Remote detection method and system of electric energy meter

Abstract

The invention relates to the field of data processing, in particular to a remote detection method and system for an electric energy meter, and the method comprises the steps: obtaining the operation data of the electric energy meter, carrying out the window division of the operation data according to a time sequence through a variational mode decomposition algorithm, obtaining a comprehensive frequency distribution curve of the operation data in a window, and generating a time-frequency diagram; the method comprises the steps of performing peak point detection based on a time-frequency diagram, determining a main frequency peak point and an interference peak point, calculating a harmonic interference degree value of the frequency of electric energy meter data based on distribution characteristics between the main frequency peak point and the interference peak point, and using change consistency and frequency amplitude of the interference degree value to calculate an abnormal degree value of an intelligent voltmeter. And the abnormal condition of the electric energy meter is judged. According to the method, the change consistency of data of different dimensions is calculated, so that single-dimension noise interference is suppressed, and the reliability of abnormal criteria is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing. In particular, it relates to a remote detection method and system for an electric energy meter. Background Art

[0002] A low-voltage metering communication device is a device used in the low-voltage part of a power system, mainly for collecting, transmitting, and processing data related to power metering. It usually combines metering and communication functions and is an important part of smart grid and Internet of Things technologies in the power system. For example, a smart electric energy meter can monitor the operating status of the device in real time and detect abnormal conditions.

[0003] The existing Chinese patent application document with the publication number CN117574192A discloses a method, system, device, and storage medium for detecting the power metering error of an electric energy meter. By obtaining the topological connection of the substation area; representing the power lines and user loads in the topological connection with lumped parameters to obtain the equivalent circuit of the substation area; based on the equivalent circuit, establishing a power balance model of the substation area according to the law of conservation of energy; collecting the electric energy meter data within multiple metering periods; using the collected electric energy meter data and the power balance model to establish a full-rank linear equation system to obtain a remote error detection model for the electric energy meter; processing the coefficient matrix of the remote error detection model for the electric energy meter to obtain a power metering correction coefficient; obtaining the relationship model between the power metering error and the power metering correction coefficient, and calculating the error detection result according to the power metering correction coefficient and the relationship model.

[0004] This application document improves the accuracy and reliability of power metering by establishing an equivalent circuit model and a power balance model of the substation area and calculating the error using the collected data and correction coefficient. Currently, when calibrating an electric energy meter, the influence of harmonic interference on metering accuracy needs to be considered. However, traditional harmonic detection methods are mainly based on the total harmonic distortion (THD). Although they can provide overall information on harmonic content, it is difficult to distinguish instantaneous interference and continuous anomalies, and there is a lack of quantitative analysis of frequency distribution similarity, resulting in insufficient detection accuracy and inability to meet the requirements of high-precision detection of metering devices in the smart grid and Internet of Things environment. Summary of the Invention

[0005] To solve the problem that harmonic interference makes it difficult for low-voltage metering devices to accurately distinguish instantaneous interference from continuous anomalies and lack quantitative analysis of frequency distribution similarity, and thus cannot meet the requirements of high-precision detection, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a remote detection method for an electric energy meter includes: obtaining the operation data of the electric energy meter, where the operation data includes voltage data and current data; using the variational mode decomposition algorithm to divide the operation data into windows according to the time series, obtaining the comprehensive frequency distribution curve of the operation data within the window, and generating a time-frequency diagram reflecting the change of the signal frequency over time based on the comprehensive frequency distribution curve; performing peak point detection based on the time-frequency diagram, determining the main frequency peak point and the interference peak point, and calculating the interference degree value of the frequency of the electric energy meter data affected by harmonics according to the distribution characteristics between the main frequency peak point and the interference peak point, where the distribution characteristics include relative interference degree and deviation degree; calculating the abnormality degree value of the intelligent voltmeter according to the change consistency and frequency amplitude of the interference degree value, and judging the abnormality situation of the electric energy meter according to the abnormality degree value.

