feat: Implement the PolyWeather dashboard including frontend components, data collection, analysis, and API endpoints.

src/analysis/deb_algorithm.py

@@ -1,6 +1,7 @@
 import os
 import json
 from datetime import datetime, timedelta
+from src.analysis.settlement_rounding import wu_round
 
 # Cross-platform file locking
 import sys
@@ -99,27 +100,39 @@ def update_daily_record(
     if date_str not in data[city_name]:
         data[city_name][date_str] = {}
 
-    # 避免无意义的频繁磁盘写入
-    old_actual = data[city_name][date_str].get("actual_high")
-    if (
-        old_actual == actual_high
-        and data[city_name][date_str].get("forecasts") == forecasts
-    ):
-        return
-
-    data[city_name][date_str]["forecasts"] = forecasts
-    data[city_name][date_str]["actual_high"] = actual_high
-    if deb_prediction is not None:
-        data[city_name][date_str]["deb_prediction"] = deb_prediction
-    if mu is not None:
-        data[city_name][date_str]["mu"] = round(mu, 2)
+    compact_probs = None
     if probabilities is not None:
         # Store compact: [{"v": 25, "p": 0.8}, ...]
-        data[city_name][date_str]["prob_snapshot"] = [
+        compact_probs = [
             {"v": p["value"], "p": p["probability"]}
             for p in probabilities[:4]
         ]
 
+    # 避免无意义的频繁磁盘写入
+    existing = data[city_name][date_str]
+    old_actual = existing.get("actual_high")
+    old_deb = existing.get("deb_prediction")
+    old_mu = existing.get("mu")
+    old_probs = existing.get("prob_snapshot")
+    next_mu = round(mu, 2) if mu is not None else None
+    if (
+        old_actual == actual_high
+        and existing.get("forecasts") == forecasts
+        and (deb_prediction is None or old_deb == deb_prediction)
+        and (mu is None or old_mu == next_mu)
+        and (compact_probs is None or old_probs == compact_probs)
+    ):
+        return
+
+    existing["forecasts"] = forecasts
+    existing["actual_high"] = actual_high
+    if deb_prediction is not None:
+        existing["deb_prediction"] = deb_prediction
+    if mu is not None:
+        existing["mu"] = next_mu
+    if probabilities is not None:
+        existing["prob_snapshot"] = compact_probs
+
     # 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）
     cutoff = (datetime.now() - timedelta(days=14)).strftime("%Y-%m-%d")
     for city in list(data.keys()):
@@ -173,7 +186,12 @@ def calculate_dynamic_weights(city_name, current_forecasts, lookback_days=7):
 
         for model in current_forecasts.keys():
             if model in past_forecasts and past_forecasts[model] is not None:
-                errors[model].append(abs(past_forecasts[model] - actual))
+                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:
@@ -182,6 +200,8 @@ def calculate_dynamic_weights(city_name, current_forecasts, lookback_days=7):
     # 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)"
 
@@ -263,8 +283,8 @@ def get_deb_accuracy(city_name):
             continue
 
         total += 1
-        deb_wu = round(deb_pred)
-        actual_wu = round(actual)
+        deb_wu = wu_round(deb_pred)
+        actual_wu = wu_round(actual)
         if deb_wu == actual_wu:
             hits += 1
         errors.append(abs(deb_pred - actual))
@@ -328,13 +348,13 @@ def get_mu_accuracy(city_name):
 
         total += 1
         mu_errors.append(abs(mu_val - actual))
-        if round(mu_val) == round(actual):
+        if wu_round(mu_val) == wu_round(actual):
             mu_hits += 1
 
         # Brier Score from probability snapshot
         prob_snap = record.get("prob_snapshot", [])
         if prob_snap:
-            actual_wu = round(actual)
+            actual_wu = wu_round(actual)
             bs = 0.0
             for entry in prob_snap:
                 predicted_p = entry.get("p", 0)

src/analysis/settlement_rounding.py

@@ -0,0 +1,20 @@
+import math
+from typing import Optional, Union
+
+
+Number = Union[int, float]
+
+
+def wu_round(value: Optional[Number]) -> Optional[int]:
+    """
+    WU 结算口径四舍五入（0.5 一律进位）:
+    - 正数: floor(x + 0.5)
+    - 负数: ceil(x - 0.5)
+    """
+    if value is None:
+        return None
+    x = float(value)
+    if x >= 0:
+        return int(math.floor(x + 0.5))
+    return int(math.ceil(x - 0.5))
+

src/analysis/trend_engine.py

@@ -13,6 +13,7 @@ from src.analysis.deb_algorithm import (
     get_deb_accuracy,
     update_daily_record,
 )
+from src.analysis.settlement_rounding import wu_round
 from src.data_collection.city_risk_profiles import get_city_risk_profile
 
