进行路径计算

app/config/hazard/__init__.py

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+# hazard 配置模块

app/config/hazard/hazard_zone_params.yaml

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+# 灾害影响范围计算参数配置
+# 2026-06-29 初始版本
+#
+# 五种灾害类型各有独立参数，未匹配到灾害类型时使用 default
+# 所有半径单位为米(m)
+#
+# 论文支撑：
+# - 滑坡/崩塌: He et al. (2023) Landslides, Corominas (1996) CGJ
+# - 泥石流: Baggio et al. (2021) NHESS, Cicoira et al. (2022) ESurf
+# - 山洪: Costache et al. (2022) STOTEN, Zhao et al. (2023) J.Hydrology
+# - 内涝: Wang et al. (2022) Water Res. Mgmt, Jamali et al. (2020) Water
+
+# ============================================
+# 滑坡 — 经验到达角法 + 地形修正
+# ============================================
+
+landslide:
+  height_drop_m: 50               # 假设崩滑高差(m)
+  reach_angle:                    # 到达角经验值(度) — He et al.(2023) 表2
+    小型: 31
+    中型: 28
+    大型: 25
+    特大型: 23
+  fan_angle: 45                   # 侧向扇形展开角(度)
+  river_erosion_enhance: 1.3      # 河流距离<200m时的增强系数
+  river_distance_threshold: 200   # 河流侵蚀增强触发距离(m)
+  fault_enhance: 1.2              # 断裂带距离<500m时的增强系数
+  fault_distance_threshold: 500   # 断裂带增强触发距离(m)
+  min_radius_m: 100               # 最小影响半径
+  max_radius_m: 2000              # 最大影响半径
+
+# ============================================
+# 泥石流 — 河流关联缓冲区
+# ============================================
+
+debris_flow:
+  base_radius_m: 200              # 基础影响半径
+  slope_factor: 10                # 坡度影响系数 (半径 = base + slope * factor)
+  river_buffer_m: 80              # 河道缓冲区宽度
+  max_river_search_m: 2000        # 最近河流搜索范围
+  min_radius_m: 100
+  max_radius_m: 2000
+
+# ============================================
+# 山洪 — 多级河流缓冲区
+# ============================================
+
+flash_flood:
+  river_dist_thresholds: [100, 300, 500]         # 距河流距离阈值(m)
+  buffer_by_level:                               # 对应各级缓冲区(m)
+    1: 500      # 干流
+    2: 300      # 一级支流
+    3: 150      # 二级支流
+    4: 80       # 三级以下
+    5: 80
+  max_river_search_m: 3000       # 最近河流搜索范围
+
+# ============================================
+# 内涝 — TWI简化 + 不透水率 + 管网修正
+# ============================================
+
+waterlogging:
+  base_radius_m: 100              # 基础积水半径
+  impervious_factor: 3            # 不透水率修正系数
+  pipe_density_factor: 400        # 管网密度修正系数
+  pipe_lower_bound: 0.3           # 管网修正下限
+  min_radius_m: 50
+  max_radius_m: 800
+
+# ============================================
+# 崩塌 — 锥体传播模型 (CONEFALL简化)
+# ============================================
+
+rockfall:
+  height_drop_m: 50               # 假设崩落高差(m)
+  reach_angle:                    # 到达角经验值(度) — Guerin et al.(2022)
+    小型: 33
+    中型: 30
+    大型: 27
+    特大型: 25
+  fault_angle_penalty: 5          # 断裂带<300m时到达角减少量(度)
+  fault_penalty_threshold: 300    # 断裂带惩罚触发距离(m)
+  fault_radius_enhance: 1.5       # 断裂带<200m时半径增强系数
+  fault_enhance_threshold: 200    # 半径增强触发距离(m)
+  min_radius_m: 50
+  max_radius_m: 1000
+
+# ============================================
+# 默认参数 — 未匹配灾害类型时的兜底
+# ============================================
+
+default:
+  radius_m: 200

app/models/hazard/__init__.py

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+from app.models.hazard.hazard_zone_calculator import HazardZoneCalculator, hazard_zone_calculator
+
+__all__ = ['HazardZoneCalculator', 'hazard_zone_calculator']

app/models/hazard/hazard_zone_calculator.py

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+"""
+灾害影响范围计算器