[0007] The effect is that: through the variational mode decomposition (VMD) algorithm, the voltage and current data collected by the electric energy meter are accurately analyzed, and the harmonic components in the signal are effectively separated. By constructing a time-frequency diagram, the change of the signal frequency over time is monitored in real time, the main frequency peak point and the interference peak point are accurately identified, and the relative interference degree and deviation degree of the harmonics are calculated, which is beneficial to providing an analysis basis for power quality analysis and abnormality detection. By analyzing the change consistency and frequency amplitude of the interference degree value, the abnormality degree value is calculated, and a threshold is set to judge the abnormal state of the electric energy meter, so as to optimize the maintenance plan, reduce the maintenance cost, improve the stability of the power system and user satisfaction. It not only meets the requirement of high-precision detection of the metering device, significantly improves the accuracy and reliability of electric energy metering, but also provides important technical support for the stable operation of the power system and power quality control.

[0008] Preferably, obtaining the comprehensive frequency distribution curve includes: Dividing the window for each operation data at a preset time interval, dividing it into multiple segments of data, using the variational mode decomposition method to decompose the operation data within each window into multiple IMF components, performing frequency analysis on each IMF component to obtain the frequency distribution curve of the IMF component, and superimposing the frequency distribution curves of all IMF components within the same window to obtain the comprehensive frequency distribution curve of each window.

[0009] The effect is that: through variational mode decomposition, different frequency components in the signal can be captured more finely. Compared with the traditional Fourier transform, variational mode decomposition provides an adaptive time-frequency analysis method without manual setting, which enables the algorithm to adapt to the complexity of the signal and effectively separate each frequency component in the signal, including the fundamental frequency and each harmonic.

[0010] Preferably, obtaining the time-frequency diagram includes: Taking the comprehensive frequency distribution curve of each window as a time node, with the horizontal axis being the corresponding time points within the window and the vertical axis being the frequency distribution at different time points, a time-frequency diagram is constructed.

[0011] Its effect is that by analyzing the time-frequency diagram, the existence of harmonic interference and its evolution over time can be clearly identified, thereby evaluating the impact of harmonics on power quality, helping to detect abnormal conditions during the operation of the electricity meter, such as instantaneous interference or continuous anomalies, and providing a data basis for fault diagnosis and maintenance.

[0012] Preferably, obtaining the interference degree value includes: Taking any moment on the time-frequency diagram as the target moment, performing peak detection on the frequency curve of the window corresponding to the target moment to obtain a peak point sequence, taking the peak point with the largest amplitude in the peak point sequence as the main frequency peak point, taking the peak points other than the main frequency peak point as interference peak points, calculating the absolute difference in amplitude between each interference peak point and the main frequency peak point, and taking the ratio between the absolute difference and the main frequency peak point as the relative interference degree; Using a negative exponential function to perform exponential decay on the relative interference degree and then summing it up to obtain the interference degree value of each electricity meter affected by harmonics at the target moment.

[0013] Its effect is that by performing peak detection on the electricity meter data on the time-frequency diagram, distinguishing the main frequency peak point and the interference peak points, calculating the relative interference degree between them, and then using a negative exponential function to perform attenuation processing on the interference degree and summing it up, thereby obtaining the interference degree value of the electricity meter affected by harmonics at a specific moment, which can effectively quantify the impact of harmonic interference, improve the accuracy and reliability of electricity metering, and enhance the accuracy of power quality monitoring.

[0014] Preferably, obtaining the interference degree value further includes: Taking any moment on the time-frequency diagram as the target moment, performing peak detection on the frequency curve of the window corresponding to the target moment to obtain a peak point sequence, taking the peak point with the largest amplitude in the peak point sequence as the main frequency peak point, taking the peak points other than the main frequency peak point as interference peak points, calculating the absolute difference in amplitude between each interference peak point and the main frequency peak point, and taking the ratio between the absolute difference and the main frequency peak point as the relative interference degree; calculating the divergence value between each interference peak point and the Gaussian function distribution of the local frequency curve corresponding to the main frequency peak point, and using the difference between the divergence value and the relative interference degree, using a negative exponential function to perform exponential decay on the difference and then summing it up to obtain the interference degree value of each electricity meter affected by harmonics at the target moment.

