Test system for single-phase electric energy meter verification

Abstract

The invention discloses a test system for single-phase electric energy meter verification, and relates to the technical field of single-phase electric energy meter verification, and the system comprises a data collection module which is used for automatically collecting the performance data of an electric energy meter, including voltage, current, power factor and active power; and the data preprocessing module is used for cleaning, converting and standardizing the original data acquired from the data acquisition module so as to eliminate noise and abnormal values in the data, and formatting the data into a form suitable for machine learning analysis. Various performance data of the electric energy meter are automatically collected through the data acquisition module, the machine learning technology is introduced, intelligent management of electric energy meter verification is achieved, verification efficiency and accuracy are improved, the performance data of the electric energy meter are automatically analyzed, potential faults are predicted, the requirement for manual intervention is reduced, the labor cost is reduced, and the working efficiency is improved. By dynamically adjusting the verification sequence and process and automatically selecting the verification method and parameters, the verification efficiency and accuracy are improved.

Description

Technical Field

[0001] The invention relates to the technical field of single-phase electric energy meter calibration, in particular to a test system for single-phase electric energy meter calibration. Background Art

[0002] Single-phase energy meter, also called single-phase meter or single-phase watt-hour meter, is a multifunctional measuring instrument used to measure and record the energy consumption in single-phase circuits. Single-phase energy meter is an instrument used to monitor and control the energy, electricity and power of single-phase loads (especially household appliances). It can measure the power, voltage, current and other parameters of single-phase circuits in real time, and has the functions of energy consumption metering, power measurement and data collection. Single-phase energy meter can accurately measure the energy consumption of single-phase circuits and provide accurate electricity consumption data. Single-phase energy meter is manufactured with advanced technology and materials, with good stability and reliability. Single-phase energy meter is small in size and easy to install. It is suitable for various single-phase circuit scenarios. Single-phase energy meter is suitable for single-phase circuits in homes, small commercial places, rural power grids, schools and hospitals, etc., and has a wide range of application scenarios.

[0003] With the rapid development of smart grid and Internet of Things technology, single-phase energy meter is an indispensable metering device in the power system. Its accuracy and reliability are crucial to the stable operation of the power system. However, the traditional single-phase energy meter calibration process usually relies on manual operation, which is not only inefficient but also prone to errors. With the increasing number of energy meters, traditional calibration methods can no longer meet the large-scale and efficient calibration requirements. Therefore, it is necessary to propose a test system for single-phase energy meter calibration to solve the problems in the existing technology. Summary of the invention

[0004] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a test system for single-phase electricity meter calibration. It can introduce machine learning technology to realize the intelligent management of electricity meter calibration, improve the calibration efficiency and accuracy, and can automatically analyze the performance data of the electricity meter and predict potential faults, thereby reducing the need for manual intervention and reducing labor costs.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a test system for single-phase electric energy meter calibration, the system comprising:

[0006] Data acquisition module, used to automatically collect performance data of the energy meter, including voltage, current, power factor and active power;

[0007] The data preprocessing module is used to clean, transform and standardize the raw data obtained from the data acquisition module to eliminate noise and outliers in the data and format the data into a form suitable for machine learning analysis;

[0008] The machine learning analysis module uses machine learning technology to perform real-time analysis on pre-processed data, identify abnormal behaviors of the energy meter, predict potential failures, and provide maintenance recommendations based on the analysis results;

[0009] The verification process optimization module is used to dynamically adjust the verification sequence and process according to the output results of the machine learning analysis module.

[0010] Furthermore, the system also includes a user interface module for providing an intuitive user interface, allowing a user to view the test results, fault prediction information and maintenance suggestions.

[0011] Furthermore, the system also includes a database for storing and managing performance data, verification results, fault prediction information and maintenance suggestions of the electric energy meter.

[0012] Furthermore, the specific execution steps of the data acquisition module include:

[0013] Initialize connection: The data acquisition module will first establish a communication connection with the single-phase energy meter to be calibrated;

[0014] Set communication parameters: According to the model and specifications of the energy meter, the data acquisition module will set the corresponding communication parameters;

[0015] Send data collection instructions: Once the connection is established and the parameters are set, the data collection module will send data collection instructions to the energy meter;

[0016] Receiving electric energy meter data: After receiving the data collection instruction, the electric energy meter will send its various performance data to the data collection module, and the data collection module will receive and store this data in real time.

[0017] Furthermore, the specific execution steps of the data acquisition module also include:

[0018] Data verification: In order to ensure the accuracy and completeness of the data, the data acquisition module will verify the received data;

[0019] Data transmission and storage: After verification, the data acquisition module will transmit the received data to the data preprocessing module or directly store it in the database;

[0020] Error handling and retry: If an error occurs during data collection, the data collection module will handle the error and record the error information for subsequent troubleshooting and maintenance;

[0021] Status monitoring and feedback: The data acquisition module monitors its own operating status and the communication status of the electric energy meter, and feeds back the relevant information to the user interface module.

[0022] Furthermore, the specific execution steps of the data preprocessing module include:

[0023] Data cleaning: Identify and process missing values, outliers, or duplicate values ​​in the data. For missing values, you can use mean, median, and mode algorithms to interpolate. For outliers, use statistical methods to identify and process them based on business scenarios and data distribution characteristics.

[0024] Data transformation: Convert data from one form to another as needed, such as converting categorical variables into numerical variables. You can also perform logarithmic transformation or Box-Cox transformation on the data to improve the distribution characteristics of the data.

[0025] Data standardization or normalization: Since the performance indicators of the electric energy meter may have different dimensions and distribution characteristics, they need to be standardized or normalized for subsequent analysis and comparison. Standardization: Use the Z-score method to convert the data into a distribution with a mean of 0 and a variance of 1. The formula is: Where X is the original data, μ is the mean, σ is the standard deviation, and normalization: map the data to the interval [0, 1] and use the maximum value normalization method. The formula is: Where X min and X max are the minimum and maximum values ​​of the data respectively.

[0026] Furthermore, the specific execution steps of the data preprocessing module also include:

[0027] Feature selection: Based on business needs and data analysis results, select features that have a significant impact on the test results to improve the efficiency and accuracy of subsequent analysis;

[0028] Data dimensionality reduction: If the data dimension is too high, it may lead to low analysis efficiency or overfitting of the model. In this case, principal component analysis and linear discriminant analysis methods are used to reduce the data dimension.

[0029] Data storage: Store the preprocessed data in a database for subsequent analysis and retrieval.

[0030] Furthermore, the specific execution steps of the machine learning analysis module include:

[0031] Data loading: Load the preprocessed dataset from the database;

[0032] Feature selection: Select features that have a significant impact on model performance based on problem requirements and data characteristics;

[0033] Model training: Use the linear regression algorithm model and training data in machine learning to train the model. During the training process, the model will learn the inherent laws and patterns of the data;

[0034] Model evaluation: After training is complete, the model is evaluated using test data to understand how the model performs on new data.

[0035] Model optimization: If the performance of the model is not ideal, you can optimize the model performance by adjusting the model parameters, adding regularization terms, and using cross-validation methods;

[0036] Result output: Output the analysis results of the model to the verification process optimization module and the user interface module.

[0037] Furthermore, the linear regression algorithm used in the machine learning analysis module is a machine learning algorithm for predicting continuous variables, which attempts to find an optimal straight line so that the sum of the distances from the data points to the straight line is minimized;

[0038] For univariate linear regression, the formula is: y = ax + b, where y is the predicted value, x is the input feature, a is the slope, and b is the intercept;

[0039] For multiple linear regression, the formula can be expanded to: y = β0 + β1x1 + β2x2 + ... + β n x n Among them, y is the predicted value, x is the input feature, β0,β1,…,β n are model parameters;

[0040] During training, the algorithm attempts to find an optimal set of β values ​​that minimizes the model's prediction error on the training data, usually by minimizing a loss function.

[0041] Furthermore, the specific execution steps of the verification process optimization module include:

[0042] Receiving analysis results: The verification process optimization module first receives the output results from the machine learning analysis module;

[0043] Analyze output results: The module analyzes the received output results, especially focusing on those information indicating possible faults or performance problems of the energy meter;

[0044] Dynamically adjust the verification order: According to the analysis results, the module will dynamically adjust the verification order of the energy meter;

[0045] Automatic selection of verification method and parameters: According to the type and specifications of the electric energy meter, the module will automatically select the most appropriate verification method and parameters;

[0046] Generate optimized verification process information: After determining the new verification order and selecting the appropriate verification method, the module will generate optimized verification process information;

[0047] Transmit information to the user interface: Finally, the module transmits the optimized verification process information to the user interface module for display.

