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 .
323 lines
12 KiB
Python
323 lines
12 KiB
Python
from __future__ import annotations
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import json
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import re
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from typing import Any, Dict, List, Optional
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CITY_AI_REQUIRED_FIELDS = [
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"metar_read_zh",
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"metar_read_en",
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"taf_read_zh",
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"taf_read_en",
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"probability_read_zh",
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"probability_read_en",
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"final_judgment_zh",
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"final_judgment_en",
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"predicted_max",
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"range_low",
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"range_high",
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"unit",
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"confidence",
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"reasoning_zh",
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"reasoning_en",
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"risks_zh",
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"risks_en",
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"model_cluster_note_zh",
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"model_cluster_note_en",
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]
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CITY_AI_STREAM_PROVIDER_FIELDS = [
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"metar_read_zh",
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"metar_read_en",
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"taf_read_zh",
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"taf_read_en",
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"probability_read_zh",
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"probability_read_en",
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"predicted_max",
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"range_low",
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"range_high",
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"unit",
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"confidence",
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"final_judgment_zh",
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"final_judgment_en",
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"reasoning_zh",
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"reasoning_en",
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]
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def _safe_float(value: Any) -> Optional[float]:
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try:
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if value is None or value == "":
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return None
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return float(value)
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except Exception:
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return None
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def _extract_ai_json_object(raw_text: str) -> Dict[str, Any]:
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text = str(raw_text or "").strip()
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if not text:
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raise ValueError("empty AI content")
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try:
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parsed = json.loads(text)
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if isinstance(parsed, dict):
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return parsed
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except Exception:
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pass
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start = text.find("{")
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end = text.rfind("}")
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if start >= 0 and end > start:
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parsed = json.loads(text[start : end + 1])
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if isinstance(parsed, dict):
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return parsed
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raise ValueError("AI content is not a JSON object")
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def _decode_json_string_fragment(fragment: str) -> str:
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safe = str(fragment or "")
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while safe.endswith("\\"):
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safe = safe[:-1]
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try:
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return str(json.loads(f'"{safe}"'))
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except Exception:
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return (
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safe.replace('\\"', '"')
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.replace("\\n", "\n")
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.replace("\\r", "\r")
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.replace("\\t", "\t")
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.replace("\\\\", "\\")
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)
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def _extract_json_string_field_fragment(raw_text: str, field: str) -> tuple[str, bool]:
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"""Best-effort extraction from a streamed/incomplete JSON object.
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DeepSeek may stream useful fields first but still end with truncated JSON.
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In that case the UI should keep the AI text already received instead of
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replacing it with the deterministic DEB/METAR fallback.
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"""
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text = str(raw_text or "")
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match = re.search(rf'"{re.escape(field)}"\s*:\s*"', text)
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if not match:
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return "", False
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idx = match.end()
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chars: List[str] = []
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escaped = False
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closed = False
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while idx < len(text):
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char = text[idx]
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idx += 1
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if escaped:
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chars.append("\\" + char)
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escaped = False
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continue
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if char == "\\":
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escaped = True
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continue
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if char == '"':
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closed = True
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break
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chars.append(char)
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if escaped:
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chars.append("\\")
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return _decode_json_string_fragment("".join(chars)).strip(), closed
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_CITY_AI_TEXT_FIELDS = {
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"metar_read_zh",
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"metar_read_en",
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"taf_read_zh",
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"taf_read_en",
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"probability_read_zh",
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"probability_read_en",
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"final_judgment_zh",
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"final_judgment_en",
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"reasoning_zh",
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"reasoning_en",
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"model_cluster_note_zh",
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"model_cluster_note_en",
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}
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_INCOMPLETE_TAIL_RE = re.compile(
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r"(?:(?:[，,；;]\s*)?(?:但|但是|不过|然而|而且|并且|因为|由于|若|如果|but|however|although|because|if|while|and)\s*)?"
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r"(?:TAF|METAR|报文|机场预报|机场报文)?\s*(?:显示|提示|预示|表明|show(?:s)?|indicate(?:s)?|suggest(?:s)?)?\s*$",
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re.IGNORECASE,
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)
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def _strip_incomplete_ai_sentence(value: Any) -> str:
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"""Remove dangling provider fragments such as "但TAF显示".
