Adjusting method and device of vehicle suspension, electronic equipment and storage medium

Abstract

The invention relates to the technical field of vehicle control, and provides a vehicle suspension adjusting method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first displacement power spectral density and a first road category corresponding to the road surface unevenness of a current driving road based on current driving state information; determining a second road category based on the current driving state information and the historical driving state information; determining a target displacement power spectral density based on the first displacement power spectral density, the first road category, the second road category, the current driving state information and the historical driving state information; based on the target displacement power spectral density, the rigidity of the suspension is controlled and adjusted, the accuracy of judging the road condition by the suspension system is improved, and riding discomfort caused by wrong road condition recognition is avoided.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a method, device, electronic device and storage medium for adjusting a vehicle suspension. Background Art

[0002] Traditional suspension stiffness adjustment relies primarily on mechanical adjustments, such as replacing springs and adjusting coil spring preload. With the rapid advancement of automotive technology, adaptive suspension stiffness adjustment has gradually replaced traditional adjustment methods. For example, the application of electronic control units (ECUs) in electronic suspension controllers enables real-time adjustment of suspension stiffness by collecting various vehicle status information through a series of high-precision sensors and the Controller Area Network (CAN) bus.

[0003] However, the process of collecting vehicle status information through sensors and then adjusting it may lead to inaccurate response to the overall vehicle situation due to inaccurate road condition recognition. Especially in complex and changeable road conditions, faced with a large amount of uncertain road surface data, it is easy to have incorrect road condition recognition, resulting in inaccurate suspension adjustment and affecting driving comfort. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a vehicle suspension adjustment method, device, electronic device and storage medium to solve the problem in the prior art that the current road conditions cannot be correctly identified, resulting in inaccurate suspension adjustment, thereby affecting driving comfort.

[0005] According to a first aspect of an embodiment of the present application, a method for adjusting a vehicle suspension is provided, comprising:

[0006] Get the vehicle's current driving status information in real time;

[0007] determining, based on the current driving state information, a first displacement power spectrum density corresponding to the road surface roughness of the current driving road and a first road category of the current driving road;

[0008] determining a second road category of the current driving road based on the current driving state information and the historical driving state information; the historical driving state information is the driving state information of the preset road section length closest to the current vehicle position updated in real time;

[0009] determining a target displacement power spectral density of the current driving road based on the first displacement power spectral density, a first road category of the current driving road, a second road category of the current driving road, current driving state information, and historical driving state information;

[0010] Based on the target displacement power spectrum density of the current driving road, the stiffness of the suspension is controlled and adjusted.

[0011] According to a second aspect of the embodiments of the present application, a vehicle suspension adjustment device is provided, comprising:

[0012] Acquisition module, real-time acquisition of the vehicle's current driving status information;

[0013] a first road spectrum determination module, which determines a first displacement power spectrum density corresponding to the road roughness of the current driving road and a first road category of the current driving road based on the current driving state information;

[0014] a second road spectrum determination module, which determines a second road category of a current driving road based on current driving state information and historical driving state information;

[0015] a target road spectrum determination module, which determines a target displacement power spectrum density of the current driving road based on the first displacement power spectrum density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information;

[0016] The adjustment module controls and adjusts the stiffness of the suspension based on the target displacement power spectrum density of the current driving road.

[0017] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0018] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0019] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows: the displacement power spectrum density of road surface unevenness is calculated by real-time acquired vehicle driving status information. As an important basis for suspension adjustment, the displacement power spectrum density can reflect the statistical characteristics of the road surface, providing more accurate and comprehensive information for suspension adjustment. Historical driving status information can also be used as prior information, combined with the real-time acquired vehicle driving status information to obtain the target displacement power spectrum density of the current driving road. By comprehensively considering multiple factors and adjusting the suspension stiffness in real time, the accuracy of the suspension system's judgment of road conditions is improved, and the accurate execution of suspension adjustment instructions is ensured. This solves the problem in the prior art that the current road conditions cannot be correctly identified in complex road conditions, resulting in inaccurate suspension adjustment and thus affecting driving comfort, and avoids riding discomfort caused by incorrect road spectrum recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 1 is a flow chart of a vehicle suspension adjustment method provided in an embodiment of the present application;

[0022] Figure 2 This is a schematic diagram of an electronic control unit of a vehicle suspension system and various sensors in a road condition acquisition device provided in an embodiment of the present application;

[0023] Figure 3 1 is a flow chart of another vehicle suspension adjustment method provided in an embodiment of the present application;

[0024] Figure 4 1 is a schematic structural diagram of a vehicle suspension adjustment device provided in an embodiment of the present application;

[0025] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0027] In related technologies, adaptive suspension stiffness adjustment uses various vehicle status information, such as vehicle height, speed, steering wheel angle, wheel cylinder pressure, and driving mode, as input signals. This information is then processed by an algorithm to generate a control signal that optimizes the vehicle's suspension control performance. This control signal then controls the on / off time or current of the solenoid valve, adjusting the inflation and deflation of the air springs and achieving real-time adjustment of suspension stiffness. However, the process of collecting vehicle status information through sensors and then adjusting the system can lead to inaccurate reflection of the vehicle's overall condition due to inaccurate road condition recognition. This is especially true in complex and changing road conditions, where large amounts of uncertain road surface data are present. This can easily lead to misidentification of road conditions, resulting in inaccurate suspension adjustment and impacting ride comfort.

