hz_crm_api/services/data/predict_edb_info.go

package data

import (
	"encoding/json"
	"errors"
	"fmt"
	"github.com/shopspring/decimal"
	"github.com/yidane/formula"
	"hongze/hz_crm_api/models/data_manage"
	"hongze/hz_crm_api/models/data_manage/request"
	"hongze/hz_crm_api/models/system"
	"hongze/hz_crm_api/utils"
	"strconv"
	"strings"
	"time"
)

// AddPredictEdbInfo 新增预测指标
func AddPredictEdbInfo(sourceEdbInfoId, classifyId int, edbName string, ruleList []request.RuleConfig, sysUserId int, sysUserName, requestBody, requestUrl string) (edbInfo *data_manage.EdbInfo, err error, errMsg string) {
	var sourceEdbInfo *data_manage.EdbInfo
	// 来源指标信息校验
	{
		sourceEdbInfo, err = data_manage.GetEdbInfoById(sourceEdbInfoId)
		if err != nil && err.Error() != utils.ErrNoRow() {
			errMsg = "新增失败"
			err = errors.New("获取来源指标失败，Err:" + err.Error())
			return
		}
		if sourceEdbInfo == nil {
			errMsg = "找不到该来源指标"
			err = nil
			return
		}
		//必须是普通的指标
		if sourceEdbInfo.EdbInfoType != 0 {
			errMsg = "来源指标异常，不是普通的指标"
			return
		}
		if !utils.InArrayByStr([]string{"日度", "周度", "月度"}, sourceEdbInfo.Frequency) {
			errMsg = "预测指标只支持选择日度、周度、月度的指标"
			return
		}
	}

	var classifyInfo *data_manage.EdbClassify
	// 来源分类信息校验
	{
		classifyInfo, err = data_manage.GetEdbClassifyById(classifyId)
		if err != nil && err.Error() != utils.ErrNoRow() {
			errMsg = "新增失败"
			err = errors.New("获取预测指标分类失败，Err:" + err.Error())
			return
		}
		if classifyInfo == nil {
			errMsg = "找不到该预测指标分类"
			err = nil
			return
		}
		//必须是预测指标分类
		if classifyInfo.ClassifyType != 1 {
			errMsg = "预测指标分类异常，不是预测指标分类"
			return
		}
	}

	edbName = strings.Trim(edbName, " ")

	edbCode := sourceEdbInfo.EdbCode + "_" + time.Now().Format(utils.FormatShortDateTimeUnSpace)

	// 判断该来源指标是否已经被引用了
	{
		//predictEdbConf, tmpErr := data_manage.GetPredictEdbConfBySourceEdbInfoId(sourceEdbInfoId)
		//if tmpErr != nil && tmpErr.Error() != utils.ErrNoRow() {
		//	errMsg = "新增失败"
		//	err = tmpErr
		//	return
		//}

		// 如果该来源指标已经被引用了，那么不允许再次使用
		//if predictEdbConf != nil {
		//	//获取预测指标详情
		//	predictEdbInfo, tmpErr := data_manage.GetEdbInfoById(predictEdbConf.PredictEdbInfoId)
		//	if tmpErr != nil {
		//		errMsg = "新增失败"
		//		err = tmpErr
		//		return
		//	}
		//	//获取预测指标的分类
		//	edbClassifyInfo, tmpErr := data_manage.GetEdbClassifyById(predictEdbInfo.ClassifyId)
		//	if tmpErr != nil {
		//		errMsg = "新增失败"
		//		err = tmpErr
		//		return
		//	}
		//	errMsg = "该指标已存在数据库，目录为：" + edbClassifyInfo.ClassifyName + "，请重新选择指标"
		//	err = errors.New(errMsg)
		//	return
		//}
	}

	//判断指标名称是否存在
	var condition string
	var pars []interface{}
	condition += " AND edb_info_type=? "
	pars = append(pars, 1)

	condition += " AND edb_name=? "
	pars = append(pars, edbName)

	count, err := data_manage.GetEdbInfoCountByCondition(condition, pars)
	if err != nil {
		errMsg = "判断指标名称是否存在失败"
		err = errors.New("判断指标名称是否存在失败,Err:" + err.Error())
		return
	}
	if count > 0 {
		errMsg = "指标名称已存在，请重新填写"
		return
	}

	timestamp := strconv.FormatInt(time.Now().UnixNano(), 10)
	edbInfo = &data_manage.EdbInfo{
		//EdbInfoId:   0,
		EdbInfoType:      1,
		SourceName:       "预测指标",
		Source:           utils.DATA_SOURCE_PREDICT,
		EdbCode:          edbCode,
		EdbName:          edbName,
		EdbNameSource:    edbName,
		Frequency:        sourceEdbInfo.Frequency,
		Unit:             sourceEdbInfo.Unit,
		StartDate:        sourceEdbInfo.StartDate,
		ClassifyId:       classifyId,
		SysUserId:        sysUserId,
		SysUserRealName:  sysUserName,
		UniqueCode:       utils.MD5(utils.DATA_PREFIX + "_" + timestamp),
		CreateTime:       time.Now(),
		ModifyTime:       time.Now(),
		MinValue:         sourceEdbInfo.MinValue,
		MaxValue:         sourceEdbInfo.MaxValue,
		CalculateFormula: sourceEdbInfo.CalculateFormula,
		EdbType:          1,
		//Sort:             sourceEdbInfo.,
		LatestDate:    sourceEdbInfo.LatestDate,
		LatestValue:   sourceEdbInfo.LatestValue,
		MoveType:      sourceEdbInfo.MoveType,
		MoveFrequency: sourceEdbInfo.MoveFrequency,
		NoUpdate:      sourceEdbInfo.NoUpdate,
		ServerUrl:     "",
	}

	// 关联关系表
	calculateMappingList := make([]*data_manage.EdbInfoCalculateMapping, 0)
	fromEdbMap := make(map[int]int)

	// 源指标关联关系表
	calculateMappingItem := &data_manage.EdbInfoCalculateMapping{
		//EdbInfoCalculateMappingId: 0,
		//EdbInfoId:                 0,
		Source:         edbInfo.Source,
		SourceName:     edbInfo.SourceName,
		EdbCode:        edbInfo.EdbCode,
		FromEdbInfoId:  sourceEdbInfo.EdbInfoId,
		FromEdbCode:    sourceEdbInfo.EdbCode,
		FromEdbName:    sourceEdbInfo.EdbName,
		FromSource:     sourceEdbInfo.Source,
		FromSourceName: sourceEdbInfo.SourceName,
		//FromTag:        "",
		Sort:       1,
		CreateTime: time.Now(),
		ModifyTime: time.Now(),
	}
	fromEdbMap[sourceEdbInfoId] = sourceEdbInfoId
	calculateMappingList = append(calculateMappingList, calculateMappingItem)

	// 预测指标配置
	predictEdbConfList := make([]*data_manage.PredictEdbConf, 0)
	for _, v := range ruleList {
		// 预测指标配置
		ruleEndDate, tmpErr := time.ParseInLocation(utils.FormatDate, v.EndDate, time.Local)
		if tmpErr != nil {
			errMsg = "规则配置的截止日期异常，请重新填写"
			return
		}
		// 没有数据，自己瞎测试
		//switch v.RuleType {
		//case 3: //3：同比
		//	v.Value = "0.1"
		//case 4: //4：同差
		//	v.Value = "10"
		//case 5: //5：环比
		//	v.Value = "0.1"
		//case 6: //6：环差
		//	v.Value = "0.1"
		//case 7: //7：N期移动均值
		//	v.Value = "5"
		//case 8: //8：N期段线性外推值
		//	v.Value = "5"
		//}
		switch v.RuleType {
		case 8: //N期段线性外推值
			valInt, tmpErr := strconv.Atoi(v.Value)
			if tmpErr != nil {
				errMsg = "N期段线性外推值的N值异常"
				return
			}
			if valInt <= 1 {
				errMsg = "N期段线性外推值的N值必须大于1"
				return
			}
		case 9: //9：动态环差
			for _, v := range v.EdbInfoIdArr {
				fromEdbMap[v.EdbInfoId] = v.EdbInfoId
			}
		}

		tmpPredictEdbConf := &data_manage.PredictEdbConf{
			PredictEdbInfoId: 0,
			SourceEdbInfoId:  sourceEdbInfoId,
			RuleType:         v.RuleType,
			//FixedValue:       v.Value,
			Value:      v.Value,
			EndDate:    ruleEndDate,
			ModifyTime: time.Now(),
			CreateTime: time.Now(),
		}

		edbInfo.EndDate = v.EndDate

		predictEdbConfList = append(predictEdbConfList, tmpPredictEdbConf)
	}
	err = data_manage.AddPredictEdb(edbInfo, calculateMappingItem, predictEdbConfList)
	if err != nil {
		errMsg = "保存失败"
		err = errors.New("保存失败，Err:" + err.Error())
		return
	}

	//新增操作日志
	{
		edbLog := new(data_manage.EdbInfoLog)
		edbLog.EdbInfoId = edbInfo.EdbInfoId
		edbLog.SourceName = edbInfo.SourceName
		edbLog.Source = edbInfo.Source
		edbLog.EdbCode = edbInfo.EdbCode
		edbLog.EdbName = edbInfo.EdbName
		edbLog.ClassifyId = edbInfo.ClassifyId
		edbLog.SysUserId = sysUserId
		edbLog.SysUserRealName = sysUserName
		edbLog.CreateTime = time.Now()
		edbLog.Content = requestBody
		edbLog.Status = "新增指标"
		edbLog.Method = requestUrl
		go data_manage.AddEdbInfoLog(edbLog)
	}

	//添加es
	AddOrEditEdbInfoToEs(edbInfo.EdbInfoId)

	return
}

// EditPredictEdbInfo 编辑预测指标
func EditPredictEdbInfo(edbInfoId, classifyId int, edbName string, ruleList []request.RuleConfig, sysUserId int, sysUserName, requestBody, requestUrl string) (edbInfo *data_manage.EdbInfo, err error, errMsg string) {
	// 指标信息校验
	{
		edbInfo, err = data_manage.GetEdbInfoById(edbInfoId)
		if err != nil && err.Error() != utils.ErrNoRow() {
			errMsg = "修改失败"
			err = errors.New("获取预测指标失败，Err:" + err.Error())
			return
		}
		if edbInfo == nil {
			errMsg = "找不到该预测指标"
			err = nil
			return
		}
		//必须是普通的指标
		if edbInfo.EdbInfoType != 1 {
			errMsg = "指标异常，不是预测指标"
			return
		}
	}

	var predictEdbConf *data_manage.PredictEdbConf
	// 指标配置信息校验
	{
		// 查找该预测指标配置
		predictEdbConfList, tmpErr := data_manage.GetPredictEdbConfListById(edbInfo.EdbInfoId)
		if tmpErr != nil && tmpErr.Error() != utils.ErrNoRow() {
			errMsg = "修改失败"
			err = errors.New("获取预测指标配置信息失败，Err:" + tmpErr.Error())
			return
		}
		if len(predictEdbConfList) == 0 {
			errMsg = "找不到该预测指标配置"
			err = nil
			return
		}
		predictEdbConf = predictEdbConfList[0]
	}

	//判断指标名称是否存在
	var condition string
	var pars []interface{}

	condition += " AND edb_info_id<>? "
	pars = append(pars, edbInfoId)

	condition += " AND edb_info_type=? "
	pars = append(pars, 1)

	condition += " AND edb_name=? "
	pars = append(pars, edbName)

	count, err := data_manage.GetEdbInfoCountByCondition(condition, pars)
	if err != nil {
		errMsg = "判断指标名称是否存在失败"
		err = errors.New("判断指标名称是否存在失败,Err:" + err.Error())
		return
	}

	if count > 0 {
		errMsg = "指标名称已存在，请重新填写"
		return
	}

	edbInfo.EdbName = edbName
	edbInfo.EdbNameSource = edbName
	edbInfo.ClassifyId = classifyId
	edbInfo.ModifyTime = time.Now()
	updateEdbInfoCol := []string{"EdbName", "EdbNameSource", "ClassifyId", "EndDate", "ModifyTime"}

	// 预测指标配置
	predictEdbConfList := make([]*data_manage.PredictEdbConf, 0)
	for _, v := range ruleList {
		// 预测指标配置
		ruleEndDate, tmpErr := time.ParseInLocation(utils.FormatDate, v.EndDate, time.Local)
		if tmpErr != nil {
			errMsg = "规则配置的截止日期异常，请重新填写"
			return
		}
		switch v.RuleType {
		case 8: //N期段线性外推值
			valInt, tmpErr := strconv.Atoi(v.Value)
			if tmpErr != nil {
				errMsg = "N期段线性外推值的N值异常"
				return
			}
			if valInt <= 1 {
				errMsg = "N期段线性外推值的N值必须大于1"
				return
			}
		case 9: //9：动态环差

		}

		tmpPredictEdbConf := &data_manage.PredictEdbConf{
			PredictEdbInfoId: edbInfoId,
			SourceEdbInfoId:  predictEdbConf.SourceEdbInfoId,
			RuleType:         v.RuleType,
			//FixedValue:       v.Value,
			Value:      v.Value,
			EndDate:    ruleEndDate,
			ModifyTime: time.Now(),
			CreateTime: time.Now(),
		}
		predictEdbConfList = append(predictEdbConfList, tmpPredictEdbConf)

		edbInfo.EndDate = v.EndDate
	}

	err = data_manage.EditPredictEdb(edbInfo, predictEdbConfList, updateEdbInfoCol)
	if err != nil {
		errMsg = "保存失败"
		err = errors.New("保存失败，Err:" + err.Error())
		return
	}

