Automatic detection method and device for route of unmanned aerial vehicle and electronic equipment

Abstract

The invention relates to an automatic detection method and device for an unmanned aerial vehicle route, and electronic equipment, relates to the field of unmanned aerial vehicles, and solves the technical problems that an existing unmanned aerial vehicle route is often unreasonable in route planning and is difficult to adapt to the requirement of quickly responding to a task to plan a route in complex environments such as multi-constraint and dynamic environments. The method comprises the following steps: acquiring position data of each waypoint in an unmanned aerial vehicle route, position data of a no-fly zone and elevation data of an area where the unmanned aerial vehicle route passes; according to the position data of each waypoint and the position data of the no-fly zone, judging whether the route of the unmanned aerial vehicle passes through the no-fly zone; according to the position data of each waypoint, judging whether the turning angle at each waypoint in the course of the unmanned aerial vehicle meets a corner constraint value; according to the position data of each waypoint and the elevation data, judging whether the route of the unmanned aerial vehicle meets a flight height constraint value or not; and judging whether the flight duration of the unmanned aerial vehicle meets a flight duration constraint value or not according to the total length of the route of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles, and in particular to an automatic detection method, device and electronic equipment for unmanned aerial vehicle routes. Background Art

[0002] In recent years, with the rapid development of drone technology, the use of drones for reconnaissance missions has become more and more widespread. In such reconnaissance missions, it is necessary to plan the drone route according to the received reconnaissance target and range, complete the reconnaissance and monitoring of the target, and collect the required reconnaissance information.

[0003] The most commonly used reconnaissance route planning method is to manually plan the UAV reconnaissance mission route on a two-dimensional digital map in the UAV ground control station software. This reconnaissance route planning method requires manually setting the latitude and longitude coordinates of multiple waypoints, and then the UAV ground control station software automatically connects the waypoints and draws the UAV flight route. The planned route is then uploaded to the UAV via a data link, and the UAV begins to fly according to the planned route.

[0004] There are several ways to manually draw and plan routes:

[0005] 1) Manually add waypoints on the map with the mouse and draw the route independently.

[0006] 2) Manually add points of interest (such as reconnaissance target points) on the map using the mouse, and then automatically generate circular waypoints around the points of interest and connect them into routes.

[0007] In some complex environments, when the UAV receives the target area or target coordinates for reconnaissance, it is necessary to quickly plan the UAV flight reconnaissance route on the two-dimensional digital map according to the target location and carry out reconnaissance activities. However, for manually planned UAV routes, unreasonable route planning is often encountered. There are the following situations:

[0008] 1) Manually added waypoints connected to form a route do not fully consider the no-fly zone constraints, which may cause some flight segments to cross the no-fly zone.

[0009] 2) Manually drawn routes do not take into account the climb or dive angle and flight altitude constraints of the drone. Limited by the personal ability of the operator or facing some complex, dynamic, multi-constrained, and highly uncertain reconnaissance environments, manually drawn routes are prone to not fully consider the impact of ground elevation and ground obstacles. Some sections are too low, causing the route to pass through ground objects such as mountains, forests, and high-rise buildings, causing flight hazards. In addition, drone routes planned by existing technologies also have drastic changes in route altitude and repeated oscillations. Dramatic altitude changes or repeated oscillations in route altitude may cause the drone to lose control. In extreme cases, it may exceed the drone's response capability, causing a safety accident in which the drone is destroyed and the people are busy.

[0010] 3) Manually draw routes without fully considering environmental constraints, complex weather area constraints, etc.

[0011] Manually drawing the UAV reconnaissance mission route requires high personal ability of personnel, is prone to omissions, is time-consuming and laborious, and is difficult to adapt to the needs of quickly responding to the reconnaissance mission route planning during reconnaissance flights. Therefore, there is an urgent need for a technical solution that can quickly and automatically detect the planned UAV route, modify unreasonable waypoints, and quickly plan a new flight route based on the mission target information. Summary of the invention

[0012] In view of the above analysis, the embodiments of the present invention aim to provide an automated detection method, device and electronic device for UAV routes, so as to solve the technical problems that the existing UAV routes often have unreasonable route planning and are difficult to adapt to the needs of rapid response to mission planning routes in complex environments such as multiple constraints and dynamics.

[0013] In a first aspect, an embodiment of the present invention provides an automated detection method for a drone route, comprising the following steps:

[0014] Step 110: Obtaining the location data of each waypoint in the drone route, the location data of the no-fly zone, and the elevation data of the area through which the drone route passes;

[0015] Step 120: judging whether the drone route crosses the no-fly zone according to the location data of each waypoint and the location data of the no-fly zone, if so, planning an obstacle avoidance route around the no-fly zone and proceeding to step 130, otherwise directly proceeding to step 130;

[0016] Step 130: judging whether the turning angle at each waypoint in the UAV route satisfies the corner constraint value according to the position data of each waypoint, and if so, directly proceeding to step 140; otherwise, planning a UAV route in which the turning angle at each waypoint satisfies the corner constraint value and proceeding to step 140;

[0017] Step 140: judging whether the UAV route satisfies the flight altitude constraint value according to the position data of each waypoint and the elevation data; if so, directly proceeding to step 150; otherwise, planning a UAV route in which the altitude values ​​of each waypoint satisfy the flight altitude constraint value and proceeding to step 150;

[0018] Step 150: According to the total length of the drone route, determine whether the drone flight time meets the flight time constraint value. If so, output the drone route, otherwise output the flight time alarm information.

[0019] Based on the further improvement of the above automatic detection method, judging whether the drone route crosses the no-fly zone according to the location data of each waypoint and the location data of the no-fly zone includes:

[0020] Get the latitude and longitude values ​​of two waypoints in any segment of the drone route;

[0021] Determining a straight line model representing the flight segment according to the latitude and longitude values ​​of the two waypoints;

[0022] Obtain the longitude and latitude values ​​of each vertex of the no-fly zone;

[0023] Whether the flight segment crosses the no-fly zone is determined according to the straight line model and the longitude and latitude values ​​of each vertex of the no-fly zone.

[0024] Based on the further improvement of the above automatic detection method, the planning of an obstacle avoidance route to bypass the no-fly zone includes:

[0025] Step B10: Obtaining the location information of the two end points of the segment of the drone route that crosses the no-fly zone;

[0026] Step B20: Determine whether the two endpoints of the flight segment crossing the no-fly zone are outside the no-fly zone. If so, proceed to step B30; otherwise, determine multiple interconnected flight segments whose two endpoints are both outside the no-fly zone from the drone route, and proceed to step B30; wherein the multiple interconnected flight segments include the flight segment crossing the no-fly zone;

[0027] Step B30: setting the two endpoints outside the no-fly zone as a starting point and an end point, and determining a passable area and a non-passable area according to the position information of the starting point, the end point and all vertices of the no-fly zone; and

[0028] Step B40: Generate an obstacle avoidance route from the starting point to the end point using a path planning algorithm based on the traversable area and the non-traversable area.

[0029] Based on the further improvement of the above automatic detection method, judging whether the turning angle at each waypoint in the UAV route satisfies the corner constraint value according to the position data of each waypoint includes:

[0030] Obtaining location information of a target waypoint in the UAV route and a first waypoint and a second waypoint adjacent to the target waypoint on both sides;

[0031] Determine the turning angle of the drone route at the target waypoint according to the position information of the target waypoint, the first waypoint, and the second waypoint;

[0032] It is determined whether the turning angle is less than a corner threshold value. If so, the turning angle at the target waypoint does not satisfy the corner constraint value. Otherwise, the turning angle at the target waypoint satisfies the corner constraint value.

[0033] Based on the further improvement of the above-mentioned automatic detection method, the UAV route in which the altitude value of each waypoint satisfies the flight altitude constraint value includes:

[0034] Determine the turning radius of the UAV at the target waypoint;

[0035] Determine an inscribed circle tangent to a first flight segment and a second flight segment according to the turning angle and the turning radius, wherein two endpoints of the first flight segment are the target waypoint and the first waypoint, and two endpoints of the second flight segment are the target waypoint and the second waypoint; and

[0036] An alternative route for the UAV at the target waypoint is determined based on the inscribed circle, wherein the turning angle at each waypoint in the alternative route is greater than or equal to the corner threshold.

[0037] Based on the further improvement of the above automatic detection method, judging whether the UAV route meets the flight altitude constraint value according to the position data of each waypoint and the elevation data includes:

[0038] According to the latitude and longitude values ​​of the two end points of any segment in the UAV route, determine whether to perform sampling on the segment and the position of each sampling point on the segment;

[0039] According to the latitude and longitude values ​​of the two endpoints and the positions of the sampling points, obtaining the elevation values ​​of the two endpoints and the sampling points;

[0040] Determine whether the difference between the elevation value of each endpoint and each sampling point and its initial altitude value meets the safety threshold range. If so, the UAV route meets the flight altitude constraint value, otherwise the UAV route does not meet the flight altitude constraint value.

[0041] Based on the further improvement of the above-mentioned automatic detection method, the UAV route in which the altitude value of each waypoint satisfies the flight altitude constraint value includes:

[0042] In response to the difference between the elevation value of any of the endpoints or any of the sampling points and its initial elevation value not satisfying a safety threshold range, increasing the initial elevation value of the corresponding endpoint or the corresponding sampling point so that the difference between the elevation value of the endpoint or the sampling point and its initial elevation value satisfies a safety threshold range; and

[0043] In response to the number of the sampling points for increasing the initial height value being greater than or equal to a preset threshold, the sampling points for increasing the initial height value are numbered according to the flight direction of the drone, and the current height of the sampling points is determined according to the numbers of the sampling points.

