A pre-check method and system for abnormal electric meter

Abstract

The present invention discloses a method and system for pre-checking abnormal electric meters, which relates to the field of electrical digital data processing, wherein the method comprises: obtaining a historical collection data set, and performing initial classification and integration on it to obtain an integrated data set; generating an electric meter stability evaluation result; using the position data of each electric meter in the historical collection data set as the first aggregate reference data; using the signal characteristics of each electric meter in the historical collection data set as the second aggregate reference data; based on this, performing correlation aggregation of the electric meter deviation data to obtain a regional aggregation result, and generating a regional signal impact identification; inputting the regional aggregation result and the electric meter stability evaluation result into an abnormal pre-check evaluation model, and outputting an abnormal identification electric meter. The technical effects such as improving the comprehensiveness and accuracy of the abnormal pre-checking of the low-voltage collective reading communication module, and then improving the abnormal pre-checking quality of the low-voltage collective reading communication module are achieved.

Description

Technical Field

[0001] The present invention relates to the field of electrical digital data processing, and in particular to a method and system for pre-checking abnormal electric meters. Background Art

[0002] With the continuous expansion of the electricity market and the increasing electricity consumption of residents, traditional manual meter reading is no longer sufficient for modern electricity management. This has necessitated the emergence of low-voltage centralized meter reading. Low-voltage centralized meter reading, also known as a low-voltage carrier automatic meter reading system, is a commonly used intelligent meter reading system in modern electricity management. Low-voltage centralized meter reading not only effectively improves the quality and efficiency of meter reading for power grid operations but also effectively reduces the workload. However, with the increasing use of low-voltage centralized meter reading, various operational issues have gradually emerged. When low-voltage centralized meter reading anomalies occur, significant manpower is required to conduct on-site readings, significantly impacting the normal operations of power supply companies. Therefore, preemptive troubleshooting of low-voltage centralized meter reading anomalies is of great practical significance.

[0003] In the prior art, there is a technical problem that the pre-checking of abnormalities of the low-voltage centralized meter reading communication module is not comprehensive enough and accurate enough, which results in a poor effect of the pre-checking of abnormalities of the low-voltage centralized meter reading communication module. Summary of the Invention

[0004] The present application provides a method and system for pre-checking abnormal electricity meters, which solves the technical problem in the prior art that the pre-checking of abnormalities of low-voltage centralized meter reading communication modules is not comprehensive and accurate, resulting in poor pre-checking effects.

[0005] In view of the above problems, the present application provides a method and system for pre-checking abnormal electricity meters.

[0006] In the first aspect, the present application provides a pre-checking method for abnormal electricity meters, wherein the method is applied to a pre-checking system for abnormal electricity meters, and the method includes: obtaining a historical collection data set through the communication module, and performing initial classification and integration on the historical collection data set to obtain an integrated data set; performing data stability analysis on the same electricity meter based on the integrated data set to generate an electricity meter stability evaluation result; using the location data of each electricity meter in the historical collection data set as the first aggregated reference data; using the signal characteristics of each electricity meter in the historical collection data set as the second aggregated reference data; performing correlation aggregation of electricity meter deviation data based on the first aggregated reference data and the second aggregated reference data to obtain a regional aggregation result, and generating a regional signal impact identification; inputting the regional aggregation result and the electricity meter stability evaluation result into an abnormal pre-checking evaluation model, and outputting an abnormal identification meter.

