Maintenance support resource dynamic rescheduling method and device based on improved particle swarm optimization

Abstract

The invention provides a maintenance support resource dynamic rescheduling method and device based on an improved particle swarm algorithm, and relates to the technical field of resource scheduling. The method comprises the following steps: determining an attribute decision set of a plurality of maintenance support tasks according to a plurality of influence factors of the plurality of maintenance support tasks and priorities of the influence factors; determining a priority order of the plurality of maintenance support tasks according to the weights of the plurality of influence factors and the attribute decision set; correcting the initial scheduling scheme population to obtain a corrected scheduling scheme population; according to each correction position code of the correction scheduling scheme population, a fitness function value corresponding to each correction position code, a code value corresponding to each correction position code and interlace operation, updating each correction position code to obtain a new scheduling scheme population, and realizing resource scheduling initial planning optimization of the maintenance support task; and when a new maintenance support task demand appears, dynamic rescheduling of the maintenance support resources is realized. Therefore, the dynamic rescheduling time of the maintenance support resources is short.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling, and in particular to a maintenance support resource dynamic rescheduling method and device based on an improved particle swarm algorithm. Background Art

[0002] Equipment maintenance and support refers to all activities carried out to maintain, restore and improve the specified technical status of equipment. Equipment maintenance and support task allocation and scheduling are important components of equipment maintenance and support, and play a vital role in whether the equipment maintenance and support command can perform its functions normally. At present, the equipment maintenance and support task allocation and scheduling technology mainly includes two parts: equipment maintenance and support task priority determination and equipment maintenance and support task scheduling. Reasonable classification of maintenance and support task priorities is a prerequisite, which can enable the maintenance and support forces to play their full role. Maintenance and support task scheduling is the key, which can respond quickly to real-time maintenance and support needs. Equipment maintenance and support appear in the form of joint support, and the collaborative support corresponding to the joint support method requires close cooperation, resource sharing, and collaborative maintenance. According to dynamic task requirements, how to quantify maintenance and support resources, reasonably allocate and dynamically schedule them, shorten the waiting time of each maintenance and support task, and give full play to the role of all maintenance and support resources is an urgent problem to be solved in the equipment maintenance and support process.

[0003] At present, there are mainly fuzzy comprehensive evaluation method and hierarchical analysis method to determine the priority of maintenance and support tasks. Decision-makers make decisions based on relevant equipment information and their own knowledge and experience. Affected by subjective factors, it is difficult to ensure the reliability and accuracy of the decision results of maintenance and support task priority. The dynamic resource scheduling planning of maintenance and support tasks is achieved through conventional scheduling planning methods. Since it is a complex planning problem to achieve resource optimization scheduling under the conditions of multiple maintenance and support tasks and multiple maintenance and support resources and the condition of allowing multiple resources to maintain a single task, it is difficult for conventional scheduling planning methods to obtain the optimization results that meet the requirements in a short time, which makes the dynamic rescheduling of maintenance and support resources take a long time. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a maintenance support resource dynamic rescheduling method and device based on an improved particle swarm algorithm, so as to solve the problem that the maintenance support resource dynamic rescheduling takes a long time.

[0005] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0006] A first aspect of the present invention provides a maintenance support resource dynamic rescheduling method based on an improved particle swarm algorithm. The maintenance support resource dynamic rescheduling method comprises:

[0007] According to the multiple influencing factors corresponding to the multiple maintenance and support tasks and the priority of each influencing factor, an attribute decision set of the multiple maintenance and support tasks is determined, where the attribute decision set is used to indicate the quantitative values ​​of the multiple influencing factors;

[0008] Determine the priority order of multiple maintenance support tasks based on the weights and attribute decision sets of multiple influencing factors;

[0009] According to the priority order and the preset length, multiple maintenance and support tasks are encoded to obtain multiple position codes, and each position code is used as a resource scheduling plan to generate an initial scheduling plan population;

[0010] According to preset requirements, preset carrying capacity, target carrying capacity of maintenance support resources, current positions of maintenance support resources and positions of maintenance support tasks corresponding to multiple maintenance support tasks, the initial scheduling scheme population is revised to obtain a revised scheduling scheme population;

[0011] According to each revised position code of the revised scheduling scheme population, the fitness function value corresponding to each revised position code, the code value corresponding to each revised position code and the crossover operation, each revised position code is updated to obtain a new scheduling scheme population;

[0012] The initial scheduling scheme population and the new scheduling scheme population are merged until the iteration condition meets the preset condition, and the optimal scheduling scheme population is obtained;

[0013] According to the optimal scheduling scheme population, multiple maintenance and support tasks are scheduled. When a new maintenance and support task is obtained, the latest maintenance and support task is determined according to the judgment status of the current maintenance and support task and the new maintenance and support task;

[0014] Determine the new maintenance support resources and the corresponding remaining carrying capacity corresponding to the latest maintenance support task, update the resource position of the new maintenance support resources, and determine the current position of the maintenance support resources in the scheduling state, where the resource position is the previous position of the maintenance support task position;

[0015] The latest maintenance support task is taken as multiple maintenance support tasks, the new maintenance support resource is taken as the maintenance support resource, the remaining carrying capacity is taken as the target carrying capacity, the current position is taken as the current position, and the next position of the resource position is taken as the maintenance support task position, and the steps of determining the attribute decision set of the multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor are returned to generate the final scheduling plan population.

[0016] A second aspect of the present application provides a maintenance support resource dynamic rescheduling device based on an improved particle swarm algorithm, the maintenance support resource dynamic rescheduling device comprising:

[0017] A first determination module is used to determine attribute decision sets of multiple maintenance and support tasks according to multiple influencing factors corresponding to the multiple maintenance and support tasks and the priority of each influencing factor, wherein the attribute decision set is used to indicate the quantitative values ​​of the multiple influencing factors;

[0018] The second determination module is used to determine the priority order of multiple maintenance support tasks according to the weights of multiple influencing factors and the attribute decision set;

[0019] The first generation module is used to encode multiple maintenance and support tasks according to the priority order and the preset length to obtain multiple position codes, and use each position code as a resource scheduling plan to generate an initial scheduling plan population;

[0020] A correction module is used to correct the initial scheduling scheme population according to preset requirements, preset carrying capacity, target carrying capacity of the maintenance support resources, current positions of the maintenance support resources and positions of the maintenance support tasks corresponding to the multiple maintenance support tasks, so as to obtain a corrected scheduling scheme population;

[0021] An updating module, used for updating each corrected position code according to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and the crossover operation, so as to obtain a new scheduling scheme population;

[0022] A population merging module is used to merge the initial scheduling solution population and the new scheduling solution population until the iteration condition meets the preset condition to obtain the optimal scheduling solution population;

[0023] The third determination module is used to schedule multiple maintenance and support tasks according to the optimal scheduling scheme population, and when a new maintenance and support task is obtained, the latest maintenance and support task is determined according to the determination status of the current maintenance and support task and the new maintenance and support task;

[0024] The fourth determination module is used to determine the new maintenance support resources and the corresponding remaining carrying amount corresponding to the latest maintenance support task, and update the resource position of the new maintenance support resources, and determine the current position of the maintenance support resources in the scheduling state, where the resource position is the previous position of the maintenance support task position;

[0025] The second generation module is used to take the latest maintenance support task as multiple maintenance support tasks, the new maintenance support resource as the maintenance support resource, the remaining carrying capacity as the target carrying capacity, the current position as the current position, and the next position of the resource position as the maintenance support task position, and return to the steps of determining the attribute decision set of multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, so as to generate the final scheduling plan population.

