A method and system for verifying an automated calibration system

Abstract

The present invention discloses a method and system for verifying an automated calibration system, which belongs to the technical field of system automatic control. The method of the present invention comprises: establishing a mathematical model for verifying an optimal frequency with the goal of minimizing the verification cost and maximizing the verification efficiency of the automated calibration system; solving the optimal frequency verification data model to determine an optimal solution; determining an optimal verification solution for the automated calibration system to be verified based on the optimal solution, and performing a period verification on the automated calibration system to be verified using the optimal verification solution. The present invention determines the optimal verification frequency of the calibration system in terms of ensuring that the verification efficiency of the automated calibration system is maximized and the comprehensive verification cost is minimized, thereby providing a theoretical basis and reference for the intelligent operation and maintenance of the automated calibration system.

Description

Technical Field

[0001] The present invention relates to the technical field of system automatic control, and more particularly, to a method and system for verifying an automated verification system. Background Art

[0002] To address the increasing demand for calibration of electric energy metering instruments such as low-voltage current transformers and single- and three-phase energy meters, State Grid Corporation of China is actively promoting the development of automated calibration systems for these instruments. Most metrology supervision departments rely on manual spot checks of transformers and single- and three-phase energy meters tested by automated calibration systems to monitor the measurement process. This method is inefficient and outdated, and struggles to match the efficiency of automated calibration systems.

[0003] The calibration process of all operations and test items on the assembly line is completed automatically. The calibration conclusion is judged by the calibration software according to the requirements of the regulations. Unqualified products must be manually re-inspected to determine the final cause of the failure. For qualified products, 10% of the total batches must be re-inspected. Therefore, the Quality Supervision Bureau also sends full-time personnel to the Metrology Center to supervise the working conditions of the assembly line. After the assembly line calibration, the qualified samples are randomly inspected, and the unqualified samples are re-inspected after the reasons are found, so as to achieve quality supervision of the assembly line to ensure the quality of the calibrated products.

[0004] However, with the surge in testing volume at each metering center, the complexity of the production line has increased accordingly. The fault point analysis and calibration test conclusions of the electric energy metering equipment are automatically generated by the system without any human intervention throughout the process. The possibility of system failure has also increased. These failures will lead to economic losses such as reduced product quality and incorrect electricity bill calculation.

[0005] Online verification technology utilizes a stable verification standard as a medium. Repeated measurements of the object being tested are performed using the verification standard. Statistical calculations establish measurement process parameters, control charts, and statistical control of the object being tested. Choosing the verification frequency is crucial for verification. While using one verification standard per test sample is the safest and most accurate approach, this approach can reduce the efficiency of automated verification systems by 50%. Therefore, it is necessary to develop reasonable verification methods that maximize verification efficiency while minimizing error correction costs. Summary of the Invention

[0006] In view of the above problems, the present invention proposes a method for verifying an automated verification system, comprising:

[0007] The goal is to establish a mathematical model for the optimal frequency of verification by minimizing the verification cost and maximizing the verification efficiency of the automated verification system.

[0008] Solve the optimal frequency verification data model and determine the optimal solution;

[0009] According to the optimal solution, the optimal verification plan for the automated verification system to be verified is determined, and the automated verification system to be verified is verified with the optimal verification plan.

[0010] Optionally, check the optimal frequency mathematical model, including: checking the comprehensive cost model, the production line verification efficiency model and the capacity ratio model;

[0011] The comprehensive verification cost model is used to calculate verification costs, false detection costs, and misdetection costs;

[0012] The pipeline verification efficiency model is used to calculate the verification efficiency of the automated verification system;

[0013] The capacity ratio model is used to calculate the capacity ratio of the automated verification system.

[0014] Optionally, the optimal frequency verification data model is solved using a Pareto fast non-dominated multi-objective optimization algorithm.

[0015] Optional, determination of the optimal verification plan, including:

[0016] Determine the parameter information of the automated verification system to be verified, and bring the parameter information and the cost of false detection into the mathematical model of the optimal verification frequency to solve it. The optimal solution obtained is the optimal verification plan.

[0017] Optional, optimal verification plan, including: optimized verification frequency of automated verification system, number of verification standards and number of single verifications.

[0018] The present invention also provides a system for verifying an automated verification system, comprising:

[0019] The model building module sets the goal of minimizing the verification cost and maximizing the verification efficiency of the automated verification system to establish a mathematical model for the optimal verification frequency;

[0020] The solution module solves the optimal frequency verification data model and determines the optimal solution;

[0021] The verification module determines the optimal verification plan for the automated verification system to be verified based on the optimal solution, and verifies the automated verification system to be verified with the optimal verification plan.

