Vehicle seat control method, system and device, storage medium and program product

Abstract

The invention discloses a vehicle seat control method, system and device, a storage medium and a program product. The method comprises the steps of receiving a self-learning diagnosis instruction sent by diagnosis equipment, wherein the self-learning diagnosis instruction is used for instructing a first vehicle to start a seat self-learning process; in response to the self-learning diagnosis instruction, at least one adjusting shaft of a seat in the first vehicle is driven to move to a mechanical limit state, and the minimum adjusting position corresponding to the at least one adjusting shaft is determined; at least one adjusting shaft is driven to reversely move from the minimum adjusting position to a mechanical limit state, and the maximum adjusting position corresponding to the at least one adjusting shaft is determined; and storing the position data of the minimum adjustment position and the maximum adjustment position, wherein the position data is used for indicating the position adjustment range of the seat in the intelligent adjustment process.

Description

Vehicle seat control methods, systems, devices, storage media, and program products Technical Field

[0001] This application relates to the field of intelligent vehicle electronic control technology, and in particular to a vehicle seat control method, system, device, storage medium, and program product. Background Technology

[0002] With the rapid development of automotive intelligent technology, intelligent seats with multi-dimensional position adjustment and memory functions have become a core configuration for improving driving and riding comfort. Through the coordinated movement of adjustment axes such as level, backrest, and height, they can adapt to the needs of users with different heights and sitting habits, and are widely used in various passenger cars and commercial vehicles.

[0003] In related technologies, manual intervention is the primary method. Operators need to manually adjust the seat to a preset initial reference position, such as an upright backrest and a horizontally centered position. Then, they trigger the learning command through physical buttons or diagnostic equipment. During the adjustment of the axis, it is necessary to manually observe whether the axis stops moving due to mechanical obstruction in order to determine whether the limit position has been reached. Then, the minimum adjustment position and the maximum adjustment position data are manually recorded.

[0004] However, the reliance on manual intervention leads to high labor costs during mass production on the production line, and the low accuracy of extreme position adjustments results in low self-learning efficiency for smart seats. Summary of the Invention

[0005] This application provides a vehicle seat control method, system, device, storage medium, and program product. The technical solution is as follows.

[0006] On one hand, a vehicle seat control method is provided, the method comprising: receiving a self-learning diagnostic command sent by a diagnostic device, the self-learning diagnostic command being used to instruct a first vehicle to initiate a seat self-learning process; responding to the self-learning diagnostic command, driving at least one adjustment axis of the seat in the first vehicle to move to a mechanical limit state, and determining a minimum adjustment position corresponding to each of the at least one adjustment axis; driving the at least one adjustment axis to move in the opposite direction from the minimum adjustment position to the mechanical limit state, and determining a maximum adjustment position corresponding to each of the at least one adjustment axis; storing position data of the minimum adjustment position and the maximum adjustment position, the position data being used to indicate the position adjustment range of the seat during the intelligent adjustment process.

[0007] On the other hand, a vehicle seat control system is provided, the system including a diagnostic device, a control unit, and a seat in a first vehicle, the control unit being connected to the diagnostic device and associated with the seat; the diagnostic device being configured to send a self-learning diagnostic command to the control unit, the self-learning diagnostic command being used to instruct the first vehicle to initiate a seat self-learning process; the control unit being configured to, in response to the self-learning diagnostic command, drive at least one adjustment axis of the seat to move to a mechanical limit state to determine a minimum adjustment position corresponding to each of the at least one adjustment axis; drive the at least one adjustment axis to move in the opposite direction from the minimum adjustment position to the mechanical limit state to determine a maximum adjustment position corresponding to each of the at least one adjustment axis; and store position data of the minimum adjustment position and the maximum adjustment position, the position data being used to indicate the position adjustment range of the seat during intelligent adjustment.

[0008] On the other hand, a vehicle seat control device is provided, the device comprising: a receiving module for receiving a self-learning diagnostic command sent by a diagnostic device, the self-learning diagnostic command being used to instruct a first vehicle to initiate a seat self-learning process; a driving module for responding to the self-learning diagnostic command to drive at least one adjustment axis of the seat in the first vehicle to move to a mechanical limit state, and determining the minimum adjustment position corresponding to each of the at least one adjustment axis; the driving module is further configured to drive the at least one adjustment axis to move in the opposite direction from the minimum adjustment position to the mechanical limit state, and determining the maximum adjustment position corresponding to each of the at least one adjustment axis; and a storage module for storing position data of the minimum adjustment position and the maximum adjustment position, the position data being used to indicate the position adjustment range of the seat during the intelligent adjustment process.

[0009] In some embodiments, the self-learning diagnostic instructions conform to the Unified Diagnostic Service (UDS) protocol.

[0010] In some embodiments, the drive module is further configured to: drive the at least one adjusting shaft to move in response to the self-learning diagnostic command, and detect a stall signal of the at least one adjusting shaft; determine the mechanical limit state based on the stall signal; and determine the minimum adjustment position when the at least one adjusting shaft moves to the mechanical limit state.

[0011] In some embodiments, the at least one adjustment axis includes a horizontal adjustment axis, a backrest adjustment axis, and a height adjustment axis; the drive module is further configured to, in response to the self-learning diagnostic command, drive the horizontal adjustment axis, the backrest adjustment axis, and the height adjustment axis to move synchronously to their respective corresponding mechanical limit states, and determine in parallel the minimum adjustment positions corresponding to the horizontal adjustment axis, the backrest adjustment axis, and the height adjustment axis; wherein, the minimum adjustment position of the horizontal adjustment axis includes the limit position where the seat is farthest from the steering wheel of the first vehicle, the minimum adjustment position of the backrest adjustment axis includes the limit position where the seat is closest to the steering wheel of the first vehicle, and the minimum adjustment position of the height adjustment axis includes the lowest limit position of the seat.

[0012] In some embodiments, the storage module is further configured to: record, in real time, status information and fault information during the self-learning process while determining the minimum adjustment position and the maximum adjustment position; the status information includes at least one of self-learning in progress, self-learning paused, and self-learning completed; the fault information includes at least one of adjustment shaft movement timeout, adjustment shaft stall signal abnormality, and diagnostic command interruption; and synchronously store the status information and the fault information to the local storage component and the remote service platform of the first vehicle; the local storage component is used for querying the first vehicle on-site through the diagnostic equipment; and the remote service platform is used for remotely tracing the cause of self-learning abnormalities.

[0013] In some embodiments, the receiving module is further configured to receive a personalized location memory instruction sent by a first user; the storage module is further configured to, in response to the personalized location memory instruction, record the currently adjusted seat position as a personalized position associated with the first user, the personalized position being located between the minimum adjustment position and the maximum adjustment position; the driving module is further configured to, when the first vehicle recognizes the first user, drive the seat to adjust to the personalized position; wherein, during the process of adjusting the seat to the personalized position, when the positional distance between the seat position and the minimum adjustment position or the maximum adjustment position is less than a distance threshold, the adjustment speed of the seat is reduced.

[0014] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the vehicle seat control method as described in any of the embodiments of this application above.

[0015] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the vehicle seat control method as described in any of the embodiments of this application above.

[0016] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the vehicle seat control methods described in the above embodiments.

[0017] The beneficial effects of the technical solution provided in this application embodiment include at least the following: by receiving self-learning diagnostic commands, the seat self-learning process is automatically triggered, eliminating the need for operators to manually adjust the seat to a preset initial reference position. This eliminates the necessity of manual intervention from the self-learning initiation stage, significantly reducing manual operation steps during mass production and lowering labor costs. By driving at least one adjustment axis of the seat to its mechanical limit state to determine the minimum and maximum adjustment positions corresponding to each adjustment axis, objective judgment based on mechanical limit states is achieved, avoiding subjective errors from manual observation and improving the accuracy of limit position adjustment. By storing position data, manual recording of position data is eliminated, further reducing manual intervention. Simultaneously, it provides a precise position range basis for subsequent intelligent seat adjustment, avoiding adjustment range deviations caused by manual recording errors. This achieves full automation of the intelligent seat self-learning process, reducing labor costs during mass production, improving the accuracy of limit position adjustment, enhancing the self-learning efficiency of the intelligent seat, and ensuring the reliability of subsequent intelligent seat adjustments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a schematic diagram of a computer system provided in an exemplary embodiment of this application; Figure 2 is a flowchart of a vehicle seat control method provided in an exemplary embodiment of this application; Figure 3 is a schematic diagram of a vehicle control system provided in an exemplary embodiment of this application; Figure 4 is a schematic diagram of the architecture of a vehicle seat control system provided in an exemplary embodiment of this application; Figure 5 is a flowchart of a vehicle seat control system provided in an exemplary embodiment of this application; Figure 6 is a structural block diagram of a vehicle seat control device provided in an exemplary embodiment of this application; Figure 7 is a structural block diagram of a terminal provided in an exemplary embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0021] It should be understood that although the terms first, second, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, a first parameter may also be referred to as a second parameter without departing from the scope of this disclosure, and similarly, a second parameter may also be referred to as a first parameter. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0022] In related technologies, manual intervention is dominant. Operators must first manually adjust the seat to a preset initial reference position, such as an upright backrest and horizontal centering. Then, a learning command is triggered via physical buttons or diagnostic equipment. During the adjustment axis movement, it is necessary to manually observe whether the axis stops moving due to mechanical obstruction to determine if the limit position has been reached, and then manually record the minimum and maximum adjustment position data. However, reliance on manual intervention leads to high labor costs during mass production on the production line, and the accuracy of limit position adjustment is low, resulting in low self-learning efficiency for intelligent seats.

