A dispatch optimization method and electronic equipment for electric energy metering instrument calibration
By sorting and optimizing the sub-batch sequences of the electrical energy metering instruments, using Pareto optimization and congestion calculations, combined with multiple transformation algorithms, the optimal sub-batch sequences are screened, and the long calibration time and cost in the existing technology are solved. The high problem realizes a scheduling method with short calibration time and low cost.
Patent Information
- Application Number
- CN202111304735.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-11-05
- Publication Date
- 2024-12-31
- Estimated Expiration
- 2041-11-05
AI Technical Summary
The prior art cannot meet the scheduling method of short calibration time and low cost of power metering instruments at the same time, resulting in difficulty in taking into account both the calibration efficiency and the cost.
By obtaining the sub-batch number of the electrical energy metering instrument, random sorting and sorting optimization are performed, and the Pareto optimization and congestion calculation formula is used to combine exchange, shift, symmetric transformation and roulette algorithms to gradually filter out the optimal and the best The sub-batch sequence is iteratively adjusted to finally determine the optimal sub-batch sequence.
It realizes the reduction of calibration costs without increasing calibration time, finds the optimal scheduling method, and ensures that both the calibration efficiency and economicality of the power metering instruments are taken into account.
Smart Images
Figure CN114066030B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of mechanical engineering technology, and in particular to a scheduling optimization method for electric energy metering instrument calibration, electronic equipment and computer-readable storage medium. Background Art
[0002] The power industry is an important basic energy industry in the development of the national economy, so the work of electric energy measurement is particularly important. Electric energy metering instruments need to pass the verification and certification of the measurement center before installation and use in my country. In the actual verification process of electric energy metering instruments, the scheduling method generally adopted is batch verification to reduce the waiting time of the machine.
[0003] Using the existing dispatching method to calibrate electric energy metering instruments can shorten the calibration time and improve the calibration efficiency, but it cannot simultaneously meet the requirement of low calibration cost. Different dispatching methods will produce different calibration times and calibration costs. The existing technology cannot find a dispatching method that can simultaneously meet the requirements of short calibration time and low calibration cost. Summary of the invention
[0004] In view of this, it is necessary to provide a scheduling optimization method, electronic device and computer-readable storage medium for calibration of electric energy metering equipment to solve the problem that the scheduling method in the prior art is not optimal enough.
[0005] In order to solve the above problems, the present invention provides a scheduling optimization method for electric energy metering instrument calibration, comprising:
[0006] Obtaining the number of sub-batches of electric energy metering equipment and the corresponding sub-batch serial numbers, and randomly sorting the sub-batch serial numbers to obtain a number of initial sub-batch sequences;
[0007] Obtaining verification time and verification cost of the several initial sub-batch sequences, and obtaining an initial optimal sub-batch sequence and an initial worst sub-batch sequence according to the verification time and verification cost;
[0008] Sorting the initial sub-batch sequences based on a preset rule to obtain a number of candidate sub-batch sequences, and optimizing the candidate sub-batch sequences to obtain a candidate optimal sub-batch sequence;
[0009] An optimal sub-batch sequence is obtained according to the candidate optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence.
[0010] Further, according to the verification time and the verification cost, an initial optimal sub-batch sequence and an initial worst sub-batch sequence are obtained, including:
[0011] According to the verification time and verification cost, and by using Pareto optimization, an initial better sub-batch sequence set and an initial worse sub-batch sequence set are obtained;
[0012] According to the initial better sub-batch sequence set and the initial worse sub-batch sequence set, and using the congestion calculation formula, an initial best sub-batch sequence and an initial worst sub-batch sequence are obtained.
[0013] Furthermore, the initial better sub-batch sequence set and the initial worse sub-batch sequence set are obtained by using Pareto optimization, including:
[0014] The Pareto optimal search is used to compare the verification time and verification cost of the several initial sub-batch sequences, and the sub-batch sequences that meet both the requirements of short verification time and low verification cost are combined into an initial better sub-batch sequence set, and the sub-batch sequences that meet both the requirements of long verification time and high verification cost are combined into an initial worse sub-batch sequence set.
