An electric energy meter informationization evaluation calibration model measurement and verification system and method

Abstract

The application discloses an electric energy meter informationization evaluation calibration model measurement and evaluation verification system and method, and the system comprises an electric energy meter informationization evaluation calibration model measurement uncertainty evaluation and control module, a load data generator based on a digital twin mixed reality technology and an electric energy meter informationization evaluation calibration model result measurement and evaluation verification method research and measurement and evaluation verification platform. The application evaluates and verifies the electric energy meter informationization calibration model in theory and practice, improves the informationization measurement accuracy and reliability to a new height, and also solves the problems of low work efficiency, high operation and electric energy meter replacement cost, high manual labor intensity, incapability of real-time monitoring of electric energy meters and timely fault reporting and early warning, incapability of effectively controlling the measurement faults and error power and the like existing in the currently generally adopted traditional manual field calibration or disassembly laboratory calibration mode.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power supply management, and particularly relates to an electric energy meter informationization evaluation calibration model measurement and verification system and method. BACKGROUND

[0002] An electric energy meter is an instrument for measuring electric energy, also known as an electric meter, a fire meter or a kilowatt-hour meter, which refers to an instrument for measuring various electrical quantities. When using an electric energy meter, attention should be paid to the fact that the electric energy meter can be directly connected to the circuit for measurement in the case of low voltage and small current. In order to make the measurement result of the electric energy meter more accurate, the electric energy meter needs to be calibrated before use.

[0003] With the development and progress of technology, the industry designs an electric energy meter informationization evaluation calibration model, which uses an electric energy meter informationization evaluation calibration model to calculate and output error data of the electric energy meter, which is different from the traditional physical transfer calibration method, and the essence is an informationization calibration method. There is no scientific and effective technical means to measure and verify the performance indicators and the accuracy of the output results of the informationization evaluation calibration model. SUMMARY

[0004] In view of the technical problems pointed out in the background art, the purpose of the present application is to provide an electric energy meter informationization evaluation calibration model measurement and verification system and method to measure and verify the electric energy meter informationization calibration model in theory and practice, and to improve the informationization measurement accuracy and reliability to a new height, and to solve the problems of low work efficiency, high operation and electric energy meter replacement cost, high labor intensity, inability to monitor the electric energy meter in real time and report faults in time, difficulty in effectively controlling the measurement failure and error power, etc. in the current widely used traditional manual field verification or laboratory calibration mode.

[0005] To achieve the above-mentioned purpose, the technical scheme provided by the present application is as follows:

[0006] First aspect

[0007] The present application provides an electric energy meter informationization evaluation calibration model measurement and verification system, characterized in that it comprises an electric energy meter informationization evaluation calibration model measurement uncertainty evaluation and control method module, a load data generator based on digital twin mixed reality technology and an electric energy meter informationization evaluation calibration model result measurement and verification method research and measurement and verification platform.

[0008] The electric energy meter informationization evaluation calibration model measurement uncertainty evaluation and control module is used to provide data input.

[0009] The load data generator based on digital twin mixed reality technology is used to generate verification data, determine simulation complex working conditions and multi-factor measurement and verification key indicators.

[0010] The electric energy meter informationization evaluation calibration model result evaluation verification method research and evaluation verification platform is used for establishing a digital simulation platform and constructing an electric energy meter informationization evaluation calibration model evaluation verification platform.

[0011] The second aspect

[0012] Corresponding to the above system, the application also provides an electric energy meter informationization evaluation calibration model evaluation verification method, comprising the following steps:

[0013] Step 1: using the electric energy meter informationization evaluation calibration model measurement uncertainty evaluation and control method to provide data input;

[0014] Step 2: using a load data generator based on digital twin hybrid reality technology to generate verification data, determine simulation complex working conditions, and multi-factor evaluation verification key indicators;

[0015] Step 3: using the electric energy meter informationization evaluation calibration model result evaluation verification method research and evaluation verification platform to establish a digital simulation platform and construct an electric energy meter informationization evaluation calibration model evaluation verification platform.

[0016] Compared with the prior art, the application has the following beneficial effects:

[0017] 1. The application establishes an electric energy meter evaluation calibration physical and mathematical model, researches the calculation and evaluation method of combined standard uncertainty and expanded uncertainty, proposes a target uncertainty control method, provides theoretical support for the optimization and improvement of physical, mathematical models and algorithms in the research process, realizes accurate evaluation and precise control of electric energy meter informationization calibration uncertainty, and guarantees the accuracy and reliability of informationization calibration.

