Merge branch 'feature/eta_1.8.2' into debug

controllers/base_from_bloomberg.go

@@ -0,0 +1,150 @@
+package controllers
+
+import (
+	"encoding/json"
+	"eta/eta_index_lib/logic"
+	"eta/eta_index_lib/models"
+	"eta/eta_index_lib/services"
+	"eta/eta_index_lib/utils"
+	"fmt"
+	"strconv"
+	"time"
+)
+
+// BloombergController Bloomberg
+type BloombergController struct {
+	BaseAuthController
+}
+
+// Add
+// @Title 新增彭博指标接口
+// @Description 新增彭博指标接口
+// @Success 200 {object} models.AddEdbInfoReq
+// @router /add [post]
+func (this *BloombergController) Add() {
+	br := new(models.BaseResponse).Init()
+	var cacheKey string
+	defer func() {
+		if br.ErrMsg == "" {
+			br.IsSendEmail = false
+		}
+		_ = utils.Rc.Delete(cacheKey)
+		this.Data["json"] = br
+		this.ServeJSON()
+	}()
+	source := utils.DATA_SOURCE_BLOOMBERG
+	var req models.AddEdbInfoReq
+	err := json.Unmarshal(this.Ctx.Input.RequestBody, &req)
+	if err != nil {
+		br.Msg = "参数解析异常！"
+		br.ErrMsg = "参数解析失败，Err:" + err.Error()
+		return
+	}
+	if req.EdbCode == "" {
+		br.Msg = "请输入指标编码！"
+		br.ErrMsg = "请输入指标编码，指标编码为空"
+		return
+	}
+	if req.Source != source {
+		br.Msg = "数据源有误"
+		br.ErrMsg = fmt.Sprintf("数据源ID不匹配, Source: %d", req.Source)
+		return
+	}
+	cacheKey = utils.CACHE_EDB_DATA_ADD + strconv.Itoa(source) + "_" + req.EdbCode
+	if !utils.Rc.IsExist(cacheKey) {
+		utils.Rc.SetNX(cacheKey, 1, 1*time.Minute)
+		err = models.AddEdbDataFromBloomberg(req.EdbCode)
+		if err != nil {
+			br.Msg = "获取指标信息失败！"
+			br.ErrMsg = "获取指标信息失败 AddEdbDataFromBloomberg，Err:" + err.Error()
+			return
+		}
+		br.Ret = 200
+		br.Success = true
+		br.Msg = "获取成功"
+	} else {
+		br.Ret = 501
+		br.Success = true
+		br.Msg = "系统处理中，请稍后重试"
+	}
+}
+
+// Refresh
+// @Title 刷新彭博指标接口
+// @Description 刷新彭博指标接口
+// @Success 200 {object} models.RefreshEdbInfoReq
+// @router /refresh [post]
+func (this *BloombergController) Refresh() {
+	br := new(models.BaseResponse).Init()
+	var cacheKey string
+	defer func() {
+		if br.ErrMsg == "" {
+			br.IsSendEmail = false
+		}
+		_ = utils.Rc.Delete(cacheKey)
+		this.Data["json"] = br
+		this.ServeJSON()
+	}()
+	source := utils.DATA_SOURCE_BLOOMBERG
+	var req models.RefreshEdbInfoReq
+	err := json.Unmarshal(this.Ctx.Input.RequestBody, &req)
+	if err != nil {
+		br.Msg = "参数解析异常！"
+		br.ErrMsg = "参数解析失败，Err:" + err.Error()
+		return
+	}
+	if req.EdbCode == "" {
+		br.Msg = "请输入指标编码！"
+		br.ErrMsg = "请输入指标编码，指标编码为空"
+		return
+	}
+	if req.EdbInfoId <= 0 {
+		br.Msg = "请输入指标ID！"
+		br.ErrMsg = "请输入指标ID"
+		return
+	}
+
+	// 获取指标详情
+	edbInfo, err := models.GetEdbInfoByEdbCode(source, req.EdbCode)
+	if err != nil {
+		br.Msg = "指标不存在！"
+		br.ErrMsg = "指标不存在"
+		return
+	}
+	cacheKey = utils.CACHE_EDB_DATA_REFRESH + strconv.Itoa(source) + "_" + req.EdbCode
+	if utils.Rc.IsExist(cacheKey) {
+		br.Ret = 501
+		br.Success = true
+		br.Msg = "系统处理中，请稍后重试"
+		return
+	}
+	dataUpdateTime := time.Now().Format(utils.FormatDateTime)
+	utils.Rc.SetNX(cacheKey, 1, 1*time.Minute)
+	err = models.RefreshEdbDataFromBloomberg(req.EdbInfoId, req.EdbCode, req.StartDate)
+	if err != nil && err.Error() != utils.ErrNoRow() {
+		br.Msg = "刷新指标信息失败！"
+		br.ErrMsg = "刷新指标信息失败 RefreshEdbDataFromBloomberg，Err:" + err.Error()
+		return
+	}
+
+	// 更新指标最大最小值
+	erDataUpdateDate, err, errMsg := models.UnifiedModifyEdbInfoMaxAndMinInfoDataUpdate(edbInfo, dataUpdateTime)
+	if err != nil {
+		br.Msg = errMsg
