feat: 各步骤产物改为原子化落盘，复用前校验 .done 完成标记

跳过条件从 Path.exists() 改为 is_file_complete()，产物成功落盘后统一补 mark_file_complete()。涉及 step1 水域掩膜、step3 去耀斑、SHP 栅格化缓存三处快路径。

step3 去耀斑新增清理分支：无 .done 标记的旧文件一律视为半成品，先删除再重跑（Kutser/Goodman/Hedley/SUGAR 四种方法一致）。

模型落盘改为 atomic_filepath：modeling_batch 批量建模与 automl_trainer 的 AutoML 结果均先写 .__wip 再原子替换，避免中断产生半个 joblib 文件。

src/core/modeling/modeling_batch.py

@@ -824,7 +824,9 @@ class WaterQualityModelingBatch:
             'metadata': metadata
         }
 
-        joblib.dump(save_data, filepath)
+        from src.utils.util import atomic_filepath
+        with atomic_filepath(str(filepath)) as _tmp:
+            joblib.dump(save_data, _tmp)
         print(f"模型已保存: {filepath}")
 
         # ═══════════════════════════════════════════════════════════

src/core/prediction/automl_trainer.py

@@ -455,11 +455,12 @@ def train_with_automl(
                     if final_model is None:
                         continue
 
-                    # 保存
+                    # 保存（★ 原子写入：先 .__wip，成功后同卷替换 + .done）
                     import joblib
+                    from src.utils.util import atomic_filepath
                     fname = f"{tgt}_{preproc}_{model_name}_AUTOML.joblib"
                     fpath = preproc_dir / fname
-                    joblib.dump({
+                    _save_data = {
                         "model": final_model,
                         "target_column_name": tgt,
                         "preprocess_method": preproc,
@@ -475,7 +476,9 @@ def train_with_automl(
                             "was_subsampled": was_sub,
                             "split_method": split_method,
                         },
-                    }, fpath)
+                    }
+                    with atomic_filepath(str(fpath)) as _tmp:
+                        joblib.dump(_save_data, _tmp)
 
                     cand = AutoMLResult(
                         success=True,

src/core/steps/glint_removal_step.py

@@ -300,6 +300,7 @@ class GlintRemovalStep:
         from src.core.utils.mask_converter import (
             prepare_water_mask_for_algorithm as _default_prepare,
         )
+        from src.utils.util import is_file_complete, mark_file_complete
 
         # 使用提供的函数或默认函数
         if _get_image_geo_info is None:
@@ -428,10 +429,19 @@ class GlintRemovalStep:
                 final_bsq = hardcoded_bsq
                 final_hdr = hardcoded_hdr
 
-            if Path(hardcoded_bsq).exists():
+            if is_file_complete(hardcoded_bsq):
-                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                print(f"检测到完整且已完成的去耀斑影像，直接使用: {hardcoded_bsq}")
                 notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
                 return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+            else:
+                # 清理上次中断遗留的半成品脏数据（无 .done 标记一律视为不完整）
+                for _stale in (hardcoded_bsq, hardcoded_hdr, hardcoded_bsq + '.hdr'):
+                    if _stale and Path(_stale).exists():
+                        print(f"清理上次中断遗留的半成品数据: {_stale}")
+                        try:
+                            Path(_stale).unlink()
+                        except Exception:
+                            pass
 
             kutser = Kutser(
                 img_path,
@@ -448,6 +458,7 @@ class GlintRemovalStep:
             if Path(hardcoded_bsq).exists():
                 _copy_hdr_info(img_path, hardcoded_bsq)
                 final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+                mark_file_complete(final)   # ★ 原子化成功标记，防半成品复用
                 notify("completed", f"去耀斑影像已生成: {final}")
                 return final
             raise RuntimeError(f"Kutser算法未生成输出文件: {hardcoded_bsq}")
@@ -466,10 +477,19 @@ class GlintRemovalStep:
                 final_bsq = hardcoded_bsq
                 final_hdr = hardcoded_hdr
 
