Power distribution data auditing method and device, electronic equipment and storage medium

Abstract

The invention discloses a power distribution data auditing method and device, electronic equipment and a storage medium, and relates to the computer technology, and the method comprises the steps: obtaining the actual configuration data of target power distribution equipment; determining standard configuration information according to the equipment identifier of the target power distribution equipment, wherein the standard configuration information comprises at least one configuration item and standard configuration data corresponding to each configuration item; determining actual configuration data corresponding to each configuration item according to the actual configuration data of the target power distribution equipment; and auditing the actual configuration data and the standard configuration data corresponding to each configuration item based on the power distribution detection model to obtain an auditing result of the target power distribution equipment. According to the method, the actual configuration data and the standard configuration data corresponding to each configuration item are audited through the power distribution detection model, so that the audit result of the target power distribution equipment is obtained, and the beneficial effects of reducing the labor cost and the error rate are achieved while the audit efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a power distribution data audit method, device, electronic device and storage medium. Background Art

[0002] With the increasing demand for electricity in modern society and the increasing scale and complexity of power grids, the auditing of power distribution data generated by each power distribution device in the power grid system has become increasingly complex. Auditing the power distribution data generated by each power distribution device is an important way to determine whether each power distribution device is operating normally. In the traditional auditing of the power distribution data generated by each power distribution device, staff are usually required to record and analyze the configuration data generated by each power distribution device separately on the service side to obtain the audit results of the power distribution data. This method has low work efficiency and is prone to omissions or human errors. Summary of the invention

[0003] The present application provides a power distribution data auditing method, device, electronic device and storage medium, which can improve the accuracy and efficiency of auditing power distribution data.

[0004] In a first aspect, the present application provides a power distribution data audit method, comprising:

[0005] Acquire actual configuration data of the target power distribution equipment; determine standard configuration information based on the equipment identification of the target power distribution equipment, wherein the standard configuration information includes at least one configuration item and standard configuration data corresponding to each configuration item; determine actual configuration data corresponding to each configuration item based on the actual configuration data of the target power distribution equipment; audit the actual configuration data and the standard configuration data corresponding to each configuration item based on a power distribution detection model to obtain an audit result of the target power distribution equipment.

[0006] In one embodiment, the actual configuration data of the target power distribution equipment is obtained, including: obtaining at least one initial configuration data, performing feature extraction on each of the initial configuration data, and obtaining a feature configuration vector; inputting each of the feature configuration vectors into a preset classification model to obtain a classification relationship between each of the initial configuration data and each power distribution equipment; for the target power distribution equipment, determining the initial configuration data corresponding to the target power distribution equipment as the actual configuration data based on the classification relationship.

[0007] In one embodiment, each of the feature configuration vectors is input into a preset classification model to obtain a classification relationship between each of the initial configuration data and each power distribution device, including: calculating the feature distance between each of the feature configuration vectors and the remaining feature matching vectors based on the preset classification model; and determining the classification relationship between each of the initial configuration data and each power distribution device based on the feature distance.

[0008] In one embodiment, determining the actual configuration data corresponding to each of the configuration items based on the actual configuration data of the target power distribution equipment includes: obtaining the actual data type corresponding to each of the actual configuration data; sequentially matching each of the actual data types with the standard data type corresponding to at least one of the configuration items; determining that the actual configuration data corresponding to the current actual data type is the actual configuration data corresponding to the current configuration item when the current actual data type matches the standard data type corresponding to the current configuration item; and obtaining the actual configuration data corresponding to each of the configuration items after all the actual configuration data are matched.

[0009] In one embodiment, the actual configuration data and the standard configuration data corresponding to each of the configuration items are audited based on the power distribution detection model to obtain the audit result of the target power distribution equipment, including: obtaining the audit rules of the current configuration item; determining the audit result of the current configuration item based on whether the actual configuration data and the standard configuration data corresponding to the current configuration item meet the audit rules; and determining the audit result of the target power distribution equipment based on the audit result of each of the configuration items.

[0010] In one embodiment, when the audit result of any configuration item is an abnormal configuration item, the method further includes: obtaining abnormal data and a configuration item identifier of the abnormal configuration item; determining the abnormal type according to the configuration item identifier; determining the abnormal cause from the abnormal type according to the abnormal data, and generating optimization suggestions for the abnormal configuration item according to the abnormal cause.

[0011] In one embodiment, the power distribution detection model is obtained in the following manner: based on the gray wolf optimization algorithm, the device coordinates corresponding to similar devices are determined from each power distribution device; the parameters to be optimized of the initial long short-term memory network algorithm are optimized according to the device coordinates, and when the parameters to be optimized reach convergence, the target long short-term memory network algorithm is obtained according to the optimization values ​​corresponding to the parameters to be optimized; the power distribution detection model is obtained according to the gray wolf optimization algorithm and the target long short-term memory network algorithm.

[0012] In a second aspect, the present application provides a power distribution data audit device, the device comprising:

[0013] A data acquisition module, used to acquire actual configuration data of the target power distribution equipment;

[0014] An information determination module, configured to determine standard configuration information according to the device identification of the target power distribution device, wherein the standard configuration information includes at least one configuration item and standard configuration data corresponding to each configuration item;

[0015] A data determination module, used to determine the actual configuration data corresponding to each of the configuration items according to the actual configuration data of the target power distribution equipment;

[0016] The data audit module is used to audit the actual configuration data and the standard configuration data corresponding to each of the configuration items based on the power distribution detection model to obtain the audit result of the target power distribution equipment.

[0017] In a third aspect, the present application further provides an electronic device, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power distribution data audit method described in any embodiment of the present application.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power distribution data audit method described in any embodiment of the present application when executed.

[0022] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the power distribution data audit method described in any embodiment of the present application.

[0023] The power distribution data auditing scheme provided in the embodiment of the present application first obtains the actual configuration data generated by the target power distribution equipment; then obtains the standard configuration data corresponding to each configuration item based on the standard configuration information of the target power distribution equipment; further determines the actual configuration data corresponding to each configuration item based on the actual configuration data of the target power distribution equipment to obtain the actual configuration data and standard configuration data corresponding to each configuration item; and further, in order to improve the auditing efficiency and accuracy of the configuration data, the present embodiment audits the actual configuration data and standard configuration data corresponding to each configuration item through the power distribution detection model to obtain the audit results of the target power distribution equipment. This embodiment solves the problems of low efficiency and low accuracy of the existing scheme due to manual auditing, and achieves the beneficial effect of reducing labor costs and error rates while improving auditing efficiency and accuracy.

[0024] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium, wherein the computer-readable storage medium may be packaged together with the processor of the power distribution data audit device, or may be packaged separately from the processor of the power distribution data audit device, which is not limited in this application.

[0025] The description of the second, third, fourth and fifth aspects of the present application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, fourth and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0026] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0027] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 It is a flow chart of the power distribution data audit method provided by the embodiment of the present application;

[0030] Figure 2 is another flow chart of the power distribution data audit method provided in an embodiment of the present application;

[0031] Figure 3 It is a structural diagram of a power distribution data audit device provided in an embodiment of the present application;

[0032] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the present application will be clearly and completely described below in conjunction with the drawings in the present embodiment. Obviously, the described embodiment is only a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0036] Figure 1 A flow chart of the power distribution data audit method provided in the embodiment of the present application, the present embodiment can be applied to the situation of auditing the power distribution data generated by each power distribution device in the large-scale, complex and changeable power distribution communication equipment scenario, the method can be executed by a power distribution data audit device, the device can be implemented in the form of hardware and / or software, and integrated in the electronic device that executes the method. Preferably, the electronic device in the embodiment of the present application can be a server, or a computer device, etc.

[0037] refer to Figure 1 The power distribution data audit method provided in this embodiment includes but is not limited to the following steps:

[0038] S110. Acquire actual configuration data of the target power distribution equipment.

[0039] The target power distribution equipment indicates the equipment in the power grid system that needs to be audited for power distribution. The purpose of auditing the power distribution equipment in this embodiment is to determine whether the target power distribution equipment is operating normally by verifying whether the power distribution data generated by the target power distribution equipment meets the specifications, so that when the audit result of the target power distribution equipment is abnormal, the target power distribution equipment needs to be promptly inspected or maintained to ensure the normal power supply of the power grid system.

[0040] In this embodiment, the indicated power distribution equipment may be all components of the power grid system. For example, it may include generators, transformers, circuit breakers, voltmeters, and protection relays, etc. Optionally, it may also include communication network equipment related to the power distribution equipment, such as switches, routing tables, etc.; the type of equipment indicated by the power distribution equipment in this embodiment is not limited here.

[0041] The above-mentioned actual configuration data indicates the configuration data generated by the target power distribution equipment during operation. Taking the target power distribution equipment as a transformer as an example, the corresponding actual configuration data may include configuration data such as the equipment voltage value, current value, power value, temperature value and load value; taking the target power distribution equipment as a protection relay as an example, the corresponding actual configuration data may include configuration data such as protection type (overvoltage, overcurrent or undervoltage), set value, action time and rated voltage value, serial port, equipment status data, network status data and network bandwidth; the data content of the actual configuration data corresponding to each specific power distribution equipment is not limited here.

[0042] S120. Determine standard configuration information according to the device identification of the target power distribution device.

[0043] The above standard configuration information is used to indicate the configuration information corresponding to the target power distribution equipment when it is operating in a standard state, wherein the current standard state may be a normal operating state.

[0044] The above-mentioned device identification is used to indicate an identification that can represent the power distribution equipment. Exemplarily, the current device identification can be implemented as a device name, a device Internet Protocol address or a device port number, and the specific embodiment of the device identification is not limited here.

[0045] Before determining the standard configuration information according to the equipment identification of the target power distribution equipment, the solution provided in this embodiment can pre-establish a standard configuration database, in which the standard configuration information corresponding to each power distribution equipment is stored, so that the corresponding standard configuration information can be determined from the standard database according to the equipment identification of the target power distribution equipment. Among them, the standard configuration information corresponding to each power distribution equipment can be understood as a standard configuration template, and the current standard configuration template can be embodied in the form of a data table. When N types of power distribution equipment are included in the power grid system, the standard configuration database should contain at least the standard configuration information corresponding to the N types of power distribution equipment, that is, one power distribution equipment pre-establishes a corresponding standard configuration information.

[0046] Specifically, in this embodiment, the standard configuration information includes at least one configuration item and the standard configuration data corresponding to each configuration item. Among them, the configuration item can be understood as attribute information, which can be reflected in the header position in the data table; the standard configuration data corresponding to each configuration item can be understood as the standard value corresponding to each attribute information when the target power distribution equipment operates normally. In this embodiment, the purpose of determining the corresponding standard configuration data for each configuration item is to provide a reference basis for subsequent comparison with the actual configuration data, thereby helping to ensure the accuracy of the configuration data audit.