	//新增操作日志
	{
		edbLog := new(data_manage.EdbInfoLog)
		edbLog.EdbInfoId = edbInfo.EdbInfoId
		edbLog.SourceName = edbInfo.SourceName
		edbLog.Source = edbInfo.Source
		edbLog.EdbCode = edbInfo.EdbCode
		edbLog.EdbName = edbInfo.EdbName
		edbLog.ClassifyId = edbInfo.ClassifyId
		edbLog.SysUserId = sysUserId
		edbLog.SysUserRealName = sysUserName
		edbLog.CreateTime = time.Now()
		edbLog.Content = requestBody
		edbLog.Status = "编辑指标"
		edbLog.Method = requestUrl
		go data_manage.AddEdbInfoLog(edbLog)
	}

	//添加es
	AddOrEditEdbInfoToEs(edbInfoId)

	// 刷新关联指标
	go EdbInfoRefreshAllFromBaseV2(edbInfo.EdbInfoId, true)
	return
}

// RefreshPredictEdbInfo 刷新预测指标
func RefreshPredictEdbInfo(edbInfoId int, refreshAll bool) (edbInfo *data_manage.EdbInfo, err error, errMsg string) {
	// 指标信息校验
	{
		edbInfo, err = data_manage.GetEdbInfoById(edbInfoId)
		if err != nil && err.Error() != utils.ErrNoRow() {
			errMsg = "刷新失败"
			err = errors.New("获取预测指标失败，Err:" + err.Error())
			return
		}
		if edbInfo == nil {
			errMsg = "找不到该预测指标"
			err = nil
			return
		}
		//必须是预测的指标
		if edbInfo.EdbInfoType != 1 {
			errMsg = "指标异常，不是预测指标"
			return
		}
	}
	err = EdbInfoRefreshAllFromBaseV2(edbInfo.EdbInfoId, refreshAll)

	return
}

// MovePredictEdbInfo 移动预测指标
func MovePredictEdbInfo(edbInfoId, classifyId, prevEdbInfoId, nextEdbInfoId int, sysUser *system.Admin, requestBody, requestUrl string) (err error, errMsg string) {
	//判断分类是否存在
	count, _ := data_manage.GetEdbClassifyCountById(classifyId)
	if count <= 0 {
		errMsg = "分类已被删除，不可移动，请刷新页面"
		return
	}

	edbInfo, err := data_manage.GetEdbInfoById(edbInfoId)
	if err != nil {
		if err != nil && err.Error() != utils.ErrNoRow() {
			errMsg = "移动失败"
			err = errors.New("获取预测指标失败，Err:" + err.Error())
			return
		}
		if edbInfo == nil {
			errMsg = "找不到该预测指标"
			err = nil
			return
		}
		return
	}

	// 移动权限校验
	button := GetEdbOpButton(sysUser, edbInfo.SysUserId, edbInfo.EdbType, edbInfo.EdbInfoType)
	if !button.MoveButton {
		errMsg = "无权限操作"
		err = nil
		return
		return
	}

	//如果改变了分类，那么移动该指标数据
	if edbInfo.ClassifyId != classifyId {
		err = data_manage.MoveEdbInfo(edbInfoId, classifyId)
		if err != nil {
			errMsg = "移动失败"
			err = errors.New("移动预测指标失败,Err:" + err.Error())
			return
		}
	}

	updateCol := make([]string, 0)
	//如果有传入 上一个兄弟节点分类id
	if prevEdbInfoId > 0 {
		prevEdbInfo, tmpErr := data_manage.GetEdbInfoById(prevEdbInfoId)
		if tmpErr != nil {
			errMsg = "移动失败"
			err = errors.New("获取上一个兄弟节点分类信息失败,Err:" + tmpErr.Error())
			return
		}
		//如果是移动在两个兄弟节点之间
		if nextEdbInfoId > 0 {
			//下一个兄弟节点
			nextEdbInfo, tmpErr := data_manage.GetEdbInfoById(nextEdbInfoId)
			if tmpErr != nil {
				errMsg = "移动失败"
				err = errors.New("获取下一个兄弟节点分类信息失败,Err:" + tmpErr.Error())
				return
			}
			//如果上一个兄弟与下一个兄弟的排序权重是一致的，那么需要将下一个兄弟（以及下个兄弟的同样排序权重）的排序权重+2，自己变成上一个兄弟的排序权重+1
			if prevEdbInfo.Sort == nextEdbInfo.Sort || prevEdbInfo.Sort == edbInfo.Sort {
				//变更兄弟节点的排序
				updateSortStr := `sort + 2`
				_ = data_manage.UpdateEdbInfoSortByClassifyId(prevEdbInfo.ClassifyId, prevEdbInfo.Sort, prevEdbInfo.EdbInfoId, updateSortStr)
			} else {
				//如果下一个兄弟的排序权重正好是上个兄弟节点 的下一层，那么需要再加一层了
				if nextEdbInfo.Sort-prevEdbInfo.Sort == 1 {
					//变更兄弟节点的排序
					updateSortStr := `sort + 1`
					_ = data_manage.UpdateEdbInfoSortByClassifyId(prevEdbInfo.ClassifyId, prevEdbInfo.Sort, prevEdbInfo.EdbInfoId, updateSortStr)
				}
			}
		}

		edbInfo.Sort = prevEdbInfo.Sort + 1
		edbInfo.ModifyTime = time.Now()
		updateCol = append(updateCol, "Sort", "ModifyTime")

	} else {
		firstClassify, tmpErr := data_manage.GetFirstEdbInfoByClassifyId(classifyId)
		if tmpErr != nil && tmpErr.Error() != utils.ErrNoRow() {
			errMsg = "移动失败"
			err = errors.New("获取获取当前父级分类下的排序第一条的分类信息失败,Err:" + err.Error())
			return
		}

		//如果该分类下存在其他分类，且第一个其他分类的排序等于0，那么需要调整排序
		if firstClassify != nil && firstClassify.Sort == 0 {
			updateSortStr := ` sort + 1 `
			_ = data_manage.UpdateEdbInfoSortByClassifyId(firstClassify.ClassifyId, 0, firstClassify.EdbInfoId-1, updateSortStr)
		}

		edbInfo.Sort = 0 //那就是排在第一位
		edbInfo.ModifyTime = time.Now()
		updateCol = append(updateCol, "Sort", "ModifyTime")
	}

	//更新
	if len(updateCol) > 0 {
		err = edbInfo.Update(updateCol)

