Automobile collision avoidance method based on fuzzy model predictive control

Abstract

The application relates to an automatic driving vehicle collision avoidance method based on fuzzy model predictive control, and belongs to the technical field of automatic driving vehicle collision avoidance. The method comprises the following steps: constructing a lateral dynamics model of an automatic driving vehicle; designing fuzzy rules according to T-S fuzzy logic, converting the lateral dynamics model into a fuzzy dynamics model based on the fuzzy rules, and discretizing the fuzzy dynamics model; designing collision avoidance constraints of the fuzzy dynamics model based on control variables and motion states; constructing a fuzzy model predictive controller based on the discretized fuzzy dynamics model and the collision avoidance constraints; optimizing and solving the fuzzy model predictive controller to obtain the control variables; and controlling the automatic driving vehicle to avoid collision based on the control variables.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of collision avoidance technology for autonomous vehicles, and particularly relates to an autonomous vehicle collision avoidance method based on fuzzy model predictive control. BACKGROUND

[0002] With the rapid development of autonomous driving technology, ensuring that autonomous vehicles safely and efficiently avoid collisions in various complex road environments has become a crucial technical challenge. In recent years, the development of autonomous vehicles has greatly improved the safety and efficiency of road traffic. Driving safety is an issue that cannot be ignored in the development process. Due to the presence of obstacles in the road, how to ensure that vehicles can effectively perceive, judge, and avoid possible collisions in various complex road environments is an important part of autonomous vehicle research. Autonomous driving technology is developing towards optimizing control algorithms, eliminating control delays, and ensuring that vehicles can efficiently and safely prevent collisions. In addition, the comfort and stability of the vehicle must be considered to avoid excessive acceleration or deceleration during collision avoidance, which may interfere with the passenger's ride experience.

[0003] Many research efforts are currently focused on the collision avoidance control field of autonomous vehicles, mainly concentrating on improving the vehicle's perception of the surrounding environment, accurately planning obstacle avoidance paths, and achieving efficient vehicle control. Autonomous vehicles are equipped with various sensors such as lidar, radar, cameras, and ultrasonic sensors to achieve all-around perception of the surrounding environment. These sensors can capture real-time road information, vehicle position, pedestrian dynamics, and other potential obstacles, providing accurate environmental data for the vehicle. Autonomous vehicles use advanced algorithms for processing and analysis to construct detailed environmental models. Based on this model, the vehicle can identify drivable areas and obstacle locations and plan the optimal driving path accordingly. Especially when encountering obstacles, the vehicle will quickly generate an obstacle avoidance trajectory to ensure that it can rejoin the predetermined nominal trajectory after avoiding obstacles, ensuring both safety and smooth driving. When planning the obstacle avoidance trajectory, autonomous vehicles use parameterized S-shaped function curves and other methods to ensure the smoothness and feasibility of the trajectory.

[0004] Design trajectory tracking control method, including control algorithm based on road geometry principle, such as pure tracking control, Stanley control, Alice control, etc. Path tracking control algorithm based on classical control theory, such as PID control, linear feedback control, etc. And path tracking control algorithm based on modern control theory, such as MPC control, LQR control, etc. At the same time, the automatic driving car will use the front feed plus the robust feedback transverse controller to realize the accurate tracking of the planning trajectory. This controller can calculate appropriate steering angle, acceleration and braking force and other control instructions based on the current state of the vehicle and the target trajectory, to ensure that the vehicle can accurately and stably follow the planned trajectory.

[0005] The application of model predictive control in automatic driving and collision avoidance control lies in its strong prediction and optimization capability. In the field of automatic driving, model predictive control predicts the future motion state of the vehicle by constructing the dynamics model of the vehicle, and optimizes the control strategy by combining real-time feedback information, to realize accurate trajectory tracking and path planning. In collision avoidance control, model predictive control can predict the relative position and speed relationship between the vehicle and the obstacle, calculate the optimal collision avoidance trajectory in advance, and control the speed, direction and other parameters of the vehicle to effectively avoid potential collisions. This control method not only improves the safety of the autonomous vehicle, but also ensures the comfort and stability during driving, providing strong support for the development of autonomous driving technology.

[0006] Most of the existing automatic driving car obstacle avoidance control technology path planning algorithms may have large calculation amount and low efficiency in complex environment, and the generated path may not be optimal. At the same time, the trajectory tracking control method may not perform well in the face of nonlinear and time-varying systems, and requires accurate parameter adjustment. Although model predictive control can avoid possible collisions by predicting the future state of the vehicle, it has high computational complexity and high accuracy requirements for the model. These problems may affect the obstacle avoidance performance and safety of the autonomous vehicle. SUMMARY

[0007] The technical problem to be solved by the present application is:

[0008] In order to avoid the shortcomings of the prior art, the present application provides an automatic driving car collision avoidance method based on fuzzy model predictive control, which solves the problem of complex calculation and error of existing model predictive control.

[0009] In order to solve the above technical problems, the technical scheme adopted by the present application is:

[0010] An automatic driving car collision avoidance method based on fuzzy model predictive control, characterized in that it comprises:

[0011] S1: Construct the lateral dynamics model of the automatic driving vehicle;

[0012] S2: design fuzzy rules according to T-S fuzzy logic, convert the lateral dynamic model into a fuzzy dynamic model based on the fuzzy rules and discretize the fuzzy dynamic model;

[0013] S3: design collision avoidance constraints of the fuzzy dynamic model based on control variables and motion states;

[0014] S4: construct a fuzzy model predictive controller based on the discretized fuzzy dynamic model and the collision avoidance constraints, optimize and solve the fuzzy model predictive controller to obtain control variables, and control the autonomous vehicle to avoid collision based on the control variables.

[0015] Further technical solutions of the present application: the lateral dynamic model of the autonomous vehicle is:

[0016]

[0017] wherein x = [y e , ψ e , v y , ω z ] T is a state vector of the vehicle system, y e is a current lateral position deviation of the vehicle, ψ e is a vehicle yaw rate error, v y is a vehicle lateral speed, and ω z is a vehicle yaw rate; u is a control variable of the vehicle model, representing a front wheel steering angle, i.e. u = δ; d is a vehicle model error, represented as d = [d y , d ψ , d v , d ω ] T , d y , d ψ , d v , d ω are errors of the four state vectors respectively; B d is a model error matrix, and C is an output matrix; A is a system matrix, and B is a control matrix, represented as:

[0018]

[0019] wherein v x is a vehicle longitudinal speed, C f and C r are turning stiffness of the front wheel and the rear wheel respectively, l f and l r represent distances between the center of mass and the front wheel and the rear wheel respectively, m and I z are mass and moment of inertia of the vehicle respectively.

[0020] The further technical scheme of the present application is that the discretized fuzzy dynamic model is specifically:

[0021] x k+1 =(I+TA k )x k +TB k u k +TB d,k d k

[0022] Wherein, T is the sampling step of the system, A k , B k and B d,k are the system matrices after fuzzy processing.

[0023] The further technical scheme of the present application is that the collision avoidance constraint includes input constraint and state constraint,

[0024] The input constraint includes input and input increment constraint, specifically:

[0025] u min <u k <u max

[0026] Δu min <Δu k <Δu max

[0027] Wherein, u min and u max are the lower limit and upper limit of the control input, Δu k is the control increment at the k step, and the upper and lower limits are Δu min and Δu max .

[0028] The state constraint includes position and velocity constraint, specifically:

[0029] v y,min <v y,k <v y,max

[0030] ψ min <ψ k <ψ max

[0031] ω min <ω k <ω max

[0032] Wherein, (·) min and (·) max respectively represent the upper boundary and lower boundary of the constraint variable.

[0033] The further technical scheme of the present application is that the fuzzy model predictive controller is optimized to obtain the control variable, and specifically:

[0034] The objective function of the fuzzy model predictive controller is designed as:

[0035]

[0036] Wherein, N p And N c Are the prediction step and the control step, Q, R and H are the weight matrices of the objective function.

[0037] The objective function is converted into an optimization problem:

[0038]

[0039] A computer system comprising one or more processors, a computer readable storage medium storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0040] A computer readable storage medium storing computer executable instructions, wherein the instructions, when executed, implement the above method.

[0041] A computer program product comprising computer executable instructions, wherein the instructions, when executed, implement the above method.

[0042] The beneficial effects of the present application are:

[0043] The automatic driving car collision avoidance method based on fuzzy model predictive control is provided, so as to realize effective obstacle avoidance of the automatic driving vehicle in the double lane transformation driving scene. A vehicle lateral dynamics model is established, the vehicle tire stiffness and inertia moment are considered, fuzzy rules are formulated, a fuzzy logic controller is designed, and the nonlinear vehicle dynamics model is fuzzed into a weighted form of a linear model. Based on the fuzzy vehicle dynamics model, a fuzzy model predictive controller is designed, the state and control variable constraints in the vehicle motion process are considered, and finally, the effective collision avoidance of the automatic driving car is realized with minimum calculation amount while ensuring the stability of the vehicle motion. Specifically as follows:

[0044] 1. The fuzzy vehicle dynamics model is established, which includes the establishment of the vehicle dynamics model and the establishment of the fuzzy controller. The nonlinear vehicle model is effectively simplified by setting the fuzzy logic rules. The fuzzy vehicle model is used to simplify the nonlinear vehicle model into a weighted sum of linear systems.

[0045] 2. By considering the constraint conditions in the vehicle collision avoidance process, a model predictive controller is designed by using the fuzzy vehicle model and the control amount and state amount constraints, so as to reduce the calculation amount and realize effective collision avoidance of the obstacle. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:

[0047] Figure 1 Flow chart of the method.

[0048] Figure 2 Vehicle motion model.

[0049] Figure 3 Motion trajectory of the collision avoidance vehicle.

[0050] Figure 4 Vehicle lateral velocity change curve.

[0051] Figure 5 Vehicle heading angle change curve.

[0052] Figure 6 Vehicle heading angle velocity change curve. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0054] In the core field of automatic driving technology, driving safety occupies a pivotal position. For the collision avoidance problem of automatic driving vehicles, due to the complexity of the nonlinear vehicle dynamics model and the diversity of obstacles in the road environment, how to effectively avoid collision and maintain stable operation of the vehicle in the presence of obstacles has become a challenging problem that needs to be solved. The present application deeply studies the collision avoidance control mechanism of the automatic driving vehicle and proposes an optimized fuzzy model predictive control algorithm. The present application establishes a series of fuzzy rules based on the Takagi-Sugeno fuzzy method, which converts the originally complex nonlinear vehicle system into a weighted sum of a series of linear affine models. This transformation not only significantly reduces the calculation cost of the algorithm, but also ensures the maintenance of control accuracy, thereby ensuring the collision avoidance performance of the automatic driving vehicle in complex road environments.

[0055] This invention provides a collision avoidance method for autonomous vehicles based on fuzzy model predictive control, which includes constructing a vehicle dynamics model, designing fuzzy logic, designing collision avoidance constraints, and designing a fuzzy model predictive controller based on the vehicle dynamics model, fuzzy logic, and collision avoidance constraints to control the autonomous vehicle to avoid collisions.

[0056] Constructing vehicle dynamics models

[0057] First, a lateral dynamics model of the autonomous vehicle is designed to better evaluate the performance of the vehicle's dynamic system and achieve precise control of the autonomous vehicle. To ensure model accuracy and simplify the subsequent design of the fuzzy model predictive controller, this invention establishes a lateral dynamics model of the vehicle, such as... Figure 2 As shown.

[0058] like Figure 2 As shown, X and Y represent the vehicle's global position in the longitudinal and lateral directions, respectively. With the vehicle's center of gravity as the reference point, l f and l r These represent the distances between the center of gravity and the front and rear wheels, respectively. And F... yf and F yr These are defined as the lateral tire forces acting on the front and rear wheels, respectively. δ is the front wheel steering angle.

[0059] The lateral position deviation and heading angle deviation of a vehicle are defined as follows:

[0060]

[0061] Where y is the vehicle's lateral position and ψ is the vehicle's heading angle. d and ψ d These are the vehicle's desired lateral position and heading angle, respectively. Therefore, the vehicle's lateral dynamics model can be expressed as:

[0062]

[0063] Among them, v x v is the longitudinal velocity of the vehicle. y Let y be the lateral velocity of the vehicle. e This is the vehicle's current lateral position deviation. ω z It is the yaw rate of the vehicle, ω d If the reference yaw rate is the vehicle's yaw rate, then the vehicle yaw rate error can be expressed as the difference ψ between the vehicle yaw rate and the reference yaw rate. e .

[0064] Assuming the heading angle error of the autonomous vehicle is small enough, further simplification yields the following approximation:

[0065]

[0066] Thus, the vehicle lateral dynamics model can be transformed as:

[0067]

[0068] In order to accurately control the vehicle acceleration, by analyzing the relationship between the vehicle tire force and the rate of change of speed, considering the relationship between the control force and the acceleration, the vehicle dynamics model is constructed as follows:

[0069]

[0070] Where m and I z are the mass and moment of inertia of the vehicle, respectively.

[0071] Under normal driving conditions of the vehicle, in order to facilitate the design of the auxiliary controller and ensure the calculation accuracy, the linear tire model is used to calculate the lateral tire force of the vehicle, which can be represented as:

[0072]

[0073] Where C f and C r are the cornering stiffness of the front and rear wheels, respectively. Based on equations (1)-(7), the lateral motion model of the vehicle can be represented as:

[0074]

[0075] Where x is the state vector of the vehicle system, represented as x = [y e , ψ e , v y , ω z ] T . u is the control quantity of the vehicle model, representing the front wheel steering angle, i.e. u = δ. A is the system matrix, B is the control matrix. d is the vehicle model error, which can be represented as d = [d y , d ψ , d v , d ω ] T , and the corresponding model error matrix can be represented as B d . The system matrix and the control matrix can be represented as:

[0076]

[0077] Design of fuzzy logic

[0078] In the actual driving process of the vehicle, due to the influence of tire and ground friction, air resistance, vehicle load and other factors, the actual performance of the lateral stiffness may present nonlinear change. By establishing the fuzzy model of the lateral stiffness and the longitudinal speed of the vehicle model, the trajectory tracking ability and the anti-collision ability of the vehicle are ensured.

[0079] In the fuzzy logic model, the longitudinal velocity and tire stiffness are usually kept within a certain range, and the designed fuzzy logic model enables us to effectively predict and control these two key parameters in the presence of uncertainty and fuzziness. Due to the physical limitations of the vehicle, the T-S fuzzy system is used to linearize the vehicle lateral dynamics model at different variable upper and lower boundaries. The range of uncertain variables in the system model is described as:

[0080]

[0081] where, * and denote the upper and lower boundaries of the fuzzy variable, respectively. In the formula, * = {v x ,1 / v x ,C f ,C r}.

[0082] The membership functions of different variables and their corresponding boundaries can be derived as:

[0083]

[0084] At the same time, in the design of the fuzzy controller, the membership function needs to meet the following conditions:

[0085]

[0086] where j = {1, 2, 3, 4}. ρ j denotes the membership variable corresponding to the membership parameter of different variables, M j,l and M j,s are the membership functions on the upper and lower boundaries of the above variables.

[0087] Based on T-S fuzzy logic, the following fuzzy rules in Table 1 are established to realize the linearization of the nonlinear model.

[0088] Table 1 Fuzzy rules

[0089]

[0090] In Table 1, s and l represent the smaller and larger values of the membership parameter values from the fuzzy controller input, respectively. At the same time, the membership parameters must satisfy:

[0091]

[0092] Under the weighted combination of the local linear models, the overall lateral dynamics model is derived as:

[0093]

[0094] where the output matrix C i and the model error matrix B d,i are defined as C i = I and B d,i = I. Furthermore, A i and B i are the state and input matrices of the local linear model.

[0095] According to equation (15), the discrete form of the vehicle system is derived based on the Euler method as:

[0096] x k+1 = (I + TA k )x k + TB k u k + TB d,k d k (16)

[0097] where T is the sampling step of the system, A k , B k and B d,k are the fuzzy processed system matrices.

[0098] Designing collision avoidance constraints for the vehicle system model

[0099] In this invention, obstacles are converted into constraint conditions in the model predictive controller. The design of the model predictive controller needs to consider state and input constraints, which are key to ensuring that the control system is both safe and efficient in practice. Input constraints are limits on control signals or manipulated variables to prevent inputs from exceeding the capabilities of the controlled vehicle actuators, leading to control failures or system instability. Input and input increment constraints are defined as:

[0100] u min < u k < u max (17)

[0101] Δu min < Δu k < Δu max (18)

[0102] where u min and u max are the lower and upper limits of the control input, and Δu k refers to the control increment at the kth step, with lower and upper bounds Δu min and Δu max . To ensure that the system does not enter an unacceptable or dangerous state that may lead to a safety accident, the position and speed of the controlled vehicle are limited to:

[0103] v y,min< v y,k < v y,max (19)

[0104] < v min < v k < v max (20)

[0105] < v min < v k < v max (21)

[0106] where (·) min and (·) max denote the upper and lower bounds of the constraint variable, respectively.

[0107] Designing fuzzy model predictive controller

[0108] In the present application, the multi-constrained fuzzy control problem is converted into a model predictive control problem. Based on the fuzzy vehicle model, an anti-collision strategy in the anti-collision scene is designed. In order to minimize the difference between the vehicle motion state and the reference state, while considering the energy cost and the driving stability, the control force and the control increment are considered in the design of the fuzzy model predictive controller. Therefore, the objective function is designed as:

[0109]

[0110] where N p and N c are the prediction step and the control step, respectively, and Q, R and H are the weight matrices of the objective function.

[0111] Therefore, according to the collision avoidance constraints of the vehicle system model, the fuzzy model predictive control problem of the autonomous vehicle is described as the following optimization problem:

[0112]

[0113] Simulation experiment

[0114] The proposed collision avoidance control technology is verified in the simulation of the autonomous driving obstacle avoidance scene of double-lane and multiple obstacles. In the simulation study, the autonomous driving vehicle will respectively use the traditional model predictive control and the improved fuzzy model predictive control in the double-lane obstacle avoidance, and verify the effectiveness of the obstacle avoidance strategy of the proposed fuzzy model predictive control algorithm.

[0115] In the double-lane and multiple obstacle avoidance scene of autonomous driving, the correct obstacle avoidance strategy needs to be completed in a short time, which is a challenge for autonomous vehicles. The vehicle motion trajectories using the traditional model predictive control and the improved fuzzy model predictive control are shown in the following Figure 3 .

[0116] As Figure 3 shown, in the process of avoiding the first obstacle, both the autonomous vehicle using traditional model predictive control and the autonomous vehicle using improved fuzzy model predictive control make collision avoidance decisions in advance and pass smoothly. However, due to the limitations of speed and distance, at t = 15 s, the vehicle trajectory using traditional model predictive control is about to collide with the second obstacle, while at the same time, due to the use of fuzzy model predictive control, the vehicle can react to the obstacle one step earlier and avoid the possible collision. Therefore, the traditional fuzzy model predictive control is difficult to avoid collision with the second obstacle during driving, which poses a threat to driving safety.

[0117] The design of the fuzzy controller helps to reduce the amount of calculation in solving the optimization problem, and at the same time makes the nominal model of the vehicle closer to the real model. During the motion of the autonomous vehicle, the speed and heading angle need to be adjusted constantly to avoid collision during the teaching process. The lateral speed and heading angle changes of the autonomous vehicle using traditional model predictive control and improved fuzzy model predictive control are shown in Figure 4 and Figure 5 .

[0118] As shown in Figure 4 , the longitudinal speed always changes within a certain range, and too large or too small longitudinal speed will pose a threat to the safety of vehicle driving. The lateral speed of the vehicle reaches a maximum of 0.2 m / s at about 8 s and 14 s, both of which occur during the obstacle avoidance process of the vehicle to the two obstacles, which is the reason for the certain overshoot in the obstacle avoidance process of the vehicle. As shown in Figure 5 , during the double-lane obstacle avoidance process, the maximum yaw angle is about 0.25 rad. After avoiding the first obstacle, the heading angle change of the autonomous vehicle using improved fuzzy model predictive control is smaller, which ensures the stability of driving. During the obstacle avoidance process of the second obstacle, the vehicle using traditional model predictive control starts to change lanes at about 14 s, which cannot completely guarantee the safe obstacle avoidance of the vehicle, while the vehicle using improved fuzzy model predictive control adjusts the heading angle from 0 rad to -0.23 rad more quickly due to the small overshoot, and makes the correct obstacle avoidance decision.

