Method, system and equipment for evaluating performance of intelligent electric meter and storage medium

Abstract

The invention discloses an intelligent electric meter performance quality evaluation method, system and device and a storage medium, and the method comprises the steps: collecting the electric energy data time series of a master meter and a sub-meter of a transformer area in different transformer areas, and carrying out the preprocessing of the collected data, and obtaining an original electric energy data time series set; performing clustering analysis on the electric energy data time series in the original electric energy data time series set; based on the electric energy data time sequence set of each category, training an evaluation model to obtain an electric energy prediction model of the corresponding category and determine a corresponding threshold index; and evaluating the performance of the intelligent electric meter based on the electric energy prediction model of each category and the corresponding threshold value index thereof. The prediction value of each type of electric energy data curve is obtained based on the clustering analysis algorithm and in combination with the long-short-term memory network model, the performance of the intelligent electric meter is judged according to the difference value between the prediction value and the actual value, and technical support is provided for operation maintenance and accurate replacement of the intelligent electric meter in a transformer area.

Description

Technical Field

[0001] The present application relates to the technical field of smart meter status evaluation, and in particular to a smart meter performance evaluation method, system, device and storage medium. Background Art

[0002] In recent years, with the rapid development of smart grids and the widespread deployment of electricity information collection systems, smart meters have been widely promoted and applied. By the first half of 2023, hundreds of millions of smart meters have been installed nationwide, gradually replacing traditional mechanical meters and making electricity measurement digital. However, as the number of smart meters installed gradually increases, how to efficiently estimate their state, detect faults, and perform operation and maintenance is a major problem currently faced.

[0003] In order to ensure the accuracy of electricity market settlement, the State Grid currently adopts the method of periodic verification and rotation to replace smart meters. The State Grid Corporation of China has been rotating electric energy meters since 2009, and has used the batch failure rate of electric energy meters as the basis for periodic rotation since 2013. The use of periodic rotation mainly brings several problems: on the one hand, there are smart meters that have reached their service life but have no faults and can continue to be used, resulting in a waste of resources. On the other hand, among the electric energy meters that have not reached the rotation cycle, some of them suddenly fail due to environmental, human and other factors, resulting in a significant increase in verification and maintenance costs and social costs.

[0004] Therefore, it is necessary to propose a more accurate method to monitor the operating status of low-voltage smart meters online, conduct quantitative analysis of the operating status of meters, and formulate corresponding meter replacement strategies based on evaluation indicators. Summary of the invention

[0005] In order to solve the problems that the existing periodic rotation method of low-voltage smart meters is not scientific and accurate enough, and the number of metering anomalies is huge, and their management consumes a lot of manpower and material resources, this application proposes a smart meter performance evaluation method, system, equipment and storage medium. This application is based on a cluster analysis algorithm and combines a long short-term memory network model to obtain the predicted value of each type of electric energy data curve, and judges the performance of the smart meter based on the difference between the predicted value and the actual value, providing technical support for the operation, maintenance and precise replacement of smart meters in the substation.

[0006] In the first aspect, the present application is implemented through the following technical solutions:

[0007] A method for evaluating the performance of a smart meter, the method comprising:

[0008] Collect the time series of electric energy data of the total meter and sub-meter in different areas respectively, and pre-process the collected data to obtain the original electric energy data time series set;

[0009] Performing cluster analysis on the electric energy data time series in the original electric energy data time series set;

[0010] Based on each category of electric energy data time series set, the evaluation model is trained respectively to obtain the electric energy prediction model of the corresponding category and determine the corresponding threshold index;

[0011] Based on various categories of electric energy prediction models and their corresponding threshold indicators, the performance of smart meters is evaluated.

