Method and system for judging operation and maintenance strategy of electric energy meter based on unbalanced sample

Abstract

The invention discloses a method and system for judging an operation and maintenance strategy of an electric energy meter based on an unbalanced sample, and relates to the technical field of online monitoring of electric power metering, and the method comprises the following steps: obtaining historical operation data of the electric energy meter, and carrying out the sample expansion of the normalized historical operation data through an improved diffusion model, and obtaining sample data; constructing a fault judgment model which comprises a feature extraction layer and a fault classification layer; the feature extraction layer is specifically used for extracting feature vectors by using a CNN-LSTM model; the fault classification layer is specifically used for calculating a fault index and an important index input model to carry out fault classification; inputting the sample data into a fault judgment model for training to obtain a final fault judgment model; obtaining to-be-evaluated operation data of the to-be-evaluated electric energy meter, inputting the to-be-evaluated operation data into the final fault judgment model, and obtaining a fault type; and obtaining an operation and maintenance strategy according to the fault type and performing judgment to obtain a judgment result of the operation and maintenance strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring of electric power metering, and mainly to a method and system for determining an operation and maintenance strategy of an electric energy meter based on unbalanced samples. Background Art

[0002] Energy meters are key devices for energy measurement in power systems. Their accuracy is directly related to electricity bill calculation and power consumption measurement. With the development of market-oriented and intelligent power systems, the operation and maintenance of energy meters has become particularly important. First, accurate metering is the foundation for fair electricity trading and is crucial for maintaining order in the power market and protecting consumer rights. Second, regular maintenance and calibration of energy meters can promptly detect and address metering errors, avoiding economic losses. Furthermore, the stable operation of energy meters is crucial for the safe and reliable power supply of the power grid. Operation and maintenance can prevent and reduce failures and improve grid efficiency. With technological advancements, the functions of energy meters are becoming increasingly complex, requiring a professional operation and maintenance team to ensure their efficient and stable operation. Therefore, the operation and maintenance of energy meters is not only a technical requirement, but also a market and legal requirement, playing a vital role in improving the operational quality and service level of the entire power system.

[0003] However, traditional electricity metering equipment inspection mainly relies on manual work, which has certain limitations and cannot meet the development requirements of power companies under the new situation; some operation and maintenance personnel rely too much on personal experience when judging operation and maintenance strategies, lack scientific basis and data analysis, resulting in operation and maintenance decision-making errors and lack of unified standards, which affects the uniformity and standardization of operation and maintenance work. The existing operation and maintenance system also lacks intelligent decision-making support functions and cannot automatically adjust operation and maintenance strategies according to operation and maintenance data and actual conditions.

[0004] For example, the Chinese invention patent with publication number "CN118885736A" discloses a "Small Sample Mechanical Fault Diagnosis Method Based on an Improved De-noising Diffusion Model," which specifically discloses "collecting and preprocessing the original vibration signals of key transmission components under different mechanical health states; using short-time Fourier transform to convert the one-dimensional time-domain signal into a two-dimensional time-frequency graph; constructing a denoising diffusion model and improving the model structure; inputting the converted time-frequency graph into the improved denoising diffusion model for training; using the trained denoising diffusion model to generate different types of samples, expanding the original data set to form a new data set; outputting the new data set to the constructed convolutional network to complete fault diagnosis." However, during the data processing process, this method needs to convert the one-dimensional time-domain signal into a two-dimensional time-frequency graph and construct a denoising diffusion model for training. This process is relatively complex and requires high computing power and data processing technology. In addition, after the fault diagnosis is completed, the method does not further provide a detailed operation and maintenance strategy formulation and judgment process. In actual application, operation and maintenance personnel may need to explore on their own or rely on experience to formulate operation and maintenance strategies, which can easily lead to strategy inconsistency and inefficiency. Summary of the Invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present application provides a method and system for determining an operation and maintenance strategy of an electric energy meter based on unbalanced samples.

[0006] The technical solution of this application is as follows: In one aspect, the present invention proposes a method for determining an operation and maintenance strategy of an electric energy meter based on an unbalanced sample, the method comprising: Acquiring historical operating data of the electric energy meter, normalizing the historical operating data to obtain normalized historical operating data; constructing an improved diffusion model, and using the improved diffusion model to perform sample expansion on the normalized historical operating data to obtain sample data; Construct a fault judgment model, including a feature extraction layer and a fault classification layer; wherein the feature extraction layer specifically uses a CNN-LSTM model to extract feature vectors of sample data; the fault classification layer specifically calculates a fault index and an importance index, and inputs the fault index and importance index into a support vector machine model for fault classification; the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and uses a hierarchical analysis method to analyze the fault type to obtain a fault weight, and combines the fault weight and the fault degree to obtain a fault index; the importance index specifically calculates the correlation coefficient of the electric energy meter, and performs weighted processing on the correlation coefficient to obtain the importance index of the electric energy meter; the sample data is input into the fault judgment model for training to obtain a final fault judgment model; Obtain the operating data to be evaluated of the electric energy meter to be evaluated, input the operating data to be evaluated into a final fault judgment model, and obtain the fault type of the fault data in the operating data to be evaluated; obtain the operation and maintenance strategy of the electric energy meter to be evaluated based on the fault type, judge the operation and maintenance strategy, and obtain a judgment result of the operation and maintenance strategy.

[0007] Preferably, the normalization process is specifically to map the historical operation data to the interval [0, 1], specifically: , ; ; ; Where, Indicates the A historical operation data sequence of an electric energy meter; Indicates the The first The vector group corresponding to the historical operation data includes the output voltage , output current , output power and power factor error ; Indicates the The first output voltage; Indicates the The first Output current; Indicates the The first Output power; Indicates the The first Power factor error; Indicates the The first The minimum value of the vector group corresponding to the historical running data; Indicates the The first The maximum value of the vector group corresponding to the historical running data; Indicates the Normalized historical operation data series of each electric energy meter; Indicates the The first A vector group corresponding to the normalized historical runs; Indicates the The first A normalized output voltage; Indicates the The first A normalized output current; Indicates the The first The normalized output power; Indicates the The first A normalized power factor error; Indicates the The index value of a historical running data series; Indicates the length of the historical running data sequence, that is, the number of vector groups; Indicates the The index value of an electric energy meter.