[0023] The vehicle seat control method provided in this embodiment automatically triggers the seat self-learning process by receiving a self-learning diagnostic command. This eliminates the need for manual adjustment of the seat to a preset initial reference position, thus removing the need for manual intervention from the self-learning initiation stage. This significantly reduces manual operation steps during mass production and lowers labor costs. By driving at least one adjustment axis of the seat to its mechanical limit state to determine the minimum and maximum adjustment positions corresponding to each axis, objective judgment based on mechanical limit states is achieved, avoiding subjective errors from manual observation and improving the accuracy of limit position adjustment. Furthermore, by storing position data, manual recording of position data is eliminated, further reducing manual intervention. This provides a precise position range basis for subsequent intelligent seat adjustment, avoiding adjustment range deviations caused by manual recording errors. This fully automates the intelligent seat self-learning process, reducing labor costs during mass production, improving the accuracy of limit position adjustment, enhancing the efficiency of intelligent seat self-learning, and ensuring the reliability of subsequent intelligent seat adjustments.

[0024] First, the computer system of this application will be described. Please refer to Figure 1, which shows a schematic diagram of a computer system provided in an exemplary embodiment of this application. The computer system includes: a terminal 10, a server 20, and a vehicle infotainment system 30.

[0025] The vehicle infotainment system 30 is used to control the first vehicle, the terminal 10 is the terminal device bound to the first vehicle, and the server 20 is the remote server of the vehicle infotainment system 30.

[0026] In some embodiments, the vehicle infotainment system 30 can control the first vehicle to achieve intelligent seat self-learning adjustment. Illustratively, the vehicle infotainment system 30 receives a self-learning diagnostic command sent by a diagnostic device, which instructs the first vehicle to initiate the seat self-learning process; in response to the self-learning diagnostic command, it drives at least one adjustment axis of the seat in the first vehicle to move to its mechanical limit state, determining the minimum adjustment position corresponding to each of the at least one adjustment axis; it drives at least one adjustment axis to move in the reverse direction from the minimum adjustment position to the mechanical limit state, determining the maximum adjustment position corresponding to each of the at least one adjustment axis; and it stores the position data of the minimum and maximum adjustment positions, which indicates the position adjustment range of the seat during the intelligent adjustment process.

[0027] The vehicle system 30 can upload location data to the server 20 to achieve cloud storage of the data, which can be used for remote diagnosis by vehicle maintenance personnel.

[0028] The vehicle system 30 can also receive control commands from the terminal 10. For example, if a user sends a personalized position memory command to the vehicle system 30 through the terminal 10, the vehicle system 30 will respond to the personalized position memory command, record the currently adjusted seat position as the personalized position associated with the user, and ensure that the personalized position is between the minimum adjustment position and the maximum adjustment position based on the position data.

[0029] It is worth noting that the above-described interaction methods are merely illustrative examples, and the embodiments of this application do not limit them.

[0030] The aforementioned terminal is optional and can be a desktop computer, laptop computer, mobile phone, tablet computer, e-book reader, Moving Picture Experts Group Audio Layer III (MP3) player, Moving Picture Experts Group Audio Layer IV (MP4) player, smart TV, smart vehicle, and other types of terminal devices. This application embodiment does not limit the specific terminal device to these types.

[0031] It is worth noting that the aforementioned servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud security, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0032] Cloud technology refers to a managed technology that unifies a series of resources such as hardware, software, and networks within a wide area network or local area network to achieve data computing, storage, processing, and sharing.

[0033] In some embodiments, the server described above can also be implemented as a node in a blockchain system.

[0034] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant regions. For example, the operational data and account information involved in this application were obtained with full authorization.

[0035] To further clarify, this application may display a prompt interface, pop-up window, or output voice prompts before and during the collection of user-related data (e.g., account information, historical operation data, and real-time operation data involved in this application). These prompt interfaces, pop-ups, or voice prompts are used to inform the user that their relevant data is being collected. This ensures that the application only begins the steps for collecting user-related data after receiving confirmation from the user regarding the prompt interface or pop-up window; otherwise (i.e., without receiving confirmation from the user), the steps for collecting user-related data end, meaning no user-related data is collected. In other words, all user data collected in this application is collected with the user's consent and authorization, and the collection, use, and processing of relevant user data must comply with the relevant laws, regulations, and standards of the relevant regions.

[0036] For illustration, please refer to Figure 2, which shows a flowchart of a vehicle seat control method provided in an exemplary embodiment of this application. The method can be executed by a terminal, by a server, or by both a terminal and a server. This embodiment of the application takes the method being executed by a terminal as an example. As shown in Figure 2, the method includes the following steps: Step 210, receiving a self-learning diagnostic command sent by a diagnostic device.

[0037] The self-learning diagnostic command is used to instruct the first vehicle to initiate the seat self-learning process.

[0038] The self-learning instructions sent by the diagnostic equipment are the start switch for the seat's self-learning process, used to trigger it by replacing manual intervention with standardized instructions.

[0039] Regarding the sender and receiver of instructions, the sender is a diagnostic device, including electrical testing equipment commonly used in automotive production lines and diagnostic instruments used in after-sales maintenance; the receiver is the seat control unit of the first vehicle, such as a zone controller. The control unit establishes a connection with the diagnostic device through a reliable communication link such as a CAN bus to ensure that the instruction transmission is without delay or loss.

[0040] Regarding the core function of the command, the self-learning diagnostic command is not only a start signal, but also implies the logic that the initial position does not need to be preset manually. Unlike manually adjusting the seat to a reference position such as upright backrest and horizontal center, the self-learning diagnostic command directly instructs the control unit to start fully automated learning, eliminating manual operation links from the source of the process, adapting to the mass production scenario of the production line, and eliminating the need for workers to manually calibrate the initial state of each seat.

[0041] In some embodiments, the self-learning diagnostic instructions conform to the Unified Diagnostic Services (UDS) protocol.

[0042] The UDS protocol defines a variety of core diagnostic services, such as fault code reading, data reading and writing, and routine control. The self-learning diagnostic command corresponds to the routine control service (service ID: 0x31), which is used to trigger the ECU to execute a specific preset process (routine), which is fully matched with the function of the start seat self-learning process.

[0043] As an illustration, the UDS routine control service distinguishes different operations through sub-function fields, such as "start routine", "stop routine", and "request routine result". The sub-function of the self-learning diagnostic command is set to "start seat self-learning routine" (such as sub-function code 0x01). After receiving the command, the seat control unit can directly identify the purpose of the command through the sub-function field without additional parsing of private fields.

[0044] To avoid command ambiguity, the self-learning diagnostic command supplements the data parameter segment of the routine control service with the unique identifier (such as hardware ID) of the Electronic Control Unit (ECU) of the seat and the type of adjustment axis to be learned, such as the combination code of "horizontal axis + backrest axis + height axis". This ensures that the control unit only drives the target adjustment axis of the target seat to perform the learning, which is in line with the diagnostic principle of UDS to accurately locate the ECU and function.

[0045] The method provided in this application uses self-learning diagnostic instructions that conform to standardized protocols such as the UDS protocol. These instructions include clear self-learning trigger identifiers, such as routine control service instructions. After receiving these instructions, the control unit can directly parse them into operation instructions to start self-learning. This eliminates the need for additional adaptation to different types of diagnostic equipment, ensuring compatibility and versatility.

[0046] Step 220: In response to the self-learning diagnostic command, drive at least one adjustment shaft of the seat in the first vehicle to move to the mechanical limit state, and determine the minimum adjustment position corresponding to each of the at least one adjustment shaft.

[0047] In some embodiments, objective judgment based on mechanical limits replaces manual observation and judgment to improve the self-learning efficiency of the seat.

[0048] After receiving the self-learning diagnostic command, the control unit immediately sends a drive signal to the seat adjustment shaft drive module, such as the adjustment shaft motor controller, without the need for manual secondary confirmation. This achieves a seamless connection between "command reception and action execution" and improves process efficiency.

[0049] Optionally, at least one adjustment axis includes at least one of a horizontal adjustment axis (controlling the seat to move forward and backward), a backrest adjustment axis (controlling the backrest angle), and a height adjustment axis (controlling the seat to rise and fall). Depending on the vehicle model requirements, single-axis or multi-axis synchronous execution can be selected. In this embodiment, multi-axis synchronous motion, such as three-axis parallel drive, is preferred to further shorten the learning time.

