An unmanned equipment-oriented mobile energy storage vehicle path planning method and device

Abstract

The application discloses a mobile energy storage vehicle path planning method and device for unmanned equipment, the path planning problem of the mobile energy storage vehicle is constructed as a two-stage mathematical programming model, first-stage constraint conditions and second-stage constraint conditions are respectively set according to the decision variables of the first stage and the second stage, the two-stage mathematical programming model is solved through an evolution algorithm, and the path planning of the mobile energy storage vehicle is obtained. The application effectively improves the planning efficiency and rationality of the path, improves the energy supplement efficiency, and provides support for the comprehensive use of future unmanned equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle scheduling, and in particular to a path planning method and device for a mobile energy storage vehicle for unmanned equipment. Background Art

[0002] Unmanned vehicles, as an emerging unmanned technology, can be widely used in scenarios where human intervention is extremely dangerous, such as disaster relief. Current unmanned systems primarily rely on electricity as their core energy source, a choice driven by its significant advantages of efficiency, cleanliness, and ease of maintenance. However, this power model also raises a key issue: the sustained operational capability and range of unmanned equipment in scenarios like disaster relief are directly limited by the capacity of their battery packs.

[0003] Currently, traditional power supply methods are difficult to implement under such conditions and cannot meet the rapid and flexible energy supply needs of unmanned equipment. In this context, the use of mobile energy storage vehicles to supply power to unmanned equipment has become a highly practical solution.

[0004] Therefore, how to invent a path planning method for mobile energy storage vehicles to effectively improve the efficiency and rationality of path planning, improve energy replenishment efficiency, and provide support for the comprehensive use of unmanned equipment in the future has become an urgent problem to be solved. Summary of the Invention

[0005] To this end, the present invention provides a path planning method and device for a mobile energy storage vehicle for unmanned equipment. By constructing a two-stage mathematical programming model and solving the two-stage mathematical programming model through an evolutionary algorithm, the path planning of the mobile energy storage vehicle is obtained, which effectively improves the efficiency and rationality of path planning, enhances the energy replenishment efficiency, and provides support for the comprehensive use of unmanned equipment in the future.

[0006] In order to achieve the above objectives, the present invention provides the following technical solutions: a path planning method for a mobile energy storage vehicle for unmanned equipment, comprising:

[0007] The path planning problem of the mobile energy storage vehicle is constructed as a two-stage mathematical programming model;

[0008] According to the decision variables of the first and second stages, set the first stage constraints and the second stage constraints respectively;

[0009] The two-stage mathematical programming model is solved by an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle.

[0010] As a preferred solution for the path planning method of a mobile energy storage vehicle for unmanned equipment, the objective function expression of the two-stage mathematical programming model is:

[0011]

[0012] Where K is the number of mobile energy storage vehicles; n k The number of tasks that need to be performed by the k-th mobile energy storage vehicle; The time it takes for the kth mobile energy storage vehicle to complete the i-th task; The i-th task completed for the k-th mobile energy storage vehicle; For the The lower edge of the time window for each task.

[0013] As a preferred solution for the path planning method of a mobile energy storage vehicle for unmanned equipment, the first-stage constraints are:

[0014]

[0015] Where x ijk and y jk is a 0-1 decision variable; x ijk The kth mobile energy storage vehicle performs the jth task after completing the ith task; y jk The jth task is performed by the kth mobile energy storage vehicle;

[0016] The constraints of the second stage are:

[0017]

[0018]

[0019] Where, E i is the energy required for the i-th task; l i is the upper edge of the time window of the i-th task; is the initial position of the kth mobile energy storage vehicle; p k is the charging center where the kth mobile energy storage vehicle is initially located; ρ is the amount of electricity consumed per unit distance by the mobile energy storage vehicle; v is the speed of the mobile energy storage vehicle; M is the maximum capacity of the mobile energy storage vehicle; P is the discharge power of the mobile energy storage vehicle; Q is the charging power of the mobile energy storage vehicle; d ij is the distance between the i-th node and the j-th node; b i is the number of the charging center closest to the i-th task node; and are the time and energy when the kth mobile energy storage vehicle enters its i-th task node; and are the time and energy when the kth mobile energy storage vehicle leaves its i-th task node; is the waiting time for the kth mobile energy storage vehicle to enter its i-th task; The number of times the k-th mobile energy storage vehicle needs to return to the charging center when performing its i-th mission.

[0020] As a preferred solution of a mobile energy storage vehicle path planning method for unmanned equipment, the steps of solving the two-stage mathematical programming model by the evolutionary algorithm are as follows:

[0021] Initializing the chromosome population through chromosome coding to obtain an initial population; initially setting the parameters of the evolutionary algorithm to obtain initial parameters;

[0022] Calculating the fitness of the initial population by improving the 2-opt operator to obtain the initial fitness;

[0023] Starting from the first chromosome in the initial population, executing a self-crossover operator to generate a crossover population;

[0024] Starting from the first chromosome in the initial population, executing the mutation operator to generate a mutant population;

[0025] Select elite individuals from the initial population, the cross population, and the variant population according to the elite population ratio in the initial parameters, and add them to the new population;

[0026] Randomly select a number of individuals from the initial population, the cross population and the variant population, and add them to the new population;

[0027] The new population is used as the initial population for the next round, and the self-crossover operator and the mutation operator are executed in sequence to iteratively generate the new population for the next round until the number of iterations reaches the maximum number of iterations in the initial parameters, and the iteration is completed, and the optimal individual is output;

[0028] The optimal individual is decoded to obtain a path planning solution for the mobile energy storage vehicle.

[0029] As a preferred solution for a mobile energy storage vehicle path planning method for unmanned equipment, in the process of calculating the fitness of the initial population through the improved 2-opt operator, the chromosome is decoded into tasks that each energy storage vehicle needs to perform; the optimal task execution order and corresponding fitness of each mobile energy storage vehicle are calculated through the improved 2-opt operator; the fitness of each mobile energy storage vehicle is summed to obtain the overall fitness of the chromosome.

[0030] The present invention also provides a path planning device for a mobile energy storage vehicle for unmanned equipment, based on the above-mentioned path planning method for a mobile energy storage vehicle for unmanned equipment, comprising:

[0031] A two-stage mathematical programming model construction module is used to construct the path planning problem of the mobile energy storage vehicle into a two-stage mathematical programming model;

[0032] A constraint condition setting module, used to set the first stage constraint conditions and the second stage constraint conditions according to the decision variables of the first stage and the second stage respectively;

[0033] The two-stage mathematical programming model solving module is used to solve the two-stage mathematical programming model through an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle.

[0034] As a preferred solution for a mobile energy storage vehicle path planning device for unmanned equipment, in the two-stage mathematical programming model construction module, the objective function expression of the two-stage mathematical programming model is:

[0035]

[0036] Where K is the number of mobile energy storage vehicles; n k The number of tasks that need to be performed by the k-th mobile energy storage vehicle; The time it takes for the kth mobile energy storage vehicle to complete the i-th task; The i-th task completed for the k-th mobile energy storage vehicle; For the The lower edge of the time window for each task.

[0037] As a preferred solution for a mobile energy storage vehicle path planning device for unmanned equipment, in the constraint setting module, the first-stage constraint is:

[0038]

[0039] Where x ijk and y jk is a 0-1 decision variable; x ijk The kth mobile energy storage vehicle performs the jth task after completing the ith task; y jk The jth task is performed by the kth mobile energy storage vehicle;

[0040] The constraints of the second stage are:

[0041]

[0042]

[0043] Where, E i is the energy required for the i-th task; l i is the upper edge of the time window of the i-th task; is the initial position of the kth mobile energy storage vehicle; p kis the charging center where the kth mobile energy storage vehicle is initially located; ρ is the amount of electricity consumed per unit distance by the mobile energy storage vehicle; v is the speed of the mobile energy storage vehicle; M is the maximum capacity of the mobile energy storage vehicle; P is the discharge power of the mobile energy storage vehicle; Q is the charging power of the mobile energy storage vehicle; d ij is the distance between the i-th node and the j-th node; b i is the number of the charging center closest to the i-th task node; and are the time and energy when the kth mobile energy storage vehicle enters its i-th task node; and are the time and energy when the kth mobile energy storage vehicle leaves its i-th task node; is the waiting time for the kth mobile energy storage vehicle to enter its i-th task; The number of times the k-th mobile energy storage vehicle needs to return to the charging center when performing its i-th mission.

[0044] As a preferred solution of a mobile energy storage vehicle path planning device for unmanned equipment, in the two-stage mathematical programming model solving module, the submodule for solving the two-stage mathematical programming model by the evolutionary algorithm includes:

[0045] The population initialization submodule is used to initialize the chromosome population through chromosome coding to obtain an initial population; and to initially set the parameters of the evolutionary algorithm to obtain initial parameters;

[0046] An initial fitness calculation submodule is used to calculate the fitness of the initial population by improving the 2-opt operator to obtain the initial fitness;

[0047] A crossover population generation submodule, configured to execute a self-crossover operator starting from the first chromosome in the initial population to generate a crossover population;

[0048] A mutant population generation submodule, configured to execute a mutation operator starting from the first chromosome in the initial population to generate a mutant population;

[0049] An elite individual selection submodule is used to select elite individuals from the initial population, the cross population and the variant population according to the elite population ratio in the initial parameters and add them to the new population;

[0050] A new population supplement submodule is used to randomly select a number of individuals from the initial population, the cross population and the variant population, and add them to the new population;

[0051] A new population iterative generation submodule is used to use the new population as the initial population for the next round, execute the self-crossover operator and the mutation operator in sequence, iteratively generate the next round of new population, until the number of iterations reaches the maximum number of iterations in the initial parameters, complete the iteration, and output the optimal individual;

[0052] The path planning solution acquisition submodule is used to decode the optimal individual and obtain the path planning solution of the mobile energy storage vehicle.

[0053] As a preferred solution for a mobile energy storage vehicle path planning device for unmanned equipment, in the initial fitness calculation submodule of the two-stage mathematical programming model solving module, in the process of calculating the fitness of the initial population through the improved 2-opt operator, the chromosome is decoded into tasks that each energy storage vehicle needs to perform; the optimal task execution order and corresponding fitness of each mobile energy storage vehicle are calculated through the improved 2-opt operator; and the fitness of each mobile energy storage vehicle is summed to obtain the overall fitness of the chromosome.

[0054] The application has the following advantages: the path planning problem of the mobile energy storage vehicle is constructed as a two-stage mathematical programming model; first stage and second stage constraint conditions are set according to the decision variables of the first stage and the second stage; the two-stage mathematical programming model is solved by an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle. The steps of the solution are as follows: the initial population is obtained by initializing the chromosome population and chromosome coding; the initial parameters are obtained by initially setting the parameters of the evolutionary algorithm; the initial fitness is obtained by calculating the fitness of the initial population by improving the 2-opt operator; starting from the first chromosome in the initial population, the self-crossing operator is executed to generate a cross population; starting from the first chromosome in the initial population, the mutation operator is executed to generate a mutation population; elite individuals are selected from the initial population, the cross population and the mutation population according to the elite population proportion in the initial parameters and added to a new population; a number of individuals are randomly selected from the initial population, the cross population and the mutation population and added to the new population; the new population is taken as the initial population of the next round, and the self-crossing operator and the mutation operator are executed in turn to iteratively generate the new population of the next round until the number of iterations reaches the maximum number of iterations in the initial parameters, the iteration is completed, and the optimal individual is output; the optimal individual is decoded to obtain the path planning scheme of the mobile energy storage vehicle. The application is more suitable for the path planning problem of the mobile energy storage vehicle for unmanned equipment. The operators designed in the algorithm are more suitable for the characteristics of the problem: open, multiple charging centers, time window constraints and consideration of material loss. The crossover operator is different from the individual-to-individual crossover in the traditional genetic algorithm, and the gene segment-to-gene segment crossover is selected, which makes the evolution of the population more efficient. The application effectively improves the planning efficiency and rationality of the path and improves the energy replenishment efficiency, providing support for the comprehensive use of unmanned equipment in the future. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and those skilled in the art can also obtain other embodiments of the drawings according to the provided drawings without creating any inventive labor.

[0056] The structures, proportions, sizes, etc. shown in the specification are only used to cooperate with the content disclosed in the specification for understanding and reading by those skilled in the art, and do not define the limiting conditions for the implementation of the application, so they do not have technical significance. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect and purpose that can be achieved by the application, should still fall within the scope covered by the disclosed technical content.

[0057] Figure 1 A flowchart of a mobile energy storage vehicle path planning method for unmanned equipment provided in Embodiment 1 of the present application;

[0058] Figure 2 A flowchart of an evolutionary algorithm in a mobile energy storage vehicle path planning method for unmanned equipment provided in Embodiment 1 of the present application;

[0059] Figure 3 A schematic diagram of a mobile energy storage vehicle path planning device architecture provided in Embodiment 2 of the present application. DETAILED DESCRIPTION

[0060] The embodiments of the present application will be described in detail by specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0061] Embodiment 1

[0062] Reference Figure 1 Embodiment 1 of the present application provides a mobile energy storage vehicle path planning method for unmanned equipment, comprising the following steps:

[0063] S1, constructing a path planning problem of a mobile energy storage vehicle as a two-stage mathematical programming model;

[0064] S2, setting first-stage constraints and second-stage constraints according to the decision variables of the first stage and the second stage, respectively;

[0065] S3, solving the two-stage mathematical programming model by an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle.

[0066] In this embodiment, in step S1, the path planning problem of the mobile energy storage vehicle is constructed as a two-stage mathematical programming model;

[0067] Specifically, the present application defines a mobile energy storage vehicle path planning problem for unmanned equipment. In this problem, the transported materials consume electrical energy during transportation, and the task to be performed is to supply energy to unmanned equipment, which is a new vehicle path problem. Specifically, in the case of known energy demand of unmanned equipment and location, the problem is to schedule a number of mobile energy storage vehicles to supply energy to unmanned equipment within a specified time window.

[0068] Based on the above definition, the problem is modeled as a two-stage mathematical programming model.

[0069] The objective function expression of the two-stage mathematical programming model is:

[0070]

[0071] Where K is the number of mobile energy storage vehicles; n k The number of tasks that need to be performed by the k-th mobile energy storage vehicle; The time it takes for the kth mobile energy storage vehicle to complete the i-th task; The i-th task completed for the k-th mobile energy storage vehicle; For the The lower edge of the time window for each task.

[0072] In this embodiment, in step S2, first-stage constraint conditions and second-stage constraint conditions are set according to the decision variables of the first and second stages respectively;

[0073] Specifically, in the first stage, the decision variable is the task that each mobile energy storage vehicle needs to perform; in the second stage, the decision variable is the order of tasks that each mobile energy storage vehicle needs to perform.

[0074] The first stage constraints are:

[0075]

[0076] Where x ijk and y jk is a 0-1 decision variable; x ijk The kth mobile energy storage vehicle performs the jth task after completing the ith task; y jk The jth task is performed by the kth mobile energy storage vehicle;

[0077] The constraints of the second stage are:

[0078]

[0079]

[0080] Where, E i is the energy required for the i-th task; l i is the upper edge of the time window of the i-th task; is the initial location of the kth mobile energy storage vehicle; p k is the charging center where the kth mobile energy storage vehicle is initially located; ρ is the amount of electricity consumed per unit distance by the mobile energy storage vehicle; v is the speed of the mobile energy storage vehicle; M is the maximum capacity of the mobile energy storage vehicle; P is the discharge power of the mobile energy storage vehicle; Q is the charging power of the mobile energy storage vehicle; dij is the distance between the i-th node and the j-th node; b i is the number of the charging center closest to the i-th task node; and are the time and energy when the kth mobile energy storage vehicle enters its i-th task node; and are the time and energy when the kth mobile energy storage vehicle leaves its i-th task node; is the waiting time for the kth mobile energy storage vehicle to enter its i-th task; The number of times the k-th mobile energy storage vehicle needs to return to the charging center when performing its i-th mission.

[0081] In this embodiment, in step S3, the two-stage mathematical programming model is solved by an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle.

[0082] Specifically, such as Figure 2 As shown, the steps of solving the two-stage mathematical programming model by the evolutionary algorithm are:

[0083] S31, initializing the chromosome population by chromosome encoding to obtain an initial population; initially setting the parameters of the evolutionary algorithm to obtain initial parameters;

[0084] Specifically, chromosomes are encoded using integer encoding. When the number of tasks is n, the chromosome size is 1×n, and the value at each position is an integer between 1 and K, indicating that the task at that position is completed by the mobile energy storage vehicle with the corresponding value. Initial parameters include: population size, maximum number of iterations, and elite population ratio.

[0085] S32, calculating the fitness of the initial population by improving the 2-opt operator to obtain an initial fitness;

[0086] Specifically, the chromosome is first decoded into the tasks that each energy storage vehicle needs to perform. Then, the improved 2-opt operator is used to find the optimal task execution order and corresponding fitness of each mobile energy storage vehicle, and the fitness of each mobile energy storage vehicle is summed to obtain the fitness of the entire chromosome. The basic steps of the improved 2-opt operator are as follows: a number of neighborhood solutions are randomly generated in the neighborhood of the current solution, and the fitness of these solutions is compared with the fitness of the current solution. If there is a neighborhood solution that is better than the current solution, then the neighborhood solution is used as the current solution. Otherwise, the current solution is used as the optimal solution. The improved 2-opt operator is encapsulated in the fitness function. At the same time, the population is iterated cyclically starting from the loop variable t = 1.

[0087] S33, starting from the first chromosome in the initial population, executing a self-crossover operator to generate a crossover population;

[0088] Specifically, starting from the first chromosome, the self-crossover operator is executed, two mobile energy storage vehicles numbered m and n are randomly selected, a random integer r is generated, r is between 1 and the minimum number of tasks of the two mobile energy storage vehicles, and r tasks are crossed between the two mobile energy storage vehicles.

[0089] Specifically, two random integers m and n are generated between 1 and K. The number of tasks assigned to the mobile energy storage vehicles numbered m and n on the current chromosome is then determined. Another random number r is then generated between 1 and the smallest of the two mobile energy storage vehicle task numbers. This prevents the number of tasks crossed from exceeding the number of tasks assigned to a particular mobile energy storage vehicle. Next, a random number r is crossed between the two mobile energy storage vehicles.

[0090] S34. Starting from the first chromosome in the initial population, executing a mutation operator to generate a mutant population;

[0091] Specifically, starting from the first chromosome, the mutation operator is executed to select the mobile energy storage vehicle number with the worst fitness, and then the task number with the worst fitness is selected. The task is randomly assigned to the task list of mobile energy storage vehicles with the same initial charging center. If there is no mobile energy storage vehicle with the same initial center, it is randomly assigned to other mobile energy storage vehicles.

[0092] Specifically, this mutation operator is similar to the single-point mutation operator. It selectively selects the mobile energy storage vehicle number with the worst fitness and the task number with the worst fitness. However, the resulting value is random. It randomly selects mobile energy storage vehicles or other mobile energy storage vehicles with the same initial charging center.

[0093] S35, selecting elite individuals from the initial population, the cross population, and the variant population according to the elite population ratio in the initial parameters, and adding them to the new population;

[0094] Specifically, the initial population, the cross population, and the mutant population are sorted together according to fitness, and elite individuals of the elite population ratio are selected starting from the individuals with the best fitness to be added to the new population.

[0095] S36, randomly selecting a number of individuals from the initial population, the cross population, and the variant population, and adding them to the new population;

[0096] Specifically, the remaining individuals in the new population are randomly selected from the initial population, the cross population, and the variant population, so as to retain a certain number of excellent individuals while enriching the diversity of the population.

[0097] S37, taking the new population as the initial population of the next round, executing the self-crossing operator and the mutation operator in turn, and iteratively generating a new population of the next round until the iteration number reaches the maximum iteration number in the initial parameter, completing the iteration, and outputting the optimal individual;

[0098] Specifically, taking the new population as the initial population of the next round of iteration, iteratively executing steps S33-S37, and the loop variable t=t+1, iteratively generating a new population of the next round until the iteration number reaches the maximum iteration number in the initial parameter, completing the iteration, and outputting the optimal individual.

[0099] S38, decoding the optimal individual to obtain the path planning scheme of the mobile energy storage vehicle.

[0100] In a possible embodiment, the comparison example of the present application with the traditional genetic algorithm is as follows:

[0101] The first example is 50 task nodes, i.e. 50 unmanned equipment. The second example is 100 task nodes.

[0102]

[0103]

[0104] Table 1 Comparison results of the present application and the traditional genetic algorithm

[0105] As can be seen from the results in Table 1, the present application is superior to the traditional genetic algorithm in terms of optimal solution quality, convergence speed and stability, but the running time is slightly longer than that of the traditional algorithm.

[0106] To sum up, the application constructs the path planning problem of the mobile energy storage vehicle as a two-stage mathematical programming model; sets the first-stage constraint condition and the second-stage constraint condition according to the decision variables of the first stage and the second stage; solves the two-stage mathematical programming model by using the evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle. The solving steps are as follows: the initial population is obtained by initializing the chromosome population and the chromosome coding; the initial parameters are obtained by initially setting the parameters of the evolutionary algorithm; the initial fitness is obtained by calculating the fitness of the initial population by using the improved 2-opt operator; the crossover population is generated by executing the self-crossing operator from the first chromosome in the initial population; the mutation population is generated by executing the mutation operator from the first chromosome in the initial population; the elite individuals are selected from the initial population, the crossover population and the mutation population according to the elite population proportion in the initial parameters, and added to the new population; a plurality of individuals are randomly selected from the initial population, the crossover population and the mutation population, and added to the new population; the new population is taken as the initial population of the next round, and the self-crossing operator and the mutation operator are executed in turn to iteratively generate the new population of the next round until the iteration number reaches the maximum iteration number in the initial parameters, the iteration is completed, and the optimal individual is output; and the optimal individual is decoded to obtain the path planning scheme of the mobile energy storage vehicle. The application is more suitable for the path planning problem of the mobile energy storage vehicle for unmanned equipment. The operators designed in the algorithm are more suitable for the characteristics of the problem: open, multiple charging centers, time window constraints and consideration of material loss. The crossover operator is different from the individual-to-individual crossover in the traditional genetic algorithm, and the gene segment-to-gene segment crossover is selected, which is more suitable for the structure of the problem solution, and this makes the evolution of the population more efficient. The application effectively improves the planning efficiency and rationality of the path, improves the energy supplement efficiency, and provides support for the comprehensive use of unmanned equipment in the future.

[0107] It should be noted that the method of the embodiment of the present disclosure can be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scenario, and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiment of the present disclosure, and the multiple devices can interact with each other to complete the method.

[0108] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] Example 2

[0110] See also Figure 3 Embodiment 2 of the present invention further provides a path planning device for a mobile energy storage vehicle for unmanned equipment, comprising:

[0111] A two-stage mathematical programming model construction module 001 is used to construct the path planning problem of the mobile energy storage vehicle into a two-stage mathematical programming model;

[0112] Constraint setting module 002, used to set first stage constraint conditions and second stage constraint conditions according to the decision variables of the first stage and the second stage respectively;

[0113] The two-stage mathematical programming model solving module 003 is used to solve the two-stage mathematical programming model through an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle.

[0114] In this embodiment, in the two-stage mathematical programming model construction module 001, the objective function expression of the two-stage mathematical programming model is:

[0115]

[0116] Where K is the number of mobile energy storage vehicles; n k The number of tasks that need to be performed by the k-th mobile energy storage vehicle; The time it takes for the kth mobile energy storage vehicle to complete the i-th task; The i-th task completed for the k-th mobile energy storage vehicle; For the The lower edge of the time window for each task.

[0117] In this embodiment, in the constraint condition setting module 002, the first stage constraint conditions are:

[0118]

[0119]

[0120] Where x ijk and y jk is a 0-1 decision variable; xijk The kth mobile energy storage vehicle performs the jth task after completing the ith task; y jk The jth task is performed by the kth mobile energy storage vehicle;

[0121] The constraints of the second stage are:

[0122]

[0123]

[0124] Where, E i is the energy required for the i-th task; l i is the upper edge of the time window of the i-th task; is the initial position of the kth mobile energy storage vehicle; p k is the charging center where the kth mobile energy storage vehicle is initially located; ρ is the amount of electricity consumed per unit distance by the mobile energy storage vehicle; v is the speed of the mobile energy storage vehicle; M is the maximum capacity of the mobile energy storage vehicle; P is the discharge power of the mobile energy storage vehicle; Q is the charging power of the mobile energy storage vehicle; d ij is the distance between the i-th node and the j-th node; b i is the number of the charging center closest to the i-th task node; and are the time and energy when the kth mobile energy storage vehicle enters its i-th task node; and are the time and energy when the kth mobile energy storage vehicle leaves its i-th task node; is the waiting time for the kth mobile energy storage vehicle to enter its i-th task; The number of times the k-th mobile energy storage vehicle needs to return to the charging center when performing its i-th mission.

[0125] In this embodiment, in the two-stage mathematical programming model solving module 003, the submodules for solving the two-stage mathematical programming model using the evolutionary algorithm include:

[0126] The population initialization submodule 031 is used to initialize the chromosome population through chromosome coding to obtain an initial population; and to initially set the parameters of the evolutionary algorithm to obtain initial parameters;

[0127] The initial fitness calculation submodule 032 is used to calculate the fitness of the initial population by improving the 2-opt operator to obtain the initial fitness;

[0128] A crossover population generation submodule 033 is configured to execute a self-crossover operator starting from the first chromosome in the initial population to generate a crossover population;

[0129] a variation population generating submodule 034, configured to execute a variation operator to generate a variation population from a first chromosome in the initial population;

[0130] an elite individual selecting submodule 035, configured to select elite individuals from the initial population, the crossover population and the variation population according to an elite population proportion in the initial parameters, and add the elite individuals to a new population;

[0131] a new population supplementing submodule 036, configured to randomly select a plurality of individuals from the initial population, the crossover population and the variation population, and add the plurality of individuals to the new population;

[0132] a new population iterative generating submodule 037, configured to take the new population as an initial population in a next round, execute the self-crossing operator and the variation operator in sequence, iteratively generate a new population in the next round, and output an optimal individual until an iteration number reaches a maximum iteration number in the initial parameters, complete the iteration, and output the optimal individual;

[0133] a path planning scheme obtaining submodule 038, configured to decode the optimal individual to obtain a path planning scheme of the mobile energy storage vehicle.

[0134] In the initial fitness calculating submodule 032 of the two-stage mathematical programming model solving module 003, in the process of calculating the fitness of the initial population by using the improved 2-opt operator, the chromosome is decoded into tasks to be performed by each energy storage vehicle; the optimal task execution order of each mobile energy storage vehicle and the corresponding fitness are obtained by using the improved 2-opt operator; and the fitness of each mobile energy storage vehicle is summed to obtain the overall fitness of the chromosome.

[0135] It should be explained that the information interaction and execution process between the modules of the system are based on the same concept as the method embodiment in Embodiment 1 of the application, and the technical effects brought by the method embodiment are the same as those of the method embodiment, and the specific content can be referred to the description of the method embodiment in the foregoing method embodiment of the application, which will not be repeated here.

[0136] Embodiment 3

[0137] Embodiment 3 of the application provides a non-transitory computer readable storage medium, and the computer readable storage medium stores a program code of a mobile energy storage vehicle path planning method for unmanned equipment, and the program code includes instructions for executing the mobile energy storage vehicle path planning method for unmanned equipment in Embodiment 1 or any possible implementation manner thereof.

[0138] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0139] Example 4

[0140] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0141] The processor and the memory communicate with each other through a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a mobile energy storage vehicle path planning method for unmanned equipment according to embodiment 1 or any possible implementation thereof.

[0142] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0143] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0144] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0145] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A path planning method for a mobile energy storage vehicle for unmanned equipment, characterized in that: include: The path planning problem of the mobile energy storage vehicle is constructed as a two-stage mathematical programming model; According to the decision variables of the first and second stages, set the first stage constraints and the second stage constraints respectively; Solving the two-stage mathematical programming model through an evolutionary algorithm to obtain a path planning for the mobile energy storage vehicle; The objective function expression of the two-stage mathematical programming model is: Where K is the number of mobile energy storage vehicles; n k The number of tasks that need to be performed by the k-th mobile energy storage vehicle; The time it takes for the kth mobile energy storage vehicle to complete the i-th task; The i-th task completed for the k-th mobile energy storage vehicle; For the The lower edge of the time window for each task; The first stage constraints are: Where x ijk and y jk is a 0-1 decision variable; x ijk The kth mobile energy storage vehicle performs the jth task after completing the ith task; y jk The jth task is performed by the kth mobile energy storage vehicle; The constraints of the second stage are: Where, E i is the energy required for the i-th task; l i is the upper edge of the time window of the i-th task; is the initial position of the kth mobile energy storage vehicle; p k is the charging center where the kth mobile energy storage vehicle is initially located; ρ is the amount of electricity consumed per unit distance by the mobile energy storage vehicle; v is the speed of the mobile energy storage vehicle; M is the maximum capacity of the mobile energy storage vehicle; P is the discharge power of the mobile energy storage vehicle; Q is the charging power of the mobile energy storage vehicle; d ij is the distance between the i-th node and the j-th node; b i is the number of the charging center closest to the i-th task node; and are the time and energy when the kth mobile energy storage vehicle enters its i-th task node; and are the time and energy when the kth mobile energy storage vehicle leaves its i-th task node; is the waiting time for the kth mobile energy storage vehicle to enter its i-th task; The number of times the k-th mobile energy storage vehicle needs to return to the charging center when performing its i-th mission.

2. A method for path planning of a mobile energy storage vehicle for unmanned equipment according to claim 1, characterized in that: The steps of solving the two-stage mathematical programming model by the evolutionary algorithm are as follows: Initializing the chromosome population through chromosome coding to obtain an initial population; initially setting the parameters of the evolutionary algorithm to obtain initial parameters; Calculating the fitness of the initial population by improving the 2-opt operator to obtain the initial fitness; Starting from the first chromosome in the initial population, executing a self-crossover operator to generate a crossover population; Starting from the first chromosome in the initial population, executing the mutation operator to generate a mutant population; Select elite individuals from the initial population, the cross population, and the variant population according to the elite population ratio in the initial parameters, and add them to the new population; Randomly select a number of individuals from the initial population, the cross population and the variant population, and add them to the new population; The new population is used as the initial population for the next round, and the self-crossover operator and the mutation operator are executed in sequence to iteratively generate the new population for the next round until the number of iterations reaches the maximum number of iterations in the initial parameters, and the iteration is completed, and the optimal individual is output; The optimal individual is decoded to obtain a path planning solution for the mobile energy storage vehicle.

3. A method for path planning of a mobile energy storage vehicle for unmanned equipment according to claim 2, characterized in that: In the process of calculating the fitness of the initial population through the improved 2-opt operator, the chromosome is decoded into the tasks that each energy storage vehicle needs to perform; the optimal task execution order and the corresponding fitness of each mobile energy storage vehicle are calculated by the improved 2-opt operator; the fitness of each mobile energy storage vehicle is summed to obtain the overall fitness of the chromosome.

