predict_edb.go 14 KB

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  1. package models
  2. import (
  3. "encoding/json"
  4. "errors"
  5. "eta/eta_index_lib/utils"
  6. "fmt"
  7. "github.com/beego/beego/v2/client/orm"
  8. "github.com/shopspring/decimal"
  9. "github.com/yidane/formula"
  10. "strings"
  11. "time"
  12. )
  13. // CalculateRule 预测指标 规则 计算
  14. type CalculateRule struct {
  15. EdbInfoId int `description:"指标id"`
  16. ConfigId int `description:"配置id"`
  17. TrendsCalculateMappingList []*PredictEdbConfCalculateMapping
  18. EdbInfoList []*EdbInfo
  19. EdbInfoIdBytes []string
  20. Formula string
  21. RuleType int `description:"预测规则,1:最新,2:固定值,3:同比,4:同差,5:环比,6:环差,7:N期移动均值,8:N期段线性外推值,9:动态环差"`
  22. EndDate string `description:"截止日期"`
  23. EdbInfoIdArr []EdbInfoFromTag `description:"指标信息"`
  24. EmptyType int `description:"空值处理类型(0查找前后35天,1不计算,2前值填充,3后值填充,4等于0)"`
  25. MaxEmptyType int `description:"MAX、MIN公式空值处理类型(1、等于0;2、跳过空值)"`
  26. }
  27. // RefreshCalculateByRuleBy9 刷新计算
  28. func RefreshCalculateByRuleBy9(rule CalculateRule) (resultDataList []*EdbInfoSearchData, err error) {
  29. o := orm.NewOrm()
  30. to, err := o.Begin()
  31. if err != nil {
  32. return
  33. }
  34. defer func() {
  35. if err != nil {
  36. to.Rollback()
  37. } else {
  38. err = to.Commit()
  39. }
  40. }()
  41. resultDataList, err = CalculateByRuleBy9(to, rule)
  42. return
  43. }
  44. // CalculateByRuleBy9 动态环差规则计算入库
  45. func CalculateByRuleBy9(to orm.TxOrmer, rule CalculateRule) (resultDataList []*EdbInfoSearchData, err error) {
  46. realSaveDataMap := make(map[string]map[int]float64)
  47. saveDataMap := make(map[string]map[int]float64)
  48. // 最小的结束日期 , 最晚的数据开始日期
  49. var minLatestDate, maxStartDate time.Time
  50. dateList := make([]string, 0) // 第一个指标的日期数据
  51. formulaStr := strings.ToUpper(rule.Formula)
  52. // 获取关联指标数据
  53. for edbInfoIndex, v := range rule.EdbInfoList {
  54. dataList, tmpErr := GetPredictEdbDataListAll(v, 1)
  55. if tmpErr != nil {
  56. err = tmpErr
  57. return
  58. }
  59. //lenData := len(dataList)
  60. for _, dv := range dataList {
  61. // 现有实际数据
  62. if val, ok := realSaveDataMap[dv.DataTime]; ok {
  63. if _, ok := val[v.EdbInfoId]; !ok {
  64. val[v.EdbInfoId] = dv.Value
  65. }
  66. } else {
  67. temp := make(map[int]float64)
  68. temp[v.EdbInfoId] = dv.Value
  69. realSaveDataMap[dv.DataTime] = temp
  70. }
  71. // 待处理的数据
  72. if val, ok := saveDataMap[dv.DataTime]; ok {
  73. if _, ok := val[v.EdbInfoId]; !ok {
  74. val[v.EdbInfoId] = dv.Value
  75. }
  76. } else {
  77. temp := make(map[int]float64)
  78. temp[v.EdbInfoId] = dv.Value
  79. saveDataMap[dv.DataTime] = temp
  80. }
  81. // 以第一个指标的日期作为基准日期
  82. if edbInfoIndex == 0 {
  83. dateList = append(dateList, dv.DataTime)
  84. tmpDate, _ := time.ParseInLocation(utils.FormatDate, dv.DataTime, time.Local)
  85. if minLatestDate.IsZero() || tmpDate.After(minLatestDate) {
  86. minLatestDate = tmpDate
  87. }
  88. if maxStartDate.IsZero() || tmpDate.Before(maxStartDate) {
  89. maxStartDate = tmpDate
  90. }
  91. }
  92. }
  93. /*if lenData > 0 {
  94. tmpLatestDate, _ := time.ParseInLocation(utils.FormatDate, dataList[lenData-1].DataTime, time.Local)
  95. if minLatestDate.IsZero() || minLatestDate.After(tmpLatestDate) {
  96. minLatestDate = tmpLatestDate
  97. }
  98. tmpStartDate, _ := time.ParseInLocation(utils.FormatDate, dataList[0].DataTime, time.Local)
  99. if maxStartDate.IsZero() || maxStartDate.Before(tmpStartDate) {
