Vehicle seat control method and device, vehicle and computer program product

Abstract

The invention 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.The method comprises the steps that sensor data information of the vehicle seat is obtained; dynamically adjusting control parameters of the vehicle seat by utilizing a preset fuzzy logic algorithm based on the sensor data information; and the vehicle seat is controlled according to the control parameters, whether the vehicle seat has potential faults or not is predicted through a preset machine learning model based on the sensor data information, and if the vehicle seat has the potential faults, maintenance reminding and / or preventive maintenance measures are / is automatically triggered. Therefore, the problems that in-vehicle space configuration is insufficient in flexibility and reliability in the prior art are solved, the situation that the seat breaks down during moving is effectively prevented, and the reliability of self-adaptive falling of the seat is improved.

Description

Technical Field

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

[0002] At present, car seat configuration mainly relies on manual adjustment, such as foldable rear seats to increase trunk space. Some high-end models are equipped with electrically adjustable seats, but they mainly adjust the seat tilt angle and fore-and-aft position. They are not flexible enough and lack intelligent monitoring and adaptive adjustment capabilities. As a result, they are prone to failure in complex usage environments and frequent operations, affecting user experience and driving safety. Summary of the invention

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

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

[0005] Optionally, when controlling the vehicle seat, it includes: monitoring the current load and 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.

[0006] Optionally, the dynamically adjusting the control parameters of the vehicle seat based on the sensor data information using a preset fuzzy logic algorithm includes: determining a current fuzzy relationship rule, wherein the current fuzzy relationship rule is converted into a fuzzy value in a fuzzy set by historical sensor data of multiple sample seats, and then the fuzzy value, 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.

[0007] Optionally, before using a preset machine learning model to predict whether the vehicle seat has a potential fault, it also includes: collecting historical fault data of multiple sample seats, and obtaining data to be trained after normalizing, denoising and processing missing values ​​of the historical fault data; dividing the data to be trained into a training set and a validation set, using the training set to train an initial machine learning model to obtain an initial machine learning model, and using the validation set to verify the prediction performance of the initial machine learning model, and when the prediction performance meets preset requirements, using the initial machine learning model as the preset machine learning model.

[0008] Optionally, when controlling the vehicle seat, it also includes: monitoring the main power status and main locking mechanism of the vehicle seat; if it is detected that the main power supply is in a fault state, switching the main power supply to a redundant power supply to power the vehicle seat, and / or, if it is detected 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 of the present application provides a control device for a vehicle seat, comprising: an acquisition module for acquiring sensor data information of a vehicle seat; an adjustment module for dynamically adjusting control parameters of the vehicle seat based on the sensor data information using a preset fuzzy logic algorithm; a control module for controlling the vehicle seat according to the control parameters, and based on the sensor data information, using a preset machine learning model to predict whether the vehicle seat has a potential fault, and if the vehicle seat has a potential fault, automatically triggering a maintenance reminder and / or preventive maintenance measures.

[0010] Optionally, the control module is further used to: monitor the current load and 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.

[0011] Optionally, the adjustment module is also used to: determine a current fuzzy relationship rule, wherein the current fuzzy relationship rule is converted into a fuzzy value in a fuzzy set by historical sensor data of multiple sample seats, and then the fuzzy value, system characteristics and a preset knowledge base are obtained according to the preset fuzzy logic algorithm; 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 using a preset machine learning model to predict whether the vehicle seat has a potential fault, the control module is also used to: collect historical fault data of multiple sample seats, and obtain data to be trained after normalizing, denoising and processing missing values ​​of the historical fault data; divide the data to be trained into a training set and a validation set, use the training set to train an initial machine learning model to obtain an initial machine learning model, and use the validation set to verify the prediction performance of the initial machine learning model; and when the prediction performance meets preset requirements, use the initial machine learning model as the preset machine learning model.

[0013] Optionally, when controlling the vehicle seat, the control module is also used to: monitor the main power status and the main locking mechanism of the vehicle seat; if it is detected that the main power supply is in a fault state, switch the main power supply to a redundant power supply to power the vehicle seat, and / or, if it is detected 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] A third aspect 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, wherein the processor executes the program to implement the vehicle seat control method as described in the above embodiment.

