458,5 +454,5 @@ class Step11MlPanel(QWidget):
 
         main_window = self.window()
         if hasattr(main_window, 'run_single_step'):
-            config = {'step11_ml': self.get_config()}
-            main_window.run_single_step('step11_ml', config)
+            config = {'step9_ml_predict': self.get_config()}
+            main_window.run_single_step('step9_ml_predict', config)

src/gui/panels/step9_panel.py

@@ -1,400 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-Step9 面板 - 自定义回归分析
-"""
-
-import os
-from typing import Dict
-
-import pandas as pd
-from PyQt5.QtWidgets import (
-    QWidget, QVBoxLayout, QGroupBox, QFormLayout, QGridLayout,
-    QHBoxLayout, QLabel, QLineEdit, QCheckBox, QPushButton,
-    QScrollArea, QMessageBox,
-)
-
-from src.gui.components.custom_widgets import FileSelectWidget
-from src.gui.styles import ModernStylesheet
-
-
-class Step9Panel(QWidget):
-    """步骤9：自定义回归分析"""
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.x_column_checkboxes: Dict[str, QCheckBox] = {}
-        self.y_column_checkboxes: Dict[str, QCheckBox] = {}
-        self.method_checkboxes: Dict[str, QCheckBox] = {}
-        self.csv_columns = []
-        self.init_ui()
-
-    def init_ui(self):
-        layout = QVBoxLayout()
-
-        hint = QLabel("指定自变量与因变量列，批量尝试不同回归方法")
-        hint.setStyleSheet("color: #666; font-size: 11px;")
-        layout.addWidget(hint)
-
-        # CSV文件选择
-        csv_group = QGroupBox("数据文件")
-        csv_layout = QVBoxLayout()
-
-        self.csv_file = FileSelectWidget(
-            "输入CSV文件:",
-            "CSV Files (*.csv);;All Files (*.*)"
-        )
-        self.csv_file.line_edit.textChanged.connect(self.on_csv_file_changed)
-        csv_layout.addWidget(self.csv_file)
-
-        self.refresh_btn = QPushButton("刷新列信息")
-        self.refresh_btn.clicked.connect(self.refresh_csv_columns)
-        csv_layout.addWidget(self.refresh_btn)
-
-        csv_group.setLayout(csv_layout)
-        layout.addWidget(csv_group)
-
-        # 自变量选择
-        x_group = QGroupBox("自变量列选择 (可多选)")
-        x_layout = QVBoxLayout()
-
-        x_scroll = QScrollArea()
-        x_scroll.setWidgetResizable(True)
-        x_scroll.setMinimumHeight(250)
-        x_scroll.setMaximumHeight(350)
-
-        x_widget = QWidget()
-        self.x_columns_layout = QGridLayout()
-        x_widget.setLayout(self.x_columns_layout)
-
-        x_scroll.setWidget(x_widget)
-        x_layout.addWidget(x_scroll)
-
-        x_btn_layout = QHBoxLayout()
-        self.x_select_all = QPushButton("全选")
-        self.x_deselect_all = QPushButton("全不选")
-        self.x_select_all.clicked.connect(lambda: self.toggle_checkboxes(self.x_column_checkboxes, True))
-        self.x_deselect_all.clicked.connect(lambda: self.toggle_checkboxes(self.x_column_checkboxes, False))
-        x_btn_layout.addWidget(self.x_select_all)
-        x_btn_layout.addWidget(self.x_deselect_all)
-        x_btn_layout.addStretch()
-        x_layout.addLayout(x_btn_layout)
-
-        x_group.setLayout(x_layout)
-        layout.addWidget(x_group)
-
-        # 因变量选择
-        y_group = QGroupBox("因变量列选择 (可多选)")
-        y_layout = QVBoxLayout()
-
-        y_scroll = QScrollArea()
-        y_scroll.setWidgetResizable(True)
-        y_scroll.setMinimumHeight(200)
-        y_scroll.setMaximumHeight(300)
-
-        y_widget = QWidget()
-        self.y_columns_layout = QGridLayout()
-        y_widget.setLayout(self.y_columns_layout)
-
-        y_scroll.setWidget(y_widget)
-        y_layout.addWidget(y_scroll)
-
-        y_btn_layout = QHBoxLayout()
-        self.y_select_all = QPushButton("全选")
-        self.y_deselect_all = QPushButton("全不选")
-        self.y_select_all.clicked.connect(lambda: self.toggle_checkboxes(self.y_column_checkboxes, True))
-        self.y_deselect_all.clicked.connect(lambda: self.toggle_checkboxes(self.y_column_checkboxes, False))
-        y_btn_layout.addWidget(self.y_select_all)
-        y_btn_layout.addWidget(self.y_deselect_all)
-        y_btn_layout.addStretch()
-        y_layout.addLayout(y_btn_layout)
-
-        y_group.setLayout(y_layout)
-        layout.addWidget(y_group)
-
-        # 回归方法选择
-        method_group = QGroupBox("回归方法选择 (可多选)")
-        method_layout = QVBoxLayout()
-
-        method_grid = QGridLayout()
-        regression_methods = [
-            'linear', 'exponential', 'power', 'logarithmic',
-            'polynomial', 'hyperbolic', 'sigmoidal'
-        ]
-
-        for i, method in enumerate(regression_methods):
-            checkbox = QCheckBox(method)
-            if method in ['linear', 'exponential', 'power', 'logarithmic']:
-                checkbox.setChecked(True)
-            self.method_checkboxes[method] = checkbox
-            method_grid.addWidget(checkbox, i // 3, i % 3)
-
-        method_layout.addLayout(method_grid)
-
-        method_btn_layout = QHBoxLayout()
-        self.method_select_all = QPushButton("全选")
-        self.method_deselect_all = QPushButton("全不选")
-        self.method_select_all.clicked.connect(lambda: self.toggle_checkboxes(self.method_checkboxes, True))
-        self.method_deselect_all.clicked.connect(lambda: self.toggle_checkboxes(self.method_checkboxes, False))
-        method_btn_layout.addWidget(self.method_select_all)
-        method_btn_layout.addWidget(self.method_deselect_all)
-        method_btn_layout.addStretch()
-        method_layout.addLayout(method_btn_layout)
-
-        method_group.setLayout(method_layout)
-        layout.addWidget(method_group)
-
-        # 输出目录
-        output_group = QGroupBox("输出设置")
-        output_layout = QFormLayout()
-
-        self.output_dir = QLineEdit()
-        self.output_dir.setText("")  # 路径由 update_from_config 根据 work_dir 自动填充
-        output_layout.addRow("输出目录名:", self.output_dir)
-
-        output_group.setLayout(output_layout)
-        layout.addWidget(output_group)
-
-        # 启用步骤
-        self.enable_checkbox = QCheckBox("启用此步骤")
-        self.enable_checkbox.setChecked(True)
-        layout.addWidget(self.enable_checkbox)
-
-        # 独立运行按钮
-        self.run_button = QPushButton("独立运行此步骤")
-        self.run_button.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
-        self.run_button.clicked.connect(self.run_step)
-        layout.addWidget(self.run_button)
-
-        layout.addStretch()
-        self.setLayout(layout)
-
-    def toggle_checkboxes(self, checkboxes_dict, checked):
-        """统一设置checkbox状态"""
-        for checkbox in checkboxes_dict.values():
-            checkbox.setChecked(checked)
-
-    def on_csv_file_changed(self):
-        """CSV文件改变时自动刷新列信息"""
-        self.refresh_csv_columns()
-
-    def refresh_csv_columns(self):
-        """刷新CSV文件的列信息"""
-        csv_path = self.csv_file.get_path()
-        if not csv_path or not os.path.exists(csv_path):
-            self.csv_columns = []
-            self.update_column_widgets()
-            return
-
-        try:
-            df = pd.read_csv(csv_path, nrows=0)
-            self.csv_columns = list(df.columns)
-            self.update_column_widgets()
-        except Exception as e:
-            self.csv_columns = []
-            self.update_column_widgets()
-            print(f"读取CSV列信息失败: {e}")
-
-    def update_column_widgets(self):
-        """更新列选择组件"""
-        for checkbox in self.x_column_checkboxes.values():
-            checkbox.setParent(None)
-        self.x_column_checkboxes.clear()
-
-        for checkbox in self.y_column_checkboxes.values():
-            checkbox.setParent(None)
-        self.y_column_checkboxes.clear()
-
-        if not self.csv_columns:
-            return
-
-        for i, col in enumerate(self.csv_columns):
-            checkbox = QCheckBox(col)
-            if any(keyword in col.lower() for keyword in ['index', 'ratio', 'normalized', 'nd', 'b']):
-                checkbox.setChecked(True)
