Intelligent Electric Energy Meter with Data Self-Check Function and Data Processing Method

Abstract

The present invention relates to the technical field of electricity meter data processing, and specifically discloses an intelligent electricity meter with a data self-checking function and a data processing method. By setting an initialization state eigenvalue acquisition module, a metering state eigenvalue acquisition module, an abnormal state eigenvalue acquisition module, and an electricity meter data abnormality judgment module, the present invention solves the problems that traditional electricity meters are limited to monitoring power usage at specified times and have limited ability to process complex data analysis. It can realize the automatic monitoring and diagnosis of the operating state of the electricity meter, improve the efficiency and accuracy of self-checking, help with early warning and taking corresponding treatment measures. By importing the intelligent electricity meter self-checking model and combining the comprehensive analysis of different eigenvalues, it can quickly locate data abnormalities in the intelligent electricity meter and provide corresponding solutions to help users promptly handle potential problems and improve the reliability and stability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter data processing, and specifically to an intelligent electric energy meter with a data self-checking function and a data processing method. Background Art

[0002] With the development of the global power system towards higher efficiency, reliability and intelligence, the demand for advanced metering equipment is increasing day by day. Intelligent electric energy meters not only provide traditional electricity consumption metering, but also support complex data processing, which is necessary for achieving highly automated and optimized power grid management. Faults or unstable states of electric energy meters can lead to economic losses and safety risks. Intelligent electric energy meters can monitor and diagnose potential problems through self-checking functions, timely feedback abnormal states, and enhance the overall reliability and safety of electric energy meters. In power trading, bill management and energy data analysis, the integrity and accuracy of data are extremely important. Intelligent electric energy meters ensure data quality through built-in data self-checking and calibration functions, and support the transparency and fairness of the power market.

[0003] Nowadays, there are still some deficiencies in the research on intelligent electric energy meters with data self-checking functions and data processing methods. Specifically, traditional electric energy meters are limited to monitoring power usage at specified times and have limited ability to process complex data analysis, resulting in slow responses when traditional electric energy meters detect system faults, inability to quickly locate the source of problems, inability to perform complex data analysis, and thus difficulty in formulating strategies and decisions based on in-depth data insights. This deficiency in data analysis also affects the long-term planning and sustainable development of the power grid. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent electric energy meter with a data self-checking function and a data processing method, which can effectively solve the problems involved in the above background art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect of the present invention, an intelligent electricity meter with a data self-checking function is provided, including an initialization state eigenvalue acquisition module, a metering state eigenvalue acquisition module, an abnormal state eigenvalue acquisition module, and an electricity meter data abnormality judgment module, where: The initialization state eigenvalue acquisition module is used to acquire the initialization state data set of the intelligent electricity meter, and based on the acquired initialization state data set of the intelligent electricity meter, comprehensively analyze to obtain the initialization state eigenvalue of the intelligent electricity meter; The metering state eigenvalue acquisition module is used to acquire the metering state data set of the intelligent electricity meter, and based on the acquired metering state data set of the intelligent electricity meter, comprehensively analyze to obtain the metering state eigenvalue of the intelligent electricity meter; The abnormal state eigenvalue acquisition module is used to acquire the abnormal state data set of the intelligent electricity meter, and based on the acquired abnormal state data set of the intelligent electricity meter, comprehensively analyze to obtain the abnormal state eigenvalue of the intelligent electricity meter; The electricity meter data abnormality judgment module is used to import the initialization state eigenvalue of the intelligent electricity meter, the metering state eigenvalue of the intelligent electricity meter, and the abnormal state eigenvalue of the intelligent electricity meter into the self-checking model of the intelligent electricity meter, issue an alarm prompt for the intelligent electricity meter with data abnormalities, and compare the solutions for the data abnormalities of the intelligent electricity meter.

[0006] As a further solution, the initialization state data set of the intelligent electricity meter specifically includes the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission of the intelligent electricity meter, and the operating speed of the initialization processor of the intelligent electricity meter.

[0007] As a further solution, based on the acquired initialization state data set of the intelligent electricity meter, comprehensively analyze to obtain the initialization state eigenvalue of the intelligent electricity meter. The specific analysis process is as follows: Based on the acquired initialization state data set of the intelligent electricity meter, comprehensively analyze to obtain the initialization state eigenvalue of the intelligent electricity meter, and the initialization state eigenvalue of the intelligent electricity meter is used as the analysis basis for judging whether there are data abnormalities in the intelligent electricity meter.

[0008] As a further solution, the specific analysis process of the initialization state eigenvalue of the intelligent electricity meter is as follows:

[0009]

[0010] In the formula, α is the initialization state eigenvalue of the intelligent electricity meter, yl is the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, dk is the network bandwidth of the initialization data transmission of the intelligent electricity meter, cy is the operating speed of the initialization processor of the intelligent electricity meter, ε1 is the compensation factor for the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter set, ε2 is the compensation factor for the network bandwidth of the initialization data transmission of the intelligent electricity meter set, and ε3 is the compensation factor for the operating speed of the initialization processor of the intelligent electricity meter set.

[0011] As a further solution, the intelligent electricity meter measurement status data set specifically includes the difference between the measured current of the intelligent electricity meter and the known load current, the difference between the measured voltage of the intelligent electricity meter and the known load voltage, and the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price.

[0012] As a further solution, based on the obtained intelligent electricity meter measurement status data set, an intelligent electricity meter measurement status characteristic value is comprehensively analyzed. The specific analysis process is as follows: Based on the obtained intelligent electricity meter measurement status data set, an intelligent electricity meter measurement status characteristic value is comprehensively analyzed. The intelligent electricity meter measurement status characteristic value is used as an analysis basis for judging whether there is data abnormality in the intelligent electricity meter.

[0013] As a further solution, the intelligent electricity meter measurement status characteristic value, the specific analysis process is as follows:

[0014]

[0015] In the formula, β is the intelligent electricity meter measurement status characteristic value, lc is the difference between the measured current of the intelligent electricity meter and the known load current, yc is the difference between the measured voltage of the intelligent electricity meter and the known load voltage, jc is the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price, σ1 is the compensation factor for the difference between the measured current of the intelligent electricity meter and the known load current, σ2 is the compensation factor for the difference between the measured voltage of the intelligent electricity meter and the known load voltage, σ3 is the compensation factor for the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price, and e is the natural constant.

[0016] As a further solution, based on the obtained intelligent electricity meter abnormal status data set, an intelligent electricity meter abnormal status characteristic value is comprehensively analyzed. The specific analysis process is as follows: Obtain the intelligent electricity meter abnormal status data set. The intelligent electricity meter abnormal status data set specifically includes the current ambient magnetic field intensity around the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times that the collected power factor of the intelligent electricity meter is lower than the normal range; Based on the obtained intelligent electricity meter abnormal status data set, an intelligent electricity meter abnormal status characteristic value is comprehensively analyzed. The intelligent electricity meter abnormal status characteristic value is used as an analysis basis for judging whether there is data abnormality in the intelligent electricity meter.

