Error calibration method and system for smart electric energy meters based on cloud computing

Abstract

The present invention relates to the field of data processing technology, and in particular to a cloud computing-based error verification method and system for smart electric energy meters. The method comprises the following steps: collecting voltage error data and current error data from a power grid to obtain the intuitive degree of fluctuation of a voltage error data segment at each moment; obtaining similar data of each voltage error data segment in each voltage error data segment at each moment; obtaining the true degree of fluctuation of the voltage error data segment at each moment based on the difference between the current error data at the moment corresponding to the voltage error data and the current error data at the moment corresponding to the similar data of the voltage error data; adaptively obtaining the number of scales of the voltage error data segment at each moment based on the true degree of fluctuation; and determining the abnormality of the voltage error data segment at each moment based on the number of scales. The present invention improves the accuracy of abnormality detection.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a cloud computing-based smart energy meter error calibration method and system. Background Art

[0002] With the development of smart grids, traditional electricity meters are no longer able to meet the increasingly complex and efficient demands of modern power systems. As electricity demand continues to grow, the reliability, accuracy, and safety requirements of power systems are becoming increasingly stringent. Traditional mechanical electricity meters not only suffer from low measurement accuracy and difficulty in real-time monitoring and calibration, but are also difficult to maintain and manage. The emergence of smart electricity meters has made electricity monitoring, data collection, transmission, and analysis more convenient. The maturity of cloud computing technology, combined with smart electricity meters, provides powerful computing power and data storage support for error detection and data verification. However, measurement errors remain a significant issue in the use of smart meters, particularly in the detection and calibration of voltage errors. If these errors are not detected and corrected, they can lead to inaccurate electricity billing and even cause power system instability.

[0003] When the multi-scale entropy algorithm performs anomaly detection on the voltage error data sequence in the power grid, it first processes the data sequence at different scales to capture the changing characteristics of the sequence at different scales, then calculates the sample entropy of the data sequence at each scale to obtain a multi-scale entropy sequence, and finally analyzes the multi-scale entropy sequence to determine whether there are abnormal fluctuations in the voltage error data sequence. However, a fixed number of scales may lead to inaccurate anomaly detection results. This is because when the number of scales is too small, it may not fully reflect the complexity of the voltage error data sequence, resulting in fewer detected anomalies. When the number of scales is too large, the amount of calculation will increase. Summary of the Invention

[0004] In order to solve the technical problems that the small number of fixed scales in the multi-scale entropy algorithm cannot fully reflect the complexity of the voltage error data sequence, resulting in fewer detected anomalies, and the large number of scales leads to an increase in calculation amount, the present invention provides a smart electricity meter error verification method and system based on cloud computing.

[0005] In a first aspect, the present invention provides a cloud computing-based smart energy meter error calibration method, which adopts the following technical solutions:

[0006] The error checking method of a smart electric energy meter based on cloud computing includes the following steps:

[0007] Collect voltage error data and current error data at each moment in the power grid; obtain voltage error data segments at each moment; obtain the intuitive degree of fluctuation of the voltage error data segments at each moment based on the interquartile range and kurtosis value corresponding to the voltage error data segments; obtain similar data of each voltage error data in the voltage error data segments at each moment based on the historical voltage error data at each moment; obtain the actual degree of fluctuation of the voltage error data segments at each moment based on the difference between the current error data corresponding to the voltage error data in the voltage error data segments and the current error data corresponding to the similar data of the voltage error data;

[0008] According to the actual fluctuation degree, the number of scales of the voltage error data segment at each moment is obtained; according to the number of scales, the sample entropy of the voltage error data segment at each moment at each scale is obtained, an abnormality judgment is made, and the smart electric energy meter is error-checked according to the abnormal situation.

[0009] The innovation of the present invention lies in obtaining the true fluctuation degree of the voltage error data segment at each moment based on the numerical characteristics of the voltage error data and the connection between the voltage error data and the current error data, thereby avoiding the influence of noise data on the fluctuation of the voltage error data. Then, based on the true fluctuation degree of the voltage error data segment at each moment, the scale number of the voltage error data segment at each moment is adaptively obtained, so that when the multi-scale entropy algorithm faces voltage error data segments with different fluctuations, the scale number can be adapted according to the true fluctuation situation, thereby improving the accuracy of anomaly detection while reducing the amount of calculation.