[0015] Furthermore, the initial optimal sub-batch sequence and the initial worst sub-batch sequence are obtained using the crowding degree calculation formula, including:
[0016] The crowding calculation formula is used to calculate the crowding values corresponding to the sub-batch sequences in the initial better sub-batch sequence set and the initial worse sub-batch sequence set. The sub-batch sequence corresponding to the maximum crowding value in the initial better sub-batch sequence set is the initial best sub-batch sequence, and the sub-batch sequence corresponding to the minimum crowding value in the initial worse sub-batch sequence set is the initial worst sub-batch sequence. The crowding calculation formula is:
[0017]
[0018] Among them, D s is the crowding value corresponding to the sth initial sub-batch sequence in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, f1 max , are the maximum verification time and maximum verification cost in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively, f1 min , are the minimum verification time and minimum verification cost in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively; f1(s+1) and f2(s+1) are the verification time and verification cost of the s+1th initial sub-batch sequence in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively; f1(s-1) and f2(s-1) are the verification time and verification cost of the s-1th initial sub-batch sequence in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively.
[0019] Furthermore, the initial sub-batch sequences are sorted based on a preset rule to obtain a number of candidate sub-batch sequences, including:
[0020] The initial sub-batch sequences are sorted by using at least one of exchange transformation, shift transformation and symmetric transformation to obtain a sorted sub-batch sequence set, and a roulette algorithm is used to select the sorted sub-batch sequence set to obtain a plurality of candidate sub-batch sequences.
[0021] Further, including:
[0022] The exchange transformation is to randomly select two sub-batch numbers in a sub-batch sequence, exchange the positions of the two sub-batch numbers, and obtain a new sub-batch sequence;
[0023] The shift transformation is to randomly select two sub-batch numbers in a sub-batch sequence, move the previous sub-batch number to the back of the next sub-batch number, and obtain a new sub-batch sequence;
[0024] The symmetric transformation is to randomly select two pairs of adjacent sub-batch numbers in a sub-batch sequence, symmetrically exchange the positions of the two pairs of adjacent sub-batch numbers, and obtain a new sub-batch sequence.
[0025] Furthermore, optimizing the plurality of candidate sub-batch sequences to obtain a candidate optimal sub-batch sequence includes:
[0026] The verification time and verification cost of the several candidate sub-batch sequences are obtained, and the candidate optimal sub-batch sequence is obtained according to the verification time and verification cost and by using Pareto optimization and congestion calculation formulas.
[0027] Further, obtaining an optimal sub-batch sequence according to the candidate optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence includes:
[0028] According to the initial optimal sub-batch sequence and the initial worst sub-batch sequence, the optimal sub-batch sequence is obtained by iterating the candidate optimal sub-batch sequence using an iterative formula, and the iterative formula is X′ i,j,k =X i,j,k +r 1,j,k (X best,j,k -|X i,j,k |)-r 2,j,k (X worst,j,k -|X i,j,k |),
[0029] Among them, X i,j,k is the optimal sub-batch sequence, X best,j,k is the initial optimal sub-batch sequence, X worst,j,k is the initial worst sub-batch sequence, j is the number of the j-th electric energy metering device in the i-th sub-batch sequence, k is the number of iterations, r 1,j,k 、r 2,j,k is the coefficient of the kth iteration of the jth electric energy meter.
[0030] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the scheduling optimization method for calibration of electric energy metering equipment as described in any of the above technical solutions is implemented.
[0031] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the scheduling optimization method for calibration of electric energy metering equipment as described in any of the above technical solutions is implemented.