+		br.ErrMsg = err.Error()
+		return
+	}
+	// 添加指标刷新成功日志
+	if erDataUpdateDate != "" {
+		_ = services.AddEdbInfoUpdateLog(edbInfo.EdbInfoId, 1, "", 1, "", 0, 0)
+	} else {
+		_ = services.AddEdbInfoUpdateLog(edbInfo.EdbInfoId, 1, "", 2, "未刷新到数据", 0, 0)
+	}
+
+	// 更新ES
+	go logic.UpdateEs(edbInfo.EdbInfoId)
+
+	br.Ret = 200
+	br.Success = true
+	br.Msg = "获取成功"
+}

models/base_from_bloomberg.go

@@ -0,0 +1,198 @@
+package models
+
+import (
+	"eta/eta_index_lib/utils"
+	"fmt"
+	"github.com/beego/beego/v2/client/orm"
+	"strconv"
+	"strings"
+	"time"
+)
+
+type BloombergData struct {
+	InputValue float64 `orm:"column(value)" description:"值"`
+	DataTime   string  `orm:"column(data_time)" description:"日期"`
+}
+
+func GetBloombergDataByCondition(condition string, pars []interface{}) (item []*BloombergData, err error) {
+	sql1 := ` SELECT * FROM base_from_bloomberg_data WHERE 1=1  `
+	o := orm.NewOrm()
+	if condition != "" {
+		sql1 += condition
+	}
+	sql := `select * from (` + sql1 + ` having 1 order by modify_time DESC ) tmp GROUP BY data_time ORDER BY data_time DESC `
+	_, err = o.Raw(sql, pars).QueryRows(&item)
+	return
+}
+
+// AddEdbDataFromBloomberg 新增Bloomberg指标数据
+func AddEdbDataFromBloomberg(edbCode string) (err error) {
+	o := orm.NewOrm()
+
+	var condition string
+	var pars []interface{}
+
+	if edbCode != "" {
+		condition += " AND index_code = ? "
+		pars = append(pars, edbCode)
+	}
+
+	bloombergDataList, err := GetBloombergDataByCondition(condition, pars)
+	if err != nil {
+		return
+	}
+
+	dataLen := len(bloombergDataList)
+
+	existMap := make(map[string]string)
+	if dataLen > 0 {
+		var isAdd bool
+		addSql := ` INSERT INTO edb_data_bloomberg (edb_info_id,edb_code,data_time,value,create_time,modify_time,data_timestamp) values `
+		for i := 0; i < dataLen; i++ {
+			item := bloombergDataList[i]
+			eDate := item.DataTime
+			sValue := utils.SubFloatToString(item.InputValue, 4)
+			if sValue != "" {
+				if _, ok := existMap[eDate]; !ok {
+					dataTime, err := time.ParseInLocation(utils.FormatDate, eDate, time.Local)
+					if err != nil {
+						return err
+					}
+					timestamp := dataTime.UnixNano() / 1e6
+					timeStr := fmt.Sprintf("%d", timestamp)
+					addSql += GetAddSql("0", edbCode, eDate, timeStr, sValue)
+					isAdd = true
+				}
+			}
+			existMap[eDate] = eDate
+		}
+		if isAdd {
+			addSql = strings.TrimRight(addSql, ",")
+			utils.FileLog.Info("addSql:" + addSql)
+			_, err = o.Raw(addSql).Exec()
+			if err != nil {
+				return err
+			}
+		}
+	}
+	return
+}
+
+// RefreshEdbDataFromBloomberg 刷新Bloomberg指标数据
+func RefreshEdbDataFromBloomberg(edbInfoId int, edbCode, startDate string) (err error) {
+	source := utils.DATA_SOURCE_BLOOMBERG
+	subSource := utils.DATA_SUB_SOURCE_EDB
+
+	o := orm.NewOrm()
+	if err != nil {
+		return
+	}
+	edbInfoIdStr := strconv.Itoa(edbInfoId)
+	//计算数据
+	var condition string
+	var pars []interface{}
+
+	if edbCode != "" {
+		condition += " AND index_code=? "
+		pars = append(pars, edbCode)
+	}
+
+	if startDate != "" {
+		condition += " AND data_time>=? "
+		pars = append(pars, startDate)
+	}
+
+	bloombergDataList, err := GetBloombergDataByCondition(condition, pars)
+	if err != nil {
+		return
+	}
+
+	// 真实数据的最大日期  ， 插入规则配置的日期
+	var realDataMaxDate, edbDataInsertConfigDate time.Time
+	var edbDataInsertConfig *EdbDataInsertConfig
+	var isFindConfigDateRealData bool //是否找到配置日期的实际数据的值