[0007] In the second aspect, the present application also provides a pre-check system for abnormal electricity meters, wherein the system includes: a data acquisition and integration module, the data acquisition and integration module is used to obtain a historical collection data set through the communication module, and perform initial classification and integration on the historical collection data set to obtain an integrated data set; a stability analysis module, the stability analysis module is used to perform data stability analysis on the same electricity meter based on the integrated data set, and generate an electricity meter stability evaluation result; a first aggregate reference data determination module, the first aggregate reference data determination module is used to use the location data of each electricity meter in the historical collection data set as the first aggregate reference data; a second aggregate reference data determination module, the second aggregate reference data determination module is used to use the signal characteristics of each electricity meter in the historical collection data set as the second aggregate reference data; an aggregation identification module, the aggregation identification module is used to perform meter deviation data association aggregation based on the first aggregate reference data and the second aggregate reference data to obtain a regional aggregation result and generate a regional signal impact identification; an abnormal pre-check evaluation module, the abnormal pre-check evaluation module is used to input the regional aggregation result and the electricity meter stability evaluation result into an abnormal pre-check evaluation model, and output an abnormal identification meter.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The communication module obtains a historical data set, performs initial classification and integration on it, and obtains an integrated data set; performs data stability analysis on the same meter based on the integrated data set, and generates a meter stability evaluation result; uses the location data of each meter in the historical data set as the first aggregate reference data; uses the signal characteristics of each meter in the historical data set as the second aggregate reference data; performs correlation aggregation of meter deviation data according to the first aggregate reference data and the second aggregate reference data, obtains a regional aggregation result, and generates a regional signal impact identifier; inputs the regional aggregation result and the meter stability evaluation result into an abnormal pre-check evaluation model, and outputs an abnormal identification meter. This achieves the goal of improving the comprehensiveness and accuracy of the abnormal pre-check of the low-voltage centralized reading communication module, thereby improving the abnormal pre-check quality of the low-voltage centralized reading communication module; at the same time, by performing intelligent and scientific abnormal pre-check of the low-voltage centralized reading communication module, the management level of the low-voltage centralized reading is improved, thereby providing strong support for the normal operation of the low-voltage centralized reading. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a method for pre-checking abnormal electricity meters is provided for this application;

[0011] Figure 2A schematic diagram of a flow chart for adjusting or maintaining the position of an abnormally marked electric meter in a pre-checking method for an abnormal electric meter according to the present application;

[0012] Figure 3 This is a flow chart of adjusting the regional position of an electric meter in a method for pre-checking an abnormal electric meter according to the present application;

[0013] Figure 4 This is a structural diagram of a system for pre-checking abnormal electricity meters in this application.

[0014] Explanation of the accompanying symbols: data collection and integration module 11, stability analysis module 12, first aggregate reference data determination module 13, second aggregate reference data determination module 14, aggregate identification module 15, abnormality pre-screening and evaluation module 16. DETAILED DESCRIPTION

[0015] The present application provides a method and system for pre-checking abnormal electric meters. It solves the technical problem in the prior art that the pre-checking of abnormalities in low-voltage centralized meter reading communication modules is not comprehensive and accurate, which results in poor pre-checking effects of abnormalities in low-voltage centralized meter reading communication modules. It achieves the goal of improving the comprehensiveness and accuracy of pre-checking abnormalities in low-voltage centralized meter reading communication modules, thereby improving the quality of pre-checking abnormalities in low-voltage centralized meter reading communication modules; at the same time, by performing intelligent and scientific pre-checking of abnormalities in low-voltage centralized meter reading communication modules, the management level of low-voltage centralized meter reading is improved, thereby providing strong support for the normal operation of low-voltage centralized meter reading.

[0016] Example 1

[0017] Please see the attached Figure 1 The present application provides a method for pre-checking abnormal electricity meters, wherein the method is applied to a pre-checking system for abnormal electricity meters, the system being communicatively connected to a communication module, and the method specifically comprises the following steps:

[0018] Step S100: obtaining a historically collected data set through the communication module, and performing initial classification and integration on the historically collected data set to obtain an integrated data set;

[0019] Specifically, multiple historically collected data are extracted from the communication module to obtain a set of historically collected data, which are then initially classified and integrated to determine an integrated data set. The communication module is in communication with the abnormal meter pre-screening system. The communication module is a low-voltage centralized meter reading communication module. Low-voltage centralized meter reading is an intelligent meter reading system composed of multiple meters, a concentrator, a collector, and a communication module. The low-voltage centralized meter reading communication module has functions such as transmitting and storing relevant data from multiple meters. The historically collected data set includes information such as meter locations, historical meter data, historical collection time, historical collection success rate, and historical meter signal strength. The integrated data set includes data information after the historically collected data set has been initially classified. For example, during the initial classification and integration of the historically collected data set, the historically collected data set can be classified according to historical collection time. The resulting integrated data set includes a set of historically collected data arranged in order of historical collection time. This achieves the technical effect of determining a set of historically collected data, performing initial classification and integration on it, and obtaining an integrated data set, providing data support for subsequent abnormality pre-screening.

