构建暴雨灾害链和地震灾害链DBN模型
This commit is contained in:
wzy-warehouse
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"""
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DBN 模型模块
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"""
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"""
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地震灾害链DBN模型模块
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"""
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from .earthquake_dbn import EarthquakeDBN, earthquake_dbn
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__all__ = [
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'EarthquakeDBN', 'earthquake_dbn',
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]
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"""
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地震灾害链DBN模型
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实现贝叶斯网络推理，预测地震触发的3类地质灾害概率：
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滑坡（landslide）、泥石流（debris_flow）、崩塌（collapse）
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"""
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import os
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import math
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import yaml
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from typing import Optional, List, Dict, Any
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from app.utils.discretizer import discretizer
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from app.repositories.dbn_repository import DbnRepository
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from app.config.paths import DBN_CONFIG_DIR, get_logger
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logger = get_logger("earthquake_dbn")
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class EarthquakeDBN:
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"""地震灾害链DBN模型"""
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# 灾害概率→离散等级的阈值映射
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HAZARD_LEVEL_THRESHOLDS = [
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(0.6, 'very_high'),
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(0.4, 'high'),
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(0.2, 'medium'),
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(0.05, 'low'),
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(0.0, 'none'),
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]
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def _probability_to_level(self, prob: float) -> str:
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"""将连续概率映射到离散等级"""
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for threshold, level in self.HAZARD_LEVEL_THRESHOLDS:
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if prob >= threshold:
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return level
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return 'none'
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def __init__(self, config_dir: Optional[str] = None):
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"""
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初始化地震DBN模型
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Args:
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config_dir: 配置文件目录，默认为 app/config/dbn
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"""
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if config_dir is None:
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config_dir = str(DBN_CONFIG_DIR)
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self.config_dir = config_dir
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self.graph_config = self._load_graph_config()
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self.cpt_config = self._load_cpt_config()
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self._build_network()
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def _load_graph_config(self) -> Dict[str, Any]:
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"""加载地震图结构配置"""
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config_path = os.path.join(self.config_dir, 'earthquake_dbn_graph.yaml')
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if not os.path.exists(config_path):
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logger.error(f"地震图结构配置文件不存在: {config_path}")
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return {}
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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def _load_cpt_config(self) -> Dict[str, Any]:
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"""加载地震CPT配置"""
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config_path = os.path.join(self.config_dir, 'earthquake_cpt_params.yaml')
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if not os.path.exists(config_path):
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logger.error(f"地震CPT配置文件不存在: {config_path}")
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return {}
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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def _build_network(self):
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"""构建贝叶斯网络结构"""
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self.trigger_nodes = self.graph_config.get('layers', {}).get('trigger', [])
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self.environment_nodes = self.graph_config.get('layers', {}).get('environment', [])
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self.hazard_nodes = self.graph_config.get('layers', {}).get('hazard', [])