[0026] Compared with the prior art, the method and device for dynamically rescheduling maintenance support resources of the improved particle swarm algorithm provided by the present invention can first determine a new scheduling scheme population to complete the initial planning based on the fitness function value and crossover operation in the genetic operator, and when a new maintenance support task is obtained, determine the latest maintenance support task, new maintenance support resources and the corresponding remaining carrying capacity, and update the resource location of the new maintenance support resource, determine the current location of the maintenance support resources in the scheduling state, and dynamically generate the final scheduling scheme population to realize the dynamic rescheduling task planning of maintenance support resources. The dynamic rescheduling task planning of maintenance support resources can shorten the time for dynamic rescheduling of maintenance support resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0028] Figure 1 The flowchart of the maintenance support resource dynamic rescheduling method based on the improved particle swarm algorithm is schematically shown;

[0029] Figure 2 A schematic diagram of the classification of maintenance support tasks is shown schematically;

[0030] Figure 3 The schematic diagram shows various factors affecting the priority of maintenance support tasks;

[0031] Figure 4 The schematic diagram of the initial maintenance support tasks and the distribution of maintenance resource locations is shown schematically;

[0032] Figure 5 Schematically shows a schematic diagram of the encoding scheme;

[0033] Figure 6 A schematic diagram of a crossover operator is shown schematically;

[0034] FIG7( a ) schematically shows a diagram of task completion rate;

[0035] FIG7( b ) schematically shows a diagram of the total task execution time;

[0036] FIG8( a ) schematically shows a Gantt chart of the execution of the initial maintenance support task;

[0037] FIG8( b ) schematically shows a Gantt chart of the execution of the initial maintenance support task;

[0038] Figure 8(c) schematically shows the Gantt chart of the initial maintenance support task execution;

[0039] Fig. 9 The schematic diagram shows that dynamic rescheduling is a schematic diagram of the distribution of maintenance support tasks and maintenance resource locations;

[0040] Fig.10 The algorithm optimization iteration curve diagram of the design algorithm in the optimization process of the maintenance resource dynamic rescheduling scheme is schematically shown;

[0041] Fig.11 A Gantt chart for dynamically rescheduling maintenance resources is schematically shown;

[0042] Fig.12 The structure diagram of the dynamic rescheduling device for maintenance support resources based on the improved particle swarm algorithm is schematically shown. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0044] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present invention should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0045] The method in the embodiment of the present invention is described in detail below.

[0046] Figure 1 The flowchart of the maintenance support resource dynamic rescheduling method for the improved particle swarm algorithm in the embodiment of the present invention is schematically shown. Figure 1 As shown, the method may include:

[0047] S101. Determine attribute decision sets of multiple maintenance and support tasks according to multiple influencing factors corresponding to multiple maintenance and support tasks and the priority of each influencing factor.

[0048] Among them, the attribute decision set is used to indicate the quantitative values ​​of multiple influencing factors.

[0049] Each maintenance support task corresponds to an attribute decision set, and multiple influencing factors corresponding to multiple maintenance support tasks include maintenance support task importance, maintenance support task environment requirements, maintenance time, and maintenance support timeliness.

[0050] Generally speaking, command equipment is the most important, combat equipment is the second, and support equipment is relatively less important. Therefore, based on the type of maintenance and support tasks and the degree of contribution of maintenance and support tasks to battlefield operations, maintenance and support tasks are divided into three categories: The first category: command equipment maintenance and support tasks and emergency combat execution equipment maintenance and support tasks (combat equipment that must immediately participate in the next military activity) are classified as emergency maintenance and support tasks; the second category: combat execution equipment maintenance and support tasks (execution equipment that does not immediately participate in combat) are classified as general maintenance and support tasks; the third category: other maintenance and support tasks (logistics equipment maintenance and support tasks, etc.) are classified as non-emergency maintenance and support tasks.

[0051] Figure 2 The diagram schematically shows the classification of maintenance and support tasks. For different types of maintenance and support tasks, the execution of maintenance and support tasks is always completed in the order of emergency maintenance and support tasks being executed first, general maintenance and support tasks being executed secondarily, and non-emergency maintenance and support tasks being executed last.

[0052] The larger the quantitative value of the importance of the maintenance support task, the higher the urgency of the task and the higher the priority; the larger the value of the environmental requirement of the maintenance support task. The larger the quantitative value of the environmental requirement of the maintenance support task, the worse the battlefield environment, the more difficult it is for maintenance resources to reach the location of the maintenance support task, and the lower the priority. The larger the quantitative value of the maintenance time, the lower the priority. The maintenance support task guarantee timeliness is the latest time when the set of resources that the maintenance support task can accept arrives to start maintenance. Therefore, the smaller the quantitative value of the maintenance support task guarantee timeliness, the earlier the maintenance support task needs to be executed, and the higher the corresponding maintenance support task priority.

[0053] The fuzzy evaluations given for each maintenance support task regarding the importance of the maintenance support task, the environmental requirements of the maintenance support task, the maintenance time and the maintenance support timeliness are converted into quantitative values ​​to form an attribute decision set of multiple maintenance support tasks. Figure 3 The schematic diagram of various influencing factors of the maintenance support task priority is shown schematically. The multiple influencing factors corresponding to multiple maintenance support tasks include the importance of maintenance support tasks, maintenance support task environmental requirements, maintenance time and maintenance support timeliness; the quantitative value of the importance of maintenance support tasks is given by the classification of maintenance support tasks, which are emergency maintenance support tasks, general maintenance support tasks, and non-emergency maintenance support tasks, and the corresponding quantitative values ​​are 3, 2, and 1. The quantitative value of the environmental requirements of the maintenance support tasks is given as bad, general, and easy according to the degree of threat from enemy artillery fire and other factors at the location of the maintenance support tasks, and the difficulty of the roads for maintenance resources to reach the location of the maintenance support tasks, and the corresponding quantitative values ​​are 3, 2, and 1.

[0054] S102: Determine the priority order of multiple maintenance and support tasks according to the weights of multiple influencing factors and the attribute decision set.

[0055] Specifically, the priority order of multiple maintenance support tasks is determined based on the weights and attribute decision sets of multiple influencing factors, including:

[0056] Step A1: Obtain the weights of the multiple influencing factors according to a correspondence table between the multiple influencing factors and the weights of the multiple influencing factors.

[0057] The correspondence table is a table in which multiple experts subjectively score the influence of multiple influencing factors of different maintenance support tasks on the priority determination of maintenance support tasks, and then combine the scores of multiple experts to give a weighted average of the scores of the influencing factors of each maintenance support task. Finally, normalization is performed to give the weights of multiple influencing factors of each maintenance support task.

[0058] Step A2: Construct an initial evaluation matrix based on the quantitative values ​​of multiple influencing factors in the attribute decision set.

[0059] The priority order of multiple maintenance support tasks can be determined based on the comprehensive evaluation method (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS).

[0060] The attribute decision set after the numerical quantification of M maintenance support tasks constitutes the initial evaluation matrix X:

[0061]

[0062] Among them, X is the initial evaluation matrix, A M is the Mth maintenance support task, a Mm is the mth influencing factor of the Mth maintenance support task, M is the number of maintenance support tasks, and m is the number of influencing factors.

[0063] Step A3: Normalize all influencing factors in the initial evaluation matrix to obtain an evaluation standard matrix.

[0064] The initial evaluation matrix is ​​normalized by the vector normalization method to eliminate the impact of the dimensions between indicators and obtain the evaluation standard matrix.

[0065] For the two influencing factors of maintenance support mission importance and maintenance support mission environment requirements, the following formula is used for normalization:

[0066]

[0067] Among them, c1bh To evaluate the hth influencing factor (maintenance support task importance and maintenance support task environment requirements) of the bth maintenance support task in the specification matrix, a1 bh is the hth influencing factor (maintenance support task importance and maintenance support task environment requirements) of the bth maintenance support task in the initial evaluation matrix, M is the number of maintenance support tasks, m is the number of influencing factors, b∈(1,M), h∈(1,m).

