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修改分辨率触发逻辑

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wzy-warehouse
2026-06-14 14:19:22 +08:00
parent 4c0edb0840
commit b269e3282d
1 changed files with 203 additions and 117 deletions
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app/services/rainfall_grid_service.py
+146 -60
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@@ -1,5 +1,5 @@
"""
降雨栅格服务
降雨栅格服务 - 内存优化版本
负责降雨插值、边缘优化、PNG生成等业务逻辑
"""
import os
@@ -42,7 +42,7 @@ class RainfallGridService:
}
# 栅格分辨率（度）
self.grid_resolution = 0.01 # 约1km
self.grid_resolution = 0.001
def _create_buffer_points(self, points_array) -> 'np.ndarray':
"""
@@ -119,16 +119,13 @@ class RainfallGridService:
def interpolate_rainfall(self, station_data: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
使用优化的反距离权重法（IDW）进行降雨插值
使用优化的反距离权重法（IDW）进行降雨插值（内存优化版本）
注意：station_data 现在包含 'rainfall'（累计降雨量）和 'duration_hours'（持续时间）
与DBN推演使用相同的降雨量计算逻辑（72小时回溯 + 3小时无雨截断）
改进：
1. 高斯核衰减替代简单幂律
2. 自适应距离阈值
3. 边缘渐变处理
4. 高斯平滑减少突变
内存优化：
1. 使用float32代替float64（内存减半）
2. 分块处理距离计算
3. 提前过滤无效站点
4. 减少中间数组
Args:
station_data: 站点数据列表，格式：
@@ -141,27 +138,29 @@ class RainfallGridService:
插值结果字典
"""
import numpy as np
from scipy.spatial import Delaunay, ConvexHull, distance_matrix
from scipy.spatial import Delaunay, ConvexHull
from scipy.ndimage import gaussian_filter
# 提取站点坐标和降雨量
points_array = np.array([[s['lon'], s['lat']] for s in station_data])
values_array = np.array([s['rainfall'] for s in station_data])
points_array = np.array([[s['lon'], s['lat']] for s in station_data], dtype=np.float32)
values_array = np.array([s['rainfall'] for s in station_data], dtype=np.float32)
# 创建栅格网格
lon_range = np.arange(
self.xian_bounds['min_lon'],
self.xian_bounds['max_lon'],
self.grid_resolution
self.grid_resolution,
dtype=np.float32
)
lat_range = np.arange(
self.xian_bounds['min_lat'],
self.xian_bounds['max_lat'],
self.grid_resolution
self.grid_resolution,
dtype=np.float32
)
grid_lon, grid_lat = np.meshgrid(lon_range, lat_range)
result = np.full_like(grid_lon, np.nan)
result = np.full_like(grid_lon, np.nan, dtype=np.float32)
# 自适应计算最大距离
actual_max_distance = self._calculate_adaptive_max_distance(points_array)
@@ -185,72 +184,159 @@ class RainfallGridService:
# 向量化判断所有网格点是否在凸包内
grid_points = np.column_stack([grid_lon.ravel(), grid_lat.ravel()])
hull_indices = tri.find_simplex(grid_points)
hull_mask = hull_indices >= 0
simplex_indices = tri.find_simplex(grid_points)
hull_mask = simplex_indices >= 0
hull_mask = hull_mask.reshape(grid_lon.shape)
# 计算置信度：基于到最近站点的距离
# 计算置信度：基于到最近站点的距离（分块处理）
grid_valid = grid_points[hull_mask.ravel()]
if len(grid_valid) > 0:
dist_to_stations = distance_matrix(grid_valid, points_array)
min_distances = np.min(dist_to_stations, axis=1)
# 分块计算距离，避免内存溢出
chunk_size = 100000 # 每次处理10万点
n_valid = len(grid_valid)
min_distances = np.zeros(n_valid, dtype=np.float32)
for i in range(0, n_valid, chunk_size):
chunk_end = min(i + chunk_size, n_valid)
chunk_points = grid_valid[i:chunk_end]
# 计算当前块到所有站点的距离
lon_diff = chunk_points[:, 0:1] - points_array[np.newaxis, :, 0]