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self.all_nodes = self.trigger_nodes + self.environment_nodes + self.hazard_nodes
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self.edges = self.graph_config.get('edges', [])
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self.node_states = self.graph_config.get('node_states', {})
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self.children = {node: [] for node in self.all_nodes}
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self.parents = {node: [] for node in self.all_nodes}
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for parent, child in self.edges:
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if parent in self.all_nodes and child in self.all_nodes:
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self.children[parent].append(child)
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self.parents[child].append(parent)
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self._build_cpt_tables()
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def _build_cpt_tables(self):
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"""构建条件概率表"""
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self.cpt_tables = {}
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for node in self.all_nodes:
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if node in self.cpt_config:
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self.cpt_tables[node] = self.cpt_config[node]
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else:
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states = self.node_states.get(node, ['no', 'yes'])
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if len(states) == 2:
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self.cpt_tables[node] = {
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'type': 'prior',
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'probabilities': [0.5, 0.5]
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}
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else:
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prob = 1.0 / len(states)
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self.cpt_tables[node] = {
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'type': 'prior',
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'probabilities': [prob] * len(states)
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}
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@staticmethod
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def estimate_seismic_intensity(magnitude: float, epicenter_distance_km: float) -> float:
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"""
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根据震级和震中距估算地震烈度
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使用中国地震烈度衰减关系
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I = 0.923 + 1.621*M - 3.494*ln(R+10)
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参考：GB 18306-2015 中国地震动参数区划图
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Args:
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magnitude: 震级（Richter）
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epicenter_distance_km: 震中距（km）
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Returns:
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估算的地震烈度（中国烈度表数值）
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"""
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if epicenter_distance_km < 0:
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epicenter_distance_km = 0
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intensity = 0.923 + 1.621 * magnitude - 3.494 * math.log(epicenter_distance_km + 10)
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# 限制在合理范围内
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return max(1.0, min(12.0, intensity))
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def _get_node_probability(self, node: str, evidence: Dict[str, str]) -> List[float]:
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"""获取节点的概率分布"""
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cpt = self.cpt_tables.get(node)
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if not cpt:
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states = self.node_states.get(node, ['no', 'yes'])
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return [1.0 / len(states)] * len(states)
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if cpt.get('type') == 'prior':
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return cpt.get('probabilities', [0.5, 0.5])
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if cpt.get('type') == 'conditional':
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return self._evaluate_conditional_probability(node, cpt, evidence)
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return [0.5, 0.5]
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def _evaluate_conditional_probability(self, node: str, cpt: Dict[str, Any],
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evidence: Dict[str, str]) -> List[float]:
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"""评估条件概率"""
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states = self.node_states.get(node, ['no', 'yes'])
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default_prob = cpt.get('default_probability', 0.02)
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rules = cpt.get('rules', [])
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for rule in rules:
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condition = rule.get('condition', {})