-            self.x_column_checkboxes[col] = checkbox
-            self.x_columns_layout.addWidget(checkbox, i // 3, i % 3)
-
-        for i, col in enumerate(self.csv_columns):
-            checkbox = QCheckBox(col)
-            if any(keyword in col.lower() for keyword in ['chl', 'tn', 'tp', 'turbidity', 'do', 'ph', 'conductivity']):
-                checkbox.setChecked(True)
-            self.y_column_checkboxes[col] = checkbox
-            self.y_columns_layout.addWidget(checkbox, i // 2, i % 2)
-
-        self.x_columns_layout.update()
-        self.y_columns_layout.update()
-
-    def get_config(self):
-        selected_x_columns = [
-            col for col, checkbox in self.x_column_checkboxes.items()
-            if checkbox.isChecked()
-        ]
-        selected_y_columns = [
-            col for col, checkbox in self.y_column_checkboxes.items()
-            if checkbox.isChecked()
-        ]
-        selected_methods = [
-            method for method, checkbox in self.method_checkboxes.items()
-            if checkbox.isChecked()
-        ]
-        if not selected_methods:
-            selected_methods = 'all'
-
-        return {
-            'csv_path': self.csv_file.get_path() or None,
-            'x_columns': selected_x_columns,
-            'y_columns': selected_y_columns,
-            'methods': selected_methods,
-            'output_dir': self.output_dir.text().strip() or None,
-            'enabled': self.enable_checkbox.isChecked()
-        }
-
-    def set_config(self, config):
-        if 'csv_path' in config:
-            self.csv_file.set_path(config['csv_path'])
-            self.refresh_csv_columns()
-
-        if 'x_columns' in config:
-            selected_x = set(config['x_columns']) if isinstance(config['x_columns'], list) else set()
-            for col, checkbox in self.x_column_checkboxes.items():
-                checkbox.setChecked(col in selected_x)
-
-        if 'y_columns' in config:
-            selected_y = set(config['y_columns']) if isinstance(config['y_columns'], list) else set()
-            for col, checkbox in self.y_column_checkboxes.items():
-                checkbox.setChecked(col in selected_y)
-
-        if 'methods' in config:
-            methods = config['methods']
-            if isinstance(methods, list):
-                selected_methods = set(methods)
-            elif methods == 'all':
-                selected_methods = set(self.method_checkboxes.keys())
-            else:
-                selected_methods = set()
-            for method, checkbox in self.method_checkboxes.items():
-                checkbox.setChecked(method in selected_methods)
-
-        if 'output_dir' in config:
-            self.output_dir.setText(config['output_dir'] or "9_Custom_Regression_Modeling")
-        if 'enabled' in config:
-            self.enable_checkbox.setChecked(config['enabled'])
-
-    def update_from_config(self, work_dir=None, pipeline=None):
-        """从全局配置自动填充训练数据和输出路径
-
-        Args:
-            work_dir: 工作目录路径
-            pipeline: Pipeline 实例（未使用，保留接口兼容性）
-        """
-        try:
-            import traceback
-
-            if work_dir:
-                self.work_dir = work_dir
-            elif hasattr(self, 'work_dir') and self.work_dir:
-                pass
-            else:
-                self.work_dir = None
-
-            # 1. 尝试从 Step8 界面读取训练光谱 CSV 路径
-            main_window = self.window()
-            if main_window and hasattr(main_window, 'step8_panel'):
-                step8_widget = main_window.step8_panel.training_data_widget
-                step8_output_path = ""
-                if hasattr(step8_widget, 'get_path'):
-                    step8_output_path = step8_widget.get_path() or ""
-
-                if step8_output_path:
-                    if not os.path.isabs(step8_output_path):
-                        step8_output_path = os.path.join(self.work_dir or '', step8_output_path).replace('\\', '/')
-                    existing = self.csv_file.get_path()
-                    if not existing or not existing.strip():
-                        self.csv_file.set_path(step8_output_path)
-
-            # 1.2 尝试从 pipeline 读取 Step 8 宽表 indices_path（优先级最高）
-            if pipeline and hasattr(pipeline, 'indices_path') and pipeline.indices_path:
-                step8_indices_path = pipeline.indices_path
-                if not os.path.isabs(step8_indices_path):
-                    step8_indices_path = os.path.join(self.work_dir or '', step8_indices_path).replace('\\', '/')
-                current_path = self.csv_file.get_path()
-                if not current_path or not current_path.strip():
-                    self.csv_file.set_path(step8_indices_path)
-                    print(f"✅ 从pipeline.indices_path回填Step8产出: {step8_indices_path}")
-
-            # 1.5 自动探测并回填 Step 8 双轨输出的 Traditional_Indices 目录
-            if self.work_dir:
-                trad_indices_dir = os.path.join(
-                    self.work_dir, "11_12_13_predictions", "Traditional_Indices"
-                )
-                if os.path.isdir(trad_indices_dir):
-                    csv_files = [
-                        f for f in os.listdir(trad_indices_dir)
-                        if f.lower().endswith('.csv')
-                    ]
-                    if csv_files:
-                        csv_files.sort()
-                        first_csv = os.path.join(trad_indices_dir, csv_files[0])
-                        existing = self.csv_file.get_path()
-                        if not existing or not existing.strip():
-                            self.csv_file.set_path(first_csv)
-                            self.refresh_csv_columns()
-                            print(f"✅ 自动探测到 Traditional_Indices 目录，加载首个CSV: {csv_files[0]}")
-
-            # 2. 自动填充输出目录（9_Custom_Regression_Modeling）
-            if self.work_dir:
-                output_dir = os.path.join(self.work_dir, "9_Custom_Regression_Modeling")
-                os.makedirs(output_dir, exist_ok=True)
-                existing_out = self.output_dir.text().strip()
-                if not existing_out:
-                    self.output_dir.setText(output_dir)
-        except Exception as e:
-            import traceback
-            print(f"【{self.__class__.__name__}】自动填充失败，跳过: {e}")
-            traceback.print_exc()
-
-    def run_step(self):
-        """独立运行步骤9"""
-        csv_path = self.csv_file.get_path()
-
-        if not csv_path:
-            QMessageBox.warning(self, "输入验证失败", "请选择输入CSV文件")
-            return
-        if not os.path.exists(csv_path):
-            QMessageBox.warning(self, "输入验证失败", "输入CSV文件不存在")
-            return
-
-        selected_x_columns = [
-            col for col, checkbox in self.x_column_checkboxes.items()
-            if checkbox.isChecked()
-        ]
-        if not selected_x_columns:
-            QMessageBox.warning(self, "输入验证失败", "请至少选择一个自变量列")
-            return
-
-        selected_y_columns = [
-            col for col, checkbox in self.y_column_checkboxes.items()
-            if checkbox.isChecked()
-        ]
-        if not selected_y_columns:
-            QMessageBox.warning(self, "输入验证失败", "请至少选择一个因变量列")
-            return
-
-        selected_methods = [
-            method for method, checkbox in self.method_checkboxes.items()
-            if checkbox.isChecked()
-        ]
-        if not selected_methods:
-            QMessageBox.warning(self, "输入验证失败", "请至少选择一种回归方法")
-            return
-
-        config = self.get_config()
-
-        parent = self.parent()
-        while parent and not hasattr(parent, 'run_single_step'):
-            parent = parent.parent()
-
-        if parent and hasattr(parent, 'run_single_step'):
-            parent.run_single_step('step9', {'step9': config})
-        else:
-            QMessageBox.critical(self, "错误", "无法找到父级GUI对象")

