Multi-source heterogeneous data management method and system based on digital twinning

Abstract

The invention provides a multi-source heterogeneous data management method and system based on digital twinning, and relates to the field of multi-source heterogeneous data management.The method comprises the steps that building units with the same energy consumption feature are clustered into a cluster according to the basic attribute and historical energy consumption of each building unit, and each cluster corresponds to an initial historical energy consumption label; predicting a target predicted energy consumption time curve S of the target building unit in the T by using an energy consumption prediction model of the digital twin platform, and further obtaining a target predicted energy consumption label BS; meanwhile, a target metering energy consumption time curve Y of the target building unit in the T is obtained to obtain a target metering energy consumption label BY, and then whether the metering energy consumption of the target building unit in the T is normal or not is determined according to BS and BY; according to the method, when metering energy consumption is abnormal, the abnormality can be determined through a digital twin platform prediction mode, the digital twin platform is prevented from using inaccurate energy consumption data, and it is ensured that an analysis processing result is normal.

Description

Technical Field

[0001] The present invention relates to the field of multi-source heterogeneous data management, and in particular to a multi-source heterogeneous data management method and system based on digital twins. Background Art

[0002] With the rapid development of building intelligence, more and more large parks have begun to use digital twin technology to monitor and display the energy consumption of buildings in the park in real time. In the existing technology, the energy consumption monitoring data of the building is usually obtained in real time, and then the energy consumption monitoring data of the building is analyzed and processed through the digital twin platform; however, the energy consumption monitoring of the building is measured by a metering device, and the metering device itself will be affected by environmental factors, such as electromagnetic interference; at the same time, the probability of failure of the metering device will increase with the increase of the service life; the above situation will seriously affect the measurement accuracy of the energy consumption metering device. If the digital twin platform uses inaccurate energy consumption data, it will cause abnormal analysis and processing results; therefore, how to judge the abnormality of the energy consumption data of the building through the digital twin platform has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is: According to a first aspect of the present application, a multi-source heterogeneous data management method based on digital twins is provided, the method comprising the following steps: S100, clustering all building units of the target building according to the basic attributes and historical energy consumption of each building unit in the target area to obtain a cluster list A = (A1, A2, ..., A i , …, A n ), i=1, 2,...,n; among them, A i is the i-th cluster obtained by clustering, n is the number of clusters obtained by clustering; the energy consumption characteristics of the building units in each cluster are the same; S200, obtaining the initial historical energy consumption label corresponding to each cluster in A, so as to obtain the initial historical energy consumption label list B corresponding to A = (B1, B2, ..., B i , …, B n ); where B i A i The corresponding initial historical energy consumption label; S300, input the basic attributes and historical energy consumption corresponding to the target building unit into the preset energy consumption prediction model of the digital twin platform to obtain the target predicted energy consumption time curve S of the target building unit within the preset time period T; wherein the start time of T is the current time T now ; S400, determining the target predicted energy consumption label BS corresponding to the target building unit in T according to S; S500, if T now =T end , then the target metering energy consumption time curve Y of the target building unit within T is obtained; where T end is the end time of T; S600, determining the target metering energy consumption label BY corresponding to the target building unit in T according to Y; S700, if BS is the same as B' and BY is the same as B', it is determined that the target building unit has normal energy consumption measured within T; otherwise, enter S800; S800, obtaining the first metering energy consumption tag of each building unit in the cluster corresponding to B' in T to determine whether the metering energy consumption of the target building unit in T is abnormal; wherein B' is the initial historical energy consumption tag corresponding to the target building unit in B.

[0004] According to another aspect of the present application, a multi-source heterogeneous data management system based on digital twins is also provided, the system comprising: a processor and a storage medium; wherein the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the multi-source heterogeneous data management method based on digital twins as described in the first aspect.

[0005] The present invention has at least the following beneficial effects: The multi-source heterogeneous data management method based on digital twins of the present invention clusters building units with the same energy consumption characteristics into a cluster according to the basic attributes and historical energy consumption of each building unit, and each cluster corresponds to an initial historical energy consumption label; uses the energy consumption prediction model of the digital twin platform to predict the target predicted energy consumption time curve S of the target building unit within T, and then obtains the target predicted energy consumption label BS; at the same time, obtains the target metered energy consumption time curve Y of the target building unit within T to obtain the target metered energy consumption label BY, and then determines whether the metered energy consumption of the target building unit within T is normal according to BS and BY; therefore, when the metered energy consumption is abnormal, the abnormality is determined by the prediction method of the digital twin platform, avoiding the digital twin platform from using inaccurate energy consumption data and ensuring that the analysis and processing results are normal. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0007] Figure 1 A flowchart of a multi-source heterogeneous data management method based on digital twins provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0008] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0009] It should be noted that, based on the present disclosure, those skilled in the art should understand that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0010] The following will refer to Figure 1 The flowchart of the multi-source heterogeneous data management method based on digital twins is shown, which introduces a multi-source heterogeneous data management method based on digital twins.

