Multi-area photovoltaic inspection path planning method and system

Abstract

The invention discloses a multi-region photovoltaic inspection path planning method and system, and the method comprises the steps: building an inspection region model, and setting the coordinates of all inspection points; setting initialization parameters of the genetic algorithm and the improved badger algorithm; solving an initial population by using a genetic algorithm; optimizing individuals in the population by using an improved badger algorithm; optimizing the individual bringing-back genetic algorithm to carry out crossover variation, sorting individuals before and after variation crossover, and taking the last 50% as a new round optimization object; iterations are repeated until the maximum number of iterations K is reached, and the solution with the lowest fitness is output as the optimal patrol path. According to the method, the problem that an existing algorithm is difficult to solve the optimal path planning of multiple regions and multiple paths is solved, the problems that a traditional path planning algorithm is low in iteration convergence speed, low in solving precision and insufficient in optimization stability are solved, the paths can be globally optimized, and the routing inspection paths with higher routing inspection efficiency are obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic inspection, and in particular relates to a multi-region photovoltaic inspection path planning method and system. Background Art

[0002] With the vigorous development of the photovoltaic industry, household rooftop micro-photovoltaics have become the main force of clean energy in rural areas. The healthy operation of photovoltaic power stations is inseparable from regular inspection and maintenance, which is the premise of efficient power generation of photovoltaic systems. However, due to the complex road conditions in rural areas, the scattered and long distances between photovoltaic power stations, it is time-consuming and laborious to find the optimal inspection path for multiple destinations during inspections. At the same time, the traditional manual inspection planning path is often to find the best path for a single person, resulting in low inspection efficiency. In addition, traditional genetic algorithms often have the disadvantages of slow convergence speed and easy to fall into local optimal solutions when seeking the optimal path. Summary of the invention

[0003] The present invention provides a multi-region photovoltaic inspection path planning method and system, which are used to solve the technical problems that the existing path planning algorithm has a slow convergence speed and is easy to fall into a local optimal solution, and the existing algorithm rarely obtains the best path in multiple regions, resulting in low inspection efficiency.

[0004] In a first aspect, the present invention provides a multi-region photovoltaic inspection path planning method, which is used for inspection rules in which multiple groups of inspection personnel start from the same location for inspection and finally return to the starting point, wherein each inspection point is inspected only once, and the inspection points of each inspection group are not repeated, and the method comprises: Step 1: Establish an inspection area model and set the coordinates of each inspection point; Step 2, setting the initialization parameters of the genetic algorithm and the improved honey badger algorithm; Step 3: Use genetic algorithm to obtain the initial population; Step 4: Use the improved honey badger algorithm to optimize the individuals in the population; Step 5: Bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the crossover, and take the last 50% as the new round of optimization objects; Step 6: Repeat steps 4 and 5 until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0005] In a second aspect, the present invention provides a multi-region photovoltaic inspection path planning system, which is used for inspection rules in which multiple groups of inspection personnel start from the same location for inspection and finally return to the starting point, wherein each inspection point is inspected only once, and the inspection points of each inspection group are not repeated, and the system includes: A construction module is configured to establish an inspection area model and set the coordinates of each inspection point; A setting module, configured to set initialization parameters of the genetic algorithm and the improved honey badger algorithm; A computing module configured to obtain an initial population using a genetic algorithm; An optimization module, configured to optimize individuals in a population using an improved honey badger algorithm; The mutation module is configured to bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the mutation crossover, and take the last 50% as the new round of optimization objects; The output module is configured to repeat iterations until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0006] In a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the multi-area photovoltaic inspection path planning method of any embodiment of the present invention.

[0007] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the multi-region photovoltaic inspection path planning method of any embodiment of the present invention.

[0008] The multi-region photovoltaic inspection path planning method and system of the present application have the following beneficial effects: Genetic algorithms are used to generate the initial running path, and the improved honey badger algorithm is used to optimize the path. Compared with traditional algorithms, it has better performance in terms of fast solving ability, solving accuracy and optimization stability; The improved honey badger algorithm has strong universality. The present invention can be combined with multiple algorithms to solve different types of problems by discretizing the position of the honey badger in the honey badger algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flowchart of a multi-region photovoltaic inspection path planning method provided by an embodiment of the present invention; Figure 2 A specific embodiment provided for an embodiment of the present invention Schematic diagram to be obtained; Figure 3 A coordinate diagram of each inspection point of a specific embodiment provided by an embodiment of the present invention; Figure 4 A path planning diagram of a specific embodiment provided by an embodiment of the present invention; Figure 5 A structural block diagram of a multi-region photovoltaic inspection path planning system provided by an embodiment of the present invention; Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] The multi-region photovoltaic inspection path planning method of the present invention is applied to the inspection rule that multiple inspection groups start from the same location for inspection and finally return to the starting point. Each inspection point is inspected only once, and the inspection points of each inspection group are not repeated.

[0013] See also Figure 1 , which shows a flow chart of a multi-region photovoltaic inspection path planning method of the present application.

[0014] like Figure 1 As shown, the multi-region photovoltaic inspection path planning method specifically includes the following steps: Step 1: Create an inspection area model and set the coordinates of each inspection point.

[0015] In this step, the rainfall of the region in the past 24 hours is retrieved and the region is divided into zones according to the rainfall. The specific zone rules are as follows: , In the formula, is the rainfall; Assume that the maximum rainfall in the region is , calculate the inspection points The road condition coefficient of the area is expressed as: , In the formula, For inspection point The road condition coefficient of the area, For inspection point The amount of rainfall in the area; The inspection area model is constructed according to the road condition coefficient and weather conditions of the area, and the coordinates of each inspection point are set.

[0016] Step 2: Set the initialization parameters of the genetic algorithm and the improved honey badger algorithm.

[0017] The initialization parameters of the genetic algorithm and the improved honey badger algorithm include the initial number of iterations , maximum number of iterations , Honey Badger's ability to obtain food , the number of individuals in the population , Number of inspection groups , Total number of inspection points , crossover probability And the probability of mutation .

[0018] Step 3: Use genetic algorithm to obtain the initial population.

[0019] In this step, assuming that the inspection starts from inspection point 1, the length of each individual in the population is The generated sequence is then partitioned in order, with each region corresponding to each inspection group. In order to balance the inspection tasks of each inspection group, each region contains inspection points. When q is not an integer, set the inspection point of the last area to , where the numbers and the order of the numbers in the area correspond to the inspection points and inspection sequence of the inspection team, where is the rounding function; Sort the individuals in the population according to their fitness, and the expression for calculating fitness is: , , , , , , , , In the formula, is the sum of the fitness of each inspection group, is the route fitness of inspection group a, is the fitness from inspection point i to inspection point j, is the path cost from inspection point i to inspection point j, is the smoothness of the path, such as Figure 2 As shown, , are constants, , They are The proportionality coefficient and The proportionality coefficient, is the distance weight between inspection point i and inspection point j, is the shortest distance between inspection point i and inspection point j, , , They are the coordinates of the previous inspection point, the current inspection point, and the next inspection point; The individuals are screened according to the preset proportion based on fitness using the preset screening rules. The screening rules are as follows: individuals with fitness in the top 50% have a 30% probability of being selected, and individuals with fitness in the bottom 50% have a 70% probability of being selected. Select among individuals Individuals are used as objects of crossover variation; The screened individuals are crossovered and mutated to generate new individuals. The crossover and mutation rules are: The Cycle Crossover (CX) operator is used for crossover operations, and a mixed combination of flipping, shuffling, and swapping is used for mutation operations; The generated new individuals and the screened individuals are sorted again by fitness, and the individuals are screened again according to the screening rules. individuals are taken as individuals to be optimized.

[0020] Step 4: Use the improved honey badger algorithm to optimize the individuals in the population.

[0021] In this step, the position update mechanism is used to optimize the individuals in the population. The position update mechanism includes: In mining mode, the honey badger position update rule is: , , , In honey-hunting mode, the honey badger's position update rule is: , The above position update rules are discretized as follows: , , In the formula, is the distance from the prey to the origin, is the distance between the honey badger’s current position and its prey, represents a set of random exchange factors, For the new location, For the prey location, is the density factor, Take 2 as a constant, A marker that can change the search direction and help escape from the local optimum. The ability to obtain food for honey badgers, For attraction, , , , , , They are The random number in .

[0022] The random number sequence of individuals in the genetic algorithm represents the corresponding solution and the position of the honey badger in the honey badger algorithm. The distance between honey badgers is defined as a series of continuous transformation operations, in which the internal points that need to be passed in the solution are transformed from one position in the corresponding solution to another position. Suppose there are two honey badgers, and their positions are: , , The distance between the two honey badgers is: , in, It is called the exchange operator, which means that the point Move to the point Locations that are close in distance.

[0023] The relationship between position and distance is defined as , means applying some commutative operator of the latter to the former.

[0024] The product of the constant coefficient and the distance is expressed as: , Among them, A represents probability, represents a swap operator that randomly draws a portion of the latter with a certain probability.

[0025] Step 5: Bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the crossover, and take the last 50% as the objects for the new round of optimization.

[0026] Step 6: Repeat steps 4 and 5 until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0027] In summary, the method of the present application establishes an inspection area model, sets the coordinates of each inspection point; sets the initialization parameters of the genetic algorithm and the improved honey badger algorithm; uses the genetic algorithm to obtain the initial population; uses the improved honey badger algorithm to optimize the individuals in the population; brings the optimized individuals back to the genetic algorithm for crossover mutation, sorts the individuals before and after the mutation crossover, and takes the last 50% as the new round of optimization objects; repeats the iteration until the maximum number of iterations K is reached, and outputs the solution with the lowest fitness as the optimal inspection path. The present invention solves the problem that the existing algorithms are difficult to solve for the optimal path planning of multiple regions and multiple paths, as well as the problems of slow iterative convergence speed, low solution accuracy and insufficient optimization stability of traditional path planning algorithms. The present invention can globally optimize the path to obtain an inspection path with higher inspection efficiency.

[0028] In a specific embodiment, in order to further verify the effectiveness of the proposed method, the present invention specifically illustrates the method through a calculation example of 25 inspection points and 3 inspection teams. The position coordinates of each inspection point are shown in Table 1. Matlab numerical simulation is used to verify the proposed mathematical model. The specific implementation steps are as follows: Table 1 Location coordinates of each inspection point Serial number X coordinate Y coordinate 1 184 124 2 126 150 3 97 134 4 115 176 5 83 177 6 149 163 7 128 120 8 165 65 9 128 49 10 104 95 11 125 90 12 75 110 13 71 131 14 84 55 15 63 166 16 75 203 17 79 226 18 103 207 19 117 230 20 46 86 21 59 139 22 51 70 23 49 50 24 23 59 25 52 210 , Step 1: Establishing the patrol area model includes constructing the road conditions and weather conditions of the area, and setting the coordinates of each patrol point. By retrieving the rainfall in the area over the past 24 hours, the area is divided according to the rainfall.

[0029] Step 2: Set the initialization parameters including the initial number of iterations , maximum number of iterations , the honey badger's ability to obtain food , the number of individuals in the population , number of inspection groups , total number of inspection points , crossover probability , mutation probability .

[0030] Step 3: Use genetic algorithm to obtain the initial population. The specific steps are as follows: Assuming that the inspection starts from inspection point 1, the generation length for each individual in the population is (2 to ), and then the generated sequence is partitioned in order. Each area corresponds to each inspection group. In order to balance the inspection task of each inspection group, each area contains When q is not an integer, set the inspection point of the last area to , the numbers and the order of the numbers in the area correspond to the inspection points and inspection sequence of the inspection group. is the rounding function.

[0031] Sort the individuals in the population according to their fitness; Individuals are selected according to a certain ratio based on fitness; Perform crossover and mutation on the screened individuals to generate new individuals; The generated new individuals and the screened individuals are sorted again by fitness, and the individuals are screened according to the rules of step 3. individuals are taken as individuals to be optimized.