+根据灾害类型和静态因子计算隐患点的影响范围(zone)和影响路径(path)
+
+论文支撑:
+- 滑坡: He et al. (2023) Landslides — 经验到达角法
+- 泥石流: Baggio et al. (2021) NHESS — 河流关联缓冲区
+- 山洪: Costache et al. (2022) STOTEN — 多级缓冲区
+- 内涝: Wang et al. (2022) Water Res. Mgmt — TWI简化法
+- 崩塌: Guerin et al. (2022) Eng. Geology — 锥体传播模型
+"""
+import math
+import os
+from typing import Optional, Tuple, Dict, Any
+
+import yaml
+
+from app.config.paths import CONFIG_DIR
+
+# 配置文件路径
+_PARAMS_PATH = os.path.join(CONFIG_DIR, 'hazard', 'hazard_zone_params.yaml')
+
+# 地球半径 (WGS84)
+_EARTH_RADIUS_M = 6371000.0
+
+
+def _load_params() -> Dict[str, Any]:
+    """加载灾害影响范围计算参数"""
+    with open(_PARAMS_PATH, 'r', encoding='utf-8') as f:
+        return yaml.safe_load(f)
+
+
+def _to_wkt_polygon(coords: list) -> str:
+    """
+    将坐标列表转换为 WKT POLYGON 字符串
+
+    Args:
+        coords: [(lon1, lat1), (lon2, lat2), ...] 首尾需闭合
+    """
+    points = ', '.join([f'{c[0]:.8f} {c[1]:.8f}' for c in coords])
+    return f'POLYGON(({points}))'
+
+
+def _to_wkt_linestring(coords: list) -> str:
+    """将坐标列表转换为 WKT LINESTRING 字符串"""
+    points = ', '.join([f'{c[0]:.8f} {c[1]:.8f}' for c in coords])
+    return f'LINESTRING({points})'
+
+
+def _offset_to_lonlat(lon: float, lat: float, dx_m: float, dy_m: float) -> Tuple[float, float]:
+    """
+    将米级偏移转换回经纬度
+
+    Args:
+        lon, lat: 中心点经纬度
+        dx_m: 东向偏移(m)
+        dy_m: 北向偏移(m)
+
+    Returns:
+        (new_lon, new_lat)
+    """
+    lat_rad = math.radians(lat)
+    dlon = math.degrees(dx_m / (_EARTH_RADIUS_M * math.cos(lat_rad)))
+    dlat = math.degrees(dy_m / _EARTH_RADIUS_M)
+    return lon + dlon, lat + dlat
+
+
+def _make_fan_wkt(lon: float, lat: float, direction_deg: float,
+                  spread_deg: float, radius_m: float,
+                  segments: int = 24) -> str:
+    """
+    创建扇形/锥形影响范围 WKT
+
+    Args:
+        lon, lat: 隐患点坐标
+        direction_deg: 扇形主方向(坡向, 0=北, 90=东)
+        spread_deg: 扇形总开角(度)
+        radius_m: 半径(m)
+        segments: 弧线段数
+
+    Returns:
+        POLYGON WKT 字符串
+    """
+    half_spread = spread_deg / 2.0
+    coords = [(lon, lat)]  # 中心点
+
+    for i in range(segments + 1):
+        angle_deg = direction_deg - half_spread + (spread_deg * i / segments)
+        angle_rad = math.radians(angle_deg)
+        dx = radius_m * math.sin(angle_rad)
+        dy = radius_m * math.cos(angle_rad)
+        coords.append(_offset_to_lonlat(lon, lat, dx, dy))
+
+    coords.append((lon, lat))  # 闭合回中心
+    return _to_wkt_polygon(coords)
+
+
+def _make_circle_wkt(lon: float, lat: float, radius_m: float,
+                     segments: int = 48) -> str:
+    """
+    创建圆形缓冲区 WKT（用正多边形近似圆）
+
+    Args:
+        lon, lat: 中心点
+        radius_m: 半径(m)
+        segments: 边数
+    """
+    coords = []
+    for i in range(segments):
+        angle_rad = 2 * math.pi * i / segments
+        dx = radius_m * math.sin(angle_rad)
+        dy = radius_m * math.cos(angle_rad)
+        coords.append(_offset_to_lonlat(lon, lat, dx, dy))
+    coords.append(coords[0])  # 闭合
+    return _to_wkt_polygon(coords)
+
+
+class HazardZoneCalculator:
+    """
+    灾害影响范围计算器
+
+    根据隐患点的灾害类型和静态因子，计算影响范围(WKT多边形)和影响路径(WKT线)。
+    每种灾害类型使用不同的经验公式，参数从 hazard_zone_params.yaml 读取。
+    """
+
+    def __init__(self):
+        self.params = _load_params()
+
+    # ==================== 公开接口 ====================
+
+    def calculate(self, source_id: int, disaster_type: str,