[0008] Preferably, the data processing module includes a first submodule, a second submodule and a third submodule; the first submodule includes an embedding unit; the second submodule includes a diffusion unit and an embedding unit; the third submodule includes an embedding unit and an LSTM unit; The input module obtains the normalized historical operation data and a preset number of diffusion steps, inputs the preset number of diffusion steps into the first submodule, and inputs the normalized historical operation data into the second submodule and the third submodule; The embedding unit of the first submodule obtains a preset number of diffusion steps and outputs the embedding result , which is the output result of the first submodule; The diffusion unit of the second submodule obtains the normalized historical operation data for diffusion, and inputs the diffusion data into the embedding unit of the second submodule, which can be expressed as follows: ; ; , ; ; Where, express The historical running data after diffusion through the preset diffusion steps is called diffusion data; The noise accumulation coefficient representing the preset number of diffusion steps; Indicates the noise ratio for the preset number of diffusion steps; The diffusion rate representing the preset number of diffusion steps; Represents the preset Gaussian noise that obeys the standard normal distribution; Indicates the The index value of the diffusion step number; Indicates the preset number of diffusion steps; Indicates the maximum value of the preset diffusion steps; Indicates the An energy meter passes The output result of the second submodule after step diffusion; represents the embedded function; The output results of the first submodule and the second submodule are subjected to residual fusion, which can be expressed as follows: ; Where, Indicates the An energy meter passes The output result of the data processing module after the first step of diffusion; represents residual fusion; The embedding unit of the third submodule divides the normalized historical operation data and performs a one-dimensional linear projection, which can be expressed as follows: ; , ; Where, Indicates the number of data blocks divided; Indicates the preset data block size; Indicates the The projection space of an electric energy meter; Indicates the The first The data after linear projection of the data blocks; Indicates the first The first Data of data blocks; Indicates the The index value of a data block; The position code of the electric energy meter is added to the vector space to obtain the position vector space; the position vector space is input into the LSTM unit of the third submodule, which is expressed as follows: ; ; ; Where, Indicates the The position vector space of each electric energy meter; Indicates positional encoding; Indicates the The first The position vector space of data blocks; A network unit representing an LSTM unit; Represents the hidden layer state set, i.e. Output result of the third submodule of each electric energy meter; Indicates the The first The output vector of the network unit of the LSTM unit of the data block.

[0009] Preferably, the encoder module includes Stacking modules, each stacking module includes a normalization layer and a multi-head self-attention layer; the output result of the data processing module and the hidden layer state set are normalized and then input into the multi-head self-attention layer, which can be expressed as: , ; ; ; ; ; ; ; ; ; Where, Indicates the An energy meter passes The output of the stacked module after step diffusion; Indicates the The value vector of a multi-head self-attention layer; Indicates the The key vector of a multi-head self-attention layer; Indicates the The query vector of a multi-head self-attention layer; represents a linear layer; Indicates the The linear transformation matrix of a value vector; Indicates the The linear transformation matrix of the key vectors; Indicates the The linear transformation matrix of the query vector; Indicates the The output of a multi-head self-attention layer; Indicates the number of heads of the multi-head attention mechanism; represents the attention mechanism; A set of query vectors representing a multi-head self-attention layer; A set of key vectors representing a multi-head self-attention layer; A set of value vectors representing a multi-head self-attention layer; Indicates the The index value of the multi-head self-attention layer; Represents a multi-head self-attention mechanism; represents the connection function; Represents the preset linear transformation matrix; The output of the stacking module is used as the input of the next stacking module, and the times, generating the output of the encoder module ; The inverse diffusion module obtains the output of the encoder module to perform sample expansion and generate sample data, which can be expressed as follows: ; ; ; Where, Indicates the Sample data generated by an electric energy meter; represents the posterior distribution; represents the mean of the posterior distribution; represents the calibration factor; represents the prediction noise; The loss function is used to train the inverse diffusion module, which is expressed as: ; Where, represents the loss function; Indicates expected value; Represents the norm.

[0010] Preferably, the feature extraction layer specifically uses a CNN-LSTM model to extract the feature vector of the sample data, which can be expressed as follows: ; Where, Indicates the The characteristic vector of an electric energy meter; Represents the CNN-LSTM model; The fault classification layer specifically calculates the fault index and importance index of the electric energy meter and inputs them into the support vector machine model for fault classification, wherein the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and is expressed as follows: ; ; Where, Indicates the fault degree of the energy meter; Represents the characteristic mean of normal samples in sample data; Represents normal samples in sample data; Indicates the number of normal samples in the sample data; Indicates the The feature vector of normal samples in the sample data; Indicates the The index value of the normal sample in the sample data; Indicates the sample data The feature vectors of samples that are similar to normal samples; Indicates the sample data samples that are similar to normal samples; Represents the maximum value of the eigenvector of the normal sample in the sample data; Represents the minimum value of the eigenvector of the normal sample in the sample data; Indicates the sample data The maximum value of the characteristic vector of samples of the same type as the normal samples; Indicates the sample data The minimum value of the characteristic vector of samples similar to normal samples; Indicates the sample data The characteristic mean of samples of the same type as the normal sample; Indicates the preset first weight; Indicates the preset second weight; represents the activation function; Indicates the sample data The index value of the sample that is similar to the normal sample.

[0011] Preferably, the fault type is analyzed using the hierarchical analysis method to obtain the fault weight, and the fault index is obtained by combining the fault weight and the fault degree, which is expressed as follows: , ; Where, represents the fault index; Represents the preset exponential decay factor; Indicates the Fault weights; Indicates the The index value of the fault weight; The important index is specifically calculated as the correlation coefficient of the electric energy meter, which includes the topological connection coefficient, the metering level coefficient and the influence coefficient. The important index of the electric energy meter is obtained by weighting the correlation coefficient, which is expressed as follows: ; ; ; ; Where, Indicates important indexes of the electric energy meter; represents the topological connectivity coefficient; Indicates the degree of the circuit measured by the electric energy meter, that is, the number of circuits connected to the electric energy meter; Indicates the mean degree of the lines connected to the current line; Indicates the metrological grade coefficient; Indicates the measurement level; Indicates the preset minimum value of the measurement grade coefficient; represents the influence coefficient; represents the activation function; Indicates the power level; Indicates the preset topological connection coefficient weight; Indicates the preset measurement grade coefficient weight; Indicates the preset influence coefficient weight; The fault index and importance index are input into the support vector machine model for fault classification, specifically: , ; Where, Indicates the status of the energy meter, where Indicates normal status. Indicates that the error exceeds the tolerance. Indicates that the DC current is open circuit, Indicates DC voltage short circuit and Indicates a short circuit in the control circuit; Indicates the status number of the energy meter; Represents a support vector machine model.

