From 1949711cda06aad01efb5adbed00b125752464dc Mon Sep 17 00:00:00 2001
From: DXC <duxingchen@iris-rs.cn>
Date: Wed, 17 Jun 2026 15:35:02 +0800
Subject: [PATCH] =?UTF-8?q?refactor:=20=E6=8F=90=E5=8F=96=20WorkspaceManag?=
 =?UTF-8?q?er=EF=BC=8C=E5=B0=86=E6=96=87=E4=BB=B6=E6=89=AB=E6=8F=8F?=
 =?UTF-8?q?=E4=B8=8E=E8=B7=AF=E5=BE=84=E4=B8=9A=E5=8A=A1=E9=80=BB=E8=BE=91?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/core/workspace_manager.py | 231 ++++++++++++++++++++++++++++
 src/gui/water_quality_gui.py  | 275 +++-------------------------------
 2 files changed, 254 insertions(+), 252 deletions(-)
 create mode 100644 src/core/workspace_manager.py

diff --git a/src/core/workspace_manager.py b/src/core/workspace_manager.py
new file mode 100644
index 0000000..52f86e0
--- /dev/null
+++ b/src/core/workspace_manager.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+工作空间管理器
+
+负责工作目录文件扫描、步骤输出路径发现、配置裁剪等业务逻辑，
+与 GUI 组件解耦，不直接引用任何 UI 类。
+"""
+
+import copy
+from pathlib import Path
+
+
+class WorkspaceManager:
+    """管理步骤默认输出路径、文件扫描与配置裁剪"""
+
+    # 白名单：科学数据格式后缀
+    SCIENTIFIC_EXTENSIONS = {'.dat', '.tif', '.tiff', '.shp'}
+    # 临时文件关键词黑名单
+    TMP_KEYWORDS = ('__tmp', '_tmp')
+    # 掩膜类型集合
+    MASK_TYPES = {'water_mask', 'glint_mask', 'boundary_mask'}
+
+    def __init__(self):
+        self.step_default_outputs = {
+            'step1': "1_water_mask/water_mask_from_ndwi.dat",
+            'step2': "2_Glint_Detection/severe_glint_area.dat",
+            'step3': "3_deglint/deglint_goodman.bsq",
+            'step4_sampling': "4_sampling/sampling_spectra.csv",
+            'step5_clean': "5_Data_Cleaning/processed_data.csv",
+            'step6_feature': "6_Spectral_Feature_Extraction/training_spectra.csv",
+            'step7_index': "7_Water_Quality_Indices/training_spectra_indices.csv",
+            'step8_ml_train': "8_Supervised_Model_Training/",
+            'step9_ml_predict': "9_ML_Prediction/",
+            'step10_watercolor': "10_WaterIndex_Images/",
+            'step11_map': "14_visualization/"
+        }
+        self.step_outputs = {}
+
+    @staticmethod
+    def _is_scientific_mask(path_str):
+        """白名单判断：只有 .dat .tif .tiff .shp 才算科学数据格式"""
+        p = Path(path_str)
+        name_lower = str(path_str).lower()
+        if any(kw in name_lower for kw in WorkspaceManager.TMP_KEYWORDS):
+            return False
+        return p.suffix.lower() in WorkspaceManager.SCIENTIFIC_EXTENSIONS
+
+    def find_step_output(self, work_path, step_id, output_type, ref_img_path=None):
+        """查找指定步骤的输出文件
+
+        Args:
+            work_path: 工作目录 Path 对象
+            step_id: 步骤 ID
+            output_type: 输出类型（如 'water_mask', 'deglint_image' 等）
+            ref_img_path: 参考影像路径（仅 output_type='reference_img' 时需要）
+
+        Returns:
+            找到的文件路径字符串，或 None
+        """
+        if step_id not in self.step_default_outputs:
+            return None
+
+        raw = self.step_default_outputs[step_id]
+
+        rel_path = None
+        if isinstance(raw, str):
+            rel_path = raw
+        elif isinstance(raw, dict):
+            rel_path = raw.get(output_type) or list(raw.values())[0]
+
+        if not rel_path:
+            return None
+
+        # 特殊处理：从 step_outputs 记录中查找实际输出路径
+        if step_id in self.step_outputs:
+            actual_outputs = self.step_outputs[step_id]
+            if output_type in actual_outputs:
+                candidate = actual_outputs[output_type]
+                if output_type in self.MASK_TYPES and not self._is_scientific_mask(candidate):
+                    pass
+                else:
+                    return candidate
+
+        if output_type == 'water_mask':
+            if rel_path:
+                mask_path = work_path / rel_path
+                if mask_path.exists():
+                    return str(mask_path)
+        elif output_type == 'reference_img':
+            if ref_img_path and Path(ref_img_path).exists():
+                return ref_img_path
+        elif output_type == 'deglint_image':
+            if rel_path:
+                deglint_path = work_path / rel_path
+                if deglint_path.exists():
+                    return str(deglint_path)
+            deglint_dir = work_path / "3_deglint"
+            if deglint_dir.exists():
+                for file_path in deglint_dir.glob("deglint_*.bsq"):
+                    return str(file_path)
+                for file_path in deglint_dir.glob("interpolated_*.bsq"):
+                    return str(file_path)
+        elif rel_path:
+            if rel_path.endswith('/'):
+                output_path = work_path / rel_path.rstrip('/')
+                if output_path.exists() and output_path.is_dir():
+                    return str(output_path)
+            else:
+                output_path = work_path / rel_path
+                if output_path.exists():
+                    return str(output_path)
+
+        return None
+
+    def scan_work_directory_for_files(self, work_path):
+        """扫描工作目录，自动发现各步骤的输出文件
