Road feeling simulation method and device for steer-by-wire vehicle, storage medium and electronic device

Abstract

The invention provides a road feeling simulation method and device for a steer-by-wire vehicle, a storage medium and an electronic device.The method comprises the steps that historical driving style data of the steer-by-wire vehicle are obtained, and the actual hand torque of a driver of the steer-by-wire vehicle is collected; the gain coefficient of the driver is calculated according to the historical driving style data, and the gain coefficient is used for representing the degree of clearness expected by the driver to the road feeling; obtaining a theoretical road feeling feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feeling feedback hand torque and the gain coefficient; and performing road feeling simulation on the steer-by-wire vehicle by adopting the expected hand torque and the actual hand torque. According to the embodiment of the invention, the technical problem that a steer-by-wire vehicle cannot transmit the road feeling according to the habit of a driver in the prior art is solved, the situation that the driver loses judgment on the dynamic state of the vehicle is avoided, the driving safety risk is reduced, and the driving safety, the control experience and the system stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicles, and in particular, to a road feel simulation method and device, a storage medium, and an electronic device for a steer-by-wire vehicle. Background Art

[0002] In related technologies, a steer-by-wire system is a system that transmits steering commands to a steering gear through electrical signals to achieve steering control of a vehicle. This system cancels the mechanical connection device between the steering wheel and the steering wheel, getting rid of various limitations of the traditional steering system. On the one hand, it reduces the complexity of the steering system, and on the other hand, it greatly improves the responsiveness of the system. At the same time, because the intermediate shaft is cancelled and there is no direct force feedback to the driver, in order to simulate the feel of the traditional steering system, a motor needs to be used to simulate the road feel.

[0003] In related technologies, road feel feedback is mainly divided into two types: one is to simulate according to a corresponding dynamic model or install corresponding sensors on the steering actuator of the steer-by-wire system to measure and obtain the road feel of the real vehicle with rack force feedback in real time, and the other is to obtain the road feel feedback torque based on experience and a simple linear dynamic model. Due to problems such as difficult installation of sensors, increased costs, inaccurate estimation of rack force, and difficult establishment of models in the current road feel feedback, the road feel feedback is not ideal, affecting the driver's judgment of road conditions and the safety of vehicle driving.

[0004] In view of the above problems existing in related technologies, no efficient and accurate solution has been found yet. Summary of the Invention

[0005] The present invention provides a road feel simulation method and device, a storage medium, and an electronic device for a steer-by-wire vehicle to solve the technical problems in related technologies.

[0006] According to an embodiment of the present invention, a road feel simulation method for a steer-by-wire vehicle is provided, including: obtaining historical driving style data of the steer-by-wire vehicle and collecting the actual hand torque of the driver of the steer-by-wire vehicle; calculating a gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity degree that the driver expects for the road feel; obtaining the theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; calculating the motor torque through closed-loop calculation by a PID controller according to the expected hand torque and the actual hand torque; and calculating the high-frequency road feel compensation torque of the vehicle; adding the motor torque and the high-frequency road feel compensation torque and performing road feel simulation on the steer-by-wire vehicle.

[0007] Optionally, calculating a gain coefficient of the driver based on the historical driving style data includes: identifying a driving habit type of the driver according to the historical driving style data; calculating a gain coefficient of the driving habit type.

[0008] Optionally, identifying a driving habit type of the driver according to the historical driving style data includes: preprocessing the historical driving style data to obtain feature parameters in a preset distribution format; selecting a target posterior probability that best matches the feature parameters from a pre-constructed set of posterior probabilities, where the pre-constructed set of posterior probabilities includes multiple posterior probabilities, and each posterior probability corresponds to a driving habit type; determining the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

[0009] Optionally, before selecting a target posterior probability that best matches the feature parameters from a pre-constructed set of posterior probabilities, the method further includes: obtaining a plurality of sample data; randomly initializing model parameters of a Gaussian mixture model, where the number of Gaussian distributions of the Gaussian mixture model is the same as the number of driving habit types; iteratively performing the following steps until the model parameters of the Gaussian mixture model meet a preset condition: calculating posterior probabilities of each sample data in the plurality of sample data belonging to each cluster in the Gaussian mixture model to obtain a probability set of all sample data; updating the model parameters of the Gaussian mixture model based on the probability set; extracting the model parameters of the Gaussian mixture model after updating; after the Gaussian mixture model is optimized, outputting the set of posterior probabilities.

[0010] Optionally, calculating a gain coefficient of the driving habit type includes: obtaining a target posterior probability of the driving habit type; calculating the gain coefficient of the driving habit type using the following formula : ; where is a gain reference coefficient corresponding to the driving habit type of the driver ; is the target posterior probability corresponding to the driving habit type of the driver of

[0011] Optionally, obtaining a theoretical road feel feedback hand torque of the vehicle includes: obtaining a rack force signal and a vehicle speed of the vehicle in real time, where the rack force signal is used to represent a force feedback of a road surface load transmitted to a steering wheel; looking up a theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

[0012] Optionally, calculating the desired hand torque according to the theoretical road feel feedback hand torque and the gain coefficient includes: obtaining a set of compensation torques of the vehicle, and superimposing the set of compensation torques and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; multiplying the intermediate hand torque by the gain coefficient to obtain the desired hand torque.