[0047] Exemplarily, taking the transformer, protection relay, and electric meter as an example to determine all the equipment types that need to be included in the standard configuration library, for each equipment type, the configuration items of each configured device are defined in detail, such as the configuration items corresponding to the transformer at least include configuration voltage, current, power, and temperature parameters, and then set standard values ​​for each configuration item (i.e., the above-mentioned standard configuration data). The standard configuration data can be determined by obtaining the historical data of each power distribution equipment in a normal state and calculating the mean of the historical data; it can also be determined by calculating the standard configuration data corresponding to each configuration item by combining the relevant data formula to obtain the standard voltage and power state of the power distribution equipment. The specific method of obtaining the standard configuration data corresponding to each configuration item is not limited here.

[0048] Optionally, when the standard configuration information corresponding to each power distribution device is stored in the standard configuration database, the data type of the current standard database can be a relational database (such as MySQL or PostgreSQL) or a non-relational database (such as MongoDB). The storage of the database can ensure efficient storage and query of data. In this embodiment, in addition to at least one configuration item and the standard configuration data corresponding to each configuration item, each standard configuration information may also include the device type and other related information to ensure the systematicness and queryability of the configuration data.

[0049] Optionally, in this embodiment, the standard configuration information of the relevant power distribution equipment may be regularly updated to ensure that it is consistent with the latest configuration requirements and best practices. The types of current updates may include newly added equipment types, newly added configuration items, and updated standard values, etc., to ensure the validity of the standard configuration database.

[0050] S130. Determine actual configuration data corresponding to each configuration item according to actual configuration data of the target power distribution device.

[0051] In this embodiment, the purpose of determining the actual configuration data corresponding to each configuration item is to associate the actual configuration data and the standard configuration data belonging to the same configuration item, thereby facilitating the sequential audit of the data contained in each configuration item of the power distribution equipment in subsequent steps, thereby helping to improve the accuracy of the audit results.

[0052] Generally, when each configuration item represents the attribute information of the power distribution equipment, the data types corresponding to different attribute information have certain differences. Therefore, in this embodiment, the method for determining the actual configuration data corresponding to each configuration item can be: obtaining the actual data type corresponding to each actual configuration data; matching each actual data type with the standard data type corresponding to at least one configuration item in sequence; when the current actual data type matches the standard data type corresponding to the current configuration item, determining that the actual configuration data corresponding to the current actual data type is the actual configuration data corresponding to the current configuration item; after all the actual configuration data are matched, obtaining the actual configuration data corresponding to each configuration item; optionally, this embodiment can also set different standard data lengths for each configuration item, so that the actual configuration data corresponding to each configuration item can be obtained by comparing the actual data length of each actual configuration data with the standard data length corresponding to each configuration item.

[0053] Optionally, when executing the audit scheme for power distribution data provided in this embodiment, two configuration sub-items may be included in each configuration item, wherein one configuration sub-item is used to represent the standard configuration data corresponding to the current configuration item, which is pre-written data; the configuration data corresponding to the other configuration sub-item in the standard configuration information is empty, so that after determining the actual configuration data corresponding to each configuration item, the actual configuration data can be filled into the configuration sub-item of the corresponding configuration item, which is the data written in real time after the current actual determination. After writing the corresponding actual configuration data to each configuration item, the information table to be audited can be obtained. The purpose of this is to make the comparison between the standard configuration data and the actual configuration data in a configuration item more intuitive, and to provide a basis for configuration comparison and analysis in subsequent steps.

[0054] S140: Audit the actual configuration data and the standard configuration data corresponding to each configuration item based on the power distribution detection model to obtain an audit result of the target power distribution equipment.

[0055] The power distribution detection model is a model that is pre-trained based on sample data. Based on the S130 example, the power distribution detection model can audit each configuration item in the audit information table in turn, thereby obtaining the audit result of each configuration item, and then obtain the audit result of the target power distribution equipment based on the audit result of each configuration item, so as to complete the audit operation on the target power distribution equipment.

[0056] This embodiment can compare the actual configuration data of each configuration item with the standard configuration data through the power distribution detection model, can accurately identify the differences and anomalies of each configuration item, and provide reliable audit results. When outputting the audit results, this embodiment can set an audit threshold or audit rule for each configuration item, so that the abnormal situation in the configuration item can be identified according to the audit threshold or audit rule corresponding to each configuration item, such as missing configuration data, non-standard values ​​or potential safety risks, etc., to ensure that the abnormality is discovered and handled in time to ensure the safe operation of the power grid system. The solution provided by this embodiment can solve the problems of low efficiency, easy errors and high maintenance costs of manual audits in the prior art. By analyzing and modeling the configuration data in an automated and intelligent manner, the automatic audit and verification of the distribution data generated by each distribution equipment can be realized, thereby improving the audit efficiency and accuracy.

[0057] A preferred embodiment, in this embodiment, the distribution detection model is obtained by fusing the Grey Wolf Optimizer (GWO) and the target long short-term memory network algorithm (LSTM) to quickly realize the audit of actual configuration data and standard configuration data, and output the audit results.