[0015] Preferably, the calculation of the divergence value includes: Gaussian fitting is performed on the local frequency curve where the main frequency peak point is located and the local frequency curve where the interference peak point is located respectively, and the local frequency distribution parameters of the main frequency peak point and the interference peak point are obtained respectively. Based on the local frequency distribution parameters, the divergence value between the two Gaussian distributions is calculated; Preferably, the abnormality degree value satisfies the following relational expression: ; In the formula, represents the abnormality degree value of the interference peak point at the th moment of the th dimensional data of the th electricity meter, represents the change consistency of the th electricity meter at the th dimensional data at the th moment, represents the change amount of the th electricity meter at the th dimensional data at the th moment.

[0016] Its effect is that by combining the change consistency and the change amount, the operation state of the electricity meter can be monitored and evaluated in real time, potential abnormalities or faults can be discovered and prevented in time, thereby improving the accuracy of electric energy metering and the reliability of the power system.

[0017] Preferably, the judging the abnormality condition of the electricity meter according to the abnormality degree value includes: In response to the abnormality degree value of any dimension being greater than or equal to the abnormality threshold, it is judged that the operation state of the electricity meter is abnormal. On the contrary, if the abnormality degree values of all dimensions are less than the abnormality threshold, it is judged that the operation state of the electricity meter is normal.

[0018] Preferably, the method further includes preprocessing the acquired operation data, where the preprocessing includes removing outliers, filling in missing data using the interpolation method, denoising the operation data, and performing normalization processing.

[0019] In a second aspect, a remote detection system for an electricity meter includes: a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned remote detection method for an electricity meter is implemented.

[0020] The present invention has the following effects: 1. The present invention accurately separates the harmonic components in the power meter data through the variational mode decomposition (VMD) algorithm. By analyzing the influence of harmonics on the main frequency, the interference degree value affected by harmonics is identified, thereby avoiding the influence of harmonics on the power meter reading, which may lead to inaccurate detection of the operating state. This not only improves the accuracy of power metering but also enhances the reliability of power data in the smart grid, meeting the requirements for high-precision power metering in the smart grid and Internet of Things environments.

[0021] 2. The present invention By using the method of quantifying the frequency distribution difference with the divergence value, the Gaussian characteristic difference between the main frequency and the interference frequency can be accurately measured, thereby improving the accuracy of harmonic interference detection. By analyzing the change consistency of data in different dimensions, the single-dimensional noise interference can be effectively suppressed, and the reliability of anomaly detection can be enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flowchart of the method from step S1 to step S4 in a remote detection method for a power meter according to an embodiment of the present invention.

[0023] Figure 2 is a block diagram of the structure of a remote detection system for a power meter according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0025] Referring to Figure 1 , a remote detection method for a power meter includes steps S1 to S4, which are specifically as follows: S1: Obtain the operating data of the power meter, where the operating data includes voltage data and current data.

[0026] Exemplarily, the operating data of multiple smart power meters are collected respectively, the collection frequency is 512 times per second, and the length of each collection is 10 seconds, so as to analyze the total operating data collected in the previous 10 seconds of the current collection time.

[0027] The method further includes preprocessing the obtained operating data, where the preprocessing includes removing outliers, filling in missing data using the interpolation method, denoising the operating data, and performing normalization processing (unifying the amplitude range of the signal), etc.

[0028] Use statistical methods (such as the 3σ rule) or machine learning methods to identify and remove these outliers. For missing data points, use interpolation methods for filling. Common interpolation methods include linear interpolation, nearest neighbor interpolation, or model-based predictive interpolation. Apply digital filters (such as low-pass filters, high-pass filters, or band-pass filters) to remove high-frequency noise in the data.

[0029] S2: Use the variational mode decomposition algorithm to divide the operating data into windows according to the time series, obtain the comprehensive frequency distribution curve of the operating data within the window, and generate a time-frequency diagram reflecting the change of the signal frequency over time based on the comprehensive frequency distribution curve.