[0012] In some implementations, the clustering analysis of the electric energy data time series in the original electric energy data time series set specifically includes:

[0013] Normalize the time series of electric energy data;

[0014] Construct the minority class cluster:

[0015] Identify minority and majority class samples;

[0016] Randomly select a sample from the minority class samples as the cluster center, calculate the Euclidean distance between the cluster center and each sample in the majority class samples, take the minimum value of the Euclidean distance between the cluster center and each sample in the majority class samples as a threshold, calculate the Euclidean distance between the cluster center and other samples in the minority class samples and obtain the maximum value of the Euclidean distance within the threshold range;

[0017] Find out whether there are samples in the minority class whose Euclidean distance to the cluster center is within the threshold range. If so, these samples and the cluster center form a cluster with the cluster center as a circle and the radius as the average value of the threshold and the maximum value; if not, the cluster center forms a cluster alone, and the cluster radius is equal to half of the threshold;

[0018] Re-determine the minority class samples and the majority class samples, and perform the above steps until the clustering of all power data time series is completed.

[0019] In some implementations, the evaluation model is trained based on each category of the electric energy data time series set, and the electric energy prediction model of the corresponding category is obtained and the corresponding threshold index is determined, which specifically includes:

[0020] First, the cluster center is selected in each category, and a related evaluation model is established, wherein the evaluation model adopts a long short-term memory neural network model; then, the evaluation model is trained using the training samples of each category, and in each training process, a section of data in the time series is used as input to predict one or more future values, and the training error is obtained by subtracting the predicted value from the actual measured value;

[0021] After each training is completed, the root mean square error is used to verify whether the trained model meets the expectations. If not, the training error is fed back to optimize the model parameters and continue the next training until the expectations are met.

[0022] The trained model is used to predict the electric energy of the test samples, and the error analysis is performed between the predicted values ​​and the actual measured values ​​to determine the threshold indicators for evaluating the electric energy performance.

[0023] In some implementations, the residual between the predicted power value and the measured power value of the test sample is used as a threshold indicator for evaluating power performance.

[0024] In some implementations, the evaluation of the performance of the smart meter based on the electric energy prediction models of various categories and their corresponding threshold indicators specifically includes:

[0025] Collect the time series of electric energy data of a smart meter and preprocess it;

[0026] Perform cluster analysis on the preprocessed electric energy data time series to determine its category;

[0027] Input the preprocessed electric energy data time series into the electric energy prediction model of the corresponding category to obtain the electric energy prediction value;

[0028] The predicted electric energy value is compared with the actual electric energy value. If the difference between them exceeds the threshold index, it is determined that the operating state of the smart meter is poor; otherwise, it is determined that the operating state of the smart meter is good.

[0029] In a second aspect, the present application proposes a smart meter performance evaluation system, the evaluation system comprising:

[0030] A data preprocessing module, which is used to preprocess the collected time series of total meter and sub-meter electric energy data of different substations to obtain a time series set of original electric energy data;

[0031] A cluster analysis module, the cluster analysis module is used to perform cluster analysis on the original electric energy data time series;

[0032] A model training module, wherein the model training module trains the evaluation model based on the time series set of electric energy data of each category, obtains the electric energy prediction model of the corresponding category and determines the corresponding threshold index;

[0033] And, a performance evaluation module, which evaluates the performance of the smart meter based on various types of electric energy prediction models and their corresponding threshold indicators.

[0034] In some embodiments, the model training module further includes:

[0035] A construction unit, wherein the construction unit first selects a time series of electric energy data corresponding to a cluster center in each category, and establishes a related evaluation model, wherein the evaluation model adopts a long short-term memory neural network model;

[0036] A parameter optimization unit, wherein the parameter optimization unit uses the training samples in each category to train the evaluation model. After each training is completed, the root mean square error is used to verify whether the trained model meets the expectations. If not, the training error of the training is fed back, the model parameters are optimized and adjusted, and the next training is continued until the training is completed if the expectations are met;

[0037] And, an error analysis unit, which uses the trained model to predict the power of the test samples in the corresponding category, performs error analysis on the predicted power value and the measured power value, and determines the threshold index for evaluating the power performance.