[0012] On the other hand, the present invention also provides a system for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples, the system comprising a data acquisition module, a fault judgment model, a data evaluation module, and a result output module, wherein: The data acquisition module is used to acquire historical operating data of the electric energy meter, normalize the historical operating data to obtain normalized historical operating data; construct an improved diffusion model, use the improved diffusion model to perform sample expansion on the normalized historical operating data to obtain sample data; and transmit the sample data to the fault judgment model; The fault judgment model module is used to construct a fault judgment model, including a feature extraction layer and a fault classification layer; wherein the feature extraction layer specifically uses the CNN-LSTM model to extract the feature vector of the sample data; the fault classification layer specifically calculates the fault index and importance index, and inputs the fault index and importance index into the support vector machine model for fault classification; the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and uses the hierarchical analysis method to analyze the fault type to obtain the fault weight, and combines the fault weight and the fault degree to obtain the fault index; the importance index specifically calculates the correlation coefficient of the electric energy meter, and performs weighted processing on the correlation coefficient to obtain the importance index of the electric energy meter; the sample data is input into the fault judgment model for training to obtain the final fault judgment model; The data evaluation module is used to obtain the operating data to be evaluated of the electric energy meter to be evaluated, input the operating data to be evaluated into the final fault judgment model, and obtain the fault type of the fault data in the operating data to be evaluated; obtain the operation and maintenance strategy of the electric energy meter to be evaluated based on the fault type, judge the operation and maintenance strategy, and obtain the judgment result of the operation and maintenance strategy; The result output module is used to output the judgment result of the operation and maintenance strategy.

[0013] Preferably, the improved diffusion model includes an input module, a data processing module, an encoder module, a reverse diffusion module and an output module, wherein: The data processing module includes a first submodule, a second submodule and a third submodule; the first submodule includes an embedding unit; the second submodule includes a diffusion unit and an embedding unit; the third submodule includes an embedding unit and an LSTM unit; The input module obtains the normalized historical operation data and a preset number of diffusion steps, inputs the preset number of diffusion steps into the first submodule, and inputs the normalized historical operation data into the second submodule and the third submodule; The embedding unit of the first submodule obtains a preset number of diffusion steps and outputs the embedding result , which is the output result of the first submodule; The diffusion unit of the second submodule obtains the normalized historical operation data for diffusion, and inputs the diffusion data into the embedding unit of the second submodule, which can be expressed as follows: ; ; , ; ; Where, express The historical running data after diffusion through the preset diffusion steps is called diffusion data; The noise accumulation coefficient representing the preset number of diffusion steps; Indicates the noise ratio for the preset number of diffusion steps; The diffusion rate representing the preset number of diffusion steps; Represents the preset Gaussian noise that obeys the standard normal distribution; Indicates the The index value of the diffusion step number; Indicates the preset number of diffusion steps; Indicates the preset total number of diffusion steps; Indicates the An energy meter passes The output result of the second submodule after step diffusion; represents the embedded function; The output results of the first submodule and the second submodule are subjected to residual fusion, which can be expressed as follows: ; Where, Indicates the An energy meter passes The output result of the data processing module after the first step of diffusion; represents residual fusion; The embedding unit of the third submodule divides the normalized historical operation data and performs a one-dimensional linear projection, which can be expressed as follows: ; , ; Where, Indicates the number of data blocks divided; Indicates the preset data block size; Indicates the The projection space of an electric energy meter; Indicates the The first The data after linear projection of the data blocks; Indicates the first The first Data of data blocks; Indicates the The index value of a data block; The position code of the electric energy meter is added to the vector space to obtain the position vector space; the position vector space is input into the LSTM unit of the third submodule, which is expressed as follows: ; ; ; Where, Indicates the The position vector space of each electric energy meter; Indicates positional encoding; Indicates the The first The position vector space of data blocks; A network unit representing an LSTM unit; Represents the hidden layer state set, i.e. Output result of the third submodule of each electric energy meter; Indicates the The first The output vector of the network unit of the LSTM unit of the data block; The encoder module includes Stacking modules, each stacking module includes a normalization layer and a multi-head self-attention layer; the output result of the data processing module and the hidden layer state set are normalized and then input into the multi-head self-attention layer, which can be expressed as: , ; ; ; ; ; ; ; ; ; Where, Indicates the An energy meter passes The output of the stacked module after step diffusion; Indicates the The value vector of a multi-head self-attention layer; Indicates the The key vector of a multi-head self-attention layer; Indicates the The query vector of a multi-head self-attention layer; represents a linear layer; Indicates the The linear transformation matrix of a value vector; Indicates the The linear transformation matrix of the key vectors; Indicates the The linear transformation matrix of the query vector; Indicates the The output of a multi-head self-attention layer; Indicates the number of heads of the multi-head attention mechanism; represents the attention mechanism; A set of query vectors representing a multi-head self-attention layer; A set of key vectors representing a multi-head self-attention layer; A set of value vectors representing a multi-head self-attention layer; Indicates the The index value of the multi-head self-attention layer; Represents a multi-head self-attention mechanism; represents the connection function; Represents the preset linear transformation matrix; The output of the stacking module is used as the input of the next stacking module, and the times, generating the output of the encoder module ; The inverse diffusion module obtains the output of the encoder module to perform sample expansion and generate sample data, which can be expressed as follows: ; ; ; Where, Indicates the Sample data generated by an electric energy meter; represents the posterior distribution; represents the mean of the posterior distribution; represents the calibration factor; represents the prediction noise; The loss function is used to train the inverse diffusion module, which is expressed as: ; Where, represents the loss function; Indicates expected value; Represents the norm.

[0014] Preferably, the feature extraction layer specifically uses a CNN-LSTM model to extract the feature vector of the sample data, which can be expressed as follows: ; Where, Indicates the The characteristic vector of an electric energy meter; Represents the CNN-LSTM model; The fault classification layer specifically calculates the fault index and importance index of the electric energy meter and inputs them into the support vector machine model for fault classification, wherein the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and is expressed as follows: ; ; Where, Indicates the fault degree of the energy meter; Represents the characteristic mean of normal samples in sample data; Represents normal samples in sample data; Indicates the number of normal samples in the sample data; Indicates the The feature vector of normal samples in the sample data; Indicates the The index value of the normal sample in the sample data; Indicates the sample data The feature vectors of samples that are similar to normal samples; Indicates the sample data samples that are similar to normal samples; Represents the maximum value of the eigenvector of the normal sample in the sample data; Represents the minimum value of the eigenvector of the normal sample in the sample data; Indicates the sample data The maximum value of the characteristic vector of samples of the same type as the normal samples; Indicates the sample data The minimum value of the characteristic vector of samples similar to normal samples; Indicates the sample data The characteristic mean of samples of the same type as the normal sample; Indicates the preset first weight; Indicates the preset second weight; represents the activation function; Indicates the sample data The index value of the sample that is similar to the normal sample.