+                  scale_grade: Optional[str],
+                  lon: float, lat: float,
+                  static_factors: Dict[str, Any]) -> Tuple[Optional[str], Optional[str]]:
+        """
+        计算单个隐患点的影响范围
+
+        Args:
+            source_id: 隐患点ID
+            disaster_type: 灾害类型 (滑坡/泥石流/山洪/内涝/崩塌)
+            scale_grade: 规模等级 (小型/中型/大型)
+            lon: 经度
+            lat: 纬度
+            static_factors: 静态因子字典，从 xian_risk_factors.static_factors 获取
+
+        Returns:
+            (zone_wkt, path_wkt) — zone为POLYGON WKT, path为LINESTRING WKT或None
+        """
+        # 选择对应灾害类型的计算方法
+        method_map = {
+            '滑坡': self._calc_landslide,
+            '泥石流': self._calc_debris_flow,
+            '山洪': self._calc_flash_flood,
+            '内涝': self._calc_waterlogging,
+            '崩塌': self._calc_rockfall,
+        }
+        method = method_map.get(disaster_type, self._calc_default)
+        return method(source_id, disaster_type, scale_grade, static_factors, lon, lat)
+
+    # ==================== 滑坡 ====================
+
+    def _calc_landslide(self, source_id: int, disaster_type: str,
+                        scale_grade: Optional[str],
+                        factors: Dict[str, Any],
+                        lon: float, lat: float) -> Tuple[Optional[str], Optional[str]]:
+        """滑坡: 经验到达角法 + 扇形展开"""
+        p = self.params['landslide']
+
+        dem = float(factors.get('dem_value', 0))
+        aspect = float(factors.get('aspect_value', 0))
+        river_dist = float(factors.get('river_distance', 9999))
+        fault_dist = float(factors.get('fault_distance', 9999))
+
+        # 到达角
+        angle_key = scale_grade if scale_grade in p['reach_angle'] else '中型'
+        reach_angle = p['reach_angle'][angle_key]
+
+        # 半径: H / tan(到达角)
+        H = p['height_drop_m']
+        radius = H / math.tan(math.radians(reach_angle))
+
+        # 修正
+        if river_dist < p['river_distance_threshold']:
+            radius *= p['river_erosion_enhance']
+        if fault_dist < p['fault_distance_threshold']:
+            radius *= p['fault_enhance']
+
+        radius = max(p['min_radius_m'], min(radius, p['max_radius_m']))
+
+        # 扇形 — 沿坡向展开
+        if aspect and aspect > 0:
+            zone_wkt = _make_fan_wkt(lon, lat, aspect, p['fan_angle'], radius)
+            # 滑坡路径: 从隐患点沿坡向延伸到堆积区边缘
+            end_lon, end_lat = _offset_to_lonlat(
+                lon, lat,
+                radius * math.sin(math.radians(aspect)),
+                radius * math.cos(math.radians(aspect))
+            )
+            path_wkt = _to_wkt_linestring([(lon, lat), (end_lon, end_lat)])
+        else:
+            zone_wkt = _make_circle_wkt(lon, lat, radius)
+            path_wkt = None
+
+        return zone_wkt, path_wkt
+
+    # ==================== 泥石流 ====================
+
+    def _calc_debris_flow(self, source_id: int, disaster_type: str,
+                           scale_grade: Optional[str],
+                           factors: Dict[str, Any],
+                           lon: float, lat: float) -> Tuple[Optional[str], Optional[str]]:
+        """
+        泥石流: 沿最近河流方向建立影响区
+
+        path_geom 为隐患点到最近河流的连线，代表潜在流动路径
+        """
+        p = self.params['debris_flow']
+
+        slope = float(factors.get('slope_value', 0))
+
+        radius = p['base_radius_m'] + slope * p['slope_factor']
+        radius = max(p['min_radius_m'], min(radius, p['max_radius_m']))
+
