An energy meter and its operating method that can adapt to different phase metering scenarios

Abstract

This invention provides an energy meter and its operating method that can adapt to different phase metering scenarios, relating to the field of energy meter metering technology. The method includes acquiring historical metering data from similar energy meters, performing feature analysis based on different metering scenarios to form scenario metering feature data; collecting real-time metering data from a target energy meter, and combining it with the scenario metering feature data for comparative analysis of metering scenarios to form real-time metering monitoring comparison data; continuously acquiring real-time metering data, and performing monitoring and analysis based on the real-time metering monitoring comparison data to form real-time metering monitoring analysis result data. This method, through the analysis and processing of historical metering data from energy meters, extracts feature information of metering data corresponding to different metering scenarios to provide adaptive metering switching for real-time metering needs, thereby efficiently and accurately achieving the switching of metering needs for different metering scenarios.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, and more specifically, to an electricity meter and its operating method that can adapt to different phase metering scenarios. Background Technology

[0002] An electricity meter is an instrument used to measure electrical energy; it is also called an energy meter, kilowatt-hour meter, or watt-hour meter, and refers to an instrument that measures various electrical quantities. Due to different metering scenarios, the parameters measured by electricity meters can vary significantly. Furthermore, as metering environments become increasingly diverse and complex, the metering functions and methods of electricity meters are gradually diversifying to fully meet metering needs in different scenarios.

[0003] Currently, in order to meet different metering scenarios and achieve broader and more diverse metering needs, the design of electricity meters is gradually becoming able to cover a variety of different metering scenario requirements. However, for different metering scenarios, electricity meters cannot yet handle the switching between metering scenarios very well. Most of the switching still relies on manual meter setting, which makes the metering switching inefficient and prone to human error.

[0004] Therefore, designing an energy meter and its operating method that can adapt to different metering scenarios is an urgent problem to be solved. By analyzing and processing the historical metering data of the energy meter, the characteristic information of the metering data corresponding to different metering scenarios can be extracted to provide adaptive automatic metering switching for real-time metering needs. This will enable efficient and accurate switching of metering needs for different metering scenarios and ensure that the energy meter can operate efficiently and stably under different metering scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a method for operating an electricity meter. By analyzing historical metering data of the same type of electricity meter based on different metering scenarios, characteristic information of the metering situation under different metering scenarios is obtained. This provides sufficient basic comparative analysis data for metering scenario identification when the target electricity meter is metered in real time, so as to ensure that the electricity meter can efficiently and quickly adjust the metering parameters and metering methods, and effectively ensure the accuracy of metering data and the stability of electricity meter operation.

[0006] The present invention also aims to provide an energy meter that can adapt to different phase metering scenarios. By configuring it to fully collect historical metering data and analyze and process the historical metering data to extract metering characteristic information under different metering scenarios, the present invention can use this metering characteristic information to analyze and judge the metering scenario under real-time metering conditions. This provides an accurate and efficient metering adjustment reference for adjusting the metering method and metering parameters of the energy meter, and provides an important and sufficient material basis for the energy meter to meet different metering scenarios and stable metering operation.

[0007] In a first aspect, the present invention provides a method for operating an electricity meter, comprising: acquiring historical metering data of similar electricity meters; performing feature analysis based on different metering scenarios to form scenario metering feature data; collecting real-time metering data of a target electricity meter; and combining the scenario metering feature data to perform comparative analysis of metering scenarios to form real-time metering monitoring comparison data; continuously acquiring real-time metering data; and performing monitoring and analysis based on the real-time metering monitoring comparison data to form real-time metering monitoring analysis result data.

[0008] In this invention, the method analyzes historical metering data of the same type of electricity meter based on different metering scenarios to obtain characteristic information of its own metering situation for different metering scenarios. Then, when the target electricity meter is metered in real time, it can provide sufficient basic comparative analysis data for metering scenario identification, so as to ensure that the electricity meter can efficiently and quickly adjust the metering parameters and metering methods, and effectively ensure the accuracy of metering data and the stability of electricity meter metering operation.

[0009] One possible approach is to acquire historical metering data from similar types of electricity meters, perform feature analysis based on different metering scenarios, and form scenario metering feature data. This includes: classifying historical metering data according to different metering scenarios to form different historical scenario metering data; for each historical scenario metering data, determining the changes in all metering parameters over time within the effective metering period to form effective change values ​​of metering parameters; and performing metering feature analysis based on the metering scenario on the effective change values ​​of metering parameters under all effective metering periods for each historical scenario metering data to form scenario metering feature data for different metering scenarios.

[0010] In this invention, historical metering data from the same type of electricity meter under different metering scenarios serves as the foundational big data for extracting metering feature data from different metering scenarios. Therefore, when performing feature extraction analysis, the acquired historical metering data should first be classified according to different metering scenarios to ensure the accuracy of feature information extraction. Of course, for electricity meters, metering data under different metering scenarios are not obtained continuously, thus forming metering data within independent effective metering time periods. The data unit for feature information extraction is based on these effective metering time periods. It is understandable that feature analysis within different independent effective metering time periods under the same metering scenario mainly focuses on the data change characteristics of metering parameters, as key parameters and data changes differ across different metering scenarios. It should be noted that the determination of the metering scenario can be a general AC metering scenario or a DC metering scenario, or it can be a metering scenario specific to the object being metered, such as household electricity or industrial electricity. This application uses general AC and DC metering scenarios as examples to illustrate the working method of electricity meters under different metering scenarios. The measurement parameters are different for the two metering scenarios. For example, AC metering mainly measures voltage and active power, while DC metering mainly measures current and total power. The focus of the measurement is different.

[0011] One possible approach is to perform a metrological feature analysis on the effective change values ​​of metrological parameters under all effective metrological time periods of each historical scenario metrological data, forming scenario metrological feature data for different metrological scenarios. This includes: performing a correlation analysis on the change of metrological parameters for each effective metrological time period of different historical scenario metrological data, forming corresponding scenario metrological correlation feature information; performing a metrological similarity analysis on all scenario metrological correlation feature information under different historical scenario metrological data, forming different similar scenario metrological correlation feature information corresponding to historical scenario metrological data; and aggregating all similar scenario metrological correlation feature information under historical scenario metrological data to form scenario metrological feature data corresponding to the metrological scenario.

[0012] In this invention, the metrological feature analysis of metrological parameters under different metrological scenarios mainly involves analyzing the correlation between the data changes of the main and non-main metrological parameters under the corresponding metrological scenarios, and extracting the feature information of the regularity of the data changes of the main metrological parameters under the metrological scenarios, so as to form scenario metrological feature data for the metrological scenarios.

[0013] One possible approach is to perform a correlation analysis based on the changes in measurement parameters for each valid measurement time period of measurement data from different historical scenarios, thereby forming corresponding scenario-based measurement correlation feature information. This includes: determining the necessary and unnecessary measurement parameters for the measurement scenario corresponding to the historical scenario measurement data; performing a measurement gap analysis on the necessary and unnecessary measurement parameters to form measurement gap correlation feature information; extracting measurement change features from the necessary measurement parameters to form necessary measurement feature information; and combining the measurement gap correlation feature information and the necessary measurement feature information for each valid measurement time period of measurement data from different historical scenarios to form scenario-based measurement correlation feature information corresponding to the valid measurement time period.

[0014] In this invention, the correlation analysis of metering parameters under different metering scenarios mainly considers that different metering scenarios have different key metering parameters, that is, distinguishing between necessary and unnecessary metering parameters. For example, for AC metering scenarios, necessary metering parameters are mainly voltage and active power, while for DC metering scenarios, necessary metering parameters are mainly current and total power. Correspondingly, since electricity meters can be used in different metering scenarios, and for different metering scenarios, the values ​​of unnecessary metering parameters (excluding necessary parameters) will also change during actual metering. However, the level of this change is significantly different from the level required for metering in the corresponding metering scenario. Characteristic analysis of the changes in these unnecessary metering parameters is also important metering characteristic information under the metering scenario. Of course, the necessary metering parameters under the metering scenario are important metering data, therefore, further metering change feature extraction is needed for these necessary metering parameters to highlight the characteristic information under the corresponding metering scenario.

[0015] As one possible approach, a measurement gap analysis is performed on necessary and unnecessary measurement parameters to generate measurement gap correlation feature information. This includes: identifying the necessary direct measurement parameters among the necessary measurement parameters in historical scenario measurement data, and extracting the necessary direct measurement change values ​​of each necessary direct measurement parameter as a function of time within the corresponding effective measurement period. Here, n represents the number of the different valid metering time periods corresponding to the historical scene metering data, and x represents the number of the necessary direct metering parameter corresponding to the historical scene metering data. The unnecessary direct metering parameters among the unnecessary metering parameters in the historical scene metering data are identified, and the changes in unnecessary metering parameters for each parameter over time are extracted within the corresponding valid metering time period. y represents the number of the non-essential direct measurement parameter corresponding to the historical scene measurement data; it represents all necessary direct measurement changes within the same valid measurement time period under the historical scene measurement data. and all non-essential measurement changes The correlation value of the measurement gap under the corresponding effective measurement period is determined according to the following formula. ,in: , , ; This represents the weighting factor of the necessary direct measurement parameter numbered x in the measurement difference. This represents the weighting factor of the non-essential direct measurement parameter y in the measurement gap. This indicates the necessary direct measurement of the difference value. This represents the quantized value of the necessary direct measurement of the difference. This indicates the difference value that is not necessarily directly measured. This represents the quantified value of the difference that is not necessarily directly measured.

[0016] In this invention, the analysis of measurement gaps between necessary and unnecessary measurement parameters mainly involves extracting the numerical differences between the measurement data of necessary and unnecessary parameters in a given measurement scenario. This difference characteristic stems from the fact that the measurement values ​​of unnecessary parameters also change to varying degrees during the actual measurement process. For example, in a DC measurement scenario, although the current does not change direction and value as significantly as AC, it still fluctuates, leading to fluctuations in AC voltage measurements. In this scenario, the values ​​of current and AC voltage differ considerably. Determining the difference by acquiring the changes in these necessary and unnecessary measurement parameters within the effective measurement time is one of the characteristics that reflects the important relationships between measurement parameters in the corresponding measurement scenario. It is important to note that parameters not directly measured are formed through direct measurement parameters and corresponding formulas; therefore, they do not have an independent impact on the gap analysis, as their influence on the gap comes from the numerical levels of the direct measurement parameters that form them. In addition, different direct measurement parameters have different impacts on the gap in the gap analysis. For example, in the DC metering scenario, current is the most important metering parameter, while DC voltage has a slightly smaller impact on current. Therefore, the impact weight of different direct measurement parameters on the gap analysis needs to be considered when conducting the gap analysis. The weighting factor can be set according to actual needs, or it can be obtained by big data analysis based on the impact of these direct measurement parameters on the metering accuracy.

[0017] As one possible implementation, necessary measurement parameters are subjected to measurement change feature extraction to form necessary measurement feature information, including: for each valid measurement time period under historical scene measurement data, based on the necessary direct measurement change value of each necessary direct measurement parameter. Determine the necessary direct measurement range for the corresponding necessary direct measurement parameters within the effective measurement period. For each valid measurement period under historical scene measurement data, determine the measurement calculation relationship of each necessary indirect measurement parameter (excluding necessary direct measurement parameters) obtained from the necessary direct measurement parameters; based on the measurement calculation relationship, determine the relationship of the corresponding necessary indirect measurement parameters from the corresponding necessary direct measurement parameters to obtain the change value. z represents the number of the necessary indirect measurement parameter under the corresponding historical measurement data; the direct acquisition change value of the necessary indirect measurement parameter within the effective measurement period is extracted. And obtain the change value by combining the corresponding relationship. Determine the bias range of different necessary indirect measurement parameters within the corresponding effective measurement time period. .

[0018] In this invention, the characteristic information of necessary metering parameters mainly considers the metering range of the parameters and the deviation data between the change value formed by the relationship of the direct metering parameters and the actual change value formed by the electricity meter. This deviation data is, on the one hand, an assessment of the deviation level under the corresponding metering parameter change range, and on the other hand, it also reflects the characteristics of the same type of electricity meter in processing non-direct metering parameter data.

[0019] As one possible implementation, a metrological similarity analysis is performed on the metrological correlation characteristics of all scenarios under different historical metrological data to form metrological correlation characteristics of different similar scenarios corresponding to historical scenario metrological data. This includes: setting a gap similarity threshold γ and a deviation similarity threshold ζ, and performing the following similarity analysis on the metrological correlation characteristics of different scenarios under historical scenario metrological data: For different scenario metrological correlation characteristics, if the corresponding metrological gap correlation value... The difference does not exceed the difference similarity threshold γ, and the bias range of each necessary indirect measurement parameter is... If the deviation overlap rate is not less than the deviation similarity threshold ζ, then the two scene measurement correlation feature information are clustered and labeled as scene measurement correlation feature information under the same similar scene measurement correlation feature information; otherwise, the two scene measurement correlation feature information are labeled as scene measurement correlation feature information under different similar scene measurement correlation feature information.

[0020] In this invention, similarity analysis is primarily evaluated through two aspects. First, the degree of similarity in the difference determines whether the changes in the measurement parameters are at the same level during two effective measurement periods; that is, whether the objects measured by the measurement parameters have similar levels of measurement data change. Second, the similarity in the deviation reflects the measurement level of the necessary measurement parameter itself. Only when both are close can the parameter levels of the measured objects be considered the same across different effective measurement periods. It should be noted that the measurement range of the necessary measurement parameter is a reference quantity used to judge the measurement scenario. Under the same measurement scenario, the measurement range of the necessary measurement parameter may differ due to the energy usage level of the measured object. Such differences are acceptable in similarity analysis. However, if the difference between the necessary and non-necessary measurement parameters, and the deviation of the necessary measurement parameter, still shows a significant difference in different measurement ranges, it indicates a significant difference in the way and effect of energy utilization by the measured object, which has a substantial impact on the measurement results.

[0021] One possible approach is to collect real-time metering data from the target electricity meter and combine it with scene metering characteristic data to conduct comparative analysis of metering scenarios, forming real-time metering monitoring comparison data. This includes: collecting real-time metering values ​​of all metering parameters of the target electricity meter, and determining the real-time metering scenario of the target electricity meter based on all necessary direct metering ranges corresponding to necessary metering parameters under the scene metering characteristic data corresponding to different metering scenarios; and matching all similar scene metering correlation characteristic information under the corresponding scene metering characteristic data based on the real-time metering scenario.

[0022] In this invention, after acquiring metering characteristic information under different metering scenarios, the main purpose is to accurately determine the metering scenario during real-time metering analysis, thereby providing a reference for the metering adjustment of the electricity meter. During the analysis, the metering characteristic information is matched using data from the initial metering stage to locate the corresponding scenario metering characteristic data, thus providing data comparison for subsequent monitoring and analysis.

[0023] As one possible implementation, real-time metering data is continuously acquired, and monitoring and analysis are performed based on the real-time metering monitoring comparison data to form real-time metering monitoring and analysis result data. This includes: acquiring the real-time metering gap correlation value, the real-time necessary direct metering range, and the real-time deviation range corresponding to the real-time metering scenario; and performing the following monitoring and analysis judgment based on all similar scenario metering correlation feature information under the matched scenario metering feature data: if any data in the real-time metering gap correlation value, the real-time necessary direct metering range, and the real-time deviation range does not correspond to any of the matched similar scenario metering correlation feature information, then the corresponding scenario metering feature data is re-determined based on the real-time necessary direct metering range; if new corresponding scenario metering feature data is determined, then all similar scenario metering correlation feature information under the scenario metering feature data is marked as real-time matching data; if new corresponding scenario metering feature data cannot be determined, then metering error information is output.

[0024] In this invention, determining the matching scene metering feature data involves continuously collecting real-time metering data for comparison and monitoring. If any metering range, difference value, or deviation exceeds the range, the metering scene needs to be re-verified. If a new scene is located, the metering parameters of the electricity meter need to be adjusted. If it remains unchanged, monitoring can continue. If the scene cannot be located, considering that the feature data is the result of big data extraction and analysis, it can be largely determined that a metering error has occurred, thus providing a metering error report.

[0025] Secondly, the present invention provides an energy meter capable of adapting to different phase metering scenarios. The energy meter capable of adapting to different phase metering scenarios is configured to: acquire historical metering data of the same type of energy meter, perform feature analysis based on different metering scenarios, and form scenario metering feature data; collect real-time metering data of the target energy meter, and perform comparative analysis of metering scenarios in combination with the scenario metering feature data, to form real-time metering monitoring comparison data; continuously acquire real-time metering data, and perform monitoring and analysis based on the real-time metering monitoring comparison data, to form real-time metering monitoring analysis result data.

[0026] In this invention, the electricity meter is configured to fully collect historical metering data and analyze and process the historical metering data to extract metering characteristic information under different metering scenarios. At the same time, it uses this metering characteristic information to analyze and judge the metering scenario under real-time metering conditions, providing an accurate and efficient metering adjustment reference for adjusting the metering method and metering parameters of the electricity meter. This provides an important and sufficient material basis for the electricity meter to meet different metering scenarios and stable metering operation.

[0027] The beneficial effects of the energy meter and its operating method that are adaptive to different phase metering scenarios provided by this invention are as follows:

[0028] This method analyzes historical metering data of similar electricity meters based on different metering scenarios to obtain characteristic information about the metering situation under different scenarios. This provides sufficient comparative analysis data for metering scenario identification when the target electricity meter is metered in real time, ensuring that the electricity meter can efficiently and quickly adjust the metering parameters and metering methods, and effectively guaranteeing the accuracy of the metering data and the stability of the metering operation.

[0029] This electricity meter is configured to fully collect historical metering data and analyze and process this data to extract metering characteristic information under different metering scenarios. At the same time, it uses this metering characteristic information to analyze and judge the metering scenario under real-time metering conditions, providing an accurate and efficient metering adjustment reference for adjusting the metering method and metering parameters. This provides an important and sufficient material basis for the electricity meter to meet different metering scenarios and stable metering operation. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the steps of an energy meter operating method that can adapt to different phase metering scenarios, as provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0033] An electricity meter is an instrument used to measure electrical energy; it is also called an energy meter, kilowatt-hour meter, or watt-hour meter, and refers to an instrument that measures various electrical quantities. Due to different metering scenarios, the parameters measured by electricity meters can vary significantly. Furthermore, as metering environments become increasingly diverse and complex, the metering functions and methods of electricity meters are gradually diversifying to fully meet metering needs in different scenarios.

[0034] Currently, in order to meet different metering scenarios and achieve broader and more diverse metering needs, the design of electricity meters is gradually becoming able to cover a variety of different metering scenario requirements. However, for different metering scenarios, electricity meters cannot yet handle the switching between metering scenarios very well. Most of the switching still relies on manual meter setting, which makes the metering switching inefficient and prone to human error.

[0035] refer to Figure 1 This invention provides a method for operating an energy meter that can adapt to different phase metering scenarios. This method analyzes historical metering data of the same type of energy meter based on different metering scenarios to obtain characteristic information of its own metering situation for different metering scenarios. Then, when the target energy meter is metered in real time, it can provide sufficient basic comparative analysis data for metering scenario identification, so as to ensure that the energy meter can efficiently and quickly adjust the metering parameters and metering methods, and effectively ensure the accuracy of metering data and the stability of energy meter metering operation.

[0036] The working method of an energy meter that can adapt to different phase metering scenarios specifically includes the following steps:

[0037] S1: Obtain historical metering data of the same type of electricity meter, perform feature analysis based on different metering scenarios, and form scenario metering feature data.

[0038] Historical metering data of similar types of electricity meters are acquired, and feature analysis is performed based on different metering scenarios to form scenario metering feature data. This includes: classifying historical metering data according to different metering scenarios to form different historical scenario metering data; for each historical scenario metering data, determining the changes of all metering parameters in sequence over time within the effective metering period to form effective changes in metering parameters; and performing metering feature analysis based on the metering scenario on the effective changes in metering parameters under all effective metering periods of each historical scenario metering data to form scenario metering feature data for different metering scenarios.

[0039] Historical metering data of the same type of electricity meter under different metering scenarios is the foundational big data for extracting metering feature data for different metering scenarios. Therefore, when performing feature extraction analysis, the acquired historical metering data should first be classified according to different metering scenarios to ensure the accuracy of feature information extraction. Of course, for electricity meters, metering data under different metering scenarios are not obtained in continuous time, thus forming metering data within independent effective metering time periods. The data unit for feature information extraction is based on the effective metering time period. It is understandable that feature analysis within different independent effective metering time periods under the same metering scenario mainly focuses on the data change characteristics of metering parameters, since key parameters and data changes differ in different metering scenarios. It should be noted that the determination of the metering scenario can be a general AC metering scenario or a DC metering scenario, or it can be a metering scenario specific to the object being metered, such as household electricity or industrial electricity. This application uses general AC metering scenarios and DC metering scenarios as examples to illustrate the working method of electricity meters under different metering scenarios. The measurement parameters are different for the two metering scenarios. For example, AC metering mainly measures voltage and active power, while DC metering mainly measures current and total power. The focus of the measurement is different.

[0040] Specifically, for each historical scenario metering data, effective changes in metering parameters over all valid metering time periods are analyzed based on metering scenario characteristics to form scenario metering characteristic data for different metering scenarios. This includes: performing correlation analysis based on changes in metering parameters over each valid metering time period of different historical scenario metering data to form corresponding scenario metering correlation characteristic information; performing metering similarity analysis on all scenario metering correlation characteristic information under different historical scenario metering data to form different similar scenario metering correlation characteristic information corresponding to historical scenario metering data; and aggregating all similar scenario metering correlation characteristic information under historical scenario metering data to form scenario metering characteristic data corresponding to the metering scenario.

[0041] The analysis of metrological characteristics of metrological parameters under different metrological scenarios mainly involves analyzing the correlation between the data changes of the main and non-main metrological parameters under the corresponding metrological scenarios, and extracting the characteristic information of the regularity of the data changes of the main metrological parameters under the metrological scenarios, so as to form scenario metrological characteristic data for the metrological scenarios.

[0042] For each valid measurement period of measurement data from different historical scenarios, a correlation analysis based on the changes in measurement parameters is performed to form corresponding scenario-based measurement correlation feature information. This includes: determining the necessary and unnecessary measurement parameters for the measurement scenario corresponding to the historical scenario measurement data; performing measurement gap analysis on the necessary and unnecessary measurement parameters to form measurement gap correlation feature information; extracting measurement change features from the necessary measurement parameters to form necessary measurement feature information; and combining the measurement gap correlation feature information and the necessary measurement feature information for each valid measurement period of measurement data from different historical scenarios to form scenario-based measurement correlation feature information corresponding to the valid measurement period.

[0043] Correlation analysis of metering parameters in different metering scenarios primarily considers the different key metering parameters for each scenario, distinguishing between necessary and unnecessary parameters. For example, in AC metering scenarios, necessary parameters mainly include voltage and active power, while in DC metering scenarios, they mainly include current and total power. Since electricity meters can be used in different metering scenarios, the values ​​of unnecessary parameters (excluding necessary ones) will also change during actual measurement. However, the level of these changes is significantly different from the levels required for measurement in the corresponding scenarios. Characteristic analysis of these changes in unnecessary parameters is crucial metering information within a given scenario. Of course, necessary metering parameters are critical data within a metering scenario, thus requiring further feature extraction to highlight their specific characteristics within that scenario.

[0044] A measurement gap analysis is performed on necessary and unnecessary measurement parameters to form measurement gap correlation feature information. This includes: identifying the necessary direct measurement parameters among the necessary measurement parameters under historical scenario measurement data, and extracting the necessary direct measurement change value of each necessary direct measurement parameter as a function of time in the corresponding effective measurement period. Here, n represents the number of the different valid metering time periods corresponding to the historical scene metering data, and x represents the number of the necessary direct metering parameter corresponding to the historical scene metering data. The unnecessary direct metering parameters among the unnecessary metering parameters in the historical scene metering data are identified, and the changes in unnecessary metering parameters for each parameter over time are extracted within the corresponding valid metering time period. y represents the number of the non-essential direct measurement parameter corresponding to the historical scene measurement data; it represents all necessary direct measurement changes within the same valid measurement time period under the historical scene measurement data. and all non-essential measurement changes The correlation value of the measurement gap under the corresponding effective measurement period is determined according to the following formula. ,in: , , ; This represents the weighting factor of the necessary direct measurement parameter numbered x in the measurement difference. This represents the weighting factor of the non-essential direct measurement parameter y in the measurement gap. This indicates the necessary direct measurement of the difference value. This represents the quantized value of the necessary direct measurement of the difference. This indicates the difference value that is not necessarily directly measured. This represents the quantified value of the difference that is not necessarily directly measured.

[0045] Metering gap analysis of necessary and unnecessary metering parameters mainly involves extracting the numerical differences between the metering data of necessary and unnecessary metering parameters in a given metering scenario. This gap information stems from the fact that the metering values ​​of unnecessary metering parameters also fluctuate to varying degrees during actual metering processes. For example, in DC metering scenarios, although current does not change direction or value as significantly as AC, it still exhibits fluctuations, leading to variations in AC voltage measurements. In such metering scenarios, the values ​​of current and AC voltage differ considerably. Determining the gap by acquiring the changes in these necessary and unnecessary metering parameters within the effective metering time is one of the key characteristics reflecting the relationships between important metering parameters in a given metering scenario. It is important to note that parameters not directly measured are derived from direct metering parameters and their corresponding formulas; therefore, indirect metering parameters do not have an independent impact on gap analysis, as their influence on the gap originates from the numerical levels of the direct metering parameters that form them. In addition, different direct measurement parameters have different impacts on the gap in the gap analysis. For example, in the DC metering scenario, current is the most important metering parameter, while DC voltage has a slightly smaller impact on current. Therefore, the impact weight of different direct measurement parameters on the gap analysis needs to be considered when conducting the gap analysis. The weighting factor can be set according to actual needs, or it can be obtained by big data analysis based on the impact of these direct measurement parameters on the metering accuracy.

[0046] The necessary measurement parameters are subjected to measurement change feature extraction to form necessary measurement feature information, including: for each valid measurement time period under historical scene measurement data, the necessary direct measurement change value of each necessary direct measurement parameter. Determine the necessary direct measurement range for the corresponding necessary direct measurement parameters within the effective measurement period. For each valid measurement period under historical scene measurement data, determine the measurement calculation relationship of each necessary indirect measurement parameter (excluding necessary direct measurement parameters) obtained from the necessary direct measurement parameters; based on the measurement calculation relationship, determine the relationship of the corresponding necessary indirect measurement parameters from the corresponding necessary direct measurement parameters to obtain the change value. z represents the number of the necessary indirect measurement parameter under the corresponding historical measurement data; the direct acquisition change value of the necessary indirect measurement parameter within the effective measurement period is extracted. And obtain the change value by combining the corresponding relationship. Determine the bias range of different necessary indirect measurement parameters within the corresponding effective measurement time period. .

[0047] For the characteristic information of necessary metering parameters, the main considerations are the metering range of the parameters and the deviation data between the change value formed by the relationship of the direct metering parameters and the actual change value formed by the electricity meter. This deviation data is, on the one hand, an assessment of the deviation level under the corresponding metering parameter change range, and on the other hand, it also reflects the characteristics of the same type of electricity meter in processing non-direct metering parameter data.

[0048] A metrological similarity analysis is performed on the metrological correlation characteristics of all scenarios under different historical metrological data to form metrological correlation characteristics of different similar scenarios corresponding to historical scenario metrological data. This includes setting a similarity threshold γ for the difference and a similarity threshold ζ for the deviation. The following similarity analysis is then performed on the metrological correlation characteristics of different scenarios under historical scenario metrological data: For different scenario metrological correlation characteristics, if the corresponding metrological difference correlation value... The difference does not exceed the difference similarity threshold γ, and the bias range of each necessary indirect measurement parameter is... If the deviation overlap rate is not less than the deviation similarity threshold ζ, then the two scene measurement correlation feature information are clustered and labeled as scene measurement correlation feature information under the same similar scene measurement correlation feature information; otherwise, the two scene measurement correlation feature information are labeled as scene measurement correlation feature information under different similar scene measurement correlation feature information.

[0049] Similarity analysis is primarily assessed through two aspects: firstly, the degree of similarity of the differences, which determines whether the changes in the measurement parameters are at the same level during two effective measurement periods—that is, whether the objects measured by the measurement parameters have similar levels of change in measurement data; and secondly, the similarity of the deviations, which reflects the measurement level of the necessary measurement parameters themselves. Only when both are close can it be considered that the parameter levels of the measured objects are the same across different effective measurement periods. It should be noted that the measurement range of necessary measurement parameters is a reference quantity used to judge the measurement scenario. Within the same measurement scenario, the measurement range of necessary measurement parameters may differ due to the energy usage level of the measured object. Such differences are acceptable in similarity analysis. However, significant differences in the level of difference between necessary and non-necessary measurement parameters, and significant differences in the deviation of necessary measurement parameters across different measurement ranges, indicate significant differences in the way and effectiveness of energy utilization by the measured object, which has a substantial impact on the measurement results.

[0050] S2: Collect real-time metering data of the target electricity meter, and combine it with scene metering characteristic data to conduct comparative analysis of metering scenarios, forming real-time metering monitoring comparison data.

[0051] Real-time metering data of the target electricity meter is collected and compared with the metering scenario feature data to form real-time metering monitoring comparison data. This includes: collecting real-time metering values ​​of all metering parameters of the target electricity meter, and determining the real-time metering scenario of the target electricity meter based on all necessary direct metering ranges corresponding to necessary metering parameters under the scene metering feature data corresponding to different metering scenarios; and matching all similar scene metering correlation feature information under the corresponding scene metering feature data based on the real-time metering scenario.

[0052] Of course, after acquiring metering characteristic information under different metering scenarios, the main purpose is to accurately determine the metering scenario during real-time metering analysis, thereby providing a reference for the metering adjustment of electricity meters. During analysis, the metering characteristic information is matched using data from the initial metering stage to locate the corresponding scenario metering characteristic data, thus providing data for comparison in subsequent monitoring and analysis.

[0053] S3: Continuously acquire real-time metering data and conduct monitoring and analysis based on real-time metering monitoring comparison data to form real-time metering monitoring analysis results data.

[0054] Continuously acquire real-time metering data and perform monitoring and analysis based on real-time metering monitoring comparison data to form real-time metering monitoring and analysis results data, including: acquiring the real-time metering gap correlation value, real-time necessary direct metering range, and real-time deviation range corresponding to the real-time metering scenario, and performing the following monitoring and analysis judgments based on all similar scenario metering correlation feature information under the matched scenario metering feature data: if any data in the real-time metering gap correlation value, real-time necessary direct metering range, and real-time deviation range does not correspond to any of the matched similar scenario metering correlation feature information, then the corresponding scenario metering feature data is re-determined based on the real-time necessary direct metering range; if new corresponding scenario metering feature data is determined, then all similar scenario metering correlation feature information under the scenario metering feature data is marked as real-time matching data; if new corresponding scenario metering feature data cannot be determined, then metering error information is output.

[0055] Determining the matching scene metering feature data involves continuously collecting real-time metering data for comparison and monitoring. If any metering range, discrepancy, or deviation exceeds the range, the metering scene needs to be recalibrated. If a new scene is located, the metering parameters of the electricity meter need to be adjusted. If it remains unchanged, monitoring can continue. However, if the scene cannot be located, considering that the feature data is the result of big data extraction and analysis, it can be largely determined that a metering error has occurred, thus providing a metering error report.

[0056] The present invention also provides an energy meter that can adapt to different phase metering scenarios. The energy meter that can adapt to different phase metering scenarios is configured to: acquire historical metering data of the same type of energy meter, perform feature analysis based on different metering scenarios to form scenario metering feature data; collect real-time metering data of the target energy meter, and perform comparative analysis of metering scenarios in combination with scenario metering feature data to form real-time metering monitoring comparison data; continuously acquire real-time metering data, and perform monitoring and analysis based on real-time metering monitoring comparison data to form real-time metering monitoring analysis result data.

[0057] This electricity meter is configured to fully collect historical metering data and analyze and process this data to extract metering characteristic information under different metering scenarios. At the same time, it uses this metering characteristic information to analyze and judge the metering scenario under real-time metering conditions, providing an accurate and efficient metering adjustment reference for adjusting the metering method and metering parameters. This provides an important and sufficient material basis for the electricity meter to meet different metering scenarios and stable metering operation.

[0058] In summary, the beneficial effects of the energy meter and its operating method that can adapt to different phase metering scenarios provided by the embodiments of the present invention are as follows:

[0059] This method analyzes historical metering data of similar electricity meters based on different metering scenarios to obtain characteristic information about the metering situation under different scenarios. This provides sufficient comparative analysis data for metering scenario identification when the target electricity meter is metered in real time, ensuring that the electricity meter can efficiently and quickly adjust the metering parameters and metering methods, and effectively guaranteeing the accuracy of the metering data and the stability of the metering operation.

[0060] This electricity meter is configured to fully collect historical metering data and analyze and process this data to extract metering characteristic information under different metering scenarios. At the same time, it uses this metering characteristic information to analyze and judge the metering scenario under real-time metering conditions, providing an accurate and efficient metering adjustment reference for adjusting the metering method and metering parameters. This provides an important and sufficient material basis for the electricity meter to meet different metering scenarios and stable metering operation.

[0061] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0062] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0063] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0064] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0065] The “protocol” mentioned in the embodiments of this application may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. The embodiments of this application do not specifically limit this.

[0066] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0067] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0068] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0069] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0070] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0071] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0072] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0073] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0075] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0078] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0079] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for operating an electricity meter, characterized in that, include: Acquire historical metering data of similar electricity meters, perform feature analysis based on different metering scenarios, and form scenario metering feature data; Real-time metering data of the target electricity meter is collected, and the metering characteristic data of the scenario is combined with the comparative analysis of the metering scenario to form real-time metering monitoring comparison data. Continuously acquire real-time metering data, and perform monitoring and analysis based on the real-time metering monitoring comparison data to form real-time metering monitoring analysis result data; This involves acquiring historical metering data from similar types of electricity meters, performing feature analysis based on different metering scenarios, and forming scenario-based metering feature data, including: The historical measurement data is classified according to different measurement scenarios to form different historical scenario measurement data; For each historical scene measurement data, determine the change values ​​of all measurement parameters in the time dimension within the effective measurement period, and form the effective change values ​​of the measurement parameters. For each historical scenario metering data, the effective change values ​​of the metering parameters under all effective metering time periods are analyzed based on the metering scenario to form scenario metering characteristic data for different metering scenarios: For each effective measurement time period of the historical scene measurement data, a correlation analysis based on the change of measurement parameters is performed to form corresponding scene measurement correlation feature information. A metrological similarity analysis is performed on all the metrological correlation feature information of the different historical scene metrological data to form metrological correlation feature information of different similar scenes corresponding to the historical scene metrological data; By aggregating all similar scene measurement correlation feature information under the historical scene measurement data, the scene measurement feature data corresponding to the measurement scene is formed.

2. The method for operating an electricity meter according to claim 1, characterized in that, The step of performing a correlation analysis based on the changes in measurement parameters for each effective measurement time period of different historical scene measurement data to form corresponding scene measurement correlation feature information includes: Determine the necessary and unnecessary metering parameters for the metering scenario corresponding to the historical scene metering data; A measurement gap analysis is performed on the necessary measurement parameters and the unnecessary measurement parameters to form measurement gap correlation feature information. The necessary measurement parameters are subjected to measurement change feature extraction to form necessary measurement feature information; For each effective measurement time period of different historical scenario measurement data, the measurement gap correlation feature information and the necessary measurement feature information are combined to form the scenario measurement correlation feature information corresponding to the effective measurement time period.

3. The method for operating an electricity meter according to claim 2, characterized in that, The step of performing a measurement gap analysis on the necessary measurement parameters and the unnecessary measurement parameters to form measurement gap correlation feature information includes: The necessary direct measurement parameters among the necessary measurement parameters under the historical scene measurement data are determined, and the necessary direct measurement change values ​​of each necessary direct measurement parameter are extracted as a function of time in the corresponding effective measurement period. , n represents the number of the different effective metering time periods corresponding to the historical scene metering data, and x represents the number of the necessary direct metering parameter corresponding to the historical scene metering data; The unnecessary direct measurement parameters among the unnecessary measurement parameters in the historical scene measurement data are identified, and the unnecessary measurement change values ​​of each unnecessary measurement parameter are extracted as a function of time in the corresponding effective measurement period. y represents the number of the non-essential direct measurement parameter corresponding to the historical scene measurement data; All necessary direct measurement changes within the same valid measurement time period under the historical scene measurement data. and all the aforementioned non-essential measurement changes Determine the correlation value of the measurement gap within the corresponding effective measurement time period. .

4. The method for operating an electricity meter according to claim 3, characterized in that, The step of extracting measurement change features from the necessary measurement parameters to form necessary measurement feature information includes: For each effective metering time period under the historical scene metering data, based on the necessary direct metering change value of each necessary direct metering parameter. Determine the necessary direct measurement range for the corresponding necessary direct measurement parameters within the effective measurement time period. ; For each effective metering time period under the historical scene metering data, determine the metering calculation relationship obtained from the necessary direct metering parameter for each necessary indirect metering parameter other than the necessary direct metering parameter; Based on the aforementioned measurement calculation relationship, the relationship between the corresponding necessary indirect measurement parameters is determined from the corresponding necessary direct measurement parameters to obtain the change value. z represents the number of the necessary indirect measurement parameter under the corresponding historical measurement data; Extract the direct acquisition change values ​​of necessary indirect measurement parameters within the effective measurement time period. And obtain the change value by combining the corresponding relationship. Determine the deviation range of different necessary indirect measurement parameters within the corresponding effective measurement time period. .

5. The method for operating an electricity meter according to claim 4, characterized in that, The step of performing metrological similarity analysis on all the metrological correlation feature information of different historical scene metrological data to form metrological correlation feature information of different similar scenes corresponding to the historical scene metrological data includes: By setting a similarity threshold γ for the difference and a similarity threshold ζ for the deviation, the following similarity analysis is performed on the correlation characteristics of different scene measurement data under the historical scene measurement data: For different scenarios of metrological correlation feature information, if the corresponding metrological gap correlation value The difference does not exceed the difference similarity threshold γ, and the bias range of each of the necessary indirect measurement parameters is... If the deviation overlap rate is not less than the deviation similarity threshold ζ, then the two scene measurement correlation feature information are clustered and labeled as scene measurement correlation feature information under the same similar scene measurement correlation feature information. Conversely, the two sets of scene measurement correlation feature information are respectively labeled as scene measurement correlation feature information under different sets of similar scene measurement correlation feature information.

6. The method of operating an electricity meter according to claim 5, characterized in that, The real-time metering data of the target electricity meter is collected, and a comparative analysis of the metering scenarios is performed in conjunction with the scene metering characteristic data to form real-time metering monitoring comparison data, including: Collect the real-time metering values ​​of all the metering parameters of the target energy meter, and determine the real-time metering scenario of the target energy meter based on all the necessary direct metering ranges corresponding to the necessary metering parameters under the scenario metering characteristic data corresponding to different metering scenarios. Based on the real-time metering scenario, match all similar scenario metering correlation feature information under the corresponding scenario metering feature data.

7. The method of operating an electricity meter according to claim 6, characterized in that, The continuous acquisition of real-time metering data, and the monitoring and analysis based on the real-time metering monitoring comparison data to form real-time metering monitoring and analysis result data, includes: Obtain the real-time metering gap correlation value, the real-time necessary direct metering range, and the real-time deviation range corresponding to the real-time metering scenario. Based on the metering correlation feature information of all similar scenarios under the matching scenario metering feature data, perform the following monitoring analysis and judgment: If any data in the real-time metering gap correlation value, the real-time necessary direct metering range, and the real-time deviation range does not correspond to any of the matched similar scenario metering correlation feature information, then the corresponding scenario metering feature data is re-determined based on the real-time necessary direct metering range: If new corresponding scene measurement feature data is determined, then all similar scene measurement correlation feature information under the scene measurement feature data is labeled as real-time matching data; If new corresponding scene measurement feature data cannot be determined, a measurement error message will be output.

8. An energy meter capable of adapting to different phase metering scenarios, characterized in that, Using the working method of the electricity meter according to claim 1, the electricity meter capable of adapting to different phase metering scenarios is configured as follows: Acquire historical metering data of similar electricity meters, perform feature analysis based on different metering scenarios, and form scenario metering feature data; Real-time metering data of the target electricity meter is collected, and the metering characteristic data of the scenario is combined with the comparative analysis of the metering scenario to form real-time metering monitoring comparison data. Continuously acquire real-time metering data, and perform monitoring and analysis based on the real-time metering monitoring comparison data to form real-time metering monitoring analysis result data.

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