[0015] Preferably, the fault type is analyzed using the hierarchical analysis method to obtain the fault weight, and the fault index is obtained by combining the fault weight and the fault degree, which is expressed as follows: , ; Where, represents the fault index; Represents the preset exponential decay factor; Indicates the Fault weights; Indicates the The index value of the fault weight; The important index is specifically calculated as the correlation coefficient of the electric energy meter, which includes the topological connection coefficient, the metering level coefficient and the influence coefficient. The important index of the electric energy meter is obtained by weighting the correlation coefficient, which is expressed as follows: ; ; ; ; Where, Indicates important indexes of the electric energy meter; represents the topological connectivity coefficient; Indicates the degree of the circuit measured by the electric energy meter, that is, the number of circuits connected to the electric energy meter; Indicates the mean degree of the lines connected to the current line; Indicates the metrological grade coefficient; Indicates the measurement level; Indicates the preset minimum value of the measurement grade coefficient; represents the influence coefficient; represents the activation function; Indicates the power level; Indicates the preset topological connection coefficient weight; Indicates the preset measurement grade coefficient weight; Indicates the preset influence coefficient weight; The fault index and importance index are input into the support vector machine model for fault classification, specifically: , ; Where, Indicates the status of the energy meter, where Indicates normal status. Indicates that the error exceeds the tolerance. Indicates that the DC current is open circuit, Indicates DC voltage short circuit and Indicates a short circuit in the control circuit; Indicates the status number of the energy meter; Represents a support vector machine model.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention provides a method and system for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples. By acquiring the historical operation data of the electric energy meter and using an improved diffusion model to expand the sample of the normalized data, the diversity and quantity of the sample data are increased, and the representativeness and usability of the data are improved. 2) This invention provides a method and system for determining the operation and maintenance strategy of an electricity meter based on unbalanced samples. This method uses a CNN-LSTM model to extract feature vectors and combines it with a support vector machine for fault classification training. This not only improves the accuracy and timeliness of fault diagnosis, but also provides more comprehensive and in-depth operation and maintenance guidance and strong guarantees. 3) The present invention provides a method and system for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples. The method introduces the hierarchical analysis method to conduct a detailed analysis of the degree of faults. The method also fully considers the attribute information and topology information of the electric energy meter to derive important indexes of the electric energy meter. The method improves the intelligence and scientific nature of the operation and maintenance strategy formulation and enhances the pertinence and effectiveness of the operation and maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 4 is a structural diagram of an improved diffusion model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0019] The present invention provides the following technical solution: a method and system for determining an operation and maintenance strategy of an electric energy meter based on unbalanced samples.

