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refactor: 重构数据库逻辑
This commit is contained in:
digua
503
electron/main/worker/query/advanced/social.ts
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503
electron/main/worker/query/advanced/social.ts
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@@ -0,0 +1,503 @@
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/**
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* 社交分析模块
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* 包含：@ 互动分析、含笑量分析
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*/
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import { openDatabase, buildTimeFilter, type TimeFilter } from '../../core'
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// ==================== @ 互动分析 ====================
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/**
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* 获取 @ 互动分析数据
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*/
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export function getMentionAnalysis(sessionId: string, filter?: TimeFilter): any {
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const db = openDatabase(sessionId)
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const emptyResult = {
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topMentioners: [],
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topMentioned: [],
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oneWay: [],
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twoWay: [],
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totalMentions: 0,
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memberDetails: [],
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}
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if (!db) return emptyResult
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// 1. 查询所有成员信息
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const members = db
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.prepare(
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`
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SELECT id, platform_id as platformId, name
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FROM member
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WHERE name != '系统消息'
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`
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)
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.all() as Array<{ id: number; platformId: string; name: string }>
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if (members.length === 0) return emptyResult
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// 2. 构建昵称到成员ID的映射（包括历史昵称）
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const nameToMemberId = new Map<string, number>()
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const memberIdToInfo = new Map<number, { platformId: string; name: string }>()
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for (const member of members) {
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memberIdToInfo.set(member.id, { platformId: member.platformId, name: member.name })
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// 当前昵称
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nameToMemberId.set(member.name, member.id)
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// 查询历史昵称
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const history = db
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.prepare(
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`
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SELECT name FROM member_name_history
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WHERE member_id = ?
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`
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)
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.all(member.id) as Array<{ name: string }>
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for (const h of history) {
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if (!nameToMemberId.has(h.name)) {
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nameToMemberId.set(h.name, member.id)
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}
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}
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}
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// 3. 查询所有消息（带时间过滤）
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const { clause, params } = buildTimeFilter(filter)
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let whereClause = clause
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if (whereClause.includes('WHERE')) {
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whereClause += " AND m.name != '系统消息' AND msg.type = 0 AND msg.content IS NOT NULL AND msg.content LIKE '%@%'"
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} else {
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whereClause = " WHERE m.name != '系统消息' AND msg.type = 0 AND msg.content IS NOT NULL AND msg.content LIKE '%@%'"
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}
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const messages = db
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.prepare(
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`
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SELECT
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msg.sender_id as senderId,
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msg.content
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FROM message msg
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JOIN member m ON msg.sender_id = m.id
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${whereClause}
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`
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)
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.all(...params) as Array<{ senderId: number; content: string }>
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// 4. 解析 @ 并构建关系矩阵
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// mentionMatrix[fromId][toId] = count
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const mentionMatrix = new Map<number, Map<number, number>>()
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const mentionedCount = new Map<number, number>() // 被 @ 的次数
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const mentionerCount = new Map<number, number>() // 发起 @ 的次数
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let totalMentions = 0
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// @ 正则：匹配 @昵称（昵称不含空格和@）
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const mentionRegex = /@([^\s@]+)/g
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for (const msg of messages) {
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const matches = msg.content.matchAll(mentionRegex)
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const mentionedInThisMsg = new Set<number>() // 避免同一消息重复计数同一人
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for (const match of matches) {
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const mentionedName = match[1]
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const mentionedId = nameToMemberId.get(mentionedName)
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// 只统计能匹配到成员的 @，且不能 @ 自己
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if (mentionedId && mentionedId !== msg.senderId && !mentionedInThisMsg.has(mentionedId)) {
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mentionedInThisMsg.add(mentionedId)
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totalMentions++
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// 更新矩阵
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if (!mentionMatrix.has(msg.senderId)) {
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mentionMatrix.set(msg.senderId, new Map())
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}
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const fromMap = mentionMatrix.get(msg.senderId)!