[0048] Compared with the prior art, the test system for single-phase electric energy meter verification has the following beneficial effects:

[0049] The present invention automatically collects various performance data of the electric energy meter through a data acquisition module, and realizes intelligent management of the electric energy meter calibration by introducing machine learning technology, thereby improving the calibration efficiency and accuracy. The system can automatically analyze the performance data of the electric energy meter and predict potential faults, thereby reducing the need for manual intervention and reducing labor costs. The calibration efficiency and accuracy are improved by dynamically adjusting the calibration sequence and process and automatically selecting the calibration method and parameters.

[0050] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 Flow chart of the test system used for single-phase electric energy meter calibration. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] A test system for single-phase electric energy meter verification, the system comprising:

[0056] Data acquisition module, used to automatically collect performance data of the energy meter, including voltage, current, power factor and active power. Data acquisition can communicate with the energy meter via wired or wireless means to ensure the real-time and accuracy of the data;

[0057] The specific execution steps of the data acquisition module include: the data acquisition module will first establish a communication connection with the single-phase electric energy meter to be calibrated, which can be done in a wired or wireless manner to ensure the real-time and accuracy of data transmission; according to the model and specifications of the electric energy meter, the data acquisition module will set corresponding communication parameters, such as baud rate, data bit, stop bit, to ensure the stability and compatibility of communication; once the connection is established and the parameters are set, the data acquisition module will send data acquisition instructions to the electric energy meter, and these instructions usually include commands requesting the electric energy meter to send its various performance data; after receiving the data acquisition instructions, the electric energy meter will send its various performance data (such as voltage, current, power factor, active power) to the data acquisition module, and the data acquisition module will receive these data in real time and store them.

[0058] The specific execution steps of the data acquisition module also include: in order to ensure the accuracy and completeness of the data, the data acquisition module will verify the received data, which can be done by calculating the checksum of the data and comparing the range of the data; after verification, the data acquisition module will transmit the received data to the data preprocessing module or directly store it in the database. The data transmission can be carried out through the internal bus, serial port, or network, depending on the design and requirements of the system; if an error occurs during the data acquisition process (such as communication interruption, data verification failure), the data acquisition module will perform error processing, such as reconnecting the electric energy meter and resending the data acquisition instruction. At the same time, the module will also record error information for subsequent troubleshooting and maintenance; the data acquisition module monitors its own operating status and the communication status of the electric energy meter, and feeds back relevant information to the user interface module, which helps users understand the operation of the system and promptly discover and solve problems.

[0059] The data preprocessing module is used to clean, transform and standardize the raw data obtained from the data acquisition module to eliminate noise and outliers in the data and format the data into a form suitable for machine learning analysis;

[0060] The specific execution steps of the data preprocessing module include: identifying and processing missing values, abnormal values ​​or duplicate values ​​in the data. For missing values, the mean, median and mode algorithms can be used for interpolation. For abnormal values, statistical methods are used for identification and processing according to business scenarios and data distribution characteristics; according to needs, data is converted from one form to another, such as converting categorical variables into numerical variables (dummy variable encoding), and data can also be logarithmically transformed or Box-Cox transformed to improve the distribution characteristics of the data;

[0061] Since the performance indicators of the electric energy meter may have different dimensions and distribution characteristics, they need to be standardized or normalized for subsequent analysis and comparison. Standardization: Use the Z-score method to convert the data into a distribution with a mean of 0 and a variance of 1. The formula is: Where X is the original data, μ is the mean, σ is the standard deviation, and normalization: map the data to the interval [0, 1] and use the maximum value normalization method. The formula is: Where X min and X max are the minimum and maximum values ​​of the data respectively.

[0062] The specific execution steps of the data preprocessing module also include: selecting features that have an important impact on the verification results according to business needs and data analysis results to improve the efficiency and accuracy of subsequent analysis; if the data dimension is too high, it may lead to low analysis efficiency or model overfitting. At this time, principal component analysis (PCA) and linear discriminant analysis (LDA) methods are used to reduce the dimension of the data; the preprocessed data is stored in a database for subsequent analysis and retrieval.

