feat: Implement AI analysis and integrate with bot listener.
This commit is contained in:
2569718930@qq.com
+70
-364
@@ -300,369 +300,66 @@ def analyze_weather_trend(weather_data, temp_symbol):
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# 兜底默认值
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first_peak_h, last_peak_h = 13, 15
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is_peak_passed = False
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if curr_temp is not None and forecast_high is not None:
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diff_max = forecast_high - curr_temp
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# 1. 气温节奏判定 (动态参考峰值时刻)
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if local_hour > last_peak_h:
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# 已经过了预报的峰值时段
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is_peak_passed = True
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if is_breakthrough:
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insights.append(f"🌡️ <b>异常高温</b>：最热的时间已经过了，但温度还是比预报高，降温可能会来得比较晚。")
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# 如果实测已经接近"任一"主流预报的最高温 (使用 min_forecast_high)
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elif max_so_far and min_forecast_high is not None and max_so_far >= min_forecast_high - 0.5:
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insights.append(f"✅ <b>今天最热已过</b>：温度已经到了预报最高值附近，接下来会慢慢降温了。")
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else:
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# 虽然时间过了，但离最高温还有差距
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insights.append(f"📉 <b>开始降温</b>：最热时段已过，现在 {curr_temp}{temp_symbol}，看起来很难再涨到预报的 {forecast_high}{temp_symbol} 了。")
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elif first_peak_h <= local_hour <= last_peak_h:
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# 正在峰值窗口内
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if is_breakthrough:
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insights.append(f"🔥 <b>极端升温</b>：正处于最热时段，温度已经超过所有预报，还在继续往上走！")
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elif max_so_far is not None and (om_today or forecast_high) - max_so_far <= 0.8:
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insights.append(f"⚖️ <b>到顶了</b>：正处于最热时段，温度基本到位（实测 {max_so_far}{temp_symbol} ≈ 预报 {om_today}{temp_symbol}），接下来会在这个水平上下浮动。")
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else:
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ref = om_today or forecast_high
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gap = ref - (max_so_far if max_so_far is not None else curr_temp)
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insights.append(f"⏳ <b>最热时段进行中</b>：虽然在最热时段了，但离预报 {ref}{temp_symbol} 还差 {gap:.1f}°，继续观察。")
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elif local_hour < first_peak_h:
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# 还没到峰值窗口
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if is_breakthrough:
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exceed = max_so_far - forecast_high
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insights.append(
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f"🔥 <b>超预报升温</b>：距最热时段还有 {first_peak_h - local_hour}h，"
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f"实测已超预报 {exceed:.1f}°（{max_so_far}{temp_symbol} vs 预报上限 {forecast_high}{temp_symbol}）。"
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)
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else:
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gap_to_high = forecast_high - (max_so_far if max_so_far is not None else curr_temp)
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if gap_to_high > 1.2:
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insights.append(f"📈 <b>还在升温</b>：离最热时段还有 {first_peak_h - local_hour} 小时，温度还会继续往上走。")
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else:
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insights.append(f"🌅 <b>快到最热了</b>：马上就要进入最热时段，温度已经接近预报高位了。")
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# --- 简化的 AI 特征提取 ---
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# 不再生成死板的分析文案，仅保留核心事实描述
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# 1. 气温节奏特征
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if local_hour > last_peak_h:
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insights.append(f"⏱️ 状态: 预报峰值时段已过 ({window})。")
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elif first_peak_h <= local_hour <= last_peak_h:
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insights.append(f"⏱️ 状态: 正处于预报最热窗口 ({window})内。")
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else:
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insights.append(f"⏱️ 状态: 距最热时段还有 {first_peak_h - local_hour}h ({window})。")
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else:
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# 回退逻辑
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insights.append(f"🌌 <b>夜间</b>：等明天太阳出来后再看新一轮升温。")
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# 2. 气温偏差特征
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if max_so_far is not None and forecast_high is not None:
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gap = max_so_far - forecast_high
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if gap > 0.5:
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insights.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})。")
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elif abs(gap) <= 1.0:
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insights.append(f"⚖️ 状态: 实测已极度接近预报峰值。")
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# 2. 湿度与露点分析 (仅在傍晚以后)
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humidity = metar.get("current", {}).get("humidity")
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dewpoint = metar.get("current", {}).get("dewpoint")
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if local_hour >= 18:
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if humidity and humidity > 80:
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insights.append(f"💦 <b>湿度很高</b>：湿度 {humidity}%，空气很潮湿，夜里热量散不掉，降温会很慢。")
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if dewpoint is not None and curr_temp - dewpoint < 2.0:
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insights.append(f"🌡️ <b>降温快到底了</b>：温度已经接近露点（空气中水汽开始凝结的温度），再往下降会很困难。")
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# 3. 气象动力特征描述 (无主观推测)
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humidity = metar.get("current", {}).get("humidity")
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if humidity and humidity > 80:
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insights.append(f"💦 湿度极高 ({humidity}%)。")
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clouds = metar.get("current", {}).get("clouds", [])
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if clouds:
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cover = clouds[-1].get("cover", "")
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c_desc = {"OVC": "全阴", "BKN": "多云", "SCT": "散云", "FEW": "少云"}.get(cover, cover)
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insights.append(f"☁️ 天空状况: {c_desc}。")
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# 3. 风力
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if wind_speed >= 15:
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insights.append(f"🌬️ <b>风很大</b>：风速 {wind_speed}kt，温度可能会忽高忽低。")
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elif wind_speed >= 10:
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insights.append(f"🍃 <b>有风</b>：风速适中 ({wind_speed}kt)，会加速空气流动，具体影响看风向。")
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wx_desc = metar.get("current", {}).get("wx_desc")
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if wx_desc:
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insights.append(f"🌧️ 天气现象: {wx_desc}。")
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# 4. 云层遮挡分析 (仅在升温期/峰值期有意义)
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clouds = metar.get("current", {}).get("clouds", [])
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if clouds and not is_peak_passed:
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main_cloud = clouds[-1]
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cover = main_cloud.get("cover", "")
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if cover == "OVC":
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insights.append(f"☁️ <b>阴天</b>：天完全被云盖住了，太阳照不进来，温度很难再往上涨了。")
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elif cover == "BKN":
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insights.append(f"🌥️ <b>云比较多</b>：天空大部分被云挡住了，日照不足，升温会比较慢。")
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elif cover in ["SKC", "CLR", "FEW"]:
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insights.append(f"☀️ <b>大晴天</b>：阳光直射，没什么云，有利于温度继续往上冲。")
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# 5. 特殊天气现象
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wx_desc = metar.get("current", {}).get("wx_desc")
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has_mgm = bool(mgm.get("current"))
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mgm_rain = mgm.get("current", {}).get("rain_24h")
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if wx_desc:
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wx_upper = wx_desc.upper().strip()
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wx_tokens = wx_upper.split()
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# 用分词匹配，避免 "METAR" 中的 "RA" 误判
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rain_codes = {"RA", "DZ", "-RA", "+RA", "-DZ", "+DZ", "TSRA", "SHRA", "FZRA", "RAIN", "DRIZZLE"}
