wzy-warehouse
721 lines
23 KiB
Python
721 lines
23 KiB
Python
"""
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||
数据库查询模块
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负责从 xian_risk_factors、xian_meteorology 等表获取数据
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"""
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import math
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from typing import Optional, List, Dict, Any
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from datetime import datetime
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from app.utils.db_helper import db_helper
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from app.config.paths import get_logger
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logger = get_logger("dbn")
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class DbnRepository:
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"""数据库查询类 - 所有DBN相关查询统一管理"""
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# ==================== 风险因子查询 ====================
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@staticmethod
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def get_all_points(region_code: Optional[str] = None) -> List[Dict[str, Any]]:
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"""
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获取所有隐患点和风险点（从 xian_risk_factors 表）
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Args:
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region_code: 行政区划代码（如 '610104'），可选，匹配隐患点.county_code 和风险点.unit_code
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Returns:
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点列表，每个元素包含：id, source_id, source_type, lon, lat, static_factors
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"""
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if region_code:
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# 通过源表的行政区划代码筛选
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sql = """
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SELECT rf.id, rf.source_id, rf.source_type, rf.lon, rf.lat, rf.static_factors
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FROM xian_risk_factors rf
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WHERE rf.is_delete = 0
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AND (
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(rf.source_type = 1 AND rf.source_id IN (
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SELECT id FROM xian_hidden_danger_spots WHERE county_id = %s AND is_delete = 0
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))
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OR
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(rf.source_type = 2 AND rf.source_id IN (
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SELECT id FROM xian_risk_spots WHERE unit_code = %s AND is_delete = 0
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))
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)
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"""
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params = (region_code, region_code)
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else:
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sql = """
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SELECT id, source_id, source_type, lon, lat, static_factors
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FROM xian_risk_factors
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WHERE is_delete = 0
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"""
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params = None
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results = db_helper.execute_query(sql, params)
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points = []
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for row in results:
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points.append({
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'id': row['id'],
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'source_id': row['source_id'],
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'source_type': row['source_type'],
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'lon': float(row['lon']) if row['lon'] else None,
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'lat': float(row['lat']) if row['lat'] else None,
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'static_factors': row.get('static_factors') or {}
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})
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return points
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@staticmethod
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def get_point_by_id(point_id: int) -> Optional[Dict[str, Any]]:
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"""
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根据ID获取单个点信息
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Args:
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point_id: xian_risk_factors 表的 ID
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Returns:
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点信息
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"""
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sql = """
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SELECT
