Electric energy meter state evaluation method and system, storage medium and electronic equipment

Abstract

The invention belongs to the field of power grid operation management, and discloses an electric energy meter state evaluation method and system, a storage medium and electronic equipment, and the method comprises the steps: dividing domestic electricity in a region, collecting the electric energy information in the region, and building an electric energy database; constructing an electric energy meter state evaluation model based on the electric energy database, and optimizing the electric energy meter state evaluation model through a genetic algorithm; performing function deepening on the optimized electric energy meter state evaluation model; performing data structure fusion on the electric energy meter state evaluation model after function deepening; and testing and evaluating the optimized electric energy meter state evaluation model, the function-deepened electric energy meter state evaluation model and the data structure-fused electric energy meter state evaluation model, and screening out an optimal electric energy meter state evaluation method. The accuracy of electric energy meter state evaluation can be improved, and the maintenance cost of the electric energy meter is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of power grid operation management, and specifically relates to an electric energy meter state evaluation method and system, a storage medium, and electronic equipment. Background Art

[0002] The existing methods for evaluating the status of electric energy meters mostly adopt traditional indicator systems and mathematical statistical methods, and use corresponding evaluation algorithms and models to evaluate the status of Class I, II, and III electric energy meters in operation, and realize system functions such as model configuration, status assessment, on-site inspection execution, and human resource allocation guidance. However, this system uses a manual weighted calculation model, which has a high labor cost and involves a relatively simple indicator system. For the electricity consumption information collection system, it only involves two indicators: power anomaly and clock anomaly, and cannot fully utilize the electricity consumption information big data. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention discloses an electric energy meter state evaluation method and system, a storage medium, and an electronic device.

[0004] The present invention is achieved through the following technical solutions:

[0005] In a first aspect, a method for evaluating the state of an electric energy meter comprises:

[0006] Divide the domestic electricity consumption by region, collect the electricity information in the region and establish an electricity database;

[0007] Building an electric energy meter state evaluation model based on the electric energy database, and optimizing the electric energy meter state evaluation model by using a genetic algorithm;

[0008] Deepen the functions of the optimized electric energy meter status evaluation model;

[0009] Carry out data structure fusion on the electric energy meter status evaluation model after function deepening;

[0010] The optimized electricity meter state evaluation model, the function-deepened electricity meter state evaluation model and the data structure-fused electricity meter state evaluation model are tested and evaluated to select the optimal electricity meter state evaluation method.

[0011] In some embodiments, the area divisions include stages and boundaries.

[0012] In some embodiments, the electric energy information includes household change errors and the number of abnormal electricity users.

[0013] In some embodiments, optimizing the electric energy meter state evaluation model through a genetic algorithm includes establishing an overall model structure through a genetic algorithm, using the overall model structure as a parent generation basis of the genetic algorithm, and realizing module functions with different effects through multi-step derivative optimization.

[0014] In some embodiments, the overall model structure includes regional modules, reasonable data thresholds, actual power usage information data, evaluation results, and abnormal data detection.

[0015] In some embodiments, a verification mechanism is established based on the optimized electric energy meter state evaluation model;

[0016] The verification mechanism includes establishing an electric energy meter operation error detection model evaluation function in the optimized electric energy meter state evaluation model.

[0017] In some embodiments, the electric energy meter operation error detection model evaluation function uses augmented reality technology to simulate the power consumption environment, collects simulation data obtained by augmented reality technology, and verifies the electric energy meter operation error monitoring model.

[0018] In some embodiments, the verification mechanism also includes an online sampling mechanism, which selects a certain proportion of electricity meter status samples for sampling and performs maintenance and comparative analysis on the sampling results.

[0019] In some embodiments, the function deepening is to deepen the application of the module functions of the optimized electric energy meter state evaluation model, including deepening the state evaluation and module switching.

[0020] In some embodiments, the data structure is integrated to integrate and transform the data structure in the electric energy meter state evaluation model.

[0021] In some embodiments, the optimized electric energy meter state evaluation model, the electric energy meter state evaluation model after function deepening, and the electric energy meter state evaluation model after data structure fusion are tested and evaluated to construct an optimization target value calculation formula:

[0022] f 1 (t) = Min[max(cim)];

[0023] Wherein, t is the individual serial number of the electric energy meter; 1≤t≤n, n is the total number of electric energy meters, i=1, 2..., n; cim is the collection time of the i-th electric energy meter; f 1 (t) represents the target value of individual t under target 1; Min[] represents the minimum value.

