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/**
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* 健康数据语音文本解析。
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*
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* 把语音识别得到的中文口语文本解析为结构化字段：
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* - 血糖（空腹 / 餐后 / 其他）
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* - 血压（高压 / 低压 / 西药 / 胰岛素）
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* - 饮食（早 / 午 / 晚餐 + 备注）
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* - 运动（类型 / 时长 / 强度）
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*
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* 兼容中文数字（六点五 / 一百二十）与阿拉伯数字（6.5 / 120）。
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* 纯前端解析，无网络依赖，识别失败时返回空对象。
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*/
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const CN_DIGIT = {
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零: 0, 〇: 0, 一: 1, 壹: 1, 幺: 1, 二: 2, 贰: 2, 两: 2,
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三: 3, 叁: 3, 四: 4, 肆: 4, 五: 5, 伍: 5, 六: 6, 陆: 6,
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七: 7, 柒: 7, 八: 8, 捌: 8, 九: 9, 玖: 9
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}
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const CN_UNIT = { 十: 10, 拾: 10, 百: 100, 佰: 100, 千: 1000, 仟: 1000, 万: 10000, 亿: 100000000 }
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/** 中文整数串 → 数字，例如 "一百二十" → 120、"八十" → 80、"十" → 10 */
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function cnIntToNumber(s) {
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let total = 0
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let section = 0
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let number = 0
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let hadUnit = false
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for (const ch of s) {
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if (CN_DIGIT[ch] !== undefined) {
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number = CN_DIGIT[ch]
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} else if (CN_UNIT[ch] !== undefined) {
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hadUnit = true
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const unit = CN_UNIT[ch]
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if (unit >= 10000) {
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section = (section + number) * unit
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total += section
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section = 0
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} else {
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if (number === 0) number = 1 // 十 = 10
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section += number * unit
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}
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number = 0
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}
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}
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const val = total + section + number
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// 没有任何单位且为纯数字串（如 "一二零"）时按位拼接更符合口语
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if (!hadUnit && s.length > 1) {
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let joined = ''
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for (const ch of s) {
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if (CN_DIGIT[ch] !== undefined) joined += CN_DIGIT[ch]
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}
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if (joined) return Number(joined)
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}
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return val
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}
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/** 中文数字串（含小数点）→ 数字，例如 "六点五" → 6.5 */
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function cnSeqToNumber(seq) {
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if (seq.includes('点')) {
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const parts = seq.split('点')
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const intPart = parts[0]
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const decPart = parts.slice(1).join('')
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const intVal = intPart ? cnIntToNumber(intPart) : 0
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let decStr = ''
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for (const ch of decPart) {
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if (CN_DIGIT[ch] !== undefined) decStr += CN_DIGIT[ch]
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}
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if (!decStr) return intVal
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return Number(`${intVal}.${decStr}`)
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}
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return cnIntToNumber(seq)
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}
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/** 是否包含中文数字字符（用于过滤“点”“重点”等误匹配） */
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function hasCnDigit(seq) {
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for (const ch of seq) {
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if (CN_DIGIT[ch] !== undefined || CN_UNIT[ch] !== undefined) return true
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}
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return false
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}
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/** 把文本中的中文数字段落替换为阿拉伯数字 */
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export function normalizeNumbers(text) {
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const raw = String(text || '')
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return raw.replace(/[零〇一壹幺二贰两三叁四肆五伍六陆七柒八捌九玖十拾百佰千仟万亿点]+/g, (m) => {
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if (!hasCnDigit(m)) return m
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const n = cnSeqToNumber(m)
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return Number.isFinite(n) ? String(n) : m
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})
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}
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/** 在文本中找到关键词后紧随的第一个数字 */
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function numAfter(text, keys) {
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for (const k of keys) {
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const i = text.indexOf(k)
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if (i >= 0) {
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const rest = text.slice(i + k.length)
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const m = rest.match(/-?\d+(?:\.\d+)?/)
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if (m) return m[0]
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}
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}
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return ''
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}
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/** 关键词后紧随的一段文本（截止到标点或下一个分隔关键词） */