[0013] Optionally, obtaining the set of compensation torques of the vehicle includes: collecting a first compensation torque output by a return torque module of the vehicle, collecting a second compensation torque output by a damping compensation module of the vehicle, and collecting a third compensation torque output by a friction compensation module of the vehicle; determining the first compensation torque, the second compensation torque, and the third compensation torque as the set of compensation torques.

[0014] Optionally, calculating the motor torque through closed-loop calculation by the PID controller according to the desired hand torque and the actual hand torque includes: calculating the motor torque using the following formula : ; where t is the closed-loop period, , represents the desired hand torque, is the actual hand torque, , , are the proportional, integral, and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the angular velocity of the steering wheel, is the angular acceleration of the steering wheel, and a, b, and c are constants.

[0015] Optionally, calculating the high-frequency road feel compensation torque of the vehicle includes: obtaining the rack force signal of the vehicle in real time, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; performing high-pass filtering on the rack force signal to extract the high-frequency road surface excitation component; determining the high-frequency road surface excitation component as the high-frequency road feel compensation torque of the vehicle.

[0016] According to another embodiment of the present invention, a road feel simulation device for a steer-by-wire vehicle is provided, including: an acquisition module, configured to obtain historical driving style data of the steer-by-wire vehicle and acquire the actual hand torque of the driver of the steer-by-wire vehicle; a first calculation module, configured to calculate a gain coefficient of the driver according to the historical driving style data, wherein the gain coefficient is used to characterize the clarity degree of the road feel expected by the driver; a second calculation module, configured to obtain the theoretical road feel feedback hand torque of the vehicle, and calculate an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; a simulation module, including: a calculation unit, configured to perform closed-loop calculation of the motor torque according to the expected hand torque and the actual hand torque through the PID controller, and calculate the high-frequency road feel compensation torque of the vehicle; a simulation unit, configured to add the motor torque and the high-frequency road feel compensation torque and perform road feel simulation on the steer-by-wire vehicle.

[0017] Optionally, the first calculation module includes: an identification unit, configured to identify the driving habit type of the driver according to the historical driving style data; a calculation unit, configured to calculate the gain coefficient of the driving habit type.

[0018] Optionally, the identification unit includes: a processing sub-unit, configured to preprocess the historical driving style data to obtain characteristic parameters in a preset distribution format; a selection sub-unit, configured to select a target posterior probability that best matches the characteristic parameters from a pre-constructed set of posterior probabilities, wherein the pre-constructed set of posterior probabilities includes multiple posterior probabilities, and each posterior probability corresponds to a driving habit type; a determination sub-unit, configured to determine the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

[0019] Optionally, the device further includes: an acquisition module, configured to obtain a plurality of sample data before the first calculation module selects a target posterior probability that best matches the characteristic parameters from a pre-constructed set of posterior probabilities; an initialization module, configured to randomly initialize the model parameters of the Gaussian mixture model, wherein the number of Gaussian distributions of the Gaussian mixture model is the same as the number of driving habit types; an iteration module, configured to iteratively execute the following steps until the model parameters of the Gaussian mixture model meet the preset conditions: calculate the posterior probability of each sample data in the plurality of sample data belonging to each cluster in the Gaussian mixture model to obtain a probability set of all sample data; update the model parameters of the Gaussian mixture model based on the probability set; extract the model parameters of the Gaussian mixture model after update; an output module, configured to output the set of posterior probabilities after the Gaussian mixture model is optimized.

[0020] Optionally, the calculation unit includes: an acquisition subunit, configured to acquire a target posterior probability of the driving habit type; a calculation subunit, configured to calculate a gain coefficient of the driving habit type by using the following formula : ; where is the driving habit type of the driver corresponding gain reference coefficient, is the driving habit type of the driver corresponding target posterior probability.

[0021] Optionally, the second calculation module includes: a first acquisition unit, configured to acquire a rack force signal and a vehicle speed of the vehicle in real time, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; a lookup unit, configured to look up a theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

[0022] Optionally, the second calculation module includes: a second acquisition unit, configured to acquire a compensation torque set of the vehicle, and superimpose the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; an operation unit, configured to multiply the intermediate hand torque by the gain coefficient to obtain an expected hand torque.

[0023] Optionally, the second acquisition unit includes: a collection subunit, configured to collect a first compensation torque output by a return torque module of the vehicle, collect a second compensation torque output by a damping compensation module of the vehicle, and collect a third compensation torque output by a friction compensation module of the vehicle; a determination subunit, configured to determine the first compensation torque, the second compensation torque, and the third compensation torque as the compensation torque set.

[0024] Optionally, the calculation unit includes: a calculation subunit, configured to calculate a motor torque by using the following formula : ; where t is a closed-loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral, and differential gains of the PID controller respectively, is a feedforward control term, , is the gain coefficient, is the angular velocity of the steering wheel, is the angular acceleration of the steering wheel, and a, b, and c are constants.

[0025] Optionally, the calculation unit includes: an acquisition subunit, configured to acquire in real time a rack force signal of the vehicle, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; a filtering subunit, configured to perform high-pass filtering on the rack force signal to extract a high-frequency road surface excitation component; and a determination subunit, configured to determine the high-frequency road surface excitation component as the high-frequency road feel compensation torque of the vehicle.