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fromMap.set(mentionedId, (fromMap.get(mentionedId) || 0) + 1)
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// 更新计数
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mentionerCount.set(msg.senderId, (mentionerCount.get(msg.senderId) || 0) + 1)
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mentionedCount.set(mentionedId, (mentionedCount.get(mentionedId) || 0) + 1)
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}
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}
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}
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if (totalMentions === 0) return emptyResult
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// 5. 构建排行榜
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const topMentioners: any[] = []
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for (const [memberId, count] of mentionerCount.entries()) {
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const info = memberIdToInfo.get(memberId)!
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topMentioners.push({
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memberId,
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platformId: info.platformId,
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name: info.name,
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count,
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percentage: Math.round((count / totalMentions) * 10000) / 100,
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})
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}
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topMentioners.sort((a, b) => b.count - a.count)
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const topMentioned: any[] = []
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for (const [memberId, count] of mentionedCount.entries()) {
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const info = memberIdToInfo.get(memberId)!
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topMentioned.push({
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memberId,
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platformId: info.platformId,
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name: info.name,
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count,
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percentage: Math.round((count / totalMentions) * 10000) / 100,
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})
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}
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topMentioned.sort((a, b) => b.count - a.count)
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// 6. 检测单向关注（舔狗检测）
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// 条件：A @ B 的比例 >= 80%（即 B @ A / A @ B < 20%）
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const oneWay: any[] = []
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const processedPairs = new Set<string>()
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for (const [fromId, toMap] of mentionMatrix.entries()) {
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for (const [toId, fromToCount] of toMap.entries()) {
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const pairKey = `${Math.min(fromId, toId)}-${Math.max(fromId, toId)}`
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if (processedPairs.has(pairKey)) continue
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processedPairs.add(pairKey)
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const toFromCount = mentionMatrix.get(toId)?.get(fromId) || 0
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const total = fromToCount + toFromCount
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// 只有总互动 >= 3 次才考虑
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if (total < 3) continue
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const ratio = fromToCount / total
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// 单向关注：一方占比 >= 80%
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if (ratio >= 0.8) {
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const fromInfo = memberIdToInfo.get(fromId)!
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const toInfo = memberIdToInfo.get(toId)!
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oneWay.push({
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fromMemberId: fromId,
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fromName: fromInfo.name,
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toMemberId: toId,
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toName: toInfo.name,
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fromToCount,
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toFromCount,
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ratio: Math.round(ratio * 100) / 100,
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})
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} else if (ratio <= 0.2) {
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// 反向单向关注
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const fromInfo = memberIdToInfo.get(fromId)!
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const toInfo = memberIdToInfo.get(toId)!
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oneWay.push({
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fromMemberId: toId,
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fromName: toInfo.name,
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toMemberId: fromId,
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toName: fromInfo.name,
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fromToCount: toFromCount,
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toFromCount: fromToCount,
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ratio: Math.round((1 - ratio) * 100) / 100,
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})
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}
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}
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}
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oneWay.sort((a, b) => b.fromToCount - a.fromToCount)
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// 7. 检测双向奔赴（CP检测）
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// 条件：双方互相 @ 总次数 >= 5 次，且比例在 30%-70% 之间
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const twoWay: any[] = []
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processedPairs.clear()
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for (const [fromId, toMap] of mentionMatrix.entries()) {
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for (const [toId, fromToCount] of toMap.entries()) {
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const pairKey = `${Math.min(fromId, toId)}-${Math.max(fromId, toId)}`
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if (processedPairs.has(pairKey)) continue
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processedPairs.add(pairKey)
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const toFromCount = mentionMatrix.get(toId)?.get(fromId) || 0
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const total = fromToCount + toFromCount
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// 总互动 >= 5 次
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if (total < 5) continue
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// 必须双方都有 @
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if (toFromCount === 0 || fromToCount === 0) continue
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const ratio = Math.min(fromToCount, toFromCount) / Math.max(fromToCount, toFromCount)
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// 平衡度 >= 30%（即 30%-100%）
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if (ratio >= 0.3) {
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const member1Info = memberIdToInfo.get(fromId)!