[0011] The multi-source heterogeneous data management method based on digital twins may include the following steps: S100, clustering all building units of the target building according to the basic attributes and historical energy consumption of each building unit in the target area to obtain a cluster list A = (A1, A2, ..., A i , …, A n ), i=1, 2,...,n; among them, A i is the i-th cluster obtained by clustering, n is the number of clusters obtained by clustering; the energy consumption characteristics of the building units in each cluster are the same.

[0012] In this embodiment, the target area may be a large office area or industrial area, and the historical energy consumption may be the energy consumption of each building unit every day or every hour in the past period of time.

[0013] Furthermore, the basic attributes include the latitude and longitude coordinates, height, azimuth angle, structure type and date information of the building unit.

[0014] In this embodiment, each building unit can be each floor, the longitude and latitude coordinates corresponding to each building are different, the height of each building unit is also different, the azimuth angle of each building unit can be understood as the offset angle from due south, and the structural type is the building structure type of the building, such as: concrete structure, steel frame structure, etc.; different structural types correspond to different insulation conditions, and the date information can clarify the specific season.

[0015] Further, step S100 may include the following steps: S110, obtaining each basic attribute corresponding to each building unit in the target area to obtain a basic attribute list set D = (D1, D2, ..., D p , …, D q ), p = 1, 2, …, q; where D p is the basic attribute list corresponding to the pth building unit, q is the number of building units in the target area; D p =(D p，1 , D p，2 , …, D p，u , …, D p，v ), u=1, 2, …, v; D p，u is the uth basic attribute corresponding to the pth building unit, and v is the number of basic attributes corresponding to each building unit.

[0016] In this embodiment, the basic attributes of each building unit may be determined according to the building parameters of each building in the target area. It can be understood that the basic attributes corresponding to each building unit include static parameters and date data.

[0017] S120, obtaining the historical energy consumption of each building unit in the target area in each preset historical time period to obtain a historical energy consumption list set E = (E1, E2, ..., E p ,…,E q ); where E p is the historical energy consumption list corresponding to the p-th building unit; E p =(E p，1 , E p，2 ,…,E p，x ,…,E p，y ), x=1, 2, …, y; E p，x is the historical energy consumption of the p-th building unit in the x-th preset historical time period.

[0018] In this embodiment, the preset historical time period can be set according to actual conditions, for example: the length of the preset historical time period is set to one day, 12 hours or 2 hours, etc.; the historical energy consumption of each building unit is recorded, and E can be obtained through the historical records; it can be understood that the historical energy consumption is the behavior data of the building unit.

[0019] S130, concatenate D and E to obtain a heterogeneous data set corresponding to each building unit, and then obtain a heterogeneous data set list F = (F1, F2, ..., F p , …, F q ); where F p is the heterogeneous data set corresponding to the p-th building unit; F p =(D p , E p ).

[0020] In this embodiment, multi-source heterogeneity of data is achieved by splicing the static data and behavioral data of each building unit; it should be noted that the parameters in the heterogeneous data group are not completely in numerical form, and the non-numerical data needs to be converted into numerical form.

[0021] S140, normalize each heterogeneous data set in F to obtain a feature vector list XL corresponding to F = (XL1, XL2, ..., XL p ,…,XL q ); Among them, XL p is the eigenvector corresponding to the pth building unit.

[0022] In this embodiment, non-numeric data can be digitized by encoding, for example, type 1 data can be encoded as 1, type 2 can be encoded as 2, etc.; it should be noted that after all the data in the heterogeneous data group are digitized, those skilled in the art can use existing normalization methods according to actual needs to normalize each heterogeneous data group in F, which will not be elaborated here.

[0023] S150, clustering XL to obtain A.