	}
	if err != nil {
		errMsg = "移动失败"
		err = errors.New("修改失败,Err:" + err.Error())
		return
	}
	//新增操作日志
	{
		edbLog := new(data_manage.EdbInfoLog)
		edbLog.EdbInfoId = edbInfo.EdbInfoId
		edbLog.SourceName = edbInfo.SourceName
		edbLog.Source = edbInfo.Source
		edbLog.EdbCode = edbInfo.EdbCode
		edbLog.EdbName = edbInfo.EdbName
		edbLog.ClassifyId = edbInfo.ClassifyId
		edbLog.SysUserId = sysUser.AdminId
		edbLog.SysUserRealName = sysUser.RealName
		edbLog.CreateTime = time.Now()
		edbLog.Content = requestBody
		edbLog.Status = "移动指标"
		edbLog.Method = requestUrl
		go data_manage.AddEdbInfoLog(edbLog)
	}
	return
}

// GetPredictEdbInfoDataList 获取预测指标的未来数据
func GetPredictEdbInfoDataList(predictEdbConf data_manage.PredictEdbConf, latestDateStr string, lastDataValue float64, endDateStr, frequency string) (predictEdbInfoData []*data_manage.EdbData, err error) {
	endDate, err := time.ParseInLocation(utils.FormatDate, endDateStr, time.Local)
	if err != nil {
		return
	}

	latestDate, err := time.ParseInLocation(utils.FormatDate, latestDateStr, time.Local)
	if err != nil {
		return
	}

	dataValue := lastDataValue
	if predictEdbConf.RuleType == 2 {
		dataValue = predictEdbConf.FixedValue
	}
	//获取后面的预测数据
	dayList := getPredictEdbDayList(latestDate, endDate, frequency)
	predictEdbInfoData = make([]*data_manage.EdbData, 0)
	lenDayList := len(dayList)
	if lenDayList > 0 {
		for i := lenDayList - 1; i >= 0; i-- {
			v := dayList[i]
			predictEdbInfoData = append(predictEdbInfoData, &data_manage.EdbData{
				EdbDataId: predictEdbConf.PredictEdbInfoId + 100000 + i,
				EdbInfoId: predictEdbConf.PredictEdbInfoId,
				DataTime:  v.Format(utils.FormatDate),
				Value:     dataValue,
			})
		}
	}

	return
}

// GetChartPredictEdbInfoDataList 获取图表的预测指标的未来数据
func GetChartPredictEdbInfoDataList(predictEdbConf data_manage.PredictEdbConf, filtrateStartDateStr, latestDateStr string, lastDataValue float64, endDateStr, frequency string) (predictEdbInfoData []*data_manage.EdbDataList, err error) {
	endDate, err := time.ParseInLocation(utils.FormatDate, endDateStr, time.Local)
	if err != nil {
		return
	}

	latestDate, err := time.ParseInLocation(utils.FormatDate, latestDateStr, time.Local)
	if err != nil {
		return
	}

	// 开始预测数据的时间
	startDate := latestDate

	// 如果有筛选时间的话
	if filtrateStartDateStr != `` {
		filtrateStartDate, tmpErr := time.ParseInLocation(utils.FormatDate, filtrateStartDateStr, time.Local)
		if tmpErr != nil {
			err = tmpErr
			return
		}
		//如果筛选时间晚于实际数据时间，那么就以筛选时间作为获取预测数据的时间
		if filtrateStartDate.After(latestDate) {
			startDate = filtrateStartDate.AddDate(0, 0, -1)
		}
	}

	dataValue := lastDataValue
	if predictEdbConf.RuleType == 2 {
		dataValue = predictEdbConf.FixedValue
	}
	//获取后面的预测数据
	dayList := getPredictEdbDayList(startDate, endDate, frequency)
	predictEdbInfoData = make([]*data_manage.EdbDataList, 0)
	for k, v := range dayList {
		predictEdbInfoData = append(predictEdbInfoData, &data_manage.EdbDataList{
			EdbDataId:     predictEdbConf.PredictEdbInfoId + 100000 + k,
			EdbInfoId:     predictEdbConf.PredictEdbInfoId,
			DataTime:      v.Format(utils.FormatDate),
			Value:         dataValue,
			DataTimestamp: v.UnixNano() / 1e6,
		})
	}
	return
}

// GetChartPredictEdbInfoDataListByConfList 获取图表的预测指标的未来数据
func GetChartPredictEdbInfoDataListByConfList(predictEdbConfList []data_manage.PredictEdbConfAndData, filtrateStartDateStr, latestDateStr, endDateStr, frequency string, realPredictEdbInfoData []*data_manage.EdbDataList) (predictEdbInfoData []*data_manage.EdbDataList, minValue, maxValue float64, err error, errMsg string) {
	endDate, err := time.ParseInLocation(utils.FormatDate, endDateStr, time.Local)
	if err != nil {
		return
	}

	latestDate, err := time.ParseInLocation(utils.FormatDate, latestDateStr, time.Local)
	if err != nil {
		return
	}

	// 开始预测数据的时间
	startDate := latestDate

	// 如果有筛选时间的话
	if filtrateStartDateStr != `` {
		filtrateStartDate, tmpErr := time.ParseInLocation(utils.FormatDate, filtrateStartDateStr, time.Local)
		if tmpErr != nil {
			err = tmpErr
			return
		}
		//如果筛选时间晚于实际数据时间，那么就以筛选时间作为获取预测数据的时间
		if filtrateStartDate.After(latestDate) {
			startDate = filtrateStartDate.AddDate(0, 0, -1)
		}
	}

	//var dateArr []string
	// 对应日期的值
	existMap := make(map[string]float64)
	for _, v := range realPredictEdbInfoData {
		//dateArr = append(dateArr, v.DataTime)
		existMap[v.DataTime] = v.Value
	}

	predictEdbInfoData = make([]*data_manage.EdbDataList, 0)
	//dataValue := lastDataValue
	//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

	for _, predictEdbConf := range predictEdbConfList {
		dataEndTime := endDate
		if predictEdbConf.EndDate.Before(dataEndTime) {
			dataEndTime = predictEdbConf.EndDate
		}

		var tmpMinValue, tmpMaxValue float64 // 当前预测结果中的最大/最小值

		dayList := getPredictEdbDayList(startDate, dataEndTime, frequency)
		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
			continue
		}

		switch predictEdbConf.RuleType {
		case 1: //1：最新
			var lastDataValue float64 //最新值
			tmpAllData := make([]*data_manage.EdbDataList, 0)
			tmpAllData = append(tmpAllData, realPredictEdbInfoData...)
			tmpAllData = append(tmpAllData, predictEdbInfoData...)
			lenTmpAllData := len(tmpAllData)
			if lenTmpAllData > 0 {
				lastDataValue = tmpAllData[lenTmpAllData-1].Value
			}
			predictEdbInfoData = GetChartPredictEdbInfoDataListByRule1(predictEdbConf.PredictEdbInfoId, lastDataValue, dayList, predictEdbInfoData, existMap)
			tmpMaxValue = lastDataValue
			tmpMinValue = lastDataValue
		case 2: //2：固定值
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			dataValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData = GetChartPredictEdbInfoDataListByRule1(predictEdbConf.PredictEdbInfoId, dataValue, dayList, predictEdbInfoData, existMap)

