Multi-unmanned aerial vehicle cooperative obstacle avoidance method and system based on Astar algorithm, electronic equipment and computer readable storage medium

Abstract

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to a multi-unmanned aerial vehicle cooperative obstacle avoidance method and system based on an Astar algorithm, electronic equipment and a computer readable storage medium. Each unmanned aerial vehicle in the system can perform path planning independently, the flight space of the unmanned aerial vehicle is divided into three-dimensional grids, and when an obstacle is detected, the coordinates, the speed and the acceleration of the grids occupied by the obstacle are calculated and sent to other unmanned aerial vehicles. And each airplane calculates the predicted coordinates of the dynamic obstacle position according to a kinematics formula. An improved Astar algorithm is used to calculate a global path, and obstacle prediction probability cost is added. And selecting the path with the minimum cost as a global path for flight. And when a new obstacle is detected or the position of the obstacle is updated, path planning is performed again. The problem that the path planning efficiency of the unmanned aerial vehicle and the success rate of obstacle avoidance are low is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle path planning, and more specifically, relates to a multi-unmanned aerial vehicle collaborative obstacle avoidance method, system, electronic device and computer-readable storage medium based on an Astar algorithm. Background Art

[0002] In the field of UAV technology, the application of multi-UAV collaborative operation is becoming more and more widespread. However, in the process of multi-UAV collaborative operation, obstacle avoidance has always been one of the key issues restricting its development. Astar algorithm is a commonly used path planning algorithm, which guides the search direction by introducing heuristic information in the search process, thereby improving the search efficiency.

[0003] Chinese invention patent CN112947406A discloses a hybrid path planning method based on FLOYD and Astar. First, a vehicle mathematical model is established and a high-precision map is constructed. Under this condition, the Floyd algorithm and the A* algorithm are combined to use a hybrid algorithm for path planning of autonomous driving vehicles. Among the common path planning algorithms, the Floyd algorithm has high time complexity, and the distance matrix and path matrix calculated by it cannot meet the high real-time requirements of autonomous driving vehicles during dynamic driving; the A* algorithm has logical design problems, and its planning results are not necessarily globally optimal.

[0004] In summary, traditional obstacle avoidance methods often use simple distance detection and deceleration strategies. This method performs poorly in multi-UAV scenarios in complex environments and is prone to problems such as collisions or path planning failures. The star algorithm also has certain limitations when dealing with multi-UAV collaborative obstacle avoidance problems. It is unable to process and use obstacle data detected by multiple UAVs, resulting in low path planning efficiency or path planning errors. Summary of the invention

[0005] The present invention aims to overcome at least one defect of the above-mentioned prior art and provide a multi-UAV collaborative obstacle avoidance method based on the Astar algorithm to solve the problem of multi-UAV collaborative obstacle avoidance. There are certain limitations in the method, and the obstacle data detected by multiple UAVs cannot be processed and used, resulting in low path planning efficiency or path planning errors.

[0006] The detailed technical scheme of the present invention is as follows:

[0007] A multi-UAV collaborative obstacle avoidance method based on Astar algorithm, the method comprising:

[0008] S1. For the area where the drone is flying, the drone establishes a continuous three-dimensional grid in the same coordinate system according to a preset determination rule, and the length, width and height of each grid are equal;

[0009] S2. When the drone detects an obstacle, it calculates the grid coordinates occupied by the obstacle based on the drone’s own position and the direction, distance, and size of the detected obstacle relative to the drone;

[0010] Furthermore, according to the obstacle position measured in three consecutive frames, the speed and acceleration of the obstacle are calculated according to the kinematic formula, and then the grid coordinates, speed and acceleration occupied by the obstacle are sent to other drones in the system through the wireless ad hoc network communication device.

[0011] Specifically, for a speed less than the threshold V min And the acceleration is less than the threshold a min The obstacles are considered as static obstacles, and the others are considered as dynamic obstacles.

[0012] S3, each drone in the system sends its own coordinates, speed and acceleration to other drones at a certain time interval T;

[0013] S4, after receiving the positions, speeds and accelerations of obstacles and aircraft sent by other drones, the drones in the system calculate the predicted coordinates of the dynamic obstacle positions and the probability of the predicted coordinates every 0.1 seconds within the time interval T;

[0014] Furthermore, the predicted coordinates of n points after the current position of the dynamic obstacle A are recorded as S A1 , S A2 ...S An , where any predicted coordinate S Ai =S A0 +v0×0.1×i+0.5×a×(0.1×i)^2,S A0 is the current coordinate of the dynamic obstacle, i is 1 to n, v0 is the current speed of the obstacle, a is the acceleration of the obstacle, and the probability of the obstacle at each predicted coordinate is recorded as P A1 , P A2 ...P An , where the probability P of any coordinate i of the obstacle Ai =1-i / 10 / T, and record the time T when the obstacle information is received A ;

[0015] The predicted coordinates S are calculated for other obstacles in the same way. bi , S Ci ...S Ni and probability P Bi , P Ci ...P Ni And record the time T when the obstacle information is received B , T C ...TN , N is the total number of drones; the received time to the current time is greater than the time threshold T max The obstacles are removed from the obstacle list, T max This is a preset time threshold. Obstacles that exceed this time threshold can be considered to have not been detected for a long time.

[0016] S5, the UAV obtains the grid where the predicted coordinates of the obstacle are located according to the predicted coordinates of the dynamic obstacle position, and plans a global path from the current position to the target position;

[0017] S6. Repeat the above steps for other drones in the system, execute the same strategy, plan the global path of all drones and fly.

[0018] Furthermore, the drone uses the improved Astar algorithm to plan a global path from the current position to the target position, including:

[0019] S51, first, the UAV searches for a search path from the current position to the target position, selects the predicted coordinates of obstacles on the search path, and participates in the calculation of the obstacle prediction probability cost P;

[0020] S52. For the grid where the obstacle prediction coordinates are located, the obstacle prediction probability cost is added to the path cost calculated by Astar, that is, F=G+H+P, where F is the total cost, G is the historical cost, H is the expected future cost, and P is the obstacle prediction probability cost in the path;

[0021] Among them, the search path contains multiple obstacle prediction coordinate points, then:

[0022] P=W*(P tA *P Aj +P tB *P Bj +...+P tN *P Nj );

[0023] W is the obstacle prediction weight, which is set according to the actual application scenario, P tA , P tB ...P tN The time weight of each obstacle is calculated as P tA =1 / (T now -T A ), where T now is the current time, P Aj , P Bj ...、P Nj Predict coordinates for obstacles on the filtered search path;

[0024] S53, UAV UAV1 selects the path with the smallest cost F as the global path for flight.

[0025] When a drone detects or receives information about a new obstacle or an updated obstacle position detected by other drones, the drone will re-use the above algorithm strategy for path planning; for any drone in the system, the position, speed, and acceleration information received from other drones will be used as obstacles for calculation.

[0026] In another aspect of the present invention, a system for implementing a multi-UAV collaborative obstacle avoidance method based on an Astar algorithm is provided, comprising:

[0027] Multiple drones with the same hardware configuration and functions, each drone has the same role and status in the system. Each drone in the system is equipped with wireless ad hoc network communication equipment and obstacle detection sensors, including but not limited to binoculars, laser radar, millimeter wave radar, and ultrasonic waves.

[0028] The wireless ad hoc network communication device sends the information of any drone in the system to all other drones in the system, and the obstacle detection sensor can detect the direction, distance and size of the obstacle relative to the drone.

[0029] Each drone in the system can perform path planning independently, and each drone runs the same algorithm.

[0030] In another aspect of the present invention, there is also provided an electronic device, comprising:

[0031] at least one processor; and

[0032] A memory storing instructions, which, when executed by the at least one processor, enables the at least one processor to execute the multi-UAV collaborative obstacle avoidance method based on the Astar algorithm as described above.

[0033] In another aspect of the present invention, a machine-readable storage medium is provided, which stores executable instructions, and when the instructions are executed, the machine executes the multi-UAV collaborative obstacle avoidance method based on the Astar algorithm as described above.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The multi-UAV collaborative obstacle avoidance method, system, electronic device and computer-readable storage medium based on the Astar algorithm provided by the present invention realizes information sharing and collaborative obstacle avoidance among UAVs by calculating the motion trajectory of obstacles in real time and sending obstacle information to other UAVs in the system, thereby improving the path planning efficiency and obstacle avoidance success rate of UAVs.

[0036] (2) The multi-UAV collaborative obstacle avoidance method, system, electronic device and computer-readable storage medium based on the Astar algorithm provided by the present invention divide the flight space of the UAVs into three-dimensional grids, making path planning more accurate and efficient. At the same time, the improved Astar algorithm is used to calculate the global path, and the obstacle probability cost is added to further improve the accuracy and efficiency of path planning. Each UAV in the system can independently perform path planning without the need for central control, thereby reducing the complexity and cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the multi-UAV collaborative obstacle avoidance method based on the Astar algorithm described in the present invention.

[0038] Figure 2 Schematic diagram of the relationship between obstacles and drones in Example 1 of the present invention.

[0039] Figure 3 It is a schematic diagram of obstacle prediction trajectory and drone path planning in Example 1 of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0043] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0044] Example 1

[0045] Ginseng Figure 1 This embodiment provides a multi-UAV collaborative obstacle avoidance method based on the Astar algorithm, the method comprising:

[0046] S1. Construct a three-dimensional grid of the area where the drone flies:

[0047] For the area where the drone flies, the drone establishes a continuous three-dimensional grid in the same coordinate system according to the preset determination rules. The length, width and height of each grid are d, that is, the flight space of the drone is divided into three-dimensional grids. The coordinates of each grid are the coordinates of the center of the grid in the coordinate system, expressed as (x, y, z);

[0048] Preferably, the drone can build a three-dimensional grid map based on the laser point cloud data according to the SLAM mapping in the robotics field.

[0049] S2. Calculate the grid coordinates occupied by the obstacle:

[0050] When UAV1 detects an obstacle, the grid coordinates occupied by the obstacle are calculated based on the UAV's own position and the direction, distance and size of the detected obstacle relative to the UAV.

[0051] Specifically, according to the obstacle position measured in three consecutive frames, the speed and acceleration of the obstacle are calculated according to the kinematic formula, and then the grid coordinates, speed and acceleration occupied by the obstacle are sent to other drones in the system through the wireless ad hoc network communication device.

[0052] Preferably, for a speed less than a threshold value V min And the acceleration is less than the threshold a min The obstacles are considered as static obstacles, and the others are considered as dynamic obstacles. min 、a min The value of is set according to the scenario. In this embodiment, V min Set to 0.3m / s, a min Set to 0.5m / s2.

[0053] S3. Information sharing between drones:

[0054] Each drone in the system sends its own coordinates, speed, and acceleration to other drones at a certain time interval T (which can be set as needed, and is set to 2S in this embodiment) to achieve information sharing between drones.

[0055] S4. Calculate the predicted coordinates of the dynamic obstacle position:

[0056] After receiving the position, velocity and acceleration of obstacles and aircraft sent by other drones (UAV2), the UAV1 in the system calculates the predicted coordinates of the dynamic obstacle position every 0.1 seconds within the next T time interval, i.e., the predicted trajectory, according to the kinematic formula s=v0×t+0.5×a×t^2. Preferably, 0.1 is 0.1s.

[0057] like Figure 2 As shown in the figure, for obstacle A, the predicted coordinates of the next n points are recorded as S A1 , S A2 .....S An , where any predicted coordinate S Ai =S A0 +v0×0.1×i+0.5×a×(0.1×i)^2,S A0 is the current coordinate of the dynamic obstacle, i is 1 to n, v0 is the current speed of the obstacle, a is the acceleration of the obstacle; at the same time, the probability of obstacle A at each coordinate is recorded as P A1 , P A2 ...P An , where the probability of any coordinate i of obstacle A is P ai =1-i / 10 / T, specifically, P A1 =0.95, P A2 =0.9, P A3 =0.85, P A4 =0.8...., and record the time T when the obstacle information is received A ;

[0058] like Figure 2 As shown, the predicted coordinates S are calculated for other obstacles B and C in the same way. Bi , S Ci and probability P Bi , P Ci And record the time T when the obstacle information is received B , T C ,Preferably, the UAV predicts obstacles within the detection range.

[0059] The received time to the current time is greater than T max The obstacles are removed from the obstacle list and do not participate in subsequent calculations. It is considered that there are no obstacles. In this embodiment, T max Set to 5s. The other drones in the system are handled the same way as UAV1.

[0060] S5. Plan the global path from the current position to the target position:

[0061] The drone obtains the grid where the predicted coordinates of the dynamic obstacle are located based on the predicted coordinates of the dynamic obstacle position, and plans a global path from the current position to the target position.

[0062] Specifically, for UAV1, the improved Astar algorithm is used to plan a global path from the current position to the target position, including;

[0063] S51, first, the UAV searches for a search path from the current position to the target position, selects the predicted coordinates of obstacles on the search path, and participates in the calculation of the obstacle prediction probability cost P;

[0064] S52. For the grid where the obstacle prediction coordinates are located, the obstacle prediction probability cost is added to the path cost calculated by Astar, that is, F=G+H+P, where F is the total cost, G is the historical cost (the cost of moving from the starting point to a specified point), H is the expected future cost (the estimated cost of moving from a specified point to the end point), and P is the obstacle prediction probability cost in the path;

[0065] Preferably, taking obstacles A, B, and C as an example, the search path includes S A1 , S A2 , S B2 , S Ci Multiple obstacle prediction coordinate points, then P = W*(P tA *(P A1 +P A2 )+P tB *P B2 +P tC *P Ci ), W is the obstacle prediction weight, which is set according to the actual application scenario, P tA , P tB , P tC It is divided into the time weights of obstacle A, obstacle B, and obstacle C, and the calculation method is P tA =1 / (T now -T A ), P tB =1 / (T now -T B ), P tC =1 / (T now -T C ) where T now For the current time, the drone selects the path with the minimum cost F as the global path for flight, such as Figure 3 As shown, obstacle prediction trajectories of UAV1 and UAV2 and the path planning of UAVs are shown.

[0066] For UAV1, when UAV1 detects or receives information about new obstacles or obstacle position updates detected by other UAVs, UAV1 uses the above algorithm strategy to plan the path again; for any UAV in the system, the position, speed, and acceleration information of other UAVs received are used as obstacles to participate in the calculation.

[0067] S6. Realize global path planning for all drones:

[0068] For other drones in the system, they follow the algorithm of the above drone UAV1, execute the same strategy, calculate their own global path and fly.

[0069] Specifically, a simulation test environment was built to verify the situation (the number of obstacles and the starting position were the same and the movement was performed with uniform acceleration), and the experimental data obtained were:

[0070]

[0071] It can be concluded that when the number of drones increases, the average number of path planning times per drone in the method of the present invention decreases compared to the prior art. Since drones share information and use the information in the calculation of path planning, the more drones there are, the more information can be detected, the more reasonable the path planning is, and the more the advantages of the present method can be reflected.

[0072] Example 2

[0073] This embodiment provides a system for implementing a multi-UAV collaborative obstacle avoidance method based on the Astar algorithm, the system comprising:

[0074] The system consists of multiple drones with the same hardware configuration and functions, and each drone has the same role and status in the system.

[0075] For each drone in the system, a wireless ad hoc network communication device and an obstacle detection sensor (including but not limited to one or more of binocular, lidar, millimeter wave radar, ultrasonic, etc.) are installed. The wireless ad hoc network communication device can send the information of any drone in the system to all other drones in the system, and the obstacle detection sensor can detect the direction, distance and size of the obstacle relative to the drone.

[0076] Each drone in the system can perform path planning independently, and each drone runs the same algorithm.

[0077] Example 3

[0078] This embodiment provides an electronic device for implementing a multi-UAV collaborative obstacle avoidance method based on an Astar algorithm, the electronic device comprising:

[0079] at least one processor; and

[0080] A memory storing instructions, which, when executed by the at least one processor, enables the at least one processor to execute the multi-UAV collaborative obstacle avoidance method based on the Astar algorithm as described above.

[0081] In this embodiment, the device includes but is not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile computing devices, smart phones, tablet computers, cellular phones, personal digital assistants (PDAs), handheld devices, messaging devices, wearable computing devices, consumer electronic devices, etc.

[0082] Example 4

[0083] This embodiment also provides a computer-readable storage medium storing executable instructions, which, when executed, enable the machine to execute the multi-UAV collaborative obstacle avoidance method based on the Astar algorithm as described above.

[0084] Specifically, a system or device equipped with a readable storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer or processor of the system or device can read and execute instructions stored in the readable storage medium.

[0085] In this case, the program code itself read from the machine-readable medium can realize the function of any one of the above embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute part of this specification.

[0086] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0087] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A multi-UAV collaborative obstacle avoidance method based on Astar algorithm, characterized in that: The method comprises: S1. For the area where the drone is flying, the drone establishes a continuous three-dimensional grid in the same coordinate system according to a preset determination rule, and the length, width and height of each grid are equal; S2. When the drone detects an obstacle, it calculates the grid coordinates occupied by the obstacle based on the drone’s own position and the direction, distance, and size of the detected obstacle relative to the drone; S3, each drone in the system sends its own coordinates, speed and acceleration to other drones at a certain time interval T; S4, after receiving the positions, speeds and accelerations of obstacles and aircraft sent by other drones, the drones in the system calculate the predicted coordinates of the dynamic obstacle positions and the probability of the predicted coordinates every 0.1 seconds within the time interval T; S5, the UAV obtains the grid where the predicted coordinates of the obstacle are located according to the predicted coordinates of the dynamic obstacle position, and plans a global path from the current position to the target position; S6. Repeat the above steps for other drones in the system, execute the same strategy, plan the global path of all drones and fly.

2. The multi-UAV collaborative obstacle avoidance method based on the Astar algorithm according to claim 1 is characterized in that: The S4 specifically includes: The predicted coordinates of n points after the current position of the dynamic obstacle are recorded as S A1 , S A2 ...S An , where any predicted coordinate S Ai =S A0 +v0×0.1×i+0.5×a×(0.1×i)^2,S A0 is the current coordinate of the dynamic obstacle, i is 1 to n, v0 is the current speed of the obstacle, a is the acceleration of the obstacle, and the probability of the obstacle at each coordinate is recorded as P A1 , P A2 ...P An , where the probability P of any coordinate i of the obstacle Ai =1-i / 10 / T, and record the time T when the obstacle information is received A ; The predicted coordinates S are calculated for other obstacles in the same way. Bi , S Ci ...S Ni and probability P Bi , P Ci ...P Ni And record the time T when the obstacle information is received B 、T C ...T N , N is the total number of drones; the received time to the current time is greater than the time threshold T max The obstacles are removed from the obstacle list.

3. The multi-UAV collaborative obstacle avoidance method based on the Astar algorithm according to claim 2 is characterized in that: Use the improved Astar algorithm to plan a global path from the current location to the target location, including: S51, first, the UAV searches for a search path from the current position to the target position, selects the predicted coordinates of obstacles on the search path, and participates in the calculation of the obstacle prediction probability cost P; S52. For the grid where the obstacle prediction coordinates are located, the obstacle prediction probability cost is added to the path cost calculated by Astar, that is, F=G+H+P, where F is the total cost, G is the historical cost, H is the expected future cost, and P is the obstacle prediction probability cost in the path; Among them, the search path contains multiple obstacle prediction coordinate points, then: P=W*(P tA *P Aj +P tB *P Bj +...+P tN *P Nj ); W is the obstacle prediction weight, which is set according to the actual application scenario, P tA , P tB ...P tN The time weight of each obstacle is calculated as P tA =1 / (T now -T A ), P tB =1 / (T now -T B )...P tN =1 / (T now -T N ), where T now is the current time, P Aj , P Bj ...P Nj Predict coordinates for obstacles on the filtered search path; S53, UAV UAV1 selects the path with the smallest cost F as the global path for flight.

4. The multi-UAV collaborative obstacle avoidance method based on the Astar algorithm according to claim 1 or 3, characterized in that: According to the obstacle position measured in three consecutive frames, the speed and acceleration of the obstacle are calculated, and then the grid coordinates, speed and acceleration occupied by the obstacle are sent to other drones in the system through the wireless ad hoc network communication device; The speed is less than the threshold V min And the acceleration is less than the threshold a min The obstacles are static obstacles, and the other obstacles are dynamic obstacles.

5. A multi-UAV collaborative obstacle avoidance system based on Astar algorithm, characterized in that: The system comprises: Multiple drones with the same hardware configuration and functions, each drone has the same role and status in the system; Each drone in the system is equipped with wireless ad hoc network communication equipment and obstacle detection sensors, including but not limited to binocular, laser radar, millimeter wave radar, and ultrasonic; The wireless ad hoc network communication device sends information about any drone in the system to all other drones in the system, and the obstacle detection sensor can detect the direction, distance and size of the obstacle relative to the drone; Each drone in the system can perform path planning independently, and each drone runs the same algorithm.

6. An electronic device, characterized in that: The electronic device comprises: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the multi-UAV collaborative obstacle avoidance method based on the Astar algorithm as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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