Intelligent electric energy meter fault data analysis method and system

Abstract

The invention relates to the technical field of data analysis, in particular to an intelligent electric energy meter fault data analysis method and system, and the method comprises the steps: determining the data state of target fault data according to an estimated abnormality degree and an influence characterization value, and determining a data correction mode according to the data state; in the partition comparison correction, a partition mode is determined based on a fault development coefficient and a period difference degree to obtain a plurality of divided areas, the area state of each divided area is determined according to the number of abnormal factors and an influence stacking degree, and a data processing mode is determined according to the area state; determining an adjustment mode according to the correction comparison coefficient under a preset correction condition; in the local correction, abnormal subdata is determined according to the fluctuation comparison degree, and data correction is performed according to the instability comparison degree; according to the invention, the accuracy of metering correction of the electric energy meter can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for analyzing fault data of a smart electric energy meter. Background Art

[0002] Smart energy meters are key devices for measuring energy consumption in power systems. Their accuracy is directly related to the economic benefits of power suppliers and users, as well as the stable operation of the power system. However, in actual use, energy meters are prone to measurement errors due to various factors, resulting in inaccurate data. Therefore, how to correct meter measurement errors and improve meter accuracy is a technical problem that needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN117572330A discloses a method and device for bidirectional compensation of electric energy meter measurement, which includes: obtaining the power consumption error curve of the product when using electricity through batch simulation, dividing the metering range of the electric energy meter into multiple power consumption compensation intervals according to the preset power consumption segmentation points; performing quadratic curve fitting on the power consumption error curve, obtaining compensation data corresponding to different power consumption compensation intervals and issuing them; obtaining the feeding error curve of the product when feeding power through batch simulation, dividing the metering range of the electric energy meter into multiple feeding compensation intervals according to the preset feeding segmentation points; performing quadratic curve fitting on the feeding error curve, obtaining compensation data corresponding to different feeding compensation intervals and issuing them; selecting the compensation data of the corresponding interval for compensation according to the type of sampled current and the compensation interval to which the sampled current belongs. It can be seen that the above technical solution has the following problems: the metering compensation method is single, and the compensation data is only compensated by the error curve, and it is impossible to adaptively adjust the compensation method according to the actual data state, resulting in poor accuracy of the electric energy meter metering compensation. Summary of the Invention

[0004] To this end, the present invention provides a method and system for analyzing fault data of an intelligent electric energy meter, so as to overcome the problem that the existing technology has a single metering compensation method, which only compensates data through an error curve and cannot adaptively adjust the correction method according to the actual data status, resulting in poor accuracy of electric energy meter metering correction.

[0005] To achieve the above objectives, the present invention provides a method for analyzing fault data of a smart energy meter, comprising:

[0006] Determine the data status of the target fault data based on the estimated abnormality and impact characterization value, and determine the data correction method based on the data status. The data correction method is partition comparison correction or local correction;

[0007] During the partition comparison and correction, the partitioning method is determined based on the fault development coefficient and the period difference to obtain several partitioned areas. The regional status of each partitioned area is determined according to the number of abnormal factors and the impact stacking degree, and the data processing method is determined according to the regional status;

[0008] The partitioning method is to perform associated partitioning according to a matching threshold or an impact correlation, and the data processing method is to perform multi-factor iterative correction or data correction according to a sub-abnormality degree;

[0009] Under the preset correction conditions, the adjustment method is determined according to the correction comparison coefficient, such as adjusting the correction factor range or the number of divided sections;

[0010] In local correction, abnormal sub-data are determined based on the fluctuation comparison degree, and data correction is performed based on the instability comparison degree.

[0011] Furthermore, if the data status of the target fault data is that the estimated abnormality is greater than or equal to the preset estimated abnormality or the impact characterization value is greater than or equal to the preset impact characterization value, the data correction method is partition comparison correction.

[0012] Furthermore, if the data state of the target fault data is that the estimated abnormality is less than the preset estimated abnormality and the impact characterization value is less than the preset impact characterization value, the data correction method is local correction.

[0013] Furthermore, the partitioning method is determined based on the fault development coefficient and the period difference, including:

[0014] If the fault development coefficient is greater than or equal to the preset fault development coefficient or the period difference is greater than or equal to the preset period difference, the partitioning method is to perform associated partitioning according to the matching threshold;

[0015] If the fault development coefficient is less than the preset fault development coefficient and the period difference is less than the preset period difference, the partitioning method is to perform associated partitioning according to the impact correlation.

[0016] Furthermore, if the regional status is that the number of abnormal factors is greater than the standard number and the impact stacking degree is greater than or equal to the preset impact stacking degree, the data processing method is multi-factor iterative correction;

[0017] In multi-factor iterative correction, the correction method is determined based on the correction dependency and factor interaction, including:

[0018] If the correction dependence is greater than or equal to the preset correction dependence or the factor interaction is greater than or equal to the preset factor interaction, the correction method is serial correction according to the correction response threshold;

[0019] If the correction dependency is less than the preset correction dependency and the factor interaction is less than the preset factor interaction, the correction method is parallel correction based on the impact coverage.

[0020] Furthermore, the serial correction according to the correction response threshold includes:

[0021] Determine the priority coefficient for correction of each abnormal factor according to the correction response threshold, and adjust the power consumption reading corresponding to each time point according to the instability offset corresponding to each characteristic factor;

[0022] For a single time point,

[0023] If the instability offset is greater than the preset instability offset, the power consumption reading corresponding to the time point is reduced and adjusted according to the dependency impact;

[0024] If the instability offset is less than the preset instability offset, the power consumption reading corresponding to the time point is increased and adjusted according to the dependency influence;

[0025] The priority coefficient of correction for a single characteristic factor is positively correlated with the correction response threshold corresponding to the abnormal factor.

[0026] Furthermore, if the correction comparison coefficient is greater than or equal to the preset correction comparison coefficient, the adjustment method is to increase the correction factor range;

[0027] The increase in the correction factor range is positively correlated with the estimated deviation coefficient.

[0028] Furthermore, if the correction comparison coefficient is less than the preset correction comparison coefficient, the adjustment method is to increase the number of sub-data;

[0029] The increase in the number of sub-data is positively correlated with the estimated deviation coefficient.

[0030] Furthermore, if the regional status is that the number of abnormal factors is equal to the standard number or the impact stacking degree is less than the preset impact stacking degree, the data processing method is to correct the data according to the sub-abnormality degree.

[0031] The present invention also provides a smart electric energy meter fault data analysis system, comprising:

[0032] The correction analysis module is used to determine the data status of the target fault data based on the estimated abnormality and the impact characterization value, and determine the data correction method based on the data status. The data correction method is partition comparison correction or local correction;

[0033] a comparison and correction module connected to the correction analysis module, for determining a partitioning method based on the fault development coefficient and the period difference to obtain a plurality of partitioned areas during the partition comparison and correction, determining the regional status of each partitioned area based on the number of abnormal factors and the degree of impact stacking, and determining a data processing method based on the regional status;

[0034] The partitioning method is to perform associated partitioning according to a matching threshold or an impact correlation, and the data processing method is to perform multi-factor iterative correction or data correction according to a sub-abnormality degree;

[0035] A correction optimization module, connected to the comparison correction module, is used to determine, under preset correction conditions, an adjustment method based on the correction comparison coefficient, such as adjustment for the correction factor range or the number of divided sections;

[0036] The local correction module is connected to the correction analysis module and is used to determine abnormal sub-data according to the fluctuation comparison degree during local correction, and to perform data correction according to the instability comparison degree.

[0037] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical solution of the present invention, the data state of the target fault data is determined according to the estimated abnormality and the impact characterization value, and the estimated abnormality and the impact characterization value are effectively reflected. The proportion of the abnormal part of the target fault data and the complexity of the influencing factors are effectively reflected, and then different data correction methods are adaptively selected according to the data state, so that the selection of data correction method is more in line with the actual application scenario, and then the partition comparison correction or local correction is reasonably selected, which can effectively improve the data quality and enhance the reliability and stability of the power system.

[0038] Furthermore, the present invention effectively reflects the actual changes in the target fault data through the fault development coefficient and the period difference, and then adaptively selects different partitioning methods based on the fault development coefficient and the period difference, avoiding the errors that may be caused by the fixed partitioning method. When the fault development coefficient or the period difference is high, the use of associated partitioning based on the matching threshold can better analyze the dynamic changes and periodic characteristics of the fault. When the fault development coefficient and the period difference are low, the use of associated partitioning based on the impact correlation can partition according to the intrinsic characteristics of the data, and can accurately adapt to the fault characteristics to optimize the data correction process, thereby effectively improving the accuracy and reliability of the electricity meter data processing and ensuring the stable operation of the power system.

[0039] Furthermore, the present invention determines the regional status of each divided area based on the number of abnormal factors and the degree of influence stacking. The number of abnormal factors and the degree of influence stacking effectively reflect the complexity of fault data in each divided area and the degree of interaction between the influencing factors. Then, different data processing methods are adaptively selected according to the regional status, which can effectively handle complex fault scenarios and reduce the errors that may be caused by a single correction method.

[0040] Furthermore, the present invention effectively reflects the sensitivity of the correction order of each abnormal factor and the correlation and synergistic effect between the factors through correction dependence and factor interaction, and then adaptively selects different correction methods according to the correction dependence and factor interaction. Serial correction based on the correction response threshold can ensure the accuracy and stability of each correction step; parallel correction based on sub-abnormality can improve correction efficiency and improve data correction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of a fault data analysis method for a smart electric energy meter according to the present invention;

[0042] Figure 2 This is a flow chart of the present invention for determining a data correction method according to a data state;

[0043] Figure 3 This is a flow chart of the present invention for determining a partitioning method based on a fault development coefficient and a period difference;

[0044] Figure 4 This is a module connection diagram of the intelligent electricity meter fault data analysis system of the present invention. DETAILED DESCRIPTION

[0045] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0048] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0049] See also Figures 1 to 3 As shown, the present invention provides a method for analyzing fault data of a smart electric energy meter, comprising:

[0050] Determine the data status of the target fault data based on the estimated abnormality and impact characterization value, and determine the data correction method based on the data status. The data correction method is partition comparison correction or local correction;

[0051] During the partition comparison and correction, the partitioning method is determined based on the fault development coefficient and the period difference to obtain several partitioned areas. The regional status of each partitioned area is determined according to the number of abnormal factors and the impact stacking degree, and the data processing method is determined according to the regional status;

[0052] The partitioning method is to perform associated partitioning according to a matching threshold or an impact correlation, and the data processing method is to perform multi-factor iterative correction or data correction according to a sub-abnormality degree;

[0053] Under the preset correction conditions, the adjustment method is determined according to the correction comparison coefficient, such as adjusting the correction factor range or the number of divided sections;

[0054] In local correction, abnormal sub-data are determined based on the fluctuation comparison degree, and data correction is performed based on the instability comparison degree.

[0055] The application scenario of the present invention is the correction of metering fault data of smart electric energy meters. The present invention includes target fault data and several standard data. The target fault data is the data that needs to be corrected when a metering fault occurs, and the standard data is the data without metering errors. The target fault data and the standard data both include the electric energy consumption reading displayed on the electric energy meter dial monitored at intervals of 5 seconds over a period of time, in kWh. The time when the data is collected is set as the time point, that is, a time point is set every 5 seconds.

