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@@ -0,0 +1,419 @@
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+package models
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+
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+import (
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+ "errors"
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+ "fmt"
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+ "github.com/beego/beego/v2/client/orm"
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+ "github.com/shopspring/decimal"
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+ "hongze/hongze_edb_lib/utils"
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+ "strconv"
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+ "strings"
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+ "time"
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+)
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+
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+// SavePredictCalculateJp 预测降频值
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+func SavePredictCalculateJp(reqEdbInfoId, classifyId int, edbName, frequency, unit, formula string, fromEdbInfo *EdbInfo, edbCode, uniqueCode string, sysUserId int, sysUserRealName string) (edbInfo *EdbInfo, latestDateStr string, latestValue float64, err error, errMsg string) {
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+ o := orm.NewOrm()
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+ to, err := o.Begin()
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+ if err != nil {
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+ return
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+ }
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+ defer func() {
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+ if err != nil {
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+ fmt.Println("SavePredictCalculateJp,Err:" + err.Error())
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+ _ = to.Rollback()
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+ } else {
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+ _ = to.Commit()
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+ }
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+ }()
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+ fmt.Println("reqEdbInfoId:", reqEdbInfoId)
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+
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+ if reqEdbInfoId <= 0 {
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+ edbInfo = &EdbInfo{
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+ //EdbInfoId: 0,
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+ EdbInfoType: 1,
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+ SourceName: utils.DATA_SOURCE_NAME_PREDICT_CALCULATE_JP,
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+ Source: utils.DATA_SOURCE_PREDICT_CALCULATE_JP,
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+ EdbCode: edbCode,
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+ EdbName: edbName,
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+ EdbNameSource: edbName,
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+ Frequency: frequency,
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+ Unit: unit,
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+ //StartDate: "",
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+ //EndDate: "",
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+ ClassifyId: classifyId,
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+ SysUserId: sysUserId,
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+ SysUserRealName: sysUserRealName,
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+ UniqueCode: uniqueCode,
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+ CreateTime: time.Now(),
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+ ModifyTime: time.Now(),
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+ MinValue: 0,
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+ MaxValue: 0,
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+ CalculateFormula: formula,
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+ EdbType: 2,
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+ Sort: 0,
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+ MoveType: 0,
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+ MoveFrequency: "",
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+ NoUpdate: 0,
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+ ServerUrl: "",
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+ EdbNameEn: "",
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+ UnitEn: "",
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+ LatestDate: "",
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+ LatestValue: 0,
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+ ChartImage: "",
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+ }
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+ newEdbInfoId, tmpErr := to.Insert(edbInfo)
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+ if tmpErr != nil {
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+ err = tmpErr
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+ return
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+ }
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+ edbInfo.EdbInfoId = int(newEdbInfoId)
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+
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+ // 添加关联关系
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+ {
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+ calculateMappingItem := &EdbInfoCalculateMapping{
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+ EdbInfoCalculateMappingId: 0,
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+ EdbInfoId: edbInfo.EdbInfoId,
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+ Source: edbInfo.Source,
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+ SourceName: edbInfo.SourceName,
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+ EdbCode: edbCode,
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+ FromEdbInfoId: fromEdbInfo.EdbInfoId,
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+ FromEdbCode: fromEdbInfo.EdbCode,
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+ FromEdbName: fromEdbInfo.EdbName,
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+ FromSource: fromEdbInfo.Source,
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+ FromSourceName: fromEdbInfo.SourceName,
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+ FromTag: "",
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+ Sort: 1,
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+ CreateTime: time.Now(),
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+ ModifyTime: time.Now(),
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+ }
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+ _, err = to.Insert(calculateMappingItem)
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+ if err != nil {
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+ return
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+ }
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+ }
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+ } else {
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+ edbInfo, err = GetEdbInfoById(reqEdbInfoId)
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+ if err != nil {
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+ if err.Error() == utils.ErrNoRow() {
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+ errMsg = `获取指标信息失败`
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+ }
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+ return
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+ }