[0063] Embodiment 2

[0064] The machine learning analysis module uses machine learning technology to perform real-time analysis on pre-processed data, identify abnormal behaviors of the energy meter, predict potential failures, and provide maintenance recommendations based on the analysis results;

[0065] The specific execution steps of the machine learning analysis module include: loading the preprocessed data set from the database; selecting features that have an important impact on the model performance based on the problem requirements and data characteristics; using the linear regression algorithm model and training data in machine learning to train the model. During the training process, the model will learn the inherent laws and patterns of the data; after the training is completed, the model is evaluated using test data to understand the performance of the model on new data. The evaluation indicators usually include accuracy, precision, recall rate, and F1 score; if the performance of the model is not ideal, the model performance can be optimized by adjusting the model parameters, adding regularization terms, and using cross-validation methods; the analysis results of the model (such as predicted values, fault prediction information) are output to the verification process optimization module and the user interface module.

[0066] The linear regression algorithm used in the machine learning analysis module is a machine learning algorithm for predicting continuous variables. It attempts to find an optimal straight line (which may be a hyperplane in multidimensional space) so that the sum of the distances from the data points to this straight line is minimized. For univariate linear regression, the formula is: y = ax + b, where y is the predicted value, x is the input feature, a is the slope, and b is the intercept. For multivariate linear regression, the formula can be expanded to: y = β0 + β1x1 + β2x2 + ... + β n x n Among them, y is the predicted value, x is the input feature, β0,β1,…,β n are model parameters (i.e. weights);

[0067] During training, the algorithm attempts to find an optimal set of β values ​​that minimizes the model's prediction error on the training data, usually by minimizing a loss function such as mean squared error.

[0068] The calibration process optimization module is used to dynamically adjust the calibration order and process according to the output results of the machine learning analysis module. For example, it gives priority to calibrating electricity meters that are predicted to have failure risks, and automatically selects the most appropriate calibration method and parameters.

[0069] The specific execution steps of the verification process optimization module include: the verification process optimization module first receives the output results from the machine learning analysis module, which generally include abnormal behavior pattern recognition, potential fault prediction, and maintenance suggestions generated based on the analysis of the electric energy meter; the module analyzes the received output results, especially focusing on information indicating that the electric energy meter may have a fault or performance problem;

[0070] Dynamically adjust the calibration order: Based on the analysis results, the module will dynamically adjust the calibration order of the energy meter. For example, for the energy meter predicted to have a risk of failure, the module will increase its calibration priority so that calibration and repair can be carried out as soon as possible. According to the type and specification of the energy meter, the module will automatically select the most appropriate calibration method and parameters, which ensures the accuracy and efficiency of the calibration process and avoids the errors that may be caused by human selection. After determining the new calibration order and selecting the appropriate calibration method, the module will generate optimized calibration process information, which includes the calibration order of each energy meter, the expected calibration time, the calibration method and parameters used.

[0071] Finally, the module transmits the optimized verification process information to the user interface module for display, so that the user can intuitively see the new verification plan and perform further operations or adjustments as needed.

[0072] Embodiment 3

[0073] The system also includes a user interface module for providing an intuitive user interface that allows users to view verification results, fault prediction information, and maintenance recommendations. The user interface supports users in performing necessary operations and settings, such as adding or deleting electric energy meters and setting verification parameters.

[0074] The user interface module provides an intuitive and user-friendly user interface, supports multiple operating platforms and devices, and is convenient for users to access and operate anytime and anywhere. It allows users to view detailed calibration results, fault prediction information, and maintenance suggestions, and supports the export and printing of results. It supports users to perform necessary operations and settings, such as adding or deleting electricity meters, setting calibration parameters, and adjusting interface layouts. It provides real-time feedback of calibration progress and abnormal situations to users, including calibration start, end, and fault warning notification information. It provides user help and document support, allowing users to quickly master system usage and operating skills. It supports user authority management and data security protection to ensure the security and stability of the system.

[0075] The specific execution steps of the user interface module include: the user enters the user name and password (or other authentication methods), the system verifies the information entered by the user, and if it is correct, allows the user to log in; otherwise, an error message is displayed, and different interface content and functions are displayed according to the user's role and permissions, and necessary information such as data lists, charts, and buttons are displayed.

