Seat adjusting method, electronic equipment, vehicle, storage medium and computer product

Abstract

The invention discloses a seat adjusting method, electronic equipment, a vehicle, a storage medium and a computer product, and relates to the technical field of vehicles, the seat adjusting method is applied to the electronic equipment, the electronic equipment comprises a wireless signal transmitting module, and the method specifically comprises the steps that the wireless signal transmitting module is controlled to transmit a plurality of wireless detection signals to a cabin of the vehicle; generating a first target living body image containing the target passenger based on the plurality of wireless detection signals, and extracting a first image feature of the first target living body image; determining a first sitting posture type of the target passenger according to the first image feature, and determining a target seat adjustment parameter according to the first sitting posture type; and adjusting a target seat where the target passenger is located according to the target seat adjustment parameter, so that the target seat enters a target seat state matched with the first sitting posture type. By adopting the method, the technical effect that the electronic equipment can accurately adjust the seat based on the sitting posture of the passenger is achieved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a seat adjustment method, an electronic device, a vehicle, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automobile industry, new energy vehicles have become the preferred means of transportation for more and more people in their daily travel.

[0003] In order to maximize the passengers' experience when riding in new energy vehicles, technicians usually install cameras in the cockpit to detect the passengers' sitting posture on the seats and adjust the seats according to the posture to make the seats fit the passengers' sitting posture better.

[0004] However, since the camera device is easily affected by ambient light when shooting the interior of the cabin, the image data obtained by the camera device is likely to be of low quality, making it difficult for the vehicle to accurately identify the sitting posture of the occupants, thereby reducing the accuracy of the vehicle's seat adjustment. Summary of the invention

[0005] The main purpose of the present application is to provide a seat adjustment method, an electronic device, a vehicle, a storage medium and a computer program product, aiming to solve the technical problem in the related art that it is difficult for a vehicle to accurately identify the sitting posture of an occupant.

[0006] To achieve the above object, the present application proposes a seat adjustment method, which is applied to an electronic device, wherein the electronic device includes a wireless signal transmission module, and the seat adjustment method includes:

[0007] Controlling the wireless signal transmitting module to transmit a plurality of wireless detection signals to the cabin of the vehicle;

[0008] generating a first target living body image including a target occupant based on the plurality of wireless detection signals, and extracting a first image feature of the first target living body image;

[0009] determining a first sitting posture type of the target occupant according to the first image feature, and determining a target seat adjustment parameter according to the first sitting posture type;

[0010] The target seat in which the target occupant is located is adjusted according to the target seat adjustment parameters so that the target seat enters a target seat state that matches the first sitting posture type.

[0011] In one embodiment, the step of determining the target seat adjustment parameter according to the first sitting posture type includes:

[0012] Determining a target seat corresponding to the target occupant, and detecting a current seat state of the target seat;

[0013] A target seat state is determined according to the first sitting posture type, and a target seat adjustment parameter is determined based on the current seat state and the target seat state.

[0014] In one embodiment, after the step of determining the target seat corresponding to the target occupant, the method further includes:

[0015] Detecting a door of the vehicle, and determining a first channel size corresponding to the target seat when detecting that the door is in an open state;

[0016] A first seat position adjustment parameter is determined according to the first channel size and a preset target channel size, and the target seat is adjusted according to the first seat position adjustment parameter so that the channel size corresponding to the target seat is switched from the first channel size to the target channel size.

[0017] In one embodiment, after the step of adjusting the target seat where the target occupant is located according to the target seat adjustment parameter, the method further includes:

[0018] generating a second target living body image including the target occupant based on the plurality of wireless detection signals, and extracting a second image feature of the second target living body image;

[0019] Determining a second sitting posture type corresponding to the target occupant according to the second image feature, and determining a function control instruction corresponding to the second sitting posture type, wherein the function control instruction is a control instruction for controlling a target function module configured in the vehicle and required to be adjusted by the target occupant;

[0020] The target functional module is controlled according to the functional control instruction so that the target functional module enters a functional operation state matching the functional control instruction.

[0021] In one embodiment, the step of determining the function control instruction corresponding to the second sitting posture type includes:

[0022] determining a preset sitting posture type that matches the second sitting posture type;

[0023] The preset function instruction matching the preset sitting posture type is determined as the function control instruction corresponding to the second sitting posture type.

[0024] In one embodiment, the step of generating a first target living body image including a target occupant based on the plurality of wireless detection signals comprises:

[0025] Detecting initial channel state information respectively matched by the plurality of wireless detection signals, and performing cleaning processing on each of the initial channel state information to obtain each complete channel state information;

[0026] Each of the complete channel state information is screened to determine target channel state information, and a first target living body image is generated based on each of the target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal detecting a target occupant.

[0027] In one embodiment, the step of detecting initial channel state information that matches each of the plurality of wireless detection signals includes:

[0028] Detecting wireless receiving signals that are matched to each of the plurality of wireless detection signals;

[0029] Determining a signal change ratio between each of the plurality of wireless detection signals and a matching wireless reception signal;

[0030] Based on each of the signal change ratios, initial channel state information that matches each of the plurality of wireless detection signals is determined.

[0031] In one embodiment, the step of generating a first target living body image based on each target channel state information includes:

[0032] Extracting one-dimensional vector features contained in each of the target channel state information;

[0033] The one-dimensional vector features are fused to obtain two-dimensional vector features, and a first target living body image is generated based on the two-dimensional vector features.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a wireless signal transmission module, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the seat adjustment method described above.

[0035] In addition, to achieve the above objectives, the present application also proposes a vehicle, which includes the electronic device as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the seat adjustment method described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the seat adjustment method described above are implemented.

