Adjusting method, device and equipment for vehicle seat and medium

Abstract

The embodiment of the invention discloses a vehicle seat adjusting method and device, equipment and a medium, and relates to the technical field of vehicle seat control. The method comprises the following steps: after a biological feature identifier of a target user passes verification, obtaining touch data of the target user on a seat adjustment interaction interface; determining an adjustment value corresponding to each part of the seat according to the touch data; and when a motor in the seat is controlled according to the adjustment numerical value so as to realize seat adjustment, updating the proportionality coefficient and the integration time in real time according to the current error change rate of the motor and the load ratio, and controlling the current of the motor through PID based on the updated proportionality coefficient and the integration time. According to the technical scheme, the proportionality coefficient and the integral time are updated through the current error change rate of the motor and the load ratio, so that PID control is more reasonable, the motor can operate stably in the seat adjusting process, and the seat adjusting error is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle seat control, and in particular to a vehicle seat adjustment method, device, equipment and medium. BACKGROUND

[0002] In the field of automobile seat control, with the development of automobile intelligence, the demand for convenient and accurate control of seat position and posture is increasing. The field has developed from early simple manual adjustment of seats to seat systems with electric adjustment functions.

[0003] Current seat adjustment schemes are usually based on traditional wired or simple wireless seat control methods. Under this scheme, users adjust the position and posture of the seat through fixed control buttons in the car, such as adjusting the forward and backward sliding of the seat, the reclining angle of the backrest, etc. However, this adjustment method is unstable when the motor load is too large, and the seat adjustment may deviate. SUMMARY

[0004] The present application provides a vehicle seat adjustment method, device, equipment and medium, which can control the motor current in real time during seat adjustment, so that the motor can run stably.

[0005] According to one aspect of the present application, a vehicle seat adjustment method is provided, the method comprising:

[0006] After the biological feature identification of the target user is verified, the touch data of the target user on the seat adjustment interactive interface is obtained;

[0007] The adjustment value corresponding to each part of the seat is determined according to the touch data;

[0008] When the motor in the seat is controlled according to the adjustment value to realize seat adjustment, the proportional coefficient and integral time are updated in real time according to the motor current error change rate and the load, and the motor current is controlled by PID based on the updated proportional coefficient and integral time.

[0009] According to another aspect of the present application, a vehicle seat adjustment device is provided, comprising:

[0010] The biological feature identification verification module is configured to obtain the touch data of the target user on the seat adjustment interactive interface after the biological feature identification of the target user is verified;

[0011] The adjustment value determination module is configured to determine the adjustment value corresponding to each part of the seat according to the touch data;

[0012] The seat adjustment module is configured to control an electric motor in a seat according to the adjustment value, to realize seat adjustment, update a proportional coefficient and an integral time according to a motor current error change rate and a load ratio in real time, and control the motor current by PID based on the updated proportional coefficient and integral time.

[0013] According to another aspect of the present application, there is provided an electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein

[0016] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the adjustment method of the vehicle seat according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to perform the adjustment method of the vehicle seat according to any one of the embodiments of the present application when executed by the processor.

[0018] The technical solution of the embodiments of the present application comprises: after the biological feature identification of the target user is verified, obtaining touch data of the target user in the seat adjustment interaction interface; determining adjustment values corresponding to each part of the seat according to the touch data; when controlling an electric motor in the seat according to the adjustment values to realize seat adjustment, updating a proportional coefficient and an integral time according to a motor current error change rate and a load ratio in real time, and controlling the motor current by PID based on the updated proportional coefficient and integral time. The technical solution updates the proportional coefficient and the integral time according to the motor current error change rate and the load ratio, so that the PID control is more reasonable, the motor can be stably operated in the seat adjustment process, and the seat adjustment error is reduced.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings described below only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 is a flow chart of a vehicle seat adjustment method according to an embodiment of the present application;

[0022] Figure 2 is a flow chart of a vehicle seat adjustment method according to an embodiment of the present application;

[0023] Figure 3 is a mobile terminal fingerprint authentication and Bluetooth communication encryption flow chart according to an embodiment of the present application;

[0024] Figure 4 is a non-linear mapping relationship between user touch input and three-dimensional posture parameters according to an embodiment of the present application;

[0025] Figure 5 is a multi-motor synchronous PID control and load adaptive parameter adjustment mechanism diagram according to an embodiment of the present application;

[0026] Figure 6 is a personalized recommendation configuration flow chart based on multi-feature clustering according to an embodiment of the present application;

[0027] Figure 7 is a whole structure diagram of a car seat remote wireless control according to an embodiment of the present application;

[0028] Figure 8 is a structure diagram of a vehicle seat adjustment device according to an embodiment of the present application;

[0029] Figure 9 is a structure diagram of an electronic device for implementing a vehicle seat adjustment method according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the person skilled in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, 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 can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment one

[0033] Figure 1 A flowchart of a method for adjusting a vehicle seat is provided for the first embodiment of the present application. The first embodiment of the present application can be applied to the case of adjusting a vehicle seat. The method can be executed by a vehicle seat adjusting device, which can be realized in the form of hardware and / or software. The vehicle seat adjusting device can be configured in an electronic device with data processing capability. As shown in the figure, the method comprises: Figure 1

[0034] S110, after the target user's biological feature identification verification is passed, obtaining the target user's touch data on the seat adjustment interaction interface.

