Method and device for analyzing metering error of electric energy meter

Abstract

The invention relates to the technical field of electric energy metering, in particular to an electric energy meter metering error analysis method and device. The method comprises the steps of obtaining feature data of a current electric energy meter; wherein the characteristic data comprises power grid data, load data, environment data and operation data; determining a multi-dimensional feature matrix based on the power grid data, the load data and the environment data; inputting the multi-dimensional feature matrix into a preset neural network model to obtain a first metering error; determining a second metering error based on the operating data; a third metering error is determined based on the first metering error and the second metering error. Therefore, the accuracy of calculating the metering error of the electric energy meter can be improved.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, and in particular to a method and apparatus for analyzing electricity meter metering errors. Background Technology

[0002] As a power system device ensuring "fair trade settlement and energy settlement," the accuracy of electricity metering directly impacts the economic interests of power supply companies and users, while also affecting the accuracy of key operations such as grid line loss analysis and renewable energy consumption monitoring. With the power system's transformation towards "high renewable energy penetration, diversified loads, and intelligent grids," traditional electricity metering error control technologies are no longer adequate for the complex and ever-changing field operating environment.

[0003] Currently, electricity metering devices have evolved from early induction-type mechanical electricity meters and traditional electronic electricity meters to smart electricity meters that integrate metering, communication, and edge computing functions. However, the metering error of smart electricity meters is affected by a combination of multiple factors. The error data obtained by existing technologies through standard meter comparison can only reflect the basic metering capability of the meter under ideal operating conditions, making it impossible to accurately calculate the meter's measurement.

[0004] Based on this, the present invention proposes a method and apparatus for analyzing metering errors in electricity meters to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention describes a method and apparatus for analyzing electricity meter metering errors, which can improve the accuracy of calculating electricity meter metering errors.

[0006] According to a first aspect, the present invention provides a method for analyzing metering errors in an electricity meter, the method comprising: Obtain the characteristic data of the current electricity meter; wherein, the characteristic data includes grid data, load data, environmental data, and operational data; Based on the power grid data, the load data, and the environmental data, a multi-dimensional feature matrix is ​​determined. The multi-dimensional feature matrix is ​​input into a preset neural network model to obtain the first measurement error; Based on the aforementioned operational data, a second measurement error is determined; Based on the first measurement error and the second measurement error, a third measurement error is determined.

[0007] According to a second aspect, the present invention provides an energy meter metering error analysis device, comprising: The acquisition unit is configured to acquire the characteristic data of the current electricity meter; wherein the characteristic data includes grid data, load data, environmental data, and operational data. The first data processing unit is configured to determine a multi-dimensional feature matrix based on the power grid data, the load data, and the environmental data. The second data processing unit is configured to input the multi-dimensional feature matrix into a preset neural network model to obtain the first measurement error; The third data processing unit is configured to determine the second measurement error based on the operating data; The fourth data processing unit is configured to determine a third measurement error based on the first measurement error and the second measurement error.

[0008] Thirdly, embodiments of this specification also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0009] Fourthly, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0010] According to the method and apparatus for analyzing electricity meter metering errors provided by the present invention, characteristic data of the current electricity meter are acquired. This characteristic data includes grid data, load data, environmental data, and operational data. Based on the grid data, load data, and environmental data, a multi-dimensional feature matrix is ​​determined. The multi-dimensional feature matrix is ​​input into a preset neural network model to obtain a first metering error. The preset neural network model utilizes its strong fitting ability for nonlinear relationships to output a first metering error that reflects the comprehensive impact of complex on-site environments (such as new energy access, load fluctuations, etc.), overcoming the limitation that traditional standard meter comparison methods are only applicable to ideal operating conditions. Simultaneously, a second metering error is independently calculated based on operational data. The second metering error characterizes the metering deviation caused by changes in the physical characteristics of the equipment itself (such as temperature drift, component aging). Then, based on the first and second metering errors, a third metering error is determined, and the final calculation result is obtained. Thus, the present invention can improve the accuracy of calculating electricity meter metering errors. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1A flowchart illustrating a method for analyzing metering errors according to one embodiment is shown. Figure 2 A schematic block diagram of an energy meter metering error analysis device according to one embodiment is shown. Detailed Implementation

[0013] The solution provided by the present invention will now be described with reference to the accompanying drawings.