[0010] Preferably, the step of obtaining the voltage error data segment at each moment includes:

[0011] The number of moments is preset as M, and a data segment consisting of the voltage error data at any moment and the voltage error data at M moments before the moment is used as the voltage error data segment at the moment.

[0012] Preferably, obtaining the intuitive fluctuation degree of the voltage error data segment at each moment includes:

[0013] ;

[0014] Where, Represents the intuitive fluctuation degree of the voltage error data segment at the pth moment; Represents the interquartile range corresponding to the voltage error data segment at the pth moment; Represents the kurtosis value of the voltage error data segment at the pth moment; || represents the absolute value sign.

[0015] According to the numerical characteristics of the voltage error data segment, the intuitive fluctuation degree of the voltage error data segment is obtained, which facilitates the subsequent adjustment of the number of scales.

[0016] Preferably, the step of obtaining similar data of each voltage error data in the voltage error data segment at each moment includes:

[0017] The voltage error data at all moments before the p-th moment are recorded as the historical voltage error data at the p-th moment; among the historical voltage error data at the p-th moment, the historical voltage error data that is consistent with the value of the m-th voltage error data in the voltage error data segment at the p-th moment is recorded as similar data to the m-th voltage error data in the voltage error data segment at the p-th moment.

[0018] This facilitates the subsequent acquisition of the true fluctuation degree of the voltage error data segment at each moment.

[0019] Preferably, obtaining the true fluctuation degree of the voltage error data segment at each moment includes:

[0020] , Represents the actual fluctuation degree of the voltage error data segment at the pth moment; Represents the intuitive fluctuation degree of the voltage error data segment at the pth moment; represents the current error data value at the moment corresponding to the m-th voltage error data in the voltage error data segment at the p-th moment; represents the mean of the current error data values ​​of all similar data of the mth voltage error data in the voltage error data segment at the pth moment at the corresponding moment; || represents the absolute value symbol; represents the number of target data in the voltage error data segment at the pth moment; exp() represents an exponential function with a natural constant as the base; Represents the number of voltage error data in the voltage error data segment at the pth moment.

[0021] The influence of noise data on the voltage error data segment is avoided, and the true fluctuation degree of the voltage error data segment at each moment is obtained.

[0022] Preferably, obtaining the target data in the voltage error data segment at the p-th moment includes:

[0023] Preset If the absolute value of the difference between the current error data at the time corresponding to the mth voltage error data in the voltage error data segment at the pth moment and the mean of the current error data at the time corresponding to all similar data of the mth voltage error data is greater than , record the mth voltage error data in the voltage error data segment at the pth moment as the target data in the voltage error data segment at the pth moment, and obtain all target data in the voltage error data segment at the pth moment.

[0024] Preferably, the step of obtaining the scale number of voltage error data segments at each moment includes:

[0025] ;

[0026] Where, The number of scales representing the voltage error data segment at the pth moment; Represents the actual fluctuation degree of the voltage error data segment at the pth moment; Represents the number of preset scales; floor() represents the floor rounding function; The value adjustment factor representing the actual fluctuation degree of the voltage error data segment at each moment.

[0027] When the multi-scale entropy algorithm faces voltage error data segments with different fluctuations, it can adapt the number of scales according to the actual fluctuation situation, thereby improving the accuracy of anomaly detection.

[0028] Preferably, the method of obtaining the sample entropy of the voltage error data segment at each time at each scale according to the number of scales, performing abnormality judgment, and performing error checking on the smart energy meter according to the abnormal situation includes:

[0029] Based on the number of scales of the voltage error data segment at each moment, a multi-scale entropy algorithm is used to obtain the sample entropy of the voltage error data segment at each scale at each moment, and linear normalization is performed on it to obtain the normalized sample entropy of the voltage error data segment at each scale at each moment. The normalized sample entropies of the voltage error data segment at each scale at each moment are used to form a multi-scale entropy sequence at each moment; the mean value of all normalized sample entropies in the multi-scale entropy sequence at each moment is recorded as the abnormality degree of the voltage error at each moment;