[0035] The target user can be a user of the vehicle, and the target user can adjust the vehicle seat through a terminal device. The terminal device can be a mobile terminal, such as a mobile phone, a smart watch, etc., or a terminal inside the vehicle.

[0036] Specifically, before the target user performs seat adjustment on the terminal device, the target user needs to verify the target user's biological feature identification on the terminal device, which can be fingerprint, iris, facial feature, etc. After the target user's biological feature identification verification is passed, the terminal device displays a seat adjustment interaction interface, which can include a three-dimensional or two-dimensional schematic diagram of the seat. The settings of the seat (such as backrest angle information, etc.) can be mapped according to the current situation of the seat. The target user can perform touch operation on the seat adjustment interaction interface, which can be forward and backward movement of the seat, backrest angle adjustment, waist support adjustment, etc. After the user performs touch operation, the target user's touch data is obtained.

[0037] S120, determining the adjustment value corresponding to each part of the seat according to the touch data.

[0038] ​Specifically, the touch data reflects the seat adjustment information required by the target user. According to the seat part operated by the user in the touch data and the adjustment amplitude of each part, the adjustment value corresponding to each part of the seat can be identified. In a specific example, if the user slowly slides from the backrest forward and backward, the determined backrest angle can be 5 degrees backward; if the user quickly slides from the backrest forward and backward, the determined backrest angle can be 10 degrees backward.

[0039] In S130, when the motor in the seat is controlled according to the adjustment value to realize seat adjustment, the proportional coefficient and the integral time are updated in real time according to the motor current error change rate and the load ratio, and the motor current is controlled by PID based on the updated proportional coefficient and integral time.

[0040] The PID is a control system that controls by proportion, integration and differentiation. In the embodiments of the present application, the PID is used to control the current of the motor in the seat.

[0041] Specifically, when the motor in the seat is controlled according to the adjustment value, the current of the motor may be overloaded due to the different weights of each user. In this case, the current of the motor is unstable and has an error. The proportional coefficient and the integral time in the PID are updated according to the current error change rate and the load ratio (the load ratio is the ratio of the actual current of the motor to the rated current), and the motor current is controlled by PID based on the updated proportional coefficient and integral time. This setting makes the proportional coefficient and the integral time adjusted according to the real-time motor current error change rate and the load ratio. When the motor current is controlled by PID, the current change of the motor is more stable, and the energy consumption robustness and adjustment stability in the motor execution process are improved.

[0042] The technical scheme of the embodiments of the present application includes: after the biological feature identification of the target user is verified, the touch data of the target user in the seat adjustment interaction interface is obtained; the adjustment value corresponding to each part of the seat is determined according to the touch data; when the motor in the seat is controlled according to the adjustment value to realize seat adjustment, the proportional coefficient and the integral time are updated in real time according to the motor current error change rate and the load ratio, and the motor current is controlled by PID based on the updated proportional coefficient and integral time. The proportional coefficient and the integral time are updated by the motor current error change rate and the load ratio, so that the PID control is more reasonable, the motor can stably run in the seat adjustment process, and the seat adjustment error is reduced.

[0043] Embodiment two

[0044] Figure 2A flowchart of a vehicle seat adjustment method provided in Embodiment Two of the present application is based on the above-mentioned embodiments and is optimized.

[0045] As shown in Figure 2 , the method of the present application specifically includes the following steps:

[0046] S210, after the biological feature identification verification of the target user is passed, acquiring touch data of the target user in the seat adjustment interactive interface.

[0047] In the present application, optionally, the biological feature identification verification process of the target user includes: acquiring fingerprint data of the target user, and performing feature extraction thereon to obtain a fingerprint vector feature group; and matching the fingerprint vector feature group and a pre-stored template feature, and determining that the biological feature identification verification of the target user is passed when a matching score is greater than a security threshold.