+	{
+		edbDataInsertConfig, err = GetEdbDataInsertConfigByEdbId(edbInfoId)
+		if err != nil && err.Error() != utils.ErrNoRow() {
+			return
+		}
+		if edbDataInsertConfig != nil {
+			edbDataInsertConfigDate = edbDataInsertConfig.Date
+		}
+	}
+
+	var existCondition string
+	var existPars []interface{}
+
+	existCondition += " AND edb_info_id=? "
+	existPars = append(existPars, edbInfoId)
+	if startDate != "" {
+		existCondition += " AND data_time>=? "
+		existPars = append(existPars, startDate)
+	}
+	//获取指标所有数据
+	existList, err := GetEdbDataByCondition(source, subSource, existCondition, existPars)
+	if err != nil {
+		return err
+	}
+	existMap := make(map[string]*EdbInfoSearchData)
+	for _, v := range existList {
+		existMap[v.DataTime] = v
+	}
+
+	addSql := ` INSERT INTO edb_data_bloomberg(edb_info_id,edb_code,data_time,value,create_time,modify_time,data_timestamp) values `
+	var isAdd bool
+	addMap := make(map[string]string)
+	for _, v := range bloombergDataList {
+		item := v
+		eDate := item.DataTime
+		sValue := utils.SubFloatToString(item.InputValue, 4)
+
+		dataTime, err := time.ParseInLocation(utils.FormatDate, eDate, time.Local)
+		if err != nil {
+			return err
+		}
+		if findItem, ok := existMap[v.DataTime]; !ok {
+			if sValue != "" {
+				timestamp := dataTime.UnixNano() / 1e6
+				timeStr := fmt.Sprintf("%d", timestamp)
+				saveValue := sValue
+
+				if _, addOk := addMap[eDate]; !addOk {
+					addSql += GetAddSql(edbInfoIdStr, edbCode, eDate, timeStr, saveValue)
+					isAdd = true
+				}
+			}
+		} else {
+			if findItem != nil && utils.SubFloatToString(findItem.Value, 4) != sValue {
+				err = ModifyEdbDataById(source, subSource, findItem.EdbDataId, sValue)
+				if err != nil {
+					return err
+				}
+			}
+		}
+		addMap[v.DataTime] = v.DataTime
+
+		// 下面代码主要目的是处理掉手动插入的数据判断
+		{
+			if realDataMaxDate.IsZero() || dataTime.After(realDataMaxDate) {
+				realDataMaxDate = dataTime
+			}
+			if edbDataInsertConfigDate.IsZero() || dataTime.Equal(edbDataInsertConfigDate) {
+				isFindConfigDateRealData = true
+			}
+		}
+	}
+
+	// 处理手工数据补充的配置
+	HandleConfigInsertEdbData(realDataMaxDate, edbDataInsertConfig, edbInfoId, source, subSource, existMap, isFindConfigDateRealData)
+
+	if isAdd {
+		addSql = strings.TrimRight(addSql, ",")
+		_, err = o.Raw(addSql).Exec()
+		if err != nil {
+			return err
+		}
+	}
+	return
+}

routers/commentsRouter.go

@@ -79,6 +79,24 @@ func init() {
             Filters: nil,
             Params: nil})
 
+    beego.GlobalControllerRouter["eta/eta_index_lib/controllers:BloombergController"] = append(beego.GlobalControllerRouter["eta/eta_index_lib/controllers:BloombergController"],
+        beego.ControllerComments{
+            Method: "Add",
+            Router: `/add`,
+            AllowHTTPMethods: []string{"post"},
+            MethodParams: param.Make(),
+            Filters: nil,
+            Params: nil})
+
+    beego.GlobalControllerRouter["eta/eta_index_lib/controllers:BloombergController"] = append(beego.GlobalControllerRouter["eta/eta_index_lib/controllers:BloombergController"],
+        beego.ControllerComments{
+            Method: "Refresh",
+            Router: `/refresh`,
+            AllowHTTPMethods: []string{"post"},
+            MethodParams: param.Make(),
+            Filters: nil,
+            Params: nil})
+
