2569718930@qq.com
a1d5435dd7
- HKO instruction 从纯否定句改为正向指导，明确告知 AI 可用数据维度 （温度、最高/最低温、湿度、10分钟风速风向） - AI 输入 current 对象新增 wind_speed_kt/wind_dir/humidity 字段， HKO 和 METAR 城市均受益 - 提示词统一补充湿度分析要求
424 lines
18 KiB
Python
424 lines
18 KiB
Python
from __future__ import annotations
|
|
|
|
import re
|
|
from datetime import datetime
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
from web.scan_city_ai_helpers import _safe_float, _truncate_ai_text
|
|
|
|
|
|
def _compact_ai_candidate(row: Dict[str, Any]) -> Dict[str, Any]:
|
|
return {
|
|
"id": row.get("id"),
|
|
"action": row.get("action"),
|
|
"side": row.get("side"),
|
|
"target_label": row.get("target_label"),
|
|
"target_value": row.get("target_value"),
|
|
"target_threshold": row.get("target_threshold"),
|
|
"target_unit": row.get("target_unit"),
|
|
"market_probability": row.get("market_probability"),
|
|
"market_event_probability": row.get("market_event_probability"),
|
|
"yes_ask": row.get("yes_ask"),
|
|
"no_ask": row.get("no_ask"),
|
|
"ask": row.get("ask"),
|
|
"spread": row.get("spread"),
|
|
"quote_age_ms": row.get("quote_age_ms"),
|
|
"cluster_role": row.get("cluster_role"),
|
|
"model_cluster_sources": _compact_ai_model_sources(row),
|
|
"metar_context": row.get("metar_context") or {},
|
|
"window_phase": row.get("window_phase"),
|
|
"peak_window_label": row.get("peak_window_label"),
|
|
"minutes_until_peak_start": row.get("minutes_until_peak_start"),
|
|
"minutes_until_peak_end": row.get("minutes_until_peak_end"),
|
|
"trend_alignment": row.get("trend_alignment"),
|
|
"tradable": row.get("tradable"),
|
|
"accepting_orders": row.get("accepting_orders"),
|
|
}
|
|
|
|
|
|
def _normalize_ai_city_key(value: Any) -> str:
|
|
return str(value or "").strip().lower().replace(" ", "").replace("-", "").replace("_", "")
|
|
|
|
|
|
def _compact_ai_model_sources(row: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
raw_sources = row.get("model_cluster_sources")
|
|
if not isinstance(raw_sources, dict):
|
|
return []
|
|
sources: List[Dict[str, Any]] = []
|
|
for name, value in raw_sources.items():
|
|
if _safe_float(value) is None:
|
|
continue
|
|
sources.append({"model": str(name), "value": value})
|
|
return sources[:12]
|
|
|
|
|
|
def _observation_sort_key(point: Dict[str, Any]) -> tuple[int, str]:
|
|
raw_time = str(point.get("time") or "").strip()
|
|
try:
|
|
parsed = datetime.fromisoformat(raw_time.replace("Z", "+00:00"))
|
|
return parsed.hour * 60 + parsed.minute, raw_time
|
|
except Exception:
|
|
pass
|
|
match = re.search(r"(\d{1,2}):(\d{2})", raw_time)
|
|
if match:
|
|
hour = max(0, min(23, int(match.group(1))))
|
|
minute = max(0, min(59, int(match.group(2))))
|
|
return hour * 60 + minute, raw_time
|
|
return 9999, raw_time
|
|
|
|
|
|
def _compact_observation_points(raw_points: Any, limit: int = 24) -> List[Dict[str, Any]]:
|
|
if not isinstance(raw_points, list):
|
|
return []
|
|
points: List[Dict[str, Any]] = []
|
|
for item in raw_points:
|
|
if isinstance(item, dict):
|
|
temp = _safe_float(item.get("temp"))
|
|
time_value = str(item.get("time") or item.get("obs_time") or item.get("time_label") or "").strip()
|
|
elif isinstance(item, (list, tuple)) and len(item) >= 2:
|
|
time_value = str(item[0] or "").strip()
|
|
temp = _safe_float(item[1])
|
|
else:
|
|
continue
|
|
if temp is None or not time_value:
|
|
continue
|
|
points.append({"time": time_value, "temp": temp})
|
|
sorted_points = sorted(points, key=_observation_sort_key)
|
|
return sorted_points[-max(1, int(limit)) :]
|
|
|
|
|
|