[0026] According to another aspect of the embodiments of the present application, there is also provided a storage medium, which includes a stored program, and when the program runs, it executes the above steps.

[0027] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; where: the memory is used to store a computer program; the processor is configured to execute the steps in the above method by running the program stored on the memory.

[0028] The embodiments of the present application also provide a computer program product containing instructions, which when running on a computer, causes the computer to execute the steps in the above method.

[0029] Advantages of the present invention: 1. Accurately identify driving habits and provide personalized road feel feedback for different drivers; 2. Utilize the posterior probability of the model algorithm to calculate in real time the gain coefficient corresponding to the driving style, and realize driver-friendly hand torque adjustment; 3. Adopt a Gaussian mixture model for clustering analysis and parameter optimization, with good adaptability to noise and abnormal data, making the steer-by-wire more stable in complex road conditions and extreme scenarios; 4. Support long-term optimization, perform long-term tracking analysis on changes in driver habits through probability modeling, and facilitate system learning and adaptive adjustment. Description of the Drawings

[0030] The drawings described herein are used to provide a further understanding of the present invention, form a part of the present application, and the illustrative embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 is a hardware structure block diagram of an automobile according to an embodiment of the present invention; Figure 2 is a flowchart of a road feel simulation method for a steer-by-wire vehicle according to an embodiment of the present invention; Figure 3 is a schematic diagram of identifying driving habit types in an embodiment of the present invention; Figure 4It is the schematic diagram of clustering analysis in the embodiments of the present invention; Figure 5 It is the mapping curve graph of the rack force and the theoretical road feel feedback hand torque under a certain vehicle speed condition in the embodiments of the present invention; Figure 6 It is the schematic diagram of calculating the expected hand torque in the embodiments of the present invention; Figure 7 It is the schematic diagram of road feel simulation based on closed-loop control in the embodiments of the present invention; Figure 8 It is the schematic diagram of the road feel simulation system in the embodiments of the present invention; Figure 9 It is the structural block diagram of a road feel simulation device for a steer-by-wire vehicle according to an embodiment of the present invention. Detailed implementation manners

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1 The method embodiment provided in the first embodiment of the present application can be executed in an automobile, a server, a processor, a controller or a similar processing device. Taking running on an automobile as an example, Figure 1 It is the hardware structural block diagram of an automobile in the embodiments of the present invention. As Figure 1 shown, the automobile may include one or more ( Figure 1Only one processor 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a field programmable gate array FPGA) and a memory 104 for storing data are shown. Optionally, the above-mentioned vehicle may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 The structure shown is only schematic and does not limit the structure of the above-mentioned vehicle. For example, the vehicle may further include more or fewer components than Figure 1 shown in, or have a different configuration from Figure 1 that shown.

[0034] The memory 104 can be used to store vehicle programs. For example, software programs and modules of application software, such as the vehicle program corresponding to the road feel simulation method of a steer-by-wire vehicle in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the vehicle program stored in the memory 104, that is, the above-mentioned method is implemented. The memory 104 may include a high-speed random access memory and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely provided with respect to the processor 102, and these remote memories can be connected to the vehicle through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by a communication provider of the vehicle. In one instance, the transmission device 106 includes a network interface controller (NIC for short), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (RF for short) module, which is used to communicate with the Internet wirelessly.

[0036] In this embodiment, a road feel simulation method for a steer-by-wire vehicle is provided. Figure 2 is a flowchart of a road feel simulation method for a steer-by-wire vehicle according to an embodiment of the present invention, as shown in Figure 2 shown, and the process includes the following steps: Step S200, obtaining historical driving style data of the steer-by-wire vehicle and collecting the actual hand torque of the driver of the steer-by-wire vehicle; The actual hand torque in this embodiment is the real-time torque of the hand force applied by the driver to the steering wheel collected by the steering wheel sensor when the driver of the steer-by-wire vehicle operates the steering wheel.

[0037] Optionally, the historical driving style data is the driving data of the steer-by-wire vehicle that reflects the driver's driving style collected before simulating the road feel of the actual hand torque collected at the current time, including vehicle speed, actual hand torque, and ideal road feel torque, and calculates the ideal road feel torque difference between the maximum ideal road feel torque and the minimum ideal road feel torque within the historical cycle time. The historical driving style data includes historical average vehicle speed, historical average actual hand torque, and ideal road feel torque difference.

[0038] Step S201, calculate the gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity degree that the driver expects for the road feel; Different drivers have different requirements for road feel. Some drivers like clear road feel, while some don't. For example, drivers who prefer economic driving (concerned about the power consumption of driving or the fuel economy) expect a smaller clarity degree for the road feel, drivers who prefer comfortable driving (concerned about driving comfort) expect a moderate clarity degree for the road feel, and drivers who prefer sporty driving (concerned about driving experience) expect a larger clarity degree for the road feel.

[0039] The road feel feedback in this embodiment is the ideal road feel simulated by the motor. By providing different road feel feedbacks for different drivers, an adaptive driver habit-based steer-by-wire road feel feedback control method is designed.