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snow_codes = {"SN", "GR", "GS", "-SN", "+SN", "BLSN", "SNOW"}
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fog_codes = {"FG", "BR", "HZ", "MIST", "FOG", "FZFG"}
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if rain_codes & set(wx_tokens):
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if has_mgm and mgm_rain and mgm_rain > 0:
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insights.append(f"🌧️ <b>在下雨</b>：已累计 {mgm_rain}mm，雨水蒸发会吸收热量，温度很难涨上去。")
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else:
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insights.append(f"🌧️ <b>在下雨</b>：METAR 探测到降水，雨水蒸发会吸收热量，升温会受阻。")
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elif snow_codes & set(wx_tokens):
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insights.append(f"❄️ <b>在下雪/冰雹</b>：温度会一直低迷。")
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elif fog_codes & set(wx_tokens):
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insights.append(f"🌫️ <b>有雾/霾</b>：阳光被挡住了，湿度也高，升温会很慢。")
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# 6. 风向分析（始终显示，风向是重要参考信息）
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# 4. 暖平流事实提取
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max_temp_time_str = metar.get("current", {}).get("max_temp_time", "")
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if max_so_far is not None and max_temp_time_str:
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try:
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# 优先 METAR，回退 MGM
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metar_wind = metar.get("current", {}).get("wind_dir")
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mgm_wind = mgm.get("current", {}).get("wind_dir")
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if metar_wind is not None:
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analysis_wind = float(metar_wind)
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wind_source = "METAR"
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elif mgm_wind is not None:
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analysis_wind = float(mgm_wind)
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wind_source = "MGM"
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else:
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analysis_wind = None
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wind_source = None
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# 两源矛盾检测
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if metar_wind is not None and mgm_wind is not None:
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metar_f = float(metar_wind)
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mgm_f = float(mgm_wind)
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diff_angle = abs(metar_f - mgm_f)
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if diff_angle > 180:
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diff_angle = 360 - diff_angle
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if diff_angle > 90:
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dirs_name = ["北", "东北", "东", "东南", "南", "西南", "西", "西北"]
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m_name = dirs_name[int((metar_f + 22.5) % 360 / 45)]
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g_name = dirs_name[int((mgm_f + 22.5) % 360 / 45)]
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insights.append(f"⚠️ <b>风向矛盾</b>：METAR 测到{m_name}风({metar_f:.0f}°)，MGM 测到{g_name}风({mgm_f:.0f}°)，相差较大，风向不稳定。")
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if analysis_wind is not None:
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wd = analysis_wind
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# 用趋势数据判断风的实际影响
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if trend_desc:
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# 从趋势中提取关键词
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is_stagnant = "停滞" in trend_desc or "持平" in trend_desc
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is_rising = "升温" in trend_desc
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is_falling = "降温" in trend_desc
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else:
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is_stagnant = False
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is_rising = False
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is_falling = False
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if 315 <= wd or wd <= 45:
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effect = ""
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if is_stagnant or is_falling:
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effect = "，升温确实被压制"
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elif is_rising:
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effect = "，但温度仍在上升"
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insights.append(f"🌬️ <b>吹北风</b>（{wind_source} {wd:.0f}°）：冷空气{effect}。")
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elif 135 <= wd <= 225:
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effect = ""
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if is_rising:
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effect = "，温度仍在上升"
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elif is_stagnant:
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effect = "，但温度已停滞"
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elif is_falling:
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effect = "，但温度已开始下降"
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insights.append(f"🔥 <b>吹南风</b>（{wind_source} {wd:.0f}°）：暖空气{effect}。")
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elif 225 < wd < 315:
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dir_name = "西南风" if wd <= 260 else ("西北风" if wd >= 280 else "西风")
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insights.append(f"🌬️ <b>吹{dir_name}</b>（{wind_source} {wd:.0f}°）。")
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elif 45 < wd < 135:
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insights.append(f"🌬️ <b>吹东风</b>（{wind_source} {wd:.0f}°）。")
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except (TypeError, ValueError):
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pass
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try:
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visibility = metar.get("current", {}).get("visibility_mi")
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if visibility is not None:
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vis_val = float(str(visibility).replace("+", "").replace("-", ""))
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if vis_val < 3 and local_hour <= 11:
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insights.append(f"🌫️ <b>早上能见度差</b>：只能看到 {vis_val} 英里远，阳光穿不透，上午升温会很慢。")
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except (TypeError, ValueError):
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pass
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# 7. 模型准确度预警（使用多模型数据）
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if is_peak_passed and max_so_far is not None:
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model_checks = []
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for m_name, m_val in mm_forecasts.items():
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if m_val is not None and m_val > max_so_far + 1.5:
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model_checks.append(f"{m_name} ({m_val}{temp_symbol})")
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# 附加源也查一下
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mb_h = mb.get("today_high")
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if mb_h and mb_h > max_so_far + 1.5:
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model_checks.append(f"MB ({mb_h}{temp_symbol})")
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nws_h = nws.get("today_high")
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if nws_h and nws_h > max_so_far + 1.5:
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model_checks.append(f"NWS ({nws_h}{temp_symbol})")
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if model_checks:
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insights.append(f"⚠️ <b>预报偏高了</b>：实测远低于 " + "、".join(model_checks) + "，这些模型今天报高了。")
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# 8. MGM 气压分析 (仅安卡拉)
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mgm_pressure = mgm.get("current", {}).get("pressure")
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if mgm_pressure is not None and not is_peak_passed:
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if mgm_pressure < 900:
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insights.append(f"📉 <b>气压偏低</b>：{mgm_pressure}hPa，可能有暖湿气流过境，有利于温度上升。")
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# 9. MGM 官方最高温交叉验证
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mgm_max = mgm.get("current", {}).get("mgm_max_temp")
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if mgm_max is not None and max_so_far is not None:
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if abs(mgm_max - max_so_far) > 1.5:
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insights.append(f"📊 <b>数据差异</b>：MGM 官方记录最高 {mgm_max}{temp_symbol}，METAR 记录 {max_so_far}{temp_symbol}，相差 {abs(mgm_max - max_so_far):.1f}°。")
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# 10. 太阳辐射分析 (Open-Meteo shortwave_radiation)
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hourly_rad = hourly.get("shortwave_radiation", [])
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sunshine_durations = daily.get("sunshine_duration", [])