[0030] Divide the window for each preset interval of the operating data, divide it into multiple segments of data, use the variational mode decomposition method to decompose the operating data within each window into multiple IMF components, perform frequency analysis on each IMF component to obtain the frequency distribution curve of the IMF component, and superimpose the frequency distribution curves of all IMF components within the same window to obtain the comprehensive frequency distribution curve of each window.

[0031] It should be noted that the preset interval is 0.5 seconds. Among them, the variational mode decomposition is used for the operating data within each window. And due to the high computational complexity and the involvement of optimization problems, the variational mode decomposition (VMD) is applicable to the situation where there are multiple frequency components in the signal and the frequencies of these components may change over time. It can accurately separate different frequency components in the signal, automatically determine the number of decomposition modes without manual setting, and is applicable to complex signals.

[0032] Taking the comprehensive frequency distribution curve of each window as the time node, with the horizontal axis being the corresponding time points within the window and the vertical axis being the frequency distribution at different time points, construct a time-frequency diagram. The time-frequency diagram can show the frequency components of the signal in different time periods.

[0033] In addition, the time-frequency diagram can also be calculated by using the short-time Fourier transform (STFT). The short-time Fourier transform divides the signal into windows of a fixed length, applies the FFT to the data of each window to obtain the frequency distribution, and then constructs a time-frequency diagram with the frequency distribution of each window as the time node. The short-time Fourier transform has a fast calculation speed, is suitable for real-time or near-real-time analysis, and is applicable to signals with relatively stable frequency components.

[0034] S3: Based on the time-frequency diagram, perform peak point detection to determine the main frequency peak point and interference peak point, and calculate the degree of interference of the frequency of the watt-hour meter data by harmonics according to the distribution characteristics between the main frequency peak point and the interference peak point. Among them, the distribution characteristics include: relative interference degree and deviation degree.

[0035] It should be noted that the main frequency peak point: represents the normal operating frequency of the power system and is the reference point for power quality analysis. The interference peak point: represents abnormal frequency components that may be caused by harmonics or other interference sources, and these components may cause errors in power metering. The relative interference degree: measures the influence of the interference peak relative to the main frequency peak. The deviation degree: represents the frequency deviation between the interference peak point and the main frequency peak point, reflecting the severity of harmonic interference. The deviation degree analysis uses the calculation method of the divergence value.

[0036] Obtaining the interference degree value includes: Taking any moment on the time-frequency diagram as the target moment, performing peak detection on the frequency curve of the corresponding window at the target moment to obtain a peak point sequence. Taking the peak point with the largest amplitude in the peak point sequence as the main frequency peak point, and taking the peak points other than the main frequency peak point as the interference peak points. Calculating the absolute difference in amplitude between each interference peak point and the main frequency peak point, and taking the ratio between the absolute difference and the main frequency peak point as the relative interference degree; Using the negative exponential function to sum after exponential decay of the relative interference degree to obtain the interference degree value of each watt-hour meter affected by harmonics at the target moment.

[0037] Specifically, the interference degree value satisfies the following relational expression: ; In the formula, represents the interference degree value of the th interference peak point of the rd dimensional data in the th watt-hour meter with respect to the main frequency peak point, represents the total number of interference peak points, represents the index of the interference peak point, represents the rd dimensional data in the th moment, the th absolute difference in amplitude between the interference peak point and the main frequency peak point, represents the value of the maximum peak point of the th dimensional data in the th moment, represents the exponential function with as the base.

[0038] That is to say, represents the th dimensional data in the th moment, the The relative interference degree between an interference peak point and the main frequency peak point. The smaller the relative interference degree, the smaller the interference. Since x represents a small response value, the influence on the main frequency is relatively low. However, if the interference frequency is closer to the main frequency, the influence on the main frequency is greater, and it is more likely to cause distortion of the main frequency.