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

[0016] In the above implementation, the sensor data information of the vehicle seat is obtained, and the control parameters of the vehicle seat are dynamically adjusted based on the sensor data information using a preset fuzzy logic algorithm, and the vehicle seat is controlled according to the control parameters. Based on the sensor data information, a preset machine learning model is used to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, maintenance reminders and / or preventive maintenance measures are automatically triggered. In this way, the problems of insufficient flexibility and reliability of the in-vehicle space configuration in the prior art are solved, and the seat is effectively prevented from malfunctioning during movement, and the reliability of the seat's adaptive folding is improved.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0021] Figure 3 It is 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, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0023] The following describes the control method, device, vehicle and computer program product of the vehicle seat of the embodiment of the present application with reference to the accompanying drawings. In view of the problem of insufficient flexibility and reliability of the in-vehicle space configuration in the prior art mentioned in the above background technology, the present application provides a method for controlling a vehicle seat, in which sensor data information of the vehicle seat is obtained, and the control parameters of the vehicle seat are dynamically adjusted based on the sensor data information using a preset fuzzy logic algorithm, the vehicle seat is controlled according to the control parameters, and based on the sensor data information, a preset machine learning model is used to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, maintenance reminders and / or preventive maintenance measures are automatically triggered. In this way, the problem of insufficient flexibility and reliability of the in-vehicle space configuration in the prior art is solved, the seat is effectively prevented from malfunctioning during movement, and the reliability of the seat's adaptive folding is improved.

[0024] Specifically, Figure 1 A schematic flow chart of a method for controlling a vehicle seat provided in an embodiment of the present application.

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

[0026] The embodiments of the present application relate to an adaptive fault prevention seat reclining mechanism, which is a mechanism that can automatically adjust its operating parameters according to usage and environmental conditions, such as motor torque, speed and power distribution to vehicle seats. The mechanism is designed with modular components to allow for quick replacement of damaged parts, minimizing maintenance time and costs, and all components and functions of the mechanism comply with the safety and quality standards of the automotive industry and have passed rigorous testing and certification to ensure the safety of the system under various conditions, the ability to adapt to high and low temperature environments, maintain stable performance, and have the ability to resist electromagnetic interference to ensure stable operation in complex environments.

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

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

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

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

[0031] Among them, the preset fuzzy logic algorithm is integrated into the electronic control unit ECU to realize the control of the vehicle seat.

[0032] Specifically, historical sensor data of multiple sample seats are collected through sensors, such as seat position, load, environmental conditions and other data, and the historical sensor data are cleaned, formatted and standardized. Features that have a significant impact on the control process are extracted from the pre-processed historical sensor data, such as the current position of the seat, movement speed, load size, etc.

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

[0034] Further construct the current fuzzy relationship rules: Based on expert knowledge (ie, preset knowledge base) and system behavior, establish the current fuzzy relationship rules, for example, if the position is HIGH and the load is LOW, reduce the seat reclining speed.

[0035] Finally, the input data is converted into fuzzy values, and the control parameters of the vehicle seat, such as motor speed, thrust, acceleration, etc., are obtained by inference output according to the current fuzzy relationship rules, 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 parameters, and based on the sensor data information, a preset machine learning model is used to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, a maintenance reminder and / or preventive maintenance measures are automatically triggered.

[0037] Optionally, in some embodiments, before using a preset machine learning model to predict whether a vehicle seat has a potential fault, it also includes: collecting historical fault data of multiple sample seats, and obtaining data to be trained after normalizing, denoising and processing missing values ​​of the historical fault data; dividing the data to be trained into a training set and a validation set, using the training set to train an initial machine learning model to obtain an initial machine learning model, and using the validation set to verify the prediction performance of the initial machine learning model; and when the prediction performance meets the preset requirements, using 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, random forest, neural network, etc., and then historical fault data of multiple sample seats are collected, and the historical fault data are normalized, denoised, and missing values ​​are processed to obtain the data to be trained, and the data to be trained is divided into a training set and a validation set. The training set is used to train the initial machine learning model to obtain the initial machine learning model, and the validation set is used to verify the prediction performance of the initial machine learning model. When the prediction performance meets the preset requirements, the initial machine learning model is used as the preset machine learning model, wherein the preset requirements are set by relevant technical personnel in this field.