[0040] Step S202, obtain the theoretical road feel feedback hand torque of the vehicle, and calculate the expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; Step S203, calculate the motor torque by closed-loop calculation of the PID (Proportional-Integral-Derivative) controller according to the expected hand torque and the actual hand torque; and calculate the high-frequency road feel compensation torque of the vehicle; Step S204, add the motor torque and the high-frequency road feel compensation torque and perform road feel simulation on the steer-by-wire vehicle; Apply the total torque obtained by adding the motor torque and the high-frequency road feel compensation torque to the motor of the steering wheel of the steer-by-wire vehicle in the reverse direction, so as to achieve road feel simulation.

[0041] Through the above steps, historical driving style data of a steer-by-wire vehicle is obtained, and the actual hand torque of the driver of the steer-by-wire vehicle is collected; a gain coefficient of the driver is calculated according to the historical driving style data, where the gain coefficient is used to characterize the clarity degree of the road feel expected by the driver; a theoretical road feel feedback hand torque of the vehicle is obtained, and an expected hand torque is calculated according to the theoretical road feel feedback hand torque and the gain coefficient; a motor torque is calculated through closed-loop calculation by a PID controller according to the expected hand torque and the actual hand torque; and a high-frequency road feel compensation torque of the vehicle is calculated; after adding the motor torque and the high-frequency road feel compensation torque, road feel simulation is performed on the steer-by-wire vehicle, and different road feel feedbacks can be provided to different drivers, so as to adapt to the driver's habits during road feel feedback control of the steer-by-wire vehicle, solve the technical problem that the steer-by-wire vehicle in the related art cannot transmit road feel according to the driver's habits, avoid the driver losing the judgment on the vehicle dynamics, reduce the driving safety risk, and improve the driving safety, handling experience and system stability.

[0042] In an implementation manner of this embodiment, calculating the gain coefficient of the driver according to the historical driving style data includes: identifying the driving habit type of the driver according to the historical driving style data, where the driving habit type includes one of the following: economy, comfort, and sport; calculating the gain coefficient of the driving habit type.

[0043] In an example, identifying the driving habit type of the driver according to the historical driving style data includes: preprocessing the historical driving style data to obtain characteristic parameters in a preset distribution format; selecting a target posterior probability that best matches the characteristic parameters from a pre-constructed set of posterior probabilities, where the pre-constructed set of posterior probabilities includes a plurality of posterior probabilities, and each posterior probability corresponds to a driving habit type; determining the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

[0044] In an example, calculating the gain coefficient of the driving habit type includes: obtaining the target posterior probability of the driving habit type; calculating the gain coefficient of the driving habit type by using the following formula : ; where is the gain reference coefficient corresponding to the driving habit type of the driver and is the target posterior probability corresponding to the driving habit type of the driver.

[0045] When the driving habit identification module identifies the driving habit type, by analyzing and identifying the driving habit of the current driver, the category with the highest probability is selected as the driving habit of the current driver. Figure 3This is the schematic diagram for identifying driving habit types in an embodiment of the present invention. The process includes: Perform data preprocessing on the collected driving data of the current driver to make its data distribution consistent with that of the training model; After selecting the feature parameters, calculate the features of the current driver The posterior probability belonging to each category: ; is the mixing coefficient of the k-th category, is the probability density of the K-th Gaussian distribution; According to the posterior probability, select the category with the largest posterior probability as the habit of the current driver for classification: ; Calculate the gain coefficient by weighted averaging the driving habit types of the current driver through the posterior probability: ; Among them, G is the calculated comprehensive gain coefficient of the current driver, is the gain reference coefficient corresponding to the k-th type of driving habit, and K (capital letter) is the total number of driving habits.

[0046] In this embodiment, before selecting the target posterior probability that best matches the feature parameters from the pre-constructed posterior probability set, it further includes: obtaining a plurality of sample data; randomly initializing the model parameters of the Gaussian mixture model, where the number of Gaussian distributions of the Gaussian mixture model is the same as the number of driving habit types; iteratively execute the following steps until the model parameters of the Gaussian mixture model meet the preset conditions: calculate the posterior probability that each sample data in the plurality of sample data belongs to each cluster in the Gaussian mixture model to obtain a probability set of all sample data; update the model parameters of the Gaussian mixture model based on the probability set; extract the model parameters of the Gaussian mixture model after updating; after the Gaussian mixture model is optimized, output the posterior probability set.

[0047] Optionally, updating the model parameters of the Gaussian mixture model based on the probability set includes: updating the mean of the Gaussian mixture model using the following formula : ; update the covariance matrix of the Gaussian mixture model using the following formula : ; update the mixing coefficient of the Gaussian mixture model using the following formula ; where N is the total number of samples of the sample data, is the sample belonging to the cluster probability, is and the sample The product of the vector values of the sample data, where the model parameters include the mean , covariance matrix , mixing coefficient , satisfying .