[0017] As a further solution, an alarm prompt is issued for the smart electricity meter with abnormal data, and a solution to the abnormal data of the smart electricity meter is compared. The specific analysis process is as follows: The initialization state characteristic value of the smart electricity meter, the metering state characteristic value of the smart electricity meter, and the abnormal state characteristic value of the smart electricity meter are imported into the self-check model of the smart electricity meter. Through comprehensive analysis, a self-check evaluation value of the smart electricity meter is obtained. The self-check evaluation value of the smart electricity meter is used as an analysis basis for judging whether there is abnormal data in the smart electricity meter; the self-check evaluation value of the smart electricity meter is compared with the self-check reference evaluation value stored in the database; if the self-check evaluation value of the smart electricity meter is higher than or equal to the self-check reference evaluation value of the smart electricity meter, there is no abnormal data in the smart electricity meter corresponding to the self-check evaluation value; if the self-check evaluation value of the smart electricity meter is lower than the self-check reference evaluation value of the smart electricity meter, there is abnormal data in the smart electricity meter corresponding to the self-check evaluation value. The difference between the self-check reference evaluation value of the smart electricity meter and the self-check evaluation value of the smart electricity meter corresponding to the smart electricity meter with abnormal data is recorded as the self-check evaluation deviation value of the smart electricity meter. The self-check evaluation deviation value of the smart electricity meter is compared with the solutions to the abnormal data of the smart electricity meter corresponding to each self-check evaluation deviation value of the smart electricity meter stored in the database to obtain the solution to the abnormal data of the smart electricity meter corresponding to the self-check evaluation deviation value of the smart electricity meter. The solution to the abnormal data of the smart electricity meter corresponding to the self-check evaluation deviation value of the smart electricity meter represents the solution to the abnormal data of the smart electricity meter with abnormal data corresponding to the self-check evaluation deviation value of the smart electricity meter.

[0018] The second aspect of the present invention provides a data processing method, including the following steps: obtaining an initialization state data set of a smart electricity meter, and through comprehensive analysis based on the obtained initialization state data set of the smart electricity meter, obtaining an initialization state characteristic value of the smart electricity meter; obtaining a metering state data set of the smart electricity meter, and through comprehensive analysis based on the obtained metering state data set of the smart electricity meter, obtaining a metering state characteristic value of the smart electricity meter; obtaining an abnormal state data set of the smart electricity meter, and through comprehensive analysis based on the obtained abnormal state data set of the smart electricity meter, obtaining an abnormal state characteristic value of the smart electricity meter; importing the initialization state characteristic value of the smart electricity meter, the metering state characteristic value of the smart electricity meter, and the abnormal state characteristic value of the smart electricity meter into the self-check model of the smart electricity meter, issuing an alarm prompt for the smart electricity meter with abnormal data, and comparing a solution to the abnormal data of the smart electricity meter.

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] (1) The present invention provides an intelligent electric energy meter with a data self-check function and a data processing method, which can realize the automatic monitoring and diagnosis of the operating state of the electric energy meter, improve the monitoring efficiency and accuracy. The comprehensive analysis based on eigenvalue can help to timely detect possible abnormal states of the intelligent electric energy meter, including abnormal power consumption, abnormal current and voltage, etc., which is helpful for early warning and taking corresponding treatment measures. By importing the self-check model of the intelligent electric energy meter and combining the comprehensive analysis of different eigenvalues, the data abnormality of the intelligent electric energy meter can be quickly located, and corresponding solutions can be provided to help users timely handle potential problems and improve the reliability and stability of the system.

[0021] (2) The present invention obtains the characteristic values of the initialization state of the intelligent electric energy meter through comprehensive analysis, and judges whether there is data abnormality in the intelligent electric energy meter. The comprehensive analysis of the characteristic values of the initialization state of the intelligent electric energy meter can timely detect potential data abnormalities, early warning of possible problems, which is beneficial to avoid potential failures and losses. It can also help to accurately diagnose the state of the intelligent electric energy meter. The automated data analysis and judgment process can improve the monitoring and diagnosis efficiency, save labor and time costs, improve the intelligent level of management, timely detect the data abnormality of the intelligent electric energy meter, reduce the possibility of accidental failures, and improve the operation stability and reliability of the equipment.

[0022] (3) The present invention obtains the characteristic values of the metering state of the intelligent electric energy meter through comprehensive analysis, and judges whether there is data abnormality in the intelligent electric energy meter, which can accurately monitor the power consumption of the intelligent electric energy meter and timely detect abnormal electricity consumption behaviors or abnormal electricity consumption. The comprehensive analysis of the characteristic values of the metering state can help to timely detect the metering abnormalities existing in the intelligent electric energy meter, including problems such as metering errors and data loss, and early warning of possible data abnormality situations. Through the comprehensive analysis of the characteristic values of the metering state, it can help to improve the accuracy and reliability of the data of the intelligent electric energy meter, ensure the authenticity of the electricity consumption data, and avoid incorrect electricity metering caused by data abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0024] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention.

[0025] Figure 2 It is a schematic diagram of the flow of the method steps of the present invention.

[0026] Figure 3 It is an image of the self-check evaluation value of the intelligent electric energy meter changing with the characteristic values of the initialization state of the intelligent electric energy meter. Detailed implementation manners

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Referring to Figure 1 As shown, the first aspect of the present invention provides an intelligent electricity meter with a data self-checking function, including an initialization state characteristic value acquisition module, a metering state characteristic value acquisition module, an abnormal state characteristic value acquisition module, and an electricity meter data abnormality judgment module.

[0029] The initialization state characteristic value acquisition module is used to acquire the initialization state data set of the intelligent electricity meter, and comprehensively analyze based on the acquired initialization state data set of the intelligent electricity meter to obtain the initialization state characteristic value of the intelligent electricity meter.