+        zone_wkt = _make_circle_wkt(lon, lat, radius)
+
+        # 查询最近河流段 + 构建完整流动路径
+        river_geom_wkt = self._get_nearest_river_geom(lon, lat, p['max_river_search_m'])
+        if river_geom_wkt:
+            # 取河流上最近点
+            river_point = self._get_nearest_river_point(lon, lat, p['max_river_search_m'])
+            if river_point:
+                path_wkt = _to_wkt_linestring([
+                    (lon, lat),
+                    (river_point[0], river_point[1])
+                ])
+            else:
+                path_wkt = river_geom_wkt  # 降级：直接展示河流段
+        else:
+            path_wkt = None
+
+        return zone_wkt, path_wkt
+
+    # ==================== 山洪 ====================
+
+    def _calc_flash_flood(self, source_id: int, disaster_type: str,
+                          scale_grade: Optional[str],
+                          factors: Dict[str, Any],
+                          lon: float, lat: float) -> Tuple[Optional[str], Optional[str]]:
+        """
+        山洪: 根据距河流距离 + 河流等级确定缓冲区
+
+        path_geom 为关联的河流段
+        """
+        p = self.params['flash_flood']
+
+        river_dist = float(factors.get('river_distance', 9999))
+
+        thresholds = p['river_dist_thresholds']
+        buffer_map = p['buffer_by_level']
+
+        # 按距离选缓冲半径
+        if river_dist < thresholds[0]:
+            radius = buffer_map[1]
+        elif river_dist < thresholds[1]:
+            radius = buffer_map[2]
+        elif river_dist < thresholds[2]:
+            radius = buffer_map[3]
+        else:
+            radius = buffer_map.get(4, 80)
+
+        zone_wkt = _make_circle_wkt(lon, lat, radius)
+
+        # 山洪路径: 关联的最近河流段（淹没路径沿河展开）
+        path_wkt = self._get_nearest_river_geom(lon, lat, p['max_river_search_m'])
+
+        return zone_wkt, path_wkt
+
+    # ==================== 内涝 ====================
+
+    def _calc_waterlogging(self, source_id: int, disaster_type: str,
+                            scale_grade: Optional[str],
+                            factors: Dict[str, Any],
+                            lon: float, lat: float) -> Tuple[Optional[str], Optional[str]]:
+        """内涝: TWI简化 + 不透水率 + 管网修正"""
+        p = self.params['waterlogging']
+
+        slope = float(factors.get('slope_value', 1))
+        impervious = float(factors.get('impervious_surface', 0))
+        pipe_density = float(factors.get('pipe_density', 0))
+
+        slope_rad = max(math.radians(slope), 0.001)
+
+        # TWI_proxy = ln(1 / tanβ)
+        twi = math.log(1.0 / math.tan(slope_rad))
+
+        radius = (p['base_radius_m'] * twi
+                  * (1.0 + impervious * p['impervious_factor'])
+                  * max(p['pipe_lower_bound'], 1.0 - pipe_density * p['pipe_density_factor']))
+
+        radius = max(p['min_radius_m'], min(radius, p['max_radius_m']))
+
+        zone_wkt = _make_circle_wkt(lon, lat, radius)
+
+        return zone_wkt, None
+
+    # ==================== 崩塌 ====================
+
+    def _calc_rockfall(self, source_id: int, disaster_type: str,
+                        scale_grade: Optional[str],
+                        factors: Dict[str, Any],
+                        lon: float, lat: float) -> Tuple[Optional[str], Optional[str]]:
+        """崩塌: 锥体传播模型 (CONEFALL简化)"""
+        p = self.params['rockfall']
+
+        aspect = float(factors.get('aspect_value', 0))
+        fault_dist = float(factors.get('fault_distance', 9999))
+
+        # 到达角