[0024] In this embodiment, the K-means clustering algorithm can be used to cluster XL. It should be noted that those skilled in the art can use the existing K-means clustering algorithm to cluster XL according to actual needs, which will not be elaborated here. It can be understood that the energy consumption characteristics of the building units in each cluster are the same. For example, the energy consumption characteristics of all building units in a certain cluster are stable energy consumption and high energy consumption, and the energy consumption characteristics of all building units in a certain cluster are large energy consumption fluctuations and low energy consumption.

[0025] S200, obtaining the initial historical energy consumption label corresponding to each cluster in A, so as to obtain the initial historical energy consumption label list B corresponding to A = (B1, B2, ..., B i , …, B n ); where B i A i The corresponding initial historical energy consumption label.

[0026] In this embodiment, since the energy consumption characteristics of all building units in each cluster are the same, it is possible to i The historical energy consumption of a building unit is randomly selected in A, and then the corresponding historical energy consumption time curve is obtained. The initial historical energy consumption label corresponding to each cluster in A is determined by the method in steps S410-S440 to obtain B.

[0027] S300, input the basic attributes and historical energy consumption corresponding to the target building unit into the preset energy consumption prediction model of the digital twin platform to obtain the target predicted energy consumption time curve S of the target building unit within the preset time period T; wherein the start time of T is the current time T now .

[0028] In this embodiment, the target building unit can be any building unit in the target area; the preset digital twin platform can display the energy consumption data of each building unit. At the same time, the digital twin platform also presets an energy consumption prediction model, which can predict the energy consumption in the future based on the basic properties and historical energy consumption of the building unit; the energy consumption prediction model can be a trained long short-term memory network (Long Short-Term Memory, LSTM), which is a special recurrent neural network (RNN) structure that can learn long-term dependent information. It performs particularly well in processing and predicting important events with long intervals and delays in time series, which makes LSTM very suitable for processing and predicting important events with long intervals and delays in time series data.

[0029] S400, according to S, determining the target predicted energy consumption label BS corresponding to the target building unit in T.

[0030] Further, step S400 may include the following steps: S410, dividing T into a number of continuous sub-time periods of equal duration, so as to obtain a sub-time period list ZT corresponding to T = (ZT1, ZT2, ..., ZT a , …, ZT b ), a=1, 2, …, b; where ZT a is the ath sub-time period obtained by dividing T, and b is the number of sub-time periods obtained by dividing T.

[0031] In this embodiment, the duration of the sub-time period can be set according to actual needs, for example, the duration of the sub-time period is 10 minutes or 5 minutes.

[0032] S420, according to S, obtain the corresponding sub-predicted energy consumption of the target building unit in each sub-time period in T, so as to obtain a sub-predicted energy consumption list ZY=(ZY1, ZY2, ..., ZYa ,…,ZY b ); Among them, ZY a For the target building unit in ZT a The corresponding sub-prediction energy consumption.

[0033] In this embodiment, the X-axis of S is the time point, and the Y-axis is the corresponding predicted energy consumption. Therefore, according to S, the corresponding sub-predicted energy consumption of the target building unit in each sub-time period in T can be obtained.

[0034] S430, based on ZY, determine the predicted average energy consumption QA= (1 / b) × ∑ b a=1 ZY a And the predicted energy consumption fluctuation rate QB = (1 / b) × ∑ b a=1 (ZY a -((1 / b)×∑ b a=1 ZY a )) 2 .

[0035] In this embodiment, the predicted average energy consumption QA can represent the predicted average energy consumption level of the target building unit within Y, and the predicted energy consumption fluctuation rate QB can represent the fluctuation of the predicted energy consumption of the target building unit within T.

[0036] S440, determining BS according to QA, QB and a preset energy consumption label mapping table; wherein the preset energy consumption label mapping table includes a plurality of rows, each row corresponding to a combination of an average energy consumption range and an energy consumption fluctuation rate range and an energy consumption label.

[0037] In this embodiment, a preset energy consumption label mapping table stores several combinations of average energy consumption ranges and energy consumption fluctuation ranges and corresponding energy consumption labels; for example, the energy consumption label corresponding to the first combination is stable high energy consumption, the energy consumption label corresponding to the second combination is low volatile high energy consumption, etc.; all combinations can be traversed, and if QA and QB both fall within the average energy consumption range and energy consumption fluctuation range of a combination, the energy consumption label corresponding to the combination is determined to be BS.

[0038] S500, if T now =T end , then the target metering energy consumption time curve Y of the target building unit within T is obtained; where T end is the end time of T.