     beego.GlobalControllerRouter["eta/eta_index_lib/controllers:CalculateController"] = append(beego.GlobalControllerRouter["eta/eta_index_lib/controllers:CalculateController"],
         beego.ControllerComments{
             Method: "Add",

routers/router.go

@@ -254,6 +254,11 @@ func init() {
 				&controllers.EdbRefreshController{},
 			),
 		),
+		beego.NSNamespace("/bloomberg",
+			beego.NSInclude(
+				&controllers.BloombergController{},
+			),
+		),
 	)
 	beego.AddNamespace(ns)
 }

utils/constants.go

@@ -102,89 +102,91 @@ const (
 	DATA_SOURCE_PREDICT_CALCULATE_ZSXY                          // 预测指数修匀->73
 	DATA_SOURCE_CALCULATE_ZDYFX                                 // 自定义分析->74
 	DATA_SOURCE_CALCULATE_RJZ                                   // 日均值计算->75
-	DATA_SOURCE_GFEX                                 = 78       // 广州期货交易所->78
-	DATA_SOURCE_ICPI                                 = 79       // ICPI消费价格指数->79
-	DATA_SOURCE_MTJH                                 = 80       // 煤炭江湖->80
-	DATA_SOURCE_CALCULATE_SUM                        = 81
-	DATA_SOURCE_CALCULATE_AVG                        = 82
+
+	DATA_SOURCE_GFEX          = 78 // 广州期货交易所->78
+	DATA_SOURCE_ICPI          = 79 // ICPI消费价格指数->79
+	DATA_SOURCE_MTJH          = 80 // 煤炭江湖->80
+	DATA_SOURCE_CALCULATE_SUM = 81
+	DATA_SOURCE_CALCULATE_AVG = 82
+	DATA_SOURCE_BLOOMBERG     = 83 // bloomberg彭博数据
 )
 
 // 指标来源的中文展示
 const (
-	DATA_SOURCE_NAME_THS                                  = `同花顺`               //同花顺
-	DATA_SOURCE_NAME_WIND                                 = `wind`              //wind
-	DATA_SOURCE_NAME_PB                                   = `彭博`                //彭博
+	DATA_SOURCE_NAME_THS                                  = `同花顺`                //同花顺
+	DATA_SOURCE_NAME_WIND                                 = `wind`                  //wind
+	DATA_SOURCE_NAME_PB                                   = `彭博`                  //彭博
 	DATA_SOURCE_NAME_CALCULATE                            = `指标运算`              //指标运算
-	DATA_SOURCE_NAME_CALCULATE_LJZZY                      = `累计值转月值`            //累计值转月
-	DATA_SOURCE_NAME_CALCULATE_TBZ                        = `同比值`               //同比值
-	DATA_SOURCE_NAME_CALCULATE_TCZ                        = `同差值`               //同差值
-	DATA_SOURCE_NAME_CALCULATE_NSZYDPJJS                  = `N数值移动平均计算`         //N数值移动平均计算
+	DATA_SOURCE_NAME_CALCULATE_LJZZY                      = `累计值转月值`          //累计值转月
+	DATA_SOURCE_NAME_CALCULATE_TBZ                        = `同比值`                //同比值
+	DATA_SOURCE_NAME_CALCULATE_TCZ                        = `同差值`                //同差值
+	DATA_SOURCE_NAME_CALCULATE_NSZYDPJJS                  = `N数值移动平均计算`     //N数值移动平均计算
 	DATA_SOURCE_NAME_MANUAL                               = `手工数据`              //手工指标
-	DATA_SOURCE_NAME_LZ                                   = `隆众`                //隆众
-	DATA_SOURCE_NAME_YS                                   = `SMM`               //有色
-	DATA_SOURCE_NAME_CALCULATE_HBZ                        = `环比值`               //环比值->12
-	DATA_SOURCE_NAME_CALCULATE_HCZ                        = `环差值`               //环差值->13
-	DATA_SOURCE_NAME_CALCULATE_BP                         = `升频`                //变频,2023-2-10 13:56:01调整为"升频"->14
-	DATA_SOURCE_NAME_GL                                   = `钢联`                //钢联->15
-	DATA_SOURCE_NAME_ZZ                                   = `郑商所`               //郑商所->16
-	DATA_SOURCE_NAME_DL                                   = `大商所`               //大商所->17
