An unmanned aerial vehicle low-altitude route planning method and device

Abstract

The application discloses a kind of unmanned aerial vehicle low-altitude air route planning method and equipment, air route planning method, comprising: obtaining the coverage data of target area;Target area is divided into multiple same size continuous square grids;Target area includes air route starting point and air route termination point;According to coverage data, the grid confidence of square grid is calculated;According to coverage data, the mesh topology of target area is constructed;According to grid confidence, the path cost of multiple topological paths in mesh topology is determined;According to path cost, the target air route from air route starting point to air route termination point is obtained.The application takes path cost as a unified selection standard, ensures that different planning designers can also obtain the same air route planning result under the same condition, and further improves the stability of planning result.

Description

Technical Field

[0001] This invention relates to the field of low-altitude flight path planning technology, and in particular to a method and equipment for low-altitude flight path planning of unmanned aerial vehicles. Background Technology

[0002] Low-altitude route planning refers to planning an optimal flight path within the low-altitude airspace that meets multiple constraints, taking into account factors such as flight safety and public impact, in order to minimize flight distance and reduce flight costs.

[0003] Currently, low-altitude flight route planners primarily employ the following methods: After determining market demand and the origin and destination locations of the route, before planning, they need to collect a series of relevant data, including population distribution, building models, road distribution, river, lake, mountain, and forest distribution, and infrastructure distribution within the proposed flight area, to provide data support for route planning. Then, by overlaying base map data and combining quantitative and qualitative analysis methods, planners need to consider numerous constraints and determine the route direction starting from the origin. For routes with multiple options, designers need to analyze and evaluate each option to select the optimal flight path. After completing the route planning, planners need to repeatedly check and adjust the planned route to gradually form the final route plan.

[0004] Therefore, in the current planning of low-altitude routes, the route direction is subjectively judged by the planners and designers. When there are multiple route options, there is a lack of unified selection criteria, which may lead to different choices by different planners and designers, resulting in low stability of the planning results. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method and equipment for low-altitude flight path planning of unmanned aerial vehicles, which can provide a unified selection standard to ensure that different planners and designers can obtain the same flight path planning results under the same conditions, thereby improving the stability of the planning results.

[0006] To solve the above problems, the present invention is implemented according to the following solution:

[0007] A method for low-altitude flight path planning for unmanned aerial vehicles is provided, including:

[0008] Acquire coverage data for the target area; the target area is divided into multiple consecutive square grids of the same size; the target area includes the start and end points of the flight path;

[0009] Calculate the raster confidence score for the square raster based on the coverage data;

[0010] Based on the coverage data, construct a mesh topology for the target area;

[0011] Based on the grid confidence level, determine the path cost of multiple topological paths in the mesh topology;

[0012] Based on the path cost, the target route from the starting point to the ending point of the route is obtained.

[0013] Compared with the prior art, the beneficial effects of the low-altitude flight path planning method for unmanned aerial vehicles of the present invention are as follows: by determining the path cost of multiple topological paths in the mesh topology and determining the target flight path based on the path cost, the path cost is used as a unified selection criterion, ensuring that different planning designers can obtain the same flight path planning results under the same conditions, thereby improving the stability of the planning results.

[0014] Optionally, the grid confidence level includes route safety, operational reliability, and public acceptability.

[0015] Optionally, calculating the raster confidence of the square raster based on the coverage data includes:

[0016] Identify the influencing factors and their corresponding weights;

[0017] Based on the coverage data, calculate the evaluation score of the square grid on the influencing factors;

[0018] The grid confidence level is determined based on the evaluation scores and weights of the influencing factors.

[0019] Optionally, constructing a mesh topology structure for the target area based on the coverage data includes:

[0020] Based on the coverage data, determine the center point of each square grid cell in the target area;

[0021] Construct a mesh topology for the target area based on the grid center point.

[0022] Optionally, constructing the mesh topology of the target area based on the grid center point includes:

[0023] Select the center point of a grid cell in the target area as the reference center point;

[0024] Determine the adjacent center points of the reference center point;

[0025] Connect the reference center point with its adjacent center points to form the topological structure of the reference center point;

[0026] Repeat the above steps for all grid center points in the target area to construct the topology of each grid center point in the target area.

[0027] The topological structure of the center point of each grid in the target area constitutes the mesh topology of the target area.

