2569718930@qq.com
664 lines
22 KiB
Python
664 lines
22 KiB
Python
import os
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import json
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from datetime import datetime, timedelta
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import requests
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from src.analysis.settlement_rounding import apply_city_settlement
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from loguru import logger
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from src.database.runtime_state import (
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DailyRecordRepository,
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STATE_STORAGE_DUAL,
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STATE_STORAGE_SQLITE,
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get_state_storage_mode,
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)
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# Cross-platform file locking
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import sys
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if sys.platform == "win32":
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import msvcrt
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def _lock_sh(f):
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msvcrt.locking(f.fileno(), msvcrt.LK_NBLCK, 1)
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def _lock_ex(f):
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msvcrt.locking(f.fileno(), msvcrt.LK_NBLCK, 1)
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def _unlock(f):
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try:
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f.seek(0)
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msvcrt.locking(f.fileno(), msvcrt.LK_UNLCK, 1)
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except Exception:
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pass
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else:
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import fcntl
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def _lock_sh(f):
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fcntl.flock(f, fcntl.LOCK_SH)
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def _lock_ex(f):
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fcntl.flock(f, fcntl.LOCK_EX)
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def _unlock(f):
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fcntl.flock(f, fcntl.LOCK_UN)
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# Simple memory cache to avoid blasting the disk if queried 10 times a minute
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_history_cache = {}
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_history_mtime = 0
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_daily_record_repo = DailyRecordRepository()
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def _sf(value):
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if value is None:
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return None
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try:
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return float(value)
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except Exception:
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return None
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def _is_excluded_model_name(model_name: str) -> bool:
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normalized = str(model_name or "").strip().lower().replace(" ", "").replace("_", "").replace("-", "")
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return "meteoblue" in normalized
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def load_history(filepath):
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global _history_cache, _history_mtime
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mode = get_state_storage_mode()
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if mode == STATE_STORAGE_SQLITE:
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try:
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data = _daily_record_repo.load_all()
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_history_cache = data
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return data
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except Exception as e:
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logger.error(f"Error loading daily records from sqlite, fallback to file: {e}")
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if not os.path.exists(filepath):
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if mode == STATE_STORAGE_DUAL:
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try:
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data = _daily_record_repo.load_all()
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_history_cache = data
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return data
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except Exception:
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return {}
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return {}
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try:
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current_mtime = os.path.getmtime(filepath)
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if current_mtime == _history_mtime and _history_cache:
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return _history_cache
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with open(filepath, "r", encoding="utf-8") as f:
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# We don't strictly need a lock for reading in Python if the write is atomic,
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# but using one prevents reading half-written JSONs.
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_lock_sh(f)
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data = json.load(f)
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_unlock(f)
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_history_cache = data
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_history_mtime = current_mtime
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return data
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except Exception as e:
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print(f"Error loading history: {e}")
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return _history_cache if _history_cache else {}
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def save_history(filepath, data):
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global _history_cache, _history_mtime
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_history_cache = data
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mode = get_state_storage_mode()
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if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}:
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try:
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_daily_record_repo.replace_all(data)
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except Exception as e:
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logger.error(f"Error saving daily records to sqlite: {e}")
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if mode == STATE_STORAGE_SQLITE:
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return
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if mode == STATE_STORAGE_SQLITE:
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return
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try:
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with open(filepath, "w", encoding="utf-8") as f:
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_lock_ex(f)
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json.dump(data, f, ensure_ascii=False, indent=2)
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_unlock(f)
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_history_mtime = os.path.getmtime(filepath)
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except Exception as e:
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print(f"Error saving history: {e}")
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def _parse_metar_row_time(row):
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"""Parse METAR row timestamp from aviationweather API payload."""
