From 6cc16c938d19e57d060c4a0a6d7a8b41deea7e19 Mon Sep 17 00:00:00 2001 From: AmandaloveYang <2569718930@qq.com> Date: Sun, 22 Feb 2026 09:54:47 +0800 Subject: [PATCH] feat: introduce `WeatherDataCollector` for multi-source weather data retrieval from OpenWeatherMap, Visual Crossing, and METAR. --- bot_listener.py | 102 +++++++++++------ src/data_collection/weather_sources.py | 149 +++++++++++++++++++++---- 2 files changed, 193 insertions(+), 58 deletions(-) diff --git a/bot_listener.py b/bot_listener.py index 182a2484..f65fb352 100644 --- a/bot_listener.py +++ b/bot_listener.py @@ -37,8 +37,10 @@ def analyze_weather_trend(weather_data, temp_symbol): forecast_highs.append(mb["today_high"]) if nws.get("today_high") is not None: forecast_highs.append(nws["today_high"]) - if mgm.get("today_high") is not None: - forecast_highs.append(mgm["today_high"]) + # 加入多模型预报 (ECMWF, GFS, ICON, GEM, JMA) + for mv in weather_data.get("multi_model", {}).get("forecasts", {}).values(): + if mv is not None: + forecast_highs.append(mv) forecast_highs = [h for h in forecast_highs if h is not None] # 取预报中的最高值作为风险防御基准 @@ -58,17 +60,25 @@ def analyze_weather_trend(weather_data, temp_symbol): local_hour = datetime.now().hour # === 模型共识评分 === + # 主要来源: 多模型预报 (ECMWF, GFS, ICON, GEM, JMA) + multi_model = weather_data.get("multi_model", {}) + mm_forecasts = multi_model.get("forecasts", {}) + labeled_forecasts = [] - om_today = daily.get("temperature_2m_max", [None])[0] - if om_today is not None: - labeled_forecasts.append(("OM", om_today)) + for model_name, model_val in mm_forecasts.items(): + if model_val is not None: + labeled_forecasts.append((model_name, model_val)) + + # 额外独立源 (如有) 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"])) - # 集合预报数据 (仅用于不确定性区间展示，不参与共识评分，避免与 OM 双重计数) + + # Open-Meteo 确定性预报（用于后续偏差检测，不重复加入共识） + om_today = daily.get("temperature_2m_max", [None])[0] + + # 集合预报数据 (仅用于不确定性区间展示) ensemble = weather_data.get("ensemble", {}) ens_median = ensemble.get("median") @@ -128,20 +138,36 @@ def analyze_weather_trend(weather_data, temp_symbol): ) # 确定性预报 vs 集合分布偏差检测 if om_today is not None: + actual_reached = max_so_far is not None and max_so_far >= om_today - 0.5 if om_today > ens_p90: - delta = om_today - ens_median - insights.append( - f"⚡ 预报偏高警告：确定性预报 {om_today}{temp_symbol} " - f"超过了集合 90% 上限 ({ens_p90}{temp_symbol})，" - f"比中位数高 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。" - ) + if actual_reached: + # 实测已达到预报值 → 确定性预报是对的，集合偏保守 + insights.append( + f"✅ 预报验证：确定性预报 {om_today}{temp_symbol} 已被实测验证 " + f"(实测最高 {max_so_far}{temp_symbol})，集合预报偏保守。" + ) + else: + # 还没到最高温，存在偏高风险 + delta = om_today - ens_median + insights.append( + f"⚡ 预报偏高警告：确定性预报 {om_today}{temp_symbol} " + f"超过了集合 90% 上限 ({ens_p90}{temp_symbol})，" + f"比中位数高 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。" + ) elif om_today < ens_p10: - delta = ens_median - om_today - insights.append( - f"⚡ 预报偏低警告：确定性预报 {om_today}{temp_symbol} " - f"低于集合 90% 下限 ({ens_p10}{temp_symbol})，" - f"比中位数低 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。" - ) + if max_so_far is not None and max_so_far >= ens_median: + # 实测已超过中位数 → 确定性预报偏低，集合更准 + insights.append( + f"✅ 预报验证：实测最高 {max_so_far}{temp_symbol} " + f"已超过确定性预报 {om_today}{temp_symbol}，集合中位数 {ens_median}{temp_symbol} 更准确。" + ) + else: + delta = ens_median - om_today + insights.append( + f"⚡ 预报偏低警告：确定性预报 {om_today}{temp_symbol} " + f"低于集合 90% 下限 ({ens_p10}{temp_symbol})，" + f"比中位数低 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。" + ) # === 核心判断：实测是否已超预报 === is_breakthrough = False @@ -224,13 +250,14 @@ def analyze_weather_trend(weather_data, temp_symbol): # 正在峰值窗口内 if is_breakthrough: insights.append(f"🔥 极端升温：正处于最热时段，温度已经超过所有预报，还在继续往上走！") - elif diff_max <= 0.8: + elif max_so_far is not None and forecast_high - max_so_far <= 0.8: insights.append(f"⚖️ 到顶了：正处于最热时段，温度基本到位，接下来会在这个水平上下浮动。") else: insights.append(f"⏳ 最热时段进行中：虽然在最热时段了，但离预报最高温还差一些，继续观察。") elif local_hour < first_peak_h: # 还没到峰值窗口 - if diff_max > 1.2: + gap_to_high = forecast_high - (max_so_far if max_so_far is not None else curr_temp) + if gap_to_high > 1.2: insights.append(f"📈 还在升温：离最热时段还有 {first_peak_h - local_hour} 小时，温度还会继续往上走。") else: insights.append(f"🌅 快到最热了：马上就要进入最热时段，温度已经接近预报高位了。") @@ -257,7 +284,7 @@ def analyze_weather_trend(weather_data, temp_symbol): # 4. 云层遮挡分析 (仅在升温期/峰值期有意义) clouds = metar.get("current", {}).get("clouds", []) - if clouds and local_hour <= last_peak_h + 1: + if clouds and not is_peak_passed: main_cloud = clouds[-1] cover = main_cloud.get("cover", "") if cover == "OVC": @@ -265,8 +292,7 @@ def analyze_weather_trend(weather_data, temp_symbol): elif cover == "BKN": insights.append(f"🌥️ 云比较多：天空大部分被云挡住了，日照不足，升温会比较慢。") elif cover in ["SKC", "CLR", "FEW"]: - if not is_peak_passed: - insights.append(f"☀️ 大晴天：阳光直射，没什么云，有利于温度继续往上冲。") + insights.append(f"☀️ 大晴天：阳光直射，没什么云，有利于温度继续往上冲。") # 5. 特殊天气现象 wx_desc = metar.get("current", {}).get("wx_desc") @@ -324,12 +350,14 @@ def analyze_weather_trend(weather_data, temp_symbol): if 315 <= wd or wd <= 45: insights.append(f"🌬️ 吹北风（{wind_source} {wd:.0f}°）：从北方来的冷空气，会压制升温。") elif 135 <= wd <= 225: - if diff_max > 0.5 or (is_breakthrough and curr_temp >= max_so_far): - if is_peak_passed and not is_breakthrough: - insights.append(f"🔥 吹南风（{wind_source} {wd:.0f}°）：南方的暖空气还在吹过来，但最热时段已过，后劲不足了。") - else: - status = "温度还有继续上涨的空间" if not is_breakthrough else "可能把温度推得更高" - insights.append(f"🔥 吹南风（{wind_source} {wd:.0f}°）：南方的暖空气正在吹过来，{status}。") + gap_to_forecast = forecast_high - (max_so_far if max_so_far is not None else curr_temp) + if is_peak_passed and not is_breakthrough: + insights.append(f"🔥 吹南风（{wind_source} {wd:.0f}°）：南方的暖空气还在吹过来，但最热时段已过，后劲不足了。") + elif gap_to_forecast > 0.5 or is_breakthrough: + status = "温度还有继续上涨的空间" if not is_breakthrough else "可能把温度推得更高" + insights.append(f"🔥 吹南风（{wind_source} {wd:.0f}°）：南方的暖空气正在吹过来，{status}。") + else: + insights.append(f"🔥 吹南风（{wind_source} {wd:.0f}°）：南方的暖空气正在吹过来，但温度已接近预报峰值。") elif 225 < wd < 315: if wd <= 260: insights.append(f"🌬️ 吹西南风（{wind_source} {wd:.0f}°）：带有一定暖湿气流，对升温有轻微帮助。") @@ -351,20 +379,22 @@ def analyze_weather_trend(weather_data, temp_symbol): except (TypeError, ValueError): pass - # 7. 模型准确度预警 + # 7. 