[0028] Optionally, the mesh topology includes multiple topology nodes and multiple topology paths;

[0029] Each grid center point corresponds to a node, and the connecting line between two nodes corresponds to a topological path.

[0030] The path types of topological paths include: vertical paths, diagonal paths, and penalty paths.

[0031] Optionally, determining the path cost of multiple topological paths in the mesh topology based on the grid confidence includes:

[0032] Determine the formula for calculating path cost based on the path type of the topological path;

[0033] Based on the calculation formula and the grid confidence level, the path cost of each topological path is calculated.

[0034] Optionally, the coverage data includes the coordinates of the starting point and ending point of the flight route;

[0035] The process of obtaining the target route from the starting point to the ending point of the route based on the path cost includes:

[0036] Based on the coordinates of the starting point and ending point of the route, determine the starting grid and ending grid of the route in multiple square grids.

[0037] The node corresponding to the center point of the starting grid of the route is determined as the starting node of the route in the mesh topology;

[0038] The node corresponding to the center point of the route termination grid in the mesh topology is determined as the route termination node;

[0039] The target route is determined based on the route cost, the route start node, and the route end node.

[0040] Optionally, determining the target route based on path cost, route start node, and route end node includes:

[0041] The shortest path algorithm is used to determine the target path under the mesh topology based on path cost;

[0042] The target path under the mesh topology is converted into the target flight path, which is the actual flight path of the unmanned aerial vehicle.

[0043] A computer device is also provided, including a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement the route planning method. Attached Figure Description

[0044] Figure 1 This is a flowchart of the route planning method of the present invention;

[0045] Figure 2 This is a schematic diagram of the topology of the present invention;

[0046] Figure 3 This is a schematic diagram of the target path of the present invention;

[0047] Figure 4 This is a schematic diagram of the target route of the present invention. Detailed Implementation

[0048] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0049] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0050] See Figure 1 As shown, the present invention provides a method for low-altitude flight path planning for unmanned aerial vehicles, comprising:

[0051] S1: Obtain coverage data for the target area; the target area includes the starting and ending points of the flight route. When planning flight routes, the impact of the surrounding environment needs to be considered. Therefore, based on the starting and ending points, the scope of the flight route planning needs to be appropriately expanded. This expansion can be based on the standards of towns, districts, and cities, meaning the target area should completely cover the starting and ending points of the flight route and extend to a certain geographical area. Coverage data includes, but is not limited to, a series of related data such as population distribution, building models, road distribution, rivers, lakes, mountains, forests, and infrastructure distribution within the target area, providing data support for flight route planning. For example, population distribution data is used to determine whether the area is densely populated, and flight routes should avoid passing through densely populated areas as much as possible. Infrastructure data includes information such as the location of infrastructure such as power lines and communication facilities. Flight routes should avoid passing through infrastructure as much as possible to avoid impacting it.

[0052] After obtaining a series of relevant data such as population distribution, building models, road distribution, river, lake, mountain and forest, and infrastructure distribution within the target area, it is necessary to process this data through a GIS (Geographic Information System) to convert data in different formats into a unified format, such as Shapefile, GeoJSON, GML, KML / KMZ, DWG, DXF, etc. By processing the data in a unified format through a GIS system, the efficiency and accuracy of data processing can be improved.

[0053] The target area is divided into multiple consecutive square grids of the same size. In this invention, the square grid size is 100 meters * 100 meters. The tools used to divide the target area into multiple square grids include, but are not limited to, GIS data processing software such as ArcGIS and QGIS, and geographic information processing databases such as PostGIS, ArcSDE, MySQL Spatial, and Oracle Spatial.

[0054] By dividing the target area into multiple square grids, complex geographic space can be abstracted into a regular grid structure, simplifying spatial analysis and calculation. At the same time, rasterization allows geographic data to be quantified and standardized, facilitating spatial analysis, influencing factor assessment, and route planning. Through rasterization, data such as population distribution, building models, road distribution, river, lake, mountain and forest, and infrastructure distribution within the target area can be processed and analyzed more efficiently, providing data support for subsequent route planning.

[0055] S2: Calculate the grid confidence score of the square grid based on the coverage data; the grid confidence score includes route safety, operational reliability, and public acceptability. That is, when calculating the grid confidence score, it is necessary to calculate the scores of the three dimensions of route safety, operational reliability, and public acceptability.