[0018] 2. The application researches the relationship between user social attributes, electric appliance loading information, running environment, space-time factors and load curve, constructs a load data generator based on digital twin hybrid reality technology, realizes digital acquisition of voltage, current, active power, reactive power, power factor and other curves of all measurement points in the transformer area, simulates different low-voltage transformer area topology structures, simulates different low-voltage transformer area fault types, simulates different low-voltage transformer area different load users, and provides multi-dimensional training data for the evaluation and verification of the electric energy meter informationization evaluation calibration model result.

[0019] 3. The application determines the electric energy meter informationization evaluation calibration model evaluation key indicators, researches the model simulation based on clustering analysis method and high-order interpolation algorithm, carries out data multi-modal coupling characteristic analysis, combines the load data generator consistent with digital space and physical, constructs the electric energy meter evaluation calibration model evaluation verification platform based on digital twin virtual reality, and realizes the evaluation and verification of the model evaluation calibration result. BRIEF DESCRIPTION OF DRAWINGS

[0020] Fig. 1 Fig. 1 shows a schematic diagram of a system module provided by an embodiment of the present application;

[0021] Fig. 2 Fig. 2 shows a schematic diagram of a system principle of an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application.

[0023] Please refer to Figs. 1-2 Fig. 1 shows an embodiment provided by the present application.

[0024] The electric energy meter informatization evaluation calibration model measurement and evaluation verification system provided by the embodiment comprises an electric energy meter informatization evaluation calibration model measurement uncertainty assessment and control method module, a load data generator based on digital twin mixed reality technology, and an electric energy meter informatization evaluation calibration model result measurement and evaluation verification method research and measurement and evaluation verification platform.

[0025] The electric energy meter informatization evaluation calibration model measurement uncertainty assessment and control module is configured to provide data input. When studying the various factors and different conditions faced when online assessing the running error of an electric energy meter, the module analyzes and identifies the uncertainty sources and uncertainty components of the electric energy meter informatization evaluation calibration model, establishes an electric energy meter evaluation calibration physical and mathematical model according to the data such as the electric quantity data, load curve, and other data in the electric energy meter and metering module, and the topology of the transformer area, studies the calculation and assessment method of combined standard uncertainty and expanded uncertainty, proposes a target uncertainty control method, provides theoretical support for the optimization and improvement of the physical and mathematical models and algorithms in the research process, realizes accurate evaluation and precise control of the uncertainty of electric energy meter informatization calibration, and ensures the accuracy and reliability of informatization calibration.

[0026] Specifically, the electric energy meter informatization evaluation calibration model measurement uncertainty assessment and control method module executes the following method.

[0027] Step 1.1. Statistical analysis is performed on the possible uncertainty sources such as the total meter error of the transformer area, low-voltage current transformer error, electric energy meter truncation error, current and voltage loop impedance, voltage fluctuation, resistance temperature coefficient, sampling time synchronization, transformer area line loss and power load characteristics, electromagnetic environment, temperature and humidity, and other uncertainty sources in the physical model establishment and data model solution link, and the uncertainty sources, uncertainty components, and correlation coefficients between the uncertainty components are analyzed and identified.

[0028] Step 1.2. Establish an uncertainty mathematical model suitable for online evaluation of electric energy meters using grey system theory, information entropy theory, Bayesian theory, Monte Carlo method, and neural network theory, and a calculation and evaluation method for combining standard uncertainty and expanded uncertainty;

[0029] Step 1.3. With the target expanded uncertainty as the optimization objective, and in combination with fuzzy theory, set and non-convex optimization techniques, the standard uncertainty components are redistributed to support continuous optimization of the hierarchical energy conservation physical model and data solving model of the transformer area.

[0030] The load data generator based on the digital twin mixed reality technology is used to generate verification data to determine the simulation of complex working conditions and multi-factor evaluation verification key indicators. It studies the relationship between user social attributes, appliance loading information, operating environment, space-time factors, and load curves, realizes multi-angle user load feature portraits, and based on the digital twin mixed reality technology, constructs a load data generator consistent with the physical device in the simulation environment, which can realize digital acquisition of voltage, current, active power, reactive power, power factor curves of all metering points in the transformer area, simulate different low-voltage transformer area topology structures, simulate different low-voltage transformer area fault types, simulate different low-voltage transformer area different load users, etc. Functions provide multi-dimensional training data for evaluation and verification of electric energy meter information evaluation calibration model results.