4. A mobile energy storage vehicle path planning device for unmanned equipment, adopting a mobile energy storage vehicle path planning method for unmanned equipment according to any one of claims 1 to 3, characterized in that: include: A two-stage mathematical programming model construction module is used to construct the path planning problem of the mobile energy storage vehicle into a two-stage mathematical programming model; A constraint condition setting module, used to set the first stage constraint conditions and the second stage constraint conditions according to the decision variables of the first stage and the second stage respectively; The two-stage mathematical programming model solving module is used to solve the two-stage mathematical programming model through an evolutionary algorithm to obtain the path planning of the mobile energy storage vehicle.

5. A mobile energy storage vehicle path planning device for unmanned equipment according to claim 4, characterized in that: In the two-stage mathematical programming model construction module, the objective function expression of the two-stage mathematical programming model is: Where K is the number of mobile energy storage vehicles; n k The number of tasks that need to be performed by the k-th mobile energy storage vehicle; The time it takes for the kth mobile energy storage vehicle to complete the i-th task; The i-th task completed for the k-th mobile energy storage vehicle; For the The lower edge of the time window for each task.

6. A mobile energy storage vehicle path planning device for unmanned equipment according to claim 5, characterized in that: In the constraint setting module, the first stage constraint is: Where x ijk and y jk is a 0-1 decision variable; x ijk The kth mobile energy storage vehicle performs the jth task after completing the ith task; y jk The jth task is performed by the kth mobile energy storage vehicle; The constraints of the second stage are: Where, E i is the energy required for the i-th task; l i is the upper edge of the time window of the i-th task; is the initial location of the kth mobile energy storage vehicle; p k is the charging center where the kth mobile energy storage vehicle is initially located; ρ is the amount of electricity consumed per unit distance by the mobile energy storage vehicle; v is the speed of the mobile energy storage vehicle; M is the maximum capacity of the mobile energy storage vehicle; P is the discharge power of the mobile energy storage vehicle; Q is the charging power of the mobile energy storage vehicle; d ij is the distance between the i-th node and the j-th node; b i is the number of the charging center closest to the i-th task node; and are the time and energy when the kth mobile energy storage vehicle enters its i-th task node; and are the time and energy when the kth mobile energy storage vehicle leaves its i-th task node; is the waiting time for the kth mobile energy storage vehicle to enter its i-th task; The number of times the k-th mobile energy storage vehicle needs to return to the charging center when performing its i-th mission.

7. A mobile energy storage vehicle path planning device for unmanned equipment according to claim 6, characterized in that: In the two-stage mathematical programming model solving module, the submodule for solving the two-stage mathematical programming model by the evolutionary algorithm includes: The population initialization submodule is used to initialize the chromosome population through chromosome coding to obtain an initial population; and to initially set the parameters of the evolutionary algorithm to obtain initial parameters; An initial fitness calculation submodule is used to calculate the fitness of the initial population by improving the 2-opt operator to obtain the initial fitness; A crossover population generation submodule, configured to execute a self-crossover operator starting from the first chromosome in the initial population to generate a crossover population; A mutant population generation submodule, configured to execute a mutation operator starting from the first chromosome in the initial population to generate a mutant population; An elite individual selection submodule is used to select elite individuals from the initial population, the cross population and the variant population according to the elite population ratio in the initial parameters and add them to the new population; A new population supplement submodule is used to randomly select a number of individuals from the initial population, the cross population and the variant population, and add them to the new population; A new population iterative generation submodule is used to use the new population as the initial population for the next round, execute the self-crossover operator and the mutation operator in sequence, iteratively generate the next round of new population, until the number of iterations reaches the maximum number of iterations in the initial parameters, complete the iteration, and output the optimal individual; The path planning solution acquisition submodule is used to decode the optimal individual and obtain the path planning solution of the mobile energy storage vehicle.

8. A path planning device for a mobile energy storage vehicle for unmanned equipment according to claim 7, characterized in that: In the initial fitness calculation submodule of the two-stage mathematical programming model solving module, in the process of calculating the fitness of the initial population through the improved 2-opt operator, the chromosome is decoded into the tasks that each energy storage vehicle needs to perform; the optimal task execution order and the corresponding fitness of each mobile energy storage vehicle are calculated by the improved 2-opt operator; and the fitness of each mobile energy storage vehicle is summed to obtain the overall fitness of the chromosome.

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