[0058] The power distribution detection model provided in this embodiment is implemented by the GWO-LSTM model, that is, GWO is integrated with LSTM, and the parameters to be optimized of LSTM are optimized by GWO, so as to improve the efficiency and accuracy of LSTM in auditing actual configuration data and standard configuration data.

[0059] Specifically, the method of obtaining the power distribution detection model in this embodiment is as follows: based on the Gray Wolf optimization algorithm, the device coordinates corresponding to similar devices are determined from each power distribution device; the parameters to be optimized of the initial long short-term memory network algorithm are optimized according to the device coordinates, and when the parameters to be optimized reach convergence, the target long short-term memory network algorithm is obtained according to the optimization values ​​corresponding to the parameters to be optimized; the power distribution detection model is obtained according to the Gray Wolf optimization algorithm and the target long short-term memory network algorithm.

[0060] GWO is responsible for the global search for the optimal combination of parameters to be optimized for LSTM. The device coordinates of the most similar distribution equipment are determined from each distribution equipment through GWO algorithm matching, that is, the location of the optimal wolf. Further, the GWO algorithm calculates the parameters to be optimized for LSTM based on the device coordinates of the most similar distribution equipment. When the parameters to be optimized reach convergence, LSTM uses the parameters to be optimized to learn and predict the distribution data. The LSTM model understands the complex dependencies between the configuration items and configuration parameters of the distribution equipment by learning the time series patterns and associations in the historical data. Use the trained LSTM model to predict the configuration data generated by the new distribution equipment. Finally, compare the results of LSTM prediction with the standard library of equipment configuration, compare the predicted value and actual value of each configuration item, and check whether it meets the range and expected value defined in the standard configuration library. When the predicted value and actual value reach convergence, it is determined that the initial LSTM training is completed and the target LSTM is obtained.

[0061] Specifically, the above method of optimizing the parameters to be optimized of the initial long short-term memory network algorithm based on the gray wolf optimization algorithm can be implemented as follows:

[0062] First, the parameters to be optimized in the initial LSTM that need to be optimized by GWO are determined. Exemplarily, the parameters to be optimized may include the number of neurons in the hidden layer, the learning rate of the LSTM, the number of iterations, etc.

[0063] Then, initialize the initial parameters of the GWO algorithm. Let the population be x = {x 1j ,x 2j ,,x Nj} T , where N represents the number of configuration items in any power distribution device, x 1j represents the state vector coordinates corresponding to the first configuration item. Among them, the position of each distribution device in space is x i , j represents the dimension number of the current power distribution equipment.

[0064] The position coordinates of each distribution device in the search space will be used as the parameters to be optimized for the LSTM model, and its fitness value will be calculated according to the following formula:

[0065]

[0066] Among them, y i represents the expected value, y i ′ represents the prediction value of the initial LSTM algorithm. The fitness value reflects the prediction error of the initial LSTM algorithm under the current hyperparameter combination. The smaller the error, the higher the fitness.

[0067] Select the location coordinates of the three distribution devices with the smallest fitness values ​​from the calculation results and assign them to xα (t), x β (t), x γ (t). Then, use the following formula to update the three position coordinates so that their fitness values ​​are always minimized:

[0070]

[0071] After each iteration, check whether the output condition is met (for example, the maximum number of iterations or the fitness converges to a certain threshold). If the condition is met, the optimization is terminated and the optimized value corresponding to each parameter to be optimized is output; otherwise, the iteration continues.

[0072] After the optimization is completed, the device coordinates x of the optimal power distribution equipment are α (t) The corresponding hyperparameters (number of hidden layer neurons, learning rate, and number of iterations) are assigned to the LSTM model. The LSTM model is trained using these optimized parameters and its performance in configuration comparison and anomaly detection is evaluated.

[0073] Finally, when testing the performance of the model, you can choose the root mean square error, mean absolute error and coefficient of determination as the main indicators for evaluating the model. The specific formula is as follows:

[0074]

[0075] Among them, n in the above is the number of input samples; RMSE is the root mean square error, MAE is the mean absolute error, R 2 represents the coefficient of determination; y i , Represents the actual value, predicted value and average value of the i-th sample respectively. The accuracy of predicting the distribution data in a certain distribution equipment is related to the root mean square error RMSE and the mean absolute error MAE value. The RMSE and MAE values ​​are inversely proportional. When the RMSE and MAE values ​​are small, it means that the model can more accurately capture the trend of real data changes. 2 Larger values ​​indicate that the model better explains the variance of the target variable. The size of these indicators can explain the predictive performance of the established model.

[0076] This embodiment uses a deep learning algorithm to train and deploy a distribution detection model, and processes distribution data by fusing the Grey Wolf Optimization (GWO) algorithm with the Long Short-Term Memory Network (LSTM) to form a GWO-LSTM model, thereby significantly improving the accuracy and speed of audit detection.

[0077] The power distribution data auditing method provided in this embodiment first obtains the actual configuration data generated by the target power distribution equipment; then obtains the standard configuration data corresponding to each configuration item based on the standard configuration information of the target power distribution equipment; further determines the actual configuration data corresponding to each configuration item based on the actual configuration data of the target power distribution equipment to obtain the actual configuration data and standard configuration data corresponding to each configuration item; and further, in order to improve the auditing efficiency and accuracy of the configuration data, this embodiment audits the actual configuration data and standard configuration data corresponding to each configuration item through the power distribution detection model to obtain the audit results of the target power distribution equipment. This embodiment solves the problems of low efficiency and low accuracy of the existing solutions due to manual auditing, and while improving the auditing efficiency and accuracy, it achieves the beneficial effect of reducing labor costs and error rates.

