A rack force estimation method and system for steer-by-wire based on a fuzzy neural network

Abstract

The application provides a rack force estimation method and system based on a fuzzy neural network for a steer-by-wire system, wherein a long short-term memory network is used to capture the long-time dependence relationship of motor torque, motor angular velocity and motor rotation angle, a fuzzy neural network model is used for fuzzy reasoning, the fuzzy neural network model uses the learning ability of a neural network to associate the fuzzy value of an input variable with the fuzzy value of estimated rack force, and reasoning for rack force estimation is completed. The application scheme can realize high-precision estimation of rack force in a complex nonlinear and multi-working-condition environment, and meets the real-time requirement of a steer-by-wire system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field related to automobile steer-by-wire, and particularly relates to a steer-by-wire rack force estimation method and system based on a fuzzy neural network. BACKGROUND

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

[0003] Steer-by-wire system is a new type of steering technology, which transmits the driver's steering intention through electronic signals and adjusts the vehicle's driving state, and is widely used in modern intelligent driving cars. However, the steer-by-wire system needs to obtain the rack force information in real time to assist in realizing the driving feedback and system control. Rack force is a key physical quantity in the steer-by-wire system, which directly affects the steering performance and driving experience of the vehicle. Rack force is the comprehensive force acting on the steering rack, including the input force from the driver's steering wheel, the road reaction force and the output force of the assist motor, and its main functions include: driving feedback: simulating the road feel in the traditional steering system, providing a real driving experience. Vehicle stability: maintaining the power balance of the steering system to ensure the stability of the vehicle under different working conditions. Steering assist control: auxiliary determination of the size and direction of motor assist to improve steering performance.

[0004] In the steer-by-wire system, due to the lack of mechanical connection, the rack force cannot be directly perceived by the mechanical path and must be obtained in real time through an accurate estimation model.

[0005] At present, the main rack force acquisition methods include:

[0006] 1. Physical modeling-based method: establish a dynamic model of the vehicle steering system to estimate the rack force. However, this method is difficult to accurately model the complex nonlinear characteristics, especially under multiple working conditions, there are significant errors.

[0007] 2. Direct measurement-based method: directly measure the rack force through sensors, but its cost is high, and the sensor has poor reliability in complex environments.

[0008] The rack force is affected by the driver's input, road characteristics, vehicle motion state and other factors, and its relationship is highly nonlinear and strongly coupled, making it difficult for traditional methods to effectively model. SUMMARY

[0009] In order to overcome the shortcomings of the prior art, the present application provides a steer-by-wire rack force estimation method and system based on a fuzzy neural network, which can realize high-precision estimation of the rack force in a complex nonlinear and multi-working condition environment, and meet the real-time requirements of the steer-by-wire system.

[0010] In order to achieve the above object, the present application adopts the following technical solutions:

[0011] In a first aspect, the present application provides a fuzzy neural network-based rack force estimation method for steer-by-wire, comprising:

[0012] Obtaining vehicle motor-related data; wherein the vehicle motor-related data at least includes motor torque, motor angular velocity and motor rotation angle;

[0013] Capturing long-time dependence of the vehicle motor-related data by using a long short-term memory network to obtain motor time series features;

[0014] Using a fuzzy neural network model to perform fuzzy reasoning on the motor time series features to obtain rack force estimation results; wherein the fuzzy neural network model uses the learning ability of a neural network to adjust the fuzzy value of the estimated rack force output based on the fuzzy value of the input variable according to the current working condition of the vehicle, through the selected membership function and the fuzzy rules corresponding to different working conditions, to complete the inference of rack force estimation.

[0015] Preferably, before processing the vehicle motor-related data using a long short-term memory network, the vehicle motor-related data is also preprocessed, and the preprocessing includes:

[0016] The motor torque, motor angular velocity and motor rotation angle are normalized respectively;

[0017] The normalized motor torque, motor angular velocity and motor rotation angle are step-sampled using a preset sliding window.

[0018] Preferably, in the fuzzy neural network model, a Gaussian function is used as the membership function to convert the input variables of the fuzzy neural network model into fuzzy values.

[0019] Preferably, in the fuzzy neural network model, the activation strength of the fuzzy rule is:

[0020]

[0021] wherein, is the activation strength, n is the number of input variables, is the input variable x i The membership degree of a fuzzy set A i .

[0022] Preferably, in the fuzzy neural network model, the rack force estimation results are obtained, specifically:

[0023]

[0024] wherein, F r is the rack force estimation result, m is the number of rules, is the activation strength of the jth rule, y j is the inferred value of the rack force according to the input of the jth rule.

[0025] Preferably, in the training of the fuzzy neural network model, the mean square error is used as the loss function.

[0026] In a second aspect, the present application provides a rack force estimation system for steer-by-wire based on fuzzy neural network, comprising:

[0027] The acquisition module is configured to acquire vehicle motor related data, wherein the vehicle motor related data at least includes motor torque, motor angular velocity and motor rotation angle;

[0028] The extraction module is configured to capture long-time dependence of the vehicle motor related data by using long short-term memory network to obtain motor time series features;

[0029] The fuzzy inference module is configured to perform fuzzy inference on the motor time series features by using a fuzzy neural network model to obtain a rack force estimation result; wherein the fuzzy neural network model uses the learning ability of the neural network to adjust the fuzzy value of the estimated rack force according to the current working condition of the vehicle based on the fuzzy value of the input variable, through the selected membership function and the fuzzy rules corresponding to different working conditions.

[0030] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, when the computer instructions are executed by the processor, the method of the first aspect is completed.

[0031] In a fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, when the computer instructions are executed by the processor, the method of the first aspect is completed.

[0032] In a fifth aspect, the present application provides a computer program product comprising a computer program, when the computer program is executed by the processor, the method of the first aspect is completed.

[0033] The above one or more technical solutions have the following beneficial effects:

[0034] In the application, the long-time dependence relationship of motor torque, motor angular velocity and motor rotation angle is captured by using a long short-term memory network, and fuzzy inference is performed by using a fuzzy neural network model.

[0035] Advantages of the additional aspects of the application will become apparent in the following description, become apparent from the following description, or be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings, which form a part of the specification, are included to provide a further understanding of the application and are incorporated herein in conjunction with the description of the application. The drawings are not to be construed as limiting the application.

[0037] Figure 1 A network structure schematic diagram of the fuzzy neural network in the embodiment of the application;

[0038] Figure 2 A flowchart of the steer-by-wire rack force estimation method based on the fuzzy neural network in the embodiment of the application. DETAILED DESCRIPTION

[0039] It should be noted that the following detailed description is merely exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs.

[0040] It should be noted that the terms used herein are merely intended to describe specific embodiments and are not intended to limit the exemplary embodiments according to the application.

[0041] The embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0042] As described in the background, the steer-by-wire system requires rack force estimation to have extremely high real-time performance, and the existing complex modeling method has high calculation cost, which is difficult to meet the requirements of embedded systems. The vehicle driving environment is complex and variable, such as high-speed driving, low-speed turning, bumpy road, etc., which requires the rack force estimation model to have strong generalization ability and robustness. The calculation resources of the embedded controller are limited, and the complexity of the model needs to be optimized. Based on the above problems, the application proposes a steer-by-wire rack force estimation method based on a fuzzy neural network, which can effectively overcome the above shortcomings and realize high-precision and real-time estimation of rack force.

[0043] The embodiment discloses a rack force estimation method for steer-by-wire based on a fuzzy neural network, comprising:

[0044] Obtain vehicle motor related data; wherein the vehicle motor related data at least includes motor torque, motor angular velocity and motor rotation angle;

[0045] Capture the long-time dependence relationship of the vehicle motor related data by using a long short-term memory network, and obtain motor time sequence characteristics;

[0046] Carry out fuzzy reasoning on the motor time sequence characteristics by using a fuzzy neural network model, and obtain rack force estimation results; wherein the fuzzy neural network model adjusts the fuzzy value of the estimated rack force output according to the current working condition of the vehicle based on the fuzzy value of the input variable by using the learning ability of the neural network, the membership function selected and the fuzzy rules corresponding to different working conditions.

[0047] The application captures the long-time dependence relationship of the motor torque, the motor angular velocity and the motor rotation angle by using a long short-term memory network, carries out fuzzy reasoning by using a fuzzy neural network model, and correlates the fuzzy value of the input variable with the fuzzy value of the estimated rack force output by using the learning ability of the neural network to complete the reasoning of the rack force estimation.

[0048] As shown in Figure 2 , a rack force estimation method for steer-by-wire based on a fuzzy neural network, specifically comprising:

[0049] Step 21: Obtain vehicle motor related data and process.

[0050] Obtain the signals of the motor in the steer-by-wire system: motor torque T m , motor angular velocity w m and motor rotation angle θ m .

[0051] Among them, the motor torque T m reflects the size of the driver input steering force input, and is a parameter directly related to the rack force;

[0052] The motor angular velocity w m is the rotation speed of the steering motor, reflecting the steering frequency of the steering wheel and the road information;

[0053] The motor rotation angle θ m reflects the rotation angle of the steering wheel and is related to the steering target.

[0054] The above signals are normalized, and the normalization formula is:

[0055]

[0056] wherein x norm is the normalized value, x min is the minimum value of the signal, x max is the maximum value of the signal, x is the signal (motor torque T m or motor angular velocity w m or motor rotation angle θ m ).

[0057] In order to capture the time correlation of the signal, the continuous time step signal is taken as the input of the long short-term memory network, a fixed length sliding window is adopted, and the data of the past set time steps is input into the long short-term memory network:

[0058]

[0059] wherein T ,,t , w m,t , θ m,t are the motor torque, angular velocity and rotation angle at t time, respectively.

[0060] The signal matrix X processed by the sliding window is input into the LSTM network, and the dimension is TxF, wherein T is the time step length, which is set to 10 in the embodiment, and F is the number of features at each time step, which is set to 3 in the embodiment.

[0061] The LSTM network is responsible for extracting the time sequence features of the input signal, and its main structure includes:

[0062] Input gate:

[0063] i t = σ(W i ·[h t-1 , x t ]+b i )

[0064] wherein i t is the input gate output at t time, σ is the Sigmoid function, W i is the input gate weight matrix, h t-1 is the hidden state at t-1 time, x t is the input at t time, and b i is the input gate bias.

[0065] Forget gate:

[0066] f t = σ(W f ·[h t-1 , x t ]+b f )

[0067] Among them, f t The output of the forget gate at time t is W. f It is the forget gate weight matrix, b f It is the forget gate bias.

[0068] Candidate memory units:

[0069]

[0070] in, W is the candidate memory unit at time t. C It is the candidate memory unit weight matrix, b C It is the candidate memory unit bias.

[0071] Current memory unit updated:

[0072]

[0073] Among them, C t C is the state of the memory unit at time t. t-1 It is the state of the memory unit at time t-1.

[0074] Output gates and hidden states:

[0075] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0076] h t =o t ·tanh(C t )

[0077] Among them, o t It is the output gate output at time t, W o It is the output gate weight matrix, b o It is the output gate bias, h t It is the hidden state at time t.

[0078] The output layer LSTM outputs the hidden state sequence H = [h] t-9 ,h t-8 ,…,h t The comprehensive representation of time series features extracted through fully connected layers is used as input to a fuzzy neural network model.

[0079] like Figure 1As shown, the fuzzy neural network model comprises an input layer, a fuzzification layer, a fuzzy inference layer, a normalization layer, and an output layer. The input layer is the starting point of the model, receiving the raw data of the motor parameters. The fuzzification layer fuzzifies the input motor parameter data, converting precise input values ​​into fuzzy sets to better handle uncertainty and fuzziness. The fuzzy inference layer uses fuzzy rules to perform inference and calculation based on the fuzzified input, deriving intermediate results. The normalization layer normalizes the results from the fuzzy inference layer, ensuring they fall within a certain range. Finally, the output layer provides the final result or decision.

[0080] The fuzzy neural network model is responsible for inferring the rack force based on the high-dimensional features extracted by the LSTM.

[0081] The main steps of the fuzzy neural network model are as follows:

[0082] Fuzzification: Input variables are transformed into fuzzy values ​​using membership functions, with the membership function being a Gaussian function.

[0083]

[0084] Where μ(x) is the membership degree, x is the input variable, c is the center of the Gaussian function, and σ is the standard deviation.

[0085] Rule-based reasoning: The activation strength of fuzzy rules is:

[0086]

[0087] in, It represents the activation intensity, and n is the number of input variables. The input variable x i For fuzzy set A i The degree of membership.

[0088] The setting of fuzzy rules needs to be based on the inherent relationship between motor torque, motor angular velocity, motor rotation angle and rack force, and also combined with the actual operating conditions of the automotive steer-by-wire system, such as:

[0089] Normal steering conditions:

[0090] Rule 1: Lower motor torque indicates less steering force input from the driver, lower motor angular velocity means slower steering wheel rotation, and a smaller motor angle reflects a smaller steering range, resulting in less rack force. This applies when the vehicle is traveling straight or making minor steering adjustments; the driver only needs to apply less force, and the system requires less rack force.

[0091] Rule two: If the motor torque is moderate, the motor angular velocity is moderate, and the motor rotation angle is in the moderate range, then the rack force is moderate. This is consistent with the scenario where the vehicle is turning normally, the driver applies appropriate force, and the system generates a corresponding moderate rack force to achieve steering.

[0092] Emergency steering working condition:

[0093] Rule three: When the motor torque is large, it means that the driver is operating the steering with great force for emergency avoidance, the motor angular velocity is high, indicating that the steering wheel is rotating quickly, and the motor rotation angle is large, indicating that the steering amplitude is large, then the rack force is large. In an emergency, the driver quickly and greatly rotates the steering wheel, and the system needs a large rack force to quickly change the vehicle's driving direction.

[0094] Special road working condition:

[0095] Rule four: If the motor torque fluctuates greatly, it means that the bumpy road will make the motor torque unstable, the motor angular velocity fluctuates greatly, indicating that the steering wheel is shaking due to the influence of the bumpy road, and the motor rotation angle also fluctuates greatly, then the rack force fluctuates greatly. When driving on a bumpy road, the change of road reaction force leads to the fluctuation of motor parameters, and then the rack force also fluctuates.

[0096] Rule five: If the motor torque continues to increase within a certain range, for example, when the vehicle is turning on a slope, it needs more steering force, the motor angular velocity is relatively stable, that is, it maintains a certain steering frequency, and the motor rotation angle gradually increases, indicating continuous steering, then the rack force gradually increases. This is applicable to the situation where the vehicle is turning on a special road, and the rack force changes according to the actual demand.

[0097] Fuzzy rules are the basis for calculating the activation strength of fuzzy rules. In actual calculation, the fuzzy values of the input variables after fuzzification, i.e. motor torque, motor angular velocity, and motor rotation angle, are substituted into the corresponding rules after being converted by the membership function. Taking the rule "if the motor torque is large, the motor angular velocity is high, and the motor rotation angle is large, then the rack force is large" as an example, the membership degree values corresponding to the motor torque, motor angular velocity, and motor rotation angle are substituted into the rule reasoning formula to calculate the activation strength. Through such calculation, the activation strength of the rule under the current input condition is obtained, which reflects the applicability of the rule under the current working condition.

[0098] The setting of fuzzy rules enables the fuzzy neural network model to better adapt to complex and variable driving conditions. Different driving scenarios such as normal driving, emergency steering, and driving on a bumpy road correspond to different combinations of fuzzy rules. When the vehicle is in different working conditions, the corresponding rules are activated, allowing the model to adjust the estimation of the rack force according to the actual situation.

[0099] Inference result calculation: The output is calculated by weighted average method:

[0100]

[0101] wherein F r is the rack force estimation result, m is the number of rules, is the activation strength of the jth rule, y j is the inferred value of the rack force according to the input of the jth rule.

[0102] Training of the fuzzy neural network model: 1000 groups of actual measurement data under different working conditions are selected as training samples, of which 800 groups are used for training and 200 groups are used for verification. The weights and fuzzy rule parameters of the fuzzy neural network are initialized, the initial learning rate is set to 0.1, and the momentum term coefficient is set to 0.9. During the training process, the learning rate is automatically adjusted according to the training error, and when the error decreases by less than 0.001 for 5 consecutive iterations, the learning rate is halved. After 5000 iterations of training, the network reaches the preset training precision, and the mean square error is less than 0.01. The known motor signal and rack force data are used to train the model, and the mean square error (MSE) is used as the loss function:

[0103]

[0104] wherein F r is the true rack force value, is the rack force value predicted by the fuzzy neural network model, and N is the number of samples

[0105] The parameters of the fuzzy neural network model are updated by the Adam optimization algorithm:

[0106]

[0107] wherein θ t is the fuzzy neural network model parameter at time t, and a is the learning rate, is the gradient of the loss function with respect to the fuzzy neural network model parameter at time t.

[0108] The embodiment also provides a rack force estimation system for steer-by-wire based on a fuzzy neural network, comprising:

[0109] An acquisition module configured to acquire vehicle motor related data; wherein the vehicle motor related data at least includes motor torque, motor angular velocity and motor rotation angle;

[0110] An extraction module configured to capture long-time dependence of the vehicle motor related data by using a long short-term memory network to obtain motor time sequence features;

[0111] The fuzzy inference module is configured to perform fuzzy inference on the motor time sequence features by using a fuzzy neural network model to obtain a rack force estimation result; wherein the fuzzy neural network model uses the learning ability of a neural network to associate fuzzy values of input variables with fuzzy values of estimated rack forces of output, and complete inference of rack force estimation.

[0112] In more embodiments, there are also provided:

[0113] An electronic device includes a memory and a processor, and computer instructions stored in the memory and run on the processor, when the computer instructions are run by the processor, the method described in embodiment one is completed. For brevity, it will not be described here.

[0114] It should be understood that in the embodiments, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-to-program gate arrays FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0115] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0116] A computer readable storage medium for storing computer instructions, when the computer instructions are executed by a processor, the method described in embodiment one is completed.

[0117] The method in embodiment one can be directly embodied as hardware processor execution completion, or executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0118] A computer program product includes a computer program, when the computer program is executed by a processor, the method described in embodiment one is implemented.

[0119] The present application also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, for example, instructions embodied in program modules, executed by devices at the target real or virtual processor to perform the processes / methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules can be combined or split between program modules as desired in various embodiments. Machine executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote memory storage devices.

[0120] Computer program code for carrying out operations of the present application can be written in one or more programming languages. These computer program codes can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program codes, when executed by the computer or other programmable data processing apparatus, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program codes can be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0121] In the context of the present application, the computer program code or related data can be carried by any suitable carrier to enable the device, apparatus or processor to perform the various processes and operations described above. Examples of the carrier include a signal, a computer readable medium, etc. Examples of the signal can include an electrical, optical, radio, sound or other forms of propagated signals, such as a carrier wave, an infrared signal, etc.

[0122] Those skilled in the art can understand that the units and algorithm steps of the examples described in conjunction with the embodiments can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0123] The above describes the specific embodiments of the present application in conjunction with the accompanying drawings, but is not a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without inventive labor are still within the scope of protection of the present application.