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probability = rule.get('probability', default_prob)
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if self._check_condition(condition, evidence):
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return [1.0 - probability, probability]
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return [1.0 - default_prob, default_prob]
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def _check_condition(self, condition: Dict[str, Any], evidence: Dict[str, str]) -> bool:
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"""检查条件是否满足"""
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for node, required_states in condition.items():
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if node not in evidence:
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return False
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evidence_state = evidence[node]
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if isinstance(required_states, list):
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if evidence_state not in required_states:
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return False
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else:
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if evidence_state != required_states:
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return False
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return True
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def predict_single_point(self, point: Dict[str, Any],
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magnitude: float,
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epicenter_distance: Optional[float] = None,
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seismic_intensity: Optional[float] = None,
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epicenter_lon: Optional[float] = None,
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epicenter_lat: Optional[float] = None) -> Dict[str, Any]:
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"""
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对单个点进行地震灾害预测
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Args:
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point: 点信息（包含 static_factors 字段，来自 xian_risk_factors 表）
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magnitude: 地震震级（Richter）
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epicenter_distance: 震中距（km），若未提供则通过震中坐标计算
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seismic_intensity: 地震烈度（中国烈度表），若未提供则自动估算
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epicenter_lon: 震中经度（可选，用于计算震中距）
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epicenter_lat: 震中纬度（可选，用于计算震中距）
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Returns:
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预测结果
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"""
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point_id = point.get('id')
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lon = point.get('lon')
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lat = point.get('lat')
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source_type = point.get('source_type')
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logger.info(f"地震预测点 ID={point_id}, source_type={source_type}")
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# 计算震中距（如果未直接提供）
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if epicenter_distance is None:
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if epicenter_lon is not None and epicenter_lat is not None:
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epicenter_distance = self._haversine_distance(
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lon, lat, epicenter_lon, epicenter_lat
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)
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logger.info(f"计算震中距: {epicenter_distance:.1f} km")
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else:
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logger.warning("未提供震中距或震中坐标，使用默认值 100km")
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epicenter_distance = 100.0
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# 估算地震烈度（如果未直接提供）
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if seismic_intensity is None:
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seismic_intensity = self.estimate_seismic_intensity(magnitude, epicenter_distance)
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logger.info(f"估算地震烈度: {seismic_intensity:.1f}")
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# 获取静态因子数据
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raw_factors = point.get('static_factors', {})
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static_factors = {
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'elevation': raw_factors.get('dem_value'),
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'slope': raw_factors.get('slope_value'),
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'aspect': raw_factors.get('aspect_value'),
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'soil_type': raw_factors.get('soil_type'),
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'lithology': raw_factors.get('lithology'),
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'landuse': raw_factors.get('landuse'),
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'terrain': raw_factors.get('landform'),
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'ndvi': raw_factors.get('vegetation_index'),
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'sand_content': raw_factors.get('soil_sand'),
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'ph': raw_factors.get('soil_ph'),
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'soil_moisture': raw_factors.get('soil_moisture'),
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'organic_carbon': raw_factors.get('organic_carbon'),
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'dist_to_river': raw_factors.get('river_distance'),
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'dist_to_fault': raw_factors.get('fault_distance'),
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}
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# 合并地震触发因子和静态因子
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all_factors = {
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'magnitude': magnitude,
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'epicenter_distance': epicenter_distance,
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'seismic_intensity': seismic_intensity,
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**static_factors
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}
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# 离散化
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evidence = discretizer.discretize_all_factors(all_factors)
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# 运行推理
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hazard_results = self._run_inference(evidence)
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# 构造输出
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result = {
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'point_id': point_id,
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'source_type': source_type,
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'lon': lon,
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'lat': lat,
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'earthquake_params': {
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'magnitude': magnitude,
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'epicenter_distance': round(epicenter_distance, 1),
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'seismic_intensity': round(seismic_intensity, 1),
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},
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'disaster_probabilities': {
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h: r['probability'] for h, r in hazard_results.items()
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},
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'disaster_levels': {
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h: r['level'] for h, r in hazard_results.items()
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}
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}
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return result
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def _haversine_distance(self, lon1: float, lat1: float,
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lon2: float, lat2: float) -> float:
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"""
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使用Haversine公式计算两点间距离
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Args:
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lon1, lat1: 点1的经纬度
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lon2, lat2: 点2的经纬度
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Returns:
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距离（km）
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"""
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R = 6371.0 # 地球半径（km）
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lat1_rad = math.radians(lat1)
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lat2_rad = math.radians(lat2)
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dlat = math.radians(lat2 - lat1)
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dlon = math.radians(lon2 - lon1)
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a = math.sin(dlat / 2) ** 2 + \
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math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(dlon / 2) ** 2
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c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
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return R * c
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def _run_inference(self, evidence: Dict[str, str]) -> Dict[str, Any]:
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"""运行贝叶斯推理"""
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hazard_probabilities = {}
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for hazard_node in self.hazard_nodes:
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prob_dist = self._get_node_probability(hazard_node, evidence)
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if len(prob_dist) >= 2:
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prob = prob_dist[1]
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else:
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prob = 0.0
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hazard_probabilities[hazard_node] = {
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'probability': round(prob, 4),
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'level': self._probability_to_level(prob)
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}
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return hazard_probabilities
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def predict(self, region_code: Optional[str] = None,
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magnitude: float = 6.0,