[0078] Figure 2 It is another flow chart of the power distribution data audit method provided in the embodiment of the present application. The embodiment of the present application is optimized on the basis of the above embodiments. The specific optimization is as follows: this embodiment is a detailed explanation of the relevant implementation process of "obtaining the actual configuration data of the target power distribution equipment" in the above embodiment, the relevant implementation process of "determining the actual configuration data corresponding to each configuration item" in the above embodiment, and the relevant implementation process of "auditing the actual configuration data and standard configuration data corresponding to each configuration item based on the power distribution detection model to obtain the audit result of the target power distribution equipment" in the above embodiment.

[0079] See also Figure 2 The power distribution data audit method provided in this embodiment includes but is not limited to the following steps:

[0080] S210: Acquire at least one initial configuration data, perform feature extraction on each initial configuration data, and obtain a feature configuration vector.

[0081] When executing the power distribution data audit scheme provided in this embodiment, since each power distribution device is in working condition, before obtaining the actual configuration data of the target power distribution device, the configuration data corresponding to each power distribution device in all access lines are actually obtained. In the current step, the actual configuration data corresponding to the subsequent target power distribution device is distinguished and the configuration data corresponding to all the initially obtained power distribution data is described as initial configuration data.

[0082] However, for at least one initial configuration data obtained, it is not clear in advance which distribution device generates each initial configuration data. Therefore, in the current step, it is necessary to classify the at least one initial configuration data obtained to determine the distribution device corresponding to each initial configuration data; for example, determine that a and b data are generated by M distribution device; c data is generated by N distribution device; d, e, f data are generated by O distribution device, etc.

[0083] Specifically, in order to facilitate the analysis of at least one initial configuration data, the collected at least one initial configuration data may be cleaned, formatted and standardized, and pre-processed such as removing redundant data and noise data to ensure the accuracy and consistency of the data. Specifically, duplicate data may be removed to ensure that the data of each configuration item in each power distribution device is unique, missing data may be removed and recorded in the report to ensure the integrity of the data, and numerical data may be standardized to make the data conform to the standard normal distribution, thereby improving the model training effect, etc.

[0084] Furthermore, in order to accurately classify at least one initial configuration data, feature extraction is first performed on each initial configuration data to obtain a feature configuration vector corresponding to each initial configuration data, and then a classification operation is performed on the pre-collected initial configuration data based on the feature configuration vector.

[0085] S211. Input each feature configuration vector into a preset classification model to obtain a classification relationship between each initial configuration data and each power distribution device.

[0086] In the current step, the preprocessed device configuration data is converted into a feature vector as an input of a preset classification model, so that the preset classification model classifies each initial configuration data and obtains a mapping relationship between each power distribution device and each initial configuration data.

[0087] The preset classification model used in this embodiment is preferably implemented by a KNN classification algorithm, that is, based on the distance or similarity between the initial configuration data in the power distribution equipment, the power distribution equipment is classified into the majority category of its K nearest neighbors. It can also be implemented by an artificial neural network algorithm, a K-nearest neighbor algorithm, or a support vector machine algorithm, etc. The specific implementation method of the preset classification model is not limited here.

[0088] In one implementation, in the above step S211, this embodiment can be implemented as follows: the feature distance between each feature configuration vector and the remaining feature matching vectors is calculated based on a preset classification model; and the classification relationship between each initial configuration data and each power distribution device is determined according to the feature distance.

[0089] The characteristic distances mentioned above may include an average distance, an average shortest distance and an average maximum distance between samples.

[0090] Specifically, in this embodiment, the method of calculating the feature distance between each feature configuration vector and the remaining feature matching vectors based on the KNN classification model can be implemented by the following formula:

[0091]

[0092] In the above formula, a(i) represents the average distance from sample feature configuration vector i to other feature configuration vector samples in the same cluster, b(i) is the average shortest distance from sample i to all samples in another cluster, and max represents the average maximum distance between a(i) and b(i).

[0093] In this embodiment, the KNN classification model is used to classify the initial configuration data generated by the power distribution equipment, so that in subsequent steps it is used to compare the actual configuration data of different types of power distribution equipment with the standard configuration information, and the subsequent GWO-LSTM model is used to achieve high-precision identification of equipment categories of different models, providing basic support for subsequent equipment configuration auditing and verification.

[0094] S212. For the target power distribution equipment, determine, according to the classification relationship, that the initial configuration data corresponding to the target power distribution equipment is the actual configuration data.

[0095] After the initial configuration data is classified, any power distribution device can be used as a target power distribution device, and the initial configuration data corresponding to the current power distribution device is the actual configuration data.

[0096] S220. Determine standard configuration information according to the device identification of the target power distribution device, where the standard configuration information includes at least one configuration item and standard configuration data corresponding to each configuration item.

[0097] S230: Obtain the actual data type corresponding to each actual configuration data.

[0098] In order to audit the initial configuration data and actual configuration data in the configuration item more accurately, in the current step, the initial configuration data and the actual configuration data belonging to the same configuration item are bound as an example, so as to facilitate the audit operation in subsequent steps.