[0068] For the two influencing factors of maintenance support mission effectiveness and maintenance time, the following formula is used for normalization:

[0069]

[0070] Among them, c2 bh To evaluate the hth influencing factor (maintenance support task effectiveness and maintenance time) of the bth maintenance support task in the specification matrix, a2 bh is the hth influencing factor (maintenance support task effectiveness and maintenance time) of the bth maintenance support task in the initial evaluation matrix, M is the number of maintenance support tasks, m is the number of influencing factors, b∈(1,M), h∈(1,m).

[0071] Step A4: Multiply each column of influencing factors in the evaluation standard matrix by the weight of the corresponding influencing factor to obtain an evaluation weighted standard matrix.

[0072] Multiply each column of the evaluation standard matrix by the weight of the corresponding influencing factor to obtain the evaluation weighted standard matrix:

[0073] E=(e bh ) M×m =(ω h ×c bh ) M×m ;

[0074] Among them, E is the evaluation weighted normative matrix, e bh To evaluate the hth influencing factor of the bth maintenance support task in the weighted specification matrix, ω h is the weight of the influencing factor, c bh To evaluate the hth influencing factor of the bth maintenance support task in the specification matrix, M is the number of maintenance support tasks, m is the number of influencing factors, b∈(1,M), h∈(1,m).

[0075] Step A5: Determine the priority order of the multiple maintenance support tasks based on the evaluation weighted specification matrix and the multiple maintenance support tasks.

[0076] Specifically, according to the evaluation weighted specification matrix and multiple maintenance support tasks, the priority order of multiple maintenance support tasks is determined, including:

[0077] Step A51: Determine the ideal solution and negative ideal solution corresponding to the evaluation weighted normative matrix.

[0078] Select the maximum value from each column of the influencing factors in the evaluation weighted norm matrix, and determine the row of factors consisting of each column of influencing factors corresponding to the maximum value as the ideal solution S + The minimum value is selected from each column of the influencing factors of the evaluation weighted norm matrix, and a row of factors consisting of each column of influencing factors corresponding to the minimum value is determined as the negative ideal solution S - .

[0079] Step A52: Determine the closeness of the multiple maintenance support tasks according to the ideal solution, the negative ideal solution and the multiple maintenance support tasks, so as to obtain the closeness of all maintenance support tasks.

[0080] According to the ideal solution, negative ideal solution and multiple maintenance support tasks, the Euclidean distance between each maintenance support task and the ideal solution and negative ideal solution is calculated, and the closeness between the maintenance support task and the ideal solution is defined as:

[0081]

[0082] Among them, Q b is the closeness, A b For each maintenance support mission, S + is an ideal solution, S - is the negative ideal solution, dis() is the Euclidean distance, b∈(1,M).

[0083] Step A53: sort the closeness of multiple maintenance and support tasks from large to small to obtain sorted closeness.

[0084] Step A54: Determine the priority order of multiple maintenance and support tasks according to the closeness of the sorting.

[0085] The larger the closeness value of the maintenance support task, the closer the evaluation index corresponding to the maintenance support task is to the ideal solution, and the higher the task priority of the maintenance support task is. The priority of the maintenance support task is determined by sorting the closeness value from large to small. Figure 4 The schematic diagram of the initial maintenance support tasks and the distribution of maintenance resource locations is shown schematically, wherein the higher the priority of the maintenance support task, the larger the circle representing the maintenance support task.

[0086] S103. Encode multiple maintenance and support tasks according to the priority order and the preset length to obtain multiple position codes, and use each position code as a resource scheduling plan to generate an initial scheduling plan population.

[0087] Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence. It searches for the optimal solution by simulating the movement of individuals in the swarm in the search space. The current position of the particle is a candidate solution to the corresponding optimization problem, and the movement process of the particle is the search process of the individual. The flight speed of the particle can be dynamically adjusted according to the historical optimal position of the particle and the historical optimal position of the population. Particles have two properties: speed and position. The speed represents the speed of movement, and the position represents the direction of movement. The optimal solution searched by each particle individually is called the individual extreme value, and the optimal individual extreme value in the particle swarm is the current global optimal solution. Iterate continuously and update the speed and position. Finally, the optimal solution that meets the termination condition is obtained. The particle swarm algorithm can converge quickly, but it is easy to fall into the local optimal solution. The improved particle swarm algorithm combined with the mutation operator and the crossover operator can maintain the diversity of the population and ensure the global optimality of the result.

[0088] The preset length is M×N, where M is the number of maintenance support tasks and N is the number of maintenance support resources.

[0089] A position code X in the discrete particle swarm algorithm i ={x i1 ,x i2 ,…,x i(M×N)} represents a solution to the maintenance support resource scheduling problem of maintenance support tasks. The position coding rule is designed as follows: Assuming that there are M maintenance support tasks, each maintenance support task corresponds to N maintenance support resource bits, and a position coding of length M×N is constructed. The coding method uses integer coding, and the coding is performed in the order of priority of the maintenance support tasks. Every N codes are a resource scheduling solution for a maintenance support task. The coding values ​​corresponding to each position coding include multiple first coding values ​​and multiple second coding values. The first coding value is 1, which indicates that the maintenance resource scheduling executes the maintenance support task, and the second coding value is 0, which indicates that the maintenance resource scheduling does not execute the maintenance support task. Among them, X i is the i-th position code, x i2 The second bit of the code for the i-th position.

[0090] Figure 5 The schematic diagram of the coding scheme is shown schematically. After random initialization, multiple position codes, i.e., multiple maintenance support resource scheduling schemes for maintenance support tasks, are constructed to form a scheduling scheme population X = {X 1 ,…,X S}, X1 is the first position code. The length of a position code is M×N, there are M maintenance support tasks, and each maintenance support task corresponds to N maintenance support resource bits, so there are S position codes in total.

[0091] S104. According to preset requirements, preset carrying capacity, target carrying capacity of maintenance support resources, current positions of maintenance support resources and positions of maintenance support tasks of the maintenance support resources corresponding to the plurality of maintenance support tasks, the initial scheduling scheme population is revised to obtain a revised scheduling scheme population.

[0092] Among them, the values ​​corresponding to each position code in the initial scheduling scheme population include multiple first code values ​​and multiple second code values, the multiple first code values ​​are used to indicate that the maintenance support resources schedule multiple maintenance support tasks, and the multiple second code values ​​are used to indicate that the maintenance support resources do not schedule multiple maintenance support tasks, and the target carrying capacity of the maintenance support resources includes the first target carrying capacity of the maintenance support resources corresponding to the multiple first code values.

[0093] Specifically, according to preset requirements, preset carrying capacity, target carrying capacity of maintenance support resources, current positions of maintenance support resources and positions of maintenance support tasks corresponding to multiple maintenance support tasks, the initial scheduling scheme population is revised to obtain a revised scheduling scheme population, including:

[0094] Step B1: Determine whether the initial scheduling scheme population meets the preset requirements.

[0095] The preset demand is the maintenance resource demand for the maintenance support task.

[0096] Determine whether the initial scheduling scheme population meets the preset requirements. If not, execute the following step B2a or step B2b; if so, execute step S105.

[0097] Step B2a: When the first target carrying capacity exceeds the preset carrying capacity, the first time sequence of the maintenance support resources arriving at the maintenance support task location is determined based on the current positions of the maintenance support resources and the maintenance support task locations corresponding to the multiple first coding values, and the first carrying capacity of the maintenance support resources is accumulated according to the first time sequence. If the first carrying capacity is equal to the preset carrying capacity, the first maintenance support resource corresponding to the first carrying capacity is determined, and the multiple first coding values ​​corresponding to the second maintenance support resources are corrected to multiple second coding values.