[0056] Table 1 lists the influencing factors and their corresponding weights for each dimension, as shown below.

[0057]

[0058] Table 1 shows the influencing factors and their corresponding weights for the three dimensions of route safety, operational reliability, and public acceptability. The influencing factors and their corresponding weights are formulated according to the "Specification for the Design of Logistics Routes for Light and Small Unmanned Aerial Vehicles in Urban Scenarios" MH / T 4054—2022 issued by the Civil Aviation Administration of China. It has strong guiding significance for the calculation of grid confidence in low-altitude route planning scenarios. However, the indicator system should be regarded as exemplary and non-restrictive. Therefore, in practical applications, influencing factors can be selected according to the actual application scenario, and the weights of each influencing factor can be dynamically adjusted. The sum of the weights of each influencing factor should be ensured to be 1.

[0059] Based on the coverage data, calculate the raster confidence score for the square raster, including:

[0060] First, determine the actual influencing factors to be selected and their corresponding weights. Then, based on the coverage data, calculate the evaluation score of the square grid on the influencing factors. This evaluation score is used to measure the importance or degree of influence of the influencing factor on the square grid. Before calculating the evaluation score, the coverage data needs to be divided into sub-data corresponding to each square grid according to the division criteria for dividing the target area into multiple square grids. All the sub-data corresponding to the square grids constitute the coverage data of the target area.

[0061] Based on the sub-data corresponding to the square grid, calculate the evaluation score for each influencing factor of the square grid. The range of evaluation scores and the scoring criteria are not constrained here. The calculation formula is as follows:

[0062]

[0063] in, Influencing factors The assessment score The influencing factors are numbered. Influencing factors The calculation results As the scoring standard, To determine the number of criteria for scoring, For when satisfy Time-related factors The score; assuming satisfy Then Values .

[0064] After calculating the evaluation score of the influencing factor for each square grid, the score of each square grid in each dimension is calculated based on the evaluation score and weight of the influencing factor.

[0065] The safety score for a flight route is calculated using the following formula:

[0066]

[0067]

[0068] in Indicates the safety level of the flight route. To score the safety of the flight route, , , , These respectively represent the safety of ground personnel, the safety of ground facilities, the difficulty of the search, and the secondary injury index. , , , These represent the weights of ground personnel safety, ground facility safety, search difficulty, and secondary injury indicators, respectively. , , , These represent the average scores of the square grids traversed by the flight path in terms of ground personnel safety, ground facility safety, search difficulty, and secondary injury indicators. The weights representing the safety level of a flight route.

[0069] The operational reliability score is calculated using the following formula:

[0070]

[0071]

[0072] in Indicates operational reliability. To score operational reliability, , , , , , These respectively represent the indicators of meteorological environment, electromagnetic environment, obstacle environment, navigation capability, communication performance, and surveillance performance. , , , , , These represent the weights of meteorological environment, electromagnetic environment, obstacle environment, navigation capability, communication performance, and surveillance performance indicators, respectively. , , , , , These represent the average scores of the square grids traversed by the flight path in terms of meteorological environment, electromagnetic environment, obstacle environment, navigation capability, communication performance, and surveillance performance, respectively. Weights that represent operational reliability.

[0073] The public acceptability score of a flight route is calculated using the following formula:

[0074]

[0075]

[0076] in Indicates public acceptability. Score for public acceptability. , These represent the privacy factor and noise factor indices, respectively. , These represent the weights of the privacy factor and noise factor indicators, respectively. , These represent the average scores of the square grids traversed by the flight path in terms of privacy and noise factors, respectively. The weight representing public acceptability.

[0077] Finally, based on the scores and weights of each dimension, the grid confidence score for each square grid cell is calculated using the following formula:

[0078]

[0079] in, For the first The confidence level of a square grid. The numbering of the square grid. Let i be the score for the i-th dimension. Let i be the weight of the i-th dimension. For the dimension number, This represents the total number of dimensions for the raster confidence score.

[0080] In the specific implementation of step S2, SQL statements can be written, using functions such as intersection, coverage, containment, and buffer analysis of PostGIS databases, among others. After running the SQL statements, the calculation of raster confidence in step S2 can be completed automatically.