[0099] Then in this embodiment, the configuration item corresponding to the actual configuration data can be determined by determining the data type. Specifically, first obtain the actual data type corresponding to each actual configuration data in the target power distribution equipment, such as integer, floating point, logical, and character types; optionally, to facilitate the distinction of the data type corresponding to each configuration item, if multiple configuration item data are of the same data type, the data length of the same data type can also be limited, thereby facilitating the determination of the unique corresponding configuration item according to the current actual data type.

[0100] S231. Match each actual data type with a standard data type corresponding to at least one configuration item in sequence.

[0101] After clarifying the actual data type corresponding to each actual configuration data, this embodiment matches the standard data type corresponding to at least one configuration item in sequence to obtain a standard data type consistent with the current actual data type.

[0102] S232: When the current actual data type matches the standard data type corresponding to the current configuration item, determine that the actual configuration data corresponding to the current actual data type is the actual configuration data corresponding to the current configuration item.

[0103] When the match is consistent, the actual configuration data corresponding to the current actual data type can be written into the current configuration item, so that the actual configuration data can be added on the basis of the standard configuration data contained in the current configuration item, which helps to accurately compare the standard configuration data and the actual configuration data in the same configuration item.

[0104] S233: After all actual configuration data are matched, the actual configuration data corresponding to each configuration item is obtained.

[0105] S240. Obtain the audit rules for the current configuration item.

[0106] For different configuration items, whether the audit result of the current configuration item is normal or abnormal can be determined by setting different audit rules.

[0107] S241. Determine the audit result of the current configuration item based on whether the actual configuration data and the standard configuration data corresponding to the current configuration item meet the audit rules.

[0108] Exemplarily, for configuration item A, the audit rules may set the data floating interval to a%. If the actual configuration data and the standard configuration data meet the current data floating interval, the audit result is determined to be normal; if the current data floating interval is not met, the audit result is determined to be abnormal; correspondingly, for configuration item B, the audit rules may set the data floating interval to b%, etc. The specific audit rules corresponding to each configuration item are not restricted here.

[0109] S242. Determine the audit result of the target power distribution equipment according to the audit result of each configuration item.

[0110] Taking the configuration items of the distribution equipment including item L as an example, when the audit results of item L are all normal, it can be determined that the audit result of the target distribution equipment is normal; when any configuration item in the audit result of item L is abnormal, it can be determined that the audit result of the target distribution equipment is abnormal.

[0111] A preferred embodiment, in this embodiment, when the audit result of any configuration item is determined to be an abnormal configuration item, the solution provided by this embodiment can also perform the following operations:

[0112] Obtain the abnormal data and configuration item identifier of the abnormal configuration item; determine the abnormal type according to the configuration item identifier; determine the abnormal cause from the abnormal type according to the abnormal data, and generate optimization suggestions for the abnormal configuration item according to the abnormal cause.

[0113] In the process of auditing each configuration item one by one, if the audit result of the current configuration item is an audit exception, the current configuration item can be marked as an abnormal configuration item. Then, when the audit of each configuration item is completed, the abnormal data and configuration item identifier corresponding to each configuration item marked as an abnormal configuration item can be obtained.

[0114] In this embodiment, the possible abnormal situations are counted in advance based on the conventional abnormal situations and after analyzing and accumulating the historical data, so as to obtain the possible abnormal types and causes of each type of power distribution equipment, and then generate the corresponding optimization method according to the historical maintenance experience, such as generating an optimization suggestion table. The cause of the abnormality can be determined from the optimization suggestion table according to the abnormal situation of the current configuration item, so as to generate optimization suggestions for the maintenance personnel for reference by the staff, which helps to improve the maintenance efficiency of the staff.

[0115] Furthermore, the abnormality type can be determined from the optimization suggestion table according to the configuration item identifier. For example, the abnormality type corresponding to the voltage meter can be the meter overvoltage and not being connected to the network.

[0116] Since the abnormal data generated when the abnormal type is different has certain differences, taking the abnormal type corresponding to the voltmeter as an example, which can be the meter overvoltage and not connected to the network, when the abnormal type is the meter overvoltage, the actual configuration data may be too large or too small, and when the abnormal type is not connected to the network, the actual configuration data of the voltmeter may not be obtained. Therefore, in this embodiment, it is possible to generate optimization suggestions for abnormal configuration items according to the abnormal cause to help staff quickly locate the cause of the failure of the power distribution equipment.

[0117] An implementation example, taking switch configuration comparison and anomaly detection as an example. Use automated scripts to extract actual configuration data from the switch, clean and standardize it to ensure data accuracy and consistency; then obtain the corresponding standard configuration information based on the device identification of the switch to clarify the configuration items that need to be audited in the switch, as well as the standard configuration data corresponding to each configuration item; then determine the actual configuration data corresponding to each configuration item; finally, based on the GWO-LSTM model that integrates the Grey Wolf Optimization Algorithm (GWO) and the Long Short-Term Memory Network (LSTM), by optimizing the hyperparameters of LSTM, accurate learning and anomaly detection of configuration data are achieved. Then, the GWO-LSTM model is used to compare and detect the actual configuration data with the standard configuration data, quickly identify abnormal configurations, and generate a detailed audit report. When abnormal configuration items are included, the audit report can also include optimization suggestions generated based on abnormal data and configuration item identification.

