A method and system for simulating and controlling a variable gain inverse model of a road spectrum time domain signal

Abstract

A road spectrum time-domain signal gain-variable inverse model simulation control method and system relates to the field of automatic control. This method addresses the problems of slow control processes and increased labor costs caused by technical deficiencies in existing control fields, such as non-online operation, inverse model failure, gain failure, and the inability of algorithms to automatically adjust according to test types. The method comprises: performing Fourier transforms on the road spectrum command signal and the collected vehicle response signal in the road spectrum time-domain signal to obtain the spectrum difference; calculating the gain value and the excitation signal required for inverse model correction, applying the excitation signal to the system, collecting the vehicle acceleration sensor output response signal, and obtaining an updated vehicle response signal through Fourier transform; performing characteristic correction based on the vehicle response signal, collecting current state information, calculating the value of the excitation signal, and performing an inverse Fourier transform to convert it into a road spectrum time-domain signal for driving a test platform. The method is also applicable to the field of vehicle fatigue testing.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a method and system for simulating and controlling a variable gain inverse model of a time-domain signal of a road spectrum. Background Art

[0002] In the control field, particularly in areas such as vehicle dynamics, tire performance evaluation, and suspension system design, it's common to simulate road data collected on actual roads using laboratory-based test equipment. This simulation allows engineers to test and optimize vehicle or component performance in a controlled environment without requiring extensive testing on actual roads. However, this type of simulation presents several issues and challenges:

[0003] First, regarding data accuracy, actual road conditions are extremely complex, including varying road materials (such as asphalt, concrete, and gravel), temperature, humidity, and wear. These factors can affect the collection of road data. Simulating these complex environmental conditions in the laboratory is extremely difficult, and therefore simulated data may not fully reflect actual conditions.

[0004] Secondly, due to equipment limitations, laboratory testing equipment, such as 4WD chassis dynamometers or tire testers, may not fully replicate the dynamic characteristics of actual roads. For example, road surface roughness and lateral force variations can be difficult to accurately simulate. 3. Control system adaptability: Vehicle control systems (such as the Electronic Stability Program (ESP) and the Anti-lock Braking System (ABS)) are typically adjusted based on actual driving conditions. In laboratory simulations, these control systems may not accurately respond to simulated road profile data. Due to the large mass of vehicles, most laboratory testing equipment is hydraulically driven. Electromechanical systems, particularly hydraulic servo systems, have relatively low inherent damping, resulting in limited control effectiveness using conventional control methods and a lag in system response relative to the given road profile signal. Furthermore, the system in the test equipment exhibits strong coupling, nonlinearity, and parameter uncertainty. Using conventional PID algorithms alone makes it difficult to accurately output the given road profile signal. Therefore, a correction compensation loop is added to the servo control loop. Here, correction compensation is an inverse model control technology based on the characteristics of the servo system. This technology obtains the input and output responses of the system and continuously corrects the excitation signal to enable the system to accurately simulate the output spectrum time domain signal.

[0005] And there are the following problems in the correction and compensation process:

[0006] First, the control is offline, not online, which increases labor costs and prolongs the test time.

[0007] Second, in the control algorithm, the gain is a constant value or is divided into several frequency bands and given several different gain values.

[0008] Gain tuning is difficult, requiring repeated manual parameter adjustments to find the optimal value. However, once the gain is set, it remains constant and, to ensure process stability, is kept relatively low. This increases the number of corrections and prolongs the test time. Even if the spectrum signal frequency band is divided into high, medium, and low bands based on process characteristics, and each band is assigned a different gain value, this does not fundamentally resolve the problem.

[0009] Third, in the control algorithm, the inverse model is not updated.

[0010] The response characteristics of test equipment are constantly changing due to time-varying factors. Using a fixed inverse model will always result in errors with the vehicle's response. These errors will accumulate and amplify as the test progresses, leading to substandard accuracy and even system divergence.

[0011] Fourth, it is not suitable for different test types.

[0012] In automotive road spectrum testing, to simulate high-frequency signals, the control cycle in the test algorithm is getting shorter and shorter. In addition, the algorithm has a large amount of computation, so it is necessary to automatically adjust the algorithm type according to different test requirements to meet different test types.

[0013] Creating a test environment that accurately simulates actual road patterns can be expensive, and setting up and maintaining such a system can be time-consuming. Furthermore, each test requires reconfiguring and recalibrating the equipment, which can lead to increased testing costs and reduced efficiency.