[0050] In some embodiments, the mechanical limit state can be identified by the control unit based on sensor feedback and logical judgment.

[0051] Optionally, the determination of mechanical limit states may include, but is not limited to, at least one of current determination and position signal determination.

[0052] Taking current judgment as an example, the current sensor detects the change in current of the motor driving the adjustment shaft. When the motor can no longer rotate due to mechanical obstruction (such as the shaft reaching the physical limit), the current will suddenly increase to a preset threshold, such as 2-3 times the normal operating current. At this time, it is determined that the mechanical limit has been reached.

[0053] Taking position signal judgment as an example, the position change of the adjustment shaft is detected by Hall sensor, infrared distance sensor, etc. When the sensor signal does not update for a preset time (such as 500 milliseconds) (i.e. the shaft stops moving), it is determined that the mechanical limit has been reached.

[0054] In some embodiments, in response to a self-learning diagnostic command, at least one adjusting shaft is driven to move, and a stall signal of at least one adjusting shaft is detected; a mechanical limit state is determined based on the stall signal; and a minimum adjusting position is determined when at least one adjusting shaft moves to the mechanical limit state.

[0055] Using the stall signal as an objective basis for determining the mechanical limit state, when the adjusting shaft moves to its physical limit (such as the end of the slide rail or the lowest point of the lifting mechanism), the shaft is mechanically blocked and cannot continue to move. The drive motor will enter a stall state due to the sudden increase in load, and the resulting stall signal can be directly mapped to the mechanical limit state, thereby accurately determining the minimum adjustment position. The entire process requires no manual observation or preset benchmarks, relying entirely on hardware detection and logical judgment.

[0056] The control unit presets the drive direction of the adjustment shaft according to the physical definition of the minimum adjustment position. For example, the horizontal adjustment shaft needs to move away from the steering wheel (corresponding to its minimum adjustment position), the height adjustment shaft needs to move downward, and the backrest adjustment shaft needs to move closer to the steering wheel. The drive direction is achieved by controlling the forward and reverse rotation of the motor (e.g., the motor rotates forward to drive the horizontal shaft away from the steering wheel, and reverses to move closer), ensuring that the adjustment shaft moves in the direction that triggers the mechanical limit from the start, reducing invalid travel.

[0057] During the movement of the adjustment shaft, the seat control unit collects physical signals related to stall in real time through dedicated sensors. These signals are direct evidence for judging mechanical limits.

[0058] After receiving stall signals (such as current data and torque data), the control unit filters out interference signals (such as instantaneous current fluctuations and slight shaft jamming) through multi-condition judgment logic to ensure that the judgment is triggered only at the actual mechanical limit, avoiding the minimum adjustment position deviation caused by misjudgment.

[0059] Relying solely on threshold judgment to filter transient interference signals may lead to misjudgments (such as a momentary jam when the adjusting shaft moves across the slide rail joint, which may cause the current to briefly exceed the threshold, but is not the mechanical limit). Therefore, a duration judgment needs to be added. The control unit will detect the duration for which the signal exceeds the threshold. Only when the duration is ≥ the preset duration (such as 300 milliseconds) will it be confirmed as a real stall. If the duration is < 300 milliseconds, it is judged as transient interference, the signal is ignored, and the adjusting shaft continues to move.

[0060] In some embodiments, position sensor data can also be used to assist in determining the mechanical limit state.

[0061] To illustrate, if the position sensor (such as a Hall sensor or infrared sensor) of the adjusting shaft does not update for 300 milliseconds (i.e., the shaft stops moving completely), and the stall signal meets the "threshold + duration" condition at this time, then it is 100% confirmed that the adjusting shaft has reached the mechanical limit state.

[0062] Once the control unit confirms through the above logic that the adjustment shaft has reached its mechanical limit, it will immediately execute the minimum adjustment position determination operation, the core of which is to accurately record the current position data.

[0063] The position sensors corresponding to the adjustment axes (such as the Hall position sensor for the horizontal axis and the infrared distance sensor for the height axis) will output the current position coordinates in real time. For example, if the horizontal axis is 80 cm away from the steering wheel and the height axis is 8 cm away from the chassis, the control unit will read the real-time data of the position sensor at the moment of determining the mechanical limit to ensure that there is no delay in data acquisition.

[0064] In some embodiments, the validity of location data can also be verified.

[0065] The control unit will verify whether the read position data is within a reasonable range. For example, the position coordinate of the horizontal axis should be between "50-100 cm". If "120 cm" is read, the data is considered abnormal. If the data is valid, it will be stored. If it is abnormal, it will be read again (up to 3 times) to avoid position data errors caused by momentary sensor failure.

[0066] The method provided in this application determines the mechanical limit state through a stall signal. The stall signal is a feedback of the physical characteristics of the motor or shaft, which is not affected by the operator's experience or attention. This can improve the objectivity of the limit position during the self-learning process. Moreover, the stall signal responds quickly with no judgment delay, and can accurately capture the instantaneous position of the shaft just reaching the limit, avoiding over-drive caused by delay, improving the shaft's lifespan. Furthermore, the detection accuracy of the stall signal is not affected by environmental interference, can adapt to complex working conditions, has high reliability, and improves the accuracy and efficiency of self-learning.

[0067] Taking at least one adjustment axis including a horizontal adjustment axis, a backrest adjustment axis and a height adjustment axis as an example, the process of determining the minimum adjustment position includes, in response to the self-learning diagnostic command, driving the horizontal adjustment axis, the backrest adjustment axis and the height adjustment axis to move synchronously to their respective mechanical limit states, and determining the minimum adjustment positions corresponding to the horizontal adjustment axis, the backrest adjustment axis and the height adjustment axis in parallel.

[0068] The minimum adjustment position of the horizontal adjustment axis includes the extreme position where the seat is farthest from the steering wheel of the first vehicle; the minimum adjustment position of the backrest adjustment axis includes the extreme position where the seat is close to the steering wheel of the first vehicle; and the minimum adjustment position of the height adjustment axis includes the lowest extreme position of the seat.

[0069] Once the self-learning diagnostic command is received and parsed by the seat control unit (such as the zone controller), the control unit will immediately start the multi-axis synchronous drive logic.

[0070] For the unified generation and distribution of drive signals, the seat learning control module in the control unit generates parallel drive signals for the horizontal adjustment axis, backrest adjustment axis, and height adjustment axis based on the same timing reference, such as the synchronous clock signal of the vehicle controller area network (CAN) bus. Unlike single-axis sequential driving, which requires waiting for the previous axis to complete before triggering the next axis, the simultaneous distribution of signals ensures that the drive motors of the three adjustment axes start and run at the same time without any sequential delay.

[0071] With the preset matching for the direction of motion, the drive signal already contains the direction of motion corresponding to the minimum adjustment position, so no manual intervention is required.

[0072] For the horizontal adjustment axis, the drive direction is set away from the steering wheel of the first vehicle, that is, the seat moves backward, with the goal of reaching the mechanical limit of rearward adjustment; for the backrest adjustment axis, the drive direction is set towards the steering wheel of the first vehicle, that is, the backrest folds forward, with the goal of reaching the mechanical limit of forward adjustment; for the height adjustment axis, the drive direction is set downward, that is, the seat is lowered as a whole, with the goal of reaching the mechanical limit of downward adjustment.

[0073] Based on the direction preset, the physical meaning of the minimum adjustment position is accurately matched, ensuring that the adjustment shaft moves in the correct direction from the start and avoiding invalid stroke.

[0074] During the synchronous movement of the three adjustment axes, the mechanical limit state of each axis is determined by independent sensor feedback and control unit logic judgment, thereby locking the minimum adjustment position.

[0075] Regarding the limit determination and minimum position determination of the horizontal adjustment shaft away from the steering wheel, after receiving the backward movement signal, the drive motor of the horizontal adjustment shaft drives the seat slide rail to slide away from the steering wheel. During the sliding process, the Hall position sensor installed at the end of the slide rail will collect the displacement data of the shaft in real time, such as updating the current position coordinates every 100 milliseconds, and transmit the data to the control unit.

[0076] When the seat slide rail reaches its physical limit for rearward adjustment, such as the metal stop at the end of the rail, the shaft cannot move further backward. The drive motor will stall due to the sudden increase in load. The control unit detects the motor current through a current sensor. When the current value suddenly increases to 2.5 times the normal operating current (a preset threshold, adaptable to different motor models), and the Hall position sensor data remains unchanged for 300 milliseconds (confirming the shaft has completely stopped), it is determined that the horizontal adjustment shaft has reached its mechanical limit, far from the steering wheel. The control unit immediately records the coordinate data fed back by the Hall position sensor at this time. For example, if the distance from the center of the steering wheel is 80 centimeters, this coordinate is the minimum adjustment position of the horizontal adjustment shaft.