src/gui/water_quality_gui.py

@@ -117,18 +117,17 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.panels.step1_panel import Step1Panel
 from src.gui.panels.step2_panel import Step2Panel
 from src.gui.panels.step3_panel import Step3Panel
-from src.gui.panels.step4_panel import Step4Panel
-from src.gui.panels.step5_panel import Step5Panel
-from src.gui.panels.step6_panel import Step6Panel  # was step8_panel
-from src.gui.panels.step7_panel import Step7Panel  # was step6_panel
-from src.gui.panels.step8_waterindex_panel import Step8WaterIndexPanel  # 水色指数反演
-from src.gui.panels.step9_concentration_panel import Step9ConcentrationPanel  # 浓度反演
-from src.gui.panels.step10_panel import Step10Panel  # was step7_panel
-from src.gui.panels.step11_ml_panel import Step11MlPanel  # ML prediction (step11_ml)
-from src.gui.panels.step14_panel import Step14Panel  # was step9_panel
+from src.gui.panels.step4_sampling_panel import Step4SamplingPanel  # 采样点布设
+from src.gui.panels.step5_clean_panel import Step5CleanPanel  # 数据清洗
+from src.gui.panels.step6_feature_panel import Step6FeaturePanel  # 光谱特征
+from src.gui.panels.step7_index_panel import Step7IndexPanel  # 水质光谱指数
+from src.gui.panels.step10_watercolor_panel import Step10WatercolorPanel  # 水色指数反演
+from src.gui.panels.step8_ml_train_panel import Step8MlTrainPanel  # 机器学习建模
+from src.gui.panels.step9_ml_predict_panel import Step9MlPredictPanel  # 机器学习预测
 from src.gui.dialogs import BandConfirmDialog, AISettingsDialog
-from src.gui.panels.visualization_panel import VisualizationPanel
-from src.gui.panels.report_generation_panel import ReportGenerationPanel
+from src.gui.panels.step11_map_panel import Step11MapPanel  # 专题图生成
+from src.gui.panels.step12_viz_panel import Step12VizPanel  # 可视化
+from src.gui.panels.step13_report_panel import Step13ReportPanel  # 报告生成
 
 # Pipeline 核心异常（用于预检弹窗）
 from src.core.pipeline.runner import PipelineHalt
@@ -1380,93 +1379,64 @@ class WaterQualityGUI(QMainWindow):
                 'deglint_goodman': '3_deglint/deglint_goodman.bsq',
                 'deglint_hedley': '3_deglint/deglint_hedley.bsq',
                 'deglint_sugar': '3_deglint/deglint_sugar.bsq',
-                'deglint_interpolated': '3_deglint/interpolated_*.bsq'  # * = interpolation_method
+                'deglint_interpolated': '3_deglint/interpolated_*.bsq'
             },
-            'step4': {
+            'step5_clean': {
                 'processed_data': '4_processed_data/processed_data.csv'
             },
-            'step5': {
+            'step6_feature': {
                 'training_spectra': '5_training_spectra/training_spectra.csv'
             },
-            'step6': {
+            'step7_index': {
                 'water_indices': '6_water_quality_indices/water_quality_indices.csv'
             },
-            'step7': {
-                'models': '7_Supervised_Model_Training/'  # 目录，包含各参数子目录
+            'step8_ml_train': {
+                'models': '7_Supervised_Model_Training/'
             },
-            'step8_non_empirical_modeling': {
-                'regression_models': '8_Regression_Modeling/'  # 目录，包含各参数子目录
-            },
-            'step9': {
-                'custom_regression_models': '9_Custom_Regression_Modeling/'  # 目录
-            },
-            'step10': {
+            'step4_sampling': {
                 'sampling_points': '10_sampling/sampling_spectra.csv'
             },
-            'step11_ml': {
-                'predictions': '11_12_13_predictions/Machine_Learning_Prediction/'  # 目录，包含机器学习预测结果
+            'step9_ml_predict': {
+                'predictions': '11_12_13_predictions/Machine_Learning_Prediction/'
             },
-            'step11': {
-                'regression_predictions': '11_12_13_predictions/Non_Empirical_Prediction/'  # 目录，包含非经验模型预测结果
-            },
-            'step12': {
-                'custom_predictions': '11_12_13_predictions/Custom_Regression_Prediction/'  # 目录，包含自定义回归预测结果
-            },
-            'step14': {
-                'distribution_maps': '14_visualization/'  # 目录，包含专题图
+            'step11_map': {
+                'distribution_maps': '14_visualization/'
             }
         }
         
         # 定义步骤间的依赖关系：{当前步骤: {输入字段: (依赖步骤, 输出类型, 面板属性名)}}
         self.step_dependencies = {
             'step2': {
-                'img_path': ('step1', 'reference_img', 'img_file'),  # 步骤2需要参考影像
-                'water_mask_path': ('step1', 'water_mask', 'water_mask_file')  # 步骤2可选水域掩膜
+                'img_path': ('step1', 'reference_img', 'img_file'),
+                'water_mask_path': ('step1', 'water_mask', 'water_mask_file')
             },
             'step3': {
-                'img_path': ('step1', 'reference_img', 'img_file'),  # 步骤3需要参考影像
-                'water_mask': ('step1', 'water_mask', 'water_mask_file'),  # 步骤3需要水域掩膜
+                'img_path': ('step1', 'reference_img', 'img_file'),
+                'water_mask': ('step1', 'water_mask', 'water_mask_file'),
             },
-            'step4': {
-                # 步骤4主要处理CSV文件，一般不依赖前面步骤的输出
+            'step6_feature': {
+                'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
+                'csv_path': ('step5_clean', 'processed_data', 'csv_file'),
+                'boundary_mask_path': ('step1', 'water_mask', 'boundary_mask_file'),
+                'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file')
             },
-            'step5': {
-                'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),  # 步骤5需要去耀斑影像
-                'csv_path': ('step4', 'processed_data', 'csv_file'),  # 步骤5需要处理后的CSV
-                'boundary_mask_path': ('step1', 'water_mask', 'boundary_mask_file'),  # 步骤5可选水体掩膜
-                'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file')  # 步骤5可选耀斑掩膜
+            'step7_index': {
+                'training_csv_path': ('step6_feature', 'training_spectra', 'output_file')
             },
-            'step6': {
-                'training_csv_path': ('step5', 'training_spectra', 'output_file')  # 步骤6需要步骤5输出的训练光谱
+            'step8_ml_train': {
+                'training_csv_path': ('step7_index', 'water_indices', 'csv_file')
             },
-            'step7': {
-                'csv_path': ('step5', 'training_spectra', 'csv_file')  # 步骤7需要训练光谱数据
+            'step4_sampling': {
+                'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
+                'water_mask_path': ('step1', 'water_mask', 'water_mask_file'),
+                'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file')
             },
-            'step8_non_empirical_modeling': {
-                'csv_path': ('step5', 'training_spectra', 'csv_file')  # 步骤8非经验建模需要训练光谱数据
+            'step9_ml_predict': {
+                'sampling_csv_path': ('step4_sampling', 'sampling_points', 'sampling_csv_file'),
+                'models_dir': ('step8_ml_train', 'models', 'models_dir_file')
             },
-            'step9': {
-                'csv_path': ('step5', 'training_spectra', 'csv_file')  # 步骤9需要训练光谱数据
-            },
-            'step10': {
-                'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),  # 步骤10需要去耀斑影像
-                'water_mask_path': ('step1', 'water_mask', 'water_mask_file'),  # 步骤10需要水域掩膜
-                'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file')  # 步骤10可选耀斑掩膜
-            },
-            'step11_ml': {
-                'sampling_csv_path': ('step10', 'sampling_points', 'sampling_csv_file'),  # 步骤11ML需要采样点
-                'models_dir': ('step7', 'models', 'models_dir_file')  # 步骤11ML需要训练好的模型
-            },
-            'step11': {
-                'sampling_csv_path': ('step10', 'sampling_points', 'sampling_csv_file'),  # 步骤11需要采样点
-                'models_dir': ('step8_non_empirical_modeling', 'regression_models', 'models_dir')  # 步骤11需要回归模型
-            },
-            'step12': {
-                'sampling_csv_path': ('step10', 'sampling_points', 'sampling_csv_file'),  # 步骤12需要采样点
-                'models_dir': ('step9', 'custom_regression_models', 'models_dir')  # 步骤12需要自定义回归模型
-            },
-            'step14': {
-                'prediction_csv_path': ('step11_ml', 'predictions', 'prediction_csv_file')  # 步骤14需要预测结果CSV
+            'step11_map': {
+                'prediction_csv_path': ('step9_ml_predict', 'predictions', 'prediction_csv_file')
             }
         }
 
