Automatic driving control method, electronic device, vehicle and storage medium

Abstract

The present application provides an autonomous driving control method, electronic device, vehicle, and storage medium. The method includes: obtaining the vehicle's current state data, which includes at least the vehicle's driving mode, current speed, and current position; when the driving mode is a specified type of autonomous driving mode, using a linear quadratic regulator optimization module based on the current speed and current position, as well as planning parameters corresponding to the autonomous driving mode, to obtain optimized control parameters; and controlling the vehicle's motion state using a PID controller based on the control parameters. In this way, autonomous driving planning control can be adaptively adjusted as the vehicle's operating state changes, which is beneficial for improving the effectiveness of autonomous driving planning control.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to an automatic driving control method, electronic equipment, a vehicle, and a storage medium. Background Art

[0002] With the development of autonomous driving technology, more and more new energy vehicles are applying intelligent driving technology, including intelligent assisted cruise control and smart parking functions. In the practical application of intelligent driving technology, the most widely used control method in the industry is to use PID (Proportional-Integral-Derivative) to control speed deviation. This control method is relatively mature, occupies little computing power, and is suitable for low-computing power platforms. Among them, the adjustment of P proportional parameters, I integral parameters, and D differential parameters is mainly determined by experience. For example, through two-dimensional or three-dimensional tables, fixed PID parameters are formulated for different vehicle speeds and different scenarios. However, the control parameters obtained based on experience are relatively fixed and it is not easy to achieve the optimal planning and control effect. In other words, the planning and control effect of autonomous driving needs to be improved. Summary of the Invention

[0003] In view of this, the purpose of the embodiments of the present application is to provide an autonomous driving control method, electronic device, vehicle and storage medium, which can improve the planning and control effect of autonomous driving.

[0004] To achieve the above technical objectives, the technical solutions adopted in this application are as follows:

[0005] In a first aspect, an embodiment of the present application provides an autonomous driving control method, the method comprising:

[0006] Acquiring current state data of the vehicle, the current state data including at least the driving mode, current speed, and current position of the vehicle;

[0007] When the driving mode is a specified type of automatic driving mode, using a linear quadratic regulator optimization module to obtain optimized control parameters based on the current vehicle speed and the current position, and planning parameters corresponding to the automatic driving mode;

[0008] Based on the control parameters, the motion state of the vehicle is controlled by a PID controller, the motion state including a desired acceleration of the vehicle.

[0009] In conjunction with the first aspect, in some optional implementations, obtaining optimized control parameters using a linear quadratic regulator optimization module based on the current vehicle speed and the current position, as well as planning parameters corresponding to the autonomous driving mode, includes:

[0010] Creating a controller model for the vehicle using a linear quadratic regulator optimization module based on a speed difference between the current vehicle speed and the planned vehicle speed in the planning parameters, and a distance difference between the current position and the planned position in the planning parameters;

[0011] Determining a gain matrix in the controller model based on a preset Riccati equation;

[0012] Based on a preset cost function and the gain matrix, output parameters of the controller model when the cost is minimized are determined as the optimized control parameters.

[0013] In conjunction with the first aspect, in some optional implementations, the controller model is:

[0014] u(t)=-K x(t)

[0015] Wherein, u(t) refers to the output parameter of the controller model and serves as the control parameter; t refers to time; K refers to the gain matrix; x(t) includes the speed difference and the distance difference;

[0016] K=R -1 B T P

[0017] R refers to the weight matrix corresponding to the control parameters;

[0018] B refers to an output matrix with the acceleration of the vehicle as the output target;

[0019] P refers to the parameter matrix obtained based on the Riccati equation;

[0020] The Riccati equation is:

[0021] P=Q+A T PA-A T PB(R+B T PB) -1 B T PA

[0022] Among them, Q includes the speed weighting matrix Q corresponding to the speed difference v And the distance weighted matrix Q corresponding to the distance difference s ;

[0023] A refers to a state matrix representing the vehicle state obtained based on the speed difference and the distance difference.

[0024] In combination with the first aspect, in some optional implementations, determining the gain matrix in the controller model based on a preset Riccati equation includes:

[0025] Calculate the P matrix based on the Riccati equation;

[0026] The calculated P matrix is ​​input into the calculation formula of the gain matrix K = R -1 B T In P, the gain matrix is ​​obtained.

[0027] In conjunction with the first aspect, in some optional implementations, determining, based on a preset cost function and the gain matrix, the output parameters of the controller model when the cost is minimized as the optimized control parameters includes:

[0028] According to the preset cost function An output parameter u(t) obtained by the controller model based on the gain matrix when the cost J is minimized is determined, and the output parameter u(t) is used as the optimized control parameter.

[0029] In conjunction with the first aspect, in some optional implementations, before obtaining the optimized control parameters using the linear quadratic regulator optimization module, the method further includes:

[0030] When the automatic driving mode is the automatic parking mode, based on the pre-established correspondence between the braking distance and the speed weighting matrix and the distance weighting matrix, the speed weighting matrix and the distance weighting matrix corresponding to the current braking distance of the vehicle are determined to form the Q matrix in the Riccati equation, wherein, when the braking distance is greater than or equal to the preset distance, the weight of the speed weighting matrix is ​​greater than or equal to the weight of the distance weighting matrix; when the braking distance is less than or equal to the preset distance, the weight of the speed weighting matrix is ​​less than the weight of the distance weighting matrix.

[0031] In conjunction with the first aspect, in some optional implementations, controlling the motion state of the vehicle by a PID controller based on the control parameter includes:

[0032] Inputting the control parameter and the speed difference into the PID controller to obtain the desired acceleration output by the PID controller;

[0033] The vehicle is controlled to travel at the desired acceleration.

[0034] In a second aspect, an embodiment of the present application further provides an electronic device, comprising a processor and a memory coupled to each other, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the electronic device executes the above-mentioned method.

[0035] In a third aspect, an embodiment of the present application further provides a vehicle, which includes a vehicle body and the above-mentioned electronic device.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium. When the computer program is run on a computer, the computer executes the above method.

[0037] The invention adopting the above technical solution has the following advantages:

[0038] In the technical solution provided in this application, when the driving mode is a specified type of autonomous driving mode, a linear quadratic regulator (LQR) optimization module is used to obtain optimized control parameters based on the current vehicle speed and position, as well as planning parameters corresponding to the autonomous driving mode. The optimized control parameters are then used to control the desired acceleration of the vehicle via a PID controller. In this solution, the LQR optimization module dynamically optimizes the control parameters based on the vehicle's current state data, and then cooperates with the PID controller to control the vehicle's acceleration. This allows the autonomous driving planning control to adaptively adjust to changes in the vehicle's operating state, improving the effectiveness of the autonomous driving planning control and addressing the issue of poor control effectiveness in different vehicle operating states caused by fixed control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present application may be further illustrated by the non-limiting embodiments provided in the accompanying drawings. It should be understood that the following drawings illustrate only certain embodiments of the present application and are therefore not to be construed as limiting the scope of the present application. It is understood that a person skilled in the art can derive other relevant drawings from these drawings without inventive effort.

[0040] Figure 1 A flowchart of the autonomous driving control method provided in an embodiment of the present application.

[0041] Figure 2 This is a schematic diagram of the structure of the LQR-PID controller provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that similar or identical parts in the drawings or descriptions are numbered the same. Implementations not shown or described in the drawings are known to those of ordinary skill in the art. In the description of this application, the terms "first," "second," etc. are used solely to distinguish descriptions and are not to be construed as indicating or implying relative importance.

[0043] Please refer to Figure 1This application also provides an autonomous driving control method that can be applied to a vehicle, and each step of the method can be performed or implemented by an electronic device in the vehicle. The electronic device serves as an electronic control system in the vehicle and has a conventional autonomous driving function. The autonomous driving control method can include the following steps:

[0044] Step 110, obtaining current state data of the vehicle, wherein the current state data includes at least the driving mode, current speed, and current position of the vehicle;

[0045] Step 120: When the driving mode is a specified type of automatic driving mode, an LQR (Linear Quadratic Regulator) optimization module is used to obtain optimized control parameters based on the current vehicle speed and the current position, as well as planning parameters corresponding to the automatic driving mode.

[0046] Step 130 : Based on the control parameters, control the motion state of the vehicle through a PID (Proportion Integration Differentiation) controller, where the motion state includes a desired acceleration of the vehicle.

[0047] The following is a detailed description of each step of the automatic driving control method:

[0048] In step 110, the vehicle can use its corresponding sensors to collect various types of vehicle operating status data in real time as current status data. Real-time collection can be performed at a fixed frequency, periodically, or at random intervals. The frequency of data collection is not specifically limited, as long as the vehicle status data can be collected and updated in real time.

[0049] In this embodiment, the current state data may include, but is not limited to, the vehicle's driving mode, current speed, and current location. The driving mode, current speed, and current location are acquired by sensors in a conventional manner. If the vehicle's driving mode is autonomous driving, the current state data may also include planning parameters for autonomous driving. Planning parameters are the speed and location calculated by the vehicle using conventional control algorithms in a specific autonomous driving mode.

[0050] If the speed difference between the current vehicle speed and the planned speed is zero, and the distance difference between the current position and the planned position is zero, the vehicle can maintain its current motion state and continue driving. In this case, there is no need to optimize the vehicle's planned position, planned speed, and other parameters. If the speed difference between the current vehicle speed and the planned speed is not zero, and / or the distance difference between the current position and the planned position is not zero, then step 120 is continued.

[0051] In step 120 , the specified type of automatic driving mode is a driving mode that requires dynamic adjustment of vehicle speed. For example, the specified type of automatic driving mode may be, but is not limited to, an adaptive cruise mode, an automatic parking mode, and the like.

[0052] As an optional implementation, in step 120, based on the current vehicle speed and the current position, and planning parameters corresponding to the autonomous driving mode, a linear quadratic regulator optimization module is used to obtain optimized control parameters, which may include:

[0053] Creating a controller model for the vehicle using a linear quadratic regulator optimization module based on a speed difference between the current vehicle speed and the planned vehicle speed in the planning parameters, and a distance difference between the current position and the planned position in the planning parameters;

[0054] Determining a gain matrix in the controller model based on a preset Riccati equation;

[0055] Based on a preset cost function and the gain matrix, output parameters of the controller model when the cost is minimized are determined as the optimized control parameters.

[0056] In this embodiment, the controller model may be:

[0057] u(t)=-K x(t)

[0058] Wherein, u(t) refers to the output parameter of the controller model and serves as the control parameter; t refers to time; K refers to the gain matrix; x(t) is composed of the speed difference and the distance difference;

[0059] K=R -1 B T P

[0060] R refers to the weight matrix corresponding to the control parameters;

[0061] B refers to an output matrix with the acceleration of the vehicle as the output target;

[0062] P refers to a parameter matrix obtained based on the Riccati equation, which is obtained by solving the Riccati equation and is used to represent the performance indicators and state feedback gains of the vehicle's autonomous driving;

[0063] The Riccati equation is:

[0064] P=Q+A T PA-A T PB(R+B T PB) -1 B TPA

[0065] Among them, Q includes the speed weighting matrix Q corresponding to the speed difference v And the distance weighted matrix Q corresponding to the distance difference s ;

[0066] A refers to a state matrix representing the vehicle state obtained based on the speed difference and the distance difference, that is, the vehicle state includes the speed difference and the distance difference of the vehicle.

[0067] In this embodiment, the Q matrix can weight the differences (or errors) in the vehicle's state variables, affecting the electronic control system's response to state errors. A larger value for the Q matrix can increase the electronic control system's sensitivity to position and velocity differences, enhancing system stability.

[0068] The R matrix can be used to weight the control input u(t), affecting its magnitude. The value of the matrix R can be calibrated; for example, a smaller value can be chosen to allow for larger control inputs, thereby increasing the responsiveness of the vehicle's electronic control system.

[0069] In this embodiment, determining the gain matrix in the controller model based on the preset Riccati equation may include:

[0070] Calculate the P matrix based on the Riccati equation;

[0071] The calculated P matrix is ​​input into the calculation formula of the gain matrix K = R -1 B T In P, the gain matrix is ​​obtained.

[0072] In this embodiment, based on a preset cost function and the gain matrix, determining the output parameters of the controller model when the cost is minimized as the optimized control parameters may include:

[0073] According to the preset cost function An output parameter u(t) obtained by the controller model based on the gain matrix when the cost J is minimized is determined, and the output parameter u(t) is used as the optimized control parameter.

[0074] In this embodiment, a preset cost function is used to calculate the output parameter u(t) when the cost J is minimum. In this way, the optimal control parameters can be obtained, which is conducive to achieving the optimal control effect of autonomous driving.

[0075] As an optional implementation, before the step of using the linear quadratic regulator optimization module to obtain optimized control parameters, the method may further include:

[0076] When the automatic driving mode is the automatic parking mode, based on the pre-established correspondence between the braking distance and the speed weighting matrix and the distance weighting matrix, the speed weighting matrix and the distance weighting matrix corresponding to the current braking distance of the vehicle are determined to form the Q matrix in the Riccati equation, wherein, when the braking distance is greater than or equal to the preset distance, the weight of the speed weighting matrix is ​​greater than or equal to the weight of the distance weighting matrix; when the braking distance is less than or equal to the preset distance, the weight of the speed weighting matrix is ​​less than the weight of the distance weighting matrix.

[0077] In this embodiment, the Q matrix is ​​composed of the matrix Q affected by the speed difference v and the matrix Q affected by the distance difference s Adjustment matrix Q v and Q s The value of can change the weight of state variables (including vehicle speed and position), the matrix Q v and Q s The value of can be determined by the braking distance. For example, when the braking distance is large and exceeds the preset distance, it means that the vehicle is far away from the braking point and needs to maintain a high speed to approach quickly. v The weight should be greater than Q s When the braking distance is small and less than the preset distance, it means that the vehicle is close to the braking point and needs to reduce the vehicle speed to improve the control accuracy of the braking position. s The weight should be greater than Q v The preset time interval can be obtained by calibration and is not specifically limited here.

[0078] In step 130, based on the control parameters, the motion state of the vehicle is controlled by a PID controller, including:

[0079] Inputting the control parameter and the speed difference into the PID controller to obtain the desired acceleration output by the PID controller, wherein the speed difference is the difference between the current vehicle speed and the planned vehicle speed in the planning parameter;

[0080] The vehicle is controlled to travel at the desired acceleration.

[0081] Understandably, the control parameters optimized by the LQR optimization module are input into the PID controller as control parameters. Furthermore, the PID controller also includes the current speed difference as input. The PID controller calculates the control parameters and speed difference to determine the desired vehicle acceleration. The vehicle's electronic control system converts this desired acceleration into wheel-end torque, controlling the vehicle's power and thus controlling the vehicle's travel at the desired acceleration.

[0082] To facilitate understanding of the implementation process of the method, the following example illustrates the execution process of the automatic driving control method using the automatic parking mode as an example:

[0083] The first step, state determination, is as follows: The vehicle's electronic control system receives the automatic parking function activation status flag to determine whether the vehicle is in automatic parking mode. It also obtains the target speed signal and target distance signal to determine whether the vehicle's current speed is the planned speed and current position is the planned position. If the vehicle is in automatic parking mode and the speed difference and distance difference are not both zero, the system proceeds to the second step.

[0084] The second step is parameter optimization: The speed and distance differences are input into the LQR optimization module to calculate the optimal P matrix and the control parameters u(t) required by the current PID controller. The planned position can be used to limit the size of the P matrix to avoid excessive acceleration due to a too large P matrix or insufficient control due to a too small P matrix.

[0085] For the longitudinal speed control of automatic parking, one of the indicators is to reduce the error between the planned speed and the actual speed. Assume that the cost function is J:

[0086]

[0087] The control parameter u(t) when the cost function is the smallest is the optimal control parameter.

[0088] The control parameters are calculated as:

[0089] u(t)=-K x(t)

[0090] Wherein, the gain matrix K = R -1 B T P needs to be solved iteratively by establishing the Riccati equation to obtain the optimal gain matrix K. The Riccati equation is:

[0091] P=Q+A T PA-A T PB(R+B T PB) -1 B T PA

[0092] Through the second step, using the LQR optimization module, the control parameters u(t) required by the current PID controller can be obtained.

[0093] The third step is vehicle speed control: input the control parameter u(t) calculated in the second step into the PID controller, and use the current speed difference as the input of the PID controller to calculate the current expected acceleration value.

[0094] Please refer to Figure 2 In this embodiment, the LQR optimization module and the PID controller can form an online optimized LQR-PID controller, which can be deployed as a software function module in the vehicle's electronic control system. In the LQR-PID controller, the vehicle's motion state is changed by utilizing the influence of the torque on the vehicle. During the acceleration and deceleration process, the LQR optimization module is online and real-time based on the speed difference e v , distance difference e s , output the optimized control parameters to the PID controller. Based on the control parameters, the PID controller outputs the desired acceleration of the vehicle, and then controls the automatic driving of the vehicle based on the desired acceleration. When the speed difference and distance difference produce new changes, the first to third steps are repeated to form a closed loop.

[0095] Based on the above design, the autonomous driving control method can be applied to the longitudinal control of automatic parking, and can solve the problem that the control parameters of the PID controller in the longitudinal control of autonomous parking cannot reach the optimal value. In addition, it can effectively improve the problem that the vehicle cannot achieve optimal control in different state scenarios due to the fixed parameters of the P matrix, and can enhance the robustness of the electronic control system.

[0096] An embodiment of the present application provides an electronic device, which serves as an electronic control system in a vehicle and may include a processor and a memory. The memory stores a computer program that, when executed by the processor, enables the electronic device to perform the corresponding steps of the aforementioned autonomous driving control method.

[0097] In this embodiment, the processor can be an integrated circuit chip with signal processing capabilities. For example, the processor can be, but is not limited to, an intelligent driving domain controller, a central processing unit (CPU), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0098] The memory may be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the storage module may be used to store planning parameters, etc. Of course, the storage module may also be used to store programs, and the processing module executes the programs after receiving an execution instruction.

[0099] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the electronic device described above can refer to the corresponding processes of each step in the aforementioned method, and will not be elaborated here.

[0100] An embodiment of the present application also provides a vehicle, which may include a vehicle body and the aforementioned electronic equipment. The vehicle adopts the aforementioned automatic driving control method. In this way, the planning and control of automatic driving can be adaptively adjusted as the vehicle's operating state changes, which is conducive to improving the planning and control effect of automatic driving.

[0101] The present application also provides a computer-readable storage medium that stores a computer program, which, when executed on a computer, causes the computer to execute the automatic driving control method described in the above embodiment.

[0102] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented through hardware or by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0103] In the embodiments provided in the present application, it should be understood that the disclosed electronic equipment and methods can also be implemented in other ways. The electronic equipment and method embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and a part of the module, program segment or code includes one or more executable instructions for implementing the specified logical function. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, the functional modules in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0104] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An automatic driving control method, characterized in that: The method comprises: Acquiring current state data of the vehicle, the current state data including at least the driving mode, current speed, and current position of the vehicle; When the driving mode is a specified type of automatic driving mode, using a linear quadratic regulator optimization module to obtain optimized control parameters based on the current vehicle speed and the current position, and planning parameters corresponding to the automatic driving mode; Based on the control parameters, the motion state of the vehicle is controlled by a PID controller, the motion state including a desired acceleration of the vehicle.

2. The method according to claim 1, characterized in that The method of obtaining optimized control parameters based on the current vehicle speed and the current position, and the planning parameters corresponding to the automatic driving mode, using a linear quadratic regulator optimization module, includes: Creating a controller model for the vehicle using a linear quadratic regulator optimization module based on a speed difference between the current vehicle speed and the planned vehicle speed in the planning parameters, and a distance difference between the current position and the planned position in the planning parameters; Determining a gain matrix in the controller model based on a preset Riccati equation; Based on a preset cost function and the gain matrix, output parameters of the controller model when the cost is minimized are determined as the optimized control parameters.

3. The method according to claim 2, characterized in that The controller model is: u(t)=-K x(t) Wherein, u(t) refers to the output parameter of the controller model and serves as the control parameter; t refers to time; K refers to the gain matrix; x(t) includes the speed difference and the distance difference; K=R -1 B T P R refers to the weight matrix corresponding to the control parameters; B refers to an output matrix with the acceleration of the vehicle as the output target; P refers to the parameter matrix obtained based on the Riccati equation; The Riccati equation is: P=Q+A T PA-A T PB(R+B T PB) -1 B T PA Among them, Q includes the speed weighting matrix Q corresponding to the speed difference v And the distance weighted matrix Q corresponding to the distance difference s ; A refers to a state matrix representing the vehicle state obtained based on the speed difference and the distance difference.

4. The method according to claim 3, characterized in that The step of determining a gain matrix in the controller model based on a preset Riccati equation includes: Calculate the P matrix based on the Riccati equation; The calculated P matrix is ​​input into the calculation formula of the gain matrix K = R -1 B T In P, the gain matrix is ​​obtained.

5. The method according to claim 3, characterized in that The step of determining the output parameters of the controller model when the cost is minimized based on the preset cost function and the gain matrix as the optimized control parameters includes: According to the preset cost function An output parameter u(t) obtained by the controller model based on the gain matrix when the cost J is minimized is determined, and the output parameter u(t) is used as the optimized control parameter.

6. The method according to claim 3, characterized in that Before obtaining the optimized control parameters by using the linear quadratic regulator optimization module, the method further includes: When the automatic driving mode is the automatic parking mode, based on the pre-established correspondence between the braking distance and the speed weighting matrix and the distance weighting matrix, the speed weighting matrix and the distance weighting matrix corresponding to the current braking distance of the vehicle are determined to form the Q matrix in the Riccati equation, wherein, when the braking distance is greater than or equal to the preset distance, the weight of the speed weighting matrix is ​​greater than or equal to the weight of the distance weighting matrix; when the braking distance is less than or equal to the preset distance, the weight of the speed weighting matrix is ​​less than the weight of the distance weighting matrix.

7. The method according to claim 1, characterized in that The controlling the motion state of the vehicle by a PID controller based on the control parameter includes: Inputting the control parameter and the speed difference into the PID controller to obtain the desired acceleration output by the PID controller, wherein the speed difference is the difference between the current vehicle speed and the planned vehicle speed in the planning parameter; The vehicle is controlled to travel at the desired acceleration.

8. An electronic device, characterized in that: The electronic device includes a processor and a memory coupled to each other, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the electronic device executes the method according to any one of claims 1 to 7.

9. A vehicle, characterized in that: The vehicle includes a vehicle body and the electronic device according to claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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