fix(runner): 14 Facade kwargs 兜底 + 4 spec parameter_map 修正 + step6_75 路由切到 indices

- 14 个 stepX_... Facade 形参表末尾加 **kwargs，杜绝 Runner 注入未声明 key 时的 TypeError（典型：step3 收到 glint_mask_path）

- runner._invoke user_overrides 合并加 v is not None and v != '' 过滤，避免 GUI 面板空值覆盖 ctx 中已写入的有效路径

- PIPELINE_STEPS 加 4 个 parameter_map 修正 ctx 字段名→形参名错位：step6_5/6_75: training_csv_path→csv_path；step8_5: models_dir→non_empirical_models_dir；step8_75: models_dir→custom_regression_dir

- step6_75 路由从 training_csv_path 切到 indices_path（requires + parameter_map 同步）；配合 skip_when_missing，未跑 step5_5 时自动 skip

- worker_thread.py: mode='full' 切到 PipelineRunner + PipelineContext 调度

src/core/pipeline/runner.py

@@ -84,11 +84,13 @@ PIPELINE_STEPS: List[StepSpec] = [
     StepSpec(
         step_id="step6_5", method_name="step6_5_non_empirical_modeling",
         requires=["training_csv_path"], produces=["models_dir"],
+        parameter_map={"training_csv_path": "csv_path"},
         description="非经验统计回归",
     ),
     StepSpec(
         step_id="step6_75", method_name="step6_75_custom_regression",
-        requires=["training_csv_path"], produces=["models_dir"],
+        requires=["indices_path"], produces=["models_dir"],
+        parameter_map={"indices_path": "csv_path"},
         description="自定义回归分析",
     ),
     StepSpec(
@@ -104,12 +106,14 @@ PIPELINE_STEPS: List[StepSpec] = [
     StepSpec(
         step_id="step8_5", method_name="step8_5_predict_with_non_empirical_models",
         requires=["sampling_csv_path", "models_dir"], produces=["prediction_dir"],
+        parameter_map={"models_dir": "non_empirical_models_dir"},
         description="非经验模型预测",
     ),
     StepSpec(
         step_id="step8_75", method_name="step8_75_predict_with_custom_regression",
         requires=["sampling_csv_path", "models_dir", "formula_csv_path"],
         produces=["prediction_dir"],
+        parameter_map={"models_dir": "custom_regression_dir"},
         description="自定义回归预测",
     ),
     StepSpec(
@@ -185,7 +189,10 @@ class PipelineRunner:
         # 2) 允许用户在 ctx.user_config[step_id] 覆盖/补充
         user_overrides = ctx.user_config.get(spec.step_id) or {}
         if isinstance(user_overrides, dict):
-            kwargs.update(user_overrides)
+            for k, v in user_overrides.items():
+                # ★ 关键防御：绝不用 GUI 的“空字符串”或 None 覆盖上游传来的有效路径
+                if v is not None and v != "":
+                    kwargs[k] = v
 
         # 3) 状态置 start
         ctx.append_log(

src/core/water_quality_inversion_pipeline_GUI.py

@@ -267,7 +267,7 @@ class WaterQualityInversionPipeline:
                                    use_ndwi: bool = False,
                                    skip_dependency_check: bool = False,
                                    generate_png: bool = True,
-                                   output_path: Optional[str] = None) -> str:
+                                   output_path: Optional[str] = None, **kwargs) -> str:
         """步骤1: 生成或设置水域mask（Facade）"""
         step_start_time = time.time()
         try:
@@ -392,7 +392,7 @@ class WaterQualityInversionPipeline:
                                max_area: Optional[int] = None,
                                buffer_size: Optional[int] = None,
                                water_mask_path: Optional[str] = None,
-                               skip_dependency_check: bool = False) -> str:
+                               skip_dependency_check: bool = False, **kwargs) -> str:
         """步骤2: 找到耀斑区域（Facade）"""
         step_start_time = time.time()
         try:
@@ -533,7 +533,7 @@ class WaterQualityInversionPipeline:
                            sugar_iter: Optional[int] = 3,
                            sugar_termination_thresh: float = 20.0,
                            output_path: Optional[str] = None,
-                           skip_dependency_check: bool = False) -> str:
+                           skip_dependency_check: bool = False, **kwargs) -> str:
         """步骤3: 去除耀斑（Facade）"""
         step_start_time = time.time()
         try:
@@ -588,7 +588,7 @@ class WaterQualityInversionPipeline:
                                   status="failed", error=str(e))
             raise
 
-    def step4_process_csv(self, csv_path: str, skip_dependency_check: bool = False) -> str:
+    def step4_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
 
@@ -615,7 +615,7 @@ class WaterQualityInversionPipeline:
                                        csv_path: Optional[str] = None,
                                        boundary_path: Optional[str] = None,
                                        glint_mask_path: Optional[str] = None,
-                                       skip_dependency_check: bool = False) -> str:
+                                       skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤5: 根据csv文件的采样点坐标，在去除耀斑的文件中统计采样点的平均光谱
 