@@ -1843,26 +1813,23 @@ class WaterQualityGUI(QMainWindow):
             "阶段一：影像预处理": [
                 ("step1", "1. 水域掩膜生成"),
                 ("step2", "2. 耀斑区域识别"),
-                ("step3", "3. 耀斑去除与修复"),
+                ("step3", "3. 耀斑去除与修复")
             ],
             "阶段二：样本数据准备": [
-                ("step4", "4. 数据标准化处理"),
-                ("step5", "5. 光谱特征提取"),
-                ("step6", "6. 水质参数指数计算"),
+                ("step4_sampling", "4. 采样点布设"),
+                ("step5_clean", "5. 数据清洗"),
+                ("step6_feature", "6. 光谱特征提取"),
+                ("step7_index", "7. 水质指数计算")
             ],
             "阶段三：模型构建与训练": [
-                ("step7", "7. 机器学习模型训练"),
-                ("step8_non_empirical_modeling", "8. 回归模型训练"),
-                ("step9", "9. 自定义回归模型训练"),
+                ("step8_ml_train", "8. 机器学习建模")
             ],
             "阶段四：预测与成果输出": [
-                ("step10", "10. 采样点布设"),
-                ("step11_ml", "11. 机器学习预测"),
-                ("step11", "12. 回归预测"),
-                ("step12", "13. 自定义回归预测"),
-                ("step14", "14. 专题图生成"),
-                ("step9_viz", "15. 可视化分析"),
-                ("step_report", "16. 分析报告生成"),
+                ("step9_ml_predict", "9. 机器学习预测"),
+                ("step10_watercolor", "10. 水色指数反演"),
+                ("step11_map", "11. 专题图生成"),
+                ("step12_viz", "12. 可视化展示"),
+                ("step13_report", "13. 分析报告生成")
             ]
         }
         
@@ -1882,11 +1849,7 @@ class WaterQualityGUI(QMainWindow):
             self.step_list.addItem(stage_item)
             
             # 添加该阶段的所有步骤
-            HIDDEN_STEP_IDS = {"step8_non_empirical_modeling", "step9", "step11", "step12"}
             for step_id, step_display in steps:
-                if step_id in HIDDEN_STEP_IDS:
-                    continue
-
                 item = QListWidgetItem(f"  └─ {step_display}")
                 item.setData(Qt.UserRole, step_id)
 
@@ -1956,38 +1919,35 @@ class WaterQualityGUI(QMainWindow):
         self.step3_panel = Step3Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step3_panel), QIcon(self.get_icon_path("3.png")), "耀斑去除")
 
-        self.step4_panel = Step4Panel()
-        self.step_stack.addTab(self.create_scroll_area(self.step4_panel), QIcon(self.get_icon_path("4.png")), "数据清洗")
+        self.step4_sampling_panel = Step4SamplingPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step4_sampling_panel), QIcon(self.get_icon_path("4.png")), "采样点布设")
 
-        self.step5_panel = Step5Panel()
-        self.step_stack.addTab(self.create_scroll_area(self.step5_panel), QIcon(self.get_icon_path("5.png")), "特征构建")
+        self.step5_clean_panel = Step5CleanPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step5_clean_panel), QIcon(self.get_icon_path("5.png")), "数据清洗")
 
-        self.step6_panel = Step6Panel()
-        self.step_stack.addTab(self.create_scroll_area(self.step6_panel), QIcon(self.get_icon_path("6.png")), "水质光谱指数计算")
+        self.step6_feature_panel = Step6FeaturePanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step6_feature_panel), QIcon(self.get_icon_path("6.png")), "光谱特征")
 
-        self.step7_panel = Step7Panel()
-        self.step_stack.addTab(self.create_scroll_area(self.step7_panel), QIcon(self.get_icon_path("7.png")), "监督建模")
+        self.step7_index_panel = Step7IndexPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step7_index_panel), QIcon(self.get_icon_path("7.png")), "水质光谱指数计算")
 
-        self.step8_waterindex_panel = Step8WaterIndexPanel()
-        self.step_stack.addTab(self.create_scroll_area(self.step8_waterindex_panel), QIcon(self.get_icon_path("6.png")), "水色指数反演")
+        self.step8_ml_train_panel = Step8MlTrainPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step8_ml_train_panel), QIcon(self.get_icon_path("8.png")), "机器学习建模")
 
-        self.step9_concentration_panel = Step9ConcentrationPanel()
-        self.step_stack.addTab(self.create_scroll_area(self.step9_concentration_panel), QIcon(self.get_icon_path("6.png")), "浓度反演")
+        self.step9_ml_predict_panel = Step9MlPredictPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step9_ml_predict_panel), QIcon(self.get_icon_path("10.png")), "机器学习预测")
 
-        self.step10_panel = Step10Panel()
-        self.step_stack.addTab(self.create_scroll_area(self.step10_panel), QIcon(self.get_icon_path("7.png")), "采样点布设")
+        self.step10_watercolor_panel = Step10WatercolorPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step10_watercolor_panel), QIcon(self.get_icon_path("10.png")), "水色指数反演")
 
-        self.step11_ml_panel = Step11MlPanel()  # ML prediction panel (step11_ml)
-        self.step_stack.addTab(self.create_scroll_area(self.step11_ml_panel), QIcon(self.get_icon_path("8.png")), "监督预测")
+        self.step11_map_panel = Step11MapPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step11_map_panel), QIcon(self.get_icon_path("10.png")), "专题图生成")
 
-        self.step14_panel = Step14Panel()
-        self.step_stack.addTab(self.create_scroll_area(self.step14_panel), QIcon(self.get_icon_path("10.png")), "专题图生成")
+        self.step12_viz_panel = Step12VizPanel()
+        self.step_stack.addTab(self.create_scroll_area(self.step12_viz_panel), QIcon(self.get_icon_path("9.png")), "可视化")
 
-        self.viz_panel = VisualizationPanel()
-        self.step_stack.addTab(self.create_scroll_area(self.viz_panel), QIcon(self.get_icon_path("9.png")), "可视化")
-
-        self.report_panel = ReportGenerationPanel(main_window=self)
-        self.step_stack.addTab(self.create_scroll_area(self.report_panel), QIcon(self.get_icon_path("10.png")), "报告生成")
+        self.step13_report_panel = Step13ReportPanel(main_window=self)
+        self.step_stack.addTab(self.create_scroll_area(self.step13_report_panel), QIcon(self.get_icon_path("10.png")), "报告生成")
         
