Step3 插值算法 OOM 修复 + 多进程加速 + 全链路累积改动（14 文件）

src/core/steps/data_preparation_step.py

@@ -21,7 +21,7 @@ class DataPreparationStep:
     @staticmethod
     def process_csv(
         csv_path: str,
-        output_dir: Union[str, Path] = "./4_processed_data",
+        output_dir: Union[str, Path] = "./5_Data_Cleaning",
         callback: Optional[Callable] = None,
     ) -> str:
         """处理CSV文件（筛选剔除异常值）"""
@@ -61,7 +61,7 @@ class DataPreparationStep:
         boundary_path: Optional[str] = None,
         glint_mask_path: Optional[str] = None,
         water_mask_path: Optional[str] = None,
-        output_dir: Union[str, Path] = "./5_training_spectra",
+        output_dir: Union[str, Path] = "./6_Spectral_Feature_Extraction",
         callback: Optional[Callable] = None,
     ) -> str:
         """根据采样点坐标在去耀斑影像中提取平均光谱"""
@@ -131,7 +131,7 @@ class DataPreparationStep:
         formula_names: Optional[List[str]] = None,
         output_file: Optional[str] = None,
         enabled: bool = True,
-        output_dir: Union[str, Path] = "./6_water_quality_indices",
+        output_dir: Union[str, Path] = "./7_Water_Quality_Indices",
         callback: Optional[Callable] = None,
     ) -> Optional[str]:
         """根据训练光谱计算水质光谱指数（使用 band_math 方法）"""

src/core/steps/modeling_step.py

@@ -135,7 +135,7 @@ class ModelingStep:
         split_methods: Optional[List[str]] = None,
         cv_folds: int = 5,
         training_csv_path: Optional[str] = None,
-        output_dir: Union[str, Path] = "./7_Supervised_Model_Training",
+        output_dir: Union[str, Path] = "./8_Supervised_Model_Training",
         callback: Optional[Callable] = None,
         _report_generator=None,
     ) -> str:
@@ -251,7 +251,7 @@ class ModelingStep:
         if output_dir is not None:
             non_empirical_dir = Path(output_dir)
         else:
-            non_empirical_dir = Path.cwd() / "8_Regression_Modeling"
+            non_empirical_dir = Path.cwd() / "8_Non_Empirical_Regression"
         non_empirical_dir.mkdir(parents=True, exist_ok=True)
 
         if preprocessing_methods is None:
@@ -430,7 +430,7 @@ def _apply_preprocessing_internal(
 
     save_path = None
     if preprocess_method == "SS":
-        models_dir = output_dir.parent.parent / "7_Supervised_Model_Training"
+        models_dir = output_dir.parent.parent / "8_Supervised_Model_Training"
         models_dir.mkdir(parents=True, exist_ok=True)
         save_path = str(models_dir / "scaler_params.pkl")
         print(f"SS预处理: scaler模型将保存到 {save_path}")

src/core/steps/prediction_step.py

@@ -259,7 +259,7 @@ class PredictionStep:
         if non_empirical_models_dir is not None:
             final_models_dir = non_empirical_models_dir
         else:
-            default_models_dir = str(Path(work_dir) / "8_Regression_Modeling")
+            default_models_dir = str(Path(work_dir) / "8_Non_Empirical_Regression")
             if Path(default_models_dir).exists():
                 final_models_dir = default_models_dir
             else: