feat: Introduce AI analysis module and refactor bot_listener.py to prepare weather data for AI-driven insights.
This commit is contained in:
2569718930@qq.com
+114
-230
@@ -15,8 +15,9 @@ from src.data_collection.weather_sources import WeatherDataCollector # type: ig
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from src.data_collection.city_risk_profiles import get_city_risk_profile, format_risk_warning # type: ignore
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def analyze_weather_trend(weather_data, temp_symbol):
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"""根据实测与预测分析气温态势，增加峰值时刻预测"""
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'''根据实测与预测分析气温态势，增加峰值时刻预测'''
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insights: List[str] = []
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ai_features: List[str] = []
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metar = weather_data.get("metar", {})
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open_meteo = weather_data.get("open-meteo", {})
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@@ -25,11 +26,14 @@ def analyze_weather_trend(weather_data, temp_symbol):
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mgm = weather_data.get("mgm", {})
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if not metar or not open_meteo:
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return ""
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return "", ""
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curr_temp = metar.get("current", {}).get("temp")
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max_so_far = metar.get("current", {}).get("max_temp_so_far") # 今日实测最高
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daily = open_meteo.get("daily", {})
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hourly = open_meteo.get("hourly", {})
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times = hourly.get("time", [])
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temps = hourly.get("temperature_2m", [])
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# === 核心：整合多源预报最高温 ===
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forecast_highs = [daily.get("temperature_2m_max", [None])[0]]
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@@ -37,293 +41,182 @@ def analyze_weather_trend(weather_data, temp_symbol):
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forecast_highs.append(mb["today_high"])
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if nws.get("today_high") is not None:
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forecast_highs.append(nws["today_high"])
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# 加入多模型预报 (ECMWF, GFS, ICON, GEM, JMA)
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for mv in weather_data.get("multi_model", {}).get("forecasts", {}).values():
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if mv is not None:
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forecast_highs.append(mv)
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forecast_highs = [h for h in forecast_highs if h is not None]
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# 取预报中的最高值作为风险防御基准
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forecast_high = max(forecast_highs) if forecast_highs else None
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# 取最低值用于判断是否“已触及预报高位”
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min_forecast_high = min(forecast_highs) if forecast_highs else forecast_high
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# 取中位数作为用户可见的"预期值"（避免极端模型误导）
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forecast_median = None
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if forecast_highs:
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sorted_fh = sorted(forecast_highs)
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forecast_median = sorted_fh[len(sorted_fh) // 2]
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forecast_median = sorted(forecast_highs)[len(forecast_highs) // 2] if forecast_highs else None
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wind_speed = metar.get("current", {}).get("wind_speed_kt", 0)
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# 获取当地时间小时
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local_time_full = open_meteo.get("current", {}).get("local_time", "")
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try:
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local_date_str = local_time_full.split(" ")[0] # YYYY-MM-DD
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local_date_str = local_time_full.split(" ")[0]
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local_hour = int(local_time_full.split(" ")[1].split(":")[0])
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except:
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from datetime import datetime
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local_date_str = datetime.now().strftime("%Y-%m-%d")
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local_hour = datetime.now().hour
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# === 模型共识评分 ===
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# 主要来源: 多模型预报 (ECMWF, GFS, ICON, GEM, JMA)
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multi_model = weather_data.get("multi_model", {})
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mm_forecasts = multi_model.get("forecasts", {})
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labeled_forecasts = []
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for model_name, model_val in mm_forecasts.items():
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if model_val is not None:
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labeled_forecasts.append((model_name, model_val))
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# 额外独立源 (如有)
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if mb.get("today_high") is not None:
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labeled_forecasts.append(("MB", mb["today_high"]))
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if nws.get("today_high") is not None:
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labeled_forecasts.append(("NWS", nws["today_high"]))
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# Open-Meteo 确定性预报（用于后续偏差检测，不重复加入共识）
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om_today = daily.get("temperature_2m_max", [None])[0]
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# 集合预报数据 (仅用于不确定性区间展示)
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ensemble = weather_data.get("ensemble", {})
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ens_median = ensemble.get("median")
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# === METAR 趋势分析 (移到前部判断降温) ===
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recent_temps = metar.get("recent_temps", [])
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trend_desc = ""
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if len(recent_temps) >= 2:
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temps_only = [t for _, t in recent_temps]
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latest_val = temps_only[0]
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prev_val = temps_only[1]
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diff = latest_val - prev_val
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if len(temps_only) >= 3:
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all_same = all(t == latest_val for t in temps_only[:3])
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all_rising = all(temps_only[i] >= temps_only[i+1] for i in range(min(3, len(temps_only)) - 1))
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all_falling = all(temps_only[i] <= temps_only[i+1] for i in range(min(3, len(temps_only)) - 1))
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trend_display = " → ".join([f"{t}{temp_symbol}@{tm}" for tm, t in recent_temps[:3]])
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if all_same: trend_desc = f"📉 温度已停滞（{trend_display}），大概率到顶。"
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elif all_rising and diff > 0: trend_desc = f"📈 仍在升温（{trend_display}）。"
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elif all_falling and diff < 0: trend_desc = f"📉 已开始降温（{trend_display}）。"
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else: trend_desc = f"📊 温度波动中（{trend_display}）。"
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elif diff == 0: trend_desc = f"📉 温度持平（最近两条都是 {latest_val}{temp_symbol}）。"
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elif diff > 0: trend_desc = f"📈 仍在升温（{prev_val} → {latest_val}{temp_symbol}）。"
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else: trend_desc = f"📉 已开始降温（{prev_val} → {latest_val}{temp_symbol}）。"
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consensus_level = "unknown"
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consensus_spread = None
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is_cooling = "降温" in trend_desc
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# === 模型共识评分 ===
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mm_forecasts = weather_data.get("multi_model", {}).get("forecasts", {})
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labeled_forecasts = [(model_name, model_val) for model_name, model_val in mm_forecasts.items() if model_val is not None]