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rf.id,
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rf.source_id,
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rf.source_type,
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rf.lon,
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rf.lat,
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rf.static_factors
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FROM xian_risk_factors rf
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WHERE rf.id = %s AND rf.is_delete = 0
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"""
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result = db_helper.execute_query_one(sql, (point_id,))
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if not result:
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return None
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return {
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'id': result['id'],
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'source_id': result['source_id'],
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'source_type': result['source_type'],
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'lon': float(result['lon']) if result['lon'] else None,
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'lat': float(result['lat']) if result['lat'] else None,
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'static_factors': result.get('static_factors') or {}
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}
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@staticmethod
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def get_points_by_ids(point_ids: List[int]) -> List[Dict[str, Any]]:
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"""
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批量获取点信息
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Args:
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point_ids: 点ID列表
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Returns:
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点信息列表
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"""
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if not point_ids:
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return []
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placeholders = ','.join(['%s'] * len(point_ids))
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sql = f"""
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SELECT
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rf.id,
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rf.source_id,
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rf.source_type,
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rf.lon,
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rf.lat,
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rf.static_factors
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FROM xian_risk_factors rf
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WHERE rf.id IN ({placeholders}) AND rf.is_delete = 0
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"""
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results = db_helper.execute_query(sql, tuple(point_ids))
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return [{
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'id': row['id'],
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'source_id': row['source_id'],
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'source_type': row['source_type'],
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'lon': float(row['lon']) if row['lon'] else None,
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'lat': float(row['lat']) if row['lat'] else None,
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'static_factors': row.get('static_factors') or {}
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} for row in results]
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@staticmethod
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def get_static_factors(point_id: int) -> Dict[str, Any]:
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"""
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获取点的静态因子数据
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Args:
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point_id: xian_risk_factors 表的 ID
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Returns:
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静态因子数据
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"""
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sql = """
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SELECT static_factors
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FROM xian_risk_factors
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WHERE id = %s AND is_delete = 0
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"""
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result = db_helper.execute_query_one(sql, (point_id,))
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if result and result.get('static_factors'):
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return result['static_factors']
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return {}
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# ==================== 降雨数据查询 ====================
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@staticmethod