+        angle_key = scale_grade if scale_grade in p['reach_angle'] else '中型'
+        reach_angle = p['reach_angle'][angle_key]
+
+        # 断裂带惩罚：减少到达角 = 更平缓 = 影响更大
+        if fault_dist < p['fault_penalty_threshold']:
+            reach_angle -= p['fault_angle_penalty']
+
+        # 半径
+        H = p['height_drop_m']
+        radius = H / math.tan(math.radians(max(reach_angle, 10)))
+
+        # 断裂带增强
+        if fault_dist < p['fault_enhance_threshold']:
+            radius *= p['fault_radius_enhance']
+
+        radius = max(p['min_radius_m'], min(radius, p['max_radius_m']))
+
+        # 锥形 — 沿坡向，窄扇形
+        if aspect and aspect > 0:
+            zone_wkt = _make_fan_wkt(lon, lat, aspect, 45, radius)
+        else:
+            zone_wkt = _make_circle_wkt(lon, lat, radius)
+
+        return zone_wkt, None
+
+    # ==================== 默认 ====================
+
+    def _calc_default(self, source_id: int, disaster_type: str,
+                       scale_grade: Optional[str],
+                       factors: Dict[str, Any],
+                       lon: float, lat: float) -> Tuple[Optional[str], Optional[str]]:
+        """未匹配灾害类型的兜底：固定半径圆形"""
+        p = self.params['default']
+        zone_wkt = _make_circle_wkt(lon, lat, p['radius_m'])
+        return zone_wkt, None
+
+    # ==================== 数据库查询辅助 ====================
+
+    @staticmethod
+    def _get_nearest_river_point(lon: float, lat: float,
+                                  max_dist_m: float) -> Optional[Tuple[float, float]]:
+        """
+        查询最近河流上距离隐患点最近的点的坐标
+        """
+        from app.utils.db_helper import db_helper
+
+        sql = """
+            SELECT ST_X(ST_ClosestPoint(r.geom, ST_SetSRID(ST_MakePoint(%s, %s), 4326))) AS rx,
+                   ST_Y(ST_ClosestPoint(r.geom, ST_SetSRID(ST_MakePoint(%s, %s), 4326))) AS ry
+            FROM xian_rivers r
+            WHERE r.is_delete = 0
+              AND ST_DWithin(r.geom::geography,
+                             ST_SetSRID(ST_MakePoint(%s, %s), 4326)::geography,
+                             %s)
+            ORDER BY ST_Distance(r.geom::geography,
+                                 ST_SetSRID(ST_MakePoint(%s, %s), 4326)::geography)
+            LIMIT 1
+        """
+        row = db_helper.execute_query_one(
+            sql, (lon, lat, lon, lat, lon, lat, max_dist_m, lon, lat)
+        )
+        if row and row['rx'] and row['ry']:
+            return float(row['rx']), float(row['ry'])
+        return None
+
+    @staticmethod
+    def _get_nearest_river_geom(lon: float, lat: float,
+                                 max_dist_m: float) -> Optional[str]:
+        """
+        查询最近河流段的 WKT
+
+        Returns:
+            LINESTRING WKT 字符串或 None
+        """
+        from app.utils.db_helper import db_helper
+
+        sql = """
+            SELECT ST_AsText(r.geom) AS wkt
+            FROM xian_rivers r
+            WHERE r.is_delete = 0
+              AND ST_DWithin(r.geom::geography,
+                             ST_SetSRID(ST_MakePoint(%s, %s), 4326)::geography,
+                             %s)
+            ORDER BY ST_Distance(r.geom::geography,
+                                 ST_SetSRID(ST_MakePoint(%s, %s), 4326)::geography)
+            LIMIT 1
+        """
+        row = db_helper.execute_query_one(sql, (lon, lat, max_dist_m, lon, lat))
+        if row and row['wkt']:
+            return row['wkt']
+        return None