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candidates = [
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row.get("reportTime"),
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row.get("receiptTime"),
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row.get("observation_time"),
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]
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for raw in candidates:
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if not raw:
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continue
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try:
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return datetime.fromisoformat(str(raw).replace("Z", "+00:00"))
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except Exception:
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continue
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obs_epoch = row.get("obsTime")
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if obs_epoch is not None:
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try:
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return datetime.utcfromtimestamp(int(obs_epoch))
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except Exception:
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pass
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return None
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def reconcile_recent_actual_highs(city_name: str, lookback_days: int = 7):
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"""
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Reconcile recent `actual_high` values using historical METAR data from
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aviationweather.gov to fix stale/wrong daily records.
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"""
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try:
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from src.data_collection.city_registry import CITY_REGISTRY, ALIASES
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city_key = str(city_name or "").strip().lower()
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city_key = ALIASES.get(city_key, city_key)
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city_meta = CITY_REGISTRY.get(city_key)
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if not isinstance(city_meta, dict):
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return {"ok": False, "reason": "unknown_city", "updated": 0}
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icao = str(city_meta.get("icao") or "").strip().upper()
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if not icao:
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return {"ok": False, "reason": "missing_icao", "updated": 0}
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tz_offset = int(city_meta.get("tz_offset") or 0)
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use_fahrenheit = bool(city_meta.get("use_fahrenheit"))
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project_root = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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history_file = os.path.join(project_root, "data", "daily_records.json")
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data = load_history(history_file)
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city_data = data.get(city_key) or {}
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if not isinstance(city_data, dict) or not city_data:
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return {"ok": True, "reason": "no_city_history", "updated": 0}
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local_now = datetime.utcnow() + timedelta(seconds=tz_offset)
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local_today = local_now.strftime("%Y-%m-%d")
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cutoff = (local_now - timedelta(days=max(lookback_days, 1) + 1)).strftime(
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"%Y-%m-%d"
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)
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target_dates = sorted(
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d for d in city_data.keys() if isinstance(d, str) and cutoff <= d < local_today
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)
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if not target_dates:
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return {"ok": True, "reason": "no_target_dates", "updated": 0}
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try:
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min_target = datetime.strptime(target_dates[0], "%Y-%m-%d")
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span_hours = int((local_now - min_target).total_seconds() / 3600) + 12
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except Exception:
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span_hours = (lookback_days + 3) * 24
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span_hours = max(72, min(240, span_hours))
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url = (
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f"https://aviationweather.gov/api/data/metar"
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f"?ids={icao}&format=json&hours={span_hours}"
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)
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resp = requests.get(url, timeout=12)
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resp.raise_for_status()
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rows = resp.json() or []
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if not isinstance(rows, list):
