Multitask electric energy meter verification scheduling method and system

Abstract

The invention relates to a multi-task electric energy meter verification scheduling method and system, and the method comprises the steps: firstly, rapidly generating an initial scheduling scheme through a greedy algorithm, preferentially distributing an electric energy meter with a verification time length to a closer and usable verification meter position, and reducing the transmission time and waiting time of the electric energy meter; and then, iterative optimization is performed on the initial scheme by using a genetic algorithm, a scheduling scheme is continuously improved, a globally optimal solution is finally obtained, and the total verification time is further shortened. According to the method and the system, an optimal scheduling strategy combining a greedy algorithm and a genetic algorithm is adopted, so that the waste of verification resources is avoided, the overall utilization efficiency of verification equipment is improved, and the overall time of electric energy meter verification is remarkably shortened.

Description

Technical Field

[0001] The invention belongs to the technical field of electric energy meter calibration, and in particular relates to a multi-task electric energy meter calibration scheduling method and system. Background Art

[0002] With the implementation of the energy-saving and people-benefiting policy of tiered electricity prices and the comprehensive construction of electricity consumption information collection systems, the demand for various types of electricity metering equipment has increased dramatically. In recent years, many provincial power companies have successively established their own provincial metering centers. With the gradual establishment and application of automated testing lines for metering automation terminals, higher requirements have been put forward for the management and verification efficiency of metering automation terminals.

[0003] Automated testing lines can not only improve verification efficiency and reduce the workload of operators, but also improve verification quality. However, factors such as the large number of metering terminals and different specifications limit the further improvement of the verification efficiency of automated testing lines.

[0004] The traditional calibration mode of electric energy metering equipment assembly line is to calibrate in batch order, that is, the calibration task of one batch is completed before the calibration of the next batch. Since each batch contains different types of concentrator terminals, the time and calibration process of each calibration link are different, and the calibration meter tray and tray device are different, resulting in limited flexibility of this scheduling model. After the calibration of some electric energy meters in the same group is completed, it is still necessary to wait for the completion of the calibration of other electric energy meters before starting a new batch of calibration, otherwise it is necessary to design extremely complex electric energy meter transportation devices and scheduling plans.

[0005] Currently, there are compatible calibration devices that can replace the meter holder according to the model of the calibrated electric energy meter, such as a quick-change meter holder and automatic plug-in device for calibrating an electric energy meter with publication number CN117849699A, which can eliminate the wiring operation by replacing the modular meter holder, and then automatically connect the wiring of the meter holder end and the test machine end through external power to achieve the purpose of quickly replacing the meter holder on the calibration production line. However, the calibration tasks of different electric energy meters take different amounts of time. For example, the calibration time of a single-phase electric energy meter is an average of 60-80 minutes, and a three-phase electric energy meter takes 2-4 hours depending on the type. The concentrator terminal takes 2-6 hours depending on the calibration item, and if a random inspection task is performed, the calibration time will generally be higher than that of a full inspection task. Therefore, using only a compatible calibration device cannot meet the scheduling requirements of complex electric energy meter calibration tasks, and still has high requirements for the complexity of the electric energy meter transmission device and the scheduling plan.

[0006] Therefore, in the automated detection line system of the metering automation terminal, it is necessary to quickly complete a large number of complex electric energy meter calibration tasks, and the reasonable allocation of calibration tasks is of great practical significance. Summary of the invention

[0007] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to at least solve one or more of the above-mentioned problems in the prior art, or to provide a multi-task electric energy meter calibration and scheduling method and system that meets one or more of the above-mentioned needs.