[0056] The target fault data and standard data correspond to a number of target monitoring data, including but not limited to the temperature, humidity, electromagnetic intensity, and harmonic content monitored every 5 seconds during the time period corresponding to the target fault data or standard data. The temperature, humidity, and electromagnetic intensity are obtained by a temperature sensor, a humidity sensor, and an electromagnetic sensor, respectively. The harmonic content is obtained by connecting a harmonic analyzer to the metering circuit where the smart electric energy meter is located. This is well understood by those skilled in the art and will not be described in detail here.

[0057] In the present invention, several historical records are correspondingly provided, and any historical record records the estimated deviation coefficient, fluctuation comparison degree, estimated abnormality, impact characterization value and fault development coefficient, etc. in the historical process of at least one correction of the measurement fault data of the smart energy meter, and each historical record corresponds to a qualified mark, which records whether the process of correction of the measurement fault data of the smart energy meter meets the user's requirements. The qualified mark can be recorded manually. It can be understood that the user can determine whether the process of correction of the measurement fault data of the smart energy meter meets the requirements based on the self-set indicators. The self-set indicators can be but not limited to the correction time, which will not be described in detail here. Among them, the correction time is the time consumed to complete the correction of the target fault data;

[0058] The preset correction condition is that the target fault data is corrected through data processing and the estimated deviation coefficient is greater than the preset estimated deviation coefficient. The estimated deviation coefficient = |adjustment reference value - preset adjustment reference value| The adjustment reference value is the standard deviation of the adjustment coefficient corresponding to each time point in the target fault data. The preset adjustment reference value is the standard deviation of the adjustment coefficient corresponding to each time point in the historical record that can meet user needs and perform multi-factor iterative correction or data correction based on sub-anomaly degree. The adjustment coefficient corresponding to a single time point = |the power consumption reading after multi-factor iterative correction or data correction based on sub-anomaly degree at that time point - the power consumption reading corresponding to that time point before correction| / the average value of the sub-anomaly degree corresponding to the target monitoring data corresponding to each characteristic factor at that time point;

[0059] The value of the preset estimated deviation coefficient can be determined by the user according to the actual application scenario. The greater the user's demand for improving the accuracy of data correction, the smaller the value of the preset estimated deviation coefficient. A value of the preset estimated deviation coefficient is provided, and the historical records of the user adjusting the correction factor range or the number of divided paragraphs are detected. The average value of the estimated deviation coefficients corresponding to the historical records that can meet the user's needs is recorded as the preset estimated deviation coefficient.

[0060] Specifically, if the data status of the target fault data is that the estimated abnormality is greater than or equal to the preset estimated abnormality or the impact characterization value is greater than or equal to the preset impact characterization value, the data correction method is partition comparison correction.

[0061] The data state includes a first data state and a second data state. The first data state is that the estimated abnormality is greater than or equal to the preset estimated abnormality or the impact characterization value is greater than or equal to the preset impact characterization value. The second data state is that the estimated abnormality is less than the preset estimated abnormality and the impact characterization value is less than the preset impact characterization value.

[0062] The target fault data is divided into n equal parts to obtain several sub-data. The value of n is positively correlated with the time length of the target fault data. The time length is the number of time points contained in the target fault data.

[0063] Estimated abnormality = number of abnormal sub-data in target fault data / total amount of sub-data in target fault data;

[0064] Abnormal sub-data refers to sub-data with a fluctuation comparison degree greater than a preset fluctuation comparison degree. For a single sub-data, the power consumption corresponding to each sub-interval in the sub-data is detected. The time period between any two adjacent time points is recorded as a sub-interval. A single sub-data contains multiple sub-intervals. The power consumption corresponding to a single sub-interval is the absolute value of the difference between the power consumption readings corresponding to the two time points corresponding to the sub-interval.

[0065] The fluctuation comparison degree corresponding to a single sub-data = the standard deviation of the power consumption corresponding to each sub-interval in the sub-data / the average power consumption corresponding to each sub-interval in the sub-data;

[0066] The value of the preset fluctuation comparison degree can be determined by the user according to the actual application scenario. The greater the user's demand for improved data correction accuracy, the smaller the value of the preset fluctuation comparison degree. A preset fluctuation comparison degree value is provided, and the average value of the fluctuation comparison degree of each sub-interval corresponding to each target correction data in the historical records that can meet the user's needs is recorded as the preset fluctuation comparison degree; the target correction data is the data obtained after correction of the target fault data;

[0067] The impact characterization value is the maximum value among the abnormal coefficients of each target monitoring data corresponding to the target fault data;

[0068] The anomaly coefficient of a single target monitoring data is the maximum value of the sub-anomaly degrees corresponding to each time point in the target monitoring data;

[0069] It is understandable that each target monitoring data corresponds to an influencing factor, which includes but is not limited to temperature, humidity, electromagnetic intensity, and harmonic content;

[0070] For a single target monitoring data, the target monitoring data is recorded as the analysis target monitoring data, and the impact factor corresponding to the analysis target monitoring data is recorded as the analysis impact factor. The sub-abnormality corresponding to a single time point in the target monitoring data = |the monitoring data value corresponding to the time point in the target monitoring data - the average value of the monitoring data values corresponding to each time point in the standard monitoring data corresponding to the analysis impact factor| / (the standard deviation of the monitoring data values corresponding to each time point in the standard monitoring data corresponding to the analysis impact factor × the average value of the monitoring data values corresponding to each time point in the standard monitoring data corresponding to the analysis impact factor);

[0071] The values of the preset estimated abnormality and the preset impact characterization value can be determined by the user according to the actual application scenario. The smaller the values of the preset estimated abnormality and the preset impact characterization value, the greater the user's need for partition comparison and correction. A preset estimated abnormality and the preset impact characterization value are provided, and the historical records of local correction are detected. The average value of the estimated abnormality corresponding to the historical records that can meet the user's needs is recorded as the preset estimated abnormality, and the average value of the impact characterization value corresponding to the historical records that can meet the user's needs is recorded as the preset impact characterization value.

[0072] Specifically, if the data state of the target fault data is that the estimated abnormality is less than the preset estimated abnormality and the impact characterization value is less than the preset impact characterization value, the data correction method is local correction.

[0073] Specifically, the partitioning method is determined based on the fault development coefficient and the period difference, including:

[0074] If the fault development coefficient is greater than or equal to the preset fault development coefficient or the period difference is greater than or equal to the preset period difference, the partitioning method is to perform associated partitioning according to the matching threshold;

[0075] If the fault development coefficient is less than the preset fault development coefficient and the period difference is less than the preset period difference, the partitioning method is to perform associated partitioning according to the impact correlation.

[0076] Among them, the fault development coefficient is the average value of the development coefficients corresponding to each sub-interval;

[0077] The development coefficient corresponding to a single subinterval = |the fluctuation contrast corresponding to the subinterval - the fluctuation contrast corresponding to the subinterval adjacent to and preceding the subinterval|. It should be noted that if there is no subinterval before a single subinterval, the development coefficient corresponding to the subinterval is 0.

[0078] The period difference is the standard deviation of the fluctuation comparison corresponding to each subinterval;

[0079] The values of the preset fault development coefficient and the preset period difference can be determined by the user according to the actual application scenario. The smaller the values of the preset fault development coefficient and the preset period difference, the greater the user's demand for associated partitioning according to the matching threshold. A value of the preset fault development coefficient and the preset period difference is provided, and the historical records of the user's associated partitioning according to the matching threshold are detected. The average value of the fault development coefficient corresponding to the historical records that can meet the user's needs is recorded as the preset fault development coefficient, and the average value of the period difference corresponding to the historical records that can meet the user's needs is recorded as the preset period difference;

[0080] Performing association partitioning according to a matching threshold includes: performing association analysis on each subinterval; when performing association analysis on a single subinterval, recording the subinterval as a target subinterval; recording subintervals outside the target subinterval that are not recorded in the partitioned area as reference subintervals; recording a combination of the reference subintervals whose matching threshold with the target subinterval is greater than a preset matching threshold and the target subinterval as a partitioned area; and continuing to perform association analysis on subintervals that are not recorded in the partitioned area until all subintervals are recorded in the partitioned area, then stopping the association analysis;

[0081] For any two subintervals, the matching threshold corresponding to the two subintervals = 1-[1 / (the absolute value of the difference in the development coefficients corresponding to the two subintervals + the absolute value of the difference in the fluctuation comparison degrees corresponding to the two subintervals + 1)]. It can be understood that the matching threshold reflects the consistency of the fluctuation and anomaly between the subintervals. Associative partitioning based on the matching threshold can divide subintervals with similar change trends into the same area, which can provide a clearer target for subsequent correction, thereby enhancing the self-consistency of the data within the area.

[0082] Performing association partitioning according to the impact correlation includes: performing correlation analysis on each subinterval, when performing correlation analysis on a single subinterval, recording the subinterval as a target subinterval, recording subintervals outside the target subinterval that are not recorded in the partitioned area as reference subintervals, recording a combination of the reference subinterval and the target subinterval whose impact correlation with the target subinterval is greater than a preset impact correlation as a partitioned area, and continuing to perform correlation analysis on the subintervals that are not recorded in the partitioned area until all subintervals are recorded in the partitioned area, then stopping the correlation analysis;

[0083] For any two subintervals, the influence correlation between the two subintervals = 1-[1 / (the absolute value of the difference between the influence reference values of the two subintervals + 1)]. It can be understood that the influence correlation reflects the intrinsic connection between different subintervals. Partitioning based on this correlation can ensure that the subintervals within the divided area have a high degree of consistency in characteristics. The data in these areas can be processed uniformly to reduce the impact of noise, thereby improving data reliability.

[0084] For a single sub-interval, the impact reference value is the average of the sub-impact reference values corresponding to each time point in the single sub-interval, and the sub-impact reference value corresponding to a single time point is the average of the sub-abnormality degrees corresponding to each target monitoring data at that time point;

[0085] The values of the preset matching threshold and the preset influence correlation can be determined by the user according to the actual application scenario. The greater the user's demand for improving the data compensation accuracy, the smaller the values of the preset matching threshold and the preset influence correlation. A value of the preset matching threshold and the preset influence correlation is provided. The historical records of the user's associating partitions according to the matching threshold are detected, and the average value of the reference matching threshold corresponding to each partitioned area in the historical records that can meet the user's needs is recorded as the preset matching threshold. The historical records of the user's associating partitions according to the influence correlation are detected, and the average value of the reference influence correlation corresponding to each partitioned area in the historical records that can meet the user's needs is recorded as the preset influence correlation. The reference matching threshold is the matching threshold corresponding to any two sub-intervals in a single partitioned area, and the reference influence correlation is the influence correlation corresponding to any two sub-intervals in a single partitioned area.

[0086] Specifically, if the regional status is that the number of abnormal factors is greater than the standard number and the impact stacking degree is greater than or equal to the preset impact stacking degree, the data processing method is multi-factor iterative correction;

[0087] In multi-factor iterative correction, the correction method is determined based on the correction dependency and factor interaction, including:

[0088] If the correction dependence is greater than or equal to the preset correction dependence or the factor interaction is greater than or equal to the preset factor interaction, the correction method is serial correction according to the correction response threshold;

[0089] If the correction dependency is less than the preset correction dependency and the factor interaction is less than the preset factor interaction, the correction method is parallel correction based on the impact coverage.

[0090] The regional status includes a first regional status and a second regional status. The first regional status is that the number of abnormal factors is greater than the standard number and the impact stacking degree is greater than or equal to the preset impact stacking degree. The second regional status is that the number of abnormal factors is equal to the standard number or the impact stacking degree is less than the preset impact stacking degree.

[0091] The number of abnormal factors corresponding to a single partition area is the total amount of abnormal factors corresponding to the partition area.

[0092] For a single influencing factor in a single divided area, detect the maximum value of the sub-anomaly coefficients corresponding to the target monitoring data of the influencing factor at each time point in the divided area and record it as the maximum sub-anomaly coefficient. If the maximum sub-anomaly coefficient corresponding to the influencing factor is greater than the preset maximum sub-anomaly coefficient, then the influencing factor is recorded as an anomaly factor.

[0093] The value of the preset maximum sub-anomaly coefficient can be determined by the user according to the actual application scenario. The greater the user's demand for improving the correction accuracy, the smaller the value of the preset maximum sub-anomaly coefficient is. A value of the preset maximum sub-anomaly coefficient is provided, and the average value of the maximum sub-anomaly coefficient corresponding to each divided area in the historical record that can meet the user's needs is recorded as the preset maximum sub-anomaly coefficient;

[0094] The standard number is 1, and the influence stacking degree corresponding to a single division area = the factor interaction degree corresponding to the division area × the correction dependence degree corresponding to the division area;

[0095] The value of the preset impact stacking degree can be determined by the user according to the actual application scenario. The smaller the value of the preset impact stacking degree, the greater the user's need for multi-factor iterative correction. A preset impact stacking degree value is provided, and the historical records of the user's multi-factor iterative correction are detected. The average value of the impact stacking degree corresponding to the historical records that can meet the user's needs is recorded as the preset impact stacking degree;

[0096] The corrected dependency is the average value of the dependency reference values corresponding to each abnormal factor in a single partitioned area. The dependency reference value corresponding to a single abnormal factor is the maximum value among the sub-dependencies corresponding to the abnormal factor and other abnormal factors.

[0097] Detect historical records that are serially corrected according to the correction response threshold and can meet the requirements and record them as reference historical records. For two abnormal factors, record them as the first factor and the second factor respectively. Detect the order in which the two abnormal factors are corrected. When the first factor in the reference historical records is corrected first over the second factor, it is recorded as the first order. When the second factor is corrected first over the first factor, it is recorded as the second order. The number of reference historical records that meet the first order is recorded as a1, and the number of reference historical records that meet the second order is recorded as a2. Sub-dependency = the larger value of a1 and a2 / (the smaller value of a1 and a2 + 1);

[0098] The factor interaction degree is the average of the sub-factor interaction degrees corresponding to each abnormal factor in a single partition area. The sub-factor interaction degree corresponding to a single abnormal factor is the average of the interaction coefficients corresponding to the abnormal factor and other abnormal factors.

[0099] The calculation formula for the interaction coefficient r corresponding to any two abnormal factors is:

[0100]

[0101] m is the number of time points in a single partition area, x k and y k are the values of the monitoring data corresponding to the kth time point in the divided area for the target monitoring data corresponding to the two abnormal factors, is x k The average value of the monitoring data corresponding to each time point in the divided area of the target monitoring data of the corresponding abnormal factor, y k The average value of the monitoring data corresponding to each time point in the divided area of the target monitoring data of the corresponding abnormal factor, k = 1, 2, 3, ..., m;

[0102] The values of the preset correction dependency and the preset factor interaction can be determined by the user according to the actual application scenario. The smaller the values of the preset correction dependency and the preset factor interaction, the greater the user's need for serial correction according to the correction response threshold. A value of the preset correction dependency and the preset factor interaction is provided, and the historical records of the user performing serial correction according to the correction response threshold are detected. The average value of the correction dependency corresponding to the historical records that can meet the user's needs is recorded as the preset correction dependency, and the average value of the factor interaction corresponding to the historical records that can meet the user's needs is recorded as the preset factor interaction;

[0103] The correction response threshold is determined by recording a single abnormal factor as the target abnormal factor and recording all abnormal factors other than the target abnormal factor as reference abnormal factors. The correction response threshold corresponding to the target abnormal factor is the average of the sub-response thresholds corresponding to the target abnormal factor and each reference abnormal factor. The sub-response threshold corresponding to the target abnormal factor and the single reference abnormal factor = the number of reference historical records for which the target abnormal factor is prioritized for correction relative to the reference abnormal factor / the total number of reference historical records.

[0104] The method for confirming the impact coverage is that, for a single divided area, each abnormal factor in the divided area is recorded as a reference factor, and the impact coverage corresponding to a single reference factor = the average value of the sub-abnormality corresponding to the target monitoring data corresponding to the reference factor at each time point in the divided area / (the average value of the interaction coefficients corresponding to the reference factor and other reference factors + 1); the value of the preset impact coverage can be determined by the user according to the actual application scenario. The greater the user's demand for improving the correction accuracy, the smaller the value of the preset impact coverage. A preset impact coverage value is provided, and the historical records of parallel correction based on the impact coverage are detected. The average value of the impact coverage corresponding to each characteristic factor in the historical records that can meet the user's needs is recorded as the preset impact coverage;

[0105] According to the parallel correction of the impact coverage, the abnormal factors with an impact coverage greater than the preset impact coverage are recorded as characteristic factors, and correction is performed simultaneously for each characteristic factor. When correcting a single characteristic factor, the power consumption reading corresponding to each time point in a single divided interval is used as the basis, and the power consumption reading corresponding to each time point is adjusted according to the instability offset to obtain the initial correction data value of the time point corresponding to the characteristic factor;

[0106] For a single time point,

[0107] If the instability deviation is greater than the preset instability deviation, the power consumption reading is adjusted downward according to the sub-abnormality;

[0108] If the instability offset is less than the preset instability offset, the power consumption reading is increased and adjusted according to the sub-abnormality;

[0109] If the instability offset is equal to the preset instability offset, no adjustment is performed;

[0110] If the instability offset is greater than the preset instability offset, the initial correction data value corresponding to the single characteristic factor = the power consumption reading - the decrease in the power consumption reading; if the instability offset is less than the preset instability offset, the initial correction data value corresponding to the single characteristic factor = the power consumption reading + the increase in the power consumption reading; if the instability offset is equal to the preset instability offset, the power consumption reading remains unchanged;

[0111] When adjusting the energy consumption reading at a time point corresponding to a single characteristic factor, the increase or decrease in the energy consumption reading at a single time point is positively correlated with the absolute value of the instability offset;

[0112] After the correction of each characteristic factor is completed simultaneously, the corrected data value corresponding to a single time point = the initial corrected data value corresponding to each characteristic factor at the time point / the number of characteristic factors.

[0113] Specifically, serial correction according to the correction response threshold includes:

[0114] Determine the priority coefficient for correction of each abnormal factor according to the correction response threshold, and adjust the power consumption reading corresponding to each time point according to the instability offset corresponding to each characteristic factor;

[0115] For a single time point,

[0116] If the instability offset is greater than the preset instability offset, the power consumption reading corresponding to the time point is reduced and adjusted according to the dependency impact;

[0117] If the instability offset is less than the preset instability offset, the power consumption reading corresponding to the time point is increased and adjusted according to the dependency influence;

[0118] The priority coefficient of correction for a single characteristic factor is positively correlated with the correction response threshold corresponding to the abnormal factor.

[0119] It can be understood that the larger the priority coefficient corresponding to a single characteristic factor is, the higher the priority of the correction order for the characteristic factor is;

[0120] When adjusting the power consumption reading at a time point corresponding to a single characteristic factor, the power consumption reading is adjusted based on the adjustment data corresponding to the characteristic factor and the degree of instability offset;

[0121] When adjusting the energy consumption reading at a time point corresponding to a single characteristic factor, the increase or decrease in the energy consumption reading at a single time point is positively correlated with the absolute value of the instability offset;

[0122] If the instability offset is equal to the preset instability offset, no adjustment is performed;

[0123] It can be understood that if there are q characteristic factors, then q adjustments are made to the power consumption reading corresponding to each time point;

[0124] After the correction of each characteristic factor is completed, the corrected data value corresponding to a single time point is the value of the power consumption reading obtained after the correction of the characteristic factor with the smallest priority coefficient is completed;

[0125] The method for confirming the adjustment data is as follows: for a single characteristic factor, the characteristic factor is recorded as the first target factor, the characteristic factor adjacent to and located before the first target factor in the reference sequence is recorded as the second target factor, and the energy consumption readings corresponding to each time point in a single divided interval are adjusted according to the dependency influence corresponding to the second target factor, and recorded as the adjustment data corresponding to the first target factor. It should be noted that if the first target factor is located at the starting position of the reference sequence, the adjustment data corresponding to the first target factor is the energy consumption readings corresponding to each time point in the single divided area;

[0126] The sequence after sorting each characteristic factor in descending order of priority coefficient is recorded as the reference sequence;

[0127] The calculation formula of the instability deviation w is: z k is the energy consumption reading corresponding to the kth time point in a single partitioned area, The average value of the power consumption readings corresponding to each time point in a single divided area;

[0128] The value of the preset instability offset can be determined by the user according to the actual application scenario. The larger the value of the preset instability offset, the greater the user's need to increase the adjustment of the power consumption reading. A preset instability offset value is provided, and the preset instability offset is 0;

[0129] The method for confirming the dependency influence is that, for a single characteristic factor, the characteristic factor is recorded as the target characteristic factor, and the characteristic factor whose priority coefficient for correction is greater than the target characteristic factor is recorded as the reference characteristic factor. The dependency influence corresponding to a single time point = 0.5 × [(the average value of the sub-dependencies corresponding to the target characteristic factor and each reference characteristic factor / the dependency reference value corresponding to the target characteristic factor) + (the average value of the interaction coefficients corresponding to the target characteristic factor and each reference characteristic factor / the sub-factor interaction degree corresponding to the target characteristic factor)] × the sub-anomaly degree corresponding to the target monitoring data corresponding to the target characteristic factor at this time point. It should be noted that if the priority coefficient for correction of a single characteristic factor is the largest, the dependency influence degree of the characteristic factor is equal to the sub-anomaly degree.

[0130] Specifically, if the correction comparison coefficient is greater than or equal to the preset correction comparison coefficient, the adjustment method is to increase the correction factor range;

[0131] The increase in the correction factor range is positively correlated with the estimated deviation coefficient.

[0132] Among them, for a single divided area, other abnormal factors other than the characteristic factor corresponding to the divided area are recorded as non-characteristic factors, and the sub-reference value corresponding to a single abnormal factor is the average value of the sub-abnormality degree corresponding to each time point of the target monitoring data corresponding to the abnormal factor in the divided area;

[0133] Correction comparison coefficient = average value of sub-reference values corresponding to each non-characteristic factor / average value of sub-reference values corresponding to each abnormal factor; It should be noted that if there is no non-characteristic factor, the correction comparison coefficient is 0;

[0134] The value of the preset correction comparison coefficient can be determined by the user according to the actual application scenario. The smaller the value of the preset correction comparison coefficient, the greater the user's need to increase the correction factor range. A preset correction comparison coefficient value is provided, and the historical records of users increasing the correction factor range are detected. The average value of the correction comparison coefficients corresponding to the historical records that can meet the user's needs is recorded as the preset correction comparison coefficient;

[0135] The correction factor range is the abnormal factors to be corrected. When increasing the correction factor range, non-characteristic factors are selected in the order of the impact on coverage from large to small and recorded in the correction factor range;

[0136] Specifically, if the correction comparison coefficient is less than the preset correction comparison coefficient, the adjustment method is to increase the number of sub-data;

[0137] The increase in the number of sub-data is positively correlated with the estimated deviation coefficient.

[0138] The number of sub-data is the total amount of sub-data obtained by dividing the target fault data into equal parts.

[0139] Specifically, if the regional status is that the number of abnormal factors is equal to the standard number or the impact stacking degree is less than the preset impact stacking degree, the data processing method is to correct the data according to the sub-abnormality degree.

[0140] Among them, when correcting data according to the sub-abnormality degree, each abnormal factor is corrected at the same time. When correcting a single abnormal factor, the power consumption reading corresponding to each time point in a single divided interval is used as the basis, and the power consumption reading corresponding to each time point is adjusted according to the instability offset degree to obtain the initial corrected data value of the time point corresponding to the abnormal factor;

[0141] For a single time point corresponding to a single abnormal factor,

[0142] If the instability offset is greater than the preset instability offset, the power consumption reading is adjusted downward according to the sub-abnormality corresponding to the target monitoring data corresponding to the abnormal factor at that time point;

[0143] If the instability offset is less than the preset instability offset, the power consumption reading is increased and adjusted according to the sub-abnormality corresponding to the target monitoring data corresponding to the abnormal factor at that time point;

[0144] If the instability offset is equal to the preset instability offset, no adjustment is performed;

[0145] If the instability offset is greater than the preset instability offset, the initial correction data value corresponding to the single abnormal factor = the power consumption reading - the decrease in the power consumption reading; if the instability offset is less than the preset instability offset, the initial correction data value corresponding to the single abnormal factor = the power consumption reading + the increase in the power consumption reading; if the instability offset is equal to the preset instability offset, the power consumption reading remains unchanged;

[0146] After the correction of each abnormal factor is completed simultaneously, the corrected data value corresponding to a single time point = the initial corrected data value corresponding to each abnormal factor at the time point / the number of abnormal factors.

[0147] Data correction is performed based on the instability comparison, including: correcting the power consumption readings corresponding to each time point in each abnormal sub-data,

[0148] When correcting a single time point in a single abnormal sub-data,

[0149] If the instability reference value is greater than the preset instability reference value, the power consumption reading corresponding to the time point is reduced and adjusted according to the instability comparison degree;

[0150] If the instability reference value is less than the preset instability reference value, the power consumption reading corresponding to the time point is increased and adjusted according to the instability comparison degree;

[0151] If the instability reference value is equal to the preset instability reference value, no adjustment is performed;

[0152] The increase or decrease in the energy consumption reading corresponding to a single time point is positively correlated with the instability comparison degree corresponding to that time point;

[0153] Each time point in the abnormal sub-data is recorded as an abnormal time point, and other time points other than the abnormal time point are recorded as normal time points.

[0154] The abnormal extreme value corresponding to a single time point is the maximum value of the sub-abnormality degree corresponding to the time point in the target monitoring data corresponding to each influencing factor;

[0155] The instability comparison degree corresponding to a single abnormal time point = |the abnormal extreme value corresponding to the abnormal time point - the average value of the abnormal extreme values corresponding to all normal time points| / the standard deviation of the abnormal extreme values corresponding to all normal time points;

[0156] If the instability reference value is greater than the preset instability reference value, the corrected data value corresponding to a single time point = the power consumption reading - the decrease in the power consumption reading; if the instability reference value is less than the preset instability reference value, the corrected data value corresponding to a single time point = the power consumption reading + the increase in the power consumption reading; if the instability reference value is equal to the preset instability reference value, the power consumption reading remains unchanged;

[0157] The calculation formula of the instability reference value p is: i is the number of time points contained in a single abnormal sub-data, and j is 1, 2, 3, ..., i, g j is the abnormal extreme value corresponding to the j-th time point in a single sub-data, is the average value of the abnormal extreme values corresponding to each time point in a single sub-data, t j is the energy consumption reading corresponding to the jth time point in a single sub-data, is the average value of the power consumption readings corresponding to each time point in a single sub-data;

[0158] The value of the preset instability reference value can be determined by the user according to the actual application scenario. The larger the value of the preset instability reference value, the greater the user's demand for increasing the adjustment of the power consumption reading. A preset instability reference value is provided, and the preset instability reference value is 0.

[0159] See also Figure 4 As shown in FIG, which is a module connection diagram of the intelligent electric energy meter fault data analysis system of the present invention, the present invention also provides a intelligent electric energy meter fault data analysis system, including:

[0160] The correction analysis module is used to determine the data status of the target fault data based on the estimated abnormality and the impact characterization value, and determine the data correction method based on the data status. The data correction method is partition comparison correction or local correction;

[0161] a comparison and correction module connected to the correction analysis module, for determining a partitioning method based on the fault development coefficient and the period difference to obtain a plurality of partitioned areas during the partition comparison and correction, determining the regional status of each partitioned area based on the number of abnormal factors and the degree of impact stacking, and determining a data processing method based on the regional status;

[0162] The partitioning method is to perform associated partitioning according to a matching threshold or an impact correlation, and the data processing method is to perform multi-factor iterative correction or data correction according to a sub-abnormality degree;

[0163] A correction optimization module, connected to the comparison correction module, is used to determine, under preset correction conditions, an adjustment method based on the correction comparison coefficient, such as adjustment for the correction factor range or the number of divided sections;

[0164] The local correction module is connected to the correction analysis module and is used to determine abnormal sub-data according to the fluctuation comparison degree during local correction, and to perform data correction according to the instability comparison degree.

[0165] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0166] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for analyzing fault data of a smart electric energy meter, characterized in that: include: Determine the data status of the target fault data based on the estimated abnormality and impact characterization value, and determine the data correction method based on the data status. The data correction method is partition comparison correction or local correction; During the partition comparison and correction, the partitioning method is determined based on the fault development coefficient and the period difference to obtain several partitioned areas. The regional status of each partitioned area is determined according to the number of abnormal factors and the impact stacking degree, and the data processing method is determined according to the regional status; The partitioning method is to perform associated partitioning according to a matching threshold or an impact correlation, and the data processing method is to perform multi-factor iterative correction or data correction according to a sub-abnormality degree; Under the preset correction conditions, the adjustment method is determined according to the correction comparison coefficient, such as adjusting the correction factor range or the number of divided sections; In local correction, abnormal sub-data are determined based on the fluctuation comparison degree, and data correction is performed based on the instability comparison degree.

2. The method for analyzing fault data of a smart energy meter according to claim 1, characterized in that: If the data status of the target fault data is that the estimated abnormality is greater than or equal to the preset estimated abnormality or the impact characterization value is greater than or equal to the preset impact characterization value, the data correction method is partition comparison correction.

3. The method for analyzing fault data of a smart energy meter according to claim 2, characterized in that: If the data status of the target fault data is that the estimated abnormality is less than the preset estimated abnormality and the impact characterization value is less than the preset impact characterization value, the data correction method is local correction.

4. The method for analyzing fault data of a smart energy meter according to claim 2, characterized in that: The partitioning method is determined based on the fault development coefficient and period difference, including: If the fault development coefficient is greater than or equal to the preset fault development coefficient or the period difference is greater than or equal to the preset period difference, the partitioning method is to perform associated partitioning according to the matching threshold; If the fault development coefficient is less than the preset fault development coefficient and the period difference is less than the preset period difference, the partitioning method is to perform associated partitioning according to the impact correlation.

5. The method for analyzing fault data of a smart energy meter according to claim 4, characterized in that: If the regional status is that the number of abnormal factors is greater than the standard number and the impact stacking degree is greater than or equal to the preset impact stacking degree, the data processing method is multi-factor iterative correction; In multi-factor iterative correction, the correction method is determined based on the correction dependency and factor interaction, including: If the correction dependence is greater than or equal to the preset correction dependence or the factor interaction is greater than or equal to the preset factor interaction, the correction method is serial correction according to the correction response threshold; If the correction dependency is less than the preset correction dependency and the factor interaction is less than the preset factor interaction, the correction method is parallel correction based on the impact coverage.

6. The method for analyzing fault data of a smart energy meter according to claim 5, characterized in that: Serial correction based on the correction response threshold includes: Determine the priority coefficient for correction of each abnormal factor according to the correction response threshold, and adjust the power consumption reading corresponding to each time point according to the instability offset corresponding to each characteristic factor; For a single time point, If the instability offset is greater than the preset instability offset, the power consumption reading corresponding to the time point is reduced and adjusted according to the dependency impact; If the instability offset is less than the preset instability offset, the power consumption reading corresponding to the time point is increased and adjusted according to the dependency influence; The priority coefficient of correction for a single characteristic factor is positively correlated with the correction response threshold corresponding to the abnormal factor.

7. The method for analyzing fault data of a smart electric energy meter according to claim 5, characterized in that: If the correction comparison coefficient is greater than or equal to the preset correction comparison coefficient, the adjustment method is to increase the correction factor range; The increase in the correction factor range is positively correlated with the estimated deviation coefficient.

8. The method for analyzing fault data of a smart energy meter according to claim 7, characterized in that: If the correction comparison coefficient is less than the preset correction comparison coefficient, the adjustment method is to increase the number of sub-data; The increase in the number of sub-data is positively correlated with the estimated deviation coefficient.

9. The method for analyzing fault data of a smart electric energy meter according to claim 5, characterized in that: If the regional status is that the number of abnormal factors is equal to the standard number or the impact stacking degree is less than the preset impact stacking degree, the data processing method is to correct the data according to the sub-abnormality degree.

10. An analysis system using the smart energy meter fault data analysis method according to any one of claims 1 to 9, characterized in that: include: The correction analysis module is used to determine the data status of the target fault data based on the estimated abnormality and the impact characterization value, and determine the data correction method based on the data status. The data correction method is partition comparison correction or local correction; a comparison and correction module connected to the correction analysis module, for determining a partitioning method based on the fault development coefficient and the period difference to obtain a plurality of partitioned areas during the partition comparison and correction, determining the regional status of each partitioned area based on the number of abnormal factors and the degree of impact stacking, and determining a data processing method based on the regional status; The partitioning method is to perform associated partitioning according to a matching threshold or an impact correlation, and the data processing method is to perform multi-factor iterative correction or data correction according to a sub-abnormality degree; A correction optimization module, connected to the comparison correction module, is used to determine, under preset correction conditions, an adjustment method based on the correction comparison coefficient, such as adjustment for the correction factor range or the number of divided sections; The local correction module is connected to the correction analysis module and is used to determine abnormal sub-data according to the fluctuation comparison degree during local correction, and to perform data correction according to the instability comparison degree.

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