[0020] Example 1 See Figure 1 This embodiment provides a method for determining an electricity meter operation and maintenance strategy based on an unbalanced sample, and the specific steps include: S1. Obtain historical operating data of the electric energy meter, and perform normalization processing on the historical operating data to obtain normalized historical operating data; the normalization processing specifically maps the historical operating data to the interval [0, 1], specifically: , ; ; ; Where, Indicates the A historical operation data sequence of an electric energy meter; Indicates the The first The vector group corresponding to the historical operation data includes the output voltage , output current , output power and power factor error ; Indicates the The first output voltage; Indicates the The first Output current; Indicates the The first Output power; Indicates the The first Power factor error; Indicates the The first The minimum value of the vector group corresponding to the historical running data; Indicates the The first The maximum value of the vector group corresponding to the historical running data; Indicates the Normalized historical operation data series of each electric energy meter; Indicates the The first A vector group corresponding to the normalized historical runs; Indicates the The first A normalized output voltage; Indicates the The first A normalized output current; Indicates the The first The normalized output power; Indicates the The first A normalized power factor error; Indicates the The index value of a historical running data series; Indicates the length of the historical running data sequence, that is, the number of vector groups; Indicates the The index value of an electric energy meter; S2. Constructing an improved diffusion model, and using the improved diffusion model to perform sample expansion on the normalized historical operation data to obtain sample data, wherein the sample data includes normal samples and fault samples; S3, please refer to Figure 2 The improved diffusion model includes an input module, a data processing module, an encoder module, a reverse diffusion module and an output module; the data processing module includes a first submodule, a second submodule and a third submodule; the first submodule includes an embedding unit; the second submodule includes a diffusion unit and an embedding unit; the third submodule includes an embedding unit and an LSTM unit; S31, the input module obtains the normalized historical operation data and a preset number of diffusion steps, inputs the preset number of diffusion steps into the first submodule, and inputs the normalized historical operation data into the second submodule and the third submodule; S32: The embedding unit of the first submodule obtains a preset number of diffusion steps and outputs the embedding result. , which is the output result of the first submodule; S33: The diffusion unit of the second submodule obtains the normalized historical operation data for diffusion, and inputs the diffusion data into the embedding unit of the second submodule, which can be expressed as: ; ; , ; ; Where, express The historical running data after diffusion through the preset diffusion steps is called diffusion data; The noise accumulation coefficient representing the preset number of diffusion steps; Indicates the noise ratio for the preset number of diffusion steps; The diffusion rate representing the preset number of diffusion steps; Represents the preset Gaussian noise that obeys the standard normal distribution; Indicates the The index value of the diffusion step number; Indicates the preset number of diffusion steps; Indicates the maximum value of the preset diffusion steps; Indicates the An energy meter passes The output result of the second submodule after step diffusion; represents the embedded function; The output results of the first submodule and the second submodule are subjected to residual fusion, which can be expressed as follows: ; Where, Indicates the An energy meter passes The output result of the data processing module after the first step of diffusion; represents residual fusion; S34, the embedding unit of the third submodule divides the normalized historical operation data and performs a one-dimensional linear projection, which is expressed as follows: ; , ; Where, Indicates the number of data blocks divided; Indicates the preset data block size; Indicates the The projection space of an electric energy meter; Indicates the The first The data after linear projection of the data blocks; Indicates the first The first Data of data blocks; Indicates the The index value of a data block; The position code of the electric energy meter is added to the vector space to obtain the position vector space; the position vector space is input into the LSTM unit of the third submodule, which is expressed as follows: ; ; ; Where, Indicates the The position vector space of each electric energy meter; Indicates positional encoding; Indicates the The first The position vector space of data blocks; A network unit representing an LSTM unit; Represents the hidden layer state set, i.e. Output result of the third submodule of each electric energy meter; Indicates the The first The output vector of the network unit of the LSTM unit of the data block; S35, the encoder module includes Stacking modules, each stacking module includes a normalization layer and a multi-head self-attention layer; the output result of the data processing module and the hidden layer state set are normalized and then input into the multi-head self-attention layer, which can be expressed as: , ; ; ; ; ; ; ; ; ; Where, Indicates the An energy meter passes The output of the stacked module after step diffusion; Indicates the The value vector of a multi-head self-attention layer; Indicates the The key vector of a multi-head self-attention layer; Indicates the The query vector of a multi-head self-attention layer; represents a linear layer; Indicates the The linear transformation matrix of a value vector; Indicates the The linear transformation matrix of the key vectors; Indicates the The linear transformation matrix of the query vector; Indicates the The output of a multi-head self-attention layer; Indicates the number of heads of the multi-head attention mechanism; represents the attention mechanism; A set of query vectors representing a multi-head self-attention layer; A set of key vectors representing a multi-head self-attention layer; A set of value vectors representing a multi-head self-attention layer; Indicates the The index value of the multi-head self-attention layer; Represents a multi-head self-attention mechanism; represents the connection function; Represents the preset linear transformation matrix; The output of the stacking module is used as the input of the next stacking module, and the times, generating the output of the encoder module ; S36, the inverse diffusion module obtains the output of the encoder module to perform sample expansion and generate sample data, which is expressed as follows: ; ; ; Where, Indicates the Sample data generated by an electric energy meter; represents the posterior distribution; represents the mean of the posterior distribution; represents the calibration factor; Represents the preset Gaussian noise that obeys the standard normal distribution, that is, the actual noise; represents the prediction noise; The loss function is used to train the inverse diffusion module, which is expressed as: ; Where, represents the loss function; Indicates expected value; represents the norm; S4. Build a fault judgment model, including a feature extraction layer and a fault classification layer; S41, the feature extraction layer specifically uses the CNN-LSTM model to extract the feature vector of the sample data, which is expressed as follows: ; Where, Indicates the The characteristic vector of an electric energy meter; Represents the CNN-LSTM model; S42, the fault classification layer specifically calculates a fault index and an importance index, and inputs the fault index and the importance index into a support vector machine model for fault classification; S421. The fault index is specifically calculated based on the characteristic vector to calculate the fault degree of the electric energy meter, which is expressed as: ; ; Where, Indicates the fault degree of the energy meter; Represents the characteristic mean of normal samples in sample data; Represents normal samples in sample data; Indicates the number of normal samples in the sample data; Indicates the The feature vector of normal samples in the sample data; Indicates the The index value of the normal sample in the sample data; Indicates the sample data The feature vectors of samples that are similar to normal samples; Indicates the sample data samples that are similar to normal samples; Represents the maximum value of the eigenvector of the normal sample in the sample data; Represents the minimum value of the eigenvector of the normal sample in the sample data; Indicates the sample data The maximum value of the characteristic vector of samples of the same type as the normal samples; Indicates the sample data The minimum value of the characteristic vector of samples similar to normal samples; Indicates the sample data The characteristic mean of samples of the same type as the normal sample; Indicates the preset first weight; Indicates the preset second weight; represents the activation function; Indicates the sample data The index value of samples that are similar to normal samples; S422, and using the analytic hierarchy process to analyze the fault type to obtain the fault weight; In this embodiment, according to the expert scoring table, the scoring rules are shown in Table 1, and the importance of each two fault types is scored to obtain the importance matrix , specifically expressed as: , , ; Where, Indicates the Rank The score values ​​of the importance of the fault degree of each two electric energy meters are listed; and are reciprocal of each other; Indicates the The row index value of the row importance matrix; Indicates the Column index value of the column importance matrix; Table 1 Expert scoring table

[0021] Solve the fourth power of the product of each row of the matrix to obtain a 4-dimensional weight vector, which can be expressed as: ; Where, Indicates the weight vectors; Perform normalization to obtain the fault weight, which can be expressed as: ; Where, Indicates the Fault weights; The fault index is obtained by combining the fault weight and fault degree, which is expressed as follows: , ; Where, represents the fault index; Represents the preset exponential decay factor; S423. The important index is specifically calculated as the correlation coefficient of the electric energy meter. The correlation coefficient includes the topological connection coefficient, the metering level coefficient, and the influence coefficient. The important index of the electric energy meter is obtained by weighting the correlation coefficient, which is expressed as follows: ; ; ; ; Where, Indicates important indexes of the electric energy meter; represents the topological connectivity coefficient; Indicates the degree of the circuit measured by the electric energy meter, that is, the number of circuits connected to the electric energy meter; Indicates the mean degree of the lines connected to the current line; Indicates the metrological grade coefficient; Indicates the measurement level; Indicates the preset minimum value of the measurement grade coefficient; represents the influence coefficient; represents the activation function; Indicates the power level; Indicates the preset topological connection coefficient weight; Indicates the preset measurement grade coefficient weight; Indicates the preset influence coefficient weight; S43: The fault index and importance index are input into a support vector machine model for fault classification, specifically: , ; Where, Indicates the status of the energy meter, where Indicates normal status. Indicates that the error exceeds the tolerance. Indicates that the DC current is open circuit, Indicates DC voltage short circuit and Indicates a short circuit in the control circuit; Indicates the status number of the energy meter; represents the support vector machine model; S5. Inputting the sample data into a fault judgment model for training to obtain a final fault judgment model; S6. Obtain the operating data to be evaluated of the electric energy meter to be evaluated, input the operating data to be evaluated into the final fault judgment model, and obtain the fault type of the fault data in the operating data to be evaluated; obtain the operation and maintenance strategy of the electric energy meter to be evaluated based on the fault type, judge the operation and maintenance strategy, and obtain a judgment result of the operation and maintenance strategy.