[0024] In some embodiments, the optimization target value calculation formula adopts the minimum verification cost, and the following formula is obtained:

[0025]

[0026] Among them, M j Y is the number of all the verification devices in the jth link; ijkIndicates whether the electric energy meter i is calibrated in the kth device of link j. If so, Y ijk is 1, otherwise Y ijk is 0; F ijk is the average cost per unit working time of the kth device in the jth link; P ijk is the calibration time of the kth device of the electric energy meter i in link j; f 2 (t) represents the target value of individual t under target 2.

[0027] In some embodiments, based on the optimization target value, the congestion degree of the electric energy meter is calculated by a congestion degree calculation formula, and the congestion degree calculation formula is:

[0028]

[0029] Where CD(t) represents the crowding distance of individual t; f g (t+1) and f g (t-1) represents the target value of individual t+1 and individual t-1 under target g; g∈(1,2,...,G), G represents the total number of targets, f g max and f g min represents the maximum target value and the minimum target value under the target g, respectively. The electric energy meter state evaluation method with the smallest error and the most evaluation functionality is selected through congestion optimization.

[0030] In a second aspect, an electric energy meter status evaluation system includes:

[0031] Data collection module, used to collect user power information and build a corresponding power database;

[0032] A model building module, used to build an electric energy meter state evaluation model and optimize the electric energy meter state evaluation model through a genetic algorithm;

[0033] Model deepening module, used to deepen the functions of the optimized electric energy meter state evaluation model;

[0034] Model fusion module, used to fuse the data structure of the electric energy meter status evaluation model after function deepening;

[0035] The model testing module is used to test and evaluate the optimized electric energy meter state evaluation model, the electric energy meter state evaluation model after function deepening and the electric energy meter state evaluation model after data structure fusion, and screen out the optimal electric energy meter state evaluation method.

[0036] In a third aspect, a computer-readable storage medium is provided, characterized in that one or more programs are stored therein, and when the one or more programs are executed, the above method can be implemented.

[0037] In a fourth aspect, a device comprises a processor, a communication interface, a memory and a communication bus; the processor, the communication interface and the memory communicate with each other via the communication bus; and the processor is used for the program stored in the above-mentioned computer-readable storage medium.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] 1. Improve the accuracy of the status assessment of the energy meter and reduce the occurrence of errors and inaccuracies;

[0040] 2. Abnormalities can be discovered and handled online in a timely manner, reducing the maintenance and management costs of the energy meter;

[0041] 3. The electric energy meter status evaluation method disclosed in the present invention improves the operational efficiency of the power company and the stability and reliability of the power system.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the present use information. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a schematic diagram of an electric energy meter status evaluation method of this embodiment.

[0045] Figure 2 It is a schematic diagram of an electric energy meter status evaluation system of this embodiment.

[0046] Figure 3 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The drawings are only for illustrative purposes and cannot be construed as limiting the present invention. To better illustrate the present embodiment, some parts of the drawings may be omitted, enlarged, or reduced, and do not represent the size of the actual product. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the drawings. The positional relationships described in the drawings are only for illustrative purposes and cannot be construed as limiting the present invention.

[0048] like Figure 1 A method for evaluating the state of an electric energy meter is shown, comprising the following steps:

[0049] S1. Regionalize household electricity consumption, collect electricity information in the area and establish an electricity database;

[0050] Specifically, the household electricity consumption is divided into substations, the boundary positions of the substations are marked, and the power information of users within the substations and at the boundary positions is collected. The power information includes the number of household power change errors and abnormal power consumption, and the number of household power change errors and abnormal power consumption is marked.

[0051] Furthermore, a substation noise level assessment mechanism is constructed to support differentiated management of substation error thresholds, including household transformer analysis, wrong wiring analysis, and cloud-edge collaboration. By using intelligent measurement switches, the collected data and the results of diagnosis by the substation noise level assessment mechanism are used to establish an electric energy database, thereby improving anomaly detection and hit rates and strengthening business governance in the substation.

[0052] S2. constructing an electric energy meter state evaluation model based on the electric energy database, and optimizing the electric energy meter state evaluation model by a genetic algorithm;

[0053] Specifically, an electric energy meter evaluation model is constructed based on the electric energy database, and an overall model structure is established through a genetic algorithm. The overall model structure serves as the parent basis of the genetic algorithm, and module functions with different effects are realized through multi-step derivative optimization. The overall model structure includes regional modules, reasonable data thresholds, actual electricity consumption information data, evaluation results and abnormal data detection, thereby realizing the optimization of the electric energy meter state evaluation model, so as to optimize the electric energy meter state evaluation method with the smallest screening error and the most evaluation functionality.

[0054] Furthermore, the abnormal data detection targets abnormally collected data and, through intelligent number supplementation and data transparent copying, compensates for the problem of insufficient calculable data and supports auxiliary positioning of abnormalities, increases the contribution of reactive power and load fluctuations to line losses, optimizes the calculation of branch losses in substations, and improves the accuracy of the electric energy meter status evaluation model.