[0028] To address the above scenario, the present application provides a vehicle suspension adjustment method. The method obtains the vehicle's current driving state information in real time. The driving state information consists of various parameters collected by sensors on the vehicle during driving. Based on the current driving state information, a first displacement power spectral density (DPSD) corresponding to the road roughness of the current road and a first road category of the current road are determined. The real-time acquired current driving state information is stored in the vehicle's suspension system database. The driving state information in the suspension system database includes both current and historical driving state information. A second road category of the current road is determined based on the driving state information in the suspension system database. The obtained second road category of the current road verifies or complements the first road category, improving road identification accuracy. Based on the first displacement power spectral density, the first and second road categories of the current road, and the driving state information in the suspension system database, a target displacement power spectral density (DPSD) for the current road is calculated to more accurately characterize the road roughness of the current road. This DPSD is used to guide vehicle suspension adjustment. Based on the target DPSD, a control algorithm adjusts the suspension stiffness to adapt to the current road conditions and improve ride comfort. The vehicle suspension adjustment method proposed in the present application calculates the displacement power spectrum density of road surface unevenness through the real-time acquisition of vehicle driving status information. As an important basis for suspension adjustment, the displacement power spectrum density can reflect the statistical characteristics of the road surface, providing more accurate and comprehensive information for suspension adjustment. It can also use historical driving status information as prior information, combined with the real-time acquisition of vehicle driving status information, to obtain the target displacement power spectrum density of the current driving road. Taking into account various factors, the suspension stiffness is adjusted in real time, and the power spectrum density is used to quantitatively analyze the road surface unevenness, thereby improving the accuracy of the suspension system's judgment of road conditions, ensuring the accurate execution of suspension adjustment instructions, avoiding ride discomfort caused by incorrect road spectrum recognition, and solving the problem in related technologies that the current road spectrum cannot be correctly identified in complex road conditions, thereby affecting driving comfort.

[0029] A method and device for adjusting a vehicle suspension according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 FIG. 1 is a flow chart of a vehicle suspension adjustment method provided in an embodiment of the present application. Figure 1 As shown, the vehicle suspension adjustment method includes:

[0031] Step 101: Acquire the current driving status information of the vehicle in real time.

[0032] In some embodiments, the vehicle's current driving status information can be obtained in real time through the vehicle's road condition collection device. The status parameters in the current driving status information may include the vertical displacement of the wheel relative to the vehicle body, the vehicle's acceleration, wheel speed, wheel force, vehicle body height, internal pressure data of the shock absorber, force data of the suspension system, torsional torque borne by the suspension system components, strain degree of the suspension system components, etc.

[0033] The vehicle's road condition collection device may include a variety of sensors, such as displacement sensors, acceleration sensors, wheel speed sensors, left front height sensors, right front height sensors, pressure sensors, force sensors, strain sensors, and torque sensors.

[0034] Specifically, the vertical displacement of the wheel relative to the vehicle body can be measured by a displacement sensor, and by detecting changes in the displacement, the unevenness of the road surface and the compression / extension of the suspension system can be reflected; the wheel speed can be measured by a wheel speed sensor, and by detecting changes in the wheel speed, it is helpful to obtain the vehicle's speed and driving status, and analyze the road condition; the acceleration of the vehicle can be measured by an acceleration sensor, and by detecting changes in the acceleration, the unevenness of the road surface and the vibration of the vehicle can be inferred; the vehicle body height can be measured by the left front height sensor and the right front height sensor to monitor changes in the vehicle body height; the internal pressure data of the shock absorber can be collected by a pressure sensor; the force data of the suspension system, such as the weight of the vehicle or the impact force caused by uneven road surface, can be collected by a force sensor; the strain degree of the suspension system components can be collected by a strain sensor, which helps to infer the force condition of the suspension system; the torsional torque of the suspension system components (such as the lower arm or swing bearing) can also be collected by a torque sensor to provide information on road friction and vehicle steering dynamics.

[0035] The ECU of the suspension system can communicate with the road condition collection device via the CAN bus, thereby obtaining the current driving status information of the vehicle sent by the road condition collection device in real time. The schematic diagram of the electronic control unit of the vehicle's suspension system and the various sensors in the road condition collection device can be shown as follows: Figure 2 As shown, the ECU 1 of the suspension system communicates with the first sensor 2, the second sensor 3, the third sensor 4, the fourth sensor 5, the fifth sensor 6, and the sixth sensor 7. The first sensor 2, the second sensor 3, the third sensor 4, the fourth sensor 5, the fifth sensor 6, and the sixth sensor 7 include, but are not limited to, a displacement sensor, an acceleration sensor, a wheel speed sensor, a left front height sensor, a right front height sensor, a pressure sensor, a force sensor, a strain sensor, a torque sensor, and the like.

[0036] The current driving status information obtained can provide the latest input data for subsequent data processing and analysis, help understand the road conditions the vehicle is experiencing, and promptly capture changes in road roughness, providing a basis for adaptive adjustment of the suspension system.

[0037] Step 102 : determining a first displacement power spectrum density corresponding to the road surface roughness of the current driving road and a first road category of the current driving road based on the current driving state information.

[0038] Specifically, road roughness is a measure of road surface smoothness, affecting vehicle stability and comfort. Displacement power spectral density is a mathematical representation of the statistical characteristics of road roughness. Fast Fourier transform analysis can be used to process current driving state information, converting the vertical displacement signal in the time domain into a frequency domain signal. The first displacement power spectral density, corresponding to the current road surface roughness, is then calculated. This first displacement power spectral density reflects the current road surface roughness characteristics and describes its distribution, providing a basis for adjusting suspension stiffness.

[0039] Different types of roads have different structural materials, so the roughness corresponding to different types of roads is also different. The current driving road can be classified according to the first displacement power spectrum density to obtain the first road category of the current driving road.