[0020] Step S200: performing data stability analysis of the same electric meter based on the integrated data set to generate an electric meter stability evaluation result;

[0021] Furthermore, step S200 of the present application further includes:

[0022] Step S210: constructing a time impact coefficient set;

[0023] Step S220: calculating the time stability influence change of the data in the integrated data set using the time influence coefficient set to obtain a calculated data set;

[0024] Step S230: performing stability evaluation using the calculation data set, and obtaining the meter stability evaluation result based on the stability evaluation result.

[0025] Specifically, the time stability impact change calculation is performed on the integrated data set according to a set of time impact coefficients to obtain a calculated data set, which is then subjected to a stability evaluation to obtain a stability evaluation result, thereby determining an electricity meter stability evaluation result. The set of time impact coefficients includes multiple preset historical collection times and multiple preset time impact coefficients corresponding to the multiple preset historical collection times. The multiple preset time impact coefficients can be used to characterize the impact of the multiple preset historical collection times on the stability of the electricity meter. The further away the preset historical collection time, the less impact the meter-related data at that preset historical collection time has on the stability of the electricity meter, and the smaller the corresponding preset time impact coefficient. The time stability impact change calculation involves matching the historical collection time of the data in the integrated data set with the multiple preset historical collection times in the set of time impact coefficients, determining a preset historical collection time that matches the historical collection time of the data in the integrated data set, and multiplying the preset time impact coefficient corresponding to the preset historical collection time with the data in the integrated data set. The calculated data set includes data information after the integrated data set has been assigned values ​​according to the set of time impact coefficients. The stability evaluation result can be used to characterize the stability of the calculated data set. The electricity meter stability evaluation result includes the stability evaluation result. The stability assignment evaluation refers to the abnormal meter pre-screening system assigning stability values ​​to the data in the calculation data set based on the fluctuations of the data in the calculation data set. For example, when performing the stability assignment evaluation on the calculation data set, if a certain data point in the calculation data set fluctuates significantly, that data point will be assigned a larger stability assignment value, and the corresponding stability assignment evaluation result will indicate that the stability of that data point is poor. This achieves the technical effect of obtaining accurate stability assignment evaluation results and improving the accuracy of the meter stability evaluation results by calculating the temporal stability influence change and performing stability assignment evaluation on the integrated data set.

[0026] Furthermore, step S200 of the present application further includes:

[0027] Step S240: collecting signal change characteristics of the electric meter on the integrated data set to obtain signal change characteristic collection results;

[0028] Step S250: performing signal acquisition change trend analysis of the electric meter based on the signal change feature acquisition result to obtain a change trend analysis result;

[0029] Step S260: Obtain the meter stability evaluation result through the change trend analysis result and the stability assignment evaluation result.

[0030] Specifically, the signal change characteristics of each electric meter in the integrated data set are collected to obtain the signal change characteristic collection results, and the meter signal collection change trends of the signal change characteristic collection results are analyzed to obtain the change trend analysis results. Combined with the obtained stability assignment evaluation results, the meter stability evaluation results are determined. The signal change characteristic collection results include signal strength change characteristics, signal integrity change characteristics and other signal change characteristic information of each electric meter in the integrated data set. The change trend analysis results include the change trends of the signal strength change characteristics, signal integrity change characteristics and other characteristics of each electric meter. The meter stability evaluation results include the change trend analysis results and the stability assignment evaluation results. The technical effect of using the integrated data set to perform data stability analysis, obtain accurate meter stability evaluation results, and thereby improve the accuracy of the abnormally identified electric meters obtained subsequently is achieved.

[0031] Step S300: using the location data of each electric meter in the historically collected data set as first aggregated reference data;

[0032] Step S400: using the signal characteristics of each electric meter in the historically collected data set as second aggregated reference data;

[0033] Step S500: performing correlation aggregation of meter deviation data based on the first aggregated reference data and the second aggregated reference data to obtain a regional aggregation result and generate a regional signal impact identifier;

[0034] Specifically, the location data of each meter in the historically collected data set is set as first aggregated reference data, and the signal characteristics of each meter in the historically collected data set are set as second aggregated reference data. Furthermore, meter deviation data analysis is performed on the second aggregated reference data based on the first aggregated reference data. Specifically, the impact of each meter's location data on its signal characteristics is analyzed to obtain a regional aggregation result and a regional signal impact indicator. The first aggregated reference data includes the location data of each meter in the historically collected data set. The second aggregated reference data includes the signal characteristics of each meter in the historically collected data set. Specifically, the second aggregated reference data includes signal characteristics such as signal strength and signal acquisition success rate for each meter in the historically collected data set. The regional aggregation result includes the location data of multiple meters whose location data affects their signal characteristics. The regional signal impact indicator can be used to indicate the degree of impact of the meter's location data on the meter's signal characteristics. For example, when a meter is inappropriately located, meter signal communication may be blocked. In this case, the meter's signal characteristics are disrupted by the meter's location data. The regional aggregation result obtained includes the meter's location data. By analyzing the influence and interference of the meter's location on its signal characteristics, we can obtain regional signal impact identification. This achieves the technical effect of obtaining reliable regional aggregation results and regional signal impact identification, thereby improving the accuracy of abnormal pre-diagnosis of low-voltage centralized meter reading communication modules.

[0035] Furthermore, step S500 of this application also includes:

[0036] Step S510: constructing a regional signal impact preset threshold, wherein the regional signal impact preset threshold corresponds to a signal constraint value;

[0037] Step S520: determining whether the regional signal impact identifier in the regional aggregation result meets the regional signal impact preset threshold;

[0038] Step S530: When the regional signal impact identifier in the regional aggregation result includes a first region that meets the regional signal impact preset threshold, an electric meter signal evaluation of the first region is performed based on the signal constraint value.

[0039] Specifically, a judgment is made as to whether the regional signal impact identifier satisfies the regional signal impact preset threshold. If the regional signal impact identifier satisfies the regional signal impact preset threshold, a first region is obtained, and the electric meter signal of the first region is evaluated according to the signal constraint value. The regional signal impact preset threshold and the signal constraint value can be adaptively set and determined. The signal constraint value includes a signal strength constraint value, a signal integrity constraint value, and a signal time period constraint value. Furthermore, the regional signal impact preset threshold and the signal constraint value have a corresponding relationship. The first region includes electric meter location data corresponding to the regional signal impact identifier that satisfies the regional signal impact preset threshold. The technical effect of screening the regional signal impact identifier according to the regional signal impact preset threshold, obtaining the first region, and evaluating the electric meter signal according to the signal constraint value is achieved, thereby improving the adaptability and accuracy of the electric meter signal evaluation.

[0040] Furthermore, step S500 of this application also includes:

[0041] Step S540: performing time period signal impact evaluation on the first area and generating an evaluation result;

[0042] Step S550: generating signal collection period identification information of the first area based on the evaluation result;

[0043] Step S560: collecting electric meter signals in the first area based on the signal collection period identification information.

[0044] Specifically, by evaluating the influence of the time period signal of the first area, an evaluation result is obtained, and then the signal collection time period identification information is generated. Further, the electric meter signal of the first area is collected according to the signal collection time period identification information. The evaluation result can be used to characterize the influence of the time period information on the signal of the first area. For example, the signal strength, signal interference, etc. of the first area may be different in different time period information. The higher the influence of the time period information on the signal of the first area, the higher the evaluation result obtained. The signal collection time period identification information includes the time period information corresponding to the evaluation result. The technical effect of collecting the electric meter signal of the first area according to the signal collection time period identification information and improving the rationality and accuracy of the electric meter signal collection in the first area is achieved.

[0045] Step S600: inputting the regional aggregation result and the meter stability evaluation result into an abnormality pre-screening evaluation model, and outputting an abnormality identification meter.

[0046] Specifically, the regional aggregation results and the meter stability evaluation results are used as input information and input into the abnormal pre-screening evaluation model to obtain abnormally identified meters. Among them, historical data query is performed according to the regional aggregation results and the meter stability evaluation results to obtain multiple historical regional aggregation results and multiple historical meter stability evaluation results. The multiple historical regional aggregation results and multiple historical meter stability evaluation results are continuously self-trained and learned until they converge to obtain the abnormal pre-screening evaluation model. The abnormal pre-screening evaluation model is a neural network model with functions such as intelligent abnormal data analysis and abnormal meter identification. The abnormal pre-screening evaluation model includes an input layer, a hidden layer, and an output layer. The abnormally identified meters include meters corresponding to multiple abnormal data in the regional aggregation results and the meter stability evaluation results. The technical effect of using the abnormal pre-screening evaluation model to obtain accurate and reliable abnormally identified meters and improve the quality of abnormal pre-screening of the low-voltage centralized meter reading communication module is achieved.

[0047] Further, as attached Figure 2 As shown, after step S600, the present application further includes:

[0048] Step S710: Generate an electricity meter maintenance list based on the electricity meter with the abnormal identification;

[0049] Step S720: performing a performance test on the abnormality-marked electric meter based on the electric meter maintenance list to obtain a performance test output result;

[0050] Step S730: adjusting the position or maintaining the abnormality-marked electric meter based on the performance test output result.

[0051] Specifically, a meter maintenance list is generated according to the abnormally marked meters, and a performance test is performed on the abnormally marked meters according to the meter maintenance list to obtain the performance test output results. Furthermore, the abnormally marked meters are repositioned or maintained according to the performance test output results. The meter maintenance list includes data information such as the meter position and basic parameters of the abnormally marked meters. The performance test includes performance tests such as temperature test, impact test, voltage impact test, current impact test, and harmonic impact test of the abnormally marked meters. The performance test output results include data information such as the functional stability, meter power consumption, and meter accuracy of the abnormally marked meters. The technical effect of performing performance tests on abnormally marked meters, adjusting the position or maintaining the abnormally marked meters according to the performance test output results, and improving the timeliness and accuracy of the position adjustment or maintenance of the abnormally marked meters is achieved.

[0052] Further, as attached Figure 3 As shown, step S730 of this application also includes:

[0053] Step S731: determining whether the performance test output result meets a first expected threshold;

[0054] Step S732: When the performance test output result fails to meet the first expected threshold, maintenance or replacement of the electric meter corresponding to the performance test output result is performed;

[0055] Step S733: When the performance test output result can meet the first expected threshold, determining whether the electric meter corresponding to the performance test output result is in the first area;

[0056] Step S734: When the meter is in the first area, the area position of the meter is adjusted.

[0057] Specifically, it is judged whether the obtained performance test output result meets the first expected threshold value. If the performance test output result does not meet the first expected threshold value, the abnormal identification meter corresponding to the performance test output result is maintained or replaced. If the performance test output result meets the first expected threshold value, it is judged whether the abnormal identification meter corresponding to the performance test output result is in the first area. If the abnormal identification meter corresponding to the performance test output result is in the first area, the regional position of the abnormal identification meter corresponding to the performance test output result is adjusted. The first expected threshold value is adaptively set and determined according to the actual needs of meter maintenance. The first area is the regional information composed of the meter position data corresponding to the regional signal impact identification that meets the preset threshold value of regional signal impact. The performance test output result is judged by the first expected threshold value and the first area, thereby adapting, reasonably maintaining and replacing, and adjusting the regional position of the abnormal identification meter, thereby improving the comprehensiveness of the abnormal pre-check of the low-voltage centralized meter reading communication module.

[0058] In summary, the method for pre-checking abnormal electricity meters provided by this application has the following technical effects:

[0059] The communication module obtains a historical data set, performs initial classification and integration on it, and obtains an integrated data set; performs data stability analysis on the same meter based on the integrated data set, and generates a meter stability evaluation result; uses the location data of each meter in the historical data set as the first aggregate reference data; uses the signal characteristics of each meter in the historical data set as the second aggregate reference data; performs correlation aggregation of meter deviation data according to the first aggregate reference data and the second aggregate reference data, obtains a regional aggregation result, and generates a regional signal impact identifier; inputs the regional aggregation result and the meter stability evaluation result into an abnormal pre-check evaluation model, and outputs an abnormal identification meter. This achieves the goal of improving the comprehensiveness and accuracy of the abnormal pre-check of the low-voltage centralized reading communication module, thereby improving the abnormal pre-check quality of the low-voltage centralized reading communication module; at the same time, by performing intelligent and scientific abnormal pre-check of the low-voltage centralized reading communication module, the management level of the low-voltage centralized reading is improved, thereby providing strong support for the normal operation of the low-voltage centralized reading.

[0060] Example 2

[0061] Based on the pre-check method of an abnormal electric meter in the above embodiment, the present invention also provides a pre-check system for an abnormal electric meter, which can be found in the attached Figure 4 , the system comprising:

[0062] A data collection and integration module 11 is configured to obtain a historically collected data set through the communication module, and to perform initial classification and integration on the historically collected data set to obtain an integrated data set;

[0063] A stability analysis module 12, configured to perform a data stability analysis on the electric meter based on the integrated data set and generate an electric meter stability evaluation result;

[0064] a first aggregated reference data determining module 13, configured to use the location data of each electric meter in the historically collected data set as first aggregated reference data;

[0065] a second aggregated reference data determining module 14, configured to use the signal characteristics of each electric meter in the historically collected data set as second aggregated reference data;

[0066] An aggregation identification module 15 is configured to perform association aggregation of meter deviation data based on the first aggregation reference data and the second aggregation reference data to obtain a regional aggregation result and generate a regional signal impact identification;

[0067] The abnormality pre-checking and evaluation module 16 is used to input the regional aggregation result and the meter stability evaluation result into an abnormality pre-checking and evaluation model, and output an abnormal identification meter.

[0068] Furthermore, the system further comprises:

[0069] a preset threshold determination module, the preset threshold determination module being used to construct a regional signal impact preset threshold, wherein the regional signal impact preset threshold corresponds to a signal constraint value;

[0070] a first judgment module, configured to judge whether the regional signal impact identifier in the regional aggregation result meets a preset regional signal impact threshold;

[0071] The electric meter signal evaluation module is used to evaluate the electric meter signal of the first area based on the signal constraint value when the regional signal impact identifier in the regional aggregation result contains a first area that meets the preset threshold of the regional signal impact.

[0072] Furthermore, the system further comprises:

[0073] A time impact coefficient determination module, wherein the time impact coefficient determination module is used to construct a time impact coefficient set;

[0074] a time-stable influence change calculation module, configured to calculate the time-stable influence change of the data in the integrated data set using the time influence coefficient set to obtain a calculated data set;

[0075] A stability assignment evaluation module is used to perform stability assignment evaluation through the calculation data set, and obtain the stability evaluation result of the electric meter based on the stability assignment evaluation result.

[0076] Furthermore, the system further comprises:

[0077] A signal change characteristic acquisition module, the signal change characteristic acquisition module is used to collect signal change characteristics of the electric meter on the integrated data set to obtain a signal change characteristic acquisition result;

[0078] A signal acquisition change trend analysis module, wherein the signal acquisition change trend analysis module is used to perform a signal acquisition change trend analysis on the electric meter based on the signal change feature acquisition result to obtain a change trend analysis result;

[0079] The electric meter stability evaluation result determination module is used to obtain the electric meter stability evaluation result through the change trend analysis result and the stability assignment evaluation result.