-	DATA_SOURCE_NAME_SH                                   = `上期所`               //上期所->18
-	DATA_SOURCE_NAME_CFFEX                                = `中金所`               //中金所->19
+	DATA_SOURCE_NAME_LZ                                   = `隆众`                  //隆众
+	DATA_SOURCE_NAME_YS                                   = `SMM`                   //有色
+	DATA_SOURCE_NAME_CALCULATE_HBZ                        = `环比值`                //环比值->12
+	DATA_SOURCE_NAME_CALCULATE_HCZ                        = `环差值`                //环差值->13
+	DATA_SOURCE_NAME_CALCULATE_BP                         = `升频`                  //变频,2023-2-10 13:56:01调整为"升频"->14
+	DATA_SOURCE_NAME_GL                                   = `钢联`                  //钢联->15
+	DATA_SOURCE_NAME_ZZ                                   = `郑商所`                //郑商所->16
+	DATA_SOURCE_NAME_DL                                   = `大商所`                //大商所->17
+	DATA_SOURCE_NAME_SH                                   = `上期所`                //上期所->18
+	DATA_SOURCE_NAME_CFFEX                                = `中金所`                //中金所->19
 	DATA_SOURCE_NAME_SHFE                                 = `上期能源`              //上期能源->20
-	DATA_SOURCE_NAME_GIE                                  = `欧洲天然气`             //欧洲天然气->21
+	DATA_SOURCE_NAME_GIE                                  = `欧洲天然气`            //欧洲天然气->21
 	DATA_SOURCE_NAME_CALCULATE_TIME_SHIFT                 = `时间移位`              //时间移位->22
 	DATA_SOURCE_NAME_CALCULATE_ZJPJ                       = `直接拼接`              //直接拼接->23
-	DATA_SOURCE_NAME_CALCULATE_LJZTBPJ                    = `累计值同比拼接`           //累计值同比拼接->24
-	DATA_SOURCE_NAME_LT                                   = `路透`                //路透->25
-	DATA_SOURCE_NAME_COAL                                 = `中国煤炭市场网`           //中国煤炭市场网->26
+	DATA_SOURCE_NAME_CALCULATE_LJZTBPJ                    = `累计值同比拼接`        //累计值同比拼接->24
+	DATA_SOURCE_NAME_LT                                   = `路透`                  //路透->25
+	DATA_SOURCE_NAME_COAL                                 = `中国煤炭市场网`        //中国煤炭市场网->26
 	DATA_SOURCE_NAME_PYTHON                               = `代码运算`              //python代码->27
 	DATA_SOURCE_NAME_PB_FINANCE                           = `彭博财务`              //彭博财务数据->28
-	DATA_SOURCE_NAME_GOOGLE_TRAVEL                        = `our world in data` //谷歌出行数据->29
+	DATA_SOURCE_NAME_GOOGLE_TRAVEL                        = `our world in data`     //谷歌出行数据->29
 	DATA_SOURCE_NAME_PREDICT                              = `预测指标`              //普通预测指标->30
-	DATA_SOURCE_NAME_PREDICT_CALCULATE                    = `预测指标运算`            //预测指标运算->31
+	DATA_SOURCE_NAME_PREDICT_CALCULATE                    = `预测指标运算`          //预测指标运算->31
 	DATA_SOURCE_NAME_PREDICT_CALCULATE_TBZ                = `预测同比`              //预测指标 - 同比值->32
 	DATA_SOURCE_NAME_PREDICT_CALCULATE_TCZ                = `预测同差`              //预测指标 - 同差值->33
 	DATA_SOURCE_NAME_MYSTEEL_CHEMICAL                     = `钢联化工`              //钢联化工->34
 	DATA_SOURCE_NAME_CALCULATE_CJJX                       = `超季节性`              //超季节性->35
-	DATA_SOURCE_NAME_EIA_STEO                             = `EIA STERO报告`       //eia stero报告->36
+	DATA_SOURCE_NAME_EIA_STEO                             = `EIA STERO报告`         //eia stero报告->36
 	DATA_SOURCE_NAME_CALCULATE_NHCC                       = `拟合残差`              //计算指标（拟合残差）->37