[0040] Bumps and impacts can cause damage to the vehicle. By determining the first displacement power spectral density and the first road category of the current road, it can help to more accurately adjust the stiffness and damping of the suspension to adapt to different road conditions, thereby improving driving comfort.

[0041] Step 103: Determine a second road category of the current driving road based on the current driving state information and the historical driving state information.

[0042] The historical driving status information is the road load data of the preset road section length closest to the current vehicle position updated in real time. For example, the preset road section length may be 1 kilometer or 2 kilometers, which is not specifically limited here.

[0043] In some embodiments, a clustering algorithm may be used to divide historical driving state information into different clusters, each cluster representing a road category, so as to determine the road category corresponding to the current driving state information, that is, the second road category of the current driving road.

[0044] By combining the current driving status information with the historical driving status information to re-categorize the current driving road, not only the current road conditions but also the road conditions at historical moments are taken into account, which helps to more accurately identify the current road type and reduce the possibility of misjudgment.

[0045] Step 104 : determining a target displacement power spectrum density of the current driving road based on the first displacement power spectrum density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information.

[0046] The first displacement power spectrum density (DPSD) represents the road roughness characteristics of the current road, determined based on the current driving state information. The first road category is the road category classified based on the first displacement power spectrum density. The second road category is a road category further classified based on the current driving state information and historical driving state information. By comprehensively considering multiple factors and adjusting the suspension stiffness in real time, the system can more accurately identify the primary road category and road spectrum information of the current road, thereby determining the target DPSD for the current road. This improves the accuracy of identifying the current road conditions and serves as a guide for adjusting the suspension stiffness.

[0047] By determining the target displacement power spectrum density of the current driving road based on the first displacement power spectrum density, the first road category, the second road category, the current driving status information and the historical driving status information, and comprehensively utilizing current and historical data, the ability to recognize and respond to complex road conditions is improved, solving the problem of inaccurate and timely adjustments under complex road conditions, and significantly improving driving comfort and safety.

[0048] Step 105 : Control and adjust the stiffness of the suspension based on the target displacement power spectrum density of the current driving road.

[0049] Specifically, suspension stiffness is the ability of a vehicle's suspension to resist deformation, that is, the degree to which it deforms when subjected to external forces. Suspension stiffness is primarily determined by the properties of the springs (including coil springs, air springs, etc.). Higher suspension stiffness makes the vehicle stiffer, reducing body movement and improving handling; lower suspension stiffness makes the vehicle softer and increases driving comfort.

[0050] Roads with different displacement power spectral densities have varying degrees of roughness. When a vehicle travels on a road with high roughness, the road presents greater resistance, requiring a lower suspension stiffness for improved driving comfort. On a road with low roughness, the road presents less resistance, requiring a higher suspension stiffness for improved stability and handling. Specifically, on relatively flat highways, the vehicle's suspension can be set to a higher stiffness to improve stability and handling; on off-road roads, where the surface roughness is high, a lower suspension stiffness is required. By controlling the air spring pressure based on the target displacement power spectral density for the current road, the suspension stiffness and height can be dynamically adjusted, optimizing the vehicle's driving performance and driving comfort to suit the current road conditions.

[0051] By adaptively adjusting the suspension stiffness based on real-time road conditions, the impact and vibration caused by uneven road surfaces can be reduced, which helps improve the vehicle's handling stability under different road conditions and reduce safety risks caused by sudden changes in road conditions.

[0052] According to the technical solution provided in the embodiment of the present application, the displacement power spectrum density of the road surface roughness is calculated by using the vehicle driving status information obtained in real time. As an important basis for suspension adjustment, the displacement power spectrum density can reflect the statistical characteristics of the road surface, providing more accurate and comprehensive information for suspension adjustment. In addition, the historical driving status information can be used as prior information, combined with the vehicle driving status information obtained in real time, to obtain the target displacement power spectrum density of the current driving road. By comprehensively considering multiple factors and adjusting the suspension stiffness in real time, the accuracy of the suspension system's judgment of the road condition is improved, and the accurate execution of the suspension adjustment instructions is ensured. This solves the problem in the prior art that the current road spectrum cannot be correctly identified in complex road conditions, resulting in inaccurate suspension adjustment, thereby affecting driving comfort.

[0053] Furthermore, in some embodiments, the various sensors of the road condition collection device can acquire raw data of driving status information in real time, including suspension system status data, wheel status data, shock absorber status data, and vehicle acceleration, vehicle height, etc. Wheel status data includes one or more of the following: vertical displacement of the wheel relative to the vehicle body, wheel speed, and wheel force; shock absorber status data may include internal shock absorber pressure data; and suspension system status data may include suspension system force data, torsional torque borne by suspension system components, and strain levels of suspension system components. Wheel forces are the forces acting on the wheels, including driving / braking forces, friction, centripetal / centrifugal forces, and other factors. The specific force conditions depend on the vehicle's operating state. Suspension system force data can include stiffness data, damping data, and spring / shock absorber parameters. Stiffness data refers to the suspension's ability to resist deformation, directly affecting vehicle handling and comfort. Damping data reflects the suspension's ability to absorb vibration energy; the higher the damping, the better the shock absorption effect. Spring / shock absorber parameters include spring stiffness and shock absorber damping coefficient, which directly affect impact absorption. The strain level of suspension system components refers to the degree of deformation of the components under external forces, i.e., the magnitude of elastic or plastic deformation of the components. The vehicle's suspension system utilizes a distributed sensor fusion structure. This distributed design allows the suspension system's electronic control unit to fully utilize multi-sensor data resources across time and space, obtaining multi-sensor observation data in a time series, and analyzing, synthesizing, controlling, and utilizing them under certain criteria. Specifically, the multi-source sensor data fusion processing process can include:

[0054] Construction and calibration of a multi-source sensing system: Select appropriate sensor types, such as displacement sensors, acceleration sensors, wheel speed sensors, left front height sensors, right front height sensors, pressure sensors, force sensors, strain sensors, torque sensors, etc., and install them in appropriate locations of the suspension system, such as coil springs and lower control arms. The electronic control unit and sensors of the suspension system can be connected via the CAN bus to build a multi-source sensing system. In addition, standard calibration methods and techniques are used to ensure that each sensor maintains accuracy when collecting data. Calibration methods include static calibration and dynamic calibration. Online multi-sensor calibration technology can be used to correct sensor installation errors and environmental influences in real time, improve data reliability, and ensure the accuracy of sensors when collecting data on the strain of the front suspension system's coil springs and lower control arm materials, the torque of each axle, and the torque of suspension system components.

[0055] Analog signal digital conversion: Amplify and filter the original analog signal collected by the sensor to improve signal quality. Use an analog-to-digital converter to convert the pre-processed analog signal into a digital signal, ensuring that the converted digital signal can accurately reflect the original analog signal, providing a basis for subsequent data processing;

[0056] Data preprocessing: Filter technology can be used to remove high-frequency noise and low-frequency drift in the signal to purify the data. Clock synchronization or timestamp technology can be used to make data from different sensors consistent on the time axis. Zero-mean processing can be performed to remove the DC component of the signal, making the signal fluctuate around zero, eliminating the DC component in the signal and improving the accuracy of subsequent processing.

[0057] Feature extraction and data processing: Extract key features from time-domain signals, such as mean, variance, skewness, peak, and energy. The extracted feature data is transmitted to the suspension system's ECU, where advanced data processing is performed. This includes removing signal glitches, extracting signal trend items, and smoothly transitioning and connecting segmented test data to obtain accurate vehicle driving response data, i.e., driving status information.

[0058] In some embodiments, determining a first displacement power spectrum density corresponding to the road roughness of the current driving road and a first road category of the current driving road based on the current driving state information includes:

[0059] Performing a fast Fourier transform on the current driving state information to obtain a corresponding first fast Fourier transform result;

[0060] Performing power spectrum density calculation on the first fast Fourier transform result to obtain a first displacement power spectrum density of the current driving road;

[0061] A first road category of the current driving road is determined based on the first displacement power spectrum density and a correspondence between the displacement power spectrum density and the road category.

[0062] Among them, the current driving status information may include suspension system status data, wheel status data, and shock absorber status data. The suspension system status data, wheel status data, and shock absorber status data can be respectively subjected to fast Fourier transform to obtain corresponding first fast Fourier transform results, and power spectrum density calculation can be performed on each first fast Fourier transform result to obtain corresponding displacement power spectrum densities. At this time, the first displacement power spectrum density of the current driving road can be obtained based on each displacement power spectrum density, and the first road category of the current driving road can be determined according to the first displacement power spectrum density and the correspondence between the displacement power spectrum density and the road category.

[0063] Optionally, the maximum displacement power spectrum density among the displacement power spectrum densities can be used as the first displacement power spectrum density; or, the average value of the displacement power spectrum densities can be calculated and used as the first displacement power spectrum density; or, based on historical experience, the weight value corresponding to each driving status information can be pre-set, and the displacement power spectrum density corresponding to each driving status information and its weight value can be weighted and summed, and the value after weighted summation can be used as the first displacement power spectrum density.

[0064] Specifically, a fast Fourier transform is performed on the current driving status information, and the time domain data in the current driving status information is converted into frequency domain data. The frequency domain data can describe the components of the data at different frequencies and identify the vibration characteristics at different frequencies, thereby obtaining the corresponding first fast Fourier transform result. The first fast Fourier transform result can be represented by FFT(data).

[0065] The power spectral density of the first fast Fourier transform result can be calculated by calculating the square of the amplitude of the first fast Fourier transform result. The calculation formula is: Where PSD(f) is the first displacement power spectrum density of the current driving road, N is the number of data points, Δt is the sampling interval, fs is the sampling frequency.

[0066] In addition, a fast Fourier transform can be performed on each parameter data in the current driving state information. After obtaining the first fast Fourier transform result corresponding to each parameter data in the current driving state information, the displacement power spectrum density corresponding to each parameter data can be obtained. In this case, the maximum displacement power spectrum density among the displacement power spectrum densities corresponding to each parameter data can be used as the first displacement power spectrum density. Alternatively, the average value of the displacement power spectrum densities corresponding to each parameter data can be calculated and used as the first displacement power spectrum density. Alternatively, based on historical experience, a weight value corresponding to each parameter data can be pre-set, and the displacement power spectrum density corresponding to each parameter data and its weight value can be weighted and summed, and the weighted sum value can be used as the first displacement power spectrum density. Then, based on the first displacement power spectrum density and the corresponding relationship between the displacement power spectrum density and the road category, the first road category of the current driving road can be determined.

[0067] By calculating the power spectral density, we can obtain the roughness characteristics of the current road surface, namely the first displacement power spectral density. This helps quantify the roughness of the current road surface and provides a basis for subsequent road surface classification. Different road categories have different road roughness, and the corresponding displacement power spectral density ranges for different road categories are also different. For example, highways generally have lower displacement power spectral densities, while off-road roads may have higher displacement power spectral densities. A correspondence between displacement power spectral density and road category can be established. Based on the pre-set correspondence between displacement power spectral density and road category, the first displacement power spectral density is mapped to determine the first road category of the current road, providing a reference for adjusting the suspension stiffness.