[0030] Preset abnormal threshold , if the abnormal degree of voltage error at any time is greater than the abnormal threshold When an abnormality is detected, the technicians are notified to check the connection status of the smart energy meter sensor on site and compare the voltage error data in the abnormal data segment with a standard calibrator. If an abnormality is found, the technicians manually adjust the hardware parameters of the energy meter or replace the faulty components, re-collect the voltage error data, and perform abnormality detection on these data until the abnormality degree of the collected voltage error data is less than the abnormal threshold. , ensure that errors are eliminated and complete the error calibration of the smart electricity meter.

[0031] Preferably, collecting voltage error data and current error data at each moment in the power grid includes:

[0032] The preset sampling frequency is 5 seconds / time. Smart electricity meters are installed at key nodes of the distribution lines in the power grid. According to the voltage sensors and current sensors in the smart electricity meters, the voltage data and current data at each moment in the power grid are collected for a total of one hour. The difference between the voltage data at each moment and the standard voltage value is recorded as the voltage error data at each moment; the difference between the current data at each moment and the standard current value is recorded as the current error data at each moment.

[0033] In a second aspect, the present invention provides a cloud computing-based smart energy meter error checking system, which adopts the following technical solutions:

[0034] The cloud computing-based smart energy meter error checking system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the cloud computing-based smart energy meter error checking method is implemented.

[0035] By adopting the above technical solution, the above-mentioned cloud computing-based smart electricity meter error verification method is generated into a computer program and stored in a memory to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0036] The present invention has the following technical effects: the purpose of the present invention is to obtain the true fluctuation degree of the voltage error data segment at each moment based on the numerical characteristics of the voltage error data and the connection between the voltage error data and the current error data, thereby avoiding the influence of noise data on the voltage error data and more realistically quantifying the fluctuation of the voltage error data segment at each moment; then, based on the true fluctuation degree of the voltage error data segment at each moment, the number of scales of the voltage error data segment at each moment is adaptively obtained, so that when the multi-scale entropy algorithm faces voltage error data segments with different fluctuations, the number of scales can be adaptively adjusted according to the true fluctuation situation, thereby improving the accuracy of anomaly detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a method for error checking a smart electric energy meter based on cloud computing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0039] The embodiment of the present invention discloses a smart energy meter error checking method based on cloud computing, referring to Figure 1 , including steps S1 to S4:

[0040] S1: Collect voltage error data and current error data in the power grid.

[0041] In the implementation of the present invention, the preset sampling frequency is 5 seconds / time, and smart electricity meters are installed at key nodes of the distribution lines in the power grid. According to the voltage sensors and current sensors in the smart electricity meters, voltage data and current data at each moment in the power grid are collected for a total of one hour; the difference between the voltage data at each moment and the standard voltage value is recorded as the voltage error data at each moment; the difference between the current data at each moment and the standard current value is recorded as the current error data at each moment.

[0042] S2: Obtain the intuitive fluctuation degree of the voltage error data segment at each moment, and obtain the similar data of each voltage error data in the voltage error data segment at each moment; obtain the actual fluctuation degree of the voltage error data segment at each moment based on the difference between the current error data at the corresponding moment of the voltage error data and the current error data at the corresponding moment of the similar data of the voltage error data.

[0043] It should be noted that the use of a fixed number of scales in the multi-scale entropy algorithm may lead to inaccurate anomaly detection results. This is because when the number of scales is too small, it may not be able to fully reflect the complexity of the voltage error data, resulting in fewer detected anomalies. When the number of scales is too large, the amount of calculation will increase. Therefore, the present invention first uses the voltage error data at each moment and the voltage error data at the previous moment as the voltage error data segment at each moment, and then analyzes the fluctuation of the voltage error data segment at each moment, so as to facilitate the subsequent adaptive acquisition of the number of scales of the voltage error data segment at each moment and judge the abnormal fluctuation of the voltage error data segment at each moment.

[0044] In an embodiment of the present invention, the preset number of moments M=300. In other embodiments, the implementer may preset the value of the number of moments M according to the specific implementation situation, and use the data segment consisting of the voltage error data at any moment and the voltage error data at M moments before the moment as the voltage error data segment at the moment.