[0022] Optionally, check the optimal frequency mathematical model, including: checking the comprehensive cost model, the production line verification efficiency model and the capacity ratio model;

[0023] The comprehensive verification cost model is used to calculate verification costs, false detection costs, and misdetection costs;

[0024] The pipeline verification efficiency model is used to calculate the verification efficiency of the automated verification system;

[0025] The capacity ratio model is used to calculate the capacity ratio of the automated verification system.

[0026] Optionally, the optimal frequency verification data model is solved using a Pareto fast non-dominated multi-objective optimization algorithm.

[0027] Optional, determination of the optimal verification plan, including:

[0028] Determine the parameter information of the automated verification system to be verified, and bring the parameter information and the cost of false detection into the mathematical model of the optimal verification frequency to solve it. The optimal solution obtained is the optimal verification plan.

[0029] Optional, optimal verification plan, including: optimized verification frequency of automated verification system, number of verification standards and number of single verifications.

[0030] The present invention determines the optimal verification frequency of the verification system while ensuring the maximum verification efficiency and the minimum comprehensive verification cost of the automated verification system, providing a theoretical basis and reference for the intelligent operation and maintenance of the automated verification system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the method of the present invention;

[0032] Figure 2 This is a model solving framework diagram for an embodiment of the method of the present invention;

[0033] Figure 3 A schematic diagram of the Pareto dominance relationship in an embodiment of the method of the present invention;

[0034] Figure 4 This is a schematic diagram of the coding strategy in an embodiment of the method of the present invention;

[0035] Figure 5 This is a schematic diagram of the crossover strategy in an embodiment of the method of the present invention;

[0036] Figure 6 This is a schematic diagram of the mutation strategy in an embodiment of the method of the present invention;

[0037] Figure 7 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0038] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0039] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0040] The present invention proposes a method for verifying an automated verification system, such as Figure 1 Shown, including:

[0041] The goal is to establish a mathematical model for the optimal frequency of verification by minimizing the verification cost and maximizing the verification efficiency of the automated verification system.

[0042] Solve the optimal frequency verification data model and determine the optimal solution;

[0043] According to the optimal solution, the optimal verification plan for the automated verification system to be verified is determined, and the automated verification system to be verified is verified with the optimal verification plan.

[0044] The present invention will be further described below with reference to specific embodiments:

[0045] In order to ensure that the measurement process of the basic error of the automated verification system is always under control, it is necessary to conduct periodic verification regularly. The frequency of periodic verification is determined by the following two methods: regular periodic verification and irregular periodic verification.

[0046] Regular inspections determine the maximum time interval between inspections based on the condition of the equipment being inspected and the metrologist's experience. Irregular inspections, however, have no time constraints and can be conducted based on the equipment's status and performance. Due to the heavy workload of the automated verification system, the project determined the optimal inspection interval for the system by minimizing its overall cost and maximizing its efficiency, while also requiring a production capacity ratio greater than 80%.

[0047] First, it is necessary to establish a mathematical model for the optimal frequency of verification, as follows:

[0048] Verify the optimal frequency mathematical model, including:

[0049] (1) Verify comprehensive costs

[0050] In the optimization model, the comprehensive verification cost mainly includes the cost of each verification, the cost of false detection caused by undetected equipment failures due to delayed verification, and the cost of incorrect detection. Its expression is as follows:

[0051] F1=min(C c +C e +C w ) (1-1)

[0052] Where: C c Verification cost for verification tasks, C e is the false detection cost, C w The cost of false detection.

[0053] The calculation expression of the verification cost is as follows:

[0054]

[0055] Where: H i represents the number of verification standards placed in the i-th verification, n is the number of verification experiments in each verification task, and the value range is n∈[1,20], and c1 is the cost of one verification experiment for one verification standard.

[0056] The calculation expression of false detection cost is as follows:

[0057]

[0058] Where: P i is the planned verification task volume for the i-th working day. N is the number of annual verifications, N = ceil (250 / T) rounded down. T is the verification cycle, with a value range of T∈[20,250]. n1 is the number of electric energy metering instruments that can be detected by a single multifunctional verification chamber on the production line, R i is the number of devices that need to be repaired on the i-th working day, which is related to the actual number of slots in the automatic inspection system, w is the false detection rate, and c2 is the cost of a single electric energy meter due to false detection.