  100. maxStartDate = tmpStartDate
  101. }
  102. }*/
  103. }
  104. // todo 数据处理,将日期内不全的数据做填补
  105. HandleDateSaveDataMap(dateList, maxStartDate, minLatestDate, realSaveDataMap, saveDataMap, rule.EdbInfoList, rule.EmptyType)
  106. // 添加数据
  107. addDataList := make([]*PredictEdbRuleData, 0)
  108. // 计算规则
  109. formulaDateSlice, formulaDateMap, err := utils.HandleFormulaJson(formulaStr, minLatestDate)
  110. if err != nil {
  111. return
  112. }
  113. //获取指标所有数据
  114. dataList := make([]*PredictEdbRuleData, 0)
  115. sql := `SELECT * FROM predict_edb_rule_data WHERE config_id = ?`
  116. _, err = to.Raw(sql, rule.ConfigId).QueryRows(&dataList)
  117. if err != nil {
  118. return
  119. }
  120. dataMap := make(map[string]*PredictEdbRuleData)
  121. for _, v := range dataList {
  122. dataMap[v.DataTime] = v
  123. }
  124. existDataMap := make(map[string]string)
  125. removeDateList := make([]string, 0) //需要移除的日期
  126. // 判断是否特殊处理max和min函数
  127. maxDealFlag := false
  128. if rule.EmptyType == 4 && rule.MaxEmptyType == 2 {
  129. maxDealFlag = true
  130. }
  131. for sk, sv := range saveDataMap {
  132. // 当空值处理类型选择了不计算时,只要有一个指标在某个日期没有值(即空值),则计算指标在该日期没有值
  133. if rule.EmptyType == 1 {
  134. if len(sv) != len(rule.EdbInfoList) {
  135. continue
  136. }
  137. }
  138. //fmt.Println(sk, sv)
  139. // 根据时间范围,选择对应的公式
  140. formulaMap := make(map[string]string)
  141. formulaStr = ""
  142. for _, fv := range formulaDateSlice {
  143. if sk < fv {
  144. if f, ok := formulaDateMap[fv]; ok {
  145. formulaStr = f
  146. formulaMap, err = utils.CheckFormula(formulaStr)
  147. if err != nil {
  148. err = fmt.Errorf("公式错误,请重新填写")
  149. return
  150. }
  151. }
  152. break
  153. }
  154. }
  155. if formulaStr == "" {
  156. continue
  157. }
  158. svMax := make(map[int]float64)
  159. if maxDealFlag {
  160. // 特殊处理max和min函数,如果原本的值为空,则选择空值参与运算
  161. if svMaxData, ok := realSaveDataMap[sk]; ok {
  162. svMax = svMaxData
  163. }
  164. }
  165. formulaStr = strings.ToUpper(formulaStr)
  166. //fmt.Println(sk, sv)
  167. formulaFormStr := ReplaceFormula(rule.EdbInfoList, sv, svMax, formulaMap, formulaStr, rule.EdbInfoIdBytes, maxDealFlag)
  168. //计算公式异常,那么就移除该指标
  169. if formulaFormStr == "" {
  170. removeDateList = append(removeDateList, sk)
  171. continue
  172. }
  173. //utils.FileLog.Info(fmt.Sprintf("formulaFormStr:%s", formulaFormStr))
  174. expression := formula.NewExpression(formulaFormStr)
  175. calResult, tmpErr := expression.Evaluate()
  176. if tmpErr != nil {
  177. // 分母为0的报错
  178. if strings.Contains(tmpErr.Error(), "divide by zero") {
  179. removeDateList = append(removeDateList, sk)
  180. continue
  181. }
  182. err = errors.New("计算失败:Err:" + tmpErr.Error() + ";formulaStr:" + formulaFormStr)
  183. //fmt.Println(err)
  184. return
  185. }
  186. calVal, tmpErr := calResult.Float64()
  187. if tmpErr != nil {
  188. err = errors.New("计算失败:获取计算值失败 Err:" + tmpErr.Error() + ";formulaStr:" + formulaFormStr)
  189. //fmt.Println(err)
  190. return
  191. }
  192. nanCheck := fmt.Sprintf("%0.f", calVal)
  193. if nanCheck == "NaN" || nanCheck == "+Inf" || nanCheck == "-Inf" {
  194. removeDateList = append(removeDateList, sk)
  195. continue
  196. }
  197. saveValue := decimal.NewFromFloat(calVal).RoundCeil(4).String() //utils.SubFloatToString(calVal, 4)
  198. existPredictEdbRuleData, ok := dataMap[sk]
  199. if !ok {
  200. dataTime, _ := time.ParseInLocation(utils.FormatDate, sk, time.Local)
  201. timestamp := dataTime.UnixNano() / 1e6