[0044] Based on further improvement of the above automatic detection method, step 140 further includes:

[0045] Step 1401: Determine whether the current altitudes of the two endpoints and the sampling points of all the flight segments in the drone route are less than the maximum flight altitude value allowed by the drone. If so, directly proceed to step 150; otherwise, modify the altitude value of the endpoint or the sampling point whose current altitude is greater than or equal to the maximum flight altitude value allowed by the drone to be the same as the maximum flight altitude value allowed by the drone and proceed to step 1402;

[0046] Step 1402: Determine whether the current altitude of the modified endpoint or the sampling point is less than the minimum flight altitude value allowed by the drone. If so, generate altitude alarm information; otherwise, go to step 150.

[0047] In a second aspect, an embodiment of the present invention provides an automatic detection device for a drone route, comprising:

[0048] An acquisition module is configured to acquire the position data of each waypoint in the UAV route, the position data of the no-fly zone, and the elevation data of the area through which the UAV route passes;

[0049] The safety detection and optimization module is configured to determine whether the drone route crosses the no-fly zone based on the location data of each waypoint and the location data of the no-fly zone, and if so, plan an obstacle avoidance route around the no-fly zone and call the corner detection and optimization module, otherwise directly call the corner detection and optimization module;

[0050] The corner detection and optimization module is configured to determine whether the turning angle at each waypoint in the UAV route satisfies the corner constraint value according to the position data of each waypoint, and if so, directly enter the call height detection and optimization module, otherwise plan the UAV route in which the turning angle at each waypoint satisfies the corner constraint value and call the height detection and optimization module;

[0051] The height detection and optimization module is configured to determine whether the UAV route meets the flight height constraint value according to the position data of each waypoint and the elevation data, and if so, directly call the flight duration detection module, otherwise plan a UAV route in which the height value of each waypoint meets the flight height constraint value and call the flight duration detection module;

[0052] The flight duration detection module is configured to determine whether the flight duration of the drone meets the flight duration constraint value based on the total length of the drone route. If so, the drone route is output, otherwise a warning message is output.

[0053] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0054] at least one processor;

[0055] A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the automated detection method for the drone route as described in any one of the first aspects of the present invention.

[0056] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0057] 1. The scheme of the present invention introduces the automated testing algorithm into the route inspection, and automatically realizes the inspection of route elevation, no-fly zone, environmental constraints, route corners, etc., to meet the needs of rapid response to mission planning routes in complex, dynamic, multi-constrained, highly uncertain environments.

[0058] 2. The solution of the present invention can provide an algorithm for quickly detecting manually planned drone routes in complex environments, and give a route optimization solution, greatly improving the efficiency of route inspection, helping drones to quickly complete their missions, and avoiding catastrophic accidents such as drone crashes.

[0059] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0061] Figure 1 A schematic flow chart of an automated detection method for a UAV route according to an embodiment of the present invention is shown.

[0062] Figure 2 A conceptual schematic diagram of a drone route in an embodiment of the present invention is shown.

[0063] Figure 3 An example of planning a route for a drone according to an embodiment of the present invention is shown.

[0064] Figure 4 An example of a target waypoint, a first waypoint, and a second waypoint according to an embodiment of the present invention is shown.

[0065] Figure 5 An example of an inscribed circle according to an embodiment of the present invention is shown.

[0066] Figure 6 An example of an alternative route according to an embodiment of the present invention is shown.

[0067] Figure 7 An example of an alternative route according to an embodiment of the present invention is shown.

[0068] Figure 8 An example of determining sampling points according to the latitude and longitude values ​​of two end points of a flight segment according to an embodiment of the present invention is shown.

[0069] Fig. 9 An example of determining the current height of an unselected sampling point according to the current height of a replacement sampling point according to an embodiment of the present invention is shown.

[0070] Fig.10 Another example of determining the current height of an unselected sampling point according to the current height of a replacement sampling point according to an embodiment of the present invention is shown.

[0071] Fig.11 An exemplary block diagram of an automatic detection device for a drone route according to an embodiment of the present invention is shown.

[0072] Fig.12 A schematic block diagram of an example electronic device 1200 is shown that can be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0073] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0074] Figure 1 FIG. 2 is a flow chart showing an automatic detection method for a drone route according to an embodiment of the present invention. Figure 1 As shown, the automatic detection method of the UAV route includes the following steps:

[0075] Step 110: Obtain the location data of each waypoint in the drone route, the location data of the no-fly zone, and the elevation data of the area through which the drone route passes.

[0076] In this embodiment, the drone route in step 110 may be a manually drawn route. In this embodiment, the manually drawn route data may be obtained through an imported route file. The route data here includes the latitude and longitude data of each waypoint in the route. Figure 2 FIG. 1 is a conceptual diagram of a drone route in an embodiment of the present invention. Figure 2 As shown in the left figure, four waypoints can be marked on the two-dimensional map first ( Figure 2 The waypoints are marked as 1, 2, 3, and 4 in the figure, and then the waypoints are connected in sequence along the flight direction by manual connection to form a route. The right figure shows the route, which includes three segments, each of which is interconnected in sequence, and each segment includes two waypoints. For example, the first segment includes waypoint 1 and waypoint 2.

[0077] In this embodiment, the no-fly zone that has been drawn on the two-dimensional map can be obtained and the longitude and latitude data of the vertices can be extracted. In this embodiment, the current bad weather area that is unfavorable for flight (for example, including rain, snow, strong wind, and cloudy weather) that has been drawn on the two-dimensional map can be obtained and the longitude and latitude data of the vertices can be extracted. In this embodiment, the current enemy reconnaissance or danger area that has been drawn on the two-dimensional map can be obtained and the longitude and latitude data of the vertices can be extracted. For the convenience of description, this article will collectively refer to dangerous areas such as no-fly zones, current bad weather areas that are unfavorable for flight, current enemy reconnaissance or danger areas as "no-fly zones". "No-fly zones" are closed areas surrounded by multiple line segments, indicating areas where drones cannot pass.

[0078] In this embodiment, the longitude and latitude data of each vertex of the no-fly zone may also be obtained by other means, such as a no-fly zone that has been stored in the system but not displayed on the two-dimensional map.

[0079] In this embodiment, a judgment can be made based on the range of the no-fly zone and the range of the two-dimensional map to calculate whether the no-fly zone is within the two-dimensional map, outside the two-dimensional map, or intersecting with the two-dimensional map. If it is within the two-dimensional map, the longitude and latitude values ​​of each vertex of the no-fly zone can be directly obtained. If it is outside the range of the two-dimensional map, the no-fly zone can be set to be ineffective and not participate in subsequent calculations. If it intersects with the two-dimensional map, the intersection of the range of the no-fly zone and the range of the two-dimensional map can be calculated first, and then the no-fly zone can be divided according to the intersection, the part within the two-dimensional map is taken, and the no-fly zone vertices are reset. Finally, the longitude and latitude values ​​of each vertex of the no-fly zone after the division can be obtained.

[0080] In this embodiment, the elevation value of the area through which the drone route passes can be obtained through the imported digital elevation model (DEM). A digital elevation model is a data model used to represent the elevation of the terrain on the earth's surface. It is stored in digital form. The data of the DEM is usually stored in the form of a raster or grid. Each cell contains an elevation value, which represents the elevation of the location on the ground. The location on the ground can be represented by latitude and longitude values.

[0081] Step 120: Based on the location data of each waypoint and the location data of the no-fly zone, determine whether the drone route crosses the no-fly zone. If so, plan a drone route that bypasses the no-fly zone and proceed to step 130; otherwise, directly proceed to step 130.

[0082] In this embodiment, each two waypoints in the drone route can be connected into a line segment and represented by a mathematical straight line model. Then, a mathematical algorithm is used to determine whether the route crosses a no-fly zone. If a route crosses or is a critical no-fly zone, a prompt can be given that a route crosses or is a critical no-fly zone, and the longitude and latitude of the illegal route can be prompted; otherwise, the process directly goes to step 130.

[0083] In some embodiments, the following methods can be used to determine whether the drone route crosses a no-fly zone:

[0084] Step A100: Obtain the latitude and longitude values ​​of two waypoints in any segment of the drone route.

[0085] Step A200: Determine a straight line model representing the flight segment according to the latitude and longitude values ​​of the two waypoints.

[0086] In this embodiment, the latitude and longitude values ​​of the waypoints can be represented by x and y respectively. x is a point on the horizontal axis X (longitude direction), y is a point on the vertical axis Y (latitude direction), and XOY forms a two-dimensional plane (O is the origin). The mathematical expression of the straight line model is y=kx+b, where k and b are constants. In this embodiment, the longitude and latitude values ​​of two waypoints in any segment can be taken and represented by (x 1 ,y 1 ) and (x 2 ,y 2 ) represents the positions of the two waypoints on the XOY plane, where x 1 <x 2 . 1 ,y 1 ) and (x 2 ,y 2 ) into the straight line model y = kx + b, we can get the values ​​of k and b. At this time, the straight line model of this segment can be expressed as: y = kx + b, x∈[x 1 ,x 2 ].

