feat: implement scan terminal dashboard system and supporting services
This commit is contained in:
2569718930@qq.com
@@ -55,14 +55,14 @@ export const DOCS_PAGES: DocsPage[] = [
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id: "core-modules",
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title: "你会在页面上看到什么",
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blocks: [
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{ type: "bullets", items: ["锚点状态：先确认当前机场主站实测、日内已见高点和结算时钟。", "当前节奏：把“此刻应到温度”和“机场实测”放在一张卡里，判断今天跑得快还是慢。", "专业气象结论条：先给今日主判断、置信度、基准/上修/下修路径和下一观测点。", "城市决策卡：从地图进入城市简报，读取 AI 机场报文解读、最高温中枢、市场温度桶和模型-市场差。", "校准模型概率 / 模型区间与分歧：概率层看当前生产概率引擎输出；EMOS / LGBM 只有在评估通过或 shadow 对照时进入解释层，模型区间用于解释分歧。", "气象证据链 / 失效条件 / 确认条件：解释为什么这么判断，以及什么情况会让判断降级。"] },
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{ type: "bullets", items: ["锚点状态：先确认当前机场主站实测、日内已见高点和结算时钟。", "当前节奏：把“此刻应到温度”和“机场实测”放在一张卡里，判断今天跑得快还是慢。", "专业气象结论条：先给今日主判断、置信度、基准/上修/下修路径和下一观测点。", "城市决策卡：从地图进入城市简报，读取结构化实况、最高温中枢、市场温度桶和模型-市场差。", "校准模型概率 / 模型区间与分歧：概率层看当前生产概率引擎输出；EMOS / LGBM 只有在评估通过或 shadow 对照时进入解释层，模型区间用于解释分歧。", "气象证据链 / 失效条件 / 确认条件：解释为什么这么判断，以及什么情况会让判断降级。"] },
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],
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},
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{
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id: "how-to-read",
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title: "如何快速读懂主站",
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blocks: [
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{ type: "steps", items: ["先看专业气象结论条或城市决策卡，确认今日主判断、最高温中枢和下一观测点。", "再看锚点状态和今日气温预测图，确认机场实测、DEB、峰值窗口和关键档位线。", "接着看 AI 机场报文解读、气象证据链、失效条件和确认条件，判断这个路径有没有被新观测破坏。", "最后看校准模型概率、模型区间、市场温度桶和模型-市场差，判断概率是否已经被市场充分计价。"] },
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{ type: "steps", items: ["先看专业气象结论条或城市决策卡，确认今日主判断、最高温中枢和下一观测点。", "再看锚点状态和今日气温预测图，确认机场实测、DEB、峰值窗口和关键档位线。", "接着看气象证据链、失效条件和确认条件，判断这个路径有没有被新观测破坏。", "最后看校准模型概率、模型区间、市场温度桶和模型-市场差，判断概率是否已经被市场充分计价。"] },
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],
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},
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],
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@@ -183,21 +183,21 @@ export const DOCS_PAGES: DocsPage[] = [
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content: {
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"zh-CN": {
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title: "城市决策卡",
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description: "这页解释地图城市决策卡如何把 AI 机场报文解读、最高温中枢、市场温度桶和模型-市场差组合成可验证判断。",
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description: "这页解释地图城市决策卡如何把结构化实况、最高温中枢、市场温度桶和模型-市场差组合成可验证判断。",
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sections: [
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{
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id: "entry-and-permission",
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title: "从地图进入决策卡",
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blocks: [
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{ type: "paragraph", text: "用户可以从地图点击城市进入城市决策卡。机会榜和日历属于 Pro 能力；地图探索和城市简报仍可作为轻量入口使用。" },
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{ type: "callout", tone: "info", title: "先天气、后市场", text: "决策卡顶部的天气判断层不读取市场价格，先用 METAR、DEB、多模型集合和 AI 解读确定最高温中枢，再把该中枢映射到市场温度桶。" },
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{ type: "callout", tone: "info", title: "先天气、后市场", text: "决策卡顶部的天气判断层不读取市场价格，先用结构化实况、DEB 和多模型集合确定最高温中枢，再把该中枢映射到市场温度桶。" },
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],
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},
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{
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id: "ai-airport-read",
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title: "AI 机场报文解读包括什么",
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id: "structured-observations",
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title: "结构化实况包括什么",
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blocks: [
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{ type: "bullets", items: ["最终判断：预计最高温中枢、上修/下修空间和当前操作口径。", "METAR 解读：报文时间、实测温度、露点/湿度、风向风速、能见度、云量、气压和 NOSIG / TAF 等机场侧信号。", "推理说明：把最新实测、DEB、多模型集群和午后对流/云雨/风向风险合并成日内节奏判断。", "模型集群备注：展示模型数量、模型区间，以及是否集中在 DEB ±2°C 内。", "风险提示：后续 METAR 或路径明显偏离时，说明应如何上调或下修。", "原始 METAR：保留原始报文，便于人工复核。"] },
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{ type: "bullets", items: ["实测锚点：当前温度、当日已见高点、观测时间和数据新鲜度。", "模型区间：DEB、多模型范围，以及模型是否明显分散。", "日内节奏：把实测路径、峰值窗口和目标温度桶放在一起对比。", "市场映射：把最高温中枢映射到 YES/NO 温度桶，并计算模型-市场差。"] },
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],
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},
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{
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@@ -213,29 +213,29 @@ export const DOCS_PAGES: DocsPage[] = [
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id: "cache-behavior",
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title: "为什么切换选项卡后不应重新空白加载",
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blocks: [
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{ type: "paragraph", text: "城市决策卡会用 city + local_date + locale + METAR signature 作为 AI 解读缓存键。METAR signature 优先使用原始报文，缺失时回退到报文时间、观测时间和温度。" },
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{ type: "bullets", items: ["页面内存缓存：保存 loading 状态、流式进度、机场报文解读片段和最终结果；从其他选项卡切回时优先恢复旧内容。", "浏览器 localStorage：保存最终成功的 AI payload，默认 TTL 为 1 小时。", "后端 AI 缓存：不再把 local_time 放入缓存键，避免同一报文因当前时间变化反复失效。", "市场扫描缓存：完整 all_buckets 结果按城市和日期缓存，默认 TTL 为 10 分钟。"] },
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{ type: "paragraph", text: "城市决策卡复用城市详情、市场扫描和图表数据缓存，不再单独请求 AI 解读。" },
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{ type: "bullets", items: ["城市详情缓存：保存实况、模型、概率和结算上下文。", "市场扫描缓存：完整 all_buckets 结果按城市和日期缓存，默认 TTL 为 10 分钟。", "前端图表缓存：切换城市或选项卡时优先复用已加载的结构化数据。"] },
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],
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},