[0014] Real-time simulation of actual road data requires high-speed data processing capabilities and precise control systems. If the laboratory equipment is not responsive enough or the control system is not precise enough, the simulation results may deviate significantly from the actual situation.

[0015] Therefore, as an important part of performance testing for new vehicles like new energy vehicles, road simulation testing in the laboratory is a key task. Simulating road data collected on actual roads using laboratory test equipment is a crucial technology that must be mastered in the control field. Summary of the Invention

[0016] The present invention aims to solve the problems of slow control process, extended test time, increased labor costs, system instability, etc. caused by technical deficiencies in the existing technology such as non-online control, inverse model failure, gain failure, and algorithm failure to automatically adjust according to the test type. To solve the above technical problems, the present invention is implemented through the following technical solutions:

[0017] Solution 1: The present invention proposes a method for simulating and controlling a time-domain signal with a variable gain inverse model, the method comprising the following steps:

[0018] S1. Perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor, respectively, to obtain a spectrum difference;

[0019] S2. Update the gain value according to the current system state, calculate the gain correction value and the excitation signal required for inverse model correction, apply the excitation signal to the system, and output the response signal. After Fourier transform, obtain the updated vehicle response signal.

[0020] S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects the current state information, updates the state information, calculates the value of the excitation signal, and performs an inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0021] Furthermore, a preferred embodiment is provided, in which the method of performing Fourier transform on the road spectrum command signal and the collected vehicle response signal in the time domain signal in S1 is:

[0022] S1.1. Determine the response characteristics of the system;

[0023] S1.2. Based on the response characteristics determined in S1, obtain the initial estimate G0(f) of the inverse model;

[0024] S1.3. Perform FFT transformation on the road spectrum time domain command signal to obtain the spectrum of the road spectrum command. The initial excitation signal is J1(f) = G0(f)R(f). The excitation signal J1(f) is converted from a frequency domain signal to a time domain signal through IFFT to excite the system.

[0025] Furthermore, a preferred embodiment is provided, in which the method for performing numerical update according to the gain of the current system state in S2 and generating the gain value and the excitation signal required for inverse model correction is:

[0026]

[0027] Where D n (f) is the driving spectrum generated during the previous iteration.

[0028] Furthermore, a preferred embodiment is provided, wherein the inverse model correction method described in S2 is:

[0029]

[0030] Where R(f) is the spectrum of the road spectrum command signal.

[0031] Furthermore, a preferred embodiment is provided, in which the method for correcting the characteristics of the vehicle response signal obtained based on the updated Fourier transform in S2 in S3 is:

[0032]

[0033] Where, N n (f) is the response spectrum deviation, M n (f) is the driving spectrum deviation.

[0034] Furthermore, a preferred embodiment is provided, in which the method for calculating the excitation signal required for inverse model correction in S2 is:

[0035]

[0036] Furthermore, a preferred embodiment is provided, wherein the state information update in S3 includes performing gain correction and inverse model correction simultaneously in the same cycle; the gain correction and inverse model correction are performed in their respective correction cycles, i.e., the gain correction is performed first; the excitation signal after gain correction is used to excite the system to generate an output response, update information The corrected excitation signal Prediction generation, i.e. steps.

[0037] Solution 2: A spectrum time-domain signal variable gain inverse model simulation control system, the system comprising:

[0038] The state information update module is used to perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference;

[0039] The excitation signal update module is used to perform numerical updates based on the gain of the current system state, calculate the excitation signal required for gain correction and inverse model correction, apply the excitation signal to the system, and output a response signal. The updated vehicle response signal is obtained through Fourier transform;

[0040] The driving module is used to perform characteristic correction on the vehicle response signal obtained based on the updated Fourier transform of the excitation signal update module, collect and update the current state information, calculate the value of the excitation signal, and perform an inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0041] Solution 3: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes any one of the methods described in Solution 1.

[0042] Solution 4: A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of Solution 1 are implemented.

[0043] The present invention is beneficial in that:

[0044] The present invention addresses technical deficiencies in existing methods, such as non-online operation, inverse model and gain updates, and the inability of the algorithm to automatically adjust to the test type. These issues can lead to slow control, extended test times, increased labor costs, and system instability. Simulating actual road conditions in a laboratory environment can improve safety, avoiding potential safety risks associated with real-world road testing, particularly during extreme driving or fault simulation.

[0045] The method described in the present invention reduces the need for expensive field testing through laboratory simulation, saving fuel, manpower and time costs. In addition, it can also reduce wear and tear on vehicles and test equipment.

[0046] The method described in the present invention provides highly repeatable test conditions through laboratory simulation, which means that the test can be run multiple times under the same conditions to verify the consistency and reliability of the results.

[0047] The method described in the present invention reduces the environmental impact through laboratory simulations because it does not require testing on real roads, thereby reducing emissions and noise pollution.

[0048] The present invention is also applicable to the field of automobile fatigue testing, as well as fatigue loading and performance testing of equipment in other fields such as transportation, aerospace, and civil engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a diagram of the automobile road spectrum test equipment described in embodiment 11.

[0050] Figure 2 This is a schematic diagram of the simulation control principle of the spectrum signal on the test platform as described in the eleventh embodiment.

[0051] Figure 3 This is a control flow chart of a method for simulating and controlling a time-domain signal with a variable gain inverse model of a road spectrum according to the first embodiment.

[0052] Figure 4 This is a schematic diagram of the time-domain signal flow of the road spectrum described in the eleventh embodiment.

[0053] Figure 1 In the figure, car 1, actuator 2, test platform 3. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the implementation methods of this application clearer, the technical solutions in the implementation methods of this application will be clearly and completely described below in combination with the drawings in the implementation methods of this application. Obviously, the described implementation methods are only part of the implementation methods of this application, not all of the implementation methods.

[0055] Implementation method 1: This implementation method provides a method for simulating and controlling a time-domain signal of a road spectrum using a variable gain inverse model. The method includes the following steps:

[0056] S1. Perform Fourier transform on the road spectrum command signal and the collected vehicle response signal in the road spectrum time domain signal to obtain the spectrum difference;

[0057] S2. Perform numerical updates based on the gain of the current system state, calculate and generate the gain value and the excitation signal required for inverse model correction, apply the excitation signal to the system, collect the output response signal of the vehicle acceleration sensor, and obtain an updated vehicle response signal through Fourier transform;

[0058] S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects current state information, calculates the value of the excitation signal, and performs an inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0059] Implementation 2: This implementation further limits the road spectrum time-domain signal variable gain inverse model simulation control method described in Implementation 1. S1: Fourier transform the road spectrum command signal in the road spectrum time-domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference.

[0060] S2. Update the gain value according to the current system state, calculate the gain correction value and the excitation signal required for inverse model correction, apply the excitation signal to the system, and output the response signal. After Fourier transform, obtain the updated vehicle response signal.

[0061] S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects the current state information, updates the state information, calculates the value of the excitation signal, and performs an inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0062] Implementation method 3: This implementation method further limits the inverse model simulation control method of the road spectrum time domain signal variable gain described in implementation method 1. In S2, the gain is numerically updated according to the current system state, and the method for generating the gain value and the excitation signal required for inverse model correction is as follows:

[0063]

[0064] Where D n (f) is the driving spectrum generated during the previous iteration.

[0065] Implementation 4: This implementation further limits the inverse model simulation control method for the time-domain signal of the road spectrum with variable gain described in Implementation 1. The inverse model correction method described in S2 is:

[0066]

[0067] Where R(f) is the spectrum of the road spectrum command signal.

[0068] Implementation 5: This implementation further limits the road spectrum time-domain signal variable gain inverse model simulation control method described in Implementation 1. The method for correcting the characteristics of the vehicle response signal obtained based on the updated leaf transform in S2 in S3 is as follows:

[0069]

[0070] Where, N n (f) is the response spectrum deviation, M n (f) is the driving spectrum deviation.

[0071] Implementation 6: This implementation further limits the road spectrum time domain signal gain inverse model simulation control method described in Implementation 1. The method for calculating the excitation signal required for inverse model correction in S2 is:

[0072]

[0073] Implementation method seven, this implementation method is a further limitation of the road spectrum time domain signal gain inverse model simulation control method described in implementation method one, the state information update described in S3 includes gain correction and inverse model correction in the same cycle at the same time; gain correction and inverse model correction are performed in their respective correction cycles, that is, gain correction is performed first; the excitation signal after gain correction is used to excite the system to generate an output response, update information The corrected excitation signal Prediction generation, i.e. steps.

[0074] Embodiment 8: This embodiment proposes a spectrum time-domain signal variable gain inverse model simulation control system, the system comprising:

[0075] The state information update module is used to perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference;

[0076] The excitation signal update module is used to perform numerical updates based on the gain of the current system state, calculate the excitation signal required for gain correction and inverse model correction, apply the excitation signal to the system, and output a response signal. The updated vehicle response signal is obtained through Fourier transform;

[0077] The driving module is used to perform characteristic correction on the vehicle response signal obtained based on the updated Fourier transform of the excitation signal update module, collect and update the current state information, calculate the value of the excitation signal, and perform an inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0078] Implementation method 9. This implementation method proposes a computer device including a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method described in any one of implementation methods 1 to 7.

[0079] Implementation 10: This implementation proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in any one of Implementation 1 to Implementation 7 are implemented.

[0080] Implementation 11: This implementation provides an example, which is used to explain the above implementations 1 to 8. Specifically, the example is as follows:

[0081] See also Figures 1 to 4 To explain this embodiment, Figure 1 The vehicle is fixed to a test platform, a mechanical device driven by multiple hydraulic actuators. During the test, several accelerometers are placed at appropriate locations on the vehicle to measure its response to road spectral signals.

[0082] See also Figure 2 As shown in the figure, the complete road spectrum time domain signal online control process consists of two parts. The first part is the state information update, which mainly includes the road spectrum error signal calculation, gain correction and inverse model correction. The road spectrum error signal is obtained by performing Fourier transform (FFT) on the road spectrum command signal in the time domain signal and the collected vehicle response signal to obtain the spectrum difference; the gain correction mainly updates the gain in the control process according to the current system state; the inverse model correction mainly corrects the characteristics according to the current system state. The second part is the excitation signal update. By collecting the current state information, the latest value of the excitation signal is calculated, and then the inverse Fourier transform (IFFT) is performed to convert it into a time domain signal to drive the test platform.

[0083] Figure 3 Flowchart of the control shown in ; Figure 4In the control process shown in FIG, the specific directions and mutual relationships of various signals are shown, where the two rectangular dotted boxes represent the same controlled object.

[0084] First, the initial conditions are:

[0085] First, the response characteristics of the system are identified by the H1 method through white powder noise, and then the initial estimate G of the inverse model is obtained based on the response characteristics. 0 (f) Based on the specific situation, give the initial value of the gain α0. The initial value can be selected in the range of 0.1 to 0.2 (for stability) or 0.4 to 0.5 (for speed).

[0086] Perform FFT transformation on the spectrum time domain command signal to obtain the spectrum R(f) of the spectrum command. Then the initial excitation signal is J1(f)=G0(f)R(f). The excitation signal J1(f) is converted from a frequency domain signal to a time domain signal through IFFT to excite the system.

[0087] Secondly, status information update:

[0088] Here, the control algorithm is divided into three working conditions according to the different test types. Different working condition algorithms are as follows: Figure 3 As shown in . Now take working condition 1 as an example to explain.

[0089] For the nth iteration process, the nth frequency domain signal J n (f) After IFFT transformation, the time domain signal is converted into the test platform to excite the corresponding vehicle acceleration output response signal Z n (t), transformed into Z by FFT n (f) Compare with the frequency domain signal of the road spectrum to form the frequency domain error signal E n (f), namely E n (f) = R(f) - Z n (f).

[0090] Generates the excitation signal required for gain values ​​and inverse model correction.

[0091]

[0092] Apply the excitation signal In the system. That is, through IFFT Convert into a time domain signal to excite the system.

[0093] Collect the response signal output by the car acceleration sensor at this time, and obtain the updated response signal through FFT transformation

[0094] Gain value correction. By the car response signal Z n(f) and the Hermitian transpose of the path spectrum instruction R(f) are used to modify the gain value.

[0095]

[0096] Inverse model correction. Based on the previous inverse model G n (f), the previous step incentive J n (f) Update the excitation signal Collecting vehicle response signals Last response signal Z n (f) Jointly modify the inverse model.

[0097]

[0098] G n+1 In the expression (f),

[0099] In terms of stimulus signal updates:

[0100] Calculate the excitation signal J n+1 times n+1 (f)

[0101]

[0102] Apply the excitation signal J n+1 (f) in the system. That is, J is transformed into n+1 (f) Convert to time domain signal excitation system

[0103] Collect the response signal output by the car acceleration sensor at this time, and obtain the updated response signal Z through FFT transformation n+1 (f).