			tmpMaxValue = dataValue
			tmpMinValue = dataValue
		case 3: //3：同比
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			tbValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleTb(predictEdbConf.PredictEdbInfoId, tbValue, dayList, frequency, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 4: //4：同差
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			tcValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleTc(predictEdbConf.PredictEdbInfoId, tcValue, dayList, frequency, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 5: //5：环比
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			hbValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleHb(predictEdbConf.PredictEdbInfoId, hbValue, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 6: //6：环差
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			hcValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleHc(predictEdbConf.PredictEdbInfoId, hcValue, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 7: //7：N期移动均值
			nValue, tmpErr := strconv.Atoi(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleNMoveMeanValue(predictEdbConf.PredictEdbInfoId, nValue, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 8: //8：N期段线性外推值
			nValue, tmpErr := strconv.Atoi(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			if nValue <= 1 {
				errMsg = `N期段线性外推值的N值必须大于1`
				err = errors.New(errMsg)
				return
			}
			predictEdbInfoData, tmpMinValue, tmpMaxValue, err = GetChartPredictEdbInfoDataListByRuleNLinearRegression(predictEdbConf.PredictEdbInfoId, nValue, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
			if err != nil {
				return
			}
		case 9: //9：动态环差”预测规则；
			//规则计算的环差值map
			hcDataMap := make(map[string]float64)
			if predictEdbConf.PredictEdbInfoId > 0 { //已经生成的动态数据
				tmpPredictEdbRuleDataList, tmpErr := data_manage.GetPredictEdbRuleDataList(predictEdbConf.PredictEdbInfoId, predictEdbConf.ConfigId, startDate.Format(utils.FormatDate), endDate.Format(utils.FormatDate))
				if tmpErr != nil {
					err = tmpErr
					return
				}
				for _, v := range tmpPredictEdbRuleDataList {
					hcDataMap[v.DataTime] = v.Value
				}
			} else { //未生成的动态数据，需要使用外部传入的数据进行计算
				if len(predictEdbConf.DataList) <= 0 {
					return
				}
				for _, v := range predictEdbConf.DataList {
					currentDate, tmpErr := time.ParseInLocation(utils.FormatDate, v.DataTime, time.Local)
					if tmpErr != nil {
						continue
					}
					// 只处理时间段内的数据
					if currentDate.Before(startDate) || currentDate.After(endDate) {
						continue
					}
					hcDataMap[v.DataTime] = v.Value
				}
			}

			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleTrendsHC(predictEdbConf.PredictEdbInfoId, dayList, realPredictEdbInfoData, predictEdbInfoData, hcDataMap, existMap)
		case 10: //10：根据 给定终值后插值 规则获取预测数据
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			finalValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleFinalValueHc(predictEdbConf.PredictEdbInfoId, finalValue, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 11: //11：根据 季节性 规则获取预测数据
			predictEdbInfoData, tmpMinValue, tmpMaxValue, err = GetChartPredictEdbInfoDataListByRuleSeason(predictEdbConf.PredictEdbInfoId, predictEdbConf.Value, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
			if err != nil {
				return
			}
		case 12: //12：根据 移动平均同比 规则获取预测数据
			var moveAverageConf MoveAverageConf
			tmpErr := json.Unmarshal([]byte(predictEdbConf.Value), &moveAverageConf)
			if tmpErr != nil {
				err = errors.New("季节性配置信息异常:" + tmpErr.Error())
				return
			}
			predictEdbInfoData, tmpMinValue, tmpMaxValue, err = GetChartPredictEdbInfoDataListByRuleMoveAverageTb(predictEdbConf.PredictEdbInfoId, moveAverageConf.NValue, moveAverageConf.Year, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
			if err != nil {
				return
			}
		case 13: //13：根据 同比增速差值 规则获取预测数据
			tmpValDecimal, tmpErr := decimal.NewFromString(predictEdbConf.Value)
			if tmpErr != nil {
				err = tmpErr
				return
			}
			tbEndValue, _ := tmpValDecimal.Float64()
			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleTbzscz(predictEdbConf.PredictEdbInfoId, tbEndValue, dayList, frequency, realPredictEdbInfoData, predictEdbInfoData, existMap)
		case 14: //14：根据 一元线性拟合 规则获取预测数据
			var ruleConf RuleLineNhConf
			err = json.Unmarshal([]byte(predictEdbConf.Value), &ruleConf)
			if err != nil {
				err = errors.New("一元线性拟合配置信息异常:" + err.Error())
				return
			}

			// 规则计算的拟合残差值map
			newNhccDataMap := make(map[string]float64)
			if predictEdbConf.PredictEdbInfoId > 0 { //已经生成的动态数据
				tmpPredictEdbRuleDataList, tmpErr := data_manage.GetPredictEdbRuleDataList(predictEdbConf.PredictEdbInfoId, predictEdbConf.ConfigId, "", "")
				if tmpErr != nil {
					err = tmpErr
					return
				}
				for _, v := range tmpPredictEdbRuleDataList {
					newNhccDataMap[v.DataTime] = v.Value
				}
			} else { //未生成的动态数据，需要使用外部传入的数据进行计算
				newNhccDataMap, err = getCalculateNhccData(append(realPredictEdbInfoData, predictEdbInfoData...), ruleConf)
				if err != nil {
					return
				}
			}

			predictEdbInfoData, tmpMinValue, tmpMaxValue, err = GetChartPredictEdbInfoDataListByRuleLineNh(predictEdbConf.PredictEdbInfoId, dayList, realPredictEdbInfoData, predictEdbInfoData, newNhccDataMap, existMap)
			if err != nil {
				return
			}

		case 15: //15：N年均值：过去N年同期均值。过去N年可以连续或者不连续，指标数据均用线性插值补全为日度数据后计算；
			predictEdbInfoData, tmpMinValue, tmpMaxValue, err = GetChartPredictEdbInfoDataListByRuleNAnnualAverage(predictEdbConf.PredictEdbInfoId, predictEdbConf.Value, dayList, realPredictEdbInfoData, predictEdbInfoData, existMap)
			if err != nil {
				return
			}

		case 16: //16：年度值倒推
			predictEdbInfoData, tmpMinValue, tmpMaxValue, err = GetChartPredictEdbInfoDataListByRuleAnnualValueInversion(predictEdbConf.PredictEdbInfoId, predictEdbConf.Value, dayList, frequency, realPredictEdbInfoData, predictEdbInfoData, existMap)
			if err != nil {
				return
			}
		}