[0032] The beneficial effect of adopting the above embodiment is: the scheduling optimization method for calibration of electric energy metering instruments provided by the present invention obtains an initial optimal sub-batch sequence and an initial worst sub-batch sequence according to the calibration time and the calibration cost in several initial sub-batch sequences, and sorts the several initial sub-batch sequences, and optimizes the obtained several alternative sub-batch sequences to obtain an alternative optimal sub-batch sequence, and obtains an optimal sub-batch sequence based on the alternative optimal sub-batch sequence and the initial optimal sub-batch sequence. The calibration time corresponding to the optimal sub-batch sequence is short and the calibration cost is low, and the scheduling method is optimal at this time. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 A flow chart of an embodiment of a scheduling optimization method for electric energy metering instrument verification provided by the present invention;
[0034] Figure 2 A schematic diagram of a sub-batch sequence initialization sorting rule provided in an embodiment of the present invention;
[0035] Figure 3 A schematic diagram of a sub-batch sequence optimization method provided in an embodiment of the present invention;
[0036] Figure 4 A schematic diagram of the exchange transformation provided in an embodiment of the present invention;
[0037] Figure 5 A schematic diagram of a shift transformation provided in an embodiment of the present invention;
[0038] Figure 6 A schematic diagram of a symmetric transformation provided in an embodiment of the present invention;
[0039] Figure 7 This is a structural block diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION
[0040] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.
[0041] The present invention provides a scheduling optimization method for electric energy metering instrument calibration, an electronic device and a computer-readable storage medium, which are described in detail below.
[0042] The embodiment of the present invention provides a scheduling optimization method for electric energy metering instrument calibration, and its flow chart is as follows: Figure 1 As shown, the scheduling optimization method for the verification of electric energy metering equipment includes:
[0043] Step S101, obtaining the number of sub-batches of electric energy metering instruments and the corresponding sub-batch serial numbers, and randomly sorting the sub-batch serial numbers to obtain a number of initial sub-batch sequences;
[0044] Step S102, obtaining the verification time and verification cost of the several initial sub-batch sequences, and obtaining the initial optimal sub-batch sequence and the initial worst sub-batch sequence according to the verification time and verification cost;
[0045] Step S103: sorting the initial sub-batch sequences based on a preset rule to obtain a number of candidate sub-batch sequences, and optimizing the candidate sub-batch sequences to obtain a candidate optimal sub-batch sequence;
[0046] Step S104: obtaining an optimal sub-batch sequence according to the candidate optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence.
[0047] In a specific embodiment, the sub-batch sequence initialization sorting rule is used to sort the sub-batch sequence numbers to obtain a number of initial sub-batch sequences. The schematic diagram of the sub-batch sequence initialization sorting rule is as follows: Figure 2 As shown, the sub-batch number in a sub-batch sequence is randomly assigned between [0,1], and the random values are arranged in descending order to obtain a corresponding sub-batch sequence.
[0048] As a preferred embodiment, obtaining the initial optimal sub-batch sequence and the initial worst sub-batch sequence according to the verification time and the verification cost includes:
[0049] According to the verification time and verification cost, and by using Pareto optimization, an initial better sub-batch sequence set and an initial worse sub-batch sequence set are obtained;
[0050] According to the initial better sub-batch sequence set and the initial worse sub-batch sequence set, and using the congestion calculation formula, an initial best sub-batch sequence and an initial worst sub-batch sequence are obtained.
[0051] It should be noted that by using the Pareto optimization and congestion calculation formula to optimize the verification time and verification cost of the sub-batch sequence, a scheduling method that simultaneously meets the requirements of short verification time and low verification cost can be obtained.
[0052] As a preferred embodiment, the initial better sub-batch sequence set and the initial worse sub-batch sequence set are obtained by using Pareto optimization, including:
[0053] The Pareto optimal search is used to compare the verification time and verification cost of the several initial sub-batch sequences, and the sub-batch sequences that meet both the requirements of short verification time and low verification cost are combined into an initial better sub-batch sequence set, and the sub-batch sequences that meet both the requirements of long verification time and high verification cost are combined into an initial worse sub-batch sequence set.
[0054] In a specific embodiment, a schematic diagram of a sub-batch sequence optimization method is shown as follows: Figure 3 As shown in the figure, the black dots are the initial optimal sub-batch sequence set, the initial optimal sub-batch sequence set contains L initial sub-batch sequences, the horizontal axis f1 represents the verification time of the sub-batch sequence, and the vertical axis f2 represents the verification cost of the sub-batch sequence.
[0055] As a preferred embodiment, the initial optimal sub-batch sequence and the initial worst sub-batch sequence are obtained by using the congestion calculation formula, including:
[0056] The crowding calculation formula is used to calculate the crowding values corresponding to the sub-batch sequences in the initial better sub-batch sequence set and the initial worse sub-batch sequence set. The sub-batch sequence corresponding to the maximum crowding value in the initial better sub-batch sequence set is the initial best sub-batch sequence, and the sub-batch sequence corresponding to the minimum crowding value in the initial worse sub-batch sequence set is the initial worst sub-batch sequence. The crowding calculation formula is:
[0057]
[0058] Among them, D s is the crowding value corresponding to the sth initial sub-batch sequence in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, f1 max , are the maximum verification time and maximum verification cost in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively, f1 min , are the minimum verification time and minimum verification cost in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively; f1(s+1) and f2(s+1) are the verification time and verification cost of the s+1th initial sub-batch sequence in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively; f1(s-1) and f2(s-1) are the verification time and verification cost of the s-1th initial sub-batch sequence in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively.
[0059] In a specific embodiment, the crowding values corresponding to L initial sub-batch sequences in the initial optimal sub-batch sequence set are calculated and compared, and the sth initial sub-batch sequence with the largest crowding value is the initial optimal sub-batch sequence.
[0060] As a preferred embodiment, the several initial sub-batch sequences are sorted based on a preset rule to obtain several candidate sub-batch sequences, including:
[0061] The initial sub-batch sequences are sorted by using at least one of exchange transformation, shift transformation and symmetric transformation to obtain a sorted sub-batch sequence set, and a roulette algorithm is used to select the sorted sub-batch sequence set to obtain a plurality of candidate sub-batch sequences.
[0062] Specifically, one of the exchange transformation, shift transformation and symmetric transformation can be selected to sort the several initial sub-batch sequences, or two of the exchange transformation, shift transformation and symmetric transformation can be selected to sort the several initial sub-batch sequences, or all three of the exchange transformation, shift transformation and symmetric transformation can be used to sort the several initial sub-batch sequences.
[0063] As a preferred embodiment, it includes:
[0064] The exchange transformation is to randomly select two sub-batch numbers in a sub-batch sequence, exchange the positions of the two sub-batch numbers, and obtain a new sub-batch sequence;
[0065] The shift transformation is to randomly select two sub-batch numbers in a sub-batch sequence, move the previous sub-batch number to the back of the next sub-batch number, and obtain a new sub-batch sequence;
[0066] The symmetric transformation is to randomly select two pairs of adjacent sub-batch numbers in a sub-batch sequence, symmetrically exchange the positions of the two pairs of adjacent sub-batch numbers, and obtain a new sub-batch sequence.
[0067] In a specific embodiment, the schematic diagram of the exchange transformation is as follows: Figure 4 As shown, the schematic diagram of the shift transformation is as follows Figure 5 As shown, the schematic diagram of the symmetry transformation is as follows Figure 6 shown.
[0068] As a preferred embodiment, optimizing the plurality of candidate sub-batch sequences to obtain the candidate optimal sub-batch sequence includes:
[0069] The verification time and verification cost of the several candidate sub-batch sequences are obtained, and the candidate optimal sub-batch sequence is obtained according to the verification time and verification cost and by using the Pareto optimization and congestion calculation formula.
[0070] It should be noted that obtaining several alternative sub-batch sequences can expand the arrangement of sub-batch numbers, reduce errors, and find the alternative optimal sub-batch sequence among several alternative sub-batch sequences, which makes it easier to find the optimal sub-batch sequence.
[0071] As a preferred embodiment, obtaining the optimal sub-batch sequence according to the candidate optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence includes:
[0072] According to the initial optimal sub-batch sequence and the initial worst sub-batch sequence, the optimal sub-batch sequence is obtained by iterating the candidate optimal sub-batch sequence using an iterative formula, and the iterative formula is:
[0073] X′ i,j,k =X i,j,k +r 1,j,k (X best,j,k -|X i,j,k |)-r 2,j,k (X worst,j,k -|X i,j,k |),
[0074] Among them, X i,j,k is the optimal sub-batch sequence, X best,j,k is the initial optimal sub-batch sequence, X worst,j,k is the initial worst sub-batch sequence, j is the number of the j-th electric energy metering device in the i-th sub-batch sequence, k is the number of iterations, r 1,j,k 、r 2,j,k is the coefficient of the kth iteration of the jth electric energy meter.
[0075] In a specific embodiment, the coefficient r 1,j,k 、r 2,j,k The value of is randomly selected between [0,1].
[0076] like Figure 7 As shown, the scheduling optimization method for the calibration of the electric energy metering device mentioned above, the present invention also provides an electronic device accordingly, the electronic device can be a computing device such as a mobile terminal, a desktop computer, a notebook, a palm computer and a server. The electronic device includes a processor 10, a memory 20 and a display 30.
[0077] In some embodiments, the memory 20 may be an internal storage unit of a computer device, such as a hard disk or memory of a computer device. In other embodiments, the memory 20 may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory 20 may also include both an internal storage unit of a computer device and an external storage device. The memory 20 is used to store application software and various types of data installed in the computer device, such as program codes for installing the computer device. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a scheduling optimization program 40 for the calibration of an electric energy metering device, and the scheduling optimization program 40 for the calibration of an electric energy metering device can be executed by the processor 10, thereby realizing the scheduling optimization method for the calibration of an electric energy metering device in each embodiment of the present invention.
[0078] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing a scheduling optimization program for calibration of electric energy metering equipment.
[0079] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the computer device and to display a visual user interface. The components 10-30 of the computer device communicate with each other through a system bus.
[0080] In one embodiment, when the processor 10 executes the scheduling optimization program 40 for verifying the electric energy metering device in the memory 20, the following steps are implemented:
[0081] Obtaining the number of sub-batches of electric energy metering equipment and the corresponding sub-batch serial numbers, and randomly sorting the sub-batch serial numbers to obtain a number of initial sub-batch sequences;
[0082] Obtaining verification time and verification cost of the several initial sub-batch sequences, and obtaining an initial optimal sub-batch sequence and an initial worst sub-batch sequence according to the verification time and verification cost;
[0083] Sorting the initial sub-batch sequences based on a preset rule to obtain a number of candidate sub-batch sequences, and optimizing the candidate sub-batch sequences to obtain a candidate optimal sub-batch sequence;
[0084] An optimal sub-batch sequence is obtained according to the candidate optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence.
[0085] This embodiment further provides a computer-readable storage medium, on which a scheduling optimization program for electric energy metering device verification is stored. When the scheduling optimization program for electric energy metering device verification is executed by a processor, the following steps are implemented:
[0086] Obtaining the number of sub-batches of electric energy metering equipment and the corresponding sub-batch serial numbers, and randomly sorting the sub-batch serial numbers to obtain a number of initial sub-batch sequences;
[0087] Obtaining verification time and verification cost of the several initial sub-batch sequences, and obtaining an initial optimal sub-batch sequence and an initial worst sub-batch sequence according to the verification time and verification cost;
[0088] Sorting the initial sub-batch sequences based on a preset rule to obtain a number of candidate sub-batch sequences, and optimizing the candidate sub-batch sequences to obtain a candidate optimal sub-batch sequence;
[0089] An optimal sub-batch sequence is obtained according to the candidate optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence.
[0090] The present invention discloses a scheduling optimization method, electronic device and computer-readable storage medium for calibration of electric energy metering instruments. The method obtains an initial optimal sub-batch sequence and an initial worst sub-batch sequence according to calibration time and calibration cost in several initial sub-batch sequences, sorts the several initial sub-batch sequences, and optimizes the obtained several alternative sub-batch sequences to obtain an alternative optimal sub-batch sequence. The optimal sub-batch sequence is obtained according to the alternative optimal sub-batch sequence, the initial optimal sub-batch sequence and the initial worst sub-batch sequence. The calibration time corresponding to the optimal sub-batch sequence is short and the calibration cost is low, and the scheduling method is optimal at this time.
[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0092] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.