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def get_nearest_station_rainfall(lon: float, lat: float,
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query_time: Optional[datetime] = None) -> Dict[str, Any]:
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"""
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获取最近雨量站的降雨数据
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Args:
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lon: 经度
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lat: 纬度
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query_time: 查询时间，若未提供则取当前时间
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Returns:
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降雨数据
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"""
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if query_time is None:
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query_time = datetime.now()
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# noinspection SqlNoDataSourceInspection
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sql = """
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WITH station_data AS (
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SELECT
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lon,
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lat,
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SUM(CAST(rainfall_1h AS DOUBLE PRECISION)) as total_rainfall,
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COUNT(*) as record_count
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FROM xian_meteorology
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WHERE datetime BETWEEN
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CAST(EXTRACT(EPOCH FROM (%s::timestamp - INTERVAL '24 hours')) AS BIGINT)
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AND CAST(EXTRACT(EPOCH FROM %s::timestamp) AS BIGINT)
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AND rainfall_1h IS NOT NULL
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AND CAST(rainfall_1h AS DOUBLE PRECISION) > 0
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GROUP BY lon, lat
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)
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SELECT
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lon,
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lat,
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total_rainfall,
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record_count,
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ST_DistanceSphere(
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ST_SetSRID(ST_MakePoint(lon, lat), 4326),
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ST_SetSRID(ST_MakePoint(%s, %s), 4326)
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) as distance
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FROM station_data
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ORDER BY distance
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LIMIT 1
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"""
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result = db_helper.execute_query_one(sql, (query_time, query_time, lon, lat))
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if result:
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return {
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'rainfall': float(result['total_rainfall']) if result['total_rainfall'] else 0.0,
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'record_count': int(result['record_count']) if result['record_count'] else 0,
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'distance': float(result['distance']) if result['distance'] else 0.0,
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'station_lon': float(result['lon']) if result['lon'] else None,
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'station_lat': float(result['lat']) if result['lat'] else None
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}
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else:
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return {
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'rainfall': 0.0,
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'record_count': 0,
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'distance': 0.0,
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'station_lon': None,
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'station_lat': None
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}
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@staticmethod
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def get_rainfall_data_with_duration(lon: float, lat: float,
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query_time: Optional[datetime] = None) -> Dict[str, Any]:
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"""
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获取降雨数据，包括累计降雨量和持续时间
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Args:
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lon: 经度
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lat: 纬度
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query_time: 查询时间，若未提供则取当前时间
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Returns:
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降雨数据：{accum_rain, duration_hours, rain_intensity}