[0039] Furthermore, in an embodiment of the present application, a preset machine learning model is used to regularly inspect the vehicle seats. If potential problems are detected with the vehicle seats, the user is prompted to perform maintenance through the vehicle's central control display screen, dashboard indicator lights, or mobile phone APP, and / or take preventive maintenance measures, such as controlling the vehicle seat to stop moving, locking the vehicle seat position, etc. At the same time, the usage and maintenance history of the vehicle seats is recorded to analyze and predict maintenance needs.

[0040] Specifically, the vehicle can automatically start at preset time intervals or trigger conditions (such as vehicle startup, after driving a certain mileage), conduct a comprehensive inspection of each key component of the seat (including but not limited to motors, transmission mechanisms, safety locks, electronic components, etc.), identify and diagnose potential problems that may exist in the vehicle seat, such as wear, looseness, malfunction, etc. At the same time, the use and maintenance history of the vehicle seat and its related systems are recorded, including but not limited to the use time, mileage, fault records, maintenance time, maintenance content, replaced parts, etc. of the seat, and a corresponding inspection report is generated. Users or maintenance personnel can query and export maintenance logs through the vehicle's central control system or remote management platform to conduct in-depth analysis of the use and maintenance of the seat, predict future maintenance needs, and formulate a reasonable maintenance plan.

[0041] Among them, in some embodiments, when controlling the vehicle seat, it includes: monitoring the current load and 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, the vehicle seat is controlled according to the preset intervention strategy.

[0042] The seat reclining mechanism includes an electronic safety locking mechanism, which includes an electronic control unit (ECU), a sensor, an actuator and a mechanical locking component.

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

[0044] Therefore, the position and status of the seat are continuously monitored through the electronic safety locking mechanism. After the seat is folded down or raised into place, the system automatically triggers the mechanical locking component to secure the seat. When there is a need to quickly enter the back seat of the vehicle or in an emergency, the system allows quick manual or automatic unlocking.

[0045] Furthermore, if the sensor detects that the current load of the vehicle seat is greater than the preset load, the seat reclining mechanism controls the vehicle seat according to the preset intervention strategy, such as the actuator controls the mechanical locking component to prevent the vehicle seat from reclining; when the current moving speed of the vehicle seat is greater than the preset moving speed, the seat reclining mechanism controls the vehicle seat according to the preset intervention strategy, such as the actuator controls the mechanical locking component to slow down the movement of the vehicle seat. After the vehicle seat is fixed, the seat reclining mechanism analyzes the vibration 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 a vehicle seat, it also includes: monitoring the main power status and the main locking mechanism of the vehicle seat; if it is detected that the main power is in a fault state, switching the main power to a redundant power supply to power the vehicle seat, and / or, if it is detected that the main locking mechanism is in a fault state, switching the main locking mechanism to a redundant locking mechanism to lock the seat.

[0047] Specifically, the embodiment of the present application designs a double locking mechanism, including a main locking mechanism and a redundant locking mechanism, to ensure that the seat can remain stable even when the main locking mechanism fails, and has a redundant power supply. When the main power supply fails, the redundant power supply can be used to keep the seat stable.

[0048] For example, the seat reclining mechanism determines whether there is a main power supply fault by detecting voltage, current or other relevant electrical parameters. Once a main power supply fault is detected, such as low voltage, current interruption or abnormal fluctuation, the main power supply is switched to the redundant power supply to power the vehicle seat. If the main locking mechanism (such as a latch, lock or gear system) is monitored to be in a faulty state, the main locking mechanism is switched to the redundant locking mechanism to lock the seat.

[0049] In an embodiment of the present application, an intuitive user interface is also provided, allowing the user to easily control the folding and locking of the seat, allowing the user to set preferences and observe the real-time status of the vehicle seat, such as the seat folding progress, safety warnings, etc., and indicating the locking status of the vehicle seat through visual and / or auditory signals, such as an LED indicator light or a buzzer, to prompt the user of the locking status of the vehicle seat.

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

[0051] In order 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, it is elaborated in detail below in conjunction with specific embodiments.

[0052] Fuzzy logic controller implementation steps:

[0053] a. Fuzzy input variables:

[0054] The actual measured values ​​of the sensors (such as seat position, load weight, moving speed, etc.) are converted 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. Construct fuzzy rules:

[0056] Design fuzzy rules to define the relationship between input and output. For example: if the position is High and the load is Low, then the thrust is Low; if the speed is Medium and the position change is Fast, then increase the motor speed.