[0048] Figure 4 Figure is the schematic diagram of the clustering analysis in the embodiment of the present invention. The clustering analysis module uses the Gaussian mixture model algorithm to cluster analyze the collected road feeling feedback torque, and divides the driver's habits into three types: economy, comfort, and sport, denoted as z1, z2, and z3. Compared with the K-Means algorithm, the Gaussian mixture model has a more flexible cluster shape processing and provides soft classification. The K-Means algorithm can only hard-classify the driver into a certain driving style, ignoring the ambiguity of the style, while the Gaussian mixture model algorithm realizes soft classification through the posterior probability, which can quantify the possibility of the driver belonging to each style and is convenient for personalized adjustment. At the same time, the Gaussian mixture model algorithm has stronger robustness and is suitable for the processing of complex data. The process of clustering analysis includes: First, preprocess the driving data of different drivers collected, remove missing values and outliers, and perform standardization processing to ensure that different features are on the same scale; Select the difference between the maximum ideal road feeling torque and the minimum ideal road feeling torque as the characteristic parameter used in the clustering analysis formula; Initialize the Gaussian mixture model, determine the number of Gaussian distributions K = 3, and randomly initialize the parameters of each Gaussian distribution, the mean , covariance matrix , mixing coefficient , satisfying ; In the Expectation step (E step), calculate the posterior probability of each sample belonging to which cluster : ; Among them, is the probability that the sample belongs to the cluster , is the probability density function of the k-th Gaussian distribution. This function represents the probability density of a certain d-dimensional vector appearing under the condition of the given mean vector and covariance matrix , is the input sample to be evaluated, represents the dimension of the input sample.

[0049] ; After obtaining the probabilities, update the model parameters of the previous round in the maximization step (M-step), and update the mean value , update the covariance matrix , update the mixing coefficients , where N is the total number of samples; Repeat the E-step and the M-step until the log-likelihood function converges, , ; According to the posterior probability , divide each sample into the cluster with the highest probability, .

[0050] In an implementation manner of this embodiment, obtaining the theoretical road feel feedback hand torque of the vehicle includes: obtaining the rack force signal and the vehicle speed of the vehicle in real time, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; looking up the theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed in the preset mapping table of the vehicle.

[0051] Perform low-pass filtering on the original rack force to eliminate high-frequency noise (such as motor vibration, road surface impact noise), and the filtering transfer function is: , where T is 0.001 s, and the filtered output is the smoothed rack force , based on the preset rack force - theoretical road feel feedback hand torque mapping curve, Figure 5 is the mapping curve of the rack force and the theoretical road feel feedback hand torque under a certain vehicle speed condition in the embodiment of the present invention. The two-dimensional linear interpolation method is used to calculate the theoretical road feel feedback hand torque corresponding to the current rack force: , where the mapping curve is generated according to the experimental calibration data and satisfies the non-linear characteristics of high gradient (enhanced road feel) at low speeds and low gradient (suppressed interference) at high speeds.

[0052] In an example, calculating the desired hand torque according to the theoretical road feel feedback hand torque and the gain coefficient includes: obtaining the compensation torque set of the vehicle, and superimposing the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; multiplying the intermediate hand torque by the gain coefficient to obtain the desired hand torque.

[0053] Optionally, the compensation torque includes: the compensation torque set output by other functional modules (such as the return-to-center torque module, the damping compensation module, the friction compensation module, etc.) .

[0054] In this embodiment, obtaining the compensation torque set of the vehicle includes: collecting a first compensation torque output by the return torque module of the vehicle, collecting a second compensation torque output by the damping compensation module of the vehicle, and collecting a third compensation torque output by the friction compensation module of the vehicle; determining the first compensation torque, the second compensation torque, and the third compensation torque as the compensation torque set.

[0055] Figure 6 FIG. 4 is a schematic diagram of calculating the expected hand torque in an embodiment of the present invention. In the road feel feedback control of the steer-by-wire system, the calculation algorithm of the expected hand torque is used as the core algorithm. Through multi-source torque fusion and driver personalized gain adjustment, an expected hand torque that conforms to the current driving scenario and driver characteristics is generated. The calculation process of the expected hand torque includes: the input signals include the rack force: the rack axial force calculated in real time by the steering actuator (such as a steering motor) , which represents the force feedback of the road surface load transmitted to the steering wheel; the compensation torque: the set of compensation torques output by other functional modules (such as the return torque module, the damping compensation module, the friction compensation module, etc.) ; the comprehensive gain coefficient: the personalized gain coefficient output based on the driver style recognition module ; taking the compensation torques calculated and output by each functional module as inputs and superimposing them on the theoretical road feel feedback hand torque as the expected hand torque; multiplying the calculated current driver comprehensive gain coefficient by the expected hand torque and outputting it.

[0056] In one example, calculating the motor torque by the PID controller in a closed loop according to the expected hand torque and the actual hand torque includes: calculating the motor torque using the following formula : ; where t is the closed-loop period, , represents the expected hand torque, is the actual hand torque, , , are respectively the proportional, integral, and differential gains of the PID controller, is the feedforward control term, , is the gain coefficient, is the angular velocity of the steering wheel, is the angular acceleration of the steering wheel, and a, b, and c are constants.

[0057] Figure 7 FIG. 5 is a schematic diagram of road feel simulation based on closed-loop control in an embodiment of the present invention. The closed-loop control module obtains the closed-loop motor torque through a feedforward PID of the expected hand torque and the driver hand torque collected in real time , The high-frequency road feel compensation torque obtained through rack force calculation is added to obtain the motor torque finally output to the motor, In one example, calculating the high-frequency road feel compensation torque of the vehicle includes: obtaining the rack force signal of the vehicle in real time, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; performing high-pass filtering on the rack force signal to extract the high-frequency road surface excitation component; and determining the high-frequency road surface excitation component as the high-frequency road feel compensation torque of the vehicle.