[0039] In this embodiment, when the current time is T endWhen the target metering energy consumption time curve Y of the target building unit within T can be obtained; Y is obtained by measuring the energy consumption of the target building unit through an energy consumption metering device, such as an electric energy meter.

[0040] S600: According to Y, determine the target metering energy consumption tag BY corresponding to the target building unit in T.

[0041] In this embodiment, after obtaining Y, BY may also be determined by the method in steps S410-S440, which will not be described in detail here.

[0042] S700, if BS is the same as B' and BY is the same as B', it is determined that the energy consumption of the target building unit is measured normally within T; otherwise, enter S800.

[0043] In this embodiment, under normal circumstances, the energy consumption characteristics of each building unit have a relatively stable energy consumption behavior pattern. Therefore, if BS is the same as B' and BY is the same as B', it means that the energy consumption behavior of the target building unit has not changed significantly compared with the historical energy consumption behavior, and it can be determined that the energy consumption of the target building unit is normal within T.

[0044] S800, obtaining the first metering energy consumption tag of each building unit in the cluster corresponding to B' in T to determine whether the metering energy consumption of the target building unit in T is abnormal; wherein B' is the initial historical energy consumption tag corresponding to the target building unit in B.

[0045] Further, step S800 may include the following steps: S810, obtaining the first metering energy consumption label of each building unit in the cluster corresponding to B' in T, so as to obtain a first metering energy consumption label list C corresponding to B' = (C1, C2, ..., C j , …, C m ), j = 1, 2, ..., m; where C j is the first metering energy consumption tag of the jth building unit in the cluster corresponding to B' in T, and m is the number of building units in the cluster corresponding to B'.

[0046] In this embodiment, the first metering energy consumption tag of each building unit in the cluster corresponding to B' in T can be obtained through the method in the above steps S410-S440.

[0047] S811, obtaining the number NUM1 of first energy consumption metering tags in C that are different from B'.

[0048] S812, if BS is the same as B', BY is different from B' and NUM1 / m<α1, then determine that the metered energy consumption of the target building unit in T is abnormal; wherein α1 is the first preset weight.

[0049] In this embodiment, α1 is a smaller value and can be set to 0.01 to 0.03; if BS is the same as B', BY is different from B' and NUM1 / m<α1, it means that the energy consumption behavior of the target building unit predicted by the digital twin platform is different from the energy consumption behavior measured by the target building unit, and, among the building units in the same cluster as the target building unit, the proportion of the number of first metering energy consumption labels that are different from B' to the total number is also very small; this situation can be considered as an abnormality of the energy consumption metering device of the target building unit itself, which is a sudden low-probability event.

[0050] S813, if BS is the same as B', BY is different from B' and NUM1 / m>α2, it is determined that the metered energy consumption of the target building unit in T is normal; wherein α2 is the second preset weight; α1<α2.

[0051] In this embodiment, α2 is a value larger than α1, and can be set to 0.4 to 0.6; if BS is the same as B', BY is different from B' and NUM1 / m>α2, it means that the energy consumption behavior of the target building unit predicted by the digital twin platform is different from the energy consumption behavior measured by the target building unit, and, among the building units in the same cluster as the target building unit, the number of first metering energy consumption tags that are different from B' accounts for a relatively large proportion of the total number; this situation can be considered that the energy consumption metering device of the target building unit is interfered by environmental factors, such as: electromagnetic interference exists in the target area, etc.

[0052] S814, if BS is the same as B', BY is different from B' and α1≤NUM1 / m≤α2, determine whether the metered energy consumption of the target building unit in T is abnormal by manual means.

[0053] In this embodiment, if BS is the same as B', BY is different from B' and α1≤NUM1 / m≤α2, there are multiple factors leading to this situation, and it is necessary to manually determine whether the metered energy consumption of the target building unit within T is abnormal.

[0054] Furthermore, after step S810, the method further includes the following steps: S820, if BS is different from B', BY is the same as B' and (m-NUM1) / m>α3, it is determined that the metered energy consumption of the target building unit within T is normal and the predicted energy consumption of the target building unit within T is abnormal; wherein α3 is the third preset weight; α3>α2.

[0055] S821, if BS is different from B', BY is the same as B' and (m-NUM1) / m≤α3, determine whether the metered energy consumption of the target building unit in T is abnormal by manual means.