[0008] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a multi-task electric energy meter calibration and scheduling method, comprising: S1. Obtain the task list of the electric energy meter to be inspected and the meter position list of the verification meter position. The task list includes the verification duration of each electric energy meter to be inspected; the meter position list includes the available time of each verification meter position. If the verification meter position is idle, the available time is the current time; if the verification meter position has been assigned a verification task, the available time is the time when the verification task is completed; S2. Select the electric energy meter to be inspected with the shortest inspection time from the task list, calculate the overall inspection time factor of the electric energy meter to be inspected relative to each inspection position according to the inspection time of the electric energy meter to be inspected and the distance of each inspection position from the electric energy meter entrance, calculate the allocation priority value of the electric energy meter to be inspected relative to each inspection position according to the overall inspection time factor and the available time of each inspection position, allocate the electric energy meter to be inspected to the inspection position with the smallest allocation priority value, and update the available time of the inspection position according to the inspection time of the electric energy meter to be inspected; repeat step S2 until all the electric energy meters to be inspected are allocated; S3, generating an initial dispatching plan for a number of electric energy meters to be inspected according to the allocation in step S2; S4. Use a genetic algorithm to iteratively optimize the initial scheduling plan to generate a global optimal scheduling plan; the fitness function of the genetic algorithm aims to minimize the total calibration time and overall calibration time factor of all the electricity meters to be inspected, while maximizing the utilization rate of the calibration meter positions.

[0009] As a preferred implementation, the verification duration is jointly generated according to the type of each electric energy meter to be verified and the type of verification task.

[0010] As a preferred implementation, the calculation formula for the overall test duration factor is: ; The calculation formula for the allocation priority value is: ; in, It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected To identify epitope The distance is the distance weight, Is the test epitope available time.

[0011] As a preferred implementation, step S4 includes the steps of: S41, setting each initial scheduling scheme as an individual, and calculating the fitness of each individual; the genome of the individual includes a plurality of genes, and each gene includes a detection epitope assigned to the electric energy meter to be detected; S42, select a number of individuals with high fitness, select some individuals from them for crossover and mutation, generate offspring individuals, and use the offspring individuals to replace individuals with lower fitness for iteration; S43. Repeat step S42 until the genetic algorithm terminates and a global optimal scheduling solution is generated.

[0012] As a further preferred implementation, the crossover in step S42 includes: Select two individuals as parents, randomly select a crossover point, swap the genes of the parents at the crossover point, and generate two offspring individuals.

[0013] As a further preferred implementation, the variation of step S42 includes: A number of individuals to be mutated are selected, and a calibration epitope assigned to an electric energy meter to be inspected among the individuals to be mutated is mutated into other calibration epitopes.

[0014] As a preferred implementation, the calculation formula of the fitness function is: ; is the total inspection duration weight of the electric energy meter to be inspected, is the utilization weight of the assay epitope, is the overall verification duration factor weight of the electric energy meter to be inspected; It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected ; is the total inspection duration weight of the electric energy meter to be inspected, is the utilization weight of the assay epitope, is the overall verification duration factor weight of the electric energy meter to be inspected; It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected To identify epitope The distance is the distance weight; is the total number of electric energy meters to be inspected; For the The first The status of a calibration position, 0 means the calibration position is free, 1 means the calibration position is occupied; M means the number of calibration compartments for electric energy meters; K means the number of calibration positions in each calibration compartment.

[0015] As a preferred implementation, the task list also includes the calibration priority of each electric energy meter to be inspected; step S2 first selects the electric energy meters to be inspected in descending order of calibration priority, and then selects the electric energy meter to be inspected with the shortest calibration time.

[0016] As a preferred implementation, when an abnormality occurs in a calibration table, the electric energy meter to be inspected that is assigned to the abnormal calibration table is reallocated to a normal calibration table through step S2.