+
+
+# 全局实例
+hazard_zone_calculator = HazardZoneCalculator()

app/repositories/hazard_repository.py

@@ -0,0 +1,120 @@
+"""
+灾害影响范围数据仓库
+负责 xian_hazard_zones 表的数据读取和写入
+"""
+from typing import List, Dict, Any, Optional
+
+from app.utils.db_helper import db_helper
+
+
+class HazardRepository:
+    """灾害影响范围数据访问层"""
+
+    # ==================== 隐患点查询 ====================
+
+    @staticmethod
+    def get_all_hidden_danger_spots() -> List[Dict[str, Any]]:
+        """
+        获取所有隐患点（含静态因子）
+
+        Returns:
+            隐患点列表，每个元素含: source_id, disaster_name, disaster_type,
+            scale_grade, county, lon, lat, static_factors
+        """
+        sql = """
+            SELECT
+                h.id AS source_id,
+                h.disaster_name,
+                h.disaster_type,
+                h.scale_grade,
+                h.county,
+                rf.lon,
+                rf.lat,
+                rf.static_factors
+            FROM xian_hidden_danger_spots h
+            JOIN xian_risk_factors rf
+                ON rf.source_id = h.id AND rf.source_type = 1
+            WHERE h.is_delete = 0
+              AND rf.is_delete = 0
+              AND h.disaster_type IN ('滑坡', '泥石流', '山洪', '内涝', '崩塌')
+              AND rf.lon IS NOT NULL
+              AND rf.lat IS NOT NULL
+            ORDER BY h.id
+        """
+        rows = db_helper.execute_query(sql)
+        return [{
+            'source_id': r['source_id'],
+            'disaster_name': r['disaster_name'],
+            'disaster_type': r['disaster_type'],
+            'scale_grade': r['scale_grade'],
+            'county': r['county'],
+            'lon': float(r['lon']),
+            'lat': float(r['lat']),
+            'static_factors': r.get('static_factors') or {}
+        } for r in rows]
+
+    # ==================== 影响范围写入 ====================
+
+    @staticmethod
+    def delete_existing_zones() -> int:
+        """逻辑删除已有影响范围记录"""
+        sql = "UPDATE xian_hazard_zones SET is_delete = 1 WHERE is_delete = 0"
+        return db_helper.execute_update(sql)
+
+    @staticmethod
+    def insert_hazard_zone(source_id: int, disaster_type: str,
+                           zone_wkt: str, path_wkt: Optional[str]) -> int:
+        """
+        插入单条影响范围记录
+
+        Args:
+            source_id: 隐患点ID
+            disaster_type: 灾害类型
+            zone_wkt: 影响范围 POLYGON WKT
+            path_wkt: 影响路径 LINESTRING WKT (可为None)
+
+        Returns:
+            新插入记录的ID
+        """
+        if path_wkt:
+            sql = """
+                INSERT INTO xian_hazard_zones (source_id, disaster_type, zone_geom, path_geom)
+                VALUES (%s, %s, ST_GeomFromText(%s, 4326), ST_GeomFromText(%s, 4326))
+                RETURNING id
+            """
+            row = db_helper.execute_query_one(sql, (source_id, disaster_type, zone_wkt, path_wkt))
+        else:
+            sql = """
+                INSERT INTO xian_hazard_zones (source_id, disaster_type, zone_geom)
+                VALUES (%s, %s, ST_GeomFromText(%s, 4326))
+                RETURNING id
+            """
+            row = db_helper.execute_query_one(sql, (source_id, disaster_type, zone_wkt))
+        return row['id'] if row else 0
+
+    @staticmethod
+    def batch_insert_zones(records: List[Dict[str, Any]]) -> int:
+        """
+        批量插入影响范围记录
+
+        Args:
+            records: [{source_id, disaster_type, zone_wkt, path_wkt}, ...]