  202. if _, existOk := existDataMap[sk]; !existOk {
  203. tmpPredictEdbRuleData := &PredictEdbRuleData{
  204. //PredictEdbRuleDataId: 0,
  205. EdbInfoId: rule.EdbInfoId,
  206. ConfigId: rule.ConfigId,
  207. DataTime: sk,
  208. Value: saveValue,
  209. CreateTime: time.Now(),
  210. ModifyTime: time.Now(),
  211. DataTimestamp: timestamp,
  212. }
  213. addDataList = append(addDataList, tmpPredictEdbRuleData)
  214. }
  215. existDataMap[sk] = sk
  216. } else {
  217. existValDecimal, tmpErr := decimal.NewFromString(existPredictEdbRuleData.Value)
  218. if tmpErr != nil {
  219. err = tmpErr
  220. return
  221. }
  222. existStr := existValDecimal.String()
  223. if existStr != saveValue {
  224. existPredictEdbRuleData.Value = saveValue
  225. existPredictEdbRuleData.ModifyTime = time.Now()
  226. _, err = to.Update(existPredictEdbRuleData, "Value", "ModifyTime")
  227. if err != nil {
  228. return
  229. }
  230. }
  231. }
  232. // 计算出来的结果集
  233. resultDataList = append(resultDataList, &EdbInfoSearchData{
  234. //EdbDataId: 0,
  235. DataTime: sk,
  236. Value: calVal,
  237. })
  238. }
  239. // 添加计算出来的值入库
  240. lenAddDataList := len(addDataList)
  241. if lenAddDataList > 0 {
  242. _, err = to.InsertMulti(lenAddDataList, addDataList)
  243. if err != nil {
  244. return
  245. }
  246. }
  247. //删除多余的值
  248. lenRemoveDateList := len(removeDateList)
  249. if lenRemoveDateList > 0 {
  250. //如果拼接指标变更了,那么需要删除所有的指标数据
  251. sql := ` DELETE FROM predict_edb_rule_data WHERE config_id = ? and data_time in (` + utils.GetOrmInReplace(lenRemoveDateList) + `) `
  252. _, err = to.Raw(sql, rule.ConfigId, removeDateList).Exec()
  253. if err != nil {
  254. err = fmt.Errorf("删除计算失败的预测规则计算指标数据失败,Err:" + err.Error())
  255. return
  256. }
  257. }
  258. return
  259. }
  260. // RefreshCalculateByRuleByLineNh 刷新动态结果计算(线性拟合)
  261. func RefreshCalculateByRuleByLineNh(predictEdbInfo EdbInfo, predictEdbConfAndDataList []*PredictEdbConfAndData, rule PredictEdbConf) (err error, errMsg string) {
  262. o := orm.NewOrm()
  263. to, err := o.Begin()
  264. if err != nil {
  265. return
  266. }
  267. defer func() {
  268. if err != nil {
  269. to.Rollback()
  270. } else {
  271. err = to.Commit()
  272. }
  273. }()
  274. err, errMsg = CalculateByRuleByRuleLineNh(to, predictEdbInfo, predictEdbConfAndDataList, rule)
  275. return
  276. }
  277. // CalculateByRuleByRuleLineNh 一元线性拟合规则计算入库
  278. func CalculateByRuleByRuleLineNh(to orm.TxOrmer, predictEdbInfo EdbInfo, predictEdbConfAndDataList []*PredictEdbConfAndData, rule PredictEdbConf) (err error, errMsg string) {
  279. var secondDataList []*EdbInfoSearchData
  280. predictEdbInfoId := predictEdbInfo.EdbInfoId // 预测指标id
  281. // 规则
  282. var ruleConf RuleLineNhConf
  283. tmpErr := json.Unmarshal([]byte(rule.Value), &ruleConf)
  284. if tmpErr != nil {
  285. errMsg = `季节性配置信息异常`
  286. err = errors.New("季节性配置信息异常:" + tmpErr.Error())
  287. return
  288. }
  289. // 获取自身指标的数据
  290. {
  291. // 来源指标
  292. var sourceEdbInfoItem *EdbInfo
  293. sql := ` SELECT * FROM edb_info WHERE edb_info_id=? `
  294. err = to.Raw(sql, rule.SourceEdbInfoId).QueryRow(&sourceEdbInfoItem)
  295. if err != nil {
  296. return
  297. }
  298. predictEdbInfo.EdbInfoId = 0
  299. secondDataList, err, _ = GetPredictDataListByPredictEdbConfList(&predictEdbInfo, sourceEdbInfoItem, predictEdbConfAndDataList, 1, ``)
  300. if err != nil {
  301. return
  302. }
  303. }
  304. lenSecondData := len(secondDataList)
  305. if lenSecondData <= 0 {
