From 6f0eaad6a8b70341cf10efdf6f8add12c06faf20 Mon Sep 17 00:00:00 2001 From: AmandaloveYang <2569718930@qq.com> Date: Sat, 21 Feb 2026 10:37:37 +0800 Subject: [PATCH] feat: Add a multi-source weather data collection module supporting OpenWeatherMap, Visual Crossing, and METAR. --- bot_listener.py | 116 +++++++++++++++++++++++++ src/data_collection/weather_sources.py | 88 +++++++++++++++++++ 2 files changed, 204 insertions(+) diff --git a/bot_listener.py b/bot_listener.py index c4175260..fadd9d6b 100644 --- a/bot_listener.py +++ b/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"🎯 模型共识：高 ({len(labeled_forecasts)}/{len(labeled_forecasts)}) — " + 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"⚖️ 模型共识：中 ({len(labeled_forecasts)}源) — " + 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"⚠️ 模型共识：低 ({len(labeled_forecasts)}源) — " + 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"📊 集合预报：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"⏰ 入场时机：理想 — {factors_str}。不确定性低，适合下注。") + elif timing_score >= 3: + insights.append(f"⏰ 入场时机：较好 — {factors_str}。可以考虑小仓位入场。") + elif timing_score >= 2: + insights.append(f"⏰ 入场时机：谨慎 — {factors_str}。建议继续观察。") + else: + insights.append(f"⏰ 入场时机：不建议 — {factors_str}。不确定性大，等更多数据。") + if not insights: return "" diff --git a/src/data_collection/weather_sources.py b/src/data_collection/weather_sources.py index fe872f69..19e35099 100644 --- a/src/data_collection/weather_sources.py +++ b/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)