Improved dynamic window approach for fast approaching of marine robots to target points, program, device and storage medium

Abstract

The application belongs to the technical field of ocean robot path planning, and particularly relates to an improved dynamic window collision avoidance method for fast approaching of an ocean robot to a target point, a program, equipment and a storage medium. In the process of performing the task of fast approaching of the ocean robot to the target point, real-time state information and obstacle information obtained are used, a dynamic action space window of the ocean robot is obtained through calculation of a dynamic prediction time based on a dynamic window algorithm, and threshold processing is performed on a distance evaluation function in consideration of the problem of excessively moving away from the obstacle in the avoidance process. The optimization processing method of the dynamic prediction time and the threshold distance function is used to solve the problem of speed reduction in approaching the temporary target point and the problem of excessively moving away from the obstacle in the collision avoidance process of the ocean robot, a more effective dangerous avoidance path is solved, the ocean robot is ensured to safely and quickly complete the obstacle avoidance process, and the dangerous avoidance efficiency and the navigation safety of the ocean robot are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ocean robot path planning, and particularly relates to an improved dynamic window collision avoidance method for ocean robots to quickly approach target points, a program, equipment and a storage medium. BACKGROUND

[0002] Ocean robots refer to various ships, unmanned boats, ocean floating structures and the like floating on the water surface in a broad sense. Ocean robots have wide application prospects and play an important role in resource detection, target search and rescue, weather monitoring and communication relay and the like. Path planning methods are one of the core technologies of ocean robots and can be divided into global path planning and local path planning. Collision avoidance planning belongs to one kind of local path planning. Ocean robots will encounter unknown obstacles and other ships not marked in a map in actual navigation. A good navigation route is helpful for ocean robots to efficiently and high-quality complete work tasks, and therefore, a dangerous avoidance method of ocean robots needs to be explored.

[0003] The document "Ship's trajectory planning for collision avoidance at sea based on modified artificial potential field" considers the speed of a dynamic target point and sets a great repulsive force field, so as to facilitate the response of an unmanned boat at an emergency moment, and proposes an improved artificial potential field method, so that a ship can safely navigate in a dynamic environment. However, the algorithm does not consider the turning ability of the unmanned boat, and the navigation safety is poor.

[0004] The document "Hierarchical Path Planning of Unmanned Surface Vehicles: A Fuzzy Artificial Potential Field Approach" introduces an adaptive fuzzy theory, divides an encounter situation into a fuzzy behavior composed of an obstacle, an encounter distance and a collision avoidance behavior, and then determines the repulsive force of the unmanned boat in different encounter situations, and proposes an improved artificial potential field method. However, the algorithm does not consider the speed change of the ocean robot in the collision avoidance process.

[0005] The document "Dynamic window based approach to mobile robot motion control in the presence of moving obstacles" models the moving obstacles as moving units and uses the DWA algorithm to predict their motion, and then forms a new virtual obstacle in the environment by the collision points of the predicted trajectories of the robot and the moving obstacles, and finally searches for a collision-free path. However, this method does not consider the complex constraints on the speed / acceleration of the marine robot.

[0006] The patent "Intelligent robot path planning algorithm combining improved JPS and dynamic window method" combines the JPS algorithm with the dynamic window method to realize global path planning and local collision avoidance planning of the intelligent robot in the grid map, and obtains a collision-free path. However, this algorithm performs local collision avoidance planning in the grid environment, and the safety of the final path is greatly affected by the modeling of the map. Moreover, the speed and acceleration changes of the marine robot are not considered.

[0007] Most of the above documents do not consider the influence of the speed change of the marine robot on the safety of collision avoidance, and in actual engineering applications, the marine robot often faces more complex and dangerous collision avoidance scenarios. The marine robot should improve the collision avoidance efficiency as much as possible to pass through the obstacle area as quickly as possible under the premise of ensuring navigation safety. SUMMARY

[0008] The purpose of the present application is to solve the problem of dangerous avoidance of the marine robot in rapid approach to the target point under the influence of dynamic coupling, and to provide an improved dynamic window collision avoidance method, program, device and storage medium for the rapid approach of the marine robot to the target point.