[0017] On the other hand, the present invention further provides a multi-task electric energy meter calibration and dispatching system, which applies a multi-task electric energy meter calibration and dispatching method as described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The multi-task electric energy meter calibration scheduling method and system of the present invention can be compatible with the simultaneous execution of multiple different electric energy meter calibration tasks, and in order to avoid the different durations of different calibration tasks affecting the overall calibration efficiency, the method and system of the present invention adopt an optimization scheduling strategy combining a greedy algorithm with a genetic algorithm, which significantly shortens the overall time of electric energy meter calibration. First, the greedy algorithm quickly generates an initial scheduling plan, and preferentially assigns electric energy meters with short and long calibration times to relatively close and available calibration tables, reducing the transportation time and waiting time of the electric energy meters. Secondly, the genetic algorithm iteratively optimizes the initial plan, continuously improves the scheduling plan, and finally obtains the global optimal solution, further shortening the total calibration time. At the same time, when assigning tasks, the scheduling algorithm will give priority to calibration tables with earlier available times, avoiding waste of resources, improving the overall utilization efficiency of calibration equipment, and making the total calibration time shorter. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the structure of the verification pipeline of an embodiment of the present invention; Figure 2 It is a flow chart of a multi-task electric energy meter calibration and scheduling method according to an embodiment of the present invention. DETAILED DESCRIPTION The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0020] Below, the specific embodiments of the present invention are described in detail with reference to the accompanying drawings. The following description with reference to the accompanying drawings is provided to assist in the understanding of the exemplary embodiments of the present invention as defined by the claims and their equivalents. It includes various specific details to assist in understanding, but they are to be regarded as exemplary only. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Moreover, in order to make the description more clear and concise, detailed descriptions of functions and structures well known in the art will be omitted.

[0021] The embodiment of the present invention provides a multi-task electric energy meter calibration scheduling method, which is applied to an automated electric energy meter calibration line system including multiple calibration cabins and multiple calibration meter positions. Each calibration meter position can place an electric energy meter for calibration. The schematic diagram of the calibration line is shown in FIG. Figure 1 As shown, the figure shows a plurality of compatibility test cabins 1 arranged in parallel, each of which is provided with a plurality of test meter positions 101. The electric energy meter is transported to the entrance 102 of the test cabin through the assembly line, and then is allocated to a specific test meter position according to the instructions of the scheduling algorithm.

[0022] The core of this scheduling method is that it does not simply calibrate according to batches or types of electric energy meters, but comprehensively considers multiple factors such as the calibration task, calibration duration, the status of the calibration meter position, and the distance, and optimizes the scheduling through an intelligent algorithm. The entire scheduling process is divided into two main stages: first, a greedy algorithm is used to quickly generate a preliminary scheduling plan; then, a genetic algorithm is used to iteratively optimize this preliminary plan to obtain a better global scheduling plan.

[0023] The process of the method is as follows Figure 2 As shown, the specific implementation is as follows: first perform step S1, obtain the task list of the electric energy meters to be inspected and the meter position list of the calibration meter position, the task list includes the calibration duration of each electric energy meter to be inspected; the meter position list includes the available time of each calibration meter position, if the calibration meter position is idle, the available time is the current time; if the calibration task has been assigned to the calibration meter position, the available time is the time when the calibration task is completed.

[0024] The task list of the energy meter to be inspected records the information of all the energy meters to be inspected. This information is not only the number of the energy meter, but also includes the type of energy meter, the type of verification task to be performed, and the estimated verification duration calculated based on this information. For example, the energy meter type can be single-phase, three-phase or concentrator, and the verification task type can be full inspection or random inspection.

[0025] The calibration task has different durations depending on the type of energy meter and whether it is a full inspection or a random inspection. The calibration duration of each energy meter can be calculated based on the type of individual energy meter and its calibration task type. For example, if a single-phase energy meter needs a full inspection, its calibration duration is 2 hours; if it is a full inspection of a three-phase energy meter, the calibration duration is about 4 hours; and for a concentrator, if it is a random inspection, the calibration duration is 24 hours. The above are just some simplified examples. In actual applications, the duration of different types of energy meters under different calibration tasks will be more finely divided according to the specific calibration plan.

[0026] In addition to the calibration task list, step S1 also obtains the calibration table list, which records the ID and status of each calibration table. Each calibration table has a unique ID and belongs to a specific calibration cabin. The table list also contains the available time of each calibration table. If a calibration table is currently idle, its available time is the current system time; if it is assigned a calibration task, the available time is the estimated completion time of the calibration task. In addition, the table list also records whether each calibration table is in a normal or abnormal state. In order to consider the distance from the electric meter to the calibration table during scheduling, the table list also records the distance of each calibration table from the electric meter entrance of the calibration cabin to which it belongs.