		// 下一个规则的开始日期
		{
			lenPredictEdbInfoData := len(predictEdbInfoData)
			if lenPredictEdbInfoData > 0 {
				tmpDataEndTime, _ := time.ParseInLocation(utils.FormatDate, predictEdbInfoData[lenPredictEdbInfoData-1].DataTime, time.Local)
				if startDate.Before(tmpDataEndTime) {
					startDate = tmpDataEndTime
				}
			}
		}

		if tmpMinValue < minValue {
			minValue = tmpMinValue
		}
		if tmpMaxValue > maxValue {
			maxValue = tmpMaxValue
		}
	}

	return
}

// GetPredictEdbDayList 获取预测指标日期列表
func getPredictEdbDayList(startDate, endDate time.Time, frequency string) (dayList []time.Time) {
	//if !utils.InArrayByStr([]string{"日度", "周度", "月度"}, frequency)
	switch frequency {
	case "日度":
		for currDate := startDate.AddDate(0, 0, 1); currDate.Before(endDate) || currDate.Equal(endDate); currDate = currDate.AddDate(0, 0, 1) {
			//周六、日排除
			if currDate.Weekday() == time.Sunday || currDate.Weekday() == time.Saturday {
				continue
			}
			dayList = append(dayList, currDate)
		}
	case "周度":
		//nextDate := startDate.AddDate(0, 0, 7)
		for currDate := startDate.AddDate(0, 0, 7); currDate.Before(endDate) || currDate.Equal(endDate); currDate = currDate.AddDate(0, 0, 7) {
			dayList = append(dayList, currDate)
		}
	case "旬度":
		for currDate := startDate.AddDate(0, 0, 1); currDate.Before(endDate) || currDate.Equal(endDate); {
			nextDate := currDate.AddDate(0, 0, 1)
			//每个月的10号、20号、最后一天，那么就写入
			if nextDate.Day() == 11 || nextDate.Day() == 21 || nextDate.Day() == 1 {
				dayList = append(dayList, currDate)
			}
			currDate = nextDate
		}
	case "月度":
		for currDate := startDate; currDate.Before(endDate) || currDate.Equal(endDate); {
			currDate = time.Date(currDate.Year(), currDate.Month(), 1, 0, 0, 0, 0, time.Now().Location()).AddDate(0, 1, -1)
			if !currDate.After(endDate) && !currDate.Equal(startDate) {
				dayList = append(dayList, currDate)
			}
			currDate = currDate.AddDate(0, 0, 1)
		}
	case "季度":
		for currDate := startDate; currDate.Before(endDate) || currDate.Equal(endDate); {
			// 每月的最后一天
			currDate = time.Date(currDate.Year(), currDate.Month(), 1, 0, 0, 0, 0, time.Now().Location()).AddDate(0, 1, -1)
			if !currDate.After(endDate) && !currDate.Equal(startDate) {
				// 季度日期就写入，否则不写入
				if currDate.Month() == 3 || currDate.Month() == 6 || currDate.Month() == 9 || currDate.Month() == 12 {
					dayList = append(dayList, currDate)
				}
			}
			currDate = currDate.AddDate(0, 0, 1)
		}
	case "半年度":
		for currDate := startDate; currDate.Before(endDate) || currDate.Equal(endDate); {
			// 每月的最后一天
			currDate = time.Date(currDate.Year(), currDate.Month(), 1, 0, 0, 0, 0, time.Now().Location()).AddDate(0, 1, -1)
			if !currDate.After(endDate) && !currDate.Equal(startDate) {
				// 半年度日期就写入，否则不写入
				if currDate.Month() == 6 || currDate.Month() == 12 {
					dayList = append(dayList, currDate)
				}
			}
			currDate = currDate.AddDate(0, 0, 1)
		}
	case "年度":
		for currDate := startDate; currDate.Before(endDate) || currDate.Equal(endDate); {
			currDate = time.Date(currDate.Year()+1, 12, 31, 0, 0, 0, 0, time.Now().Location())
			if !currDate.After(endDate) && !currDate.Equal(startDate) {
				dayList = append(dayList, currDate)
			}
		}
	}
	return
}

// GetPredictDataListByPredictEdbInfoId 根据预测指标id获取预测指标的数据
func GetPredictDataListByPredictEdbInfoId(edbInfoId int, startDate, endDate string, isTimeBetween bool) (edbInfo *data_manage.EdbInfo, dataList []*data_manage.EdbDataList, sourceEdbInfoItem *data_manage.EdbInfo, predictEdbConf *data_manage.PredictEdbConf, err error, errMsg string) {
	edbInfo, err = data_manage.GetEdbInfoById(edbInfoId)
	if err != nil {
		errMsg = `获取预测指标信息失败`
		return
	}
	dataList, sourceEdbInfoItem, predictEdbConf, err, errMsg = GetPredictDataListByPredictEdbInfo(edbInfo, startDate, endDate, isTimeBetween)
	return
}

// GetPredictDataListByPredictEdbInfo 根据预测指标信息获取预测指标的数据
func GetPredictDataListByPredictEdbInfo(edbInfo *data_manage.EdbInfo, startDate, endDate string, isTimeBetween bool) (dataList []*data_manage.EdbDataList, sourceEdbInfoItem *data_manage.EdbInfo, predictEdbConf *data_manage.PredictEdbConf, err error, errMsg string) {
	// 非计算指标，直接从表里获取数据
	if edbInfo.EdbType != 1 {
		if !isTimeBetween { //如果不是区间数据，那么就结束日期为空
			endDate = ``
		}
		return GetPredictCalculateDataListByPredictEdbInfo(edbInfo, startDate, endDate)
	}
	// 查找该预测指标配置
	predictEdbConfList, err := data_manage.GetPredictEdbConfListById(edbInfo.EdbInfoId)
	if err != nil && err.Error() != utils.ErrNoRow() {
		errMsg = "获取预测指标配置信息失败"
		return
	}
	if len(predictEdbConfList) == 0 {
		errMsg = "获取预测指标配置信息失败"
		err = errors.New(errMsg)
		return
	}
	predictEdbConf = predictEdbConfList[0]

	// 来源指标
	sourceEdbInfoItem, err = data_manage.GetEdbInfoById(predictEdbConf.SourceEdbInfoId)
	if err != nil {
		if err.Error() == utils.ErrNoRow() {
			errMsg = "找不到来源指标信息"
			err = errors.New(errMsg)
		}
		return
	}

	allDataList := make([]*data_manage.EdbDataList, 0)
	//获取指标数据（实际已生成）
	dataList, err = data_manage.GetEdbDataList(sourceEdbInfoItem.Source, sourceEdbInfoItem.EdbInfoId, startDate, endDate)
	if err != nil {
		return
	}
	// 如果选择了日期，那么需要筛选所有的数据，用于未来指标的生成
	if startDate != `` {
		allDataList, err = data_manage.GetEdbDataList(sourceEdbInfoItem.Source, sourceEdbInfoItem.EdbInfoId, "", "")
		if err != nil {
			return
		}
	} else {
		allDataList = dataList
	}
	// 获取预测指标未来的数据
	predictDataList := make([]*data_manage.EdbDataList, 0)

	endDateStr := edbInfo.EndDate //预测指标的结束日期

	if isTimeBetween { //如果是时间区间，那么
		reqEndDateTime, _ := time.ParseInLocation(utils.FormatDate, endDate, time.Local)
		endDateTime, _ := time.ParseInLocation(utils.FormatDate, edbInfo.EndDate, time.Local)
		// 如果选择的时间区间结束日期 晚于 当天，那么预测数据截止到当天
		if reqEndDateTime.Before(endDateTime) {
			endDateStr = endDate
		}
	}
	//predictDataList, err = GetChartPredictEdbInfoDataList(*predictEdbConf, startDate, sourceEdbInfoItem.LatestDate, sourceEdbInfoItem.LatestValue, endDateStr, edbInfo.Frequency)
	predictEdbConfDataList := make([]data_manage.PredictEdbConfAndData, 0)
	for _, v := range predictEdbConfList {
		predictEdbConfDataList = append(predictEdbConfDataList, data_manage.PredictEdbConfAndData{
			ConfigId:         v.ConfigId,
			PredictEdbInfoId: v.PredictEdbInfoId,
			SourceEdbInfoId:  v.SourceEdbInfoId,
			RuleType:         v.RuleType,
			FixedValue:       v.FixedValue,
			Value:            v.Value,
			EndDate:          v.EndDate,
			ModifyTime:       v.ModifyTime,
			CreateTime:       v.CreateTime,
			DataList:         make([]*data_manage.EdbDataList, 0),
		})
	}
	//var predictMinValue, predictMaxValue float64
	predictDataList, _, _, err, _ = GetChartPredictEdbInfoDataListByConfList(predictEdbConfDataList, startDate, sourceEdbInfoItem.LatestDate, endDateStr, edbInfo.Frequency, allDataList)
	if err != nil {
		return
	}
	dataList = append(dataList, predictDataList...)
	//if len(predictDataList) > 0 {
	//	// 如果最小值 大于 预测值，那么将预测值作为最小值数据返回
	//	if edbInfo.MinValue > predictMinValue {
	//		edbInfo.MinValue = predictMinValue
	//	}
	//
	//	// 如果最大值 小于 预测值，那么将预测值作为最大值数据返回
	//	if edbInfo.MaxValue < predictMaxValue {
	//		edbInfo.MaxValue = predictMaxValue
	//	}
	//}
	return
}

// GetChartDataList 通过完整的预测数据 进行 季节性图、公历、农历处理
func GetChartDataList(dataList []*data_manage.EdbDataList, chartType int, calendar, latestDateStr, startDate string) (resultDataList interface{}, err error) {
	startDateReal := startDate
	calendarPreYear := 0
	if calendar == "农历" {
		newStartDateReal, err := time.Parse(utils.FormatDate, startDateReal)
		if err != nil {
			fmt.Println("time.Parse:" + err.Error())
		}
		calendarPreYear = newStartDateReal.Year() - 1
		newStartDateReal = newStartDateReal.AddDate(-1, 0, 0)
		startDateReal = newStartDateReal.Format(utils.FormatDate)
	}

	// 曲线图
	if chartType == 1 {
		resultDataList = dataList
		return
	}

	//实际数据的截止日期
	latestDate, tmpErr := time.Parse(utils.FormatDate, latestDateStr)
	if tmpErr != nil {
		err = errors.New(fmt.Sprint("获取最后实际数据的日期失败,Err:" + tmpErr.Error() + ";LatestDate:" + latestDateStr))
		return
	}
	latestDateYear := latestDate.Year() //实际数据截止年份

	if calendar == "农历" {
		if len(dataList) <= 0 {
			resultDataList = data_manage.EdbDataResult{}
		} else {
			result, tmpErr := data_manage.AddCalculateQuarterV4(dataList)
			if tmpErr != nil {
				err = errors.New("获取农历数据失败,Err:" + tmpErr.Error())
				return
			}

			// 处理季节图的截止日期
			for k, edbDataItems := range result.List {
				var cuttingDataTimestamp int64

				// 切割的日期时间字符串
				cuttingDataTimeStr := latestDate.AddDate(0, 0, edbDataItems.BetweenDay).Format(utils.FormatDate)
				//如果等于最后的实际日期，那么遍历找到该日期对应的时间戳，并将其赋值为 切割时间戳
				if edbDataItems.Year >= latestDateYear {
					for _, tmpData := range edbDataItems.Items {
						if tmpData.DataTime == cuttingDataTimeStr {
							cuttingDataTimestamp = tmpData.DataTimestamp
							break
						}
					}
				}
				edbDataItems.CuttingDataTimestamp = cuttingDataTimestamp
				result.List[k] = edbDataItems
			}

			if result.List[0].Year != calendarPreYear {
				itemList := make([]*data_manage.EdbDataList, 0)
				items := new(data_manage.EdbDataItems)
				//items.Year = calendarPreYear
				items.Items = itemList

				newResult := new(data_manage.EdbDataResult)
				newResult.List = append(newResult.List, items)
				newResult.List = append(newResult.List, result.List...)
				resultDataList = newResult
			} else {
				resultDataList = result
			}
		}

	} else {
		currentYear := time.Now().Year()

		quarterDataList := make([]*data_manage.QuarterData, 0)
		quarterMap := make(map[int][]*data_manage.EdbDataList)
		var quarterArr []int

		for _, v := range dataList {
			itemDate, tmpErr := time.Parse(utils.FormatDate, v.DataTime)
			if tmpErr != nil {
				err = errors.New("季度指标日期转换,Err:" + tmpErr.Error() + ";DataTime:" + v.DataTime)
				return
			}
			year := itemDate.Year()
			newItemDate := itemDate.AddDate(currentYear-year, 0, 0)
			timestamp := newItemDate.UnixNano() / 1e6
			v.DataTimestamp = timestamp
			if findVal, ok := quarterMap[year]; !ok {
				quarterArr = append(quarterArr, year)
				findVal = append(findVal, v)
				quarterMap[year] = findVal
			} else {
				findVal = append(findVal, v)
				quarterMap[year] = findVal
			}
		}
		for _, v := range quarterArr {
			itemList := quarterMap[v]
			quarterItem := new(data_manage.QuarterData)
			quarterItem.Year = v
			quarterItem.DataList = itemList

			//如果等于最后的实际日期，那么将切割时间戳记录
			if v == latestDateYear {
				var cuttingDataTimestamp int64
				for _, tmpData := range itemList {
					if tmpData.DataTime == latestDateStr {
						cuttingDataTimestamp = tmpData.DataTimestamp
						break
					}
				}
				quarterItem.CuttingDataTimestamp = cuttingDataTimestamp
			} else if v > latestDateYear {
				//如果大于最后的实际日期，那么第一个点就是切割的时间戳
				if len(itemList) > 0 {
					quarterItem.CuttingDataTimestamp = itemList[0].DataTimestamp - 100
				}
			}
			quarterDataList = append(quarterDataList, quarterItem)
		}
		resultDataList = quarterDataList
	}
	return
}

