feat: Implement Dynamic Ensemble Blending (DEB) algorithm with historical data management, dynamic weight calculation, and accuracy tracking.

src/analysis/deb_algorithm.py

@@ -37,6 +37,11 @@ _history_cache = {}
 _history_mtime = 0
 
 
+def _is_excluded_model_name(model_name: str) -> bool:
+    normalized = str(model_name or "").strip().lower().replace(" ", "").replace("_", "").replace("-", "")
+    return "meteoblue" in normalized
+
+
 def load_history(filepath):
     global _history_cache, _history_mtime
     if not os.path.exists(filepath):
@@ -100,6 +105,11 @@ def update_daily_record(
     if date_str not in data[city_name]:
         data[city_name][date_str] = {}
 
+    # 统一过滤已弃用模型，避免历史/展示残留
+    forecasts = {
+        k: v for k, v in (forecasts or {}).items() if not _is_excluded_model_name(k)
+    }
+
     compact_probs = None
     if probabilities is not None:
         # Store compact: [{"v": 25, "p": 0.8}, ...]
@@ -124,6 +134,13 @@ def update_daily_record(
     ):
         return
 
+    # 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
+    if old_actual is not None and actual_high is not None:
+        try:
+            actual_high = max(float(old_actual), float(actual_high))
+        except Exception:
+            pass
+
     existing["forecasts"] = forecasts
     existing["actual_high"] = actual_high
     if deb_prediction is not None:
@@ -155,6 +172,12 @@ def calculate_dynamic_weights(city_name, current_forecasts, lookback_days=7):
     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]

src/analysis/trend_engine.py

@@ -6,12 +6,14 @@ for both Telegram bot and web dashboard.
 """
 
 import math
+from datetime import datetime, timezone, timedelta
 from typing import List, Optional, Tuple, Dict, Any
 
 from src.analysis.deb_algorithm import (
     calculate_dynamic_weights,
     get_deb_accuracy,
     update_daily_record,
+    _is_excluded_model_name,
 )
 from src.analysis.settlement_rounding import wu_round
 from src.data_collection.city_risk_profiles import get_city_risk_profile
@@ -89,7 +91,7 @@ def analyze_weather_trend(
 
     mm_forecasts = weather_data.get("multi_model", {}).get("forecasts", {})
     for m_name, m_val in mm_forecasts.items():
-        if m_val is not None:
+        if m_val is not None and not _is_excluded_model_name(m_name):
             current_forecasts[m_name] = _sf(m_val)
 
     forecast_highs = [h for h in current_forecasts.values() if h is not None]
@@ -100,18 +102,54 @@ def analyze_weather_trend(
 
     wind_speed = metar.get("current", {}).get("wind_speed_kt", 0)
 
-    # === Local time ===
-    local_time_full = open_meteo.get("current", {}).get("local_time", "")
-    try:
-        local_date_str = local_time_full.split(" ")[0]
-        time_parts = local_time_full.split(" ")[1].split(":")
-        local_hour = int(time_parts[0])
-        local_minute = int(time_parts[1]) if len(time_parts) > 1 else 0
-    except Exception:
-        from datetime import datetime
-        local_date_str = datetime.now().strftime("%Y-%m-%d")
-        local_hour = datetime.now().hour
-        local_minute = datetime.now().minute
+    # === Local time/date (do not trust cached Open-Meteo local_time for date key) ===
+    utc_offset = _sf(open_meteo.get("utc_offset"))
+    if utc_offset is None and city_name:
+        try:
+            from src.data_collection.city_registry import CITY_REGISTRY
+
+            city_meta = CITY_REGISTRY.get(str(city_name).lower())
+            if isinstance(city_meta, dict):
+                utc_offset = _sf(city_meta.get("tz_offset"))
+        except Exception:
+            pass
+
+    city_now = None
+    if utc_offset is not None:
+        try:
+            city_now = datetime.now(timezone.utc).astimezone(
+                timezone(timedelta(seconds=int(utc_offset)))
+            )
+        except Exception:
+            city_now = None
+
+    local_time_full = str((open_meteo.get("current") or {}).get("local_time") or "").strip()
+    if city_now is not None:
+        local_date_str = city_now.strftime("%Y-%m-%d")
+        local_hour = city_now.hour
+        local_minute = city_now.minute
+    else:
+        try:
+            local_date_str = local_time_full.split(" ")[0]
+            time_parts = local_time_full.split(" ")[1].split(":")
+            local_hour = int(time_parts[0])
+            local_minute = int(time_parts[1]) if len(time_parts) > 1 else 0
+        except Exception:
+            fallback_now = datetime.now()
+            local_date_str = fallback_now.strftime("%Y-%m-%d")
+            local_hour = fallback_now.hour
+            local_minute = fallback_now.minute
+
+    # Use METAR observation date in city local time when available (reliable for actual_high date key).
+    metar_obs_time_raw = str(metar.get("observation_time") or "").strip()
+    if metar_obs_time_raw and utc_offset is not None:
+        try:
+            metar_obs_dt = datetime.fromisoformat(metar_obs_time_raw.replace("Z", "+00:00"))
+            local_date_str = metar_obs_dt.astimezone(
+                timezone(timedelta(seconds=int(utc_offset)))
+            ).strftime("%Y-%m-%d")
+        except Exception:
+            pass
     local_hour_frac = local_hour + local_minute / 60
 
     # === DEB ===