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epicenter_distance: Optional[float] = None,
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seismic_intensity: Optional[float] = None,
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epicenter_lon: Optional[float] = None,
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epicenter_lat: Optional[float] = None) -> List[Dict[str, Any]]:
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"""
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对所有点进行地震灾害预测
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Args:
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region_code: 行政区划代码（可选）
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magnitude: 地震震级（默认6.0）
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epicenter_distance: 震中距（km，可选）
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seismic_intensity: 地震烈度（可选）
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epicenter_lon: 震中经度（可选）
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epicenter_lat: 震中纬度（可选）
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Returns:
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预测结果列表
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"""
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points = DbnRepository.get_all_points(region_code)
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if not points:
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logger.warning(f"没有找到点数据，region_code={region_code}")
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return []
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logger.info(f"地震灾害预测：共 {len(points)} 个点，震级 M{magnitude}")
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results = []
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for point in points:
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try:
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result = self.predict_single_point(
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point,
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magnitude=magnitude,
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epicenter_distance=epicenter_distance,
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seismic_intensity=seismic_intensity,
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epicenter_lon=epicenter_lon,
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epicenter_lat=epicenter_lat
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)
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results.append(result)
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except Exception as e:
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logger.error(f"预测点 {point.get('id')} 失败: {e}")
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results.append({
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'point_id': point.get('id'),
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'source_type': point.get('source_type'),
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'lon': point.get('lon'),
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'lat': point.get('lat'),
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'error': str(e)
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})
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return results
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def get_model_info(self) -> Dict[str, Any]:
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"""获取模型信息"""
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return {
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'model_type': 'earthquake',
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'trigger_nodes': self.trigger_nodes,
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'environment_nodes': self.environment_nodes,
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'hazard_nodes': self.hazard_nodes,
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'edges': self.edges,
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'node_states': self.node_states
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}
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# 创建全局实例
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earthquake_dbn = EarthquakeDBN()
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@@ -0,0 +1,11 @@
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"""
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暴雨灾害链DBN模型模块
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数据库查询统一由 app.repositories.dbn_repository 提供
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离散化工具由 app.utils.discretizer 提供
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空间计算由 app.utils.spatial_utils 提供
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"""
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from .rainfall_dbn import RainfallDBN, rainfall_dbn
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__all__ = [
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'RainfallDBN', 'rainfall_dbn',
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]
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@@ -0,0 +1,394 @@
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"""
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暴雨灾害链DBN模型
|
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实现贝叶斯网络推理，预测5类灾害概率
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"""
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import os
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import yaml
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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.discretizer import discretizer