-	DATA_SOURCE_NAME_COM_TRADE                            = `UN`                //联合国商品贸易数据->38
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_NSZYDPJJS          = `预测N数值移动平均计算`       //预测指标 - N数值移动平均计算 -> 39
+	DATA_SOURCE_NAME_COM_TRADE                            = `UN`                    //联合国商品贸易数据->38
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_NSZYDPJJS          = `预测N数值移动平均计算` //预测指标 - N数值移动平均计算 -> 39
 	DATA_SOURCE_NAME_CALCULATE_ADJUST                     = `数据调整`              //数据调整->40
-	DATA_SOURCE_NAME_SCI                                  = `SCI`               //卓创数据（红桃三）->41
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZZY              = `预测累计值转月值`          //预测指标 - 累计值转月->42
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_HBZ                = `预测环比值`             //预测指标 - 环比值->43
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_HCZ                = `预测环差值`             //预测指标 - 环差值->44
+	DATA_SOURCE_NAME_SCI                                  = `SCI`                   //卓创数据（红桃三）->41
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZZY              = `预测累计值转月值`      //预测指标 - 累计值转月->42
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_HBZ                = `预测环比值`            //预测指标 - 环比值->43
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_HCZ                = `预测环差值`            //预测指标 - 环差值->44
 	DATA_SOURCE_NAME_PREDICT_CALCULATE_BP                 = `预测升频`              //预测指标 - 升频->45
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_TIME_SHIFT         = `预测时间移位`            //预测指标 - 时间移位->46
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_ZJPJ               = `预测直接拼接`            //预测指标 - 直接拼接->47
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZTBPJ            = `预测累计值同比拼接`         //预测指标 - 累计值同比拼接->48
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_CJJX               = `预测超季节性`            //预测指标 - 超季节性->49
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_NHCC               = `预测拟合残差`            //预测指标 - 计算指标（拟合残差）->50
-	DATA_SOURCE_NAME_CALCULATE_JP                         = `降频`                //降频->51
-	DATA_SOURCE_NAME_CALCULATE_NH                         = `年化`                //年化->52
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_TIME_SHIFT         = `预测时间移位`          //预测指标 - 时间移位->46
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_ZJPJ               = `预测直接拼接`          //预测指标 - 直接拼接->47
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZTBPJ            = `预测累计值同比拼接`    //预测指标 - 累计值同比拼接->48
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_CJJX               = `预测超季节性`          //预测指标 - 超季节性->49
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_NHCC               = `预测拟合残差`          //预测指标 - 计算指标（拟合残差）->50
+	DATA_SOURCE_NAME_CALCULATE_JP                         = `降频`                  //降频->51
+	DATA_SOURCE_NAME_CALCULATE_NH                         = `年化`                  //年化->52
 	DATA_SOURCE_NAME_CALCULATE_KSZS                       = `扩散指数`              //扩散指数->53
 	DATA_SOURCE_NAME_PREDICT_CALCULATE_JP                 = `预测降频`              //预测指标 - 计算指标(降频)->54
 	DATA_SOURCE_NAME_PREDICT_CALCULATE_NH                 = `预测年化`              //预测指标 - 计算指标(年化)->55
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_KSZS               = `预测扩散指数`            //预测指标 - 计算指标(扩散指数)->56