[0031] Specifically, the load data generator based on the digital twin mixed reality technology executes the following method:

[0032] Step 2.1. According to the actual transformer area metering device configuration and operating conditions, study the relationship between user social attributes, appliance loading information, operating environment, etc. and load curves, construct multi-angle user load feature portraits through SOM network clustering analysis, classification regression machine learning algorithm, and determine the key feature quantities of model evaluation;

[0033] Step 2.2. Based on the digital twin mixed reality technology, abstract the physical feature model of the actual low-voltage transformer area, construct a low-voltage transformer area digital simulation model, which can realize simulation of different transformer area types, different load users, different transformer area topology structures, and different transformer area fault types;

[0034] Step 2.3. In the simulation environment, construct a load data generator consistent with the low-voltage transformer area power consumption characteristics, realize digital acquisition of voltage, current, active power, reactive power, and power factor of all metering points in the transformer area, simulate different low-voltage transformer area topology structures, simulate different low-voltage transformer area fault types, simulate different low-voltage transformer area different load users, and provide multi-dimensional training data for evaluation and verification of electric energy meter information evaluation calibration model results.

[0035] It should be noted that the load data generator based on the digital twin mixed reality technology is a set of load data generator consistent with the physical device.

[0036] The evaluation and calibration model result evaluation and verification method research and evaluation and verification platform for electric energy meter informationization is used to establish a digital simulation platform and construct an electric energy meter informationization evaluation and calibration model evaluation and verification platform. Based on the research results of the electric energy meter evaluation and calibration model and uncertainty, the key indicators of the electric energy meter informationization evaluation and calibration model evaluation are determined, the model simulation based on clustering analysis and high-order interpolation algorithm is researched, the data multi-modal coupling characteristic analysis is carried out, the digital space and the physical consistent load data generator are combined, the electric energy meter evaluation and calibration model evaluation and verification platform based on digital twin virtual reality is constructed, and the evaluation and verification of the model evaluation and calibration results is realized.

[0037] Specifically, the electric energy meter informationization evaluation and calibration model result evaluation and verification method research and evaluation and verification platform executes the following method:

[0038] Step 3.1 Based on the research results of the electric energy meter informationization evaluation and calibration model and uncertainty, the characteristics of various indicators including mean absolute error, mean variance and root mean square error are researched, and appropriate evaluation and verification evaluation indicators are designed. On this basis, the surface verification, Turing test, t-test method, confidence interval method and Bayesian hypothesis testing method are comprehensively designed, and the evaluation and verification method suitable for the evaluation and calibration model is designed.

[0039] Step 3.2 Based on the evaluation index design and result evaluation and verification method, the load data generator is combined to construct the electric energy meter informationization evaluation and calibration model evaluation and verification platform with on-demand reconstruction, node flexible expansion, load adjustable control, real-time power flow calculation, dynamic fault simulation, virtual-real bidirectional linkage and state accurate visualization.

[0040] Step 3.3 Based on the electric energy meter informationization evaluation and calibration model evaluation and verification platform, according to the research evaluation and verification method, the electric energy meter evaluation and calibration model result is evaluated by using the data provided by the load data generator based on the digital twin mixed reality technology, and the calibration model result and uncertainty analysis result are verified on the actual physical node of the electric energy meter informationization evaluation and calibration model evaluation and verification platform.

[0041] Corresponding to the above system, the embodiment also provides an electric energy meter informationization evaluation and calibration model evaluation and verification method, characterized in that it comprises the following steps:

[0042] Step 1: Use the electric energy meter informationization evaluation and calibration model measurement uncertainty assessment and control method to provide data input;

[0043] Step 2: Generate verification data using a load data generator based on digital twin mixed reality technology to determine simulation complex working conditions, multi-factor evaluation verification key indicators;

[0044] Step 3: Establish a digital simulation platform using an electric energy meter information evaluation calibration model result evaluation verification method and an evaluation verification platform to build an electric energy meter information evaluation calibration model evaluation verification platform.

[0045] The step 1 includes the following:

[0046] Step 1.1 In the physical model establishment and data model solution link, statistical analysis is performed on possible sources of uncertainty such as transformer total meter error, low-voltage current transformer error, electric energy meter truncation error, current and voltage loop impedance, voltage fluctuation, resistance temperature coefficient, sampling time synchronization, transformer line loss and power load characteristics, electromagnetic environment, temperature and humidity, etc. The uncertainty sources, uncertainty components and correlation coefficients between the uncertainty components are analyzed and determined.