+
+        Returns:
+            discovered_outputs: dict, {step_id: {output_type: path_str}}
+        """
+        discovered_outputs = {}
+
+        subdirs = {
+            '1_water_mask': 'step1',
+            '2_Glint_Detection': 'step2',
+            '3_deglint': 'step3',
+            '5_Data_Cleaning': 'step5_clean',
+            '6_Spectral_Feature_Extraction': 'step6_feature',
+            '7_Water_Quality_Indices': 'step7_index',
+            '8_Supervised_Model_Training': 'step8_ml_train',
+            '8_Regression_Modeling': 'step8_ml_train',
+            '13_Custom_Regression': 'step13',
+            '9_ML_Prediction': 'step9_ml_predict',
+            '11_12_13_predictions/Non_Empirical_Prediction': 'step11_map',
+            '13_Custom_Regression/Custom_Regression_Prediction': 'step13',
+            '14_visualization': 'step13_report',
+            '10_geotiff_batch_rendering': 'step11_map'
+        }
+
+        for subdir, step_ids in subdirs.items():
+            subdir_path = work_path / subdir
+            if not subdir_path.exists():
+                continue
+
+            if isinstance(step_ids, str):
+                step_ids = [step_ids]
+
+            for file_path in subdir_path.rglob('*'):
+                if file_path.is_file():
+                    file_name = file_path.name.lower()
+
+                    for step_id in step_ids:
+                        if step_id not in discovered_outputs:
+                            discovered_outputs[step_id] = {}
+
+                        if 'water_mask' in file_name and step_id == 'step1':
+                            if self._is_scientific_mask(file_path):
+                                discovered_outputs[step_id]['water_mask'] = str(file_path)
+                        elif 'glint' in file_name and 'mask' in file_name and step_id == 'step2':
+                            if self._is_scientific_mask(file_path):
+                                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_sampling':
+                            discovered_outputs[step_id]['processed_data'] = str(file_path)
+                        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_feature':
+                            discovered_outputs[step_id]['water_indices'] = str(file_path)
+                        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 ['step9_ml_predict', 'step11_map', 'step12_viz']:
+                            discovered_outputs[step_id]['predictions'] = str(file_path)
+
+        for step_id, outputs in discovered_outputs.items():
+            if step_id not in self.step_outputs:
+                self.step_outputs[step_id] = {}
+            self.step_outputs[step_id].update(outputs)
+
+        return discovered_outputs
+
+    def update_step_outputs(self, step_name, work_path):
+        """更新指定步骤的输出路径记录"""
+        if step_name not in self.step_default_outputs:
+            return
+
+        step_outputs = self.step_default_outputs[step_name]
+
+        for output_type, relative_path in step_outputs.items():
+            if '*' in relative_path:
+                pattern_path = work_path / relative_path.replace('*', '*')
+                matching_files = list(pattern_path.parent.glob(pattern_path.name))
+                if matching_files:
+                    latest_file = max(matching_files, key=lambda p: p.stat().st_mtime)
+                    self.step_outputs[step_name][output_type] = str(latest_file)
+            else:
+                output_path = work_path / relative_path
+                if output_path.exists():
+                    self.step_outputs[step_name][output_type] = str(output_path)
+
+    @staticmethod
+    def prune_config_for_prediction_mode(config: dict) -> dict:
+        """Prediction-only 模式：禁用训练相关步骤，保留预测和成图步骤。
+
+        被禁用的 step dict 中统一写入 'enabled': False，
+        这些配置最终传给 PipelineRunner，Runner 会跳过它们。
+        同时，被跳过的步骤的 required_input_files 在 build_missing_items
+        中不会被检查，从而自然规避了"CSV 缺失"等训练模式下的误报。
+
+        Args:
+            config: 完整配置字典（来自 get_current_config）
+
+        Returns:
+            裁剪后的 config（深拷贝，原 config 不被修改）
+        """
+        cfg = copy.deepcopy(config)
+
+        training_steps = [
+            "step4",
+            "step5",
+            "step7",
+            "step6",
+            "step8_non_empirical_modeling",
+            "step9",
+        ]
+        for step_id in training_steps:
+            step_cfg = cfg.setdefault(step_id, {})
+            step_cfg["enabled"] = False
+
+        return cfg
diff --git a/src/gui/water_quality_gui.py b/src/gui/water_quality_gui.py
index 7fecefd..88417a1 100644
--- a/src/gui/water_quality_gui.py
+++ b/src/gui/water_quality_gui.py
@@ -158,6 +158,7 @@ from src.gui.core.worker_thread import (
 from src.gui.core.preflight_dialog import PreflightDialog
 from src.gui.core.pipeline_mode_dialog import PipelineModeDialog
 from src.gui.core.viz_thread import VisualizationWorkerThread, _viz_training_spectra_csv_path
+from src.core.workspace_manager import WorkspaceManager
 