[0098] The second maintenance support resource is a maintenance support resource other than the first maintenance support resource.

[0099] When the initial scheduling scheme population does not meet the preset requirements, for the resource scheduling scheme corresponding to the position code, the first target carrying capacity of the maintenance and support resources corresponding to multiple first code values, that is, the carrying capacity of code value 1 exceeds the resource demand of the maintenance and support task, and their resource carrying capacities are accumulated one by one in the order of the time when the resources arrive at the maintenance and support task location. When the total resource carrying capacity just meets the preset carrying capacity, the remaining uncalled maintenance and support resources with a code value of 1 are deleted, that is, the code value in the corresponding position code is corrected from 1 to 0.

[0100] Step B2b: When the first target carrying capacity does not exceed the preset carrying capacity, determine the second time sequence of the maintenance support resources arriving at the maintenance support task location based on the current positions of the maintenance support resources corresponding to the multiple second coding values ​​and the maintenance support task location, and accumulate the second carrying capacity of the maintenance support resources according to the second time sequence. If the second carrying capacity is equal to the preset carrying capacity, determine the third maintenance support resource corresponding to the second carrying capacity, and correct the multiple second coding values ​​corresponding to the fourth maintenance support resource to multiple first coding values.

[0101] Among them, the fourth maintenance guarantee resource is the maintenance guarantee resource other than the third maintenance guarantee resource.

[0102] When the population of the initial scheduling scheme does not meet the preset demand, for the resource scheduling scheme corresponding to the position code, the first target carrying capacity of the maintenance and support resources corresponding to the multiple first code values, that is, the carrying capacity of the code value 1, is lower than the resource demand of the maintenance and support task. The time for the remaining resources, that is, the time for the resources corresponding to the code value 0 to arrive at the maintenance and support task location is calculated, and new maintenance and support resources are added to the location of the maintenance task in order of arrival time, that is, the code value is corrected from 0 to 1, and the total resource carrying capacity of the corresponding maintenance and support resources is calculated. When the total resource carrying capacity meets the preset carrying capacity, stop adding new maintenance and support resources.

[0103] S105. According to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and the crossover operation, each corrected position code is updated to obtain a new scheduling scheme population.

[0104] Specifically, according to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and the crossover operation, each corrected position code is updated to obtain a new scheduling scheme population, including:

[0105] Step C1: Determine the speed coding according to each modified position coding of the modified scheduling scheme population, the fitness function value corresponding to each modified position coding and the scaling factor.

[0106] Specifically, the speed coding is determined according to each corrected position coding of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position coding, and the scaling factor, including:

[0107] Step C11: Decode each modified position code of the modified scheduling scheme population, and calculate the first fitness function value corresponding to each modified position code.

[0108] The particle swarm algorithm needs to simulate the movement of resource scheduling schemes in the search space to find the optimal solution by updating the population and each particle position code. First, decode each modified position code of the modified scheduling scheme population, and calculate the first fitness function value corresponding to each modified position code. Among them, one position code corresponds to one first fitness function.

[0109] The fitness function is the iterative criterion of the entire particle swarm algorithm. The better the fitness of the particle, the closer its position code is to the optimal solution of the problem. At the same time, in the process of algorithm iteration, other particle codes will gradually approach it, so that the code converges to an optimal solution. For the task scheduling model in the present invention, the optimization goal is to ensure that the total execution time of the maintenance and support tasks is the shortest while ensuring the maximum overall completion rate of the maintenance and support tasks. The formula for calculating fitness is as follows:

[0110] fitness={maxRate,minTime};

[0111] Among them, Rate is the completion rate of the maintenance support task, and Time is the total execution time of the maintenance support task.

[0112] Step C12: Determine the optimal position coding and the optimal resource scheduling scheme coding according to the first fitness function value.

[0113] According to the conditions that the completion rate of the maintenance and support tasks is the largest and the total execution time of the maintenance and support tasks is the shortest, the best one is selected from all the first fitness functions. According to the conditions that the completion rate of the maintenance and support tasks is the largest and the total execution time of the maintenance and support tasks is the shortest, the best first fitness function of multiple iterations is selected. According to the best first fitness function, the best position coding and the best resource scheduling scheme coding can be decided.

[0114] Step C13: Determine the speed coding according to the optimal position coding, the optimal resource scheduling scheme coding and the scaling factor.

[0115] The velocity coding of particles is constructed through the following mutation operator, that is, the optimal moving direction of particles. Specifically, the velocity coding is determined according to the optimal position coding, the optimal resource scheduling scheme coding and the scaling factor using the following formula:

[0116] V i,G=X i,G +ξ·(X gbest -X i,G )+ξ·(X pbest -X r1,G );

[0117] Among them, V i,G is the speed code, i.e., the speed code corresponding to the i-th position code in the G-th iteration, X i,G is the i-th position code in the G-th iteration, ξ is the scaling factor, and the scaling factor ranges from [0, 1], X gbest is the optimal position code, that is, the overall historical optimal resource scheduling scheme position code of the population during the algorithm iteration, X pbest is the optimal resource scheduling scheme code, that is, the optimal resource scheduling scheme code of the particle individual history, X r1,G is the r1th position code in the Gth iteration, V i,G = {v i1 ,…,v ij ,…,v i(M×N)}, X i,G ={x i1 ,…,x ij ,…,x i(M×N)},v ij is the number of bits of speed code, i.e., the jth bit of speed code corresponding to the i-th position code in the G-th iteration, x ij is the code value corresponding to each modified position code, that is, the j-th code value of the ith position code.

[0118] Step C2: According to the code values ​​corresponding to the speed code and each corrected position code, each corrected position code is updated to obtain an updated scheduling scheme population.

[0119] The speed code includes multiple bits of speed code.

[0120] Specifically, according to the code values ​​corresponding to the speed code and each corrected position code, each corrected position code is updated to obtain an updated scheduling scheme population, including:

[0121] According to the multi-bit speed code and the code value corresponding to each modified position code, each modified position code is updated to obtain the updated scheduling scheme population:

[0122]

[0123] Among them, s(v ij ) is the mapping value of velocity encoding, v ij Multiple bits for speed encoding, is the intermediate value of the velocity encoding mapping process, x ijis the code value corresponding to each correction position code, ~x ij To invert the code value corresponding to each corrected position code, i is the i-th position code, and j is the j-th bit in the multi-bit speed code.

[0124] The modulus value of each bit in the speed code|v ij The larger the | is, the greater the deviation of the bit position encoding from the optimal scheduling solution is, and it is mapped to a value closer to 1 through the first formula above; in the second formula above, s(v i,j ) is closer to 1, the greater the probability that the position code of this bit is inverted, and the closer the updated position code is to the optimal scheduling solution.

[0125] Step C3: Perform a cross operation on the update position codes of the updated scheduling scheme population to obtain a new scheduling scheme population.

[0126] Based on the updated scheduling scheme population after the position coding scheme is updated, a crossover operation of the position coding is performed to increase the diversity of the resource scheduling population scheme again, and the crossover operator adopts an exponential crossover method. Figure 6 The schematic diagram of the crossover operator is shown schematically, where the position code X i,G An integer K is randomly selected on [1, M×N] as the starting position of the crossover, and then another integer k is randomly selected on [1, M×N] as the number of codes that need to be crossovered. Finally, an exponential crossover is performed to generate a new scheduling plan population corresponding to the new maintenance support task.