[0027] When executing step S1, the method of this embodiment reads the basic information of each calibration table from the database or configuration file, and sets the status of all calibration tables to idle, and the available time is set to the current time. When an electric energy meter is assigned to a calibration table, the status of the calibration table will be immediately updated to occupied, and the available time will be updated to the current time plus the calibration duration of the electric energy meter. When a calibration table completes the calibration task, the status of the table is updated to idle, and the available time is updated to the current time. If a calibration table fails, the method of this embodiment will mark the abnormal state of the calibration table as abnormal, and subsequent allocation tasks will avoid this abnormal table. When the abnormal table is repaired, the system will receive a recovery signal, and update its abnormal state to normal, and then update the available time according to its current state. The distance between the calibration table and the entrance of the electric energy meter is usually a fixed value, which is determined when the system is configured and stored in the database or configuration file. The scheduling algorithm uses this distance information when calculating the allocation priority value.

[0028] S2. Select the electric energy meter to be inspected with the shortest inspection time from the task list, calculate the overall inspection time factor of the electric energy meter to be inspected relative to each inspection position according to the inspection time of the electric energy meter to be inspected and the distance of each inspection position from the electric energy meter entrance, calculate the allocation priority value of the electric energy meter to be inspected relative to each inspection position according to the overall inspection time factor and the available time of each inspection position, allocate the electric energy meter to be inspected to the inspection position with the smallest allocation priority value, and update the available time of the inspection position according to the inspection time of the electric energy meter to be inspected; repeat step S2 until all the electric energy meters to be inspected are allocated; In step S2 of this embodiment, a greedy algorithm is used to perform preliminary task allocation. The core idea of ​​the greedy algorithm is to preferentially allocate the electric energy meter with the shortest calibration time to the most suitable calibration position at present.

[0029] Specifically, step S2 first selects an electric energy meter to be inspected from the task list, and the selection strategy is to select the electric energy meter to be inspected with the shortest inspection time; if there are multiple electric energy meters with the same inspection time, first-come-first-served, random selection and other strategies can be used for selection.

[0030] Then, for the selected electric energy meter to be inspected Step S2 calculates its relative to each test epitope The overall assay duration factor is calculated and the assignment priority value is calculated to decide which assay epitope to assign it to.

[0031] Among them, the calculation formula of the overall test duration factor is: ; The calculation formula for the allocation priority value is: ; in, It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected To identify epitope The distance is the distance weight, and its specific value can be determined by empirical value, experimental method, adaptive adjustment or joint optimization with other parameters. Is the test epitope available time.

[0032] Next, the system will check the energy meter Assign to the assay epitope with the smallest assignment priority value.

[0033] Through the calculation of the above-mentioned overall calibration time factor, it is possible to consider both the calibration time of the electric energy meter and the distance from the electric energy meter to the calibration position when allocating the calibration position. Specifically, the overall calibration time factor combines the two factors of calibration time and distance to form a comprehensive indicator. This indicator reflects the potential impact on the efficiency of the overall calibration process after an electric energy meter is assigned to a certain calibration position. Through the overall calibration time factor, the method of this embodiment can always allocate electric energy meters with shorter calibration times to closer calibration positions, so that electric energy meters with shorter calibration times can quickly enter and exit closer calibration positions, reducing the impact of transportation time on the overall time consumption of the calibration task. If a calibration position is far away, due to the long transportation time of the electric energy meter, the overall calibration time factor may be relatively large, thereby reducing its priority for selection.

[0034] The allocation priority value further considers the available time of the calibration station on the basis of the overall calibration duration factor. It comprehensively considers the following three factors: the available time of the calibration station, the calibration duration of the electric energy meter, and the distance from the electric energy meter to the calibration station. Giving priority to the calibration station with earlier available time can make the completion time of all calibration tasks as early as possible. Giving priority to the electric energy meter with shorter calibration duration is also conducive to the electric energy meter with shorter calibration duration to quickly enter and exit the place close to the entrance to complete the calibration. Since the distance from the electric energy meter to the calibration station will increase the time the electric energy meter is on the road, giving priority to the calibration station with a closer distance can also give priority to quickly completing the task with shorter calibration duration.

[0035] In an electric energy meter to be inspected After being assigned to a verification table, the system updates the available time of the verification table, and updates its available time to the time when all the currently assigned verification tasks are completed.

