2569718930@qq.com
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
# 城市温度市场 - 数据偏差风险档案
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# 基于 METAR 机场站与市区实际温度的系统性差异
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from src.data_collection.city_registry import CITY_REGISTRY
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# Generate profiles from registry
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CITY_RISK_PROFILES = {
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cid: {
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"risk_level": info["risk_level"],
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"risk_emoji": info["risk_emoji"],
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"icao": info["icao"],
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"airport_name": info["airport_name"],
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"distance_km": info["distance_km"],
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"warning": info["warning"],
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# Backwards compatibility flags if needed
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"typical_bias_f": info.get("typical_bias_f", 0.0),
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"elevation_diff_m": info.get("elevation_diff_m", 0),
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"bias_direction": info.get("bias_direction", None),
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"season_notes": info.get("season_notes", None),
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}
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for cid, info in CITY_REGISTRY.items()
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}
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def get_city_risk_profile(city: str) -> dict:
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"""获取城市的风险档案"""
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city_lower = city.lower().strip()
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city_key = city_lower
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return CITY_RISK_PROFILES.get(city_key)
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def format_risk_warning(profile: dict, temp_symbol: str) -> str:
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"""格式化风险警告信息"""
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if not profile:
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return ""
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lines = []
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# 风险等级标题
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risk_labels = {"high": "高危", "medium": "中危", "low": "低危"}
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risk_label = risk_labels.get(profile["risk_level"], "未知")
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lines.append(f"⚠️ <b>数据偏差风险</b>: {profile['risk_emoji']} {risk_label}")
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# 机场信息
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lines.append(f" 📍 机场: {profile['airport_name']} ({profile['icao']})")
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lines.append(f" 📏 距市区: {profile['distance_km']}km")
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# 典型偏差
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if profile["typical_bias_f"] >= 1.0:
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lines.append(f" 📊 偏差: ±{profile['typical_bias_f']}{temp_symbol}")
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# 偏差方向说明
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if profile["bias_direction"]:
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lines.append(f" 💡 {profile['bias_direction']}")
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# 特别警告
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if profile["warning"]:
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lines.append(f" 🚨 {profile['warning']}")
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return "\n".join(lines)
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