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],
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},
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"en-US": {
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title: "City Decision Cards",
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description: "How the city card combines the AI airport read, expected-high center, market bucket mapping, and model-market difference into a verifiable decision.",
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description: "How the city card combines structured observations, expected-high center, market bucket mapping, and model-market difference into a verifiable decision.",
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sections: [
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{
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id: "entry-and-permission",
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title: "Opening a card from the map",
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blocks: [
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{ type: "paragraph", text: "Users can click a city on the map to open its city decision card. The opportunity board and calendar are Pro surfaces; map exploration and city briefs remain the lightweight entry point." },
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{ type: "callout", tone: "info", title: "Weather first, market second", text: "The weather decision layer does not use market price input. It first sets the expected-high center from METAR, DEB, the model cluster, and AI reasoning, then maps that center to the relevant market bucket." },
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{ type: "callout", tone: "info", title: "Weather first, market second", text: "The weather decision layer does not use market price input. It first sets the expected-high center from structured observations, DEB, and the model cluster, then maps that center to the relevant market bucket." },
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],
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},
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{
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id: "ai-airport-read",
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title: "What the AI airport read contains",
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id: "structured-observations",
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title: "What structured observations contain",
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blocks: [
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{ type: "bullets", items: ["Final judgment: expected-high center, upside/downside room, and the working decision.", "METAR read: report time, observed temperature, dew point / humidity, wind, visibility, clouds, pressure, and NOSIG / TAF airport-side signals.", "Reasoning: combines live observations, DEB, the model cluster, and convective / cloud / wind risks into an intraday pace read.", "Model-cluster note: model count, model range, and whether the cluster sits within DEB ±2°C.", "Risk notes: how later METAR/path breaks should raise or lower the high center.", "Raw METAR: preserved so the read can be manually audited."] },
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{ type: "bullets", items: ["Observation anchor: current temperature, daily high so far, observation time, and freshness.", "Model range: DEB, multi-model range, and whether the model cluster is dispersed.", "Intraday pace: live path, peak window, and target temperature bucket in one comparison.", "Market mapping: maps the expected-high center to YES/NO buckets and calculates the model-market difference."] },
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],
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},
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{
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@@ -249,10 +249,10 @@ export const DOCS_PAGES: DocsPage[] = [
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},
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{
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id: "cache-behavior",
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title: "Why tab switching should not blank the AI read",
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title: "Why tab switching should not blank the card",
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blocks: [
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{ type: "paragraph", text: "The card keys AI reads by city + local_date + locale + METAR signature. The signature prefers the raw report and falls back to report time, observation time, and temperature." },
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{ type: "bullets", items: ["In-page memory cache: stores loading state, stream progress, airport-read snippets, and final results so returning from another tab restores prior content first.", "Browser localStorage: stores final successful AI payloads for one hour by default.", "Backend AI cache: excludes local_time from the key so the same report does not expire merely because the current clock changed.", "Market-scan cache: stores full all_buckets results by city and date for 10 minutes by default."] },
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{ type: "paragraph", text: "The card reuses city detail, market scan, and chart-data caches. It no longer makes a separate AI-read request." },
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{ type: "bullets", items: ["City detail cache: stores observations, models, probabilities, and settlement context.", "Market-scan cache: stores full all_buckets results by city and date for 10 minutes by default.", "Frontend chart cache: reuses already loaded structured data when switching cities or tabs."] },
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],
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},
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],
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