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const member2Info = memberIdToInfo.get(toId)!
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twoWay.push({
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member1Id: fromId,
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member1Name: member1Info.name,
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member2Id: toId,
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member2Name: member2Info.name,
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member1To2: fromToCount,
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member2To1: toFromCount,
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total,
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balance: Math.round(ratio * 100) / 100,
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})
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}
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}
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}
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twoWay.sort((a, b) => b.total - a.total)
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// 8. 构建成员详情（每个成员的 @ 关系 TOP 5）
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const memberDetails: any[] = []
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for (const member of members) {
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const memberId = member.id
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const info = memberIdToInfo.get(memberId)!
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// 该成员最常 @ 的人
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const topMentionedByThis: any[] = []
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const toMap = mentionMatrix.get(memberId)
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if (toMap) {
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for (const [toId, count] of toMap.entries()) {
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const toInfo = memberIdToInfo.get(toId)!
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topMentionedByThis.push({
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fromMemberId: memberId,
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fromName: info.name,
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toMemberId: toId,
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toName: toInfo.name,
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count,
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})
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}
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topMentionedByThis.sort((a, b) => b.count - a.count)
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}
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// 最常 @ 该成员的人
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const topMentionersOfThis: any[] = []
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for (const [fromId, toMap] of mentionMatrix.entries()) {
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const count = toMap.get(memberId)
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if (count) {
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const fromInfo = memberIdToInfo.get(fromId)!
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topMentionersOfThis.push({
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fromMemberId: fromId,
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fromName: fromInfo.name,
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toMemberId: memberId,
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toName: info.name,
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count,
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})
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}
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}
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topMentionersOfThis.sort((a, b) => b.count - a.count)
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// 只有有数据的成员才添加
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if (topMentionedByThis.length > 0 || topMentionersOfThis.length > 0) {
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memberDetails.push({
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memberId,
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name: info.name,
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topMentioned: topMentionedByThis.slice(0, 5),
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topMentioners: topMentionersOfThis.slice(0, 5),
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})
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}
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}
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return {
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topMentioners,
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topMentioned,
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oneWay,
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twoWay,
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totalMentions,
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memberDetails,
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}
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}
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// ==================== 含笑量分析 ====================
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/**
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* 将关键词转换为正则表达式模式
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*/
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function keywordToPattern(keyword: string): string {
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// 转义特殊字符
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const escaped = keyword.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')
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// 特殊处理一些关键词的变体
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if (keyword === '哈哈') {
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return '哈哈+'
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}
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return escaped
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}
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/**
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* 获取含笑量分析数据
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* @param sessionId 会话ID
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* @param filter 时间过滤
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* @param keywords 自定义关键词列表（可选，默认使用内置列表）
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*/
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export function getLaughAnalysis(sessionId: string, filter?: TimeFilter, keywords?: string[]): any {
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const db = openDatabase(sessionId)
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const emptyResult = {
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rankByRate: [],
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rankByCount: [],
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typeDistribution: [],
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totalLaughs: 0,
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totalMessages: 0,
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groupLaughRate: 0,
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}
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if (!db) return emptyResult
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// 使用传入的关键词或默认关键词
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const laughKeywords = keywords && keywords.length > 0 ? keywords : []
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// 构建正则表达式
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const patterns = laughKeywords.map(keywordToPattern)
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const laughRegex = new RegExp(`(${patterns.join('|')})`, 'gi')
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// 查询所有消息
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const { clause, params } = buildTimeFilter(filter)
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let whereClause = clause
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if (whereClause.includes('WHERE')) {
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whereClause += " AND m.name != '系统消息' AND msg.type = 0 AND msg.content IS NOT NULL"
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} else {
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whereClause = " WHERE m.name != '系统消息' AND msg.type = 0 AND msg.content IS NOT NULL"
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}
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const messages = db
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.prepare(
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`
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SELECT
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msg.sender_id as senderId,
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msg.content,
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m.platform_id as platformId,
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m.name
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FROM message msg
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JOIN member m ON msg.sender_id = m.id
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${whereClause}
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`
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)
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.all(...params) as Array<{
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senderId: number
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content: string
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platformId: string
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name: string
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}>
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if (messages.length === 0) return emptyResult
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// 统计数据
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const memberStats = new Map<
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number,
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{
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platformId: string
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name: string
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laughCount: number
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messageCount: number
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keywordCounts: Map<string, number> // 每个关键词的计数
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}
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>()
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const typeCount = new Map<string, number>()
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let totalLaughs = 0
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for (const msg of messages) {
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// 初始化成员统计
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if (!memberStats.has(msg.senderId)) {
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memberStats.set(msg.senderId, {
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platformId: msg.platformId,
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name: msg.name,
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laughCount: 0,
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messageCount: 0,
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keywordCounts: new Map(),
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})
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}
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const stats = memberStats.get(msg.senderId)!