Claims
1. A scheduling optimization method for electric energy metering instrument calibration, characterized in that: include: Obtaining the number of sub-batches of electric energy metering equipment and the corresponding sub-batch serial numbers, and randomly sorting the sub-batch serial numbers to obtain a number of initial sub-batch sequences; Obtaining verification time and verification cost of the several initial sub-batch sequences, and obtaining an initial superior sub-batch sequence set and an initial inferior sub-batch sequence set according to the verification time and verification cost and by using Pareto optimization; The crowding calculation formula is used to calculate the crowding values corresponding to the sub-batch sequences in the initial better sub-batch sequence set and the initial worse sub-batch sequence set. The sub-batch sequence corresponding to the maximum crowding value in the initial better sub-batch sequence set is the initial best sub-batch sequence, and the sub-batch sequence corresponding to the minimum crowding value in the initial worse sub-batch sequence set is the initial worst sub-batch sequence. The crowding calculation formula is: ， in, is the first in the initial better sub-batch sequence set or the first in the initial worse sub-batch sequence set The crowding value corresponding to the initial sub-batch sequence is , are the maximum verification time and maximum verification cost in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively. , are the minimum verification time and minimum verification cost in the initial better sub-batch sequence set or the initial worse sub-batch sequence set, respectively. , are the first and last sub-batch sequences in the initial better sub-batch sequence set or the first and last sub-batch sequences in the initial worse sub-batch sequence set. The verification time and cost of the initial sub-batch sequence, , are the first and last sub-batch sequences in the initial better sub-batch sequence set or the first and last sub-batch sequences in the initial worse sub-batch sequence set. The verification time and cost of the initial sub-batch sequence; The plurality of initial sub-batch sequences are sorted by using at least one of exchange transformation, shift transformation and symmetric transformation to obtain a sorted sub-batch sequence set, and a plurality of candidate sub-batch sequences are selected from the sorted sub-batch sequence set by using a roulette algorithm; Obtaining the verification time and verification cost of the plurality of candidate sub-batch sequences, and obtaining the candidate optimal sub-batch sequence according to the verification time and verification cost and by using Pareto optimization and congestion calculation formulas; According to the initial optimal sub-batch sequence and the initial worst sub-batch sequence, the optimal sub-batch sequence is obtained by iterating the candidate optimal sub-batch sequence using an iterative formula, and the iterative formula is: ， in, is the optimal sub-batch sequence, is the initial optimal sub-batch sequence, is the initial worst sub-batch sequence, j is the number of the j-th electric energy metering device in the i-th sub-batch sequence, k is the number of iterations, , is the coefficient of the kth iteration of the jth electric energy meter.
2. The scheduling optimization method for electric energy metering instrument calibration according to claim 1 is characterized in that: The initial better sub-batch sequence set and the initial worse sub-batch sequence set are obtained by using Pareto optimization, including: The Pareto optimal search is used to compare the verification time and verification cost of the several initial sub-batch sequences, and the sub-batch sequences that meet both the requirements of short verification time and low verification cost are combined into an initial better sub-batch sequence set, and the sub-batch sequences that meet both the requirements of long verification time and high verification cost are combined into an initial worse sub-batch sequence set.
3. The scheduling optimization method for electric energy metering instrument calibration according to claim 1 is characterized in that: include: The exchange transformation is to randomly select two sub-batch numbers in a sub-batch sequence, exchange the positions of the two sub-batch numbers, and obtain a new sub-batch sequence; The shift transformation is to randomly select two sub-batch numbers in a sub-batch sequence, move the previous sub-batch number to the back of the next sub-batch number, and obtain a new sub-batch sequence; The symmetric transformation is to randomly select two pairs of adjacent sub-batch numbers in a sub-batch sequence, symmetrically exchange the positions of the two pairs of adjacent sub-batch numbers, and obtain a new sub-batch sequence.
4. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the scheduling optimization method for calibration of electric energy metering equipment as described in any one of claims 1-3 is implemented.
5. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the scheduling optimization method for calibration of electric energy metering equipment as described in any one of claims 1-3 is implemented.