[0038] In some embodiments, the performance evaluation module further includes:

[0039] A prediction unit, wherein the prediction unit inputs the electric energy data time series which has been preprocessed by the preprocessing module and whose category is determined by the clustering analysis module into an electric energy prediction model of a corresponding category to obtain an electric energy prediction value;

[0040] And, a comparison unit, which compares the electric energy prediction value output by the prediction unit with the electric energy actual measurement value. If the difference exceeds the threshold index, it is determined that the operating state of the smart meter is poor, otherwise it is determined that the operating state of the smart meter is good.

[0041] In a third aspect, the present application proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0042] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0043] The present application proposes a method, system, device and storage medium for evaluating the performance of smart meters. The cluster analysis algorithm based on Euclidean distance is used to perform cluster analysis on the metering data of each site to obtain different categories (i.e., obviously different) of smart meter electric energy data time series. Then, the electric energy data time series of each category is used to train the long short-term memory network model to obtain the electric energy prediction model corresponding to the category. The threshold index is set according to the difference between the electric energy prediction value and the measured value as the evaluation criterion for determining the performance of the smart meter, providing technical support for the operation, maintenance and accurate replacement of smart meters in the substation area.

[0044] The smart meter performance evaluation method, system, device and storage medium proposed in the present application have a wider scope of application and stronger versatility than the existing status evaluation that is only applicable to one type of meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0046] Figure 1 This is a flow chart of the evaluation method of an embodiment of the present application;

[0047] Figure 2 This is a functional block diagram of an evaluation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0049] Example:

[0050] The existing periodic rotation method of low-voltage smart meters easily leads to waste of resources. In addition, the number of metering anomalies is huge, and managing them consumes a lot of manpower and material resources. In view of this, this embodiment proposes a method for evaluating the performance of smart meters.

[0051] like Figure 1 As shown, the method proposed in this embodiment specifically includes the following steps:

[0052] Step 100, respectively collect time series of electric energy data of the total meter and sub-meter in different areas, and pre-process them to obtain a time series set of original electric energy data.

[0053] The step 100 specifically includes the following sub-steps:

[0054] Step 101, respectively collect time series of electric energy data of the total meter and sub-meter in different substations.

[0055] Step 102, filtering the collected raw electric energy data time series to filter out possible interference and noise such as incompleteness and inconsistency in the metering data, so as to provide a more accurate and reliable data basis for subsequent clustering and model training of electric energy curves of electric meters.

[0056] Step 200 , performing cluster analysis on the electric energy data time series in the original electric energy data time series set.

[0057] The step 200 specifically includes the following sub-steps:

[0058] Step 201, cluster analysis is performed on the electric energy data time series in the original electric energy data time series set to generate a sample data set. The specific process is:

[0059] First, in order to eliminate the dimensional relationship between data variables, the data needs to be normalized and preprocessed to [-1,1].

[0060] Then, we construct the minority cluster. Let the data set be X, the data feature dimension be n, and the minority class samples be represented as E = {e1, e2, ..., e c}, the majority class F = {f1,f2,...,f m}. c is the number of samples in the minority class, and m is the number of samples in the majority class; among them, abnormal data is the minority class, and normal data is the majority class. Randomly select a sample from the minority class E as the cluster center e a , and then calculate the following related parameters for discrimination.

[0061] The Euclidean distance between the cluster center and the majority class samples is shown in formula (1).

[0062]

[0063] The threshold χ is the distance between the cluster center and the majority class sample closest to it, as shown in formula (2).

[0064] χ=min{D(e a ,f i )},i∈{1,2,…m} (2)

[0065] The distance between the cluster center and other minority class samples is calculated as shown in formula (3), and then the maximum value of the distance in the interval (0, χ] is obtained by formula (4), which is recorded as d.

[0066]

[0067] d=max{D(ea ,e j )|0≤D(e a ,e j )≤χ},j∈{1,2,…(c-1)} (4)

[0068] The cluster radius R is shown in the following formula (5):

[0069]

[0070] According to formula (1) to formula (5), we can get a a As the center, R is the radius of the minority class cluster. Then, find out whether there are any samples in the remaining minority class samples whose Euclidean distance from the cluster center is less than or equal to the threshold χ. If so, the value of the maximum distance between these minority class samples and the cluster center is recorded as d, thus forming a cluster with the cluster center as the center and the radius R as the average value of χ and d; if not, the cluster center forms a cluster alone, and the radius R is equal to After that, the above contents are repeated until all clusters are formed. In order to further reduce the training time of the prediction model, this embodiment uses the construction covering algorithm of Euclidean distance to perform cluster analysis on the historical power time series of the smart meter according to the similarity of the power data curve.

