Method and device for controlling multifunctional direction deflector rod with information display function

Abstract

The invention provides a multifunctional direction deflector rod control method with information display. The method comprises the steps that S1, angular velocity and acceleration data of a deflector rod are collected in real time; s2, detecting a finger contact position and a sliding track of a driver to obtain operation action information of the driver, confirming a physical gear state of a driving lever, constructing a feature vector by utilizing vehicle speed, a steering wheel steering angle and driving mode information, and establishing an intention recognition model by combining a long-short-term memory network with an attention mechanism, inputting the feature vectors into an intention recognition model for recognition, iteratively updating parameters of the long-short-term memory network through an Adam optimization algorithm, and performing compensation by using a temperature compensation model to improve the sensing precision of the intention recognition model; s3, based on the recognition result of the intention recognition model, a control interface matched with the current vehicle state is dynamically generated and displayed on a touch screen at the top end of the shifting rod; and S4, performing operation on the touch screen, triggering tactile feedback in a grading manner according to the operation completion degree, and performing visual confirmation through the state change of the touch screen.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile electronic control, and in particular to a method and device for controlling an intelligent steering lever with an information display function. Background Art

[0002] Traditional vehicle steering stalks typically utilize mechanical structures, offer limited functionality, and lack intuitive status feedback, requiring drivers to rely on the instrument panel or audio prompts to confirm successful operation. With the increasing electronicization of modern vehicles, more functions are integrated into modern vehicles, and the fixed operation logic of traditional stalks is no longer sufficient. This forces drivers to memorize complex control combinations, increasing cognitive load and the risk of misoperation while driving.

[0003] Therefore, there is an urgent need for an intelligent steering lever control method and device that can integrate information display function to meet the needs of modern automobile driving. Summary of the Invention

[0004] The present invention provides a multifunctional steering lever control method and device with information display, which solves the problems of the prior art such as single function of the steering lever, non-intuitive operation, easy misoperation and low intelligent level of human-computer interaction.

[0005] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a method for controlling a multi-function directional control lever with information display, comprising the following steps: S1, collects the angular velocity and acceleration data of the lever in real time; S2, using the capacitive touch module to detect the driver's finger contact position and sliding trajectory to obtain the driver's operation action information and confirm the physical gear status of the lever. The vehicle's CAN bus transmits information about vehicle speed, steering wheel angle, and driving mode to construct a feature vector. This is then combined with a long-short-term memory network (LSTM) and an attention mechanism to establish an intent recognition model. This feature vector is then fed into the model for recognition. The Adam optimization algorithm is used to iteratively update the LSTM network parameters, and a temperature compensation model is used to compensate for this, improving the model's perception accuracy. S3: Based on the recognition results of the intent recognition model, the touch screen at the top of the lever dynamically generates and displays a control interface that matches the current vehicle status, allowing the driver to quickly obtain information; S4, performs operations on the touch screen, triggers tactile feedback according to the level of operation completion, and provides visual confirmation through changes in the touch screen status.

[0006] Preferably, the specific process of denoising in step S2 is: denoising, correcting and state estimating the collected angular velocity and acceleration data by using the Kalman filter algorithm, converting the observation data containing noise and uncertainty into more accurate and smooth system state information, The specific process of coordinate system conversion is as follows: based on the mechanical structure parameters of the lever, a coordinate system conversion function θ=f(ω,α,t) is established to convert the collected angular velocity ω and acceleration α data into the actual rotation angle of the lever.