         # 连接Tab切换信号，实现双向同步（必须在step_stack创建后）
         self.step_stack.currentChanged.connect(self.on_tab_changed)
@@ -2126,22 +2086,11 @@ class WaterQualityGUI(QMainWindow):
         
         # 根据步骤ID查找对应的tab索引
         step_id_to_tab = {
-            'step1': 0,
-            'step2': 1,
-            'step3': 2,
-            'step4': 3,
-            'step5': 4,
-            'step6': 5,
-            'step7': 6,
-            'step8_non_empirical_modeling': 7,
-            'step9': 8,
-            'step10': 9,
-            'step11_ml': 10,
-            'step11': 11,
-            'step12': 12,
-            'step14': 13,
-            'step9_viz': 14,
-            'step_report': 15,
+            'step1': 0, 'step2': 1, 'step3': 2, 'step4_sampling': 3,
+            'step5_clean': 4, 'step6_feature': 5, 'step7_index': 6,
+            'step8_ml_train': 7, 'step9_ml_predict': 8,
+            'step10_watercolor': 9, 'step11_map': 10,
+            'step12_viz': 11, 'step13_report': 12,
         }
         
         if item_data in step_id_to_tab:
@@ -2155,24 +2104,13 @@ class WaterQualityGUI(QMainWindow):
         if index < 0:
             return
         
-        # Tab索引到步骤ID的反向映射
+        # Tab索引到步骤ID的反向映射（13个Tab，index 0-12）
         tab_to_step_id = {
-            0: 'step1',
-            1: 'step2',
-            2: 'step3',
-            3: 'step4',
-            4: 'step5',
-            5: 'step6',
-            6: 'step7',
-            7: 'step8_non_empirical_modeling',
-            8: 'step9',
-            9: 'step10',
-            10: 'step11_ml',
-            11: 'step11',
-            12: 'step12',
-            13: 'step14',
-            14: 'step9_viz',
-            15: 'step_report',
+            0: 'step1', 1: 'step2', 2: 'step3', 3: 'step4_sampling',
+            4: 'step5_clean', 5: 'step6_feature', 6: 'step7_index',
+            7: 'step8_ml_train', 8: 'step9_ml_predict',
+            9: 'step10_watercolor', 10: 'step11_map',
+            11: 'step12_viz', 12: 'step13_report',
         }
         
         if index not in tab_to_step_id:
@@ -2191,53 +2129,27 @@ class WaterQualityGUI(QMainWindow):
                 self.step_list.setCurrentRow(row)
                 break
 
-        # Step2 切换时自动填充数据流转路径
-        if index == 1:
-            self.step2_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
+        # 面板自动填充：统一 mapping 覆盖 index 0-12
+        mapping = {
+            0: (self.step1_panel, "Step1"),
+            1: (self.step2_panel, "Step2"),
+            2: (self.step3_panel, "Step3"),
+            3: (self.step4_sampling_panel, "Step4"),
+            4: (self.step5_clean_panel, "Step5"),
+            5: (self.step6_feature_panel, "Step6"),
+            6: (self.step7_index_panel, "Step7"),
+            7: (self.step8_ml_train_panel, "Step8"),
+            8: (self.step9_ml_predict_panel, "Step9"),
+            9: (self.step10_watercolor_panel, "Step10"),  # 水色指数反演
+            10: (self.step11_map_panel, "Step11"),        # 专题图生成
+            11: (self.step12_viz_panel, "Step12"),
+            12: (self.step13_report_panel, "Step13")
+        }
 
-        # Step3 切换时自动填充数据流转路径
-        elif index == 2:
-            self.step3_panel.update_from_config(work_dir=self.work_dir)
-
-        # Step4 切换时自动填充输出路径
-        elif index == 3:
-            self.step4_panel.update_from_config(work_dir=self.work_dir)
-
-        # Step5 切换时自动填充数据流转路径
-        elif index == 4:
-            self.step5_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step6（水质光谱指数）切换时自动填充输出路径
-        elif index == 5:
-            self.step6_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step7（监督建模）切换时自动填充训练数据和输出路径
-        elif index == 6:
-            self.step7_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step8 水色指数反演切换时自动填充光谱数据和输出路径
-        elif index == 7:
-            self.step8_waterindex_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step9 浓度反演切换时自动填充 QAA 结果和输出路径
-        elif index == 8:
-            self.step9_concentration_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step10（采样点布设）切换时自动填充掩膜和输出路径
-        elif index == 9:
-            self.step10_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step11（机器学习预测）切换时自动填充采样光谱和模型目录
-        elif index == 10:
-            self.step11_ml_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # Step14（专题图生成）切换时自动填充预测结果目录
-        elif index == 11:
-            self.step14_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
-
-        # 可视化分析面板切换时自动推断图像目录并加载目录树
-        elif index == 12:
-            self.viz_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
+        if index in mapping:
+            panel, _ = mapping[index]
+            if hasattr(panel, 'update_from_config'):
+                panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline)
 
     def apply_stylesheet(self):
         """应用样式表 - 应用现代化设计风格"""
@@ -2276,24 +2188,22 @@ class WaterQualityGUI(QMainWindow):
                 self.step2_panel.set_config(config['step2'])
             if 'step3' in config:
                 self.step3_panel.set_config(config['step3'])
-            if 'step4' in config:
-                self.step4_panel.set_config(config['step4'])
-            if 'step5' in config:
-                self.step5_panel.set_config(config['step5'])
-            if 'step6' in config:
-                self.step6_panel.set_config(config['step6'])
-            if 'step7' in config:
-                self.step7_panel.set_config(config['step7'])
-            if 'step10' in config:
-                self.step10_panel.set_config(config['step10'])
-            if 'step11_ml' in config:
-                self.step11_ml_panel.set_config(config['step11_ml'])
-            if 'step14' in config:
-                self.step14_panel.set_config(config['step14'])
-            if 'visualization' in config:
-                self.viz_panel.set_config(config['visualization'])
-            if 'report_generation' in config:
-                self.report_panel.set_config(config['report_generation'])
+            if 'step4_sampling' in config:
+                self.step4_sampling_panel.set_config(config['step4_sampling'])
+            if 'step5_clean' in config:
+                self.step5_clean_panel.set_config(config['step5_clean'])
+            if 'step6_feature' in config:
+                self.step6_feature_panel.set_config(config['step6_feature'])
+            if 'step7_index' in config:
+                self.step7_index_panel.set_config(config['step7_index'])
+            if 'step9_ml_predict' in config:
+                self.step9_ml_predict_panel.set_config(config['step9_ml_predict'])
+            if 'step11_map' in config:
+                self.step11_map_panel.set_config(config['step11_map'])
+            if 'step12_viz' in config:
+                self.step12_viz_panel.set_config(config['step12_viz'])
+            if 'step13_report' in config:
+                self.step13_report_panel.set_config(config['step13_report'])
             
             self.config_file = file_path
             self.log_message(f"已加载配置: {file_path}", "info")
@@ -2330,15 +2240,15 @@ class WaterQualityGUI(QMainWindow):
             'step1': self.step1_panel.get_config(),
             'step2': self.step2_panel.get_config(),
             'step3': self.step3_panel.get_config(),
-            'step4': self.step4_panel.get_config(),
-            'step5': self.step5_panel.get_config(),
-            'step6': self.step6_panel.get_config(),
-            'step7': self.step7_panel.get_config(),
-            'step10': self.step10_panel.get_config(),
-            'step11_ml': self.step11_ml_panel.get_config(),
-            'step14': self.step14_panel.get_config(),
-            'visualization': self.viz_panel.get_config(),
-            'report_generation': self.report_panel.get_config(),
+            'step4_sampling': self.step4_sampling_panel.get_config(),
+            'step5_clean': self.step5_clean_panel.get_config(),
+            'step6_feature': self.step6_feature_panel.get_config(),
+            'step7_index': self.step7_index_panel.get_config(),
+            'step8_ml_train': self.step8_ml_train_panel.get_config(),
+            'step9_ml_predict': self.step9_ml_predict_panel.get_config(),
+            'step11_map': self.step11_map_panel.get_config(),
+            'step12_viz': self.step12_viz_panel.get_config(),
+            'step13_report': self.step13_report_panel.get_config(),
         }
         return config
 