+
+        Returns:
+            成功插入的记录数
+        """
+        count = 0
+        for rec in records:
+            hid = HazardRepository.insert_hazard_zone(
+                rec['source_id'],
+                rec['disaster_type'],
+                rec['zone_wkt'],
+                rec.get('path_wkt')
+            )
+            if hid > 0:
+                count += 1
+        return count
+
+
+# 全局实例
+hazard_repository = HazardRepository()

app/script/calculate_hazard_zones.py

@@ -0,0 +1,115 @@
+"""
+计算所有隐患点的灾害影响范围并填充 xian_hazard_zones 表
+
+仅计算隐患点 (xian_hidden_danger_spots)，不计算风险点。
+灾害类型包括: 滑坡、泥石流、山洪、内涝、崩塌
+
+"""
+import sys
+import os
+from datetime import datetime
+
+# 确保项目根目录在 sys.path 中
+_project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+if _project_root not in sys.path:
+    sys.path.insert(0, _project_root)
+
+from app.utils.logger import LoggerManager
+from app.repositories.hazard_repository import hazard_repository
+from app.models.hazard.hazard_zone_calculator import hazard_zone_calculator
+
+_BATCH_SIZE = 200  # 每处理200条输出一次进度
+
+logger = LoggerManager.get_logger("hazard_zones",
+                                  os.path.join(_project_root, "logs"))
+
+
+def main():
+    logger.info("=" * 60)
+    logger.info("开始计算隐患点灾害影响范围")
+    logger.info(f"启动时间: {datetime.now().isoformat()}")
+    logger.info("=" * 60)
+
+    # 1. 获取所有隐患点
+    spots = hazard_repository.get_all_hidden_danger_spots()
+    total = len(spots)
+    logger.info(f"查询到 {total} 个隐患点")
+    if total == 0:
+        logger.warning("没有隐患点数据，退出")
+        return
+
+    # 按灾害类型统计
+    type_counts = {}
+    for s in spots:
+        dt = s['disaster_type']
+        type_counts[dt] = type_counts.get(dt, 0) + 1
+    logger.info(f"灾害类型分布: {type_counts}")
+
+    # 2. 逐点计算
+    records = []
+    success_count = 0
+    skip_count = 0
+    error_count = 0
+
+    for i, spot in enumerate(spots):
+        sid = spot['source_id']
+        dtype = spot['disaster_type']
+        dname = spot['disaster_name']
+        scale = spot.get('scale_grade')
+
+        try:
+            zone_wkt, path_wkt = hazard_zone_calculator.calculate(
+                source_id=sid,
+                disaster_type=dtype,
+                scale_grade=scale,
+                lon=spot['lon'],
+                lat=spot['lat'],
+                static_factors=spot['static_factors']
+            )
+
+            if zone_wkt:
+                records.append({
+                    'source_id': sid,
+                    'disaster_type': dtype,
+                    'zone_wkt': zone_wkt,
+                    'path_wkt': path_wkt
+                })
+                success_count += 1
+            else:
+                skip_count += 1
+                logger.debug(f"跳过 [{sid}] {dname}({dtype}): 无法计算影响范围")
+
+        except Exception as e:
+            error_count += 1
+            logger.error(f"计算失败 [{sid}] {dname}({dtype}): {e}")
+
+        # 进度输出
+        if (i + 1) % _BATCH_SIZE == 0 or (i + 1) == total:
+            logger.info(f"进度: {i + 1}/{total} "
+                        f"(成功:{success_count} 跳过:{skip_count} 失败:{error_count})")
+
+    # 3. 写入数据库
+    logger.info(f"计算完成。开始写入数据库，共 {len(records)} 条记录")
+
+    # 先逻辑删除旧数据
+    deleted = hazard_repository.delete_existing_zones()
+    logger.info(f"已标记 {deleted} 条旧记录为删除")
+
+    # 批量写入新数据
+    inserted = hazard_repository.batch_insert_zones(records)
+    logger.info(f"成功写入 {inserted} 条新记录")
+
+    # 4. 汇总
+    logger.info("=" * 60)
+    logger.info("执行完成")
+    logger.info(f"  隐患点总数: {total}")
+    logger.info(f"  计算成功:   {success_count}")
+    logger.info(f"  跳过:       {skip_count}")
+    logger.info(f"  计算失败:   {error_count}")
+    logger.info(f"  写入记录:   {inserted}")
+    logger.info(f"完成时间: {datetime.now().isoformat()}")
+    logger.info("=" * 60)
+
+
+if __name__ == '__main__':
+    main()