[0047] Step 1.2 A mathematical model for online evaluation of electric energy meters is established using gray system theory, information entropy theory, Bayesian theory, Monte Carlo method and neural network theory, as well as a calculation and evaluation method for combined standard uncertainty and expanded uncertainty.

[0048] Step 1.3 With the target expanded uncertainty as the optimization target, combined with fuzzy theory and non-convex optimization techniques, the standard uncertainty components are redistributed to support the continuous optimization of hierarchical and graded transformer energy conservation physical models and data solution models.

[0049] The step 2 includes the following:

[0050] Step 2.1 According to the actual transformer metering device configuration and operation, the relationship between user social attributes, appliance loading information, operating environment and load curve is studied, and through the study of SOM network clustering analysis, classification regression machine learning algorithm, a multi-angle user load feature portrait is constructed to determine the key feature quantities of model evaluation.

[0051] Step 2.2 Based on digital twin mixed reality technology, the physical feature model of the actual low-voltage transformer is abstracted to construct a low-voltage transformer digital simulation model, which can realize simulation of different transformer types, different load users, different transformer topological structures and different transformer fault types.

[0052] Step 2.3. In the simulation environment, a load data generator consistent with the power utilization characteristics of a low-voltage transformer area is constructed to realize digital acquisition of voltage, current, active power, reactive power and power factor of all metering points in the transformer area, simulate different low-voltage transformer area topological structures, simulate different low-voltage transformer area fault types, simulate different low-voltage transformer area users, and provide multi-dimensional training data for evaluation and verification of the results of the electric energy meter informationization evaluation and calibration model.

[0053] The step 3 comprises the following:

[0054] Step 3.1. Based on the electric energy meter informationization evaluation and calibration model and the research results of uncertainty analysis, the characteristics of various indexes including mean absolute error, mean variance and root mean square error are researched, appropriate evaluation indexes for evaluation and verification are designed, and on this basis, surface verification, Turing test, t-test method, confidence interval method and Bayesian hypothesis testing method are comprehensively designed to design an evaluation and verification method suitable for the evaluation and calibration model.

[0055] Step 3.2. Based on the evaluation index design and result evaluation and verification method, a load data generator is combined to construct an electric energy meter informationization evaluation and calibration model evaluation and verification platform with on-demand reconstruction, node flexible expansion, load adjustable control, real-time power flow calculation, dynamic fault simulation, virtual-real bidirectional linkage and state accurate visualization.

[0056] Step 3.3. Based on the electric energy meter informationization evaluation and calibration model evaluation and verification platform, the calibration model results are evaluated according to the research evaluation and verification method by using the data provided by the load data generator based on digital twin mixed reality technology, and the calibration model results and uncertainty analysis results are verified on the actual physical nodes of the electric energy meter informationization evaluation and calibration model evaluation and verification platform.

[0057] The load data generator based on digital twin mixed reality technology is a set of load data generators consistent with the physical devices.

[0058] It should be noted that:

[0059] 1. The present application establishes an electric energy meter evaluation and calibration physical and mathematical model, researches the calculation and evaluation method of combined standard uncertainty and expanded uncertainty, proposes a target uncertainty control method, provides theoretical support for optimization and improvement of physical, mathematical models and algorithms in the research process, realizes accurate evaluation and precise control of electric energy meter informationization calibration uncertainty, and guarantees the accuracy and reliability of informationization calibration.

[0060] 2. The application builds a load data generator based on digital twin hybrid reality technology by studying the relationship between user social attributes, appliance loading information, operating environment, space-time factors and load curve, realizes the functions of digital acquisition of voltage, current, active power, reactive power, power factor curves of all metering points in the transformer area, simulation of different low-voltage transformer area topology structure, simulation of different low-voltage transformer area fault types, simulation of different low-voltage transformer area different load users, etc., and provides multi-dimensional training data for the evaluation and verification of the calibration model results of the electric energy meter informatization evaluation.

[0061] 3. The application determines the key indicators of the electric energy meter informatization evaluation calibration model evaluation, studies the model simulation based on clustering analysis method and high-order interpolation algorithm, carries out data multi-modal coupling characteristic analysis, combines the load data generator consistent with digital space and physics, builds the electric energy meter evaluation calibration model evaluation and verification platform based on digital twin virtual reality, and realizes the evaluation and verification of the model evaluation and calibration results.

[0062] The basic principles, main features and advantages of the application are shown and described above. It should be understood by those skilled in the art that the application is not limited by the above examples, the above examples and descriptions in the specification are only preferred examples of the application, and are not intended to limit the application, various changes and improvements of the application can be made without departing from the spirit and scope of the application, and these changes and improvements all fall within the scope of the claimed application. The scope of protection of the application is defined by the appended claims and their equivalents.