lat_diff = chunk_points[:, 1:2] - points_array[np.newaxis, :, 1]
distances = np.sqrt(lon_diff**2 + lat_diff**2)
# 记录最小距离
min_distances[i:chunk_end] = np.min(distances, axis=1)
# 释放临时数组
del lon_diff, lat_diff, distances
# 创建置信度掩码（距离越远，置信度越低）
confidence = np.ones(len(grid_points))
confidence = np.ones(len(grid_points), dtype=np.float32)
confidence[hull_mask.ravel()] = np.exp(-min_distances / actual_max_distance)
confidence_mask = confidence.reshape(grid_lon.shape)
else:
confidence_mask = np.ones_like(grid_lon)
confidence_mask = np.ones_like(grid_lon, dtype=np.float32)
except Exception as e:
self.logger.warning(f"凸包计算失败: {e}，使用全区域插值")
hull_mask = np.ones_like(grid_lon, dtype=bool)
confidence_mask = np.ones_like(grid_lon)
confidence_mask = np.ones_like(grid_lon, dtype=np.float32)
else:
hull_mask = np.ones_like(grid_lon, dtype=bool)
confidence_mask = np.ones_like(grid_lon)
confidence_mask = np.ones_like(grid_lon, dtype=np.float32)
# 向量化计算所有网格点到所有站点的距离
lon_diff = grid_lon[:, :, np.newaxis] - points_array[np.newaxis, np.newaxis, :, 0]
lat_diff = grid_lat[:, :, np.newaxis] - points_array[np.newaxis, np.newaxis, :, 1]
distances = np.sqrt(lon_diff**2 + lat_diff**2)
# 获取凸包内网格点坐标
grid_points = np.column_stack([grid_lon.ravel(), grid_lat.ravel()])
if hull_mask is not None:
# 只计算凸包内网格点到站点的距离
hull_point_indices = np.where(hull_mask.ravel())[0]
grid_points_hull = grid_points[hull_point_indices]
n_hull_points = len(grid_points_hull)
self.logger.info(f"凸包内网格点数量: {n_hull_points}, 总网格点: {grid_lon.size}")
else:
# 如果凸包掩码不可用，使用所有网格点
grid_points_hull = grid_points
hull_point_indices = np.arange(len(grid_points))
n_hull_points = len(grid_points_hull)
# 过滤超出最大距离的站点
valid_mask = distances <= actual_max_distance
# 分块计算凸包内网格点到所有站点的距离
chunk_size = 50000 # 每次处理5万点
result_hull = np.full(n_hull_points, np.nan, dtype=np.float32)
# 对于每个网格点，检查是否有有效站点
has_valid_stations = np.any(valid_mask, axis=2)
# 用于记录最后一个块的has_valid_stations_chunk
last_has_valid_stations_chunk = None
# 合并凸包掩码和有效站点掩码
final_mask = hull_mask & has_valid_stations
for i in range(0, n_hull_points, chunk_size):
chunk_end = min(i + chunk_size, n_hull_points)
chunk_points = grid_points_hull[i:chunk_end]
chunk_size_actual = chunk_end - i
# 避免除零
distances = np.where(valid_mask, distances, np.inf)
distances = np.maximum(distances, 1e-10)
# 计算当前块到所有站点的距离
lon_diff_chunk = chunk_points[:, 0:1] - points_array[np.newaxis, :, 0]
lat_diff_chunk = chunk_points[:, 1:2] - points_array[np.newaxis, :, 1]
distances_chunk = np.sqrt(lon_diff_chunk**2 + lat_diff_chunk**2)
# 优化的权重计算：结合幂律和高斯衰减
power = 2.0
power_weights = 1.0 / (distances ** power)
gaussian_weights = np.exp(-0.5 * (distances / (actual_max_distance * 0.5)) ** 2)
# 过滤超出最大距离的站点
valid_mask_chunk = distances_chunk <= actual_max_distance
# 混合权重：距离越远，高斯权重占比越大
distance_ratio = distances / actual_max_distance
mix_factor = np.clip(distance_ratio, 0, 1)
weights = (1 - mix_factor) * power_weights + mix_factor * gaussian_weights
# 对于每个网格点，检查是否有有效站点
has_valid_stations_chunk = np.any(valid_mask_chunk, axis=1)