[0056] In this embodiment, if BS is different from B' and BY is the same as B', it is possible that the prediction model has made a prediction error; however, it is also possible that when the external environmental factors undergo significant changes, the metering device also has an abnormality; α3 can be set to a larger value, for example: α3 can be set to 0.98 to 0.99; in the above case, if (m-NUM1) / m>α3, it means that the energy consumption behaviors of the building units in the same cluster of the target building unit have all undergone significant changes, and therefore, it can be determined that the predicted energy consumption of the target building unit in T is abnormal; if (m-NUM1) / m≤α3, it means that the energy consumption behaviors of a certain number of building units in the same cluster of the target building unit have all undergone significant changes, and there are multiple factors that may cause this situation, and it is necessary to manually determine whether the metered energy consumption of the target building unit in T is abnormal.

[0057] Further, after step S700 and before step S800, the method further includes the following steps: S710: If BS is different from B' and BY is different from B', determine whether the metered energy consumption of the target building unit in T is abnormal by manual means.

[0058] In this embodiment, this situation may occur because both the metering device and the prediction model are abnormal, and it is necessary to manually determine whether the metered energy consumption of the target building unit within T is abnormal.

[0059] In this embodiment, building units with the same energy consumption characteristics are clustered into a cluster based on the basic attributes and historical energy consumption of each building unit, and each cluster corresponds to an initial historical energy consumption label; the energy consumption prediction model of the digital twin platform is used to predict the target predicted energy consumption time curve S of the target building unit within T, and then obtain the target predicted energy consumption label BS; at the same time, the target metered energy consumption time curve Y of the target building unit within T is obtained to obtain the target metered energy consumption label BY, and then determine whether the metered energy consumption of the target building unit within T is normal based on BS and BY; therefore, when the metered energy consumption is abnormal, the abnormality is determined by the prediction method of the digital twin platform, avoiding the digital twin platform from using inaccurate energy consumption data and ensuring that the analysis and processing results are normal.

[0060] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0061] An embodiment of the present invention also provides a multi-source heterogeneous data management system based on digital twins, the system comprising: a processor and a storage medium; wherein the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the multi-source heterogeneous data management method based on digital twins as described in the above embodiments.

[0062] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A multi-source heterogeneous data management method based on digital twins, characterized in that: The method comprises the following steps: S100, clustering all building units of the target building according to the basic attributes and historical energy consumption of each building unit in the target area to obtain a cluster list A = (A1, A2, ..., A i , …, A n ), i=1, 2,...,n; among them, A i is the i-th cluster obtained by clustering, n is the number of clusters obtained by clustering; the energy consumption characteristics of the building units in each cluster are the same; S200, obtaining the initial historical energy consumption label corresponding to each cluster in A, so as to obtain the initial historical energy consumption label list B corresponding to A = (B1, B2, ..., B i , …, B n ); where B i A i The corresponding initial historical energy consumption label; S300, input the basic attributes and historical energy consumption corresponding to the target building unit into the preset energy consumption prediction model of the digital twin platform to obtain the target predicted energy consumption time curve S of the target building unit within the preset time period T; wherein the start time of T is the current time T now ; S400, determining the target predicted energy consumption label BS corresponding to the target building unit in T according to S; S500, if T now =T end , then the target metering energy consumption time curve Y of the target building unit within T is obtained; where T end is the end time of T; S600, determining the target metering energy consumption label BY corresponding to the target building unit in T according to Y; S700, if BS is the same as B' and BY is the same as B', it is determined that the target building unit has normal energy consumption measured within T; otherwise, enter S800; S800, obtaining the first metering energy consumption tag of each building unit in the cluster corresponding to B' in T to determine whether the metering energy consumption of the target building unit in T is abnormal; wherein B' is the initial historical energy consumption tag corresponding to the target building unit in B.

2. The multi-source heterogeneous data management method based on digital twins according to claim 1 is characterized in that: Step S800 includes the following steps: S810, obtaining the first metering energy consumption label of each building unit in the cluster corresponding to B' in T, so as to obtain a first metering energy consumption label list C corresponding to B' = (C1, C2, ..., C j , …, C m ), j = 1, 2, ..., m; where C j is the first metering energy consumption label of the jth building unit in the cluster corresponding to B' in T, and m is the number of building units in the cluster corresponding to B'; S811, obtaining the number NUM1 of first energy consumption metering tags in C that are different from B'; S812, if BS is the same as B', BY is different from B' and NUM1 / m<α1, then determine that the metered energy consumption of the target building unit in T is abnormal; wherein α1 is the first preset weight; S813, if BS is the same as B', BY is different from B' and NUM1 / m>α2, then it is determined that the metered energy consumption of the target building unit in T is normal; wherein α2 is the second preset weight; α1<α2; S814, if BS is the same as B', BY is different from B' and α1≤NUM1 / m≤α2, determine whether the metered energy consumption of the target building unit in T is abnormal by manual means.