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if mb.get("today_high") is not None: labeled_forecasts.append(("MB", mb["today_high"]))
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if nws.get("today_high") is not None: labeled_forecasts.append(("NWS", nws["today_high"]))
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if len(labeled_forecasts) >= 2:
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f_values = [v for _, v in labeled_forecasts]
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f_max = max(f_values)
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f_min = min(f_values)
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consensus_spread = f_max - f_min
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f_avg = sum(f_values) / len(f_values)
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# 动态阈值：华氏度场景用更大的容差
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is_f = (temp_symbol == "°F")
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tight_threshold = 1.5 if is_f else 0.8 # 高共识
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mid_threshold = 3.0 if is_f else 1.5 # 中共识
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consensus_spread = max(f_values) - min(f_values)
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tight_threshold = 1.5 if temp_symbol == "°F" else 0.8
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mid_threshold = 3.0 if temp_symbol == "°F" else 1.5
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parts = " | ".join([f"{name} {val}{temp_symbol}" for name, val in labeled_forecasts])
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if consensus_spread <= tight_threshold:
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consensus_level = "high"
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insights.append(
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f"🎯 <b>模型共识：高 ({len(labeled_forecasts)}/{len(labeled_forecasts)})</b> — "
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f"{parts}，极差仅 {consensus_spread:.1f}°，预报高度一致。"
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)
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msg = f"🎯 <b>模型共识：高 ({len(labeled_forecasts)}源)</b> — {parts}，极差仅 {consensus_spread:.1f}°，高度一致。"
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elif consensus_spread <= mid_threshold:
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consensus_level = "medium"
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insights.append(
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f"⚖️ <b>模型共识：中 ({len(labeled_forecasts)}源)</b> — "
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f"{parts}，极差 {consensus_spread:.1f}°，有轻微分歧。"
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)
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msg = f"⚖️ <b>模型共识：中 ({len(labeled_forecasts)}源)</b> — {parts}，极差 {consensus_spread:.1f}°，有轻微分歧。"
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else:
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consensus_level = "low"
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# 找出最高和最低的源
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highest = max(labeled_forecasts, key=lambda x: x[1])
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lowest = min(labeled_forecasts, key=lambda x: x[1])
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insights.append(
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f"⚠️ <b>模型共识：低 ({len(labeled_forecasts)}源)</b> — "
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f"{parts}，极差 {consensus_spread:.1f}°！"
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f"{highest[0]} 最高 ({highest[1]}{temp_symbol}) vs {lowest[0]} 最低 ({lowest[1]}{temp_symbol})，不确定性大。"
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)
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msg = f"⚠️ <b>模型共识：低 ({len(labeled_forecasts)}源)</b> — {parts}，极差 {consensus_spread:.1f}°！{highest[0]} 最高 vs {lowest[0]} 最低。"
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# 移交 AI 处理，不再给用户直接显示博弈区间
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ai_features.append(msg)
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elif len(labeled_forecasts) == 1:
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name, val = labeled_forecasts[0]
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insights.append(
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f"📡 <b>仅1个预报源 ({name} {val}{temp_symbol})</b> — 无法交叉验证，共识评分不可用。"
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)
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msg = f"📡 <b>仅1个预报源 ({labeled_forecasts[0][0]} {labeled_forecasts[0][1]}{temp_symbol})</b>"
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# 移交 AI 处理，不再给用户直接显示博弈区间
