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MiMo AI 能力扩展：TAF解读、概率分布解读、异常检测、市场概览

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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 .
This commit is contained in:
2569718930@qq.com
2026-05-14 22:41:31 +08:00
parent 6c08a68413
commit 2ee00f8016
13 changed files with 823 additions and 55 deletions
Download Patch File Download Diff File Expand all files Collapse all files
web/scan_terminal_service.py
+51 -41
View File
@@ -51,6 +51,7 @@ from web.scan_terminal_ai_compact import (
_compact_hourly_context,
_compact_intraday_context,
_compact_observation_points,
_compact_probability_context,
_compact_taf_context,
_compact_vertical_context,
build_scan_ai_prompt,
@@ -177,7 +178,7 @@ SCAN_CITY_AI_RETRY_ON_STREAM_PARSE_ERROR = str(
).strip().lower() in {"1", "true", "yes", "on"}
SCAN_AI_CACHE_TTL_SEC = max(
30,
int(os.getenv("POLYWEATHER_SCAN_AI_CACHE_TTL_SEC", "1800")),
int(os.getenv("POLYWEATHER_SCAN_AI_CACHE_TTL_SEC", "3600")),
)
SCAN_AI_MAX_ROWS = _env_int("POLYWEATHER_SCAN_AI_MAX_ROWS", 40, min_value=1)
SCAN_AI_MAX_TOKENS = _env_int(
@@ -194,7 +195,7 @@ SCAN_CITY_AI_MAX_TOKENS = _env_int(
)
SCAN_CITY_AI_STREAM_MAX_TOKENS = _env_int(
"POLYWEATHER_SCAN_CITY_AI_STREAM_MAX_TOKENS",
min(SCAN_CITY_AI_MAX_TOKENS, 900),
min(SCAN_CITY_AI_MAX_TOKENS, 1200),
min_value=400,
max_value=64000,
)
@@ -393,6 +394,11 @@ def _build_city_ai_prompt(data: Dict[str, Any]) -> Dict[str, Any]:
"station_label": airport_current.get("station_label"),
},
"taf": _compact_taf_context(data.get("taf")),
"probability": _compact_probability_context(
data.get("probabilities") if isinstance(data.get("probabilities"), dict) else None,
data.get("deb") if isinstance(data.get("deb"), dict) else None,
data.get("temp_symbol"),
),
"vertical_profile_signal": _compact_vertical_context(
data.get("vertical_profile_signal")
),
@@ -425,47 +431,39 @@ def _scan_city_ai_cache_key(ai_input: Dict[str, Any]) -> str:
observation_anchor = ai_input.get("observation_anchor") if isinstance(ai_input.get("observation_anchor"), dict) else {}
is_airport_metar = observation_anchor.get("is_airport_metar") is not False
airport_current = ai_input.get("airport_current") if isinstance(ai_input.get("airport_current"), dict) else {}
current_obs = ai_input.get("current") if isinstance(ai_input.get("current"), dict) else {}
metar_context = ai_input.get("metar_context") if isinstance(ai_input.get("metar_context"), dict) else {}
observation_obs = (
ai_input.get("metar_today_obs") or ai_input.get("metar_recent_obs") or []
if is_airport_metar
else ai_input.get("settlement_today_obs") or ai_input.get("settlement_recent_obs") or []
)
observation_fingerprint = {
"stale_for_today": metar_context.get("stale_for_today"),
"last_observation_time": metar_context.get("last_observation_time"),
"last_time": metar_context.get("last_time"),
"last_temp": metar_context.get("last_temp"),
"max_time": metar_context.get("max_time"),
"max_temp": metar_context.get("max_temp"),
"airport_obs_time": airport_current.get("obs_time"),
"airport_report_time": airport_current.get("report_time"),
"airport_receipt_time": airport_current.get("receipt_time"),
"airport_temp": airport_current.get("temp"),
"airport_max_so_far": airport_current.get("max_so_far"),
"current_obs_time": current_obs.get("obs_time"),
"current_report_time": current_obs.get("report_time"),
"current_temp": current_obs.get("temp"),
"current_max_so_far": current_obs.get("max_so_far"),
}
key_payload = {
"prompt_version": SCAN_CITY_AI_PROMPT_VERSION,
"schema_version": ai_input.get("schema_version"),
"model": SCAN_CITY_AI_MODEL,
"city": ai_input.get("city"),
"local_date": ai_input.get("local_date"),
"deb": (ai_input.get("deb") or {}).get("prediction") if isinstance(ai_input.get("deb"), dict) else None,
