166 lines
5.0 KiB
JavaScript
166 lines
5.0 KiB
JavaScript
/**
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* 岗位数据分析模块
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*/
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const jobAnalyzer = {
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/**
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* 清洗无效薪资数据
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* @param {Array} jobs 原始岗位数据
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* @returns {Array} 有效岗位数据
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*/
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cleanData: (jobs) => {
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if (!Array.isArray(jobs)) return []
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return jobs.filter(job =>
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Number(job.minSalary) > 0 &&
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Number(job.maxSalary) > 0
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)
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},
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/**
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* 执行完整分析流程
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* @param {Array} jobs 原始岗位数据
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* @param {Object} options 配置选项
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* @returns {Object} 分析结果
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*/
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analyze: (jobs, options = {
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verbose: false
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}) => {
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// 数据校验
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if (!Array.isArray(jobs)) {
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throw new Error('Invalid jobs data format')
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}
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// 数据清洗
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const validJobs = jobAnalyzer.cleanData(jobs)
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if (validJobs.length === 0) {
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return {
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warning: 'No valid job data available'
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}
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}
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// 执行分析
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const results = {
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salary: jobAnalyzer.analyzeSalaries(validJobs),
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categories: jobAnalyzer.countCategories(validJobs),
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experience: jobAnalyzer.analyzeExperience(validJobs),
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areas: jobAnalyzer.analyzeAreas(validJobs)
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}
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// 按需控制台输出
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if (options.verbose) {
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jobAnalyzer.printResults(results)
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}
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return results
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},
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/** 薪资分析 */
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analyzeSalaries: (jobs) => {
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const stats = jobs.reduce((acc, job) => {
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acc.totalMin += job.minSalary
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acc.totalMax += job.maxSalary
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acc.highPay += (job.maxSalary >= 10000) ? 1 : 0
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return acc
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}, {
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totalMin: 0,
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totalMax: 0,
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highPay: 0
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})
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return {
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avgMin: Math.round(stats.totalMin / jobs.length),
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avgMax: Math.round(stats.totalMax / jobs.length),
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highPayRatio: Math.round((stats.highPay / jobs.length) * 100)
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}
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},
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/** 岗位类别统计 */
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countCategories: (jobs) => {
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return jobs.reduce((map, job) => {
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map[job.jobCategory] = (map[job.jobCategory] || 0) + 1
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return map
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}, {})
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},
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/** 经验要求分析 */
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analyzeExperience: (jobs) => {
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return jobs.reduce((stats, job) => {
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const label = job.experIenceLabel || '未知'
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stats[label] = (stats[label] || 0) + 1
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return stats
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}, {})
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},
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/** 地区分布分析 */
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analyzeAreas: (jobs) => {
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return jobs.reduce((map, job) => {
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const area = job.jobLocationAreaCodeLabel || '未知'
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map[area] = (map[area] || 0) + 1
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return map
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}, {})
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},
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/** 格式化输出结果 */
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printResults: (results) => {
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console.log('【高薪岗位分析】')
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console.log(`- 平均月薪范围：${results.salary.avgMin}k ~ ${results.salary.avgMax}k`)
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console.log(`- 月薪≥10k的岗位占比：${results.salary.highPayRatio}%`)
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console.log('\n【热门岗位类别】')
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console.log(Object.entries(results.categories)
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.sort((a, b) => b[1] - a[1])
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.map(([k, v]) => `- ${k} (${v}个)`)
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.join('\n'))
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console.log('\n【经验要求分布】')
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console.log(Object.entries(results.experience)
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.map(([k, v]) => `- ${k}: ${v}个`)
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.join('\n'))
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console.log('\n【工作地区分布】')
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console.log(Object.entries(results.areas)
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.sort((a, b) => b[1] - a[1])
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.map(([k, v]) => `- ${k}: ${v}个`)
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.join('\n'))
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},
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/** 合并所有统计属性并添加类型前缀 */
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_mergeAllStats: (results) => {
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const merged = {}
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// 合并岗位类别（添加前缀）
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Object.entries(results.categories).forEach(([k, v]) => {
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merged[`岗位:${k}`] = v
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})
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// 合并工作地区（添加前缀）
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Object.entries(results.areas).forEach(([k, v]) => {
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merged[`地区:${k}`] = v
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})
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// 合并经验要求（添加前缀）
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Object.entries(results.experience).forEach(([k, v]) => {
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merged[`经验:${k}`] = v
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})
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return merged
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},
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/** 全属性统一排序输出 */
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printUnifiedResults: (results, options = {
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log: false
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}) => {
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const mergedData = jobAnalyzer._mergeAllStats(results)
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const sortedEntries = Object.entries(mergedData)
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.sort((a, b) => b[1] - a[1])
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if (options.log) {
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// 格式化输出
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console.log('【全维度排序分析】')
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console.log(sortedEntries
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.map(([k, v]) => `- ${k}: ${v}个`)
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.join('\n'))
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}
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return sortedEntries
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}
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}
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export default jobAnalyzer |