diff --git a/src/vue2/src/components/layout/Header.vue b/src/vue2/src/components/layout/Header.vue index 95ad165..8a55325 100644 --- a/src/vue2/src/components/layout/Header.vue +++ b/src/vue2/src/components/layout/Header.vue @@ -10,6 +10,9 @@ 处理流程 + + 统计界面 + diff --git a/src/vue2/src/router/index.js b/src/vue2/src/router/index.js index e12d486..55afb29 100644 --- a/src/vue2/src/router/index.js +++ b/src/vue2/src/router/index.js @@ -1,6 +1,7 @@ import Vue from 'vue' import VueRouter from 'vue-router' import Process from '../views/Process.vue' +import StatisticsPage from '../views/Statistics.vue' Vue.use(VueRouter) @@ -15,6 +16,11 @@ const routes = [ name: 'Process', component: Process }, + { + path: '/statistics', + name: 'StatisticsPage', + component: StatisticsPage + }, { path: '*', redirect: '/process' diff --git a/src/vue2/src/utils/textAnalysis.js b/src/vue2/src/utils/textAnalysis.js deleted file mode 100644 index 6bba0bd..0000000 --- a/src/vue2/src/utils/textAnalysis.js +++ /dev/null @@ -1,819 +0,0 @@ -/** - * 文本分析工具函数 - * 提供词频统计、趋势分析、搭配词分析等功能 - */ -// import _ from 'lodash' - -// 中文停用词列表（常见的虚词、助词等） -const chineseStopWords = [ - '的', '了', '和', '是', '在', '我', '有', '就', '不', '人', '都', - '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', - '着', '没有', '看', '好', '自己', '这', '那', '这个', '那个', '啊', - '吧', '呢', '啦', '呀', '吗', '哦', '哪', '对', '可以', '他', '她', - '它', '这些', '那些', '把', '让', '向', '往', '是否', '什么', '怎么', - '如何', '为', '为了', '依', '从', '当', '来', '被' -] - -// 英文停用词列表 -const englishStopWords = [ - 'a', 'an', 'the', 'and', 'or', 'but', 'if', 'because', 'as', 'what', - 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', - 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', - 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', - 'theirs', 'themselves', 'this', 'that', 'these', 'those', 'am', 'is', 'are', - 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', - 'does', 'did', 'doing', 'would', 'should', 'could', 'ought', 'i\'m', 'you\'re', - 'he\'s', 'she\'s', 'it\'s', 'we\'re', 'they\'re', 'i\'ve', 'you\'ve', 'we\'ve', - 'they\'ve', 'i\'d', 'you\'d', 'he\'d', 'she\'d', 'we\'d', 'they\'d', 'i\'ll', - 'you\'ll', 'he\'ll', 'she\'ll', 'we\'ll', 'they\'ll', 'isn\'t', 'aren\'t', - 'wasn\'t', 'weren\'t', 'hasn\'t', 'haven\'t', 'hadn\'t', 'doesn\'t', 'don\'t', - 'didn\'t', 'won\'t', 'wouldn\'t', 'shan\'t', 'shouldn\'t', 'can\'t', 'cannot', - 'couldn\'t', 'mustn\'t', 'let\'s', 'that\'s', 'who\'s', 'what\'s', 'here\'s', - 'there\'s', 'when\'s', 'where\'s', 'why\'s', 'how\'s', 'of', 'on', 'at', 'in', - 'to', 'for', 'with', 'by', 'about', 'against', 'between', 'into', 'through', - 'during', 'before', 'after', 'above', 'below', 'from', 'up', 'down', 'out', - 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', - 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', - 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', - 'so', 'than', 'too', 'very' -] - -// 合并停用词列表 -const stopWordsList = [...chineseStopWords, ...englishStopWords] - -/** - * 分词处理函数 - * 简单按空格和标点符号拆分 - * - * @param {string} text - 输入文本 - * @param {object} options - 分词选项 - * @returns {Array} - 分词结果数组 - */ -export const tokenize = (text, options = {}) => { - const defaultOptions = { - minLength: 2, // 最小词长 - filterStopWords: true, // 是否过滤停用词 - language: 'zh' // 语言 - } - - const opts = { ...defaultOptions, ...options } - - if (!text || typeof text !