[0022] Example 2 This embodiment provides a system for determining an operation and maintenance strategy of an electric energy meter based on unbalanced samples. The system includes a data acquisition module, a fault judgment model, a data evaluation module, and a result output module, wherein: The data acquisition module is used to acquire historical operating data of the electric energy meter, normalize the historical operating data to obtain normalized historical operating data; construct an improved diffusion model, use the improved diffusion model to perform sample expansion on the normalized historical operating data to obtain sample data; and transmit the sample data to the fault judgment model; The fault judgment model module is used to construct a fault judgment model, including a feature extraction layer and a fault classification layer; wherein the feature extraction layer specifically uses the CNN-LSTM model to extract the feature vector of the sample data; the fault classification layer specifically calculates the fault index and importance index, and inputs the fault index and importance index into the support vector machine model for fault classification; the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and uses the hierarchical analysis method to analyze the fault type to obtain the fault weight, and combines the fault weight and the fault degree to obtain the fault index; the importance index specifically calculates the correlation coefficient of the electric energy meter, and performs weighted processing on the correlation coefficient to obtain the importance index of the electric energy meter; the sample data is input into the fault judgment model for training to obtain the final fault judgment model; The data evaluation module is used to obtain the operating data to be evaluated of the electric energy meter to be evaluated, input the operating data to be evaluated into the final fault judgment model, and obtain the fault type of the fault data in the operating data to be evaluated; obtain the operation and maintenance strategy of the electric energy meter to be evaluated based on the fault type, judge the operation and maintenance strategy, and obtain the judgment result of the operation and maintenance strategy; The result output module is used to output the judgment result of the operation and maintenance strategy.

[0023] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for determining an operation and maintenance strategy of an electric energy meter based on unbalanced samples, characterized in that: The method comprises: Acquire historical operation data of the electric energy meter, perform normalization processing on the historical operation data, and obtain normalized historical operation data; construct an improved diffusion model, and use the improved diffusion model to perform sample expansion on the normalized historical operation data to obtain sample data; Construct a fault judgment model, including a feature extraction layer and a fault classification layer; wherein the feature extraction layer specifically uses a CNN-LSTM model to extract a feature vector of sample data; the fault classification layer specifically calculates a fault index and an important index, and inputs the fault index and the important index into a support vector machine model for fault classification; the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and uses a hierarchical analysis method to analyze the fault type to obtain a fault weight, and combines the fault weight and the fault degree to obtain a fault index; the important index specifically calculates the correlation coefficient of the electric energy meter, and performs weighted processing on the correlation coefficient to obtain an important index of the electric energy meter; the sample data is input into the fault judgment model for training to obtain a final fault judgment model; The operating data to be evaluated of the electric energy meter to be evaluated is obtained, and the operating data to be evaluated is input into a final fault judgment model to obtain the fault type of the fault data in the operating data to be evaluated; the operation and maintenance strategy of the electric energy meter to be evaluated is obtained according to the fault type, and the operation and maintenance strategy is judged to obtain a judgment result of the operation and maintenance strategy.

2. The method for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 1, characterized in that: The normalization process is specifically to map the historical operation data to the interval [0, 1], specifically: ， ； ； ； In the formula, Indicates A historical operation data sequence of an electric energy meter; Indicates The energy meter The vector group corresponding to the historical operation data includes the output voltage , output current , output power and power factor error ; Indicates The energy meter Output voltage; Indicates The energy meter Output current; Indicates The energy meter Output power; Indicates The energy meter Power factor error; Indicates The energy meter The minimum value of the vector group corresponding to the historical running data; Indicates The energy meter The maximum value of the vector group corresponding to the historical running data; Indicates The normalized historical operation data sequence of each electric energy meter; Indicates The energy meter A vector group corresponding to the normalized historical runs; Indicates The energy meter A normalized output voltage; Indicates The energy meter A normalized output current; Indicates The energy meter The normalized output power; Indicates The energy meter A normalized power factor error; Indicates The index value of a historical running data series; Indicates the length of the historical running data sequence, that is, the number of vector groups; Indicates The index value of an electric energy meter.

3. The method for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 1, characterized in that: The improved diffusion model includes an input module, a data processing module, an encoder module, a reverse diffusion module and an output module, wherein: The data processing module includes a first submodule, a second submodule and a third submodule; the first submodule includes an embedding unit; the second submodule includes a diffusion unit and an embedding unit; the third submodule includes an embedding unit and an LSTM unit; The input module obtains the normalized historical operation data and the preset diffusion steps, inputs the preset diffusion steps into the first submodule, and inputs the normalized historical operation data into the second submodule and the third submodule; The embedding unit of the first submodule obtains a preset number of diffusion steps and outputs an embedding result , which is the output result of the first submodule; The diffusion unit of the second submodule obtains the normalized historical operation data for diffusion, and inputs the diffusion data into the embedding unit of the second submodule, which is expressed as: ； ； ， ； ； In the formula, express The historical operation data after diffusion through the preset diffusion steps is called diffusion data; The noise accumulation coefficient representing the preset number of diffusion steps; Indicates the noise ratio for the preset diffusion steps; The diffusion rate representing the preset number of diffusion steps; Represents the preset Gaussian noise that obeys the standard normal distribution; Indicates The index value of the diffusion step number; Indicates the preset number of diffusion steps; Indicates the maximum value of the preset diffusion steps; Indicates The energy meter passes The output result of the second submodule after step diffusion; represents the embedded function; The output results of the first submodule and the second submodule are residually fused, which can be expressed as: ； In the formula, Indicates The energy meter passes The output result of the data processing module after the step diffusion; represents residual fusion; The embedding unit of the third submodule segments the normalized historical operation data and performs a one-dimensional linear projection, which is expressed as follows: ； ， ； In the formula, Indicates the number of data blocks divided; Indicates the preset data block division size; Indicates Projection space of an electric energy meter; Indicates The energy meter The data after linear projection of the data blocks; Indicates the split The energy meter Data of data blocks; Indicates The index value of a data block; The position code of the electric energy meter is added to the vector space to obtain the position vector space; the position vector space is input into the LSTM unit of the third submodule, which is expressed as follows: ； ； ； In the formula, Indicates The position vector space of the electric energy meters; Indicates positional encoding; Indicates The energy meter The position vector space of data blocks; A network unit representing an LSTM unit; represents the set of hidden layer states, i.e. The output result of the third submodule of an electric energy meter; Indicates The energy meter The output vector of the network unit of the LSTM unit of the data block.