[0045] It should be further explained that when analyzing the fluctuation of data in the voltage error data segment at each moment, it is necessary to refer to the interquartile range corresponding to the voltage error data segment at each moment. It is known that the interquartile range in a set of data is the difference between the upper quartile and the lower quartile, which represents the dispersion range of the data in the middle part of a set of data. If the interquartile range is larger, it means that the data in the middle part is more dispersed, and the fluctuation degree of the set of data is also greater. Therefore, if the interquartile range corresponding to the voltage error data segment at each moment is larger, it means that the intuitive fluctuation degree of the voltage error data segment is greater; and the kurtosis value of the voltage error data segment at each moment is obtained. If the kurtosis of the voltage error data segment at any moment is The closer the value is to the kurtosis value of the normal distribution (generally 3 is used as a reference), the more stable the data distribution in the voltage error data segment at that moment is; the smaller the kurtosis value is than the normal distribution, the more chaotic the data distribution in the voltage error data segment at that moment is, and the greater the intuitive fluctuation degree of the voltage error data segment at this moment is; the greater the kurtosis value is than the normal distribution, the more extreme values ​​there are in the data of the voltage error data segment at that moment, and the greater the intuitive fluctuation degree of the voltage error data segment at this moment is; therefore, the present invention obtains the intuitive fluctuation degree of the voltage error data segment at each moment based on the interquartile range corresponding to the voltage error data segment at each moment and the kurtosis value of the voltage error data segment at each moment,

[0046] In an embodiment of the present invention, the intuitive fluctuation degree of the voltage error data segment at each moment is obtained:

[0047] ;

[0048] Where, Represents the intuitive fluctuation degree of the voltage error data segment at the pth moment; Represents the interquartile range corresponding to the voltage error data segment at the pth moment; represents the kurtosis value of the voltage error data segment at the p-th moment. It should be noted that obtaining the kurtosis value of the voltage error data segment is a well-known technique and will not be described in detail here; The larger the value of , the more dispersed the data in the voltage error data segment at the p-th moment is, and the greater the intuitive fluctuation degree of the voltage error data segment at the p-th moment is. The larger the value of , the greater the intuitive fluctuation degree of the voltage error data segment at the pth moment.

[0049] It should be noted that the intuitive fluctuation degree of the voltage error data segment at each moment is known to be obtained. This indicator intuitively quantifies the fluctuation of the voltage error data segment based on the numerical change of the voltage error data. However, when collecting voltage data in the power grid, the surrounding environment (electromagnetic interference, equipment noise, etc.) may cause noise data to be generated. Therefore, noise also exists in the voltage error data, resulting in the voltage error noise data being closer to the data value. Therefore, the presence of noise data will reduce the accuracy of the intuitive fluctuation degree of the voltage error data segment at each moment. Therefore, the influence of noise needs to be addressed.

[0050] Since voltage fluctuations in the power grid usually cause current fluctuations, if the power grid voltage drops, the load current will change accordingly, and the two are usually positively correlated. Therefore, there is a similar change trend between the voltage error data and the current error data. When the voltage error increases, the current error usually increases, and vice versa. Therefore, the present invention distinguishes the noise data by analyzing the change characteristics between all voltage error data in the voltage error data segment at each moment and the current error data at the corresponding moment. Among them, the greater the correlation between the voltage error data in the voltage error data segment at each moment and the current error data at the corresponding moment, the less likely it is that noise data exists in the voltage error data segment at each moment, and the greater the credibility of the intuitive fluctuation degree of the voltage error data segment at each moment.

[0051] It should be further explained that when analyzing the correlation between the voltage error data in the voltage error data segment at each moment and its corresponding current error data, the historical voltage error data at each moment is first obtained, and then the similar data of each voltage error data in the voltage error data segment at each moment is obtained therefrom. If the difference between the current error data at the moment corresponding to any voltage error data and the mean value of the current error data at the moment corresponding to the similar data of the voltage error data is smaller, it means that under similar voltage error conditions, the current error data is also relatively stable and consistent. At this time, the voltage error data is not affected by noise. Therefore, if all voltage error data in the voltage error data segment at each moment are not affected by noise, the true fluctuation degree of the voltage error data segment at each moment is more accurate.