[0077] Regarding the limit determination and minimum position determination of the backrest adjustment shaft in the direction of the steering wheel, after receiving the forward folding signal, the drive motor of the backrest adjustment shaft drives the backrest to rotate around the rotation axis in the direction of the steering wheel. During the rotation, the angle sensor installed at the backrest rotation shaft will collect the tilt angle data of the backrest in real time (such as the current angle is 85°, 84°, etc.) and transmit it to the control unit synchronously.

[0078] When the backrest rotates forward to its physical stop position, such as when the bottom of the backrest contacts the limiting protrusion of the seat cushion, the shaft cannot continue to rotate forward, and the drive motor also enters a stall state. The control unit detects a sudden increase in motor current through the current sensor (same as the horizontal axis threshold logic), and the angle sensor data remains unchanged for 300 milliseconds (e.g., stable at 60°). This indicates that the backrest adjustment shaft has reached its mechanical limit state close to the steering wheel. The control unit records the angle data at this time (e.g., "60°", with the seat cushion horizontal as the 0° reference). This angle is the minimum adjustment position of the backrest adjustment shaft.

[0079] Regarding the limit determination and minimum position determination of the downward movement direction of the height adjustment shaft, after receiving the downward movement signal, the drive motor of the height adjustment shaft drives the seat lifting mechanism to descend in the vertical direction. During the descent, the infrared distance sensor installed on the lifting mechanism support will collect the distance data between the bottom of the seat and the vehicle chassis in real time (such as the current distance is 15 cm, 14 cm, etc.) and transmit it to the control unit.

[0080] When the seat descends to its lowest physical limit, such as the lowest support point of the lifting mechanism, the shaft can no longer descend, causing the drive motor to stall. The control unit detects a sudden increase in motor current, and if the infrared distance sensor data remains stable at a certain value (e.g., "8 cm") for 300 milliseconds, it determines that the height adjustment shaft has reached its mechanical limit for downward movement. The control unit records the distance data at this time (e.g., "8 cm"), which is the minimum adjustment position of the height adjustment shaft.

[0081] The method provided in this application, through the synchronous movement of three adjustment axes, independent determination of limits, and locking of the minimum adjustment position, enables parallel processing to replace serial processing, significantly improving self-learning efficiency and adapting to mass production on production lines; avoiding errors caused by manual intervention and ensuring the accuracy of the minimum position; adapting to the learning needs of multiple scenarios, offering greater flexibility. If a certain vehicle model only needs to learn the "horizontal + height axes" (such as a fixed backrest model), the control unit can use the adjustment axis type parameters in the self-learning diagnostic instructions to synchronously drive the target axis movement without modifying the core logic, adapting to the seat configuration requirements of different vehicle models; through the logic of synchronous driving, independent determination, and parallel recording, it achieves automated and precise determination of the minimum adjustment position, while solving the defects of low efficiency and large errors in related technologies.

[0082] Step 230: Drive at least one adjustment shaft to move in the reverse direction from the minimum adjustment position to the mechanical limit state, and determine the maximum adjustment position corresponding to each of the at least one adjustment shaft.

[0083] In some embodiments, the continuous determination of the minimum adjustment position and the maximum adjustment position is achieved through automatic reverse motion.

[0084] Regarding the triggering logic for reverse movement, there is no need to manually send reverse drive commands. After the control unit determines the minimum adjustment position of a certain adjustment axis, it immediately and automatically switches the drive direction. For example, the horizontal adjustment axis switches from away from the steering wheel to closer to the steering wheel, realizing the automatic connection between minimum position determination and reverse movement initiation, avoiding the inefficiency of manual switching of adjustment direction required by existing technologies.

[0085] For the reuse judgment of mechanical limit states, the judgment method of the mechanical limit state of the maximum adjustment position is consistent with that of the mechanical limit state of the minimum adjustment position (current judgment or position signal judgment) to ensure that the judgment standard is consistent. For example, when the horizontal adjustment shaft moves in the opposite direction to the closest position to the steering wheel, the motor current suddenly increases, and the control unit judges that the mechanical limit has been reached, ensuring that the judgment accuracy of the maximum adjustment position is consistent with that of the minimum adjustment position.

[0086] The definition and determination of the maximum adjustment position corresponds inversely to the minimum adjustment position. The horizontal adjustment axis is at its maximum position closest to the steering wheel, the backrest adjustment axis is at its maximum position furthest from the steering wheel, and the height adjustment axis is at its maximum position at the highest position of the seat. The control unit records the position data at this time, which is the maximum adjustment position of the adjustment axis. This completes the determination of the full adjustment range of the minimum and maximum adjustment positions of a single axis.

[0087] In some embodiments, initial environmental data can be collected by multiple source sensors on the seat before driving at least one adjustment shaft to its mechanical limit.

[0088] The multi-source sensors include a pressure sensor matrix, an infrared distance sensor, and an angle sensor. The initial environmental data includes whether the seat is under load and the initial distance between the seat and the steering wheel of the first vehicle.

[0089] If a load is detected on the seat (e.g., someone is sitting on it), the self-learning process is paused and a load warning signal is sent to the diagnostic device to prevent the load from affecting the accuracy of the limit position judgment.

[0090] In some embodiments, after collecting initial environmental data through multi-source sensors, if the seat is found to be unloaded, the approximate movement range of the adjustment shaft is predicted based on the initial distance between the seat and the steering wheel collected by the infrared distance sensor. If the initial distance is too close, the horizontal adjustment shaft is preferentially controlled to move away from the steering wheel direction, thereby shortening the time it takes for the adjustment shaft to reach its mechanical limit state.

[0091] In some embodiments, during the process of driving at least one adjusting shaft to its mechanical limit, the speed of the adjusting shaft can also be adjusted based on the current temperature environment of the first vehicle (obtained by an in-vehicle temperature sensor).

[0092] As an illustration, when the temperature is below -20℃ or above 80℃, reduce the movement speed of the adjustment shaft, such as from 100% of the normal speed to 60%, to avoid movement jamming caused by shaft lubrication failure under extreme temperatures and to improve the stability of the self-learning process.

[0093] Step 240: Store the position data of the minimum adjustment position and the maximum adjustment position.

[0094] Position data is used to indicate the range of position adjustment of the seat during the intelligent adjustment process.

[0095] Regarding the data storage implementation method, the control unit stores the minimum and maximum adjustment positions of each adjustment axis in local storage components, such as electrically erasable programmable read-only memory (EEPROM) or flash memory. In some scenarios, it can also be synchronously uploaded to a remote service platform.

[0096] Local storage facilitates subsequent local retrieval by the vehicle, such as real-time range query when adjusting seats. Remote storage facilitates production line traceability or after-sales troubleshooting, such as verifying consistency of learning data during mass production. Moreover, the storage process does not require manual entry, avoiding data recording errors.

[0097] The position data indicates the range of seat adjustment during the intelligent adjustment process. When the user adjusts the seat later (such as manual adjustment or automatic memory recall), the control unit will compare the current adjustment position with the stored minimum-maximum position range in real time. If it approaches the limit position, it will automatically slow down or stop to avoid the adjustment shaft exceeding the mechanical range, which may cause motor damage or shaft jamming. At the same time, this range is also the basis for personalized position memory. For example, the comfortable position remembered by the user must be within this range to ensure the safety and accuracy of subsequent intelligent adjustments.

[0098] In some embodiments, the stored location data can be reused long-term and updated only when the vehicle experiences severe vibrations (such as a collision) or periodic inspections (such as every 3 months), without having to re-perform self-learning every time the seat is used, thus balancing data accuracy and usage efficiency.

[0099] Optionally, once the minimum and maximum adjustment positions of any adjustment axis are determined, the position data corresponding to that adjustment axis can be stored immediately without waiting for other adjustment axes to complete the determination of their minimum and maximum adjustment positions.

[0100] In some embodiments, after storing the position data, an intermediate adjustment position of the seat is determined based on the position data, such as 50% between the minimum and maximum adjustment positions, and at least one adjustment axis is driven to move to the intermediate adjustment position.

[0101] In some embodiments, during subsequent intelligent seat adjustment of the first vehicle, the range of motion of the adjustment shaft is limited to the interval between the minimum and maximum adjustment positions to prevent the adjustment shaft from exceeding its mechanical limits and causing damage.

[0102] In some embodiments, during the process of determining the minimum adjustment position and the maximum adjustment position, the status information and fault information during the self-learning process are recorded in real time, and the status information and fault information are synchronously stored in the local storage component of the first vehicle and the remote service platform.

[0103] Optionally, the status information includes at least one of self-learning in progress, self-learning paused, and self-learning completed, and the fault information includes at least one of adjusting shaft movement timeout, adjusting shaft stall signal abnormality, and diagnostic command interruption.

[0104] The local storage component is used for on-site queries via diagnostic equipment in the first vehicle, while the remote service platform is used for remote tracing of the causes of self-learning anomalies.