[0007] Preferably, the specific process of fitting the trajectory with the cubic spline interpolation function is as follows: using the cubic spline interpolation function S(t)=a+bt+ct²+dt³(t∈[ , ]) (1) Trajectory fitting is performed on discrete angle data to smooth the discrete angle data collected by the sensor, eliminate noise and mutation, fill in missing values, and ensure the continuity and accuracy of the data. Where a, b, c, and d in the above formula (1) are polynomial coefficients. and are adjacent sampling time points; the function S (t) satisfies S ( )= 、S ( )= 、S'( )=S'( )、S''( )=S''( ), where S ( )= 、S ( )= 、S'( )=S'( )、S''( )=S''( ) represents the discrete angles at different moments. The equality of its first-order derivatives ensures the continuity of velocity, and the equality of its second-order derivatives ensures the continuity of acceleration, thus ensuring that the fitting trajectory transitions smoothly at the segmentation points without sudden jitter. Preferably, the specific process of constructing the feature vector is: The vehicle CAN bus is used to obtain real-time vehicle speed v, steering angle δ, and driving mode information m, and construct a feature vector X = [ω, α, v, δ, m]; Among them, ω is the angular velocity of the lever collected by the gyroscope, α is the acceleration of the lever collected by the accelerometer, v is the real-time vehicle speed obtained through the vehicle CAN bus, δ is the steering wheel angle, and m is the driving mode information. By using a long short-term memory network combined with an attention mechanism to establish an intent recognition model, the feature vector X is input into the intent recognition model for recognition. The input layer of the intent recognition model receives the feature vector X, and the hidden layer of the intent recognition model captures the temporal dependency relationship presented by the following formula through the gating unit:

[0008] , where t in the above formula (2) represents the current moment of the sequence; It is the hidden state of the previous moment and stores historical key information. Enter data for the current moment; 、 、 、 It is a trainable weight matrix, which is used for the calculation of forget gate, input gate, candidate memory, and output gate respectively; 、 、 、 is the corresponding bias term; is the Sigmoid activation function used for gating, and tanh is the hyperbolic tangent activation function used to generate candidate memory-enhanced nonlinearities; Represents element-wise multiplication; It is the output of the forget gate, which controls the cell state at the previous moment; Information retention; Is the input gate output, which determines the candidate memory The amount written to the cell state; The current cell state is updated by adding the historical cell state filtered by the forget gate and the candidate memory filtered by the input gate; Is the output gate output, controlling the cell state Output to the current hidden state The amount of information (the cell state is filtered by the output gate and obtained through tanh processing, and passed to the next moment or used as model output); The output layer of the intent recognition model outputs the operation intention probability distribution P (y|X) through the softmax function. Through transfer learning, the pre-trained driving behavior is used to identify the parameters of the intent recognition model, initialize the long short-term memory network, and fine-tune it on the target dataset; The attention mechanism is used to dynamically weight the input features. The calculation formula (3) is: Attention Q , K , V )=softmax ( )V (3) ； Where Q, K, and V in the above formula (3) are query, key, and value matrices respectively, and d k is the key vector dimension; The intent recognition model is trained using the cross entropy loss function: L = log( ) (4), where N in the above formula (4) is the total number of samples, n is the number of categories, is the true label of the jth class of the i-th sample, is the probability that the intent recognition model predicts that the i-th sample belongs to the j-th class, The Adam optimization algorithm is used to minimize the loss function and iteratively update the parameters of the long short-term memory network to achieve high-precision perception of the lever by the intent recognition model.

[0009] Preferably, the specific process of updating the parameters of the long short-term memory network is: when it is detected that the angular velocity changes in N consecutive sampling periods are all less than the threshold ε, the zero bias compensation value is updated. (5), Among them, the above formula (5) is the gyroscope zero bias correction value at the current moment, is the estimated value of the gyroscope bias at the previous moment, is the measured angular velocity of the gyroscope at the current moment, and N is the sliding average window size; The specific process of temperature compensation by the temperature compensation model is: substitute the real-time collected ambient temperature T into the quadratic function relationship between the zero bias compensation value and temperature of the temperature compensation model: = + T+ T² (6), calculate the zero bias compensation value at the corresponding temperature to compensate for the accumulated angle integral error caused by the zero bias drift of the gyroscope with temperature changes, thereby correcting the zero bias drift caused by temperature in real time. Among them, the above formula (6) is the zero bias compensation value at T, is the reference zero bias, is the coefficient of the first-order term, To reflect the linear change rate of zero bias with temperature, the quadratic term coefficient captures the nonlinear characteristics of the zero bias and temperature curve; The zero bias compensation value is then collected from the angular velocity measured by the gyroscope in real time Subtract it from the original value, and the corrected angular velocity is ; Finally, use the corrected angular velocity Angle integral calculation is performed to avoid angle errors caused by the accumulation of zero bias compensation values, ensuring the accuracy of the lever motion state measurement.

