UAV Trajectory Planning Method and System Based on a New Adaptive Anti-Collision Model

Abstract

The present invention belongs to the technical field of UAV trajectory planning, and discloses a UAV trajectory planning method and system based on a novel adaptive anti-collision model, including generating an initial trajectory; constructing a speed adaptive conical anti-collision model; segmenting the initial trajectory, each segment of the initial trajectory being represented by a set of control points, and calculating the relative trajectory convex hull of the two; obtaining the normal vector and safety margin for separating the relative trajectory convex hull and the anti-collision model; using the normal vector and safety margin to construct linear inequalities, and taking the linear inequalities as collision avoidance constraints to perform trajectory optimization to obtain the final trajectory. The anti-collision model based on the dynamic adjustment of the UAV speed in the present invention can more accurately simulate and compensate for the influence of the downwash effect on the adjacent UAVs, and greatly improves the reliability and flexibility of the UAV system in complex environments.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) trajectory planning, and particularly to a UAV trajectory planning method and system based on a novel adaptive anti-collision model. Background Art

[0002] The statements in this section merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of technology, UAVs have been widely used in many fields such as logistics distribution, agricultural monitoring, disaster rescue, and military reconnaissance. Due to the complexity of tasks, more and more task scenarios adopt multi-UAV systems. To ensure the safe and efficient operation of the system, the cooperative cooperation between multi-UAVs has become one of the key technologies. However, although the current methods perform well in avoiding static obstacles, there are still many deficiencies in dealing with complex situations in a dynamic environment, especially considering the unique downwash effect of UAVs, that is, the significant impact on other UAVs below or adjacent during the flight of UAVs due to the generation of airflows. When the downwash effect acts on adjacent UAVs, it will increase the control difficulty and may even lead to a collision risk. Especially during dense formation flight or when multiple UAVs perform tasks in the same limited area, the downwash effect will seriously affect the overall safety and efficiency of the system.

[0004] Most traditional anti-collision models use elliptical or other simple geometric shapes to define the safety area around UAVs. This method is difficult to accurately simulate the downwash effect because of the insufficient coincidence with the specific airflow shape; moreover, the intensity of the downwash effect is closely related to the speed of UAVs, and the traditional model fails to fully consider this point, resulting in possible misjudgments during high-speed flight or close-range operation, thus increasing the potential risks in practical applications. Although the traditional model brings a greater safety margin, it makes some areas that could originally pass normally be prohibited from passing due to being too conservative, reducing the overall efficiency of the task to a certain extent. Therefore, it is necessary to develop an anti-collision model that can better simulate the downwash effect to meet the need of improving the quality of multi-UAV cooperative operations. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a UAV trajectory planning method and system based on a novel adaptive anti-collision model. Considering the influence of UAV speed on the intensity of the downwash effect, a dynamic adjustment anti-collision model based on UAV speed is proposed, which can more accurately simulate and compensate for the influence of the downwash effect on adjacent UAVs, greatly improving the reliability and flexibility of the UAV system in complex environments.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for unmanned aerial vehicle (UAV) trajectory planning based on a novel adaptive anti-collision model, comprising the following steps:

[0008] Generate an initial trajectory according to environmental information and the previous trajectory.

[0009] Construct a speed adaptive cone anti-collision model based on the speed information of the UAV.

[0010] Segment the initial trajectory, where each segment of the initial trajectory is represented by a set of control points. Calculate the set of control points of the relative trajectory of each pair of UAVs, and then obtain the convex hull of their relative trajectories.

[0011] Obtain the closest point pair between the convex hull of the relative trajectory and the anti-collision model, calculate the vector between the closest point pair, find the normal vector of the hyperplane separating the convex hull of the relative trajectory and the anti-collision model, and then obtain the safety margin between the UAVs according to the hyperplane separation theorem.

[0012] Construct a linear inequality using the normal vector and the safety margin, and use the linear inequality as a collision avoidance constraint to optimize the trajectory to obtain the final trajectory.

[0013] As an alternative implementation, the cone anti-collision model has the horizontal plane where the UAV is located as the base of the cone, the real-time position of the UAV as the center of the base circle, and the vertex of the cone is located below the base.

