修改启动前检查方式

app/core/launcher.py

@@ -9,7 +9,6 @@ from app.core.venv_manager import check_virtualenv
 from app.core.dependency_manager import check_dependencies
 from app.utils.logger import get_logger
 from app.utils.thread_pool_manager import block_main_thread, thread_pool_manager
-from app.core.rainfall_manager import rainfall_manager
 
 
 class AppLauncher:
@@ -52,11 +51,13 @@ class AppLauncher:
 
 def start():
     """启动应用服务"""
+    from app.core.rainfall_manager import rainfall_manager
+
     logger = get_logger()
-    
+
     # 启动降雨站点监测
     logger.info("启动降雨站点监测服务...")
     rainfall_manager.monitoring_rainfall_station_id('2025-09-16 20:00:00')
-    
+
     # 阻塞主线程，防止程序立即退出
     block_main_thread()

app/core/rainfall_manager.py

@@ -8,8 +8,6 @@ from typing import Optional
 
 from app.utils import thread_pool_manager
 from app.utils.logger import get_logger
-from app.repositories.rainfall_repository import rainfall_repository
-from app.services.rainfall_grid_service import rainfall_grid_service
 
 
 class RainfallManager:
@@ -42,12 +40,14 @@ class RainfallManager:
     def _monitoring_loop(self, initial_query_time: datetime):
         """
         监测循环，定期检查最大ID是否改变
-        
+
         Args:
             initial_query_time: 初始查询时间
         """
+        from app.repositories.rainfall_repository import rainfall_repository
+
         query_time = initial_query_time
-        
+
         while True:
             try:
                 # 查询当前时间窗口内的最大ID
@@ -78,14 +78,17 @@ class RainfallManager:
     def _generate_rainfall_grid_task(self, query_time: datetime, max_id: int):
         """
         生成降雨栅格的任务函数
-        
+
         Args:
             query_time: 查询时间
             max_id: 最大ID
         """
+        from app.repositories.rainfall_repository import rainfall_repository
+        from app.services.rainfall_grid_service import rainfall_grid_service
+
         try:
             self.logger.info(f"开始生成降雨栅格，查询时间: {query_time}, ID: {max_id}")
-            
+
             # 1. 查询雨量站点数据
             station_data = rainfall_repository.get_rainfall_stations_data(query_time)
             

app/services/rainfall_grid_service.py

@@ -8,17 +8,7 @@ from datetime import datetime
 from typing import Optional, List, Dict, Any, Tuple
 from io import BytesIO
 
-import numpy as np
-from scipy.spatial import Delaunay, ConvexHull
-from scipy.interpolate import griddata
-from scipy.ndimage import gaussian_filter
-from PIL import Image
-import matplotlib.pyplot as plt
-from matplotlib.colors import ListedColormap, BoundaryNorm
-
-from config import settings
 from app.utils.logger import get_logger
-from app.utils.redis_helper import redis_helper
 
 
 class RainfallGridService:
@@ -54,16 +44,18 @@ class RainfallGridService:
         # 栅格分辨率（度）
         self.grid_resolution = 0.01  # 约1km
     
-    def _create_buffer_points(self, points_array: np.ndarray) -> np.ndarray:
+    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)
         
@@ -89,37 +81,39 @@ class RainfallGridService:
     
     def _calculate_adaptive_max_distance(
         self,
-        points_array: np.ndarray,
+        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
-        from scipy.spatial import distance_matrix
         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))
     
@@ -131,13 +125,17 @@ class RainfallGridService:
         2. 自适应距离阈值
         3. 边缘渐变处理
         4. 高斯平滑减少突变
-        
+
         Args:
             station_data: 站点数据列表
-            
+
         Returns:
             插值结果字典
         """
+        import numpy as np
+        from scipy.spatial import Delaunay, ConvexHull, distance_matrix
+        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])
@@ -184,7 +182,6 @@ class RainfallGridService:
                 hull_mask = hull_mask.reshape(grid_lon.shape)
                 
                 # 计算置信度：基于到最近站点的距离
-                from scipy.spatial import distance_matrix
                 grid_valid = grid_points[hull_mask.ravel()]
                 if len(grid_valid) > 0:
                     dist_to_stations = distance_matrix(grid_valid, points_array)
@@ -289,29 +286,35 @@ class RainfallGridService:
     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['lon_range']
             lat_range = grid_data['lat_range']
-            
+
             # 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)
             
@@ -373,17 +376,20 @@ class RainfallGridService:
             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, 
+    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

start.py

@@ -3,13 +3,15 @@
 使用 Dynaconf 进行环境隔离配置
 """
 from pathlib import Path
-from app.core.launcher import AppLauncher
 
 # 添加项目根目录到Python路径
 project_root = Path(__file__).parent
 
 
 if __name__ == "__main__":
+    # 延迟导入，确保在依赖安装前不会导入第三方库
+    from app.core.launcher import AppLauncher
+
     # 创建并运行启动器
     launcher = AppLauncher(project_root)
     launcher.run()