def _compact_ai_text(value: Any, limit: int = 700) -> Optional[str]:
|
|
text = _truncate_ai_text(value, limit).strip()
|
|
return text or None
|
|
|
|
|
|
def _compact_hourly_context(raw_hourly: Any) -> Dict[str, Any]:
|
|
if not isinstance(raw_hourly, dict):
|
|
return {}
|
|
times = raw_hourly.get("times") or raw_hourly.get("time") or []
|
|
temps = raw_hourly.get("temps") or raw_hourly.get("temperature_2m") or []
|
|
radiation = raw_hourly.get("radiation") or raw_hourly.get("shortwave_radiation") or []
|
|
if not isinstance(times, list) or not isinstance(temps, list):
|
|
return {}
|
|
|
|
points: List[Dict[str, Any]] = []
|
|
for idx, raw_time in enumerate(times):
|
|
temp = _safe_float(temps[idx] if idx < len(temps) else None)
|
|
if temp is None:
|
|
continue
|
|
time_text = str(raw_time or "").strip()
|
|
if "T" in time_text:
|
|
time_text = time_text.split("T", 1)[1][:5]
|
|
elif len(time_text) > 5:
|
|
time_text = time_text[:5]
|
|
point: Dict[str, Any] = {"time": time_text, "temp": temp}
|
|
rad = _safe_float(radiation[idx] if isinstance(radiation, list) and idx < len(radiation) else None)
|
|
if rad is not None:
|
|
point["radiation"] = rad
|
|
points.append(point)
|
|
|
|
if not points:
|
|
return {}
|
|
max_point = max(points, key=lambda item: _safe_float(item.get("temp")) or -999.0)
|
|
sample_indexes = {
|
|
idx
|
|
for idx in range(len(points))
|
|
if idx % 2 == 0 or idx >= len(points) - 4 or points[idx] is max_point
|
|
}
|
|
samples = [points[idx] for idx in sorted(sample_indexes)][-14:]
|
|
return {
|
|
"sample_count": len(points),
|
|
"forecast_hourly_max": max_point,
|
|
"samples": samples,
|
|
}
|
|
|
|
|
|
def _compact_taf_context(raw_taf_data: Any) -> Dict[str, Any]:
|
|
if not isinstance(raw_taf_data, dict):
|
|
return {}
|
|
signal = raw_taf_data.get("signal") if isinstance(raw_taf_data.get("signal"), dict) else {}
|
|
source = signal or raw_taf_data
|
|
raw_taf = raw_taf_data.get("raw_taf") or source.get("raw_taf")
|
|
compact: Dict[str, Any] = {
|
|
"available": bool(source.get("available") or raw_taf),
|
|
"raw_taf": _compact_ai_text(raw_taf, 900),
|
|
"issue_time": raw_taf_data.get("issue_time") or source.get("issue_time"),
|
|
"valid_time_from": raw_taf_data.get("valid_time_from") or source.get("valid_time_from"),
|
|
"valid_time_to": raw_taf_data.get("valid_time_to") or source.get("valid_time_to"),
|
|
"peak_window": source.get("peak_window"),
|
|
"suppression_level": source.get("suppression_level"),
|
|
"disruption_level": source.get("disruption_level"),
|
|
"wind_shift": source.get("wind_shift"),
|
|
"wind_regimes": source.get("wind_regimes"),
|
|
"summary_zh": _compact_ai_text(source.get("summary_zh"), 260),
|
|
"summary_en": _compact_ai_text(source.get("summary_en"), 260),
|
|
}
|
|
segments = source.get("segments") if isinstance(source.get("segments"), list) else []
|
|
markers = source.get("markers") if isinstance(source.get("markers"), list) else []
|
|
if segments:
|
|
compact["segments"] = segments[:3]
|
|
if markers:
|
|
compact["markers"] = markers[:4]
|
|
return {key: value for key, value in compact.items() if value not in (None, "", [])}
|
|
|
|
|
|
def _compact_vertical_context(raw_vertical: Any) -> Dict[str, Any]:
|
|
if not isinstance(raw_vertical, dict):
|
|
return {}
|
|
keys = [
|
|
"source",
|
|
"window_start",
|
|
"window_end",
|
|
"suppression_risk",
|
|
"trigger_risk",
|
|
"mixing_strength",
|
|
"shear_risk",
|
|
"heating_setup",
|
|
"heating_score",
|
|
"summary_zh",
|
|
"summary_en",
|
|
]
|
|
compact: Dict[str, Any] = {}