[0068] By performing fast Fourier transform on the current driving state information, the first displacement power spectrum density and the first road category are obtained, thereby achieving real-time acquisition of the first displacement power spectrum density and the first road category. Both the first displacement power spectrum density and the first road category can reflect the actual situation of the current driving road conditions in real time, ensuring the real-time and authenticity of the first displacement power spectrum density and the first road category.

[0069] In some embodiments, determining the second road category of the current driving road based on the current driving state information and the historical driving state information includes:

[0070] Cluster the historical driving status information to obtain the centroid data of each corresponding historical driving status information;

[0071] A second road category of the current driving road is determined based on the current driving state information and centroid data of each type of corresponding historical driving state information.

[0072] Specifically, by clustering the historical driving state information, aggregate classes of historical driving state information corresponding to each category can be obtained. Each aggregate class corresponds to a road category, and each aggregate class of historical driving state information corresponding to a category corresponds to multiple pieces of historical driving state information. Optionally, each parameter in each piece of historical driving state information can be clustered separately to obtain an aggregate class corresponding to each parameter.

[0073] By calculating the mean of all historical driving status information in each aggregation class, the centroid data of each corresponding historical driving status information can be obtained. The centroid data can represent the typical characteristics of the corresponding road category and indicate the "average" or "center" position of an aggregation class.

[0074] In some embodiments, the distance or similarity between the current driving status information and the centroid data of each corresponding historical driving status information can be calculated, and the current driving status information can be classified into the closest category based on the distance or similarity, so that the road category corresponding to this category is determined as the second road category of the current driving road.

[0075] Specifically, the distance between the centroid data of each type of corresponding historical driving state information and the current driving state information can be calculated as follows:

[0076]

[0077] Among them, d(D,X i ) is the distance between the centroid data of the historical driving state information corresponding to the i-th category and the current driving state information, X ij is the jth parameter of the centroid data of the historical driving state information corresponding to the i-th category, D is the current driving state information, n is the number of parameters in the driving state information, X i is the centroid data of the historical driving status information corresponding to the i-th category, Y j is the weight of the jth parameter in the driving status information.

[0078] Through the above formula, the distance d (D, X) between the centroid data of each corresponding historical driving state information and the current driving state information can be obtained. i ), and by min(d(D,X i )) can find the centroid data of the historical driving status information corresponding to the road category closest to the current driving status information, determine the aggregation class closest to the current driving status information, and determine the category corresponding to the aggregation class as the second road category of the current driving road.

[0079] In addition, the weight of each parameter in the driving state information can be determined based on the degree of influence of each parameter in the driving state information on the driving quality of the vehicle. The degree of influence of each parameter in the driving state information on the driving quality of the vehicle can be obtained through multiple experiments or by using the Delphi method to prepare a questionnaire and distribute it to multiple professionals in the field of suspension systems, thereby determining the degree of influence of each parameter in the driving state information (i.e., the vertical displacement of the wheel relative to the vehicle body, the acceleration of the vehicle, the wheel speed, the wheel force, the vehicle body height, the internal pressure data of the shock absorber, the force data of the suspension system, the torsional torque borne by the suspension system components, the strain degree of the suspension system components, etc.) on the driving quality of the vehicle. Among them, Y j ∈[0,1],

[0080] By combining historical driving status information, more comprehensive information is provided, the accuracy of identifying current road conditions is improved, and the possibility of misjudgment caused by driving status information at a single moment is reduced.

[0081] In some embodiments, determining a target displacement power spectral density for the current driving road based on the first displacement power spectral density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information includes:

[0082] When the first road category and the second road category of the current driving road are the same, determining the first displacement power spectrum density as the target displacement power spectrum density of the current driving road;

[0083] When the first road category of the current driving road is different from the second road category of the current driving road, a target displacement power spectrum density of the current driving road is determined based on the current driving state information and the historical driving state information.

[0084] In some embodiments, if the first road category and the second road category of the current driving road are consistent, it indicates that there is a high degree of consistency between the current driving state information and the historical driving state information. In this case, the first displacement power spectrum density can be directly used as the target displacement power spectrum density.

[0085] If the first and second road categories differ, the current road condition may be complex, and the driving state information at a single moment is insufficient to characterize the current road condition. Therefore, historical driving state information can be used in combination with the current driving state information for comprehensive consideration. Specifically, Bayesian statistical methods can be applied to dynamically adjust the road spectrum probability distribution based on the current and historical driving state information. This yields a probability distribution for each road type, from which the displacement power spectrum density corresponding to the road category with the highest probability is selected as the target displacement power spectrum density.

[0086] By acquiring current driving status information in real time and combining it with historical driving status information closest to the current vehicle location, a continuously updated dataset can be formed. Combining the category determination results obtained from real-time data (the first road category) with the category determination results obtained from reference historical data (the second road category) helps reduce the false positive rate and improve the accuracy of road spectrum recognition for the current road.

[0087] In addition, the user's subjective opinion can also be considered as to whether to refer to historical driving status information for road spectrum recognition of the current driving road. When the first road category of the current driving road and the second road category of the current driving road are different, the category comparison result (i.e., the first road category of the current driving road and the second road category of the current driving road are different) can be used to remind the user through the vehicle's central control display screen. After receiving the category comparison result, the user can decide whether to re-recognize the road spectrum of the current driving road. If an instruction is received from the user indicating that the road spectrum recognition of the current driving road needs to be re-performed, the road spectrum of the current driving road is re-evaluated based on the Bayesian statistical method with reference to the historical driving status information. If an instruction is received from the user indicating that the road spectrum recognition of the current driving road does not need to be re-performed, the first displacement power spectral density continues to be determined as the target displacement power spectral density of the current driving road, thereby achieving stiffness adjustment of the vehicle suspension based on the user's wishes.