[0048] Exemplarily, Figure 3 A flowchart of fingerprint authentication and Bluetooth communication encryption for a mobile terminal. The biological feature identification verification process of the target user first captures a fingerprint image through a capacitive fingerprint sensor at a resolution of 500 dpi, performs hash feature extraction and compression dimension reduction processing on the image data inside the terminal, obtains a fingerprint vector feature group F = {f1, f2, …, fn}, and uses a symmetric encryption algorithm (such as AES-256) to symmetrically encrypt the vector group, the key being dynamically generated by an entropy source in the TEE (Trusted Execution Environment), being valid only in the current session, and ensuring that the data cannot be reused. After encryption, the fingerprint vector feature group and the pre-stored template feature are matched based on the following formula: n

[0049]

[0050] where f i is the current fingerprint collection feature value, t i is the pre-stored template corresponding feature value, n is the number of feature dimensions, and ω is the matching weight coefficient set by the system, which is set to 0.6 by default. The formula combines the normalized Jaccard similarity and the average difference inverse factor, the former evaluating the coincidence degree of the two feature groups in the value domain distribution, and the latter measuring the detailed matching of the microstructure. The double-channel structure makes the comparison result take into account both robustness and sensitivity. When the matching score S exceeds the security threshold θ = 0.85, it is determined that the biological feature identification verification of the target user is passed.

[0051] ​Optionally, after the biological feature identification of the target user is verified, the method further includes: generating a dynamic key after integrity checking of the terminal device identification and the biological feature identification verification information of the target user; and encrypting communication information based on the dynamic key when communicating with the terminal device for seat adjustment.

[0052] For example, after the biological feature identification of the target user is verified, the mobile terminal triggers an encrypted connection request, and transmits the device identification code and the authentication success flag to the vehicle-mounted control unit. The vehicle-mounted system verifies the data integrity through HMAC-SHA3, and establishes a 128-bit dynamic key based thereon, which is used for encryption of the Bluetooth 5.2 low-power communication link. The average response time of the entire verification and connection initialization process is controlled within 185 ms, which meets the demand for remote and instant control, and ensures the identity uniqueness, anti-replay attack capability and anti-middleman attack capability. The authentication process realizes a terminal-level zero-trust start mechanism, and provides a trusted access basis for subsequent human-computer interaction control and dynamic feedback loop.

[0053] For example, the seat adjustment interaction interface is a three-dimensional posture simulation module constructed based on a Unity3D engine, and real-time visual mapping of five-dimensional parameters, such as forward and backward sliding, lifting adjustment, backrest inclination, seat cushion pitch and waist support, is realized through GPU accelerated rendering. Figure 4 A schematic diagram of a nonlinear mapping relationship between user touch input and three-dimensional posture parameters (the three-dimensional posture parameters are adjustment parameters of parts of the seat). The user performs gesture interaction through a multi-point touch screen, captures multiple frames of touch data on each dragging path in real time, and maps two-dimensional screen coordinate changes to three-dimensional posture transformation parameters by using a trajectory fitting algorithm. In modeling, the seat posture state quantity vector is defined as where x represents forward and backward sliding displacement, h is vertical lifting height, is the backrest inclination, is the seat cushion pitch angle, is the waist support extension amount.

[0054] S220, determining the adjustment values of the parts of the seat according to the touch data.

[0055] For example, to improve the mapping accuracy between touch interaction and physical action, a nonlinear mapping transformation function T: R 2 → R 5 is constructed, and a neural spline interpolation algorithm is used to analyze the multi-point touch gesture path. Optionally, according to the touch data, the adjustment values of the parts of the seat are determined, including: the adjustment values of the parts of the seat are determined according to the following formula:

[0056]

[0057] wherein θ i represents the adjustment value of the ith seat position, m is the number of touch nodes collected in the drag trajectory in the touch data, (u j ,v j ) represents the displacement increment of the jth touch point in the screen coordinate system, φ ij is a kernel function group constructed for the dimension to which the ith seat position belongs, λ i is a touch sensitivity gain factor, δ i is the current position offset of the ith seat position.

[0058] The mapping function can adaptively correct the input response under different user operation styles, effectively suppress the posture error caused by slight jitter through the weighted average processing based on the change rate of the touch trajectory, and improve the adjustment accuracy and control linearity. The final generated adjustment value corresponding to each position is associated with the target user ID and transmitted to the vehicle-mounted control unit through the encrypted Bluetooth channel, which is analyzed by the vehicle-mounted control unit and further driven by the PID closed-loop controller under the CAN bus to execute the adjustment instruction. At the system level, the design builds a seamless mapping relationship between visual perception, touch mapping and physical execution, ensuring the intuitiveness and accuracy of human-computer interaction, and providing a high-response and high-consistency input portal for the entire control chain.

[0059] S230, when controlling the motor in the seat according to the adjustment value to realize seat adjustment, if it is determined that the load ratio of the current is greater than the preset load threshold, a parameter adjustment suppression value is determined in real time according to the motor current error change rate and the load ratio.

[0060] To ensure the synchronization accuracy of seat position and posture adjustment and the stability of motor response, a dynamic parameter adjustment mechanism is introduced on the basis of traditional PID closed-loop control, the proportional coefficient K p and the integral time T i are adaptively optimized by real-time monitoring of the motor load current change trend. Figure 5 is a schematic diagram of multi-motor synchronous PID control and load adaptive parameter adjustment mechanism. The current sampling values of each adjustment motor transmitted by the CAN bus are used as the core feedback signal, and a double-channel state evaluation model is constructed combined with the displacement data returned by the SPI interface. When the actual current I act of a certain adjustment motor continuously exceeds 15% of its rated value I rated , that is, the condition of I is met, the system considers that there is an overload trend or mechanism damping abnormality, and automatically triggers the fuzzy adjustment module.

[0061] Further, a fuzzy control rule library is constructed based on an empirical rule, to error change rate (error change rate is the change rate of current error, which can be the difference between actual current and ideal control current) Δe and current load ratio For input variables, proportional coefficient K p And integral time T i Joint correction is realized. To avoid regulation overshoot or response lag, a parameter adjustment suppression function Ψ is introduced to suppress the adjustment step, which is:

[0062]

[0063] Wherein, the calculation result of Ψ is the parameter adjustment suppression value, γ and η are system empirical coefficients, ρ represents the load ratio, Δe is the error derivative term, and Ψ is used as a dynamic gain adjuster to suppress the amplitude of control parameter adjustment. When ρ gradually increases and the change rate of Δe slows down, the value of Ψ decreases, thereby slowing down the adjustment speed of K p And T i , to prevent the system from oscillating under transient disturbance.

[0064] In S240, the proportional coefficient and the integral time are updated based on the parameter adjustment suppression value, and the motor current is controlled by PID based on the updated proportional coefficient and integral time.

[0065] For example, the update formula of the proportional coefficient and the integral time is:

[0066]

[0067] Wherein, μ and ν are respectively the proportional and integral adjustment sensitivity coefficients, and Φ can be equal to Ψ by controlling the directionality of gain and integral response time. When the load increases, the driving response strength is slowed down and the integral correction time is lengthened, to avoid system cumulative error amplification and motor temperature rise risk. The dynamic adjustment mechanism takes load sensing as the trigger core, combines fuzzy control and suppression parameter adjustment model, effectively improves the energy consumption robustness and regulation stability of the motor during execution, ensures that the synchronous error is controlled within 0.5mm during the collaborative operation of multiple mechanisms, and supports the reliability and safety of the physical execution layer in the whole seat three-dimensional posture control chain.

[0068] In the embodiments of the application, optionally, in the process of controlling the motor in the seat according to the parameter adjustment value, the method further comprises: collecting pressure data through a pressure sensor arranged at the connection between the seat and the vehicle; judging whether there is a risk of impact according to a reference pressure value, a current pressure value reflected by the pressure data and a pressure change rate; if there is, locking the locking groove of the motor output shaft through the electromagnetic brake, and / or triggering the safety belt pretensioning device, so that the safety belt can be tightened.

[0069] To prevent seat adjustment process from causing human injury due to mechanical jamming, misoperation or collision event, a high-speed response safety locking mechanism can be triggered based on the pressure signal. A multi-point stress field monitoring network of the seat and vehicle body connection state is constructed by arranging high-sensitivity strain pressure sensors at the ends of the slide rails, the roots of the lifting columns and the rotary joint positions. The pressure signals collected by each adjustment channel are sent to the vehicle-mounted control unit in real time, and whether an abnormal impact or structural loosening occurs is determined by an integrated logic judgment model. The pressure abnormality criterion function Φ is defined as follows:

[0070]

[0071] where n is the number of pressure sensing points, P i represents the current pressure value of the i-th point, P i,ref is the reference pressure value under the historical stable working condition of the position, and ξ is the static deviation weight factor, represents the rate of change of pressure with time. When the value of Φ exceeds the set threshold Φ crit , it is determined that there is a risk of impact; that is, the current state is a potential impact state, triggering a double safety linkage process. The first response is performed by the electromagnetic brake module, which uses a permanent magnet attraction type electromagnetic brake. After the control unit sends a locking signal, the excitation coil completes energy magnetization within 50 milliseconds, drives the internal spring hook structure to instantaneously engage the locking groove on the output shaft of the adjustment motor, realizes rigid locking of the mechanism, and blocks any subsequent displacement. The second response is that the control unit sends a collision warning frame to the vehicle body main control ECU through the CAN-FD bus. The frame contains the current Φ value and the abnormal position index code, and simultaneously triggers the seat belt pretensioning device to tighten 5 cm in advance to prevent the occupant from being injured due to instantaneous inertial displacement. The response cycle of the entire process does not exceed 80 milliseconds. To improve the accuracy and stability of the overall decision, the reference pressure P i,ref of each sensor is updated before each adjustment, and the dynamic correction is performed by the exponential weighted average algorithm, so that the criterion function Φ can automatically adapt to the stress characteristics of different occupant sizes and seat positions, and maintain high sensitivity and low false alarm rate to abnormal events. The safety locking mechanism, identity authentication, posture mapping and motor control link form a closed loop linkage, constituting a fast protection path from signal monitoring to physical response, significantly enhancing the fault tolerance and occupant protection capability of the system.

[0072] In the embodiments of the application, optionally, after the seat adjustment is completed, the method further comprises: to realize the seat position and posture personalized configuration recommendation mechanism for multiple users, a high-dimensional multi-feature clustering algorithm is designed to solve the slow convergence and cluster center drift problems of traditional K-means when the sample number increases or the class boundary is blurred. Figure 6A flowchart is configured for personalized recommendation based on multi-feature clustering. After each seat adjustment is completed, the vehicle-mounted control unit automatically uploads the user identity ID, height h, weight w, and current adjustment parameters S = [x, y, θ1, θ2, l] to the cloud server, where x and y are front and rear sliding amounts and lifting heights, θ1 and θ2 are angles of the backrest and the cushion, and l is a waist support protrusion amount. The system normalizes these data and constructs a user feature vector Enter the clustering model for preference classification.

[0073] To overcome the problem of sensitivity of K-means to initial centers, a dynamic density weight factor δ i is introduced as a regulation factor for the convergence contribution of each sample to the cluster center. Specifically, a feature vector can be constructed according to the seat setting information and body attribute information of each user, and the dynamic density weight factor can be calculated based on the feature vector according to the following formula:

[0074]

[0075] where δ i is the dynamic density weight factor, u i is the feature vector of the i-th user sample, m is the total number of samples, and α is the density sensitivity coefficient. This factor is used to measure the local density of a sample in the feature space. The larger the value, the more isolated the sample distribution, and the more it should affect the update of the cluster center, thereby improving the contribution of the edge sample to the final cluster center.

[0076] Based on the dynamic density weight factor and the feature vector of each user, clustering is performed, and the cluster center is updated based on the following formula:

[0077]

[0078] where C is the new cluster center of the k-th class after the t+1 iteration, C k is the index set of all samples currently belonging to the k-th class. This formula performs density-aware adjustment on the equal-weighted mean update of the traditional K-means in each iteration, thereby enhancing the clustering robustness under the condition of uneven distribution of user preference features and significantly reducing the recommendation error in the cold start stage.

[0079] Further, the target cluster center with the highest matching degree with the feature vector of the subsequent user is determined among the cluster centers obtained after clustering, and the initial parameter setting of the seat used by the subsequent user is performed according to the seat setting information corresponding to the target cluster center.

[0080] In determining the target cluster center with the highest matching degree with the feature vector of the subsequent user among the cluster centers obtained after clustering, the initial feature vector unew Match the Euclidean distance with all cluster centers, and select the center with the smallest distance as the target cluster center.

[0081] The method further includes taking the historical average parameter configuration corresponding to the target cluster center as a recommended reference. The recommended parameter is displayed in a three-dimensional model manner through a human-computer interaction interface, allowing the user to fine-tune the position based on the default recommendation, thereby realizing the rapid construction of individualized initialization and the non-susceptible switching of user experience without affecting the PID synchronous adjustment link. This clustering mechanism and the seat motor control, posture rendering and biological authentication modules constitute a complete intelligent adjustment closed loop, realizing the data-driven user memory and dynamic adaptation capability.

[0082] Figure 7 The overall structure diagram of the remote wireless control of the automobile seat, in terms of communication connection and identity authentication, the technical scheme of the embodiment of the application establishes a low-power wireless connection through the built-in Bluetooth 5.2 module of the mobile terminal and the vehicle-mounted control unit. When the connection is established, the mobile terminal sends a pairing request containing a device identification code, and the vehicle-mounted system generates a 128-bit dynamic encryption key after verifying the legality of the identification code. At the same time, a biological feature authentication module integrated in the security chip of the mobile terminal is introduced, with fingerprint recognition as the only basis. The capacitive fingerprint sensor captures fingerprint images at a resolution of 500 dpi, and after hash feature extraction, compression and dimensionality reduction, a fingerprint vector feature group is obtained, which is then encrypted using AES-256. The key is dynamically generated by the entropy source in the TEE. The user's identity is compared through a specific feature matching function, and only when the matching score exceeds the safety threshold, the system determines that the authentication is passed and generates a communication permission, and then establishes an encrypted connection. This technical scheme effectively improves the security of the vehicle-mounted system communication connection and the accuracy of user identity recognition, realizes a terminal-level zero-trust start mechanism, and provides a trusted access basis for subsequent control links, with an average response time controlled within 185 ms, meeting the remote real-time control requirements, and also having the ability of identity uniqueness, anti-replay attack and anti-middleman attack.

[0083] In the human-computer interaction link, a three-dimensional posture simulation module based on the Unity3D engine is introduced. With the help of GPU accelerated rendering, real-time visualization mapping of five-dimensional parameters such as seat forward and backward sliding, height adjustment, backrest inclination, cushion pitch, and waist support is realized. Users interact through multi-touch screens, and the system captures touch data, maps two-dimensional screen coordinate changes to three-dimensional posture transformation parameters using trajectory fitting algorithms, and constructs a non-linear mapping transformation function and an adjustment response function. This function analyzes multi-touch gesture paths using neural spline interpolation algorithms, extracts sliding trajectory features using a Gaussian-radial hybrid kernel function group, and adaptively corrects input responses to suppress posture errors caused by minor jitter. The final five-dimensional control instruction vector is associated with the user ID and transmitted via encrypted Bluetooth channels. This allows users to intuitively control seat position and posture, improves the mapping accuracy between touch interaction and physical action, enhances the intuitiveness and accuracy of human-computer interaction, and provides a high-response, high-consistency input portal for the entire control chain.

[0084] For seat position and posture adjustment control, a dynamic parameter adjustment mechanism is introduced based on traditional PID closed-loop control. A dual-channel state evaluation model is constructed using CAN bus transmitted adjustment motor current sampling values and SPI interface returned displacement data to monitor motor load current trend in real time. When the actual motor current continuously exceeds 15% of the rated value, the fuzzy adjustment module is triggered, and based on the fuzzy control rule base, the error change rate and the current load ratio are input variables to jointly correct the proportional coefficient and integral time. At the same time, a parameter adjustment suppression function is introduced to avoid adjustment overshoot or response lag. Through this dynamic adjustment mechanism, the energy consumption robustness and adjustment stability of the motor during execution are improved, ensuring that the synchronization error is controlled within 0.5mm during multi-mechanism cooperative operation, and the reliability and safety of the physical execution layer are guaranteed.

[0085] In terms of safety protection, a high-speed response safety locking mechanism based on pressure signal triggering is introduced. High-sensitivity strain pressure sensors are placed at the slide rail end, lift column root, and rotary joint positions to construct a multi-point stress field monitoring network, real-time collect pressure signals and send them to the vehicle control unit, and determine abnormal conditions through an integrated logic judgment model. When the pressure abnormality criterion function value exceeds the set threshold, the dual safety linkage process is triggered: the permanent magnet attraction type electromagnetic brake of the electromagnetic brake module locks the adjustment motor output shaft within 50 milliseconds; at the same time, the control unit sends a collision warning frame to the vehicle body main control ECU through the CAN-FD bus, triggering the seat belt pretensioning device to tighten 5 centimeters in advance. In addition, the system updates the reference pressure of each sensor before each adjustment to improve decision accuracy and response stability. This mechanism forms a closed loop with other control links, significantly enhancing the system's fault tolerance and occupant protection capabilities.

[0086] In the aspect of personalized configuration recommendation, a high-dimensional multi-feature clustering algorithm is designed. After each adjustment is completed, the vehicle control unit uploads the user ID, height, weight and current adjustment parameters to the cloud server, constructs a user feature vector and performs normalization processing, and enters the clustering model. A dynamic density weight factor is introduced to overcome the problem of sensitivity to initial center of traditional K-means algorithm, and to enhance the clustering robustness under the condition of uneven distribution of user preference features. After a new user completes fingerprint authentication and device recognition, the system matches the initial feature vector of the new user with the cluster center in Euclidean distance, selects the historical average parameter configuration corresponding to the cluster center with the smallest distance as the recommended reference, and displays it in a three-dimensional model. The user can fine-tune based on the default recommendation. This realizes the data-driven user memory and dynamic adaptation ability, and improves the user experience.

[0087] Embodiment three

[0088] Figure 8 A structural schematic diagram of an adjusting device of a vehicle seat is provided for embodiment three of the present application. The device can perform the adjusting method of the vehicle seat provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the performing method. As shown in Figure 8 The device comprises:

[0089] The biometric feature identification verification module 310 is configured to, after the biometric feature identification verification of the target user is passed, acquire touch data of the target user in the seat adjustment interaction interface.