[0058] The high-frequency compensation torque calculation method is to perform high-pass filtering on the rack force signal (cutoff frequency 50 Hz) to extract the high-frequency road surface excitation component: ; where is the cutoff frequency, is the high-frequency gain coefficient.

[0059] The calculated is added to the high-frequency compensation torque to obtain the final road feel feedback torque output to the motor, realizing road feel simulation.

[0060] Adopting the solution of this embodiment, based on the anti-integral saturation strategy, when the error continuously exceeds the limit, the integral term is frozen to avoid output saturation and improve the dynamic response; adopting a parameter adaptive mechanism: the PID gain is dynamically adjusted with the vehicle speed, enhancing the proportional control at low speeds and strengthening the differential damping at high speeds; adopting a feedforward-feedback decoupling design: the feedforward term dominates the steady-state tracking, and the PID feedback suppresses the disturbance, and the two are complementary in the frequency domain (the feedforward compensates for the low frequency and the feedback suppresses the high frequency); adopting high-frequency compensation optimization: using phase lead correction to adjust the phase of to ensure matching with the motor response characteristics and avoid resonance. This module significantly improves the tracking accuracy of road feel feedback and the high-frequency detail restoration ability through feedforward-feedback collaborative control and high-frequency dynamic compensation, enhancing the driver's driving experience and the real-time performance and accuracy of dynamic feedback torque calculation.

[0061] The solution of this embodiment provides a road feel simulation control method for a steer-by-wire system that adapts to the driver's habits. Figure 8It is the schematic diagram of the road feeling simulation system in the embodiment of the present invention, including a clustering analysis module, a driver habit identification module, an expected hand torque calculation module, and a closed-loop control module; the clustering analysis module first collects the driving data of different drivers at different vehicle speeds, performs clustering analysis on the collected data, and after obtaining different driving habits, the driver habit identification module combines the driving data of the current driver to identify the current driver, obtains the driving habit of the current driver, brings the driving habit of the current driver into the classification obtained by clustering analysis to get a comprehensive gain coefficient, the expected hand torque calculation module calculates the expected hand torque suitable for the current driver by undertaking the compensation torques of other modules (such as return-to-center compensation torque, damping compensation torque), combining the rack force, vehicle speed, and comprehensive gain coefficient, etc., and the closed-loop control module combines the expected hand torque with the actual hand torque of the driver collected in real time, and through a feedforward PID, combines parameters such as vehicle speed, upper rotation motor speed, and high-frequency compensation torque to obtain a closed-loop motor torque, and adds it to the high-frequency road feeling compensation torque obtained by the rack force to obtain the final motor control torque output to the motor, and transmits it to the HWA road feeling simulation motor to complete the road feeling simulation.

[0062] The clustering analysis module offline collects the driving data of different drivers at different vehicle speeds, takes the collected road feeling feedback torque as the ideal road feeling feedback torque of the driver at the current vehicle speed, performs clustering analysis on the collected driving data, and divides the driver habits into three types: economy, comfort, and sport; The driver habit identification module identifies the driving data of the current driver and obtains the gain coefficient corresponding to the driving habit of the current driver; The expected hand torque calculation module obtains the target hand torque by looking up the table of vehicle speed through the rack force signal estimated by the downward rotation, converts the outputs of modules such as the return-to-center module and the damping module into hand torque and adds them to the target hand torque, and substitutes the gain coefficient of the driver habit into this step to obtain the final expected hand torque; The closed-loop control module obtains a closed-loop motor torque through a feedforward PID by combining the expected hand torque with the hand torque of the driver collected in real time, and adds it to the high-frequency road feeling compensation torque obtained by the rack force to obtain the final motor torque output to the motor, and transmits it to the HWA road feeling simulation motor to complete the road feeling simulation; This embodiment can also dynamically update the model of the target posterior probability according to the long-term behavior data of the driver to ensure the accuracy and personalization of habit determination.

[0063] Adopting the road feeling simulation control method of the steer-by-wire system with adaptive driver habits in this embodiment can solve the problem of the lack of road feeling transmission in steer-by-wire that exists in traditional steering, avoid the driver losing the judgment of the vehicle dynamics, reduce the driving safety risk, and improve the driving safety, handling experience, and system stability.

[0064] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0065] Embodiment 2 In this embodiment, a road feeling simulation device for a steer-by-wire vehicle is further provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0066] Figure 9 is a structural block diagram of a road feeling simulation device for a steer-by-wire vehicle according to an embodiment of the present invention. As Figure 9 shown, the device includes: An acquisition module 91, configured to obtain historical driving style data of the steer-by-wire vehicle and collect the actual hand torque of the driver of the steer-by-wire vehicle; A first calculation module 92, configured to calculate a gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity degree of the road feeling expected by the driver; A second calculation module 93, configured to obtain the theoretical road feeling feedback hand torque of the vehicle, and calculate an expected hand torque according to the theoretical road feeling feedback hand torque and the gain coefficient; A simulation module 94, including: a calculation unit, configured to calculate the motor torque through closed-loop calculation by the PID controller according to the expected hand torque and the actual hand torque; and calculate the high-frequency road feeling compensation torque of the vehicle; a simulation unit, configured to add the motor torque and the high-frequency road feeling compensation torque and perform road feeling simulation on the steer-by-wire vehicle.

[0067] Optionally, the first calculation module includes: an identification unit, configured to identify the driving habit type of the driver according to the historical driving style data; a calculation unit, configured to calculate the gain coefficient of the driving habit type.