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if hourly_rad and times:
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# 计算今天已经过去的小时的累计辐射 vs 全天预测总辐射
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today_total_rad = 0.0
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today_so_far_rad = 0.0
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today_peak_rad = 0.0
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today_peak_hour = ""
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max_h = int(max_temp_time_str.split(":")[0])
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max_temp_rad = 0.0
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hourly_rad = hourly.get("shortwave_radiation", [])
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for t_str, rad in zip(times, hourly_rad):
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if t_str.startswith(local_date_str) and rad is not None:
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today_total_rad += rad
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hour_val = int(t_str.split("T")[1][:2])
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if hour_val <= local_hour:
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today_so_far_rad += rad
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if rad > today_peak_rad:
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today_peak_rad = rad
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today_peak_hour = t_str.split("T")[1][:5]
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if t_str.startswith(local_date_str) and int(t_str.split("T")[1][:2]) == max_h:
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max_temp_rad = rad if rad is not None else 0.0
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break
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if max_temp_rad < 50:
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insights.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)。")
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except: pass
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if today_total_rad > 0:
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rad_pct = today_so_far_rad / today_total_rad * 100
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if not is_peak_passed and local_hour >= 8:
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# 白天升温期：报告太阳能量进度
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if rad_pct < 30 and local_hour >= 12:
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insights.append(f"🌤️ <b>日照不足</b>：到目前为止只吸收了全天 {rad_pct:.0f}% 的太阳能量，云层可能在严重削弱日照。")
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# 检测"暖平流型"高温：峰值温度出现在太阳辐射极低的时段
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max_temp_time_str = metar.get("current", {}).get("max_temp_time", "")
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if max_so_far is not None and max_temp_time_str:
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try:
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max_h = int(max_temp_time_str.split(":")[0])
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# 找到最高温时段对应的辐射值
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max_temp_rad = 0.0
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for t_str, rad in zip(times, hourly_rad):
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if t_str.startswith(local_date_str) and rad is not None:
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h = int(t_str.split("T")[1][:2])
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if h == max_h:
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max_temp_rad = rad
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break
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if max_temp_rad < 50 and today_peak_rad > 200:
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insights.append(
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f"🌙 <b>暖平流驱动</b>：最高温出现在 {max_temp_time_str}，"
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f"当时太阳辐射仅 {max_temp_rad:.0f} W/m²（峰值 {today_peak_rad:.0f} W/m²），"
|
||||
f"说明气温是被暖空气推高的，而不是被太阳晒热的。"
|
||||
)
|
||||
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 is_breakthrough:
|
||||
# 模型全部预测错了，共识一致但方向错误，不加分
|
||||
timing_factors.append("⚠️模型已失效")
|
||||
elif 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")
|
||||
elif consensus_level == "low":
|
||||
timing_factors.append("模型分歧大")
|
||||
else:
|
||||
timing_factors.append("仅单源")
|
||||
|
||||
if max_so_far is not None and forecast_high is not None and (is_peak_passed or hours_to_peak <= 3):
|
||||
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。")
|
||||
|
||||
# === 明日预览：当今日峰值已过，自动显示明天的模型共识 ===
|
||||
if is_peak_passed:
|
||||
tomorrow_forecasts = {}
|
||||
tomorrow_date = None
|
||||
|
||||
# 从 multi_model 的 daily_forecasts 中取明天数据
|
||||