[0080] Furthermore, the system further comprises:

[0081] An electric meter maintenance list determination module, the electric meter maintenance list determination module is used to generate an electric meter maintenance list according to the abnormality-marked electric meter;

[0082] A performance test module, configured to perform a performance test on the abnormality-marked electric meter based on the electric meter maintenance list to obtain a performance test output result;

[0083] An adjustment and maintenance module is used to adjust or maintain the position of the abnormality-marked electric meter based on the output result of the performance test.

[0084] Furthermore, the system further comprises:

[0085] A second judgment module, the second judgment module is used to judge whether the performance test output result meets a first expected threshold;

[0086] a maintenance and replacement module, configured to maintain or replace the electric meter corresponding to the performance test output result when the performance test output result fails to meet the first expected threshold;

[0087] a third judgment module, configured to, when the performance test output result satisfies the first expected threshold, determine whether the electric meter corresponding to the performance test output result is in the first area;

[0088] The regional position adjustment module is used to adjust the regional position of the electric meter when the electric meter is in the first area.

[0089] Furthermore, the system further comprises:

[0090] a time period signal impact evaluation module, configured to perform a time period signal impact evaluation on the first area and generate an evaluation result;

[0091] a signal collection period identification information determination module, the signal collection period identification information determination module being configured to generate signal collection period identification information of the first area based on the evaluation result;

[0092] An electric meter signal acquisition module is configured to acquire electric meter signals in the first area based on the signal acquisition period identification information.

[0093] The present application provides a method for pre-checking abnormal electric meters, wherein the method is applied to a pre-checking system for abnormal electric meters, and the method includes: obtaining a historical collection data set through the communication module, and performing initial classification and integration on the data set to obtain an integrated data set; performing data stability analysis on the same electric meter based on the integrated data set to generate an electric meter stability evaluation result; using the location data of each electric meter in the historical collection data set as the first aggregate reference data; using the signal characteristics of each electric meter in the historical collection data set as the second aggregate reference data; performing correlation aggregation of electric meter deviation data according to the first aggregate reference data and the second aggregate reference data to obtain a regional aggregation result, and generating a regional signal impact identifier; inputting the regional aggregation result and the electric meter stability evaluation result into an abnormal pre-checking evaluation model to output an abnormal identification meter. This solves the technical problem in the prior art that the abnormal pre-checking of low-voltage centralized meter reading communication modules is not comprehensive and accurate, which results in poor abnormal pre-checking effect of low-voltage centralized meter reading communication modules. The goal is to improve the comprehensiveness and accuracy of the abnormality pre-checking of the low-voltage centralized reading communication module, thereby improving the quality of the abnormality pre-checking of the low-voltage centralized reading communication module; at the same time, by conducting intelligent and scientific abnormality pre-checking of the low-voltage centralized reading communication module, the management level of the low-voltage centralized reading is improved, thereby providing strong support for the normal work of the low-voltage centralized reading.