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ai_features.append(msg)
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# === 博弈区间提醒 (基于 WU 四舍五入结算) ===
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# === 博弈区间提醒 ===
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if len(labeled_forecasts) >= 2:
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import math
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wu_round = lambda v: math.floor(v + 0.5)
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settlement_vals = sorted(set(wu_round(v) for _, v in labeled_forecasts))
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unit_short = temp_symbol
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# 如果实测已超所有预报，用实测值重新评估博弈区间
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if max_so_far is not None and forecast_high is not None and max_so_far > forecast_high + 0.5:
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actual_settled = wu_round(max_so_far)
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if actual_settled not in settlement_vals:
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all_vals = sorted(set(settlement_vals + [actual_settled]))
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else:
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all_vals = settlement_vals
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insights.append(
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f"🎲 <b>博弈区间</b>：模型预报已失效！实测最高 {max_so_far}{unit_short} → WU <b>{actual_settled}{unit_short}</b>，"
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f"但温度仍可能继续变化。"
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)
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msg = f"🎲 <b>博弈区间</b>：预报已失效！实测最高 {max_so_far}{temp_symbol} → WU <b>{actual_settled}{temp_symbol}</b>，温度仍可能波动。"
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elif len(settlement_vals) == 1:
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insights.append(f"🎲 <b>博弈区间</b>：{len(labeled_forecasts)}个模型全部指向 <b>{settlement_vals[0]}{unit_short}</b> 结算。")
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msg = f"🎲 <b>博弈区间</b>：模型全部指向 <b>{settlement_vals[0]}{temp_symbol}</b> 结算。"
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elif len(settlement_vals) == 2:
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insights.append(f"🎲 <b>博弈区间</b>：温度在 <b>{settlement_vals[0]}{unit_short}</b> 和 <b>{settlement_vals[1]}{unit_short}</b> 之间博弈。")
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msg = f"🎲 <b>博弈区间</b>：在 <b>{settlement_vals[0]}{temp_symbol}</b> 和 <b>{settlement_vals[1]}{temp_symbol}</b> 之间博弈。"
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elif len(settlement_vals) == 3:
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insights.append(f"🎲 <b>博弈区间</b>：温度在 <b>{settlement_vals[0]}{unit_short}</b>、<b>{settlement_vals[1]}{unit_short}</b>、<b>{settlement_vals[2]}{unit_short}</b> 之间博弈。")
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msg = f"🎲 <b>博弈区间</b>：在 <b>{settlement_vals[0]}{temp_symbol}</b>、<b>{settlement_vals[1]}{temp_symbol}</b>、<b>{settlement_vals[2]}{temp_symbol}</b> 之间博弈。"
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else:
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insights.append(f"🎲 <b>博弈区间</b>：模型分歧太大，结算还不确定。")
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# 集合预报区间 (独立于共识评分显示)
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msg = f"🎲 <b>博弈区间</b>：模型分歧太大，结算还不确定。"
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# 移交 AI 处理，不再给用户直接显示博弈区间
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ai_features.append(msg)
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# === 集合预报区间 (去除了啰嗦的预报验证) ===
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ensemble = weather_data.get("ensemble", {})
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ens_p10 = ensemble.get("p10")
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ens_p90 = ensemble.get("p90")
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ens_median = ensemble.get("median")
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om_today = daily.get("temperature_2m_max", [None])[0]
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if ens_p10 is not None and ens_p90 is not None and ens_median is not None:
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ens_range = ens_p90 - ens_p10
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insights.append(
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f"📊 <b>集合预报</b>：中位数 {ens_median}{temp_symbol}，"
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f"90% 区间 [{ens_p10}{temp_symbol} - {ens_p90}{temp_symbol}]，"
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f"波动幅度 {ens_range:.1f}°。"