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+ if edbInfo.EdbInfoType != 1 {
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+ errMsg = `该指标不是预测指标`
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+ err = errors.New(errMsg)
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+ return
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+ }
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+
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+ //判断计算指标是否被更换
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+ var existCondition string
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+ var existPars []interface{}
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+ existCondition += " AND edb_info_id=? AND from_edb_info_id=? "
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+ existPars = append(existPars, edbInfo.EdbInfoId, fromEdbInfo.EdbInfoId)
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+
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+ count, tmpErr := GetEdbInfoCalculateCountByCondition(existCondition, existPars)
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+ if tmpErr != nil {
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+ err = errors.New("判断指标是否改变失败,Err:" + tmpErr.Error())
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+ return
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+ }
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+ if count > 0 { // 指标未被替换，无需重新计算
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+ return
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+ }
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+
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+ //删除，计算指标关联的，基础指标的关联关系
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+ sql := ` DELETE FROM edb_info_calculate_mapping WHERE edb_info_id = ? `
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+ _, err = to.Raw(sql, edbInfo.EdbInfoId).Exec()
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+ if err != nil {
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+ err = errors.New("删除计算指标关联关系失败,Err:" + err.Error())
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+ return
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+ }
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+
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+ //清空原有已经入库的数据
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+ tableName := GetEdbDataTableName(edbInfo.Source)
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+ sql = ` DELETE FROM ` + tableName + ` WHERE edb_info_id = ? `
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+ _, err = to.Raw(sql, edbInfo.EdbInfoId).Exec()
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+ if err != nil {
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+ return
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+ }
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+
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+ //关联关系
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+ {
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+ calculateMappingItem := &EdbInfoCalculateMapping{
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+ EdbInfoCalculateMappingId: 0,
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+ EdbInfoId: edbInfo.EdbInfoId,
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+ Source: edbInfo.Source,
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+ SourceName: edbInfo.SourceName,
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+ EdbCode: edbCode,
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+ FromEdbInfoId: fromEdbInfo.EdbInfoId,
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+ FromEdbCode: fromEdbInfo.EdbCode,
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+ FromEdbName: fromEdbInfo.EdbName,
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+ FromSource: fromEdbInfo.Source,
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+ FromSourceName: fromEdbInfo.SourceName,
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+ FromTag: "",
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+ Sort: 1,
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+ CreateTime: time.Now(),
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+ ModifyTime: time.Now(),
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+ }
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+ _, err = to.Insert(calculateMappingItem)
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+ if err != nil {
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+ return
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+ }
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+ }
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+
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+ }
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+
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+ // 计算数据
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+ latestDateStr, latestValue, err = refreshAllPredictCalculateJp(to, edbInfo.EdbInfoId, edbInfo.Source, fromEdbInfo, edbCode, edbInfo.Frequency, formula)
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+
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+ return
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+}
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+
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+// RefreshAllPredictCalculateJp 刷新全部预测降频值数据
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+func RefreshAllPredictCalculateJp(edbInfoId, source int, fromEdbInfo *EdbInfo, edbCode, edbFrequency, formula string) (latestDateStr string, latestValue float64, err error) {
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+ o := orm.NewOrm()
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+ to, err := o.Begin()
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+ if err != nil {
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+ return
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+ }
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+ defer func() {
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+ if err != nil {
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+ fmt.Println("RefreshAllCalculateJp,Err:" + err.Error())
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+ _ = to.Rollback()
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+ } else {
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+ _ = to.Commit()
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+ }
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+ }()
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+
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+ // 计算数据
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+ latestDateStr, latestValue, err = refreshAllPredictCalculateJp(to, edbInfoId, source, fromEdbInfo, edbCode, edbFrequency, formula)
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+ return
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+}
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+
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+// refreshAllPredictCalculateJp 刷新预测降频数据
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+func refreshAllPredictCalculateJp(to orm.TxOrmer, edbInfoId, source int, fromEdbInfo *EdbInfo, edbCode, edbFrequency, formula string) (latestDateStr string, latestValue float64, err error) {
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+ edbInfoIdStr := strconv.Itoa(edbInfoId)
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+ //获取源指标数据
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+ fmt.Println("EdbInfoId:", fromEdbInfo.EdbInfoId)
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+ dataList, err := GetPredictEdbDataListAll(fromEdbInfo, 1)
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+ if err != nil {