[0014] Figure 1 This diagram illustrates a flow chart of a method for analyzing metering errors according to one embodiment. It is understood that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities. Figure 1 As shown, the method includes: Step 100: Obtain the characteristic data of the current electricity meter; wherein, the characteristic data includes grid data, load data, environmental data, and operation data; Step 102: Determine a multi-dimensional feature matrix based on power grid data, load data, and environmental data; Step 104: Input the multi-dimensional feature matrix into the preset neural network model to obtain the first measurement error; Step 106: Determine the second measurement error based on the operational data; Step 108: Determine the third measurement error based on the first measurement error and the second measurement error.

[0015] In this embodiment, the characteristic data of the current electricity meter is acquired; wherein, the characteristic data includes grid data, load data, environmental data, and operational data; based on the grid data, load data, and environmental data, a multi-dimensional feature matrix is ​​determined; the multi-dimensional feature matrix is ​​input into a preset neural network model to obtain the first metering error; the preset neural network model utilizes the model's strong fitting ability for nonlinear relationships to output the first metering error, which reflects the comprehensive impact of complex on-site environments (such as new energy access, load fluctuations, etc.), thus overcoming the limitation that the traditional standard meter comparison method is only applicable to ideal operating conditions. Simultaneously, a second metering error is independently calculated based on the operational data; the second metering error is used to characterize the metering deviation caused by changes in the physical characteristics of the equipment itself (such as temperature drift, component aging), and then, based on the first and second metering errors, a third metering error is determined, which is the final calculation result. Thus, this invention can improve the accuracy of calculating the metering error of the electricity meter.

[0016] In this embodiment, a multi-dimensional feature matrix is ​​input into a preset neural network model to obtain the first measurement error, fully utilizing the neural network's ability to fit nonlinear relationships. This preset model requires training with a large amount of historical data (such as the correspondence between feature data and actual errors under different operating conditions) and can learn the error patterns under the coupled effects of multiple factors such as power grid fluctuations, load changes, and environmental interference. Therefore, it can effectively reflect the actual measurement deviation of the electricity meter in complex field environments.

[0017] In one embodiment of the present invention, the power grid data includes voltage, voltage deviation rate, frequency, and subharmonic content; the load data includes current, load factor, and power factor; the environmental data includes ambient temperature and relative humidity; and the operating data includes the actual value of the sampling resistor, the actual turns ratio of the current transformer, the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual power grid voltage.

[0018] In this embodiment, the specific parameter selection for grid data, load data, environmental data, and operational data determines the influencing factors of the electricity meter's metering principle. Among these, voltage deviation rate and frequency reflect the impact of grid stability on the metering benchmark; subharmonic content relates to metering accuracy under nonlinear loads; load factor and power factor affect the operating point of metering components; ambient temperature and humidity directly cause component parameter drift; and operational data from sampling resistors, transformers, and ADC chips are the main sources of error in the current and voltage measurement links. The selection of these parameters ensures the comprehensiveness of the characteristic data.

[0019] In one embodiment of the present invention, determining a second measurement error based on operational data includes: The first error factor is determined based on the actual value of the sampling resistor and the actual ratio of the current transformer. The second error influence factor is determined based on the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual grid voltage. The second measurement error is determined based on the first error influence factor and the second error influence factor.

[0020] In this embodiment, firstly, based on the actual value of the sampling resistor and the actual transformation ratio of the current transformer, a first error influence factor is calculated. Then, combined with the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual grid voltage, a second error influence factor is determined. Finally, by integrating the first and second error influence factors, a second measurement error is obtained. The second measurement error is used to characterize the measurement deviation caused by changes in the physical characteristics of the equipment itself (such as temperature drift and device aging).