 
 class WaterQualityGUI(QMainWindow):
@@ -183,10 +184,10 @@ class WaterQualityGUI(QMainWindow):
         # 训练数据模式状态
         self.has_training_data = True  # 默认有训练数据
 
-        # 步骤输出路径记录
-        self.step_outputs = {}  # 记录每个步骤的输出路径
+        # 工作空间管理器（文件扫描、路径发现、配置裁剪）
+        self.workspace_manager = WorkspaceManager()
 
-        # 定义步骤依赖关系和标准输出路径
+        # 定义步骤依赖关系
         self._init_step_dependencies()
 
         self.init_ui()
@@ -198,22 +199,7 @@ class WaterQualityGUI(QMainWindow):
         QTimer.singleShot(100, self.init_workspace)
 
     def _init_step_dependencies(self):
-        """初始化步骤依赖关系和标准输出路径"""
-        # 定义每个步骤的标准输出路径模式（相对于工作目录）
-        self.step_default_outputs = {
-            'step1': "1_water_mask/water_mask_from_ndwi.dat",
-            'step2': "2_Glint_Detection/severe_glint_area.dat",
-            'step3': "3_deglint/deglint_goodman.bsq",
-            'step4_sampling': "4_sampling/sampling_spectra.csv",
-            'step5_clean': "5_Data_Cleaning/processed_data.csv",
-            'step6_feature': "6_Spectral_Feature_Extraction/training_spectra.csv",
-            'step7_index': "7_Water_Quality_Indices/training_spectra_indices.csv",
-            'step8_ml_train': "8_Supervised_Model_Training/",
-            'step9_ml_predict': "9_ML_Prediction/",
-            'step10_watercolor': "10_WaterIndex_Images/",
-            'step11_map': "14_visualization/"
-        }
-        
+        """初始化步骤依赖关系"""
         # 依赖关系字典结构:
         # '当前步骤ID': { '依赖参数名': ('上游步骤ID', '上游输出类型/Key', '当前步骤接收该路径的组件属性名') }
         self.step_dependencies = {
@@ -1081,7 +1067,11 @@ class WaterQualityGUI(QMainWindow):
         
         dependencies = self.step_dependencies[step_id]
         filled_count = 0
-        
+
+        ref_img_path = None
+        if hasattr(self, 'step1_panel'):
+            ref_img_path = self.step1_panel.img_file.get_path()
+
         for input_field, (dep_step, output_type, panel_attr) in dependencies.items():
             # 检查面板是否有对应的属性
             if not hasattr(panel, panel_attr):
@@ -1101,7 +1091,7 @@ class WaterQualityGUI(QMainWindow):
                 continue
 