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rows = []
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daily_max_c = {}
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for row in rows:
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if not isinstance(row, dict):
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continue
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temp = row.get("temp")
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if temp is None:
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continue
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obs_dt = _parse_metar_row_time(row)
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if obs_dt is None:
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continue
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local_dt = obs_dt + timedelta(seconds=tz_offset)
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d = local_dt.strftime("%Y-%m-%d")
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if d < cutoff or d >= local_today:
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continue
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try:
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t = float(temp)
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except Exception:
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continue
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prev = daily_max_c.get(d)
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if prev is None or t > prev:
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daily_max_c[d] = t
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updated = 0
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for d in target_dates:
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t_c = daily_max_c.get(d)
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if t_c is None:
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continue
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corrected = round(t_c * 9 / 5 + 32, 1) if use_fahrenheit else round(t_c, 1)
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rec = city_data.get(d) or {}
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old = rec.get("actual_high")
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try:
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old_val = float(old) if old is not None else None
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except Exception:
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old_val = None
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if old_val is None or abs(old_val - corrected) >= 0.1:
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rec["actual_high"] = corrected
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city_data[d] = rec
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updated += 1
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if updated > 0:
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data[city_key] = city_data
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save_history(history_file, data)
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return {
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"ok": True,
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"updated": updated,
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"scanned_dates": len(target_dates),
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"metar_rows": len(rows),
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"icao": icao,
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}
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except Exception as e:
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return {"ok": False, "reason": str(e), "updated": 0}
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def update_daily_record(
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city_name,
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date_str,
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forecasts,
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actual_high,
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deb_prediction=None,
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mu=None,
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probabilities=None,
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probability_features=None,
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shadow_probabilities=None,
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calibration_summary=None,
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):
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"""
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保存/更新某城市某天的各个模型预报与最终实测值
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forecasts: dict, 例如 {"ECMWF": 28.5, "GFS": 30.0, ...}
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actual_high: float, 最终实测最高温
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deb_prediction: float, DEB 融合预测值（用于准确率追踪）
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mu: float, 概率引擎中心值（用于 μ MAE 追踪）
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probabilities: list[dict], 概率分布快照（用于 Brier Score 校准）
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例如 [{"value": 25, "probability": 0.8}, {"value": 26, "probability": 0.2}]
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"""
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project_root = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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history_file = os.path.join(project_root, "data", "daily_records.json")
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mode = get_state_storage_mode()
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data = load_history(history_file)