[0105] By recording fault information and status information, the entire self-learning process can be traced. By recording status information (reflecting process progress) and fault information (locating the cause of abnormality) in real time and storing them synchronously on both local and remote carriers, the shortcomings of no record in the self-learning process and the need to disassemble the seat for troubleshooting after a fault are solved.

[0106] After the self-learning diagnostic command is parsed by the seat control unit and the adjustment shaft drive is activated, the information recording module will be activated simultaneously, realizing seamless binding between the self-learning process and information recording, and achieving data recording without delay or omission.

[0107] Regarding the triggering timing, the control unit sends a record start command to the information recording module at the same time as sending a drive signal to the adjustment axis, ensuring that the first action of self-learning (the adjustment axis starts to move) is included in the recording range, thus avoiding the loss of information in the early stages of the process.

[0108] Regarding the recording frequency, status information is updated according to process nodes. For example, if the process enters "adjustment axis in motion", "self-learning" is recorded immediately. Fault information is triggered in real time according to abnormal events. For example, if adjustment axis timeout is detected, "motion timeout" is recorded immediately. There is no need for fixed periodic polling, which reduces the computing power consumption of the control unit and ensures the timeliness of information.

[0109] For information association dimensions, each record is automatically appended with a timestamp, a unique identifier for the seat ECU (such as a hardware ID), and the type of adjustment axis. For example, "2025-10-01 14:30:05.123 | ECU_ID:12345 | Adjustment axis: Horizontal axis | Status: Self-learning". Subsequent queries can use these dimensions to accurately locate information about a specific moment, a specific seat, and a specific axis, avoiding confusion.

[0110] Status information is used to track whether self-learning is progressing normally, and fault information is used to locate the root cause of anomalies.

[0111] For the self-learning process in the status information, the triggering conditions include the adjustment axis starting to move (regardless of whether it has reached its limit) and no fault being detected; this is used to indicate that the self-learning process is progressing normally. At this time, the record will be updated with the current moving axis and the duration of movement, which makes it easier to determine whether the process is stuck at a certain stage.

[0112] For the self-learning pause in the status information, the triggering conditions include the detection of non-fatal interference, such as a sudden load on the seat or a temporary disconnection of the diagnostic equipment that is restored within 3 seconds. The control unit pauses the movement of the adjustment axis. This is used to indicate that the process is temporarily interrupted but can be resumed. The record will indicate the reason for the pause. After the interference is eliminated, the process can continue from the paused node without restarting.

[0113] For the self-learning completion status information, the triggering conditions include that the minimum / maximum adjustment position of all target adjustment axes has been determined and there are no faults; it is used to indicate that the process has ended normally, and the record will summarize the total learning time and the position data of each axis to form a complete process closed loop record.

[0114] For the adjustment shaft movement timeout in the fault information, the triggering conditions include the adjustment shaft moving in a preset direction, but exceeding the timeout threshold. For example, the horizontal shaft is preset to reach the limit within 30 seconds, but no stall signal is detected after 40 seconds of actual movement. The fault may be due to the adjustment shaft slide rail being stuck (such as being blocked by foreign objects), insufficient power of the drive motor, or a faulty stall signal detection sensor. The record will include the movement time and the current position to help troubleshoot whether the shaft is stuck (the position is not moving) or the sensor is not detecting a signal (the position is still moving but there is no stall).

[0115] For abnormal stall signal of the adjusting shaft, the triggering conditions include the adjusting shaft moving to the vicinity of the physical limit, but no stall signal that meets the standard is detected, such as no sudden increase in current, or the duration of the sudden increase is less than 300 milliseconds; the cause of the fault may be a fault in the stall signal detection sensor (such as a current sensor), an incorrect setting of the motor stall current threshold, or the adjusting shaft not actually reaching the limit (such as assembly deviation of the limit parts). The record will include real-time current data and position data, which will directly narrow down the troubleshooting scope to the sensor or limit parts.

[0116] For diagnostic command interruptions, the triggering conditions include a sudden interruption of communication between the seat control unit and the diagnostic equipment during the self-learning process, such as loss of CAN bus signal, power failure of the diagnostic equipment, and exceeding the reconnection threshold, such as failure to restore communication within 5 seconds. The cause of the fault may be poor contact of the communication line, fault of the diagnostic equipment, or fluctuation of vehicle power supply. The record will include the process node at the time of the interruption, and it can be determined whether the learning needs to be restarted after the communication is restored.

[0117] For local storage, automotive-grade non-volatile storage components, such as EEPROM and Flash chips, can be used as storage media. These components are characterized by data retention during power outages, resistance to high and low temperatures (-40℃~150℃), suitability for complex vehicle operating conditions, and sufficient storage capacity.

[0118] As an illustration, the storage format is a structured format of "timestamp + information type + associated dimension + content", for example: 2025-10-01 14:30:05.123 | Status | ECU_ID:12345 | Axis: Horizontal axis | Content: In self-learning, has been moving for 15 seconds. This format can be directly parsed by diagnostic equipment without complex decoding.

[0119] Regarding the access method of local storage, the diagnostic equipment at the vehicle site (such as production line electrical testers and after-sales diagnostic instruments) is connected to the seat control unit via the CAN bus and sends a UDS data read command to retrieve the information stored locally. For example, if a production line worker finds that a seat has failed to learn, he can use the diagnostic instrument to read the record on the spot and see "Fault: Horizontal axis movement timeout" within 10 seconds without disassembling the seat for inspection.

[0120] Regarding the synchronization method for remote storage, the seat control unit uses the vehicle's vehicle networking module, such as 4G / 5G, to synchronize the recorded information to the remote service platform in real time. If the current network signal is weak, it will first cache the data locally and then upload it in batches after the signal is restored, ensuring that no data is lost.

[0121] For remote storage, the remote platform can store data in a hierarchical structure of "Vehicle VIN (unique identifier); Seat ECUID; Self-learning batch; Record time", for example, "VIN:LFV2A21KXXXXXXXXX; ECU_ID:12345; Batch:2025100101; Record:2025-10-01 14:30:05", supporting filtering and querying by vehicle model, production batch, fault type, and other dimensions.

[0122] Regarding the remote storage access method, after-sales engineers and R&D personnel can view all the self-learning records of the seat by entering the vehicle's VIN code or ECU ID through the remote platform web page or application. For example, if a batch of vehicles frequently experiences abnormal backrest shaft stall signal, R&D personnel can export the records of that batch through the platform in batches to analyze whether the stall current threshold setting is too low, without having to go to the site to check each vehicle individually.

[0123] The method provided in this application embodiment allows production line workers to quickly locate problems, shorten production line downtime, and improve on-site troubleshooting efficiency by reading locally stored fault information through diagnostic equipment without disassembling the seats. After a user's vehicle seat learning fails, after-sales engineers can remotely retrieve records from a remote platform to determine the cause of the fault in advance, supporting remote fault location for after-sales service.

[0124] In some embodiments, a personalization process is also included after storing location data.

[0125] Indicatively, the system receives a personalized position memory command sent by the first user; in response to the personalized position memory command, it records the currently adjusted seat position as the personalized position associated with the first user, the personalized position being between the minimum adjustment position and the maximum adjustment position; and when the first vehicle recognizes the first user, it drives the seat to adjust to the personalized position.

[0126] Specifically, during the process of adjusting the seat to a personalized position, when the positional distance between the seat position and the minimum or maximum adjustment position is less than a distance threshold, the seat adjustment speed is reduced.

[0127] Through the logic of user-initiated memory, automatic vehicle adaptation, and safe speed reduction adjustment, the minimum-maximum adjustment range obtained through self-learning is transformed into a personalized driving experience, while ensuring the safety and comfort of the adjustment process.

[0128] Personalized location memory commands are requests initiated by users to store their current comfortable location.

[0129] The first user refers to the occupants of the first vehicle, such as the driver or a fixed front passenger. The scenario in which they send the instruction is that the seat has been adjusted to a position that is subjectively comfortable for the user. For example, after the driver has adjusted the backrest angle and seat height, they hope that they will not need to make the same adjustments again when they get in the car next time.

[0130] The commands are sent through the human-machine interface of the first vehicle, covering a variety of operation methods that users are accustomed to, ensuring convenience, including but not limited to physical button triggering, voice command triggering, mobile terminal triggering and other command sending methods.

[0131] Taking physical button triggering as an example, a personalized memory button is set on the side of the seat or the center console. After the user adjusts the position, press and hold the button for 2 seconds to send a personalized position memory command. Taking voice command triggering as an example, the user can say "memorize my seat position" through the vehicle's voice assistant. After the voice module recognizes the command, it forwards the personalized position memory command to the seat control unit. Taking mobile terminal triggering as an example, the user can click "memorize current position" in the seat settings interface through the vehicle's supporting software. The software transmits the command to the vehicle control unit through the vehicle networking module.

[0132] It is worth noting that the above-mentioned instruction triggering methods are merely illustrative examples, and the embodiments of this application do not limit them.

[0133] In some embodiments, after receiving a command, the seat control unit will first check whether the current seat is in a stable state. If there is no adjustment shaft moving or no fault alarm, the command will only be responded to when the state is stable, so as to avoid memory position deviation caused by accidental touch during adjustment.