|
|
for key in keys:
|
|
value = raw_vertical.get(key)
|
|
if isinstance(value, str):
|
|
value = _compact_ai_text(value, 280)
|
|
if value not in (None, "", []):
|
|
compact[key] = value
|
|
return compact
|
|
|
|
|
|
def _compact_intraday_context(raw_intraday: Any) -> Dict[str, Any]:
|
|
if not isinstance(raw_intraday, dict):
|
|
return {}
|
|
compact: Dict[str, Any] = {}
|
|
for key in [
|
|
"headline",
|
|
"headline_en",
|
|
"confidence",
|
|
"base_case_bucket",
|
|
"upside_bucket",
|
|
"downside_bucket",
|
|
"next_observation_time",
|
|
"peak_window",
|
|
]:
|
|
value = raw_intraday.get(key)
|
|
if isinstance(value, str):
|
|
value = _compact_ai_text(value, 220)
|
|
if value not in (None, "", []):
|
|
compact[key] = value
|
|
signals = raw_intraday.get("signal_contributions")
|
|
if isinstance(signals, list):
|
|
compact["signal_contributions"] = [
|
|
{
|
|
"label": item.get("label"),
|
|
"label_en": item.get("label_en"),
|
|
"direction": item.get("direction"),
|
|
"strength": item.get("strength"),
|
|
"summary": _compact_ai_text(item.get("summary"), 180),
|
|
"summary_en": _compact_ai_text(item.get("summary_en"), 180),
|
|
}
|
|
for item in signals[:4]
|
|
if isinstance(item, dict)
|
|
]
|
|
return compact
|
|
|
|
|
|
def _build_metar_decision_context(data: Dict[str, Any]) -> Dict[str, Any]:
|
|
today_obs = _compact_observation_points(data.get("metar_today_obs"), 36)
|
|
recent_obs = _compact_observation_points(data.get("metar_recent_obs"), 12)
|
|
settlement_obs = _compact_observation_points(data.get("settlement_today_obs"), 36)
|
|
airport_current = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {}
|
|
metar_status = data.get("metar_status") if isinstance(data.get("metar_status"), dict) else {}
|
|
|
|
source_obs = today_obs or recent_obs or settlement_obs
|
|
trend_source = recent_obs or source_obs[-4:]
|
|
last_point = source_obs[-1] if source_obs else {}
|
|
first_trend = trend_source[0] if trend_source else {}
|
|
last_trend = trend_source[-1] if trend_source else {}
|
|
max_point = None
|
|
for point in source_obs:
|
|
if max_point is None or float(point["temp"]) >= float(max_point["temp"]):
|
|
max_point = point
|
|
|
|
last_temp = _safe_float(last_point.get("temp"))
|
|
first_temp = _safe_float(first_trend.get("temp"))
|
|
trend_last_temp = _safe_float(last_trend.get("temp"))
|
|
trend_delta = (
|
|
trend_last_temp - first_temp
|
|
if trend_last_temp is not None and first_temp is not None and len(trend_source) >= 2
|
|
else None
|
|
)
|
|
station = data.get("risk") if isinstance(data.get("risk"), dict) else {}
|
|
current = data.get("current") if isinstance(data.get("current"), dict) else {}
|
|
settlement_station = data.get("settlement_station") if isinstance(data.get("settlement_station"), dict) else {}
|
|
settlement_source = str(
|
|
current.get("settlement_source")
|
|
or settlement_station.get("settlement_source")
|
|
or "metar"
|
|
).strip().lower()
|
|
is_hko = settlement_source == "hko"
|
|
source_label = "HKO" if is_hko else "METAR"
|
|
return {
|
|
"source": source_label,
|
|
"is_airport_metar": not is_hko,
|
|
"station": (
|
|
current.get("station_code")
|
|
or settlement_station.get("settlement_station_code")
|
|
or station.get("icao")
|
|
or airport_current.get("station_code")
|
|
),
|
|
"station_label": (
|
|
current.get("station_name")
|
|
or settlement_station.get("settlement_station_label")
|
|
or station.get("airport")
|
|
or airport_current.get("station_label")
|
|
),
|
|
"today_obs": today_obs[-12:],
|
|
"recent_obs": recent_obs[-8:],