 
@@ -348,7 +349,7 @@ def analyze_weather_trend(
                 ai_features.append(f"🎲 数学概率分布：{prob_str}")
 
     elif is_dead_market:
-        settled_wu = round(max_so_far) if max_so_far is not None else 0
+        settled_wu = wu_round(max_so_far) if max_so_far is not None else 0
         dead_msg = f"🎲 <b>结算预测</b>：已锁定 {settled_wu}{temp_symbol} (死盘确认)"
         insights.append(dead_msg)
         ai_features.append("🎲 状态: 确认死盘，结算已无悬念。")
@@ -375,7 +376,7 @@ def analyze_weather_trend(
 
     # === Settlement boundary ===
     if max_so_far is not None:
-        settled = round(max_so_far)
+        settled = wu_round(max_so_far)
         fractional = max_so_far - int(max_so_far)
         dist_to_boundary = abs(fractional - 0.5)
         if dist_to_boundary <= 0.3:
@@ -437,7 +438,7 @@ def analyze_weather_trend(
         ai_features.append(f"🌡️ 当前实测温度: {cur_temp}{temp_symbol}。")
     if max_so_far is not None:
         ai_features.append(
-            f"🏔️ 今日实测最高温: {max_so_far}{temp_symbol} (WU结算={round(max_so_far)}{temp_symbol})。"
+            f"🏔️ 今日实测最高温: {max_so_far}{temp_symbol} (WU结算={wu_round(max_so_far)}{temp_symbol})。"
         )
     if city_name:
         _profile = get_city_risk_profile(city_name)
@@ -486,7 +487,7 @@ def analyze_weather_trend(
                 for t, p in sorted_probs[:4]
             ]
         elif is_dead_market and max_so_far is not None:
-            _prob_list = [{"value": round(max_so_far), "probability": 1.0}]
+            _prob_list = [{"value": wu_round(max_so_far), "probability": 1.0}]
 
         update_daily_record(
             city_name,
@@ -524,7 +525,7 @@ def analyze_weather_trend(
         "forecast_miss_deg": forecast_miss_deg,
         "max_so_far": max_so_far,
         "cur_temp": cur_temp,
-        "wu_settle": round(max_so_far) if max_so_far is not None else None,
+        "wu_settle": wu_round(max_so_far) if max_so_far is not None else None,
     }
     display_str = "\n".join(insights) if insights else ""
     return display_str, "\n".join(ai_features), structured
@@ -543,12 +544,12 @@ def calculate_prob_distribution(
         # 0.5 * (1 + erf( (x-m)/(s*sqrt(2)) ))
         return 0.5 * (1 + math.erf((x - m) / (sigma * math.sqrt(2))))
 
-    min_possible_wu = round(max_so_far) if max_so_far is not None else -999
+    min_possible_wu = wu_round(max_so_far) if max_so_far is not None else -999
     probs = {}
     
     # Range: mu +/- 3 sigma or at least +/- 2 degrees
     search_range = max(2, int(sigma * 2.5))
-    target_mu = round(mu)
+    target_mu = wu_round(mu)
     
     for n in range(target_mu - search_range, target_mu + search_range + 1):
         if n < min_possible_wu:

src/data_collection/weather_sources.py

@@ -40,6 +40,19 @@ class WeatherDataCollector:
         "munich": ["EDDM", "EDMO", "EDJA"],
     }
 
+    # Meteoblue 仅在增益最大的城市启用（减少配额消耗与冗余请求）
+    METEOBLUE_PRIORITY_CITIES = {
+        "london",
+        "paris",
+        "seoul",
+        "toronto",
+        "buenos aires",
+        "wellington",
+        "lucknow",
+        "sao paulo",
+        "munich",
+    }
+
     def __init__(self, config: dict):
         self.config = config
         weather_cfg = config.get("weather", {})
@@ -1503,14 +1516,15 @@ class WeatherDataCollector:
                     # 获取时区偏移以过滤 METAR
                     utc_offset = open_meteo.get("utc_offset", 0)
 
-                mb_data = self.fetch_from_meteoblue(
-                    lat,
-                    lon,
-                    timezone_name=open_meteo.get("timezone", "UTC"),
-                    use_fahrenheit=use_fahrenheit,
-                )
-                if mb_data:
-                    results["meteoblue"] = mb_data
+                if city_lower in self.METEOBLUE_PRIORITY_CITIES:
+                    mb_data = self.fetch_from_meteoblue(
+                        lat,
+                        lon,
+                        timezone_name=open_meteo.get("timezone", "UTC"),
+                        use_fahrenheit=use_fahrenheit,
+                    )
+                    if mb_data:
+                        results["meteoblue"] = mb_data
 
                 # 对美国城市，额外获取 NWS 高精预报
                 if use_fahrenheit: