A method and device for determining the amount of spare parts carried

Abstract

The present application discloses a method for determining the carrying capacity of spare parts, comprising the following steps: determining the upper limit of the carrying capacity of each important spare part according to the carrying capacity; determining the lower limit of the carrying capacity of each important spare part according to the set guarantee probability; within the range of the upper and lower limits of the carrying capacity, using an improved genetic algorithm to traverse the carrying capacity of all important spare parts in the range, and obtaining the task completion of each important spare part carrying capacity plan through a binomial probability calculation method, and obtaining the optimal spare parts carrying capacity plan corresponding to the highest task completion. The present invention also includes a device for implementing the above method. The present application solves the problem that the current research on the spare parts carrying capacity method is not comprehensive enough, resulting in poor task completion.

Description

Technical Field

[0001] The present application relates to the field of integrated support technology, and in particular to a method and device for determining the amount of spare parts carried. Background Art

[0002] Spare parts are an indispensable resource in maintenance support and an important factor in determining whether maintenance tasks can be successfully completed. Scientific and efficient spare parts support is an important guarantee for improving the success rate of maintenance. Therefore, how to determine the type and quantity of spare parts required for the task is a key issue. Unscientific spare parts configuration, on the one hand, is likely to cause spare parts shortages and reduce the probability of support; on the other hand, some spare parts may be over-configured, resulting in a waste of resources. Therefore, the study of the carrying capacity of spare parts is particularly important.

[0003] There are many studies on the amount of spare parts to be carried, but most of them are limited to exponentially distributed spare parts. Moreover, for comprehensive support, the type and quantity of spare parts are equally important. Most studies study the two separately. As a necessity for performing tasks, the type selection of spare parts and the decision on the quantity of spare parts should belong to the same set of processes. Replacing spare parts is the main means of maintenance. However, due to the limited storage space for spare parts and the high cost of support, in order to achieve satisfactory task completion, it is necessary to determine a reasonable amount of spare parts to be carried. Therefore, how to comprehensively consider the type and quantity to determine the amount of spare parts to be carried is an important guarantee for the effective execution of maintenance tasks. Summary of the invention

[0004] The embodiments of the present application provide a method and device for determining the amount of spare parts carried. The purpose of this patent is to solve the problem that the current research on the method of carrying spare parts is not comprehensive enough, resulting in poor task completion. A spare parts carrying amount calculation model based on importance and task completion is proposed. Under certain storage space constraints, the final spare parts carrying amount is obtained through this model.

[0005] A method for determining the amount of spare parts carried provided in an embodiment of the present application comprises the following steps:

[0006] Determine the upper limit of the carrying capacity of each important spare part based on the carrying capacity; determine the lower limit of the carrying capacity of each important spare part based on the set guarantee probability;

[0007] Within the upper and lower limits of the carrying capacity, an improved genetic algorithm is used to traverse the carrying capacity of all important spare parts within the range, and the task completion degree of each important spare parts carrying capacity plan is obtained by a binomial probability calculation method, so as to obtain the optimal spare parts carrying capacity plan corresponding to the highest task completion degree;

[0008] For an important spare part, the task completion degree refers to the probability that at least k spare parts among n+k important spare parts can work normally within the working time T, k is the number of single-machine installations of this important spare part, and n is the number of carrying units of this important spare part; when the important spare parts carrying quantity plan includes multiple important spare parts, the task completion degree of the important spare parts carrying quantity plan refers to the product of the task completion degrees of various important spare parts.

[0009] Preferably, m indicators for evaluating the importance of spare parts are set, an evaluation matrix of n spare parts is constructed, and the importance score of each spare part is obtained through a comprehensive evaluation analysis method; spare parts with scores exceeding a set threshold are regarded as the important spare parts.

[0010] Preferably, the carrying capacity is the sum of multiple carriers, and the volumes of the multiple carriers are different; and determining the upper limit of the carrying capacity of each important spare part according to the carrying capacity specifically includes the following steps:

[0011] Sort the spare parts according to the total number of spare parts of each type that can be placed on the carrier, and configure the largest carrier for the spare parts that can hold the least number of spare parts;

[0012] and / or

[0013] The carriers are weightedly allocated according to the importance scores of the spare parts, and spare parts with high importance scores are configured with more carriers.

[0014] Preferably, the lower limit of the carrying capacity is the spare parts demand calculated according to the spare parts life distribution model under the set guarantee probability condition; the life distribution model represents the relationship between the spare parts demand and the guarantee probability, and the life distribution model includes at least one of the exponential life distribution, Weibull life distribution and normal life distribution.

[0015] Preferably, the improved genetic algorithm specifically comprises the following steps:

[0016] Construct the initial population according to the upper and lower limits of the amount of important spare parts carried;

[0017] Calculate the fitness of the initial population, which is the task completion degree corresponding to the amount of important spare parts carried;

[0018] Sort by individual utility, take the individual with the highest fitness and copy it as the next generation, and the other individuals will crossover and mutate;

[0019] Calculate the fitness of the next generation population until the optimal important spare parts carrying capacity plan is obtained.

[0020] Preferably, when the spare parts include electronic spare parts, mechanical spare parts and electromechanical spare parts, the task completion of the important spare parts carrying capacity plan of electronic spare parts, electromechanical spare parts and mechanical spare parts are calculated respectively, and the first task completion degree, the second task completion degree and the third task completion degree are obtained respectively; the task completion degree of the total important spare parts carrying capacity plan is the product of the first task completion degree, the second task completion degree and the third task completion degree.

[0021] The present application also proposes a device for determining the amount of spare parts carried, which is used to implement the method described in any embodiment of the present application, comprising an upper limit module, a lower limit module, and an optimization module;

[0022] The upper limit module is used to determine the upper limit of the carrying capacity of each important spare part according to the carrying capacity;

[0023] The lower limit module is used to determine the lower limit of the carrying amount of each important spare part according to the set guarantee probability;

[0024] The optimization module is used to use an improved genetic algorithm to traverse the carrying capacity of all important spare parts within the upper and lower limits of the carrying capacity, obtain the task completion degree of each important spare parts carrying capacity plan through a binomial probability calculation method, and obtain the optimal spare parts carrying capacity plan corresponding to the highest task completion degree.

[0025] Preferably, it also includes an evaluation module and a selection module;

[0026] The evaluation module is used to set m indicators for evaluating the importance of spare parts, construct an evaluation matrix for n spare parts, and obtain the importance score of each spare part through a comprehensive evaluation and analysis method;

[0027] The selection module is used to select the spare parts with scores exceeding a set threshold as the important spare parts.

[0028] The present application also proposes a computer-readable storage medium on which a computer program is stored, wherein when the program is executed by a processor, the method described in any embodiment of the present application is implemented.

[0029] The present application also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present application when executing the computer program.

[0030] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0031] The present invention takes the task completion as the goal and the upper and lower limits of important spare parts as the initial conditions to obtain the best spare parts carrying plan. In a further embodiment, the present invention establishes a spare parts carrying volume model based on the comprehensive evaluation analysis method (FCE) and task completion. At the same time, the model takes into account the packing problem of spare parts, customizes the packing situation according to the size of the spare parts box, packs the spare parts according to the rules, and obtains the final spare parts carrying plan. While ensuring the probability of spare parts protection, the spare parts packing problem is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 This is a flow chart of an embodiment of the method of this application;

[0034] Figure 2 This is a flow chart of another embodiment of the method of the present application;

[0035] Figure 3 A flowchart of a specific embodiment of the steps for selecting important spare parts in this application;

[0036] Figure 4 A flowchart of a specific embodiment of the steps of using the improved genetic algorithm for this application;

[0037] Figure 5 To show the convergence of the algorithm of the embodiment of the present application;

[0038] Figure 6 This is an embodiment of the device of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0040] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0041] Figure 1 This is a flow chart of an embodiment of the method of this application.

[0042] A method for determining the amount of spare parts carried provided in an embodiment of the present application comprises the following steps:

[0043] Step 101: Determine the upper limit of the carrying capacity of each important spare part according to the carrying capacity;

[0044] Preferably, the carrying capacity is the sum of multiple carriers, and the volumes of the multiple carriers are different; and determining the upper limit of the carrying capacity of each important spare part according to the carrying capacity specifically includes the following steps:

[0045] Rule 1: Sort the spare parts according to the total number of each type of spare parts that can be placed on the carrier, and configure the largest carrier for the spare parts that can accommodate the least number of spare parts; and / or: Rule 2: Weightedly allocate carriers according to the importance scores of the spare parts, and spare parts with high importance scores are configured with more carriers.

[0046] It should be noted that in this step, the "carrier" is a volumetric object used to load or store spare parts, such as a spare parts box or a spare parts grid that further divides the spare parts box. For example, formulate spare parts packing rules, divide the space of the box according to the packaging parameters of the spare parts, formulate spare parts packing rules according to the size of the box, pack the spare parts according to the rules, and obtain the upper limit of the carrying capacity of important spare parts. According to the above-mentioned Rule 1 and / or Rule 2, for the selected important spare parts, the total number of spare parts of a single type that can be placed is calculated according to the number of spare parts boxes, and the spare parts are placed according to the rules, and finally the maximum number of each spare part that can be placed in the spare parts cabinet is obtained, that is, the upper limit of the carrying capacity of important spare parts is obtained.

[0047] In a specific embodiment of the present application, the spare parts box is divided into n spare parts grids by taking half of its length as the side and the height of the spare parts box as the height, and taking 80 mm as the interval;

[0048] Sort the spare parts in ascending order according to the total number of spare parts of each type that can be placed in the spare parts box, and place the spare parts with the least number of spare parts in the spare parts box with the largest volume first;

[0049] The number of spare parts boxes is allocated according to the importance of spare parts, and the largest spare parts box will be used first to hold the least number of spare parts.

[0050] It should also be noted that if the number of selected spare parts types is greater than the number of spare parts boxes, the number of spare parts grids in the spare parts boxes is divided according to the rules of step 101 of the present application.

[0051] Step 102: Determine the lower limit of the carrying amount of each important spare part according to the set guarantee probability;

[0052] Preferably, the lower limit of the carrying capacity is the spare parts demand calculated according to the spare parts life distribution model under the set guarantee probability condition. The life distribution model represents the relationship between the spare parts demand and the guarantee probability. Generally, and applicable to different types of spare parts, the life distribution model includes at least one of exponential life distribution, Weibull life distribution and normal life distribution.

[0053] It should be noted that the relationship between the number of spare parts and the probability of task completion is calculated using different types of spare parts life distribution models, so as to obtain the optimal spare parts carrying plan, as follows:

[0054] According to the types of important spare parts obtained, spare parts are divided into three types: exponential life distribution, Weibull life distribution and normal life distribution. Because there is a quantitative relationship between the demand for spare parts, the guarantee probability and the spare parts life distribution model, under the set guarantee probability, the demand for spare parts can be predicted according to the spare parts life distribution model, so as to obtain the minimum spare parts carrying quantity under the guarantee probability.

[0055] Exponential distribution: N i is the number of single machine installations of the i-th spare part, T i is the working time, λ i is the failure rate, P i is the probability of spare parts support, s i is the demand for this type of spare parts. N i is the number of single machine installations of the i-th spare part, T i is the working time, λ i is the failure rate, P i is the probability of spare parts support, s i The demand for this type of spare parts.

[0056]

[0057] Weibull distribution: The number of single machine installations of spare part l is N l , the shape parameter of the spare part is β l , the scale parameter is θ l , the position parameter is 0, and the working time is T l , then the spare parts demand is:

[0058]

[0059] Normal distribution: The number of single machine installations of spare part j is N j , the average life of the spare part is E j , with variance σ j , working time is T j , then the spare parts demand is:

[0060]

[0061] In the above model, The corresponding probability is P i The normal quantile value (or percentile point) of .

[0062] Step 103: within the upper and lower limits of the carrying capacity, an improved genetic algorithm is used to traverse the carrying capacity of all important spare parts within the range, and the task completion degree of each important spare parts carrying capacity plan is obtained by a binomial probability calculation method, so as to obtain the optimal spare parts carrying capacity plan corresponding to the highest task completion degree;

[0063] The binomial distribution is a statistical distribution with two possible outcomes. For example, if the number of trials is n, the outcome of each trial can be "success" or "failure". The probability of success is represented by p, and failure is (1-p). So if there are n trials and the probability of success i times is: The probability of at least i successes is In the calculation of the number of important spare parts carried, the reliability of spare parts (the ability of spare parts to perform specified functions without failure within a certain period of time and under certain conditions) is used to represent p. The reliability formula of electronic spare parts is: -λT ; The reliability formula of mechanical parts is: The reliability formula of electromechanical components is: Substituting the spare parts reliability formula into the binomial distribution formula can obtain the probability that at least k spare parts work normally to ensure task completion (task completion degree).