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_KSZS               = `预测扩散指数`          //预测指标 - 计算指标(扩散指数)->56
 	DATA_SOURCE_NAME_BAIINFO                              = `百川盈孚`              //百川盈孚 ->57
 	DATA_SOURCE_NAME_STOCK_PLANT                          = `存量装置`              //存量装置 ->58
-	DATA_SOURCE_NAME_CALCULATE_CORRELATION                = `相关性计算`             //相关性计算->59
-	DATA_SOURCE_NAME_NATIONAL_STATISTICS                  = `国家统计局`             //国家统计局->60
-	DATA_SOURCE_NAME_CALCULATE_LJZZJ                      = `累计值转季值`            //累计值转季 -> 61
-	DATA_SOURCE_NAME_CALCULATE_LJZ                        = `累计值`               //累计值 -> 62
-	DATA_SOURCE_NAME_CALCULATE_LJZNCZJ                    = `年初至今累计值`           //累计值(年初至今） -> 63
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZZJ              = `预测累计值转季值`          //预测指标 - 累计值转季->64
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZ                = `预测累计值`             //预测指标 - 累计值 -> 65
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZNCZJ            = `预测年初至今累计值`         //预测指标 - 累计值(年初至今） -> 66
-	DATA_SOURCE_NAME_CALCULATE_STANDARD_DEVIATION         = `标准差`               //标准差->67
-	DATA_SOURCE_NAME_CALCULATE_PERCENTILE                 = `百分位`               //百分位->68
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_STANDARD_DEVIATION = `预测标准差`             //预测标准差->69
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_PERCENTILE         = `预测百分位`             //预测百分位->70
+	DATA_SOURCE_NAME_CALCULATE_CORRELATION                = `相关性计算`            //相关性计算->59
+	DATA_SOURCE_NAME_NATIONAL_STATISTICS                  = `国家统计局`            //国家统计局->60
+	DATA_SOURCE_NAME_CALCULATE_LJZZJ                      = `累计值转季值`          //累计值转季 -> 61
+	DATA_SOURCE_NAME_CALCULATE_LJZ                        = `累计值`                //累计值 -> 62
+	DATA_SOURCE_NAME_CALCULATE_LJZNCZJ                    = `年初至今累计值`        //累计值(年初至今） -> 63
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZZJ              = `预测累计值转季值`      //预测指标 - 累计值转季->64
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZ                = `预测累计值`            //预测指标 - 累计值 -> 65
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_LJZNCZJ            = `预测年初至今累计值`    //预测指标 - 累计值(年初至今） -> 66
+	DATA_SOURCE_NAME_CALCULATE_STANDARD_DEVIATION         = `标准差`                //标准差->67
+	DATA_SOURCE_NAME_CALCULATE_PERCENTILE                 = `百分位`                //百分位->68
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_STANDARD_DEVIATION = `预测标准差`            //预测标准差->69
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_PERCENTILE         = `预测百分位`            //预测百分位->70
 	DATA_SOURCE_NAME_FUBAO                                = `富宝数据`              //富宝数据->71
 	DATA_SOURCE_NAME_CALCULATE_ZSXY                       = `指数修匀`              //指数修匀->72
-	DATA_SOURCE_NAME_PREDICT_CALCULATE_ZSXY               = `预测指数修匀`            //预测指数修匀->73
-	DATA_SOURCE_NAME_CALCULATE_ZDYFX                      = `自定义分析`             //自定义分析->74
+	DATA_SOURCE_NAME_PREDICT_CALCULATE_ZSXY               = `预测指数修匀`          //预测指数修匀->73
+	DATA_SOURCE_NAME_CALCULATE_ZDYFX                      = `自定义分析`            //自定义分析->74
 	DATA_SOURCE_NAME_YONYI                                = `涌益咨询`              // 涌益咨询
 	DATA_SOURCE_NAME_ICPI                                 = "ICPI消费价格指数"
 	DATA_SOURCE_NAME_FENWEI                               = `汾渭数据` // 汾渭煤炭