[0039] By identifying and quantifying harmonic interference, the electricity metering can be more accurately evaluated and corrected, thereby improving the accuracy of electricity metering, contributing to the monitoring and analysis of the power quality of the power system, identifying potential harmonic problems, and providing important technical support for the stable operation of the smart grid and power quality control.

[0040] In addition, in another embodiment, it further includes: Taking any moment on the time-frequency diagram as the target moment, performing peak detection on the frequency curve of the window corresponding to the target moment to obtain a peak point sequence. Taking the peak point with the largest amplitude in the peak point sequence as the main frequency peak point, and taking the peak points other than the main frequency peak point as interference peak points. Calculating the absolute difference in amplitude between each interference peak point and the main frequency peak point, and taking the ratio between the absolute difference and the main frequency peak point as the relative interference degree; Calculating the divergence value between the Gaussian function distributions of the local frequency curves corresponding to each interference peak point and the main frequency peak point, and respectively mapping the divergence value and the relative interference degree using the negative exponential function, multiplying the mapped results as the comprehensive weight, and adding the comprehensive weights of all interference peak points as the interference degree value of each electricity meter affected by harmonics at the target moment.

[0041] Specifically, the interference degree value satisfies the following relational expression: ; In the formula, represents the interference degree value of the th interference peak point on the main frequency peak point in the th dimension data of the th electricity meter at the represents the total number of interference peak points, represents the index of the interference peak point, represents the th dimension data of the th smart electricity meter at the th moment, the th interference peak point and the divergence value between the Gaussian functions corresponding to the main frequency peak point, represents the th dimension data at the th moment, the The absolute difference in amplitude between an interference peak point and the main frequency peak point represents the value of the maximum peak point at the moment, represents an exponential function with as the base.

[0042] Furthermore, the widths of different frequencies are different (i.e., it can be regarded as the degree of dispersion or the range of frequency distribution), which means that the influence ranges of these frequencies are different. Then, for the local frequency curve between the interference peak point and the two peak points on the left and right at the moment, the mean variance is calculated for the local frequency curve to obtain the corresponding Gaussian function. Similarly, for the local frequency curve between the maximum peak point and the two peak points on the left and right, the Gaussian function corresponding to the maximum peak point is obtained. If the two Gaussian functions are more similar, then the divergence value is smaller, indicating a greater interference weight.

[0043] That is to say, reflects the similarity degree of the local frequency distribution curves corresponding to the two peak points. A smaller divergence value indicates a high similarity degree between the two frequency distributions. That is to say, the frequency distribution of the th interference peak point is close to the frequency distribution of the main frequency peak point. A larger divergence value indicates a greater difference between the two frequency distributions. By using the divergence value, the influence of harmonic interference on the main frequency can be measured.

[0044] Obtaining the divergence value includes the steps of: Performing Gaussian fitting on the local frequency curve where the main frequency peak point is located and the local frequency curve where the interference peak point is located respectively, obtaining the local frequency distribution parameters of the main frequency peak point and the interference peak point respectively, and calculating the divergence value between the two Gaussian distributions based on the local frequency distribution parameters; Among them, the divergence value satisfies the following relational expression: ; In the formula, represents the th dimension data of the th electricity meter at the moment between the th interference peak point and the divergence value between the Gaussian functions corresponding to the main frequency peak point, represents the natural logarithm function, represents the standard deviation of the Gaussian distribution of the main frequency peak point, represents the standard deviation of the Gaussian distribution of the interference peak point represents the mean of the Gaussian distribution of the main frequency peak point, represents the mean of the Gaussian function distribution of the interference peak point.

[0045] That is to say, among them, the local frequency distribution parameters are the mean and the standard deviation. The mean and the standard deviation describe the position and width of the Gaussian distribution respectively, and reflect the central tendency and dispersion degree of the signal at a specific frequency.

[0046] That is to say, a smaller divergence value means that the frequency distribution of the interference peak point is similar to that of the main frequency peak point, and the harmonic interference is small; on the contrary, a larger divergence value means that the frequency distribution of the interference peak point is quite different from that of the main frequency peak point, indicating a large harmonic interference. By calculating the divergence value, it can be used to guide the optimization and adjustment of the power system to reduce harmonic interference and improve the accuracy of electric energy metering and the quality of electric energy.