-            if Path(hardcoded_bsq).exists():
+            if is_file_complete(hardcoded_bsq):
-                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                print(f"检测到完整且已完成的去耀斑影像，直接使用: {hardcoded_bsq}")
                 notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
                 return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+            else:
+                # 清理上次中断遗留的半成品脏数据（无 .done 标记一律视为不完整）
+                for _stale in (hardcoded_bsq, hardcoded_hdr, hardcoded_bsq + '.hdr'):
+                    if _stale and Path(_stale).exists():
+                        print(f"清理上次中断遗留的半成品数据: {_stale}")
+                        try:
+                            Path(_stale).unlink()
+                        except Exception:
+                            pass
 
             goodman = Goodman(
                 img_path,
@@ -489,6 +509,7 @@ class GlintRemovalStep:
             del corrected_bands
 
             final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+            mark_file_complete(final)   # ★ 原子化成功标记，防半成品复用
             notify("completed", f"去耀斑影像已生成: {final}")
             return final
 
@@ -504,10 +525,19 @@ class GlintRemovalStep:
                 final_bsq = hardcoded_bsq
                 final_hdr = hardcoded_hdr
 
-            if Path(hardcoded_bsq).exists():
+            if is_file_complete(hardcoded_bsq):
-                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                print(f"检测到完整且已完成的去耀斑影像，直接使用: {hardcoded_bsq}")
                 notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
                 return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+            else:
+                # 清理上次中断遗留的半成品脏数据（无 .done 标记一律视为不完整）
+                for _stale in (hardcoded_bsq, hardcoded_hdr, hardcoded_bsq + '.hdr'):
+                    if _stale and Path(_stale).exists():
+                        print(f"清理上次中断遗留的半成品数据: {_stale}")
+                        try:
+                            Path(_stale).unlink()
+                        except Exception:
+                            pass
 
             hedley = Hedley(
                 img_path,
@@ -521,6 +551,7 @@ class GlintRemovalStep:
             if Path(hardcoded_bsq).exists():
                 _copy_hdr_info(img_path, hardcoded_bsq)
                 final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+                mark_file_complete(final)   # ★ 原子化成功标记，防半成品复用
                 notify("completed", f"去耀斑影像已生成: {final}")
                 return final
             raise RuntimeError(f"Hedley算法未生成输出文件: {hardcoded_bsq}")
@@ -547,10 +578,19 @@ class GlintRemovalStep:
                 final_bsq = hardcoded_bsq
                 final_hdr = hardcoded_hdr
 
-            if Path(hardcoded_bsq).exists():
+            if is_file_complete(hardcoded_bsq):
-                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                print(f"检测到完整且已完成的去耀斑影像，直接使用: {hardcoded_bsq}")
                 notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
                 return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+            else:
+                # 清理上次中断遗留的半成品脏数据（无 .done 标记一律视为不完整）
+                for _stale in (hardcoded_bsq, hardcoded_hdr, hardcoded_bsq + '.hdr'):
+                    if _stale and Path(_stale).exists():
+                        print(f"清理上次中断遗留的半成品数据: {_stale}")
+                        try:
+                            Path(_stale).unlink()
+                        except Exception:
+                            pass
 
             if sugar_bounds is None:
                 sugar_bounds = [(1, 2)]
@@ -569,6 +609,7 @@ class GlintRemovalStep:
             if Path(hardcoded_bsq).exists():
                 _copy_hdr_info(img_path, hardcoded_bsq)
                 final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+                mark_file_complete(final)   # ★ 原子化成功标记，防半成品复用
                 notify("completed", f"去耀斑影像已生成: {final}")
                 return final
             raise RuntimeError(f"SUGAR算法未生成输出文件: {hardcoded_bsq}")

src/core/steps/water_mask_step.py

@@ -46,6 +46,7 @@ class WaterMaskStep:
             dat 格式的水域掩膜文件路径
         """
         from src.utils.extract_water_area import rasterize_shp, ndwi
+        from src.utils.util import is_file_complete, mark_file_complete
         from src.core.utils.preview_generator import (
             generate_image_preview,
             generate_water_mask_overlay,
@@ -85,12 +86,13 @@ class WaterMaskStep:
             ndwi_output_path = output_path or str(water_mask_dir / "water_mask_from_ndwi.dat")
             os.makedirs(Path(ndwi_output_path).parent, exist_ok=True)
 