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stats.messageCount++
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// 匹配笑声关键词
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const matches = msg.content.match(laughRegex)
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if (matches) {
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stats.laughCount += matches.length
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totalLaughs += matches.length
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// 统计类型分布
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for (const match of matches) {
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// 归类到对应的关键词类型
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let matchedType = '其他'
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for (const keyword of laughKeywords) {
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const pattern = new RegExp(`^${keywordToPattern(keyword)}$`, 'i')
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if (pattern.test(match)) {
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matchedType = keyword
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break
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}
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}
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typeCount.set(matchedType, (typeCount.get(matchedType) || 0) + 1)
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// 记录到成员的关键词计数
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stats.keywordCounts.set(matchedType, (stats.keywordCounts.get(matchedType) || 0) + 1)
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}
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}
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}
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const totalMessages = messages.length
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if (totalLaughs === 0) return emptyResult
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// 构建排行榜
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const rankItems: any[] = []
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for (const [memberId, stats] of memberStats.entries()) {
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if (stats.laughCount > 0) {
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// 构建该成员的关键词分布（按原始关键词顺序）
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const keywordDistribution: Array<{ keyword: string; count: number; percentage: number }> = []
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for (const keyword of laughKeywords) {
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const count = stats.keywordCounts.get(keyword) || 0
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if (count > 0) {
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keywordDistribution.push({
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keyword,
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count,
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percentage: Math.round((count / stats.laughCount) * 10000) / 100,
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})
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}
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}
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// 处理"其他"类型
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const otherCount = stats.keywordCounts.get('其他') || 0
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if (otherCount > 0) {
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keywordDistribution.push({
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keyword: '其他',
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count: otherCount,
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percentage: Math.round((otherCount / stats.laughCount) * 10000) / 100,
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})
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}
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rankItems.push({
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memberId,
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platformId: stats.platformId,
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name: stats.name,
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laughCount: stats.laughCount,
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messageCount: stats.messageCount,
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laughRate: Math.round((stats.laughCount / stats.messageCount) * 10000) / 100,
|
||||
percentage: Math.round((stats.laughCount / totalLaughs) * 10000) / 100,
|
||||
keywordDistribution,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// 按含笑率排序
|
||||
const rankByRate = [...rankItems].sort((a, b) => b.laughRate - a.laughRate)
|
||||
// 按贡献度（绝对数量）排序
|
||||
const rankByCount = [...rankItems].sort((a, b) => b.laughCount - a.laughCount)
|
||||
|
||||
// 构建类型分布
|
||||
const typeDistribution: any[] = []
|
||||
for (const [type, count] of typeCount.entries()) {
|
||||
typeDistribution.push({
|
||||
type,
|
||||
count,
|
||||
percentage: Math.round((count / totalLaughs) * 10000) / 100,
|
||||
})
|
||||
}
|
||||
typeDistribution.sort((a, b) => b.count - a.count)
|
||||
|
||||
return {
|
||||
rankByRate,
|
||||
rankByCount,
|
||||
typeDistribution,
|
||||
totalLaughs,
|
||||
totalMessages,
|
||||
groupLaughRate: Math.round((totalLaughs / totalMessages) * 10000) / 100,
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user