[0021] In one embodiment of the present invention, the second measurement error is determined by the following formula:

[0022] In the formula, The first factor affecting error, This is the actual value of the sampling resistor. This is the nominal value of the sampling resistor. The temperature coefficient of the sampling resistor, This represents the deviation between the actual temperature and the nominal temperature. This represents the actual turns ratio of the current transformer. This refers to the nominal turns ratio of the current transformer. For the preset nonlinear error of the current transformer, This is the second error factor. This is the actual value of the voltage divider resistor. This is the nominal value of the voltage divider resistor. The temperature coefficient of the voltage divider resistor. For the quantization error of the ADC chip, For the zero drift error of the ADC chip, This is the actual measured grid voltage. This refers to the gain error of the ADC chip. This is the second error factor. This is the second measurement error. This is an inherent error.

[0023] In this embodiment, the first error influence factor is used to reflect the error of the current loop, and the second error influence factor is used to reflect the error of the voltage loop. The creativity of this formula lies in both using the square root of the sum of squares to reflect the orthogonality of the current and voltage loop errors, and incorporating inherent errors, which improves the accuracy of calculating the second measurement error. Among them, the temperature coefficient of the sampling resistor (e.g., ±25ppm / ℃, that is, the resistance changes by 0.000025 for every 1℃ change in temperature), the deviation between the actual temperature and the nominal temperature. For example, if the nominal temperature is designed to be 25℃ and the actual temperature is 50℃, then the deviation between the actual temperature and the nominal temperature = 25℃, the zero drift error of the ADC chip (e.g., ±2mV, a fixed deviation that exists regardless of the input voltage), the gain error of the ADC chip (e.g., ±0.05%, the proportional deviation during the input signal amplification process), and the inherent error, that is, the fixed error in the above equation, can be obtained by linear fitting through historical data.

[0024] In one embodiment of the present invention, determining a third measurement error based on a first measurement error and a second measurement error includes: The third measurement error is calculated by multiplying the first measurement error by the first weight and the second measurement error by the second weight. The sum of the first weight and the second weight is one. The first weight is used to characterize the confidence level of the first measurement error, and the second weight is used to characterize the confidence level of the second measurement error.

[0025] In this embodiment, the first weight and the second weight are adaptively adjusted based on the confidence levels of the two error calculation methods. When the variance of the first measurement error is small (i.e., the model prediction is more stable), the first weight increases; conversely, if the variance of the second measurement error is smaller (i.e., the hardware parameter calculation is more reliable), the second weight increases. This weighting method based on the statistical characteristics of the data avoids the subjectivity of fixed weights, allowing the third measurement error to preferentially adopt more reliable error sources, thus improving the accuracy of the calculation result (the third measurement error).

[0026] In one embodiment of the present invention, the first weight is determined by the following formula:

[0027] In the formula, As the first weight, The first measurement variance, For the first The difference between the model prediction error and the actual error in the first measurement error sample. This is the average of all deviations in the first measurement error sample. The number of samples for the first measurement error. The second measurement variance, The number of samples for the second measurement error. This is the average of all deviations in the second measurement error sample. This is the average of all deviations for the second sample.

[0028] In this embodiment, the innovative aspect of these two measurement error variance formulas is that they use the measurement values ​​of high-level standard meters as a reference. First, the actual error of the meter under analysis is calculated, and then this is used as a benchmark to calculate the deviation. This ensures that the variance is no longer a meaningless number, but rather reflects the deviation and fluctuation between the error analysis method and the actual situation, meeting the professional needs of metrology calibration. For the first error of the neural network model and the second error of the hardware formula, the variance is calculated using completely consistent logic, providing a quantitative comparison index for the stability of the two types of errors, allowing for the determination of which method is more reliable. During small sample testing, an unbiased estimate of "dividing by n-1" is used to avoid underestimating error fluctuations, closely reflecting the actual situation of limited samples on-site, and improving the accuracy of the calculation results (third measurement error).

[0029] In one embodiment of the present invention, before determining the third measurement error based on the first measurement error and the second measurement error, the method further includes: When the absolute value of the difference between the first measurement error and the second measurement error is greater than the first preset value, the step "obtain the characteristic data of the current electricity meter" is executed again until the number of cycles reaches the second preset value.

[0030] In this embodiment, re-collecting data when the difference between the first measurement error and the second measurement error exceeds a first preset value is an anomaly verification procedure. The purpose of this procedure is to eliminate abnormal data caused by accidental factors (such as instantaneous power grid fluctuations, temporary sensor malfunctions, etc.) and ensure the accuracy of the input feature data. Setting a second preset value to limit the number of loops is to avoid getting stuck in an infinite loop, while obtaining reliable data through a limited number of retries, ultimately improving the accuracy and timeliness of calculating the third measurement error.