[0071] Step 202: divide the sample data set generated by clustering into a training sample set and a validation sample set according to a preset ratio. For example, 70% of the sample data is used as a training sample set and 30% is used as a validation sample set.

[0072] Step 300: Based on each category of the electric energy data time series set, the evaluation model is trained respectively to obtain the electric energy prediction model of the corresponding category and determine the corresponding threshold index. This embodiment can use but is not limited to the long short-term memory neural network model as the evaluation model, and continuously train the model according to the electric energy data time series of the substation to obtain the corresponding electric energy prediction model. The specific process is as follows:

[0073] Step 301, first select the electric energy data time series corresponding to the cluster center in each category, and establish a related evaluation model. This embodiment can use but is not limited to the long short-term memory neural network model as the evaluation model. The evaluation model is trained using the training sample data of each category. During the model training process, a section of data in the time series is used as input, and one or more values ​​are predicted in front of the window. Each time the predicted value is subtracted from the actual electric energy measurement value to obtain the training error, a complete training is completed.

[0074] Step 302: After each training is completed, the root mean square error shown in formula (6) is used to verify whether the trained model meets the expectations. If not, the training error is fed back to optimize the model parameters and continue the next training until the expectations are met.

[0075]

[0076] In the formula, num represents the number of training samples; y i is the actual value of the i-th data; is the model prediction value for the i-th data.

[0077] Step 303, use the trained model to predict the power of the test sample set, perform error analysis on the predicted value and the actual measured value, and determine the threshold index for evaluating the power performance. This embodiment assumes that the power meter is in good operating condition during the model establishment process, then the residual corresponding to the test sample set is within the normal range, which is used as the threshold of the evaluation model. The calculation formula for the prediction residual is as follows:

[0078] error=W p -W r (7)

[0079] Where: error is the residual of electric energy data; W p is the electric energy data predicted by the model; W r It is the actual electric energy data of the smart meter.

[0080] Step 400, based on various types of electric energy prediction models and their corresponding threshold indicators, the performance of the smart meter is evaluated.

[0081] The step 400 specifically includes the following steps:

[0082] Step 401, collecting the time series of electric energy data of a smart meter and preprocessing it;

[0083] Step 402: Perform cluster analysis on the preprocessed electric energy data time series to determine the category to which it belongs.

[0084] Step 403: input the preprocessed electric energy data time series into the electric energy prediction model of the corresponding category to obtain the electric energy prediction value.

[0085] Step 404, compare the predicted power value with the measured power value, if the difference exceeds the threshold index, it is determined that the operation status of the smart meter is poor, otherwise it is determined that the operation status of the smart meter is good.

[0086] The evaluation method proposed in this embodiment is based on the Euclidean distance cluster analysis algorithm. By performing cluster analysis on the data measured by the smart meters at each site, the electric energy data time series of several categories of smart meters with obvious differences are obtained. Then, the long short-term memory network model is used to train each category of the electric energy data time series respectively, so as to obtain the predicted value of each category of the electric energy data time series. Finally, based on the data obtained in the actual substation, the threshold is set by using the difference between the predicted electric energy value and the actual value to determine the evaluation index of the performance of the smart meter, which provides technical support for the operation, maintenance and precise replacement of smart meters in the substation, and improves the accuracy of fault analysis and positioning.

[0087] Based on the same technical concept as above, this embodiment also proposes a smart meter performance evaluation system, such as Figure 2 As shown, the evaluation system proposed in this embodiment includes:

[0088] The data preprocessing module is used to preprocess the collected time series of total meter and sub-meter electric energy data of different substations to obtain the original electric energy data time series set. The data preprocessing module can also be used for the smart meter electric energy data time series for later performance evaluation. Specifically, a filtering algorithm can be used to process the collected electric energy data time series to filter out the interference and noise in the metering data and improve the reliability of the data, as described in the above steps 101-102, which will not be repeated here.

