Unmanned aerial vehicle inspection sequence planning method and system for disaster situation data acquisition and return

Abstract

The invention discloses an unmanned aerial vehicle routing inspection sequence planning method and system for disaster situation data acquisition and return. The method comprises the steps of establishing an unmanned aerial vehicle routing inspection sequence planning task scene for unmanned aerial vehicle disaster situation data acquisition and return; in the task scene, based on a multi-path traveling salesman problem, establishing a post-disaster data acquisition and return task system model; and solving the post-disaster data acquisition and return task system model by adopting a multi-strategy improved grey wolf optimization algorithm to obtain an unmanned aerial vehicle inspection sequence scheme, and the method and the system can realize unmanned aerial vehicle inspection sequence planning.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) path planning, and relates to a UAV inspection sequence planning method and system for disaster data collection and return. Background Art

[0002] Drones, with their exceptional maneuverability and efficient data transmission capabilities, have become an indispensable tool for post-disaster inspections. Their robust terrain adaptability enables them to conduct inspections in complex environments, collecting and transmitting critical disaster information. However, the complexity of disaster sites presents multiple challenges for drone operations. First, damaged communication infrastructure leads to signal interruptions in some areas, severely impacting the reliability of data transmission. Second, the post-disaster environment results in an irregular distribution of patrol points. This, coupled with the uneven distribution of damaged communication base stations, reduces the overall coverage of the communication system, severely impacting drone inspection efficiency and the timeliness of data transmission. Although the inspection sequence optimization problem is formally similar to the classic traveling salesman problem, its application to this task scenario has certain limitations. The Grey Wolf Optimizer (GWO), an efficient metaheuristic algorithm proposed by Mirjalili et al. in 2014, implements an optimization search by simulating the hunting behavior of gray wolf packs. However, when dealing with such complex optimization problems, the traditional GWO algorithm is prone to problems such as a decrease in population diversity and becoming trapped in local optima in the later stages of iteration. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method and system for drone inspection sequence planning for disaster data collection and return, which can realize drone inspection sequence planning.

[0004] To achieve the above objectives, the present invention discloses a method for planning the inspection sequence of drones for disaster data collection and return, comprising:

[0005] Establish a mission scenario for drone inspection sequence planning for drone disaster data collection and return;

[0006] In the described mission scenario, a post-disaster data collection and return mission system model is established based on the multi-path traveling salesman problem.

[0007] The multi-strategy improved grey wolf optimization algorithm is used to solve the post-disaster data collection and return task system model and obtain the drone inspection sequence plan.

[0008] The further improvement of the drone inspection sequence planning method for disaster data collection and return transmission described in the present invention is:

[0009] Furthermore, the process of establishing the task scenario of drone inspection sequence planning for drone disaster data collection and return is as follows:

[0010] 1a) Assume the size of the flight space

[0011] 1b) The communication area is abstracted as a communication point. Each communication point is equipped with equipment for providing data backhaul services. The communication point set R = {r1, r2, ... r n ,...r N}, r n Indicates the nth communication point, the position coordinates of the nth communication point are (x n ,y n ), N represents the number of communication points;

[0012] 1c) The inspection area is abstracted into a patrol point, and the patrol point set C = {c1, c2, ..., c m ,...,c M}, c m Indicates the mth cruise point, the position coordinates of the mth cruise point are (x m ,y m ), M represents the number of cruise points;

[0013] 1d) There is a drone in the scene. Assume that the drone's speed V is constant, the drone's flight altitude H is constant, and the drone's starting point q is constant. start =(x s ,y s ), the inspection path is represented by an ordered sequence of K path points, and the path set is represented as P = {p1, p2, ..., p k ...,p K};

[0014] 1f) The drone starts from the starting point q start Departure, patrol the cruise point with index m∈{1,2,...,M} and collect data. During the patrol, the drone selects the communication point with index n∈{1,2,...,N} to transmit data back. After the patrol, it returns to the starting point q start .

[0015] Furthermore, the objective function of the post-disaster data collection and return task system model is:

[0016]

[0017] Among them, f revebue represents the return mission benefit of the drone, f cost represents the cost of drone flight time,

[0018] Furthermore, the constraints of the post-disaster data collection and return task system model are:

[0019]

[0020] Among them, constraints C1 and C2 indicate that each cruise point can only be visited once, and only one of the two flight modes is used between the two mission points; constraint C3 is used to ensure that there is no loop in the complete path of the drone; constraint C4 indicates that all cruise points in the scene are inspected; constraint C5 indicates that and It is a 0, 1 variable, and the edge access has only two states: 0 and 1.

[0021] Furthermore, the initial population of the multi-strategy improved grey wolf optimization algorithm is improved based on the Piecewise chaotic mapping.

[0022] Furthermore, the expression of the piecewise chaotic map is:

[0023]

[0024] Among them, x t+1 The value generated for the next iteration, x i is the current iteration value, β represents the control parameter, β∈[0,1].

[0025] The present invention discloses a UAV inspection sequence planning system for disaster data collection and return, comprising:

[0026] The first module is used to establish a task scenario for drone disaster data collection and return drone inspection sequence planning;

[0027] The second establishment module is used to establish a post-disaster data collection and return task system model based on the multi-path traveling salesman problem in the task scenario;

[0028] The solution module is used to adopt a multi-strategy improved grey wolf optimization algorithm to solve the post-disaster data collection and return task system model and obtain a drone inspection sequence plan.

[0029] The further improvement of the drone inspection sequence planning system for disaster data collection and return transmission described in the present invention is:

[0030] Furthermore, the objective function of the post-disaster data collection and return task system model is:

[0031]

[0032] Among them, f revenueIndicates the return mission benefit of the drone, f cost represents the cost of drone flight time,

[0033] The constraints of the post-disaster data collection and return task system model are:

[0034]

[0035] Among them, constraints C1 and C2 indicate that each cruise point can only be visited once, and only one of the two flight modes is used between the two mission points; constraint C3 is used to ensure that there is no loop in the complete path of the drone; constraint C4 indicates that all cruise points in the scene are inspected; constraint C5 indicates that and It is a 0, 1 variable, and the edge access has only two states: 0 and 1.

[0036] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for planning the sequence of drone inspections for disaster data collection and return are implemented.

[0037] The present invention discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the drone inspection sequence planning method for disaster data collection and return are implemented.

[0038] The present invention has the following beneficial effects:

[0039] The method and system for planning the drone inspection sequence for disaster data collection and feedback described in the present invention adopt a multi-strategy improved gray wolf optimization algorithm during specific operation to solve the post-disaster data collection and feedback task system model and obtain a drone inspection sequence plan. The multi-strategy improved gray wolf optimization algorithm improves the optimization ability and the balance between global search ability and local search efficiency, thereby solving the problem of multi-task inspection sequence planning for data collection and feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 It is the algorithm flow chart of the present invention;

[0042] Figure 2 It is a task scenario diagram of the present invention;

[0043] Figure 3 Mapping histogram for the initial population in the present invention;

[0044] Figure 4 It is a graph of the convergence factor iteration curve in the present invention;

[0045] Figure 5 This is a simulation diagram of the inspection scene in the present invention;

[0046] Figure 6 This is a simulation diagram of the inspection sequence planning of the UAV in the present invention;

[0047] Figure 7 This is the iterative curve diagram of the objective function of the drone inspection sequence planning in the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0050] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0052] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0053] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0054] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0055] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0056] Example 1

[0057] refer to Figure 1 The method for planning the inspection sequence of drones for disaster data collection and return transmission according to the present invention comprises the following steps:

[0058] 1) Establish a mission scenario for drone inspection sequence planning for drone disaster data collection and return transmission;

[0059] 2) Based on the multi-path traveling salesman problem, a post-disaster data collection and return task system model is established;

[0060] 3) A multi-strategy improved grey wolf optimization algorithm is used to generate a drone inspection sequence plan.

[0061] according to Figure 2 The task scenario diagram shown and Figure 5 In the UAV inspection mission scenario shown in the figure, the specific operations of step 1) are as follows:

[0062] 1a) Flight space size It does not include threat sources;

[0063] 1b) The communication area is abstracted as a communication point. Each communication point is equipped with a set of equipment for providing data backhaul services. The communication point set R = {r1, r2, ... r n ,...r N}, r n Indicates the nth communication point, the position coordinates of the nth communication point are (x n ,y n ), N represents the number of communication points;

[0064] 1c) The inspection area is abstracted into a patrol point, and the patrol point set C = {c1, c2, ..., c m ,...,c M}, c m Indicates the mth cruise point, the position coordinates of the mth cruise point are (x m ,y m ), M represents the number of cruise points;

[0065] 1d) There is a single quadrotor drone in the scene. Assume that the drone's speed V is constant, the drone's flight altitude H is constant, and the drone's starting point q is constant. start =(x s ,y s ), the inspection path is represented by an ordered sequence of K path points, and the path set is represented as P = {p1, p2, ..., p k ...,p K};

[0066] 1f) The drone starts from the starting point q start Departure, patrol the cruise point with index m∈{1,2,...,M} and collect data. During the patrol, the drone selects the communication point with index n∈{1,2,...,N} to transmit data back. After the patrol, it returns to the starting point q start .

[0067] The specific operations of step 2) are:

[0068] 2a) Let c i and c j For any two cruise points, let the decision variable representing the path mode be and (1≤i,j≤M, i,j∈(0,1), where, Represents the decision variable of the cruise path, indicating that the UAV moves from c i to c j Whether to select the cruise path mode, i.e. Indicates that this section of the path adopts the cruise mode path, and the drone has visited the cruise point c i Then visit c j , represents the decision variable of the communication path, when When , it means that this section of the path adopts the communication mode, and the UAV has visited the cruise point c i Then visit the communication point first, and then visit the next cruise point c j ; It is stipulated that when a UAV flies from a cruising point to the next cruising point, it must adopt one of the two flight modes, namely: for any pair of c i and c j ,have

[0069] 2b) The objective function for optimizing the UAV flight time and return mission revenue is expressed as: Among them, f revenue Indicates the return mission benefit of the drone, f cost represents the cost of drone flight time,

[0070] 2c) To ensure that the drone can complete the inspection task as required, the following constraints are given:

[0071]

[0072] Among them, constraints C1 and C2 indicate that each cruise point can only be visited once, and only one of the two flight modes is used between the two mission points; constraint C3 is used to ensure that there is no loop in the complete path of the drone; constraint C4 indicates that all cruise points in the scene are inspected; constraint C5 indicates that and It is a 0, 1 variable, and the edge access has only two states: 0 and 1.

[0073] The specific operations of step 3) are:

[0074] 4a) Initialize the algorithm parameters based on the multi-strategy improved gray wolf optimization;

[0075] 4b) Based on the UAV’s position, combined with the flight mission and model constraints, a piecewise chaos strategy is used in the search space to improve the initial population;

[0076] 4c) Calculating the objective function value for each gray wolf individual, and determining the objective function value based on the top three gray wolf individuals in the ranking results;

[0077] 4d) calculating coefficient vectors A and C based on the nonlinear convergence factor α;

[0078] 4e) Introduce the Levy flight strategy, update the individual position, perform selection comparison, and determine the position of the optimal individual;

[0079] 4f) When the maximum number of iterations is reached, the iteration stops and the optimal inspection order of the drones is obtained.

[0080] The specific operations of step 3a) are:

[0081] In the process of initializing the algorithm parameters of the gray wolf optimization based on multi-strategy improvement, the corresponding parameters include population size N, maximum number of iterations MaxIier, inertia weight ω and dimension Dim.

[0082] The specific operations of step 3b) are:

[0083] The initial population of the improved grey wolf optimization algorithm based on Piecewise chaotic mapping, the initial population X i ={x i1 ,x i2 ,...,x ij ,...x iD}, (i = 1, 2, ..., NP; j = 1, 2, ..., Dim) are randomly generated in the solution space, where NP is the population size. The present invention introduces piecewise chaotic mapping to generate the initial population of the gray wolf optimization algorithm, which can better make the gray wolf population spread throughout the entire search space. The mathematical expression of piecewise chaotic mapping is:

[0084]

[0085] Among them, x t+1 The value generated for the next iteration, x i is the current iteration value, β represents the control parameter, β∈[0,1], reference Figure 3 The population distribution mapping histogram shown in the figure shows that the generated initialized population is more evenly distributed in the solution space.

[0086] The specific operation of step 3c) is: calculating the objective function value of each individual in the wolf pack according to the objective function, and determining the three gray wolves with the best ranking according to the objective function value as: alpha wolf, beta wolf and detla wolf.

[0087] The specific operations of step 3d) are:

[0088] When wolves hunt their prey, they will chase, approach, surround, harass, and attack. The mathematical model of the process of wolves chasing, approaching, and surrounding their prey is:

[0089] X(t+1)=X prey (t)-A·D

[0090] D=|C·X prey (t)-X(t)|

[0091] A=2a·r1-a

[0092] C=2·r2

[0093]

[0094] Among them, X and X prey Represent the position vectors of the gray wolf and prey, respectively. A and C are coefficient vectors, r1 and r2 are random vectors in the closed interval [0,1]. t and T represent the current and maximum iterations, respectively. The value of coefficient a decreases linearly from 2 to 0.

[0095] After the above process, the wolf pack will attack the prey. This process is led by the alpha wolf, beta wolf and delta wolf. The omega wolf will update its position based on the positions of the alpha wolf, beta wolf and delta wolf. The mathematical model of this process is:

[0096]

[0097] The present invention proposes a strategy to improve the nonlinear convergence factor; in the gray wolf optimization algorithm, when the parameter A<1, the gray wolf moves closer to the current optimal solution position and conducts local development; when A>1, the gray wolf moves away from the current optimal solution position and searches for prey in the entire search area. The value of A in the original algorithm changes with the change of the convergence factor a. However, in the standard gray wolf optimization algorithm, the value of a decreases linearly from 2 to 0, and its change pattern fails to fully reflect the different optimization focuses in the early and late stages of the iteration. To this end, the present invention designs an improved nonlinear convergence factor, which dynamically adjusts the value range of A through nonlinear changes to better adapt to the optimization needs of different stages. Reference Figure 4 The convergence curve is shown in the iterative graph, and the mathematical expression of the improved nonlinear factor is:

[0098]

[0099] from Figure 4 It can be seen that the improved convergence factor obviously decays more slowly with the increase of iteration number, while the convergence factor in the late iteration obviously decays more rapidly with the increase of iteration number.

[0100] The specific operations of step 3e) are:

[0101] The Lévy flight strategy is introduced to improve the search strategy of the Gray Wolf Optimization Algorithm. For the Gray Wolf Optimization Algorithm, achieving a balance between global exploration and local exploitation is key to rapid algorithm convergence and effectively avoiding local optima. The introduction of the Lévy flight strategy diversifies the population's search patterns, preventing premature convergence to local optima while improving search efficiency. Its mathematical expression is: Among them, μ obeys the normal distribution N(0,σ 2 ), v obeys the standard normal distribution N(0,1); the gray wolf algorithm is optimized using the Lévy flight strategy, and the positions of gray wolf individuals are subjected to Lévy flight mutation in the iterative search. The specific method is: after the algorithm updates all individuals, the Lévy flight strategy is used to update the individual positions once. The first step of the update process is: Where: Where: t represents the current iteration number, x t is the mutation position of the gray wolf population after the tth iteration, T represents the maximum number of iterations, x best represents the global optimal solution under the current number of iterations, Represents the corresponding point-to-point multiplication. By achieving a better balance between global exploration and local development, Levy flight effectively improves the overall performance of the swarm intelligence optimization algorithm.

[0102] The specific operation of step 3f) is: reaching the maximum number of iterations and obtaining the most effective inspection order of the drones.

[0103] Simulation experiment

[0104] The software environment for experimental verification of this invention is Matlab R2020a and WINDOWS10 operating system, and the hardware environment is AMD Ryzen7-6000, 16GB physical memory. The algorithm parameters are defined as follows: starting point (10, 10, 10), maximum number of iterations MaxIter is 500, and population size N is 100. The simulation environment is shown in the figure below. Figure 5 The inspection sequence planning diagram of the improved gray wolf optimization algorithm proposed in this invention in the simulation environment is shown in Figure 6 As shown, the objective function curve is as follows Figure 7 shown.

[0105] Example 2

[0106] The UAV inspection sequence planning system for disaster data collection and return transmission of the present invention includes:

[0107] The first module is used to establish a task scenario for drone disaster data collection and return drone inspection sequence planning;

[0108] The second establishment module is used to establish a post-disaster data collection and return task system model based on the multi-path traveling salesman problem in the task scenario;

[0109] The solution module is used to adopt a multi-strategy improved grey wolf optimization algorithm to solve the post-disaster data collection and return task system model and obtain a drone inspection sequence plan.

[0110] In this embodiment, the objective function of the post-disaster data collection and return task system model is:

[0111]

[0112] Among them, f revebue represents the return mission benefit of the drone, f cost represents the cost of drone flight time,

[0113] The constraints of the post-disaster data collection and return task system model are:

[0114]

[0115] Among them, constraints C1 and C2 indicate that each cruise point can only be visited once, and only one of the two flight modes is used between the two mission points; constraint C3 is used to ensure that there is no loop in the complete path of the drone; constraint C4 indicates that all cruise points in the scene are inspected; constraint C5 indicates that and It is a 0, 1 variable, and the edge access has only two states: 0 and 1.

[0116] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0117] Example 3

[0118] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for planning a drone inspection sequence for disaster data collection and return. For example, the method includes: establishing a task scenario for planning a drone inspection sequence for disaster data collection and return; establishing a post-disaster data collection and return task system model based on the multi-path traveling salesman problem within the task scenario; and solving the post-disaster data collection and return task system model using a multi-strategy improved grey wolf optimization algorithm to obtain a drone inspection sequence plan. The memory may include internal memory, such as a high-speed random access memory (RAM), or non-volatile memory, such as at least one disk drive. The processor, network interface, and memory are interconnected via an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnect standard bus, an extended industrial standard architecture bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, or the like. The memory is used to store programs. Specifically, the programs may include program code, which includes computer operating instructions. The memory may include both internal memory and non-volatile memory, and provides instructions and data to the processor.

[0119] Example 4

[0120] A computer-readable storage medium stores a computer program. When executed by a processor, the computer program implements the steps of the method for planning a drone inspection sequence for disaster data collection and return, including: establishing a task scenario for planning a drone inspection sequence for disaster data collection and return; in the task scenario, establishing a post-disaster data collection and return task system model based on the multi-path traveling salesman problem; and using a multi-strategy improved gray wolf optimization algorithm to solve the post-disaster data collection and return task system model to obtain a drone inspection sequence plan. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory and / or cache memory, etc. The non-volatile memory may include read-only memory, hard disk, flash memory, optical disk, magnetic disk, etc.

[0121] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0125] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0126] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0127] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for planning the inspection sequence of drones for disaster data collection and return, characterized by: include: Establish a mission scenario for drone inspection sequence planning for drone disaster data collection and return; In the described mission scenario, a post-disaster data collection and return mission system model is established based on the multi-path traveling salesman problem. The multi-strategy improved grey wolf optimization algorithm is used to solve the post-disaster data collection and return task system model and obtain the drone inspection sequence plan.