             # 查找依赖步骤的输出文件
-            output_path = self.find_step_output(work_path, dep_step, output_type)
+            output_path = self.workspace_manager.find_step_output(work_path, dep_step, output_type, ref_img_path=ref_img_path)
 
             if output_path and Path(output_path).exists():
                 # ★ 兼容 FileSelectWidget 与原生 QLineEdit
@@ -1132,173 +1122,6 @@ class WaterQualityGUI(QMainWindow):
         }
         return panel_map.get(step_id)
 
-    def find_step_output(self, work_path, step_id, output_type):
-        """查找指定步骤的输出文件"""
-        if step_id not in self.step_default_outputs:
-            return None
-
-        raw = self.step_default_outputs[step_id]
-
-        # ★ 兼容扁平化后的纯字符串路径格式
-        rel_path = None
-        if isinstance(raw, str):
-            rel_path = raw
-        elif isinstance(raw, dict):
-            rel_path = raw.get(output_type) or list(raw.values())[0]
-
-        if not rel_path:
-            return None
-
-        # ★ 掩膜类型列表：这些类型只接受科学数据格式
-        mask_types = {'water_mask', 'glint_mask', 'boundary_mask'}
-        # ★ 白名单机制：只允许 .dat .tif .tiff .shp，拒绝其他一切格式
-        scientific_extensions = {'.dat', '.tif', '.tiff', '.shp'}
-        # ★ 临时文件关键词黑名单
-        tmp_keywords = ('__tmp', '_tmp')
-
-        def _is_scientific_mask(path_str):
-            """白名单判断：只有 .dat .tif .tiff .shp 才算科学数据格式"""
-            p = Path(path_str)
-            name_lower = str(path_str).lower()
-            # 拒绝临时文件
-            if any(kw in name_lower for kw in tmp_keywords):
-                return False
-            # 白名单校验
-            return p.suffix.lower() in scientific_extensions
-
-        # 特殊处理：从step_outputs记录中查找实际输出路径
-        if step_id in self.step_outputs:
-            actual_outputs = self.step_outputs[step_id]
-            if output_type in actual_outputs:
-                candidate = actual_outputs[output_type]
-                # ★ 掩膜类型白名单二次校验：不在白名单内的一律拒绝
-                if output_type in mask_types and not _is_scientific_mask(candidate):
-                    # 非科学格式被拒绝，不使用 step_outputs 中的值
-                    pass
-                else:
-                    return candidate
-
-        # 根据输出类型查找对应的文件
-        if output_type == 'water_mask':
-            # 水域掩膜：直接用统一路径
-            if rel_path:
-                mask_path = work_path / rel_path
-                if mask_path.exists():
-                    return str(mask_path)
-        elif output_type == 'reference_img':
-            # 参考影像：从step1的配置中获取用户输入的影像路径
-            if hasattr(self, 'step1_panel'):
-                img_path = self.step1_panel.img_file.get_path()
-                if img_path and Path(img_path).exists():
-                    return img_path
-        elif output_type == 'deglint_image':
-            # 去耀斑影像：直接用统一路径
-            if rel_path:
-                deglint_path = work_path / rel_path
-                if deglint_path.exists():
-                    return str(deglint_path)
-            # 还要检查 Kutser 算法输出与插值方法生成的文件
-            deglint_dir = work_path / "3_deglint"
-            if deglint_dir.exists():
-                for file_path in deglint_dir.glob("deglint_*.bsq"):
-                    return str(file_path)
-                for file_path in deglint_dir.glob("interpolated_*.bsq"):
-                    return str(file_path)
-        elif rel_path:
-            # 直接匹配的输出类型（统一使用 rel_path）
-            if rel_path.endswith('/'):
-                # 是目录
-                output_path = work_path / rel_path.rstrip('/')
-                if output_path.exists() and output_path.is_dir():
-                    return str(output_path)
-            else:
-                # 是文件
-                output_path = work_path / rel_path
-                if output_path.exists():
-                    return str(output_path)
-
-        return None
-