[0036] After the above calculation, step S2 will select a calibration meter position for each electric energy meter to be inspected, which is both available and relatively close and can better balance the working time of each calibration meter position.

[0037] The above step S2 will be repeated repeatedly until all the electric energy meters to be inspected are assigned to a verification position.

[0038] Step S3: Generate an initial scheduling plan based on the allocation in step S2.

[0039] The initial dispatching plan allocates all the electric energy meters to be inspected to specific inspection positions, and the overall inspection time required to complete the entire dispatching plan is relatively short.

[0040] Step S2 will be executed several times, and each execution will assign all the electric energy meters to be inspected to a verification table position, so that step S3 can generate several initial scheduling schemes, and these several initial scheduling schemes are used as the initial population for subsequent optimization. In this embodiment, the number of initial scheduling schemes is specifically provided. Through steps S2 and S3, this embodiment generates 100 initial scheduling schemes.

[0041] S4. Use a genetic algorithm to iteratively optimize the initial scheduling plan to generate a global optimal scheduling plan; the fitness function of the genetic algorithm aims to minimize the total calibration time and overall calibration time factor of all the electricity meters to be inspected, while maximizing the utilization rate of the calibration meter positions.

[0042] Among them, the genetic algorithm can simulate natural selection and genetic mechanism, and through selection, crossover, mutation and other operations, it continuously iterates and finally finds a scheduling solution close to the global optimal solution. In step S3, each initial scheduling solution is encoded as an individual, and the genome of the individual consists of N genes. Specifically, N is the number of electric energy meters to be inspected, each gene corresponds to an electric energy meter to be inspected, and the value of the gene represents the ID of the inspection epitope assigned to the electric energy meter. The gene code of the energy meter to be inspected is ,in For electric meter The assigned test cabin number (1 to M, M is the total number of test cabins), It's an electric meter The epitope number assigned to it (1 to K, where K is the number of epitopes in each assay chamber).

[0043] Therefore, the genome of a single solution can be expressed as:

[0044] The genetic algorithm of step S4 has a preset fitness function, which is used to evaluate the quality of each individual. In this embodiment, the fitness function comprehensively considers three optimization objectives: minimizing the total calibration time of all electricity meters to be inspected, maximizing the utilization rate of the calibration table positions, and minimizing the overall calibration time factor of all electricity meters to be inspected.

[0045] This embodiment provides a calculation formula for the fitness function, which is specifically: ; in is the total inspection duration weight of the electric energy meter to be inspected, is the utilization weight of the assay epitope, is the overall verification time factor weight of the electric energy meter to be tested; It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected To identify epitope The distance is the distance weight; is the total number of electric energy meters to be inspected; For the The first The status of a calibration position, 0 means the calibration position is free, 1 means the calibration position is occupied; M means the number of calibration compartments for electric energy meters; K means the number of calibration positions in each calibration compartment.

[0046] Add up the fitness of all individuals, that is, all initial scheduling plans, to get the total fitness ,but ,in is the population size, that is, the number of initial scheduling solutions.

[0047] The genetic operations of genetic algorithms include selection, crossover and mutation.

[0048] The selection operation calculates the probability of each individual being selected based on its fitness value. The higher the fitness value, the greater the probability of the individual being selected.

[0049] Specifically, the selection probability is allocated according to the fitness of each individual. The higher the fitness, the greater the probability of selection and the greater the chance of survival. The selection probability of an individual is: ; By randomly selecting intervals, we select from individuals with high probability (high fitness), and prepare for crossover and mutation operations to generate new and better scheduling solutions.

[0050] The crossover operation is specifically to randomly select an intersection point for the two selected parent individuals, exchange the gene fragments before or after the intersection point, and generate two offspring individuals. For example, if the parent individuals are [A, B, C, D, E, F] and [1, 2, 3, 4,5, 6], and the intersection point is 3, then the offspring individuals are [A, B, C, 4, 5, 6] and [1, 2, 3, D, E, F]. In this embodiment, for the scheduling scheme encoded as an individual, the specific execution method of the crossover operation is to select two scheduling schemes and a certain intersection point between the two, and all the electric energy meters to be inspected after the intersection point and the inspection table position allocated to each electric energy meter to be inspected are completely exchanged from the two scheduling schemes.

