feat: Add a multi-source weather data collection module supporting OpenWeatherMap, Visual Crossing, and METAR.

bot_listener.py

@@ -57,6 +57,73 @@ def analyze_weather_trend(weather_data, temp_symbol):
         local_date_str = datetime.now().strftime("%Y-%m-%d")
         local_hour = datetime.now().hour
 
+    # === 模型共识评分 ===
+    labeled_forecasts = []
+    om_today = daily.get("temperature_2m_max", [None])[0]
+    if om_today is not None:
+        labeled_forecasts.append(("OM", om_today))
+    if mb.get("today_high") is not None:
+        labeled_forecasts.append(("MB", mb["today_high"]))
+    if nws.get("today_high") is not None:
+        labeled_forecasts.append(("NWS", nws["today_high"]))
+    if mgm.get("today_high") is not None:
+        labeled_forecasts.append(("MGM", mgm["today_high"]))
+    # 集合预报中位数 (如果有)
+    ensemble = weather_data.get("ensemble", {})
+    ens_median = ensemble.get("median")
+    if ens_median is not None:
+        labeled_forecasts.append(("ENS", ens_median))
+
+    consensus_level = "unknown"
+    consensus_spread = None
+    if len(labeled_forecasts) >= 2:
+        f_values = [v for _, v in labeled_forecasts]
+        f_max = max(f_values)
+        f_min = min(f_values)
+        consensus_spread = f_max - f_min
+        f_avg = sum(f_values) / len(f_values)
+
+        # 动态阈值：华氏度场景用更大的容差
+        is_f = (temp_symbol == "°F")
+        tight_threshold = 1.5 if is_f else 0.8   # 高共识
+        mid_threshold = 3.0 if is_f else 1.5      # 中共识
+
+        parts = " | ".join([f"{name} {val}{temp_symbol}" for name, val in labeled_forecasts])
+        
+        if consensus_spread <= tight_threshold:
+            consensus_level = "high"
+            insights.append(
+                f"🎯 <b>模型共识：高 ({len(labeled_forecasts)}/{len(labeled_forecasts)})</b> — "
+                f"{parts}，极差仅 {consensus_spread:.1f}°，预报高度一致。"
+            )
+        elif consensus_spread <= mid_threshold:
+            consensus_level = "medium"
+            insights.append(
+                f"⚖️ <b>模型共识：中 ({len(labeled_forecasts)}源)</b> — "
+                f"{parts}，极差 {consensus_spread:.1f}°，有轻微分歧。"
+            )
+        else:
+            consensus_level = "low"
+            # 找出最高和最低的源
+            highest = max(labeled_forecasts, key=lambda x: x[1])
+            lowest = min(labeled_forecasts, key=lambda x: x[1])
+            insights.append(
+                f"⚠️ <b>模型共识：低 ({len(labeled_forecasts)}源)</b> — "
+                f"{parts}，极差 {consensus_spread:.1f}°！"
+                f"{highest[0]} 最高 ({highest[1]}{temp_symbol}) vs {lowest[0]} 最低 ({lowest[1]}{temp_symbol})，不确定性大。"
+            )
+
+        # 集合预报区间 (如果有)
+        ens_p10 = ensemble.get("p10")
+        ens_p90 = ensemble.get("p90")
+        if ens_p10 is not None and ens_p90 is not None and ens_median is not None:
+            ens_range = ens_p90 - ens_p10
+            insights.append(
+                f"📊 <b>集合预报</b>：中位数 {ens_median}{temp_symbol}，"
+                f"90% 区间 [{ens_p10}{temp_symbol} - {ens_p90}{temp_symbol}]，"
+                f"波动幅度 {ens_range:.1f}°。"
+            )
+
     # === 核心判断：实测是否已超预报 ===
     is_breakthrough = False
     if max_so_far is not None and forecast_high is not None:
@@ -341,6 +408,55 @@ def analyze_weather_trend(weather_data, temp_symbol):
                     except (ValueError, IndexError):
                         pass
 