[0127] S106. The initial scheduling solution population and the new scheduling solution population are merged until the iteration condition satisfies the preset condition, thereby obtaining the optimal scheduling solution population.

[0128] For the initial scheduling plan population and the new scheduling plan population, by comparing the fitness values ​​corresponding to each position code, the old and new populations are merged to achieve re-optimization of the scheduling plan population and achieve elite retention of the better resource scheduling plan.

[0129] By comparing the fitness values ​​corresponding to each position code, the new and old populations are merged, specifically including: sorting all the fitness values ​​of the determined initial scheduling plan population and all the fitness values ​​of the new scheduling plan population from large to small, and proposing the scheduling plan in the scheduling plan population corresponding to the second half of the fitness values ​​to achieve population merging.

[0130] When the iteration condition of the improved particle swarm algorithm meets the preset conditions, the iteration is stopped, the position code with the optimal fitness value is obtained, the position code is decoded, and the optimal scheduling solution population obtained by the iterative optimization of the algorithm is output.

[0131] S107. Scheduling multiple maintenance and support tasks according to the optimal scheduling scheme population. When a new maintenance and support task is obtained, determining the latest maintenance and support task according to the determination status of the current maintenance and support task and the new maintenance and support task.

[0132] Among them, the determination status includes the executing status and the unexecuted status. The determination status also includes the executed status, and the unexecuted status also includes the unexecutable status. The unexecutable maintenance support task cannot guarantee the time constraint. For maintenance support resources, there are three states: scheduling state, waiting state and execution state.

[0133] During the execution of the resource scheduling plan for the given initial maintenance support task, new maintenance support task requirements will arise. At this time, based on the resource scheduling plan for the initial maintenance support task, the maintenance status of the current maintenance support task will be determined and the information of the maintenance support resources will be updated. Then, the improved particle swarm algorithm designed will be used to realize the re-planning of maintenance support tasks and the re-scheduling of resources, and dynamically allocate new maintenance support resources.

[0134] Specifically, multiple maintenance support tasks are scheduled according to the optimal scheduling scheme population. When a new maintenance support task is obtained, the latest maintenance support task is determined according to the judgment status of the current maintenance support task and the new maintenance support task, including:

[0135] According to the optimal scheduling scheme population, multiple maintenance and support tasks are scheduled. When a new maintenance and support task is obtained, the current maintenance and support task in the executing state, the current maintenance and support task in the unexecuting state, and the new maintenance and support task are combined to form the latest maintenance and support task.

[0136] Re-make priority decisions, delete the maintenance and support tasks in execution that do not meet the priority constraints, and release the occupied maintenance and support resources.

[0137] S108: Determine the new maintenance support resources and the corresponding remaining carrying capacity corresponding to the latest maintenance support task, update the resource position of the maintenance support resources, and determine the current position of the maintenance support resources in the scheduling state.

[0138] Among them, the resource location is the previous location of the maintenance and support task location.

[0139] The calculation formula for the current position of the maintenance support resources in the dispatch state is as follows:

[0140]

[0141] in, is the current position of the maintenance support resources in the scheduling state, that is, the position of the resource set when a new maintenance support task demand arrives, is the resource location of the maintenance support resource, that is, the location of the resource set before scheduling, s v To maintain the speed of resources, time insert is the new maintenance support task time, time s The time to start scheduling maintenance support resources.

[0142] S109, taking the latest maintenance support task as multiple maintenance support tasks, taking the new maintenance support resource as the maintenance support resource, taking the remaining carrying capacity as the target carrying capacity, taking the current position as the current position, taking the next position of the resource position as the maintenance support task position, and returning to the step of determining the attribute decision set of the multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, so as to generate the final scheduling plan population.

[0143] There are currently two main strategies for dynamic task replanning: one is to dynamically repair the existing plan, which has less damage to the original plan, but only focuses on local impact and it is difficult to maximize the overall benefit of the task; the other is to completely reschedule the unexecuted tasks and the dynamically inserted new tasks, and regenerate a new scheduling plan. This plan can ensure the overall maximization of the scheduling plan, but it relatively consumes a lot of computing resources. The present invention is aimed at the dynamic planning of maintenance and support tasks with priorities. It is often difficult to simultaneously ensure the task priority constraints and the maximization of the overall benefit by performing dynamic repairs, so a complete rescheduling strategy is adopted to obtain better maintenance and support task guarantee benefits.

[0144] The latest maintenance support task is taken as multiple maintenance support tasks, the new maintenance support resource is taken as the maintenance support resource, the remaining carrying capacity is taken as the target carrying capacity, the current position is taken as the current position, and the next position of the resource position is taken as the maintenance support task position, and the process returns to step S101, i.e., the step of determining the attribute decision set of the multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, so as to generate a final scheduling scheme population, which includes the dynamic rescheduling execution scheme of the maintenance support resources of the maintenance support tasks.

[0145] In order to verify the maintenance support resource dynamic rescheduling method of the improved particle swarm algorithm proposed in the present invention, the algorithm parameters shown in Table 1, Table 2, and Table 3 and the expert scoring table shown in Table 4 are used to verify the reliability of the present invention, and the relevant performances of the existing particle swarm algorithm, the improved particle swarm algorithm and the present invention are compared; at the same time, the new maintenance support task simulation example shown in Table 6 is used to verify the reliability of the present invention in maintenance support task replanning and resource rescheduling:

[0146] Table 1 Initial maintenance support mission requirements

[0147]

[0148]

[0149] Table 2 Initial resource set configuration table

[0150]

[0151] Table 3 Algorithm parameters

[0152]

[0153] Table 4 Expert scoring table

[0154]

[0155] Experimental results analysis:

[0156] According to the data input from the above table to Table 4, the priority ranking result of the maintenance and support tasks can be obtained as 5→7→1→6→2→3→8→9→4→10. Referring to the algorithm performance comparison chart shown in Figure 7 and the task execution Gantt chart obtained by different algorithms shown in Figure 8, the improved particle swarm algorithm 1 is a maintenance and support equipment resource scheduling method based on the improved particle swarm algorithm of the new position coding update method, and the improved particle swarm algorithm 2 is a maintenance and support resource scheduling method based on the improved particle swarm algorithm 1 combined with the mutation operator and the crossover operator.

[0157] FIG7(a) schematically shows a task completion rate diagram. FIG7(a) shows that the maintenance support resource dynamic rescheduling method of an improved particle swarm algorithm proposed in the present invention, the discrete particle swarm algorithm and the improved particle swarm algorithm can all obtain the highest initial task completion rate within a limited number of iterations; FIG7(b) schematically shows a task total execution time diagram. FIG7(b) shows that compared with the discrete particle swarm algorithm, both improved methods have improved optimization effects, but the improved particle swarm algorithm 1 can only converge to a better result in the later stage of the algorithm, while the improved particle swarm algorithm 2 with the addition of mutation operator and crossover operator can obtain the best resource scheduling scheme optimization in the early stage, proving that the proposed new position coding update method combined with the mutation operator and crossover operator is conducive to obtaining a better maintenance support task resource scheduling scheme.

[0158] FIG8(a) schematically shows the execution Gantt chart of the initial maintenance support task (general particle swarm), FIG8(b) schematically shows the execution Gantt chart of the initial maintenance support task (improved particle swarm 1), and FIG8(c) schematically shows the execution Gantt chart of the initial maintenance support task (improved particle swarm 2); wherein one color represents the maintenance execution of a maintenance support task, the start of each color rectangle is the moment when the maintenance support resource arrives at the location of the maintenance support task, and the end of the rectangle is the end moment when the maintenance support task completes the maintenance; each row is a maintenance support task sequence arranged by a maintenance support resource.

[0159] Comparison and analysis of optimization results of different algorithms:

[0160] Table 5 Comparison of algorithm optimization results

[0161]

[0162] When new maintenance support tasks are required, the optimization results and analysis of dynamic scheduling of maintenance resources are as follows:

[0163] Table 6 New maintenance support mission requirements

[0164]

[0165]

[0166] Based on the initial maintenance support task resource scheduling scheme given by the improved particle swarm algorithm 2, the task status is determined first, and the maintenance support task 2 that does not meet the priority constraint is deleted. The priority decision results of the remaining maintenance support tasks are 15→7→11→1→6→12→3→13→8→9→4→14→10. Fig. 9 The diagram schematically shows the dynamic rescheduling as a distribution diagram of maintenance support tasks and maintenance resource locations. It is a distribution diagram of new maintenance support tasks and maintenance support resources after dynamic planning task preprocessing when new maintenance support task requirements arise. The higher the priority of the maintenance support task, the larger the circle representing the maintenance support task.

[0167] Fig.10 The algorithm optimization iteration curve diagram of the design algorithm in the optimization process of the dynamic rescheduling scheme of maintenance resources is schematically shown. Fig.10 (a) is the iteration curve of the task completion rate of the dynamically adjusted task, that is, the iteration curve of the task completion rate of the new maintenance support task during the algorithm iteration process. It can be seen that during the algorithm iteration optimization process, the maintenance resource scheduling scheme given can gradually find a scheme with a task completion rate of 1, that is, it can enable all new maintenance support tasks to be executed; Fig.10(b) is the iteration curve of the dynamically adjusted task execution time, that is, the iteration curve of the total execution time of the new maintenance and support tasks during the algorithm iteration process. When the task completion rate remains unchanged, the algorithm iteration optimization process can always optimize in the direction of making the total execution time of the maintenance and support tasks shorter and shorter. When the task completion rate changes to 1, the resource scheduling plan with the shortest execution time for all maintenance and support tasks can be quickly found and finally converged. Fig.11 The Gantt chart of dynamic rescheduling of maintenance resources is schematically shown, where the starting time is the time when dynamic rescheduling starts when a new maintenance support task is required, one color represents the maintenance execution of a new maintenance support task, the starting time of each color rectangle is the time when the maintenance support resource arrives at the location of the new maintenance support task, and the end of the rectangle is the end time when the new maintenance support task completes maintenance; each row is a new maintenance support task sequence arranged by dynamic rescheduling of maintenance support resources. From the figure, it can be explained that the maintenance support resource dynamic rescheduling method of the improved particle swarm algorithm proposed by the present invention can still quickly optimize the maintenance support resource dynamic rescheduling scheme of the maintenance support task with the highest task completion rate and the shortest maintenance time after the new maintenance support task is required.

[0168] Based on the above Figure 1It can be seen from the implementation method that the embodiment of the present invention updates each corrected position code according to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and the crossover operation to obtain a new scheduling scheme population; merges the initial scheduling scheme population and the new scheduling scheme population until the iteration condition meets the preset condition to obtain the optimal scheduling scheme population; schedules multiple maintenance and support tasks according to the optimal scheduling scheme population, and when a new maintenance and support task is obtained, determines the latest maintenance and support task according to the judgment state of the current maintenance and support task and the new maintenance and support task; determines the latest maintenance and support task The method further comprises the following steps: taking the new maintenance support resources and the corresponding remaining carrying capacity corresponding to the maintenance support tasks, updating the resource position of the new maintenance support resources, determining the current position of the maintenance support resources in the scheduling state, and the resource position being the previous position of the maintenance support task position; taking the latest maintenance support tasks as multiple maintenance support tasks, the new maintenance support resources as maintenance support resources, the remaining carrying capacity as the target carrying capacity, the current position as the current position, and the next position of the resource position as the maintenance support task position; and returning to the step of determining the attribute decision set of the multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, so as to generate the final scheduling solution population. In this way, a new scheduling scheme population can be determined based on the fitness function value and crossover operation in the genetic operator to complete the initial planning. When a new maintenance and support task is obtained, the latest maintenance and support task, new maintenance and support resources and the corresponding remaining carrying capacity are determined, and the resource position of the new maintenance and support resources is updated. The current position of the maintenance and support resources in the scheduling state is determined, and the final scheduling scheme population is dynamically generated to realize the dynamic rescheduling task planning of maintenance and support resources. The dynamic rescheduling task planning of maintenance and support resources can shorten the time for dynamic rescheduling of maintenance and support resources.

[0169] Based on the same inventive concept, as an implementation of the above-mentioned maintenance support resource dynamic rescheduling method of an improved particle swarm algorithm, an embodiment of the present invention further provides a maintenance support resource dynamic rescheduling device of an improved particle swarm algorithm. Fig.12 is a structural diagram of the device in the embodiment of the present invention, see Fig.12 As shown, the device may include:

[0170] The first determination module 1201 is used to determine the attribute decision sets of the multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, where the attribute decision sets are used to indicate the quantitative values ​​of the multiple influencing factors;

[0171] The second determination module 1202 is used to determine the priority order of multiple maintenance support tasks according to the weights of multiple influencing factors and the attribute decision set;

[0172] The first generation module 1203 is used to encode multiple maintenance and support tasks according to the priority order and the preset length to obtain multiple position codes, and use each position code as a resource scheduling plan to generate an initial scheduling plan population;

[0173] The correction module 1204 is used to correct the initial scheduling scheme population according to the preset demand, preset carrying capacity, target carrying capacity of the maintenance support resources, current position of the maintenance support resources and the position of the maintenance support tasks corresponding to the multiple maintenance support tasks, so as to obtain a corrected scheduling scheme population;

[0174] An updating module 1205 is used to update each corrected position code according to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and the crossover operation to obtain a new scheduling scheme population;

[0175] The population merging module 1206 is used to merge the initial scheduling solution population and the new scheduling solution population until the iteration condition meets the preset condition to obtain the optimal scheduling solution population;

[0176] The third determination module 1207 is used to schedule multiple maintenance and support tasks according to the optimal scheduling scheme population, and when a new maintenance and support task is obtained, determine the latest maintenance and support task according to the determination status of the current maintenance and support task and the new maintenance and support task;

[0177] The fourth determination module 1208 is used to determine the new maintenance support resource and the corresponding remaining carrying amount corresponding to the latest maintenance support task, and update the resource position of the new maintenance support resource, and determine the current position of the maintenance support resource in the scheduling state, where the resource position is the previous position of the maintenance support task position;

[0178] The second generation module 1209 is used to take the latest maintenance and support task as multiple maintenance and support tasks, the new maintenance and support resources as maintenance and support resources, the remaining carrying capacity as the target carrying capacity, the current position as the current position, and the next position of the resource position as the maintenance and support task position, and return to the step of determining the attribute decision set of multiple maintenance and support tasks based on multiple influencing factors corresponding to the multiple maintenance and support tasks and the priority of each influencing factor, so as to generate a final scheduling plan population.

[0179] The second determination module 1202 is specifically used to obtain the weights of multiple influencing factors according to a correspondence table between multiple influencing factors and the weights of multiple influencing factors; construct an initial evaluation matrix according to the quantitative values ​​of multiple influencing factors in the attribute decision set; normalize all the influencing factors in the initial evaluation matrix respectively to obtain an evaluation standard matrix; multiply each column of the influencing factors in the evaluation standard matrix by the weight of the corresponding influencing factor to obtain an evaluation weighted standard matrix; determine the priority order of multiple maintenance and support tasks according to the evaluation weighted standard matrix and multiple maintenance and support tasks.

