""" 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 import json from datetime import datetime from typing import Optional, List, Dict, Any, Tuple from io import BytesIO from app.utils.logger import get_logger class RainfallGridService: """降雨栅格服务""" def __init__(self): """初始化服务""" self.logger = get_logger() # 国标12小时累计降雨量等级和颜色映射 self.rainfall_levels = { 'levels': [0, 0.1, 5, 15, 30, 70, 140], 'colors': [ (200, 200, 200, 0), # 无雨 (0, 0, 255, 0.4 * 255), # 小雨 (0.1-5mm) (0, 255, 255, 0.5 * 255), # 中雨 (5-15mm) (0, 255, 0, 0.89 * 255), # 大雨 (15-30mm) (255, 255, 0, 0.7 * 255), # 暴雨 (30-70mm) (255, 165, 0, 0.8 * 255), # 大暴雨 (70-140mm) (255, 0, 0, 0.9 * 255), # 特大暴雨 (140m+) ], 'labels': ['无雨', '小雨', '中雨', '大雨', '暴雨', '大暴雨', '特大暴雨'] } # 西安地区大致边界（用于栅格范围） self.xian_bounds = { 'min_lon': 107, 'max_lon': 110, 'min_lat': 33, 'max_lat': 35, } # 栅格分辨率（度） self.grid_resolution = 0.001 def _create_buffer_points(self, points_array) -> 'np.ndarray': """ 创建缓冲点：在原始站点外围生成虚拟点以扩展插值区域 Args: points_array: 原始站点坐标数组 Returns: 缓冲点坐标数组 """ import numpy as np # 计算站点分布的中心 center = np.mean(points_array, axis=0) # 在站点外围生成缓冲点（沿着各个方向扩展） buffer_points = [] num_angles = 120 # 每隔3度生成一个缓冲点 for angle_deg in range(0, 360, 360 // num_angles): angle_rad = np.radians(angle_deg) # 在凸包边界外扩展 for scale in [1.05, 1.1, 1.15]: # 找到该方向上最远的站点 direction = np.array([np.cos(angle_rad), np.sin(angle_rad)]) projections = points_array @ direction max_idx = np.argmax(projections) # 在该方向上扩展 base_point = points_array[max_idx] buffer_point = center + (base_point - center) * scale buffer_points.append(buffer_point) return np.array(buffer_points) def _calculate_adaptive_max_distance( self, points_array, base_distance: float = 0.3, min_distance: float = 0.15, max_distance: float = 0.5 ) -> float: """ 根据站点密度自适应计算最大影响距离 Args: points_array: 站点坐标数组 base_distance: 基础距离 min_distance: 最小距离 max_distance: 最大距离 Returns: 自适应的最大影响距离 """ import numpy as np from scipy.spatial import distance_matrix if len(points_array) < 3: return base_distance # 计算站点间的平均距离 dist_matrix = distance_matrix(points_array, points_array) # 排除对角线（自身距离为0） np.fill_diagonal(dist_matrix, np.inf) avg_distance = np.mean(np.min(dist_matrix, axis=1)) # 根据平均距离调整max_distance adaptive_distance = avg_distance * 3 # 约3倍平均站点间距 # 限制在合理范围内 return float(np.clip(adaptive_distance, min_distance, max_distance)) 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（内存优化版本） 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： [ {'lon': x, 'lat': y, 'rainfall': z, 'duration_hours': h}, ... ] Returns: 插值结果字典 """ import numpy as np 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], 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, dtype=np.float32 ) lat_range = np.arange( self.xian_bounds['min_lat'], self.xian_bounds['max_lat'], self.grid_resolution, dtype=np.float32 ) grid_lon, grid_lat = np.meshgrid(lon_range, lat_range) 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) self.logger.info(f"使用最大影响距离: {actual_max_distance:.3f} 度") # 计算站点的凸包（带边缘缓冲） hull_mask = None confidence_mask = None if len(points_array) >= 3: try: # 创建缓冲站点：在原始站点外围添加虚拟点 buffer_points = self._create_buffer_points(points_array) # 合并原始站点和缓冲站点 all_points = np.vstack([points_array, buffer_points]) # 计算凸包 hull = ConvexHull(all_points) hull_points = all_points[hull.vertices] tri = Delaunay(hull_points) # 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()]) 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（分块处理） grid_valid = grid_points[hull_mask.ravel()] if len(grid_valid) > 0: # 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), 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, 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, dtype=np.float32) else: hull_mask = np.ones_like(grid_lon, dtype=bool) confidence_mask = np.ones_like(grid_lon, dtype=np.float32) # 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) # 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) # 用于记录最后一个块的has_valid_stations_chunk last_has_valid_stations_chunk = None 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 # 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_stations_chunk = np.any(valid_mask_chunk, axis=1) # 避免除零 distances_chunk = np.where(valid_mask_chunk, distances_chunk, np.inf) distances_chunk = np.maximum(distances_chunk, 1e-10) # 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) # 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: valid_rainfall = result[final_mask] if len(valid_rainfall) > 0: mean_rainfall = np.mean(valid_rainfall) # 根据置信度调整结果，低置信度区域向均值靠拢 adjusted_result = result * confidence_mask + mean_rainfall * (1 - confidence_mask) result = np.where(final_mask, adjusted_result, np.nan) # 应用高斯平滑减少边缘突变 result = gaussian_filter(result, sigma=1.0) # 处理NaN值 result = np.nan_to_num(result, nan=0.0) return { 'grid_values': result, 'grid_lon': grid_lon, 'grid_lat': grid_lat, 'lon_range': lon_range, 'lat_range': lat_range, } def optimize_edges(self, grid_data: Dict[str, Any], station_data: List[Dict[str, Any]]) -> Dict[str, Any]: """ 优化栅格边缘（已在插值时处理，此方法保留用于向后兼容） Args: grid_data: 插值结果 station_data: 站点数据 Returns: 优化后的栅格数据 """ # 由于interpolate_rainfall已经包含了边缘优化和平滑处理 # 这里不再重复处理，直接返回 return grid_data def save_rainfall_grid_png(self, grid_data: Dict[str, Any], max_id: int) -> Optional[str]: """ 将降雨栅格保存为PNG图片（背景透明） Args: grid_data: 栅格数据 max_id: 最大ID Returns: PNG文件相对路径，失败返回None """ import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap, BoundaryNorm from PIL import Image from config import settings try: grid_values = grid_data['grid_values'] 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'] colors = self.rainfall_levels['colors'] cmap = ListedColormap([ tuple(c / 255.0 for c in color) for color in colors ]) norm = BoundaryNorm(levels, cmap.N) # 创建图形（设置dpi确保不拉伸） fig, ax = plt.subplots(1, 1, figsize=(10, 10), dpi=100) # 绘制栅格 im = ax.pcolormesh( lon_range, lat_range, grid_values, cmap=cmap, norm=norm, shading='auto' ) # 设置透明背景 fig.patch.set_alpha(0) ax.patch.set_alpha(0) # 移除坐标轴 ax.set_axis_off() # 调整布局，去除白边 plt.tight_layout(pad=0) # 构建文件路径 file_store_dir = settings.FILE_STORE_DIR grid_dir_template = settings.RAIN_STATION_GRID_DIR # 替换:id为实际的max_id grid_dir = grid_dir_template.replace(':id', str(max_id)) # 完整路径 full_dir = os.path.join(file_store_dir, grid_dir.lstrip('/')) # 创建目录 os.makedirs(full_dir, exist_ok=True) # 保存PNG（使用PIL确保透明度） png_path = os.path.join(full_dir, 'grid.png') # 先保存到缓冲区 buf = BytesIO() plt.savefig(buf, format='png', transparent=True, bbox_inches='tight', pad_inches=0) buf.seek(0) # 使用PIL打开并重新保存，确保透明度正确 img = Image.open(buf) img.save(png_path, 'PNG') buf.close() plt.close(fig) # 返回相对路径（相对于FILE_STORE_DIR），统一使用正斜杠 relative_path = os.path.join(grid_dir, 'grid.png').replace('\\', '/') saved_path = png_path.replace('\\', '/') self.logger.info(f"PNG图片已保存: {saved_path}") return relative_path except Exception as e: self.logger.error(f"保存PNG图片失败: {e}", exc_info=True) return None def store_to_redis(self, png_path: str, max_id: int, query_time, station_data: List[Dict[str, Any]]): """ 将栅格信息存储到Redis Args: png_path: PNG文件相对路径 max_id: 最大ID query_time: 查询时间（datetime对象或字符串） station_data: 站点数据 """ from config import settings from app.utils.redis_helper import redis_helper try: redis_key = settings.REDIS_RAIN_STATION_GRID_KEY redis_identifier_key = settings.REDIS_RAIN_STATION_IDENTIFIER_KEY # 处理query_time，可能是datetime对象或字符串 if isinstance(query_time, datetime): query_time_str = query_time.isoformat() else: query_time_str = str(query_time) # 构建辅助前端定位的信息 grid_info = { 'id': max_id, 'png_path': png_path, # 相对路径 'query_time': query_time_str, 'resolution': self.grid_resolution, # 分辨率 'station_count': len(station_data), # 站点数量 # Cesium需要的定位信息 'cesium_config': { 'rectangle': { 'west': self.xian_bounds['min_lon'], 'south': self.xian_bounds['min_lat'], 'east': self.xian_bounds['max_lon'], 'north': self.xian_bounds['max_lat'], }, # 图片尺寸（需要读取实际图片） 'width': int((self.xian_bounds['max_lon'] - self.xian_bounds['min_lon']) / self.grid_resolution), 'height': int((self.xian_bounds['max_lat'] - self.xian_bounds['min_lat']) / self.grid_resolution), } } # 存储到Redis redis_helper.set(redis_key, json.dumps(grid_info)) redis_helper.set(redis_identifier_key, max_id) self.logger.info(f"栅格信息已存储到Redis，key: {redis_key}, id: {max_id}") except Exception as e: self.logger.error(f"存储到Redis失败: {e}", exc_info=True) # 创建全局实例 rainfall_grid_service = RainfallGridService()