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function textAfter(text, keys, stopKeys = []) {
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for (const k of keys) {
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const i = text.indexOf(k)
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if (i >= 0) {
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let rest = text.slice(i + k.length)
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// 去掉口语连接词
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rest = rest.replace(/^[是为吃了喝了吃的喝的有打了用了：:，,、。\s]+/, '')
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let end = rest.length
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const stopRe = /[，,。.；;！!？?\n]/
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const mStop = rest.match(stopRe)
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if (mStop && mStop.index < end) end = mStop.index
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for (const sk of stopKeys) {
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const si = rest.indexOf(sk)
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if (si >= 0 && si < end) end = si
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}
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const seg = rest.slice(0, end).trim()
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if (seg) return seg
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}
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}
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return ''
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}
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/**
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* 解析血糖：返回 { fasting_blood_sugar, postprandial_blood_sugar, other_blood_sugar }
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* 例："空腹六点五餐后八点二" → { fasting:6.5, postprandial:8.2 }
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*/
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export function parseGlucose(raw) {
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const t = normalizeNumbers(raw)
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const result = {}
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const fasting = numAfter(t, ['空腹'])
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const post = numAfter(t, ['餐后', '饭后'])
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const other = numAfter(t, ['其他', '随机', '睡前', '凌晨', '夜间', '晚上'])
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if (fasting) result.fasting_blood_sugar = fasting
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if (post) result.postprandial_blood_sugar = post
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if (other) result.other_blood_sugar = other
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// 没有关键词但只有一个数字 → 视为“其他血糖”
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if (!fasting && !post && !other) {
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const nums = t.match(/\d+(?:\.\d+)?/g)
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if (nums && nums.length === 1) result.other_blood_sugar = nums[0]
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}
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return result
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}
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/**
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* 解析血压：返回 { systolic_pressure, diastolic_pressure, western_medicine, insulin }
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* 例："高压一百二低压八十" → { systolic:120, diastolic:80 }
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*/
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export function parseBloodPressure(raw) {
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const t = normalizeNumbers(raw)
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const result = {}
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let sys = numAfter(t, ['高压', '收缩压', '收缩'])
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let dia = numAfter(t, ['低压', '舒张压', '舒张'])
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if (!sys || !dia) {
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const nums = (t.match(/\d{2,3}/g) || [])
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.map(Number)
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.filter((n) => n >= 30 && n <= 300)
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if (!sys && !dia && nums.length >= 2) {
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sys = String(nums[0])
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dia = String(nums[1])
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} else if (!sys && nums.length === 1 && nums[0] >= 90) {
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sys = String(nums[0])
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}
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}
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// 高压应不低于低压，顺序异常时交换
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if (sys && dia && Number(sys) < Number(dia)) {
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const tmp = sys
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sys = dia
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dia = tmp
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}
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if (sys) result.systolic_pressure = sys
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if (dia) result.diastolic_pressure = dia
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const insulin = textAfter(raw, ['胰岛素'], ['高压', '低压', '血压', '西药', '备注'])
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if (insulin) result.insulin = insulin
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const western = textAfter(raw, ['西药', '降糖药', '口服药'], ['胰岛素', '高压', '低压', '血压', '备注'])
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if (western) result.western_medicine = western
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return result
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}
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const DIET_MARKERS = [
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{ keys: ['早餐', '早饭', '早上', '早点', '早晨'], field: 'breakfast_foods' },
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{ keys: ['午餐', '午饭', '中午'], field: 'lunch_foods' },
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{ keys: ['晚餐', '晚饭', '晚上', '夜宵', '夜里'], field: 'dinner_foods' }
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]
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function cleanFoodSeg(seg) {
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return String(seg || '')
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.replace(/^[是为吃了吃的喝了喝的有：:，,、。\s]+/, '')
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.replace(/[。.\s]+$/, '')
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.trim()
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}
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/**
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* 解析饮食：返回 { breakfast_foods, lunch_foods, dinner_foods, note }
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* 例："早餐鸡蛋粥中午米饭炒青菜晚上面条" → 分别归位
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* 无餐别关键词时整句写入 note