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if city_name not in data:
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data[city_name] = {}
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if date_str not in data[city_name]:
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data[city_name][date_str] = {}
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# 统一过滤已弃用模型，避免历史/展示残留。
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# 对同一天的多次刷新，保留历史上已经拿到的模型值，避免某次上游短暂缺失
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# 把已有 forecast 整体覆盖成更稀疏的新字典，导致历史图断线。
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next_forecasts = {
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k: v for k, v in (forecasts or {}).items() if not _is_excluded_model_name(k)
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}
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compact_probs = None
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if probabilities is not None:
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# Store compact: [{"v": 25, "p": 0.8}, ...]
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compact_probs = [
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{"v": p["value"], "p": p["probability"]}
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for p in probabilities[:4]
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]
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compact_features = None
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if isinstance(probability_features, dict) and probability_features:
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compact_features = {
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"raw_mu": _sf(probability_features.get("raw_mu")),
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"raw_sigma": _sf(probability_features.get("raw_sigma")),
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"deb_prediction": _sf(probability_features.get("deb_prediction")),
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"ens_median": _sf(probability_features.get("ens_median")),
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"ensemble_spread": _sf(probability_features.get("ensemble_spread")),
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"max_so_far": _sf(probability_features.get("max_so_far")),
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"max_so_far_gap": _sf(probability_features.get("max_so_far_gap")),
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"peak_status": probability_features.get("peak_status"),
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}
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compact_shadow_probs = None
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if shadow_probabilities is not None:
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compact_shadow_probs = [
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{"v": p["value"], "p": p["probability"]}
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for p in shadow_probabilities[:4]
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]
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compact_calibration = None
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if isinstance(calibration_summary, dict) and calibration_summary:
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compact_calibration = {
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"mode": calibration_summary.get("mode"),
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"engine": calibration_summary.get("engine"),
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"version": calibration_summary.get("calibration_version"),
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"source": calibration_summary.get("calibration_source"),
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"raw_mu": _sf(calibration_summary.get("raw_mu")),
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"raw_sigma": _sf(calibration_summary.get("raw_sigma")),
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"calibrated_mu": _sf(calibration_summary.get("calibrated_mu")),
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"calibrated_sigma": _sf(calibration_summary.get("calibrated_sigma")),
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}
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# 避免无意义的频繁磁盘写入
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existing = data[city_name][date_str]
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old_actual = existing.get("actual_high")
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old_deb = existing.get("deb_prediction")
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old_mu = existing.get("mu")
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old_probs = existing.get("prob_snapshot")
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old_shadow_probs = existing.get("shadow_prob_snapshot")
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old_forecasts = existing.get("forecasts") if isinstance(existing.get("forecasts"), dict) else {}
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merged_forecasts = dict(old_forecasts)
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for model_name, model_value in next_forecasts.items():
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if model_value is not None:
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merged_forecasts[model_name] = model_value
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elif model_name not in merged_forecasts:
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merged_forecasts[model_name] = model_value
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next_mu = round(mu, 2) if mu is not None else None