-            if Path(ndwi_output_path).exists():
+            if is_file_complete(ndwi_output_path):
-                print(f"检测到已存在的NDWI掩膜文件，直接使用: {ndwi_output_path}")
+                print(f"检测到完整且已完成的NDWI掩膜文件，直接使用: {ndwi_output_path}")
                 notify("skipped", f"水域掩膜已设置: {ndwi_output_path}")
                 return ndwi_output_path
 
             ndwi(img_path, ndwi_threshold, ndwi_output_path)
+            mark_file_complete(ndwi_output_path)
 
             if generate_png:
                 overlay_path = water_mask_dir / "water_mask_overlay.png"
@@ -119,8 +121,8 @@ class WaterMaskStep:
             shp_output_path = output_path or str(water_mask_dir / "water_mask_from_shp.dat")
             os.makedirs(Path(shp_output_path).parent, exist_ok=True)
 
-            if Path(shp_output_path).exists():
+            if is_file_complete(shp_output_path):
-                print(f"检测到已存在的栅格化掩膜文件，直接使用: {shp_output_path}")
+                print(f"检测到完整且已完成的栅格化掩膜文件，直接使用: {shp_output_path}")
                 notify("skipped", f"水域掩膜已设置: {shp_output_path}")
                 if generate_png:
                     overlay_path = water_mask_dir / "water_mask_overlay.png"
@@ -130,6 +132,7 @@ class WaterMaskStep:
 
             safe_mask_path = os.path.abspath(mask_path).replace("\\", "/")
             rasterize_shp(safe_mask_path, shp_output_path, img_path)
+            mark_file_complete(shp_output_path)
 
             if generate_png:
                 overlay_path = water_mask_dir / "water_mask_overlay.png"

src/core/utils/mask_converter.py

@@ -94,6 +94,7 @@ def _convert_shp_to_mask(shp_path: str, img_path: str,
                           callback=None) -> np.ndarray:
     """将 shapefile 栅格化为掩膜数组"""
     from src.utils.extract_water_area import rasterize_shp
+    from src.utils.util import is_file_complete, mark_file_complete
 
     safe_shp_path = os.path.abspath(shp_path).replace('\\', '/')
     shp_name = Path(safe_shp_path).stem
@@ -103,9 +104,9 @@ def _convert_shp_to_mask(shp_path: str, img_path: str,
     else:
         temp_mask_path = f"/tmp/water_mask_{shp_name}.dat"
 
-    # 缓存：已栅格化则直接读取
+    # 缓存：已完成（带 .done 标记）才直接读取，否则重新栅格化
-    if Path(temp_mask_path).exists():
+    if is_file_complete(temp_mask_path):
-        print(f"使用已存在的栅格化掩膜: {temp_mask_path}")
+        print(f"使用已完成的栅格化掩膜: {temp_mask_path}")
         return _load_raster_mask(temp_mask_path, image_shape[0], image_shape[1])
 
     # 需要栅格化
@@ -114,6 +115,7 @@ def _convert_shp_to_mask(shp_path: str, img_path: str,
 
     print(f"正在将 SHP 栅格化: {safe_shp_path}")
     rasterize_shp(safe_shp_path, temp_mask_path, img_path)
+    mark_file_complete(temp_mask_path)
 
     return _load_raster_mask(temp_mask_path, image_shape[0], image_shape[1])