== 'string') { - return [] - } - - // 预处理文本，统一替换标点符号为空格，转小写 - let processedText = text.toLowerCase() - .replace(/[.,?!;:()[\]{}""''「」『』【】《》〈〉]/g, ' ') - .replace(/\s+/g, ' ') - .trim() - - // 按空格分词（对英文、数字有效） - let tokens = processedText.split(' ') - - // 对于中文，执行额外的字符级拆分 - if (opts.language === 'zh') { - // 处理中文字符 - tokens = tokens.reduce((result, token) => { - // 如果当前token是纯中文 - if (/^[\u4e00-\u9fa5]+$/.test(token)) { - // 双字组合 - for (let i = 0; i < token.length - 1; i++) { - result.push(token.substring(i, i + 2)) - } - - // 三字组合 - if (token.length >= 3) { - for (let i = 0; i < token.length - 2; i++) { - result.push(token.substring(i, i + 3)) - } - } - - // 四字组合 - if (token.length >= 4) { - for (let i = 0; i < token.length - 3; i++) { - result.push(token.substring(i, i + 4)) - } - } - - // 单字也添加进来 - for (let i = 0; i < token.length; i++) { - result.push(token[i]) - } - } else { - // 非中文直接添加 - result.push(token) - } - return result - }, []) - } - - // 过滤掉长度小于最小词长的词 - tokens = tokens.filter(token => token.length >= opts.minLength) - - // 如果需要过滤停用词 - if (opts.filterStopWords) { - tokens = tokens.filter(token => !stopWordsList.includes(token)) - } - - return tokens -} - -/** - * 词频分析 - * - * @param {Array} segments - 文本段落数组 - * @param {Object} options - 分析选项 - * @returns {Array} - 词频统计结果 - */ -export const analyzeTermFrequency = (segments, options = {}) => { - const defaultOptions = { - minLength: 2, - filterStopWords: true, - language: 'zh', - limit: 100 // 返回的最大词汇数量 - } - - const opts = { ...defaultOptions, ...options } - - if (!segments || !Array.isArray(segments) || segments.length === 0) { - return [] - } - - // 所有段落的文本内容 - const allText = segments.map(segment => segment.text).join(' ') - - // 分词 - const tokens = tokenize(allText, { - minLength: opts.minLength, - filterStopWords: opts.filterStopWords, - language: opts.language - }) - - // 统计词频 - const frequencyMap = tokens.reduce((acc, token) => { - acc[token] = (acc[token] || 0) + 1 - return acc - }, {}) - - // 转换为数组并排序 - const sortedTerms = Object.entries(frequencyMap) - .map(([term, count]) => ({ - term, - count, - percentage: count / tokens.length - })) - .sort((a, b) => b.count - a.count) - .slice(0, opts.limit) - - return sortedTerms -} - -/** - * 趋势分析 - * - * @param {Array} segments - 文本段落数组 - * @param {Array} terms - 要分析趋势的词汇列表 - * @param {Object} options - 分析选项 - * @returns {Object} - 趋势分析结果 - */ -export const analyzeTermTrends = (segments, terms, options = {}) => { - const defaultOptions = { - minLength: 2, - filterStopWords: true, - language: 'zh', - normalization: 'relative' // 'raw' 或 'relative' - } - - const opts = { ...defaultOptions, ...options } - - if (!segments || !Array.isArray(segments) || segments.length === 0) { - return { terms: [], data: [] } - } - - if (!terms || !Array.isArray(terms) || terms.length === 0) { - return { terms: [], data: [] } - } - - // 分析每个段落中各词汇的出现频率 - const trendsData = segments.map(segment => { - // 分词 - const tokens = tokenize(segment.text, { - minLength: opts.minLength, - filterStopWords: opts.filterStopWords, - language: opts.language - }) - - // 计算该段落的总词数（用于相对频率） - const totalTokens = tokens.length - - // 统计该段落中每个指定词汇的出现频率 - const segmentFrequencies = {} - - // 初始化所有词汇的频率为0 - terms.forEach(term => { - segmentFrequencies[term] = 0 - }) - - // 统计词频 - tokens.forEach(token => { - if (terms.includes(token)) { - segmentFrequencies[token]++ - } - }) - - // 如果使用相对频率，将词频除以总词数 - if (opts.normalization === 'relative' && totalTokens > 0) { - Object.keys(segmentFrequencies).forEach(term => { - segmentFrequencies[term] = segmentFrequencies[term] / totalTokens - }) - } - - return { - position: segment.position, - id: segment.id, - frequencies: segmentFrequencies - } - }) - - // 组织数据格式，适合图表展示 - const result = { - terms: terms, - data: trendsData - } - - return result -} - -/** - * 搭配词分析 - * - * @param {Array} segments - 文本段落数组 - * @param {String} targetTerm - 目标词汇 - * @param {Object} options - 分析选项 - * @returns {Array} - 搭配词分析结果 - */ -export const analyzeCollocates = (segments, targetTerm, options = {}) => { - const defaultOptions = { - minLength: 2, - filterStopWords: true, - language: 'zh', - window: 5, // 上下文窗口大小 - limit: 50 // 返回的最大搭配词数量 - } - - const opts = { ...defaultOptions, ...options } - - if (!segments || !Array.isArray(segments) || segments.length === 0) { - return [] - } - - if (!targetTerm || typeof targetTerm !== 'string') { - return [] - } - - let collocatesMap = {} - - // 遍历每个段落 - segments.forEach(segment => { - // 分词 - const tokens = tokenize(segment.text, { - minLength: 1, // 设为1以捕获所有可能的词 - filterStopWords: false, // 暂时不过滤停用词 - language: opts.language - }) - - // 查找目标词在token数组中的所有位置 - const targetIndices = [] - tokens.forEach((token, index) => { - if (token === targetTerm) { - targetIndices.push(index) - } - }) - - // 对于每个目标词出现的位置，收集其上下文中的词 - targetIndices.forEach(targetIndex => { - // 计算上下文窗口的起止位置 - const start = Math.max(0, targetIndex - opts.window) - const end = Math.min(tokens.length - 1, targetIndex + opts.window) - - // 收集上下文中的词 - for (let i = start; i <= end; i++) { - if (i !== targetIndex) { // 排除目标词自身 - const collocate = tokens[i] - - // 忽略长度小于最小长度的词 - if (collocate.length < opts.minLength) { - continue - } - - // 如果需要过滤停用词 - if (opts.filterStopWords && stopWordsList.includes(collocate)) { - continue - } - - // 统计搭配词出现次数 - collocatesMap[collocate] = (collocatesMap[collocate] || 0) + 1 - } - } - }) - }) - - // 转换为数组并排序 - const sortedCollocates = Object.entries(collocatesMap) - .map(([term, count]) => ({ - term, - count - })) - .sort((a, b) => b.count - a.count) - .slice(0, opts.limit) - - return sortedCollocates -} - -/** - * 上下文分析 - * - * @param {Array} segments - 文本段落数组 - * @param {String} targetTerm - 目标词汇 - * @param {Object} options - 分析选项 - * @returns {Array} - 上下文分析结果 - */ -export const analyzeContexts = (segments, targetTerm, options = {}) => { - const defaultOptions = { - window: 8, // 上下文窗口大小（左侧和右侧各多少个字符） - limit: 50 // 返回的最大上下文数量 - } - - const opts = { ...defaultOptions, ...options } - - if (!segments || !Array.isArray(segments) || segments.length === 0) { - return [] - } - - if (!targetTerm || typeof targetTerm !== 'string') { - return [] - } - - const contexts = [] - - // 遍历每个段落 - segments.forEach(segment => { - const text = segment.text - const segmentId = segment.id - - // 查找目标词在文本中的所有位置 - let position = 0 - let foundPos = text.indexOf(targetTerm, position) - - while (foundPos !