[0047] The divergence value and the relative interference degree are two indicators for measuring interference from different perspectives. The divergence value reflects the difference in the frequency distribution shape between the interference frequency band and the main frequency, while the relative interference degree reflects the difference in amplitude between the interference frequency band and the main frequency. Combining the two can comprehensively consider the frequency distribution shape and amplitude difference, so as to more comprehensively and accurately evaluate the degree of harmonic interference.

[0048] S4: Calculate the abnormality degree value of the intelligent voltmeter according to the change consistency and frequency amplitude of the interference degree value, and judge the abnormality of the electric energy meter according to the abnormality degree value.

[0049] Specifically, the abnormality degree satisfies the following relational expression: ; In the formula, represents the abnormality degree value of the interference peak point at the th dimension data of the th electric energy meter at the th moment, represents the change consistency of the th electric energy meter at the th moment for the th dimension data, represents the change amount of the interference degree value of the th electric energy meter at the th moment for the

[0050] That is to say, by analyzing the different dimension data at the th moment and the Take the absolute value of the difference between the harmonic interference degree values at different times to obtain the change amount of the interference degree value of the data in the th dimension at two adjacent times, and then calculate the variance value of the change amounts of the interference degree values at all adjacent times to obtain the change consistency at the th time.

[0051] Specifically, the change amount of the interference degree value satisfies the following relational expression: ; In the formula, represents the change amount of the harmonic interference degree value of the data in the th dimension at the th time and the th time, represents the interference degree value of the th watt-hour meter affected by harmonics at the th time, represents the interference degree value of the th watt-hour meter affected by harmonics at the th time.

[0052] That is to say, by calculating the absolute value of the difference, the change of the data in a single dimension at consecutive time points can be monitored. If the absolute value of the difference is abnormally large, it may indicate that some abnormal event or interference has occurred between these two time points.

[0053] The abnormal degree value not only depends on the change amplitude (change amount) of the frequency distribution, but also depends on the change consistency. If the change of the frequency distribution is consistent at multiple time points, then this change may be normal or expected, so the abnormal degree value will be lower. On the contrary, if the change is inconsistent, that is, the change is significant at some time points and small at other time points, then this may indicate an abnormal situation and the abnormal degree value will be higher.

[0054] In response to the abnormal degree value of any dimension being greater than or equal to the abnormal threshold, it is determined that the operating state of the watt-hour meter is abnormal. On the contrary, if the abnormal degree values of all dimensions are less than the abnormal threshold, it is determined that the operating state of the watt-hour meter is normal.

[0055] Exemplarily, the abnormal threshold is 1.5, and the implementer can adjust it according to specific circumstances.

[0056] The present invention also provides a remote detection system for a watt-hour meter. As Figure 2As shown, the system includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a remote detection method for an electric energy meter according to the first aspect of the present invention is implemented. The system also includes a communication bus, a communication interface, and other components well-known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0057] It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several variations and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent shall be subject to the appended claims.

Claims

1. A remote detection method for an electric energy meter, characterized in that, Comprising: Obtain the operation data of the electricity meter, wherein the operation data includes voltage data and current data; Use the variational mode decomposition algorithm to divide the operation data into windows according to the time series, obtain the comprehensive frequency distribution curve of the operation data within the window, and generate a time-frequency diagram reflecting the change of signal frequency over time based on the comprehensive frequency distribution curve; Based on the time-frequency diagram, perform peak point detection to determine the main frequency peak point and interference peak points, and calculate the interference degree value of the frequency of the electricity meter data affected by harmonics according to the distribution characteristics between the main frequency peak point and the interference peak points, wherein the distribution characteristics include relative interference degree and deviation degree; Calculate the abnormality degree value of the intelligent voltage meter according to the change consistency and frequency amplitude of the interference degree value, and judge the abnormal situation of the electricity meter according to the abnormality degree value.