[0089] Cluster analysis module, which is used to perform cluster analysis on the original power data time series. The specific clustering process is as described in the above steps 201-202, and will not be repeated here. The cluster analysis can also perform adaptive clustering on the newly collected power data time series, that is, identify the category of the power data time series.

[0090] The model training module trains the evaluation model based on the time series set of electric energy data of each category, obtains the electric energy prediction model of the corresponding category and determines the corresponding threshold index.

[0091] And, a performance evaluation module, which evaluates the performance of the smart meter based on various categories of electric energy prediction models and their corresponding threshold indicators.

[0092] Furthermore, the model training module also includes:

[0093] A construction unit is constructed, which first selects the time series of electric energy data corresponding to the cluster center in each category and establishes a related evaluation model. A long short-term memory neural network model is used as the evaluation model.

[0094] The parameter optimization unit uses the training samples in each category to train the evaluation model. After each training, the root mean square error is used to verify whether the trained model meets the expectations. If so, the training is completed. Otherwise, the training error is fed back to optimize the model parameters and continue with the next training.

[0095] And, an error analysis unit, which uses the trained model to predict the power of the test sample, performs error analysis between the predicted value and the actual measured value, and determines a threshold indicator for evaluating the power performance.

[0096] Furthermore, the performance evaluation module also includes:

[0097] The prediction unit inputs the electric energy data time series processed by the preprocessing module and whose category is determined by the clustering analysis module into the electric energy prediction model of the corresponding category to obtain the electric energy prediction value.

[0098] And, a comparison unit, which compares the electric energy prediction value output by the prediction unit with the electric energy actual measurement value. If the difference exceeds a threshold indicator, it is determined that the operating state of the smart meter is poor, otherwise it is determined that the operating state of the smart meter is good.

[0099] This embodiment uses actual area data to verify the above evaluation method proposed in this embodiment, as follows:

[0100] Aiming at the problem of error estimation of smart grid operation in a typical distribution network area in a certain region, the evaluation method proposed in this embodiment is used to evaluate two types of smart energy meters, AFPM and PZ. The distribution network area is equipped with a master smart energy meter and 125 sub-smart energy meters. The training data uses the historical operation data of smart energy meters from April to June 2022, with a sampling frequency of 15 minutes / time; the sample test data uses the smart grid operation input data from May 1 to 15, 2022. The error estimation results shown in Table 1 are obtained.

[0101] Table 1. Estimation results of operation errors of smart energy meters in distribution network areas

[0102]

[0103] It can be seen from Table 1 that the evaluation method proposed in this embodiment has a good effect and provides technical support for operation and maintenance detection and fault diagnosis.

[0104] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for evaluating the performance of a smart meter, characterized in that: The evaluation methods include: Collect the time series of electric energy data of the total meter and sub-meter in different areas respectively, and pre-process the collected data to obtain the original electric energy data time series set; Performing cluster analysis on the electric energy data time series in the original electric energy data time series set; Based on each category of electric energy data time series set, the evaluation model is trained respectively to obtain the electric energy prediction model of the corresponding category and determine the corresponding threshold index; Based on various categories of electric energy prediction models and their corresponding threshold indicators, the performance of smart meters is evaluated.

2. A method for evaluating the performance of a smart meter according to claim 1, characterized in that: The cluster analysis of the electric energy data time series in the original electric energy data time series set specifically includes: Normalize the time series of electric energy data; Construct the minority class cluster: Identify minority and majority class samples; Randomly select a sample from the minority class samples as the cluster center, calculate the Euclidean distance between the cluster center and each sample in the majority class samples, take the minimum value of the Euclidean distance between the cluster center and each sample in the majority class samples as a threshold, calculate the Euclidean distance between the cluster center and other samples in the minority class samples and obtain the maximum value of the Euclidean distance within the threshold range; Find out whether there are samples in the minority class whose Euclidean distance to the cluster center is within the threshold range. If so, these samples and the cluster center form a cluster with the cluster center as a circle and the radius as the average value of the threshold and the maximum value; if not, the cluster center forms a cluster alone, and the cluster radius is equal to half of the threshold; Re-determine the minority class samples and the majority class samples, and perform the above steps until the clustering of all power data time series is completed.