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+ return
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+ }
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+ var dateArr []string
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+ dataMap := make(map[string]*EdbInfoSearchData)
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+ fromDataMap := make(map[string]float64)
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+ //来源指指标数据
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+ for _, v := range dataList {
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+ dateArr = append(dateArr, v.DataTime)
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+ dataMap[v.DataTime] = v
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+ fromDataMap[v.DataTime] = v.Value
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+ }
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+ fmt.Println("source:", source)
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+
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+ //获取降频指标所有数据
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+ existDataList, err := GetAllEdbDataListByTo(to, edbInfoId, source)
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+ if err != nil {
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+ return
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+ }
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+ //计算指标的map
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+ existDataMap := make(map[string]*EdbData, 0)
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+ for _, v := range existDataList {
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+ existDataMap[v.DataTime] = v
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+ }
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+
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+ latestDateStr = fromEdbInfo.LatestDate
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+
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+ tableName := GetEdbDataTableName(utils.DATA_SOURCE_PREDICT_CALCULATE_JP)
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+ addSql := ` INSERT INTO ` + tableName + ` (edb_info_id,edb_code,data_time,value,create_time,modify_time,data_timestamp) values `
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+ var isAdd bool
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+
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+ //existMap := make(map[string]string)
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+ dataLen := len(dataList)
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+ if dataLen <= 0 {
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+ return
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+ }
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+ startDataTime, _ := time.ParseInLocation(utils.FormatDate, dataList[0].DataTime, time.Local)
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+ endDataTime, _ := time.ParseInLocation(utils.FormatDate, dataList[dataLen-1].DataTime, time.Local)
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+
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+ var lastValue float64 // 最近的值
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+ var nextEndDate time.Time // 下一个节点的日期
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+ weekDayDataList := make([]float64, 0)
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+ for currStartDataTime := startDataTime; !currStartDataTime.After(endDataTime); currStartDataTime = currStartDataTime.AddDate(0, 0, 1) {
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+ // 将当前数据加入到 weekDayDataList
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+ if tmpData, ok := dataMap[currStartDataTime.Format(utils.FormatDate)]; ok {
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+ tmpValue := decimal.NewFromFloat(tmpData.Value)
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+ tmpValueFloat, _ := tmpValue.Round(4).Float64()
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+ weekDayDataList = append(weekDayDataList, tmpValueFloat)
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+ }
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+ // 如果下个节点的日期不存在,那么就先给赋值(兼容时间区间内只有一组数据的情况)
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+ if nextEndDate.IsZero() {
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+ nextEndDate = utils.GetFrequencyEndDay(currStartDataTime, edbFrequency)
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+ }
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+
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+ // 日期处理过滤
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+ switch edbFrequency {
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+ case "周度":
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+ if currStartDataTime.Weekday() != 0 {
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+ //不是周日,代表需要进入下一个循环获取数据并计算
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+ continue
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+ } else {
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+ //记录下一个结束节点的日期
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+ nextEndDate = currStartDataTime.AddDate(0, 0, 7)
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+ }
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+ case "旬度":
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+ nextDay := currStartDataTime.AddDate(0, 0, 1)
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+ if nextDay.Day() != 1 && nextDay.Day() != 11 && nextDay.Day() != 21 {
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+ //不是每月10、20、最后一天,代表需要进入下一个循环获取数据并计算
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+ continue
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+ } else {
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+ //记录下一个结束节点的日期
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+ if nextDay.Day() == 1 || nextDay.Day() == 11 {
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+ //月初或者月末的时候,加10天就好了
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+ nextEndDate = nextDay.AddDate(0, 0, 9)
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+ } else {
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+ tmpNextMonth := nextDay.AddDate(0, 1, 0)
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+ nextEndDate = time.Date(tmpNextMonth.Year(), tmpNextMonth.Month(), 1, 0, 0, 0, 0, time.Local).AddDate(0, 0, -1)
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+ }
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+ }
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+ case "月度":
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+ nextDay := currStartDataTime.AddDate(0, 0, 1)
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+ if nextDay.Day() != 1 {
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+ //不是每月最后一天,代表需要进入下一个循环获取数据并计算
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+ continue
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+ } else {
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+ //记录下一个结束节点的日期
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+ nextEndDate = nextDay.AddDate(0, 1, -1)
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+ }
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+ case "季度":
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+ nextDay := currStartDataTime.AddDate(0, 0, 1)
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+ if (nextDay.Month() == 1 || nextDay.Month() == 4 || nextDay.Month() == 7 || nextDay.Month() == 10) && nextDay.Day() == 1 {
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+ //记录下一个结束节点的日期
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+ nextEndDate = nextDay.AddDate(0, 3, -1)