[0031] In one embodiment of the present invention, when the third measurement error is greater than the third preset value, the correction coefficient of the first weight is obtained, the correction coefficient is multiplied by the first weight to obtain the correction weight, and the first weight in the step "multiply the first measurement error by the first weight and the second measurement error by the second weight to calculate the third measurement error" is replaced by the correction weight to obtain the fourth measurement error.

[0032] In this embodiment, when the third measurement error exceeds a third preset value, it means that the current fusion result has exceeded the acceptable error range, and a weight correction mechanism needs to be activated to optimize the result. Specifically, the correction coefficient of the first weight is obtained, which dynamically reflects the actual reliability of the first measurement error in the current scenario; then, the correction coefficient is multiplied by the original first weight to obtain the correction weight, thereby adjusting the proportion of the first measurement error in the fusion calculation; finally, the correction weight replaces the original first weight, and the calculation of "first measurement error multiplied by correction weight plus second measurement error multiplied by second weight" is re-executed to obtain the fourth measurement error. In this way, the problem of excessive error caused by unreasonable initial weights under extreme working conditions is effectively avoided, further improving the reliability of measurement error calculation.

[0033] In one embodiment of the present invention, the correction weight is determined by the following formula:

[0034] In the formula, To adjust the weights, For the weighting factor of the harmonic scene, For the harmonic cumulative integral term, For the weighting factors of the load scenario, The load rate at the current moment. For the temperature scenario, This is the cumulative integral term for temperature offset. for Harmonic content at any given time For harmonic memory constants, For ambient temperature, For a historic moment, For the current moment, It is an exponential decay factor. The temperature decay factor, These are nonlinear coefficients. This is the amplitude limiting function.

[0035] In this embodiment, the innovation of the above formula is threefold: First, it dynamically integrates multiple scenarios, breaking through the limitations of the existing technology's "single-factor static weighting." It integrates three types of electricity meter operating parameters—harmonics, load factor, and temperature—making the weight allocation no longer singular and capable of responding to complex scenarios involving multiple coupled factors in the power grid. Second, it addresses the cumulative effect. The harmonic term, through integration and exponential decay factors, reflects the characteristic that "high harmonics have a stronger impact in the near term and their impact diminishes in the long term." The temperature term, through integration, nonlinear coefficients, and decay factors, accurately captures the physical law of "slow temperature drift and hysteresis memory," solving the problem of insufficient description of "long-term cumulative error sources" in traditional methods. Third, it enhances engineering practicality. The load factor, as a real-time term, quickly responds to instantaneous load changes. The influence degree of each scenario is flexibly adjusted through weight factors, and the limiting function ensures that the weights are within a reasonable range, avoiding weight overflow under extreme operating conditions that could lead to inaccurate error analysis, ultimately improving the accuracy of calculating the third metering error.

[0036] In one embodiment of the present invention, when the number of cycles reaches a second preset value, the third measurement error for each cycle is counted, and the third measurement error for each cycle is averaged to obtain the final result.

[0037] In this embodiment, traditional electricity meter error analysis often relies on single calculations, which are easily affected by random factors (such as instantaneous grid fluctuations and temporary device noise), leading to result deviations. After the number of cycles reaches a preset value, the third metering error obtained from multiple calculations is averaged, which can effectively reduce the impact of random errors: multiple cycles can include a wider range of operating condition combinations, and statistical averaging can allow accidental deviations to cancel each other out, resulting in a final result that is closer to the long-term stable level of the true error, ultimately improving the accuracy of calculating the third metering error.