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)
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# 确定性预报 vs 集合分布偏差检测
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msg1 = f"📊 <b>集合预报</b>：中位数 {ens_median}{temp_symbol}，90% 区间 [{ens_p10}{temp_symbol} - {ens_p90}{temp_symbol}]。"
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if not is_cooling: insights.append(msg1)
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ai_features.append(msg1)
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if om_today is not None:
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actual_reached = max_so_far is not None and max_so_far >= om_today - 0.5
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if om_today > ens_p90:
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if actual_reached:
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# 实测已达到预报值 → 确定性预报是对的，集合偏保守
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insights.append(
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f"✅ <b>预报验证</b>：确定性预报 {om_today}{temp_symbol} 已被实测验证 "
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f"(实测最高 {max_so_far}{temp_symbol})，集合预报偏保守。"
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)
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else:
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# 还没到最高温，存在偏高风险
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delta = om_today - ens_median
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insights.append(
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f"⚡ <b>预报偏高警告</b>：确定性预报 {om_today}{temp_symbol} "
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f"超过了集合 90% 上限 ({ens_p90}{temp_symbol})，"
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f"比中位数高 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。"
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)
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elif om_today < ens_p10:
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if max_so_far is not None and max_so_far >= ens_median:
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# 实测已超过中位数 → 确定性预报偏低，集合更准
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insights.append(
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f"✅ <b>预报验证</b>：实测最高 {max_so_far}{temp_symbol} "
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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。"
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)
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else:
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delta = ens_median - om_today
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insights.append(
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f"⚡ <b>预报偏低警告</b>：确定性预报 {om_today}{temp_symbol} "
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f"低于集合 90% 下限 ({ens_p10}{temp_symbol})，"
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f"比中位数低 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。"
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)
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# === 核心判断：实测是否已超预报 ===
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is_breakthrough = False
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# METAR 趋势分析（最近 3-4 条报文）
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recent_temps = metar.get("recent_temps", []) # [("15:00", 5), ("14:20", 5), ("14:00", 3)] 倒序
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trend_desc = ""
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if len(recent_temps) >= 2:
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temps_only = [t for _, t in recent_temps] # 倒序：最新在前
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latest_val = temps_only[0]
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prev_val = temps_only[1]
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diff = latest_val - prev_val
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if len(temps_only) >= 3:
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# 3 条以上：判断整体趋势
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all_same = all(t == latest_val for t in temps_only[:3])
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all_rising = all(temps_only[i] >= temps_only[i+1] for i in range(min(3, len(temps_only)) - 1))
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all_falling = all(temps_only[i] <= temps_only[i+1] for i in range(min(3, len(temps_only)) - 1))
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trend_display = " → ".join([f"{t}{temp_symbol}@{tm}" for tm, t in recent_temps[:3]])
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if all_same:
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trend_desc = f"📉 温度已停滞（{trend_display}），大概率到顶。"
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elif all_rising and diff > 0:
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trend_desc = f"📈 仍在升温（{trend_display}）。"
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elif all_falling and diff < 0:
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trend_desc = f"📉 已开始降温（{trend_display}）。"
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else:
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trend_desc = f"📊 温度波动中（{trend_display}）。"
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elif diff == 0:
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trend_desc = f"📉 温度持平（最近两条都是 {latest_val}{temp_symbol}）。"
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elif diff > 0:
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trend_desc = f"📈 仍在升温（{prev_val} → {latest_val}{temp_symbol}）。"
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else:
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trend_desc = f"📉 已开始降温（{prev_val} → {latest_val}{temp_symbol}）。"
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if om_today > ens_p90 and (max_so_far is None or max_so_far < om_today - 0.5):
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||||
msg2 = f"⚡ <b>预报偏高警告</b>：确定性预报 {om_today}{temp_symbol} 超集合90%上限！更可能接近 {ens_median}{temp_symbol}。"
|
||||
if not is_cooling: insights.append(msg2)
|
||||
ai_features.append(msg2)
|
||||
elif om_today < ens_p10 and (max_so_far is None or max_so_far < ens_median):
|
||||
msg2 = f"⚡ <b>预报偏低警告</b>：确定性预报 {om_today}{temp_symbol} 低于集合90%下限！更可能接近 {ens_median}{temp_symbol}。"
|
||||
if not is_cooling: insights.append(msg2)
|
||||
ai_features.append(msg2)
|
||||
|
||||
# === 实测已超预报 & 趋势输出 ===
|
||||
if max_so_far is not None and forecast_high is not None:
|
||||
if max_so_far > forecast_high + 0.5:
|
||||
is_breakthrough = True
|
||||
exceed_by = max_so_far - forecast_high
|
||||
# 合并为一条：事实 + 趋势（不给主观建议）
|
||||
bt_msg = (
|
||||
f"🚨 <b>实测已超预报</b>：{max_so_far}{temp_symbol} 超过预报上限 "
|
||||
f"{forecast_high}{temp_symbol}（+{exceed_by:.1f}°）。"
|
||||
)
|
||||
if trend_desc:
|
||||
bt_msg += f"\n{trend_desc}"
|
||||
bt_msg = f"🚨 <b>实测已超预报</b>：{max_so_far}{temp_symbol} 超过上限 {forecast_high}{temp_symbol}（+{exceed_by:.1f}°）。"
|
||||
if trend_desc: bt_msg += f"\n{trend_desc}"
|
||||
insights.append(bt_msg)
|
||||
ai_features.append(f"🚨 异常: 实测已冲破所有预报上限 ({max_so_far}{temp_symbol} vs {forecast_high}{temp_symbol})。")
|
||||
ai_features.append(trend_desc)
|
||||
else:
|
||||
if trend_desc:
|
||||
insights.append(trend_desc)
|
||||
ai_features.append(trend_desc)
|
||||
elif trend_desc:
|
||||
insights.append(trend_desc)
|
||||
ai_features.append(trend_desc)
|
||||
|
||||
# === 结算取整分析 (Wunderground 四舍五入到整数) ===
|
||||
# === 结算取整分析 ===
|
||||
if max_so_far is not None:
|
||||
settled = round(max_so_far)
|
||||
fractional = max_so_far - int(max_so_far)
|
||||
# 离取整边界的距离
|
||||
dist_to_boundary = abs(fractional - 0.5)
|
||||
|
||||
if dist_to_boundary <= 0.3:
|
||||
# 在边界附近 (X.2 ~ X.8)，取整结果可能随时翻转
|
||||
if fractional < 0.5:
|
||||
insights.append(
|
||||
f"⚖️ <b>结算边界</b>：当前最高 {max_so_far}{temp_symbol} → "
|
||||
f"WU 结算 <b>{settled}{temp_symbol}</b>，"
|
||||
f"但只差 <b>{0.5 - fractional:.1f}°</b> 就会进位到 {settled + 1}{temp_symbol}！"
|
||||
)
|
||||
msg = f"⚖️ <b>结算边界</b>：当前最高 {max_so_far}{temp_symbol} → WU 结算 <b>{settled}{temp_symbol}</b>，但只差 <b>{0.5 - fractional:.1f}°</b> 就会进位到 {settled + 1}{temp_symbol}！"
|
||||
else:
|
||||
insights.append(
|
||||
f"⚖️ <b>结算边界</b>：当前最高 {max_so_far}{temp_symbol} → "
|
||||
f"WU 结算 <b>{settled}{temp_symbol}</b>，"
|
||||
f"刚刚越过进位线，再降 <b>{fractional - 0.5:.1f}°</b> 就会回落到 {settled - 1}{temp_symbol}。"
|
||||
)
|
||||
msg = f"⚖️ <b>结算边界</b>：当前最高 {max_so_far}{temp_symbol} → WU 结算 <b>{settled}{temp_symbol}</b>，刚刚越过进位线，再降 <b>{fractional - 0.5:.1f}°</b> 就会回落到 {settled - 1}{temp_symbol}。"
|
||||
insights.append(msg)
|
||||
ai_features.append(msg)
|
||||
|
||||
# --- 峰值时刻预测逻辑 (仍以 Open-Meteo 逐小时数据为准) ---
|
||||
hourly = open_meteo.get("hourly", {})
|
||||
times = hourly.get("time", [])
|
||||
temps = hourly.get("temperature_2m", [])
|
||||
|
||||
# === 峰值时刻预测 (只在还没过峰值时显示) ===
|
||||
peak_hours = []
|
||||
om_high = daily.get("temperature_2m_max", [None])[0]
|
||||
if times and temps and om_high is not None:
|
||||
if times and temps and om_today is not None:
|
||||
for t_str, temp in zip(times, temps):
|
||||
if t_str.startswith(local_date_str):
|
||||
if abs(temp - om_high) <= 0.2:
|
||||
hour = t_str.split("T")[1][:5]
|
||||
peak_hours.append(hour)
|
||||
|
||||
# 确定用于逻辑判断的峰值小时
|
||||
if t_str.startswith(local_date_str) and abs(temp - om_today) <= 0.2:
|
||||
peak_hours.append(t_str.split("T")[1][:5])