[0032] Step 4: Use the improved honey badger algorithm to optimize the individuals in the population.

[0033] Step 5: Bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the crossover, and take the last 50% as the objects for the new round of optimization.

[0034] Step 6: Repeat steps 4 and 5 until the maximum number of iterations K=100 is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0035] The embodiment is solved using Matlab to obtain the solution: X={6,19,17,25,16,18,4,2,7,3,5,15,21,13,12,10,11,20,24,23,22,14,9,8}.

[0036] Therefore, the inspection route is: Inspection route 1: 1-6-19-17-25-16-18-4-2-1; Inspection route 2: 1-7-3-5-15-21-13-12-10-1; Inspection route 3: 1-11-20-24-23-22-14-9-8-1; Inspection point coordinate diagram Figure 3 As shown in the figure, the path planning diagram is as follows Figure 4 shown.

[0037] See also Figure 5 , which shows a structural block diagram of a multi-region photovoltaic inspection path planning system of the present application.

[0038] like Figure 5 As shown, the multi-region photovoltaic inspection path planning system 200 includes a construction module 210, a setting module 220, a calculation module 230, an optimization module 240, a variation module 250 and an output module 260.

[0039] Among them, the construction module 210 is configured to establish an inspection area model and set the coordinates of each inspection point; the setting module 220 is configured to set the initialization parameters of the genetic algorithm and the improved honey badger algorithm; the calculation module 230 is configured to use the genetic algorithm to obtain the initial population; the optimization module 240 is configured to use the improved honey badger algorithm to optimize the individuals in the population; the mutation module 250 is configured to bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the mutation crossover, and take the last 50% as the new round of optimization objects; the output module 260 is configured to repeat the iteration until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0040] It should be understood that Figure 5 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 5 The modules in it will not be described in detail here.

[0041] In some other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the multi-region photovoltaic inspection path planning method in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: Step 1: Establish an inspection area model and set the coordinates of each inspection point; Step 2, setting the initialization parameters of the genetic algorithm and the improved honey badger algorithm; Step 3: Use genetic algorithm to obtain the initial population; Step 4: Use the improved honey badger algorithm to optimize the individuals in the population; Step 5: Bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the crossover, and take the last 50% as the new round of optimization objects; Step 6: Repeat steps 4 and 5 until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0042] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the multi-region photovoltaic inspection path planning system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the multi-region photovoltaic inspection path planning system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0043] Figure 6 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 6 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 6 In the example, the bus connection is used. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the multi-region photovoltaic inspection path planning method of the above-mentioned method embodiment is implemented. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the multi-region photovoltaic inspection path planning system. The output device 340 may include a display device such as a display screen.

[0044] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0045] As an implementation mode, the electronic device is applied to a multi-region photovoltaic inspection path planning system, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: Step 1: Establish an inspection area model and set the coordinates of each inspection point; Step 2, setting the initialization parameters of the genetic algorithm and the improved honey badger algorithm; Step 3: Use genetic algorithm to obtain the initial population; Step 4: Use the improved honey badger algorithm to optimize the individuals in the population; Step 5: Bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the crossover, and take the last 50% as the new round of optimization objects; Step 6: Repeat steps 4 and 5 until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

[0046] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-region photovoltaic inspection path planning method, characterized in that: An inspection rule for multiple inspection groups starting from the same location for inspection and finally returning to the starting point, wherein each inspection point is inspected only once and the inspection points of each inspection group are not repeated, the method includes: Step 1: Establish an inspection area model and set the coordinates of each inspection point; Step 2, setting the initialization parameters of the genetic algorithm and the improved honey badger algorithm; Step 3: Use genetic algorithm to obtain the initial population; Step 4: Use the improved honey badger algorithm to optimize the individuals in the population; Step 5: Bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the crossover, and take the last 50% as the new round of optimization objects; Step 6: Repeat steps 4 and 5 until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

2. A multi-region photovoltaic inspection path planning method according to claim 1, characterized in that: In step 1, the establishment of the inspection area model and setting of the coordinates of each inspection point include: Retrieve the past 24-hour rainfall in the region and divide the region into zones according to the rainfall. The specific zoning rules are as follows: ， In the formula, is the rainfall; Assume that the maximum rainfall in the region is , calculate the inspection points The road condition coefficient of the area is expressed as: ， In the formula, For inspection point The road condition coefficient of the area, For inspection point The amount of rainfall in the area; The inspection area model is constructed according to the road condition coefficient and weather conditions of the area, and the coordinates of each inspection point are set.