[0090] The adjustment value determination module 320 is configured to determine adjustment values corresponding to each part of the seat according to the touch data.

[0091] The seat adjustment module 330 is configured to, when controlling the motor in the seat according to the adjustment values to realize seat adjustment, update the proportional coefficient and the integral time in real time according to the motor current error change rate and the load ratio, and control the motor current through PID based on the updated proportional coefficient and integral time.

[0092] The technical scheme of the embodiment of the application comprises: a biological feature identifier verification module 310, configured to acquire touch data of a target user on a seat adjustment interaction interface after biological feature identifier verification of the target user is passed; a parameter value determination module 320, configured to determine parameter values corresponding to each part of the seat according to the touch data; and a seat adjustment module 330, configured to control a motor in the seat according to the parameter values to realize seat adjustment, and update a proportional coefficient and an integral time according to a motor current error change rate and a load ratio in real time, and control the motor current by PID based on the updated proportional coefficient and integral time. The technical scheme updates the proportional coefficient and the integral time according to the motor current error change rate and the load ratio, so that the PID control is more reasonable, the motor can stably operate in the seat adjustment process, and the seat adjustment error is reduced.

[0093] In the embodiment of the application, optionally, the biological feature identifier verification process of the target user comprises:

[0094] Fingerprint data of the target user is acquired, and feature extraction is performed on the fingerprint data to obtain a fingerprint vector feature group;

[0095] The fingerprint vector feature group and a pre-stored template feature are matched, and when a matching score is greater than a security threshold, it is determined that the biological feature identifier verification of the target user is passed.

[0096] In the embodiment of the application, optionally, the apparatus further comprises:

[0097] A dynamic key generation module, configured to generate a dynamic key after integrity check on a terminal device identifier and biological feature identifier verification information of the target user;

[0098] An encrypted communication module, configured to encrypt communication information based on the dynamic key when communicating with the terminal device for seat adjustment.

[0099] In the embodiment of the application, optionally, the parameter value determination module 320 comprises:

[0100] A parameter value determination unit, configured to determine the parameter values corresponding to each part of the seat according to the following formula:

[0101]

[0102] wherein, θ i represents the parameter value of the i th seat part, m is the number of touch nodes collected in the drag track in the touch data, (u j ,v j ) represents the displacement increment of the j th touch point in the screen coordinate system, φ ij is a kernel function group constructed for the dimension to which the i th seat part belongs, and λi δ is the touch sensitivity gain factor. i This represents the current position offset of the i-th seat part.

[0103] Optionally, in this embodiment of the application, the seat adjustment module 330 includes:

[0104] The parameter adjustment suppression value calculation unit is used to determine the parameter adjustment suppression value in real time based on the motor current error change rate and the load ratio if the load ratio of the determined current is greater than the preset load threshold.

[0105] The current control unit is used to update the proportional coefficient and integral time based on the parameter adjustment suppression value, and to control the motor current through PID control based on the updated proportional coefficient and integral time.

[0106] Optionally, in this embodiment of the application, the device further includes:

[0107] The pressure data acquisition module is used to acquire pressure data through pressure sensors located at the connection between the seat and the vehicle.

[0108] The impact risk assessment module is used to determine whether there is an impact risk based on the reference pressure value, as well as the current pressure value and pressure change rate reflected by the pressure data.

[0109] The risk handling module is used to lock the locking groove of the motor output shaft via an electromagnetic brake, and / or trigger the seat belt pretensioning device to tighten the seat belt, if present.

[0110] Optionally, in this embodiment of the application, the device further includes:

[0111] The dynamic density weighting factor calculation module is used to construct feature vectors based on each user's seat setting information and body attribute information, and calculate the dynamic density weighting factor based on the feature vectors using the following formula:

[0112]

[0113] Where, δ i For dynamic density weighting factor, u i Let represent the feature vector of the i-th user sample, m be the total number of samples, and α be the density sensitivity coefficient;

[0114] The clustering module is used to perform clustering based on each user's dynamic density weight factor and feature vector, and updates the cluster centers based on the following formula:

[0115]

[0116] in, Let C represent the new cluster center of class k after the (t+1)th iteration.k a set of all sample indexes currently belonging to the kth class;

[0117] The initial parameter setting module is configured to determine a target cluster center with the highest matching degree with the feature vector of the subsequent user from the cluster centers obtained after clustering, and perform initial parameter setting on the seat used by the subsequent user according to the seat setting information corresponding to the target cluster center.

[0118] The adjusting device of the vehicle seat provided in the embodiments of the present application can perform the adjusting method of the vehicle seat provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the performing method.

[0119] Embodiment Four

[0120] Figure 9 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0121] As shown in Figure 9 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0123] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the adjustment method of the vehicle seat.

[0124] In some embodiments, the adjustment method of the vehicle seat can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the adjustment method of the vehicle seat described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the adjustment method of the vehicle seat by any other appropriate means, such as by means of firmware.

[0125] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0126] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package and partially on a remote machine or entirely on a remote machine or server.

[0127] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0129] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0130] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0131] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, and the present disclosure is not limited in this regard.

[0132] The specific embodiments described above are not intended to limit the scope of the present disclosure. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present disclosure. Any further modifications, equivalents, and / or alternatives come within the scope of the present disclosure as recited by the claims.

Claims

1. A method for adjusting a vehicle seat, characterized in that, include: After the target user's biometric identification is verified, the touch data of the target user on the seat adjustment interface is obtained; The adjustment values ​​for each part of the seat are determined based on the touch data. When controlling the motor in the seat according to the adjustment value to achieve seat adjustment, the proportional coefficient and integral time are updated in real time according to the motor current error change rate and load ratio, and the motor current is controlled by PID based on the updated proportional coefficient and integral time.

2. The method according to claim 1, characterized in that, The biometric verification process for target users includes: Obtain the fingerprint data of the target user and extract its features to obtain fingerprint vector feature groups; The fingerprint vector feature group is matched with the pre-stored template features. When the matching score is greater than the security threshold, the biometric identification of the target user is confirmed to be successful.

3. The method according to claim 2, characterized in that, After the target user's biometric identification is verified, the method further includes: After verifying the integrity of the terminal device identifier and the target user's biometric identifier verification information, a dynamic key is generated; The communication information is encrypted using this dynamic key when communicating with the terminal device for seat adjustment.

4. The method according to claim 1, characterized in that, The adjustment values ​​for each part of the seat are determined based on the touch data, including: The adjustment values ​​for each part of the seat are determined using the following formula: Where, θ i This represents the adjustment value of the i-th seat part, where m is the number of touch nodes collected in the drag trajectory of the touch data, (u j ,v j φ represents the displacement increment of the j-th touch point in the screen coordinate system. ij λ is a set of kernel functions constructed for the dimension to which the i-th seat part belongs. i δ is the touch sensitivity gain factor. i This represents the current position offset of the i-th seat part.

5. The method according to claim 1, characterized in that, The proportional coefficient and integral time are updated in real time based on the motor current error change rate and load ratio. Based on the updated proportional coefficient and integral time, the motor current is controlled by a PID controller, including: If the current load ratio is determined to be greater than the preset load threshold, the parameter adjustment suppression value is determined in real time based on the motor current error change rate and the load ratio. The proportional coefficient and integral time are updated based on the parameter adjustment suppression value, and the motor current is controlled by PID based on the updated proportional coefficient and integral time.

6. The method according to claim 1, characterized in that, In the process of controlling the motor in the seat according to the adjustment value, the method further includes: Pressure data is collected by pressure sensors placed at the connection between the seat and the vehicle. Determine whether there is a risk of impact based on the reference pressure value, as well as the current pressure value and pressure change rate reflected in the pressure data; If present, the locking groove of the motor output shaft is locked by an electromagnetic brake, and / or the seat belt pretensioning device is triggered to tighten the seat belt.

7. The method according to claim 1, characterized in that, After the seat adjustment is completed, the method further includes: Feature vectors are constructed based on each user's seat settings and body attribute information, and dynamic density weighting factors are calculated based on the feature vectors using the following formula: Where, δ i For dynamic density weighting factor, u i Let represent the feature vector of the i-th user sample, m be the total number of samples, and α be the density sensitivity coefficient; Clustering is performed based on the dynamic density weight factor and feature vector of each user, and the cluster centers are updated according to the following formula: in, Let C represent the new cluster center of class k after the (t+1)th iteration. k This is the set of indices of all samples currently belonging to class k. The target cluster center with the highest matching degree with the feature vector of the subsequent user is determined among the cluster centers obtained after clustering. The initial parameters of the seats used by the subsequent users are set according to the seat setting information corresponding to the target cluster center.

8. A vehicle seat adjustment device, characterized in that, include: The biometric identification verification module is used to obtain the target user's touch data on the seat adjustment interaction interface after the target user's biometric identification is verified. The adjustment value determination module is used to determine the adjustment values ​​corresponding to each part of the seat based on the touch data; The seat adjustment module is used to control the motor in the seat according to the adjustment value, so that when adjusting the seat, the proportional coefficient and integral time are updated in real time according to the motor current error change rate and load ratio, and the motor current is controlled by PID based on the updated proportional coefficient and integral time.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the vehicle seat adjustment method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for adjusting a vehicle seat according to any one of claims 1-7.

Images