[0057] c. Apply fuzzy rules:

[0058] Using fuzzy sets and predefined fuzzy rules, a fuzzy reasoning process is performed to transform the fuzzified input into the fuzzified output.

[0059] d. Defuzzification:

[0060] Convert the fuzzy output into precise values, which can be the duty cycle of the motor's PWM signal, current size, etc. The pseudo code of the fuzzy logic controller is as follows:

[0061] Pseudo

[0062] Copy

[0063] function FuzzyController(sensor_inputs):

[0064] / / Fuzzy input

[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 the 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 model predictions.

[0079] b. Data preprocessing: including normalization, noise removal, and filling missing values.

[0080] c. Model selection: Choose a machine learning model that is appropriate for the problem. For example, for a continuous control problem, you might choose linear regression or a neural network.

[0081] d. Model training: Use historical failure data of vehicle seats to train the machine learning model and adjust the model parameters to minimize the prediction error.

[0082] e. Model evaluation and optimization: Use the validation set to evaluate model performance and optimize the model as needed.

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

[0084] Example machine learning model pseudocode:

[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 corresponding controller according to 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 control signal to the actuator

[0107] execute_seat_movement(control_signal)

[0108] / / Monitor system status and user feedback

[0109] monitor_system_health_and_feedback()

[0110] According to the vehicle seat control method proposed in the embodiment of the present application, the sensor data information of the vehicle seat is obtained, and the control parameters of the vehicle seat are dynamically adjusted based on the sensor data information using a preset fuzzy logic algorithm, and the vehicle seat is controlled according to the control parameters. Based on the sensor data information, a preset machine learning model is used to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, maintenance reminders and / or preventive maintenance measures are automatically triggered. In this way, the problems of insufficient flexibility and reliability of the in-vehicle space configuration in the prior art are solved, and the seat is effectively prevented from malfunctioning when moving, and the reliability of the seat's adaptive folding is improved.

[0111] Next, the control device for the vehicle seat proposed in accordance with the embodiment of the present application will be described with reference to the accompanying drawings.

[0112] Figure 2 It is a block diagram of a control device for a vehicle seat according to an embodiment of the present application.

[0113] like Figure 2 As shown, the control device 10 of the vehicle seat includes: an acquisition module 100 , an adjustment module 200 and a control module 300 .

[0114] Among them, the acquisition module 100 is used to acquire sensor data information of the vehicle seat; the adjustment module 200 is used to dynamically adjust the control parameters of the vehicle seat based on the sensor data information using a preset fuzzy logic algorithm; the control module 300 is used to control the vehicle seat according to the control parameters, and based on the sensor data information, use a preset machine learning model to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, it automatically triggers maintenance reminders and / or preventive maintenance measures.

[0115] Optionally, in some embodiments, the control module 300 is also used to: monitor the current load and 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.

[0116] Optionally, in some embodiments, the adjustment module 200 is also used to: determine a current fuzzy relationship rule, wherein the current fuzzy relationship rule is obtained by converting historical sensor data of multiple sample seats into fuzzy values ​​in a fuzzy set according to a preset fuzzy logic algorithm, a fuzzy value, a system characteristic and a preset knowledge base; generate a fuzzified output according to the current fuzzy relationship rule and sensor data information, and convert the fuzzified output into a control parameter.

[0117] Optionally, in some embodiments, before using a preset machine learning model to predict whether a vehicle seat has a potential fault, the control module 300 is also used to: collect historical fault data of multiple sample seats, and obtain data to be trained after normalizing, denoising and processing missing values ​​of the historical fault data; divide the data to be trained into a training set and a validation set, use the training set to train an initial machine learning model to obtain an initial machine learning model, and use the validation set to verify the prediction performance of the initial machine learning model; and when the prediction performance meets the preset requirements, use the initial machine learning model as the preset machine learning model.

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

[0119] It should be noted that the above explanation of the embodiment of the vehicle seat control method is also applicable to the vehicle seat control device of this embodiment, which will not be repeated here.