[0051] The mutation operation is used to introduce the diversity of genetic algorithm iterations to prevent the algorithm from falling into a local optimal solution. Specifically, an individual is randomly selected, a gene of the individual is randomly selected, and the value of the gene is randomly modified to the value of another gene. For example, for individuals [A, B, C, D, E, F], the third gene is selected for mutation, and C is modified to F. Then the mutated individuals are [A, B, F, D, E, F]. In this embodiment, for the scheduling scheme encoded as an individual, the specific execution method of the mutation operation is to randomly select any gene and modify the calibration epitope assigned to the energy meter to be inspected corresponding to the gene to other calibration epitopes.

[0052] After the crossover and mutation operations are completed, the individual with the highest current fitness is selected to enter the next iteration. The genetic algorithm will repeat the selection, crossover and mutation operations until the termination condition is met. The termination condition can be that the preset maximum number of iterations is reached, the fitness value no longer changes significantly in several consecutive generations, or a satisfactory solution that meets the preset conditions is found.

[0053] Finally, the global optimal scheduling scheme for the calibration of the current batch of electricity meters is obtained. This scheme is used for calibration scheduling of electricity meters, which is compatible with the simultaneous execution of multiple calibration tasks of different electricity meters. After the calibration tasks of electricity meters of different durations are optimized and scheduled by combining the greedy algorithm with the genetic algorithm, the scheduling scheme of all electricity meters to be inspected can be optimized under the optimized operation of efficiently utilizing the calibration equipment and reducing the transmission time, thereby obtaining the shortest overall completion time of the calibration tasks.

[0054] An improved embodiment of the present invention also takes into account that when different types of electric meters are delivered for calibration, the urgency of the order may be different from the business conditions of regular business, resulting in the possibility that the later-delivered electric meters may be inserted into the front of the calibration queue without considering the duration of the calibration.

[0055] Therefore, this improved embodiment sets a verification priority for each electric energy meter to be inspected, and includes the verification priority of each electric energy meter to be inspected in the task list.

[0056] In this embodiment, step S2 first selects the energy meter to be inspected according to the order of inspection priority from high to low, and first selects the energy meter to be inspected with the highest priority. For energy meters to be inspected with the same priority, the energy meter to be inspected with the shortest inspection duration is selected according to the order of inspection duration.

[0057] Another improved embodiment of the present invention also takes into account the situation where a calibration table position is abnormal or faulty. During the execution of step S2 or during the calibration of the electric energy meter, if a calibration table position is abnormal or faulty, the relevant electric meter will be removed from the cabin and rearranged through step S2.

[0058] On the other hand, the present invention also provides a multi-task electric energy meter calibration dispatching system, which applies a multi-task electric energy meter calibration dispatching method as described in the above embodiment. The system includes Figure 1 The electric energy meter calibration line, information acquisition unit, allocation unit, scheduling scheme generation unit and optimization unit are shown.

[0059] The information acquisition unit is used to obtain the task list of the electric energy meter to be inspected and the table position list of the calibration table position from the electric energy meter calibration pipeline.

[0060] The allocation unit is used to allocate all electric energy meters to the calibration meter positions through step S2 of the above method.

[0061] The dispatching scheme generating unit is used to generate initial dispatching schemes for a number of electric energy meters to be inspected according to the allocation of the allocating unit.

[0062] The optimization unit is used to perform genetic algorithm optimization on the initial allocation plan through step S4 of the above method to generate a global optimal scheduling plan, and then the electric energy meter verification pipeline executes the global optimal scheduling plan to complete the verification of the electric energy meter.