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+ } else {
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+ //不是3,6,9,12 月份的最后一天,代表需要进入下一个循环获取数据并计算
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+ continue
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+ }
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+ case "年度":
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+ if currStartDataTime.Month() == 12 && currStartDataTime.Day() == 31 {
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+ //记录下一个结束节点的日期
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+ nextEndDate = currStartDataTime.AddDate(1, 0, 0)
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+ } else {
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+ //不是每年的12-31日,代表需要进入下一个循环获取数据并计算
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+ continue
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+ }
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+ default:
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+ err = errors.New("错误的频度:" + edbFrequency)
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+ return
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+ }
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+
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+ // 当前时间段内的数据计算,得出实际值
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+ var currVal float64
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+ lenWeekDayDataList := len(weekDayDataList)
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+ // 如果这个时间区间内没有数据,那么就采用上一个时间区间的值
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+ if len(weekDayDataList) <= 0 {
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+ currVal = lastValue
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+ } else {
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+ if formula == "期末值" {
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+ currVal = weekDayDataList[lenWeekDayDataList-1]
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+ } else {
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+ // 平均值
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+ sumValDeci := decimal.NewFromFloat(0)
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+ for _, v := range weekDayDataList {
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+ tmpValDeci := decimal.NewFromFloat(v)
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+ sumValDeci = sumValDeci.Add(tmpValDeci)
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+ }
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+ lenDeci := decimal.NewFromInt(int64(lenWeekDayDataList))
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+ currVal, _ = sumValDeci.Div(lenDeci).Round(4).Float64()
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+ }
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+ }
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+
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+ // 给实际日期数据的值赋值
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+ if fromEdbInfo.LatestDate == currStartDataTime.Format(utils.FormatDate) {
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+ latestValue = currVal
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+ }
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+
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+ // 判断降频指标是否存在数据
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+ if existData, ok := existDataMap[currStartDataTime.Format(utils.FormatDate)]; ok {
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+ // 处理降频数据的值
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+ existValStr := existData.Value
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+ existValDeci, tmpErr := decimal.NewFromString(existValStr)
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+ if tmpErr != nil {
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+ err = tmpErr
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+ return
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+ }
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+ existVal, _ := existValDeci.Round(4).Float64()
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+ // 判断降频数据的值 与 当前计算出来的结果, 如果两个数据结果不相等的话,那么就修改咯
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+ if existVal != currVal {
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+ err = ModifyEdbDataById(source, existData.EdbDataId, fmt.Sprint(currVal))
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+ if err != nil {
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+ return
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+ }
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+ }
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+ } else {
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+ // 直接入库
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+ timestamp := currStartDataTime.UnixNano() / 1e6
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+ timestampStr := fmt.Sprintf("%d", timestamp)
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+ addSql += GetAddSql(edbInfoIdStr, edbCode, currStartDataTime.Format(utils.FormatDate), timestampStr, fmt.Sprint(currVal))
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+ isAdd = true
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+ }
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+
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+ // 一轮结束后,数据清空
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+ weekDayDataList = make([]float64, 0)
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+ }
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+
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+ // 最后已有的日期处理完成后,需要对剩余不在时间段内的数据做处理
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+ if len(weekDayDataList) > 0 {
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+ // 当前时间段内的数据计算,得出实际值
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+ var currVal float64
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+ lenWeekDayDataList := len(weekDayDataList)
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+ // 如果这个时间区间内没有数据,那么就采用上一个时间区间的值
|
|
|
+ if len(weekDayDataList) < 0 {
|
|
|
+ currVal = lastValue
|
|
|
+ } else {
|
|
|
+ if formula == "期末值" {
|
|
|
+ currVal = weekDayDataList[lenWeekDayDataList-1]
|
|
|
+ } else {
|
|
|
+ // 平均值
|
|
|
+ sumValDeci := decimal.NewFromFloat(0)
|
|
|
+ for _, v := range weekDayDataList {
|
|
|
+ tmpValDeci := decimal.NewFromFloat(v)
|
|
|
+ sumValDeci = sumValDeci.Add(tmpValDeci)
|
|
|
+ }
|
|
|
+ lenDeci := decimal.NewFromInt(int64(lenWeekDayDataList))
|
|
|
+ currVal, _ = sumValDeci.Div(lenDeci).Round(4).Float64()
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 判断降频指标是否存在数据
|
|
|
+ if existData, ok := existDataMap[nextEndDate.Format(utils.FormatDate)]; ok {
|
|
|
+ // 处理降频数据的值
|
|
|
+ existValStr := existData.Value
|
|
|
+ existValDeci, tmpErr := decimal.NewFromString(existValStr)
|
|
|
+ if tmpErr != nil {
|
|
|
+ err = tmpErr
|
|
|
+ return
|
|
|
+ }
|
|
|
+ existVal, _ := existValDeci.Round(4).Float64()
|
|
|
+ // 判断降频数据的值 与 当前计算出来的结果, 如果两个数据结果不相等的话,那么就修改咯
|
|
|
+ if existVal != currVal {
|
|
|
+ err = ModifyEdbDataById(source, existData.EdbDataId, fmt.Sprint(currVal))
|
|
|
+ if err != nil {
|
|
|
+ return
|
|
|
+ }
|
|
|
+ }
|
|
|
+ } else {
|
|
|
+ // 直接入库
|
|
|
+ timestamp := nextEndDate.UnixNano() / 1e6
|
|
|
+ timestampStr := fmt.Sprintf("%d", timestamp)
|
|
|
+ addSql += GetAddSql(edbInfoIdStr, edbCode, nextEndDate.Format(utils.FormatDate), timestampStr, fmt.Sprint(currVal))
|
|
|
+ isAdd = true
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if isAdd {
|
|
|
+ addSql = strings.TrimRight(addSql, ",")
|
|
|
+ _, err = to.Raw(addSql).Exec()
|
|
|
+ }
|
|
|
+
|
|
|
+ return
|
|
|
+}
|