3. A multi-region photovoltaic inspection path planning method according to claim 1, characterized in that: In step 2, the initialization parameters of the genetic algorithm and the improved honey badger algorithm include the initial number of iterations , maximum number of iterations , Honey Badger's ability to obtain food , the number of individuals in the population , Number of inspection groups , Total number of inspection points , crossover probability And the probability of mutation .

4. A multi-region photovoltaic inspection path planning method according to claim 3, characterized in that: In step 3, the method of obtaining an initial population using a genetic algorithm includes: Assuming that the inspection starts from inspection point 1, the generation length for each individual in the population is The generated sequence is then partitioned in order, with each region corresponding to each inspection group. In order to balance the inspection tasks of each inspection group, each region contains inspection points. When q is not an integer, set the inspection point of the last area to , where the numbers and the order of the numbers in the area correspond to the inspection points and inspection sequence of the inspection team, where is the rounding function; Sort the individuals in the population according to their fitness, and the expression for calculating fitness is: ， ， ， ， ， ， ， ， In the formula, is the sum of the fitness of each inspection group, is the route fitness of inspection group a, is the fitness from inspection point i to inspection point j, is the path cost from inspection point i to inspection point j, is the smoothness of the path, , are constants, , They are The proportionality coefficient and The proportionality coefficient, is the distance weight between inspection point i and inspection point j, is the shortest distance between inspection point i and inspection point j, , , They are the coordinates of the previous inspection point, the current inspection point, and the next inspection point; The individuals are screened according to the preset proportion based on fitness using the preset screening rules. The screening rules are as follows: individuals with fitness in the top 50% have a 30% probability of being selected, and individuals with fitness in the bottom 50% have a 70% probability of being selected. Select among individuals Individuals are used as objects of crossover variation; Perform crossover and mutation on the screened individuals to generate new individuals; The generated new individuals and the screened individuals are sorted again by fitness, and the individuals are screened again according to the screening rules. individuals are taken as individuals to be optimized.

5. A multi-region photovoltaic inspection path planning method according to claim 1, characterized in that: In step 4, the optimization of individuals in the population using the improved honey badger algorithm includes: The position update mechanism is used to optimize individuals in the population. The position update mechanism includes: In mining mode, the honey badger position update rule is: ， ， ， In the formula, is the initial iteration number, is the maximum number of iterations; In honey-hunting mode, the honey badger's position update rule is: ， In the formula, is the distance from the prey to the origin, is the distance between the honey badger’s current position and its prey, represents a set of random exchange factors, For the new location, For the prey location, is the density factor, Take 2 as a constant, A marker that can change the search direction and help escape from the local optimum. The ability to obtain food for honey badgers, For attraction, , , , , , They are The random number in .

6. A multi-region photovoltaic inspection path planning system, characterized in that: Inspection rules for multiple groups of inspection personnel starting from the same location for inspection and finally returning to the starting point, wherein each inspection point is inspected only once and the inspection points of each inspection group are not repeated, the system includes: A construction module is configured to establish an inspection area model and set the coordinates of each inspection point; A setting module, configured to set initialization parameters of the genetic algorithm and the improved honey badger algorithm; A computing module configured to obtain an initial population using a genetic algorithm; An optimization module, configured to optimize individuals in a population using an improved honey badger algorithm; The mutation module is configured to bring the optimized individuals back to the genetic algorithm for crossover mutation, sort the individuals before and after the mutation crossover, and take the last 50% as the new round of optimization objects; The output module is configured to repeat iterations until the maximum number of iterations K is reached, and then output the solution with the lowest fitness as the optimal patrol path.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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