[0064] For the i-th important spare part, the task completion degree refers to the probability P that at least k of the n+k important spare parts can work normally within the working time T. i , k is the number of single-machine installations of the i-th important spare part, and n is the number of vehicles carrying the i-th important spare part. When the important spare parts carrying quantity plan includes multiple important spare parts, the task completion degree of the important spare parts carrying quantity plan refers to the product of the task completion degrees of various important spare parts.

[0065] Preferably, when the spare parts include electronic spare parts, electromechanical spare parts and mechanical spare parts, the task completion of the important spare parts carrying amount plan of electronic spare parts, mechanical spare parts and electromechanical spare parts is calculated respectively to obtain the first task completion degree, the second task completion degree and the third task completion degree respectively;

[0066] When the spare part type is an electronic spare part, calculate the task completion degree of the i-th spare part among all the electronic spare parts in the important spare parts, n 1i is the carrying capacity of the important electronic spare parts, k 1i is the number of single-machine installations of the i-th important electronic spare part, λ iis the failure rate of the i-th electronic spare part. If there are q electronic spare parts among the important spare parts, the task completion formula of the i-th electronic spare part is:

[0067]

[0068] Then, the completion degree of the first task is:

[0069]

[0070] When the spare part type is a mechanical spare part, calculate the task completion degree of the jth spare part among all the mechanical spare parts in the important spare parts, n 2j is the carrying capacity of the important mechanical spare parts, k 2j is the number of single-machine installations of the jth important mechanical spare part, μ j is the average life of the jth mechanical spare part, σ j is the life variance of the jth mechanical spare part. Assuming there are s mechanical spare parts among the important spare parts, the task completion formula of the jth mechanical spare part is:

[0071]

[0072] Then, the completion degree of the second task is:

[0073]

[0074] When the spare part type is electromechanical spare parts, calculate the task completion degree of the mth spare part among all electromechanical spare parts in the important spare parts, n 3m is the carrying capacity of the important electromechanical spare parts, k 3m is the number of single-machine installations of the mth important electromechanical spare part, β m is the shape parameter of the mth electromechanical spare part, θ m is the scale parameter of the mth electromechanical spare part. Assuming there are y electromechanical spare parts among the important spare parts, the task completion formula of the mth electromechanical spare part is:

[0075]

[0076] The completion degree of the third task is:

[0077]

[0078] Finally, the total task completion of the program is

[0079] P=P1×P2×P3

[0080] According to the task completion calculation method, the fitness corresponding to each spare parts carrying scheme is calculated, and the individual with the highest fitness is taken out for replication, and the remaining individuals are mutated, crossed, and selected to generate the next generation. In the present invention, the crossover is a single-point crossover, the mutation probability is 0.9, and the selection operator is a roulette selection. Each generation of population is calculated until the maximum number of iterations is reached. Finally, the optimal spare parts carrying scheme is obtained.

[0081] Figure 2 This is a flow chart of another method embodiment of the present application. Another method embodiment of the present application includes steps 201 to 204, wherein:

[0082] Step 201, set m indicators for evaluating the importance of spare parts, construct an evaluation matrix of n spare parts, and obtain the importance score of each spare part through comprehensive evaluation and analysis; spare parts with scores exceeding the set threshold are regarded as the important spare parts (note that the meanings of m and n in step 201 are different from those in other steps, see below for details Figure 3 of the embodiment of the invention).

[0083] The comprehensive evaluation analysis method (FCE) described in this application refers to a method of converting multiple indicators into an indicator that can reflect the comprehensive situation for analysis, as detailed below.

[0084] Step 202, same as step 101;

[0085] Step 203, same as step 102;

[0086] Step 204 is the same as step 103.

[0087] Figure 3 This is a flowchart of a specific embodiment of step 201 of selecting important spare parts in this application.

[0088] The information covered in each spare part index is selected as the evaluation index that affects the determination of spare parts types, and the scoring criteria of the index are set. The types of important spare parts are selected while retaining the index information. The spare parts evaluation index system is established using five indicators: spare parts reliability, key parts, fragility, economy, and single machine installation number. The scoring criteria are shown in Table 1.

[0089] Table 1 Spare parts evaluation index system

[0090]

[0091] Equipment set X = {x1,x2,x3,...,x n}, the evaluation index is E = {e1,e2,e3,...,e m}, then the evaluation vector is x i ={x i1 ,x i2 ,x i3,...,x im}, then the initial evaluation matrix is:

[0092]

[0093] After determining the comprehensive evaluation index system and the initial evaluation matrix, it is necessary to collect the index scores. Since the evaluation results of the indicators of different dimensions are not of practical significance and value after integration, the data needs to be standardized when the indicators are integrated. The indicators are converted from actual values ​​to evaluation values ​​through standardization. The standardization method for positive indicators is:

[0094]

[0095] The standardized processing method for reverse indicators and dimensionless indicators is:

[0096]

[0097] After processing, the standardized matrix I' is obtained.

[0098]

[0099] Then, the eigenvalue λ of the matrix is ​​obtained by calculating the correlation coefficient matrix R t and the eigenvector e t (t=1,2,3,…,m), using the eigenvalue λ t The contribution rate C of each indicator can be calculated t and weight w t .

[0100]

[0101] Where: r ij is the correlation coefficient between indicator i and indicator j.

[0102]

[0103] The matrix calculation formula can be used to find the eigenvalue λ of the matrix t , then the contribution rate of each indicator C t and weight w t for:

[0104]

[0105]

[0106] Using the eigenvector e of the correlation coefficient matrix t And the standardized matrix I' can get the score matrix S:

[0107] S=I'*[e1 e2 … em ]

[0108] According to the score matrix and the weight of each indicator, the final score S can be obtained. Z :

[0109] S Z =S×W

[0110] Where: W is the weight w t vector.

[0111] Sort by the size of the score, and classify spare parts with a score above 0.5 as important spare parts, spare parts with a score between 0.1 and 0.5 as general spare parts, and spare parts with a score below 0.1 as unimportant spare parts.

[0112] Figure 4 The flowchart of a specific embodiment of step 103 or step 204 using the improved genetic algorithm for the present application.

[0113] Preferably, the improved genetic algorithm specifically comprises the following steps:

[0114] Step 401, constructing an initial population according to the upper and lower limits of the amount of important spare parts carried;

[0115] Step 402: Calculate the fitness of the initial population, where the fitness is the task completion degree corresponding to the amount of important spare parts carried;

[0116] Step 403: Sort the individuals according to their utility, take the individual with the highest fitness and copy it as the next generation, and perform crossover and mutation on the other individuals;

[0117] Step 404: Calculate the fitness of the next generation population.

[0118] Step 405: Continue iterating until the optimal plan for carrying the amount of important spare parts is obtained.

[0119] Figure 5 To show the convergence of the algorithm of the embodiment of the present application.

[0120] Example

[0121] It is known that a certain task requires equipment 1, equipment 2, and equipment 3 to complete together. There are 10 A spare parts boxes, 7 B spare parts boxes, 1 C spare parts box, 1 D spare parts box, and 1 E spare parts box. The size of A spare parts box is 560mm long, 180mm wide, and 60mm high; the size of B spare parts box is 560mm long, 260mm wide, and 90mm high; the size of C spare parts box is 650mm long, 540mm wide, and 200mm high; the size of D spare parts box is 650mm long, 540mm wide, and 240mm high; the size of E spare parts box is 650mm long, 540mm wide, and 320mm high. The working time T is 1000 hours, and the set guarantee probability is 0.95. The spare parts list is shown in Table 2.

[0122] Table 2 Spare parts list

[0123]

[0124]

[0125] According to step 201, the evaluation index system constructs an initial matrix, normalizes and standardizes the initial matrix, and then obtains the final score.

[0126] The initial matrix I is obtained through the evaluation index system:

[0127]

[0128] The initial matrix is ​​standardized to obtain the standardized matrix I', and the relationship matrix R and eigenvalue λ of the standardized matrix I' are obtained by calculation. t and the eigenvector e t Finally, follow the steps to get the comprehensive score S of the spare parts. Z As shown in Table 3.

[0129] Table 3 Spare parts comprehensive score

[0130]

[0131]

[0132] The spare parts are sorted according to the scores, and the spare parts with scores above 0.5 are regarded as important spare parts. The finally obtained important spare parts category P = {spare part 4, spare part 5, spare part 14, spare part 15, spare part 23, spare part 24}.

[0133] Divide the spare parts box space:

[0134] Table 4 Number of spare parts compartments in each spare parts box

[0135]

[0136] Allocate and pack spare parts boxes according to rules one and two.

[0137] Table 5 Upper limit of the amount of spare parts that can be carried

[0138]

[0139]

[0140] According to the working time T of the spare parts guarantee probability task, the spare parts parameters in Table 2 are substituted into the calculation formula for the lower limit of the spare parts carrying quantity to obtain the lower limit of the spare parts carrying quantity.

[0141] Table 6 Upper and lower limits of spare parts carrying capacity

[0142]

[0143] 600 groups of random data are selected as the population within the upper and lower limits of the amount of important spare parts carried. 20 groups of data are selected from these 600 groups as the initial population. The number of iterations is 100. The fitness of each group of data in the initial population is calculated according to the task completion calculation method. The individuals with the highest fitness are taken out for replication, and the remaining individuals are mutated, crossed, and selected to generate the next generation. Each generation of population is calculated until the maximum number of iterations is reached. The convergence of the improved genetic algorithm is as follows: Figure 5 As shown, the optimal spare parts carrying plan is finally obtained.

[0144] The task completion rate of the optimal spare parts carrying solution is: P = 90.4%, and the spare parts carrying solution is

[0145] Table 7 Optimal amount of spare parts to carry

[0146]

[0147] Figure 6 This is an embodiment of the device of the present application.

[0148] The present application also proposes a device for determining the amount of spare parts carried, which is used to implement the method described in any embodiment of the present application, comprising an upper limit module 601, a lower limit module 602, and an optimization module 603;

[0149] The upper limit module is used to determine the upper limit of the carrying capacity of each important spare part according to the portable capacity; for example, the space of the box is divided according to the packaging parameters of the spare parts, the spare parts packing rules are formulated according to the size of the box, and the spare parts are packed according to the rules to obtain the upper limit of the carrying capacity of the important spare parts. As described in step 101 of the present application, the feature is that the spare parts packing rules are formulated according to the number of spare parts that can be equipped in the spare parts box and the importance of the spare parts, taking into account both the large-volume spare parts and the importance of the spare parts.

[0150] The lower limit module is used to determine the lower limit of the carrying capacity of each important spare part according to the set protection probability; for example, as described in step 102 of the present application, according to the set protection probability, based on the relationship formula between the life distribution of various types of spare parts and the number of spare parts, the lower limit of the carrying capacity of important spare parts can be calculated.

[0151] The optimization module is used to use an improved genetic algorithm to traverse the carrying capacity of all important spare parts within the upper and lower limits of the carrying capacity, obtain the task completion of each important spare parts carrying capacity plan through a binomial probability calculation method, and obtain the optimal spare parts carrying capacity plan corresponding to the highest task completion. As described in step 103 of the present application, the task completion is calculated using probability calculation, and the spare parts plan is optimized using the task completion as the fitness function.

[0152] Preferably, it also includes an evaluation module 604 and a selection module 605;

[0153] The evaluation module is used to set m indicators for evaluating the importance of spare parts, construct an evaluation matrix for n spare parts, and obtain the importance score of each spare part through a comprehensive evaluation analysis method; for example, as described in step 201 of the present application, a spare part index system is established: the information covered in each spare part index is selected as the evaluation index affecting the determination of the spare part type, and the scoring criteria of the index are set. According to the scoring criteria, the index information can be retained, and then the type of important spare parts can be selected. Furthermore, the final score of each spare part is obtained by using the comprehensive evaluation analysis method and the spare part index system, and the score is recorded as the importance of the spare part.

[0154] The selection module is used to select spare parts with scores exceeding a set threshold as the important spare parts. For example, according to the descending order of importance, spare parts with importance greater than 0.5 are obtained as important spare parts.

[0155] The present application also proposes a computer-readable storage medium on which a computer program is stored, wherein when the program is executed by a processor, the method described in any embodiment of the present application is implemented.

[0156] The present application also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present application when executing the computer program.

[0157] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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.) containing computer-usable program code.

[0158] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 processes in the flowchart and / or block diagram. 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.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0162] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0163] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0164] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0165] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for determining the amount of spare parts carried, characterized in that: The following steps are involved: The carrying capacity is the sum of the multiple carriers, and the multiple carriers have different volumes; Determining the upper limit of the carrying capacity of each important spare part according to the carrying capacity specifically comprises the following steps: sorting the spare parts according to the total number of each type of spare parts that can be placed on the carrier, and configuring the largest carrier for the spare parts that can be placed in the least number; and / or weighting the carriers according to the importance scores of the spare parts, so that the number of carriers configured for the spare parts with high importance scores is large; Determine the minimum carrying capacity of each important spare part based on the set guarantee probability; Within the upper and lower limits of the carrying capacity, an improved genetic algorithm is used to traverse the carrying capacity of all important spare parts within the range, and the task completion degree of each important spare parts carrying capacity plan is obtained by a binomial probability calculation method, so as to obtain the optimal spare parts carrying capacity plan corresponding to the highest task completion degree; For an important spare part, the task completion degree refers to the time within which the task is completed. n + k There are no less than k The probability that a spare part can work properly, k The number of single-machine installations of this important spare part, n is the number of important spare parts to be carried; when the important spare parts carrying quantity plan includes multiple important spare parts, the task completion degree of the important spare parts carrying quantity plan refers to the product of the task completion degrees of various important spare parts; The improved genetic algorithm specifically includes the following steps: constructing an initial population according to the upper and lower limits of the important spare parts carrying capacity; calculating the fitness of the initial population, where the fitness is the task completion degree corresponding to the important spare parts carrying capacity; sorting according to individual practicality, taking the individual with the highest fitness to copy as the next generation, and performing crossover and mutation on other individuals; calculating the fitness of the next generation population until the optimal important spare parts carrying capacity plan is obtained.

2. The method according to claim 1, characterized in that: set up m An indicator to evaluate the importance of spare parts, construct n The evaluation matrix of spare parts is constructed, and the importance score of each spare part is obtained through comprehensive evaluation and analysis; Spare parts with scores exceeding a set threshold are regarded as the important spare parts.

3. The method according to claim 1, characterized in that: The lower limit of the carrying capacity is the spare parts demand calculated according to the spare parts life distribution model under the set guarantee probability condition; the life distribution model represents the relationship between the spare parts demand and the guarantee probability, and the life distribution model includes at least one of the exponential life distribution, the Weibull life distribution and the normal life distribution.

4. The method according to claim 1, characterized in that: When the spare parts include electronic spare parts, electromechanical spare parts and mechanical spare parts, the task completion of the important spare parts carrying quantity plan of electronic spare parts, mechanical spare parts and electromechanical spare parts is calculated respectively, and the first task completion degree, the second task completion degree and the third task completion degree are obtained respectively; The task completion rate of the total important spare parts carrying capacity plan is the product of the first task completion rate, the second task completion rate, and the third task completion rate.

5. A device for determining the amount of spare parts carried, used to implement the method described in any one of claims 1 to 4, characterized in that: Contains upper limit module, lower limit module and optimization module; The upper limit module is used to determine the upper limit of the carrying capacity of each important spare part according to the carrying capacity; The lower limit module is used to determine the lower limit of the carrying amount of each important spare part according to the set guarantee probability; The optimization module is used to use an improved genetic algorithm to traverse the carrying capacity of all important spare parts within the upper and lower limits of the carrying capacity, obtain the task completion degree of each important spare parts carrying capacity plan through a binomial probability calculation method, and obtain the optimal spare parts carrying capacity plan corresponding to the highest task completion degree.

6. The device according to claim 5, characterized in that It also includes evaluation module and selection module; The evaluation module is used to set m An indicator to evaluate the importance of spare parts, construct n The evaluation matrix of spare parts is constructed, and the importance score of each spare part is obtained through comprehensive evaluation and analysis; The selection module is used to select the spare parts with scores exceeding a set threshold as the important spare parts.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

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