[0010] Preferably, if the lever operation intention model recognizes that the driver intends to turn on the turn signal, the touch screen on the top of the lever will display the turn signal's on status and flashing frequency information; if the lever operation intention model determines that the driving mode is to be adjusted, the touch screen will display the currently available driving mode options and their corresponding icons and brief descriptions, so that the driver can intuitively understand and select the required function.

[0011] Preferably, different tactile feedback is triggered according to different levels of operation completion, and the operation completion levels are divided into excellent, qualified and need improvement levels; when the operation score is excellent, the feedback goal is to confirm that the operation is completed accurately; When the score is qualified, the feedback goal is to confirm that the operation is completed but to indicate room for improvement; When the rating is for improvement, the feedback goal is to indicate that operational deviations need to be corrected.

[0012] A multifunctional directional control device with information display includes: a three-axis gyroscope, an accelerometer, a capacitive touch module, a sensor, a touch screen, a tactile feedback module and a controller. The three-axis gyroscope, capacitive touch module, sensor, touch screen, and tactile feedback module are all connected to the controller for communication, and the capacitive touch module is arranged inside the touch screen.

[0013] An electronic device includes a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor executes the computer program, the steps of a method for controlling a multifunctional directional control lever with information display are implemented.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for controlling a multifunctional directional control lever with information display.

[0015] Compared with the existing technology, the beneficial effect of the present invention is that through the intelligent matching of dynamic display and vehicle status, the driver's recognition efficiency of the direction lever function is significantly improved, the operation is more intuitive and accurate, and the misoperation rate is effectively reduced by more than 40%. At the same time, the line of sight deviation time is reduced. On the basis of retaining the physical operation habits of the traditional lever, the high-complexity control requirements of intelligent connected vehicles are taken into account, thereby improving driving safety and human-computer interaction experience as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart of a method for controlling a multi-function steering lever with information display; Figure 2 Schematic diagram of the multi-function steering stalk controls with information display. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Reference Figure 1 and Figure 2 A first aspect of the present invention provides a method for controlling a multi-function directional control lever with information display, comprising the following steps: S1 uses a three-axis gyroscope and accelerometer to collect real-time data on the lever's angular velocity ω and acceleration α. ​​The gyroscopes are strategically positioned within the lever to ensure they can accurately capture minute changes in the lever's motion in three dimensions, providing precise basic data for subsequent operational analysis. S2 uses the capacitive touch module to detect the driver's finger contact position and sliding trajectory to obtain the driver's operating action information, and uses the Hall sensor to confirm the physical gear status of the lever (such as the high and low beam switching gear, the intermittent / low speed / high speed gear of the wiper, etc.). The capacitive touch module can still accurately obtain the driver's operating action information even in bumpy vehicle conditions; Using vehicle speed, steering wheel angle, and driving mode information transmitted via the vehicle's CAN bus, a feature vector is constructed. An intent recognition model is established using a long-short-term memory network combined with an attention mechanism. This feature vector is then input into the intent recognition model to identify operational characteristics (such as the angle change rate of the shift lever and the duration of the operation). The parameters of the long-short-term memory network are iteratively updated using the Adam optimization algorithm, and a temperature compensation model is used to improve the perception accuracy of the intent recognition model. S3: Based on the recognition results of the intent recognition model, the touch screen at the top of the lever dynamically generates and displays a control interface that matches the current vehicle status, allowing the driver to quickly obtain information; S4, the tactile feedback module triggers tactile feedback to the controller according to the level of operation completion, and visually confirms the change in the touch screen state.

[0020] Furthermore, the three-axis gyroscope and accelerometer collect the paddle shifter angular velocity ω and acceleration α in real time. After denoising and coordinate system conversion using the Kalman filter algorithm, the trajectory is fitted using a cubic spline interpolation function. The vehicle speed, steering wheel angle, and driving information m transmitted via the vehicle's CAN bus are used to construct a feature vector X. An intention recognition model is established using a long short-term memory (LSTM) network combined with an attention mechanism. The feature vector X is input into the intention recognition model, and the parameters of the LSTM network are iteratively updated using the Adam optimization algorithm. Compensation is also performed using a temperature compensation model, enabling the intention recognition model to achieve high-precision perception of the paddle shifter.