[0059] The cost of false detection is a function of the verification cycle and is negatively correlated. The formula is as follows:

[0060] C w =f w (N) (1-4)

[0061] (2) Production line verification efficiency

[0062] The verification efficiency of the low-voltage current transformer automated verification line is calculated by taking the minimum verification efficiency of 250 working days as the overall verification efficiency of the verification system. The expression is as follows:

[0063]

[0064] Where: M i is the number of finished products tested on the i-th working day, that is, the number of qualified products; Cap is the production capacity, the unit is a fixed value of units per hour; T i The effective working hours for a single working day are expressed as follows:

[0065] M i =(P i -H i *n-n1*R i )*(1-w) (1-6)

[0066] Where: H i The number of verification standards delivered on the i-th working day.

[0067] T i =8-∑t i (1-7)

[0068] Where: ∑t i The total downtime of any single machine that is not caused by failure of the machine itself.

[0069] (3) Capacity ratio

[0070] The capacity ratio of the automated verification system is the ratio of actual capacity to designed capacity, which is required to be greater than 80%. The expression is as follows:

[0071]

[0072] Where: Cap d The annual design capacity of the verification system; Cap a The annual actual capacity of the system. The annual design capacity is composed of the annual cycle rotation number, the annual business expansion and new additions, and the annual number of failures. That is, the standard value of annual design capacity = annual cycle rotation number + annual business expansion and new additions + annual number of failures.

[0073] in:

[0074] Annual cycle rotation number = number of operating electric energy metering devices in the province (based on the average annual operating number of the past three years (including the current year) provided by the marketing system) * annual rotation rate (calculated based on the 8-year calibration cycle required by the current calibration regulations, the annual rotation rate is 12.5%).

[0075] Annual business expansion increases = number of electric energy metering devices in operation across the province * business expansion growth rate (the business expansion growth rate shall be based on the average annual growth rate of the past three years (excluding the current year) provided in the marketing system).

[0076] Annual number of failures = number of electric energy metering devices in operation in the province * annual average failure rate (usually 2%).

[0077] The calculation method is shown in 1-9.

[0078] Cap d =P total *r rot +P total *r inc +P total *r mal (1-9)

[0079] Where: P total is the number of electric energy metering devices in operation in the province, r rot is the annual rotation rate, r inc is the business expansion growth rate of the provincial metrology center, r mal is the average annual failure rate.

[0080] Annual actual production capacity Cap a The calculation formula is as follows:

[0081]

[0082] Secondly, the optimal frequency mathematical model is verified, including:

[0083] (1) Pareto-based fast non-dominated multi-objective optimization (NSGA2) multi-objective optimization algorithm

[0084] Since this problem has two optimization objectives, namely the comprehensive cost of automated verification system verification and verification efficiency, this paper adopts the Pareto-based fast non-dominated multi-objective optimization (NSGA2) multi-objective optimization algorithm to solve the model, and finally obtains a series of non-dominated solutions that meet the constraints. The algorithm framework is as follows Figure 2 shown.

[0085] The above algorithm: imitates the biological evolution process, as follows:

[0086] a) First, there is a biological population (corresponding to the population initialization of the algorithm)

[0087] b) Gene exchange between organisms. Generally speaking (individuals with good traits + individuals with good traits, their offspring are likely to also have good traits). This process corresponds to the crossover of the algorithm.

[0088] c) In addition, to promote population evolution, the genes of some individuals will mutate, thus promoting the development of the population. This process corresponds to the mutation of the algorithm.

[0089] d) Combine the parental population and the offspring population, and select those with good traits. This process corresponds to the combination of populations PB and PC in the algorithm, and the advantage is to prevent excellent individuals from "dying" during the evolution process.

[0090] e) Since this problem is to find the optimal solution for two objectives, it is easy to compare the advantages and disadvantages of a single objective, but for two objectives, it is necessary to judge the changes in the quality of population individuals. Therefore, non-dominated sorting and crowding degree calculation are used.

[0091] f) During the evolution process, it is easy to generate many individuals with similar traits, especially in discrete problems. In this case, it is not conducive to gene exchange, and thus not conducive to population evolution. Therefore, a perturbed population is added, that is, some abnormal individuals are removed, and some new individuals are added to increase the diversity within the population and promote population evolution. Through the above process, the population continuously evolves forward, and finally a population with excellent traits (here it is the optimization objective) is obtained.