[0087] In this embodiment, the latitude and longitude values ​​of the waypoints can be represented by x and y respectively. x is a point on the horizontal axis X (longitude direction), y is a point on the vertical axis Y (latitude direction), and XOY forms a two-dimensional plane (O is the origin). The mathematical expression of the straight line model is x=k'y+b', where k' and b' are constants. In this embodiment, the longitude and latitude values ​​of two waypoints in any segment can be taken and represented by (y 1 ,x 1 ) and (y 2 ,x 2 ) represents the positions of the two waypoints on the XOY plane, where y 1 <y 2 . 1 ,x 1 ) and (y 2 ,x 2 ) into the straight line model x = k'y + b', we can get the values ​​of k' and b'. At this time, the straight line model of this segment can be expressed as: x = k'y + b', y∈[y 1 ,y 2 ].

[0088] Step A300: Obtain the longitude and latitude values ​​of each vertex of the no-fly zone.

[0089] Step A400: Determine whether the flight segment crosses the no-fly zone based on the straight line model and the longitude and latitude values ​​of each vertex of the no-fly zone.

[0090] In this embodiment, the safety of the flight segment can be determined based on the positional relationship between the straight line model of the flight segment and the no-fly zone. Specifically, if the straight line model of the flight segment is y=kx+b, then when the no-fly zone is above or below the straight line model of the flight segment, the flight segment does not pass through the no-fly zone, otherwise the flight segment passes through the no-fly zone. If the straight line model of the flight segment is x=k'y+b', then when the no-fly zone is to the left or right of the straight line model of the flight segment, the flight segment does not pass through the no-fly zone, otherwise the flight segment passes through the no-fly zone.

[0091] In some embodiments, step A400 may include the following steps:

[0092] Step A10: Mapping the original longitude value or the original latitude value of the no-fly zone vertex onto the straight line model to obtain the mapped latitude value or the mapped longitude value of the no-fly zone vertex.

[0093] Step A20: Compare the mapped latitude value of the no-fly zone vertex with the original latitude value or the mapped longitude value with the original longitude value.

[0094] Step A30: Determine the safety of the flight segment according to the comparison results of the mapped latitude values ​​and the original latitude values ​​or the comparison results of the mapped longitude values ​​and the original longitude values ​​of all the no-fly zone vertices.

[0095] The following describes steps A10 to A30 by assuming that the straight line model of the flight segment is y=kx+b.

[0096] First, obtain the longitude and latitude values ​​of all vertices in the no-fly zone, and use x' and y' to represent the longitude and latitude values ​​of the vertices. x' is the point on the horizontal axis X (longitude direction), and y' is the point on the vertical axis Y (latitude direction).

[0097] After that, substitute x' into the straight line model of the current flight segment: y=kx+b, and calculate the y" value. Compare the y" value with the y' value to get the comparison result. If the y' values ​​of all vertices in the no-fly zone are greater than the y" value, it means that the no-fly zone is above this flight segment and this flight segment does not pass through the no-fly zone. If the y' values ​​of all vertices in the no-fly zone are less than the y" value, it means that the no-fly zone is below this flight segment and this flight segment does not pass through the no-fly zone. If it is calculated that the y' values ​​of some no-fly zone vertices are greater than the y" value, and the y' values ​​of some no-fly zone vertices are less than the y" value, it means that this flight segment crosses the no-fly zone. If it is calculated that the y' values ​​of some no-fly zone vertices are equal to the y" value, and the y' values ​​of all other no-fly zone vertices are greater than or less than the y" value, it means that this flight segment is a critical no-fly zone. If this flight segment passes through or is a critical no-fly zone, this flight segment is not safe, otherwise this flight segment is safe.

[0098] In this embodiment, the y" value obtained by the above calculation is the mapped latitude value of the vertex of the no-fly zone.

[0099] In this embodiment, two adjacent waypoints in the route can be combined into a flight segment, and each flight segment is calculated according to this algorithm. If all flight segments do not cross the no-fly zone, then the route does not cross the no-fly zone; if one flight segment crosses the no-fly zone or the critical no-fly zone, then the route has crossed the no-fly zone or the critical no-fly zone.

[0100] The following describes steps A10 to A30 by assuming that the straight line model of the flight segment is x=k'y+b'.

[0101] First, obtain the longitude and latitude values ​​of all vertices in the no-fly zone, and use x' and y' to represent the longitude and latitude values ​​of the vertices. x' is the point on the horizontal axis X (longitude direction), and y' is the point on the vertical axis Y (latitude direction).

[0102] After that, substitute y' into the straight line model of the current flight segment: x = k'y + b', and calculate the x" value. Compare the x" value with the x' value to get the comparison result. If the x' values ​​of all vertices in the no-fly zone are greater than the x" value, it means that the no-fly zone is on the right side of this flight segment, and this flight segment does not pass through the no-fly zone. If the x' values ​​of all vertices in the no-fly zone are less than the x" value, it means that the no-fly zone is on the left side of this flight segment, and this flight segment does not pass through the no-fly zone. If it is calculated that the x' values ​​of some no-fly zone vertices are greater than the x" value, and the x' values ​​of some no-fly zone vertices are less than the x" value, it means that this flight segment crosses the no-fly zone. If it is calculated that the x' values ​​of some no-fly zone vertices are equal to the x" value, and the x' values ​​of all other no-fly zone vertices are greater than or less than the x" value, it means that this flight segment is a critical no-fly zone. If this flight segment passes through or is a critical no-fly zone, this flight segment is not safe, otherwise this flight segment is safe.

[0103] In this embodiment, the x" value obtained by the above calculation is the mapped longitude value of the vertex of the no-fly zone.

[0104] In this embodiment, two adjacent waypoints in the route can be combined into a flight segment, and each flight segment is calculated according to this algorithm. If all flight segments do not cross the no-fly zone, then the route does not cross the no-fly zone; if one flight segment crosses the no-fly zone or the critical no-fly zone, then the route has crossed the no-fly zone or the critical no-fly zone.

[0105] Returning to step 120, if a flight segment passes through or is critical to a no-fly zone, an obstacle avoidance algorithm may be used to plan a UAV route that bypasses the no-fly zone and proceed to step 130. In this embodiment, the flight segment that passes through or is critical to a no-fly zone and the area where the no-fly zone is located may first be divided using a grid method. Afterwards, an intelligent obstacle avoidance algorithm may be used to plan a new flight segment that bypasses the no-fly zone.

[0106] In some embodiments, a new flight segment that bypasses a no-fly zone may be planned by the following method, thereby replacing a flight segment that passes through or is critical to a no-fly zone:

[0107] Step B10: Obtain the location information of the two end points of the segment of the planned route of the drone that passes through the no-fly zone.

[0108] Step B20: Determine whether the two endpoints of the flight segment passing through the no-fly zone are outside the no-fly zone. If so, proceed to step B30; otherwise, determine multiple interconnected flight segments whose two endpoints are both outside the no-fly zone from the planned UAV route, and proceed to step B30.

[0109] In this embodiment, it is possible to determine whether a waypoint is outside the no-fly zone based on the latitude and longitude data of the endpoint of the flight segment that passes through the no-fly zone and the latitude and longitude data of the vertex of the no-fly zone. In some embodiments, the following method can be used to determine whether a waypoint of a flight segment that passes through the no-fly zone is outside the no-fly zone:

[0110] Step B1: Draw a ray from the endpoint along the target direction.

[0111] In some embodiments, a ray may be drawn from the end point of the flight segment that passes through the no-fly zone to the right along the longitude direction.

[0112] In this embodiment, the latitude and longitude values ​​of the endpoint of the flight segment that passes through the no-fly zone can be represented by x and y values ​​respectively. x is a point on the horizontal axis X (longitude direction), y is a point on the vertical axis Y (latitude direction), and XOY constitutes the plane where the two-dimensional map is located (O is the origin). Draw a ray from the endpoint of the flight segment that passes through the no-fly zone to the right. The mathematical model of the ray is expressed as: y = kx + b, where k = 0, x>x 1 , x 1 is the longitude value of the endpoint. Then connect the vertices of the no-fly zone into line segments and intersect the ray with the edge of the no-fly zone, and calculate the number of intersections between the ray and the edge of the no-fly zone.

[0113] In some embodiments, a ray may be drawn along the longitude direction from the end point of the flight segment that passes through the no-fly zone to the left.

[0114] In this embodiment, the latitude and longitude values ​​of the endpoint of the flight segment that passes through the no-fly zone can be represented by x and y values ​​respectively. x is a point on the horizontal axis X (longitude direction), y is a point on the vertical axis Y (latitude direction), and XOY constitutes the plane where the two-dimensional map is located (O is the origin). Draw a ray from the endpoint of the flight segment that passes through the no-fly zone to the left. The mathematical model of the ray is expressed as: y = kx + b, where k = 0, x < x 1 , x 1is the longitude value of the endpoint. Then connect the vertices of the no-fly zone into line segments and intersect the ray with the edge of the no-fly zone, and calculate the number of intersections between the ray and the edge of the no-fly zone.

[0115] In some embodiments, a ray may be drawn upward along the latitude direction from the end point of the flight segment that passes through the no-fly zone.

[0116] In this embodiment, the latitude and longitude values ​​of the endpoint of the flight segment that passes through the no-fly zone can be represented by x and y values ​​respectively. x is a point on the horizontal axis X (longitude direction), y is a point on the vertical axis Y (latitude direction), and XOY constitutes the plane where the two-dimensional map is located (O is the origin). Draw a ray upward from the endpoint of the flight segment that passes through the no-fly zone. The mathematical model of the ray is expressed as: x = k'y + b', where k' = 0, y> y 1 ,y 1 The latitude value of the endpoint is then represented. Then the vertices of the no-fly zone are connected into line segments and the rays are intersected with the edges of the no-fly zone, and the number of intersections between the rays and the edges of the no-fly zone is calculated.

[0117] In some embodiments, a ray may be drawn downward in the latitude direction from the end point of the flight segment that passes through the no-fly zone.

[0118] In this embodiment, the latitude and longitude values ​​of the endpoint of the flight segment that passes through the no-fly zone can be represented by x and y values ​​respectively. x is a point on the horizontal axis X (longitude direction), y is a point on the vertical axis Y (latitude direction), and XOY constitutes the plane where the two-dimensional map is located (O is the origin). Draw a ray downward from the endpoint of the flight segment that passes through the no-fly zone. The mathematical model of the ray is expressed as: x = k'y + b', where k' = 0, y> y 1 ,y 1 The latitude value of the endpoint is then represented. Then the vertices of the no-fly zone are connected into line segments and the rays are intersected with the edges of the no-fly zone, and the number of intersections between the rays and the edges of the no-fly zone is calculated.

[0119] Step B2: judging whether the endpoint is outside the no-fly zone according to the number of intersections between the ray and the line segments of the no-fly zone.

[0120] In this embodiment, whether the waypoint is outside the no-fly zone can be determined based on the number of intersections between the ray and the edge of the no-fly zone.

[0121] In some embodiments, step B2 may include:

[0122] Step B101: Determine whether the intersection point of the ray and the line segment of the no-fly zone contains the vertex of the no-fly zone. If so and the number of intersection points that are the vertices of the no-fly zone is an odd number, proceed to step B102; otherwise, proceed to step B103.

[0123] Step B102: Determine whether the number of intersections between the ray and the line segments of the no-fly zone minus 1 is an odd number. If so, the endpoint is within the no-fly zone; otherwise, the endpoint is outside the no-fly zone.

[0124] Step B103: Determine whether the number of intersections between the ray and the line segments of the no-fly zone is an odd number. If so, the endpoint is within the no-fly zone; otherwise, the endpoint is outside the no-fly zone.

[0125] Specifically, it includes the following situations:

[0126] 1. If there is no vertex of the no-fly zone among the intersections of the waypoint's ray and the edge of the no-fly zone, the waypoint can be judged whether it is outside the no-fly zone based on the parity of the number of intersections between the waypoint's ray and the edge of the no-fly zone. Specifically, if the number of intersections between the waypoint's ray and the edge of the no-fly zone is even, the waypoint is outside the no-fly zone. If the number of intersections between the waypoint's ray and the edge of the no-fly zone is odd, the waypoint is inside the no-fly zone.

[0127] 2. If the intersection of the waypoint's ray and the edge of the no-fly zone contains a vertex of the no-fly zone, and the number of intersections that are vertices of the no-fly zone is even, then the waypoint can be judged whether it is outside the no-fly zone based on the parity of the number of intersections between the waypoint's ray and the edge of the no-fly zone. Specifically, if the number of intersections between the waypoint's ray and the edge of the no-fly zone is even, then the waypoint is outside the no-fly zone. If the number of intersections between the waypoint's ray and the edge of the no-fly zone is odd, then the waypoint is inside the no-fly zone.

[0128] 3. If the intersection of the waypoint's ray and the edge of the no-fly zone contains a vertex of the no-fly zone, and the number of intersections that are vertices of the no-fly zone is odd, then the waypoint can be judged to be outside the no-fly zone based on the parity of the number of intersections between the waypoint's ray and the edge of the no-fly zone minus 1. Specifically, if the number of intersections between the waypoint's ray and the edge of the no-fly zone minus 1 is an even number, then the waypoint is outside the no-fly zone. If the number of intersections between the waypoint's ray and the edge of the no-fly zone minus 1 is an odd number, then the waypoint is inside the no-fly zone.

[0129] In this embodiment, any of the above described embodiments can be used to determine whether the two endpoints of the flight segment that passes through the no-fly zone are outside the no-fly zone. If either of the two endpoints of the flight segment that passes through the no-fly zone is inside the no-fly zone, other waypoints outside the no-fly zone can be found from the planned route of the drone. In some embodiments, waypoints outside the no-fly zone can be found in the upstream adjacent waypoints and downstream adjacent waypoints of the flight segment that passes through the no-fly zone. The upstream and downstream here are relative to the flight direction of the drone. Figure 2For example, if the flying direction of the drone is from left to right, the upstream adjacent waypoint of waypoint 2 is waypoint 1, and the downstream adjacent waypoint of waypoint 3 is waypoint 4. In this embodiment, any of the above-described embodiments can be used to determine whether the upstream waypoint or the downstream waypoint is outside the no-fly zone. Figure 3 An example of a UAV route planning according to an embodiment of the present invention is shown below. Figure 3 The process of searching for a waypoint outside the no-fly zone among the upstream waypoints and the downstream waypoints in this embodiment is described. Figure 3 As shown in the figure, the known flight segment that passes through the no-fly zone is indicated in red, namely the flight segment consisting of waypoints 2 and 3. Figure 3 It can be seen that both waypoints 2 and 3 are in the no-fly zone, so it is necessary to find other waypoints outside the no-fly zone on the route. First, for waypoint 2, its upstream adjacent waypoint is waypoint 1, so we can first determine whether waypoint 1 is outside the no-fly zone. The determination method can adopt any of the embodiments described above. Figure 3 In the example, waypoint 1 is outside the no-fly zone, so a waypoint outside the no-fly zone has been found in the upstream direction. Next, for waypoint 3, its downstream adjacent waypoint is waypoint 4, so we can first determine whether waypoint 4 is outside the no-fly zone. The determination method can adopt any of the embodiments described above. Figure 3 In the example, waypoint 4 is still in the no-fly zone, so it is necessary to continue to search for waypoints outside the no-fly zone in the downstream direction. Since the adjacent waypoint downstream of waypoint 4 is waypoint 5, it is possible to determine whether waypoint 5 is outside the no-fly zone. The determination method can adopt any of the embodiments described above. Figure 3 In the example, waypoint 5 is outside the no-fly zone, so a waypoint outside the no-fly zone has been found in the downstream direction. The route composed of waypoint 1 to waypoint 5 is a multi-segment interconnected flight segment in which both endpoints are outside the no-fly zone in this embodiment.

[0130] Step B30: setting the two endpoints outside the no-fly zone as a starting point and an end point, and determining a passable area and a non-passable area according to the position information of the starting point, the end point and all vertices of the no-fly zone.

[0131] In this embodiment, after finding two waypoints outside the no-fly zone, the waypoint in the upstream direction can be set as the starting point, and the waypoint in the downstream direction can be set as the end point. Figure 3 In the example, waypoint 1 is the starting point and waypoint 5 is the end point. After that, you can traverse the starting point, end point, and all the no-fly zones, and select the maximum and minimum longitude values, as well as the maximum and minimum latitude values. A rectangular area can be enclosed according to the maximum longitude value, the minimum longitude value, the maximum latitude value, and the minimum latitude value. After that, the area occupied by the no-fly zone in the rectangular area can be set as an inaccessible area, and the other areas outside the no-fly zone can be set as a passable area.

[0132] In some embodiments, the rectangular area can be grid-divided according to a preset grid resolution. For example, a grid can be divided into 100 meters * 100 meters each, and the grid can completely cover the no-fly zone, the starting point, and the end point. After the grid is divided, each grid can be numbered, for example, (0,0) is used to represent the first grid in the lower left corner, and the grids are numbered with (0,1), (0,2), etc. to the right, and (1,0), (2,0), etc. to the top. The grid number and the longitude and latitude coordinate values ​​of the four corners of the grid can be recorded simultaneously for each grid. Then, according to the longitude and latitude coordinate values ​​of the starting point and the end point, the grid where the starting point and the end point are located are calculated and recorded as the starting point grid and the end point grid. After that, all grids can be traversed to calculate whether each grid coincides with or intersects with the no-fly zone. Shapely in the python toolbox can be used to quickly determine whether two polygons have overlapping areas or intersect. Finally, the grids that intersect or overlap with the no-fly zone are marked as 1 (indicating that there is an obstacle and it is not passable), otherwise the grids are marked as 0 (indicating that there is no obstacle and it is passable).

[0133] Step B40: Generate an obstacle avoidance route from the starting point to the end point using a path planning algorithm based on the traversable area and the non-traversable area.

[0134] In this embodiment, a path planning algorithm may be used to plan a path from a starting grid to an end grid, avoiding grids with obstacles, and passing through grids.

[0135] In some embodiments, a deep reinforcement learning (DRL) algorithm may be used for optimal path planning. Specifically, the DRL algorithm may be used to construct a deep Q-network (DQN). In order to prevent the DRL algorithm from oscillating infinitely and not converging during the calculation process, the present invention optimizes the DRL algorithm as follows:

[0136] 1. Dynamically adjust the grid resolution. First, you can calculate the distance from the start point to the end point. If the distance between the two waypoints is too large (for example, greater than or equal to 30 kilometers), in order to avoid the DRL algorithm not converging for a long time, you can divide the grid into 1 km * 1 km. If the distance between the two waypoints is less than the threshold (for example, 30 kilometers), you can divide the grid into 100 meters * 100 meters, thereby reducing the number of grids at large distances.

[0137] 2. Increase the number of neural network layers. In order to improve the optimization ability of the DRL algorithm, the number of DQN neural network layers can be increased from three to five, and the number of neurons can be increased.

[0138] In some embodiments, the path planning algorithm includes any one of the following: A* path planning algorithm, bidirectional A* path planning algorithm, Dijkstra algorithm.

[0139] Step 130: Based on the position data of each waypoint, determine whether the turning angle at each waypoint in the UAV route satisfies the corner constraint value. If so, directly proceed to step 140. Otherwise, plan a UAV route in which the turning angle at each waypoint satisfies the corner constraint value and proceed to step 140.

[0140] In this embodiment, it is possible to detect whether the turning angle at each waypoint in the drone route is greater than or equal to a corner threshold (e.g., 120°). If so, directly proceed to step 140, otherwise, the corner at the turning point of the drone route is smoothed.

[0141] In some embodiments, the following method can be used to determine whether the turning angle at each waypoint in the drone route meets the corner constraint value:

[0142] Step C100: Acquire the location information of a target waypoint in the UAV route and a first waypoint and a second waypoint adjacent to the target waypoint on both sides.

[0143] The target waypoint in this embodiment can be any other waypoint in the drone route except the first waypoint and the last waypoint. Figure 2 In the example of the right figure, both waypoint 2 and waypoint 3 can be the target waypoints in this embodiment. In this embodiment, the two sides of the target waypoint refer to the upstream direction and downstream direction of the target waypoint. The upstream and downstream here are relative to the flight direction of the drone. Figure 2 For example, the flight direction of the drone is from left to right. If waypoint 2 is selected as the target waypoint, the waypoint adjacent to waypoint 2 in the upstream direction is waypoint 1, and the waypoint adjacent to waypoint 2 in the downstream direction is waypoint 3.

[0144] The first waypoint in this embodiment may be a waypoint adjacent to the target waypoint in the upstream direction. Figure 2 For example, if waypoint 2 is selected as the target waypoint, the first waypoint is waypoint 1. In this embodiment, the second waypoint can be a waypoint adjacent to the target waypoint in the downstream direction. Figure 2 Taking the example of as an example, if waypoint 2 is selected as the target waypoint, the second waypoint is waypoint 3.

[0145] Step C200: Determine the turning angle of the drone route at the target waypoint based on the position information of the target waypoint, the first waypoint, and the second waypoint.

[0146] In this embodiment, the distance between the target waypoint and the first waypoint, the distance between the target waypoint and the second waypoint, and the distance between the first waypoint and the second waypoint can be calculated based on the latitude and longitude values ​​of the target waypoint, the first waypoint, and the second waypoint. Figure 4 An example of a target waypoint, a first waypoint, and a second waypoint according to an embodiment of the present invention is shown. Figure 4 As shown, the target waypoint is waypoint A, the first waypoint is waypoint B, the second waypoint is waypoint C, the distance of segment AB is b, the distance of segment AC is c, and the distance of segment BC is a. The angle θ between segment AB and AC at point A can be calculated according to the following formula:

[0147] cosθ=(b 2 +c 2 -a 2 ) / 2bc

[0148] The calculated angle θ is the turning angle of the UAV route at waypoint A.

[0149] Step C300: Determine whether the turning angle is less than a corner threshold value. If so, the turning angle at the target waypoint does not satisfy the corner constraint value. Otherwise, the turning angle at the target waypoint satisfies the corner constraint value.

[0150] The corner threshold in this embodiment can be any turning angle greater than or equal to the minimum turning angle allowed by the drone itself. For example, if the minimum turning angle allowed by the drone itself is 120 degrees, the corner threshold can be 120 degrees or 130 degrees. In some embodiments, the corner threshold is equal to the minimum turning angle allowed by the drone itself, such as 120 degrees.

[0151] Returning to step 130, if it is detected that a turning angle of a waypoint in the drone route is less than the corner threshold, the turning angle at the waypoint may be smoothed.

[0152] In some embodiments, the corners of the drone's flight path can be smoothed by the following method:

[0153] Step C400: Determine the turning radius of the UAV at the target waypoint.

[0154] In this embodiment, when it is determined that the turning angle at the target waypoint is less than the corner threshold, the minimum turning radius of the drone at the target waypoint can be calculated. In some embodiments, the turning radius of the drone at the target waypoint is greater than or equal to the minimum turning radius of the drone at the target waypoint, wherein the minimum turning radius of the drone at the target waypoint is determined by the flight speed of the drone at the target waypoint and the maximum roll angle of the drone. The minimum turning radius R of the drone at the target waypoint can be calculated by the following formula:

[0155] R=v 2 / (g×tanφ)

[0156] Among them, g is the acceleration of gravity, v is the flight speed of the UAV at the target waypoint, and Φ is the maximum roll angle of the UAV.

[0157] Step C500: determining an inscribed circle tangent to the first flight segment and the second flight segment according to the turning angle and the turning radius.

[0158] In this embodiment, the first flight segment may be a flight segment consisting of a target waypoint and a first waypoint, and the second flight segment may be a flight segment consisting of a target waypoint and a second waypoint. The coordinates of the center point of the inscribed circle tangent to both the first flight segment and the second flight segment may be calculated through the angle between the first flight segment and the second flight segment and the turning radius of the drone at the target waypoint. Figure 5 An example of an inscribed circle according to an embodiment of the present invention is shown. Figure 5 As shown, the target waypoint is waypoint A, the first waypoint is waypoint B, the second waypoint is waypoint C, the AB segment is the first segment, the AC segment is the second segment, the turning radius of the drone at waypoint A is r, and the angle between the first segment and the second segment is θ. Based on the known radius R and angle θ, the coordinates of the center point O of the inscribed circle tangent to both AC and AB can be calculated. Specifically, the distance from the coordinates of point O to the perpendicular lines of the two line segments AC and AB is r, which can be obtained:

[0159]

[0160] Among them, the distance of segment AB is b, the distance of segment AC is c, the distance of segment BC is a, and X and Y are the coordinates of the center point O. After obtaining the coordinates of the center point O, a circle with a radius of r can be drawn from the center point O, which is tangent to AC and AB respectively. According to the coordinates of the center point O, the coordinates of point A and the angle θ, the radius r is known, and the coordinates of the tangent points A1 and A2 of the inscribed circle with AC and AB can be obtained respectively.

[0161] Step C600: Determine an alternative route for the UAV at the target waypoint based on the inscribed circle.

[0162] In this embodiment, an inscribed circle can be used to find a route for replacing the target waypoint. The turning angle at each waypoint in the alternative route is greater than the corner threshold. In some embodiments, step C600 may include the following steps:

[0163] Step C10: Acquire position information of a first tangent point and a second tangent point, wherein the first tangent point is the tangent point between the inscribed circle and the first flight segment, and the second tangent point is the tangent point between the inscribed circle and the second flight segment.

[0164] Step C20: intercepting an arc from the first tangent point to the second tangent point from the inscribed circle.

[0165] Step C30: Select one or more replacement points on the arc.

[0166] Step C40: sequentially connect the first tangent point, the one or more replacement points, and the second tangent point to obtain an alternative route.

[0167] Figure 6 An example of an alternative route according to an embodiment of the present invention is shown. Figure 6 Steps C10 to C40 are explained. It should be noted that: Figure 6 Yes Figure 5 Further processing of the example shown.

[0168] According to the above Figure 5 From the description, we can know that the first tangent point is the tangent point A1 between the inscribed circle and the AB line segment, and the second tangent point is the tangent point A2 between the inscribed circle and the AC line segment. After obtaining the coordinates of the tangent points A1 and A2 between the inscribed circle and AC and AB, we can intercept the arc between the tangent point A1 and the tangent point A2. Figure 5 In the figure, the dotted line crossing the intersection points A1 and A2 represents the interception direction of the inscribed circle, and the arc below the dotted line is the intercepted arc. Then, one or more points on the arc can be selected as new waypoints to replace waypoint A. Figure 6 In the example, two points A3 and A4 are selected from the arc as new waypoints to replace waypoint A. Finally, point A1, point A3, point A4, and point A2 are connected in sequence to obtain a new route, which is the alternative route in this embodiment.

[0169] In some embodiments, step C600 may include the following steps:

[0170] Step C11: Acquire position information of a first tangent point and a second tangent point, wherein the first tangent point is the tangent point between the inscribed circle and the first flight segment, and the second tangent point is the tangent point between the inscribed circle and the second flight segment.

[0171] Step C22: Connect the target waypoint and the center of the inscribed circle to obtain a connecting line segment.

[0172] Step C33: Obtain the intersection point of the connecting line segment and the inscribed circle.

[0173] Step C44: sequentially connect the first tangent point, the intersection point, and the second tangent point to obtain an alternative route.

[0174] Figure 7 An example of an alternative route according to an embodiment of the present invention is shown. Figure 7Steps C11 to C44 are described. It should be noted that: Figure 7 Yes Figure 5 Further processing of the example shown.

[0175] According to the above Figure 5 From the description, we can see that the first tangent point is the tangent point A1 between the inscribed circle and the AB line segment, and the second tangent point is the tangent point A2 between the inscribed circle and the AC line segment. After obtaining the coordinates of the tangent points A1 and A2, we can connect waypoint A and the center point O of the inscribed circle to obtain line segment AO. Then we can find the intersection point A3 of line segment AO and the inscribed circle. Specifically, first, based on the coordinates of waypoint A and the center point O, we can calculate the expression of line segment AO: y=kx+b, where x is longitude and y is latitude. Since point A3 is on line segment AO, y satisfies 3 =kx 3 +b(x 3 ,y 3 is the coordinate of point A3), and point A3 is also on the inscribed circle, so r is also satisfied. 2 =(x 3 -x 0 ) 2 +(y 3 -y 0 ) 2 (x 0 ,y 0 is the coordinate of the center point O), and A3 satisfies the condition that it is between point O and point A; the coordinate of the unique point A3 can be obtained according to the above two equations and the condition that A3 satisfies. Finally, a new route is obtained by connecting point A1, point A3, and point A2 in sequence, and the new route is the alternative route in this embodiment.

[0176] In some embodiments, the altitude value of the alternative route described in the above embodiments is the same as the altitude value of the target waypoint.

[0177] Step 140: Based on the position data of each waypoint and the elevation data, determine whether the UAV route meets the flight altitude constraint value. If so, directly proceed to step 150. Otherwise, plan a UAV route in which the altitude value of each waypoint meets the flight altitude constraint value and proceed to step 150.

[0178] In this embodiment, it is possible to detect whether the difference between the altitude value of the drone route and the elevation value of the area through which the route passes is greater than or equal to a height threshold (for example, 500 meters). If so, directly enter step 140, otherwise the altitude of the drone route is optimized and the optimized route is smoothed.

[0179] In some embodiments, the following method can be used to determine whether the drone route meets the flight altitude constraint value:

[0180] Step D100: Determine whether to perform sampling on any segment in the UAV route and the position of each sampling point on the segment according to the latitude and longitude values ​​of the two end points of the segment.

[0181] In this embodiment, whether to perform sampling on the flight segment can be determined based on the distance between the two endpoints of the flight segment. In some embodiments, it can be determined whether the distance between the two endpoints of the flight segment is less than a distance threshold (e.g., 1 kilometer). If it is less than the distance threshold, sampling is not performed; otherwise, sampling is performed.

[0182] In some embodiments, the absolute values ​​of the longitude difference and the latitude difference of the two endpoints of the flight segment may be calculated first; then, it is determined whether the absolute values ​​of the longitude difference and the latitude difference are both less than a distance threshold (e.g., 1 km). If the absolute values ​​of the longitude difference and the latitude difference are both less than the distance threshold, no sampling is performed; otherwise, sampling is performed. Exemplarily, the absolute values ​​of the longitude difference and the latitude difference of the two endpoints of the flight segment may be calculated first; then, it is determined which one of the absolute values ​​of the longitude difference and the latitude difference is greater; then, it is determined whether the greater one of the absolute values ​​of the longitude difference and the latitude difference is less than the distance threshold. If the greater one of the absolute values ​​of the longitude difference and the latitude difference is less than the distance threshold, no sampling is performed; otherwise, sampling is performed.

[0183] In this embodiment, after determining to sample the flight segment, the location of the sampling point on the flight segment can be determined according to the latitude and longitude values ​​of the two endpoints of the flight segment. Specifically, the location of the sampling point on the flight segment can be determined according to the distance between the two endpoints of the flight segment. For example, if the distance between the two endpoints of the flight segment is 6d, then starting from the starting point of the flight segment, a point can be sampled every distance d, and a total of 5 sampling points can be obtained.

[0184] In some embodiments, determining the position of the sampling point on the flight segment according to the latitude and longitude values ​​of the two end points of the flight segment includes:

[0185] First, the absolute value of the longitude difference and the absolute value of the latitude difference of the two end points of the segment can be calculated; then, the larger of the absolute value of the longitude difference and the absolute value of the latitude difference is selected; finally, along the selected longitude or latitude direction, from the starting point to the end point of the segment, sampling is performed on the segment at every preset distance. Figure 8 FIG. 2 shows an example of determining sampling points according to the latitude and longitude values ​​of the two end points of a flight segment according to an embodiment of the present invention. Figure 8 As shown in the figure, for the segment consisting of waypoints A and B, the absolute value of the longitude difference between waypoints A and B (i.e., horizontal span) is greater than the absolute value of the latitude difference (i.e., vertical span), so we can sample every 1 km along the longitude direction on segment AB. Since waypoint A is the starting point and waypoint B is the end point in segment AB, we can start from waypoint A and make the first sampling point at a distance of 1 km from waypoint A in the longitude direction. Figure 8Marked as waypoint 1. Afterwards, a second sampling point can be made at a distance of 1 km from waypoint 1 in the longitude direction. Figure 8 is marked as waypoint 2, and so on.

[0186] Step D200: acquiring the elevation values ​​of the two endpoints and each of the sampling points according to the latitude and longitude values ​​of the two endpoints and the positions of each of the sampling points.

[0187] In this embodiment, according to the latitude and longitude values ​​of the waypoints on the route and the latitude and longitude values ​​of the sampling points, the imported DEM is indexed to obtain the elevation values ​​of the waypoints and the sampling points.

[0188] Step D300: Determine whether the difference between the elevation value of each endpoint and each sampling point and its initial elevation value meets the safety threshold range. If so, the drone route meets the flight altitude constraint value, otherwise the drone route does not meet the flight altitude constraint value.

[0189] The initial altitude value of the drone route refers to the altitude generated by the system by default, which is usually a fixed value (for example, 3000 meters). The initial altitude values ​​of each waypoint on the drone route are fixed values. In this embodiment, it can be determined whether the difference between the elevation value of the endpoint or sampling point of the flight segment and its initial altitude value meets the safety threshold range. For example, in this example, it can be determined whether the difference between the initial altitude value of the endpoint or sampling point of the flight segment and its elevation value (3000-DEM) is greater than the safety threshold (for example, 500 meters). If so, the initial altitude value of the endpoint or sampling point meets the safety threshold range, otherwise it does not meet the safety threshold range.

[0190] Returning to step 140, if it is detected that the initial height value of an endpoint or sampling point in the drone route does not meet the safety threshold range, the height value of the endpoint or sampling point can be adjusted and the height of the adjusted sampling point can be smoothed.

[0191] In some embodiments, the method of adjusting the altitude value of the route endpoint or the sampling point and smoothing the adjusted altitude of the sampling point includes:

[0192] Step D300: In response to the difference between the elevation value of any of the endpoints or any of the sampling points and its initial height value not satisfying the safety threshold range, the initial height value of the corresponding endpoint or the corresponding sampling point is increased so that the difference between the elevation value of the endpoint or the sampling point and its initial height value satisfies the safety threshold range.

[0193] In this embodiment, if the difference between the calculated elevation value of the endpoint or sampling point and its initial elevation value meets the safety threshold range, the initial elevation value of the endpoint or sampling point is maintained unchanged. If the difference between the calculated elevation value of the endpoint or sampling point and its initial elevation value does not meet the safety threshold range, the elevation value of the endpoint or sampling point is modified to the elevation value plus the safety threshold so that the elevation value of the endpoint or sampling point meets the safety threshold range. For example, if the difference between the initial elevation value of the endpoint or sampling point of the flight segment and its elevation value is less than the safety threshold (3000-DEM<500), the current elevation value of the endpoint or sampling point can be set to its elevation value plus the safety threshold (DEM+500), and this sampling point is inserted into the waypoint, and the latitude, longitude and altitude values ​​of this sampling point are recorded.

[0194] Step D400: In response to the number of the sampling points for increasing the initial height value being greater than or equal to a preset threshold, the sampling points for increasing the initial height value are numbered according to the flight direction of the UAV, and the current height of the sampling points is determined according to the numbers of the sampling points.

[0195] In this embodiment, it can be determined whether the number of sampling points that increase the initial altitude value is greater than or equal to a preset threshold value (for example, 3). If so, the sampling points that increase the initial altitude value are numbered, otherwise they are not numbered and the current altitudes of the sampling points are maintained unchanged. For example, if there are five sampling points in segment AB, the current altitudes of 3 sampling points are the initial altitude values, and the current altitudes of 2 sampling points are greater than the initial altitude values, since the number of sampling points whose current altitudes are greater than the initial altitude values ​​is less than 3 (the preset threshold value), these 2 sampling points are not numbered, and the current altitudes of the 2 sampling points are maintained unchanged.

[0196] In this embodiment, if the number of sampling points for adding the initial height value is greater than or equal to a preset threshold, the sampling points for adding the initial height value may be numbered according to the flight direction of the drone. Figure 8 An example of numbering the sampling points according to an embodiment of the present invention is also shown. Figure 8 As shown in , if there are 5 sampling points between segment AB, and the heights of all sampling points are greater than the initial height value, then starting from starting point A, along the direction A→B, the 5 sampling points can be numbered 1, 2, 3, 4, and 5 in sequence. Figure 8 In the figure, the sampling point numbered 1 is closest to the starting point, and the sampling point numbered 5 is closest to the end point. The direction A→B is the flight direction of the drone in segment AB.

[0197] In this embodiment, the current height of the sampling point can be determined according to the number of the sampling point. Exemplarily, the sampling points whose absolute values ​​of the number distances are within a threshold range (e.g., less than or equal to 2) can be selected as a group of sampling points to be optimized, and then the current height of the sampling point with the smallest height in the group of sampling points to be optimized is modified to the summed average of the current heights of multiple sampling points with the highest heights in the group of sampling points to be optimized.

[0198] In some embodiments, determining the current height of the sampling point according to the number of the sampling point comprises the following steps:

[0199] Step D10: Select N sampling points with the largest height values ​​from the sampling points with increased initial height values, where N is an integer greater than or equal to 1.

[0200] In this embodiment, the current heights of the N sampling points may be maintained unchanged, that is, the heights after the initial height value is increased.

[0201] Step D20: for the unselected sampling points, determine their current heights according to the distances between their numbers and the numbers of the N sampling points.

[0202] In this embodiment, the distance between the numbers of the sampling points refers to the absolute value of the difference between the numbers of the sampling points. For example, the number distance between the sampling point numbered 1 and the sampling point numbered 2 is 1. In step D20, for the unselected sampling points, a sampling point whose number distance is within a threshold range (for example, less than or equal to 2) can be selected from the N sampling points as the target sampling point, and then the current height of the unselected sampling point is modified to the summed average of the current heights of the target sampling points.

[0203] In some embodiments, step D20 includes:

[0204] Step D202: Selecting a sampling point with the closest number distance from the N sampling points as a replacement sampling point for the unselected sampling point.

[0205] In this embodiment, for an unselected sampling point, a sampling point with the closest serial number to the unselected sampling point may be selected from the N sampling points as a replacement sampling point for the sampling point.

[0206] Step D204: Determine the current height of the unselected sampling point according to the current height of the replacement sampling point.

[0207] In this embodiment, for unselected sampling points, their current heights may be modified to be the same as the current heights of the sampling points they replace.

[0208] Fig. 9An example of determining the current height of an unselected sampling point according to the current height of a replacement sampling point according to an embodiment of the present invention is shown. Fig. 9 Step D204 is described.

[0209] exist Fig. 9 In the left figure, there are 5 sampling points between the flight segments AB, and the height of each sampling point is greater than the initial height value. After numbering the 5 sampling points, we get sampling point 1, sampling point 2, sampling point 3, sampling point 4 and sampling point 5 respectively. The height values ​​of sampling point 1, sampling point 2, sampling point 3, sampling point 4 and sampling point 5 are as follows: Fig. 9 As shown in the right figure, the height value of sampling point 3 is less than the height value of sampling point 2, less than the height value of sampling point 1, less than the height value of sampling point 4, less than the height value of sampling point 5. First, three sampling points with the largest height values ​​(N=3) can be selected from the five sampling points, namely sampling point 1, sampling point 4 and sampling point 5, and the height values ​​of these three sampling points remain unchanged. Afterwards, for sampling point 3, the sampling point with the closest number distance can be selected from sampling point 1, sampling point 4 and sampling point 5, namely sampling point 4. Subsequently, the height value of sampling point 3 is modified to be the same as the height value of sampling point 4, and a new sampling point 3' is obtained to replace sampling point 3. For sampling point 2, the sampling point with the closest number distance can be selected from sampling point 1, sampling point 4 and sampling point 5, namely sampling point 1. Subsequently, the height value of sampling point 2 is modified to be the same as the height value of sampling point 1, and a new sampling point 2' is obtained to replace sampling point 2. Finally, sampling point 1, sampling point 2', sampling point 3', sampling point 4 and sampling point 5 are connected in sequence to form a highly smooth new route in the present invention.

[0210] Fig.10 Another example of determining the current height of an unselected sampling point according to the current height of a replacement sampling point according to an embodiment of the present invention is shown.

[0211] exist Fig.10 In the left figure, there are 5 sampling points between the flight segments AB, and the height of each sampling point is greater than the initial height value. After numbering the 5 sampling points, we get sampling point 1, sampling point 2, sampling point 3, sampling point 4 and sampling point 5 respectively. The height values ​​of sampling point 1, sampling point 2, sampling point 3, sampling point 4 and sampling point 5 are as follows: Fig.10As shown in the right figure in , that is, the height value of sampling point 2 is less than the height value of sampling point 4, less than the height value of sampling point 1, less than the height value of sampling point 3, less than the height value of sampling point 5. First, the three sampling points with the largest height values ​​(N=3) can be selected from the five sampling points, namely sampling point 1, sampling point 3 and sampling point 5, and the height values ​​of these three sampling points remain unchanged. Afterwards, for sampling point 2, the sampling points with the closest number distance can be selected from sampling point 1, sampling point 3 and sampling point 5, namely sampling point 1 and sampling point 2. Subsequently, the height value of sampling point 2 is modified to be the same as the smaller of the height values ​​of sampling point 1 and sampling point 2, and a new sampling point 2' is obtained to replace sampling point 2. For sampling point 4, the sampling points with the closest number distance can be selected from sampling point 1, sampling point 3 and sampling point 5, namely sampling point 3 and sampling point 5. Subsequently, the height value of sampling point 4 is modified to be the same as the smaller of the height values ​​of sampling point 3 and sampling point 5, and a new sampling point 4' is obtained to replace sampling point 4. Finally, sampling point 1, sampling point 2', sampling point 3, sampling point 4', and sampling point 5 are sequentially connected together to form a highly smooth new route in the present invention.

[0212] Returning to step 140, in some embodiments, step 140 further includes:

[0213] Step 1401: Determine whether the current altitudes of the two endpoints and each sampling point of all flight segments in the UAV route are less than the maximum flight altitude value allowed by the UAV. If so, directly proceed to step 150; otherwise, modify the altitude value of the endpoint or the sampling point whose current altitude is greater than or equal to the maximum flight altitude value allowed by the UAV to be the same as the maximum flight altitude value allowed by the UAV and proceed to step 1402.

[0214] The maximum flight altitude value here refers to the maximum flight altitude value of the drone, for example, 10,000 meters. In this embodiment, it is possible to detect whether the current altitude of all waypoints and sampling points is greater than the maximum flight altitude value. If so, the current altitude is modified to the maximum flight altitude value; otherwise, the current altitude remains unchanged.

[0215] Step 1402: Determine whether the current altitude of the modified endpoint or the sampling point is less than the minimum flight altitude value allowed by the drone. If so, generate altitude alarm information; otherwise, go to step 150.

[0216] The minimum flight altitude value here refers to the minimum safe altitude value for the drone to fly, for example, 100 meters. In this embodiment, it can be determined whether the maximum flight altitude value is greater than the sum of the elevation value of the waypoint modified to the maximum flight altitude value and the minimum flight altitude value. If so, no alarm information is generated; otherwise, an alarm information is generated.

[0217] Step 150: According to the total length of the drone route, determine whether the drone flight time meets the flight time constraint value. If so, output the drone route, otherwise output the flight time alarm information.

[0218] In this embodiment, the length of the drone route can be calculated, and the flight time can be calculated based on the route length. Then, the flight time is detected based on the current fuel level of the drone to see if it is less than the maximum allowed flight time. If so, the new route after detection optimization is displayed on the two-dimensional map; otherwise, a duration alarm message is output.

[0219] Fig.11 FIG. 2 shows an exemplary block diagram of an automatic detection device for a drone route according to an embodiment of the present invention. Fig.11 As shown, the automatic detection device 300 for the drone route includes: an acquisition module 1101, which is configured to obtain the position data of each waypoint in the drone route, the position data of the no-fly zone, and the elevation data of the area through which the drone route passes; a safety detection and optimization module 1102, which is configured to determine whether the drone route crosses the no-fly zone based on the position data of each waypoint and the position data of the no-fly zone, and if so, plan an obstacle avoidance route that bypasses the no-fly zone and call the corner detection and optimization module, otherwise directly call the corner detection and optimization module; a corner detection and optimization module 1103, which is configured to determine whether the turning angle at each waypoint in the drone route meets the corner constraint value based on the position data of each waypoint, If so, directly enter and call the altitude detection and optimization module, otherwise plan a drone route in which the turning angles at each waypoint satisfy the corner constraint value and call the altitude detection and optimization module; the altitude detection and optimization module 1104 is configured to determine whether the drone route satisfies the flight altitude constraint value based on the position data of each waypoint and the elevation data, and if so, directly call the flight duration detection module, otherwise plan a drone route in which the altitude values ​​of each waypoint satisfy the flight altitude constraint value and call the flight duration detection module; the flight duration detection module 1105 is configured to determine whether the drone flight duration satisfies the flight duration constraint value based on the total length of the drone route, and if so, output the drone route, otherwise output a warning message.

[0220] It should be understood that Fig.11 The various modules of the apparatus 1100 shown in FIG. 1 can be used in conjunction with the reference Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features and advantages described above for the method are also applicable to the device 1100 and the modules included therein. For the sake of brevity, some operations, features and advantages are not repeated here.

[0221] Fig.12 1 shows a schematic block diagram of an example electronic device 1200 that can be used to implement embodiments of the present disclosure. Fig.12 , a structural block diagram of an electronic device 1200 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein. Fig.12 As shown, the electronic device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. In RAM 1203, various programs and data required for the operation of the device 1200 can also be stored. The computing unit 1201, ROM 1202, and RAM 1203 are connected to each other via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204. Multiple components in the device 1200 are connected to the I / O interface 1205, including: an input unit 1206, such as a keyboard, a mouse, etc.; an output unit 1207, such as various types of displays, speakers, etc.; a storage unit 1208, such as a disk, an optical disk, etc.; and a communication unit 1209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1209 allows the device 1200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0222] The computing unit 1201 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1201 performs the various methods and processes described above, such as the automated detection method of the drone route. For example, in some embodiments, the automated detection method of the drone route may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1208. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1200 via the ROM 1202 and / or the communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the gift package recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to perform an automated detection method of a drone route in any other suitable manner (e.g., by means of firmware).

[0223] An embodiment of the present invention provides a non-transient computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute any of the above-described methods for automatically detecting the route of a drone. In this article, one or more computer-readable non-transient storage media may include one or more semiconductor-based or other integrated circuits (ICs) (e.g., field programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard disk drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical disk drives, floppy disks, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM drives, and any other suitable computer-readable non-transient storage media. The computer-readable non-transient storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile.

[0224] An embodiment of the present invention provides a computer program product, comprising program code instructions. When the program product is executed by a computer, the program code instructions enable the computer to execute any one of the above-described methods for automatically detecting drone routes.

[0225] Compared with the prior art, the embodiments of the present invention can achieve at least one of the following beneficial effects:

[0226] 1. The scheme of the present invention introduces the automated testing algorithm into the route inspection, and automatically realizes the inspection of route elevation, no-fly zone, environmental constraints, route corners, etc., to meet the needs of rapid response to mission planning routes in complex, dynamic, multi-constrained, highly uncertain environments.

[0227] 2. The solution of the present invention can provide an algorithm for quickly detecting manually planned drone routes in complex environments, and give a route optimization solution, greatly improving the efficiency of route inspection, helping drones to quickly complete their missions, and avoiding catastrophic accidents such as drone crashes.

[0228] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An automated detection method for drone routes, characterized in that: The following steps are involved: Step 110: Obtaining the location data of each waypoint in the drone route, the location data of the no-fly zone, and the elevation data of the area through which the drone route passes; Step 120: judging whether the drone route crosses the no-fly zone according to the location data of each waypoint and the location data of the no-fly zone, if so, planning an obstacle avoidance route around the no-fly zone and proceeding to step 130, otherwise directly proceeding to step 130; Step 130: judging whether the turning angle at each waypoint in the UAV route satisfies the corner constraint value according to the position data of each waypoint, and if so, directly proceeding to step 140; otherwise, planning a UAV route in which the turning angle at each waypoint satisfies the corner constraint value and proceeding to step 140; Step 140: judging whether the UAV route satisfies the flight altitude constraint value according to the position data of each waypoint and the elevation data; if so, directly proceeding to step 150; otherwise, planning a UAV route in which the altitude values ​​of each waypoint satisfy the flight altitude constraint value and proceeding to step 150; Step 150: According to the total length of the drone route, determine whether the drone flight time meets the flight time constraint value. If so, output the drone route, otherwise output the flight time alarm information.

2. The automated detection method according to claim 1, characterized in that: According to the location data of each waypoint and the location data of the no-fly zone, determining whether the drone route crosses the no-fly zone includes: Get the latitude and longitude values ​​of two waypoints in any segment of the drone route; Determining a straight line model representing the flight segment according to the latitude and longitude values ​​of the two waypoints; Obtain the longitude and latitude values ​​of each vertex of the no-fly zone; Whether the flight segment crosses the no-fly zone is determined according to the straight line model and the longitude and latitude values ​​of each vertex of the no-fly zone.

3. The automated detection method according to claim 1, characterized in that: The planning of an obstacle avoidance route that circumvents the no-fly zone includes: Step B10: Obtaining the location information of the two end points of the segment of the drone route that crosses the no-fly zone; Step B20: Determine whether the two endpoints of the flight segment crossing the no-fly zone are outside the no-fly zone. If so, proceed to step B30; otherwise, determine multiple interconnected flight segments whose two endpoints are both outside the no-fly zone from the drone route, and proceed to step B30; wherein the multiple interconnected flight segments include the flight segment crossing the no-fly zone; Step B30: setting the two endpoints outside the no-fly zone as a starting point and an end point, and determining a passable area and a non-passable area according to the position information of the starting point, the end point and all vertices of the no-fly zone; and Step B40: Generate an obstacle avoidance route from the starting point to the end point using a path planning algorithm based on the traversable area and the non-traversable area.

4. The automated detection method according to claim 1, characterized in that: According to the position data of each waypoint, judging whether the turning angle at each waypoint in the route of the UAV satisfies the corner constraint value includes: Obtaining location information of a target waypoint in the UAV route and a first waypoint and a second waypoint adjacent to the target waypoint on both sides; Determine the turning angle of the drone route at the target waypoint according to the position information of the target waypoint, the first waypoint, and the second waypoint; It is determined whether the turning angle is less than a corner threshold value. If so, the turning angle at the target waypoint does not satisfy the corner constraint value. Otherwise, the turning angle at the target waypoint satisfies the corner constraint value.

5. The automated detection method according to claim 4, characterized in that: The UAV route in which the altitude values ​​of each waypoint are planned to meet the flight altitude constraint value includes: Determine the turning radius of the UAV at the target waypoint; Determine an inscribed circle tangent to a first flight segment and a second flight segment according to the turning angle and the turning radius, wherein two endpoints of the first flight segment are the target waypoint and the first waypoint, and two endpoints of the second flight segment are the target waypoint and the second waypoint; and An alternative route for the UAV at the target waypoint is determined according to the inscribed circle, wherein the turning angle at each waypoint in the alternative route is greater than or equal to the corner threshold.

6. The automated detection method according to claim 1, characterized in that: Judging whether the route of the UAV satisfies the flight altitude constraint value according to the position data of each waypoint and the altitude data includes: According to the latitude and longitude values ​​of the two end points of any segment in the UAV route, determine whether to perform sampling on the segment and the position of each sampling point on the segment; According to the latitude and longitude values ​​of the two endpoints and the positions of the sampling points, obtaining the elevation values ​​of the two endpoints and the sampling points; Determine whether the difference between the elevation value of each endpoint and each sampling point and its initial altitude value meets the safety threshold range. If so, the UAV route meets the flight altitude constraint value, otherwise the UAV route does not meet the flight altitude constraint value.

7. The automated detection method according to claim 6, characterized in that: The UAV route in which the altitude values ​​of each waypoint are planned to meet the flight altitude constraint value includes: In response to the difference between the elevation value of any of the endpoints or any of the sampling points and its initial elevation value not satisfying a safety threshold range, increasing the initial elevation value of the corresponding endpoint or the corresponding sampling point so that the difference between the elevation value of the endpoint or the sampling point and its initial elevation value satisfies a safety threshold range; and In response to the number of the sampling points for increasing the initial height value being greater than or equal to a preset threshold, the sampling points for increasing the initial height value are numbered according to the flight direction of the drone, and the current height of the sampling points is determined according to the numbers of the sampling points.

8. The automated detection method according to claim 7, characterized in that: Step 140 also includes: Step 1401: Determine whether the current altitudes of the two endpoints and the sampling points of all the flight segments in the drone route are less than the maximum flight altitude value allowed by the drone. If so, directly proceed to step 150; otherwise, modify the altitude value of the endpoint or the sampling point whose current altitude is greater than or equal to the maximum flight altitude value allowed by the drone to be the same as the maximum flight altitude value allowed by the drone and proceed to step 1402; Step 1402: Determine whether the current altitude of the modified endpoint or the sampling point is less than the minimum flight altitude value allowed by the drone. If so, generate altitude alarm information; otherwise, go to step 150.

9. An automatic detection device for a drone route, characterized in that: include: An acquisition module is configured to acquire the position data of each waypoint in the UAV route, the position data of the no-fly zone, and the elevation data of the area through which the UAV route passes; The safety detection and optimization module is configured to determine whether the drone route crosses the no-fly zone based on the location data of each waypoint and the location data of the no-fly zone, and if so, plan an obstacle avoidance route around the no-fly zone and call the corner detection and optimization module, otherwise directly call the corner detection and optimization module; The corner detection and optimization module is configured to determine whether the turning angle at each waypoint in the UAV route satisfies the corner constraint value according to the position data of each waypoint, and if so, directly enter the call height detection and optimization module, otherwise plan the UAV route in which the turning angle at each waypoint satisfies the corner constraint value and call the height detection and optimization module; The height detection and optimization module is configured to determine whether the UAV route meets the flight height constraint value according to the position data of each waypoint and the elevation data, and if so, directly call the flight duration detection module, otherwise plan a UAV route in which the height value of each waypoint meets the flight height constraint value and call the flight duration detection module; The flight duration detection module is configured to determine whether the flight duration of the drone meets the flight duration constraint value based on the total length of the drone route. If so, the drone route is output, otherwise a warning message is output.

10. An electronic device comprising: at least one processor; A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the automated detection method for the drone route according to any one of claims 1-8.

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