Multi-unmanned aerial vehicle low-altitude coverage type search task planning method in urban environment

Abstract

The invention discloses a multi-unmanned aerial vehicle low-altitude coverage type search task planning method in an urban environment, and relates to the field of unmanned aerial vehicle task planning, and the method comprises the steps: building an initial grid map of a search region according to unmanned aerial vehicle constraints and map digital information; disassembling full-coverage scanning search into ground scanning and building scanning, and classifying map elements into a to-be-searched ground and a to-be-scanned building according to a grid height relationship; calculating the to-be-scanned area of the ground area and the to-be-scanned area of the building surface, and completing the number distribution of the unmanned aerial vehicles by taking the area proportion as a distribution criterion; establishing a ground area division optimization model and realizing ground scanning task allocation based on sub-area iteration; and establishing an improved multi-traveling salesman model and solving based on a genetic algorithm to realize building scanning task allocation. According to the invention, task planning of each unmanned aerial vehicle can be realized in a multi-unmanned aerial vehicle coverage reconnaissance task scene oriented to an urban environment.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) mission planning, and in particular relates to a method for planning a multi-UAV low-altitude coverage search mission in an urban environment. Background Art

[0002] As the center of economy and culture, cities have important strategic significance. However, urban environments are usually densely built and have complex terrain. Traditional high-altitude photoelectric reconnaissance methods are prone to information loss due to line of sight obstruction. Therefore, using small drones to conduct low-altitude all-round detection in urban areas can significantly improve the comprehensiveness and accuracy of information collection, which has important application value.

[0003] Current research on urban target search mission planning mainly focuses on the mission planning of ground target search, which usually adopts a fixed altitude UAV flight strategy and performs mission planning under this premise. However, this strategy does not fully consider the problem of buildings blocking ground targets at a specific perspective, resulting in incomplete information collection in some areas. In addition, buildings in urban environments may serve as hiding places for target personnel, and their surfaces also need to be scanned. Therefore, search strategies that only target ground areas are difficult to meet the comprehensive search needs in complex urban environments. In summary, a new search strategy is needed that can fully consider the search needs of ground areas and building surfaces, combine the advantages of multi-UAV collaboration, and realize efficient and comprehensive low-altitude coverage search tasks in urban environments. Summary of the invention

[0004] Purpose of the invention: To solve the above technical problems, the purpose of the present invention is to provide a method for planning low-altitude coverage search tasks for multiple UAVs in an urban environment. The grid unit length is established based on the scanning width to ensure the accuracy of the sensor, and the classification extraction of the ground and buildings is achieved through the height relationship. The urban coverage search is decomposed into ground scanning and building scanning, and the number of UAVs performing the corresponding tasks is allocated with the shortest mission time as the goal, and finally the mission planning of each UAV is completed respectively.

[0005] Technical solution: To achieve the above purpose, the present invention provides the following technical solution:

[0006] In a first aspect, a method for planning a multi-UAV low-altitude coverage search mission in an urban environment is provided, comprising the following steps:

[0007] Create an initial grid map of the search area;

[0008] The search task is split into ground scanning and building scanning, and the grids in the initial grid map are divided into ground grids and building grids;

[0009] Based on the grid point division results, a grid map for mission planning is established;

[0010] The areas to be scanned for ground scanning and building scanning are calculated respectively, and the number of drones for ground scanning and building scanning is allocated based on the area ratio.

[0011] Furthermore, an initial grid map of the search area is established based on the drone constraints and the map digital information, including:

[0012] First, obtain the DEM map and DSM map of the same accuracy in the search area;

[0013] Then, calculate the length of the grid cells in the initial grid map:

[0014] l X = l Y =Wη

[0015] Among them, l X and l Y are the horizontal and vertical axis lengths of the grid unit, respectively; W is the scanning width of the sensor when the UAV is flying at an altitude of h above the ground; η is the lateral overlap rate during scanning;

[0016] Then, judge l X and l Y The relationship between the accuracy of DEM and DSM maps is as follows: X and l Y If the accuracy of the DEM and DSM maps is less than l, the DEM and DSM maps are interpolated respectively to make the distance between two adjacent points in the horizontal and vertical directions in the DEM and DSM maps less than l X and l Y ;

[0017] Finally, calculate the DEM height h of each grid (i, j) in the initial grid map E (i,j) and DSM height h S (i,j), the calculation formulas are:

[0018]

[0019] Among them, H E (x,y) and H S (x, y) are the DEM height and DSM height corresponding to the point (x, y), respectively, and N is the number of points in the DEM map and DSM map that meet the constraints.

[0020] Furthermore, the grid partitioning discriminant is:

[0021]

[0022] Among them, hE (i,j) is the DEM height of grid (i,j), h S (i, j) is the DSM height of grid (i, j), Δh1 is the discrimination height, building is the building grid set, and ground is the ground grid set.

[0023] Furthermore, based on the grid point division results, a grid map for mission planning is established, including:

[0024] Set the height of all grids in ground to 0, and set the height of the grid corresponding to grid (i, j) in building to h(i, j) = h S (i,j)-h E (i,j)-Δh1;

[0025] Classify the grids in building and get the building set B = {B1,...,B k ,...,B M}, the classification method is:

[0026]

[0027] Where k = 1, 2, ..., M, M is the total number of buildings, n k and m k are the number of grids contained horizontally and vertically by the k-th building in the initial grid map, respectively. and are the row index and column index of the lower left corner grid of the building in the initial grid map, respectively, and k1 and k2 are the classification coefficients;

[0028] Finally, B k The grid heights of all grids in the grid are updated to H k =max(h(i,j)),(i,j)∈B k , and obtain the final grid map for mission planning.

[0029] Furthermore, the areas to be scanned for ground scanning and building scanning are calculated respectively, and the number of drones for ground scanning and building scanning is allocated based on the area ratio as the allocation criterion, including:

[0030] Calculate the area S to be scanned for ground scanning according to the number of ground grids G , the calculation formula is:

[0031] S G =Ml X l Y

[0032] Where M is the number of grids in the ground set, l X and l Y are the horizontal and vertical lengths of the grid unit, respectively;

[0033] The surface area S to be scanned for the building is calculated based on the height relationship between the building outer grid and its adjacent outer grid. B , and its calculation formula is:

[0034]

[0035] In the formula For building B k The area to be scanned.

[0036] Furthermore, the number of drones for ground scanning and building scanning is allocated based on the area ratio as the allocation criterion, including:

[0037] The number of drones scanning the ground N GU and the number of drones N scanning the building BU Respectively expressed as:

[0038]

[0039] N BU =N U -N GU

[0040] Among them, N U is the total number of drones, and represent rounding down and rounding up respectively; ε is the dispersion coefficient of the building, satisfying ε>1.

[0041] Furthermore, the method further comprises:

[0042] The allocation of ground scanning tasks is iteratively implemented based on the sub-region area, including:

[0043] Step 1: According to the number of drones N scanning the ground GU , the area covered by the ground grid set ground is initially divided to obtain a sub-area set Each sub-region is a rectangle and there is no overlap between any two sub-regions, q = 1, 2, ..., N GU ;

[0044] Step 2: Calculate the drone U q Fly to sub-region Q q The time and scan sub-area Q of the center q The sum of time T q , the calculation formula is:

[0045]

[0046] Where V is the speed of the drone, L q For U q Starting point distance Q q Euclidean distance of the center point, S q Q q area;

[0047] Step 3: Get the time set Find max(T) and min(T) and the corresponding sub-area index respectively max and index min ;

[0048] Step 4: Calculate the difference Δt=max(T)-min(T). If Δt is less than the preset value or the number of iterations is greater than the maximum number of iterations, terminate and output the current sub-region set.

[0049] Step 5: Follow the steps from index min To index max The transmission direction of the sub-area index min and sub-regions of the pathway (excluding index max ) to add a row or column outward from one edge to obtain a new set of sub-regions and return to Step 2.

[0050] Furthermore, the method further comprises:

[0051] An improved multi-traveling salesman model is established and solved based on a genetic algorithm to achieve the allocation of building scanning tasks, including:

[0052] Build an improved multiple traveling salesman model:

[0053]

[0054] Among them, seq i For UAV i The task sequence, ind v Refers to seq i The vth element represents the number of the building in the building set B. seq i | is seq i The number of elements in

[0055] Buildings and Flight time between As edge weights, buildings Scan time As the node weight, f(seq i ) is calculated as:

[0056]

[0057] In the formula, l k,k+1 For buildings and The distance between them, V is the flight speed, For buildings The area to be scanned;

[0058] The task sequence of the drone scanning the building is obtained by using the genetic algorithm to solve the task sequence.

[0059] On the second aspect, a computer-readable storage medium storing one or more programs is also provided, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the multi-UAV low-altitude coverage search mission planning method in an urban environment as described above.

[0060] According to a third aspect, an electronic device is provided, comprising one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the multi-UAV low-altitude coverage search mission planning method in an urban environment as described above.

[0061] Beneficial effects: The present invention proposes a multi-UAV low-altitude coverage search task planning method in an urban environment, which fully considers the establishment of a grid map based on a scan width constraint, and realizes the rapid decomposition of the ground and buildings according to the grid map. The method considers the relationship between the flight altitude of the UAV and the accuracy of the sensor, so that the UAV can maintain the same flight altitude to the ground during the entire mission; a strategy is designed in which the ground and buildings are searched and scanned by two different teams of UAVs respectively, and the original coupling problem is processed in layers, reducing the difficulty of problem solving; and based on this, a UAV quantity allocation method and a task execution timing optimization model are designed to minimize the task time. Based on the present invention, the rapid allocation of coverage scanning tasks for UAV formations can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention provides a multi-UAV low-altitude coverage search mission planning flow chart for urban environments.

[0063] Figure 2 This is the DEM diagram used in this example.

[0064] Figure 3is a schematic diagram of the DSM used in this example.

[0065] Figure 4 This is a schematic diagram of ground and building distribution obtained using the method in this example.

[0066] Figure 5 This is the final grid map used for mission planning obtained using the method in this example.

[0067] Figure 6 It is the transmission direction.

[0068] Figure 7 It is a flowchart based on sub-region area iteration provided by the present invention.

[0069] Figure 8 This is a schematic diagram of the sub-region division result obtained using the method in this example. DETAILED DESCRIPTION

[0070] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below in conjunction with a specific embodiment. In this example, there are 4 drones searching an urban area.

[0071] like Figure 1 As shown, this embodiment provides a method for planning a multi-UAV low-altitude coverage search mission in an urban environment, including:

[0072] Step 1: Create a raster map based on drone constraints and map digital information.

[0073] First, obtain the DEM map and DSM map of the same accuracy in the search area. In this example, the obtained DEM map and DSM map are as follows: Figure 2 , Figure 3 As shown, the map is a map after longitude and latitude are converted into length, wherein the unit length of the z-axis is m, and the unit lengths of the x-axis and y-axis are 8 m, and each map has 60×60=3600 data points.

[0074] Then, calculate the length of the grid cell in the grid map, and the calculation relationship is as follows:

[0075] l X = l Y =Wη

[0076] Among them, l X and l Y are the horizontal and vertical axis lengths of the grid unit, respectively. W is the scanning width of the sensor when the UAV is flying at an altitude of h. η is the lateral overlap rate during scanning. W is 20 meters and η is 80%. Then l X = l Y = 16 meters. X = lY =16 is greater than 8 meters, there is no need to interpolate the DEM and DSM maps.

[0077] Finally, a grid map is established based on the grid unit length, and the DEM height h of each grid (i, j) in the grid map is calculated. E (i,j) and DSM height h S (i,j), the calculation formulas are:

[0078]

[0079] Among them, H E (x,y) and H S (x, y) are the DEM height and DSM height corresponding to the point (x, y) respectively, N is the number of points that meet the constraints in the DEM map and DSM map, N = 4.