[0068] Optionally, the recognition unit includes: a processing subunit, configured to preprocess the historical driving style data to obtain feature parameters in a preset distribution format; a selection subunit, configured to select, from a pre-constructed set of posterior probabilities, a target posterior probability that best matches the feature parameters, where the pre-constructed set of posterior probabilities includes a plurality of posterior probabilities, and each posterior probability corresponds to a driving habit type; a determination subunit, configured to determine the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

[0069] Optionally, the apparatus further includes: an acquisition module, configured to acquire a plurality of sample data before the first calculation module selects, from a pre-constructed set of posterior probabilities, a target posterior probability that best matches the feature parameters; an initialization module, configured to randomly initialize the model parameters of a Gaussian mixture model, where the number of Gaussian distributions of the Gaussian mixture model is the same as the number of driving habit types. An iteration module, configured to iteratively execute the following steps until the model parameters of the Gaussian mixture model meet preset conditions: calculate, in the Gaussian mixture model, the posterior probability that each sample data in the plurality of sample data belongs to each cluster, to obtain a probability set of all sample data; update the model parameters of the Gaussian mixture model based on the probability set; extract the model parameters of the Gaussian mixture model after the update; an output module, configured to output the set of posterior probabilities after the Gaussian mixture model is optimized.

[0070] Optionally, the calculation unit includes: an acquisition subunit, configured to acquire the target posterior probability of the driving habit type; a calculation subunit, configured to calculate the gain coefficient of the driving habit type using the following formula : ; where is the gain reference coefficient corresponding to the driving habit type of the driver and is the target posterior probability corresponding to the driving habit type of the driver.

[0071] Optionally, the second calculation module includes: a first acquisition unit, configured to acquire, in real time, the rack force signal and the vehicle speed of the vehicle, where the rack force signal is used to represent the force feedback of the road surface load transmitted to the steering wheel; a search unit, configured to search, in a preset mapping table of the vehicle, for a theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed.

[0072] Optionally, the second calculation module includes: a second acquisition unit, configured to acquire a set of compensation torques of the vehicle, and superimpose the set of compensation torques and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; an operation unit, configured to multiply the intermediate hand torque by the gain coefficient to obtain an expected hand torque.

[0073] Optionally, the second acquisition unit includes: a collection subunit, configured to collect a first compensation torque output by the vehicle's return torque module, collect a second compensation torque output by the vehicle's damping compensation module, and collect a third compensation torque output by the vehicle's friction compensation module; a determination subunit, configured to determine the first compensation torque, the second compensation torque, and the third compensation torque as the set of compensation torques.

[0074] Optionally, the calculation unit includes: a calculation subunit, configured to calculate the motor torque using the following formula : ; where t is the closed-loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral, and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the angular velocity of the steering wheel, is the angular acceleration of the steering wheel, and a, b, and c are constants.

[0075] Optionally, the calculation unit includes: an acquisition subunit, configured to acquire the rack force signal of the vehicle in real time, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; a filtering subunit, configured to perform high-pass filtering on the rack force signal to extract the high-frequency road surface excitation component; a determination subunit, configured to determine the high-frequency road surface excitation component as the high-frequency road feel compensation torque of the vehicle.

[0076] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above-mentioned modules are all located in the same processor; or, the above-mentioned various modules are separately located in different processors in any combination form.

[0077] Embodiment 3 An embodiment of the present invention further provides a storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0078] Optionally, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S1. Obtain the historical driving style data of the steer-by-wire vehicle and collect the actual hand torque of the driver of the steer-by-wire vehicle; S2. Calculate the gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity of the road feel expected by the driver; S3. Obtain the theoretical road feel feedback hand torque of the vehicle, and calculate the expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; S4. Calculate the motor torque through closed-loop calculation by a PID controller according to the expected hand torque and the actual hand torque; and calculate the high-frequency road feel compensation torque of the vehicle; S5. Add the motor torque and the high-frequency road feel compensation torque and perform road feel simulation on the steer-by-wire vehicle.

[0079] Optionally, in this embodiment, the above storage medium may include but is not limited to: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0080] An embodiment of the present invention also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0081] Optionally, the above electronic device may further include a transmission device and an input / output device, where the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0082] Optionally, in this embodiment, the above processor may be configured to execute the following steps through a computer program: S1. Obtain the historical driving style data of the steer-by-wire vehicle and collect the actual hand torque of the driver of the steer-by-wire vehicle; S2. Calculate the gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity of the road feel expected by the driver; S3. Obtain the theoretical road feel feedback hand torque of the vehicle, and calculate the expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; S4. Calculate the motor torque through closed-loop calculation by a PID controller based on the desired hand torque and the actual hand torque; and calculate the high-frequency road feel compensation torque of the vehicle; S5. Add the motor torque and the high-frequency road feel compensation torque, and then perform road feel simulation on the steer-by-wire vehicle.

[0083] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated herein.

[0084] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by means of software plus a general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0086] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless otherwise clearly specified in the context, the singular forms "a", "an", and "the" as used herein may also include the plural forms. The terms "include", "comprise", "contain", and "have" are inclusive and thus specify the presence of the stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be executed in the specific order described or illustrated, unless the execution order is clearly specified. It should also be understood that alternative or additional steps can be used.