  306. return
  307. }
  308. newNhccDataMap, err, errMsg := getCalculateNhccData(secondDataList, ruleConf)
  309. if err != nil {
  310. return
  311. }
  312. //将最后计算出来的结果数据处理(新增入库、编辑日期的值、删除日期)
  313. {
  314. // 获取需要预测的日期
  315. startDateStr := secondDataList[lenSecondData-1].DataTime
  316. startDate, _ := time.ParseInLocation(utils.FormatDate, startDateStr, time.Local)
  317. //endDate, _ := time.ParseInLocation(utils.FormatDate, ruleConf.EndDate, time.Local)
  318. endDate := rule.EndDate
  319. dayList := getPredictEdbDayList(startDate, endDate, predictEdbInfo.Frequency, predictEdbInfo.DataDateType)
  320. if len(dayList) <= 0 { // 如果未来没有日期的话,那么就退出当前循环,进入下一个循环
  321. return
  322. }
  323. //获取该配置的所有数据
  324. dataList := make([]*PredictEdbRuleData, 0)
  325. sql := `SELECT * FROM predict_edb_rule_data WHERE config_id = ?`
  326. _, err = to.Raw(sql, rule.ConfigId).QueryRows(&dataList)
  327. if err != nil {
  328. return
  329. }
  330. dataMap := make(map[string]*PredictEdbRuleData)
  331. for _, v := range dataList {
  332. dataMap[v.DataTime] = v
  333. }
  334. //需要移除的日期
  335. removeDateList := make([]string, 0)
  336. // 已经操作过的日期
  337. existDataMap := make(map[string]string)
  338. // 添加数据
  339. addDataList := make([]*PredictEdbRuleData, 0)
  340. for _, currentDate := range dayList {
  341. // 动态拟合残差值数据
  342. currentDateStr := currentDate.Format(utils.FormatDate)
  343. val, ok := newNhccDataMap[currentDateStr]
  344. // 找不到数据,那么就移除该日期的数据
  345. if !ok {
  346. removeDateList = append(removeDateList, currentDateStr)
  347. continue
  348. }
  349. saveValue := decimal.NewFromFloat(val).RoundCeil(4).String() //utils.SubFloatToString(calVal, 4)
  350. existPredictEdbRuleData, ok := dataMap[currentDateStr]
  351. if !ok {
  352. timestamp := currentDate.UnixNano() / 1e6
  353. if _, existOk := existDataMap[currentDateStr]; !existOk {
  354. tmpPredictEdbRuleData := &PredictEdbRuleData{
  355. //PredictEdbRuleDataId: 0,
  356. EdbInfoId: predictEdbInfoId,
  357. ConfigId: rule.ConfigId,
  358. DataTime: currentDateStr,
  359. Value: saveValue,
  360. CreateTime: time.Now(),
  361. ModifyTime: time.Now(),
  362. DataTimestamp: timestamp,
  363. }
  364. addDataList = append(addDataList, tmpPredictEdbRuleData)
  365. }
  366. existDataMap[currentDateStr] = currentDateStr
  367. } else {
  368. existValDecimal, tmpErr := decimal.NewFromString(existPredictEdbRuleData.Value)
  369. if tmpErr != nil {
  370. err = tmpErr
  371. return
  372. }
  373. existStr := existValDecimal.String()
  374. if existStr != saveValue {
  375. existPredictEdbRuleData.Value = saveValue
  376. existPredictEdbRuleData.ModifyTime = time.Now()
  377. _, err = to.Update(existPredictEdbRuleData, "Value", "ModifyTime")
  378. if err != nil {
  379. return
  380. }
  381. }
  382. }
  383. }
  384. // 添加计算出来的值入库
  385. lenAddDataList := len(addDataList)
  386. if lenAddDataList > 0 {
  387. _, err = to.InsertMulti(lenAddDataList, addDataList)
  388. if err != nil {
  389. return
  390. }
  391. }
  392. //删除多余的值
  393. lenRemoveDateList := len(removeDateList)
  394. if lenRemoveDateList > 0 {
  395. //如果拼接指标变更了,那么需要删除所有的指标数据
  396. sql := ` DELETE FROM predict_edb_rule_data WHERE config_id = ? and data_time in (` + utils.GetOrmInReplace(lenRemoveDateList) + `) `
  397. _, err = to.Raw(sql, rule.ConfigId, removeDateList).Exec()
  398. if err != nil {
  399. err = fmt.Errorf("删除计算失败的预测规则计算指标数据失败,Err:" + err.Error())
  400. return
  401. }
  402. }
  403. }
  404. return
  405. }