[0088] In some embodiments, determining a target displacement power spectrum density of a current driving road based on current driving state information and historical driving state information includes:

[0089] Calculating the road roughness of the current road based on the current driving state information and the historical driving state information to obtain the second displacement power spectrum density of the current road;

[0090] The second displacement power spectrum density of the current driving road is determined as the target displacement power spectrum density of the current driving road.

[0091] Specifically, the current driving state information and the historical driving state information may be stored in the suspension system database and may be stored in the form of a time series, and each driving state information includes parameters measured by a plurality of sensors.

[0092] The fuzzy clustering algorithm or the K-means clustering algorithm can be used to cluster the historical driving state information in the suspension system database to obtain multiple clusters, each of which corresponds to a road category.

[0093] Road roughness is an indicator that measures the smoothness of the road surface and affects the stability and comfort of vehicle driving. The displacement power spectrum density is a mathematical representation of the statistical characteristics of road roughness. Therefore, by calculating the road roughness of the current road, the displacement power spectrum density of the current road can be obtained.

[0094] When calculating the second displacement power spectrum density, for each aggregation class, the ratio of the number of corresponding driving state information to the total number of driving state information can be calculated, thereby obtaining the initial probability distribution corresponding to each class and the prior probability; and constructing a likelihood function to represent the probability of observing the current driving state when a certain type of road is given; combining the current driving state information with the prior probability, using the Bayesian formula to calculate the posterior probability, that is, the probability of belonging to each road type when the current driving state is observed, according to the posterior probability, select the most likely road category, that is, the aggregation class with the largest probability value, and use the centroid data corresponding to the aggregation class with the largest probability value as the target driving state information, calculate the displacement power spectrum density corresponding to the target driving state information, and obtain the second displacement power spectrum density of the current driving road.

[0095] In some embodiments, as Figure 3 As shown, based on the current driving state information and the historical driving state information, the road roughness of the current driving road is calculated to obtain the second displacement power spectrum density of the current driving road, including:

[0096] Step 301: cluster the historical driving state information and determine the initial probability distribution corresponding to each clustered category;

[0097] Step 302: adjusting the initial probability distribution corresponding to each category based on the current driving state information to obtain the posterior probability distribution corresponding to each category;

[0098] Step 303: If the change range of the posterior probability distribution within the preset time period is less than the preset range, determine the third road category of the current driving road based on the posterior probability distribution;

[0099] Step 304: determining centroid data of the driving state information corresponding to the third road category based on the driving state information corresponding to the third road category;

[0100] Step 305, performing a fast Fourier transform on the centroid data to obtain a corresponding second fast Fourier transform result;

[0101] Step 306 : Perform power spectrum density calculation on the second fast Fourier transform result to obtain a second displacement power spectrum density of the current driving road.

[0102] In some embodiments, a fuzzy clustering algorithm or a K-means algorithm can be used to cluster historical driving state information to obtain multiple clusters, each corresponding to a road category. Optionally, during clustering, each parameter in the historical driving state information can be clustered separately to obtain multiple clusters. For each cluster, the proportion of the corresponding historical driving state information to the total amount of historical driving state information is calculated to obtain the initial probability distribution corresponding to each cluster, i.e., the prior probability.

[0103] The road surface conditions in a certain area have a certain degree of similarity, which conforms to the statistical law. i ) as the prior probability of road spectrum recognition of the current road W, the current driving state information is used as the observation data D, and the likelihood function P(D|X i ), that is, in a given road category X i Under the condition of , the probability of observing the previous driving state information D is true.

[0104] According to the clustering algorithm, Xi is the centroid data of the driving state information corresponding to the i-th category, and the distance between the observation data D and the centroid data Xi of the driving state information corresponding to the i-th category is d(D,Xi), that is: where X ij is the jth parameter of the centroid data of the driving status information corresponding to the i-th category, Y j is the weight of the jth parameter in the driving state information, and n is the number of parameters in the driving state information.

[0105] pass You can get the given road category X i The probability that the previous driving state information D is true is obtained in the case of . By storing the current driving state information acquired in real time into the suspension system database and clustering it, the centroid data of each cluster can be continuously updated. When the observed data D is known to be true, the probability distribution of the re-evaluated observed data D, that is, the posterior probability P(X i |D), the calculation formula is as follows: Where P(X i ) is the prior probability, P(D|X i ) is a given road category X i In the case of , the probability of observing the previous driving state information D is true, P(D) is the sum of the probabilities of the observed data D under each road category, and the posterior probability P(X i |D) is the observed data D and X i The probability of being of the same category.

[0106] P(D) can be calculated by the following formula: P(D) = ∑P(D|X i)·P(X i ).

[0107] As time goes by, the current driving status information obtained in real time is stored in the vehicle's suspension system database, and the corresponding posterior probability distributions of various types are updated accordingly. After multiple iterations, the posterior probability distribution tends to be stable.

[0108] When the variation of the posterior probability distribution within the preset time period is less than the preset amplitude, the posterior probability distribution can be considered to be stable. The preset time period can be 30 seconds and the preset amplitude can be 10%. Compare the corresponding posterior probability distributions of various types, and according to max(P(X i ))Determine the most likely road category, that is, the third road category of the current road.

[0109] The average or center point of all driving state information belonging to the third road category is calculated to obtain the centroid data of the driving state information corresponding to the third road category as the representative feature of this category. A fast Fourier transform is performed on the centroid data of the driving state information corresponding to the third road category, converting the centroid data from the time domain to the frequency domain to obtain the vibration characteristics at different frequencies. The corresponding second fast Fourier transform results are then subjected to power spectral density calculation to obtain the second displacement power spectral density, which reflects the roughness characteristics of the current road surface. The second displacement power spectral density provides quantified road surface roughness information, facilitating subsequent preemptive adjustments to the suspension stiffness to improve driving comfort.