[0052] In an embodiment of the present invention, voltage error data at all moments before the p-th moment is recorded as historical voltage error data at the p-th moment; among the historical voltage error data at the p-th moment, historical voltage error data that is consistent with the value of the m-th voltage error data in the voltage error data segment at the p-th moment is recorded as similar data to the m-th voltage error data in the voltage error data segment at the p-th moment; and the average value of the current error data at the corresponding moments of all similar data of the m-th voltage error data in the voltage error data segment at the p-th moment is obtained;

[0053] Preset In other embodiments, the implementer may preset If the absolute value of the difference between the current error data at the time corresponding to the mth voltage error data in the voltage error data segment at the pth moment and the mean of the current error data at the time corresponding to all similar data of the mth voltage error data is greater than , record the mth voltage error data in the voltage error data segment at the pth moment as the target data in the voltage error data segment at the pth moment; similarly, obtain all target data in the voltage error data segment at the pth moment;

[0054] It should be noted that if the target data in the voltage error data segment at the pth moment is less, it means that the voltage error data in the voltage error data segment at the pth moment is less affected by noise, and the actual fluctuation degree of the voltage error data segment at the pth moment is more accurate.

[0055] In an embodiment of the present invention, the actual fluctuation degree of the voltage error data segment at each moment is obtained:

[0056] ；

[0057] Where, Represents the actual fluctuation degree of the voltage error data segment at the pth moment; Represents the intuitive fluctuation degree of the voltage error data segment at the pth moment; represents the current error data value at the moment corresponding to the m-th voltage error data in the voltage error data segment at the p-th moment; represents the mean of the current error data values ​​of all similar data of the mth voltage error data in the voltage error data segment at the pth moment at the corresponding moment; || represents the absolute value symbol; represents the number of target data in the voltage error data segment at the pth moment; exp() represents an exponential function with a natural constant as the base; represents the number of voltage error data in the voltage error data segment at the pth moment;

[0058] The larger the value of , the more fluctuating the value change of the voltage error data in the voltage error data segment at the p-th moment is, and the greater the actual fluctuation degree of the voltage error data segment at the p-th moment is;

[0059] The smaller the value of , the smaller the difference between the current error data corresponding to each voltage error data in the voltage error data segment at the pth moment and the current error data mean corresponding to all similar data of each voltage error data. In this case, the greater the correlation between the voltage error data in the voltage error data segment at the pth moment and its corresponding current error data, the smaller the possibility of noise data in the voltage error data segment at the pth moment. Therefore, the greater the authenticity of the intuitive fluctuation degree of the voltage error data segment at the pth moment. The smaller the value of , the less voltage error data in the voltage error data segment at the pth moment is affected by noise, that is, The smaller the value of , the greater the authenticity of the intuitive fluctuation degree of the voltage error data segment at the p-th moment, and the greater the true fluctuation degree of the voltage error data segment at the p-th moment;

[0060] When the value of is larger, there may be noise data in the voltage error data segment at the pth moment, then the authenticity of the intuitive fluctuation degree of the voltage error data segment at the pth moment is smaller, then the actual fluctuation degree of the voltage error data segment at the pth moment is smaller.

[0061] S3: adaptively obtaining the scale number of the voltage error data segment at each moment according to the actual fluctuation degree of the voltage error data segment at each moment.

[0062] It should be noted that if the true fluctuation degree of the voltage error data segment at each moment is greater, it means that the voltage error data has fluctuated, and there may be some sudden abnormal situations. In order to promptly discover and deal with these potential problems, it is necessary to assign more scales to the data segments with large true fluctuation degrees, so as to facilitate data analysis from multiple different angles. If the true fluctuation degree of the voltage error data segment at each moment is smaller, in order to improve the efficiency of the algorithm, it can be appropriately assigned fewer scales.

[0063] In the embodiment of the present invention, the preset number of scales R=6. In other embodiments, implementers may preset the value of R according to specific implementation conditions.

