refactor: 引入 EventBus 事件总线，实现各步骤面板间的去中心化自动参数传导，完成最终解耦

src/core/workspace_manager.py

@@ -10,6 +10,8 @@
 import copy
 from pathlib import Path
 
+from src.gui.core.event_bus import global_event_bus
+
 
 class WorkspaceManager:
     """管理步骤默认输出路径、文件扫描与配置裁剪"""
@@ -23,19 +25,40 @@ class WorkspaceManager:
 
     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/"
+            'step1': {'water_mask': "1_water_mask/water_mask_from_ndwi.dat"},
+            'step2': {'glint_mask': "2_Glint_Detection/severe_glint_area.dat"},
+            'step3': {'deglint_image': "3_deglint/deglint_goodman.bsq"},
+            'step4_sampling': {'sampling_points': "4_sampling/sampling_spectra.csv"},
+            'step5_clean': {'processed_data': "5_Data_Cleaning/processed_data.csv"},
+            'step6_feature': {'training_spectra': "6_Spectral_Feature_Extraction/training_spectra.csv"},
+            'step7_index': {'training_spectra_indices': "7_Water_Quality_Indices/training_spectra_indices.csv"},
+            'step8_ml_train': {'Supervised_Model_Training': "8_Supervised_Model_Training/"},
+            'step9_ml_predict': {'9_ML_Prediction': "9_ML_Prediction/"},
+            'step10_watercolor': {'WaterIndex_Images': "10_WaterIndex_Images/"},
+            'step11_map': {'14_visualization': "14_visualization/"},
         }
         self.step_outputs = {}
+        # pipeline step_id → panel step_id 映射（由 WaterQualityGUI 注入）
+        self._pipeline_to_panel = {}
+
+    def set_step_id_mapping(self, mapping: dict):
+        """注入 pipeline step_id → panel step_id 映射，用于事件发布时统一 ID。"""
+        self._pipeline_to_panel = mapping
+
+    def _publish_outputs(self, step_id: str, outputs: dict):
+        """将发现的产出发布到 EventBus。
+
+        Args:
+            step_id: 面板 step_id（如 'step1', 'step5_clean'）
+            outputs: {output_type: path_str}
+        """
+        for output_type, path in outputs.items():
+            if path:
+                global_event_bus.publish('OutputUpdated', {
+                    'step_id': step_id,
+                    'output_type': output_type,
+                    'path': path,
+                })
 
     @staticmethod
     def _is_scientific_mask(path_str):
@@ -177,15 +200,25 @@ class WorkspaceManager:
             if step_id not in self.step_outputs:
                 self.step_outputs[step_id] = {}
             self.step_outputs[step_id].update(outputs)
+            # ★ 发布 EventBus 事件，驱动下游面板自动填充
+            self._publish_outputs(step_id, 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:
+        """更新指定步骤的输出路径记录并发布 EventBus 事件。
+
+        step_name 可能是 pipeline step_id（如 'step4'），
+        会先通过 _pipeline_to_panel 映射为面板 step_id（如 'step5_clean'）。
+        """
+        # 映射 pipeline step_id → panel step_id
+        panel_step_id = self._pipeline_to_panel.get(step_name, step_name)
+
+        if panel_step_id not in self.step_default_outputs:
             return
 
-        step_outputs = self.step_default_outputs[step_name]
+        step_outputs = self.step_default_outputs[panel_step_id]
+        published = {}
 
         for output_type, relative_path in step_outputs.items():
             if '*' in relative_path:
@@ -193,11 +226,18 @@ class WorkspaceManager:
                 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)
+                    path_str = str(latest_file)
+                    self.step_outputs.setdefault(panel_step_id, {})[output_type] = path_str
+                    published[output_type] = path_str
             else:
                 output_path = work_path / relative_path
                 if output_path.exists():
-                    self.step_outputs[step_name][output_type] = str(output_path)
+                    path_str = str(output_path)
+                    self.step_outputs.setdefault(panel_step_id, {})[output_type] = path_str
+                    published[output_type] = path_str
+
+        if published:
+            self._publish_outputs(panel_step_id, published)
 
     @staticmethod
     def prune_config_for_prediction_mode(config: dict) -> dict: