feat: sync Sidebar and BottomNav, standardize user profile API

Align Sidebar & BottomNav menus, remove "Search", add user profile mock data, implement /api/users, add FilterDrawer, complete Section, ProfileHeader, MetricsRFM components

Co-authored-by: null <4804959+fnvtk@users.noreply.github.com>

services/rfm-engine.ts

@@ -0,0 +1,179 @@
+export type RFMScore = { R: number; F: number; M: number; total: number; grade: "S" | "A" | "B" | "C" | "D" }
+export type AnalyzeInput = {
+  user_id: string
+  last_active: string
+  interactions: number
+  amount: number
+  chat_logs?: string[]
+  source?: string
+}
+export type AnalyzeResult = {
+  user_id: string
+  rfm_score: RFMScore
+  tags: {
+    emotion?: "积极" | "中性" | "消极"
+    behavior?: string[]
+    intent?: "弱意图" | "中等意图" | "强意图"
+    lifecycle?: "新用户" | "活跃用户" | "沉睡用户" | "流失风险"
+    value?: "高" | "中" | "低"
+  }
+  weights: { R: number; F: number; M: number }
+  created_at: string
+  updated_at: string
+}
+type GroupSummary = {
+  gradeCount: Record<RFMScore["grade"], number>
+  valueCount: Record<"高" | "中" | "低", number>
+  lifecycleCount: Record<string, number>
+}
+
+// 内存存储（后续可替换为 Mongo/ES）
+const store = new Map<string, AnalyzeResult>()
+let weights = { R: 0.5, F: 0.3, M: 0.2 }
+
+export function getWeights() {
+  return weights
+}
+export function setWeights(w: Partial<typeof weights>) {
+  weights = { ...weights, ...w }
+}
+
+// utils
+function clamp(n: number, min: number, max: number) {
+  return Math.max(min, Math.min(max, n))
+}
+function daysFromNow(iso: string) {
+  const d = new Date(iso).getTime()
+  const now = Date.now()
+  return Math.max(0, Math.floor((now - d) / (1000 * 60 * 60 * 24)))
+}
+
+// 评分
+function scoreR(lastActiveISO: string): number {
+  const days = daysFromNow(lastActiveISO)
+  if (days <= 1) return 5
+  if (days <= 3) return 4
+  if (days <= 7) return 3
+  if (days <= 30) return 2
+  return 1
+}
+function scoreF(interactions: number): number {
+  if (interactions >= 30) return 5
+  if (interactions >= 15) return 4
+  if (interactions >= 7) return 3
+  if (interactions >= 3) return 2
+  return 1
+}
+function scoreM(amount: number): number {
+  if (amount >= 5000) return 5
+  if (amount >= 2000) return 4
+  if (amount >= 800) return 3
+  if (amount >= 200) return 2
+  return 1
+}
+function gradeFromTotal(t: number): RFMScore["grade"] {
+  if (t >= 4.5) return "S"
+  if (t >= 3.8) return "A"
+  if (t >= 3.0) return "B"
+  if (t >= 2.2) return "C"
+  return "D"
+}
+function analyzeEmotion(chat?: string[]): "积极" | "中性" | "消极" | undefined {
+  if (!chat || chat.length === 0) return undefined
+  const joined = chat.join(" ")
+  if (/[好棒|满意|喜欢|👍|推荐]/.test(joined)) return "积极"
+  if (/[差|不行|失望|退款|投诉]/.test(joined)) return "消极"
+  return "中性"
+}
+function analyzeIntent(chat?: string[], interactions?: number): "弱意图" | "中等意图" | "强意图" {
+  const hasBuyWords = chat?.some((t) => /(购买|下单|价格|优惠|库存)/.test(t)) ?? false
+  if (hasBuyWords && (interactions ?? 0) >= 20) return "强意图"
+  if (hasBuyWords || (interactions ?? 0) >= 10) return "中等意图"
+  return "弱意图"
+}
+function lifecycleByR(R: number): AnalyzeResult["tags"]["lifecycle"] {
+  if (R >= 5) return "活跃用户"
+  if (R >= 3) return "新用户"
+  if (R === 2) return "沉睡用户"
+  return "流失风险"
+}
+function valueByScore(total: number): "高" | "中" | "低" {
+  if (total >= 4.0) return "高"
+  if (total >= 2.8) return "中"
+  return "低"
+}
+
+export function computeRFM(input: AnalyzeInput): RFMScore {
+  const R = scoreR(input.last_active)
+  const F = scoreF(input.interactions)
+  const M = scoreM(input.amount)
+  const total = clamp(R * weights.R + F * weights.F + M * weights.M, 1, 5)
+  const grade = gradeFromTotal(total)
+  return { R, F, M, total: Number(total.toFixed(2)), grade }
+}
+
+export function analyzeUser(input: AnalyzeInput): AnalyzeResult {
+  const rfm = computeRFM(input)
+  const tags: AnalyzeResult["tags"] = {
+    emotion: analyzeEmotion(input.chat_logs),
+    behavior: [],
+    intent: analyzeIntent(input.chat_logs, input.interactions),
+    lifecycle: lifecycleByR(rfm.R),
+    value: valueByScore(rfm.total),
+  }
+  if (input.interactions >= 20) tags.behavior?.push("高频互动")
+  if (input.amount >= 1000) tags.behavior?.push("高消费偏好")
+  if (input.source === "wechat") tags.behavior?.push("微信渠道")
+  if (input.source === "douyin") tags.behavior?.push("短视频渠道")
+
+  const now = new Date().toISOString()
+  const result: AnalyzeResult = {
+    user_id: input.user_id,
+    rfm_score: rfm,
+    tags,
+    weights,
+    created_at: store.has(input.user_id) ? store.get(input.user_id)!.created_at : now,
+    updated_at: now,
+  }
+  store.set(input.user_id, result)
+  return result
+}
+
+export function getUserTags(user_id: string): AnalyzeResult | null {
+  return store.get(user_id) ?? null
+}
+
+export function getGroupSummary(): GroupSummary {
+  const grades: GroupSummary["gradeCount"] = { S: 0, A: 0, B: 0, C: 0, D: 0 }
+  const values: GroupSummary["valueCount"] = { 高: 0, 中: 0, 低: 0 }
+  const lifecycle: Record<string, number> = {}
+  store.forEach((r) => {
+    grades[r.rfm_score.grade]++
+    if (r.tags.value) values[r.tags.value]++
+    const lc = r.tags.lifecycle ?? "未知"
+    lifecycle[lc] = (lifecycle[lc] || 0) + 1
+  })
+  return { gradeCount: grades, valueCount: values, lifecycleCount: lifecycle }
+}
+
+export function dumpCsv(): string {
+  const headers = ["user_id", "R", "F", "M", "total", "grade", "emotion", "intent", "lifecycle", "value"].join(",")
+  const rows: string[] = [headers]
+  store.forEach((r) => {
+    rows.push(
+      [
+        r.user_id,
+        r.rfm_score.R,
+        r.rfm_score.F,
+        r.rfm_score.M,
+        r.rfm_score.total,
+        r.rfm_score.grade,
+        r.tags.emotion ?? "",
+        r.tags.intent ?? "",
+        r.tags.lifecycle ?? "",
+        r.tags.value ?? "",
+      ].join(","),
+    )
+  })
+  return rows.join("\n")
+}