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The city AI endpoint can fall back to partially streamed JSON. If the stream
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stops in the middle of a string, keeping the whole fragment produces broken
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UI text. Prefer the last complete sentence/clause; if the whole field is too
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incomplete, let schema completion use the deterministic fallback.
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"""
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text = re.sub(r"\s+", " ", str(value or "")).strip()
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if not text:
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return ""
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if re.search(r"[。！？.!?]$", text):
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return text
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stripped = _INCOMPLETE_TAIL_RE.sub("", text).rstrip(" ，,；;：:")
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if stripped != text:
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text = stripped.strip()
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if not text:
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return ""
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if re.search(r"[。！？.!?]$", text):
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return text
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return text.rstrip(" ，,；;：:") + ("." if re.search(r"[A-Za-z]$", text) else "。")
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# If the provider stopped after a semicolon/comma-delimited clause, keep the
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# complete preceding clause instead of showing a dangling tail.
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for punct in ("；", ";", "。", "！", "？", ".", "!", "?"):
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idx = text.rfind(punct)
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if idx >= 0:
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candidate = text[: idx + 1].strip()
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if len(candidate) >= 8:
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return candidate.rstrip("；;") + ("." if punct == ";" else "。" if punct == "；" else "")
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# If no dangling connector is visible, keep the provider text and add only
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# terminal punctuation. This avoids replacing otherwise useful long reads
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# just because the JSON string missed its closing quote.
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return text.rstrip(" ，,；;：:") + ("." if re.search(r"[A-Za-z]$", text) else "。")
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def _extract_json_number_field_from_fragment(raw_text: str, field: str) -> Optional[float]:
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text = str(raw_text or "")
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match = re.search(rf'"{re.escape(field)}"\s*:\s*(-?\d+(?:\.\d+)?)', text)
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if not match:
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return None
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return _safe_float(match.group(1))
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def _extract_city_ai_partial_fields(raw_text: str) -> Dict[str, Any]:
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text = str(raw_text or "")
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if not text.strip():
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return {}
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out: Dict[str, Any] = {}
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for field in (
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"metar_read_zh",
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"metar_read_en",
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"final_judgment_zh",
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"final_judgment_en",
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"reasoning_zh",
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"reasoning_en",
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"model_cluster_note_zh",
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"model_cluster_note_en",
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"confidence",
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"unit",
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):
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value, closed = _extract_json_string_field_fragment(text, field)
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if value and field in _CITY_AI_TEXT_FIELDS and not closed:
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value = _strip_incomplete_ai_sentence(value)
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if value:
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out[field] = value
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for field in ("predicted_max", "range_low", "range_high"):
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value = _extract_json_number_field_from_fragment(text, field)
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if value is not None:
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out[field] = value
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return out
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def _truncate_ai_text(value: Any, limit: int = 800) -> str:
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text = re.sub(r"\s+", " ", str(value or "")).strip()
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if len(text) <= limit:
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return text
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return text[: max(0, limit - 1)].rstrip() + "…"
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def _extract_provider_content(data: Any) -> str:
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if not isinstance(data, dict):
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return ""
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choices = data.get("choices") or []
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if not choices or not isinstance(choices[0], dict):
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return ""
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message = choices[0].get("message") or {}
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if not isinstance(message, dict):
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return ""
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return str(message.get("content") or "")
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def _extract_provider_stream_delta(data: Any) -> str:
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if not isinstance(data, dict):