[0030] Specifically, the intelligent electricity meter initialization status data set specifically includes the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the data transmission network bandwidth during the initialization of the intelligent electricity meter, and the operating speed of the initialization processor of the intelligent electricity meter. The number of abnormal parts in the initialization circuit connection of the intelligent electricity meter refers to the number of parts with abnormal circuit connections found during the initialization of the intelligent electricity meter. Abnormal circuit connections may include problems such as poor wiring, short circuits, and open circuits, which can cause the electricity meter to malfunction. Counting the number of abnormal parts in the circuit connection can help determine whether there are wiring problems with the device. The data transmission network bandwidth during the initialization of the intelligent electricity meter refers to the bandwidth of the data transmission network required by the intelligent electricity meter during initialization. The larger the data transmission network bandwidth, the more data can be supported for transmission, ensuring the transmission speed and stability of the data. Understanding the bandwidth of the data transmission network can evaluate the quality of the network and whether it meets the data transmission requirements of the intelligent electricity meter. The operating speed of the initialization processor of the intelligent electricity meter refers to the operating speed of the processor during the initialization of the intelligent electricity meter. The faster the processor operates, the faster it can process and calculate data, improving the response speed and performance of the intelligent electricity meter. Understanding the processor operating speed helps evaluate the performance level and processing ability of the device. The number of abnormal parts in the initialization circuit connection of the intelligent electricity meter is obtained through the monitoring system built into the intelligent electricity meter or dedicated electricity meter monitoring equipment. The monitoring equipment can be connected to the communication interface of the electricity meter to obtain the number of abnormal parts in the circuit connection by reading error codes or fault information. The data transmission network bandwidth during the initialization of the intelligent electricity meter is obtained through dedicated network monitoring equipment or network analyzers to obtain the bandwidth of the data transmission network. It is connected to the data transmission network through a network port, and the network bandwidth information is obtained by monitoring the data packet transmission situation and bandwidth utilization rate. The operating speed of the initialization processor of the intelligent electricity meter is obtained through dedicated debugging tools or monitoring equipment to obtain the processor operating speed information. It is connected to the processor interface of the electricity meter to obtain the processor operating speed information by reading the working frequency or performance monitoring data of the processor.

[0031] It should be explained that an increase in the number of abnormal parts in the above-mentioned circuit connection may lead to an increase in the load of the data transmission network, and a larger bandwidth is required to support the transmission of abnormal data. If there are more abnormal parts in the circuit connection, a higher data transmission network bandwidth may be required to ensure the stability and efficiency of data transmission. The larger the bandwidth of the data transmission network, the faster data can be transmitted, enabling the processor to receive and process data faster. If the bandwidth of the data transmission network is limited, it may affect the operating speed of the processor, resulting in delays and reduced efficiency in processing data. An increase in the number of abnormal parts in the circuit connection may lead to delays in data transmission and increased complexity in data processing, affecting the operating speed of the processor. If the operating speed of the processor is too slow, it may not be able to process a large amount of abnormal data in a timely manner, resulting in performance degradation and untimely responses.

[0032] Further, based on the obtained intelligent electricity meter initialization status data set, the initialization status eigenvalue of the intelligent electricity meter is obtained through comprehensive analysis. The specific analysis process is as follows: Based on the obtained intelligent electricity meter initialization status data set, the initialization status eigenvalue of the intelligent electricity meter is obtained through comprehensive analysis. The initialization status eigenvalue of the intelligent electricity meter is used as the analysis basis for judging whether there is data abnormality in the intelligent electricity meter.

[0033] In a specific embodiment, by comprehensively analyzing multiple eigenvalues, the initialization status of the intelligent electricity meter can be evaluated more comprehensively and objectively, and whether there is data abnormality can be accurately judged. Using the eigenvalue as the judgment criterion can achieve automatic anomaly detection, reduce the burden of manual inspection, improve the processing efficiency, monitor and analyze the initialization status eigenvalue of the intelligent electricity meter in real time, and discover anomalies in a timely manner, so that measures can be taken early to prevent the problem from deteriorating further and ensure the operation stability of the electricity meter.

[0034] It should be explained that by comprehensively analyzing the initialization status eigenvalue of the intelligent electricity meter to judge whether there is data abnormality in the intelligent electricity meter, comprehensively analyzing the eigenvalue of the initialization status of the intelligent electricity meter can timely discover potential data abnormalities, give early warnings about possible problems, be conducive to avoiding potential failures and losses, and can also help accurately diagnose the status of the intelligent electricity meter. The automated data analysis and judgment process can improve the efficiency of monitoring and diagnosis, save labor and time costs, improve the intelligent level of management, timely discover data abnormalities of the intelligent electricity meter, reduce the possibility of accidental failures, and improve the operation stability and reliability of the equipment.

[0035] Further, the specific analysis process of the initialization status eigenvalue of the intelligent electricity meter is as follows:

[0036]

[0037] Where α is the initialization status eigenvalue of the intelligent electricity meter, yl is the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, dk is the bandwidth of the initialization data transmission network of the intelligent electricity meter, cy is the operation speed of the initialization processor of the intelligent electricity meter, ε1 is the compensation factor for the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, ε2 is the compensation factor for the bandwidth of the initialization data transmission network of the intelligent electricity meter, and ε3 is the compensation factor for the operation speed of the initialization processor of the intelligent electricity meter.

[0038] It should be noted that the above-mentioned initialization state characteristic values of the intelligent electricity meter are calculated through the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, and the operating speed of the initialization processor of the intelligent electricity meter. Normalize the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, and the operating speed of the initialization processor of the intelligent electricity meter. Monitor the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, which can promptly detect and locate possible connection problems or damaged components, helping to accurately diagnose and solve potential hardware faults, ensuring the normal operation of the device. The network bandwidth of the initialization data transmission network of the intelligent electricity meter reflects the efficiency and speed of data transmission, and can evaluate whether the network bandwidth is sufficient to support the data transmission requirements of the intelligent electricity meter and whether there are transmission bottlenecks, helping to optimize the network configuration and improve the data transmission performance of the intelligent electricity meter. The operating speed of the initialization processor of the intelligent electricity meter is an important indicator to measure the operating efficiency of the processor. Monitoring this indicator can evaluate the working state and performance of the processor, ensure that the operating speed of the processor is within the normal range, improve the response speed and operation efficiency of the intelligent electricity meter, and ensure the accurate acquisition and processing of electricity meter data. Monitoring and analyzing the initialization state characteristic values of the intelligent electricity meter helps to detect potential problems early, take maintenance measures in advance, reduce the impact brought by hardware faults and data transmission problems, contribute to preventive maintenance, reduce maintenance costs, and extend the service life of the intelligent electricity meter. Regularly analyzing the data of the initialization state characteristic values of the intelligent electricity meter can help to discover performance bottlenecks and potential problems, provide strong support for subsequent optimization measures, improve the connection parts, optimize the network bandwidth, and accelerate the operating speed of the processor, enhancing the overall operating performance and efficiency of the intelligent electricity meter. The compensation factors for the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, and the operating speed of the initialization processor of the intelligent electricity meter are obtained from the database. Based on historical data, establish a mapping set between the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, the operating speed of the initialization processor of the intelligent electricity meter in historical measurements and the compensation factors for the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, the operating speed of the initialization processor of the intelligent electricity meter, and obtain the compensation factors for the number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, the operating speed of the initialization processor of the intelligent electricity meter corresponding to the current number of abnormal parts in the initialization circuit connection of the intelligent electricity meter, the network bandwidth of the initialization data transmission network of the intelligent electricity meter, the operating speed of the initialization processor of the intelligent electricity meter.