[0009] The improved dynamic window collision avoidance method for the rapid approach of the marine robot to the target point comprises the following steps:

[0010] Step 1: During the process of performing the task of rapid approach to the target point, the marine robot acquires the state information of the marine robot itself and the detected obstacle information at the current time;

[0011] Step 2: According to the distance between the marine robot and the nearest obstacle, determine the action set V a (t) that the marine robot can take to avoid collision with the obstacle by emergency stopping;

[0012] Step 3: According to the distances between the marine robot and the target point and the nearest obstacle, calculate the dynamic window duration;

[0013] Step 4: According to the dynamic window duration, obtain the action set V d (t) of the marine robot in the next decision period under the maximum acceleration constraint;

[0014] Step 5: According to the speed limit set V of the marine robot S , take the intersection of V S , V a (t) and V d (t) as the action space window of the marine robot at the current time t;

[0015] Step 6: Sample the action space window, calculate the evaluation value for each group of speed vectors, take the speed vector with the maximum corresponding evaluation value, calculate the heading angle, and take the speed vector and the heading angle as the collision avoidance action of the marine robot;

[0016] Step 7: After the marine robot performs the collision avoidance action, if the target point has not been reached, return to step 1.

[0017] Further, V a (t) in step 2 is specifically represented as:

[0018]

[0019] Wherein, v(t), θ(t), ω(t), respectively represent the linear speed, heading angle, angular speed, linear acceleration and angular acceleration of the marine robot at the current time t; d1(t) represents the distance between the marine robot and the nearest obstacle at the current time t.

[0020] Further, the dynamic window length T DWA (t+Δt) in step 3 is specifically:

[0021]

[0022] Wherein, k1 is a distance adjustment coefficient; d0 is a safety distance between the marine robot and the obstacle; d2(t) is the distance between the marine robot and the target point at the current time t.

[0023] Further, V d (t) in step 4 is specifically:

[0024]

[0025] Wherein, and represent the maximum linear acceleration and the maximum angular acceleration of the marine robot.

[0026] Further, V S in step 5 is specifically:

[0027] V S = {(v(t+Δt), ω(t+Δt)) v(t+Δt) ∈ [vmin v max ω(t+Δt)∈[ω min ,ω max}

[0028] where v min and ω min denote the minimum linear and angular velocities of the marine robot; v max and ω max denote the maximum linear and angular velocities of the marine robot.

[0029] Further, the method of sampling the action space window and calculating the evaluation value for each group of velocity vector (v(t+Δt), ω(t+Δt)) in step 6 is specifically as follows:

[0030] Step 6.1: Determine the prediction step t0=n·Δt, n is an integer, and initialize t1=t+Δt;

[0031] Step 6.2: Simulate the motion of the marine robot according to the linear and angular accelerations of the marine robot and Update the state information of the marine robot at time t1+Δt [x(t1+Δt), y(t1+Δt), v(t1+Δt), θ(t1+Δt), ω(t1+Δt)];

[0032] x(t1+Δt)=x(t1)+v(t1)·cos(θ(t1))·Δt

[0033] y(t1+Δt)=y(t1)+v(t1)·sin(θ(t1))·Δt

[0034]

[0035] θ(t1+Δt)=θ(t1)+ω(t1)·Δt

[0036]

[0037] Step 6.3: If t1+Δt

[0038] Otherwise, calculate the evaluation value G according to the state information of the marine robot at time t+t0;

[0039] G=σ(α·heading+β·dist+γ·velocity)

[0040] Wherein, σ, α, β, γ are coefficients; heading is a heading evaluation function, which is used to evaluate the angle difference between the position [x(t+t0), y(t+t0)] of the marine robot at t+t0 and the target point; dist is an obstacle distance evaluation function, which is used to evaluate the distance between the position [x(t+t0), y(t+t0)] of the marine robot and the nearest obstacle; velocity is a velocity evaluation function, which is used to evaluate the velocity [θ(t+t0), ω(t+t0)] of the marine robot.

[0041] Further, the obstacle distance evaluation function dist in step 6.3 is threshold processed, specifically:

[0042]

[0043] Wherein, R N is the radius of the marine robot, which is half of the longitudinal ship length; μ is a distance safety factor, if the environment around the marine robot is a narrow channel, then μ=2; if the marine robot is in a dynamic crowded environment, then μ=3; if the marine robot is in an open water area, then μ=4.

[0044] A computer device / apparatus / system, comprising a memory, a processor and a computer program stored on the memory, the processor executes the computer program to implement the steps of the improved dynamic window collision avoidance method for the marine robot to quickly approach the target point.

[0045] A computer readable storage medium, having a computer program / instruction stored thereon, the computer program / instruction is executed by a processor to implement the steps of the improved dynamic window collision avoidance method for the marine robot to quickly approach the target point.

[0046] A computer program product, comprising a computer program / instruction, the computer program / instruction is executed by a processor to implement the steps of the improved dynamic window collision avoidance method for the marine robot to quickly approach the target point.

[0047] The beneficial effects of the present application are:

[0048] The application utilizes the real-time state information and obstacle information obtained in the process of performing the task of quickly approaching the target point by the marine robot, obtains the dynamic action space window of the marine robot based on the dynamic window algorithm through calculating the dynamic prediction time, solves the problem of reducing speed near the target point in the process of danger avoidance of the marine robot, considers the problem of excessively moving away from the obstacle in the avoidance process, and makes threshold processing for the distance evaluation function, which not only guarantees the safety of the collision avoidance process, but also improves the navigation efficiency of the marine robot and the adaptability to the dense collision avoidance environment. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is the overall principle flowchart of the application.

[0050] Figure 2 is a dynamic window schematic diagram.

[0051] Figure 3 is a marine robot motion model diagram.

[0052] Figure 4 is a marine robot candidate trajectory cluster diagram.

[0053] Figure 5 is a marine robot azimuth angle evaluation schematic diagram.

[0054] Figure 6 is a marine robot collision avoidance scene one.

[0055] Figure 7 is a marine robot collision avoidance scene two.

[0056] Figure 8 is a marine robot collision avoidance scene three.

[0057] Figure 9 is the overall system architecture diagram of the application. DETAILED DESCRIPTION

[0058] The application will be further described below in combination with the drawings.

[0059] The application aims to solve the problem of danger avoidance of the ocean robot in rapid approach to the target point under the influence of dynamic coupling, and the existing danger avoidance method does not consider the problem of excessive distance from the obstacle and speed reduction of the ocean robot in the process of approaching the target point, therefore, the application provides an improved dynamic window collision avoidance method for the ocean robot in rapid approach to the target point based on dynamic prediction time and threshold distance function, which is used for solving the problem of speed reduction in approaching the temporary target point and the problem of excessive distance from the obstacle in the process of collision avoidance of the ocean robot, and improves the danger avoidance efficiency and navigation safety of the ocean robot.

[0060] Step 1: In the process of the ocean robot performing the task of rapid approach to the target point, the current t time ocean robot state information [x(t), y(t), v(t), θ(t), ω(t)] and the detected obstacle information are acquired;

[0061] The ocean robot is loaded with a binocular vision system, a Beidou integrated machine and a combined navigation system, and can obtain the obstacle information around the ocean robot and the position, attitude and speed information of the ocean robot in real time; the obstacle information includes static obstacle information and dynamic obstacle (navigation ship) information around the ocean robot; [x(t), y(t)] represents the position of the ocean robot at the current t time, v(t), θ(t) and ω(t) represent the linear speed, heading angle and angular speed of the ocean robot at the current t time, respectively;

[0062] Step 2: According to the distance d1(t) between the ocean robot and the nearest obstacle, the action set V a (t) that the ocean robot can avoid collision with the obstacle by emergency stop is determined;

[0063]

[0064] Wherein, And x(t), y(t) represent the linear acceleration and angular acceleration of the ocean robot at the current t time;

[0065] Step 3: According to the distance d2(t) between the ocean robot and the target point and the distance d1(t) between the ocean robot and the nearest obstacle, the dynamic window length T DWA (t+Δt) is calculated;

[0066]

[0067] Wherein, k1 is a distance adjustment coefficient; d0 is the safety distance between the ocean robot and the obstacle; k1·T DWA (t) is a position prediction, which represents the possibility of exceeding the target point, and does not necessarily really exceed the target point, which is an approximate position prediction method. After the dynamic processing of the prediction time, the ocean robot faces Figure 8In the engineering scenario three shown, it is possible to maintain high-speed navigation when approaching the temporary target point, which improves the navigation efficiency of the marine robot and the collision avoidance safety in encountering situations.

[0068] Step 4: According to the dynamic window length T DWA (t+Δt), obtain the action set V of the marine robot in the next decision cycle under the maximum acceleration constraint d (t);

[0069]

[0070] in, and Indicates the maximum linear acceleration and maximum angular acceleration of the marine robot;

[0071] Step 5: Set V according to the speed limit of the marine robot S , take V S 、V a (t) and V d The intersection of (t) is used as the action space window V of the marine robot at the current time t r (t);

[0072] V S ={(v(t+Δt),ω(t+Δt))v(t+Δt)∈[v min ,v max ],ω(t+Δt)∈[ω min ,ω max ]}

[0073] Among them, v min With ω min represents the minimum linear velocity and minimum angular velocity of the marine robot; v max With ω max Indicates the maximum linear velocity and maximum angular velocity of the marine robot;

[0074] Step 6: Adjust the action space window V r (t) is sampled, and the evaluation value is calculated for each set of velocity vectors (v(t+Δt), ω(t+Δt)), the velocity vector with the largest corresponding evaluation value is taken, the heading angle θ(t+Δt) is calculated, and [v(t+Δt), θ(t+Δt), ω(t+Δt)] is used as the collision avoidance action of the marine robot;

[0075] For a set of velocity vectors (v(t+Δt),ω(t+Δt)), the method for calculating the evaluation value is as follows:

[0076] Step 6.1: Determine the prediction step size t0 = n·Δt, where n is an integer between 10 and 30, and initialize t1 = t+Δt.

[0077] Step 6.2: According to the linear acceleration and angular acceleration of the marine robot With Simulate the motion of the marine robot, update the state information of the marine robot itself at time t1+Δt [x(t1+Δt), y(t1+Δt), v(t1+Δt), θ(t1+Δt), ω(t1+Δt)];

[0078] The trajectory of the marine robot can be approximated as a uniform straight line motion in a very short time. As shown in Figure 3 X-Y is the geodetic coordinate system, x robot -y robot is the body coordinate system, and θ represents the heading angle of the marine robot. At this time, the kinematic model of the marine robot can be represented by the following formula:

[0079]

[0080] In the formula, x(t), y(t) are the Cartesian coordinates of the marine robot, which can be expressed as u is the longitudinal linear velocity of the marine robot in the forward direction, v is the transverse linear velocity of the marine robot perpendicular to the forward direction; ψ(t) is the heading angle of the marine robot, is the derivative of the heading angle of the marine robot, i.e. the angular velocity r(t); the velocity vector of the marine robot in the Cartesian coordinate system can be represented as represents the velocity of the marine robot in the x-axis direction, represents the velocity of the marine robot in the y-axis direction. To represent the dynamic constraint, the longitudinal velocity of the marine robot satisfies 0≤u(t)≤u max , the angular velocity satisfies -r max ≤r(t)≤r max .

[0081] Generally, the state of the marine robot at a certain time is represented by p(t) and v(t) at that time, and the two variables of the sampling action (v, ω) in the DWA algorithm are added to obtain the state, and the mathematical expression is [x(t), y(t), θ(t), v, ω] T . The state transition equation of the marine robot can be obtained:

[0082] x(t1+Δt)=x(t1)+v(t1)·cos(θ(t1))·Δt

[0083] y(t1+Δt)=y(t1)+v(t1)·sin(θ(t1))·Δt

[0084]

[0085] θ(t1+Δt) = θ(t1) + ω(t1) · Δt

[0086]

[0087] Step 6.3: If t1+Δt < t+t0, then let t1 = t1+Δt, return to step 6.2;

[0088] Otherwise, calculate the evaluation value G according to the state information of the marine robot at t+t0;

[0089] G = σ(α · heading + β · dist + γ · velocity)

[0090] Wherein, σ represents a normalization coefficient; heading is a heading evaluation function, which is used to evaluate the angle difference between the position [x(t+t0), y(t+t0)] of the marine robot at t+t0 and the target point, as shown by θ in the following formula: Figure 5 θ is the deviation of the heading of the trajectory end of the marine robot and the target heading, and the smaller θ is, the smaller the deviation is. However, the evaluation function is to find the maximum value, so θ needs to be handled as a supplementary angle. α is a heading evaluation coefficient, and the larger α is, the closer the selected trajectory is to the target heading.