[0064] Get the scale number of voltage error data segments at each moment:

[0065] ;

[0066] Where, The number of scales representing the voltage error data segment at the pth moment; Represents the actual fluctuation degree of the voltage error data segment at the pth moment; Represents the number of preset scales; floor() represents the floor rounding function; the greater the actual fluctuation degree of the voltage error data segment at the pth moment, the more scales are required; Represents the value adjustment factor of the actual fluctuation degree of the voltage error data segment at each moment; preset In other embodiments of the invention, the implementer may preset The value of The value range of is [0,1], so The value range of is adjusted to [0.5, 1.5] to ensure that the number of scales of the voltage error data segments at each moment is at least 3.

[0067] S4: judging the abnormality of the voltage error data segment at each moment according to the number of scales of the voltage error data segment at each moment.

[0068] It should be noted that the optimized multi-scale entropy algorithm is used to detect anomalies in the voltage error data. It is known that the number of scales of the voltage error data segment at each moment is adaptively obtained. Therefore, based on the number of scales and the multi-scale entropy algorithm, the sample entropy of the voltage error data segment at each moment at each scale is obtained. The sample entropy is an indicator for measuring data complexity. The higher the value, the more irregular or complex the data and the higher the potential fault risk. Therefore, if the mean value of the sample entropy of the voltage error data segment at all scales at any moment is larger, it means that an anomaly has occurred in the voltage error data segment at that moment.

[0069] In an embodiment of the present invention, the number of scales of the voltage error data segment at each moment is obtained; based on the number of scales, a multi-scale entropy algorithm is used to obtain the sample entropy of the voltage error data segment at each moment at each scale, and linear normalization processing is performed on the obtained sample entropy to obtain the normalized sample entropy of the voltage error data segment at each moment at each scale; the normalized sample entropy of the voltage error data segment at each moment at each scale is used to form a multi-scale entropy sequence at each moment;

[0070] The mean of all normalized sample entropies in the multi-scale entropy sequence at each moment is recorded as the abnormality degree of the voltage error at each moment;

[0071] Preset abnormal threshold In other embodiments, the implementer may preset the threshold value according to the specific implementation situation. If the abnormality of the voltage error at any time is greater than the abnormal threshold When the voltage error data segment at that moment is abnormal, it means that the voltage error data segment at that moment has abnormal fluctuations in voltage error data. There may be a potential fault risk in the power grid, and relevant technical personnel should be notified in time for inspection and maintenance.

[0072] Specifically, the technicians check the connection status of the smart energy meter sensor on site and use a standard calibrator to compare the voltage error data in the abnormal data segment. If an abnormality is found, the technicians manually adjust the energy meter hardware parameters or replace the faulty components, re-collect the voltage error data, and perform anomaly detection on these data until the abnormality level of the collected voltage error data is less than the abnormal threshold. , ensure that errors are eliminated and complete the error calibration of the smart electricity meter.

[0073] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart energy meter error calibration method based on cloud computing, characterized in that: Including steps: Collect voltage error data and current error data at each moment in the power grid; obtain the voltage error data segment at each moment; obtain the intuitive fluctuation degree of the voltage error data segment at each moment based on the interquartile range and kurtosis value corresponding to the voltage error data segment, satisfying: ； Represents the intuitive fluctuation degree of the voltage error data segment at the pth moment; Represents the interquartile range corresponding to the voltage error data segment at the pth moment; represents the kurtosis value of the voltage error data segment at the pth moment; || represents the absolute value sign; Based on the historical voltage error data at each moment, similar data of each voltage error data in the voltage error data segment at each moment is obtained, including: The voltage error data at all moments before the p-th moment are recorded as the historical voltage error data at the p-th moment; among the historical voltage error data at the p-th moment, the historical voltage error data that is consistent with the value of the m-th voltage error data in the voltage error data segment at the p-th moment is recorded as the similar data of the m-th voltage error data in the voltage error data segment at the p-th moment; Obtaining the true fluctuation degree of the voltage error data segment at each moment based on the difference between the current error data corresponding to the voltage error data in the voltage error data segment and the current error data corresponding to similar data of the voltage error data includes: , Represents the actual fluctuation degree of the voltage error data segment at the pth moment; Represents the intuitive fluctuation degree of the voltage error data segment at the pth moment; represents the current error data value at the moment corresponding to the m-th voltage error data in the voltage error data segment at the p-th moment; represents the mean of the current error data values ​​at the corresponding moments of all similar data of the mth voltage error data in the voltage error data segment at the pth moment; represents the number of target data in the voltage error data segment at the pth moment; exp() represents an exponential function with a natural constant as the base; Represents the number of voltage error data in the voltage error data segment at the pth moment; according to the actual fluctuation degree, obtain the number of scales of the voltage error data segment at each moment; according to the number of scales, obtain the sample entropy of the voltage error data segment at each moment at each scale, perform abnormality judgment, and perform error verification on the smart electricity meter according to the abnormal situation.