[0118] The power distribution data audit method provided in this embodiment audits the actual configuration data and standard configuration data corresponding to each configuration item through the power distribution detection model to obtain the audit results of the target power distribution equipment. Once an abnormality is detected, the cause of the abnormality will be analyzed based on the abnormal data and configuration item identification, and corresponding optimization suggestions will be generated based on the abnormal cause, including but not limited to configuration modification, software and hardware updates, etc., to improve the operating efficiency and safety of the equipment. Through the solution provided in this embodiment, the audit efficiency and audit accuracy of power distribution data are significantly improved, while reducing labor costs and error rates, and is suitable for the audit of power distribution data in large-scale power distribution equipment.

[0119] Figure 3 Schematic diagram of a power distribution data audit device provided in an embodiment of the present application, which is suitable for executing the power distribution data audit method provided in an embodiment of the present application. Figure 3 As shown, the device may specifically include: a data acquisition module 310, an information determination module 320, a data determination module 330 and a data audit module 340, wherein:

[0120] The data acquisition module 310 is used to acquire the actual configuration data of the target power distribution equipment;

[0121] An information determination module 320, configured to determine standard configuration information according to the device identification of the target power distribution device, wherein the standard configuration information includes at least one configuration item and standard configuration data corresponding to each configuration item;

[0122] A data determination module 330, configured to determine actual configuration data corresponding to each of the configuration items according to actual configuration data of the target power distribution device;

[0123] The data audit module 340 is used to audit the actual configuration data and the standard configuration data corresponding to each of the configuration items based on the power distribution detection model to obtain the audit result of the target power distribution equipment.

[0124] The power distribution data auditing device provided in this embodiment first obtains the actual configuration data generated by the target power distribution equipment; then obtains the standard configuration data corresponding to each configuration item according to the standard configuration information of the target power distribution equipment; further determines the actual configuration data corresponding to each configuration item according to the actual configuration data of the target power distribution equipment, so as to obtain the actual configuration data and standard configuration data corresponding to each configuration item; and further, in order to improve the auditing efficiency and accuracy of the configuration data, this embodiment audits the actual configuration data and standard configuration data corresponding to each configuration item through the power distribution detection model, so as to obtain the audit results of the target power distribution equipment. This embodiment solves the problems of low efficiency and low accuracy of the existing scheme due to manual auditing, and achieves the beneficial effect of reducing labor costs and error rates while improving the auditing efficiency and accuracy.

[0125] In one embodiment, the data acquisition module 310 includes: a feature extraction unit, a relationship acquisition unit and a data determination unit, wherein:

[0126] A feature extraction unit, used to obtain at least one initial configuration data, perform feature extraction on each of the initial configuration data, and obtain a feature configuration vector;

[0127] A relationship obtaining unit, used for inputting each of the characteristic configuration vectors into a preset classification model to obtain a classification relationship between each of the initial configuration data and each power distribution device;

[0128] A data determination unit is used to determine, for the target power distribution equipment, according to the classification relationship, that the initial configuration data corresponding to the target power distribution equipment is the actual configuration data.

[0129] In one embodiment, the relationship acquisition unit is specifically used to calculate the feature distance between each of the feature configuration vectors and the remaining feature matching vectors based on the preset classification model; and determine the classification relationship between each of the initial configuration data and each distribution device according to the feature distance.

[0130] In one embodiment, the data determination module 330 includes a type acquisition unit and a type matching unit, wherein:

[0131] A type acquisition unit, used to acquire the actual data type corresponding to each of the actual configuration data;

[0132] A type matching unit, used to sequentially match each of the actual data types with a standard data type corresponding to at least one of the configuration items;

[0133] The data determination unit is further configured to determine that the actual configuration data corresponding to the current actual data type is the actual configuration data corresponding to the current configuration item when the current actual data type matches the standard data type corresponding to the current configuration item;

[0134] The data determination unit is further configured to obtain the actual configuration data corresponding to each of the configuration items after all the actual configuration data are matched.

[0135] In one embodiment, the data audit module 340 includes a rule acquisition unit and a result determination unit, wherein:

[0136] A rule acquisition unit is used to obtain the audit rules of the current configuration item;

[0137] A result determination unit, configured to determine an audit result of the current configuration item according to whether the actual configuration data and the standard configuration data corresponding to the current configuration item satisfy the audit rule;

[0138] The result determination unit is further used to determine the audit result of the target power distribution equipment according to the audit result of each of the configuration items.

[0139] In one embodiment, when the audit result of any configuration item is an abnormal configuration item, the device further includes: a suggestion generation module, wherein:

[0140] The suggestion generation module is specifically used to obtain the abnormal data and configuration item identifier of the abnormal configuration item; determine the abnormal type according to the configuration item identifier; determine the abnormal cause from the abnormal type according to the abnormal data, and generate optimization suggestions for the abnormal configuration item according to the abnormal cause.

[0141] In one embodiment, the power distribution detection model is obtained in the following manner: based on the gray wolf optimization algorithm, the device coordinates corresponding to similar devices are determined from each power distribution device; the parameters to be optimized of the initial long short-term memory network algorithm are optimized according to the device coordinates, and when the parameters to be optimized reach convergence, the target long short-term memory network algorithm is obtained according to the optimization values ​​corresponding to the parameters to be optimized; the power distribution detection model is obtained according to the gray wolf optimization algorithm and the target long short-term memory network algorithm.

[0142] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0143] An embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power distribution data audit method described in any embodiment of the present application.

[0144] An embodiment of the present application further provides a computer-readable medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power distribution data audit method described in any embodiment of the present application when executed.

