Control method and device of vehicle seat, vehicle and computer program product

Abstract

The application relates to the technical field of vehicle seats, in particular to a control method and device of a vehicle seat, a vehicle and a computer program product, wherein the method comprises the following steps: acquiring sensor data information of the vehicle seat; dynamically adjusting control parameters of the vehicle seat by using a preset fuzzy logic algorithm based on the sensor data information; controlling the vehicle seat according to the control parameters, and predicting whether the vehicle seat has a potential fault by using a preset machine learning model based on the sensor data information; if the vehicle seat has a potential fault, automatically triggering a maintenance reminder and / or a preventive maintenance measure. Thus, the problem of insufficient flexibility and reliability of in-vehicle space configuration in the prior art is solved, the seat failure during movement is effectively prevented, and the reliability of seat self-adaptive folding is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle seats, and in particular to a control method and device for a vehicle seat, a vehicle, and a computer program product. BACKGROUND

[0002] Currently, the configuration of an automobile seat mainly relies on manual adjustment, such as folding a rear seat to increase the space of a trunk. Some high-end vehicles are equipped with electrically adjustable seats, but the adjustment is mainly for the inclination angle and the front and rear position of the seat, and the flexibility is insufficient. There is a lack of intelligent monitoring and adaptive adjustment capability, which leads to faults under complex use environments and frequent operations, and affects user experience and driving safety. SUMMARY

[0003] The present application provides a control method and device for a vehicle seat, a vehicle, and a computer program product to solve the problem of insufficient flexibility and reliability of in-vehicle space configuration in the prior art.

[0004] The first aspect of the present application provides a control method for a vehicle seat, comprising the following steps: obtaining sensor data information of a vehicle seat; dynamically adjusting control parameters of the vehicle seat based on the sensor data information by using a preset fuzzy logic algorithm; controlling the vehicle seat according to the control parameters, and predicting whether there is a potential fault in the vehicle seat by using a preset machine learning model based on the sensor data information. If there is a potential fault in the vehicle seat, a maintenance reminder and / or preventive maintenance measures are automatically triggered.

[0005] Optionally, when the vehicle seat is controlled, the current load and the current moving speed of the vehicle seat are monitored. If the current load is greater than a preset load or the current moving speed is greater than a preset moving speed, the vehicle seat is controlled according to a preset intervention strategy.

[0006] Optionally, the step of dynamically adjusting the control parameters of the vehicle seat based on the sensor data information by using the preset fuzzy logic algorithm comprises the following steps: determining a current fuzzy relationship rule, wherein the current fuzzy relationship rule is obtained from fuzzy values in a fuzzy set after historical sensor data of a plurality of sample seats are converted, according to the preset fuzzy logic algorithm, the fuzzy values, system characteristics, and a preset knowledge base; generating a fuzzified output according to the current fuzzy relationship rule and the sensor data information, and converting the fuzzified output into the control parameters.

[0007] Optionally, before predicting whether the vehicle seat has a potential fault by using the preset machine learning model, the method further comprises: collecting historical fault data of a plurality of sample seats, and obtaining training data after normalizing, denoising and missing value processing of the historical fault data; dividing the training data into a training set and a validation set, training an initial machine learning model by using the training set to obtain the initial machine learning model, and verifying the prediction performance of the initial machine learning model by using the validation set, and taking the initial machine learning model as the preset machine learning model when the prediction performance meets a preset requirement.

[0008] Optionally, when controlling the vehicle seat, the method further comprises: monitoring a main power supply state and a main locking mechanism of the vehicle seat; if it is monitored that the main power supply is in a fault state, switching the main power supply to a redundant power supply to supply power to the vehicle seat, and / or if it is monitored that the main locking mechanism is in a fault state, switching the main locking mechanism to a redundant locking mechanism to lock the seat.

[0009] The second aspect embodiment of the present application provides a control device of a vehicle seat, comprising: an acquisition module configured to acquire sensor data information of a vehicle seat; an adjustment module configured to dynamically adjust a control parameter of the vehicle seat by using a preset fuzzy logic algorithm based on the sensor data information; and a control module configured to control the vehicle seat according to the control parameter, and predict whether the vehicle seat has a potential fault by using a preset machine learning model based on the sensor data information, and automatically trigger a maintenance reminder and / or a preventive maintenance measure if the vehicle seat has a potential fault.