+        # 11. 入场时机信号
+        hours_to_peak = first_peak_h - local_hour if local_hour < first_peak_h else 0
+        
+        # 综合评分：距离峰值越近 + 共识越高 + 实测越接近预报 → 越适合入场
+        timing_score = 0
+        timing_factors = []
+        
+        if is_peak_passed:
+            timing_score += 3
+            timing_factors.append("最热已过")
+        elif hours_to_peak <= 2:
+            timing_score += 2
+            timing_factors.append(f"距峰值{hours_to_peak}h")
+        elif hours_to_peak <= 4:
+            timing_score += 1
+            timing_factors.append(f"距峰值{hours_to_peak}h")
+        else:
+            timing_factors.append(f"距峰值{hours_to_peak}h")
+        
+        if consensus_level == "high":
+            timing_score += 2
+            timing_factors.append("模型一致")
+        elif consensus_level == "medium":
+            timing_score += 1
+            timing_factors.append("模型小分歧")
+        else:
+            timing_factors.append("模型分歧大")
+        
+        if max_so_far is not None and forecast_high is not None:
+            gap = abs(max_so_far - forecast_high)
+            if gap <= 0.5:
+                timing_score += 2
+                timing_factors.append("实测≈预报")
+            elif gap <= 1.5:
+                timing_score += 1
+                timing_factors.append(f"差{gap:.1f}°")
+            else:
+                timing_factors.append(f"差{gap:.1f}°")
+        
+        factors_str = "，".join(timing_factors)
+        if timing_score >= 5:
+            insights.append(f"⏰ <b>入场时机：理想</b> — {factors_str}。不确定性低，适合下注。")
+        elif timing_score >= 3:
+            insights.append(f"⏰ <b>入场时机：较好</b> — {factors_str}。可以考虑小仓位入场。")
+        elif timing_score >= 2:
+            insights.append(f"⏰ <b>入场时机：谨慎</b> — {factors_str}。建议继续观察。")
+        else:
+            insights.append(f"⏰ <b>入场时机：不建议</b> — {factors_str}。不确定性大，等更多数据。")
+
     if not insights:
         return ""
         

src/data_collection/weather_sources.py

@@ -535,6 +535,89 @@ class WeatherDataCollector:
             logger.error(f"Open-Meteo forecast failed: {e}")
             return None
 
+    def fetch_ensemble(
+        self,
+        lat: float,
+        lon: float,
+        use_fahrenheit: bool = False,
+    ) -> Optional[Dict]:
+        """
+        从 Open-Meteo Ensemble API 获取 51 成员集合预报
+        用于计算预报不确定性范围（散度）
+        """
+        try:
+            url = "https://ensemble-api.open-meteo.com/v1/ensemble"
+            params = {
+                "latitude": lat,
+                "longitude": lon,
+                "daily": "temperature_2m_max",
+                "timezone": "auto",
+                "forecast_days": 3,
+                "_t": int(time.time()),
+            }
+            if use_fahrenheit:
+                params["temperature_unit"] = "fahrenheit"
+            else:
+                params["temperature_unit"] = "celsius"
+
+            response = self.session.get(
+                url,
+                params=params,
+                headers={"Cache-Control": "no-cache"},
+                timeout=self.timeout,
+            )
+            response.raise_for_status()
+            data = response.json()
+
+            daily = data.get("daily", {})
+            # 每个成员都会返回一组 temperature_2m_max
+            # 格式: {"time": [...], "temperature_2m_max_member01": [...], ...}
+            today_highs = []
+            for key, values in daily.items():
+                if key.startswith("temperature_2m_max") and key != "temperature_2m_max":
+                    if values and values[0] is not None:
+                        today_highs.append(values[0])
+            
+            # 也检查非成员键（有些返回格式不同）
+            if not today_highs:
+                raw_max = daily.get("temperature_2m_max", [])
+                if isinstance(raw_max, list) and raw_max:
+                    if isinstance(raw_max[0], list):
+                        # 嵌套列表格式: [[member1_day1, member1_day2], [member2_day1, ...]]
+                        today_highs = [m[0] for m in raw_max if m and m[0] is not None]
+                    elif raw_max[0] is not None:
+                        today_highs = [raw_max[0]]
+
+            if len(today_highs) < 3:
+                logger.warning(f"Ensemble 数据不足: 仅获取 {len(today_highs)} 个成员")
+                return None
+
+            today_highs.sort()
+            n = len(today_highs)
+            median = today_highs[n // 2]
+            p10 = today_highs[max(0, int(n * 0.1))]
+            p90 = today_highs[min(n - 1, int(n * 0.9))]
+
+            result = {
+                "source": "ensemble",
+                "members": n,
+                "median": round(median, 1),
+                "p10": round(p10, 1),
+                "p90": round(p90, 1),
+                "min": round(today_highs[0], 1),
+                "max": round(today_highs[-1], 1),
+                "unit": "fahrenheit" if use_fahrenheit else "celsius",
+            }
+
+            logger.info(
+                f"📊 Ensemble ({n} members): median={median:.1f}, "
+                f"p10={p10:.1f}, p90={p90:.1f}"
+            )
+            return result
+        except Exception as e:
+            logger.warning(f"Ensemble API 请求失败: {e}")
+            return None
+
     def fetch_from_meteoblue(
         self,
         lat: float,
@@ -807,6 +890,11 @@ class WeatherDataCollector:
                     nws_data = self.fetch_nws(lat, lon)
                     if nws_data:
                         results["nws"] = nws_data
+                
+                # 集合预报 (所有城市通用，用于不确定性分析)
+                ens_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit)
+                if ens_data:
+                    results["ensemble"] = ens_data
             else:
                 # Open-Meteo 失败时，仍然尝试获取 METAR 和 NWS
                 metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)