mm_daily = multi_model.get("daily_forecasts", {})
|
||||
mm_dates = multi_model.get("dates", [])
|
||||
|
||||
if len(mm_dates) >= 2:
|
||||
tomorrow_date = mm_dates[1]
|
||||
tomorrow_forecasts = mm_daily.get(tomorrow_date, {})
|
||||
|
||||
# 明天的 Open-Meteo 预报
|
||||
tomorrow_om = None
|
||||
om_max_list = daily.get("temperature_2m_max", [])
|
||||
om_dates = daily.get("time", [])
|
||||
if len(om_max_list) >= 2:
|
||||
tomorrow_om = om_max_list[1]
|
||||
if tomorrow_date is None and len(om_dates) >= 2:
|
||||
tomorrow_date = om_dates[1]
|
||||
|
||||
if tomorrow_date and (tomorrow_forecasts or tomorrow_om is not None):
|
||||
# 格式化日期 (02-24)
|
||||
date_short = tomorrow_date[5:] if tomorrow_date else "明天"
|
||||
|
||||
preview_parts = [f"\n📋 <b>明日预览 ({date_short})</b>"]
|
||||
|
||||
if tomorrow_om is not None:
|
||||
preview_parts.append(f"📊 Open-Meteo 预报: {tomorrow_om}{temp_symbol}")
|
||||
|
||||
if tomorrow_forecasts:
|
||||
t_values = list(tomorrow_forecasts.values())
|
||||
t_max = max(t_values)
|
||||
t_min = min(t_values)
|
||||
t_spread = t_max - t_min
|
||||
|
||||
is_f = (temp_symbol == "°F")
|
||||
tight = 1.5 if is_f else 0.8
|
||||
mid = 3.0 if is_f else 1.5
|
||||
|
||||
parts = " | ".join([f"{name} {val}{temp_symbol}" for name, val in tomorrow_forecasts.items()])
|
||||
|
||||
if t_spread <= tight:
|
||||
preview_parts.append(f"🎯 模型共识：高 — {parts}，极差 {t_spread:.1f}°")
|
||||
elif t_spread <= mid:
|
||||
preview_parts.append(f"⚖️ 模型共识：中 — {parts}，极差 {t_spread:.1f}°")
|
||||
else:
|
||||
highest = max(tomorrow_forecasts.items(), key=lambda x: x[1])
|
||||
lowest = min(tomorrow_forecasts.items(), key=lambda x: x[1])
|
||||
preview_parts.append(
|
||||
f"⚠️ 模型共识：低 — {parts}，极差 {t_spread:.1f}°！"
|
||||
f"{highest[0]} 最高 vs {lowest[0]} 最低"
|
||||
)
|
||||
|
||||
insights.extend(preview_parts)
|
||||
# 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:
|
||||
insights.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})。")
|
||||
|
||||
if not insights:
|
||||
return ""
|
||||
|
||||
return "\n💡 <b>态势分析</b>\n" + "\n".join(insights)
|
||||
return "\n".join(insights)
|
||||
|
||||
def start_bot():
|
||||
config = load_config()
|
||||
@@ -1007,21 +704,30 @@ def start_bot():
|
||||
if cloud_desc:
|
||||
msg_lines.append(f" {prefix} {cloud_desc} | 👁️ {vis or 10}mi | 💨 {wind or 0}kt")
|
||||
|
||||
# --- 5. 态势分析 ---
|
||||
trend_insights = analyze_weather_trend(weather_data, temp_symbol)
|
||||
if trend_insights:
|
||||
clean_insights = trend_insights.replace("💡 <b>态势分析</b>", "").strip()
|
||||
if clean_insights:
|
||||
msg_lines.append(f"\n💡 <b>分析</b>:")
|
||||
for line in clean_insights.split("\n"):
|
||||
if line.strip():
|
||||
msg_lines.append(f"- {line.strip()}")
|
||||
# --- 5. 态势特征提取 ---
|
||||
feature_str = analyze_weather_trend(weather_data, temp_symbol)
|
||||
if feature_str:
|
||||
# 仅将最核心的信息展示给用户作为"态势分析"
|
||||
# 但后面会把更全的数据传给 AI
|
||||
msg_lines.append(f"\n💡 <b>分析</b>:")
|
||||
for line in feature_str.split("\n"):
|
||||
if line.strip():
|
||||
msg_lines.append(f"- {line.strip()}")
|
||||
|
||||
# --- 6. Groq AI 数据分析 ---
|
||||
# --- 6. Groq AI 深度分析 ---
|
||||
try:
|
||||
from src.analysis.ai_analyzer import get_ai_analysis
|
||||
# Groq 极快，通常不用发送“正在分析”的提示，直接拼接
|
||||
ai_result = get_ai_analysis(clean_insights, city_name, temp_symbol)
|
||||
# 构建更全的背景数据给 AI
|
||||
# 包含风力、能见度、多源分歧等原始结论
|
||||
ai_context = feature_str
|
||||
|
||||
# 补充多模型分歧
|
||||
mm = weather_data.get("multi_model", {})
|
||||
if mm.get("forecasts"):
|
||||
mm_str = " | ".join([f"{k}:{v}{temp_symbol}" for k,v in mm["forecasts"].items() if v])
|
||||
ai_context += f"\n模型分歧: {mm_str}"
|
||||
|
||||
ai_result = get_ai_analysis(ai_context, city_name, temp_symbol)
|
||||
if ai_result:
|
||||
msg_lines.append(f"\n{ai_result}")
|
||||
except Exception as e:
|
||||
|
||||
@@ -20,21 +20,26 @@ def get_ai_analysis(weather_insights: str, city_name: str, temp_symbol: str) ->
|
||||
}
|
||||
|
||||
prompt = f"""
|
||||
你是一个专业的天气衍生品（如 Polymarket）交易员。你的任务是根据当前天气数据推测今日最高温度趋势，进行交易决策。
|
||||
请严格根据以下我提供的【{city_name}】的实时天气数据和规则策略进行分析。
|
||||
你是一个专业的天气衍生品（如 Polymarket）交易员。你的任务是分析当前天气特征，判断今日实测最高温是否能达到或超过预报中的【最高值】。
|
||||
|
||||
【参考数据与态势】
|
||||
请综合以下提供的【{city_name}】气象特征进行深度推理。
|
||||
|
||||
【气象特征与事实】
|
||||
{weather_insights}
|
||||
|
||||
【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。
|
||||
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. 语言必须极端简练，直击要害，整体不超过60个字。
|
||||
2. 必须给出一个明确的操作建议（针对“今天温度是否会涨到预报峰值”）。结论可以是：下注YES、下注NO、或 观望。
|
||||
3. 必须包含 1-10 的信心指数。
|
||||
4. 严格按照以下HTML格式输出:
|
||||
1. **禁止废话**，整体控制在 80 字以内。
|
||||
2. 严格按照以下 HTML 格式输出:
|
||||
|
||||
🤖 <b>Groq AI 决策</b>
|
||||
- 💡 逻辑: [一句话说明核心支撑逻辑]
|
||||
- 🎯 建议: <b>[下注YES / 下注NO / 观望]</b> (信心: [1-10]/10)
|
||||
- 💡 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。]
|
||||
- ⏰ 时机: [理想 / 较好 / 谨慎 / 不建议] (信心: [1-10]/10)
|
||||
"""
|
||||
|
||||
payload = {
|
||||
|
||||
Reference in New Issue
Block a user