[0092] (2) Introduction to the Pareto dominance relationship

[0093] The Pareto dominance relationship is as Figure 3 shown: For a minimization multi-objective optimization problem, for n objective components f i (x), i = 1...n, arbitrarily given three points (X1, Y1), (X2, Y2) and (X3, Y3).

[0094] a) Since X1 < X3, but Y3 < Y1, so in this solution problem, it is impossible to judge which one is better between point 1 and point 3. Therefore, point 1 and point 3 are non-dominated solutions to each other, that is, they do not dominate each other.

[0095] b) Since X1 < X2 and Y1 < Y2, so in this solution problem, point 1 has better traits than point 2. Therefore, point 1 dominates point 2, or point 2 is dominated by point 1.

[0096] Finding all non-dominated solutions is the population with excellent traits we need. In addition, due to the large solution space of multi-objective optimization, there is no good exact algorithm (which can obtain the optimal solution 100%) to solve it, and the evolutionary algorithm in this paper is mostly used for solving. However, the evolutionary algorithm is an approximate optimization algorithm, that is, it does not guarantee that the optimal solution can be obtained finally, and it can only be said that an approximate optimal or sub-optimal solution close to the optimal solution is obtained.

[0097] (3) Encoding

[0098] Encode the initialized population individuals, with each individual represented by a chromosome. The genes on the chromosome are the solution variables of our problem, so each individual can represent the solution to a problem. Since this article is interested in the frequency of verification, the number of verifications per time, and the number of mutual inductors verified each time, this article adopts the following encoding strategy:

[0099] Step 1: Initialize the verification frequency T, T∈[20,250].

[0100] Step 2: Randomly initialize the number of verification passes n each time, n∈[1,20].

[0101] Step 3: Randomly initialize the number H of verification standards for the electric energy metering equipment placed for each verification.

[0102] Step 4: From the above steps, we can know that the length of chromosome is 3, that is, one chromosome includes 3 gene loci.

[0103] Through the above steps, we can get the initialized individuals, such as Figure 4 shown.

[0104] (4) Cross

[0105] The purpose of crossover is to obtain individuals with better traits through gene exchange between populations. Through crossover operation, the global search ability of the population can be improved, allowing the population to search in the solution space. According to the specific characteristics of the problem, this paper adopts the multi-point crossover method. The graphical representation of the crossover operation is as follows Figure 5 As shown, the crossover gene position is randomly generated, and then the corresponding gene is replaced to obtain the crossover individual.

[0106] (5) Variation

[0107] Mutation brings some additional genetic composition to the population to enhance the diversity of the population, which helps the population escape from the local optimum and improves the local search ability of the population. The graphical representation of the mutation operation is as follows Figure 6 As shown, the mutated gene positions 1 and 3 are randomly generated, and then the mutated genes are mutated according to the characteristics of the problem to obtain the mutated individuals.

[0108] (6) Disturbing populations

[0109] For the model solved in this paper, since its solution space is not continuous, it is easy to produce many identical individuals during the evolution process, which is not conducive to genetic exchange and population evolution, and can easily cause the population to fall into a local optimum. The identical individuals in the evolving population are removed and new individuals are added to improve the diversity of the population. The perturbation strategy is as follows:

[0110] Step 1: Delete the same individuals in the evolved population. Determine whether the population size has reached the evolved population size. If so, proceed to step 2. Otherwise, add a perturbation population.

[0111] Step 2: Randomly generate a new population and add it to the population in step 1 so that the population size reaches the evolutionary population size, and return to step 1.

[0112] (7) Selection operator

[0113] The purpose of the selection operator is to select relatively good individuals in the evolving population, and then evolve the next generation of population, continuously improving the quality of each generation. In multi-objective optimization algorithms, non-dominated solutions are considered relatively good individuals. However, during the evolution process, the number of non-dominated solutions may be insufficient or exceed the number of the original population. Therefore, this paper uses the crowding calculation method to screen the population participating in the next generation of evolution. The steps of the selection operator are as follows:

[0114] Step 1: Get the current population P and the population Q after crossover and mutation. The population size is popnum.

[0115] Step 2: Merge populations P and Q to obtain the final population P_last, and then perform fast non-dominated solution sorting on population P_last.

[0116] Step 3: According to the crowding calculation, select popnum individuals from the population P_last to participate in the evolution of the next generation population.