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"""
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if query_time is None:
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query_time = datetime.now()
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# 查找最近的雨量站
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# noinspection SqlNoDataSourceInspection
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sql = """
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SELECT lon, lat, dist
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FROM (
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SELECT DISTINCT lon, lat,
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ST_DistanceSphere(
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ST_SetSRID(ST_MakePoint(lon, lat), 4326),
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ST_SetSRID(ST_MakePoint(%s, %s), 4326)
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) as dist
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FROM xian_meteorology
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) t
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WHERE dist < 50000
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ORDER BY dist
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LIMIT 1
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"""
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station = db_helper.execute_query_one(sql, (lon, lat))
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if not station:
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return {'accum_rain': 0.0, 'duration_hours': 0, 'rain_intensity': 0.0}
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station_lon = station['lon']
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station_lat = station['lat']
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# 查询该站点的降雨时序数据
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# noinspection SqlNoDataSourceInspection
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sql = """
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SELECT
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datetime,
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CAST(rainfall_1h AS DOUBLE PRECISION) as rainfall
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FROM xian_meteorology
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WHERE lon = %s AND lat = %s
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AND datetime BETWEEN
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CAST(EXTRACT(EPOCH FROM (%s::timestamp - INTERVAL '72 hours')) AS BIGINT)
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AND CAST(EXTRACT(EPOCH FROM %s::timestamp) AS BIGINT)
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ORDER BY datetime DESC
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"""
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results = db_helper.execute_query(sql, (station_lon, station_lat, query_time, query_time))
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if not results:
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return {'accum_rain': 0.0, 'duration_hours': 0, 'rain_intensity': 0.0}
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||
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# 计算累计降雨量和持续时间
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accum_rain = 0.0
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duration_hours = 0
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consecutive_no_rain = 0
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||
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for row in results:
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rainfall = float(row['rainfall']) if row['rainfall'] else 0.0
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if rainfall > 0:
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accum_rain += rainfall
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duration_hours += 1
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consecutive_no_rain = 0
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||
else:
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||
consecutive_no_rain += 1
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||
if consecutive_no_rain >= 3:
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break
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if accum_rain > 0:
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duration_hours += 1
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||
|
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rain_intensity = accum_rain / duration_hours if duration_hours > 0 else 0.0
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||
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||
return {
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||
'accum_rain': accum_rain,
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'duration_hours': duration_hours,
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||
'rain_intensity': rain_intensity
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||
}
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||
|
||
# ---- 批量降雨查询（性能优化） ----
|
||
|
||
_cached_stations: Optional[List[Dict[str, Any]]] = None
|
||
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||
@classmethod
|
||
def _ensure_stations_cached(cls) -> List[Dict[str, Any]]:
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"""一次性加载所有气象站点坐标到内存（188个站点，约2KB）"""
|
||
if cls._cached_stations is not None:
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||
return cls._cached_stations
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||
sql = "SELECT DISTINCT lon, lat FROM xian_meteorology"
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||
cls._cached_stations = db_helper.execute_query(sql)
|
||
logger.info(f"已缓存 {len(cls._cached_stations)} 个气象站点坐标")
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||
return cls._cached_stations
|
||
|
||
@staticmethod
|
||
def _haversine_distance(lon1: float, lat1: float, lon2: float, lat2: float) -> float:
|
||
"""Haversine公式计算两点间距离（米）"""
|
||
R = 6371000
|
||
phi1, phi2 = math.radians(lat1), math.radians(lat2)
|
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dphi = math.radians(lat2 - lat1)
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dlam = math.radians(lon2 - lon1)
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||
a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlam / 2) ** 2
|
||
return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
|
||
|
||
@classmethod
|
||
def _find_nearest_station(cls, lon: float, lat: float) -> Optional[Dict[str, Any]]:
|
||
"""在缓存的站点中找最近的一个（纯Python，微秒级）"""
|
||
stations = cls._ensure_stations_cached()
|
||
if not stations:
|
||
return None
|
||
best = None
|
||
best_dist = float('inf')
|
||
for s in stations:
|
||
d = cls._haversine_distance(lon, lat, s['lon'], s['lat'])
|
||
if d < best_dist:
|
||
best_dist = d
|
||
best = s
|
||
if best_dist > 50000:
|
||
return None
|
||
return {'lon': best['lon'], 'lat': best['lat'], 'dist': best_dist}
|
||
|
||
@classmethod
|
||
def get_rainfall_data_batch(cls, points: List[Dict[str, Any]],
|
||
query_time: Optional[datetime] = None) -> Dict[str, Dict[str, Any]]:
|
||
"""
|
||
批量获取多个点的降雨数据（2次DB查询替代 N×2次）
|
||
|
||
Args:
|
||
points: 预测点列表，每个含 {'id': str, 'lon': float, 'lat': float}
|
||
query_time: 查询时间
|
||
|
||
Returns:
|
||
{point_id: {accum_rain, duration_hours, rain_intensity}}
|
||
"""
|
||
if query_time is None:
|
||
query_time = datetime.now()
|
||
|
||
# 结果模板（无数据时的默认值）
|
||
default = {'accum_rain': 0.0, 'duration_hours': 0, 'rain_intensity': 0.0}
|
||
result: Dict[str, Dict[str, Any]] = {}
|
||
|
||
# 1. 为每个点找最近站点（纯Python，瞬间完成）
|
||
station_to_points: Dict[tuple, List[str]] = {}
|
||
for p in points:
|
||
station = cls._find_nearest_station(p['lon'], p['lat'])
|
||
if station is None:
|
||
result[p['id']] = default.copy()
|
||
continue
|
||
key = (station['lon'], station['lat'])
|
||
station_to_points.setdefault(key, []).append(p['id'])
|
||
|
||
if not station_to_points:
|
||
return result
|
||
|
||
# 2. 一次批量查所有站点的72小时降雨数据
|
||
station_keys = list(station_to_points.keys())
|
||
placeholders = ', '.join(['(%s, %s)'] * len(station_keys))
|
||
params: List[Any] = []
|
||
for slon, slat in station_keys:
|
||
params.extend([slon, slat])
|
||
params.extend([query_time, query_time])
|
||
|
||
# noinspection SqlNoDataSourceInspection
|
||
sql = f"""
|
||
SELECT lon, lat, datetime, CAST(rainfall_1h AS DOUBLE PRECISION) as rainfall
|
||
FROM xian_meteorology
|
||
WHERE (lon, lat) IN ({placeholders})
|
||
AND datetime BETWEEN
|
||
CAST(EXTRACT(EPOCH FROM (%s::timestamp - INTERVAL '72 hours')) AS BIGINT)
|
||
AND CAST(EXTRACT(EPOCH FROM %s::timestamp) AS BIGINT)
|
||
ORDER BY lon, lat, datetime DESC
|
||
"""
|
||
rows = db_helper.execute_query(sql, tuple(params))
|
||
|
||
# 3. 按站点分组，计算累计降雨量和持续时间
|
||
from itertools import groupby
|
||
station_rainfall: Dict[tuple, Dict[str, Any]] = {}
|
||
for (slon, slat), group in groupby(rows, key=lambda r: (r['lon'], r['lat'])):
|
||
accum_rain = 0.0
|
||
duration_hours = 0
|
||
consecutive_no_rain = 0
|
||
for row in group:
|
||
rainfall = float(row['rainfall']) if row['rainfall'] else 0.0
|
||
if rainfall > 0:
|
||
accum_rain += rainfall
|
||
duration_hours += 1
|
||
consecutive_no_rain = 0
|
||
else:
|
||
consecutive_no_rain += 1
|
||
if consecutive_no_rain >= 3:
|
||
break
|
||
if accum_rain > 0:
|
||
duration_hours += 1
|
||
intensity = accum_rain / duration_hours if duration_hours > 0 else 0.0
|
||
station_rainfall[(slon, slat)] = {
|
||
'accum_rain': accum_rain,
|
||
'duration_hours': duration_hours,
|
||
'rain_intensity': intensity
|
||
}
|
||
|
||
# 4. 分发给各预测点
|
||
for (slon, slat), point_ids in station_to_points.items():
|
||
rain_data = station_rainfall.get((slon, slat), default)
|
||
for pid in point_ids:
|
||
result[pid] = rain_data
|
||
|
||
return result
|
||
|
||
# ==================== 空间查询 ====================
|
||
|
||
@staticmethod
|
||
def get_nearest_station(lon: float, lat: float,
|
||
station_type: str = 'meteorology') -> Optional[Dict[str, Any]]:
|
||
"""
|
||
获取最近的气象站点
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
station_type: 站点类型
|
||
|
||
Returns:
|
||
最近站点信息
|
||
"""
|
||
sql = """
|
||
SELECT DISTINCT ON (lon, lat)
|
||
lon,
|
||
lat,
|
||
ST_DistanceSphere(
|
||
ST_SetSRID(ST_MakePoint(lon, lat), 4326),
|
||
ST_SetSRID(ST_MakePoint(%s, %s), 4326)
|
||
) as distance
|
||
FROM xian_meteorology
|
||
WHERE is_delete = 0
|
||
ORDER BY lon, lat, distance
|
||
LIMIT 1
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat))
|
||
|
||
if result:
|
||
return {
|
||
'lon': float(result['lon']),
|
||
'lat': float(result['lat']),
|
||
'distance': float(result['distance'])
|
||
}
|
||
return None
|
||
|
||
@staticmethod
|
||