[0080] Step 2: Split the scanning search into ground scanning and building scanning, and divide the grids in the raster map into ground grids and building grids according to the grid height relationship, and then split the map into the ground to be searched and the building to be scanned, including:

[0081] 1. In this embodiment, the scanning tasks of the ground area and the building area are respectively assigned to different drone formations for execution; this strategy hierarchically processes the original integrated coupling solution problem, reducing the complexity of problem solving.

[0082] 2. According to h E (i,j) and h S The size relationship of (i, j) generates the building grid and the ground grid, and the discriminant is:

[0083]

[0084] Wherein Δh1 is the determination height, building refers to the building grid set to be scanned, and ground refers to the ground grid set to be scanned. In this example, Δh1=10.

[0085] 3. In the grid map, set the height of all grids in ground to 0, and set the height of the grid corresponding to grid (i, j) in building to h(i, j) = h S (i,j)-h E (i,j)-Δh1. Figure 4 This is a schematic diagram of the ground and building distribution, where the grey grids are building grids and the white grids are ground grids.

[0086] 4. Classify the grids in building to obtain the building set B = {B1,...,B k ,...,BM}, M is the total number of buildings, and the classification method is:

[0087]

[0088] where n k and m k Building B k The number of grid cells contained horizontally and vertically in the grid map. and Building B in the grid map k The row and column indices of the lower left grid corner.

[0089] 5. Let L k , W k and H k B k The corresponding horizontal length, vertical length and height are calculated as follows:

[0090]

[0091] 6. B k All grid heights in are updated to H k , and the final grid map for mission planning is obtained as Figure 5 Table 1 shows the length, width and height of each building in this embodiment.

[0092] Table 1 Length, width and height of buildings

[0093] Serial number L / m W / m H / m 1 48 64 81 2 64 80 70 3 48 64 56 4 48 96 70 5 64 32 8 6 64 80 44 7 80 64 24 8 48 64 26.5 9 64 48 20 10 90 48 69

[0094] Step 3: Calculate the area to be scanned on the ground and the area to be scanned on the building surface, and use the area ratio as the allocation criterion to complete the allocation of the number of drones, specifically:

[0095] 1. Calculate the ground area to be scanned S according to the number of ground grids G , the calculation formula is:

[0096] S G =Ml X l Y

[0097] Where M is the number of grids in the ground set. In this example, M = 749. Then S G =191744m 2 .

[0098] 2. The surface area of ​​the building to be scanned is only its side surface, not the upper and lower bottom surfaces. Considering the case of two adjacent buildings, their overlapping edges will reduce the surface area to be scanned. Therefore, the surface area of ​​the building to be scanned cannot be obtained by simply adding up all the building surfaces. The surface area of ​​the building to be scanned is calculated based on the height relationship between the outer grid of the building and its adjacent outer grid. The discrete calculation formula is as follows:

[0099]

[0100] in For building B k The surface area to be scanned can be obtained by B =118652m 2 .

[0101] 3. Since the total number of drones is N U =4, then the number of drones performing ground scanning and building scanning is N GU ,N BU It can be expressed as:

[0102]

[0103] N BU =N U -N GU

[0104] in, and denotes rounding down and rounding up respectively, ε is the building dispersion coefficient, in this example, ε=2, then N GU =2,N BU =2.

[0105] Step 4: Iterate the ground scanning task allocation based on the sub-region area; specifically include:

[0106] Step 1: First, according to the number of drones N scanning the ground GU , initially divide the total area into a set of sub-areas Each sub-region Q q It should be a regular rectangle with no overlap between sub-areas;

[0107] Step 2: Calculate the drone U q Fly to sub-region Q q The time and scan sub-area Q of the center q The sum of time T q , the calculation formula is:

[0108]

[0109] Where V is the speed of the drone, L q For Uq Starting point distance Q q Euclidean distance of the center point, S q Q q The area to be scanned.

[0110] Step 3: Get the time set Find max(T) and min(T) and the corresponding sub-area index respectively max and index min ;

[0111] Step 4: Calculate the difference Δt=max(T)-min(T). If Δt is less than the preset value or the number of iterations is greater than the maximum number of iterations, terminate and output the current sub-region set.

[0112] Step 5: Follow the steps below: Figure 6 As shown from index min To index max The transmission direction of the sub-area index min and sub-regions of the pathway (excluding index max ) to add a row or column outward from one edge to obtain a new set of sub-regions and return to Step 2.

[0113] In order to explain the above process more clearly, the flow chart is as follows Figure 7 shown.

[0114] In this example, the starting point of each drone is the lower left corner of the map, the flight speed is 5m / s, the maximum number of iterations is 10, the initial division is equal to the left and right, and the difference preset value is 1min. The sub-area distribution corresponding to drone 1 and drone 2 can be obtained as follows Figure 8 As shown, at this time Δt=48.3 seconds.

[0115] Step 5: Establish an improved multi-traveling salesman model and solve the building scanning task allocation based on the genetic algorithm; specifically:

[0116] Build an improved multiple traveling salesman model:

[0117]

[0118] Among them, seq i Refers to the task sequence containing the building number, corresponding to the UAV U i Done. seq i Expressed as ind v Refers to seq i The vth element represents the number of the building in the building set B. For seqi The number of elements in .

[0119] Buildings and Flight time between As edge weights, buildings Scan time As the node weight, f(seq i ) is calculated as:

[0120]

[0121] In the formula, l k,k+1 For buildings and The distance between them, V is the flight speed, For buildings The area to be scanned.

[0122] Finally, the task sequence is used as the genetic code to solve the genetic algorithm to obtain the task sequence of the drone scanning the building, that is, each drone performs the search task in the order of scanning the building.

[0123] In this example, the number of iterations of the genetic algorithm is set to 5000, the initial population size is 100, the crossover probability is 0.4, and the mutation probability is 0.1. The task sequences of UAV 3 and UAV 4 are {5, 10, 3, 7, 4} and {9, 8, 1, 6, 2} respectively. At this time, the time difference between the two UAVs to complete the tasks is 18.5 seconds.

[0124] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the use. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the use methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

[0125] Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the above-mentioned multi-UAV low-altitude coverage search route planning method in an urban environment.

[0126] Based on the same technical solution, the present invention also discloses a computing device, including one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above-mentioned multi-UAV low-altitude coverage search route planning method in an urban environment.

[0127] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A method for planning a multi-UAV low-altitude coverage search mission in an urban environment, characterized in that: The method comprises: Create an initial grid map of the search area; The search task is split into ground scanning and building scanning, and the grids in the initial grid map are divided into ground grids and building grids; Based on the grid point division results, a grid map for mission planning is established; The areas to be scanned for ground scanning and building scanning are calculated respectively, and the number of drones for ground scanning and building scanning is allocated based on the area ratio.

2. The method according to claim 1, characterized in that Create an initial grid map of the search area based on the drone constraints and map digital information, including: First, obtain the DEM map and DSM map of the same accuracy in the search area; Then, calculate the length of the grid cells in the initial grid map: l X =l Y =Wη Among them, l X and l Y are the horizontal and vertical axis lengths of the grid unit, respectively; W is the scanning width of the sensor when the UAV is flying at an altitude of h above the ground; η is the lateral overlap rate during scanning; Then, judge l X and l Y The relationship between the accuracy of DEM and DSM maps is as follows: X and l Y If the accuracy of the DEM and DSM maps is less than l, the DEM and DSM maps are interpolated respectively to make the distance between two adjacent points in the horizontal and vertical directions in the DEM and DSM maps less than l X and l Y ; Finally, calculate the DEM height h of each grid (i, j) in the initial grid map E (i,j) and DSM height h S (i,j), the calculation formulas are: Among them, H E (x,y) and H S (x, y) are the DEM height and DSM height corresponding to the point (x, y), respectively, and N is the number of points in the DEM map and DSM map that meet the constraints.

