fix: Kriging 整体拟合+变异函数容错+自适应分层抽样+高斯平滑+掩膜修复

src/postprocessing/map.py

@@ -672,7 +672,52 @@ class ContentMapper:
                 grid_y = grid_yy[:, 0]
                 total_cells = len(grid_x) * len(grid_y)
 
-                # ── 1) 全局变异函数拟合（多模型容错回退）──
+                # ── 1) 全局变异函数拟合（自适应分层抽样防 OOM）──
+                # 变异函数拟合内存 ≈ N²×8 bytes。预留 1.5GB 安全上限 → N_max≈14000
+                _vario_pts = points
+                _vario_vals = values
+                # 从系统内存动态估算可容纳的最大点数
+                try:
+                    import psutil
+                    _avail_gb = psutil.virtual_memory().available / (1024**3)
+                except Exception:
+                    _avail_gb = 4.0  # 保守兜底
+                # 计算安全点数：留 50% 余量给 Python 开销
+                _MAX_VARIOGRAM_PTS = int(np.sqrt(max(_avail_gb * 0.5, 0.5) * 1024**3 / 8))
+                _MAX_VARIOGRAM_PTS = np.clip(_MAX_VARIOGRAM_PTS, 3000, 20000)
+
+                if len(points) > _MAX_VARIOGRAM_PTS:
+                    # 空间分层抽样：网格覆盖全水域，每格保留1点
+                    _grid = int(np.ceil(np.sqrt(_MAX_VARIOGRAM_PTS)))
+                    _x_bins = np.linspace(points[:, 0].min(), points[:, 0].max(), _grid + 1)
+                    _y_bins = np.linspace(points[:, 1].min(), points[:, 1].max(), _grid + 1)
+                    _keep = np.zeros(len(points), dtype=bool)
+                    for _ix in range(_grid):
+                        for _iy in range(_grid):
+                            _cell = (
+                                (points[:, 0] >= _x_bins[_ix]) &
+                                (points[:, 0] < _x_bins[_ix + 1]) &
+                                (points[:, 1] >= _y_bins[_iy]) &
+                                (points[:, 1] < _y_bins[_iy + 1])
+                            )
+                            if _cell.any():
+                                _keep[np.where(_cell)[0][0]] = True
+                    _kept_idx = np.where(_keep)[0]
+                    if len(_kept_idx) < _MAX_VARIOGRAM_PTS:
+                        _remaining = np.where(~_keep)[0]
+                        _extra = np.random.default_rng(42).choice(
+                            _remaining,
+                            min(_MAX_VARIOGRAM_PTS - len(_kept_idx), len(_remaining)),
+                            replace=False,
+                        )
+                        _kept_idx = np.concatenate([_kept_idx, _extra])
+                    _vario_pts = points[_kept_idx]
+                    _vario_vals = values[_kept_idx]
+                    print(f"  [Kriging] 采样点 {len(points)} > {_MAX_VARIOGRAM_PTS}"
+                          f"(可用内存 {_avail_gb:.1f}GB)，"
+                          f"空间分层抽样 → {len(_kept_idx)} 点"
+                          f"（网格 {_grid}×{_grid}，全覆盖）")
+
                 _vario_models = [
                     ('spherical',   {'nugget': 1e-6}),
                     ('exponential', {'nugget': 1e-6}),
@@ -684,7 +729,7 @@ class ContentMapper:
                 for _vm_name, _vm_kw in _vario_models:
                     try:
                         ok_model = OrdinaryKriging(
-                            points[:, 0], points[:, 1], values,
+                            _vario_pts[:, 0], _vario_pts[:, 1], _vario_vals,
                             variogram_model=_vm_name,
                             verbose=False, enable_plotting=False,
                             **_vm_kw,