@@ -666,7 +666,7 @@ class WaterQualityInversionPipeline:
                                                formula_names: Optional[List[str]] = None,
                                                output_file: Optional[str] = None,
                                                enabled: bool = True,
-                                               skip_dependency_check: bool = False) -> str:
+                                               skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤5.5: 根据训练光谱计算水质光谱指数
 
@@ -710,7 +710,7 @@ class WaterQualityInversionPipeline:
                            split_methods: List[str] = None,
                            cv_folds: int = 5,
                            training_csv_path: Optional[str] = None,
-                           skip_dependency_check: bool = False) -> str:
+                           skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤6: 使用采样点的平均光谱和对应的实测值建立机器学习模型，保存模型权重
         
@@ -753,7 +753,7 @@ class WaterQualityInversionPipeline:
                                        chunk_size: int = 1000,
                                        water_mask_path: Optional[str] = None,
                                        glint_mask_path: Optional[str] = None,
-                                       skip_dependency_check: bool = False) -> str:
+                                       skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤7: 生成根据水域掩膜内且耀斑掩膜外的采样点，统计采样点的平均光谱
         
@@ -795,7 +795,7 @@ class WaterQualityInversionPipeline:
                                     models_dir: Optional[str] = None,
                                     metric: str = 'test_r2',
                                     prediction_column: str = 'prediction',
-                                    skip_dependency_check: bool = False) -> Dict[str, str]:
+                                    skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
         """
         步骤8: 将训练好的最佳机器学习模型应用到采样点的平均光谱上，预测水质参数
         
@@ -835,7 +835,7 @@ class WaterQualityInversionPipeline:
                                          diffusion_n_neighbors: int = 15,
                                          cmap: Optional[str] = None,
                                          expand_ratio: float = 0.05,
-                                         skip_dependency_check: bool = False) -> str:
+                                         skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤9: 根据采样点的坐标和反演的实测参数，以及水域掩膜，通过插值的方法，得到水质参数的可视化分布图
         
@@ -1764,7 +1764,7 @@ class WaterQualityInversionPipeline:
                                       window: int = 5,
                                       output_dir: Optional[str] = None,
                                       enabled: bool = True,
-                                      skip_dependency_check: bool = False) -> Dict[str, str]:
+                                      skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
         """
         步骤6.5: 非经验统计回归模型训练
         
@@ -1812,7 +1812,7 @@ class WaterQualityInversionPipeline:
                                    methods: Union[str, List[str]] = 'all',
                                    output_dir: Optional[str] = None,
                                    enabled: bool = True,
-                                   skip_dependency_check: bool = False) -> str:
+                                   skip_dependency_check: bool = False, **kwargs) -> str:
         """
         步骤6.75: 使用自定义回归方法分析指标与目标参数之间的关系
         """
@@ -1991,7 +1991,7 @@ class WaterQualityInversionPipeline:
                                                metric: str = 'Average Accuracy(%)',
                                                prediction_column: str = 'prediction',
                                                enabled: bool = True,
-                                               skip_dependency_check: bool = False) -> Dict[str, str]:
+                                               skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
         """
         步骤8.5: 使用非经验统计回归模型进行参数预测
 
@@ -2028,7 +2028,7 @@ class WaterQualityInversionPipeline:
                                                output_dir: Optional[str] = None,
                                                filename_prefix: str = "custom_regression_prediction",
                                                enabled: bool = True,
-                                               skip_dependency_check: bool = False) -> Dict[str, str]:
+                                               skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
         """
         步骤8.75: 使用自定义回归模型进行参数预测
         

src/gui/core/worker_thread.py

@@ -247,16 +247,34 @@ class WorkerThread(QThread):
             mpl_prev = None
         try:
             from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
+            from src.core.pipeline.runner import PipelineRunner
+            from src.core.pipeline.context import PipelineContext
+
             self.pipeline = WaterQualityInversionPipeline(work_dir=self.work_dir)
 
             if self.mode == 'full':
-                self.log_message.emit("开始运行完整流程...", "info")
+                self.log_message.emit("开始运行完整流程 (Runner 调度模式)...", "info")
-                self.step_count = 0
-
                 if hasattr(self.pipeline, 'set_callback'):
                     self.pipeline.set_callback(self.pipeline_callback)
 
-                self.pipeline.run_full_pipeline(self.config)
+                # 构造上下文 (Ctx)，将 config 整体注入 user_config
+                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('step9', {}).get('boundary_shp_path'),
+                    formula_csv_path=self.config.get('step8_75', {}).get('formula_csv_path'),
+                    work_dir=self.work_dir,
+                    user_config=self.config
+                )
+
+                # 启动新调度器
+                runner = PipelineRunner(self.pipeline)
+                result_ctx = runner.run(ctx)
+
+                if result_ctx.last_error:
+                    raise RuntimeError(f"流水线执行失败: {result_ctx.last_error}")
 
                 self.progress_update.emit(100, "流程执行完成")
                 self.finished.emit(True, "完整流程执行成功！")