-    def scan_work_directory_for_files(self, work_path):
-        """扫描工作目录，自动发现各步骤的输出文件"""
-        discovered_outputs = {}
-        
-        # 扫描各个子目录
-        subdirs = {
-            '1_water_mask': 'step1',
-            '2_Glint_Detection': 'step2',
-            '3_deglint': 'step3',
-            '5_Data_Cleaning': 'step5_clean',
-            '6_Spectral_Feature_Extraction': 'step6_feature',
-            '7_Water_Quality_Indices': 'step7_index',
-            '8_Supervised_Model_Training': 'step8_ml_train',
-            '8_Regression_Modeling': 'step8_ml_train',
-            '13_Custom_Regression': 'step13',
-            '9_ML_Prediction': 'step9_ml_predict',
-            '11_12_13_predictions/Non_Empirical_Prediction': 'step11_map',
-            '13_Custom_Regression/Custom_Regression_Prediction': 'step13',
-            '14_visualization': 'step13_report',
-            '10_geotiff_batch_rendering': 'step11_map'
-        }
-        
-        for subdir, step_ids in subdirs.items():
-            subdir_path = work_path / subdir
-            if not subdir_path.exists():
-                continue
-            
-            if isinstance(step_ids, str):
-                step_ids = [step_ids]
-            
-            # 扫描该目录下的文件
-            for file_path in subdir_path.rglob('*'):
-                if file_path.is_file():
-                    file_name = file_path.name.lower()
-                    
-                    # 根据文件名模式判断输出类型
-                    for step_id in step_ids:
-                        if step_id not in discovered_outputs:
-                            discovered_outputs[step_id] = {}
-
-                        # ★ 掩膜文件白名单过滤：只有 .dat .tif .tiff .shp 才通过，拒绝 .hdr .xml .png 等
-                        scientific_extensions = {'.dat', '.tif', '.tiff', '.shp'}
-                        tmp_keywords = ('__tmp', '_tmp')
-
-                        def _is_scientific_mask(path_str):
-                            """白名单判断：拒绝 .hdr .xml 临时文件等，只接受科学数据格式"""
-                            p = Path(path_str)
-                            name_lower = str(path_str).lower()
-                            if any(kw in name_lower for kw in tmp_keywords):
-                                return False
-                            return p.suffix.lower() in scientific_extensions
-
-                        # 匹配不同的文件类型
-                        if 'water_mask' in file_name and step_id == 'step1':
-                            if _is_scientific_mask(file_path):
-                                discovered_outputs[step_id]['water_mask'] = str(file_path)
-                        elif 'glint' in file_name and 'mask' in file_name and step_id == 'step2':
-                            if _is_scientific_mask(file_path):
-                                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_sampling':
-                            discovered_outputs[step_id]['processed_data'] = str(file_path)
-                        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_feature':
-                            discovered_outputs[step_id]['water_indices'] = str(file_path)
-                        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 ['step9_ml_predict', 'step11_map', 'step12_viz']:
-                            discovered_outputs[step_id]['predictions'] = str(file_path)
-        
-        # 更新内部记录
-        for step_id, outputs in discovered_outputs.items():
-            if step_id not in self.step_outputs:
-                self.step_outputs[step_id] = {}
-            self.step_outputs[step_id].update(outputs)
-        
-        return discovered_outputs
-
     def auto_populate_all_steps(self):
         """自动填充所有步骤的输入路径"""
         work_dir = getattr(self, 'work_dir', './work_dir')
@@ -1309,7 +1132,7 @@ class WaterQualityGUI(QMainWindow):
             return
         