[0091] dist is an obstacle distance evaluation function, which is used to evaluate the distance between the position [x(t+t0), y(t+t0)] of the marine robot and the nearest obstacle, and β is an obstacle distance evaluation coefficient. The larger β is, the farther the selected trajectory is from the obstacle. velocity is a speed evaluation function, which is used to evaluate the speed [θ(t+t0), ω(t+t0)] of the marine robot, and γ is a speed evaluation coefficient. The larger γ is, the larger the speed of the selected trajectory is, and the faster the trajectory can approach the target point.

[0092] The obstacle distance evaluation function is thresholded, which aims to make the marine robot select a more safe and effective dangerous avoidance route and avoid reducing the collision avoidance efficiency by being too far away from the obstacle.

[0093]

[0094] Wherein, R N is the radius of the marine robot, which is half of the longitudinal length of the ship; μ is a distance safety factor. If the marine robot is in a narrow channel, then μ = 2; if the marine robot is in a dynamic crowded environment, then μ = 3; and if the marine robot is in an open water area, then μ = 4. After thresholding the obstacle distance evaluation function, the marine robot can select a more effective obstacle avoidance route and improve the efficiency of dangerous avoidance of the marine robot when facing the engineering scenarios one and two shown in the following formulas: Figure 6 、 Figure 7 ​

[0095] The evaluation function is normalized, and the specific steps are as follows:

[0096]

[0097]

[0098]

[0099] In the formula, n is the number of all trajectories of sampling, and i is the number of the current trajectory to be evaluated.

[0100] Step 7: The marine robot performs the collision avoidance action, and after moving for Δt time with [v(t+Δt), θ(t+Δt), ω(t+Δt)], if the target point is not reached, return to step 1.

[0101] As shown in Figure 9 The collision avoidance system for the marine robot to quickly approach the target point includes:

[0102] (1) Information data processing module: transmit the marine environment information of the marine robot sailing in the sea area, accept the task information, and process and optimize various data, update the marine environment information regularly, and provide the time-varying marine environment information and the current pose information for the danger avoidance module.

[0103] The above-mentioned marine environment information refers to the static obstacle information and dynamic navigation ship information around the marine robot.

[0104] (2) Danger avoidance module: according to the received task instruction, the current state of the marine robot and the dynamic constraint, calculate the dynamic decision period, generate a candidate trajectory cluster, determine the avoidance action to be taken by the marine robot through optimization, and regularly update the candidate trajectory cluster and the current position of the marine robot according to the information transmitted by the information data processing module, and search out the danger avoidance path according to the method.

[0105] The danger avoidance module constantly provides the latest optimal danger avoidance action scheme according to the updated marine environment information and the real-time position of the marine robot.

[0106] (3) Environment perception module: obtain the obstacle information around the current position through the underwater sonar and the vision system.

[0107] (4) Positioning module: obtain the pose information of the marine robot through the Beidou integrated machine and the integrated navigation, and transmit it to the information data processing module to provide time-varying position information to the danger avoidance module.

[0108] (5) Marine robot control module: obtain the avoidance action instruction, execute the steering mechanism, and sail according to the specified instruction.

[0109] In summary, in the process of performing the task of quickly approaching the target point by the marine robot, the real-time state information and the obstacle information obtained are used, the dynamic action space window of the marine robot is obtained by calculating the dynamic prediction time based on the dynamic window algorithm (DWA), the problem of reducing speed near the target point in the process of danger avoidance of the marine robot is solved, the problem of excessively moving away from the obstacle in the avoidance process is considered, the threshold processing is performed on the distance evaluation function, and therefore the safety of the collision avoidance process is ensured, and the navigation efficiency of the marine robot and the adaptability to the dense collision avoidance environment are improved.

[0110] The preferred embodiments of the present application have been described above with the aid of drawing; the present application can be variously changed and altered by those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An improved dynamic window collision avoidance method for a marine robot to quickly approach a target point, characterized in that: The following steps are involved: Step 1: When the marine robot is executing the task of rapidly approaching the target point, it obtains the current state information of the marine robot itself and the information of the detected obstacles; Step 2: Based on the distance between the marine robot and the nearest obstacle, determine the action set V that the marine robot can use to avoid collision with the obstacle by emergency stopping. a (t); Among them, v(t), θ(t), ω(t), They represent the linear velocity, heading angle, angular velocity, linear acceleration and angular acceleration of the marine robot at the current moment t respectively; d1(t) represents the distance between the marine robot and the nearest obstacle at the current moment t; Step 3: Calculate the dynamic window duration T based on the distance between the marine robot and the target point and the nearest obstacle DWA (t+Δt); Where k1 is the distance adjustment coefficient; d0 is the safe distance between the marine robot and the obstacle; d2(t) is the distance between the marine robot and the target point at the current moment t; Step 4: According to the dynamic window duration, obtain the action set V of the marine robot in the next decision cycle under the maximum acceleration constraint d (t); in, and represents the maximum linear acceleration and maximum angular acceleration of the marine robot; Step 5: Set V according to the speed limit of the marine robot S , take V S 、V a (t) and V d The intersection of (t) is used as the action space window of the marine robot at the current moment t; Step 6: Sample the action space window, calculate the evaluation value for each set of velocity vectors, take the velocity vector with the largest corresponding evaluation value, calculate the heading angle, and use the velocity vector and heading angle as the collision avoidance action of the marine robot; Step 7: After the marine robot performs the collision avoidance action, if it fails to reach the target point, it returns to step 1.

2. The improved dynamic window collision avoidance method for a marine robot to quickly approach a target point according to claim 1, characterized in that: In step 5, V S Specifically: V S ={(v(t+Δt),ω(t+Δt))|v(t+Δt)∈[v min ,v max ],ω(t+Δt)∈[ω min ,ω max ]} Among them, v min With ω min represents the minimum linear velocity and minimum angular velocity of the marine robot; v max With ω max Indicates the maximum linear velocity and maximum angular velocity of the marine robot.

3. The improved dynamic window collision avoidance method for a marine robot to quickly approach a target point according to claim 2, characterized in that: In step 6, the action space window is sampled, and the method for calculating the evaluation value for each set of velocity vectors (v(t+Δt), ω(t+Δt)) is specifically as follows: Step 6.1: Determine the prediction step size t0 = n·Δt, where n is an integer, and initialize t1 = t+Δt; Step 6.2: According to the linear acceleration and angular acceleration of the marine robot and Simulate the motion of the marine robot and update the marine robot's own state information at time t1+Δt [x(t1+Δt), y(t1+Δt), v(t1+Δt), θ(t1+Δt), ω(t1+Δt)]; x(t1+Δt)=x(t1)+v(t1)·cos(θ(t1))·Δt y(t1+Δt)=y(t1)+v(t1)·sin(θ(t1))·Δt θ(t1+Δt)=θ(t1)+ω(t1)·Δt Step 6.3: If t1 + Δt < t + t0, set t1 = t1 + Δt and return to step 6.2; Otherwise, the evaluation value G is calculated based on the state information of the marine robot at time t+t0; G=σ(α·heading+β·dist+γ·velocity) Among them, σ, α, β, and γ are coefficients; heading is the azimuth evaluation function, which is used to evaluate the angular difference between the position of the marine robot at time t+t0 [x(t+t0), y(t+t0)] and the target point; dist is the obstacle distance evaluation function, which is used to evaluate the distance between the position of the marine robot [x(t+t0), y(t+t0)] and the nearest obstacle; velocity is the speed evaluation function, which is used to evaluate the speed of the marine robot [θ(t+t0), ω(t+t0)].

4. The improved dynamic window collision avoidance method for a marine robot to quickly approach a target point according to claim 3, characterized in that: In step 6.3, the obstacle distance evaluation function dist is thresholded, specifically: Among them, R N is the radius of the marine robot, which is half of the longitudinal length of the ship; μ is the distance safety factor. If the marine robot is in a narrow channel, μ = 2; if the marine robot is in a dynamic crowded environment, μ = 3; if the marine robot is in open waters, μ = 4.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

6. 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 4 are implemented.

7. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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