WO2023142382A1 - 一种基于卷积神经网络的电动座椅调节方法、装置及车辆 - Google Patents

一种基于卷积神经网络的电动座椅调节方法、装置及车辆 Download PDF

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WO2023142382A1
WO2023142382A1 PCT/CN2022/103036 CN2022103036W WO2023142382A1 WO 2023142382 A1 WO2023142382 A1 WO 2023142382A1 CN 2022103036 W CN2022103036 W CN 2022103036W WO 2023142382 A1 WO2023142382 A1 WO 2023142382A1
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WIPO (PCT)
Prior art keywords
sitting posture
electric seat
user
information
neural network
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PCT/CN2022/103036
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English (en)
French (fr)
Inventor
赵子龙
苏雯
关忠旭
杨慧凯
韩新立
王鹏鹏
贺明明
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中国第一汽车股份有限公司
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Publication of WO2023142382A1 publication Critical patent/WO2023142382A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0268—Non-manual adjustments, e.g. with electrical operation with logic circuits using sensors or detectors for adapting the seat or seat part, e.g. to the position of an occupant
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0272—Non-manual adjustments, e.g. with electrical operation with logic circuits using sensors or detectors for detecting the position of seat parts
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/037—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for occupant comfort, e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present application relates to the technical field of automobile electric seats, in particular to an electric seat adjustment method, device and vehicle based on a convolutional neural network.
  • the purpose of the present invention is to provide a convolutional neural network-based electric seat adjustment method to at least solve one of the above-mentioned technical problems.
  • One aspect of the present invention provides a convolutional neural network-based electric seat adjustment method, the convolutional neural network-based electric seat adjustment method comprising:
  • the body information of the user is input to the trained convolutional neural network model, thereby obtaining the best sitting posture;
  • the optimal sitting posture is sent to the driving device, so that the driving device drives the electric seat to move according to the optimal sitting posture.
  • the convolutional neural network-based electric seat adjustment method further includes:
  • the adjusted sitting posture is sent to the driving device, so that the driving device drives the electric seat to move according to the adjusted sitting posture.
  • the judging whether the user needs to re-adjust the electric seat according to the user's sitting posture information includes:
  • the judging whether the user deviates from the optimal electric seat position according to the user's sitting posture information includes:
  • the body information includes at least weight information and height information.
  • the body information further includes cervical spine condition information and/or lumbar spine condition information.
  • the convolutional neural network-based electric seat adjustment method further includes :
  • the electric seat lumbar airbag inflation signal is generated, and the electric seat lumbar airbag inflation signal is transmitted to the lumbar airbag inflation control unit of the electric seat, so that the lumbar airbag
  • the inflation control unit inflates the lumbar airbags.
  • the present application also provides a convolutional neural network-based electric seat adjustment device, the convolutional neural network-based electric seat adjustment device comprising:
  • a body information acquisition module the body information acquisition module is used to acquire the user's body information
  • a network model acquisition module the network model acquisition module is used to obtain a trained convolutional neural network model
  • An optimal sitting posture acquisition module the optimal sitting posture acquisition module is used to input the user's body information into the trained convolutional neural network model, so as to obtain the optimal sitting posture;
  • a sending module the sending module is used to send the optimal sitting posture to the driving device, so that the driving device can drive the electric seat to move according to the optimal sitting posture.
  • the present application also provides a vehicle, the vehicle comprising:
  • the deformation sensor is arranged on the electric seat, and is used to obtain deformation information of the electric seat;
  • the pressure sensor is arranged on the electric seat, and is used to obtain force information of a user on the electric seat;
  • An electric seat adjustment device based on a convolutional neural network is the above-mentioned electric seat adjustment device based on a convolutional neural network.
  • the vehicle further includes:
  • a lumbar airbag assembly located on the power seat;
  • the cervical spine airbag assembly is located on the electric seat.
  • the convolutional neural network-based electric seat adjustment method of this application starts from the perspective of improving user health, comfort, and intelligent experience.
  • the system can be calculated according to the network model.
  • the user's height, weight and other information are used to recommend the best sitting posture, thereby helping the user maintain a good and comfortable sitting posture while driving a vehicle, and reduce the harm of sitting for a long time.
  • FIG. 1 is a schematic flow chart of an electric seat adjustment method based on a convolutional neural network according to an embodiment of the present application.
  • FIG. 2 is a schematic diagram of electronic equipment for implementing the convolutional neural network-based electric seat adjustment method shown in FIG. 1 .
  • Fig. 3 is a schematic diagram of an electric seat device according to an embodiment of the present application.
  • FIG. 1 is a schematic flow chart of an electric seat adjustment method based on a convolutional neural network according to an embodiment of the present application.
  • the electric seat adjustment method based on convolutional neural network as shown in Figure 1 includes:
  • Step 1 Obtain the user's physical information
  • Step 2 Obtain the trained convolutional neural network model
  • Step 3 Input the user's body information into the trained convolutional neural network model to obtain the best sitting posture
  • Step 4 Send the optimal sitting posture to the driving device, so that the driving device drives the electric seat to move according to the optimal sitting posture.
  • the convolutional neural network-based electric seat adjustment method of this application starts from the perspective of improving user health, comfort, and intelligent experience.
  • the system can be calculated according to the network model.
  • the user's height, weight and other information are used to recommend the best sitting posture, thereby helping the user maintain a good and comfortable sitting posture while driving a vehicle, and reduce the harm of sitting for a long time.
  • the method for adjusting the electric seat based on convolutional neural network further includes:
  • the adjusted sitting posture is sent to the driving device, so that the driving device drives the electric seat to move according to the adjusted sitting posture.
  • the sitting posture information is calculated based on the force situation and the position situation.
  • a sensor group is arranged on the seat, which can sense the relative position and pressure value.
  • the sensor group arranged on the seat includes a pressure sensor and a deformation sensor.
  • the pressure sensor Through the pressure sensor, the pressure distribution information of the seat when the user is sitting can be obtained.
  • the deformation sensor Through the deformation sensor, it can be sensed that the seat is under the force of the user.
  • the position of the seat after the stress and the original position of the seat can be obtained through the deformation information, and the relative position of the seat can be obtained through the position of the seat after the force and the original position of the seat, Then the contour surface information of the seat surface can be obtained correspondingly through the relative position;
  • the force information at each sensing point can be obtained correspondingly through the pressure value
  • the sitting posture information is formed.
  • the optimal position of the seat can be dynamically adjusted, ensuring that the user is always in a good sitting posture, and improving the health and comfort of the user when driving the vehicle for a long time.
  • judging whether the user needs to re-adjust the electric seat according to the user's sitting posture information includes:
  • the sitting posture will be calculated according to the current surface information and force information
  • judging whether the user needs to re-adjust the electric seat according to the user's sitting posture information includes:
  • each sensor point will have the force situation of the measurement point.
  • the surface shape of the seat at this time can be detected (note: this surface is a curved surface, not a curve, that is, a contour surface), thereby identifying the driver's sitting posture;
  • the next step is to calculate the fitting degree of the sitting posture and determine the best sitting posture.
  • the specific implementation is as follows:
  • the trained neural network model can calculate the best ergonomic sitting posture according to the driver's height and weight
  • the body information includes at least weight information and height information.
  • the body information further includes cervical spine status information and/or lumbar spine status information.
  • users may have cervical spondylosis or lumbar spondylosis, resulting in the need for some special seat positions.
  • cervical spondylosis or lumbar spondylosis By setting information about cervical spondylosis or lumbar spondylosis, two more can be considered when generating convolutional neural networks. Dimensions, allowing for more accurate secondary adjustments.
  • the method for adjusting the electric seat based on the convolutional neural network further includes:
  • the electric seat lumbar airbag inflation signal is generated, and the electric seat lumbar airbag inflation signal is transmitted to the electric seat lumbar airbag inflation control unit, so that the lumbar airbag inflation control unit The unit inflates the lumbar airbags.
  • the waist airbag can provide a support for the user's waist, so that the user can be in a more comfortable posture.
  • the method for adjusting the electric seat based on the convolutional neural network further includes:
  • the electric seat cervical spine airbag inflation signal is generated, and the electric seat cervical spine airbag inflation signal is transmitted to the cervical spine airbag inflation control unit of the electric seat, so that the cervical spine airbag inflation control unit The unit inflates the cervical airbags.
  • the cervical spine airbag can provide a support for the user's cervical spine, so that the user can be in a more comfortable posture.
  • the present application also provides an electric seat adjustment device based on a convolutional neural network.
  • the electric seat adjustment device based on a convolutional neural network includes a body information acquisition module, a network model acquisition module, an optimal sitting posture acquisition module, and a sending module, wherein, the body information acquisition module is used to obtain the user's body information; the network model acquisition module is used to obtain the trained convolutional neural network model; the best sitting posture acquisition module is used to input the user's body information into The trained convolutional neural network model is used to obtain the optimal sitting posture; the sending module is used to send the optimal sitting posture to the driving device, so that the driving device drives the electric seat to move according to the optimal sitting posture.
  • the present application also provides a vehicle, the vehicle includes an electric seat 11, a pressure sensor 12, a deformation sensor (not shown in the figure) and an electric seat adjustment device based on a convolutional neural network, see FIG. 3,
  • the pressure sensor is arranged on the electric seat for obtaining the force information of the user on the electric seat;
  • the deformation sensor is arranged on the electric seat for obtaining the deformation of the electric seat Information
  • the electric seat adjustment device based on convolutional neural network is the electric seat adjustment device based on convolutional neural network as described above.
  • the vehicle further includes a lumbar airbag assembly and/or a cervical spine airbag assembly, and the lumbar airbag assembly is located on the electric seat; the cervical spine airbag assembly is located on the electric seat.
  • the trained convolutional neural network model is obtained by the following method:
  • the first step is to establish a training set and a test set of optimal sitting posture data for people of different heights and weights based on ergonomic principles and the actual structure and material of the car seat.
  • the training set and the test set are completely independent.
  • the second step is to use the training set for convolutional neural network model training. Because the structure and material of the seat are different, users of different heights and weights have different optimal sitting postures and stress situations when sitting on it. By training a convolutional neural network model, height and weight can be associated with the optimal position of the seat.
  • the third step is to use the test set to test the convolutional neural network model after each round of training in the training set.
  • the optimal sitting posture calculated by the convolutional neural network model based on height and weight coincides with the optimal position of the actual seat
  • the degree reaches more than 98% it is considered that the model training is completed, and the training step ends. If the coincidence degree is lower than 98%, continue to use the training set for a new round of training until the coincidence degree reaches more than 98%.
  • the user when the user uses the method of the present application for the first time, the user needs to input data such as height, weight, cervical spine, lumbar spine, etc. into the system through the entertainment host. According to the user's input, the system can store multiple sets of height and weight data to match the needs of multiple users.
  • the user can also input the above-mentioned data such as height, weight, cervical spine, lumbar spine, etc., or directly read the previously input information through face recognition, fingerprint recognition, etc.
  • the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • the processor executes the computer program, the above electric seat adjustment based on the convolutional neural network is realized. method.
  • the present application also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the above electric seat adjustment method based on a convolutional neural network can be realized.
  • Fig. 2 is an exemplary structural diagram of an electronic device capable of implementing a method for adjusting an electric seat based on a convolutional neural network according to an embodiment of the present application.
  • the electronic device includes an input device 501 , an input interface 502 , a central processing unit 503 , a memory 504 , an output interface 505 and an output device 506 .
  • the input interface 502, the central processing unit 503, the memory 504 and the output interface 505 are connected to each other through the bus 507, and the input device 501 and the output device 506 are respectively connected to the bus 507 through the input interface 502 and the output interface 505, and then connected to other components of the electronic device. Component connections.
  • the input device 504 receives input information from the outside, and transmits the input information to the central processing unit 503 through the input interface 502; the central processing unit 503 processes the input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently store the output information in the memory 504, and then transmit the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for the user to use.
  • the electronic device shown in FIG. 2 can also be implemented as including: a memory storing computer-executable instructions; and one or more processors, which can Realize the automobile cockpit interaction method described in conjunction with FIG. 1 .
  • the electronic device shown in FIG. 2 may be implemented to include: a memory 504 configured to store executable program codes; one or more processors 503 configured to run the executable code stored in the memory 504
  • the program code is used to execute the vehicle cockpit interaction method in the above embodiment.
  • a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • Memory may include non-permanent storage in computer readable media, in the form of random access memory (RAM) and/or nonvolatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer readable media.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash random access memory
  • Computer-readable media include both permanent and non-permanent, removable and non-removable media, which may be implemented by any method or technology for storage of information.
  • Information may be computer readable instructions, data structures, modules of a program, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), data versatile disc (DVD) or other optical storage, A magnetic tape cartridge, tape disk storage or other magnetic storage device, or any other non-transmission medium, that may be used to store information that can be accessed by a computing device.
  • PRAM phase change memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • RAM random access memory
  • ROM read only memory
  • EEPROM electrically erasable programmable read-only
  • the embodiments of the present application may be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • a computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of code that includes one or more Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks identified in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block in the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or overall flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations. implemented, or may be implemented by a combination of special purpose hardware and computer instructions.
  • the processor referred to in this embodiment may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit , ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the like.
  • the memory can be used to store computer programs and/or modules, and the processor realizes various functions of the device/terminal device by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory.
  • the memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playback function, an image playback function, etc.); The data created by the use (such as audio data, phone book, etc.) and so on.
  • the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, internal memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • non-volatile memory such as hard disk, internal memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • the integrated module/unit of the device/terminal device is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present invention realizes all or part of the processes in the methods of the above-mentioned embodiments, and can also be completed by instructing related hardware through computer programs, and the computer programs can be stored in a computer-readable storage medium.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form.
  • the computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electrical carrier signal telecommunication signal and software distribution medium, etc.
  • the embodiments of the present application may be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • a computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.

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Abstract

一种基于卷积神经网络的电动座椅调节方法、装置及车辆。基于卷积神经网络的电动座椅调节方法包括：获取使用者的身体信息；获取经过训练的卷积神经网络模型；将使用者的身体信息输入至经过训练的卷积神经网络模型，从而获取最佳坐姿；将最佳坐姿发送给驱动装置，从而使驱动装置根据最佳坐姿驱动电动座椅运动。

Description

一种基于卷积神经网络的电动座椅调节方法、装置及车辆 技术领域
本申请涉及汽车电动座椅技术领域，具体涉及一种基于卷积神经网络的电动座椅调节方法、装置及车辆。
背景技术
为了提升用户在日常驾乘车辆时的舒适性，电动座椅上出现了许多人性化的设计，如多向座椅调节功能、座椅调节位置记忆功能、座椅加热功能、座椅通风功能等。
然而，现有技术中，大多数研究均是考虑如何提升用户个性化设置的舒适性体验，而没有考虑用户健康性、智能化体验的需求。
因此，希望有一种技术方案来解决或至少减轻现有技术的上述不足。
发明内容
本发明的目的在于提供一种基于卷积神经网络的电动座椅调节方法来至少解决上述的一个技术问题。
本发明的一个方面，提供一种基于卷积神经网络的电动座椅调节方法，所述基于卷积神经网络的电动座椅调节方法包括：
获取使用者的身体信息；
获取经过训练的卷积神经网络模型；
将所述使用者的身体信息输入至所述经过训练的卷积神经网络模型，从 而获取最佳坐姿；
将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动。
可选地，在所述驱动电动座椅运动后，所述基于卷积神经网络的电动座椅调节方法进一步包括：
获取使用者的坐姿信息；
根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节，若是，则
根据使用者的坐姿信息以及最佳坐姿获取调整坐姿；
将所述调整坐姿发送给驱动装置，从而使驱动装置根据所述调整坐姿驱动电动座椅运动。
可选地，所述根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节包括：
获取使用者的受力信息；
根据使用者的受力信息获取使用者的坐姿信息；
根据使用者的坐姿信息判断使用者是否偏离所述最佳电动座椅位置，若是，则
根据使用者的坐姿信息以及最佳坐姿获取调整坐姿。
可选地，
所述根据使用者的坐姿信息判断使用者是否偏离所述最佳电动座椅位置包括：
获取所述最佳坐姿；
将所述使用者的坐姿信息与所述最佳坐姿进行对比拟合，获得二者的偏差；
判断二者的偏差是否超过预设阈值且超过预设阈值的时间超过预设时间，若是，则
根据使用者的坐姿信息以及最佳坐姿获取调整坐姿。
可选地，所述身体信息至少包括体重信息、身高信息。
可选地，所述身体信息进一步包括颈椎状况信息以和/或腰椎状况信息。
可选地，在所述将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动之后，所述基于卷积神经网络的电动座椅调节方法进一步包括：
当所述身体信息中的腰椎状况信息符合预设条件时，生成电动座椅腰部气囊充气信号，并将电动座椅腰部气囊充气信号传递给电动座椅的腰部气囊充气控制单元，从而使腰部气囊充气控制单元为腰部气囊充气。
本申请还提供了一种基于卷积神经网络的电动座椅调节装置，所述基于卷积神经网络的电动座椅调节装置包括：
身体信息获取模块，所述身体信息获取模块用于获取使用者的身体信息；
网络模型获取模块，所述网络模型获取模块用于获取经过训练的卷积神经网络模型；
最佳坐姿获取模块，所述最佳坐姿获取模块用于将所述使用者的身体信息输入至所述经过训练的卷积神经网络模型，从而获取最佳坐姿；
发送模块，所述发送模块用于将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动。
本申请还提供了一种车辆，所述车辆包括：
电动座椅；
形变传感器，所述形变传感器布置在所述电动座椅上，用于获取电动座椅的形变信息；
压力传感器，所述压力传感器布置在所述电动座椅上，用于获取位于电动座椅上的使用者的受力信息；
基于卷积神经网络的电动座椅调节装置，所述基于卷积神经网络的电动座椅调节装置为如上所述的基于卷积神经网络的电动座椅调节装置。
可选地，所述车辆进一步包括：
腰部气囊组件，所述腰部气囊组件位于所述电动座椅上；和/或，
颈椎气囊组件，所述颈椎气囊组件位于所述电动座椅上。
有益效果
本申请的基于卷积神经网络的电动座椅调节方法从提升用户健康性、舒适性以及智能化体验角度出发，通过预先训练一个卷积神经网络模型，经过该网络模型的计算后，系统可根据用户的身高、体重等信息进行最佳的坐姿推荐，进而帮助用户在驾乘车辆时保持良好且舒适的坐姿，降低久坐的危害。
附图说明
图1是本申请一实施例的基于卷积神经网络的电动座椅调节方法的流程示意图。
图2是用于实现图1所示的基于卷积神经网络的电动座椅调节方法的电子设备示意图。
图3是本申请一实施例的电动座椅的装置示意图。
具体实施方式
为使本申请实施的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行更加详细的描述。在附图中，自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。所描述的实施例是本申请一部分实施例，而不是全部的实施例。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。下面结合附图对本申请的实施例进行详细说明。
图1是本申请一实施例的基于卷积神经网络的电动座椅调节方法的流程示意图。
如图1所示的基于卷积神经网络的电动座椅调节方法包括：
步骤1：获取使用者的身体信息；
步骤2：获取经过训练的卷积神经网络模型；
步骤3：将使用者的身体信息输入至经过训练的卷积神经网络模型，从而获取最佳坐姿；
步骤4：将最佳坐姿发送给驱动装置，从而使驱动装置根据最佳坐姿驱动电动座椅运动。
本申请的基于卷积神经网络的电动座椅调节方法从提升用户健康性、舒适性以及智能化体验角度出发，通过预先训练一个卷积神经网络模型，经过该网络模型的计算后，系统可根据用户的身高、体重等信息进行最佳的坐姿推荐，进而帮助用户在驾乘车辆时保持良好且舒适的坐姿，降低久坐的危害。
在本实施例中，在驱动电动座椅运动后，所述基于卷积神经网络的电动座椅调节方法进一步包括：
获取使用者的坐姿信息；
根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节，若是，则
根据使用者的坐姿信息以及最佳坐姿获取调整坐姿；
将所述调整坐姿发送给驱动装置，从而使驱动装置根据所述调整坐姿驱动电动座椅运动。
在本实施例中，坐姿信息是通过受力情况和位置情况计算出来的。
合不合格需要看坐姿信息。
首先，座椅上布置传感器组，可以感知相对位置和压力值。具体而言，在座椅上布置的传感器组包括压力传感器以及形变传感器，通过压力传感器能够获取到使用者乘坐时座椅的压力分布信息，通过形变传感器可以感知到座椅在受到使用者的力后的形变信息，通过形变信息即可以获得座椅在受力后的位置以及座椅的原始位置，通过座椅在受力后的位置以及座椅的原始位置及可以获得座椅的相对位置，继而通过相对位置可以对应获取座椅表面的轮廓曲面信息；
在本实施例中，通过压力值可以对应获取的是每一个传感点处的受力信息；
将曲面信息和受力信息融合在一起后，从而形成坐姿信息。
采用这种方式，可以动态调节座椅的最佳位置，保证用户始终处于一个良好的坐姿状态，提升了用户长时间驾乘车辆时的健康性和舒适性。
在本实施中，根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节包括：
获取使用者的受力信息；
根据使用者的受力信息获取使用者的坐姿信息；
根据使用者的坐姿信息判断使用者是否偏离最佳电动座椅位置，若是，则
根据使用者的坐姿信息以及最佳坐姿获取调整坐姿。
在本实施例中，会根据当前曲面信息和受力信息计算出坐姿情况；
将当前坐姿情况与最佳坐姿进行对比拟合，获得二者的偏差；
根据偏差可以反推出如何调整曲面信息和受力信息可更加接近最佳坐姿；
从而实现座椅调整。
在本实施例中，根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节包括：
获取使用者的受力信息；
根据使用者的受力信息获取使用者的坐姿信息；
根据使用者的坐姿信息判断使用者是否偏离最佳电动座椅位置，若是，则
根据使用者的坐姿信息以及最佳坐姿获取调整坐姿。
参见图3，具体而言，当驾驶员坐在座椅上时，每个传感器点都会有该测量点的受力情况。各行列传感器之间存在相对位置，根据相对位置可检测出座椅此时的表面形状(注：此表面是一个曲面，而非曲线，即轮廓曲面)，从而识别出驾驶员的坐姿；
通过上述的方法获取驾驶员的坐姿和受力情况后，接下来进行坐姿拟合度计算和最佳坐姿判断，具体实施如下：
通过智能感知座椅表面各采集点的数据推算出整个座椅表面的受力情况和用户的坐姿；
已训练完成的神经网络模型可根据驾驶员身高体重计算出符合人体工程学的最佳坐姿；
将智能感知座椅表面检测到的驾驶员实际受力情况和坐姿与网络模型计算出的受力情况和坐姿进行方差计算。当实际值与理论值的差值大于理论值的10％，认为坐姿不合格；差值小于理论值的10％，认为坐姿处于各个范围内。
在本实施例中，身体信息至少包括体重信息、身高信息。
在本实施例中，身体信息进一步包括颈椎状况信息以和/或腰椎状况信息。
在某些情况下，使用者有可能具有颈椎病或者腰椎病，导致需要一些特殊的座椅位置，通过设置有颈椎病或者腰椎病的信息，可以在进行卷积神经网络生成时多考虑两个维度，从而实现更准确的辅助调整。
在本实施例中，在将最佳坐姿发送给驱动装置，从而使驱动装置根据最佳坐姿驱动电动座椅运动之后，基于卷积神经网络的电动座椅调节方法进一 步包括：
当身体信息中的腰椎状况信息符合预设条件时，生成电动座椅腰部气囊充气信号，并将电动座椅腰部气囊充气信号传递给电动座椅的腰部气囊充气控制单元，从而使腰部气囊充气控制单元为腰部气囊充气。
采用这种方式，可以通过腰部气囊为使用者的腰部提供一个支撑，从而使得使用者处于更为舒适的姿势中。
在其他实施例中，在将最佳坐姿发送给驱动装置，从而使驱动装置根据最佳坐姿驱动电动座椅运动之后，基于卷积神经网络的电动座椅调节方法进一步包括：
当身体信息中的颈椎状况信息符合预设条件时，生成电动座椅颈椎气囊充气信号，并将电动座椅颈椎气囊充气信号传递给电动座椅的颈椎气囊充气控制单元，从而使颈椎气囊充气控制单元为颈椎气囊充气。
采用这种方式，可以通过颈椎气囊为使用者的颈椎提供一个支撑，从而使得使用者处于更为舒适的姿势中。
本申请还提供了一种基于卷积神经网络的电动座椅调节装置，所述基于卷积神经网络的电动座椅调节装置包括身体信息获取模块、网络模型获取模块、最佳坐姿获取模块以及发送模块，其中，身体信息获取模块用于获取使用者的身体信息；网络模型获取模块用于获取经过训练的卷积神经网络模型；最佳坐姿获取模块用于将所述使用者的身体信息输入至所述经过训练的卷积神经网络模型，从而获取最佳坐姿；发送模块用于将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动。
参见图2，本申请还提供了一种车辆，车辆包括电动座椅11、压力传感器12、形变传感器(图中未示出)以及基于卷积神经网络的电动座椅调节装置，参见图3，在本实施例中，压力传感器布置在电动座椅上，用于获取位于电动座椅上的使用者的受力信息；形变传感器布置在所述电动座椅上，用于获取电动座椅的形变信息，基于卷积神经网络的电动座椅调节装置为如上所述的基于卷积神经网络的电动座椅调节装置。
在本实施例中，车辆进一步包括腰部气囊组件和/或颈椎气囊组件，腰部气囊组件位于电动座椅上；颈椎气囊组件位于电动座椅上。
在本实施例中，经过训练的卷积神经网络模型采用如下方法获取：
第一步，根据人体工学原理以及汽车座椅实际的结构和材质，建立不同身高、体重人群的最佳坐姿数据训练集和测试集，训练集与测试集完全独立。
第二步，使用训练集进行卷积神经网络模型训练。因为座椅的结构和材 质不同，不同身高、体重的用户坐在上面的最佳坐姿和受力情况不同。通过训练卷积神经网络模型，可以将身高、体重与座椅的最佳位置建立起关联。
第三步，训练集每训练完成一轮后，使用测试集对卷积神经网络模型进行测试，当卷积神经网络模型根据身高、体重计算出来的最佳坐姿与实际座椅的最佳位置重合度达到98％以上时，认为模型训练完成，结束训练步骤。如果重合度低于98％，则继续使用训练集进行新一轮的训练，直至重合度达到98％以上。
在本实施例中，使用者初次使用本申请的方法时，使用者需要将身高、体重、颈椎、腰椎等数据通过娱乐主机输入到系统中。系统中可以根据用户的输入，存储多组身高、体重数据，用于匹配多位用户的需求。
在后续的使用中，使用者还可以进行上述的身高、体重、颈椎、腰椎等数据的输入，也可以通过人脸识别、指纹识别等直接读取之前输入的信息。
需要说明的是，前述对方法实施例的解释说明也适用于本实施例的装置，此处不再赘述。
本申请还提供了一种电子设备，包括存储器、处理器以及存储在存储器中并能够在处理器上运行的计算机程序，处理器执行计算机程序时实现如上的基于卷积神经网络的电动座椅调节方法。
本申请还提供了一种计算机可读存储介质，计算机可读存储介质存储有计算机程序，计算机程序被处理器执行时能够实现如上的基于卷积神经网络的电动座椅调节方法。
图2是能够实现根据本申请一个实施例提供的基于卷积神经网络的电动座椅调节方法的电子设备的示例性结构图。
如图2所示，电子设备包括输入设备501、输入接口502、中央处理器503、存储器504、输出接口505以及输出设备506。其中，输入接口502、中央处理器503、存储器504以及输出接口505通过总线507相互连接，输入设备501和输出设备506分别通过输入接口502和输出接口505与总线507连接，进而与电子设备的其他组件连接。具体地，输入设备504接收来自外部的输入信息，并通过输入接口502将输入信息传送到中央处理器503；中央处理器503基于存储器504中存储的计算机可执行指令对输入信息进行处理以生成输出信息，将输出信息临时或者永久地存储在存储器504中，然后通过输出接口505将输出信息传送到输出设备506；输出设备506将输出信息输出到电子设备的外部供用户使用。
也就是说，图2所示的电子设备也可以被实现为包括：存储有计算机可执行指令的存储器；以及一个或多个处理器，该一个或多个处理器在执行计算机可执行指令时可以实现结合图1描述的汽车座舱交互方法。
在一个实施例中，图2所示的电子设备可以被实现为包括：存储器504， 被配置为存储可执行程序代码；一个或多个处理器503，被配置为运行存储器504中存储的可执行程序代码，以执行上述实施例中的汽车座舱交互方法。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动，媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数据多功能光盘(DVD)或其他光学存储、磁盒式磁带、磁带磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。
本领域技术人员应明白，本申请的实施例可提供为方法、系统或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
此外，显然“包括”一词不排除其他单元或步骤。装置权利要求中陈述的多个单元、模块或装置也可以由一个单元或总装置通过软件或硬件来实现。
附图中的流程图和框图，图示了按照本申请各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，模块、程序段、或代码的一部分包括一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地标识的方框实际上可以基本并行地执行，他们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或总流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
在本实施例中所称处理器可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处 理器或者该处理器也可以是任何常规的处理器等。
存储器可用于存储计算机程序和/或模块，处理器通过运行或执行存储在存储器内的计算机程序和/或模块，以及调用存储在存储器内的数据，实现装置/终端设备的各种功能。存储器可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据手机的使用所创建的数据(比如音频数据、电话本等)等。此外，存储器可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
在本实施例中，装置/终端设备集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，计算机程序包括计算机程序代码，计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。计算机可读介质可以包括：能够携带计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减。本申请虽然以较佳实施例公开如上，但其实并不是用来限定本申请，任何本领域技术人员在不脱离本申请的精神和范围内，都可以做出可能的变动和修改，因此，本申请的保护范围应当以本申请权利要求所界定的范围为准。
本领域技术人员应明白，本申请的实施例可提供为方法、系统或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
此外，显然“包括”一词不排除其他单元或步骤。装置权利要求中陈述的多个单元、模块或装置也可以由一个单元或总装置通过软件或硬件来实现。
虽然，上文中已经用一般性说明及具体实施方案对本发明作了详尽的描述，但在本发明基础上，可以对之作一些修改或改进，这对本领域技术人员而言是显而易见的。因此，在不偏离本发明精神的基础上所做的这些修改或改进，均属于本发明要求保护的范围。

Claims (10)

  1. 一种基于卷积神经网络的电动座椅调节方法，其特征在于，所述基于卷积神经网络的电动座椅调节方法包括：
    获取使用者的身体信息；
    获取经过训练的卷积神经网络模型；
    将所述使用者的身体信息输入至所述经过训练的卷积神经网络模型，从而获取最佳坐姿；
    将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动。
  2. 如权利要求1所述的基于卷积神经网络的电动座椅调节方法，其特征在于，在所述驱动电动座椅运动后，所述基于卷积神经网络的电动座椅调节方法进一步包括：
    获取使用者的坐姿信息；
    根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节，若是，则
    根据使用者的坐姿信息以及最佳坐姿获取调整坐姿；
    将所述调整坐姿发送给驱动装置，从而使驱动装置根据所述调整坐姿驱动电动座椅运动。
  3. 如权利要求2所述的基于卷积神经网络的电动座椅调节方法，其特征在于，所述根据使用者的坐姿信息判断使用者是否需要重新进行电动座椅调节包括：
    获取使用者的受力信息；
    根据使用者的受力信息获取使用者的坐姿信息；
    根据使用者的坐姿信息判断使用者是否偏离所述最佳电动座椅位置，若是，则
    根据使用者的坐姿信息以及最佳坐姿获取调整坐姿。
  4. 如权利要求3所述的基于卷积神经网络的电动座椅调节方法，其特征在于，所述根据使用者的坐姿信息判断使用者是否偏离所述最佳电动座椅位置包括：
    获取所述最佳坐姿；
    将所述使用者的坐姿信息与所述最佳坐姿进行对比拟合，获得二者的偏差；
    判断二者的偏差是否超过预设阈值且超过预设阈值的时间超过预设时间，若是，则
    根据使用者的坐姿信息以及最佳坐姿获取调整坐姿。
  5. 如权利要求4所述的基于卷积神经网络的电动座椅调节方法，其特征在于，所述身体信息至少包括体重信息、身高信息。
  6. 如权利要求5所述的基于卷积神经网络的电动座椅调节方法，其特征在于，所述身体信息进一步包括颈椎状况信息以和/或腰椎状况信息。
  7. 如权利要求6所述的基于卷积神经网络的电动座椅调节方法，其特征在于，在所述将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动之后，所述基于卷积神经网络的电动座椅调节方法进一步包括：
    当所述身体信息中的腰椎状况信息符合预设条件时，生成电动座椅腰部气囊充气信号，并将电动座椅腰部气囊充气信号传递给电动座椅的腰部气囊充气控制单元，从而使腰部气囊充气控制单元为腰部气囊充气。
  8. 一种基于卷积神经网络的电动座椅调节装置，其特征在于，所述基于卷积神经网络的电动座椅调节装置包括：
    身体信息获取模块，所述身体信息获取模块用于获取使用者的身体信息；
    网络模型获取模块，所述网络模型获取模块用于获取经过训练的卷积神经网络模型；
    最佳坐姿获取模块，所述最佳坐姿获取模块用于将所述使用者的身体信 息输入至所述经过训练的卷积神经网络模型，从而获取最佳坐姿；
    发送模块，所述发送模块用于将所述最佳坐姿发送给驱动装置，从而使驱动装置根据所述最佳坐姿驱动电动座椅运动。
  9. 一种车辆，其特征在于，所述车辆包括：
    电动座椅；
    压力传感器，所述压力传感器布置在所述电动座椅上，用于获取位于电动座椅上的使用者的受力信息；
    形变传感器，所述形变传感器布置在所述电动座椅上，用于获取电动座椅的形变信息；
    基于卷积神经网络的电动座椅调节装置，所述基于卷积神经网络的电动座椅调节装置为如权利要求8所述的基于卷积神经网络的电动座椅调节装置。
  10. 如权利要求9所述的车辆，其特征在于，所述车辆进一步包括：
    腰部气囊组件，所述腰部气囊组件位于所述电动座椅上；和/或，
    颈椎气囊组件，所述颈椎气囊组件位于所述电动座椅上。
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