3. The multi-source heterogeneous data management method based on digital twins according to claim 2 is characterized in that: After step S810, the method further includes the following steps: S820, if BS is different from B', BY is the same as B' and (m-NUM1) / m>α3, it is determined that the metered energy consumption of the target building unit within T is normal and the predicted energy consumption of the target building unit within T is abnormal; wherein α3 is the third preset weight; α3>α2; S821, if BS is different from B', BY is the same as B' and (m-NUM1) / m≤α3, determine whether the metered energy consumption of the target building unit in T is abnormal by manual means.

4. The multi-source heterogeneous data management method based on digital twins according to claim 1 is characterized in that: After step S700 and before step S800, the method further includes the following steps: S710: If BS is different from B' and BY is different from B', determine whether the metered energy consumption of the target building unit in T is abnormal by manual means.

5. The multi-source heterogeneous data management method based on digital twins according to claim 1 is characterized in that: Step S100 includes the following steps: S110, obtaining each basic attribute corresponding to each building unit in the target area to obtain a basic attribute list set D = (D1, D2, ..., D p , …, D q ), p = 1, 2, …, q; where D p is the basic attribute list corresponding to the pth building unit, q is the number of building units in the target area; D p =(D p，1 , D p，2 , …, D p，u , …, D p，v ), u=1, 2, …, v; D p，u is the uth basic attribute corresponding to the pth building unit, and v is the number of basic attributes corresponding to each building unit; S120, obtaining the historical energy consumption of each building unit in the target area in each preset historical time period to obtain a historical energy consumption list set E = (E1, E2, ..., E p , …, E q ); where E p is the historical energy consumption list corresponding to the p-th building unit; E p =(E p，1 , E p，2 , …, E p，x , …, E p，y ), x=1, 2, …, y; E p，x is the historical energy consumption of the p-th building unit in the x-th preset historical time period; S130, concatenate D and E to obtain a heterogeneous data set corresponding to each building unit, and then obtain a heterogeneous data set list F = (F1, F2, ..., F p , …, F q ); where F p is the heterogeneous data set corresponding to the p-th building unit; F p =(D p , E p ); S140, normalize each heterogeneous data set in F to obtain a feature vector list XL corresponding to F = (XL1, XL2, ..., XL p ,…,XL q ); Among them, XL p is the eigenvector corresponding to the pth building unit; S150, clustering XL to obtain A.

6. The multi-source heterogeneous data management method based on digital twins according to claim 1 is characterized in that: BS is obtained through the following steps: S410, dividing T into a number of continuous sub-time periods of equal duration, so as to obtain a sub-time period list ZT corresponding to T = (ZT1, ZT2, ..., ZT a , …, ZT b ), a=1, 2, …, b; where ZT a is the ath sub-time period obtained by dividing T, and b is the number of sub-time periods obtained by dividing T; S420, according to S, obtain the corresponding sub-predicted energy consumption of the target building unit in each sub-time period in T, so as to obtain a sub-predicted energy consumption list ZY=(ZY1, ZY2, ..., ZY a ,…,ZY b ); Among them, ZY a For the target building unit in ZT a The corresponding sub-prediction energy consumption; S430, based on ZY, determine the predicted average energy consumption QA= (1 / b) × ∑ b a=1 ZY a And the predicted energy consumption fluctuation rate QB = (1 / b) × ∑ b a=1 (ZY a -((1 / b)×∑ b a=1 ZY a )) 2 ; S440, determining BS according to QA, QB and a preset energy consumption label mapping table; wherein the preset energy consumption label mapping table includes a plurality of rows, each row corresponding to a combination of an average energy consumption range and an energy consumption fluctuation rate range and an energy consumption label.

7. The multi-source heterogeneous data management method based on digital twins according to claim 1 is characterized in that: Basic attributes include the building unit's latitude and longitude coordinates, height, azimuth angle, structure type, and date information.

8. The multi-source heterogeneous data management method based on digital twins according to claim 1 is characterized in that: The building unit is a floor of a corresponding building.

9. A multi-source heterogeneous data management system based on digital twins, characterized in that: The system includes: a processor and a storage medium; wherein, the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the multi-source heterogeneous data management method based on digital twins as described in any one of claims 1-8.

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