@@ -2385,13 +2295,15 @@ class WaterQualityGUI(QMainWindow):
             'step1': self.step1_panel,
             'step2': self.step2_panel,
             'step3': self.step3_panel,
-            'step4': self.step4_panel,
-            'step5': self.step5_panel,
-            'step6': self.step6_panel,
-            'step7': self.step7_panel,
-            'step10': self.step10_panel,
-            'step11_ml': self.step11_ml_panel,
-            'step14': self.step14_panel,
+            'step4_sampling': self.step4_sampling_panel,
+            'step5_clean': self.step5_clean_panel,
+            'step6_feature': self.step6_feature_panel,
+            'step7_index': self.step7_index_panel,
+            'step8_ml_train': self.step8_ml_train_panel,
+            'step9_ml_predict': self.step9_ml_predict_panel,
+            'step11_map': self.step11_map_panel,
+            'step12_viz': self.step12_viz_panel,
+            'step13_report': self.step13_report_panel,
         }
         return panel_map.get(step_id)
 
@@ -2483,17 +2395,17 @@ class WaterQualityGUI(QMainWindow):
             '1_water_mask': 'step1',
             '2_glint': 'step2',
             '3_deglint': 'step3',
-            '4_processed_data': 'step4',
-            '5_training_spectra': 'step5',
-            '6_water_quality_indices': 'step6',
-            '7_Supervised_Model_Training': 'step7',
-            '8_Regression_Modeling': 'step8_non_empirical_modeling',
-            '9_Custom_Regression_Modeling': 'step9',
-            '10_sampling': 'step10',
-            '11_12_13_predictions/Machine_Learning_Prediction': 'step11_ml',
-            '11_12_13_predictions/Non_Empirical_Prediction': 'step11',
-            '11_12_13_predictions/Custom_Regression_Prediction': 'step12',
-            '14_visualization': 'step14'
+            '4_processed_data': 'step4_sampling',
+            '5_training_spectra': 'step5_clean',
+            '6_water_quality_indices': 'step6_feature',
+            '7_Supervised_Model_Training': 'step7_index',
+            '8_Regression_Modeling': 'step8_ml_train',
+            '9_Custom_Regression_Modeling': 'step9_ml_predict',
+            '11_12_13_predictions/Machine_Learning_Prediction': 'step9_ml_predict',
+            '11_12_13_predictions/Non_Empirical_Prediction': 'step11_map',
+            '11_12_13_predictions/Custom_Regression_Prediction': 'step12_viz',
+            '14_visualization': 'step13_report',
+            '10_geotiff_batch_rendering': 'step11_map'
         }
         
         for subdir, step_ids in subdirs.items():
@@ -2535,15 +2447,15 @@ class WaterQualityGUI(QMainWindow):
                                 discovered_outputs[step_id]['glint_mask'] = str(file_path)
                         elif 'deglint' in file_name and step_id == 'step3':
                             discovered_outputs[step_id]['deglint_image'] = str(file_path)
-                        elif 'processed_data' in file_name and step_id == 'step4':
+                        elif 'processed_data' in file_name and step_id == 'step4_sampling':
                             discovered_outputs[step_id]['processed_data'] = str(file_path)
-                        elif 'training_spectra' in file_name and step_id == 'step5':
+                        elif 'training_spectra' in file_name and step_id == 'step5_clean':
                             discovered_outputs[step_id]['training_spectra'] = str(file_path)
-                        elif 'water_quality_indices' in file_name and step_id == 'step6':
+                        elif 'water_quality_indices' in file_name and step_id == 'step6_feature':
                             discovered_outputs[step_id]['water_indices'] = str(file_path)
-                        elif 'sampling_spectra' in file_name and step_id == 'step10':
+                        elif 'sampling_spectra' in file_name and step_id == 'step4_sampling':
                             discovered_outputs[step_id]['sampling_points'] = str(file_path)
-                        elif file_name.endswith('.csv') and step_id in ['step11_ml', 'step11', 'step12']:
+                        elif file_name.endswith('.csv') and step_id in ['step9_ml_predict', 'step11_map', 'step12_viz']:
                             discovered_outputs[step_id]['predictions'] = str(file_path)
         
         # 更新内部记录
@@ -2566,8 +2478,8 @@ class WaterQualityGUI(QMainWindow):
         # 首先扫描工作目录发现已有的输出文件
         self.scan_work_directory_for_files(work_path)
         
-        step_order = ['step2', 'step3', 'step4', 'step5', 'step6', 'step7', 'step8_non_empirical_modeling', 'step9',
-                     'step10', 'step11_ml', 'step11', 'step12', 'step14']
+        step_order = ['step2', 'step3', 'step4_sampling', 'step5_clean', 'step6_feature', 'step7_index',
+                     'step8_ml_train', 'step9_ml_predict', 'step11_map', 'step12_viz', 'step13_report']
         
         filled_count = 0
         for step_id in step_order:
@@ -2588,12 +2500,15 @@ class WaterQualityGUI(QMainWindow):
         panels_with_dependencies = [
             ('step2', self.step2_panel),
             ('step3', self.step3_panel),
-            ('step5', self.step5_panel),
-            ('step6', self.step6_panel),
-            ('step7', self.step7_panel),
-            ('step10', self.step10_panel),
-            ('step11_ml', self.step11_ml_panel),
-            ('step14', self.step14_panel)
+            ('step4_sampling', self.step4_sampling_panel),
+            ('step5_clean', self.step5_clean_panel),
+            ('step6_feature', self.step6_feature_panel),
+            ('step7_index', self.step7_index_panel),
+            ('step8_ml_train', self.step8_ml_train_panel),
+            ('step9_ml_predict', self.step9_ml_predict_panel),
+            ('step11_map', self.step11_map_panel),
+            ('step12_viz', self.step12_viz_panel),
+            ('step13_report', self.step13_report_panel),
         ]
         
         for step_id, panel in panels_with_dependencies:
@@ -2663,10 +2578,10 @@ class WaterQualityGUI(QMainWindow):
             self.statusBar().showMessage(f"工作目录: {dir_path}")
             
             # 同步到可视化面板
-            if hasattr(self, 'viz_panel'):
-                self.viz_panel.set_work_dir(dir_path)
-            if hasattr(self, 'report_panel'):
-                self.report_panel.set_work_dir(dir_path)
+            if hasattr(self, 'step12_viz_panel'):
+                self.step12_viz_panel.set_work_dir(dir_path)
+            if hasattr(self, 'step13_report_panel'):
+                self.step13_report_panel.set_work_dir(dir_path)
     
     def open_work_directory(self):
         """打开工作目录"""
@@ -2985,11 +2900,11 @@ class WaterQualityGUI(QMainWindow):
         
         # 准备实际运行配置（排除未启用的步骤）
         worker_config = copy.deepcopy(config)
-        step6_cfg = worker_config.get('step6')
+        step6_cfg = worker_config.get('step6_feature')
         if step6_cfg:
             enabled = step6_cfg.pop('enabled', True)
             if not enabled:
-                worker_config.pop('step6', None)
+                worker_config.pop('step6_feature', None)
 