weights = np.where(valid_mask, weights, 0)
# 避免除零
distances_chunk = np.where(valid_mask_chunk, distances_chunk, np.inf)
distances_chunk = np.maximum(distances_chunk, 1e-10)
# 加权平均
weighted_sum = np.sum(weights * values_array[np.newaxis, np.newaxis, :], axis=2)
weight_total = np.sum(weights, axis=2)
# 优化的权重计算：结合幂律和高斯衰减
power = 2.0
power_weights_chunk = 1.0 / (distances_chunk ** power)
gaussian_weights_chunk = np.exp(-0.5 * (distances_chunk / (actual_max_distance * 0.5)) ** 2)
# 计算基础插值结果
with np.errstate(divide='ignore', invalid='ignore'):
result = np.where(
final_mask & (weight_total > 0),
weighted_sum / weight_total,
np.nan
)
# 混合权重：距离越远，高斯权重占比越大
distance_ratio_chunk = distances_chunk / actual_max_distance
mix_factor_chunk = np.clip(distance_ratio_chunk, 0, 1)
weights_chunk = (1 - mix_factor_chunk) * power_weights_chunk + mix_factor_chunk * gaussian_weights_chunk
weights_chunk = np.where(valid_mask_chunk, weights_chunk, 0)
# 加权平均
weighted_sum_chunk = np.sum(weights_chunk * values_array[np.newaxis, :], axis=1)
weight_total_chunk = np.sum(weights_chunk, axis=1)
# 计算当前块的插值结果
with np.errstate(divide='ignore', invalid='ignore'):
chunk_result = np.where(
has_valid_stations_chunk & (weight_total_chunk > 0),
weighted_sum_chunk / weight_total_chunk,
np.nan
)
# 存储结果
result_hull[i:chunk_end] = chunk_result
# 记录最后一个块的has_valid_stations_chunk
last_has_valid_stations_chunk = has_valid_stations_chunk
# 释放临时数组
del lon_diff_chunk, lat_diff_chunk, distances_chunk, valid_mask_chunk
del power_weights_chunk, gaussian_weights_chunk, weights_chunk
del weighted_sum_chunk, weight_total_chunk, chunk_result
# 将凸包内点的结果映射回完整网格
result = np.full_like(grid_lon, np.nan, dtype=np.float32)
result.ravel()[hull_point_indices] = result_hull
# 构建完整网格的有效掩码（凸包内且有有效站点）
# 注意：这里需要重新计算所有凸包内点的有效站点掩码
# 由于分块处理，我们需要重新计算完整掩码
has_valid_stations_full = np.zeros_like(grid_lon, dtype=bool)
# 重新计算所有凸包内点的有效站点掩码（内存优化：分块计算）
if n_hull_points > 0:
# 分块计算有效站点掩码
chunk_size_mask = 100000 # 每次处理10万点
for i in range(0, n_hull_points, chunk_size_mask):
chunk_end = min(i + chunk_size_mask, n_hull_points)
chunk_points = grid_points_hull[i:chunk_end]
# 计算当前块到所有站点的距离
lon_diff_chunk = chunk_points[:, 0:1] - points_array[np.newaxis, :, 0]
lat_diff_chunk = chunk_points[:, 1:2] - points_array[np.newaxis, :, 1]
distances_chunk = np.sqrt(lon_diff_chunk**2 + lat_diff_chunk**2)
# 过滤超出最大距离的站点
valid_mask_chunk = distances_chunk <= actual_max_distance
# 对于每个网格点，检查是否有有效站点
has_valid_chunk = np.any(valid_mask_chunk, axis=1)
# 存储结果
has_valid_stations_full.ravel()[hull_point_indices[i:chunk_end]] = has_valid_chunk
# 释放临时数组
del lon_diff_chunk, lat_diff_chunk, distances_chunk, valid_mask_chunk, has_valid_chunk
final_mask = hull_mask & has_valid_stations_full
# 应用置信度调整：边缘区域向邻近值渐变
if confidence_mask is not None:
@@ -310,8 +396,8 @@ class RainfallGridService:
try:
grid_values = grid_data['grid_values']
lon_range = grid_data['lon_range']
lat_range = grid_data['lat_range']
lon_range = grid_data['grid_lon']
lat_range = grid_data['grid_lat']
# 创建自定义颜色映射
levels = self.rainfall_levels['levels']