3. A method for evaluating the performance of a smart meter according to claim 2, characterized in that: The evaluation model is trained based on each category of electric energy data time series set, and the electric energy prediction model of the corresponding category is obtained and the corresponding threshold index is determined, which specifically includes: First, the cluster center is selected in each category, and a related evaluation model is established, wherein the evaluation model adopts a long short-term memory neural network model; then, the evaluation model is trained using the training samples of each category, and in each training process, a section of data in the time series is used as input to predict one or more future values, and the training error is obtained by subtracting the predicted value from the actual measured value; After each training is completed, the root mean square error is used to verify whether the trained model meets the expectations. If not, the training error is fed back to optimize the model parameters and continue the next training until the expectations are met. The trained model is used to predict the electric energy of the test samples, and the error analysis is performed between the predicted values ​​and the actual measured values ​​to determine the threshold indicators for evaluating the electric energy performance.

4. A method for evaluating the performance of a smart meter according to claim 3, characterized in that: The residual between the predicted power value and the measured power value of the test sample is used as the threshold indicator for evaluating power performance.

5. A method for evaluating the performance of a smart meter according to any one of claims 1 to 4, characterized in that: The electric energy prediction models based on various categories and their corresponding threshold indicators are used to evaluate the performance of smart meters, including: Collect the time series of electric energy data of a smart meter and preprocess it; Perform cluster analysis on the preprocessed electric energy data time series to determine its category; Input the preprocessed electric energy data time series into the electric energy prediction model of the corresponding category to obtain the electric energy prediction value; The predicted electric energy value is compared with the actual electric energy value. If the difference between them exceeds the threshold index, it is determined that the operating state of the smart meter is poor; otherwise, it is determined that the operating state of the smart meter is good.

6. A smart meter performance evaluation system, characterized in that: The evaluation system comprises: A data preprocessing module, which is used to preprocess the collected time series of total meter and sub-meter electric energy data of different substations to obtain a time series set of original electric energy data; A cluster analysis module, the cluster analysis module is used to perform cluster analysis on the original electric energy data time series; A model training module, wherein the model training module trains the evaluation model based on the time series set of electric energy data of each category, obtains the electric energy prediction model of the corresponding category and determines the corresponding threshold index; And, a performance evaluation module, which evaluates the performance of the smart meter based on various types of electric energy prediction models and their corresponding threshold indicators.

7. A smart meter performance evaluation system according to claim 6, characterized in that: The model training module also includes: A construction unit, wherein the construction unit first selects a time series of electric energy data corresponding to a cluster center in each category, and establishes a related evaluation model, wherein the evaluation model adopts a long short-term memory neural network model; A parameter optimization unit, wherein the parameter optimization unit uses the training samples in each category to train the evaluation model. After each training is completed, the root mean square error is used to verify whether the trained model meets the expectations. If not, the training error of the training is fed back, the model parameters are optimized and adjusted, and the next training is continued until the training is completed if the expectations are met; And, an error analysis unit, which uses the trained model to predict the power of the test samples in the corresponding category, performs error analysis on the predicted power value and the measured power value, and determines the threshold index for evaluating the power performance.

8. A smart meter performance evaluation system according to claim 6, characterized in that: The performance evaluation module also includes: A prediction unit, wherein the prediction unit inputs the electric energy data time series which has been preprocessed by the preprocessing module and whose category is determined by the clustering analysis module into an electric energy prediction model of a corresponding category to obtain an electric energy prediction value; And, a comparison unit, which compares the electric energy prediction value output by the prediction unit with the electric energy actual measurement value. If the difference exceeds the threshold index, it is determined that the operating state of the smart meter is poor, otherwise it is determined that the operating state of the smart meter is good.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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