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from app.repositories.dbn_repository import DbnRepository
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from app.config.paths import DBN_CONFIG_DIR, get_logger
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logger = get_logger("dbn")
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class RainfallDBN:
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"""暴雨灾害链DBN模型"""
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# 灾害概率→离散等级的阈值映射
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HAZARD_LEVEL_THRESHOLDS = [
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(0.6, 'very_high'),
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(0.4, 'high'),
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(0.2, 'medium'),
|
||||
(0.05, 'low'),
|
||||
(0.0, 'none'),
|
||||
]
|
||||
|
||||
def _probability_to_level(self, prob: float) -> str:
|
||||
"""将连续概率映射到离散等级"""
|
||||
for threshold, level in self.HAZARD_LEVEL_THRESHOLDS:
|
||||
if prob >= threshold:
|
||||
return level
|
||||
return 'none'
|
||||
|
||||
def __init__(self, config_dir: Optional[str] = None):
|
||||
"""
|
||||
初始化DBN模型
|
||||
|
||||
Args:
|
||||
config_dir: 配置文件目录
|
||||
"""
|
||||
if config_dir is None:
|
||||
config_dir = str(DBN_CONFIG_DIR)
|
||||
|
||||
self.config_dir = config_dir
|
||||
self.graph_config = self._load_graph_config()
|
||||
self.cpt_config = self._load_cpt_config()
|
||||
|
||||
# 构建贝叶斯网络结构
|
||||
self._build_network()
|
||||
|
||||
def _load_graph_config(self) -> Dict[str, Any]:
|
||||
"""加载图结构配置"""
|
||||
config_path = os.path.join(self.config_dir, 'rainfall_dbn_graph.yaml')
|
||||
|
||||
if not os.path.exists(config_path):
|
||||
logger.error(f"图结构配置文件不存在: {config_path}")
|
||||
return {}
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
return config
|
||||
|
||||
def _load_cpt_config(self) -> Dict[str, Any]:
|
||||
"""加载CPT配置"""
|
||||
config_path = os.path.join(self.config_dir, 'rainfall_cpt_params.yaml')
|
||||
|
||||
if not os.path.exists(config_path):
|
||||
logger.error(f"CPT配置文件不存在: {config_path}")
|
||||
return {}
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
return config
|
||||
|
||||
def _build_network(self):
|
||||
"""构建贝叶斯网络结构"""
|
||||
# 获取节点列表
|
||||
self.trigger_nodes = self.graph_config.get('layers', {}).get('trigger', [])
|
||||
self.environment_nodes = self.graph_config.get('layers', {}).get('environment', [])
|
||||
self.hazard_nodes = self.graph_config.get('layers', {}).get('hazard', [])
|
||||
|
||||
# 获取所有节点
|
||||
self.all_nodes = self.trigger_nodes + self.environment_nodes + self.hazard_nodes
|
||||
|
||||
# 获取边关系
|
||||
self.edges = self.graph_config.get('edges', [])
|
||||
|
||||
# 获取节点状态
|
||||
self.node_states = self.graph_config.get('node_states', {})
|
||||
|
||||
# 构建父子关系
|
||||
self.children = {node: [] for node in self.all_nodes}
|
||||
self.parents = {node: [] for node in self.all_nodes}
|
||||
|
||||
for parent, child in self.edges:
|
||||
if parent in self.all_nodes and child in self.all_nodes:
|
||||
self.children[parent].append(child)
|
||||
self.parents[child].append(parent)
|
||||
|
||||
# 构建CPT表
|
||||
self._build_cpt_tables()
|
||||
|
||||
def _build_cpt_tables(self):
|
||||
"""构建条件概率表"""
|
||||
self.cpt_tables = {}
|
||||
|
||||
for node in self.all_nodes:
|
||||
if node in self.cpt_config:
|
||||
self.cpt_tables[node] = self.cpt_config[node]
|
||||
else:
|
||||
# 如果没有配置，使用均匀分布
|
||||
states = self.node_states.get(node, ['no', 'yes'])
|
||||
if len(states) == 2:
|
||||
# 二值节点
|
||||
self.cpt_tables[node] = {
|
||||
'type': 'prior',
|
||||
'probabilities': [0.5, 0.5]
|
||||
}
|
||||
else:
|
||||
# 多值节点
|
||||
prob = 1.0 / len(states)
|
||||
self.cpt_tables[node] = {
|
||||
'type': 'prior',
|
||||
'probabilities': [prob] * len(states)
|
||||
}
|
||||
|
||||
def _get_node_probability(self, node: str, evidence: Dict[str, str]) -> List[float]:
|
||||
"""
|
||||
获取节点的概率分布
|
||||
|
||||
Args:
|
||||
node: 节点名称
|
||||
evidence: 证据字典
|
||||
|
||||
Returns:
|
||||
概率分布列表
|
||||
"""
|
||||
cpt = self.cpt_tables.get(node)
|
||||
if not cpt:
|
||||
states = self.node_states.get(node, ['no', 'yes'])
|
||||
return [1.0 / len(states)] * len(states)
|
||||
|
||||
# 如果是先验概率
|
||||
if cpt.get('type') == 'prior':
|
||||
return cpt.get('probabilities', [0.5, 0.5])
|
||||
|
||||
# 如果是条件概率
|
||||
if cpt.get('type') == 'conditional':
|
||||
return self._evaluate_conditional_probability(node, cpt, evidence)
|
||||
|
||||
return [0.5, 0.5]
|
||||
|
||||
def _evaluate_conditional_probability(self, node: str, cpt: Dict[str, Any],
|
||||
evidence: Dict[str, str]) -> List[float]:
|
||||
"""
|
||||
评估条件概率
|
||||
|
||||
Args:
|
||||
node: 节点名称
|
||||
cpt: CPT配置
|
||||
evidence: 证据字典
|
||||
|
||||
Returns:
|
||||
概率分布列表
|
||||
"""
|
||||
states = self.node_states.get(node, ['no', 'yes'])
|
||||
default_prob = cpt.get('default_probability', 0.05)
|
||||
|
||||
# 检查规则
|
||||
rules = cpt.get('rules', [])
|
||||
for rule in rules:
|
||||
condition = rule.get('condition', {})
|
||||
probability = rule.get('probability', default_prob)
|
||||
|
||||
# 检查是否满足条件
|
||||
if self._check_condition(condition, evidence):
|
||||
# 返回 [P(no), P(yes)]
|
||||
return [1.0 - probability, probability]
|
||||
|
||||
# 如果没有匹配的规则，返回默认概率
|
||||
return [1.0 - default_prob, default_prob]
|
||||
|
||||
def _check_condition(self, condition: Dict[str, Any], evidence: Dict[str, str]) -> bool:
|
||||
"""
|
||||
检查条件是否满足
|
||||
|
||||
Args:
|
||||
condition: 条件字典
|
||||
evidence: 证据字典
|
||||
|
||||
Returns:
|
||||
是否满足
|
||||
"""
|
||||