[0039] A metering state characteristic value acquisition module is used to acquire the intelligent electricity meter metering state data set, and based on the acquired intelligent electricity meter metering state data set, comprehensively analyze to obtain the intelligent electricity meter metering state characteristic value.

[0040] Specifically, the intelligent electricity meter measurement status data set specifically includes the difference between the measured current of the intelligent electricity meter and the known load current, the difference between the measured voltage of the intelligent electricity meter and the known load voltage, and the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price. The difference between the measured current of the intelligent electricity meter and the known load current represents the deviation between the current value measured by the electricity meter and the actual load current value. If this difference is too large, it may indicate problems with the measurement accuracy of the electricity meter and calibration or repair is required. The difference between the measured voltage of the intelligent electricity meter and the known load voltage represents the deviation between the voltage value measured by the electricity meter and the actual load voltage value. If this difference exceeds the normal range, it may indicate accuracy problems with the electricity meter. The difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price represents the deviation between the electricity price measured by the electricity meter and the actual electricity price. If this difference is significantly inconsistent, it may indicate errors in the electricity price calculation of the electricity meter and adjustment or correction may be required. Equipment for obtaining the measured current and voltage values of the intelligent electricity meter can use professional testing instruments such as current clamps and voltmeters, which are connected to the current and voltage input ports of the electricity meter to read the current and voltage values being measured by the electricity meter. Equipment for obtaining the current and voltage values of the known load can use equipment such as load ammeters and load voltmeters, which are connected to the load ports to obtain the actual current and voltage values of the load. Equipment for obtaining the current actual electricity price obtains the current electricity price information in real time through data acquisition equipment connected to the power system, such as intelligent electricity meters and intelligent grid monitoring systems.

[0041] It should be explained that the accuracy of the measured current and voltage of the above intelligent electricity meter is affected by voltage fluctuations and load changes. If the voltage fluctuations are large or the load changes frequently, it may cause a large deviation between the measured values of current and voltage and the actual values, thereby affecting the measurement accuracy. If the difference between the measured values of current and voltage and the known load values is large, further analysis of the voltage stability and load change conditions may be required. The difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price reflects the accuracy of electricity billing. If there is a large difference between the electricity price calculation and the actual electricity price, it may indicate errors in the electricity price calculation rules or parameter settings, resulting in a mismatch between the electricity charges paid by users and the actual electricity consumed. Monitoring the change in the electricity price difference can help detect electricity price calculation problems in a timely manner and ensure the accuracy of electricity charges. If the differences between the measured current of the intelligent electricity meter and the known load current, the measured voltage of the intelligent electricity meter and the known load voltage, and the measured electricity price of the intelligent electricity meter and the current actual electricity price are too large, they all indicate the inaccuracy of the intelligent electricity meter measurement data and the measurement data of the intelligent electricity meter needs to be adjusted in a timely manner.

[0042] Further, based on the obtained dataset of the metering status of the smart electricity meter, the characteristic values of the metering status of the smart electricity meter are comprehensively analyzed. The specific analysis process is as follows: Based on the obtained dataset of the metering status of the smart electricity meter, the characteristic values of the metering status of the smart electricity meter are comprehensively analyzed. The characteristic values of the metering status of the smart electricity meter are used as the analysis basis for judging whether there is data abnormality in the smart electricity meter.

[0043] In a specific embodiment, by comprehensively analyzing the metering status data and extracting representative characteristic values, it is possible to help screen out the most important indicators and characteristics, thereby more effectively describing the metering status of the smart electricity meter, reducing the data redundancy. After converting the data into characteristic values, the dimension and complexity of the data can be greatly reduced, making the data easier to process and analyze. The extracted characteristic values can better reflect the characteristics of the smart electricity meter, which is conducive to data visualization and intuitive analysis. Using the characteristic values as the basis for judging whether the metering status of the smart electricity meter is abnormal, abnormal data points can be discovered through the relationships and change trends between the characteristic values, and potential problems or errors can be discovered in advance, which helps to take timely measures to process the abnormal data, ensuring the accuracy and reliability of the metering data. Based on the analysis results of the characteristic values, more comprehensive data basis and statistical analysis reports can be provided for managers, helping them make more accurate and scientific decisions. Characteristic value analysis can discover general problems and rules, providing important references for the maintenance and optimization of smart electricity meters.

[0044] It should be explained that by comprehensively analyzing to obtain the characteristic values of the metering status of the smart electricity meter and judging whether there is data abnormality in the smart electricity meter, the electricity consumption situation of the smart electricity meter can be accurately monitored, and abnormal electricity consumption behaviors or abnormal electricity consumption amounts can be discovered in time. Comprehensively analyzing the metering status characteristic values can help to timely discover the metering abnormalities existing in the smart electricity meter, including problems such as metering errors and data loss, and early warning of possible data abnormality situations. Through the comprehensive analysis of the metering status characteristic values, it is possible to help improve the accuracy and reliability of the smart electricity meter data, ensure the authenticity of the electricity consumption data, and avoid incorrect electricity metering caused by data abnormality.

[0045] Further, the specific analysis process of the characteristic values of the metering status of the smart electricity meter is as follows:

[0046]

[0047] Wherein, β is the metering state characteristic value of the smart electricity meter, lc is the difference between the metering current of the smart electricity meter and the known load current, yc is the difference between the metering voltage of the smart electricity meter and the known load voltage, jc is the difference between the metering electricity price of the smart electricity meter and the current actual electricity price, σ1 is the compensation factor for the difference between the metering current of the smart electricity meter and the known load current, σ2 is the compensation factor for the difference between the metering voltage of the smart electricity meter and the known load voltage, σ3 is the compensation factor for the difference between the metering electricity price of the smart electricity meter and the current actual electricity price, and e is the natural constant.