[0117] Finally, determine the optimal frequency for automated verification system checks, including:

[0118] To verify the feasibility of the above mathematical model and calculation method, we take a provincial metrology center as an example. Under normal circumstances, each test of the automated verification system is carried out with each bin fully loaded, that is, no bin is empty. The required parameter information is as follows:

[0119] (1) c1 (cost of one verification test for one verification standard) / yuan;

[0120] (2) n1 (the number of electric energy metering instruments that can be inspected in a single inspection chamber of the production line);

[0121] (3) Number of warehouses to be inspected;

[0122] (4)R i (Number of equipment requiring maintenance on the i-th working day) value range;

[0123] (5) w (false positive rate) empirical value;

[0124] (6) c2 (cost of misdetection of a single electric energy meter) / yuan;

[0125] (7) Cap (production capacity), unit: units per hour;

[0126] (8)Cap d Annual designed production capacity;

[0127] (9)P total (Average number of measuring instruments in operation in the past three years);

[0128] (10)r inc (Average business expansion growth rate over the past three years);

[0129] (11) Average time required for maintenance of the inspection warehouse.

[0130] Since the false detection cost is a function of the verification cycle N and is negatively correlated, assuming that the false detection cost is 100,000, we get Equation 1-11.

[0131] C w =100000 / N (1-11)

[0132] By substituting the above parameters and calculating with the Pareto-based fast non-dominated multi-objective optimization (NSGA2) multi-objective optimization algorithm, the optimal verification frequency, the number of verification standards, and the number of single verifications of the automated verification system can be obtained.

[0133] Traditional verification involves manually rechecking the wiring of 10% of the total batch size on a manual calibration table. However, this verification model has a low degree of automation and a long verification interval. If a problem with the verification system causes a false positive or misdetection, recalling the tested samples for retesting consumes significant manpower and time. Using a single verification standard for each test sample is the safest and most accurate verification method, but this approach can reduce the efficiency of automated verification systems by 50%.

[0134] The verification method based on the Pareto fast non-dominated multi-objective optimization algorithm can determine the verification frequency, number of verification standards, and number of verifications under the conditions of maximum verification efficiency and minimum overall cost. The existing verification plan is optimized to obtain the optimal verification method.

[0135] The present invention also provides a system 200 for verifying an automated verification system, such as Figure 7 As shown, including:

[0136] The model building module 201 establishes a mathematical model for optimal verification frequency with the goal of minimizing the verification cost and maximizing the verification efficiency of the automated verification system;

[0137] A solution module 202 solves the optimal frequency verification data model and determines the optimal solution;

[0138] The verification module 203 determines the optimal verification solution for the automated verification system to be verified based on the optimal solution, and verifies the automated verification system to be verified using the optimal verification solution.

[0139] Among them, the optimal frequency mathematical model is checked, including: checking the comprehensive cost model, the production line verification efficiency model and the capacity ratio model;

[0140] The comprehensive verification cost model is used to calculate verification costs, false detection costs, and misdetection costs;

[0141] The pipeline verification efficiency model is used to calculate the verification efficiency of the automated verification system;

[0142] The capacity ratio model is used to calculate the capacity ratio of the automated verification system.

[0143] Among them, the optimal frequency verification data model is solved using the Pareto fast non-dominated multi-objective optimization algorithm.

[0144] The determination of the optimal verification plan specifically includes:

[0145] Determine the parameter information of the automated verification system to be verified, and bring the parameter information and the cost of false detection into the mathematical model of the optimal verification frequency to solve it. The optimal solution obtained is the optimal verification plan.

[0146] Among them, the optimal verification plan includes: the optimized verification frequency of the automated verification system, the number of verification standards and the number of single verifications.

[0147] The present invention determines the optimal verification frequency of the verification system while ensuring the maximum verification efficiency and the minimum comprehensive verification cost of the automated verification system, providing a theoretical basis and reference for the intelligent operation and maintenance of the automated verification system.