[0055] In addition, a verification mechanism is established for the optimized electric energy meter state evaluation model. The verification mechanism is to establish an electric energy meter operation error detection model evaluation function in the optimized electric energy meter state evaluation model, use augmented reality (AR) technology to simulate the power consumption environment, collect simulation data obtained by augmented reality technology simulation, and verify the electric energy meter operation error monitoring model.

[0056] Furthermore, the verification mechanism also includes an online sampling mechanism, which selects a certain proportion of electricity meter status samples for sampling, maintains and compares the sampling results, and provides technical support for the supervision and inspection of sampling calibration and status replacement work in cities and counties.

[0057] S3. Deepen the function of the optimized electric energy meter status evaluation model;

[0058] Specifically, the function deepening starts from the usage needs of grassroots personnel and the management needs of municipal and provincial companies, deepens the application of the module functions of the optimized electricity meter status evaluation model, deepens the status evaluation according to different levels of user management, and is equipped with a switching application function. According to the use of the electricity meter status evaluation method by different types of work, targeted switching of different modules is added, thereby improving the versatility of the electricity meter status evaluation.

[0059] S4. Perform data structure fusion on the electric energy meter status evaluation model after function deepening;

[0060] Specifically, the data structure in the electric energy meter status evaluation model is integrated and transformed to realize the business integration of the electric energy meter evaluation model and module switching and the electric energy meter evaluation model system of the present invention, establish data integration, and enhance the support capability for inaccurate replacement business.

[0061] S5. Test and evaluate the optimized electricity meter state evaluation model, the electricity meter state evaluation model after function deepening, and the electricity meter state evaluation model after data structure fusion, and select the optimal electricity meter state evaluation method.

[0062] Specifically, the optimized electric energy meter state evaluation model, the electric energy meter state evaluation model after function deepening, and the electric energy meter state evaluation model after data structure fusion are tested and evaluated, and the accuracy of the test structure is tested repeatedly to optimize the target value. The calculation formula is:

[0063] f 1 (t) = Min [max (cim)]; (1)

[0064] Wherein, t is the individual serial number of the electric energy meter; 1≤t≤n, n is the total number of electric energy meters, i=1, 2..., n; cim is the collection time of the i-th electric energy meter; f 1 (t) represents the target value of individual t under target 1; Min[] is the minimum value, and the calculation formula for the optimized target value with the minimum inspection cost is:

[0065]

[0066] Among them, M j Y is the number of all the verification devices in the jth link; ijkIndicates whether the electric energy meter i is calibrated in the kth device of link j. If so, it is 1, otherwise it is 0; F ijk is the average cost per unit working time of the kth device in the jth link; P ijk is the calibration time of the kth device of the electric energy meter i in link j; f 2 (t) represents the target value of individual t under target 2. The congestion degree of the electric energy meter is calculated by the congestion degree calculation formula. The congestion degree calculation formula is:

[0067]

[0068] Where CD(t) represents the crowding distance of individual t; f g (t+1) and f g (t-1) represents the target value of individual t+1 and individual t-1 under target g; g∈(1,2,...,G), G represents the total number of targets, f g max and f g min represents the maximum target value and the minimum target value under the target g, respectively. The electric energy meter state evaluation method with the smallest error and the most evaluation functionality is selected through congestion optimization.

[0069] like Figure 2 The electric energy meter status evaluation system shown includes a data acquisition module, a model construction module, a model supervision and verification module, a model deepening and fusion module and a model testing module.

[0070] Specifically, the data acquisition module is used to collect user power information to build a corresponding power database;

[0071] A model building module, used to build an electric energy meter state evaluation model and optimize the electric energy meter state evaluation model through a genetic algorithm;

[0072] Model deepening module, used to deepen the functions of the optimized electric energy meter state evaluation model;

[0073] Model fusion module, used to fuse the data structure of the electric energy meter status evaluation model after function deepening;

[0074] The model testing module is used to test and evaluate the optimized electric energy meter state evaluation model, the electric energy meter state evaluation model after function deepening and the electric energy meter state evaluation model after data structure fusion, and screen out the optimal electric energy meter state evaluation method.

[0075] The embodiment of the present disclosure further provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0076] The memory is a computer-readable storage medium for storing one or more programs.

[0077] The processor is used to execute the program stored in the computer-readable storage medium.

[0078] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or may exist independently without being assembled into the device / apparatus.