[0048] It should be noted that the above-mentioned metering status characteristic values of the intelligent electricity meter are calculated through the differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price. Normalize the differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price. Monitoring the differences between the metering current of the intelligent electricity meter and the known load current and the metering voltage and the known load voltage can promptly detect current and voltage abnormalities, such as overload, underload, short circuit, etc., which helps to quickly discover equipment failures or abnormal situations, conduct timely processing, and avoid further damage to the equipment or potential safety hazards. The difference between the metering electricity price of the intelligent electricity meter and the current actual electricity price can reflect the accuracy of electricity billing. Monitoring this indicator can help detect whether there are errors or deviations in electricity price metering, which helps to ensure that users are accurately billed. By monitoring and comparing the metering status characteristic values, data errors or abnormal situations that may occur during the metering process of the intelligent electricity meter, such as measurement errors, transmission errors, etc., can be promptly discovered, and then processed and corrected in a timely manner to ensure the accuracy and reliability of metering data. Calculating the differences of the metering status characteristic values can conduct data analysis in aspects such as current, voltage, and electricity price, helping users and management agencies understand real-time and historical energy usage situations. Based on the analysis results, energy-saving plans can be formulated, energy management strategies can be optimized, energy utilization efficiency can be improved, energy consumption can be reduced. Monitoring the differences of the metering status characteristic values of the intelligent electricity meter helps to early warn of potential problems, realize preventive maintenance and management, promptly discover and solve metering problems, avoid the decline or damage of the electricity meter performance, extend the equipment life, and reduce energy resource waste. The compensation factors for the differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price are obtained from the database. According to historical data, a mapping set of the differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price and the compensation factors for the differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price is established, and the compensation factors for the differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price corresponding to the current differences between the metering current of the intelligent electricity meter and the known load current, the metering voltage of the intelligent electricity meter and the known load voltage, and the metering electricity price of the intelligent electricity meter and the current actual electricity price are obtained.It should be noted that the differences between the measured current of the smart electricity meter and the known load current, the measured voltage of the smart electricity meter and the known load voltage, and the measured electricity price of the smart electricity meter and the current actual electricity price are the absolute values of the differences between the measured current of the smart electricity meter and the known load current, the measured voltage of the smart electricity meter and the known load voltage, and the measured electricity price of the smart electricity meter and the current actual electricity price.

[0049] An abnormal state eigenvalue acquisition module, which is used to acquire the abnormal state data set of the smart electricity meter, and based on the acquired abnormal state data set of the smart electricity meter, comprehensively analyze to obtain the abnormal state eigenvalue of the smart electricity meter.

[0050] Specifically, based on the acquired abnormal state data set of the smart electricity meter, comprehensively analyze to obtain the abnormal state eigenvalue of the smart electricity meter. The specific analysis process is as follows: acquire the abnormal state data set of the smart electricity meter. The abnormal state data set of the smart electricity meter specifically includes the current ambient magnetic field intensity around the smart electricity meter, the number of unregistered opening times of the smart electricity meter per unit time, and the number of times that the power factor collected by the smart electricity meter is lower than the normal range. The current ambient magnetic field intensity around the smart electricity meter refers to the magnetic field intensity level around the smart electricity meter. A high-intensity magnetic field may affect the normal operation of the electricity meter, resulting in inaccurate data collection or metering errors. Monitoring the ambient magnetic field intensity can help evaluate the degree of external interference received by the electricity meter. The number of unregistered opening times of the smart electricity meter per unit time indicates the number of times the smart electricity meter is opened by unauthorized personnel or for other reasons within a certain period of time. This may imply potential safety problems and may also lead to data tampering or inaccuracy. Monitoring this indicator can help identify potential safety hazards. The number of times that the power factor collected by the smart electricity meter is lower than the normal range refers to the ratio between the useful power and the apparent power in the circuit. A power factor lower than the normal range may mean that there are problems with the data measured by the electricity meter or that there are devices with poor power factors in the circuit. Monitoring this indicator can help detect potential power quality problems. A magnetic field intensity detection instrument is used to measure the magnetic field intensity around the smart electricity meter, an opening detector is used to detect whether the smart electricity meter is abnormally opened, and a power factor monitoring device is used to monitor the power factor in the circuit; based on the acquired abnormal state data set of the smart electricity meter, comprehensively analyze to obtain the abnormal state eigenvalue of the smart electricity meter. The abnormal state eigenvalue of the smart electricity meter is used as the analysis basis for judging whether there is data abnormality in the smart electricity meter.

[0051] It should be noted that the above-mentioned high-intensity magnetic field may affect the internal electronic components of the smart electricity meter, resulting in inaccurate data collection. If the surrounding magnetic field strength exceeds a certain level, it may cause abnormal data in the electricity meter, including the situation where the power factor is lower than the normal range. If the smart electricity meter is frequently opened by unauthorized personnel, it may lead to data tampering or damage, and the power factor may be lower than the normal range because the normal operation of the electricity meter is interfered. A power factor lower than the normal range may imply problems in the circuit, which may be caused by external interference, equipment failure, or data tampering. The data accuracy of the electricity meter may be affected, and the number of opening times may also increase. The abnormal data of the smart electricity meter will be caused by the increase in the current surrounding magnetic field strength of the smart electricity meter, the number of unregistered opening times of the smart electricity meter per unit time, and the number of times the power factor collected by the smart electricity meter is lower than the normal range.

[0052] It should be noted that by comprehensively analyzing the relationships between the above different indicators, the abnormal state of the smart electricity meter can be understood more comprehensively. By combining the magnetic field strength, the number of opening times, and the power factor data, it is possible to more accurately determine whether there is an abnormality in the electricity meter. It is also possible to integrate the information of multiple indicators into a comprehensive metric, improving the accuracy of abnormal state judgment. The characteristic value can better reflect the overall abnormal situation of the electricity meter and be used as a basis for judging whether there is data abnormality in the smart electricity meter, which can help quickly identify problems and take timely measures for repair, thereby reducing potential data errors or losses. Continuous monitoring and prevention of the abnormal state of the smart electricity meter can be achieved, detecting the abnormal state early and taking corresponding measures, which can improve the reliability and stability of the electricity meter data. The analysis based on the characteristic value can provide data support for the management and maintenance of the smart electricity meter, helping to make more scientific decisions and measures, and improving the efficiency and reliability of the electricity meter system.

[0053] It should be noted that the characteristic value of the abnormal state of the above-mentioned smart electricity meter has the following specific analysis process:

[0054]

[0055] In the formula, γ is the characteristic value of the abnormal state of the smart electricity meter, cc is the current surrounding magnetic field strength of the smart electricity meter, kg is the number of unregistered opening times of the smart electricity meter per unit time, gy is the number of times the power factor collected by the smart electricity meter is lower than the normal range, τ1 is the compensation factor for the current surrounding magnetic field strength of the smart electricity meter, τ2 is the compensation factor for the number of unregistered opening times of the smart electricity meter per unit time, τ3 is the compensation factor for the number of times the power factor collected by the smart electricity meter is lower than the normal range, and e is the natural constant.

[0056] It should be noted that the above abnormal state characteristic values of the intelligent electricity meter are calculated through the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range. Normalize the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range. Monitoring the abnormal state characteristic values of the intelligent electricity meter helps to achieve abnormal early warning and management. By monitoring and analyzing indicators such as magnetic field intensity, opening times, and power factor, potential abnormalities in the electricity meter can be detected in advance, and measures can be taken in a timely manner to avoid equipment failures or data anomalies, ensuring the normal operation of the electricity meter and data accuracy. Monitoring the abnormal state characteristic values can achieve the maintenance optimization of the intelligent electricity meter, timely discover and handle abnormal states, help extend the equipment life, improve the performance stability of the equipment, reduce maintenance costs, and ensure the long-term reliable operation of the electricity meter. Monitoring the ambient magnetic field intensity around the intelligent electricity meter can help detect whether there is an impact of external magnetic fields on the normal operation of the electricity meter. Abnormal magnetic fields may cause inaccurate data collection of the electricity meter or even damage the equipment. By monitoring the magnetic field intensity, potential safety hazards can be detected in a timely manner, ensuring the normal operation of the electricity meter and data accuracy. Monitoring the number of unregistered opening times per unit time can be used to detect whether the electricity meter has been maliciously opened or tampered with. If the electricity meter frequently experiences unauthorized opening behavior, there may be a risk of data tampering. Monitoring this indicator can timely discover potential tampering behavior and ensure the security and reliability of the data. Monitoring the number of times the collected power factor is lower than the normal range helps to evaluate the performance status of the electricity meter. The power factor is an important indicator to measure the efficiency of electricity use. A low power factor may lead to energy waste and an increase in the grid load. Monitoring the number of abnormal power factor times can timely discover performance problems of the electricity meter, take measures for adjustment and optimization, and improve energy utilization efficiency. The compensation factors for the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range are obtained from the database. Based on historical data, a mapping set of the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range in historical measurements and the compensation factors for the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range is established to obtain the compensation factors for the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range corresponding to the current ambient magnetic field intensity of the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times the power factor collected by the intelligent electricity meter is lower than the normal range.

[0057] The electric energy meter data anomaly judgment module is used to import the initialization state characteristic values, metering state characteristic values, and anomaly state characteristic values of the intelligent electric energy meter into the intelligent electric energy meter self-check model, issue an alarm prompt for the intelligent electric energy meter with data anomalies, and compare the solutions for the data anomalies of the intelligent electric energy meter.

[0058] Specifically, for the intelligent electric energy meter with data anomalies, an alarm prompt is issued and the solution for the data anomalies of the intelligent electric energy meter is compared. The specific analysis process is as follows: The initialization state characteristic values, metering state characteristic values, and anomaly state characteristic values of the intelligent electric energy meter are imported into the intelligent electric energy meter self-check model, and the self-check evaluation value of the intelligent electric energy meter is obtained through comprehensive analysis. The self-check evaluation value of the intelligent electric energy meter is used as the analysis basis for judging whether there are data anomalies in the intelligent electric energy meter; the self-check evaluation value of the intelligent electric energy meter is compared with the self-check reference evaluation value stored in the database; if the self-check evaluation value of the intelligent electric energy meter is higher than or equal to the self-check reference evaluation value of the intelligent electric energy meter, then there are no data anomalies in the intelligent electric energy meter corresponding to the self-check evaluation value; if the self-check evaluation value of the intelligent electric energy meter is lower than the self-check reference evaluation value of the intelligent electric energy meter, then there are data anomalies in the intelligent electric energy meter corresponding to the self-check evaluation value. The difference between the self-check reference evaluation value and the self-check evaluation value of the intelligent electric energy meter corresponding to the intelligent electric energy meter with data anomalies is recorded as the self-check evaluation deviation value of the intelligent electric energy meter. The self-check evaluation deviation value of the intelligent electric energy meter is compared with the solutions for the data anomalies of the intelligent electric energy meter corresponding to each self-check evaluation deviation value stored in the database to obtain the solution for the data anomalies of the intelligent electric energy meter corresponding to the self-check evaluation deviation value of the intelligent electric energy meter. The solution for the data anomalies of the intelligent electric energy meter corresponding to the self-check evaluation deviation value of the intelligent electric energy meter represents the solution for the data anomalies of the intelligent electric energy meter with data anomalies corresponding to the self-check evaluation deviation value of the intelligent electric energy meter.

[0059] In a specific embodiment, by comparing the self-check evaluation value with the reference evaluation value, the system can quickly identify smart electricity meters with abnormal data, thus promptly issuing an alarm prompt to alert relevant personnel and prompt them to take necessary actions. Obtaining a specific solution for data anomalies based on the evaluation deviation value can help accurately diagnose problems existing in smart electricity meters, contribute to targeted resolution of specific data anomalies, improve repair efficiency and accuracy. By analyzing the self-check model of smart electricity meters and comparing data, the system can automatically identify data anomalies and provide corresponding solutions, reducing the need for manual intervention, improving processing efficiency and accuracy. By continuously comparing the evaluation values and tracking the deviation values, the system can continuously monitor the data anomalies of smart electricity meters, promptly discover and solve potential problems, ensuring the accuracy and reliability of electricity meter data. The solution obtained based on the evaluation deviation value can help further optimize the maintenance and management strategies of smart electricity meters. By accumulating solutions for data anomalies, a more perfect maintenance system can be established, improving the overall maintenance effect.

[0060] It should be noted that the specific analysis process of the above self-check model for smart electricity meters is as follows:

[0061]

[0062] In the formula, δ is the self-check evaluation value of the smart electricity meter, α is the initialization state characteristic value of the smart electricity meter, β is the metering state characteristic value of the smart electricity meter, and γ is the abnormal state characteristic value of the smart electricity meter.

[0063] In a specific embodiment, the initialization state characteristic value, metering state characteristic value, and abnormal state characteristic value of the smart electricity meter are imported into the self-check model of the smart electricity meter, and the self-check evaluation value of the smart electricity meter is obtained through comprehensive analysis. The self-check model of the smart electricity meter can more comprehensively analyze the overall state of the smart electricity meter, contribute to identifying possible problems in multiple aspects, and improve the comprehensiveness and accuracy of detection. Considering that different characteristic values can reflect multiple aspects of the state of the smart electricity meter, the self-check evaluation value of the smart electricity meter can more comprehensively reflect the operation of the smart electricity meter, detect whether there are data anomalies in the smart electricity meter from multiple angles, reduce the omission of possible abnormal situations, and also contribute to improving the accuracy of detection and reducing unnecessary troubles and costs caused by misjudgment. The self-check evaluation value of the smart electricity meter can provide timely feedback, enabling potential problems to be discovered before data anomalies occur, contributing to taking necessary maintenance measures in advance to prevent the occurrence of data anomalies and ensuring the normal operation of the smart electricity meter. The self-check evaluation value obtained through comprehensive analysis can help identify the performance bottlenecks and problem points of the smart electricity meter, providing an important reference for subsequent optimization and improvement, and contributing to continuously optimizing the performance and stability of the smart electricity meter.

[0064] Such as Figure 3As shown, it is an image of the self - test evaluation value of the intelligent electricity meter changing with the characteristic value of the initialization state of the intelligent electricity meter. Here, the x - axis represents the characteristic value of the initialization state of the intelligent electricity meter, and the y - axis represents the self - test evaluation value of the intelligent electricity meter. This can help intuitively understand how the characteristic value of the initialization state of the intelligent electricity meter affects the self - test evaluation value of the intelligent electricity meter. The larger the characteristic value of the initialization state of the intelligent electricity meter, the larger the self - test evaluation value of the intelligent electricity meter, indicating that the self - test state of the intelligent electricity meter is better. As the characteristic value of the initialization state of the intelligent electricity meter increases, the influence of the characteristic value of the initialization state of the intelligent electricity meter on the self - test evaluation value of the intelligent electricity meter gradually increases. Set the characteristic value of the metering state of the intelligent electricity meter to 2 and keep it unchanged, and set the characteristic value of the abnormal state of the intelligent electricity meter to 2 and keep it unchanged. Only change the size of the characteristic value of the initialization state of the intelligent electricity meter. The example values of the characteristic value of the initialization state of the intelligent electricity meter are as follows:

[0065] Table 1: Example values of the characteristic value of the initialization state of the intelligent electricity meter in the self - test evaluation value of the intelligent electricity meter

[0066] n α β γ δ 1 1 2 2 4.0000 2 2 2 2 7.0000 3 3 2 2 12.0000

[0067] It should be noted that the larger the characteristic value of the abnormal state of the intelligent electricity meter, the larger the self - test evaluation value of the intelligent electricity meter, indicating that the self - test state of the intelligent electricity meter is better. As the characteristic value of the abnormal state of the intelligent electricity meter increases, the influence of the characteristic value of the abnormal state of the intelligent electricity meter on the self - test evaluation value of the intelligent electricity meter gradually weakens. Set the characteristic value of the initialization state of the intelligent electricity meter to 1 and keep it unchanged, set the characteristic value of the metering state of the intelligent electricity meter to 2 and keep it unchanged. Only change the size of the characteristic value of the abnormal state of the intelligent electricity meter. The example values of the characteristic value of the abnormal state of the intelligent electricity meter are as follows:

[0068] Table 2: Example values of the characteristic value of the abnormal state of the intelligent electricity meter in the self - test evaluation value of the intelligent electricity meter

[0069]

[0070]

[0071] Refer to Figure 2As shown in the figure, the second aspect of the present invention provides a data processing method, including the following steps: obtaining an initialization status data set of an intelligent electricity meter, and based on the obtained initialization status data set of the intelligent electricity meter, comprehensively analyzing to obtain an initialization status eigenvalue of the intelligent electricity meter; obtaining a metering status data set of the intelligent electricity meter, and based on the obtained metering status data set of the intelligent electricity meter, comprehensively analyzing to obtain a metering status eigenvalue of the intelligent electricity meter; obtaining an abnormal status data set of the intelligent electricity meter, and based on the obtained abnormal status data set of the intelligent electricity meter, comprehensively analyzing to obtain an abnormal status eigenvalue of the intelligent electricity meter; importing the initialization status eigenvalue, the metering status eigenvalue and the abnormal status eigenvalue of the intelligent electricity meter into the self-check model of the intelligent electricity meter, sending an alarm prompt for the intelligent electricity meter with data anomalies and comparing the solutions for the data anomalies of the intelligent electricity meter.

[0072] It should be noted that by providing an intelligent electricity meter and a data processing method with a data self-check function as described above, the automatic monitoring and diagnosis of the operating status of the electricity meter can be realized, improving the monitoring efficiency and accuracy. The comprehensive analysis based on the eigenvalues can help to timely detect possible abnormal states of the intelligent electricity meter, including abnormal power consumption, abnormal current and voltage, etc., which is helpful for early warning and taking corresponding treatment measures. By importing the self-check model of the intelligent electricity meter and combining the comprehensive analysis of different eigenvalues, the data anomalies of the intelligent electricity meter can be quickly located and corresponding solutions can be provided to help users timely handle potential problems and improve the reliability and stability of the system.

[0073] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all belong to the protection scope of the present invention.

Claims

1. An intelligent electricity meter with a data self-checking function, characterized in that, It includes an initialization state eigenvalue acquisition module, a metering state eigenvalue acquisition module, an abnormal state eigenvalue acquisition module, and an electric energy meter data abnormality judgment module, where: The initialization state eigenvalue acquisition module is used to acquire the initialization state data set of the intelligent electric energy meter, and based on the acquired initialization state data set of the intelligent electric energy meter, comprehensively analyze to obtain the initialization state eigenvalue of the intelligent electric energy meter; The metering state eigenvalue acquisition module is used to acquire the metering state data set of the intelligent electric energy meter, and based on the acquired metering state data set of the intelligent electric energy meter, comprehensively analyze to obtain the metering state eigenvalue of the intelligent electric energy meter; The abnormal state eigenvalue acquisition module is used to acquire the abnormal state data set of the intelligent electric energy meter, and based on the acquired abnormal state data set of the intelligent electric energy meter, comprehensively analyze to obtain the abnormal state eigenvalue of the intelligent electric energy meter; The electric energy meter data abnormality judgment module is used to import the initialization state eigenvalue of the intelligent electric energy meter, the metering state eigenvalue of the intelligent electric energy meter, and the abnormal state eigenvalue of the intelligent electric energy meter into the self-check model of the intelligent electric energy meter, issue an alarm prompt for the intelligent electric energy meter with data abnormalities, and compare the solutions for the data abnormalities of the intelligent electric energy meter; The specific analysis process of the initialization state eigenvalue of the intelligent electric energy meter is as follows: The initialization state eigenvalue of the intelligent electric energy meter is calculated by the number of abnormal parts in the initialization circuit connection of the intelligent electric energy meter, the network bandwidth of the initialization data transmission of the intelligent electric energy meter, and the operating speed of the initialization processor of the intelligent electric energy meter; The specific analysis process of the metering state eigenvalue of the intelligent electric energy meter is as follows: The metering state eigenvalue of the intelligent electric energy meter is calculated by the difference between the metering current of the intelligent electric energy meter and the known load current, the difference between the metering voltage of the intelligent electric energy meter and the known load voltage, and the difference between the metering electricity price of the intelligent electric energy meter and the current actual electricity price.

2. The intelligent electric energy meter with a data self-checking function according to claim 1, wherein: The initialization state data set of the intelligent electric energy meter specifically includes the number of abnormal parts in the initialization circuit connection of the intelligent electric energy meter, the network bandwidth of the initialization data transmission of the intelligent electric energy meter, and the operating speed of the initialization processor of the intelligent electric energy meter.