[0180] The second determination module 1202 determines the priority order of multiple maintenance support tasks based on the evaluation weighted specification matrix and multiple maintenance support tasks, including: determining the ideal solution and the negative ideal solution corresponding to the evaluation weighted specification matrix; determining the closeness of multiple maintenance support tasks based on the ideal solution, the negative ideal solution and the multiple maintenance support tasks to obtain the closeness of all maintenance support tasks; sorting the closeness of multiple maintenance support tasks from large to small to obtain the sorted closeness; and determining the priority order of multiple maintenance support tasks according to the sorted closeness.

[0181] The values ​​corresponding to each position code in the initial scheduling scheme population in the first generation module 1203 include multiple first code values ​​and multiple second code values, the multiple first code values ​​are used to indicate that the maintenance support resource schedules multiple maintenance support tasks, the multiple second code values ​​are used to indicate that the maintenance support resource does not schedule multiple maintenance support tasks, and the target carrying capacity of the maintenance support resource includes the first target carrying capacity of the maintenance support resource corresponding to the multiple first code values.

[0182] The correction module 1204 is specifically used to determine whether the initial scheduling scheme population meets the preset demand; if not, when the first target carrying capacity exceeds the preset carrying capacity, according to the current positions and the maintenance and support task positions of the maintenance and support resources corresponding to the multiple first code values, determine the first time sequence of the maintenance and support resources arriving at the maintenance and support task position, and accumulate the first carrying capacity of the maintenance and support resources according to the first time sequence; if the first carrying capacity is equal to the preset carrying capacity, determine the first maintenance and support resource corresponding to the first carrying capacity, correct the multiple first code values ​​corresponding to the second maintenance and support resource to multiple second code values, and the second maintenance and support resource is a maintenance and support resource other than the first maintenance and support resource; when the first target carrying capacity does not exceed the preset carrying capacity, according to the current positions and the maintenance and support task positions of the maintenance and support resources corresponding to the multiple second code values, determine the second time sequence of the maintenance and support resources arriving at the maintenance and support task position, and accumulate the second carrying capacity of the maintenance and support resources according to the second time sequence; if the second carrying capacity is equal to the preset carrying capacity, determine the third maintenance and support resource corresponding to the second carrying capacity, correct the multiple second code values ​​corresponding to the fourth maintenance and support resource to multiple first code values, and the fourth maintenance and support resource is a maintenance and support resource other than the third maintenance and support resource.

[0183] The updating module 1205 is used to determine the speed coding according to the corrected position coding of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position coding and the scaling factor; update each corrected position coding according to the speed coding and the coding value corresponding to each corrected position coding to obtain an updated scheduling scheme population; and perform cross operation on the updated position coding of the updated scheduling scheme population to obtain a new scheduling scheme population.

[0184] The updating module 1205 determines the speed coding according to each corrected position coding of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position coding and the scaling factor, including: decoding each corrected position coding of the corrected scheduling scheme population, and calculating the first fitness function value corresponding to each corrected position coding; determining the optimal position coding and the optimal resource scheduling scheme coding according to the first fitness function value; determining the speed coding according to the optimal position coding, the optimal resource scheduling scheme coding and the scaling factor.

[0185] The updating module 1205, the speed code includes multiple bits of the speed code, and according to the speed code and the code values ​​corresponding to each corrected position code, each corrected position code is updated to obtain an updated scheduling scheme population, including: according to the multiple bits of the speed code and the code values ​​corresponding to each corrected position code, each corrected position code is updated to obtain an updated scheduling scheme population:

[0186]

[0187] Among them, s(v ij) is the mapping value of velocity encoding, v ij Multiple bits for speed encoding, is the intermediate value of the velocity encoding mapping process, x (ij) is the code value corresponding to each correction position code, ~x (ij) To invert the code value corresponding to each corrected position code, i is the i-th position code, and j is the j-th bit in the multi-bit speed code.

[0188] The third determination module 1207 is specifically used to schedule multiple maintenance and support tasks according to the optimal scheduling scheme population. When a new maintenance and support task is obtained, the current maintenance and support task in the executing state, the current maintenance and support task in the unexecuting state, and the new maintenance and support task are combined to form the latest maintenance and support task. The judgment status includes the executing state and the unexecuting state.

[0189] It should be pointed out here that the above description of the embodiment of the dynamic rescheduling device for maintenance and support resources of the improved particle swarm algorithm is similar to the description of the embodiment of the dynamic rescheduling method for maintenance and support resources of the improved particle swarm algorithm, and has similar beneficial effects as the embodiment of the dynamic rescheduling method for maintenance and support resources of the improved particle swarm algorithm. For technical details not disclosed in the embodiment of the dynamic rescheduling device for maintenance and support resources of the improved particle swarm algorithm of the embodiment of the present invention, please refer to the description of the embodiment of the dynamic rescheduling method for maintenance and support resources of the improved particle swarm algorithm of the present invention for understanding.

[0190] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A maintenance support resource dynamic rescheduling method based on an improved particle swarm algorithm, characterized in that: The maintenance support resource dynamic rescheduling method comprises: Determine attribute decision sets of the multiple maintenance support tasks according to the multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, wherein the attribute decision sets are used to indicate the quantitative values ​​of the multiple influencing factors; Determining the priority order of multiple maintenance support tasks according to the weights of multiple influencing factors and the attribute decision set; According to the priority order and the preset length, the plurality of maintenance and support tasks are encoded to obtain a plurality of position codes, and each position code is used as a resource scheduling scheme to generate an initial scheduling scheme population; According to preset requirements and preset carrying capacity of maintenance support resources corresponding to the plurality of maintenance support tasks, target carrying capacity of the maintenance support resources, current positions of the maintenance support resources and positions of the maintenance support tasks, the initial scheduling scheme population is modified to obtain a modified scheduling scheme population; According to each modified position code of the modified scheduling scheme population, the fitness function value corresponding to each modified position code, the code value corresponding to each modified position code and the crossover operation, each modified position code is updated to obtain a new scheduling scheme population; Merging the initial scheduling solution population and the new scheduling solution population until an iteration condition satisfies a preset condition, thereby obtaining an optimal scheduling solution population; According to the optimal scheduling scheme population, the plurality of maintenance and support tasks are scheduled, and when a new maintenance and support task is acquired, the latest maintenance and support task is determined according to the determination status of the current maintenance and support task and the new maintenance and support task; Determine the new maintenance support resource and the corresponding remaining carrying capacity corresponding to the latest maintenance support task, update the resource position of the new maintenance support resource, and determine the current position of the maintenance support resource in the scheduling state, where the resource position is the previous position of the maintenance support task position; The latest maintenance support task is taken as the multiple maintenance support tasks, the new maintenance support resource is taken as the maintenance support resource, the remaining carrying capacity is taken as the target carrying capacity, the current position is taken as the current position, and the next position of the resource position is taken as the maintenance support task position, and the step of determining the attribute decision set of multiple maintenance support tasks according to multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor is returned to generate a final scheduling solution population.

2. The maintenance support resource dynamic rescheduling method according to claim 1, characterized in that: Determining the priority order of multiple maintenance support tasks according to the weights of multiple influencing factors and the attribute decision set includes: Obtaining the weights of the multiple influencing factors according to a correspondence table between the multiple influencing factors and the weights of the multiple influencing factors; constructing an initial evaluation matrix according to the quantitative values ​​of the multiple influencing factors in the attribute decision set; Normalizing all influencing factors in the initial evaluation matrix to obtain an evaluation standard matrix; Multiplying each column of influencing factors in the evaluation standard matrix by the weight of the corresponding influencing factors to obtain an evaluation weighted standard matrix; The priority order of the plurality of maintenance support tasks is determined according to the evaluation weighted specification matrix and the plurality of maintenance support tasks.