2. The error checking method for smart electric energy meters based on cloud computing according to claim 1, characterized in that: The step of obtaining the voltage error data segment at each moment includes: The number of moments is preset to M, and a data segment consisting of the voltage error data at any moment and the voltage error data at the M moments before it is used as the voltage error data segment at that moment.

3. The error checking method for smart electric energy meters based on cloud computing according to claim 1, characterized in that: The acquisition of target data in the voltage error data segment at the p-th moment includes: Preset If the absolute value of the difference between the current error data at the time corresponding to the mth voltage error data in the voltage error data segment at the pth moment and the mean of the current error data at the time corresponding to all similar data of the mth voltage error data is greater than , record the mth voltage error data in the voltage error data segment at the pth moment as the target data in the voltage error data segment at the pth moment, and obtain all target data in the voltage error data segment at the pth moment.

4. The error checking method for smart electric energy meters based on cloud computing according to claim 1, characterized in that: The step of obtaining the number of scales of voltage error data segments at each moment includes: ； Where, The number of scales representing the voltage error data segment at the pth moment; Represents the actual fluctuation degree of the voltage error data segment at the pth moment; Represents the number of preset scales; floor() represents the floor rounding function; The value adjustment factor representing the actual fluctuation degree of the voltage error data segment at each moment.

5. The error checking method for smart electric energy meters based on cloud computing according to claim 1, characterized in that: The method of obtaining the sample entropy of the voltage error data segment at each time at each scale according to the number of scales, performing abnormality judgment, and performing error checking on the smart energy meter according to the abnormal situation includes: Based on the number of scales of the voltage error data segment at each moment, a multi-scale entropy algorithm is used to obtain the sample entropy of the voltage error data segment at each scale at each moment, and linear normalization is performed on it to obtain the normalized sample entropy of the voltage error data segment at each scale at each moment. The normalized sample entropies of the voltage error data segment at each scale at each moment are used to form a multi-scale entropy sequence at each moment; the mean value of all normalized sample entropies in the multi-scale entropy sequence at each moment is recorded as the abnormality degree of the voltage error at each moment; Preset abnormal threshold , if the abnormal degree of voltage error at any time is greater than the abnormal threshold When an abnormality is detected, the technicians are notified to check the connection status of the smart energy meter sensor on site and compare the voltage error data in the abnormal data segment with a standard calibrator. If an abnormality is found, the technicians manually adjust the hardware parameters of the energy meter or replace the faulty components, re-collect the voltage error data, and perform abnormality detection on these data until the abnormality degree of the collected voltage error data is less than the abnormal threshold. , ensure that errors are eliminated and complete the error calibration of the smart electricity meter.

6. The error checking method for smart electric energy meters based on cloud computing according to claim 1, characterized in that: The collecting of voltage error data and current error data at each moment in the power grid includes: The preset sampling frequency is 5 seconds / time. Smart electricity meters are installed at key nodes of the distribution lines in the power grid. According to the voltage sensors and current sensors in the smart electricity meters, the voltage data and current data at each moment in the power grid are collected for a total of one hour. The difference between the voltage data at each moment and the standard voltage value is recorded as the voltage error data at each moment; the difference between the current data at each moment and the standard current value is recorded as the current error data at each moment.

7. The intelligent energy meter error calibration system based on cloud computing is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the cloud computing-based smart energy meter error verification method according to any one of claims 1 to 6 is implemented.

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