[0087] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A road feeling simulation method for a steer-by-wire vehicle, characterized in that, Including: Obtaining historical driving style data of a steer-by-wire vehicle and collecting the actual hand torque of the driver of the steer-by-wire vehicle; Calculating a gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity degree that the driver expects for road feel; Obtaining the theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; Calculating the motor torque through closed-loop calculation by a PID controller according to the expected hand torque and the actual hand torque; and calculating the high-frequency road feel compensation torque of the vehicle; Adding the motor torque and the high-frequency road feel compensation torque and performing road feel simulation on the steer-by-wire vehicle.

2. The method according to claim 1, wherein Obtaining the theoretical road feel feedback hand torque of the vehicle includes: Real-time obtaining the rack force signal and vehicle speed of the vehicle, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; Looking up the theoretical road feel feedback hand torque matching the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

3. The method according to claim 1, wherein Calculating the expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient includes: Obtaining a set of compensation torques of the vehicle, and superimposing the set of compensation torques and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; Multiplying the intermediate hand torque by the gain coefficient to obtain the expected hand torque.

4. The method according to claim 3, characterized in that, Obtaining the set of compensation torques of the vehicle includes: Collecting a first compensation torque output by a return-to-center torque module of the vehicle, collecting a second compensation torque output by a damping compensation module of the vehicle, and collecting a third compensation torque output by a friction compensation module of the vehicle; Determining the first compensation torque, the second compensation torque, and the third compensation torque as the set of compensation torques.

5. The method according to claim 1, wherein Calculating the motor torque through closed-loop calculation by a PID controller according to the expected hand torque and the actual hand torque includes: Calculate the motor torque using the following formula :[[]]END]] ； where t is the closed-loop period, , represents the desired hand torque, is the actual hand torque, , , are the proportional, integral, and derivative gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the angular velocity of the steering wheel, is the angular acceleration of the steering wheel, and a, b, c are constants.

6. The method according to claim 1, wherein Calculating the high-frequency road feel compensation torque of the vehicle includes: Real-time obtaining the rack force signal of the vehicle, where the rack force signal is used to characterize the force feedback of the road surface load transmitted to the steering wheel; Performing high-pass filtering on the rack force signal to extract a high-frequency road surface excitation component; Determining the high-frequency road surface excitation component as the high-frequency road feel compensation torque of the vehicle.

7. The method according to claim 1, characterized in that, Calculating the gain coefficient of the driver according to the historical driving style data includes: Identifying the driving habit type of the driver according to the historical driving style data; Calculating the gain coefficient of the driving habit type.

8. The method according to claim 7, characterized in that Identifying the driving habit type of the driver according to the historical driving style data includes: Performing preprocessing on the historical driving style data to obtain characteristic parameters in a preset distribution format; Selecting a target posterior probability that best matches the characteristic parameters from a pre-constructed set of posterior probabilities, where the pre-constructed set of posterior probabilities includes a plurality of posterior probabilities, and each posterior probability corresponds to a driving habit type; Determining the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

9. The method according to claim 8, characterized in that Before selecting the target posterior probability that best matches the characteristic parameters from the pre-constructed set of posterior probabilities, the method further includes: Obtaining a plurality of sample data; Randomly initialize the model parameters of the Gaussian mixture model, where the number of Gaussian distributions in the Gaussian mixture model is the same as the number of driving habit types; Iteratively execute the following steps until the model parameters of the Gaussian mixture model meet the preset conditions: Calculate the posterior probability that each sample data in the multiple sample data belongs to each cluster in the Gaussian mixture model to obtain the probability set of all sample data; Update the model parameters of the Gaussian mixture model based on the probability set; Extract the model parameters of the Gaussian mixture model after the update; After the Gaussian mixture model is optimized, output the posterior probability set.

10. The method according to claim 7, wherein Calculating the gain coefficient of the driving habit type includes: Obtain the target posterior probability of the driving habit type; The gain coefficient of the driving habit type is calculated using the following formula :[[]]END]] ； Among them, is the driving habit type of the driver corresponding gain reference coefficient, is the driving habit type of the driver corresponding target posterior probability.

11. A road feeling simulation device for a steer-by-wire vehicle, characterized in that, Including: An acquisition module, configured to obtain historical driving style data of the steer-by-wire vehicle and collect the actual hand torque of the driver of the steer-by-wire vehicle; A first calculation module, configured to calculate the gain coefficient of the driver according to the historical driving style data, where the gain coefficient is used to characterize the clarity degree that the driver expects for the road feeling; A second calculation module, configured to obtain the theoretical road feeling feedback hand torque of the vehicle, and calculate the expected hand torque according to the theoretical road feeling feedback hand torque and the gain coefficient; A simulation module, including: a calculation unit, configured to calculate the motor torque through closed-loop calculation by a PID controller according to the expected hand torque and the actual hand torque; and calculate the high-frequency road feeling compensation torque of the vehicle; a simulation unit, configured to add the motor torque and the high-frequency road feeling compensation torque and perform road feeling simulation on the steer-by-wire vehicle.

12. A storage medium, characterized in that, A computer program is stored in the storage medium, where the computer program is configured to execute the method described in any one of claims 1 to 10 when running.

13. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method described in any one of claims 1 to 10.

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