Claims

1. An electric energy meter information evaluation and calibration model evaluation and verification system, characterized in that: It includes the measurement uncertainty assessment and control method module of the electric energy meter informatization evaluation and calibration model, the load data generator based on digital twin mixed reality technology, and the research and evaluation verification method of the electric energy meter informatization evaluation and calibration model results and the evaluation verification platform; The measurement uncertainty assessment and control module of the electric energy meter information evaluation and calibration model is used to provide data input; The load data generator based on digital twin mixed reality technology is used to generate verification data and determine key indicators for simulating complex working conditions and multi-factor evaluation verification; The research on the evaluation and verification method of the results of the electric energy meter informatization evaluation and calibration model and the evaluation and verification platform are used to establish a digital simulation platform and construct an evaluation and verification platform for the electric energy meter informatization evaluation and calibration model; The measurement uncertainty assessment and control method module of the electric energy meter information evaluation and calibration model performs the following method: Step 1.1 Statistically analyze the possible sources of uncertainty during the physical model establishment and data model solution, including substation total meter error, low-voltage current transformer error, energy meter truncation error, current and voltage loop impedance, voltage fluctuation, resistance temperature coefficient, sampling time synchronization, substation line loss and power load characteristics, electromagnetic environment, and temperature and humidity. Analyze and clarify the uncertainty sources, uncertainty components, and correlation coefficients between the uncertainty components. Step 1.2: Use grey system theory, information entropy theory, Bayesian theory, Monte Carlo method, and neural network theory to establish a mathematical model for uncertainty and a calculation and evaluation method for the combined standard uncertainty and expanded uncertainty suitable for online evaluation of electric energy meters. Step 1.3: Using the target expanded uncertainty as the optimization objective, combine the fuzzy theory collection and non-convex optimization techniques to redistribute the standard uncertainty components to support the continuous optimization of the energy conservation physical model and data solution model for the hierarchical and graded substations. The research on the evaluation and verification method of the electric energy meter information-based evaluation and calibration model results and the evaluation and verification platform implement the following methods: Step 3.1: Based on the information-based evaluation and calibration model of the energy meter and the research results of uncertainty analysis, various indicators and their characteristics, including mean absolute error, mean square error, and root mean square error, are studied, and appropriate evaluation and verification indicators are designed. Based on this, a comprehensive evaluation and verification method, including surface verification, Turing test, t-test, confidence interval method, and Bayesian hypothesis test, is designed to suit the evaluation and calibration model. Step 3.2: Based on the evaluation index design and result evaluation and verification methods, and in combination with the load data generator, a platform for evaluating and verifying the information-based evaluation and calibration model of electric energy meters is constructed, featuring on-demand reconfiguration, flexible node expansion, load adjustment and control, real-time flow calculation, dynamic fault simulation, bidirectional virtual-real linkage, and accurate and visual status. Step 3.3 Using the data provided by the load data generator of the digital twin mixed reality technology, based on the evaluation and verification platform of the electric energy meter informationization evaluation and calibration model, the results of the electric energy meter evaluation and calibration model are evaluated according to the research evaluation and verification method, and the calibration model results and uncertainty analysis results are verified on the actual physical nodes of the evaluation and verification platform of the electric energy meter informationization evaluation and calibration model.

2. The electric energy meter information evaluation and calibration model evaluation and verification system according to claim 1 is characterized in that: The load data generator based on digital twin mixed reality technology performs the following method: Step 2.1 Based on the actual metering device configuration and operation status of the substation, the relationship between user social attributes, appliance loading information, operating environment factors, and load curves is studied. By studying SOM network cluster analysis and classification regression machine learning algorithms, a multi-angle user load characteristic profile is constructed to determine the key characteristic quantities for model evaluation; Step 2.2: Based on digital twin mixed reality technology, the physical characteristic model of the actual low-voltage substation is abstracted to construct a digital simulation model of the low-voltage substation. This model can simulate different substation types, different load users, different substation topologies, and different substation fault types. Step 2.3 In the simulation environment, construct a load data generator consistent with the power consumption characteristics of the low-voltage substation to realize the digital acquisition of voltage, current, active power, reactive power, and power factor of all metering points under the substation. Simulate different low-voltage substation topologies, simulate different low-voltage substation fault types, and simulate different load users in different low-voltage substations to provide multi-dimensional training data for the evaluation and verification of the results of the electric energy meter information evaluation and calibration model.

