Unmanned aerial vehicle path optimization method and device in strong denial space, equipment and medium

Abstract

The invention relates to an unmanned aerial vehicle path optimization method and device in a strong denial space, equipment and a medium, and the method comprises the steps: obtaining a task region in the strong denial space and information related to the task region, and carrying out the optimization of the path of an unmanned aerial vehicle in the task region according to the information related to the task region; performing path planning under a threat area constraint condition and an unmanned aerial vehicle attribute constraint condition to obtain a first planning path, obtaining a plurality of threat areas meeting the threat area constraint condition in the task area, obtaining a threat window according to the distance between each threat area and the first planning path, and obtaining a second planning path according to the distance between each threat area and the first planning path; secondary path planning is conducted on the first planning path located in the threat window, an optimized unmanned aerial vehicle planning path is obtained, and the route after secondary path planning is in a spiral shape. By adopting the method, the survival rate of the unmanned aerial vehicle in the strong denial space can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of drone swarm control, and in particular to a method, device, equipment and medium for optimizing the path of drones in a strongly denied space. Background Art

[0002] Building a strong denial zone for drone systems is a common defensive measure for the defending side, while the attacking side needs to face the practical problem of performing reconnaissance and positioning, interference suppression, target indication, direct strikes, coordinated confrontation and other tasks under strong denial conditions. It can be foreseen that the confrontation between drone systems around regional denial and counter-denial will inevitably be a future trend.

[0003] The path planning of an aircraft is essentially to find an optimal or feasible flight trajectory from a starting point to one or more target points within a specified mission area and under known constraints. However, most of the existing path planning methods are designed for obstacle avoidance and shortest path, and algorithm research focuses on the complexity, real-time performance, resource consumption rate and other aspects of the relevant algorithms. The modeling of actual application scenarios is relatively idealized, which reduces the survival rate of drones when the existing drone path planning methods are applied in strongly denied spaces. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, equipment and medium for optimizing the path of drones in a strongly denied space that can improve the survival rate of drones in response to the above-mentioned technical problems.

[0005] A method for optimizing a drone path in a strongly denied space, the method comprising:

[0006] Acquire a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0007] According to the information related to the mission area, path planning is performed within the mission area under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes to obtain a first planned path;

[0008] Obtaining a plurality of threat areas satisfying the threat area constraint condition in the mission area;

[0009] Obtaining a threat window according to the distance between each of the threat areas and the first planned path;

[0010] A secondary path planning is performed on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0011] In one embodiment, the threat zone constraint condition comes from the range of the opponent's ground-based air defense radar and the counter-attack system.

[0012] In one embodiment, path planning is performed under the constraints of the threat area and the drone's own attributes, using any one of the algorithms including the shortest tangent method, genetic algorithm, RRT fast retrieval random tree algorithm, and drone intelligent avoidance decision algorithm based on deep reinforcement learning.

[0013] In one embodiment, a plurality of threat areas satisfying the threat area constraint condition are obtained in the mission area, including:

[0014] Gridding the task area according to the task area information;

[0015] The attributes of each grid are judged according to the constraint conditions of the threat zone to obtain a plurality of grids with threat attributes;

[0016] Adjacent grids with threat attributes are merged using a clustering algorithm to obtain a plurality of threat areas.

[0017] In one embodiment, obtaining the threat window according to the distance between each of the threat areas and the first planned path includes:

[0018] The first planned path is composed of multiple point coordinates, and the threat distance between each point coordinate and the boundary of the adjacent threat area is calculated in sequence;

[0019] Determine whether the corresponding point coordinates are threat points or safety points according to the first preset threshold and the threat distance;

[0020] According to the continuous threat points, corresponding preliminary threat window center line segments are formed. If the distance between two adjacent preliminary threat window center lines is less than a second preset value, the two preliminary threat window center line segments are merged to obtain a threat window center line segment.

[0021] The threat window is generated according to the threat window center line segment.

[0022] In one embodiment, generating the threat window according to the threat window center line segment includes:

[0023] For each of the threat window center line segments, dynamically determine the width of the threat window based on the minimum distance between each point on the center line segment and the adjacent threat area;

[0024] The threat window is generated according to the center line segment and width of each threat window.

[0025] In one embodiment, the motion equation of the drone within the threat window is expressed as:

[0026] xk=Rvsin(ωT)

[0027] yk=Rhcos(ωT)

[0028] zk=V 变 T

[0029] In the above formula, the parameter that can be adjusted according to the maneuverability of the drone is the flight speed V 变 , horizontal spiral radius Rh and vertical spiral radius Rv.

[0030] The present application also provides a device for optimizing the path of a drone in a strongly denied space, the device comprising:

[0031] An information acquisition module, used for acquiring a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0032] A first path planning module is used to perform path planning in the mission area according to the information related to the mission area, under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes, to obtain a first planned path;

[0033] A threat region obtaining module, used for obtaining a plurality of threat regions satisfying the threat region constraint conditions within the mission region;

[0034] A threat window obtaining module, used for obtaining a threat window according to the distance between each of the threat areas and the first planned path;

[0035] The secondary path planning module is used to perform secondary path planning on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0036] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Acquire a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0038] According to the information related to the mission area, path planning is performed within the mission area under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes to obtain a first planned path;

[0039] Obtaining a plurality of threat areas satisfying the threat area constraint condition in the mission area;

[0040] Obtaining a threat window according to the distance between each of the threat areas and the first planned path;

[0041] A secondary path planning is performed on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0043] Acquire a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0044] According to the information related to the mission area, path planning is performed within the mission area under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes to obtain a first planned path;

[0045] Obtaining a plurality of threat areas satisfying the threat area constraint condition in the mission area;

[0046] Obtaining a threat window according to the distance between each of the threat areas and the first planned path;

[0047] A secondary path planning is performed on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0048] The above-mentioned method, device, equipment and medium for optimizing the path of a drone in a strongly denied space obtains the mission area and information related to the mission area in the strongly denied space, performs path planning in the mission area according to the information related to the mission area, under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes, obtains the first planned path, obtains multiple threat areas that meet the constraint conditions of the threat area in the mission area, obtains the threat window according to the distance between each threat area and the first planned path, performs secondary path planning on the first planned path in the threat window, obtains the optimized drone planned path, wherein the route after the secondary path planning is spiral. The use of this method can effectively improve the survival rate of drones in strongly denied spaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flowchart of a method for optimizing a drone path in a strongly denied space in one embodiment;

[0050] Figure 2 A schematic diagram of a spiral speed change maneuver in a threat window area in one embodiment;

[0051] Figure 3 It is a structural block diagram of a UAV path optimization device in a strongly denied space in one embodiment;

[0052] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] In the prior art, most of the aircraft (UAV) path planning methods are designed for obstacle avoidance and shortest path, and the algorithm research focuses on the complexity, real-time performance, resource consumption rate and other aspects of the relevant algorithms, and the modeling of the actual application scenarios is relatively idealized. This makes the existing UAV path planning methods used in strongly denied spaces have a low survival rate of UAVs. In this application, Figure 1 As shown, a method for optimizing the path of a UAV in a strongly denied space is provided, which specifically includes the following steps:

[0055] Step S100, obtaining a task area and information related to the task area, wherein the task area is located in a strong denial space.

[0056] Step S110 , performing path planning in the mission area according to information related to the mission area, under the constraint conditions of the threat area and the UAV's own attribute constraints, to obtain a first planned path.

[0057] Step S120: obtaining a plurality of threat areas satisfying the threat area constraint condition in the task area.

[0058] Step S130, obtaining a threat window according to the distance between each threat area and the first planned path.

[0059] Step S140, performing secondary path planning on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0060] In this embodiment, based on the relationship between the first planned path and multiple threat areas in the strong denial space, a threat window is divided on the first planned path, and secondary path planning is performed within the threat window, so that the UAV can better respond to various threats when passing through the threat window, thereby improving the survival rate of the UAV when performing tasks in the strong denial space.

[0061] In step S100, the method is aimed at the scenario where a drone or aircraft performs a mission in a strongly denied space, so before performing the first path planning, it is necessary to obtain relevant information in the mission area in order to obtain a reasonable and efficient first planned path. In this embodiment, the relevant information includes geographic information, such as terrain, precise geographic coordinates and boundary coordinates of the mission area, weather conditions, physical obstacle information, adversary threat information, mission target information, etc.

[0062] In step S110, when performing the first path planning, it is necessary to clarify the threat area in the mission area so that the first path planning can effectively avoid any threatening area. According to the scenario and object of the application of this method, the threat area constraint condition comes from the range of action of the opponent's ground air defense radar and the counter-attack system. Further, it is the detection area of ​​the opponent's ground air defense radar set in the mission area and the attack distance of the counter-attack system.

[0063] In this embodiment, path planning is performed under the constraints of the threat area and the drone's own attributes, using any one of the algorithms including the shortest tangent method, genetic algorithm, RRT fast retrieval random tree algorithm, and drone intelligent avoidance decision algorithm based on deep reinforcement learning.

[0064] Furthermore, the UAV's own attribute constraints include speed constraints, turning radius constraints, stall radius constraints, climbing constraints, onboard resource constraints, and collision avoidance constraints. These constraints are set according to the specific conditions of the UAV performing the mission.

[0065] At the same time, it can be seen that when the first path planning is carried out, the path planning methods used are designed for obstacle avoidance and shortest path. For example, for the modeling of the threat zone, although static threats and dynamic threats are distinguished, the setting of the threat zone still draws clear geometric boundaries, and the selection of navigation points in flight is also a binary choice, that is, two possible choices: feasible and infeasible. However, in the actual confrontation environment, the causes of the threat zone are complex. Some threats come from static mountains and landforms, which are actually physical obstacles. Some threats come from the deployment of enemy defense forces. In addition, in the same threat area, multiple systems of denial defense equipment may be deployed, including navigation denial, firepower strikes, electromagnetic confrontation, people in the loop and other forms, which together constitute the defense force of the threat zone. Under the synergistic effect of these forces, the boundaries of the threat zone will inevitably become very vague and dynamically changing, and it is impossible to be simple and clear. Therefore, for the UAV path planning algorithm under strong denial conditions, it is necessary to further consider the impact of these boundary areas.

[0066] Therefore, in this method, in response to the above situation, based on the first planned path, the threat window is further divided, and the planned path within the threat window is planned for the second time to avoid possible threats, thereby improving the survival rate of the drone.

[0067] In step 120, the region is further refined according to the threat constraint condition in the task area to obtain multiple threat areas that meet the threat area constraint condition. Specifically, the task area is gridded according to the task area information, the attribute of each grid is judged according to the threat area constraint condition to obtain multiple grids with threat attributes, and then the adjacent grids with threat attributes are merged by using a clustering algorithm to obtain multiple threat areas.

[0068] In this embodiment, the threat area is circular.

[0069] Furthermore, when the mission area information is gridded, the grid size is set according to the UAV's flight accuracy, threat detection accuracy, and mission requirements to ensure that the divided network is fine enough to reflect the details of the threat area. When judging the threat attributes of each grid, it can be judged based on the detection area of ​​the opponent's ground defense radar and the attack distance of the counterattack system. When the center point of a grid falls within the radar's detection range or attack distance, the grid is marked as "having threat data."

[0070] Specifically, a spatial clustering algorithm (such as DBSCAN) is used to merge adjacent grids with threat attributes. The definition of adjacent grids can be based on the geometric proximity relationship of the grids or the same threat source. Through clustering analysis, these adjacent threat grids are merged to form multiple threat areas.

[0071] In this method, the concept of threat window is established. When the flight path of the UAV's mission area is obtained by running the above algorithm, the threat window is divided according to the Euclidean space distance relationship between the planned path and the threat area. That is, when some sections in the planned path may overlap with the space of the threat area, it is judged that the path is within the threat window, which means that there is a certain survival risk for the UAV flying in this path.

[0072] Specifically, in step S130, according to the distance between each threat area and the first planned path, a threat window is obtained, including: the first planned path is composed of a plurality of point coordinates, the threat distance between each point coordinate and the boundary of the adjacent threat area is calculated in turn, the corresponding point coordinate is judged as a threat point or a safety point according to a first preset threshold and the threat distance, a corresponding preliminary threat window center line segment is formed according to continuous threat points, and if the distance between two adjacent preliminary threat window center lines is less than a second preset value, the two preliminary threat window center line segments are merged to obtain a threat window center line segment, and a threat window is generated according to the threat window center line segment.

[0073] In this embodiment, it can be understood that the first planned path may be composed of multiple discrete coordinate points, and the threat distance between each coordinate point and the boundary of the adjacent threat area is calculated in turn. The threat distance here refers to the minimum distance between the coordinate point and the boundary of the adjacent threat area.

[0074] In some embodiments, the first preset value may be set to 3 kilometers. When the threat distance is less than 3 kilometers, the point is determined as a threat point. Otherwise, the point is a safe point.

[0075] In this embodiment, when merging the center line segments of two preliminary threat windows, 0.5 km may be used as the second preset value, and two preliminary threat windows whose distance is less than the second preset value may be merged, thereby forming a complete threat window.

[0076] In this embodiment, the threat window is generated according to the center line segment of the threat window, including: for each center line segment of the threat window, based on the minimum distance from each point on the center line segment to the adjacent threat area, the width of the threat window is dynamically determined, and the threat window is generated according to the center line segment and width of each threat window.

[0077] Specifically, when the distance from the center line segment to the adjacent threat area is small, the width of the threat window can be adjusted to be larger according to the actual situation to provide sufficient safety margin. When the distance is large, the window width can be reduced accordingly.

[0078] For the path segments within the threat window, secondary planning is required to further design a path with certain penetration performance. In this method, the proposed three-dimensional spiral speed change maneuver is the path form of secondary planning. This path form is an effective form of penetration movement, which can be understood as an active interference movement under the opponent's aiming pressure. This situation is similar to the UAV combat environment under strong denial conditions. The flight of UAVs in the denied space may inevitably be subject to the pressure of ground radar scanning or tracking lock.

[0079] like Figure 2As shown in the figure, the division of the threat window and the changes in the UAV's track within the threat window are described. The green part in the figure shows that the UAV is within the normal planned path, and the track movement is expressed as a smooth straight line. The main contradiction at this stage is to shorten the flight range, save fuel consumption, increase the flight speed and shorten the mission execution time. The red part in the figure shows that the UAV is in the threat window area, and the track movement is expressed as a spiral curve. At this time, the main contradiction of the UAV is to maximize the survival rate, and some non-critical indicators can be sacrificed for this, such as increasing power energy consumption, increasing the flight range and extending the mission execution time.

[0080] Specifically, when performing secondary path planning in the threat window, in the track coordinate system OX k Y k Z k Under this condition, the motion equation of the UAV in the threat window is expressed as:

[0081] xk=Rvsin(ωT)

[0082] yk=Rhcos(ωT)

[0083] zh=V 变 T

[0084] In the above formula, the parameter that can be adjusted according to the maneuverability of the drone is the flight speed V 变 , horizontal spiral radius Rh and vertical spiral radius Rv. Among them, the specific settings of flight speed and horizontal / vertical spiral radius must meet the flight performance constraints of the UAV itself and the corrected mission time, terrain obstacle avoidance and other requirements. In general, the flight speed V becomes a required adjustment parameter, and the horizontal and vertical spiral radii are optional adjustment parameters. When the vertical spiral radius Rv takes a larger value, the elevation of the UAV changes dramatically when performing spiral motion, which increases the difficulty of detection by ground radar. When the horizontal spiral radius Rh takes a larger value, the movement of the UAV appears to be similar to a Z shape.

[0085] Preferably, in the threat window area, under the premise of satisfying the maneuverability of the aircraft itself, the flight speed V is multiplied by 0.8 times the maximum flight speed. 变 Get the value.

[0086] In the above-mentioned method for optimizing the path of a drone in a strongly denied space, the method, device, equipment and medium for optimizing the path of a drone in a strongly denied space obtain the mission area and information related to the mission area in the strongly denied space, and perform path planning in the mission area according to the information related to the mission area, under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes, to obtain the first planned path, obtain multiple threat areas that meet the constraint conditions of the threat area in the mission area, obtain the threat window according to the distance between each threat area and the first planned path, perform secondary path planning on the first planned path in the threat window, and obtain the optimized drone planned path, wherein the route after the secondary path planning is spiral. The use of this method can effectively improve the survival rate of drones in strongly denied spaces.

[0087] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0088] In one embodiment, Figure 3 As shown, a UAV path optimization device in a strong denial space is provided, including: an information acquisition module 200, a first path planning module 210, a threat area acquisition module 220, a threat window acquisition module 230 and a secondary path planning module 240, wherein:

[0089] An information acquisition module 200, used to acquire a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0090] A first path planning module 210 is used to perform path planning in the mission area according to the information related to the mission area, under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes, to obtain a first planned path;

[0091] A threat region obtaining module 220, configured to obtain a plurality of threat regions satisfying the threat region constraint conditions in the mission region;

[0092] A threat window obtaining module 230, configured to obtain a threat window according to the distance between each of the threat areas and the first planned path;

[0093] The secondary path planning module 240 is used to perform secondary path planning on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0094] For the specific definition of the drone path optimization device in the strongly denied space, please refer to the definition of the drone path optimization method in the strongly denied space above, which will not be repeated here. Each module in the above-mentioned drone path optimization device in the strongly denied space can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0095] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the path of a drone in a strongly denied space is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0096] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0097] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0098] Acquire a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0099] According to the information related to the mission area, path planning is performed within the mission area under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes to obtain a first planned path;

[0100] Obtaining a plurality of threat areas satisfying the threat area constraint condition in the mission area;

[0101] Obtaining a threat window according to the distance between each of the threat areas and the first planned path;

[0102] A secondary path planning is performed on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0103] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0104] Acquire a task area and information related to the task area, wherein the task area is located in a strong denial space;

[0105] According to the information related to the mission area, path planning is performed within the mission area under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes to obtain a first planned path;

[0106] Obtaining a plurality of threat areas satisfying the threat area constraint condition in the mission area;

[0107] Obtaining a threat window according to the distance between each of the threat areas and the first planned path;

[0108] A secondary path planning is performed on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

[0109] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0110] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing the path of a drone in a strongly denied space, characterized in that: The method comprises: Acquire a task area and information related to the task area, wherein the task area is located in a strong denial space; According to the information related to the mission area, path planning is performed within the mission area under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes to obtain a first planned path; Obtaining a plurality of threat areas satisfying the threat area constraint condition in the mission area; Obtaining a threat window according to the distance between each of the threat areas and the first planned path; A secondary path planning is performed on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

2. The UAV path optimization method according to claim 1, characterized in that: The threat zone constraints come from the range of action of the adversary's ground-based air defense radar and the counter-attack system.

3. The UAV path optimization method according to claim 1, characterized in that: Path planning is performed under the constraints of the threat area and the drone's own attributes, using any of the algorithms including the shortest tangent method, genetic algorithm, RRT fast retrieval random tree algorithm, and drone intelligent avoidance decision algorithm based on deep reinforcement learning.

4. The UAV path optimization method according to claim 3, characterized in that: A plurality of threat areas satisfying the threat area constraint conditions are obtained in the mission area, including: Gridding the task area according to the task area information; The attributes of each grid are judged according to the constraint conditions of the threat zone to obtain a plurality of grids with threat attributes; Adjacent grids with threat attributes are merged using a clustering algorithm to obtain a plurality of threat areas.

5. The method for optimizing the path of a UAV according to claim 4, characterized in that: Obtaining a threat window according to the distance between each of the threat areas and the first planned path includes: The first planned path is composed of multiple point coordinates, and the threat distance between each point coordinate and the boundary of the adjacent threat area is calculated in sequence; Determine whether the corresponding point coordinates are threat points or safety points according to the first preset threshold and the threat distance; According to the continuous threat points, corresponding preliminary threat window center line segments are formed. If the distance between two adjacent preliminary threat window center lines is less than a second preset value, the two preliminary threat window center line segments are merged to obtain a threat window center line segment; The threat window is generated according to the threat window center line segment.

6. The method for optimizing the path of a UAV according to claim 5, characterized in that: The generating the threat window according to the threat window center line segment comprises: For each of the threat window center line segments, dynamically determine the width of the threat window based on the minimum distance between each point on the center line segment and the adjacent threat area; The threat window is generated according to the center line segment and width of each threat window.

7. The method for optimizing the path of a UAV according to any one of claims 1 to 6, characterized in that: The motion equation of the drone within the threat window is expressed as: xk=Rvsin(ωT) yk=Rhcos(ωT) zk=V 变 T In the above formula, the parameter that can be adjusted according to the maneuverability of the drone is the flight speed V 变 , horizontal spiral radius Rh and vertical spiral radius Rv.

8. A drone path optimization device in a strongly denied space, characterized in that: The device comprises: An information acquisition module, used for acquiring a task area and information related to the task area, wherein the task area is located in a strong denial space; A first path planning module is used to perform path planning in the mission area according to the information related to the mission area, under the constraint conditions of the threat area and the constraint conditions of the drone's own attributes, to obtain a first planned path; A threat region obtaining module, used for obtaining a plurality of threat regions satisfying the threat region constraint conditions within the mission region; A threat window obtaining module, used for obtaining a threat window according to the distance between each of the threat areas and the first planned path; The secondary path planning module is used to perform secondary path planning on the first planned path within the threat window to obtain an optimized UAV planned path, wherein the route after the secondary path planning is spiral.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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