|
|
"settlement_today_obs": settlement_obs[-12:],
|
|
"obs_count": len(source_obs),
|
|
"last_time": last_point.get("time"),
|
|
"last_temp": last_temp,
|
|
"max_temp": _safe_float((max_point or {}).get("temp")),
|
|
"max_time": (max_point or {}).get("time"),
|
|
"trend_delta": trend_delta,
|
|
"stale_for_today": bool(metar_status.get("stale_for_today")),
|
|
"available_for_today": bool(metar_status.get("available_for_today")),
|
|
"last_observation_time": metar_status.get("last_observation_time"),
|
|
"airport_current_temp": _safe_float(airport_current.get("temp")),
|
|
"airport_max_so_far": _safe_float(airport_current.get("max_so_far")),
|
|
"airport_obs_time": airport_current.get("obs_time"),
|
|
"airport_report_time": airport_current.get("report_time"),
|
|
"airport_raw_metar": airport_current.get("raw_metar"),
|
|
"airport_wx_desc": airport_current.get("wx_desc"),
|
|
"airport_cloud_desc": airport_current.get("cloud_desc"),
|
|
"airport_visibility_mi": _safe_float(airport_current.get("visibility_mi")),
|
|
"airport_wind_speed_kt": _safe_float(airport_current.get("wind_speed_kt")),
|
|
"airport_wind_dir": _safe_float(airport_current.get("wind_dir")),
|
|
"airport_humidity": _safe_float(airport_current.get("humidity")),
|
|
}
|
|
|
|
|
|
def _city_observation_anchor(data: Dict[str, Any]) -> Dict[str, Any]:
|
|
current = data.get("current") if isinstance(data.get("current"), dict) else {}
|
|
settlement_station = data.get("settlement_station") if isinstance(data.get("settlement_station"), dict) else {}
|
|
airport_current = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {}
|
|
risk = data.get("risk") if isinstance(data.get("risk"), dict) else {}
|
|
source = str(
|
|
current.get("settlement_source")
|
|
or settlement_station.get("settlement_source")
|
|
or "metar"
|
|
).strip().lower()
|
|
is_hko = source == "hko"
|
|
if is_hko:
|
|
station_code = (
|
|
current.get("station_code")
|
|
or settlement_station.get("settlement_station_code")
|
|
or "HKO"
|
|
)
|
|
station_label = (
|
|
current.get("station_name")
|
|
or settlement_station.get("settlement_station_label")
|
|
or "Hong Kong Observatory"
|
|
)
|
|
return {
|
|
"source": "hko",
|
|
"source_label": "Hong Kong Observatory",
|
|
"is_airport_metar": False,
|
|
"station_code": station_code,
|
|
"station_label": station_label,
|
|
"read_label_zh": "香港天文台观测解读",
|
|
"read_label_en": "Hong Kong Observatory observation read",
|
|
"instruction_zh": (
|
|
"该城市使用香港天文台/HKO 官方站点观测，提供温度、今日最高/最低温、相对湿度、"
|
|
"10分钟平均风速和风向数据。它不是机场 METAR，不得使用 METAR、TAF、机场报文、报文时间等称谓，"
|
|
"应使用「观测时间」「风速」「风向」「湿度」等官方气象站用语。"
|
|
"观测解读必须具体说明：最新观测时间、温度、风向风速对升温路径的影响（增温/降温/中性）、"
|
|
"湿度是否可能压制日间升温。涉及风时必须说明具体风向及来源（如「偏北风」「海风/陆风」）。"
|
|
"如果有今日最高温和最低温数据，应结合判断温度走势。"
|
|
),
|
|
"instruction_en": (
|
|
"This city uses Hong Kong Observatory/HKO official station observations, providing temperature, "
|
|
"today's high/low, relative humidity, 10-min mean wind speed and direction. "
|
|
"It is NOT an airport METAR; do not use METAR, TAF, airport bulletin, or report time terminology. "
|
|
"Use terms like observation time, wind speed, wind direction, humidity. "
|
|
"The observation read must be specific: latest observation time, temperature, whether the wind "
|
|
"direction tends to warm, cool or be neutral for today's high path and why, and whether humidity "
|
|
"may suppress daytime heating. When mentioning wind, specify the direction and source "
|
|
"(e.g. northerly, sea breeze/land breeze). If today's high/low are available, use them to assess the trend."