[0038] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0039] According to another embodiment, the present invention provides an energy meter metering error analysis device. Figure 2A schematic block diagram of an electricity meter metering error analysis device according to one embodiment is shown. It will be understood that this device can be implemented by any apparatus, device, platform, or cluster of devices with computing and processing capabilities. Figure 2 As shown, the device includes: an acquisition unit 200, a first data processing unit 202, a second data processing unit 204, a third data processing unit 206, and a fourth data processing unit 208. The main functions of each component are as follows: The acquisition unit 200 is configured to acquire the characteristic data of the current electricity meter; wherein, the characteristic data includes grid data, load data, environmental data, and operating data; The first data processing unit 202 is configured to determine a multi-dimensional feature matrix based on the power grid data, the load data, and the environmental data. The second data processing unit 204 is configured to input the multi-dimensional feature matrix into a preset neural network model to obtain the first measurement error; The third data processing unit 206 is configured to determine the second measurement error based on the operating data; The fourth data processing unit 208 is configured to determine a third measurement error based on the first measurement error and the second measurement error.

[0040] In one embodiment of the present invention, the power grid data includes voltage, voltage deviation rate, frequency, and subharmonic content; the load data includes current, load factor, and power factor; the environmental data includes ambient temperature and relative humidity; and the operating data includes the actual value of the sampling resistor, the actual turns ratio of the current transformer, the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual power grid voltage.

[0041] In one embodiment of the present invention, the third data processing unit 206 is configured to perform the following operations: Based on the actual value of the sampling resistor and the actual transformation ratio of the current transformer, the first error influence factor is determined. Based on the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual grid voltage, a second error influence factor is determined. The second measurement error is determined based on the first error influence factor and the second error influence factor.

[0042] In one embodiment of the present invention, the second measurement error is determined by the following formula:

[0043] In the formula, This is the first error influence factor. This is the actual value of the sampling resistor. This is the nominal value of the sampling resistor. The temperature coefficient of the sampling resistor, This represents the deviation between the actual temperature and the nominal temperature. This refers to the actual turns ratio of the current transformer. This refers to the nominal turns ratio of the current transformer. For the preset nonlinear error of the current transformer, This is the second error influence factor. This is the actual value of the voltage divider resistor. This is the nominal value of the voltage divider resistor. The temperature coefficient of the voltage divider resistor. For the quantization error of the ADC chip, For the zero drift error of the ADC chip, This is the actual measured grid voltage. This refers to the gain error of the ADC chip. This is the second error influence factor. This is the second measurement error. This is an inherent error.

[0044] In one embodiment of the present invention, the fourth data processing unit 208 is configured to perform the following operations: The third measurement error is obtained by multiplying the first measurement error by the first weight and the second measurement error by the second weight; wherein the sum of the first weight and the second weight is one, the first weight is used to characterize the confidence level of the first measurement error, and the second weight is used to characterize the confidence level of the second measurement error.

[0045] In one embodiment of the present invention, the first weight is determined by the following formula:

[0046] In the formula, For the first weight, The first measurement variance, For the first The difference between the model prediction error and the actual error in the first measurement error sample. This is the average of all deviations in the first measurement error sample. The number of samples for the first measurement error. The second measurement variance, The number of samples for the second measurement error. This is the average of all deviations in the second measurement error sample. This is the average of all deviations for the second sample.

[0047] In one embodiment of the present invention, the apparatus further includes a fifth data processing unit, the fifth data processing unit being configured to perform the following operations: When the absolute value of the difference between the first metering error and the second metering error is greater than the first preset value, the step "obtain the characteristic data of the current electricity meter" is executed again until the number of cycles reaches the second preset value.

[0048] In one embodiment of the present invention, the correction weight is determined by the following formula:

[0049] In the formula, To adjust the weights, For the weighting factor of the harmonic scene, For the harmonic cumulative integral term, For the weighting factors of the load scenario, The load rate at the current moment. For the temperature scenario, This is the cumulative integral term for temperature offset. for Harmonic content at any given time For harmonic memory constants, For ambient temperature, For a historic moment, For the current moment, It is an exponential decay factor. The temperature decay factor, These are nonlinear coefficients. This is the amplitude limiting function.

[0050] In one embodiment of the present invention, the apparatus further includes a sixth data processing unit, the sixth data processing unit being configured to perform the following operations: When the number of cycles reaches the second preset value, the third measurement error for each cycle is calculated, and the third measurement error for each cycle is averaged to obtain the final result.