[0081] By calculating the grid confidence score of a square grid, scores across three dimensions—route safety, operational reliability, and public acceptability—can be comprehensively considered, providing a multi-dimensional evaluation basis for route planning. This multi-dimensional evaluation method ensures that routes achieve an optimal balance in terms of safety, reliability, and public acceptability, improving the safety and reliability of unmanned aerial vehicles (UAVs) flying at low altitudes.

[0082] S3: Based on the coverage data, construct the mesh topology of the target area, including:

[0083] First, based on the coverage data, i.e., based on the sub-data of each square grid, the center point of each square grid is determined. This center point is the geometric center point of each square grid. The geometric center point is the average position of all points within the square grid, which can balance the influence of various parts within the square grid and ensure that the overall characteristics of the square grid can be accurately reflected when planning flight routes in the future. When determining the grid center point, the geometric center point of each square grid can be calculated quickly in batches using GIS data processing software and geographic information processing databases. The calculation function for the geometric center point of the square grid is not constrained here.

[0084] After determining the center point of each square grid, select any grid center point in the target area as the reference center point, and determine the adjacent center points of the reference center point. Connect the reference center point with its adjacent center points to form the topology of the reference center point. Repeat the above steps for all grid center points in the target area to construct the topology of each grid center point in the target area. The topology of each grid center point in the target area constitutes the mesh topology of the target area. That is, by constructing the pairwise connections between grid center points in the target area, a mesh topology covering all grid center points in the target area can be constructed. This mesh topology includes the pairwise connections between all grid center points in the target area.

[0085] Construct the topology of each grid center point using the following formula:

[0086]

[0087] in, As the reference center point The topology, Center point The set of adjacent center points, As the reference center point The adjacency matrix of an undirected graph. The grid number corresponding to the reference center point. As the reference center point The adjacent center points, As the reference center point and adjacent center points The connecting edge, As the reference center point The number of adjacent center points, Figure 2 As the reference center point A schematic diagram of the topology. Figure 2 In this context, j1-j8 are the adjacent center points of the reference center point j.

[0088] In the specific implementation of step S3, SQL statements can be written, using functions such as intersection, contact, containment, and line segment generation from the PostGIS database, among others. After running the SQL statements, the construction of the mesh topology of the target area in step S3 can be completed automatically.

[0089] By constructing a mesh topology for the target area, the connection relationships between the center points of the square grids can be clearly defined, thus clarifying the connection relationships between each square grid in the target area. This facilitates path search and optimization during route planning. The mesh topology represents the spatial relationships between the center points of the grids through nodes and connecting edges. In other words, the mesh topology can be used to represent the spatial relationships between each square grid. This allows route planning to be performed within the node network of the mesh topology, with each node representing a square grid. This reduces computational complexity and improves the efficiency of route planning.

[0090] S4: Determine the path cost of multiple topological paths in the mesh topology based on the grid confidence level; the mesh topology includes multiple topological nodes and multiple topological paths, each grid center point corresponds to a topological node, and the connection line between two topological nodes corresponds to a topological path, that is, each topological path can be represented as... The path types of topological paths include vertical paths, diagonal paths, and penalty paths.

[0091] by Figure 2 The illustrated topology diagram explains the path types of the topology paths. Figure 2 j and These are some of the nodes in a mesh topology, and the edges connecting the nodes are the topological paths. Figure 2 In , , , , , , , This refers to a portion of the topological paths in a mesh topology, where... Figure 2 middle , , , These four topological paths correspond to vertical paths. , , , The path type corresponding to the four topological paths is a diagonal path; when any one of the two nodes constituting the topological path has a grid confidence score equal to the maximum grid confidence score, the path type of this topological path is preferentially defined as a penalty path, that is, when At that time, topological path The path type is preferentially defined as a penalty path, where, Let be the grid confidence score of the square grid corresponding to node j. For nodes The grid confidence level of the corresponding square grid. This represents the maximum confidence level of the raster.