3. The electric energy meter information evaluation and calibration model evaluation and verification system according to claim 1 is characterized in that: The load data generator based on digital twin mixed reality technology is a load data generator that is consistent with the physical device.

4. A method for evaluating and verifying an electric energy meter information-based evaluation and calibration model, characterized in that: The steps include: Step 1: Provide data input using the measurement uncertainty assessment and control method of the electric energy meter information evaluation and calibration model; Step 2: Generate verification data using a load data generator based on digital twin mixed reality technology to determine key indicators for simulating complex working conditions and multi-factor evaluation verification; Step 3: Establish a digital simulation platform using the research on the evaluation and verification method of the electric energy meter informatization evaluation and calibration model results and the evaluation and verification platform to construct the evaluation and verification platform for the electric energy meter informatization evaluation and calibration model; The step 1 includes the following: Step 1.1 Statistically analyze the possible sources of uncertainty during the physical model establishment and data model solution, including substation total meter error, low-voltage current transformer error, energy meter truncation error, current and voltage loop impedance, voltage fluctuation, resistance temperature coefficient, sampling time synchronization, substation line loss and power load characteristics, electromagnetic environment, and temperature and humidity. Analyze and clarify the uncertainty sources, uncertainty components, and correlation coefficients between the uncertainty components. Step 1.2: Use grey system theory, information entropy theory, Bayesian theory, Monte Carlo method, and neural network theory to establish a mathematical model for uncertainty and a calculation and evaluation method for the combined standard uncertainty and expanded uncertainty suitable for online evaluation of electric energy meters. Step 1.3: Using the target expanded uncertainty as the optimization objective, combine the fuzzy theory collection and non-convex optimization techniques to redistribute the standard uncertainty components to support the continuous optimization of the energy conservation physical model and data solution model for the hierarchical and graded substations. The step 3 includes the following: Step 3.1: Based on the information-based evaluation and calibration model of the energy meter and the research results of uncertainty analysis, various indicators and their characteristics, including mean absolute error, mean square error, and root mean square error, are studied, and appropriate evaluation and verification indicators are designed. Based on this, a comprehensive evaluation and verification method, including surface verification, Turing test, t-test, confidence interval method, and Bayesian hypothesis test, is designed to suit the evaluation and calibration model. Step 3.2: Based on the evaluation index design and result evaluation and verification methods, and in combination with the load data generator, a platform for evaluating and verifying the information-based evaluation and calibration model of electric energy meters is constructed, featuring on-demand reconfiguration, flexible node expansion, load adjustment and control, real-time flow calculation, dynamic fault simulation, bidirectional virtual-real linkage, and accurate and visual status. Step 3.3 Using the data provided by the load data generator of the digital twin mixed reality technology, based on the evaluation and verification platform of the electric energy meter informationization evaluation and calibration model, the results of the electric energy meter evaluation and calibration model are evaluated according to the research evaluation and verification method, and the calibration model results and uncertainty analysis results are verified on the actual physical nodes of the evaluation and verification platform of the electric energy meter informationization evaluation and calibration model.

5. The method for evaluating and verifying an electric energy meter information-based evaluation and calibration model according to claim 4, characterized in that: The step 2 includes the following: Step 2.1 Based on the actual metering device configuration and operation status of the substation, the relationship between user social attributes, appliance loading information, operating environment factors, and load curves is studied. By studying SOM network cluster analysis and classification regression machine learning algorithms, a multi-angle user load characteristic profile is constructed to determine the key characteristic quantities for model evaluation; Step 2.2: Based on digital twin mixed reality technology, the physical characteristic model of the actual low-voltage substation is abstracted to construct a digital simulation model of the low-voltage substation. This model can simulate different substation types, different load users, different substation topologies, and different substation fault types. Step 2.3 In the simulation environment, construct a load data generator consistent with the power consumption characteristics of the low-voltage substation to realize the digital acquisition of voltage, current, active power, reactive power, and power factor of all metering points under the substation. Simulate different low-voltage substation topologies, simulate different low-voltage substation fault types, and simulate different load users in different low-voltage substations to provide multi-dimensional training data for the evaluation and verification of the results of the electric energy meter information evaluation and calibration model.

6. The method for evaluating and verifying an electric energy meter information-based evaluation and calibration model according to claim 4, characterized in that: The load data generator based on digital twin mixed reality technology is a load data generator that is consistent with the physical device.

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