[0014] As an alternative implementation, the distance from the vertex of the cone anti-collision model to the horizontal plane where the UAV is located changes with the change of the UAV speed.

[0015] As an alternative implementation, the current position of the UAV and environmental information are used to generate the initial trajectory during the first planning.

[0016] As an alternative implementation, the convex hull property of the Bernstein polynomial is used to eliminate the non-convex constraints of the set of control points of the relative trajectory.

[0017] As an alternative implementation, the GJK algorithm is used to find the closest point pair between the convex hull of the relative trajectory and the anti-collision model.

[0018] In a second aspect, the present invention provides a UAV trajectory planning system based on a novel adaptive anti-collision model, comprising:

[0019] An initial trajectory generation module, configured to: generate an initial trajectory according to environmental information and the previous trajectory;

[0020] A model construction module, configured to: construct a speed adaptive cone anti-collision model based on the speed information of the UAV;

[0021] A trajectory convex hull calculation module, configured to: segment an initial trajectory, where each segment of the initial trajectory is represented by a set of control points, calculate a set of control points of the relative trajectory of each pair of unmanned aerial vehicles, and thereby obtain the relative trajectory convex hull of the two;

[0022] A normal vector and safety margin calculation module, configured to: obtain the closest point pair between the relative trajectory convex hull and the anti-collision model, calculate the vector between the closest point pair, find the normal vector of the hyperplane separating the relative trajectory convex hull and the anti-collision model, and thereby obtain the safety margin between the unmanned aerial vehicles according to the hyperplane separation theorem;

[0023] A trajectory optimization module, configured to: construct linear inequalities using the normal vector and the safety margin, use the linear inequalities as collision avoidance constraints, perform trajectory optimization, and obtain the final trajectory.

[0024] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in the first aspect is completed.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first aspect is completed.

[0026] In a fifth aspect, the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, the method described in the first aspect is implemented.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] The present disclosure proposes a method and system for unmanned aerial vehicle trajectory planning based on a novel adaptive anti-collision model. Innovatively, the traditional anti-collision model is improved to a cone dynamically adjusted based on the flight speed of the unmanned aerial vehicle. This model can more accurately simulate and compensate for the influence of the downwash effect on adjacent unmanned aerial vehicles by adjusting the distance from the cone vertex to the bottom surface in real time according to the speed of the unmanned aerial vehicle, effectively solving the problem of insufficient accuracy in the traditional model, and thus significantly improving the safety of multi-unmanned aerial vehicle systems in dense formation flight; compared with the traditional anti-collision model, the present invention is more in line with the actual situation of the downwash airflow. The shape of the cone provides more flight margins, improving the anti-lock flexibility of unmanned aerial vehicles when meeting in narrow situations and enhancing the overall mission efficiency; specifically considering the influence of the speed of unmanned aerial vehicles on the intensity of the downwash effect, the anti-collision model can not only effectively predict and avoid potential collisions, but also ensure that each unmanned aerial vehicle maintains an optimal safety distance within its operating range, which greatly improves the reliability and flexibility of the system in complex environments.

[0029] Advantages of additional aspects of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not unduly limit the present invention.

[0031] Figure 1 Flow chart of the UAV trajectory planning method based on a new type of adaptive anti-collision model provided in Embodiment 1 of the present invention Figure 1 ;

[0032] Figure 2 Flow chart of the UAV trajectory planning method based on a new type of adaptive anti-collision model provided in Embodiment 1 of the present invention Figure 2 ;

[0033] Figure 3 Schematic diagram of the new type of adaptive anti-collision model provided in Embodiment 1 of the present invention;

[0034] Figure 4 Experimental simulation result diagram of the obstacle environment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0038] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0039] Embodiment 1

[0040] AsFigure 1-2 As shown in the figure, this embodiment provides a UAV trajectory planning method based on a new type of adaptive anti-collision model, including the following steps:

[0041] Step 1: Generate an initial trajectory according to the environmental information and the previous trajectory.

[0042] Step 2: Construct a speed adaptive cone anti-collision model according to the speed information of the UAV.

[0043] Step 3: Segment the initial trajectory. Each segment of the initial trajectory is represented by a set of control points. Calculate the set of control points of the relative trajectory of each pair of UAVs, and then obtain the convex hull of their relative trajectories.

[0044] Step 4: Obtain the closest point pair between the relative trajectory convex hull and the anti-collision model, calculate the vector between the closest point pair, find the normal vector of the hyperplane separating the relative trajectory convex hull and the anti-collision model, and then obtain the safety margin between UAVs according to the hyperplane separation theorem.

[0045] Step 5: Use the normal vector and the safety margin to construct a linear inequality, use the linear inequality as a collision avoidance constraint, perform trajectory optimization, and obtain the final trajectory.

[0046] The specific solution of the present invention is as follows:

[0047] Step 1: Adopt an online distributed replanning method. When replanning the trajectory at each step, first generate a new initial trajectory based on the previous trajectory and environmental information. Use the current position of the UAV at the first planning.

[0048] Step 2: Take the horizontal plane where each UAV is located as the plane where the bottom surface of the cone is located, generate a cone with the real-time position of the UAV as the center of the bottom surface and the vertex located below the bottom surface, and regard this cone as the anti-collision model of the UAV. For UAV and UAV at the anti-collision model between moments The specific formula is as follows:

[0049]

[0050] Among them, are respectively the real-time three-dimensional coordinate values of UAV at moment, is the bottom radius of the cone at moment, is the height of the cone at moment.

[0051] When using replanning, obtain UAV At the speed at the moment , and formulate a strategy for the height of the cone to be dynamically and adaptively adjusted with the speed according to different speed values. The specific formula is as follows:

[0052]

[0053] To ensure that the drone and the drone At the moment has a reasonable safety margin, it is necessary to ensure that the minimum value of the drone At the moment to the edge of the anti-collision model also meets the requirements of the safety margin, that is:

[0054]

[0055] Among them, are the effective radii of the drone and the drone respectively. To achieve more safety margins and higher flexibility in the trajectory planning of multiple drones in a dense area, the equal sign can be taken during actual operation.

[0056] Step 3: After obtaining the initial trajectories of the drones according to Step 1, divide these trajectories into multiple segments, each segment represented by a set of control points. For each pair of drones and the drone for which collision avoidance needs to be considered, calculate the set of control points of their relative trajectories. As long as there is no intersection between their relative positions and the anti-collision model, unobstructed passage between the drones can be achieved, which is expressed by the formula:

[0057]

[0058] Among them, is the number of steps for replanning, is the trajectory of the drone at the step of replanning.

[0059] This can be specifically achieved by subtracting the control points of the th segment of the drone from the control points of the corresponding th segment of the drone

[0060]

[0061]

[0062] Among them, is the convex hull of the relative trajectory control points between the UAVs and . is the UAV the th segment of the th trajectory point on the is the UAV set, is the convex hull operator that returns the convex hull of the input set.

[0063] Step 4: Once the convex hull of the relative positions of the two UAVs is obtained, it can be used to detect whether the two UAVs will collide. Figure 3 is a schematic diagram of the new speed adaptive anti-collision model designed by the present invention. In the figure, the red area is the anti-collision model of the UAV , and the orange area is the convex hull of the relative positions of the two UAVs and and . If the convex hull of the relative positions of the two UAVs has no intersection with their anti-collision model, it means that in this step, the UAVs and and will not collide.

[0064] To ensure that the convex hull of the relative trajectory does not intersect with the anti-collision model , a hyperplane separating the two sets needs to be found. First, the normal vector of the hyperplane needs to be found. By using the GJK algorithm, the closest point pair between the convex hull of the relative trajectory and the anti-collision model is found. Suppose it is and , and the normal vector can be obtained by calculating the vector between the closest point pair:

[0065]

[0066] To ensure that there is a sufficient safety distance between the UAVs, a safety margin needs to be calculated. This margin takes into account not only the distance of the closest point pair but also the shape of the anti-collision model. According to the hyperplane separation theorem, the safety margin can be defined as:

[0067]

[0068] Among them, is the initial control point generated using the trajectory from the previous step.

[0069] Step 5: Using the normal vector and safety margin obtained in Step 4, a set of linear inequalities can be constructed to describe the linear safety corridor. Specifically, for each pair of UAVs for which collision avoidance needs to be considered and UAV , the following linear inequalities are defined:

[0070]

[0071] Finally, by incorporating the linear inequalities obtained in Step 5 into the quadratic programming problem as part of the collision avoidance constraints, it is possible to ensure that there are no collisions between UAVs during the trajectory optimization phase, and there is an appropriate safety margin to avoid the influence of the downwash effect. Figure 4 This is the experimental simulation result graph of the obstacle environment of the present invention. The circles in the figure are UAVs, the lines with the same color as the center of the circle are the optimized trajectories of the corresponding UAVs, the positions where the circles are located are the end points of the optimized trajectories of the UAVs, and the other ends of the lines with the same color as the center of the circle are the starting points of the optimized trajectories of the UAVs. The trajectory planning method of the present invention performs trajectory planning for pairs of UAVs, where the starting point of one UAV is the end point of the other UAV. It can be seen that the algorithm proposed by the present invention can be applied to the trajectory planning of multiple UAVs and has good effects.

[0072] Embodiment 2

[0073] This embodiment provides a UAV trajectory planning system based on a novel adaptive anti-collision model, including:

[0074] An initial trajectory generation module, configured to: generate an initial trajectory according to environmental information and the previous trajectory;

[0075] A model construction module, configured to: construct a speed adaptive conical anti-collision model according to the speed information of the UAV;

[0076] A trajectory convex hull calculation module, configured to: segment the initial trajectory, each segment of the initial trajectory is represented by a set of control points, calculate the set of control points of the relative trajectory of each pair of UAVs, and then obtain the relative trajectory convex hull of the two;

[0077] A normal vector and safety margin calculation module, configured to: obtain the closest point pair between the relative trajectory convex hull and the anti-collision model, calculate the vector between the closest point pair, find the normal vector of the hyperplane separating the relative trajectory convex hull and the anti-collision model, and then obtain the safety margin between the UAVs according to the hyperplane separation theorem;

[0078] A trajectory optimization module, configured to: construct linear inequalities by using normal vectors and safety margins, use the linear inequalities as collision avoidance constraints, perform trajectory optimization, and obtain a final trajectory.

[0079] It should be noted here that the above modules correspond to the steps described in Embodiment 1. The examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of a system, can be executed in a computer system such as a set of computer-executable instructions.

[0080] In more embodiments, there is also provided:

[0081] An electronic device, including a memory and a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be elaborated here.

[0082] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0083] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0084] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the method described in Embodiment 1.

[0085] The method in Embodiment 1 can be directly implemented by a hardware processor, or can be implemented by a combination of hardware and software modules in the processor. The software modules may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0086] A computer program product, including a computer program, which, when executed by a processor, implements the method described in Embodiment 1.

[0087] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the processes / methods described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided among program modules as needed. The machine-executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0088] The computer program code for implementing the method of the present invention can be written in one or more programming languages. This computer program code can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the computer, partially on the computer, as a stand-alone software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server.

[0089] In the context of the present invention, the computer program code or related data can be carried by any suitable carrier so that a device, apparatus, or processor can perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals can include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, etc.

[0090] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0091] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that based on the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A method for UAV trajectory planning based on a new type of adaptive anti-collision model, characterized in that, Including the following steps: Generate an initial trajectory based on environmental information and the trajectory of the previous step; Construct a speed-adaptive cone anti-collision model according to the speed information of the UAV; Segment the initial trajectory, each segment of the initial trajectory is represented by a set of control points, calculate the set of control points of the relative trajectory of each pair of UAVs, and then obtain the convex hull of the relative trajectory between the two; Obtain the closest point pair between the convex hull of the relative trajectory and the anti-collision model, calculate the vector between the closest point pair, find the normal vector of the hyperplane separating the convex hull of the relative trajectory and the anti-collision model, and then obtain the safety margin between UAVs according to the hyperplane separation theorem; Construct a linear inequality using the normal vector and the safety margin, use the linear inequality as a collision avoidance constraint, perform trajectory optimization, and obtain the final trajectory; The cone anti-collision model has the horizontal plane where the UAV is located as the bottom surface of the cone, the real-time position of the UAV as the center of the bottom surface, and the vertex of the cone is located below the bottom surface; The distance from the vertex of the cone anti-collision model to the horizontal plane where the UAV is located changes with the change of the UAV speed; For unmanned aerial vehicles and unmanned aerial vehicles The anti-collision model between moments The specific formula is as follows: Among them, are respectively the three-dimensional coordinate values of the drone at the real-time moment, is the bottom radius of the cone at the moment, is the height of the cone at the moment; Obtain the drone when re-planning is adopted at the speed at the moment , and formulate a strategy for the height of the cone to be dynamically and adaptively adjusted with the speed according to different speed values. The specific formula is as follows: To ensure a reasonable safety margin for the unmanned aerial vehicle (UAV) and the UAV at time, it is necessary to ensure that the minimum value from the UAV to the edge of the anti-collision model at time also meets the requirements of the safety margin, that is: Among them, are respectively the effective radii of the unmanned aerial vehicle and the unmanned aerial vehicle ​ 2. The method for UAV trajectory planning based on the novel adaptive anti-collision model according to claim 1, characterized in that, When planning for the first time, use the current position of the UAV and environmental information to generate an initial trajectory.

3. The method for UAV trajectory planning based on the new adaptive anti-collision model according to claim 1, characterized in that Use the convex hull property of the Bernstein polynomial to eliminate the non-convex constraints of the set of control points of the relative trajectory.

4. The method for UAV trajectory planning based on the new adaptive anti-collision model according to claim 1, characterized in that, Use the GJK algorithm to find the closest point pair between the convex hull of the relative trajectory and the anti-collision model.

5. A UAV trajectory planning system based on a new type of adaptive anti-collision model, characterized in that, Including: An initial trajectory generation module, configured to: generate an initial trajectory based on environmental information and the trajectory of the previous step; A model construction module, configured to: construct a speed-adaptive cone anti-collision model according to the speed information of the UAV; A trajectory convex hull calculation module, configured to: segment the initial trajectory, each segment of the initial trajectory is represented by a set of control points, calculate the set of control points of the relative trajectory of each pair of UAVs, and then obtain the convex hull of the relative trajectory between the two; A normal vector and safety margin calculation module, configured to: obtain the closest point pair between the convex hull of the relative trajectory and the anti-collision model, calculate the vector between the closest point pair, find the normal vector of the hyperplane separating the convex hull of the relative trajectory and the anti-collision model, and then obtain the safety margin between UAVs according to the hyperplane separation theorem; A trajectory optimization module, configured to: construct a linear inequality using the normal vector and the safety margin, use the linear inequality as a collision avoidance constraint, perform trajectory optimization, and obtain the final trajectory; The cone anti-collision model has the horizontal plane where the UAV is located as the bottom surface of the cone, the real-time position of the UAV as the center of the bottom surface, and the vertex of the cone is located below the bottom surface; The distance from the vertex of the cone anti-collision model to the horizontal plane where the UAV is located changes with the change of the UAV speed; For drones and drones The anti-collision model between moments is as follows: The specific formula is as follows: Among them, are the real-time three-dimensional coordinate values of the drone at moment respectively, is the bottom radius of the cone at moment, is the height of the cone at moment; Obtain the drone when re-planning is adopted At the speed at the moment , and formulate a strategy for the height of the cone to be dynamically and adaptively adjusted with the speed according to different speed values. The specific formula is as follows: To ensure a reasonable safety margin for the drone and the drone at time, it is necessary to ensure that the minimum value from the drone at time to the edge of the anti-collision model also meets the requirements of the safety margin, that is: Among them, are the effective radii of the UAV and the UAV respectively.

6. An electronic device, characterized in that, Including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in any one of claims 1-4 is completed.

7. A computer-readable storage medium, characterized in that, For storing computer instructions, when the computer instructions are executed by the processor, the method described in any one of claims 1-4 is completed.

8. A computer program product, characterized in that, Including a computer program, when the computer program is executed by the processor, the method described in any one of claims 1-4 is implemented.

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