4. The method for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 3, characterized in that: The encoder module includes The output result of the data processing module and the hidden layer state set are normalized and then input into the multi-head self-attention layer, which can be expressed as: ， ； ； ； ； ； ； ； ； ； In the formula, Indicates The energy meter passes The output of the stacked module after step diffusion; Indicates The value vector of the multi-head self-attention layer; Indicates The key vector of a multi-head self-attention layer; Indicates The query vector of the multi-head self-attention layer; represents a linear layer; Indicates The linear transformation matrix of a value vector; Indicates The linear transformation matrix of the key vectors; Indicates The linear transformation matrix of the query vector; Indicates The output of a multi-head self-attention layer; Indicates the number of heads of the multi-head attention mechanism; represents the attention mechanism; A set of query vectors representing the multi-head self-attention layer; A set of key vectors representing a multi-head self-attention layer; A set of value vectors representing a multi-head self-attention layer; Indicates The index value of the multi-head self-attention layer; Represents a multi-head self-attention mechanism; represents the connection function; Represents a preset linear transformation matrix; The output of the stacking module is used as the input of the next stacking module, iteratively Second, generate the output of the encoder module ; The inverse diffusion module obtains the output of the encoder module to perform sample expansion and generate sample data, which can be expressed as follows: ； ； ； In the formula, Indicates Sample data generated by an electric energy meter; represents the posterior distribution; represents the mean of the posterior distribution; represents the calibration factor; represents the prediction noise; The loss function is used to train the inverse diffusion module, which is expressed as: ； In the formula, represents the loss function; Indicates expected value; Represents the norm.

5. The method for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 4, characterized in that: The feature extraction layer specifically uses the CNN-LSTM model to extract the feature vector of the sample data, which is expressed as follows: ； In the formula, Indicates The characteristic vector of an electric energy meter; Represents the CNN-LSTM model; The fault classification layer specifically calculates the fault index and importance index of the electric energy meter and inputs the support vector machine model for fault classification, wherein the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and is expressed as: ； ； In the formula, Indicates the fault degree of the energy meter; Represents the characteristic mean of normal samples in sample data; Represents normal samples in sample data; Indicates the number of normal samples in the sample data; Indicates The feature vector of normal samples in the sample data; Indicates The index value of the normal sample in the sample data; Indicates the sample data The feature vectors of samples that are similar to normal samples; Indicates the sample data samples that are similar to normal samples; Represents the maximum value of the eigenvector of the normal sample in the sample data; Represents the minimum value of the eigenvector of the normal sample in the sample data; Indicates the sample data The maximum value of the feature vector of samples of the same type as the normal samples; Indicates the sample data The minimum value of the feature vector of samples of the same type as the normal samples; Indicates the sample data The characteristic mean of samples of the same type as normal samples; Indicates the preset first weight; represents a preset second weight; represents the activation function; Indicates the sample data The index value of the sample that is similar to the normal sample.

6. A method for determining an operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 5, characterized in that: The fault type is analyzed by using the hierarchical analysis method to obtain the fault weight, and the fault index is obtained by combining the fault weight and the fault degree, which is expressed as follows: ， ； In the formula, represents the fault index; Represents the preset exponential decay factor; Indicates Fault weights; Indicates The index value of the fault weight; The important index is specifically calculated as the correlation coefficient of the electric energy meter, the correlation coefficient includes the topological connection coefficient, the metering level coefficient and the influence coefficient, and the important index of the electric energy meter is obtained by weighting the correlation coefficient, which is expressed as follows: ； ； ； ； In the formula, Indicates important indexes of the electric energy meter; represents the topological connectivity coefficient; Indicates the degree of the line measured by the electric energy meter, that is, the number of lines connected to the electric energy meter; Represents the mean value of the degree of the lines connected to the current line; It represents the metrological grade coefficient; Indicates the measurement level; Indicates the preset minimum value of the measurement grade coefficient; represents the influence coefficient; represents the activation function; Indicates the power level; Indicates the preset topological connection coefficient weight; Indicates the preset measurement grade coefficient weight; Indicates the preset influence coefficient weight; The fault index and importance index are input into the support vector machine model for fault classification, specifically: ， ； In the formula, Indicates the status of the energy meter, where: Indicates normal status. Indicates that the error exceeds the tolerance. Indicates that the DC current is open circuit, Indicates DC voltage short circuit and Indicates that the control circuit is short-circuited; Indicates the status number of the electric energy meter; Represents a support vector machine model.

7. A judgment system for electric energy meter operation and maintenance strategy based on unbalanced samples, characterized in that: The system includes a data acquisition module, a fault judgment model module, a data evaluation module and a result output module, wherein: The data acquisition module is used to acquire the historical operation data of the electric energy meter, normalize the historical operation data, and obtain the normalized historical operation data; construct an improved diffusion model, use the improved diffusion model to perform sample expansion on the normalized historical operation data, and obtain sample data; transmit the sample data to the fault judgment model; The fault judgment model module is used to construct a fault judgment model, including a feature extraction layer and a fault classification layer; wherein the feature extraction layer specifically uses the CNN-LSTM model to extract the feature vector of the sample data; the fault classification layer specifically calculates the fault index and the importance index, and inputs the fault index and the importance index into the support vector machine model for fault classification; the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and uses the hierarchical analysis method to analyze the fault type to obtain the fault weight, and combines the fault weight and the fault degree to obtain the fault index; the importance index specifically calculates the correlation coefficient of the electric energy meter, and performs weighted processing on the correlation coefficient to obtain the importance index of the electric energy meter; the sample data is input into the fault judgment model for training to obtain the final fault judgment model; The data evaluation module is used to obtain the operating data to be evaluated of the electric energy meter to be evaluated, input the operating data to be evaluated into the final fault judgment model, and obtain the fault type of the fault data in the operating data to be evaluated; obtain the operation and maintenance strategy of the electric energy meter to be evaluated according to the fault type, judge the operation and maintenance strategy, and obtain the judgment result of the operation and maintenance strategy; The result output module is used to output the judgment result of the operation and maintenance strategy.