"observation_source": observation_anchor.get("source") or ("metar" if is_airport_metar else "official"),
"station": observation_anchor.get("station_code"),
"metar": airport_current.get("raw_metar") if is_airport_metar else None,
"observation_fingerprint": observation_fingerprint,
"obs": observation_obs,
"raw_metar": airport_current.get("raw_metar") if is_airport_metar else None,
"obs_time": airport_current.get("obs_time") or metar_context.get("last_observation_time"),
"stale_for_today": metar_context.get("stale_for_today"),
}
raw = json.dumps(key_payload, sort_keys=True, ensure_ascii=False, default=str)
return "city-ai:" + hashlib.sha256(raw.encode("utf-8")).hexdigest()
def _quick_metar_cache_key(data: Dict[str, Any]) -> str:
airport_current = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {}
current = data.get("current") if isinstance(data.get("current"), dict) else {}
observation_anchor = data.get("observation_anchor") if isinstance(data.get("observation_anchor"), dict) else {}
raw_metar = airport_current.get("raw_metar") or current.get("raw_metar")
obs_time = airport_current.get("obs_time") or current.get("obs_time")
if raw_metar and obs_time:
finger = {
"city": data.get("name"),
"raw_metar": raw_metar,
"obs_time": obs_time,
"station": observation_anchor.get("station_code"),
"prompt_version": SCAN_CITY_AI_PROMPT_VERSION,
}
raw = json.dumps(finger, sort_keys=True, ensure_ascii=False, default=str)
return "city-ai:" + hashlib.sha256(raw.encode("utf-8")).hexdigest()
return ""
def _sse_event(event: str, payload: Dict[str, Any]) -> str:
return (
f"event: {event}\n"
@@ -492,15 +490,19 @@ def _cache_city_ai_payload(
data: Dict[str, Any],
generated_at: str,
ai_raw: Dict[str, Any],
quick_key: str = "",
) -> None:
entry = {
"expires_at": time.time() + SCAN_AI_CACHE_TTL_SEC,
"generated_at": generated_at,
"city": data.get("name"),
"city_display_name": data.get("display_name"),
"payload": ai_raw,
}
with _SCAN_CITY_AI_CACHE_LOCK:
_SCAN_CITY_AI_CACHE[cache_key] = {
"expires_at": time.time() + SCAN_AI_CACHE_TTL_SEC,
"generated_at": generated_at,
"city": data.get("name"),
"city_display_name": data.get("display_name"),
"payload": ai_raw,
}
_SCAN_CITY_AI_CACHE[cache_key] = entry
if quick_key and quick_key != cache_key:
_SCAN_CITY_AI_CACHE[quick_key] = entry
def _is_city_ai_fallback(ai_raw: Any) -> bool:
@@ -590,9 +592,12 @@ def stream_scan_city_ai_forecast_payload(
)
ai_input = _build_city_ai_prompt(data)
cache_key = _scan_city_ai_cache_key(ai_input)
quick_key = _quick_metar_cache_key(data)
if not force_refresh:
with _SCAN_CITY_AI_CACHE_LOCK:
cached = _SCAN_CITY_AI_CACHE.get(cache_key)
cached = _SCAN_CITY_AI_CACHE.get(cache_key) or (
_SCAN_CITY_AI_CACHE.get(quick_key) if quick_key else None
)
if cached and cached.get("expires_at", 0) >= time.time():
yield _sse_event(
"final",
@@ -803,6 +808,7 @@ def stream_scan_city_ai_forecast_payload(
data=data,
generated_at=generated_at,
ai_raw=ai_raw,
quick_key=quick_key,
)
yield _sse_event(
"final",
@@ -914,9 +920,12 @@ def build_scan_city_ai_forecast_payload(
)
ai_input = _build_city_ai_prompt(data)
cache_key = _scan_city_ai_cache_key(ai_input)
quick_key = _quick_metar_cache_key(data)
if not force_refresh:
with _SCAN_CITY_AI_CACHE_LOCK:
cached = _SCAN_CITY_AI_CACHE.get(cache_key)
cached = _SCAN_CITY_AI_CACHE.get(cache_key) or (
_SCAN_CITY_AI_CACHE.get(quick_key) if quick_key else None
)
if cached and cached.get("expires_at", 0) >= time.time():
logger.info(
"scan city AI forecast cache hit city={} model={}",
@@ -1058,6 +1067,7 @@ def build_scan_city_ai_forecast_payload(
data=data,
generated_at=generated_at,
ai_raw=ai_raw,
quick_key=quick_key,
)
logger.info(
"scan city AI forecast complete city={} duration_ms={} model={} confidence={}",