Claims

1. A method for estimating rack force of steer-by-wire based on fuzzy neural network, characterized in that, The method comprises: acquiring vehicle motor related data; wherein the vehicle motor related data at least includes motor torque, motor angular velocity and motor rotation angle; capturing long-short time dependence of the vehicle motor related data by using a long-short time memory network to obtain motor time sequence features; performing fuzzy reasoning on the motor time sequence features by using a fuzzy neural network model to obtain rack force estimation results; wherein the fuzzy neural network model adjusts fuzzy values of estimated rack forces output according to current vehicle working conditions based on fuzzy values of input variables by using learning ability of the neural network, through selected membership functions and fuzzy rules corresponding to different working conditions, to complete inference of rack force estimation; wherein in the fuzzy neural network model, a Gaussian function is used as a membership function to convert input variables of the fuzzy neural network model into fuzzy values; in the fuzzy neural network model, an activation strength of a fuzzy rule is: wherein, is the activation strength, is the number of input variables, is the input variable membership of the fuzzy set to the fuzzy set. in the fuzzy neural network model, the rack force estimation results are obtained, specifically as follows: wherein, is the rack force estimate, is the number of rules, is the activation strength of the j th rule, is the inferred value of the rack force according to the input for the j th rule.

2. A method of rack force estimation for steer-by-wire based on fuzzy neural network according to claim 1, characterized in that, before processing the vehicle motor related data by using the long-short time memory network, the method further comprises preprocessing the vehicle motor related data, and the preprocessing comprises: performing normalization processing on the motor torque, the motor angular velocity and the motor rotation angle respectively; performing step sampling processing on the normalized motor torque, the motor angular velocity and the motor rotation angle by using a preset sliding window.

3. A method of rack force estimation for steer-by-wire based on fuzzy neural network according to claim 1, characterized in that, In the training of the fuzzy neural network model, mean square error is used as a loss function.

4. A fuzzy neural network based rack force estimation system for steer-by-wire steering, characterized by, The method comprises: an acquisition module configured to acquire vehicle motor related data; wherein the vehicle motor related data at least includes motor torque, motor angular velocity and motor rotation angle; an extraction module configured to capture long-short time dependence of the vehicle motor related data by using a long-short time memory network to obtain motor time sequence features; a fuzzy reasoning module configured to perform fuzzy reasoning on the motor time sequence features by using a fuzzy neural network model to obtain rack force estimation results; wherein the fuzzy neural network model adjusts fuzzy values of estimated rack forces output according to current vehicle working conditions based on fuzzy values of input variables by using learning ability of the neural network, through selected membership functions and fuzzy rules corresponding to different working conditions, to complete inference of rack force estimation; wherein in the fuzzy neural network model, a Gaussian function is used as a membership function to convert input variables of the fuzzy neural network model into fuzzy values; in the fuzzy neural network model, an activation strength of a fuzzy rule is: wherein, is the activation strength, is the number of input variables, is the input variable membership of the fuzzy set to the fuzzy set. in the fuzzy neural network model, the rack force estimation results are obtained, specifically as follows: wherein, is the rack force estimate, is the number of rules, is the activation strength of the j th rule, is the inferred value of the rack force from the input for the j th rule.

5. An electronic device, comprising: The computer program product comprises a memory and a processor, and computer instructions stored in the memory and running on the processor, and when the computer instructions are run by the processor, the method of any one of claims 1-3 is completed.

6. A computer-readable storage medium, characterized in that, The computer program product is used for storing computer instructions, and when the computer instructions are executed by the processor, the method of any one of claims 1-3 is completed.

7. A computer program product, characterised in that, A computer program comprising computer program elements which, when executed by a processor, perform the method according to any one of claims 1 to 3. A computer program comprising computer program elements which, when executed by a processor, perform the method according to any one of claims 1 to 3.

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