[0010] Optionally, the control module is further configured to: monitor a current load and a current moving speed of the vehicle seat; and control the vehicle seat according to a preset intervention strategy if the current load is greater than a preset load or the current moving speed is greater than a preset moving speed.

[0011] Optionally, the adjustment module is further configured to: determine a current fuzzy relationship rule, wherein the current fuzzy relationship rule is obtained according to the preset fuzzy logic algorithm, fuzzy values in a fuzzy set and a preset knowledge base after historical sensor data of a plurality of sample seats are converted into the fuzzy values; generate a fuzzified output according to the current fuzzy relationship rule and the sensor data information, and convert the fuzzified output into the control parameter.

[0012] Optionally, before predicting whether the vehicle seat has a potential fault by using the preset machine learning model, the control module is further configured to: collect historical fault data of a plurality of sample seats, and obtain training data after normalizing, denoising and missing value processing of the historical fault data; divide the training data into a training set and a validation set, train an initial machine learning model by using the training set, obtain the initial machine learning model, and verify the prediction performance of the initial machine learning model by using the validation set, and when the prediction performance meets a preset requirement, use the initial machine learning model as the preset machine learning model.

[0013] Optionally, when controlling the vehicle seat, the control module is further configured to: monitor a main power supply state and a main locking mechanism of the vehicle seat; if it is monitored that the main power supply is in a fault state, switch the main power supply to a redundant power supply to supply power to the vehicle seat, and / or if it is monitored that the main locking mechanism is in a fault state, switch the main locking mechanism to a redundant locking mechanism to lock the seat.

[0014] The third aspect of the embodiments of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the control method of the vehicle seat as described in the above embodiments.

[0015] The fourth aspect of the embodiments of the present application provides a computer program product having a computer program stored thereon, which is executed by a processor to implement the control method of the vehicle seat as described in the above embodiments.

[0016] In the above embodiments, sensor data information of the vehicle seat is acquired, a control parameter of the vehicle seat is dynamically adjusted based on the sensor data information by using a preset fuzzy logic algorithm, the vehicle seat is controlled according to the control parameter, and whether the vehicle seat has a potential fault is predicted based on the sensor data information by using a preset machine learning model, and if the vehicle seat has a potential fault, a maintenance reminder and / or a preventive maintenance measure are automatically triggered. Thus, the problem of insufficient flexibility and reliability of in-vehicle space configuration in the prior art is solved, and the reliability of seat self-adaptive folding is improved.

[0017] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0019] Figure 1 a flowchart of a control method of a vehicle seat according to an embodiment of the present application;

[0020] Figure 2 an example diagram of a control device of a vehicle seat according to an embodiment of the present application;

[0021] Figure 3 a schematic diagram of a vehicle structure according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] Embodiments of the present application are described in detail below with reference to the accompanying drawings, in which the same or similar components have the same or similar designations and functions throughout various figures and / or portions of the drawings. Embodiments described below are exemplary and are intended to be illustrative of the present application rather than limiting.

[0023] A control method, device, vehicle, and computer program product of a vehicle seat according to embodiments of the present application are described below with reference to the accompanying drawings. In view of the insufficient flexibility and reliability of in-vehicle space configuration in the prior art mentioned in the background, the present application provides a control method of a vehicle seat, in which sensor data information of the vehicle seat is obtained, a preset fuzzy logic algorithm is used to dynamically adjust control parameters of the vehicle seat based on the sensor data information, the vehicle seat is controlled according to the control parameters, and a preset machine learning model is used to predict whether the vehicle seat has a potential fault based on the sensor data information. If the vehicle seat has a potential fault, a maintenance reminder and / or preventive maintenance measures are automatically triggered. Thus, the problem of insufficient flexibility and reliability of in-vehicle space configuration in the prior art is solved, effectively preventing the seat from malfunctioning when moving, and improving the reliability of the self-adaptive folding seat.

[0024] Specifically, Figure 1 a flowchart of a control method of a vehicle seat according to an embodiment of the present application.

[0025] As Figure 1 shown, the control method of the vehicle seat includes the following steps:

[0026] The self-adaptive fault prevention seat folding mechanism according to the embodiments of the present application is a mechanism that can automatically adjust its operating parameters such as motor torque, speed, and power distribution to the vehicle seat according to usage and environmental conditions. The mechanism is designed with modular components, allowing quick replacement of damaged parts, minimizing maintenance time and cost, and all components and functions of the mechanism meet the safety and quality standards of the automotive industry and pass strict testing and certification, ensuring the safety of the system under various conditions, adapting to high and low temperature environments, maintaining stable performance, resisting electromagnetic interference, and ensuring stable operation in complex environments.

[0027] Specifically, in step S101, sensor data information of a vehicle seat is acquired.

[0028] The seat folding mechanism of the vehicle is equipped with multiple intelligent sensors for real-time monitoring of sensor data information such as the position, load, usage frequency, and environmental conditions (such as temperature and humidity) of the seat.

[0029] In step S102, the control parameters of the vehicle seat are dynamically adjusted based on the sensor data information using a preset fuzzy logic algorithm.

[0030] Optionally, in some embodiments, the control parameters of the vehicle seat are dynamically adjusted based on the sensor data information using a preset fuzzy logic algorithm, including: determining a current fuzzy relationship rule, wherein the current fuzzy relationship rule is obtained from the fuzzy values in the fuzzy set after the historical sensor data of multiple sample seats are converted, according to the preset fuzzy logic algorithm, the fuzzy values, system characteristics, and a preset knowledge base; generating a fuzzified output according to the current fuzzy relationship rule and the sensor data information, and converting the fuzzified output into control parameters.

[0031] The preset fuzzy logic algorithm is integrated into an electronic control unit (ECU) for controlling the vehicle seat.

[0032] Specifically, the historical sensor data of multiple sample seats, such as the position, load, and environmental conditions of the seat, are collected by sensors, and the historical sensor data is cleaned, formatted, and standardized, and features that have a significant impact on the control process, such as the current position, motion speed, and load size of the seat, are extracted from the preprocessed historical sensor data.

[0033] Further define the fuzzy set: define the fuzzy set (such as LOW, MEDIUM, HIGH) for each input variable (such as position, speed, load).

[0034] Further construct the current fuzzy relationship rule: based on expert knowledge (i.e., a preset knowledge base) and system behavior, establish the current fuzzy relationship rule, for example, if the position is HIGH and the load is LOW, reduce the seat folding speed.

[0035] Finally, the input data is converted into fuzzy values, and the control parameters of the vehicle seat, such as motor speed, thrust, and acceleration, are obtained by reasoning output according to the current fuzzy relationship rule, so as to control the vehicle seat based on the control parameters.

[0036] In step S103, the vehicle seat is controlled according to the control parameter, and whether the vehicle seat has a potential fault is predicted based on the sensor data information and by using a preset machine learning model, and if the vehicle seat has a potential fault, a maintenance reminder and / or a preventive maintenance measure is automatically triggered.

[0037] Optionally, in some embodiments, before predicting whether the vehicle seat has a potential fault by using the preset machine learning model, the method further includes: collecting historical fault data of a plurality of sample seats, and obtaining training data after normalizing, denoising and missing value processing of the historical fault data; dividing the training data into a training set and a validation set, training an initial machine learning model by using the training set to obtain the initial machine learning model, and verifying the prediction performance of the initial machine learning model by using the validation set, and when the prediction performance meets a preset requirement, taking the initial machine learning model as the preset machine learning model.

[0038] Specifically, first, a machine learning model suitable for the problem type is selected, such as a decision tree, a random forest, a neural network, etc., second, historical fault data of a plurality of sample seats is collected, and training data is obtained after normalizing, denoising and missing value processing of the historical fault data, and the training data is divided into a training set and a validation set, an initial machine learning model is trained by using the training set to obtain the initial machine learning model, and the prediction performance of the initial machine learning model is verified by using the validation set, and when the prediction performance meets a preset requirement, the initial machine learning model is taken as the preset machine learning model, wherein the preset requirement is set by a person skilled in the art.

[0039] Further, in the embodiments of the present application, the vehicle seat is detected periodically by using the preset machine learning model, if the vehicle seat has a potential problem, the user is prompted to maintain and / or take preventive maintenance measures, such as controlling the vehicle seat to stop moving, locking the position of the vehicle seat, etc., through the central control display screen of the vehicle, the instrument panel indicator light or the mobile phone APP, etc., and at the same time, the use and maintenance history of the vehicle seat is recorded for analyzing and predicting the maintenance demand.

[0040] Specifically, the vehicle can automatically start at preset time intervals or trigger conditions (such as vehicle start, after driving a certain mileage), and comprehensively detect each key component of the seat (including but not limited to motor, transmission mechanism, safety lock, electronic components, etc.), identify and diagnose potential problems that may exist in the vehicle seat, such as wear, looseness, abnormal function, etc. At the same time, record the use and maintenance history information of the vehicle seat and its related systems, including but not limited to the use time of the seat, the driving mileage, the fault record, the maintenance time, the maintenance content, the replaced parts, etc., and generate the corresponding detection report. Users or maintenance personnel can query and export the maintenance log through the vehicle's central control system or remote management platform, in order to analyze the use and maintenance of the seat in depth, predict future maintenance needs, and develop a reasonable maintenance plan.

[0041] In some embodiments, when controlling the vehicle seat, the method comprises: monitoring the current load and the current moving speed of the vehicle seat; if the current load is greater than the preset load, or the current moving speed is greater than the preset moving speed, controlling the vehicle seat according to the preset intervention strategy.

[0042] In the seat folding mechanism, an electronic safety locking mechanism is included, which comprises an electronic control unit (ECU), sensors, actuators, and mechanical locking components.

[0043] The electronic control unit serves as an intelligent monitoring and decision-making center, receiving sensor signals and controlling the locking of the vehicle seat; the sensors include position sensors, load sensors, speed sensors, etc., detecting the state of the vehicle seat and environmental conditions; the actuators, such as electric motors, servo motors or electromagnets, are used to drive the locking and unlocking actions; the mechanical locking components, such as latches, locks or gear systems, cooperate with the actuators to fix or release the seat.

[0044] In this way, the electronic safety locking mechanism continuously monitors the position and state of the seat, and after the seat is folded or raised in place, the system automatically triggers the mechanical locking components to fix the seat, allowing quick manual or automatic unlocking in the case of needing to quickly enter the rear row of the vehicle or in emergency situations.

[0045] Further, if the sensor detects that the current load of the vehicle seat is greater than the preset load, the seat folding mechanism controls the vehicle seat according to the preset intervention strategy, for example, the actuator controls the mechanical locking component to prevent the vehicle seat from folding; when the current moving speed of the vehicle seat is greater than the preset moving speed, the seat folding mechanism controls the vehicle seat according to the preset intervention strategy, for example, the actuator controls the mechanical locking component to slow down the movement of the vehicle seat. After the vehicle seat is fixed, the seat folding mechanism analyzes the vibrations that may be caused by road conditions in real time to ensure the stability of the vehicle seat during vehicle driving.

[0046] Optionally, in some embodiments, when controlling the vehicle seat, further comprising: monitoring the main power supply state and the main locking mechanism of the vehicle seat; if the main power supply is detected to be in a failure state, switching the main power supply to the redundant power supply to power the vehicle seat, and / or if the main locking mechanism is detected to be in a failure state, switching the main locking mechanism to the redundant locking mechanism to lock the seat.

[0047] In particular, the embodiments of the present application design a double locking mechanism, including a main locking mechanism and a redundant locking mechanism, to ensure that the seat remains stable even when the main locking mechanism fails, and has a redundant power supply, which can be used to keep the seat stable when the main power supply fails.

[0048] For example, the seat folding mechanism detects voltage, current or other related electrical parameters to determine whether the main power supply is faulty, and once it detects that the main power supply is faulty, such as low voltage, current interruption or abnormal fluctuation, it switches the main power supply to the redundant power supply to power the vehicle seat, and if the main locking mechanism (e.g. such as a latch, lock or gear system) is detected to be in a failure state, it switches the main locking mechanism to the redundant locking mechanism to lock the seat.

[0049] In the embodiments of the present application, an intuitive user interface is also provided, which allows users to easily control the folding and locking of the seat, allows users to set preferences and observe the real-time status of the vehicle seat, such as seat folding progress, safety warnings, etc., and indicates the locking status of the vehicle seat through visual and / or audible signals, such as LED indicators or buzzers, to prompt the user of the locking status of the vehicle seat.

[0050] In the embodiments of the present application, the system is integrated with other intelligent systems of the vehicle (such as infotainment systems) to provide collaborative safety and convenience functions.

[0051] To enable those skilled in the art to further understand the implementation method of the fuzzy logic algorithm and machine learning model of the embodiments of the present application, the following will be described in detail in conjunction with specific embodiments.

[0052] Fuzzy logic controller implementation steps:

[0053] a. Fuzzify input variables:

[0054] Convert the actual measurement values of the sensors (such as seat position, load weight, movement speed, etc.) into fuzzy values. For example, the readings of the position sensor are converted into fuzzy sets such as {VeryLow, Low, Medium, High, VeryHigh}.

[0055] b. Build fuzzy rules:

[0056] Fuzzy rules are designed to define the relationship between inputs and outputs. For example: if position is High and load is Low, then thrust is Low; if speed is Medium and position change is Fast, then increase motor speed.

[0057] c. Apply Fuzzy Rules:

[0058] Using fuzzy sets and predefined fuzzy rules, a fuzzy inference process is performed to convert fuzzified inputs into fuzzified outputs.

[0059] d. Defuzzify:

[0060] Convert the fuzzy outputs into precise numerical values, which can be motor PWM signal duty cycle, current size, etc. Example Fuzzy Logic Controller Pseudo Code:

[0061] Pseudo

[0062] Copy

[0063] function FuzzyController(sensor_inputs):

[0064] / / Fuzzify inputs

[0065] position_fuzzy = fuzzify(sensor_inputs.position)

[0066] load_fuzzy = fuzzify(sensor_inputs.load)

[0067] speed_fuzzy = fuzzify(sensor_inputs.speed),

[0068] / / Apply Fuzzy Rules

[0069] rules = [

[0070] (High, Low) => (Low),

[0071] ... / / Other rules ]

[0073] output_fuzzy = apply_rules(position_fuzzy, load_fuzzy, speed_fuzzy, rules)

[0074] / / Defuzzify output

[0075] control_signal = defuzzify(output_fuzzy)

[0076] return control_signal

[0077] The implementation steps of the machine learning model are as follows:

[0078] a. Data collection and feature extraction: Collect sensor data and extract features that help the model make predictions.

[0079] b. Data preprocessing: Including normalization, noise removal, missing value filling, etc.

[0080] c. Model selection: Select a machine learning model suitable for the problem. For example, for continuous control problems, linear regression or neural networks may be selected.

[0081] d. Model training: Train the machine learning model using historical fault data of vehicle seats, adjust model parameters to minimize prediction error.

[0082] e. Model evaluation and optimization: Evaluate the model performance using the validation set and optimize the model as needed.

[0083] f. Model deployment: Deploy the trained model to the ECU, receive input from sensors in real time and output control signals.

[0084] Example machine learning model pseudo code:

[0085] Pseudo

[0086] Copy

[0087] function MLController(sensor_inputs):

[0088] / / Data preprocessing

[0089] features = preprocess(sensor_inputs)

[0090] / / Model prediction

[0091] predicted_output = machine_learning_model.predict(features)

[0092] / / Convert model output to control signal

[0093] control_signal = convert_output_to_signal(predicted_output)

[0094] return control_signal

[0095] Electronic Control Unit (ECU) integrated into the seat reclining mechanism:

[0096] Pseudo

[0097] Copy

[0098] function OperateSeatMechanism():

[0099] while seat_operational:

[0100] sensor_inputs = get_sensor_data()

[0101] / / Call the appropriate controller based on the selected control strategy

[0102] if using_fuzzy_logic:

[0103] control_signal = FuzzyController(sensor_inputs)

[0104] elif using_machine_learning:

[0105] control_signal = MLController(sensor_inputs)

[0106] / / Send the control signal to the actuator

[0107] execute_seat_movement(control_signal)

[0108] / / Monitor system health and user feedback

[0109] monitor_system_health_and_feedback()

[0110] According to the control method of the vehicle seat provided in the embodiments of the present application, sensor data information of the vehicle seat is acquired, a control parameter of the vehicle seat is dynamically adjusted based on the sensor data information by using a preset fuzzy logic algorithm, the vehicle seat is controlled according to the control parameter, and whether the vehicle seat has a potential fault is predicted based on the sensor data information by using a preset machine learning model; if the vehicle seat has a potential fault, a maintenance reminder and / or a preventive maintenance measure are automatically triggered. Thus, the problem of insufficient flexibility and reliability of the in-vehicle space in the prior art is solved, and the reliability of the seat self-adaptive folding is improved.

[0111] Next, the control device of the vehicle seat provided in the embodiments of the present application is described with reference to the accompanying drawings.

[0112] Figure 2 FIG. 1 is a block schematic diagram of the control device of the vehicle seat in the embodiments of the present application.

[0113] As shown in FIG. 1, the control device 10 of the vehicle seat includes an acquisition module 100, an adjustment module 200 and a control module 300. Figure 2 The acquisition module 100 is configured to acquire sensor data information of the vehicle seat.

[0114] The adjustment module 200 is configured to dynamically adjust a control parameter of the vehicle seat based on the sensor data information by using a preset fuzzy logic algorithm. The control module 300 is configured to control the vehicle seat according to the control parameter, and predict whether the vehicle seat has a potential fault based on the sensor data information by using a preset machine learning model; if the vehicle seat has a potential fault, a maintenance reminder and / or a preventive maintenance measure are automatically triggered.

[0115] Optionally, in some embodiments, the control module 300 is further configured to monitor a current load and a current moving speed of the vehicle seat; if the current load is greater than a preset load or the current moving speed is greater than a preset moving speed, the vehicle seat is controlled according to a preset intervention strategy.

[0116] Optionally, in some embodiments, the adjustment module 200 is further configured to determine a current fuzzy relationship rule, wherein the current fuzzy relationship rule is obtained according to a preset fuzzy logic algorithm, fuzzy values in a fuzzy set and a preset knowledge base after historical sensor data of a plurality of sample seats are converted into the fuzzy values; a fuzzified output is generated according to the current fuzzy relationship rule and the sensor data information, and the fuzzified output is converted into the control parameter.

[0117] Optionally, in some embodiments, before predicting whether the vehicle seat has a potential fault by using the preset machine learning model, the control module 300 is further configured to: collect historical fault data of a plurality of sample seats, and obtain training data after normalizing, denoising and missing value processing of the historical fault data; divide the training data into a training set and a validation set, train an initial machine learning model by using the training set, obtain the initial machine learning model, and verify the prediction performance of the initial machine learning model by using the validation set, and when the prediction performance meets a preset requirement, use the initial machine learning model as the preset machine learning model.

[0118] Optionally, in some embodiments, when controlling the vehicle seat, the control module 300 is further configured to: monitor the main power supply state and the main locking mechanism of the vehicle seat; if it is monitored that the main power supply is in a fault state, switch the main power supply to a redundant power supply to supply power to the vehicle seat, and / or if it is monitored that the main locking mechanism is in a fault state, switch the main locking mechanism to a redundant locking mechanism to lock the seat.

[0119] It should be noted that the foregoing explanation and description of the control method for the vehicle seat also applies to the control device for the vehicle seat of this embodiment, which will not be described here.

[0120] The control device for the vehicle seat provided by the embodiments of the present application acquires sensor data information of the vehicle seat, dynamically adjusts control parameters of the vehicle seat based on the sensor data information by using a preset fuzzy logic algorithm, controls the vehicle seat according to the control parameters, and predicts whether the vehicle seat has a potential fault by using a preset machine learning model based on the sensor data information. If the vehicle seat has a potential fault, a maintenance reminder and / or a preventive maintenance measure are automatically triggered. Thus, the problem of insufficient flexibility and reliability of the in-vehicle space in the prior art is solved, and the reliability of the self-adaptive folding of the seat is improved.

[0121] Figure 3 The vehicle provided by the embodiments of the present application is shown in the structural schematic diagram. The vehicle can include:

[0122] The memory 301, the processor 302, and the computer program stored in the memory 301 and executable on the processor 302.

[0123] The processor 302 implements the control method for the vehicle seat provided in the above embodiments when executing the program.

[0124] Further, the vehicle further includes:

[0125] The communication interface 303 is configured to communicate between the memory 301 and the processor 302.

[0126] a memory 301 for storing a computer program executable on the processor 302.

[0127] The memory 301 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0128] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 3 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0129] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can complete communication between each other through an internal interface.

[0130] The processor 302 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0131] The embodiments of the present application also provide a computer program product, which has a computer program stored thereon, and the program is executed by a processor to implement the control method of the vehicle seat as above.

[0132] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example two, three or the like, unless explicitly stated otherwise.

[0133] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features of the application, and do not imply or connote relative importance or a specific order of precedence. Thus, features defined with "first", "second", etc. can include at least one of the features, either explicitly or implicitly.

[0134] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts or otherwise described herein are not necessarily performed in the order shown or discussed, including, for example, as performed by a computer processor. Alternate implementations can perform functions or steps described in different orders, including substantially concurrently, or in reverse order.

[0135] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer program product" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer program product include the following: an electronic connection having one or more wires (electronic apparatus), a portable computer diskette (magnetic apparatus), a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM or Flash memory), an optical fiber apparatus, and a portable compact disc read-only memory (CDROM). Additionally, the computer program product can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for example, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0136] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0137] Those of skill in the art could readily implement the above described example methods with all or a portion of the disclosed steps carried out by a program for use with or in connection with the relevant hardware. Such a program can be stored on any computer readable medium including a non-transitory computer readable medium such as a storage device or discette, that can direct a computer or other similar electronic device to function in a particular manner, such that the computer or other electronic device can be caused to carry out a method of the present application.

[0138] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can exist physically separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer program product.

[0139] The computer program product mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A control method of a vehicle seat, characterized by, The method comprises the following steps: obtaining sensor data information of a vehicle seat; dynamically adjusting control parameters of the vehicle seat based on the sensor data information by using a preset fuzzy logic algorithm; controlling the vehicle seat according to the control parameters, and predicting whether the vehicle seat has a potential fault by using a preset machine learning model based on the sensor data information, and if the vehicle seat has a potential fault, automatically triggering a maintenance reminder and / or a preventive maintenance measure; when controlling the vehicle seat, comprising: monitoring a current load and a current moving speed of the vehicle seat; if the current load is greater than a preset load or the current moving speed is greater than a preset moving speed, controlling the vehicle seat according to a preset intervention strategy.

2. The method of claim 1, wherein, The step of dynamically adjusting the control parameters of the vehicle seat based on the sensor data information by using the preset fuzzy logic algorithm comprises: determining a current fuzzy relationship rule, wherein the current fuzzy relationship rule is obtained by converting historical sensor data of a plurality of sample seats into fuzzy values in a fuzzy set, and then obtaining the fuzzy values, system characteristics and a preset knowledge base according to the preset fuzzy logic algorithm; generating a fuzzified output according to the current fuzzy relationship rule and the sensor data information, and converting the fuzzified output into the control parameters.

3. The method of claim 1, wherein, Before predicting whether the vehicle seat has a potential fault by using the preset machine learning model, further comprising: collecting historical fault data of a plurality of sample seats, and obtaining training data after normalizing, denoising and missing value processing the historical fault data; dividing the training data into a training set and a validation set, training an initial machine learning model by using the training set to obtain the initial machine learning model, and verifying the prediction performance of the initial machine learning model by using the validation set, and if the prediction performance meets a preset requirement, taking the initial machine learning model as the preset machine learning model.

4. The method of claim 1, wherein, When controlling the vehicle seat, further comprising: monitoring a main power supply state and a main locking mechanism of the vehicle seat; if it is monitored that the main power supply is in a fault state, switching the main power supply to a redundant power supply to supply power to the vehicle seat, and / or if it is monitored that the main locking mechanism is in a fault state, switching the main locking mechanism to a redundant locking mechanism to lock the seat.

5. A control device of a vehicle seat characterized by comprising: comprising: an obtaining module configured to obtain sensor data information of a vehicle seat; an adjusting module configured to dynamically adjust control parameters of the vehicle seat based on the sensor data information by using a preset fuzzy logic algorithm; a control module configured to control the vehicle seat according to the control parameters, and predict whether the vehicle seat has a potential fault by using a preset machine learning model based on the sensor data information, and if the vehicle seat has a potential fault, automatically trigger a maintenance reminder and / or a preventive maintenance measure; The control module is further configured to monitor a current load and a current moving speed of the vehicle seat; if the current load is greater than a preset load or the current moving speed is greater than a preset moving speed, control the vehicle seat according to a preset intervention strategy.

6. The apparatus of claim 5, wherein, The adjusting module is further configured to: determining a current fuzzy relationship rule, wherein the current fuzzy relationship rule is converted from historical sensor data of a plurality of sample seats into fuzzy values in a fuzzy set, and the fuzzy values, system characteristics and a preset knowledge base are obtained according to the preset fuzzy logic algorithm; generating a fuzzified output according to the current fuzzy relationship rule and the sensor data information, and converting the fuzzified output into the control parameter.

7. A vehicle characterized by comprising: A computer program product comprising a memory, a processor and a computer program stored on the memory and loadable on the processor, the processor executing the program to implement the control method of the vehicle seat according to any one of claims 1-4.

8. A computer program product, the computer program product storing a computer program, characterized in that, The program is executed by the processor to implement the control method of the vehicle seat according to any one of claims 1-4.

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