[0110] By comprehensively utilizing historical and current driving status information, the ability to identify and respond to complex road conditions is improved. This not only solves the problem of inaccurate and in-time adjustments caused by relying solely on real-time data, but also, by introducing Bayesian statistical theory, enhances the robustness and adaptability of the vehicle suspension system, improves driving comfort and safety, and enables the vehicle suspension system to better adapt to various complex driving environments.

[0111] In some embodiments, controlling and adjusting the stiffness of the suspension based on the target displacement power spectrum density of the current driving road includes:

[0112] Determining a desired stiffness of the current driving road based on a target displacement power spectral density of the current driving road;

[0113] Determining a corresponding stiffness control instruction for the suspension based on the expected stiffness of the current driving road and the current stiffness of the suspension;

[0114] The opening of the solenoid valve of the suspension is adjusted based on the stiffness control command to adjust the stiffness of the suspension.

[0115] In some embodiments, the desired stiffness of the current driving road is determined based on the target displacement power spectrum density of the current driving road and the preset correspondence between the displacement power spectrum density and the desired stiffness. The preset correspondence between the displacement power spectrum density and the desired stiffness is shown in the following table:

[0116] Table 1 Correspondence between the preset displacement power spectrum density and the expected stiffness

[0117] Displacement power spectral density (m2*s) Expected stiffness (N / m) (10-4,10-2] 100-500 (0.01,0.1] 500-1000 (0.1,1] 1000-2000 >1 >2000

[0118] Roads with different displacement power spectral densities require different suspension stiffnesses. The expected stiffness allows the vehicle to quickly adapt to the corresponding road conditions. The expected stiffness for roads with different displacement power spectral densities is derived from empirical data or simulation tests and represents the optimal suspension stiffness required for the vehicle on roads with corresponding displacement power spectral densities. The expected stiffness provides a clear target value for shock absorber adjustments.

[0119] When the suspension stiffness needs to be increased, the ECU controls the solenoid valve to open, allowing compressed air to flow from the air compressor or air tank into the air spring. When the suspension stiffness needs to be reduced, the ECU controls the solenoid valve to open the exhaust hole of the air spring to release the internal gas. The current stiffness of the suspension can be monitored in real time by sensors, and the desired stiffness can be compared with the current stiffness through the control algorithm to determine the stiffness control instruction corresponding to the suspension. Specifically, the control algorithm can be a proportional-integral-derivative (PID) algorithm. The PID algorithm updates the output of the controller by calculating the incremental and cumulative deviations of the deviation to obtain the stiffness control instruction, which specifically includes: Where ΔP is the change in the opening of the solenoid valve, Kp, Ki, and Kd are the proportional, integral, and differential coefficients of the PID controller, e is the error between the desired stiffness and the current stiffness of the suspension, and ∫edt is the integral of the error. is the rate of change of the error. ΔP is used as the stiffness control command to adjust the opening of the suspension solenoid valve to adjust the stiffness of the suspension.

[0120] By controlling the opening of the solenoid valve to adjust the inflation and deflation of the air spring, the stiffness index of the suspension system is adjusted to reduce the "overcharging" and "over-discharging" phenomena.

[0121] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0122] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0123] Figure 4 Schematic diagram of a vehicle suspension adjustment device provided in an embodiment of the present application. Figure 4 As shown, the adjusting device of the vehicle suspension includes:

[0124] Acquisition module 401, real-time acquisition of the vehicle's current driving status information;

[0125] A first determining module 402 determines, based on current driving state information, a first displacement power spectrum density corresponding to the road roughness of a current driving road and a first road category of the current driving road;

[0126] The second determining module 403 determines a second road category of the current driving road based on the current driving state information and the historical driving state information; the historical driving state information is the driving state information of the preset road section length closest to the current vehicle position updated in real time;

[0127] A third determining module 404 determines a target displacement power spectrum density of the current driving road based on the first displacement power spectrum density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information;

[0128] The adjustment module 405 controls and adjusts the stiffness of the suspension based on the target displacement power spectrum density of the current driving road.

[0129] In some embodiments, the first determination module 402 is used to perform a fast Fourier transform on the current driving state information to obtain a corresponding first fast Fourier transform result; perform a power spectrum density calculation on the first fast Fourier transform result to obtain a first displacement power spectrum density of the current driving road; and determine the first road category of the current driving road based on the first displacement power spectrum density and the correspondence between the displacement power spectrum density and the road category.

[0130] In some embodiments, the second determination module 403 is used to cluster the historical driving status information to obtain the centroid data of each type of corresponding historical driving status information; based on the current driving status information and the centroid data of each type of corresponding historical driving status information, determine the second road category of the current driving road.

[0131] In some embodiments, the third determination module 404 is used to determine the first displacement power spectrum density as the target displacement power spectrum density of the current driving road when the first road category and the second road category of the current driving road are the same; and to determine the target displacement power spectrum density of the current driving road based on the current driving state information and the historical driving state information when the first road category of the current driving road and the second road category of the current driving road are different.

[0132] In some embodiments, the third determination module 404 is used to calculate the road roughness of the current driving road based on the current driving state information and the historical driving state information to obtain the second displacement power spectrum density of the current driving road; and determine the second displacement power spectrum density of the current driving road as the target displacement power spectrum density of the current driving road.