3. The maintenance support resource dynamic rescheduling method according to claim 2, characterized in that: Determining the priority order of the plurality of maintenance support tasks according to the evaluation weighted specification matrix and the plurality of maintenance support tasks includes: Determining an ideal solution and a negative ideal solution corresponding to the evaluation weighted normative matrix; Determining the closeness of the plurality of maintenance support tasks according to the ideal solution, the negative ideal solution and the plurality of maintenance support tasks, so as to obtain the closeness of all maintenance support tasks; Sorting the closeness of the plurality of maintenance support tasks from largest to smallest to obtain sorted closeness; According to the closeness of the sorting, the priority order of the multiple maintenance and support tasks is determined.

4. The maintenance support resource dynamic rescheduling method according to claim 1, characterized in that: The values ​​corresponding to the position codes in the initial scheduling scheme population include multiple first code values ​​and multiple second code values, the multiple first code values ​​are used to indicate that the maintenance and support resources schedule the multiple maintenance and support tasks, and the multiple second code values ​​are used to indicate that the maintenance and support resources do not schedule the multiple maintenance and support tasks, and the target carrying capacity of the maintenance and support resources includes the first target carrying capacity of the maintenance and support resources corresponding to the multiple first code values.

5. The maintenance support resource dynamic rescheduling method according to claim 4, characterized in that: The method of modifying the initial scheduling scheme population according to the preset requirements and preset carrying capacity of the maintenance support resources corresponding to the plurality of maintenance support tasks, the target carrying capacity of the maintenance support resources, the current position of the maintenance support resources and the position of the maintenance support tasks to obtain a modified scheduling scheme population includes: Determining whether the initial scheduling scheme population meets the preset requirements; If not, when the first target carrying capacity exceeds the preset carrying capacity, according to the current positions of the maintenance support resources corresponding to the multiple first code values ​​and the maintenance support task positions, determine the first time sequence of the maintenance support resources arriving at the maintenance support task positions, and accumulate the first carrying capacity of the maintenance support resources according to the first time sequence; if the first carrying capacity is equal to the preset carrying capacity, determine the first maintenance support resource corresponding to the first carrying capacity, correct the multiple first code values ​​corresponding to the second maintenance support resource to the multiple second code values, and the second maintenance support resource is the maintenance support resource other than the first maintenance support resource; When the first target carrying capacity does not exceed the preset carrying capacity, the second time sequence of the maintenance support resource arriving at the maintenance support task position is determined according to the current position of the maintenance support resource corresponding to the multiple second coding values ​​and the maintenance support task position, and the second carrying capacity of the maintenance support resource is accumulated according to the second time sequence. If the second carrying capacity is equal to the preset carrying capacity, the third maintenance support resource corresponding to the second carrying capacity is determined, and the multiple second coding values ​​corresponding to the fourth maintenance support resource are corrected to the multiple first coding values. The fourth maintenance support resource is a maintenance support resource other than the third maintenance support resource.

6. The maintenance support resource dynamic rescheduling method according to claim 1, characterized in that: The method of updating each corrected position code according to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and the crossover operation to obtain a new scheduling scheme population includes: Determine the speed code according to each modified position code of the modified scheduling scheme population, the fitness function value corresponding to each modified position code, and the scaling factor; According to the code values ​​corresponding to the speed code and the corrected position codes, the corrected position codes are updated to obtain an updated scheduling scheme population; A cross operation is performed on the update position codes of the updated scheduling scheme population to obtain the new scheduling scheme population.

7. The maintenance support resource dynamic rescheduling method according to claim 6, characterized in that: The determining of the speed coding according to each modified position coding of the modified scheduling scheme population, the fitness function value and the scaling factor corresponding to each modified position coding comprises: Decoding each modified position code of the modified scheduling scheme population, and calculating a first fitness function value corresponding to each modified position code; Determining an optimal position code and an optimal resource scheduling scheme code according to the first fitness function value; The speed code is determined according to the optimal position code, the optimal resource scheduling scheme code and the scaling factor.

8. The maintenance support resource dynamic rescheduling method according to claim 6, characterized in that: The speed code includes multiple bits of speed code, and the updating of each modified position code according to the code value corresponding to the speed code and each modified position code to obtain an updated scheduling scheme population includes: According to the multiple bits of the speed code and the code values ​​corresponding to the modified position codes, the modified position codes are updated to obtain the updated scheduling scheme population: Among them, s(v ij ) is the mapping value of the speed code, v ij A number of bits encoding the speed, is the intermediate value of the speed encoding mapping process, x ij is the code value corresponding to each correction position code, ~x ij To invert the code value corresponding to each modified position code, i is the i-th position code, and j is the j-th bit in the multiple bits of the speed code.

9. The maintenance support resource dynamic rescheduling method according to claim 8, characterized in that: The determination state includes an executing state and a non-executing state. The scheduling of the multiple maintenance and support tasks according to the optimal scheduling scheme population, and when a new maintenance and support task is acquired, determining the latest maintenance and support task according to the determination state of the current maintenance and support task and the new maintenance and support task, include: The multiple maintenance and support tasks are scheduled according to the optimal scheduling scheme population. When a new maintenance and support task is obtained, the current maintenance and support task in an executing state, the current maintenance and support task in an unexecuted state, and the new maintenance and support task are combined to form the latest maintenance and support task.

10. A maintenance support resource dynamic rescheduling device based on an improved particle swarm algorithm, characterized in that: The maintenance support resource dynamic rescheduling device comprises: A first determination module is used to determine an attribute decision set of multiple maintenance support tasks according to multiple influencing factors corresponding to the multiple maintenance support tasks and the priority of each influencing factor, wherein the attribute decision set is used to indicate the quantitative values ​​of the multiple influencing factors; A second determination module is used to determine the priority order of multiple maintenance support tasks according to the weights of multiple influencing factors and the attribute decision set; A first generating module is used to encode the plurality of maintenance and support tasks according to the priority order and the preset length to obtain a plurality of position codes, and use each position code as a resource scheduling scheme to generate an initial scheduling scheme population; A correction module, configured to correct the initial scheduling scheme population according to preset requirements and preset carrying capacity of the maintenance support resources corresponding to the multiple maintenance support tasks, the target carrying capacity of the maintenance support resources, the current position of the maintenance support resources and the position of the maintenance support tasks, so as to obtain a corrected scheduling scheme population; An updating module, used to update each corrected position code according to each corrected position code of the corrected scheduling scheme population, the fitness function value corresponding to each corrected position code, the code value corresponding to each corrected position code and a crossover operation, so as to obtain a new scheduling scheme population; A population merging module, used to merge the initial scheduling solution population and the new scheduling solution population until an iteration condition satisfies a preset condition, thereby obtaining an optimal scheduling solution population; A third determination module is used to schedule the multiple maintenance and support tasks according to the optimal scheduling scheme population, and when a new maintenance and support task is obtained, determine the latest maintenance and support task according to the determination state of the current maintenance and support task and the new maintenance and support task; A fourth determination module is used to determine the new maintenance support resource and the corresponding remaining carrying amount corresponding to the latest maintenance support task, and update the resource position of the new maintenance support resource, and determine the current position of the maintenance support resource in the scheduling state, wherein the resource position is the previous position of the maintenance support task position; The second generation module is used to take the latest maintenance and support task as the multiple maintenance and support tasks, the new maintenance and support resource as the maintenance and support resource, the remaining carrying capacity as the target carrying capacity, the current position as the current position, and the next position of the resource position as the maintenance and support task position, and return to the step of determining the attribute decision set of multiple maintenance and support tasks according to multiple influencing factors corresponding to the multiple maintenance and support tasks and the priority of each influencing factor, so as to generate a final scheduling plan population.

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