== -1 && contexts.length < opts.limit) { - // 计算上下文窗口的起止位置 - const startPos = Math.max(0, foundPos - opts.window) - const endPos = Math.min(text.length, foundPos + targetTerm.length + opts.window) - - // 提取上下文 - const before = text.substring(startPos, foundPos) - const after = text.substring(foundPos + targetTerm.length, endPos) - - // 添加到结果列表 - contexts.push({ - segmentId, - position: foundPos, - before, - term: targetTerm, - after - }) - - // 继续查找下一个位置 - position = foundPos + targetTerm.length - foundPos = text.indexOf(targetTerm, position) - } - }) - - return contexts.slice(0, opts.limit) -} - -/** - * 生成词云数据 - * @param {Array} termFrequency - 术语频率数据，包含 term 和 frequency 字段 - * @param {Number} maxCount - 最大显示术语数量 - * @param {Object} options - 配置选项 - * @param {Number} options.minSize - 最小字体大小 - * @param {Number} options.maxSize - 最大字体大小 - * @returns {Array} 词云数据 - */ -export function generateWordCloudData(termFrequency, maxCount = 100, options = {}) { - if (!termFrequency || termFrequency.length === 0) { - return [] - } - - const { minSize = 12, maxSize = 32 } = options - const sortedData = [...termFrequency].sort((a, b) => b.frequency - a.frequency) - const slicedData = sortedData.slice(0, maxCount) - - // 找出最大和最小频率 - const maxFreq = slicedData[0].frequency - const minFreq = slicedData[slicedData.length - 1].frequency - - // 计算每个词的大小 - return slicedData.map(item => { - // 根据频率计算字体大小（线性映射） - let size = minSize - if (maxFreq !== minFreq) { - size = minSize + ((item.frequency - minFreq) / (maxFreq - minFreq)) * (maxSize - minSize) - } - - return { - name: item.term, - value: item.frequency, - size: Math.round(size) - } - }) -} - -/** - * 提取关键词 - * @param {String} text - 文本内容 - * @param {Object} options - 配置选项 - * @param {Number} options.count - 提取关键词数量 - * @returns {Array} 关键词数组 - */ -export function extractKeywords(text, options = {}) { - const { count = 10 } = options - - // 这里是简化的实现，实际应用中可能需要更复杂的算法 - if (!text) return [] - - // 将文本分词并计算频率（简化实现） - const words = text.split(/\s+|，|。|；|：|！|？|,|\.|;|:|!|\?/) - .filter(word => word.length > 1) // 过滤掉单字和空字符 - - const wordFreq = {} - words.forEach(word => { - wordFreq[word] = (wordFreq[word] || 0) + 1 - }) - - // 转换为数组并排序 - const sortedWords = Object.keys(wordFreq) - .map(term => ({ term, score: wordFreq[term] })) - .sort((a, b) => b.score - a.score) - .slice(0, count) - - return sortedWords -} - -/** - * 情感分析 - * @param {String} text - 文本内容 - * @returns {Object} 情感分析结果 - */ -export function analyzeSentiment(text) { - if (!text) { - return { - score: 0, - label: '中性', - positiveCount: 0, - negativeCount: 0, - neutralCount: 0 - } - } - - // 简化的情感词典 - const positiveWords = [ - '优秀', '良好', '满意', '喜欢', '赞', '好', '优化', '提高', '增长', - '成功', '积极', '优点', '强', '高效', '精确', '合适', '顺利' - ] - - const negativeWords = [ - '差', '失败', '缺点', '问题', '错误', '缺陷', '不良', '不足', - '弱', '低效', '不精确', '不合适', '困难', '危险', '降低', '减少' - ] - - // 简单统计正负面词语出现次数 - let positiveCount = 0 - let negativeCount = 0 - let neutralCount = 0 - - // 分词（简化实现） - const words = text.split(/\s+|，|。|；|：|！|？|,|\.|;|:|!|\?/) - .filter(word => word.length > 0) - - words.forEach(word => { - if (positiveWords.some(pw => word.includes(pw))) { - positiveCount++ - } else if (negativeWords.some(nw => word.includes(nw))) { - negativeCount++ - } else { - neutralCount++ - } - }) - - // 计算情感得分：范围从-1到1，-1表示极度负面，1表示极度正面 - const totalWords = positiveCount + negativeCount + neutralCount - let score = 0 - - if (totalWords > 0) { - score = (positiveCount - negativeCount) / totalWords - } - - // 确定情感标签 - let label = '中性' - if (score > 0.6) label = '非常积极' - else if (score > 0.2) label = '积极' - else if (score > -0.2) label = '中性' - else if (score > -0.6) label = '消极' - else label = '非常消极' - - return { - score, - label, - positiveCount, - negativeCount, - neutralCount - } -} - -/** - * 根据词语类别分组术语 - * @param {Array} termFrequency - 术语频率数据 - * @param {Object} categories - 类别词典，键为类别名称，值为该类别下的词语数组 - * @returns {Object} 按类别分组的术语 - */ -export function groupTermsByCategory(termFrequency, categories) { - if (!termFrequency || !categories) { - return {} - } - - const result = {} - - // 初始化结果对象 - Object.keys(categories).forEach(category => { - result[category] = [] - }) - - // 遍历术语频率数据 - termFrequency.forEach(item => { - const { term } = item - - // 检查该术语属于哪个类别 - Object.keys(categories).forEach(category => { - const categoryWords = categories[category] - // 如果术语在类别词语列表中，或者术语包含类别词语中的某个词 - if (categoryWords.includes(term) || categoryWords.some(word => term.includes(word))) { - result[category].push(item) - } - }) - }) - - return result -} - -/** - * 计算文本相似度 - * @param {String} text1 - 第一个文本 - * @param {String} text2 - 第二个文本 - * @returns {Object} 相似度分析结果 - */ -export function calculateTextSimilarity(text1, text2) { - if (!text1 || !text2) { - return { - similarity: 0, - commonWords: [], - uniqueWords1: [], - uniqueWords2: [] - } - } - - // 简化实现，将文本分词 - const words1 = text1.split(/\s+|，|。|；|：|！|？|,|\.|;|:|!|\?/) - .filter(word => word.length > 1) - const words2 = text2.split(/\s+|，|。|；|：|！|？|,|\.|;|:|!|\?/) - .filter(word => word.length > 1) - - // 计算词频 - const freq1 = {} - const freq2 = {} - - words1.forEach(word => { - freq1[word] = (freq1[word] || 0) + 1 - }) - - words2.forEach(word => { - freq2[word] = (freq2[word] || 0) + 1 - }) - - // 找出共有词和独有词 - const commonWords = [] - const uniqueWords1 = [] - const uniqueWords2 = [] - - Object.keys(freq1).forEach(word => { - if (freq2[word]) { - commonWords.push({ - term: word, - freq1: freq1[word], - freq2: freq2[word] - }) - } else { - uniqueWords1.push({ - term: word, - frequency: freq1[word] - }) - } - }) - - Object.keys(freq2).forEach(word => { - if (!freq1[word]) { - uniqueWords2.push({ - term: word, - frequency: freq2[word] - }) - } - }) - - // 使用Jaccard相似度计算文本相似度 - const allWords = new Set([...Object.keys(freq1), ...Object.keys(freq2)]) - const intersection = commonWords.length - const similarity = intersection / allWords.size - - return { - similarity, - commonWords, - uniqueWords1, - uniqueWords2 - } -} - -/** - * 将术语频率数据转换为CSV格式 - * @param {Array} termFrequency - 术语频率数据 - * @returns {String} CSV格式的数据 - */ -export function exportTermFrequencyToCsv(termFrequency) { - if (!termFrequency || !termFrequency.length) { - return '' - } - - // 创建CSV头 - let csvContent = '术语,频率,百分比\n' - - // 计算总频率 - const totalFrequency = termFrequency.reduce((sum, item) => sum + item.frequency, 0) - - // 添加每一行数据 - termFrequency.forEach(item => { - const percentage = ((item.frequency / totalFrequency) * 100).toFixed(2) - csvContent += `${item.term},${item.frequency},${percentage}%\n` - }) - - return csvContent -} - -/** - * 生成模拟的词频数据（用于测试） - * @param {Number} count - 词条数量 - * @returns {Array} 模拟的词频数据 - */ -export function generateMockTermFrequency(count = 50) { - const terms = [ - '数据', '分析', '报告', '增长', '下降', '趋势', '预测', '计划', - '指标', '目标', '完成', '销售', '产品', '市场', '客户', '服务', - '研发', '技术', '创新', '战略', '管理', '团队', '协作', '效率', - '质量', '成本', '收入', '利润', '预算', '投资', '回报', '风险', - '机会', '挑战', '问题', '解决', '方案', '实施', '评估', '改进', - '优化', '整合', '扩展', '收缩', '专注', '多元', '流程', '体系', - '标准', '规范', '合规', '审计', '监控', '决策', '执行', '反馈', - '调整', '变革', '稳定', '增值', '季度', '年度', '月度', '周期', - '短期', '长期', '快速', '稳健', '领先', '落后', '核心', '边缘', - '内部', '外部', '上游', '下游', '供应', '需求', '库存', '周转', - '提高', '降低', '强化', '弱化', '轻量', '重量', '敏捷', '刚性', - '柔性', '弹性', '创造', '破坏', '构建', '拆解', '增加', '减少' - ] - - // 确保词条数量不超过可用词条 - const actualCount = Math.min(count, terms.length) - const selectedTerms = [] - - // 随机选择词条 - while (selectedTerms.length < actualCount) { - const randomIndex = Math.floor(Math.random() * terms.length) - const term = terms[randomIndex] - - if (!selectedTerms.find(item => item.term === term)) { - // 生成随机频率，使前面的项有更高的频率（模拟长尾分布） - const frequency = Math.floor(Math.random() * 100) + - Math.floor(Math.random() * (100 - selectedTerms.length * 2)) - - selectedTerms.push({ - term, - frequency - }) - } - } - - // 按频率降序排序 - return selectedTerms.sort((a, b) => b.frequency - a.frequency) -} - -/** - * 生成词汇关联网络数据 - * - * @param {Array} segments - 文本段落数组 - * @param {Array} terms - 要分析关系的词汇列表 - * @param {Object} options - 分析选项 - * @returns {Object} - 关联网络数据 - */ -export const generateTermLinks = (segments, terms, options = {}) => { - const defaultOptions = { - minCooccurrence: 2, // 最小共现次数 - window: 50, // 共现窗口大小（段落内字符数） - maxLinks: 50 // 最大链接数 - } - - const opts = { ...defaultOptions, ...options } - - if (!segments || !Array.isArray(segments) || segments.length === 0) { - return { nodes: [], links: [] } - } - - if (!terms || !Array.isArray(terms) || terms.length < 2) { - return { nodes: [], links: [] } - } - - // 初始化共现矩阵 - const cooccurrenceMatrix = {} - terms.forEach(term1 => { - cooccurrenceMatrix[term1] = {} - terms.forEach(term2 => { - if (term1 !== term2) { - cooccurrenceMatrix[term1][term2] = 0 - } - }) - }) - - // 统计词汇共现次数 - segments.forEach(segment => { - const text = segment.text - - // 对每对词汇，检查它们是否在窗口内共现 - terms.forEach(term1 => { - // 找出term1在文本中的所有位置 - let positions1 = [] - let pos = 0 - while (text.indexOf(term1, pos) !== -1) { - const foundPos = text.indexOf(term1, pos) - positions1.push(foundPos) - pos = foundPos + term1.length - } - - // 对于term1的每个位置，检查其他词是否在窗口内 - terms.forEach(term2 => { - if (term1 !== term2) { - // 找出term2在文本中的所有位置 - let positions2 = [] - pos = 0 - while (text.indexOf(term2, pos) !== -1) { - const foundPos = text.indexOf(term2, pos) - positions2.push(foundPos) - pos = foundPos + term2.length - } - - // 检查term1和term2是否在窗口内共现 - positions1.forEach(pos1 => { - positions2.forEach(pos2 => { - if (Math.abs(pos1 - pos2) <= opts.window) { - cooccurrenceMatrix[term1][term2]++ - } - }) - }) - } - }) - }) - }) - - // 生成节点和链接数据 - const nodes = terms.map(term => ({ - name: term, - value: 1 // 可以替换为词频 - })) - - let links = [] - - // 添加链接 - terms.forEach(term1 => { - terms.forEach(term2 => { - if (term1 !== term2 && cooccurrenceMatrix[term1][term2] >= opts.minCooccurrence) { - links.push({ - source: term1, - target: term2, - value: cooccurrenceMatrix[term1][term2] - }) - } - }) - }) - - // 按共现次数排序并限制链接数量 - links = links.sort((a, b) => b.value - a.value).slice(0, opts.maxLinks) - - return { nodes, links } -} \ No newline at end of file diff --git a/src/vue2/src/views/Statistics.vue b/src/vue2/src/views/Statistics.vue new file mode 100644 index 0000000..a69307d --- /dev/null +++ b/src/vue2/src/views/Statistics.vue @@ -0,0 +1,500 @@ + + + + + \ No newline at end of file