fix: ML Pipeline 缺失值填充 + 推理端外部模型 dict 兼容

- modeling_batch: Pipeline 首步新增 SimpleImputer(median) 填充 NaN
- inference_batch: 外部模型支持完整 dict（含 metadata/train_wavelengths），
  兼容旧版裸 Pipeline 对象
- step9_ml_predict_panel: 模型加载保留完整 dict 而非仅 model 对象，
  确保推理端可从 train_wavelengths 做光谱重采样

src/core/modeling/modeling_batch.py

@@ -20,6 +20,7 @@ from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, Extra
 from sklearn.tree import DecisionTreeRegressor
 from sklearn.neural_network import MLPRegressor
 from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
 from joblib import parallel_backend
 # 第三方模型导入
 # try:
@@ -611,6 +612,7 @@ class WaterQualityModelingBatch:
         # ============ 关键：把预处理器塞进 Pipeline ============
         preproc = get_preprocessing_transformer(preprocess_method)
         pipeline = Pipeline([
+            ('imputer', SimpleImputer(strategy='median')),
             ('preproc', preproc),
             ('model', base_model),
         ])

src/core/prediction/inference_batch.py

@@ -48,9 +48,15 @@ class WaterQualityInference:
 
         # 规范化 loaded_model_data：始终为 dict，确保 ['model'] 访问不崩溃
         if external_model is not None:
-            # 外部传入的是裸模型对象 → 包装为 dict，统一后续 .get('model') 访问
-            self.loaded_model_data = {'model': external_model, 'preprocess_method': 'None'}
-            print(f"  外部模型已规范化: type={type(external_model).__name__}")
+            # ★ 外部模型可能是完整 dict（含 model + metadata + train_wavelengths），
+            #   也可能是裸 Pipeline 对象（旧版兼容）
+            if isinstance(external_model, dict) and 'model' in external_model:
+                self.loaded_model_data = external_model
+                print(f"  外部模型已规范化: dict (含 metadata)")
+            else:
+                self.loaded_model_data = {'model': external_model,
+                                          'preprocess_method': 'None'}
+                print(f"  外部模型已规范化: type={type(external_model).__name__}")
         else:
             self.loaded_model_data = None
 

src/gui/panels/step9_ml_predict_panel.py

@@ -253,13 +253,14 @@ class Step9MlPredictPanel(QWidget):
                 try:
                     loaded = joblib.load(joblib_path)
                     if isinstance(loaded, dict) and "model" in loaded:
-                        model_obj = loaded["model"]
+                        # ★ 保留完整 dict（含 metadata / train_wavelengths），
+                        #   推理端需要 train_wavelengths 做光谱重采样
+                        models_found[subdir_name] = loaded
                     elif hasattr(loaded, "predict"):
-                        model_obj = loaded
+                        models_found[subdir_name] = loaded
                     else:
                         errors.append(f"{subdir_name}: 无法识别的格式 {type(loaded).__name__}")
                         continue
-                    models_found[subdir_name] = model_obj
                 except Exception as e:
                     errors.append(f"{subdir_name}: {type(e).__name__}: {e}")