模型准确度预警（使用多模型数据） if is_peak_passed and max_so_far is not None: model_checks = [] - if om_high and om_high > max_so_far + 1.5: - model_checks.append(f"Open-Meteo ({om_high}{temp_symbol})") + for m_name, m_val in mm_forecasts.items(): + if m_val is not None and m_val > max_so_far + 1.5: + model_checks.append(f"{m_name} ({m_val}{temp_symbol})") + # 附加源也查一下 mb_h = mb.get("today_high") if mb_h and mb_h > max_so_far + 1.5: - model_checks.append(f"Meteoblue ({mb_h}{temp_symbol})") + model_checks.append(f"MB ({mb_h}{temp_symbol})") nws_h = nws.get("today_high") if nws_h and nws_h > max_so_far + 1.5: model_checks.append(f"NWS ({nws_h}{temp_symbol})") if model_checks: - insights.append(f"⚠️ 预报偏高了：实测远低于 " + "、".join(model_checks) + "，这些预报今天报高了。") + insights.append(f"⚠️ 预报偏高了：实测远低于 " + "、".join(model_checks) + "，这些模型今天报高了。") # 8. MGM 气压分析 (仅安卡拉) mgm_pressure = mgm.get("current", {}).get("pressure") diff --git a/src/data_collection/weather_sources.py b/src/data_collection/weather_sources.py index 19e35099..4aa5e73b 100644 --- a/src/data_collection/weather_sources.py +++ b/src/data_collection/weather_sources.py @@ -369,14 +369,33 @@ class WeatherDataCollector: "station_name": latest.get("istasyonAd") or latest.get("adi") or latest.get("merkezAd") or "Ankara Esenboğa" } - # 2. 每日预报 - daily_resp = self.session.get(f"{base_url}/tahminler/gunluk?istno={istno}", headers=headers, timeout=self.timeout) - if daily_resp.status_code == 200: - forecasts = daily_resp.json() - if forecasts and isinstance(forecasts, list): - today = forecasts[0] - results["today_high"] = today.get("enYuksekGun1") - results["today_low"] = today.get("enDusukGun1") + # 2. 每日预报（尝试两个可能的 API 路径） + forecast_urls = [ + f"{base_url}/tahminler/gunluk?istno={istno}", + f"https://servis.mgm.gov.tr/api/tahminler/gunluk?istno={istno}", + ] + for forecast_url in forecast_urls: + try: + daily_resp = self.session.get(forecast_url, headers=headers, timeout=self.timeout) + if daily_resp.status_code == 200: + forecasts = daily_resp.json() + if forecasts and isinstance(forecasts, list): + today = forecasts[0] + high_val = today.get("enYuksekGun1") + low_val = today.get("enDusukGun1") + if high_val is not None: + results["today_high"] = high_val + results["today_low"] = low_val + logger.info(f"📋 MGM 每日预报: 最高 {high_val}°C, 最低 {low_val}°C (from {forecast_url})") + break + else: + # 记录所有可用字段，方便调试 + available_keys = [k for k in today.keys() if "yuksek" in k.lower() or "sicaklik" in k.lower() or "gun" in k.lower()] + logger.warning(f"MGM 每日预报: enYuksekGun1 为空，可用字段: {available_keys}") + else: + logger.debug(f"MGM forecast URL {forecast_url} returned {daily_resp.status_code}") + except Exception as e: + logger.debug(f"MGM forecast URL {forecast_url} failed: {e}") return results if "current" in results else None except Exception as e: @@ -618,6 +637,86 @@ class WeatherDataCollector: logger.warning(f"Ensemble API 请求失败: {e}") return None + def fetch_multi_model( + self, + lat: float, + lon: float, + use_fahrenheit: bool = False, + ) -> Optional[Dict]: + """ + 从 Open-Meteo 获取多个独立 NWP 模型的预报 + 用于真正的多模型共识评分 + + 模型列表: + - ECMWF IFS (欧洲中期天气预报中心) + - GFS (美国 NOAA) + - ICON (德国气象局 DWD) + - GEM (加拿大气象局) + - JMA (日本气象厅) + """ + try: + url = "https://api.open-meteo.com/v1/forecast" + models = "ecmwf_ifs025,gfs_seamless,icon_seamless,gem_seamless,jma_seamless" + params = { + "latitude": lat, + "longitude": lon, + "daily": "temperature_2m_max", + "models": models, + "timezone": "auto", + "forecast_days": 1, + "_t": int(time.time()), + } + if use_fahrenheit: + params["temperature_unit"] = "fahrenheit" + + response = self.session.get( + url, + params=params, + headers={"Cache-Control": "no-cache"}, + timeout=self.timeout, + ) + response.raise_for_status() + data = response.json() + + # Open-Meteo 多模型返回格式: + # "daily": { + # "temperature_2m_max_ecmwf_ifs025": [12.3], + # "temperature_2m_max_gfs_seamless": [11.8], + # ... + # } + daily = data.get("daily", {}) + + model_labels = { + "ecmwf_ifs025": "ECMWF", + "gfs_seamless": "GFS", + "icon_seamless": "ICON", + "gem_seamless": "GEM", + "jma_seamless": "JMA", + } + + forecasts = {} + for model_key, label in model_labels.items(): + key = f"temperature_2m_max_{model_key}" + values = daily.get(key, []) + if values and values[0] is not None: + forecasts[label] = round(values[0], 1) + + if not forecasts: + logger.warning("Multi-model: 无有效模型数据") + return None + + labels_str = ", ".join([f"{k}={v}" for k, v in forecasts.items()]) + logger.info(f"🔬 Multi-model ({len(forecasts)}个): {labels_str}") + + return { + "source": "multi_model", + "forecasts": forecasts, # {"ECMWF": 12.3, "GFS": 11.8, ...} + "unit": "fahrenheit" if use_fahrenheit else "celsius", + } + except Exception as e: + logger.warning(f"Multi-model API 请求失败: {e}") + return None + def fetch_from_meteoblue( self, lat: float, @@ -725,22 +824,23 @@ class WeatherDataCollector: """ 使用 Open-Meteo Geocoding API 获取城市坐标 (免费, 无需 Key) """ - # 预设常用城市坐标，避免网络波动导致启动失败 + # 坐标使用 METAR 机场位置（Polymarket 以机场数据结算） static_coords = { - "london": {"lat": 51.5074, "lon": -0.1278}, - "new york": {"lat": 40.7128, "lon": -74.0060}, + "london": {"lat": 51.5053, "lon": 0.0553}, # EGLC London City + "paris": {"lat": 49.0097, "lon": 2.5478}, # LFPG Charles de Gaulle + "new york": {"lat": 40.7750, "lon": -73.8750}, # KLGA LaGuardia "new york's central park": {"lat": 40.7812, "lon": -73.9665}, - "nyc": {"lat": 40.7128, "lon": -74.0060}, - "seattle": {"lat": 47.6062, "lon": -122.3321}, - "chicago": {"lat": 41.8781, "lon": -87.6298}, - "dallas": {"lat": 32.7767, "lon": -96.7970}, - "miami": {"lat": 25.7617, "lon": -80.1918}, - "atlanta": {"lat": 33.7490, "lon": -84.3880}, - "seoul": {"lat": 37.5665, "lon": 126.9780}, - "toronto": {"lat": 43.6532, "lon": -79.3832}, - "ankara": {"lat": 39.9334, "lon": 32.8597}, - "wellington": {"lat": -41.2865, "lon": 174.7762}, - "buenos aires": {"lat": -34.6037, "lon": -58.3816}, + "nyc": {"lat": 40.7750, "lon": -73.8750}, # KLGA LaGuardia + "seattle": {"lat": 47.4499, "lon": -122.3118}, # KSEA Sea-Tac + "chicago": {"lat": 41.9769, "lon": -87.9081}, # KORD O'Hare + "dallas": {"lat": 32.8459, "lon": -96.8509}, # KDAL Love Field + "miami": {"lat": 25.7933, "lon": -80.2906}, # KMIA International + "atlanta": {"lat": 33.6367, "lon": -84.4281}, # KATL Hartsfield-Jackson + "seoul": {"lat": 37.4691, "lon": 126.4510}, # RKSI Incheon + "toronto": {"lat": 43.6759, "lon": -79.6294}, # CYYZ Pearson + "ankara": {"lat": 40.1281, "lon": 32.9950}, # LTAC Esenboğa + "wellington": {"lat": -41.3272, "lon": 174.8053}, # NZWN Wellington + "buenos aires": {"lat": -34.8222, "lon": -58.5358}, # SAEZ Ezeiza } normalized_city = city.lower().strip() @@ -895,6 +995,11 @@ class WeatherDataCollector: ens_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit) if ens_data: results["ensemble"] = ens_data + + # 多模型预报 (所有城市通用，用于共识评分) + mm_data = self.fetch_multi_model(lat, lon, use_fahrenheit=use_fahrenheit) + if mm_data: + results["multi_model"] = mm_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)