[0021] Furthermore, the specific process of using the Kalman filter algorithm for denoising is as follows: the collected raw data (such as the angular velocity and acceleration of the gyroscope) is denoised, corrected, and state estimated by the Kalman filter algorithm, converting the observation data containing noise and uncertainty into more accurate and smooth system state information; The specific process of coordinate system conversion is as follows: Based on the mechanical structure parameter data (initial attribute data) of the lever, a coordinate system conversion function θ=f(ω,α,t) is established. Combined with time t and the mechanical structure parameter data of the lever, the collected angular velocity ω and acceleration α data are converted into the actual rotation angle of the lever; Furthermore, the specific process of fitting the trajectory of the cubic spline interpolation function is as follows: using the cubic spline interpolation function S(t)=a+bt+ct²+dt³(t∈[ , ]) (1), Perform trajectory fitting on discrete angle data (i.e. error data in the original data) to smooth the discrete angle data collected by the sensor, eliminate noise and mutations, fill in missing values, and ensure data continuity and accuracy. Where a, b, c, and d in the above formula (1) are polynomial coefficients. and are adjacent sampling time points; the function S (t) satisfies S ( )= 、S ( )= 、S'( )=S'( )、S''( )=S''( ), where S ( )= 、S ( )= 、S'( )=S'( )、S''( )=S''( ) represents the discrete angles at different moments. The equality of its first-order derivatives ensures the continuity of velocity, and the equality of its second-order derivatives ensures the continuity of acceleration, thereby ensuring that the fitting trajectory has a smooth transition at the segmentation points without sudden changes or jitters.

[0022] Furthermore, the specific process of constructing the feature vector is: The feature vector X = [ω, α, v, δ, m] is constructed using the real-time vehicle speed v, steering angle δ, and driving mode information m transmitted by the vehicle CAN bus; Among them, ω is the angular velocity of the lever collected by the gyroscope, α is the acceleration of the lever collected by the accelerometer, v is the real-time vehicle speed obtained through the vehicle CAN bus, δ is the steering wheel angle, and m is the driving mode information. By using a long short-term memory network combined with an attention mechanism to establish an intent recognition model, the feature vector X is input into the intent recognition model for recognition. The input layer of the intent recognition model receives the feature vector X, and the hidden layer of the intent recognition model captures the temporal dependency relationship presented by the following formula through the gating unit:

[0023] Wherein, t in the above formula (2) represents the current moment of the sequence; It is the hidden state of the previous moment and stores historical key information. Enter data for the current moment; 、 、 、 It is a trainable weight matrix, which is used for the calculation of forget gate, input gate, candidate memory, and output gate respectively; 、 、 、 is the corresponding bias term; is the Sigmoid activation function used for gating, and tanh is the hyperbolic tangent activation function used to generate candidate memory-enhanced nonlinearities; Represents element-wise multiplication; It is the output of the forget gate, which controls the cell state at the previous moment; Information retention; Is the input gate output, which determines the candidate memory The amount of new information written to the cell state (based on the current input and the historical hidden state); The current cell state is updated by adding the historical cell state filtered by the forget gate and the candidate memory filtered by the input gate; Is the output gate output, controlling the cell state Output to the current hidden state The amount of information (the cell state is filtered by the output gate and obtained through tanh processing, and passed to the next moment or used as model output); The output layer of the intent recognition model outputs the operation intention probability distribution P (y|X) through the softmax function, where the dataset y∈{ (turn signal), (wipers), (cruise control), ..., (custom function)}; Through transfer learning, the pre-trained driving behavior is used to identify the intent recognition model parameters, initialize the long short-term memory network, and fine-tune it on the target dataset in the dataset (such as turn signals, wipers, cruise control, and custom functions); The attention mechanism is used to dynamically weight the input features. The calculation formula (3) is: Attention Q , K , V )=softmax ( ) V (3) ； Where Q, K, and V in the above formula (3) are query, key, and value matrices respectively, and d k is the key vector dimension; The intent recognition model is trained using the cross entropy loss function: L = log( ) (4), where N in the above formula (4) is the total number of samples, n is the number of categories, is the true label of the jth class of the i-th sample, is the probability that the intent recognition model predicts that the i-th sample belongs to the j-th class, The Adam optimization algorithm is used to minimize the loss function and iteratively update the parameters of the long short-term memory network, which can achieve high-precision perception of the lever by the intent recognition model.

[0024] Furthermore, the specific process of updating the parameters of the long short-term memory network is as follows: when it is detected that the angular velocity change in N consecutive sampling periods is less than the threshold ε, the zero bias compensation value is updated. (5), Among them, the above formula (5) is the gyroscope zero bias correction value at the current moment, is the estimated value of the gyroscope bias at the previous moment, is the measured angular velocity of the gyroscope at the current moment, and N is the sliding average window size; The specific process of temperature compensation by the temperature compensation model is: substitute the real-time collected ambient temperature T into the quadratic function relationship between the zero bias compensation value and temperature of the temperature compensation model: = + T+ T² (6), calculate the zero bias compensation value at the corresponding temperature to compensate for the accumulated angle integral error caused by the zero bias drift of the gyroscope with temperature changes, thereby correcting the zero bias drift caused by temperature in real time. Among them, the above formula (6) is the zero bias compensation value at T, is the reference zero bias, is the coefficient of the first-order term, To reflect the linear change rate of zero bias with temperature, the quadratic term coefficient captures the nonlinear characteristics of the zero bias and temperature curve; The zero bias compensation value is then collected from the angular velocity measured by the gyroscope in real time Subtract it from the original value, and the corrected angular velocity is ; Finally, use the corrected angular velocity Angle integral calculation is performed to avoid angle errors caused by the accumulation of zero bias compensation values, ensuring the accuracy of the lever motion state measurement.

[0025] Furthermore, if the intention recognition model identifies that the driver intends to turn on the turn signal, the touch screen on the top of the lever will display the turn signal's on status and flashing frequency information; if the lever operation intention model determines that it is to adjust the driving mode, the touch screen will display the currently available driving mode options and their corresponding icons and brief descriptions, so that the driver can intuitively understand and select the required function.

[0026] Step S3 includes the following steps: based on the operation intention recognition result y∈{ , ,…, }, select the interface type T (y) corresponding to the task from the predefined interface template library; Combined with the vehicle state parameters S = [v, δ, m, Light, Wiper, …] (7), the Bezier curve interpolation algorithm is used to generate the transition animation; Among them, the above formula (7) vis the real-time vehicle speed obtained through the vehicle CAN bus, δ is the steering wheel angle, m is the driving mode (such as economy, sport, etc.), Light represents the light status (low beam, turn signal, etc.), Wiper refers to the wiper working status (off, high speed, etc.), and "..." includes other extended features such as accelerator pedal position and brake status. The position and size of each display element are calculated by the interface layout engine to meet the following requirements: Layout( T, S )={ E 1( x 1, y 1, w 1, h 1), E 2( x 2, y 2, w 2, h 2),…, En ( xn,yn,wn,hn )} in, T Indicates the type of task interface. S Indicates the vehicle status parameters input on the screen, E1 Indicates the first display element, ( x 1, y 1) is the first coordinate, ( w 1, h 1) is the first width × height dimension, E2 Indicates the second display element, ( x 2, y 2) is the second coordinate, ( w 2, h 2) is the second width × height dimension, E3 Indicates the n Display elements, ( xn , yn ) is the n coordinates, ( wn , hn ) is the n Dimensions of width × height; The element position change of the transition animation satisfies: P ( t )= +3 t +3 (1 t ) + ( t ∈[0,1]) (8), Among them, t in the above formula (8) represents the curve from the starting point To the end The traversal progress, 、 、 、 are the coordinates of the control points; By color mapping function f ( v , m ) Generate an ergonomic interface tone that meets:

[0027] The interactive elements in the autonomous driving mode are dynamically adjusted using the tactile feedback threshold function g(v) to meet the following requirements: g ( v )= v + (9), Among them, the above formula (9) 、 To calibrate the coefficients to ensure stronger feedback at high speeds; Interface update frequency Dynamic adjustment based on vehicle speed: = , Ensures smoother visual feedback at high speeds.