[0076] Users interact with the system by clicking buttons, selecting options, and entering data. The system performs corresponding functions based on the user's operations, such as data query, editing, and deletion. The system provides feedback to the user on the operation results, such as displaying success or failure information, and providing the user with operation prompts or help information when necessary. If the user chooses to log out, the system clears the user's login status.

[0077] The system also includes a database for storing and managing the performance data, calibration results, fault prediction information and maintenance recommendations of the electricity meter. The database design adopts efficient data storage and retrieval technology to ensure the integrity and security of the data.

[0078] The database should design a reasonable table structure to store the performance data, calibration results, fault prediction information and maintenance suggestions of the electricity meter. For example, the following tables can be designed: Electricity meter information table: stores the basic information of the electricity meter, such as ID, model, and production date; Performance data table: stores the real-time performance data of the electricity meter, such as voltage, current, power factor, and data collection time; Calibration result table: records the results of each calibration, including calibration time, calibration method, and calibration result; Fault prediction table: stores fault information predicted by the machine learning model, such as fault type and prediction time; Maintenance suggestion table: stores maintenance suggestions generated based on the analysis results, such as recommended maintenance time and maintenance method.

[0079] By setting primary keys, foreign keys and constraints, the integrity and consistency of data are ensured, and data security is ensured by using data encryption and access control technologies.

[0080] Adopt appropriate data storage strategies, such as partitioning and indexing, to improve data retrieval efficiency, back up the database regularly to ensure data reliability and recoverability, and be able to quickly restore data and reduce losses when failures occur.

[0081] As the number of electricity meters increases, the database may face performance bottlenecks. In this case, you can increase the number of database servers or adopt a distributed database architecture to achieve horizontal expansion and improve the system's throughput and response speed. On a single database server, you can upgrade the hardware, optimize the database configuration, improve the server's processing power, and achieve vertical expansion.

[0082] Ensure real-time synchronization of data between the data acquisition module, data preprocessing module, and machine learning analysis module to obtain the latest electricity meter performance data and analysis results in a timely manner. According to business needs, regularly update the data in the database, such as adding new electricity meter information and updating calibration results.

[0083] Monitor the performance of the database in real time, including response time, throughput, and CPU utilization indicators, promptly identify and resolve performance issues, regularly review database access rights and security settings to ensure that only authorized users can access and operate data in the database, and record database access and operation logs for tracing and analysis when problems arise.

[0084] Through the expansion of the above aspects, an efficient, secure and scalable database system is constructed to provide stable data support for the single-phase electricity meter verification and testing system.

[0085] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the same elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A test system for single-phase electric energy meter verification, characterized in that: The system includes: Data acquisition module, used to automatically collect performance data of the energy meter, including voltage, current, power factor and active power; The data preprocessing module is used to clean, transform and standardize the raw data obtained from the data acquisition module to eliminate noise and outliers in the data and format the data into a form suitable for machine learning analysis; The machine learning analysis module uses machine learning technology to perform real-time analysis on pre-processed data, identify abnormal behaviors of the energy meter, predict potential failures, and provide maintenance recommendations based on the analysis results; The verification process optimization module is used to dynamically adjust the verification sequence and process according to the output results of the machine learning analysis module.

2. The test system for single-phase electric energy meter verification according to claim 1, characterized in that: The system also includes a user interface module for providing an intuitive user interface that allows the user to view the test results, failure prediction information and maintenance recommendations.

3. The test system for single-phase electric energy meter verification according to claim 1, characterized in that: The system also includes a database for storing and managing performance data, verification results, fault prediction information, and maintenance recommendations of the electric energy meters.

4. The test system for single-phase electric energy meter verification according to claim 1, characterized in that: The specific execution steps of the data acquisition module include: Initialize connection: The data acquisition module will first establish a communication connection with the single-phase energy meter to be calibrated; Set communication parameters: According to the model and specifications of the energy meter, the data acquisition module will set the corresponding communication parameters; Send data collection instructions: Once the connection is established and the parameters are set, the data collection module will send data collection instructions to the energy meter; Receiving electric energy meter data: After receiving the data collection instruction, the electric energy meter will send its various performance data to the data collection module, and the data collection module will receive and store this data in real time.