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if (
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old_actual == actual_high
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and old_forecasts == merged_forecasts
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and (deb_prediction is None or old_deb == deb_prediction)
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and (mu is None or old_mu == next_mu)
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and (compact_probs is None or old_probs == compact_probs)
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and (
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compact_shadow_probs is None
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or old_shadow_probs == compact_shadow_probs
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)
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and (
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compact_features is None
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or existing.get("probability_features") == compact_features
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)
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and (
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compact_calibration is None
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or existing.get("probability_calibration") == compact_calibration
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)
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):
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return
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# actual_high 应该是日内最高温，理论上不应下降；防止异常写入覆盖已确认高值
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if old_actual is not None and actual_high is not None:
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try:
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actual_high = max(float(old_actual), float(actual_high))
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except Exception:
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pass
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|
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existing["forecasts"] = merged_forecasts
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existing["actual_high"] = actual_high
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if deb_prediction is not None:
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existing["deb_prediction"] = deb_prediction
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if mu is not None:
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existing["mu"] = next_mu
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if probabilities is not None:
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existing["prob_snapshot"] = compact_probs
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if compact_features is not None:
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existing["probability_features"] = compact_features
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if shadow_probabilities is not None:
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existing["shadow_prob_snapshot"] = compact_shadow_probs
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if compact_calibration is not None:
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existing["probability_calibration"] = compact_calibration
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|
|
|
# 自动清理：只保留最近 14 天的记录（DEB 只用 7 天，14 天留足余量）
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cutoff = (datetime.now() - timedelta(days=14)).strftime("%Y-%m-%d")
|
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for city in list(data.keys()):
|
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old_dates = [d for d in data[city] if d < cutoff]
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for d in old_dates:
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del data[city][d]
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|
|
|
if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}:
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|
try:
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_daily_record_repo.upsert_record(city_name, date_str, existing)
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|
cutoff = (datetime.now() - timedelta(days=14)).strftime("%Y-%m-%d")
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_daily_record_repo.delete_older_than(cutoff)
|
|
except Exception as e:
|
|
logger.error(f"Error upserting daily record to sqlite city={city_name} date={date_str}: {e}")
|
|
if mode == STATE_STORAGE_SQLITE:
|
|
raise
|
|
|
|
if mode != STATE_STORAGE_SQLITE:
|
|
save_history(history_file, data)
|
|
|
|
|
|
def calculate_dynamic_weights(city_name, current_forecasts, lookback_days=7):
|
|
"""
|
|
计算动态权重融合 (Dynamic Ensemble Blending, DEB)
|
|
根据过去 N 天各模型的 Mean Absolute Error (MAE) 计算倒数权重
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|
返回: blended_high (融合预报值), weights_info (权重展示字符串)
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|
"""
|
|
project_root = os.path.dirname(
|
|
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|
)
|
|
history_file = os.path.join(project_root, "data", "daily_records.json")
|
|
data = load_history(history_file)
|
|
|
|
current_forecasts = {
|
|
k: v
|
|
for k, v in (current_forecasts or {}).items()
|
|
if not _is_excluded_model_name(k)
|
|
}
|
|
|
|
if city_name not in data or not data[city_name]:
|
|
# 没有历史数据，返回简单的平均/中位数
|
|
valid_vals = [v for v in current_forecasts.values() if v is not None]
|
|
if not valid_vals:
|
|
return None, "暂无模型数据"
|
|
avg = sum(valid_vals) / len(valid_vals)
|
|
return round(avg, 1), "等权平均(历史数据不足)"
|
|
|
|
# 获取过去 lookback_days 天的有 actual_high 的记录
|
|
city_data = data[city_name]
|
|