[0028] Furthermore, different tactile feedback levels are triggered based on the degree of operation completion, which is categorized as excellent, acceptable, and needs improvement. When the operation score is ≥0.8 (excellent), the feedback goal is to confirm the precise completion of the operation. A single pure sine wave vibration with an amplitude of 0.15mm, a frequency of 100Hz, and a duration of 50ms is provided, triggered once the lever returns to its original position. When the score is 0.5-0.8 (qualified), the feedback goal is to confirm the operation is completed but indicate room for improvement. The feedback is provided by a sine wave with an amplitude of 0.3mm and a superposition of 80Hz and 160Hz, with double 100ms vibration feedback at 20ms intervals. The trigger is delayed by 10ms after the operation is completed. When the score is < 0.5 (for improvement), the feedback goal is to prompt that the operational deviation needs to be corrected. This is triggered immediately by a single strong vibration with a sine wave of 0.5mm amplitude, 60Hz, and a phase offset of π / 2, which gradually weakens at the end of 150ms. If the touch screen does not respond to the secondary confirmation interface, feedback is repeated every 500ms to correct the operation.

[0029] The operation completion function is: (10) Among them, Sim in the above formula (10) is the similarity function (value ranges from 0 to 1), is the calibration factor (usually =2, =5), is the standard deviation of the operation trajectory, The actual operation is time-consuming. is the standard operation time, and σ is the standard deviation.

[0030] when When is excellent, when Qualified when Time for improvement.

[0031] The second aspect of the present invention further provides a multifunctional direction lever control device with information display, comprising: a three-axis gyroscope, an accelerometer, a capacitive touch module, a sensor, a touch screen, a tactile feedback module and a controller. The three-axis gyroscope, capacitive touch module, sensor, touch screen, and tactile feedback module are all connected to the controller for communication, and the capacitive touch module is arranged inside the touch screen.

[0032] The third aspect of the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the steps of the multi-function directional control lever with information display are implemented.

[0033] A fourth aspect of the present invention further provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, the steps of the method for controlling a multi-function directional control lever with information display are implemented.

[0034] The touchscreen dynamically generates its UI layout based on the vehicle's real-time status. For example, when the vehicle is driving at low speeds, the touchscreen prioritizes the turn signal options; at high speeds, the touchscreen prioritizes driving safety-related functions, enhancing operational convenience.

[0035] The tactile feedback module triggers differentiated vibration feedback based on different operation types, such as short, long, strong, and weak vibrations. Based on user feedback and driving scenario requirements, the vibration parameters are appropriately adjusted to provide a more intuitive and comfortable operation feedback experience.

[0036] The activated function icons and related parameters (such as steering angle values) are highlighted in real time on the touch screen, ensuring that the icons and parameters are displayed clearly, accurately and timely, making it easier for the driver to quickly obtain information.

[0037] Control commands are sent to the body controller via the vehicle's CAN bus on the touch screen, ensuring the timeliness and accuracy of data transmission and providing key information for comprehensive judgment of driving scenarios. The CAN FD protocol of the vehicle's CAN bus can ensure the reliability and efficiency of control command transmission, achieving precise control of vehicle-related functions.

[0038] The touch screen continuously monitors the completion of an operation. If the completion rate doesn't reach the set threshold, a secondary confirmation prompt is triggered. Reasonable completion thresholds can be set, and secondary confirmation can be performed using various methods such as sound, vibration, and on-screen prompts to avoid misoperation.

[0039] The touchscreen allows for detailed operation logs to be recorded for driving habit analysis and system self-optimization. Designing a rational log format and storage method, and using data analysis techniques to uncover the potential value of operational data, provides a basis for system function optimization and personalized settings.

[0040] The intelligent steering stalk control device with information display function provided by this invention can be used in conjunction with an OTA upgrade module, which receives new function configurations and updates the intent recognition model in real time. Establishing a secure and reliable OTA upgrade mechanism ensures stability and data security during the upgrade process, enabling the system to promptly adapt to new driving needs and function expansions.

[0041] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0042] In particular, according to some embodiments of the present disclosure, the process described above can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0043] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0044] In some embodiments of the present disclosure, a computer-readable signal medium may include a mission data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated mission data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or convey a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0045] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital task data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks), as well as any currently known or later developed networks.