3. The method according to claim 1, characterized in that The grid partitioning discriminant is: Among them, h E (i,j) is the DEM height of grid (i,j), h S (i, j) is the DSM height of grid (i, j), Δh1 is the discrimination height, building is the building grid set, and ground is the ground grid set.

4. The method according to claim 3, characterized in that Based on the grid point division results, a grid map for mission planning is established, including: Set the height of all grids in ground to 0, and set the height of the grid corresponding to grid (i, j) in building to h(i, j) = h S (i,j)-h E (i,j)-Δh1; Classify the grids in building and get the building set B = {B1,...,B k ,...,B M }, the classification method is: Where k = 1, 2, ..., M, M is the total number of buildings, n k and m k are the number of grids contained horizontally and vertically by the k-th building in the initial grid map, respectively. and are the row index and column index of the lower left corner grid of the building in the initial grid map, respectively, and k1 and k2 are the classification coefficients; Finally, B k The grid heights of all grids in the grid are updated to H k =max(h(i,j)),(i,j)∈B k , and obtain the final grid map for mission planning.

5. The method according to claim 3, characterized in that: The areas to be scanned for ground scanning and building scanning are calculated respectively, and the number of drones for ground scanning and building scanning is allocated based on the area ratio as the allocation criterion, including: Calculate the area S to be scanned for ground scanning according to the number of ground grids G , the calculation formula is: S G =Ml X l Y Where M is the number of grids in the ground set, l X and l Y are the horizontal and vertical lengths of the grid unit, respectively; The surface area S to be scanned for the building is calculated based on the height relationship between the building outer grid and its adjacent outer grid. B , and its calculation formula is: In the formula For building B k The area to be scanned.

6. The method according to claim 5, characterized in that The number of drones for ground scanning and building scanning is allocated based on the area ratio, including: The number of drones scanning the ground N GU and the number of drones N scanning the building BU Respectively expressed as: Among them, N U is the total number of drones, and represent rounding down and rounding up respectively; ε is the dispersion coefficient of the building, satisfying ε>

1.

7. The method according to claim 1, characterized in that The method further comprises: The allocation of ground scanning tasks is iteratively implemented based on the sub-region area, including: Step 1: According to the number of drones N scanning the ground GU , the area covered by the ground grid set ground is initially divided to obtain a sub-area set Each sub-region is a rectangle and there is no overlap between any two sub-regions, q = 1, 2, ..., N GU ; Step 2: Calculate the drone U q Fly to sub-region Q q The time and scan sub-area Q of the center q The sum of time T q , the calculation formula is: Where V is the speed of the drone, L q For U q Starting point distance Q q Euclidean distance of the center point, S q Q q area; Step 3: Get the time set Find max(T) and min(T) and the corresponding sub-area index respectively max and index min ; Step 4: Calculate the difference Δt=max(T)-min(T). If Δt is less than the preset value or the number of iterations is greater than the maximum number of iterations, terminate and output the current sub-region set. Step 5: Follow the steps from index min To index max The transmission direction of the sub-area index min Add a row or column outward from an edge of the sub-region of the path to obtain a new sub-region set and return to Step 2; the sub-region of the path does not contain index max .

8. The method according to claim 1, characterized in that The method further comprises: An improved multi-traveling salesman model is established and solved based on a genetic algorithm to achieve the allocation of building scanning tasks, including: Build an improved multiple traveling salesman model: Among them, seq i For UAV i The task sequence, ind v Refers to seq i The vth element represents the number of the building in the building set B. seq i | is seq i The number of elements in Buildings and Flight time between As edge weights, buildings Scan time As the node weight, f(seq i ) is calculated as: In the formula, l k,k+1 For buildings and The distance between them, V is the flight speed, For buildings The area to be scanned; The task sequence of the drone scanning the building is obtained by using the genetic algorithm to solve the task sequence.

9. A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, characterized in that: When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: The method comprises one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as claimed in any one of claims 1 to 8.

Images