for node, required_states in condition.items():
|
||||
if node not in evidence:
|
||||
return False
|
||||
|
||||
evidence_state = evidence[node]
|
||||
|
||||
# 如果required_states是列表，检查是否在列表中
|
||||
if isinstance(required_states, list):
|
||||
if evidence_state not in required_states:
|
||||
return False
|
||||
else:
|
||||
# 如果是单个值，检查是否相等
|
||||
if evidence_state != required_states:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def predict_single_point(self, point: Dict[str, Any],
|
||||
rainfall: Optional[float] = None,
|
||||
duration: Optional[float] = None,
|
||||
query_time: Optional[datetime] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
对单个点进行预测
|
||||
|
||||
Args:
|
||||
point: 点信息（包含 static_factors 字段）
|
||||
rainfall: 累计降雨量（可选）
|
||||
duration: 持续时间（可选）
|
||||
query_time: 查询时间（可选）
|
||||
|
||||
Returns:
|
||||
预测结果
|
||||
"""
|
||||
point_id = point.get('id')
|
||||
lon = point.get('lon')
|
||||
lat = point.get('lat')
|
||||
source_type = point.get('source_type')
|
||||
|
||||
logger.info(f"预测点 ID={point_id}, source_type={source_type}")
|
||||
|
||||
# 获取降雨数据
|
||||
if rainfall is not None and duration is not None:
|
||||
rain_intensity = rainfall / duration if duration > 0 else 0.0
|
||||
rainfall_data = {
|
||||
'accum_rain': rainfall,
|
||||
'duration_hours': duration,
|
||||
'rain_intensity': rain_intensity
|
||||
}
|
||||
else:
|
||||
rainfall_data = DbnRepository.get_rainfall_data_with_duration(lon, lat, query_time)
|
||||
|
||||
# 获取静态因子数据（从 point 的 static_factors 字段）
|
||||
raw_factors = point.get('static_factors', {})
|
||||
static_factors = {
|
||||
'elevation': raw_factors.get('dem_value'),
|
||||
'slope': raw_factors.get('slope_value'),
|
||||
'aspect': raw_factors.get('aspect_value'),
|
||||
'soil_type': raw_factors.get('soil_type'),
|
||||
'lithology': raw_factors.get('lithology'),
|
||||
'landuse': raw_factors.get('landuse'),
|
||||
'terrain': raw_factors.get('landform'),
|
||||
'impervious': raw_factors.get('impervious_surface'),
|
||||
'ndvi': raw_factors.get('vegetation_index'),
|
||||
'sand_content': raw_factors.get('soil_sand'),
|
||||
'ph': raw_factors.get('soil_ph'),
|
||||
'soil_moisture': raw_factors.get('soil_moisture'),
|
||||
'organic_carbon': raw_factors.get('organic_carbon'),
|
||||
'dist_to_river': raw_factors.get('river_distance'),
|
||||
'dist_to_fault': raw_factors.get('fault_distance'),
|
||||
'pipe_density': raw_factors.get('pipe_density')
|
||||
}
|
||||
|
||||
# 合并所有因子
|
||||
all_factors = {
|
||||
'rain_intensity': rainfall_data.get('rain_intensity', 0.0),
|
||||
'duration': rainfall_data.get('duration_hours', 0),
|
||||
'accum_rain': rainfall_data.get('accum_rain', 0.0),
|
||||
**static_factors
|
||||
}
|
||||
|
||||
# 离散化
|
||||
evidence = discretizer.discretize_all_factors(all_factors)
|
||||
|
||||
# 运行推理
|
||||
hazard_results = self._run_inference(evidence)
|
||||
|
||||
# 构造输出
|
||||
result = {
|
||||
'point_id': point_id,
|
||||
'source_type': source_type,
|
||||
'lon': lon,
|
||||
'lat': lat,
|
||||
'disaster_probabilities': {
|
||||
h: r['probability'] for h, r in hazard_results.items()
|
||||
},
|
||||
'disaster_levels': {
|
||||
h: r['level'] for h, r in hazard_results.items()
|
||||
}
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def _run_inference(self, evidence: Dict[str, str]) -> Dict[str, Any]:
|
||||
"""
|
||||
运行贝叶斯推理
|
||||
|
||||
Args:
|
||||
evidence: 证据字典
|
||||
|
||||
Returns:
|
||||
灾害概率字典，每个值包含 probability 和 level
|
||||
"""
|
||||
hazard_probabilities = {}
|
||||
|
||||
for hazard_node in self.hazard_nodes:
|
||||
# 获取灾害节点的概率
|
||||
prob_dist = self._get_node_probability(hazard_node, evidence)
|
||||
|
||||
# 取发生概率（第二个状态）
|
||||
if len(prob_dist) >= 2:
|
||||
prob = prob_dist[1]
|
||||
else:
|
||||
prob = 0.0
|
||||
|
||||
hazard_probabilities[hazard_node] = {
|
||||
'probability': round(prob, 4),
|
||||
'level': self._probability_to_level(prob)
|
||||
}
|
||||
|
||||
return hazard_probabilities
|
||||
|
||||
def predict(self, region_code: Optional[str] = None,
|
||||
rainfall: Optional[float] = None,
|
||||
duration: Optional[float] = None,
|
||||
timestamp: Optional[datetime] = None) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
预测灾害概率
|
||||
|
||||
Args:
|
||||
region_code: 行政区划代码（可选）
|
||||
rainfall: 累计降雨量（可选，全局值）
|
||||
duration: 持续时间（可选，全局值）
|
||||
timestamp: 时间（可选）
|
||||
|
||||
Returns:
|
||||
预测结果列表
|
||||
"""
|
||||
# 1. 获取点列表
|
||||
points = DbnRepository.get_all_points(region_code)
|
||||
|
||||
if not points:
|
||||
logger.warning(f"没有找到点数据，region_code={region_code}")
|
||||
return []
|
||||
|
||||
logger.info(f"共找到 {len(points)} 个点")
|
||||
|
||||
# 2. 对每个点进行预测
|
||||
results = []
|
||||
for point in points:
|
||||
try:
|
||||
result = self.predict_single_point(
|
||||
point,
|
||||
rainfall=rainfall,
|
||||
duration=duration,
|
||||
query_time=timestamp
|
||||
)
|
||||
results.append(result)
|
||||
except Exception as e:
|
||||
logger.error(f"预测点 {point.get('id')} 失败: {e}")
|
||||
results.append({
|
||||
'point_id': point.get('id'),
|
||||
'source_type': point.get('source_type'),
|
||||
'lon': point.get('lon'),
|
||||
'lat': point.get('lat'),
|
||||
'error': str(e)
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
def get_model_info(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取模型信息
|
||||
|
||||
Returns:
|
||||
模型信息字典
|
||||
"""
|
||||
return {
|
||||
'trigger_nodes': self.trigger_nodes,
|
||||
'environment_nodes': self.environment_nodes,
|
||||
'hazard_nodes': self.hazard_nodes,
|
||||
'edges': self.edges,
|
||||
'node_states': self.node_states
|
||||
}
|
||||
|
||||
|
||||
# 创建全局实例
|
||||
rainfall_dbn = RainfallDBN()
|
||||
Reference in New Issue
Block a user