|
||||
|
||||
if peak_hours:
|
||||
first_peak_h = int(peak_hours[0].split(":")[0])
|
||||
last_peak_h = int(peak_hours[-1].split(":")[0])
|
||||
|
||||
window = f"{peak_hours[0]} - {peak_hours[-1]}" if len(peak_hours) > 1 else peak_hours[0]
|
||||
insights.append(f"⏱️ <b>预计最热时段</b>：今天 <b>{window}</b>。")
|
||||
|
||||
if last_peak_h < 6:
|
||||
insights.append(f"⚠️ <b>提示</b>：预测最热在凌晨，后续气温可能一路走低。")
|
||||
elif local_hour < first_peak_h and (max_so_far is None or max_so_far < forecast_high):
|
||||
target_temp = om_today if om_today is not None else forecast_high
|
||||
insights.append(f"🎯 <b>关注重点</b>：看看那个时段温度能不能真的到 {target_temp}{temp_symbol}。")
|
||||
if local_hour <= last_peak_h:
|
||||
if last_peak_h < 6:
|
||||
ai_features.append(f"⚠️ <b>提示</b>：预测最热在凌晨，后续气温可能一路走低。")
|
||||
elif local_hour < first_peak_h and (max_so_far is None or max_so_far < forecast_high):
|
||||
target_temp = om_today if om_today is not None else forecast_high
|
||||
ai_features.append(f"🎯 <b>关注重点</b>：看看那个时段能否涨到 {target_temp}{temp_symbol}。")
|
||||
|
||||
# 写给AI
|
||||
if local_hour > last_peak_h: ai_features.append(f"⏱️ 状态: 预报峰值时段已过 ({window})。")
|
||||
elif first_peak_h <= local_hour <= last_peak_h: ai_features.append(f"⏱️ 状态: 正处于预报最热窗口 ({window})内。")
|
||||
else: ai_features.append(f"⏱️ 状态: 距最热时段还有 {first_peak_h - local_hour}h ({window})。")
|
||||
else:
|
||||
# 兜底默认值
|
||||
first_peak_h, last_peak_h = 13, 15
|
||||
|
||||
# --- 简化的 AI 特征提取 (不对用户双重显示，仅供 AI 使用) ---
|
||||
ai_features = list(insights)
|
||||
# 不再生成死板的分析文案，仅保留核心事实描述
|
||||
|
||||
# 1. 气温节奏特征
|
||||
if local_hour > last_peak_h:
|
||||
ai_features.append(f"⏱️ 状态: 预报峰值时段已过 ({window})。")
|
||||
elif first_peak_h <= local_hour <= last_peak_h:
|
||||
ai_features.append(f"⏱️ 状态: 正处于预报最热窗口 ({window})内。")
|
||||
else:
|
||||
ai_features.append(f"⏱️ 状态: 距最热时段还有 {first_peak_h - local_hour}h ({window})。")
|
||||
|
||||
# 2. 气温偏差特征
|
||||
if max_so_far is not None and forecast_high is not None:
|
||||
gap = max_so_far - forecast_high
|
||||
if gap > 0.5:
|
||||
ai_features.append(f"🚨 异常: 实测已冲破所有预报上限 ({max_so_far}{temp_symbol} vs {forecast_high}{temp_symbol})。")
|
||||
elif abs(gap) <= 1.0:
|
||||
ai_features.append(f"⚖️ 状态: 实测已极度接近预报峰值。")
|
||||
|
||||
# 3. 气象动力特征描述 (无主观推测)
|
||||
# === 其他 AI 专供的事实特征 ===
|
||||
if wind_speed:
|
||||
wind_dir = metar.get("current", {}).get("wind_dir", "未知")
|
||||
ai_features.append(f"🌬️ 当下风况: 约 {wind_speed}kt (方向 {wind_dir}°)。")
|
||||
humidity = metar.get("current", {}).get("humidity")
|
||||
if humidity and humidity > 80:
|
||||
ai_features.append(f"💦 湿度极高 ({humidity}%)。")
|
||||
if humidity and humidity > 80: ai_features.append(f"💦 湿度极高 ({humidity}%)。")
|
||||
|
||||
clouds = metar.get("current", {}).get("clouds", [])
|
||||
if clouds:
|
||||
@@ -332,10 +225,8 @@ def analyze_weather_trend(weather_data, temp_symbol):
|
||||
ai_features.append(f"☁️ 天空状况: {c_desc}。")
|
||||
|
||||
wx_desc = metar.get("current", {}).get("wx_desc")
|
||||
if wx_desc:
|
||||
ai_features.append(f"🌧️ 天气现象: {wx_desc}。")
|
||||
if wx_desc: ai_features.append(f"🌧️ 天气现象: {wx_desc}。")
|
||||
|
||||
# 4. 暖平流事实提取
|
||||
max_temp_time_str = metar.get("current", {}).get("max_temp_time", "")
|
||||
if max_so_far is not None and max_temp_time_str:
|
||||
try:
|
||||
@@ -350,13 +241,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
|
||||
ai_features.append(f"🌙 动力事实: 最高温出现在低辐射时段 ({max_temp_time_str}, 辐射{max_temp_rad:.0f}W/m²)。")
|
||||
except: pass
|
||||
|
||||
# 5. 结算判定
|
||||
if max_so_far is not None:
|
||||
settled = round(max_so_far)
|
||||
fractional = max_so_far - int(max_so_far)
|
||||
if abs(fractional - 0.5) <= 0.2:
|
||||
ai_features.append(f"⚖️ 结算事实: 当前最高 {max_so_far}{temp_symbol} 处于进位关键点 ({settled}{temp_symbol})。")
|
||||
|
||||
display_str = "\n".join(insights) if insights else ""
|
||||
return display_str, "\n".join(ai_features)
|
||||
|
||||
|
||||
@@ -30,15 +30,16 @@ def get_ai_analysis(weather_insights: str, city_name: str, temp_symbol: str) ->
|
||||
【分析重点】
|
||||
1. **动力来源**：对比太阳辐射(W/m²)与最高温出现时间。如果低辐射时段气温冲高，说明是强暖平流，预报往往低估这种惯性。
|
||||
2. **阻碍因子**：由于高湿度(>80%)、降水或全阴天气导致的升温失速。
|
||||
3. **模型 spread**：多模型极差如果很大，说明结算极具博弈价值。
|
||||
3. **结算推演**：根据我提供给你的【博弈区间】以及【当前所处时段(是否过了最热期)】推断并告诉我最终结算温度落在哪个区间的希望更大。
|
||||
4. **结算边界**：如果当前温度处于 X.5 这种进位/舍位边缘，需特别预警。
|
||||
|
||||
【输出要求】
|
||||
1. **禁止废话**，整体控制在 80 字以内。
|
||||
1. **禁止废话**，整体控制在 100 字以内。
|
||||
2. 严格按照以下 HTML 格式输出:
|
||||
|
||||
🤖 <b>Groq AI 决策</b>
|
||||
- 💡 逻辑: [简述动力来源/阻碍因子。例如：暖平流强势推高，且辐射极低时段创新高，极大概率超预报。]
|
||||
- 🎲 盘口: [一句话指出结算在哪里博弈，以及目前是否到了最热时段。例如：距最热时段还有3小时，目前预计在27或28之间博弈，有突破可能。]
|
||||
- 💡 逻辑: [不要重复模版例子！请使用一句话提炼机场实测(如风速风向、云量、气温变化趋势)及热力动力因子。例如：实测吹强劲西南风(15kt)伴随云量减少，辐射加热强劲，破预报阻力非常小。]
|
||||
- 🎯 信心: [1-10]/10
|
||||
"""
|
||||
|
||||
|
||||
Reference in New Issue
Block a user