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return ""
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choices = data.get("choices") or []
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if not choices or not isinstance(choices[0], dict):
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text = data.get("text") or data.get("content")
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return str(text or "")
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delta = choices[0].get("delta") or {}
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if isinstance(delta, dict):
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content = delta.get("content")
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if content:
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return str(content)
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message = choices[0].get("message") or {}
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if isinstance(message, dict):
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content = message.get("content")
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if content:
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return str(content)
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text = choices[0].get("text") or data.get("text") or data.get("content")
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return str(text or "")
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def _provider_response_meta(data: Any) -> Dict[str, Any]:
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if not isinstance(data, dict):
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return {}
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choices = data.get("choices") or []
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first = choices[0] if choices and isinstance(choices[0], dict) else {}
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return {
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"usage": data.get("usage"),
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"finish_reason": first.get("finish_reason"),
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}
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def _city_ai_response_example(unit: str) -> Dict[str, Any]:
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return {
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"metar_read_zh": f"最新观测显示 37.0{unit or '°C'}，观测时间 04:30Z；风和云量暂未显示强降温信号，后续观测用于确认升温路径。",
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"metar_read_en": f"The latest observation shows 37.0{unit or '°C'} at 04:30Z; wind and cloud signals do not yet show a strong cooling break, so later observations should confirm the warming path.",
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"taf_read_zh": "TAF 预报 14-16Z BECMG 18012KT，午后风向切换为海风可能抑制升温。",
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"taf_read_en": "TAF shows BECMG 18012KT during 14-16Z; afternoon onshore wind shift may cap warming.",
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"probability_read_zh": f"概率分布偏右，最高概率桶 42-43{unit or '°C'}（~35%），上方尾部延至 45{unit or '°C'}。",
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"probability_read_en": f"Distribution skews right; peak bucket 42-43{unit or '°C'} (~35%), upper tail to 45{unit or '°C'}.",
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"final_judgment_zh": f"预计最高温暂以 43.0{unit or '°C'} 附近为中枢。",
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"final_judgment_en": f"The expected daily high is centered near 43.0{unit or '°C'}.",
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"predicted_max": 43.0,
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"range_low": 42.0,
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"range_high": 44.0,
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"unit": unit or "°C",
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"confidence": "medium",
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"reasoning_zh": "DEB 与多数模型集中在同一温区，最新观测仍处于上午升温路径。",
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"reasoning_en": "DEB and most models cluster in the same temperature band, while the latest observation remains on a morning warming path.",
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"risks_zh": ["若后续观测升温放缓或云雨增强，需要下调中枢。"],
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"risks_en": ["If later observations show slower warming or stronger cloud/rain, revise the center lower."],
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"model_cluster_note_zh": "7/8 个模型落在 DEB ±2°C 内，模型支撑较集中。",
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"model_cluster_note_en": "7/8 models are within DEB ±2°C, so model support is clustered.",
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}
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def _city_ai_stream_response_example(unit: str) -> Dict[str, Any]:
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return {
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"metar_read_zh": f"最新 METAR 显示 37.0{unit or '°C'}，报文时间 04:30Z；风和云量暂未显示强降温信号，后续报文用于确认升温路径。",
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"metar_read_en": f"The latest METAR shows 37.0{unit or '°C'} at 04:30Z; wind and cloud signals do not yet show a strong cooling break, so later reports should confirm the warming path.",
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"taf_read_zh": "TAF 预报 14-16Z BECMG 18012KT，午后风向转为海风可能抑制升温，需关注。",
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"taf_read_en": "TAF shows BECMG 18012KT during 14-16Z; afternoon onshore wind shift may cap further warming.",
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"probability_read_zh": f"概率分布偏右，最高概率桶 42-43{unit or '°C'}（~35%），上方尾部延至 45{unit or '°C'}。",
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"probability_read_en": f"Distribution skews right; peak bucket 42-43{unit or '°C'} (~35%), upper tail extends to 45{unit or '°C'}.",
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"predicted_max": 43.0,
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"range_low": 42.0,
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"range_high": 44.0,
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"unit": unit or "°C",
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"confidence": "medium",
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"final_judgment_zh": f"预计最高温暂以 43.0{unit or '°C'} 附近为中枢。",
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"final_judgment_en": f"The expected daily high is centered near 43.0{unit or '°C'}.",
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"reasoning_zh": "当前观测仍贴近上午升温路径，若后续风向转为海风或云雨增强，再下修最高温中枢。",
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"reasoning_en": "The latest observation still fits the morning warming path; revise the daily-high center lower if later wind turns onshore or cloud/rain strengthens.",
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}
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