[0120] According to the vehicle seat control device proposed in the embodiment of the present application, the sensor data information of the vehicle seat is obtained, and the control parameters of the vehicle seat are dynamically adjusted based on the sensor data information using a preset fuzzy logic algorithm, and the vehicle seat is controlled according to the control parameters. Based on the sensor data information, a preset machine learning model is used to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, maintenance reminders and / or preventive maintenance measures are automatically triggered. In this way, the problems of insufficient flexibility and reliability in the configuration of the vehicle space in the prior art are solved, and the seat is effectively prevented from malfunctioning when moving, and the reliability of the seat's adaptive folding is improved.

[0121] Figure 3 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

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

[0123] When the processor 302 executes the program, the vehicle seat control method provided in the above embodiment is implemented.

[0124] Furthermore, the vehicle also includes:

[0125] The communication interface 303 is used for communication between the memory 301 and the processor 302 .

[0126] The memory 301 is used to store computer programs that can be run on the processor 302 .

[0127] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as 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 communicate with 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. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only 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 communicate with each other through an internal interface.

[0130] The processor 302 may 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] An embodiment of the present application also provides a computer program product, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned vehicle seat control method is implemented.

[0132] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0133] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0134] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer program product for use with an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer program product" can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). Furthermore, the computer program product may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example by optically scanning the paper or other medium and then editing, interpreting or, if necessary, processing in another suitable manner, and then storing it in a computer memory.

[0136] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0137] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer program product, which, when executed, includes one or a combination of the steps of the method embodiment.

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

[0139] The computer program product mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for controlling a vehicle seat, characterized in that: The following steps are involved: Obtain sensor data information of vehicle seats; Dynamically adjusting the control parameters of the vehicle seat using a preset fuzzy logic algorithm based on the sensor data information; The vehicle seat is controlled according to the control parameters, and based on the sensor data information, a preset machine learning model is used to predict whether the vehicle seat has a potential fault. If the vehicle seat has a potential fault, maintenance reminders and / or preventive maintenance measures are automatically triggered.

2. The method according to claim 1, characterized in that When controlling the vehicle seat, including: monitoring a current load and a current movement 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.

3. The method according to claim 1, characterized in that The dynamically adjusting the control parameters of the vehicle seat based on the sensor data information using a preset fuzzy logic algorithm includes: 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 then, according to the preset fuzzy logic algorithm, the fuzzy values, system characteristics and a preset knowledge base are obtained; 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.

4. The method according to claim 1, characterized in that Before predicting whether the vehicle seat has a potential fault using a preset machine learning model, the method further includes: Collecting historical fault data of multiple sample seats, and obtaining data to be trained after normalizing, denoising and processing missing values ​​of the historical fault data; The data to be trained is divided into a training set and a validation set, the training set is used to train the initial machine learning model to obtain the initial machine learning model, and the validation set is used to verify the prediction performance of the initial machine learning model. When the prediction performance meets the preset requirements, the initial machine learning model is used as the preset machine learning model.

5. The method according to claim 1, characterized in that When controlling the vehicle seat, the method further includes: monitoring a main power status and a main locking mechanism of the vehicle seat; If it is detected that the main power supply is in a fault state, the main power supply is switched to a redundant power supply to power the vehicle seat, and / or, if it is detected that the main locking mechanism is in a fault state, the main locking mechanism is switched to a redundant locking mechanism to lock the seat.

6. A control device for a vehicle seat, characterized in that: include: An acquisition module, used to acquire sensor data information of a vehicle seat; An adjustment module, configured to dynamically adjust control parameters of the vehicle seat using a preset fuzzy logic algorithm based on the sensor data information; A control module is used to control the vehicle seat according to the control parameters, and based on the sensor data information, use a preset machine learning model to predict whether the vehicle seat has a potential fault, and if the vehicle seat has a potential fault, automatically trigger a maintenance reminder and / or preventive maintenance measures.

7. The device according to claim 6, characterized in that The control module is also used for: monitoring a current load and a current movement 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.

8. The device according to claim 6, characterized in that The adjustment module is further used for: 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 then, according to the preset fuzzy logic algorithm, the fuzzy values, system characteristics and a preset knowledge base are obtained; 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.

9. A vehicle, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle seat control method as claimed in any one of claims 1 to 5.

10. A computer program product, wherein the computer program product stores a computer program, characterized in that: When the program is executed by a processor, the vehicle seat control method as described in any one of claims 1 to 5 is implemented.

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