[0134] After the seat control unit responds to the command, it immediately collects the current real-time position data through the position sensors of each adjustment axis, such as the Hall position sensor of the horizontal axis and the angle sensor of the backrest axis. The data accuracy is consistent with the previous self-learning, ensuring that the recorded personalized position is consistent with the actual adjustment position of the user.

[0135] The control unit compares the current position with the minimum and maximum adjustment positions stored in the previous self-learning system. Only when the minimum adjustment position < current position < maximum adjustment position will it be recorded as the personalized position associated with the first user. If the user accidentally adjusts the seat to a position close to the limit, such as when the horizontal axis is almost touching the minimum position away from the steering wheel, the control unit will prompt "The current position is close to the adjustment limit. It is recommended to adjust it and then memorize it" through the central control screen or voice prompt, so as to avoid safety risks caused by the position being too close to the limit during subsequent adjustments.

[0136] Once the legitimate location is determined, the control unit will bind and store the personalized location data with the first user's identity identifier. The association methods for the identity identifier include: associating with the car key, if the first user unlocks the vehicle with a dedicated car key, the personalized location is directly bound to the ID of that key; associating with the user account, if the first user logs into a dedicated account (such as a car manufacturer's cloud account) in the vehicle's central control, the personalized location is stored under that account; and associating with biometrics, by recognizing the first user's sitting posture characteristics through a seat pressure sensor, or by recognizing facial features through an in-vehicle camera, to achieve keyless / accountless identity association.

[0137] Regarding the identification method for the first user, the vehicle can complete the identity determination before and after the user gets in the vehicle through a combination of active recognition and passive perception, without requiring the user to manually trigger the adjustment.

[0138] Taking key recognition as an example, when the first user approaches the vehicle with their exclusive car key (e.g., at a distance of ≤1 meter), the vehicle recognizes the key ID through the Passive Entry Passive Start (PEPS) system. After matching the key with the first user's personalized location, the seat adjustment is initiated in advance (the seat begins to move towards the personalized position when the user opens the door). Taking account recognition as an example, after the first user gets into the car, they log into their exclusive account on the central control. Once the system logs in successfully, it immediately retrieves the personalized location associated with that account. Taking biometric recognition as an example, after the user sits in the seat, the seat pressure sensor collects the pressure distribution of the sitting posture (compared with the memorized sitting posture features of the first user), or the in-car camera captures the face and compares it with a pre-stored face. Once recognition is successful, adjustment is triggered.

[0139] In some embodiments, if the personalized locations of multiple users are detected, the personalized locations are sorted based on the usage frequency of each user; when the first vehicle starts and no specific user is identified, the drive adjustment shaft moves to the personalized location with the highest usage frequency to adapt to the needs of most scenarios.

[0140] Once the recognition is successful, the control unit will drive the seat adjustment in a multi-axis coordinated motion manner, rather than adjusting one axis sequentially, to ensure adjustment efficiency and comfort.

[0141] To optimize the adjustment sequence, dimensions that affect the user's seating position can be adjusted first, such as moving the horizontal axis away from the steering wheel to give the user more legroom, and then adjusting detailed comfort dimensions such as backrest angle and seat height.

[0142] The distance threshold is a safe warning distance calibrated based on the mechanical motion characteristics of the seat adjustment axis and the user's perceived comfort. It is not a fixed value, but is related to the type of adjustment axis and the adjustment range.

[0143] For example, for linear adjustment axes (horizontal axis, height axis), the distance threshold is set to 5%-10% of the total adjustment range between the minimum / maximum adjustment position and the personalized position, or a fixed physical distance. For example, the minimum adjustment position of the horizontal axis is 80 cm (away from the steering wheel), and the maximum adjustment position is 40 cm (close to the steering wheel). If the personalized position is 60 cm (in the middle), when the position moves from 65 cm to 60 cm during adjustment, if it is close to the maximum adjustment position (40 cm), then the distance threshold is set to 2 cm (i.e., the speed is reduced when the position is ≤42 cm).

[0144] For the rotary adjustment axis (backrest axis), the distance threshold is set to an angle difference of 2° to 3°. For example, the minimum adjustment position of the backrest axis is 60° (close to the steering wheel), the maximum adjustment position is 120° (away from the steering wheel), and the personalized position is 90°. When adjusted to 118° (2° away from the maximum position), speed reduction is triggered.

[0145] The threshold can be fine-tuned using vehicle diagnostic equipment based on the mechanical stiffness of the seats in different models. For example, sports seats have high mechanical strength, so the threshold can be set lower; comfort seats can have a higher threshold.

[0146] The speed reduction logic conforms to smooth deceleration rather than abrupt stopping. The control unit reduces speed by calculating the position difference in real time and dynamically adjusting the motor drive signal to avoid sudden speed changes that may cause user discomfort.

[0147] The method provided in this application embodiment, through a closed-loop logic of user command triggering, limit range constraint, contactless recognition adjustment, and safe speed reduction protection, can not only ensure the adjustment safety during the intelligent seat adjustment process based on self-learning results, but also avoid mechanical damage based on the dynamic adjustment of adjustment speed, thereby improving user comfort.

[0148] In summary, the method provided in this application automatically triggers the seat's self-learning process by receiving a self-learning diagnostic command. This eliminates the need for manual adjustment of the seat to a preset initial reference position, thus removing the need for manual intervention from the self-learning initiation stage. This significantly reduces manual operation steps during mass production and lowers labor costs. By driving at least one adjustment axis of the seat to its mechanical limit state to determine the minimum and maximum adjustment positions corresponding to each axis, objective judgment based on mechanical limit states is achieved, avoiding subjective errors from manual observation and improving the accuracy of limit position adjustment. Furthermore, by storing position data, manual recording of position data is eliminated, further reducing manual intervention. This provides a precise position range basis for subsequent intelligent seat adjustment, avoiding deviations in adjustment range caused by manual recording errors. This fully automates the intelligent seat's self-learning process, reducing labor costs during mass production, improving the accuracy of limit position adjustment, enhancing the efficiency of intelligent seat self-learning, and ensuring the reliability of subsequent intelligent seat adjustments.

[0149] In some embodiments, this application also provides a vehicle seat control system. Please refer to FIG3. FIG3 is a schematic diagram of a vehicle control system provided in an exemplary embodiment of this application. As shown in FIG3, the system includes a diagnostic device 310, a control unit 320 and a seat 330 in a first vehicle. The control unit 320 is connected to the diagnostic device 310 and associated with the seat 330.

[0150] The diagnostic device 310 is configured to send self-learning diagnostic commands to the control unit 320.

[0151] The self-learning diagnostic command is used to instruct the first vehicle to initiate the seat self-learning process.

[0152] The diagnostic device is the sole entry point for sending self-learning diagnostic commands to the control unit, providing standardized and compatible command triggering capabilities to avoid process chaos caused by inconsistent triggering methods.

[0153] Optionally, the diagnostic equipment includes, but is not limited to, an instruction generation module, a communication module, and a human-computer interaction module.

[0154] The instruction generation module is used to generate self-learning diagnostic instructions based on the Unified Diagnostic Service (UDS) protocol. Instruction parameters can be configured, such as configuring the adjustment axis type and specifying the drive level / backrest / height axis; configuring the timeout threshold and setting the maximum allowed time for the adjustment axis movement to avoid jamming.

[0155] The communication module uses the CAN bus, which is common in the automotive electronics field, as the physical transmission medium. It is equipped with a CAN controller and transceiver to ensure the real-time performance and reliability of command transmission, and to prevent data tampering through Cyclic Redundancy Check (CRC) verification.

[0156] The human-machine interaction module includes a display screen and operation buttons, such as a physical button to start self-learning and a parameter setting touch screen, allowing production line workers or after-sales personnel to select learning parameters, such as learning only the horizontal axis; and to view the command sending status, such as "command sent" and "control unit received".

[0157] The control unit 320 is configured to, in response to a self-learning diagnostic command, drive at least one adjustment axis of the seat 330 to a mechanical limit state to determine the minimum adjustment position corresponding to each of the at least one adjustment axis; drive at least one adjustment axis to move in the reverse direction from the minimum adjustment position to the mechanical limit state to determine the maximum adjustment position corresponding to each of the at least one adjustment axis; and store position data of the minimum adjustment position and the maximum adjustment position.

[0158] Position data is used to indicate the range of position adjustment of the seat during the intelligent adjustment process.

[0159] Optionally, the control unit includes a main processor, a drive module, a signal detection module, a storage module, a communication module, etc.

[0160] The main processor is responsible for coordinating the entire process, parsing the self-learning diagnostic instructions sent by the diagnostic equipment, and scheduling the work of other modules.

[0161] The drive module receives control signals from the main processor and outputs constant power drive signals to the seat adjustment shaft motor to control the motor's forward and reverse rotation, corresponding to the movement direction of the adjustment shaft; the signal detection module includes a current sensor and a data receiving interface to collect the movement status signals of the adjustment shaft in real time.