         # 工作线程内创建 Pipeline，避免主线程阻塞及 Qt5Agg 子线程绘图卡死
         self.worker = WorkerThread(work_dir, worker_config, mode='full', skip_list=skip_list)
@@ -3219,19 +3134,18 @@ class WaterQualityGUI(QMainWindow):
     def update_ui_for_training_mode(self):
         """根据训练数据模式更新UI状态"""
         # 需要禁用的步骤ID（对应无训练数据模式下需要禁用的步骤）
-        disabled_step_ids = ['step4', 'step5', 'step6', 'step7', 'step8_non_empirical_modeling', 'step9']
+        disabled_step_ids = ['step4_sampling', 'step5_clean', 'step6_feature', 'step7_index', 'step9_ml_predict']
 
         # 更新标签页的启用/禁用状态
-        step_id_to_tab = {
-            'step1': 0, 'step2': 1, 'step3': 2, 'step4': 3,
-            'step5': 4, 'step6': 5, 'step7': 6, 'step8_non_empirical_modeling': 7,
-            'step9': 8, 'step10': 9, 'step11_ml': 10, 'step11': 11,
-            'step12': 12, 'step14': 13, 'step9_viz': 14
+        step_id_to_tab_training = {
+            'step1': 0, 'step2': 1, 'step3': 2, 'step4_sampling': 3,
+            'step5_clean': 4, 'step6_feature': 5, 'step7_index': 6, 'step9_ml_predict': 7,
+            'step10_watercolor': 9, 'step11_map': 10, 'step12_viz': 11, 'step13_report': 12
         }
 
         for step_id in disabled_step_ids:
-            if step_id in step_id_to_tab:
-                tab_index = step_id_to_tab[step_id]
+            if step_id in step_id_to_tab_training:
+                tab_index = step_id_to_tab_training[step_id]
                 if tab_index < self.step_stack.count():
                     self.step_stack.setTabEnabled(tab_index, self.has_training_data)
 

src/postprocessing/map.py

@@ -7,7 +7,7 @@ from typing import Optional, Tuple
 from pyproj import CRS, Transformer
 import matplotlib.pyplot as plt
 import matplotlib.patches as patches
-from matplotlib.ticker import FuncFormatter
+from matplotlib.ticker import FuncFormatter, MaxNLocator
 from matplotlib_scalebar.scalebar import ScaleBar
 from scipy.interpolate import griddata
 from scipy import ndimage
@@ -63,6 +63,72 @@ PARAMS_CMAP = {
 }
 
 
+# ── 水色指数英文名 → 中文标题映射（精确整词匹配）────────────────────
+# 优先于动态拼接；当文件名恰好命中完整 key 时使用
+INDEX_TITLE_MAP = {
+    # Chl_a 叶绿素a
+    "Chl_Conc_NDCI":          "叶绿素a浓度估算_NDCI模型",
+    "Chl_MM12NDCI":           "叶绿素a相对指数_Matthews12模型",
+    "Chl_Conc_Gao":           "叶绿素a浓度估算_Gao模型",
+    "Chl_Conc_QAA":           "叶绿素a浓度估算_QAA模型",
+    # BGA 蓝藻/藻蓝蛋白
+    "BGA_Go04MCI":            "蓝藻相对指数_Gower04模型",
+    "BGA_PC_Conc_Mishra":     "藻蓝蛋白浓度估算_Mishra模型",
+    "BGA_Conc":               "蓝藻浓度估算",
+    "BGA_Am09KBBI":           "蓝藻相对指数_Am09模型",
+    # Turb 浊度
+    "Turb_Dox02NIRoverRed":   "水体浊度指数_Doxaran02模型",
+    "Turbidity":              "水体浊度",
+    "Turb_Conc":              "浊度估算",
+    # TSM 悬浮物
+    "TSM_Conc_Bowling":       "总悬浮物浓度估算_Bowling模型",
+    "TSM_Conc":               "总悬浮物浓度估算",
+    # CDOM 有色溶解有机物
+    "CDOM_Conc":              "有色溶解有机物浓度估算",
+    # WI 水色指数综合
+    "WaterIndex":             "水色指数",
+    "NDCI":                   "归一化叶绿素差值指数_NDCI",
+    "MCI":                    "最大Chlorophyll指数_MCI",
+}
+
+# ── 关键词 → 中文词根映射（用于动态拼接）────────────────────────────
+# 顺序即优先级：长词优先，短词兜底
+PART_NAME_MAP = [
+    # 1. 指数/浓度类（最高优先级，描述输出类型）
+    ("Conc",                  "浓度估算"),
+    ("_Conc",                 "浓度估算"),
+    ("Concentration",         "浓度"),
+    ("Index",                 "指数"),
+    ("_Index",                "指数"),
+    # 2. 模型/方法标识（次高优先级）
+    ("NDCI",                  "NDCI模型"),
+    ("MCI",                   "MCI模型"),
+    ("FLH",                   "FLH荧光基线"),
+    ("QAA",                   "QAA模型"),
+    ("Go04",                  "Gower04"),
+    ("MM12",                  "Matthews12"),
+    ("Gao",                   "Gao模型"),
+    ("Dox02",                 "Doxaran02"),
+    ("Bowling",               "Bowling模型"),
+    ("Mishra",                "Mishra模型"),
+    ("Am09",                  "Am09"),
+    ("KBBI",                  "KBBI"),
+    ("PC",                    "藻蓝蛋白"),
+    # 3. 参数大类（核心水质指标）
+    ("BGA",                   "蓝藻相对指数"),
+    ("Chl",                   "叶绿素a"),
+    ("Chl_a",                 "叶绿素a"),
+    ("Turb",                  "浊度"),
+    ("TSM",                   "总悬浮物"),
+    ("CDOM",                  "有色溶解有机物"),
+    ("DO",                    "溶解氧"),
+    ("pH",                    "pH值"),
+    ("NH3",                   "氨氮"),
+    ("NO3",                   "硝态氮"),
+    ("TDS",                   "溶解性总固体"),
+]
+
+
 class ContentMapper:
     def __init__(self, input_crs='EPSG:32651', output_crs='EPSG:4326'):
         """
@@ -97,6 +163,63 @@ class ContentMapper:
 
         print(f"坐标转换设置: {input_crs} -> {output_crs}")
 
+    # ── 内部工具 ─────────────────────────────────────────────────────
+    @staticmethod
+    def _get_chinese_title(stem: str) -> str:
+        """
+        根据 GeoTIFF 文件名 stem 返回中文图表标题（绝对唯一）。
+
+        匹配策略：
+          1. 精确整词命中 INDEX_TITLE_MAP
+          2. 中文分类前缀 + 原始模型后缀（绝对唯一保证）
+          3. 未匹配任何关键词 → 返回原英文 stem
+
+        Parameters
+        ----------
+        stem : str
+            GeoTIFF 文件名（不含路径和扩展名）
+
+        Returns
+        -------
+        str
+            中文标题；若未匹配则返回英文 stem
+        """
+        # 策略1：精确整词匹配（优先级最高）
+        if stem in INDEX_TITLE_MAP:
+            return INDEX_TITLE_MAP[stem]
+
+        # 策略2：中文分类前缀 + 原始模型后缀（确保绝对唯一）
+        category = ""
+        suffix = ""
+
+        if stem.startswith("BGA_PC_Conc"):
+            category = "藻蓝蛋白浓度估算"
+            suffix = stem.replace("BGA_PC_Conc_", "")
+        elif stem.startswith("BGA"):
+            category = "蓝藻相对指数"
+            suffix = stem.replace("BGA_", "")
+        elif stem.startswith("Chl_Conc"):
+            category = "叶绿素a浓度估算"
+            suffix = stem.replace("Chl_Conc_", "")
+        elif stem.startswith("Chl"):
+            category = "叶绿素a相对指数"
+            suffix = stem.replace("Chl_", "")
+        elif stem.startswith("Turb_Conc"):
+            category = "浊度浓度估算"
+            suffix = stem.replace("Turb_Conc_", "")
+        elif stem.startswith("Turb"):
+            category = "浊度相对指数"
+            suffix = stem.replace("Turb_", "")
+        elif stem.startswith("TSM_Conc"):
+            category = "总悬浮物浓度估算"
+            suffix = stem.replace("TSM_Conc_", "")
+        else:
+            # 兜底机制：未知分类直接使用原名
+            category = "水质参数"
+            suffix = stem
+
+        return f"{category}_{suffix}"
+
     def _extract_param_name(self, csv_file):
         """
         从CSV文件名或内容中提取参数名称
@@ -2080,12 +2203,15 @@ class ContentMapper:
         str
             输出图片路径
         """
-        # ── 输出路径自动派生 ──────────────────────────────────────────
-        if output_file is None:
+        # ── 始终从路径提取 stem（供后续中文标题和文件派生使用）──────────
         stem = Path(raster_tif_path).stem
+
+        # ── 输出路径自动派生（中文文件名）──────────────────────────────
+        if output_file is None:
+            chinese_title = self._get_chinese_title(stem)
             out_dir = Path(raster_tif_path).parent / 'visualization'
             out_dir.mkdir(parents=True, exist_ok=True)
-            output_file = str(out_dir / f"{stem}_map.png")
+            output_file = str(out_dir / f"{chinese_title}_专题图.png")
 
