diff --git a/src/postprocessing/map.py b/src/postprocessing/map.py
index 0681b2e..a939555 100644
--- a/src/postprocessing/map.py
+++ b/src/postprocessing/map.py
@@ -815,35 +815,6 @@ class ContentMapper:
         )
         return np.array(z), bx_min, bx_max, by_min, by_max
 
-
-def _local_krige_block_worker(args):
-    """单个局部克里金块任务（独立进程入口，必须为模块级函数）"""
-    (local_pts, local_vals, sub_grid_x, sub_grid_y,
-     bx_min, bx_max, by_min, by_max,
-     grid_dx, grid_dy, block_ix, block_iy, total) = args
-
-    import numpy as np
-    from pykrige.ok import OrdinaryKriging
-
-    if len(local_pts) < 3:
-        return (None, bx_min, bx_max, by_min, by_max)
-
-    print(f"  [LocalKrige] 块 ({block_iy},{block_ix}) [{block_iy * 100 + block_ix}/{total}] "
-          f"采样点={len(local_pts)}, 网格={len(sub_grid_x)}×{len(sub_grid_y)}")
-
-    ok = OrdinaryKriging(
-        local_pts[:, 0], local_pts[:, 1], local_vals,
-        variogram_model='spherical',
-        verbose=False,
-        enable_plotting=False,
-    )
-    z, ss = ok.execute(
-        'grid', sub_grid_x, sub_grid_y,
-        backend='loop',
-        n_closest_points=min(15, len(local_pts)),
-    )
-    return (np.array(z), bx_min, bx_max, by_min, by_max)
-
     @staticmethod
     def _idw_interpolation(points, values, grid_xx, grid_yy,
                            power=2, n_neighbors=15):
@@ -3304,3 +3275,36 @@ if __name__ == "__main__":
     #     resolution=50
     # )
     # """)
+
+
+# ═══════════════════════════════════════════════════════════════
+# 模块级函数：多进程 worker（必须在类外部定义，供 Pool.map 使用）
+# ═══════════════════════════════════════════════════════════════
+
+def _local_krige_block_worker(args):
+    """单个局部克里金块任务（独立进程入口，必须为模块级函数）"""
+    (local_pts, local_vals, sub_grid_x, sub_grid_y,
+     bx_min, bx_max, by_min, by_max,
+     grid_dx, grid_dy, block_ix, block_iy, total) = args
+
+    import numpy as np
+    from pykrige.ok import OrdinaryKriging
+
+    if len(local_pts) < 3:
+        return (None, bx_min, bx_max, by_min, by_max)
+
+    print(f"  [LocalKrige] 块 ({block_iy},{block_ix}) [{block_iy * 100 + block_ix}/{total}] "
+          f"采样点={len(local_pts)}, 网格={len(sub_grid_x)}×{len(sub_grid_y)}")
+
+    ok = OrdinaryKriging(
+        local_pts[:, 0], local_pts[:, 1], local_vals,
+        variogram_model='spherical',
+        verbose=False,
+        enable_plotting=False,
+    )
+    z, ss = ok.execute(
+        'grid', sub_grid_x, sub_grid_y,
+        backend='loop',
+        n_closest_points=min(15, len(local_pts)),
+    )
+    return (np.array(z), bx_min, bx_max, by_min, by_max)