// GetPredictCalculateDataListByPredictEdbInfo 根据预测运算指标信息获取预测指标的数据
func GetPredictCalculateDataListByPredictEdbInfo(edbInfo *data_manage.EdbInfo, startDate, endDate string) (dataList []*data_manage.EdbDataList, sourceEdbInfoItem *data_manage.EdbInfo, predictEdbConf *data_manage.PredictEdbConf, err error, errMsg string) {
	dataList, err = data_manage.GetEdbDataList(edbInfo.Source, edbInfo.EdbInfoId, startDate, endDate)
	return
}

// GetCalculateByRuleByNineParams 获取预测规则9的计算参数
func GetCalculateByRuleByNineParams(req request.RuleConfig) (formula string, edbInfoList []*data_manage.EdbInfo, edbInfoIdBytes []string, err error, errMsg string) {
	formula = req.Value
	formula = strings.Replace(formula, "（", "(", -1)
	formula = strings.Replace(formula, "）", ")", -1)
	formula = strings.Replace(formula, "，", ",", -1)
	formula = strings.Replace(formula, "。", ".", -1)
	formula = strings.Replace(formula, "%", "*0.01", -1)

	//检验公式
	var checkFormulaStr string
	for _, tmpEdbInfoId := range req.EdbInfoIdArr {
		checkFormulaStr += tmpEdbInfoId.FromTag + ","
		edbInfoIdBytes = append(edbInfoIdBytes, tmpEdbInfoId.FromTag)
	}
	formulaMap := CheckFormula(formula)
	for _, tmpFormula := range formulaMap {
		if !strings.Contains(checkFormulaStr, tmpFormula) {
			errMsg = "公式错误，请重新填写"
			return
		}
	}

	//关联的指标信息
	edbInfoList = make([]*data_manage.EdbInfo, 0)

	for _, tmpEdbInfoId := range req.EdbInfoIdArr {
		fromEdbInfo, tmpErr := data_manage.GetEdbInfoById(tmpEdbInfoId.EdbInfoId)
		if tmpErr != nil {
			if tmpErr.Error() == utils.ErrNoRow() {
				err = errors.New("指标 " + strconv.Itoa(tmpEdbInfoId.EdbInfoId) + " 不存在")
			} else {
				err = errors.New("获取指标失败:Err:" + tmpErr.Error())
			}
			errMsg = "数据计算失败"
			return
		}
		edbInfoList = append(edbInfoList, fromEdbInfo)
	}
	ok, _ := CheckFormula2(edbInfoList, formulaMap, formula, edbInfoIdBytes)
	if !ok {
		errMsg = "生成计算指标失败，请使用正确的计算公式"
		err = errors.New(errMsg)
	}
	return
}

// CalculateByRuleByNine 动态环差规则计算入库
func CalculateByRuleByNine(formulaStr string, edbInfoList []*data_manage.EdbInfo, edbInfoIdBytes []string) (dataList []*data_manage.EdbDataList, err error) {
	realSaveDataMap := make(map[string]map[int]float64)
	saveDataMap := make(map[string]map[int]float64)
	dateList := make([]string, 0) //日期

	formulaStr = strings.ToUpper(formulaStr)
	// 获取关联指标数据
	for edbInfoIndex, v := range edbInfoList {
		sourceDataList, _, _, tmpErr, _ := GetPredictDataListByPredictEdbInfo(v, "", "", false)
		if tmpErr != nil {
			err = tmpErr
			return
		}
		dataMap := make(map[string]float64)
		for _, dv := range sourceDataList {
			// 实际数据
			if val, ok := realSaveDataMap[dv.DataTime]; ok {
				if _, ok := val[v.EdbInfoId]; !ok {
					val[v.EdbInfoId] = dv.Value
				}
			} else {
				temp := make(map[int]float64)
				temp[v.EdbInfoId] = dv.Value
				realSaveDataMap[dv.DataTime] = temp
			}

			// saveDataMap 待计算的数据
			if val, ok := saveDataMap[dv.DataTime]; ok {
				if _, ok := val[v.EdbInfoId]; !ok {
					val[v.EdbInfoId] = dv.Value
				}
			} else {
				temp2 := make(map[int]float64)
				temp2[v.EdbInfoId] = dv.Value
				saveDataMap[dv.DataTime] = temp2
			}

			// 以第一个指标的日期作为基准日期
			if edbInfoIndex == 0 {
				dateList = append(dateList, dv.DataTime)
			}
		}
		item := new(CalculateItems)
		item.EdbInfoId = v.EdbInfoId
		item.DataMap = dataMap
	}

	//数据处理，将日期内不全的数据做补全
	handleDateSaveDataMap(dateList, realSaveDataMap, saveDataMap, edbInfoList)

	// 添加数据
	dataList = make([]*data_manage.EdbDataList, 0)

	// 计算规则
	formulaMap := CheckFormula(formulaStr)

	existDataMap := make(map[string]string)

	for k, date := range dateList {
		sv := saveDataMap[date]
		//fmt.Println(date, sv)

		formulaFormStr := ReplaceFormula(edbInfoList, sv, formulaMap, formulaStr, edbInfoIdBytes)
		if formulaFormStr == `` {
			//计算公式异常，那么就移除该指标
			continue
		}

		//fmt.Println(fmt.Sprintf("formulaFormStr:%s", formulaFormStr))
		expression := formula.NewExpression(formulaFormStr)
		calResult, tmpErr := expression.Evaluate()
		if tmpErr != nil {
			// 分母为0的报错
			if strings.Contains(tmpErr.Error(), "divide by zero") {
				continue
			}
			err = errors.New("计算失败：Err:" + tmpErr.Error() + ";formulaStr:" + formulaFormStr)
			return
		}
		calVal, tmpErr := calResult.Float64()
		if tmpErr != nil {
			err = errors.New("计算失败：获取计算值失败 Err:" + tmpErr.Error() + ";formulaStr:" + formulaFormStr)
			fmt.Println(err)
			return
		}

		saveValue, _ := decimal.NewFromFloat(calVal).RoundCeil(4).Float64() //utils.SubFloatToString(calVal, 4)
		dataTime, _ := time.Parse(utils.FormatDate, date)
		timestamp := dataTime.UnixNano() / 1e6

		if _, existOk := existDataMap[date]; !existOk {
			tmpPredictEdbRuleData := &data_manage.EdbDataList{
				EdbDataId:     k,
				EdbInfoId:     0,
				DataTime:      date,
				DataTimestamp: timestamp,
				Value:         saveValue,
			}
			dataList = append(dataList, tmpPredictEdbRuleData)
		}
		existDataMap[date] = date
	}
	return
}