[0094] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] This specification and drawings are merely exemplary illustrations of the present application. If modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A method for pre-checking abnormal electric meters, characterized in that: The method is applied to an intelligent monitoring system, the intelligent monitoring system is communicatively connected with a communication module, and the method comprises: S100: obtaining a historically collected data set through the communication module, and initially classifying and integrating the historically collected data set to obtain an integrated data set; S200: performing data stability analysis of the same electric meter based on the integrated data set to generate an electric meter stability evaluation result; S300: Using the location data of each electric meter in the historical collection data set as first aggregated reference data; S400: Using the signal characteristics of each electric meter in the historical collection data set as second aggregated reference data; S500: Based on the first aggregated reference data and the second aggregated reference data, the meter deviation data is associated and aggregated to obtain a regional aggregation result, and a regional signal impact identifier is generated, wherein the process of associating and aggregating the meter deviation data includes: performing meter deviation data analysis on the second aggregated reference data according to the first aggregated reference data, analyzing the influence of the location data of each meter on the signal characteristics of each meter; the regional aggregation result includes the location data of multiple meters whose location data influences the signal characteristics of the meter; the regional signal impact identifier is used to characterize the influence of the location data of the meter on the signal characteristics of the meter; S600: Input the regional aggregation result and the meter stability evaluation result into an abnormal pre-check evaluation model, and output an abnormal identification meter; Among them, S500 also includes: Constructing a regional signal impact preset threshold, wherein the regional signal impact preset threshold corresponds to a signal constraint value, and the signal constraint value includes a signal strength constraint value, a signal integrity constraint value, and a signal period constraint value; Determining whether the regional signal impact identifier in the regional aggregation result meets the regional signal impact preset threshold; When the regional signal impact identifier in the regional aggregation result includes a first region that meets the regional signal impact preset threshold, an electric meter signal evaluation is performed in the first region based on the signal constraint value; Among them, S600 and later also include: S710: Generate an electric meter maintenance list according to the abnormal identification electric meter; S720: Performing a performance test on the abnormality-marked electric meter based on the electric meter maintenance list to obtain a performance test output result; S730: Adjusting or maintaining the abnormality-marked electric meter based on the performance test output result; Among them, S730 also includes: Determining whether the performance test output result meets a first expected threshold; When the performance test output result fails to meet the first expected threshold, maintenance or replacement of the electric meter corresponding to the performance test output result is performed; When the performance test output result can meet the first expected threshold, determining whether the electric meter corresponding to the performance test output result is in the first area; When the electric meter is in the first area, the electric meter is adjusted to a regional position; Among them, S500 also includes: Performing a time period signal impact evaluation on the first area to generate an evaluation result; generating signal collection period identification information of the first area based on the evaluation result; The electric meter signals in the first area are collected based on the signal collection period identification information.

2. The method according to claim 1, characterized in that The S200 also includes: Build time impact coefficient set; Calculating the time stability influence change of the data in the integrated data set by using the time influence coefficient set to obtain a calculated data set; A stability evaluation is performed using the calculation data set, and the electric meter stability evaluation result is obtained based on the stability evaluation result.

3. The method according to claim 2, characterized in that The S200 also includes: Collecting signal change characteristics of the electric meter on the integrated data set to obtain a signal change characteristic collection result; Based on the signal change feature acquisition result, a signal acquisition change trend analysis of the electric meter is performed to obtain a change trend analysis result; The electric meter stability evaluation result is obtained through the change trend analysis result and the stability assignment evaluation result.

4. A pre-check system for abnormal electric meters, characterized in that: The system is communicatively connected with the communication module to execute the method according to any one of claims 1 to 3, and the system comprises: A data collection and integration module, the data collection and integration module is used to obtain a historical collection data set through the communication module, and perform initial classification and integration on the historical collection data set to obtain an integrated data set; A stability analysis module, the stability analysis module is used to perform data stability analysis of the same electric meter based on the integrated data set to generate an electric meter stability evaluation result; a first aggregated reference data determination module, the first aggregated reference data determination module being configured to use the location data of each electric meter in the historically collected data set as first aggregated reference data; a second aggregated reference data determination module, the second aggregated reference data determination module being configured to use the signal characteristics of each electric meter in the historical collection data set as second aggregated reference data; An aggregation identification module, the aggregation identification module is used to perform associated aggregation of meter deviation data based on the first aggregation reference data and the second aggregation reference data to obtain a regional aggregation result and generate a regional signal impact identification, wherein the process of performing associated aggregation of meter deviation data includes: performing meter deviation data analysis on the second aggregation reference data according to the first aggregation reference data, analyzing the influence of the location data of each meter on the signal characteristics of each meter; the regional aggregation result includes the location data of multiple meters whose location data influences the signal characteristics of the meter; the regional signal impact identification is used to characterize the influence of the location data of the meter on the signal characteristics of the meter; The abnormal pre-checking and evaluation module is used to input the regional aggregation result and the meter stability evaluation result into an abnormal pre-checking and evaluation model, and output an abnormal identification meter.

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