2. The remote detection method of an electric energy meter according to claim 1, characterized in that, Obtain the comprehensive frequency distribution curve, including: Divide the window for each operation data at a preset time interval into multiple segments of data. Use the variational mode decomposition method to decompose the operation data within each window into multiple IMF components, perform frequency analysis on each IMF component to obtain the frequency distribution curve of the IMF component, and superimpose the frequency distribution curves of all IMF components within the same window to obtain the comprehensive frequency distribution curve of each window.

3. The remote detection method of an electric energy meter according to claim 1, characterized in that, Obtain the time-frequency diagram including: Take the comprehensive frequency distribution curve of each window as the time node, the horizontal axis as the corresponding time point within the window, and the vertical axis as the frequency distribution at different time points to construct the time-frequency diagram.

4. A remote detection method for an electric energy meter according to claim 1, characterized in that, Obtain the interference degree value including: Take any moment on the time-frequency diagram as the target moment, perform peak detection on the frequency curve of the window corresponding to the target moment to obtain a peak point sequence, take the peak point with the largest amplitude in the peak point sequence as the main frequency peak point, take the peak points other than the main frequency peak point as interference peak points, calculate the absolute difference in amplitude between each interference peak point and the main frequency peak point, and take the ratio between the absolute difference and the main frequency peak point as the relative interference degree; Sum the exponentially decayed relative interference degrees using the negative exponential function to obtain the interference degree value of each electricity meter affected by harmonics at the target moment.

5. A remote detection method for an electric energy meter according to claim 1, characterized in that, Obtaining the interference degree value further includes: taking any moment on the time-frequency diagram as the target moment, performing peak detection on the frequency curve of the window corresponding to the target moment to obtain a peak point sequence, taking the peak point with the largest amplitude in the peak point sequence as the main frequency peak point, taking the peak points other than the main frequency peak point as interference peak points, calculating the absolute difference in amplitude between each interference peak point and the main frequency peak point, and taking the ratio between the absolute difference and the main frequency peak point as the relative interference degree; calculating the divergence value between the divergence value of each interference peak point and the Gaussian function distribution of the local frequency curve corresponding to the main frequency peak point, and respectively mapping the divergence value and the relative interference degree using the negative exponential function, multiplying the mapped results as the comprehensive weight, and adding the comprehensive weights of all interference peak points as the interference degree value of each watt-hour meter affected by harmonics at the target moment.

6. The remote detection method of an electric energy meter according to claim 5, wherein The said The calculation of the divergence value includes: Perform Gaussian fitting on the local frequency curve where the main frequency peak point is located and the local frequency curve where the interference peak point is located respectively, obtain the local frequency distribution parameters of the main frequency peak point and the interference peak point, and calculate the divergence value between the two Gaussian distributions based on the local frequency distribution parameters.

7. A remote detection method for an electric energy meter according to claim 1, characterized in that, The abnormality degree value satisfies the following relational expression: ； Wherein, represents the abnormality degree value of the interference peak point at the th time for the th dimensional data of the th electric energy meter, represents the change consistency at the th time for the th dimensional data of the th electric energy meter, represents the change amount at the th time for the th dimensional data of the th electric energy meter.

8. A remote detection method for an electric energy meter according to claim 1, characterized in that The judging the abnormal situation of the electricity meter according to the abnormality degree value includes: In response to the abnormality degree value of any dimension being greater than or equal to the abnormality threshold, judge that the operation state of the electricity meter is abnormal. On the contrary, if the abnormality degree values of all dimensions are less than the abnormality threshold, judge that the operation state of the electricity meter is normal.

9. A remote detection method for an electric energy meter according to claim 1, characterized in that, The method further includes preprocessing the obtained operation data, wherein the preprocessing includes removing outliers, filling in missing data using the interpolation method, denoising the operation data, and performing normalization processing.

10. A remote detection system for an electric energy meter, characterized in that, Comprising: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the remote detection method of the electricity meter according to any one of claims 1-9 is implemented.

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