3. The intelligent electric energy meter with a data self-checking function according to claim 2, characterized in that: Based on the acquired initialization state data set of the intelligent electric energy meter, comprehensively analyze to obtain the initialization state eigenvalue of the intelligent electric energy meter. The specific analysis process is as follows: Based on the acquired initialization state data set of the intelligent electric energy meter, comprehensively analyze to obtain the initialization state eigenvalue of the intelligent electric energy meter. The initialization state eigenvalue of the intelligent electric energy meter is used as an analysis basis for judging whether there are data abnormalities in the intelligent electric energy meter.

4. The intelligent electric energy meter with a data self-checking function according to claim 3, characterized in that: The initialization state eigenvalue of the intelligent electric energy meter further includes: In the formula, α is the initialization state eigenvalue of the intelligent electric energy meter, yl is the number of abnormal parts in the initialization circuit connection of the intelligent electric energy meter, dk is the network bandwidth of the initialization data transmission of the intelligent electric energy meter, cy is the operating speed of the initialization processor of the intelligent electric energy meter, ε1 is the compensation factor for the number of abnormal parts in the initialization circuit connection of the intelligent electric energy meter set, ε2 is the compensation factor for the network bandwidth of the initialization data transmission of the intelligent electric energy meter set, and ε3 is the compensation factor for the operating speed of the initialization processor of the intelligent electric energy meter set.

5. The intelligent electric energy meter with a data self-checking function according to claim 1, characterized in that: The intelligent electricity meter measurement status data set specifically includes the difference between the measured current of the intelligent electricity meter and the known load current, the difference between the measured voltage of the intelligent electricity meter and the known load voltage, and the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price.

6. The intelligent electric energy meter with a data self-checking function according to claim 5, characterized in that: Based on the obtained intelligent electricity meter measurement status data set, the intelligent electricity meter measurement status characteristic value is comprehensively analyzed. The specific analysis process is as follows: Based on the obtained intelligent electricity meter measurement status data set, the intelligent electricity meter measurement status characteristic value is comprehensively analyzed. The intelligent electricity meter measurement status characteristic value is used as the analysis basis for judging whether there is data abnormality in the intelligent electricity meter.

7. The intelligent electric energy meter with a data self-checking function according to claim 6, characterized in that: The intelligent electricity meter measurement status characteristic value also includes: In the formula, β is the intelligent electricity meter measurement status characteristic value, lc is the difference between the measured current of the intelligent electricity meter and the known load current, yc is the difference between the measured voltage of the intelligent electricity meter and the known load voltage, jc is the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price, σ1 is the compensation factor for the difference between the measured current of the intelligent electricity meter and the known load current, σ2 is the compensation factor for the difference between the measured voltage of the intelligent electricity meter and the known load voltage, σ3 is the compensation factor for the difference between the measured electricity price of the intelligent electricity meter and the current actual electricity price, and e is the natural constant.

8. The intelligent electric energy meter with a data self-checking function according to claim 1, characterized in that: Based on the obtained intelligent electricity meter abnormal status data set, the intelligent electricity meter abnormal status characteristic value is comprehensively analyzed. The specific analysis process is as follows: Obtain the intelligent electricity meter abnormal status data set. The intelligent electricity meter abnormal status data set specifically includes the current ambient magnetic field intensity around the intelligent electricity meter, the number of unregistered opening times of the intelligent electricity meter per unit time, and the number of times that the collected power factor of the intelligent electricity meter is lower than the normal range. Based on the obtained intelligent electricity meter abnormal status data set, the intelligent electricity meter abnormal status characteristic value is comprehensively analyzed. The intelligent electricity meter abnormal status characteristic value is used as the analysis basis for judging whether there is data abnormality in the intelligent electricity meter.

9. The intelligent electric energy meter with a data self-checking function according to claim 1, characterized in that: For the intelligent electricity meter with data abnormality, an alarm prompt is issued and the solution to the data abnormality of the intelligent electricity meter is compared. The specific analysis process is as follows: Import the intelligent electricity meter initialization status characteristic value, the intelligent electricity meter measurement status characteristic value, and the intelligent electricity meter abnormal status characteristic value into the intelligent electricity meter self-check model, and comprehensively analyze to obtain the intelligent electricity meter self-check evaluation value. The intelligent electricity meter self-check evaluation value is used as the analysis basis for judging whether there is data abnormality in the intelligent electricity meter; Compare the intelligent electricity meter self-check evaluation value with the intelligent electricity meter self-check reference evaluation value stored in the database; If the intelligent electricity meter self-check evaluation value is higher than or equal to the intelligent electricity meter self-check reference evaluation value, the intelligent electricity meter corresponding to the intelligent electricity meter self-check evaluation value has no data abnormality; If the self-check evaluation value of the smart electricity meter is lower than the self-check reference evaluation value of the smart electricity meter, there is data abnormality in the smart electricity meter corresponding to the self-check evaluation value of the smart electricity meter. Denote the difference between the self-check reference evaluation value of the smart electricity meter and the self-check evaluation value of the smart electricity meter corresponding to the smart electricity meter with data abnormality as the self-check evaluation deviation value of the smart electricity meter. Compare the self-check evaluation deviation value with the smart electricity meter data abnormality solutions corresponding to each self-check evaluation deviation value stored in the database to obtain the smart electricity meter data abnormality solution corresponding to the self-check evaluation deviation value of the smart electricity meter. The smart electricity meter data abnormality solution corresponding to the self-check evaluation deviation value of the smart electricity meter represents the smart electricity meter data abnormality solution for the smart electricity meter with data abnormality corresponding to the self-check evaluation deviation value of the smart electricity meter.

10. A data processing method, characterized in that, Applied to the smart electricity meter with data self-check function according to any one of claims 1-9, it includes the following steps: Obtain the smart electricity meter initialization state data set, and based on the obtained smart electricity meter initialization state data set, comprehensively analyze to obtain the smart electricity meter initialization state characteristic value; Obtain the smart electricity meter metering state data set, and based on the obtained smart electricity meter metering state data set, comprehensively analyze to obtain the smart electricity meter metering state characteristic value; Obtain the smart electricity meter abnormal state data set, and based on the obtained smart electricity meter abnormal state data set, comprehensively analyze to obtain the smart electricity meter abnormal state characteristic value; Import the smart electricity meter initialization state characteristic value, the smart electricity meter metering state characteristic value and the smart electricity meter abnormal state characteristic value into the smart electricity meter self-check model, issue an alarm prompt for the smart electricity meter with data abnormality and compare to obtain the smart electricity meter data abnormality solution.

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