[0092] Based on the grid confidence level, determine the path cost of multiple topological paths in the mesh topology, including:

[0093] Based on the path type in the topology, the formula for calculating path cost is determined. The formulas for calculating path cost for different path types are as follows:

[0094]

[0095] in, For nodes and nodes The connecting edges are called topological paths. This refers to the number of the square grid cell corresponding to the node. For nodes The adjacent nodes, For nodes The number of adjacent nodes, Topology path Path cost, For nodes The grid confidence level of the corresponding square grid. For nodes The grid confidence level of the corresponding square grid. This represents the maximum confidence level of the raster. The penalty cost parameter can be set to a larger value depending on the situation. Since the shortest path algorithm is used in route planning, the larger the penalty cost parameter, the greater the path cost and the stronger the constraint on the grid confidence. This square grid belongs to a strictly controlled area and must be avoided. It indicates that when automatically planning routes, the greater the path cost of the topological path, the greater the risk of the topological path. That is, the topological path with the lower path cost is preferred during planning. Therefore, this penalty cost parameter does not have specific constraints and can be set in combination with the higher-level constraints of route planning and actual needs.

[0096] In the specific implementation of step S4, SQL statements can be written, using functions such as line segment length calculation from the PostGIS database, which include but are not limited to. After running the SQL statements, the path cost calculation for each topological path in step S4 can be completed automatically.

[0097] By determining the path cost of the topology path based on grid confidence, grid confidence can be effectively incorporated into path planning considerations, ensuring that routes avoid high-risk areas during planning. In particular, by introducing penalty paths, the likelihood of routes passing through high-risk areas can be strictly controlled, further improving route safety. The path cost calculation method makes route planning more intelligent and refined, enabling dynamic adjustments to routes based on actual conditions.

[0098] S5: Based on the path cost, obtain the target route from the starting point to the ending point of the route, including:

[0099] Obtain the coordinates of the starting point and ending point of the flight route from the coverage data. Based on the coordinates of the starting point and ending point of the flight route, determine the starting grid and ending grid of the flight route from multiple square grids in the target area. Specifically, the square grid in the target area that covers the coordinates of the starting point of the flight route is used as the starting grid of the flight route, and the square grid in the target area that covers the coordinates of the ending point of the flight route is used as the ending grid of the flight route.

[0100] Next, based on the route start grid and route end grid, the route start node and route end node are determined in the mesh topology. Since the center point of each grid corresponds to a node in the mesh topology, the node corresponding to the center point of the route start grid in the mesh topology is taken as the route start node, and the node corresponding to the center point of the route end grid in the mesh topology is taken as the route end node.

[0101] Finally, based on the path cost, the route's origin node, and the route's termination node, the target route is determined, including:

[0102] The shortest path algorithm is used to determine the target path under the mesh topology based on the path cost of each square grid in the target area. The shortest path algorithm used here is the Dijkstra algorithm commonly used in existing technologies. In existing technologies, the shortest path algorithm is based on the actual distance between nodes to determine the shortest path. However, this invention also determines the target path based on the path cost. Since the path cost is calculated based on the grid confidence, the final target path is the optimal path that comprehensively considers the three factors of path cost, grid confidence, and path distance.

[0103] Because the mesh topology represents the spatial relationship between grid center points through nodes and connecting edges, the target path is the planar projection path of the flight path. That is, the target path under the mesh topology needs to be converted into the target flight path. This is achieved by replacing the coordinates of the starting point, the starting grid, and the starting node of the flight path, as well as the coordinates of the ending point, the ending grid, and the ending node of the flight path with the coordinate information in the coverage data, and then obtaining the target flight path. This target flight path is the actual flight path of the unmanned aerial vehicle.

[0104] In the specific implementation step S5, the target route can be automatically calculated by writing SQL statements and using functions such as pgr_dijkstra, which include but are not limited to the shortest path analysis of the PostGIS database.

[0105] By employing a shortest path algorithm combined with path cost, an optimal route can be obtained that comprehensively considers path cost, grid confidence, and path distance. This method not only ensures the shortest path but also minimizes the risk of high-risk areas, thereby improving the safety and reliability of the route.

[0106] This invention determines the path cost of multiple topological paths in a mesh topology and determines the target route based on the path cost. By using path cost as a unified selection criterion, it ensures that different planners and designers can obtain the same route planning results under the same conditions, thereby improving the stability of the planning results.

[0107] After obtaining the target flight path, visualization technology is used to programmatically visualize the low-altitude flight path. For details, please refer to... Figure 3 This is a schematic diagram of the target path of the present invention. The visualization technology may include, but is not limited to, WebGL technologies such as Baidu MapVGL, Gaode LOCA, and Mapbox, as well as other GIS visualization software. By visualizing the target route, technicians can clearly see the flight path of the unmanned aerial vehicle.