[0046] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to detecting a query operation on a production collaboration document in the switch production line management application, determines the network connection status of the switch production line management application; in response to determining that the network connection status of the switch production line management application represents an offline state, replaces the web page entry information corresponding to the production collaboration document with target entry file information, and loads target web page resource information to display the web page of the production collaboration document offline in the switch production line management application, wherein the target entry file information is file information of a pre-downloaded entry file corresponding to the web page of the production collaboration document, and the target web page resource information is locally stored resource information corresponding to the web page; in response to determining that the network connection status of the switch production line management application represents an online state and the web page resource information corresponding to the production collaboration document is not stored locally, downloads the web page resource information of the web page from the production line document server, wherein the web page resource information includes the entry file and resource information; displays the web page of the production collaboration document in the switch production line management application according to the web page resource information, and stores the web page resource information in a local database.

[0047] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including product-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling a multi-function steering lever with information display, characterized in that: The following steps are involved: S1, collects the angular velocity and acceleration data of the lever in real time; S2, using the capacitive touch module to detect the driver's finger contact position and sliding trajectory to obtain the driver's operation action information and confirm the physical gear status of the lever. The vehicle's CAN bus transmits information about vehicle speed, steering wheel angle, and driving mode to construct a feature vector. This is then combined with a long-short-term memory network (LSTM) and an attention mechanism to establish an intent recognition model. This feature vector is then fed into the model for recognition. The Adam optimization algorithm is used to iteratively update the LSTM network parameters, and a temperature compensation model is used to compensate for this, improving the model's perception accuracy. S3: Based on the recognition results of the intent recognition model, the touch screen at the top of the lever dynamically generates and displays a control interface that matches the current vehicle status, allowing the driver to quickly obtain information; S4, performs operations on the touch screen, triggers tactile feedback according to the level of operation completion, and provides visual confirmation through changes in the touch screen status.

2. The method for controlling a multi-function direction lever with information display according to claim 1, wherein: The specific process of denoising in step S2 is: denoising, correcting and state estimating the collected angular velocity and acceleration data through the Kalman filter algorithm, converting the observation data containing noise and uncertainty into more accurate and smooth system state information. The specific process of coordinate system conversion is as follows: based on the mechanical structure parameters of the lever, a coordinate system conversion function θ=f(ω,α,t) is established to convert the collected angular velocity ω and acceleration α data into the actual rotation angle of the lever.

3. The method for controlling a multi-function direction lever with information display according to claim 1, wherein: The specific process of fitting the trajectory of the cubic spline interpolation function is as follows: using the cubic spline interpolation function S (t) = a + bt + ct² + dt³ (t∈[ , ]) (1) Trajectory fitting is performed on discrete angle data to smooth the discrete angle data collected by the sensor, eliminate noise and mutation, fill in missing values, and ensure the continuity and accuracy of the data. Where a, b, c, and d in the above formula (1) are polynomial coefficients. and are adjacent sampling time points; the function S(t) satisfies S ( )= 、S ( )= 、S'( )=S'( )、S''( )=S''( ), where S ( )= 、S ( )= 、S'( )=S'( )、S''( )=S''( ) represents the discrete angles at different moments. The equality of its first-order derivatives ensures the continuity of velocity, and the equality of its second-order derivatives ensures the continuity of acceleration, thereby ensuring that the fitting trajectory has a smooth transition at the segmentation points without sudden changes or jitters.