[0063] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-task electric energy meter calibration and scheduling method, characterized in that: include: S1. Obtain a task list of the electric energy meter to be inspected and a table position list of the verification table position, wherein the task list includes the verification duration of each electric energy meter to be inspected; The epitope list includes the available time of each verification epitope. If the verification epitope is idle, the available time is the current time; if the verification epitope has been assigned a verification task, the available time is the time when the verification task is completed; S2. Select the electric energy meter to be inspected with the shortest inspection time from the task list, calculate the overall inspection time factor of the electric energy meter to be inspected relative to each inspection position according to the inspection time of the electric energy meter to be inspected and the distance of each inspection position from the electric energy meter entrance, calculate the allocation priority value of the electric energy meter to be inspected relative to each inspection position according to the overall inspection time factor and the available time of each inspection position, allocate the electric energy meter to be inspected to the inspection position with the smallest allocation priority value, and update the available time of the inspection position according to the inspection time of the electric energy meter to be inspected; repeat step S2 until all the electric energy meters to be inspected are allocated; S3, generating an initial dispatching plan for a number of electric energy meters to be inspected according to the allocation in step S2; S4. Use a genetic algorithm to iteratively optimize the initial scheduling plan to generate a global optimal scheduling plan; the fitness function target of the genetic algorithm is to minimize the total calibration time and the overall calibration time factor of all the electric energy meters to be inspected, while maximizing the utilization rate of the calibration meter positions.

2. A multi-task electric energy meter calibration and scheduling method as claimed in claim 1, characterized in that: The verification duration is jointly generated according to the type of each electric energy meter to be verified and the type of verification task.

3. A multi-task electric energy meter calibration and scheduling method as claimed in claim 1, characterized in that: The calculation formula of the overall test duration factor is: ； The calculation formula of the allocation priority value is: ； Among them, the It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected To identify epitope The distance is the distance weight, Is the test epitope available time.

4. A multi-task electric energy meter calibration and scheduling method as claimed in claim 1, characterized in that: Step S4 comprises the steps of: S41, setting each of the initial scheduling schemes as an individual, and calculating the fitness of each of the individuals; the genome of the individual includes a plurality of genes, and each of the genes includes a detection epitope assigned to the electric energy meter to be detected; S42, selecting a number of individuals with high fitness, selecting some individuals from them for crossover and mutation, generating offspring individuals, and using the offspring individuals to replace individuals with lower fitness for iteration; S43. Repeat step S42 until the genetic algorithm terminates and a global optimal scheduling solution is generated.

5. A multi-task electric energy meter calibration and dispatching method as claimed in claim 4, characterized in that: The crossover in step S42 includes: Two individuals are selected as parent individuals, a crossover point is randomly selected, and the genes of the parent individuals are exchanged at the crossover point to generate two offspring individuals.

6. A multi-task electric energy meter calibration and dispatching method as claimed in claim 4, characterized in that: Variations of step S42 include: A number of individuals to be mutated are selected, and a verification epitope assigned to an electric energy meter to be inspected among the individuals to be mutated is mutated into other verification epitopes.

7. A multi-task electric energy meter calibration and dispatching method as claimed in claim 1, characterized in that: The calculation formula of the fitness function is: ； Said is the total inspection duration weight of the electric energy meter to be inspected, is the utilization weight of the assay epitope, is the overall verification duration factor weight of the electric energy meter to be inspected; Said It is the electric energy meter to be inspected The verification time is long, It is the electric energy meter to be inspected To identify epitope The distance is the distance weight; is the total number of electric energy meters to be inspected; For the The first The status of a calibration position, 0 means the calibration position is free, 1 means the calibration position is occupied; M means the number of calibration compartments for electric energy meters; K means the number of calibration positions in each calibration compartment.

8. A multi-task electric energy meter calibration and dispatching method as claimed in claim 1, characterized in that: The task list also includes the inspection priority of each electric energy meter to be inspected; step S2 first selects the electric energy meters to be inspected in order from high to low according to the inspection priority, and then selects the electric energy meter to be inspected with the shortest inspection time.

9. A multi-task electric energy meter calibration and scheduling method as claimed in claim 1, characterized in that: When an abnormality occurs in the calibration table, the electric energy meter to be inspected allocated to the abnormal calibration table is reallocated to the normal calibration table through step S2.

10. A multi-task electric energy meter calibration and dispatching system, characterized in that: A multi-task electric energy meter calibration and scheduling method as described in any one of claims 1 to 9 is applied.

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