[0133] In some embodiments, the third determination module 404 is used to cluster historical driving status information and determine the initial probability distribution corresponding to each category obtained by clustering; adjust the initial probability distribution corresponding to each category according to the current driving status information to obtain the posterior probability distribution corresponding to each category; when the change amplitude of the posterior probability distribution within a preset time length is less than the preset amplitude, determine the third road category of the current driving road according to the posterior probability distribution; determine the centroid data of the driving status information corresponding to the third road category according to the driving status information corresponding to the third road category; perform fast Fourier transform on the centroid data to obtain the corresponding second fast Fourier transform result; perform power spectral density calculation on the second fast Fourier transform result to obtain the second displacement power spectral density of the current driving road.

[0134] In some embodiments, the adjustment module 405 is used to determine the expected stiffness of the current driving road based on the target displacement power spectrum density of the current driving road; determine the stiffness control instruction corresponding to the suspension based on the expected stiffness of the current driving road and the current stiffness of the suspension; and adjust the opening of the solenoid valve of the suspension based on the stiffness control instruction to adjust the stiffness of the suspension.

[0135] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0136] Figure 5 Schematic diagram of the electronic device 5 provided in the embodiment of the present application. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable by the processor 501. When the processor 501 executes the computer program 503, the steps of the above-mentioned method embodiments are implemented. Alternatively, when the processor 501 executes the computer program 503, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0137] The electronic device 5 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 5 may include but is not limited to a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 5This is merely an example of the electronic device 5 and does not limit the electronic device 5 . The electronic device 5 may include more or fewer components than shown in the figure, or different components.

[0138] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0139] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. The memory 502 can also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0140] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0142] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for adjusting a vehicle suspension, characterized in that: include: Get the vehicle's current driving status information in real time; determining, based on the current driving state information, a first displacement power spectrum density corresponding to the road surface roughness of the current driving road and a first road category of the current driving road; determining a second road category of the current driving road based on the current driving state information and historical driving state information; the historical driving state information is driving state information of a preset road section length closest to the current vehicle position that is updated in real time; determining a target displacement power spectral density of the current driving road based on the first displacement power spectral density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information; Based on the target displacement power spectrum density of the current driving road, the stiffness of the suspension is controlled and adjusted.

2. The method according to claim 1, characterized in that The determining, based on the current driving state information, a first displacement power spectrum density corresponding to the road surface roughness of the current driving road and a first road category of the current driving road comprises: Performing a fast Fourier transform on the current driving state information to obtain a corresponding first fast Fourier transform result; performing power spectral density calculation on the first fast Fourier transform result to obtain a first displacement power spectral density of the current driving road; A first road category of the current driving road is determined based on the first displacement power spectrum density and a correspondence between the displacement power spectrum density and the road category.

3. The method according to claim 1, characterized in that The determining the second road category of the current driving road based on the current driving state information and the historical driving state information includes: Clustering the historical driving state information to obtain centroid data of each type of historical driving state information; A second road category of the current driving road is determined based on the current driving state information and centroid data of the various types of corresponding historical driving state information.

4. The method according to claim 1, wherein The determining, based on the first displacement power spectrum density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information, a target displacement power spectrum density of the current driving road includes: When the first road category and the second road category of the current driving road are the same, determining the first displacement power spectral density as a target displacement power spectral density of the current driving road; In a case where the first road category of the current driving road and the second road category of the current driving road are different, a target displacement power spectrum density of the current driving road is determined based on the current driving state information and the historical driving state information.

5. The method according to claim 4, characterized in that The determining the target displacement power spectrum density of the current driving road based on the current driving state information and the historical driving state information includes: Calculating the road roughness of the current driving road based on the current driving state information and the historical driving state information to obtain a second displacement power spectrum density of the current driving road; The second displacement power spectrum density of the current driving road is determined as the target displacement power spectrum density of the current driving road.

6. The method according to claim 5, characterized in that The calculating the road roughness of the current driving road based on the current driving state information and the historical driving state information to obtain a second displacement power spectrum density of the current driving road includes: Clustering the historical driving state information to determine an initial probability distribution corresponding to each clustered category; Adjusting the initial probability distribution corresponding to each category according to the current driving state information to obtain the posterior probability distribution corresponding to each category; When the change amplitude of the posterior probability distribution within the preset time period is less than the preset amplitude, determining the third road category of the current travel road according to the posterior probability distribution; determining centroid data of the driving state information corresponding to the third road category according to the driving state information corresponding to the third road category; Performing a fast Fourier transform on the centroid data to obtain a corresponding second fast Fourier transform result; Perform power spectrum density calculation on the second fast Fourier transform result to obtain a second displacement power spectrum density of the current driving road.

7. The method according to any one of claims 1 to 6, characterized in that The controlling and adjusting the stiffness of the suspension based on the target displacement power spectrum density of the current driving road includes: determining a desired stiffness of the current driving road based on a target displacement power spectrum density of the current driving road; determining a stiffness control instruction corresponding to the suspension based on the expected stiffness of the current driving road and the current stiffness of the suspension; The opening of the solenoid valve of the suspension is adjusted based on the stiffness control instruction to adjust the stiffness of the suspension.

8. A vehicle suspension adjustment device, characterized in that: include: Acquisition module, real-time acquisition of the vehicle's current driving status information; a first determining module, configured to determine, based on the current driving state information, a first displacement power spectrum density corresponding to the road surface roughness of the current driving road and a first road category of the current driving road; a second determining module, configured to determine a second road category of a current driving road based on the current driving state information and the historical driving state information; The historical driving status information is the driving status information of the preset road section length closest to the current vehicle position updated in real time; a third determining module, determining a target displacement power spectral density of the current driving road based on the first displacement power spectral density, the first road category of the current driving road, the second road category of the current driving road, the current driving state information, and the historical driving state information; The adjustment module controls and adjusts the stiffness of the suspension based on the target displacement power spectrum density of the current driving road.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images