4. The method for controlling a multi-function direction lever with information display according to claim 1, wherein: The specific process of constructing the feature vector is: The vehicle CAN bus is used to obtain real-time vehicle speed v, steering angle δ, and driving mode information m, and construct a feature vector X = [ω, α, v, δ, m]; Among them, ω is the angular velocity of the lever collected by the gyroscope, α is the acceleration of the lever collected by the accelerometer, v is the real-time vehicle speed obtained through the vehicle CAN bus, δ is the steering wheel angle, and m is the driving mode information. By using a long short-term memory network combined with an attention mechanism to establish an intent recognition model, the feature vector X is input into the intent recognition model for recognition. The input layer of the intent recognition model receives the feature vector X, and the hidden layer of the intent recognition model captures the temporal dependency relationship presented by the following formula through the gating unit: ， Wherein, t in the above formula (2) represents the current moment of the sequence; It is the hidden state of the previous moment and stores historical key information. Enter data for the current moment; 、 、 、 It is a trainable weight matrix, which is used for the calculation of forget gate, input gate, candidate memory, and output gate respectively; 、 、 、 is the corresponding bias term; is the Sigmoid activation function used for gating, and tanh is the hyperbolic tangent activation function used to generate candidate memory-enhanced nonlinearities; Represents element-wise multiplication; It is the output of the forget gate, which controls the cell state at the previous moment; Information retention; Is the input gate output, which determines the candidate memory The amount written to the cell state; The current cell state is updated by adding the historical cell state filtered by the forget gate and the candidate memory filtered by the input gate; Is the output gate output, controlling the cell state Output to the current hidden state The amount of information (the cell state is filtered by the output gate and obtained through tanh processing, and passed to the next moment or used as model output); The output layer of the intention recognition model outputs the probability distribution of the operation intention P (y|X) through the softmax function. The intention recognition model parameters are identified by using the pre-trained driving behavior through transfer learning, and the long short-term memory network is initialized and fine-tuned on the target dataset. The attention mechanism is used to dynamically weight the input features. The calculation formula (3) is: Attention( Q , K , V )=softmax ( ) V (3) ； Where Q, K, and V in the above formula (3) are query, key, and value matrices respectively, and d k is the key vector dimension; the intent recognition model training adopts the cross entropy loss function: L = log( ) (4), where N in the above formula (4) is the total number of samples, n is the number of categories, is the true label of the jth class of the i-th sample, The intention recognition model predicts the probability that the i-th sample belongs to the j-th category, and uses the Adam optimization algorithm to minimize the loss function, iteratively update the parameters of the long short-term memory network, and realize the high-precision perception of the lever by the intention recognition model.

5. The method for controlling a multi-function direction lever with information display according to claim 4, wherein: The specific process of updating the parameters of the long short-term memory network is as follows: when it is detected that the angular velocity change in N consecutive sampling periods is less than the threshold ε, the zero bias compensation value is updated. （5）， Among them, the above formula (5) is the gyroscope zero bias correction value at the current moment, is the estimated value of the gyroscope bias at the previous moment, is the measured angular velocity of the gyroscope at the current moment, and N is the sliding average window size; The specific process of temperature compensation by the temperature compensation model is: substitute the real-time collected ambient temperature T into the quadratic function relationship between the zero bias compensation value and temperature of the temperature compensation model: = + T+ T² (6), calculate the zero bias compensation value at the corresponding temperature to compensate for the accumulated angle integral error caused by the zero bias drift of the gyroscope with temperature changes, thereby correcting the zero bias drift caused by temperature in real time. Among them, the above formula (6) is the zero bias compensation value at T, is the reference zero bias, is the coefficient of the first-order term, To reflect the linear change rate of zero bias with temperature, the quadratic term coefficient captures the nonlinear characteristics of the zero bias and temperature curve; The zero bias compensation value is then collected from the angular velocity measured by the gyroscope in real time Subtract it from the original value, and the corrected angular velocity is ; Finally, use the corrected angular velocity Angle integral calculation is performed to avoid angle errors caused by the accumulation of zero bias compensation values, ensuring the accuracy of the lever motion state measurement.

6. The method for controlling a multi-function direction lever with information display according to claim 1, wherein: If the stalk operation intention model recognizes that the driver intends to turn on the turn signal, the touch screen on the top of the stalk will display the turn signal's on status and flashing frequency information; If the lever operation intention model determines that the driving mode is to be adjusted, the touch screen displays the currently available driving mode options and their corresponding icons and brief descriptions, allowing the driver to intuitively understand and select the required function.

7. The method for controlling a multi-function direction lever with information display according to claim 1, wherein: Different tactile feedback is triggered based on the degree of operation completion, which is divided into excellent, qualified, and needs improvement. When the operation score is excellent, the feedback goal is to confirm that the operation is completed accurately. When the score is qualified, the feedback goal is to confirm that the operation is completed but to indicate room for improvement; When the rating is for improvement, the feedback goal is to indicate that operational deviations need to be corrected.

8. A device for controlling a multi-function directional control lever with information display according to any one of claims 1 to 7, comprising: Three-axis gyroscope, accelerometer, capacitive touch module, sensor, touch screen, tactile feedback module and controller, The three-axis gyroscope, capacitive touch module, sensor, touch screen, and tactile feedback module are all connected to the controller for communication, and the capacitive touch module is arranged inside the touch screen.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the method for controlling a multi-function directional control lever with information display as claimed in 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 for controlling a multi-function directional control lever with information display as claimed in any one of claims 1 to 7 are implemented.

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