增加按分类与成立年份浏览的 ARR 披露表
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@@ -1,6 +1,7 @@
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import { useEffect, useMemo, useRef, useState } from "react";
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import { useRouter } from "next/router";
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import IncomeDistribution from "./IncomeDistribution";
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import ArrDisclosures from "./ArrDisclosures";
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import {
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categorySummary, evidenceUrl, technicalReviewLabels, reviewCompanyIds, type ResearchEvidence, type ResearchReview, type ResearchSnapshot,
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} from "@/lib/ai-research";
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@@ -154,6 +155,7 @@ export default function AICompanyResearch() {
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</>}
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{tab === "revenue" && <>
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<IncomeDistribution data={data} onSelect={setSelected} />
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<ArrDisclosures data={data} onSelect={setSelected} />
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<h3>企业披露与估算的历史里程碑</h3>
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<p className="research-notice">这一组包含 {disclosedCompanies} 家公司的披露或估算信号;企业披露含公司或交易当事方公开材料,披露主体与审计状态见证据。多为异时点历史里程碑,期间收入不直接年化,ARR 与年化收入口径仍待核对,暂不计算其同期领域均值。币种未确认的记录仅显示原值,不绘制美元长度条。上面的平台MRR统计与这一组分开。</p>
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<div className="research-revenue-chart" aria-label="不同时间的公开收入里程碑,线性长度,非同行排名">{observed.map((o) => <div key={o.id}>
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@@ -0,0 +1,41 @@
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import { useState } from "react";
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import { evidenceUrl, type ResearchSnapshot } from "@/lib/ai-research";
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import { arrDisclosures } from "@/lib/research-statistics";
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const labels: Record<string, string> = {
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signal: "企业或当事方披露", estimate: "第三方估算",
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historical: "历史披露", "historical-estimate": "历史估算",
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};
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export default function ArrDisclosures({ data, onSelect }: { data: ResearchSnapshot; onSelect: (id: string) => void }) {
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const [category, setCategory] = useState("");
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const [year, setYear] = useState("");
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const rows = arrDisclosures(data, { category, foundedYear: year ? Number(year) : undefined });
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const companies = new Set(rows.map((row) => row.company.id));
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const categories = new Map(data.categories.map((item) => [item.id, item.name]));
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const evidence = new Map(data.evidence.map((item) => [item.id, item]));
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return <section aria-label="ARR披露分类表">
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<h3>2025—2026 年成立公司的 ARR 披露</h3>
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<p className="research-notice">这是跨时点证据表,共 {companies.size} 家公司、{rows.length} 条记录;同一公司的历史里程碑逐条保留,不能相加或视为独立公司。ARR 标签沿用原始披露,未证明经常性收入定义或审计状态;不纳入 MRR 图表,不计算同期行业均值或造假概率。仅收录明确标注的 ARR;定义冲突、目标、承诺、客户和前雇主收入排除,缺失不作零。</p>
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<div className="research-filters">
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<label>业务分类<select value={category} onChange={(event) => setCategory(event.target.value)}><option value="">全部分类</option>{data.categories.map((item) => <option key={item.id} value={item.id}>{item.name}</option>)}</select></label>
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<label>成立年份<select value={year} onChange={(event) => setYear(event.target.value)}><option value="">2025 与 2026</option><option value="2025">2025</option><option value="2026">2026</option></select></label>
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</div>
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<div className="research-table-wrap"><table>
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<caption>金额保留原币种、下界和估算身份;日期栏保留业务截至日缺项</caption>
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<thead><tr><th>公司 / 分类</th><th>收费与交付模式</th><th>原始 ARR 声明</th><th>时点</th><th>身份 / 限制</th><th>证据</th></tr></thead>
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<tbody>{rows.map(({ company, observation: item }) => {
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const source = evidence.get(item.evidence);
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const url = source && evidenceUrl(source.url);
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return <tr key={item.id}>
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<td><button onClick={() => onSelect(company.id)}>{company.name}</button><small>{categories.get(company.cat)} · {company.year}</small></td>
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<td>{company.businessModel ?? company.price}</td>
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<td>{item.bound !== "=" && item.bound}{new Intl.NumberFormat("zh-CN", { maximumFractionDigits: 2 }).format(item.value)} {item.unit}<small>{item.kind}</small></td>
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<td>{item.timing}</td><td>{labels[item.selection]}<small>{item.reason}</small></td>
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<td>{url ? <a href={url} target="_blank" rel="noopener noreferrer">{item.evidence} ↗</a> : item.evidence}</td>
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</tr>;
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})}</tbody>
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</table></div>
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{!rows.length && <p>当前筛选没有符合条件的已记录 ARR 声明;不代表这些公司收入为零。</p>}
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</section>;
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}
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@@ -1,6 +1,20 @@
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import type { ResearchCompany, ResearchObservation, ResearchSnapshot } from "./ai-research";
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export type IncomePoint = { company: ResearchCompany; observation: ResearchObservation; value: number };
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/** An evidence ledger, never a comparable sample: retain historical claims and estimates separately. */
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export function arrDisclosures(snapshot: ResearchSnapshot, options: { category?: string; foundedYear?: number } = {}) {
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const companies = new Map(snapshot.companies.map((company) => [company.id, company]));
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return snapshot.observations.flatMap((observation) => {
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const company = companies.get(observation.company);
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if (!company || ![2025, 2026].includes(company.year ?? 0) || observation.metric !== "arr" ||
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!["signal", "estimate", "historical", "historical-estimate"].includes(observation.selection) ||
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!Number.isFinite(observation.value) || observation.value < 0 ||
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(options.category && company.cat !== options.category) ||
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(options.foundedYear && company.year !== options.foundedYear)) return [];
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return [{ company, observation }];
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});
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}
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export type IncomeStats = {
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n: number; mean: number; median: number; min: number; max: number;
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sampleDeviation: number | null; cv: number | null; meanMedianRatio: number | null;
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