[0145] Reference below Figure 4 , Figure 4 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, and shows a schematic diagram of the structure of a computer system 500 suitable for implementing the electronic device in an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0146] like Figure 4 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0147] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0148] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present application are executed.

[0149] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, etc., or any suitable combination of the above.

[0150] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] The modules and / or units involved in the embodiments described in the present application may be implemented by software or hardware. The modules and / or units described may also be arranged in a processor, for example, it may be described as follows: a processor includes a data acquisition module, an information determination module, a data determination module, and a data audit module. The names of these modules do not, in some cases, constitute limitations on the modules themselves.

[0152] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: obtaining actual configuration data of the target power distribution device; determining standard configuration information according to the device identification of the target power distribution device, the standard configuration information including at least one configuration item and standard configuration data corresponding to each configuration item; determining actual configuration data corresponding to each configuration item according to the actual configuration data of the target power distribution device; auditing the actual configuration data and the standard configuration data corresponding to each configuration item based on the power distribution detection model to obtain the audit result of the target power distribution device.

[0153] According to the technical solution of this embodiment, the actual configuration data and standard configuration data corresponding to each configuration item are audited through the power distribution detection model to obtain the audit results of the target standard power distribution equipment. The solution provided by this embodiment solves the problems of low efficiency and low accuracy of manual auditing in the existing solution, and achieves the beneficial effect of reducing labor costs and error rates while improving audit efficiency and accuracy.

[0154] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A power distribution data audit method, characterized in that: include: Obtaining actual configuration data of the target power distribution equipment; Determine standard configuration information according to the device identification of the target power distribution device, wherein the standard configuration information includes at least one configuration item and standard configuration data corresponding to each configuration item; Determine actual configuration data corresponding to each of the configuration items according to actual configuration data of the target power distribution equipment; The actual configuration data and the standard configuration data respectively corresponding to each of the configuration items are audited based on the power distribution detection model to obtain an audit result of the target power distribution equipment.

2. The power distribution data audit method according to claim 1, characterized in that: The obtaining of actual configuration data of the target power distribution equipment includes: Acquire at least one initial configuration data, perform feature extraction on each of the initial configuration data, and obtain a feature configuration vector; Input each of the feature configuration vectors into a preset classification model to obtain a classification relationship between each of the initial configuration data and each power distribution device; For the target power distribution equipment, the initial configuration data corresponding to the target power distribution equipment is determined as the actual configuration data according to the classification relationship.

3. The power distribution data audit method according to claim 2, characterized in that: The step of inputting each of the feature configuration vectors into a preset classification model to obtain a classification relationship between each of the initial configuration data and each power distribution device includes: Calculate the feature distance between each of the feature configuration vectors and the remaining feature matching vectors based on the preset classification model; The classification relationship between each of the initial configuration data and each power distribution device is determined according to the characteristic distance.

4. The power distribution data audit method according to claim 1, characterized in that: The determining the actual configuration data corresponding to each of the configuration items according to the actual configuration data of the target power distribution equipment includes: Obtaining the actual data type corresponding to each of the actual configuration data; Matching each of the actual data types with a standard data type corresponding to at least one of the configuration items in sequence; When the current actual data type matches the standard data type corresponding to the current configuration item, determining that the actual configuration data corresponding to the current actual data type is the actual configuration data corresponding to the current configuration item; After all the actual configuration data are matched, the actual configuration data corresponding to each configuration item is obtained.

5. The power distribution data audit method according to claim 1, characterized in that: The auditing of the actual configuration data and the standard configuration data respectively corresponding to each of the configuration items based on the power distribution detection model to obtain the audit result of the target power distribution equipment includes: Get the audit rules of the current configuration item; Determine an audit result of the current configuration item according to whether the actual configuration data and the standard configuration data corresponding to the current configuration item meet the audit rule; The audit result of the target power distribution device is determined according to the audit result of each of the configuration items.

6. The power distribution data audit method according to claim 1, characterized in that: When the audit result of any configuration item is an abnormal configuration item, the method further includes: Obtaining abnormal data and a configuration item identifier of the abnormal configuration item; Determine the exception type according to the configuration item identifier; The cause of the exception is determined from the exception type according to the exception data, and an optimization suggestion for the abnormal configuration item is generated according to the exception cause.

7. The power distribution data audit method according to claim 1, characterized in that: The power distribution detection model is obtained in the following way: Determine the device coordinates corresponding to similar devices from each power distribution device based on the Grey Wolf Optimization Algorithm; Optimizing the parameters to be optimized of the initial long short-term memory network algorithm according to the device coordinates, and obtaining the target long short-term memory network algorithm according to the optimization values ​​corresponding to the parameters to be optimized when the parameters to be optimized reach convergence; The power distribution detection model is obtained according to the grey wolf optimization algorithm and the target long short-term memory network algorithm.

8. A power distribution data audit device, characterized in that: include: A data acquisition module, used to acquire actual configuration data of the target power distribution equipment; An information determination module, configured to determine standard configuration information according to the device identification of the target power distribution device, wherein the standard configuration information includes at least one configuration item and standard configuration data corresponding to each configuration item; A data determination module, used to determine the actual configuration data corresponding to each of the configuration items according to the actual configuration data of the target power distribution equipment; The data audit module is used to audit the actual configuration data and the standard configuration data corresponding to each of the configuration items based on the power distribution detection model to obtain the audit result of the target power distribution equipment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the power distribution data audit method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the power distribution data audit method as described in any one of claims 1 to 7 is implemented.

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