[0162] The storage module integrates a non-volatile memory chip to store the minimum and maximum adjustment positions obtained through self-learning, and the data is not lost after power failure.

[0163] The communication module is matched with the CAN bus communication module of the diagnostic equipment to realize command reception and status feedback, such as sending "self-learning in progress" or "self-learning completed" status to the diagnostic equipment.

[0164] Each seat corresponds to at least one adjustment shaft, which is the mechanical basis for the seat to adjust its position. There are usually three types (which can be added or removed depending on the vehicle model configuration). Each type of shaft has a drive motor, a mechanical transmission structure, and physical limits.

[0165] Horizontal adjustment shaft: includes slide rail (the track for the seat to slide back and forth), transmission gear (connecting the motor and slide rail), and slide rail end stop (physical limiting structure). When the motor drives the gear to rotate, it drives the seat to move along the slide rail until it reaches the stop and can no longer move (triggers stall).

[0166] The backrest adjustment shaft includes a rotating shaft (connecting the backrest and seat cushion), a worm gear transmission mechanism (reducing speed and increasing torque to ensure smooth adjustment of the backrest angle), and a backrest limiting protrusion (physical limit). The motor drives the worm to rotate, causing the backrest to rotate around the rotating shaft. Once it reaches the protrusion, it cannot continue to rotate (triggers a stall).

[0167] The height adjustment shaft includes a lifting support column (a retractable support column that supports the seat), a screw drive structure (which converts the motor's rotational motion into linear lifting), and a support column minimum limit ring (physical limit). The motor drives the screw to rotate, causing the support column to extend or retract. When it reaches the limit ring, it cannot continue (triggers a stall).

[0168] For other implementation details, please refer to the above embodiments, which will not be repeated here.

[0169] In summary, the system provided in this application automatically triggers the seat's self-learning process by sending self-learning diagnostic commands through diagnostic equipment. This eliminates the need for manual adjustment of the seat to a preset initial reference position, thus removing the need for manual intervention from the self-learning initiation stage. This significantly reduces manual operation steps during mass production and lowers labor costs. By driving at least one adjustment axis of the seat to its mechanical limit state through the control unit, the minimum and maximum adjustment positions corresponding to each adjustment axis are determined. This achieves objective judgment based on mechanical limit states, avoiding subjective errors from manual observation and improving the accuracy of limit position adjustment. Furthermore, by storing position data, manual recording of position data is eliminated, further reducing manual intervention. Simultaneously, it provides a precise position range basis for subsequent intelligent seat adjustment, avoiding adjustment range deviations caused by manual recording errors. This achieves full automation of the intelligent seat's self-learning process, reducing labor costs during mass production, improving the accuracy of limit position adjustment, enhancing the efficiency of intelligent seat self-learning, and ensuring the reliability of subsequent intelligent seat adjustments.

[0170] In conjunction with the above embodiments, this application provides a UDS-based diagnostic self-learning software control method and system for automotive intelligent seats. By introducing the UDS diagnostic protocol and automatic seat learning strategy, intelligent learning and adaptive adjustment of seat parameters are achieved, solving the problems of insufficient personalization and poor adaptability.

[0171] As an illustration, the system includes smart seats and area controllers, diagnostic equipment, and a remote service platform.

[0172] The smart seat features seat memory and incorporates multi-source data, including but not limited to a pressure sensor matrix (8 sensors per square centimeter), infrared distance sensors, and angle sensors, to comprehensively capture seat position data. The Hall effect sensor maintains ±1% linearity within the -40℃ to 150℃ range, meeting automotive-grade temperature stability requirements.

[0173] The area controller is an intelligent controller based on a central computing platform, used to handle vehicle-domain related functions.

[0174] Diagnostic equipment includes, but is not limited to, diagnostic host computers, diagnostic instruments, and production line electrical testing equipment, which are used to send diagnostic commands related to seat learning.

[0175] The seat self-learning control software is deployed in the area control.

[0176] The seat self-learning control software includes a UDS diagnostic control module, a data acquisition module, a seat learning control module, and a fault log processing module.

[0177] The UDS diagnostic control module, based on the UDS diagnostic protocol defined by the ISO 14229 standard, is integrated into the seat control system. It supports 26 diagnostic services, including fault code reading (0x19), data reading and writing (0x22 / 0x2E), routine control (0x31 service), and secure access (0x27). It processes commands sent by diagnostic devices, and the system uses a CAN bus as the physical layer communication medium to ensure the real-time performance and reliability of diagnostic message transmission.

[0178] The data acquisition module is used to collect data from the seat motor and serves as the data source for the seat learning module.

[0179] The seat learning control module drives the horizontal axis to its minimum position, the backrest axis to its minimum position, and the height axis to its minimum position. After each axis reaches its stall point and completes the stall, it automatically performs the reverse action. After the horizontal axis reaches its maximum position, the backrest axis reaches its maximum position, and the height axis reaches its maximum position, the learning is completed, and the corresponding control source ZCUL_DriverSeatControlSource=0x4: Study is sent to the bus.

[0180] Minimum position point determination: The minimum position point of the backrest axis is the frontmost part of the seat (closest to the steering wheel), the minimum position point of the horizontal axis is the rearmost part of the seat (away from the steering wheel), and the minimum position point of the height axis is the bottommost part of the seat.

[0181] The seat's horizontal, backrest, and height axes are learned synchronously. Once any one of these axes has been learned, its maximum and minimum position data should be stored immediately.

[0182] The seat level, backrest, and height axes are learned synchronously. After storage, they are restored and driven together to the middle position (e.g., 50%). If the backrest has not been learned after the level and height axes have been learned, they will return to the middle position together after the backrest axis has been learned.

[0183] The log processing module records the learning status and the reason for failure if it fails. The log data can be saved to EEPROM and remote data platform. The seat learning status and faults can be queried through the diagnostic instrument and remote data platform to help analyze and trace problems.

[0184] For illustrative purposes, please refer to Figure 4, which is a schematic diagram of a vehicle seat control system architecture provided in an exemplary embodiment of this application. As shown in Figure 4, the system includes a remote service platform 401, a diagnostic device 402, a smart seat 403, and a region controller 404.

[0185] The area controller 404 includes a seat learning control module, a fault log processing module, a UDS diagnostic control module, and a data acquisition module.

[0186] Based on the vehicle seat control system shown in Figure 4, the control flowchart for seat learning is shown in Figure 5. The process includes: Step 501, reading the diagnostic configuration; Step 502, determining whether the seat learning function is enabled, and if so, proceeding to Step 503; Step 503, sending UDS diagnostic command 10 03 to start the End of Line (EOL) session mode; Step 504, determining whether the security check has passed, and if so, proceeding to Step 505; Step 505, sending UDS diagnostic command 31 01 53 87 to start seat learning; Step 506, providing feedback on the seat results.

[0187] In some embodiments, the above-mentioned seat recording result feedback includes the seat learning status and the reason for learning failure.

[0188] Optionally, learning status feedback includes, but is not limited to, success, learning in progress, learning interrupted, and failure.

[0189] Indicatively, the learning status feedback includes, but is not limited to, voltage abnormality, diagnostic cancellation of learning, manual interruption of learning, horizontal Hall effect travel abnormality, backrest Hall effect travel abnormality, tilt angle Hall effect travel abnormality, height Hall effect travel abnormality, horizontal alignment failure, backrest return failure, tilt angle return failure, height return failure, horizontal operation timeout failure, backrest operation timeout failure, tilt angle operation timeout failure, height operation timeout failure, horizontal Hall effect open / closed loss, backrest Hall effect abnormal loss, tilt angle Hall effect abnormal loss, height Hall effect abnormal loss.

[0190] Figure 6 is a structural block diagram of a vehicle seat control device provided in an exemplary embodiment of this application. As shown in Figure 6, the device includes the following parts: a receiving module 610, used to receive a self-learning diagnostic command sent by a diagnostic device, the self-learning diagnostic command being used to instruct a first vehicle to start a seat self-learning process; a driving module 620, used to respond to the self-learning diagnostic command to drive at least one adjustment axis of the seat in the first vehicle to move to a mechanical limit state, and determine the minimum adjustment position corresponding to each of the at least one adjustment axis; the driving module 620 is also used to drive the at least one adjustment axis to move in the opposite direction from the minimum adjustment position to the mechanical limit state, and determine the maximum adjustment position corresponding to each of the at least one adjustment axis; and a storage module 630, used to store position data of the minimum adjustment position and the maximum adjustment position, the position data being used to indicate the position adjustment range of the seat during the intelligent adjustment process.

[0191] In some embodiments, the self-learning diagnostic instructions conform to the Unified Diagnostic Service (UDS) protocol.

[0192] In some embodiments, the drive module 620 is further configured to: drive the at least one adjusting shaft to move in response to the self-learning diagnostic command, and detect a stall signal of the at least one adjusting shaft; determine the mechanical limit state based on the stall signal; and determine the minimum adjustment position when the at least one adjusting shaft moves to the mechanical limit state.