         # ── 读取 GeoTIFF（优先 rasterio，备选 GDAL）──────────────────
         tif_path = Path(raster_tif_path)
@@ -2214,6 +2340,10 @@ class ContentMapper:
             param_name = self._extract_param_name(str(tif_path))
             cmap = self._get_colormap(param_name)
 
+        # ── 中文标题（文件名汉化 + 绘图标题）──────────────────────────
+        # 用户显式传入 title 时直接使用；否则用中文映射
+        chinese_title = self._get_chinese_title(stem) if not title else title
+
         # ── 计算空间范围（extent）──────────────────────────────────────
         # 优先使用 rasterio 原生 bounds，保证坐标轴为真实 UTM 米
         # GDAL 回退使用 GeoTransform 计算
@@ -2251,23 +2381,24 @@ class ContentMapper:
         safe_figsize = (safe_w, safe_h)
         fig, ax = plt.subplots(figsize=safe_figsize)
 
-        # 计算有效值统计（使用 nanpercentile 精准锁定水体内部，排除陆地 NoData 干扰）
+        # 计算有效值统计（2σ 标准差拉伸，排除长尾异常值干扰）
         valid = array[~np.isnan(array)]
         if valid.size == 0:
             raise ValueError("GeoTIFF 中没有有效数据（全部为 NoData）")
 
-        vmin = float(np.nanpercentile(array, 2))
-        vmax = float(np.nanpercentile(array, 98))
-        data_range = vmax - vmin
+        mean_val = float(np.nanmean(array))
+        std_val = float(np.nanstd(array))
+        vmin = max(float(np.nanmin(array)), mean_val - 2 * std_val)
+        vmax = min(float(np.nanmax(array)), mean_val + 2 * std_val)
 
-        if data_range < 1e-9:
-            center = float(np.nanmean(array))
+        if (vmax - vmin) < 1e-9:
+            center = mean_val
             exp = max(abs(center) * 0.01, 1e-9)
             vmin = center - exp
             vmax = center + exp
 
-        print(f"[visualize_raster] 分位数拉伸: P2={vmin:.4f}, P98={vmax:.4f}，"
-              f"有效像元: {valid.size}/{array.size}")
+        print(f"[visualize_raster] 2σ 拉伸: vmin={vmin:.4f}, vmax={vmax:.4f}，"
+              f"mean={mean_val:.4f}, std={std_val:.4f}，有效像元: {valid.size}/{array.size}")
 
         # ── 栅格绘图 ─────────────────────────────────────────────────
         # 使用 masked array：NaN 区域自动不显示
@@ -2321,21 +2452,16 @@ class ContentMapper:
         ax.grid(True, linestyle='--', linewidth=0.5, alpha=0.4, color='gray')
         ax.set_axisbelow(True)
 
-        # ── 标题 ─────────────────────────────────────────────────────
-        if title:
-            ax.set_title(title, fontsize=13, fontweight='bold', pad=10)
-        elif param_name:
-            ax.set_title(param_name, fontsize=13, fontweight='bold', pad=10)
+        # ── 标题（中文）──────────────────────────────────────────────
+        ax.set_title(chinese_title, fontsize=13, fontweight='bold', pad=10)
 
-        # ── 颜色条 ───────────────────────────────────────────────────
+        # ── 颜色条（工业级样式：extend 三角 + MaxNLocator 刻度防重叠）─────────
         if show_colorbar and im is not None:
             try:
-                cbar = plt.colorbar(im, ax=ax, shrink=0.55, aspect=35, pad=0.02)
+                cbar = fig.colorbar(im, ax=ax, shrink=0.55, aspect=35, pad=0.02, extend='both')
                 cbar.set_label('Index Value', fontsize=10)
-                if data_range > 1e-9:
-                    ticks = np.linspace(vmin, vmax, 6)
-                    cbar.set_ticks(ticks)
-                    cbar.set_ticklabels([f'{t:.3f}' for t in ticks])
+                cbar.locator = MaxNLocator(nbins=6)
+                cbar.update_ticks()
                 print("[visualize_raster] 颜色条添加成功")
             except Exception as e:
                 print(f"[visualize_raster] 颜色条添加失败: {e}")

tmp_concentration_rescue.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Step9 面板 - 浓度反演（基于 QAA 物理反演的二次反演）
+Step10 面板 - 浓度反演（基于 QAA 物理反演的二次反演）
 """
 
 import os
@@ -18,8 +18,8 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 
-class Step9ConcentrationPanel(QWidget):
-    """步骤9：浓度反演（物理模型二次反演）"""
+class Step10ConcentrationPanel(QWidget):
+    """步骤10：浓度反演（物理模型二次反演）"""
     def __init__(self, parent=None):
         super().__init__(parent)
         self.init_ui()
@@ -27,7 +27,7 @@ class Step9ConcentrationPanel(QWidget):
     def init_ui(self):
         layout = QVBoxLayout()
 
-        title = QLabel("步骤9：浓度反演（物理模型二次反演）")
+        title = QLabel("步骤10：浓度反演（物理模型二次反演）")
         title.setFont(QFont("Arial", 12, QFont.Bold))
         layout.addWidget(title)
 

tmp_watercolor_rescue.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Step8 面板 - 水色指数反演（直接处理去耀斑 BSQ 影像）
+Step9 面板 - 水色指数反演（直接处理去耀斑 BSQ 影像）
 
 将 waterindex.csv 中的公式直接应用于去耀斑高光谱影像，
 输出各水质参数指数的 GeoTIFF 栅格图像。
@@ -98,8 +98,8 @@ class WaterIndexWorker(QThread):
         self.progress.emit(msg, pct)
 
 
-class Step8WaterIndexPanel(QWidget):
-    """步骤8：水色指数反演（直接处理 BSQ 影像）"""
+class Step11WaterColorPanel(QWidget):
+    """步骤11：水色指数反演（直接处理 BSQ 影像）"""
 
     def __init__(self, parent=None):
         super().__init__(parent)
@@ -115,7 +115,7 @@ class Step8WaterIndexPanel(QWidget):
         layout = QVBoxLayout()
 
         # ---- 标题 ----
-        title = QLabel("步骤8：水色指数反演（高光谱影像直接处理）")
+        title = QLabel("步骤11：水色指数反演（高光谱影像直接处理）")
         title.setFont(QFont("Arial", 12, QFont.Bold))
         layout.addWidget(title)