[0104] In summary, each correction is completed in two stages, namely, formulas (1) to (4). In the first stage, the current excitation signal is used to stimulate the test platform to generate the vehicle acceleration response signal, formula (1). Based on this, the gain and inverse model are corrected, namely formulas (2) to (3). The second stage is to update the excitation signal and generate the next corrected excitation signal, namely formula (4). In actual testing, some frequency bands or frequency points may meet the technical indicators. In this case, these frequency bands or points are windowed, and the previous excitation value is maintained. Only other frequency bands or points are corrected.

[0105] In the status information update, there are three working condition algorithms: Working condition one, gain correction and inverse model correction are performed simultaneously in the same cycle, which is suitable for tests that require a short test control cycle and good controller hardware, and can provide accurate vehicle fatigue test results; Working condition two, gain correction and inverse model correction are performed in their respective correction cycles, that is, gain correction is performed first, and the excitation signal after gain correction is used to excite the system to generate an output response. Then use this response to perform inverse model correction. In this way, gain correction and impedance correction are performed in sequence, and the two are independent to avoid mutual influence, but the number of times the system is excited increases. It is suitable for test equipment that has no requirements for the total test time and has average controller hardware performance; Working condition three, update information Not by the corrected stimulus signal The stimulus test platform is collected from the car acceleration, but the stimulus signal is corrected Prediction generation, i.e. It is suitable for tests that have requirements on the total test time but not on the control accuracy, and can quickly provide approximate test results.

[0106] Those skilled in the art will understand that the above description is only a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or coupled in various ways, even if such a combination or coupling is not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

[0107] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A method for simulating and controlling a variable gain inverse model of a time-domain signal of a road spectrum, characterized in that: The method comprises the following steps: S1. Perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor, respectively, to obtain a spectrum difference; S2. Update the gain value according to the current system state, calculate the gain correction value and the excitation signal required for inverse model correction, apply the excitation signal to the system, and output the response signal. After Fourier transform, obtain the updated vehicle response signal. S3: Based on the vehicle response signal obtained by the updated Fourier transform in S2, the characteristics are corrected, the current state information is collected and updated, the value of the excitation signal is calculated, and the inverse Fourier transform is performed to convert it into a road spectrum time domain signal for driving the test platform; In S2, the gain of the current system state is numerically updated to generate the gain value and the excitation signal required for inverse model correction as follows: (1) Where, is the driving spectrum generated in the previous iteration process, is the gain value, is the frequency domain error signal, is the inverse model; The inverse model modification method described in S2 is: (2) Where, is the spectrum of the road spectrum command signal, For the car to respond to the signal, For the last car response signal, It is a road map instruction.

2. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1, characterized in that: In S1, the method of Fourier transforming the road spectrum command signal and the collected vehicle response signal in the time domain signal is as follows: S1.

1. Determine the response characteristics of the system; S1.

2. Based on the response characteristics determined in S1, obtain the initial estimate of the inverse model ; S1.

3. Perform FFT transformation on the time domain command signal of the road spectrum to obtain the spectrum of the road spectrum command. The initial excitation signal is: , the excitation signal The system is excited by converting the frequency domain signal into the time domain signal through IFFT.

3. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1, characterized in that: The method for correcting the characteristics of the vehicle response signal obtained by the updated Fourier transform in S2 in S3 is: (3) Where, , , is the response spectrum deviation, is the driving spectrum deviation.

4. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1, characterized in that: The method for calculating the excitation signal required for inverse model correction in S2 is: (4)。 5. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1, characterized in that: The state information update described in S3 includes the gain correction and the inverse model correction being performed simultaneously in the same cycle, the gain correction and the inverse model correction being performed in their respective correction cycles, that is, the gain correction is performed first, and the excitation signal after the gain correction is used to excite the system to generate an output response, and the information is updated. , the corrected excitation signal Prediction generation, i.e. steps.

6. A road spectrum time domain signal variable gain inverse model simulation control system, characterized in that: The system is implemented based on the control method according to any one of claims 1 to 5, and the system includes: The state information update module is used to perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference; The excitation signal update module is used to perform numerical updates based on the gain of the current system state, calculate the excitation signal required for gain correction and inverse model correction, apply the excitation signal to the system, and output a response signal. The updated vehicle response signal is obtained through Fourier transform; The driving module is used to perform characteristic correction on the vehicle response signal obtained based on the updated Fourier transform of the excitation signal update module, collect and update the current state information, calculate the value of the excitation signal, and perform an inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

7. A computer device comprising a memory and a processor, characterized in that: A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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