[0148] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0149] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0152] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0153] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for verifying an automated verification system, the method comprising: The goal is to establish a mathematical model for the optimal frequency of verification by minimizing the verification cost and maximizing the verification efficiency of the automated verification system. Solve the verification optimal frequency data model and determine the optimal solution; Determine the optimal verification plan for the automated verification system to be verified based on the optimal solution, and verify the automated verification system to be verified using the optimal verification plan; The mathematical model for checking the optimal frequency includes: a comprehensive cost model for checking, a production line verification efficiency model, and a production capacity ratio model; The comprehensive verification cost model is used to calculate verification costs, false detection costs, and misdetection costs; The pipeline verification efficiency model is used to calculate the verification efficiency of the automated verification system; The capacity ratio model is used to calculate the capacity ratio of the automated verification system; A mathematical model for the optimal frequency of verification is established, as follows: Verify the optimal frequency mathematical model, including: (1) Verify the comprehensive cost: In the optimization model, the comprehensive verification cost mainly includes the cost of each verification, the cost of false detection caused by undetected equipment failures due to delayed verification, and the cost of incorrect detection. Its expression is as follows: F1=min(C c +C e +C w ) (1-1) Where: C c Verification cost for verification tasks, C e is the false detection cost, C w is the cost of false detection; The calculation expression of the verification cost is as follows: n is the number of experiments to be verified in each verification task, and its value range is n∈[1,20]. c1 is the cost of one verification experiment for one verification standard. H i The number of verification standards placed for the i-th verification; The calculation expression of false detection cost is as follows: Where: P i is the planned verification task volume for the i-th working day, N is the number of annual verifications, N = ceil (250 / T), n1 is the number of electric energy metering instruments that can be detected by a single multifunctional verification bin on the production line, R i is the number of devices that need to be repaired on the i-th working day, w is the false detection rate, and c2 is the cost of a single electric energy meter due to false detection; The cost of false detection is a function of the verification cycle and is negatively correlated. The formula is as follows: C w =f w (N) (1-4) (2) Production line verification efficiency; The minimum verification efficiency of 250 working days is taken as the overall verification efficiency of the verification system, and the expression is as follows: Where: M i is the number of finished products tested on the i-th working day, that is, the number of qualified products; Cap is the production capacity, in units of units per hour; T i The effective working hours for a single working day are expressed as follows: M i =(P i -H i *n-n1*R i )*(1-w) (1-6) T i =8-∑t i (1-7) Where: ∑t i The total downtime of any single machine that is not caused by the machine's own failure; (3) capacity ratio; The capacity ratio of the automated verification system is the ratio of the actual capacity to the designed capacity, expressed as follows: Where: Cap d The annual design capacity of the verification system; Cap a The annual actual capacity of the system; the annual design capacity is composed of the annual cycle rotation number, the annual business expansion and new additions, and the annual number of failures, that is, the standard value of annual design capacity = annual cycle rotation number + annual business expansion and new additions + annual number of failures; in: Annual cycle rotation number = number of electric energy metering devices in operation in the province * annual rotation rate; Annual business expansion number = number of electric energy metering devices in operation in the province * business expansion growth rate; Annual failure number = number of electric energy metering devices in operation in the province * annual average failure rate; The calculation method of annual design capacity is as follows; Cap d =P total *r rot +P total *r inc +P total *r mal (1-9) Where: P total is the number of electric energy metering devices in operation in the province, r rot is the annual rotation rate, r inc is the business expansion growth rate of the province's metrology centers, r mal is the average annual failure rate; Annual actual production capacity Cap a The calculation formula is as follows:

2. The method according to claim 1, wherein the optimal frequency verification data model is solved using a Pareto fast non-dominated multi-objective optimization algorithm.

3. The method according to claim 1, wherein determining the optimal verification solution specifically comprises: Determine the parameter information of the automated verification system to be verified, and bring the parameter information and the cost of false detection into the mathematical model of the optimal verification frequency to solve it. The optimal solution obtained is the optimal verification plan.

4. The method according to claim 1, wherein the optimal verification scheme comprises: Optimize the verification frequency, number of verification standards and number of single verifications of the automated verification system.

5. A system for verifying an automated verification system, the system being based on the method of claim 1, comprising: The model building module sets the goal of minimizing the verification cost and maximizing the verification efficiency of the automated verification system to establish a mathematical model for the optimal verification frequency; The solution module solves the verification optimal frequency data model and determines the optimal solution; The verification module determines the optimal verification plan for the automated verification system to be verified based on the optimal solution, and verifies the automated verification system to be verified with the optimal verification plan.

6. The system according to claim 5, wherein the optimal frequency verification data model is solved using a Pareto fast non-dominated multi-objective optimization algorithm.

7. The system according to claim 6, wherein determining the optimal verification solution specifically comprises: Determine the parameter information of the automated verification system to be verified, and bring the parameter information and the cost of false detection into the mathematical model of the optimal verification frequency to solve it. The optimal solution obtained is the optimal verification plan.

8. The system according to claim 6, wherein the optimal verification solution comprises: Optimize the verification frequency, number of verification standards and number of single verifications of the automated verification system.

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