[0108] See Figure 4The diagram shows the target route of this invention. As can be seen from the diagram, the target route scores similarly in the three dimensions of route safety, operational reliability, and public acceptability. This means that the target route performs relatively well in all dimensions and is a route with good overall performance, which can meet the requirements of multiple dimensions at the same time.

[0109] The present invention also provides a computer device, including a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the above-described route planning method.

[0110] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0111] The memory can be used to store the computer program or module. The processor implements various functions of the route planning method by running or executing the computer program or module stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0112] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for low altitude route planning for unmanned aerial vehicles, characterized in that, The method comprises the following steps: acquiring coverage data of a target area; dividing the target area into a plurality of continuous square grids of the same size, wherein the target area comprises a route starting point and a route ending point; calculating grid confidence of the square grids according to the coverage data; constructing a mesh topology of the target area according to the coverage data, wherein each square grid corresponds to a node in the mesh topology; path types of topological paths in the mesh topology comprise a vertical path, a diagonal path and a penalty path; and path costs of a plurality of topological paths in the mesh topology are determined according to the grid confidence and the path types of the topological paths; the determination of the path costs of the plurality of topological paths in the mesh topology according to the grid confidence and the path types of the topological paths comprises: determining a calculation formula of the path costs according to the path types of the topological paths, wherein the calculation formulas of the path costs of different path types are as follows: wherein, is the connection edge of the node and the node , i.e. the topology path, is the number of the square grid corresponding to the node, is the adjacent node of the node , is the number of the adjacent nodes of the node , is the path cost of the topology path , is the grid confidence of the square grid corresponding to the node , is the grid confidence of the square grid corresponding to the node , is the maximum value of the grid confidence, is the penalty cost parameter; calculating the path costs of each topological path according to the calculation formula and the grid confidence; obtaining a target route from the route starting point to the route ending point according to the path costs by using a shortest path algorithm.

2. The method of claim 1, wherein, The grid confidence comprises route safety, operation reliability and public acceptability.

3. The method of claim 2, wherein, The calculation of the grid confidence of the square grids according to the coverage data comprises: determining influencing factors and corresponding weights; calculating evaluation scores of the square grids on the influencing factors according to the coverage data; determining the grid confidence according to the evaluation scores and the weights corresponding to the influencing factors.

4. The method of claim 1, wherein, The construction of the mesh topology of the target area according to the coverage data comprises: determining grid center points corresponding to each square grid in the target area according to the coverage data; constructing the mesh topology of the target area according to the grid center points.

5. The method of claim 4, wherein, The construction of the mesh topology of the target area according to the grid center points comprises the following steps: step 1: selecting a grid center point in the target area as a reference center point; step 2: determining adjacent center points of the reference center point; step 3: connecting the reference center point and the adjacent center points to form a topology structure of the reference center point; step 4: repeating steps 1 to 3 for all grid center points in the target area to construct a topology structure of each grid center point in the target area; step 5: constructing the mesh topology of the target area according to the topology structures of each grid center point in the target area.

6. The method of claim 4, wherein, The mesh topology comprises a plurality of topological nodes and a plurality of topological paths; each grid center point corresponds to a node, and a connection line between two nodes corresponds to a topological path.

7. The method of claim 6, wherein, The coverage data comprises route starting point coordinates and route ending point coordinates; The obtaining of the target route from the route starting point to the route ending point according to the path costs by using the shortest path algorithm comprises: determining a route starting grid and a route ending grid in the plurality of square grids according to the route starting point coordinates and the route ending point coordinates; determining a node corresponding to a grid center point of the route starting grid in the mesh topology as a route starting node; determining a node corresponding to a grid center point of the route ending grid in the mesh topology as a route ending node; The shortest path algorithm is used to determine the target route according to the path cost, the route start node and the route end node.

8. The method of claim 7, wherein, The shortest path algorithm is used to determine the target route according to the path cost, the route start node and the route end node. The shortest path algorithm is used to determine the target path in the mesh topology based on the path cost. The target path in the mesh topology is converted into the target route, and the target route is the actual driving route of the unmanned aerial vehicle.

9. A computer device, comprising: The computer device comprises a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, which is loaded and executed by the processor to implement the route planning method according to any one of claims 1 to 8.

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