[0079] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the state of an electric energy meter, characterized in that: include: Divide the domestic electricity consumption by region, collect the electricity information in the region and establish an electricity database; Building an electric energy meter state evaluation model based on the electric energy database, and optimizing the electric energy meter state evaluation model by using a genetic algorithm; Deepen the functions of the optimized electric energy meter status evaluation model; Carry out data structure fusion on the electric energy meter status evaluation model after function deepening; The optimized electricity meter state evaluation model, the function-deepened electricity meter state evaluation model and the data structure-fused electricity meter state evaluation model are tested and evaluated to select the optimal electricity meter state evaluation method.

2. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: The area division includes areas and boundaries.

3. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: The electric energy information includes household change errors and the number of abnormal electricity users.

4. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: Optimizing the electric energy meter state evaluation model through a genetic algorithm includes establishing a total model structure through a genetic algorithm, using the total model structure as a parent generation basis of the genetic algorithm, and realizing module functions with different effects through multi-step derivative optimization.

5. The method for evaluating the state of an electric energy meter according to claim 4, characterized in that: The overall model structure includes regional modules, reasonable data thresholds, actual electricity consumption information data, evaluation results and abnormal data detection.

6. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: The method further comprises: Establish a verification mechanism based on the optimized electricity meter status evaluation model; The verification mechanism includes establishing an electric energy meter operation error detection model evaluation function in the optimized electric energy meter state evaluation model.

7. The method for evaluating the state of an electric energy meter according to claim 6, characterized in that: The electric energy meter operation error detection model evaluation function uses augmented reality technology to simulate the power consumption environment, collects simulation data obtained by augmented reality technology simulation, and verifies the electric energy meter operation error monitoring model.

8. The method for evaluating the state of an electric energy meter according to claim 6, characterized in that: The verification mechanism also includes an online sampling mechanism, which selects a certain proportion of electric energy meter status samples for sampling and performs maintenance and comparative analysis on the sampling results.

9. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: The function deepening is to deepen the application of the module functions of the optimized electric energy meter state evaluation model, including deepening state evaluation and module switching.

10. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: The data structure fusion is to integrate and transform the data structure in the electric energy meter state evaluation model.

11. The method for evaluating the state of an electric energy meter according to claim 1, characterized in that: Test and evaluate the optimized electric energy meter status evaluation model, the electric energy meter status evaluation model after function deepening, and the electric energy meter status evaluation model after data structure fusion, and construct the optimization target value calculation formula: f1(t)=Min[max(cim)]; Wherein, t is the individual serial number of the electric energy meter; 1≤t≤n, n is the total number of electric energy meters, i=1, 2..., n; cim is the collection time of the i-th electric energy meter; f1(t) represents the target value of individual t under target 1; Min[] is the minimum value.

12. The method for evaluating the state of an electric energy meter according to claim 11, characterized in that: The optimization target value calculation formula adopts the minimum verification cost, and the following formula is obtained: Among them, M j is the number of all the verification devices in the jth link; Y ijk Indicates whether the energy meter i is calibrated in the kth device of link j. If so, Y ijk is 1, otherwise Y ijk is 0; F ijk is the average cost per unit working time of the kth device in the jth link; P ijk is the calibration time of the kth device of the electric energy meter i in link j; f2(t) represents the target value of individual t under target 2.

13. The method for evaluating the state of an electric energy meter according to claim 12, characterized in that: Based on the optimization target value, the congestion degree of the electric energy meter is calculated by the congestion degree calculation formula, and the congestion degree calculation formula is: Where CD(t) represents the crowding distance of individual t; f g (t+1) and f g (t-1) represents the target value of individual t+1 and individual t-1 under target g; g∈(1,2,...,G), G represents the total number of targets, f g max and f g min represents the maximum target value and the minimum target value under the target g, respectively. The electric energy meter state evaluation method with the smallest error and the most evaluation functionality is selected through congestion optimization.

14. An electric energy meter status evaluation system, characterized in that: include: Data collection module, used to collect user power information and build a corresponding power database; A model building module, used to build an electric energy meter state evaluation model and optimize the electric energy meter state evaluation model through a genetic algorithm; Model deepening module, used to deepen the functions of the optimized electric energy meter state evaluation model; Model fusion module, used to fuse the data structure of the electric energy meter status evaluation model after function deepening; The model testing module is used to test and evaluate the optimized electric energy meter state evaluation model, the electric energy meter state evaluation model after function deepening and the electric energy meter state evaluation model after data structure fusion, and screen out the optimal electric energy meter state evaluation method.

15. A computer-readable storage medium, characterized in that: One or more programs are stored, and when the one or more programs are executed, the method described in any one of claims 1 to 13 can be implemented.

16. A device comprising a processor, a communication interface, a memory and a communication bus; the processor, the communication interface and the memory communicate with each other via the communication bus; characterized in that: The processor is used to execute the program stored in the computer-readable storage medium according to claim 15.

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