2569718930@qq.com
2ee00f8016
AI 解读字段扩展:
- 新增 taf_read_zh/en：解读机场预报中影响今日峰值窗口的变化
- 新增 probability_read_zh/en：描述概率分布形态（最高桶、偏左/偏右）
- stream max_tokens 900→1200 容纳新输出字段
- 缓存 key 简化为 METAR原文+观测时间，大幅提升命中率
- 兜底函数补全 TAF 和概率字段的确定性生成
异常检测:
- 纯数学计算，零 AI 延迟：实测温度 vs 全部模型预测上下限
- 三级告警：breakout_above / breakout_below / deviation
市场概览:
- 新增 POST /api/scan/terminal/overview（MiMo 批量解读，缓存10分钟）
- 前端 MarketOverviewBanner 可折叠横幅（顶栏与标签栏之间）
- 移动端适配 640px/768px 断点，暗色/亮色双主题
Scope-risk: MEDIUM — 170 测试通过，TypeScript 零错误，ruff 零告警
Tested: python -m pytest -q (170 passed), npx tsc --noEmit (0 errors), ruff check .
103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
"""Anomaly detection — pure math, no AI call.
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Flags cities where current observations deviate from model predictions.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from web.scan_city_ai_helpers import _safe_float
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def _check_city_anomaly(
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data: Dict[str, Any],
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*,
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high_temp_threshold: float = 2.0,
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) -> Optional[Dict[str, Any]]:
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"""Return anomaly flag if current observation breaks model cluster bounds."""
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current = data.get("current") if isinstance(data.get("current"), dict) else {}
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airport = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {}
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multi = data.get("multi_model") if isinstance(data.get("multi_model"), dict) else {}
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deb = data.get("deb") if isinstance(data.get("deb"), dict) else {}
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observed = _safe_float(current.get("temp") or airport.get("temp"))
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if observed is None:
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return None
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model_highs = [
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_safe_float(v)
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for v in multi.values()
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if _safe_float(v) is not None
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]
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deb_pred = _safe_float(deb.get("prediction"))
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if deb_pred is not None:
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model_highs.append(deb_pred)
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if not model_highs:
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return None
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model_max = max(model_highs)
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model_min = min(model_highs)
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model_median = sorted(model_highs)[len(model_highs) // 2]
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delta_above_max = observed - model_max
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delta_below_min = model_min - observed
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delta_from_median = observed - model_median
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anomaly: Optional[Dict[str, Any]] = None
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if delta_above_max > high_temp_threshold:
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anomaly = {
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"level": "breakout_above",
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"observed": observed,
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"model_max": model_max,
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"delta": round(delta_above_max, 1),
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"model_count": len(model_highs),
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}
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elif delta_below_min > high_temp_threshold:
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anomaly = {
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"level": "breakout_below",
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"observed": observed,
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"model_min": model_min,
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"delta": round(delta_below_min, 1),
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"model_count": len(model_highs),
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}
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elif abs(delta_from_median) > 1.5:
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anomaly = {
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"level": "deviation",
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"observed": observed,
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"model_median": model_median,
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"delta": round(delta_from_median, 1),
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"model_count": len(model_highs),
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}
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if anomaly:
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anomaly.update(
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{
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"city": data.get("name") or data.get("city"),
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"local_date": data.get("local_date"),
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"temp_unit": data.get("temp_symbol", "°C"),
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"deb_prediction": deb_pred,
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}
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)
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return anomaly
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def detect_scan_terminal_anomalies(
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rows: List[Dict[str, Any]],
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*,
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high_temp_threshold: float = 2.0,
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) -> List[Dict[str, Any]]:
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"""Scan all terminal rows and return anomaly flags."""
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anomalies = []
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for row in rows:
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if not isinstance(row, dict):
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continue
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city_data = row.get("city_data") or row
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flag = _check_city_anomaly(city_data, high_temp_threshold=high_temp_threshold)
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if flag:
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flag["row_id"] = row.get("row_id") or row.get("id")
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anomalies.append(flag)
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return anomalies
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