|
|
),
|
|
}
|
|
return {
|
|
"source": "metar",
|
|
"source_label": "METAR",
|
|
"is_airport_metar": True,
|
|
"station_code": risk.get("icao") or airport_current.get("station_code"),
|
|
"station_label": risk.get("airport") or airport_current.get("station_label"),
|
|
"read_label_zh": "机场报文解读",
|
|
"read_label_en": "airport-bulletin read",
|
|
"instruction_zh": "该城市使用机场 METAR/TAF 作为日内实况证据。",
|
|
"instruction_en": "This city uses airport METAR/TAF as intraday observation evidence.",
|
|
}
|
|
|
|
|
|
def _compact_ai_city_group(rows: List[Dict[str, Any]]) -> Dict[str, Any]:
|
|
first = rows[0]
|
|
return {
|
|
"city": first.get("city"),
|
|
"city_display_name": first.get("city_display_name") or first.get("display_name") or first.get("city"),
|
|
"selected_date": first.get("selected_date") or first.get("local_date"),
|
|
"local_time": first.get("local_time"),
|
|
"temp_symbol": first.get("temp_symbol") or first.get("target_unit"),
|
|
"current_temp": first.get("current_temp"),
|
|
"current_max_so_far": first.get("current_max_so_far"),
|
|
"window_phase": first.get("window_phase"),
|
|
"remaining_window_minutes": first.get("remaining_window_minutes"),
|
|
"peak_window_label": first.get("peak_window_label"),
|
|
"minutes_until_peak_start": first.get("minutes_until_peak_start"),
|
|
"minutes_until_peak_end": first.get("minutes_until_peak_end"),
|
|
"metar_context": first.get("metar_context") or {},
|
|
"model_cluster": {
|
|
"core_low": first.get("cluster_core_low"),
|
|
"core_high": first.get("cluster_core_high"),
|
|
"median": first.get("cluster_median"),
|
|
"deb_reference": first.get("cluster_deb_reference"),
|
|
"model_count": first.get("cluster_model_count"),
|
|
"sources": _compact_ai_model_sources(first),
|
|
},
|
|
"contracts": [_compact_ai_candidate(row) for row in rows],
|
|
}
|
|
|
|
|
|
def build_scan_ai_prompt(payload: Dict[str, Any], *, max_rows: int) -> Dict[str, Any]:
|
|
raw_rows = [
|
|
row
|
|
for row in (payload.get("rows") or [])[:max_rows]
|
|
if isinstance(row, dict) and row.get("id")
|
|
]
|
|
grouped: Dict[str, List[Dict[str, Any]]] = {}
|
|
for row in raw_rows:
|
|
key = "|".join(
|
|
[
|
|
_normalize_ai_city_key(row.get("city") or row.get("city_display_name")),
|
|
str(row.get("selected_date") or row.get("local_date") or ""),
|
|
]
|
|
)
|
|
grouped.setdefault(key, []).append(row)
|
|
cities = [_compact_ai_city_group(rows) for rows in grouped.values() if rows]
|
|
sent_contracts = sum(len(city.get("contracts") or []) for city in cities)
|
|
return {
|
|
"schema_version": "city_forecast_v1",
|
|
"snapshot_id": payload.get("snapshot_id"),
|
|
"generated_at": payload.get("generated_at"),
|
|
"summary": payload.get("summary") or {},
|
|
"filters": payload.get("filters") or {},
|
|
"city_count": len(cities),
|
|
"candidate_row_count": len(raw_rows),
|
|
"cities": cities,
|
|
"_polyweather_input_meta": {
|
|
"sent_cities": len(cities),
|
|
"sent_contracts": sent_contracts,
|
|
},
|
|
}
|