[0193] In some embodiments, the at least one adjustment axis includes a horizontal adjustment axis, a backrest adjustment axis, and a height adjustment axis; the drive module 620 is further configured to, in response to the self-learning diagnostic command, drive the horizontal adjustment axis, the backrest adjustment axis, and the height adjustment axis to move synchronously to their respective corresponding mechanical limit states, and determine in parallel the minimum adjustment positions corresponding to the horizontal adjustment axis, the backrest adjustment axis, and the height adjustment axis; wherein, the minimum adjustment position of the horizontal adjustment axis includes the limit position where the seat is farthest from the steering wheel of the first vehicle, the minimum adjustment position of the backrest adjustment axis includes the limit position where the seat is closest to the steering wheel of the first vehicle, and the minimum adjustment position of the height adjustment axis includes the lowest limit position of the seat.

[0194] In some embodiments, the storage module 630 is further configured to: record, in real time, status information and fault information during the self-learning process while determining the minimum adjustment position and the maximum adjustment position; the status information includes at least one of self-learning in progress, self-learning paused, and self-learning completed; the fault information includes at least one of adjustment shaft movement timeout, adjustment shaft stall signal abnormality, and diagnostic command interruption; and synchronously store the status information and the fault information to a local storage component and a remote service platform of the first vehicle; the local storage component is used for querying the first vehicle on-site through the diagnostic equipment; and the remote service platform is used for remotely tracing the cause of self-learning abnormalities.

[0195] In some embodiments, the receiving module 610 is further configured to receive a personalized location memory instruction sent by the first user; the storage module 630 is further configured to, in response to the personalized location memory instruction, record the currently adjusted seat position as a personalized position associated with the first user, the personalized position being located between the minimum adjustment position and the maximum adjustment position; the driving module 620 is further configured to, when the first vehicle recognizes the first user, drive the seat to adjust to the personalized position; wherein, during the process of the seat adjusting to the personalized position, when the positional distance between the seat position and the minimum adjustment position or the maximum adjustment position is less than a distance threshold, the adjustment speed of the seat is reduced.

[0196] In summary, the device provided in this application automatically triggers the seat self-learning process by receiving self-learning diagnostic commands. This eliminates the need for manual adjustment of the seat to a preset initial reference position, thus removing the need for manual intervention from the self-learning initiation stage. This significantly reduces manual operation steps during mass production and lowers labor costs. By driving at least one adjustment axis of the seat to its mechanical limit state to determine the minimum and maximum adjustment positions corresponding to each axis, objective judgment based on mechanical limit states is achieved, avoiding subjective errors from manual observation and improving the accuracy of limit position adjustment. Furthermore, by storing position data, manual recording of position data is eliminated, further reducing manual intervention. This provides a precise position range basis for subsequent intelligent seat adjustment, avoiding adjustment range deviations caused by manual recording errors. This fully automates the intelligent seat self-learning process, reducing labor costs during mass production, improving the accuracy of limit position adjustment, enhancing the efficiency of intelligent seat self-learning, and ensuring the reliability of subsequent intelligent seat adjustments.

[0197] It should be noted that the vehicle seat control device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0198] Figure 7 shows a structural block diagram of a terminal 700 provided in an exemplary embodiment of this application. The terminal 700 may be a smartphone, tablet computer, MP3 player, MP4 player, laptop computer, or desktop computer. The terminal 700 may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other names.

[0199] Typically, terminal 700 includes a processor 701 and a memory 702.

[0200] Processor 701 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 701 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). Processor 701 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 701 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 701 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0201] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 702 are used to store at least one instruction, which is executed by the processor 701 to implement the vehicle seat control method provided in the method embodiments of this application.

[0202] In some embodiments, terminal 700 may also include other components 703. Those skilled in the art will understand that the structure shown in FIG7 does not constitute a limitation on terminal 700 and may include more or fewer components than shown, or combine certain components, or adopt different component arrangements.

[0203] Embodiments of this application also provide a computer device, which can be implemented as a terminal or server as shown in FIG1. ​​The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement the vehicle seat control method provided in the above-described method embodiments.

[0204] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the vehicle seat control method provided in the above-described method embodiments.

[0205] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle seat control method provided in the above-described method embodiments.

[0206] Optionally, the computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments in this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0207] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0208] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle seat control method, characterized in that, The method includes: receiving a self-learning diagnostic command sent by a diagnostic device, the self-learning diagnostic command being used to instruct a first vehicle to initiate a seat self-learning process; responding to the self-learning diagnostic command, driving at least one adjustment axis of the seat in the first vehicle to move to a mechanical limit state, and determining the minimum adjustment position corresponding to each of the at least one adjustment axis; driving the at least one adjustment axis to move in the opposite direction from the minimum adjustment position to the mechanical limit state, and determining the maximum adjustment position corresponding to each of the at least one adjustment axis; and storing position data of the minimum adjustment position and the maximum adjustment position, the position data being used to indicate the position adjustment range of the seat during the intelligent adjustment process.

2. The method according to claim 1, characterized in that, The self-learning diagnostic instructions conform to the Unified Diagnostic Service (UDS) protocol.

3. The method according to claim 1, characterized in that, The step of driving at least one adjustment shaft of the seat in the first vehicle to move to a mechanical limit state in response to the self-learning diagnostic command, and determining the minimum adjustment position corresponding to each of the at least one adjustment shaft, includes: driving the at least one adjustment shaft to move in response to the self-learning diagnostic command, and detecting a stall signal of the at least one adjustment shaft; determining the mechanical limit state based on the stall signal; and determining the minimum adjustment position when the at least one adjustment shaft moves to the mechanical limit state.

4. The method according to any one of claims 1 to 3, characterized in that, The at least one adjustment axis includes a horizontal adjustment axis, a backrest adjustment axis, and a height adjustment axis; the step of driving at least one adjustment axis of the seat in the first vehicle to move to its mechanical limit state in response to the self-learning diagnostic command, and determining the minimum adjustment position corresponding to each of the at least one adjustment axis, includes: driving the horizontal adjustment axis, the backrest adjustment axis, and the height adjustment axis to move synchronously to their respective mechanical limit states in response to the self-learning diagnostic command, and determining the minimum adjustment position corresponding to each of the horizontal adjustment axis, the backrest adjustment axis, and the height adjustment axis in parallel; wherein, the minimum adjustment position of the horizontal adjustment axis includes the limit position of the seat away from the steering wheel of the first vehicle, the minimum adjustment position of the backrest adjustment axis includes the limit position of the seat close to the steering wheel of the first vehicle, and the minimum adjustment position of the height adjustment axis includes the lowest limit position of the seat.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: during the process of determining the minimum adjustment position and the maximum adjustment position, recording status information and fault information in real time during the self-learning process; the status information includes at least one of self-learning in progress, self-learning paused, and self-learning completed; the fault information includes at least one of adjustment shaft movement timeout, adjustment shaft stall signal abnormality, and diagnostic command interruption; and synchronously storing the status information and the fault information to the local storage component of the first vehicle and the remote service platform; the local storage component is used for querying the first vehicle on-site through the diagnostic equipment; and the remote service platform is used for remotely tracing the cause of self-learning abnormalities.

6. The method according to any one of claims 1 to 3, characterized in that, After storing the position data of the minimum adjustment position and the maximum adjustment position, the method further includes: receiving a personalized position memory command sent by a first user; in response to the personalized position memory command, recording the currently adjusted seat position as a personalized position associated with the first user, wherein the personalized position is located between the minimum adjustment position and the maximum adjustment position; and driving the seat to adjust to the personalized position when the first vehicle recognizes the first user; wherein, during the process of adjusting the seat to the personalized position, when the positional distance between the seat position and the minimum adjustment position or the maximum adjustment position is less than a distance threshold, the adjustment speed of the seat is reduced.

7. A vehicle seat control system, characterized in that, The system includes a diagnostic device, a control unit, and a seat in a first vehicle. The control unit is connected to the diagnostic device and associated with the seat. The diagnostic device is configured to send the self-learning diagnostic command to the control unit, the self-learning diagnostic command being used to instruct the first vehicle to initiate a seat self-learning process. The control unit is configured to, in response to the self-learning diagnostic command, drive at least one adjustment axis of the seat to move to a mechanical limit state to determine the minimum adjustment position corresponding to each of the at least one adjustment axis; drive the at least one adjustment axis to move in the opposite direction from the minimum adjustment position to the mechanical limit state to determine the maximum adjustment position corresponding to each of the at least one adjustment axis; and store position data of the minimum adjustment position and the maximum adjustment position, the position data being used to indicate the position adjustment range of the seat during the intelligent adjustment process.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to implement the vehicle seat control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one computer program, which is loaded and executed by a processor to implement the vehicle seat control method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the vehicle seat control method as described in any one of claims 1 to 6.

Images