[0119] Precise control of the yaw rate is crucial to ensure the stable operation and precise control of the vehicle. The change of the yaw rate of the autonomous vehicle is shown in Figure 6 .

[0120] As shown in Figure 6 , the yaw rate of the vehicle reaches -0.03 rad / s at 8 s, at which time the vehicle is performing obstacle avoidance and lane changing motion, and after the first obstacle avoidance is completed, the yaw rate tends to 0 rad / s, but due to the change of state during obstacle avoidance, it cannot completely reach 0 rad / s.

[0121] The automatic driving vehicle using the fuzzy model predictive control changes with a smaller jitter of 0 rad / s; and starts the second obstacle avoidance movement within 13-14 s. The course angle speed changes within-0.03 rad / s to 0.02 rad / s.

[0122] Compared with the method of the application, the traditional fuzzy model predictive control algorithm is difficult to effectively complete the obstacle avoidance in a complex scene when dealing with a nonlinear system. The fuzzy model predictive control obstacle avoidance technology provided by the application greatly reduces the calculation amount of the system while ensuring effective obstacle avoidance, has stronger adaptability to the uncertainty and complexity of the system, reduces the dependence of the controller on the accurate model of the system, and improves the robustness of the system.

[0123] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the application, and these modifications or replacements should be covered within the protection scope of the application.

Claims

1. An automatic driving vehicle collision avoidance method based on fuzzy model predictive control, characterized in that, The method comprises the following steps: S1: constructing a lateral dynamics model of an autonomous vehicle; wherein is a state vector of the vehicle system, is a current lateral position error of the vehicle, is a vehicle yaw rate error, is a vehicle lateral velocity, is a yaw rate of the vehicle; is a control variable of the vehicle model, representing a front wheel steering angle, i.e. ; is a vehicle model error, representing , are errors of the four state vectors, respectively; is a model error matrix, is an output matrix; is a system matrix, is a control matrix, representing wherein, is the vehicle longitudinal speed, and are the cornering stiffness of the front and rear wheels, respectively, and denote the distance between the center of mass position and the front and rear wheels, respectively, and are the vehicle mass and moment of inertia, respectively; S2: designing fuzzy rules according to T-S fuzzy logic, converting the lateral dynamics model into a fuzzy dynamics model based on the fuzzy rules, and discretizing the fuzzy dynamics model; the discretized fuzzy dynamics model is specifically as follows: wherein, is the sampling step size of the system, , and is the blurred system matrix; S3: designing collision avoidance constraints of the fuzzy dynamics model based on control variables and motion states; the collision avoidance constraints comprise input constraints and state constraints: the input constraints comprise input and input increment constraints, and are specifically as follows: wherein, and are lower and upper limits of the control input quantity, refers to the control increment at the step , whose upper and lower bounds are and ; the state constraints comprise position and velocity constraints, and are specifically as follows: wherein and respectively represent upper and lower bounds of the constraint variable; S4: constructing a fuzzy model predictive controller based on the discretized fuzzy dynamics model and the collision avoidance constraints, solving the fuzzy model predictive controller to obtain control variables, and controlling the autonomous vehicle to avoid collision based on the control variables; the solving of the fuzzy model predictive controller to obtain the control variables is specifically as follows: designing an objective function of the fuzzy model predictive controller: wherein, and are a prediction step and a control step, respectively, , and are weight matrices of the objective function; converting the objective function into an optimization problem: 。 2. A computer system, characterized by The method comprises the following steps: one or more processors, a computer readable storage medium, and one or more programs stored in the computer readable storage medium, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of claim 1.

3. A computer-readable storage medium, characterized in that The computer executable instructions are stored in the computer readable storage medium, and the instructions are executed to implement the method of claim 1.

4. A computer program product, characterized by The computer executable instructions are stored in the computer readable storage medium, and the instructions are executed to implement the method of claim 1.

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