def get_distance_to_river(lon: float, lat: float) -> float:
|
||
"""
|
||
计算点到最近河流的距离
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
|
||
Returns:
|
||
距离（米）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
MIN(ST_DistanceSphere(
|
||
ST_SetSRID(ST_MakePoint(%s, %s), 4326),
|
||
geom
|
||
)) as min_distance
|
||
FROM xian_rivers
|
||
WHERE is_delete = 0
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat))
|
||
|
||
if result and result['min_distance']:
|
||
return float(result['min_distance'])
|
||
return 0.0
|
||
|
||
@staticmethod
|
||
def get_distance_to_fault(lon: float, lat: float) -> float:
|
||
"""
|
||
计算点到最近断裂带的距离
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
|
||
Returns:
|
||
距离（米）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
MIN(ST_DistanceSphere(
|
||
ST_SetSRID(ST_MakePoint(%s, %s), 4326),
|
||
geom
|
||
)) as min_distance
|
||
FROM xian_fault_lines
|
||
WHERE is_delete = 0
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat))
|
||
|
||
if result and result['min_distance']:
|
||
return float(result['min_distance'])
|
||
return 0.0
|
||
|
||
@staticmethod
|
||
def get_pipe_density(lon: float, lat: float, buffer_radius: float = 500.0) -> float:
|
||
"""
|
||
计算点周围缓冲区内的管网密度
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
buffer_radius: 缓冲区半径（米）
|
||
|
||
Returns:
|
||
管网密度（m/m²）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
COALESCE(SUM(ST_Length(wp.geom::geography)), 0) as total_length
|
||
FROM xian_water_pipe wp
|
||
WHERE wp.is_delete = 0
|
||
AND ST_DWithin(
|
||
ST_SetSRID(ST_MakePoint(%s, %s), 4326)::geography,
|
||
wp.geom::geography,
|
||
%s
|
||
)
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat, buffer_radius))
|
||
|
||
if result and result['total_length']:
|
||
total_length = float(result['total_length'])
|
||
buffer_area = math.pi * buffer_radius ** 2
|
||
density = total_length / buffer_area
|
||
return density
|
||
return 0.0
|
||
|
||
@staticmethod
|
||
def get_river_density(lon: float, lat: float, buffer_radius: float = 1000.0) -> float:
|
||
"""
|
||
计算点周围缓冲区内的河流密度
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
buffer_radius: 缓冲区半径（米）
|
||
|
||
Returns:
|
||
河流密度（m/m²）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
COALESCE(SUM(ST_Length(r.geom::geography)), 0) as total_length
|
||
FROM xian_rivers r
|
||
WHERE r.is_delete = 0
|
||
AND ST_DWithin(
|
||
ST_SetSRID(ST_MakePoint(%s, %s), 4326)::geography,
|
||
r.geom::geography,
|
||
%s
|
||
)
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat, buffer_radius))
|
||
|
||
if result and result['total_length']:
|
||
total_length = float(result['total_length'])
|
||
buffer_area = math.pi * buffer_radius ** 2
|
||
density = total_length / buffer_area
|
||
return density
|
||
return 0.0
|
||
|
||
@staticmethod
|
||
def get_point_elevation(lon: float, lat: float) -> Optional[float]:
|
||
"""
|
||
获取点的高程值
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
|
||
Returns:
|
||
高程值（米）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
ST_Value(rast, ST_SetSRID(ST_MakePoint(%s, %s), 4326)) as elevation
|
||
FROM xian_dem
|
||
WHERE ST_Intersects(rast, ST_SetSRID(ST_MakePoint(%s, %s), 4326))
|
||
LIMIT 1
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat, lon, lat))
|
||
|
||
if result and result['elevation']:
|
||
return float(result['elevation'])
|
||
return None
|
||
|
||
@staticmethod
|
||
def get_point_slope(lon: float, lat: float) -> Optional[float]:
|
||
"""
|
||
获取点的坡度值
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
|
||
Returns:
|
||
坡度值（度）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
ST_Value(rast, ST_SetSRID(ST_MakePoint(%s, %s), 4326)) as slope
|
||
FROM xian_slope
|
||
WHERE ST_Intersects(rast, ST_SetSRID(ST_MakePoint(%s, %s), 4326))
|
||
LIMIT 1
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat, lon, lat))
|
||
|
||
if result and result['slope']:
|
||
return float(result['slope'])
|
||
return None
|
||
|
||
@staticmethod
|
||
def get_point_aspect(lon: float, lat: float) -> Optional[float]:
|
||
"""
|
||
获取点的坡向值
|
||
|
||
Args:
|
||
lon: 经度
|
||
lat: 纬度
|
||
|
||
Returns:
|
||
坡向值（度）
|
||
"""
|
||
sql = """
|
||
SELECT
|
||
ST_Value(rast, ST_SetSRID(ST_MakePoint(%s, %s), 4326)) as aspect
|
||
FROM xian_aspect
|
||
WHERE ST_Intersects(rast, ST_SetSRID(ST_MakePoint(%s, %s), 4326))
|
||
LIMIT 1
|
||
"""
|
||
|
||
result = db_helper.execute_query_one(sql, (lon, lat, lon, lat))
|
||
|
||
if result and result['aspect']:
|
||
return float(result['aspect'])
|
||
return None
|
||
|
||
@staticmethod
|
||
def save_inference_result(event_type: str, occurred_time, operation_type: str,
|
||
condition: dict, result: list) -> int:
|
||
"""
|
||
保存推理结果到 inference_result 表
|
||
|
||
Args:
|
||
event_type: 事件类型（'rainfall' 或 'earthquake'）
|
||
occurred_time: 事件发生时间
|
||
operation_type: 操作类型
|
||
condition: 输入条件（JSON）
|
||
result: 预测结果列表（JSON）
|
||
|
||
Returns:
|
||
新插入记录的 ID
|
||
"""
|
||
import json
|
||
sql = """
|
||
INSERT INTO xian_inference_result (event_type, occurred_time, operation_type, condition, result)
|
||
VALUES (%s, %s, %s, %s::jsonb, %s::jsonb)
|
||
RETURNING id
|
||
"""
|
||
row = db_helper.execute_query_one(sql, (
|
||
event_type,
|
||
occurred_time,
|
||
operation_type,
|
||
json.dumps(condition, ensure_ascii=False),
|
||
json.dumps(result, ensure_ascii=False)
|
||
))
|
||
return row['id'] if row else 0
|
||
|
||
|
||
# 创建全局实例
|
||
dbn_repository = DbnRepository()
|