[0051] In one embodiment of the present invention, the apparatus further includes a seventh data processing unit, the seventh data processing unit being configured to perform the following operations: When the third measurement error is greater than the third preset value, obtain the correction coefficient of the first weight, multiply the correction coefficient by the first weight to obtain the correction weight, and replace the first weight in the step "multiply the first measurement error by the first weight and add the second measurement error by the second weight to calculate the third measurement error" with the correction weight to obtain the fourth measurement error.

[0052] According to another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform a combination Figure 1 The method described.

[0053] According to another embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements a combination... Figure 1 The method described.

[0054] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0055] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0056] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing metering errors in electricity meters, characterized in that, include: Obtain the characteristic data of the current electricity meter; wherein, the characteristic data includes grid data, load data, environmental data, and operational data; Based on the power grid data, the load data, and the environmental data, a multi-dimensional feature matrix is ​​determined. The multi-dimensional feature matrix is ​​input into a preset neural network model to obtain the first measurement error; Based on the aforementioned operational data, a second measurement error is determined; Based on the first measurement error and the second measurement error, a third measurement error is determined.

2. The method according to claim 1, characterized in that, The power grid data includes voltage, voltage deviation rate, frequency, and subharmonic content; the load data includes current, load factor, and power factor; the environmental data includes ambient temperature and relative humidity; and the operating data includes the actual value of the sampling resistor, the actual ratio of the current transformer, the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual power grid voltage.

3. The method according to claim 2, characterized in that, The determination of the second measurement error based on the operational data includes: Based on the actual value of the sampling resistor and the actual transformation ratio of the current transformer, the first error influence factor is determined. Based on the actual value of the voltage divider resistor, the quantization error of the ADC chip, and the actual grid voltage, a second error influence factor is determined. The second measurement error is determined based on the first error influence factor and the second error influence factor.

4. The method according to claim 3, characterized in that, The second measurement error is determined by the following formula: In the formula, This is the first error influence factor. This is the actual value of the sampling resistor. This is the nominal value of the sampling resistor. The temperature coefficient of the sampling resistor, This represents the deviation between the actual temperature and the nominal temperature. This refers to the actual turns ratio of the current transformer. This refers to the nominal turns ratio of the current transformer. For the preset nonlinear error of the current transformer, This is the second error influence factor. This is the actual value of the voltage divider resistor. This is the nominal value of the voltage divider resistor. The temperature coefficient of the voltage divider resistor. For the quantization error of the ADC chip, For the zero drift error of the ADC chip, This is the actual measured grid voltage. This refers to the gain error of the ADC chip. This is the second error influence factor. This is the second measurement error. This is an inherent error.

5. The method according to claim 1, characterized in that, The determination of the third measurement error based on the first measurement error and the second measurement error includes: The third measurement error is obtained by multiplying the first measurement error by the first weight and the second measurement error by the second weight; wherein the sum of the first weight and the second weight is one, the first weight is used to characterize the confidence level of the first measurement error, and the second weight is used to characterize the confidence level of the second measurement error.

6. The method according to claim 5, characterized in that, The first weight is determined by the following formula: In the formula, For the first weight, The first measurement variance, For the first The difference between the model prediction error and the actual error in the first measurement error sample. This is the average of all deviations in the first measurement error sample. The number of samples for the first measurement error. The second measurement variance, The number of samples for the second measurement error. This is the average of all deviations in the second measurement error sample. This is the average of all deviations for the second sample.

7. The method according to claim 1, characterized in that, Before determining the third measurement error based on the first measurement error and the second measurement error, the method further includes: When the absolute value of the difference between the first metering error and the second metering error is greater than the first preset value, the step "obtain the characteristic data of the current electricity meter" is executed again until the number of cycles reaches the second preset value.

8. A device for analyzing metering errors in electricity meters, characterized in that, include: The acquisition unit is configured to acquire the characteristic data of the current electricity meter; wherein the characteristic data includes grid data, load data, environmental data, and operational data. The first data processing unit is configured to determine a multi-dimensional feature matrix based on the power grid data, the load data, and the environmental data. The second data processing unit is configured to input the multi-dimensional feature matrix into a preset neural network model to obtain the first measurement error; The third data processing unit is configured to determine the second measurement error based on the operating data; The fourth data processing unit is configured to determine a third measurement error based on the first measurement error and the second measurement error.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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