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*/
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export function parseDiet(raw) {
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const t = String(raw || '').trim()
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const result = {}
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const hits = []
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DIET_MARKERS.forEach((m) => {
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for (const k of m.keys) {
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const idx = t.indexOf(k)
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if (idx >= 0) {
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hits.push({ idx, field: m.field, klen: k.length })
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break
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}
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}
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})
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if (!hits.length) {
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if (t) result.note = t
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return result
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}
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hits.sort((a, b) => a.idx - b.idx)
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hits.forEach((h, i) => {
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const start = h.idx + h.klen
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const end = i + 1 < hits.length ? hits[i + 1].idx : t.length
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const seg = cleanFoodSeg(t.slice(start, end))
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if (seg && !result[h.field]) result[h.field] = seg
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})
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// 第一个餐别关键词之前的内容作为备注
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const head = cleanFoodSeg(t.slice(0, hits[0].idx))
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if (head) result.note = head
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return result
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}
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const EXERCISE_FILLERS = [
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'今天', '我', '做了', '做', '进行了', '进行', '运动了', '运动', '锻炼了', '锻炼',
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'了', '大概', '左右', '持续', '差不多', '一共', '总共', '的', '走了', '打了', '跑了'
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]
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/**
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* 解析运动：返回 { exercise_type, duration, intensity }
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* 例："散步三十分钟中强度" → { type:'散步', duration:30, intensity:2 }
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*/
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export function parseExercise(raw) {
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const t = normalizeNumbers(raw)
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const result = {}
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let durMatch = t.match(/(\d+(?:\.\d+)?)\s*(个小时|小时|钟头|时|分钟|分)/)
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if (durMatch) {
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const val = parseFloat(durMatch[1])
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const isHour = /个小时|小时|钟头|时/.test(durMatch[2])
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result.duration = String(Math.round(isHour ? val * 60 : val))
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}
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if (/高强度|剧烈|很累|大汗|气喘/.test(t)) result.intensity = 3
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else if (/中强度|中等强度|中等|有点累|微微出汗|微汗/.test(t)) result.intensity = 2
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else if (/低强度|轻松|轻微|溜达|缓慢/.test(t)) result.intensity = 1
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// 运动类型：移除时长、强度、填充词后剩余文本
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let type = t
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if (durMatch) type = type.replace(durMatch[0], '')
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type = type
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.replace(/高强度|中强度|中等强度|低强度|剧烈|很累|大汗|气喘|有点累|微微出汗|微汗|轻松|轻微|缓慢/g, '')
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.replace(/\d+(?:\.\d+)?/g, '')
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.replace(/[，,。.；;、\s]+/g, '')
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EXERCISE_FILLERS.forEach((f) => {
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type = type.split(f).join('')
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})
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type = type.trim()
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if (type && type.length <= 20) result.exercise_type = type
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return result
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}
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const FIELD_LABELS = {
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fasting_blood_sugar: '空腹',
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postprandial_blood_sugar: '餐后',
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other_blood_sugar: '其他血糖',
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systolic_pressure: '高压',
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diastolic_pressure: '低压',
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western_medicine: '西药',
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insulin: '胰岛素',
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breakfast_foods: '早餐',
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lunch_foods: '午餐',
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dinner_foods: '晚餐',
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note: '备注',
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exercise_type: '运动',
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duration: '时长',
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intensity: '强度'
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}
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const INTENSITY_LABELS = { 1: '低强度', 2: '中强度', 3: '高强度' }
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/** 把解析结果转成可读的反馈文案，例如 "空腹6.5 · 餐后8.2" */
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export function summarizeParsed(parsed) {
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const parts = []
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Object.keys(parsed).forEach((k) => {
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const label = FIELD_LABELS[k] || k
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let val = parsed[k]
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if (k === 'intensity') val = INTENSITY_LABELS[val] || val
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parts.push(`${label}${val}`)
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})
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return parts.join(' · ')
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}
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