         # 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)
+        self.workspace_manager.scan_work_directory_for_files(work_path)
         
         step_order = ['step2', 'step3', 'step4_sampling', 'step5_clean', 'step6_feature', 'step7_index',
                      'step8_ml_train', 'step9_ml_predict', 'step11_map', 'step12_viz', 'step13_report']
@@ -1612,41 +1435,6 @@ class WaterQualityGUI(QMainWindow):
 
         return True
 
-    # ------------------------------------------------------------------
-    # ★ 全流程模式动态裁剪
-    # ------------------------------------------------------------------
-
-    def _prune_config_for_prediction_mode(self, config: dict) -> dict:
-        """Prediction-only 模式：禁用训练相关步骤，保留预测和成图步骤。
-
-        被禁用的 step dict 中统一写入 'enabled': False，
-        这些配置最终传给 PipelineRunner，Runner 会跳过它们。
-        同时，被跳过的步骤的 required_input_files 在 build_missing_items
-        中不会被检查，从而自然规避了"CSV 缺失"等训练模式下的误报。
-
-        Args:
-            config: 完整配置字典（来自 get_current_config）
-
-        Returns:
-            裁剪后的 config（深拷贝，原 config 不被修改）
-        """
-        cfg = copy.deepcopy(config)
-
-        # 在每个训练相关步骤的 dict 中写入 enabled=False
-        training_steps = [
-            "step4",                          # CSV 实测数据清洗
-            "step5",                          # 实测点光谱提取（→ training_csv_path）
-            "step7",                          # ML 监督建模
-            "step6",                          # 水质指数计算（辅助训练）
-            "step8_non_empirical_modeling",    # 非经验回归建模
-            "step9",                          # 自定义回归建模
-        ]
-        for step_id in training_steps:
-            step_cfg = cfg.setdefault(step_id, {})
-            step_cfg["enabled"] = False
-
-        return cfg
-
     def run_full_pipeline(self):
         """运行完整流程"""
         if not PIPELINE_AVAILABLE:
@@ -1665,7 +1453,7 @@ class WaterQualityGUI(QMainWindow):
         # ── 1) 运行前智能预检与自动回填（硬盘已有产物自动跳过） ──
         work_path = Path(work_dir)
         self.log_message("正在进行运行前环境预检与自动扫描...", "info")
-        self.scan_work_directory_for_files(work_path)
+        self.workspace_manager.scan_work_directory_for_files(work_path)
         self.auto_populate_all_steps()
         self.log_message("✓ 预检完成：已扫描工作目录并自动回填已落盘的产物", "info")
 
@@ -1685,7 +1473,7 @@ class WaterQualityGUI(QMainWindow):
 
         # ── 2.1) ★ 根据模式动态裁剪配置 ──
         if selected_mode == "prediction_only":
-            config = self._prune_config_for_prediction_mode(config)
+            config = self.workspace_manager.prune_config_for_prediction_mode(config)
             self.log_message("[模式选择] 已裁剪训练相关步骤（step4/5/7/8），进入仅预测模式", "info")
 
         # ── 3) ★ 一次性全预检 + 用户交互式决策 ──
@@ -1798,38 +1586,21 @@ class WaterQualityGUI(QMainWindow):
         work_path = Path(work_dir)
         
         # 根据步骤名称和约定路径，记录实际输出
-        if step_name not in self.step_outputs:
-            self.step_outputs[step_name] = {}
-        
+        if step_name not in self.workspace_manager.step_outputs:
+            self.workspace_manager.step_outputs[step_name] = {}
+
         # 扫描工作目录，更新该步骤的输出路径
-        self.update_step_outputs(step_name, work_path)
+        self.workspace_manager.update_step_outputs(step_name, work_path)
         
         # 自动填充依赖该步骤输出的后续步骤
         self.auto_populate_dependent_steps(step_name)
 
-    def update_step_outputs(self, step_name, work_path):
-        """更新指定步骤的输出路径记录"""
-        if step_name not in self.step_default_outputs:
-            return
-        
-        step_outputs = self.step_default_outputs[step_name]
-        
-        for output_type, relative_path in step_outputs.items():
-            if '*' in relative_path:
-                # 处理通配符路径
-                pattern_path = work_path / relative_path.replace('*', '*')
-                matching_files = list(pattern_path.parent.glob(pattern_path.name))
-                if matching_files:
-                    # 选择最新的文件
-                    latest_file = max(matching_files, key=lambda p: p.stat().st_mtime)
-                    self.step_outputs[step_name][output_type] = str(latest_file)
-            else:
-                output_path = work_path / relative_path
-                if output_path.exists():
-                    self.step_outputs[step_name][output_type] = str(output_path)
-
     def auto_populate_dependent_steps(self, completed_step):
         """自动填充依赖于已完成步骤的后续步骤"""
+        ref_img_path = None
+        if hasattr(self, 'step1_panel'):
+            ref_img_path = self.step1_panel.img_file.get_path()
+
         for step_id, dependencies in self.step_dependencies.items():
             for input_field, (dep_step, output_type, panel_attr) in dependencies.items():
                 if dep_step == completed_step:
@@ -1841,7 +1612,7 @@ class WaterQualityGUI(QMainWindow):
                         if not file_widget.get_path().strip():
                             work_dir = getattr(self, 'work_dir', './work_dir')
                             work_path = Path(work_dir)
-                            output_path = self.find_step_output(work_path, dep_step, output_type)
+                            output_path = self.workspace_manager.find_step_output(work_path, dep_step, output_type, ref_img_path=ref_img_path)
                             if output_path and Path(output_path).exists():
                                 file_widget.set_path(output_path)
                                 self.log_message(f"步骤完成后自动填充 {step_id}.{input_field}: {output_path}", "info")