2. The method for planning the inspection sequence of drones for disaster data collection and return according to claim 1 is characterized in that: The process of establishing the task scenario of drone inspection sequence planning for drone disaster data collection and return is as follows: 1a) Assume the size of the flight space 1b) The communication area is abstracted as a communication point. Each communication point is equipped with equipment for providing data backhaul services. The communication point set R = {r1, r2, ... r n ,...r N }, r n Indicates the nth communication point, the position coordinates of the nth communication point are (x n ,y n ), N represents the number of communication points; 1c) The inspection area is abstracted into a patrol point, and the patrol point set C = {c1, c2, ..., c m ,...,c M }, c m Indicates the mth cruise point, the position coordinates of the mth cruise point are (x m ,y m ), M represents the number of cruise points; 1d) There is a drone in the scene. Assume that the drone's speed V is constant, the drone's flight altitude H is constant, and the drone's starting point q is constant. start =(x s ,y s ), the inspection path is represented by an ordered sequence of K path points, and the path set is represented as P = {p1, p2, ..., p k ...,p K }; 1f) The drone starts from the starting point q start Departure, patrol the cruise point with index m∈{1,2,...,M} and collect data. During the patrol, the drone selects the communication point with index n∈{1,2,...,N} to transmit data back. After the patrol, it returns to the starting point q start .

3. The method for planning the inspection sequence of drones for disaster data collection and return according to claim 1 is characterized in that: The objective function of the post-disaster data collection and return task system model is: Among them, f revenue Indicates the return mission benefit of the drone, f cost represents the cost of drone flight time, 4. The method for planning the inspection sequence of drones for disaster data collection and return according to claim 1 is characterized in that: The constraints of the post-disaster data collection and return task system model are: Among them, constraints C1 and C2 indicate that each cruise point can only be visited once, and only one of the two flight modes is used between the two mission points; constraint C3 is used to ensure that there is no loop in the complete path of the drone; constraint C4 indicates that all cruise points in the scene are inspected; constraint C5 indicates that and It is a 0, 1 variable, and the edge access has only two states: 0 and 1.

5. The method for planning the inspection sequence of drones for disaster data collection and return according to claim 1 is characterized in that: The initial population of the multi-strategy improved grey wolf optimization algorithm is improved based on the Piecewise chaotic mapping.

6. The method for planning the inspection sequence of drones for disaster data collection and return according to claim 1 is characterized in that: The expression of the Piecewise chaotic map is: Among them, x t+1 The value generated for the next iteration, x i is the current iteration value, β represents the control parameter, β∈[0,1].

7. A drone inspection sequence planning system for disaster data collection and return, characterized by: include; The first module is used to establish a task scenario for drone disaster data collection and return drone inspection sequence planning; The second establishment module is used to establish a post-disaster data collection and return task system model based on the multi-path traveling salesman problem in the task scenario; The solution module is used to adopt a multi-strategy improved grey wolf optimization algorithm to solve the post-disaster data collection and return task system model and obtain a drone inspection sequence plan.

8. The drone inspection sequence planning system for disaster data collection and return according to claim 7 is characterized in that: The objective function of the post-disaster data collection and return task system model is: Among them, f revenue Indicates the return mission benefit of the drone, f cost represents the cost of drone flight time, The constraints of the post-disaster data collection and return task system model are: Among them, constraints C1 and C2 indicate that each cruise point can only be visited once, and only one of the two flight modes is used between the two mission points; constraint C3 is used to ensure that there is no loop in the complete path of the drone; constraint C4 indicates that all cruise points in the scene are inspected; constraint C5 indicates that and It is a 0, 1 variable, and the edge access has only two states: 0 and 1.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the drone inspection sequence planning method for disaster data collection and return are implemented as described in any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the drone inspection sequence planning method for disaster data collection and return are implemented as described in any one of claims 1 to 6.

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