8. The system for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 7, characterized in that: The improved diffusion model includes an input module, a data processing module, an encoder module, a reverse diffusion module and an output module, wherein: The data processing module includes a first submodule, a second submodule and a third submodule; the first submodule includes an embedding unit; the second submodule includes a diffusion unit and an embedding unit; the third submodule includes an embedding unit and an LSTM unit; The input module obtains the normalized historical operation data and the preset diffusion steps, inputs the preset diffusion steps into the first submodule, and inputs the normalized historical operation data into the second submodule and the third submodule; The embedding unit of the first submodule obtains a preset number of diffusion steps and outputs an embedding result , which is the output result of the first submodule; The diffusion unit of the second submodule obtains the normalized historical operation data for diffusion, and inputs the diffusion data into the embedding unit of the second submodule, which is expressed as: ； ； ， ； ； In the formula, express The historical operation data after diffusion through the preset diffusion steps is called diffusion data; The noise accumulation coefficient representing the preset number of diffusion steps; Indicates the noise ratio for the preset diffusion steps; The diffusion rate representing the preset number of diffusion steps; Represents the preset Gaussian noise that obeys the standard normal distribution; Indicates The index value of the diffusion step number; Indicates the preset number of diffusion steps; Indicates the maximum value of the preset diffusion steps; Indicates The energy meter passes The output result of the second submodule after step diffusion; represents the embedded function; The output results of the first submodule and the second submodule are residually fused, which can be expressed as: ； In the formula, Indicates The energy meter passes The output result of the data processing module after the step diffusion; represents residual fusion; The embedding unit of the third submodule segments the normalized historical operation data and performs a one-dimensional linear projection, which is expressed as follows: ； ， ； In the formula, Indicates the number of data blocks divided; Indicates the preset data block division size; Indicates Projection space of an electric energy meter; Indicates The energy meter The data after linear projection of the data blocks; Indicates the split The energy meter Data of data blocks; Indicates The index value of a data block; The position code of the electric energy meter is added to the vector space to obtain the position vector space; the position vector space is input into the LSTM unit of the third submodule, which is expressed as follows: ； ； ； In the formula, Indicates The position vector space of the electric energy meters; Indicates positional encoding; Indicates The energy meter The position vector space of data blocks; A network unit representing an LSTM unit; represents the set of hidden layer states, i.e. The output result of the third submodule of an electric energy meter; Indicates The energy meter The output vector of the network unit of the LSTM unit of the data block; The encoder module includes The output result of the data processing module and the hidden layer state set are normalized and then input into the multi-head self-attention layer, which can be expressed as: ， ； ； ； ； ； ； ； ； ； In the formula, Indicates The energy meter passes The output of the stacked module after step diffusion; Indicates The value vector of the multi-head self-attention layer; Indicates The key vector of a multi-head self-attention layer; Indicates The query vector of the multi-head self-attention layer; represents a linear layer; Indicates The linear transformation matrix of a value vector; Indicates The linear transformation matrix of the key vectors; Indicates The linear transformation matrix of the query vector; Indicates The output of a multi-head self-attention layer; Indicates the number of heads of the multi-head attention mechanism; represents the attention mechanism; A set of query vectors representing the multi-head self-attention layer; A set of key vectors representing a multi-head self-attention layer; A set of value vectors representing a multi-head self-attention layer; Indicates The index value of the multi-head self-attention layer; Represents a multi-head self-attention mechanism; represents the connection function; Represents a preset linear transformation matrix; The output of the stacking module is used as the input of the next stacking module, iteratively Second, generate the output of the encoder module ; The inverse diffusion module obtains the output of the encoder module to perform sample expansion and generate sample data, which can be expressed as follows: ； ； ； In the formula, Indicates Sample data generated by an electric energy meter; represents the posterior distribution; represents the mean of the posterior distribution; represents the calibration factor; represents the prediction noise; The loss function is used to train the inverse diffusion module, which is expressed as: ； In the formula, represents the loss function; Indicates expected value; Represents the norm.

9. The system for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 8, characterized in that: The feature extraction layer specifically uses the CNN-LSTM model to extract the feature vector of the sample data, which is expressed as follows: ； In the formula, Indicates The characteristic vector of an electric energy meter; Represents the CNN-LSTM model; The fault classification layer specifically calculates the fault index and importance index of the electric energy meter and inputs the support vector machine model for fault classification, wherein the fault index specifically calculates the fault degree of the electric energy meter based on the feature vector, and is expressed as: ； ； In the formula, Indicates the fault degree of the energy meter; Represents the characteristic mean of normal samples in sample data; Represents normal samples in sample data; Indicates the number of normal samples in the sample data; Indicates The feature vector of normal samples in the sample data; Indicates The index value of the normal sample in the sample data; Indicates the sample data The feature vectors of samples that are similar to normal samples; Indicates the sample data samples that are similar to normal samples; Represents the maximum value of the eigenvector of the normal sample in the sample data; Represents the minimum value of the eigenvector of the normal sample in the sample data; Indicates the sample data The maximum value of the feature vector of samples of the same type as the normal samples; Indicates the sample data The minimum value of the feature vector of samples of the same type as normal samples; Indicates the sample data The characteristic mean of samples of the same type as normal samples; Indicates the preset first weight; represents a preset second weight; represents the activation function; Indicates the sample data The index value of the sample that is similar to the normal sample.

10. The system for determining the operation and maintenance strategy of an electric energy meter based on unbalanced samples according to claim 7, characterized in that: The fault type is analyzed by using the hierarchical analysis method to obtain the fault weight, and the fault index is obtained by combining the fault weight and the fault degree, which is expressed as follows: ， ； In the formula, represents the fault index; Represents the preset exponential decay factor; Indicates Fault weights; Indicates The index value of the fault weight; The important index is specifically calculated as the correlation coefficient of the electric energy meter, the correlation coefficient includes the topological connection coefficient, the metering level coefficient and the influence coefficient, and the important index of the electric energy meter is obtained by weighting the correlation coefficient, which is expressed as follows: ； ； ； ； In the formula, Indicates important indexes of the electric energy meter; represents the topological connectivity coefficient; Indicates the degree of the line measured by the electric energy meter, that is, the number of lines connected to the electric energy meter; Represents the mean value of the degree of the lines connected to the current line; It represents the metrological grade coefficient; Indicates the measurement level; Indicates the preset minimum value of the measurement grade coefficient; represents the influence coefficient; represents the activation function; Indicates the power level; Indicates the preset topological connection coefficient weight; Indicates the preset measurement grade coefficient weight; Indicates the preset influence coefficient weight; The fault index and importance index are input into the support vector machine model for fault classification, specifically: ， ； In the formula, Indicates the status of the energy meter, where: Indicates normal status. Indicates that the error exceeds the tolerance. Indicates that the DC current is open circuit, Indicates DC voltage short circuit and Indicates that the control circuit is short-circuited; Indicates the status number of the electric energy meter; Represents a support vector machine model.

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