sorted_dates = sorted(city_data.keys(), reverse=True)
|
|
|
|
# 我们只用真正结清（或者有比较准确最高温）的历史来算误差
|
|
# 这边简化：凡是有 actual_high 的都算进去
|
|
errors = {model: [] for model in current_forecasts.keys()}
|
|
|
|
days_used = 0
|
|
for date_str in sorted_dates:
|
|
# 跳过今天，今天还没出最终结果
|
|
if date_str == datetime.now().strftime("%Y-%m-%d"):
|
|
continue
|
|
|
|
record = city_data[date_str]
|
|
actual = record.get("actual_high")
|
|
past_forecasts = record.get("forecasts", {})
|
|
|
|
if actual is None:
|
|
continue
|
|
|
|
for model in current_forecasts.keys():
|
|
if model in past_forecasts and past_forecasts[model] is not None:
|
|
try:
|
|
pv = float(past_forecasts[model])
|
|
av = float(actual)
|
|
except (TypeError, ValueError):
|
|
continue
|
|
errors[model].append(abs(pv - av))
|
|
|
|
days_used += 1
|
|
if days_used >= lookback_days:
|
|
break
|
|
|
|
# 如果有效历史天数 < 2 天，还是使用等权
|
|
if days_used < 2:
|
|
valid_vals = [v for v in current_forecasts.values() if v is not None]
|
|
if not valid_vals:
|
|
return None, f"暂无有效模型数据(由于仅{days_used}天历史)"
|
|
avg = sum(valid_vals) / len(valid_vals)
|
|
return round(avg, 1), f"等权平均(由于仅{days_used}天历史)"
|
|
|
|
# 计算 MAE
|
|
maes = {}
|
|
for model, err_list in errors.items():
|
|
if err_list:
|
|
maes[model] = sum(err_list) / len(err_list)
|
|
else:
|
|
# 如果某个新模型没有历史数据，给它一个平均误差
|
|
maes[model] = 2.0
|
|
|
|
# 计算权重（用 MAE 的倒数，误差越小权重越大；加 0.1 防止除以0）
|
|
inverse_errors = {
|
|
m: 1.0 / (mae + 0.1)
|
|
for m, mae in maes.items()
|
|
if current_forecasts.get(m) is not None
|
|
}
|
|
|
|
total_inv = sum(inverse_errors.values())
|
|
if total_inv == 0:
|
|
return None, "权重计算异常"
|
|
|
|
weights = {m: inv / total_inv for m, inv in inverse_errors.items()}
|
|
|
|
# 计算加权最高温
|
|
blended_high = 0.0
|
|
for m in weights.keys():
|
|
blended_high += current_forecasts[m] * weights[m]
|
|
|
|
# 格式化权重信息，挑选前权重最高的2-3个模型展示
|
|
sorted_models = sorted(weights.items(), key=lambda x: x[1], reverse=True)
|
|
weight_str_parts = []
|
|
for m, w in sorted_models[:3]:
|
|
weight_str_parts.append(f"{m}({w * 100:.0f}%,MAE:{maes[m]:.1f}°)")
|
|
|
|
return round(blended_high, 1), " | ".join(weight_str_parts)
|
|
|
|
|
|
def get_deb_accuracy(city_name):
|
|
"""
|
|
计算 DEB 融合预测的历史准确率
|
|
返回: (hit_rate, mae, total_days, details_str) 或 None
|
|
- hit_rate: WU 结算命中率 (DEB 四舍五入 == 实测四舍五入)
|
|
- mae: 平均绝对误差
|
|
- total_days: 有效天数
|
|
- details_str: 格式化的展示字符串
|
|
"""
|
|
project_root = os.path.dirname(
|
|
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|
)
|
|
history_file = os.path.join(project_root, "data", "daily_records.json")
|
|
data = load_history(history_file)
|
|
|
|
if city_name not in data:
|
|
return None
|
|
|
|
city_data = data[city_name]
|
|
today_str = datetime.now().strftime("%Y-%m-%d")
|
|
|
|
hits = 0
|
|
total = 0
|
|
errors = []
|
|
|
|
for date_str in sorted(city_data.keys()):
|
|
if date_str == today_str:
|
|
continue # 跳过今天，还没结算
|
|
record = city_data[date_str]
|
|
deb_pred = record.get("deb_prediction")
|
|
actual = record.get("actual_high")
|
|
|
|
if deb_pred is None or actual is None:
|
|
continue
|
|
|
|
try:
|
|
deb_pred = float(deb_pred)
|
|
actual = float(actual)
|
|
except Exception:
|
|
continue
|
|
|
|
total += 1
|
|
deb_wu = apply_city_settlement(city_name, deb_pred)
|
|
actual_wu = apply_city_settlement(city_name, actual)
|
|
if deb_wu == actual_wu:
|
|
hits += 1
|
|
errors.append(abs(deb_pred - actual))
|
|
|
|
if total == 0:
|
|
return None
|
|
|
|
hit_rate = hits / total * 100
|
|
mae = sum(errors) / len(errors)
|
|
|
|
details_str = (
|
|
f"过去{total}天 WU命中 {hits}/{total} ({hit_rate:.0f}%) | MAE: {mae:.1f}°"
|
|
)
|
|
|
|
return hit_rate, mae, total, details_str
|
|
|
|
|
|
def get_mu_accuracy(city_name):
|
|
"""
|
|
评估概率引擎 μ 的历史准确性
|
|
返回: (mu_mae, mu_hit_rate, brier_score, total_days, details_str) 或 None
|
|
|
|
- mu_mae: μ 与实际最高温的平均绝对误差
|
|
- mu_hit_rate: round(μ) 命中 WU 结算值的比率
|
|
- brier_score: 概率分布的 Brier Score (越低越好)
|
|
对于每天，取概率最高的预测值，计算 (p - outcome)² 的平均值
|
|
- total_days: 有效统计天数
|
|
"""
|
|
project_root = os.path.dirname(
|
|
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|
)
|
|
history_file = os.path.join(project_root, "data", "daily_records.json")
|
|
data = load_history(history_file)
|
|
|
|
if city_name not in data:
|
|
return None
|
|
|
|
city_data = data[city_name]
|
|
today_str = datetime.now().strftime("%Y-%m-%d")
|
|
|
|
mu_errors = []
|
|
mu_hits = 0
|
|
brier_scores = []
|
|
total = 0
|
|
|
|
for date_str in sorted(city_data.keys()):
|
|
if date_str == today_str:
|
|
continue
|
|
record = city_data[date_str]
|
|
actual = record.get("actual_high")
|
|
mu_val = record.get("mu")
|
|
|
|
if actual is None or mu_val is None:
|
|
continue
|
|
|
|
try:
|
|
actual = float(actual)
|
|
mu_val = float(mu_val)
|
|
except Exception:
|
|
continue
|
|
|
|
total += 1
|
|
mu_errors.append(abs(mu_val - actual))
|
|
if apply_city_settlement(city_name, mu_val) == apply_city_settlement(city_name, actual):
|
|
mu_hits += 1
|
|
|
|
# Brier Score from probability snapshot
|
|
prob_snap = record.get("prob_snapshot", [])
|
|
if prob_snap:
|
|
actual_wu = apply_city_settlement(city_name, actual)
|
|
bs = 0.0
|
|
for entry in prob_snap:
|
|
predicted_p = entry.get("p", 0)
|
|
outcome = 1.0 if entry.get("v") == actual_wu else 0.0
|
|
bs += (predicted_p - outcome) ** 2
|
|
brier_scores.append(bs)
|
|
|
|
if total == 0:
|
|
return None
|
|
|
|
mu_mae = sum(mu_errors) / len(mu_errors)
|
|
mu_hr = mu_hits / total * 100
|
|
avg_brier = sum(brier_scores) / len(brier_scores) if brier_scores else None
|
|
|
|
details_parts = [
|
|
f"μ准确率: 过去{total}天",
|
|
f"WU命中 {mu_hits}/{total} ({mu_hr:.0f}%)",
|
|
f"MAE: {mu_mae:.1f}°",
|
|
]
|
|
if avg_brier is not None:
|
|
details_parts.append(f"Brier: {avg_brier:.3f}")
|
|
|
|
return mu_mae, mu_hr, avg_brier, total, " | ".join(details_parts)
|