5. The test system for single-phase electric energy meter verification according to claim 4, characterized in that: The specific execution steps of the data acquisition module also include: Data verification: In order to ensure the accuracy and completeness of the data, the data acquisition module will verify the received data; Data transmission and storage: After verification, the data acquisition module will transmit the received data to the data preprocessing module or directly store it in the database; Error handling and retry: If an error occurs during data collection, the data collection module will handle the error and record the error information for subsequent troubleshooting and maintenance; Status monitoring and feedback: The data acquisition module monitors its own operating status and the communication status of the electric energy meter, and feeds back the relevant information to the user interface module.

6. The test system for single-phase electric energy meter verification according to claim 1, characterized in that: The specific execution steps of the data preprocessing module include: Data cleaning: Identify and process missing values, outliers, or duplicate values ​​in the data. For missing values, you can use mean, median, and mode algorithms to interpolate. For outliers, use statistical methods to identify and process them based on business scenarios and data distribution characteristics. Data transformation: Convert data from one form to another as needed, such as converting categorical variables into numerical variables. You can also perform logarithmic transformation or Box-Cox transformation on the data to improve the distribution characteristics of the data. Data standardization or normalization: Since the performance indicators of the electric energy meter may have different dimensions and distribution characteristics, they need to be standardized or normalized for subsequent analysis and comparison. Standardization: Use the Z-score method to convert the data into a distribution with a mean of 0 and a variance of 1. The formula is: Where X is the original data, μ is the mean, σ is the standard deviation, and normalization: map the data to the interval [0, 1] and use the maximum value normalization method. The formula is: Where X min and X max are the minimum and maximum values ​​of the data respectively.

7. The test system for single-phase electric energy meter verification according to claim 6, characterized in that: The specific execution steps of the data preprocessing module also include: Feature selection: Based on business needs and data analysis results, select features that have a significant impact on the test results to improve the efficiency and accuracy of subsequent analysis; Data dimensionality reduction: If the data dimension is too high, it may lead to low analysis efficiency or overfitting of the model. In this case, principal component analysis and linear discriminant analysis methods are used to reduce the data dimension. Data storage: Store the preprocessed data in a database for subsequent analysis and retrieval.

8. The test system for single-phase electric energy meter verification according to claim 1, characterized in that: The specific execution steps of the machine learning analysis module include: Data loading: Load the preprocessed dataset from the database; Feature selection: Select features that have a significant impact on model performance based on problem requirements and data characteristics; Model training: Use the linear regression algorithm model and training data in machine learning to train the model. During the training process, the model will learn the inherent laws and patterns of the data; Model evaluation: After training is complete, the model is evaluated using test data to understand how the model performs on new data. Model optimization: If the performance of the model is not ideal, you can optimize the model performance by adjusting the model parameters, adding regularization terms, and using cross-validation methods; Result output: Output the analysis results of the model to the verification process optimization module and the user interface module.

9. The test system for single-phase electric energy meter verification according to claim 8, characterized in that: The linear regression algorithm used in the machine learning analysis module is a machine learning algorithm for predicting continuous variables, which attempts to find an optimal straight line so that the sum of the distances from the data points to the straight line is minimized; For univariate linear regression, the formula is: y = ax + b, where y is the predicted value, x is the input feature, a is the slope, and b is the intercept; For multiple linear regression, the formula can be expanded to: y = β0 + β1x1 + β2x2 + ... + β n x n Among them, y is the predicted value, x is the input feature, β0,β1,…,β n are model parameters; During training, the algorithm attempts to find an optimal set of β values ​​that minimizes the model's prediction error on the training data, usually by minimizing the loss function.

10. The test system for single-phase electric energy meter verification according to claim 1, characterized in that: The specific execution steps of the verification process optimization module include: Receiving analysis results: The verification process optimization module first receives the output results from the machine learning analysis module; Analyze output results: The module analyzes the received output results, especially focusing on those information indicating possible faults or performance problems of the energy meter; Dynamically adjust the verification order: According to the analysis results, the module will dynamically adjust the verification order of the energy meter; Automatic selection of verification method and parameters: According to the type and specifications of the electric energy meter, the module will automatically select the most appropriate verification method and parameters; Generate optimized verification process information: After determining the new verification order and selecting the appropriate verification method, the module will generate optimized verification process information; Transmit information to the user interface: Finally, the module transmits the optimized verification process information to the user interface module for display.

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