[0038] The seat adjustment method provided in the embodiment of the present application is applied to an electronic device, which includes a wireless signal transmission module, and transmits multiple wireless detection signals to the vehicle's cabin by controlling the wireless signal transmission module; generates a first target living image including a target occupant based on the multiple wireless detection signals, and extracts a first image feature of the first target living image; determines a first sitting posture type of the target occupant according to the first image feature, and determines a target seat adjustment parameter according to the first sitting posture type; and adjusts the target seat where the target occupant is located according to the target seat adjustment parameter, so that the target seat enters a target seat state that matches the first sitting posture type.

[0039] In this embodiment, during operation, the electronic device first controls its configured wireless signal transmission module to transmit a multi-channel wireless detection signal into the vehicle cabin. Thereafter, the electronic device generates a first target living image including the target occupant in the cabin based on the multi-channel wireless detection signal, and extracts a first image feature included in the first target living image. Thereafter, the electronic device determines a first sitting posture type adopted by the target occupant in the target seat according to the first image feature, and determines a target seat adjustment parameter for adjusting the target seat according to the first sitting posture type. Finally, the electronic device adjusts the target seat according to the target seat adjustment parameter, so that the target seat enters a target seat state that matches the first sitting posture type adopted by the target occupant.

[0040] In this way, the present application solves the technical problem in the related art that it is difficult for a vehicle to accurately identify the sitting posture of an occupant. That is, the present application utilizes the characteristic that a wireless detection signal will generate different channel state information when it comes into contact with a living body, and can accurately identify the sitting posture type of a target occupant in a vehicle cabin, thereby determining seat adjustment parameters according to the sitting posture type, and adjusting the target seat to a target seat state that matches the sitting posture type according to the seat adjustment parameters, thereby avoiding the situation where the camera device is unable to accurately identify the sitting posture of the occupant due to the influence of ambient light, and achieving the technical effect of enabling the electronic device to accurately adjust the seat based on the sitting posture of the occupant. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a schematic diagram of the structure of an electronic device involved in an embodiment of the seat adjustment method of the present application;

[0044] Figure 2 A schematic diagram of a process flow provided for the first embodiment of the seat adjustment method of the present application;

[0045] Figure 3 A schematic diagram of a first target living body image involved in an embodiment of the seat adjustment method of the present application;

[0046] Figure 4 A brief flowchart of the seat adjustment method of this application;

[0047] Figure 5 This is a schematic diagram of the module structure of the vehicle warning device according to an embodiment of the present application;

[0048] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the seat adjustment method in the embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] In this embodiment, for ease of description, please refer to Figure 1 , Figure 1 The structure diagram of the electronic device involved in the seat adjustment method of the present application is as follows: Figure 1As shown, an electronic device with a wireless signal transmission module configured inside, or a mobile terminal, data storage control terminal, PC and other terminals connected to an electronic control unit supporting the electronic device are explained as the execution subject. Among them, the wireless signal transmission module includes two parts: TCAM (Telematics & Connectivity Antenna Module, smart antenna module) and DHU (Digital Cockpit Head Unit, digital cockpit unit), among which TCAM includes a first Wifi Service, Core Service, MCU (Microcontroller Unit, micro control unit), and similarly, the DHU includes a second Wifi Service, QNX Service (QNX virtual machine), Android Service (Android subsystem), Algorithm Service (data processing unit) and MCU.

[0053] Based on the above electronic device, the overall concept of the seat adjustment method of the present application is proposed here.

[0054] With the continuous development of the automobile industry, new energy vehicles have become the preferred means of transportation for more and more people in their daily travel. In order to maximize the passengers' experience when riding in new energy vehicles, technicians usually install a camera in the cockpit to detect the sitting posture of the passengers on the seats through the camera, and adjust the seats according to the sitting posture so that the seats can better fit the sitting posture of the passengers. However, since the camera is easily affected by ambient light during the process of shooting the interior of the cockpit, it is easy for the camera to obtain low-quality image data, which makes it difficult for the vehicle to accurately identify the sitting posture of the passengers, thereby reducing the accuracy of the vehicle's seat adjustment.

[0055] In response to the above phenomenon, the present application provides a seat adjustment method, which is applied to an electronic device, and the electronic device includes a wireless signal transmission module. The seat adjustment method includes: controlling the wireless signal transmission module to transmit multiple wireless detection signals to the vehicle cabin; generating a first target living image including a target occupant based on the multiple wireless detection signals, and extracting a first image feature of the first target living image; determining a first sitting posture type of the target occupant according to the first image feature, and determining a target seat adjustment parameter according to the first sitting posture type; adjusting the target seat where the target occupant is located according to the target seat adjustment parameter, so that the target seat enters a target seat state that matches the first sitting posture type.

[0056] In this way, the present application solves the technical problem in the related art that it is difficult for a vehicle to accurately identify the sitting posture of an occupant. That is, the present application utilizes the characteristic that a wireless detection signal will generate different channel state information when it comes into contact with a living body, and can accurately identify the sitting posture type of a target occupant in a vehicle cabin, thereby determining seat adjustment parameters according to the sitting posture type, and adjusting the target seat to a target seat state that matches the sitting posture type according to the seat adjustment parameters, thereby avoiding the situation where the camera device is unable to accurately identify the sitting posture of the occupant due to the influence of ambient light, and achieving the technical effect of enabling the electronic device to accurately adjust the seat based on the sitting posture of the occupant.

[0057] Based on the overall concept of the seat adjustment method of the present application, the present application embodiment provides a seat adjustment method, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the seat adjustment method of the present application.

[0058] In this embodiment, the seat adjustment method is applied to an electronic device, the electronic device includes a wireless signal transmission module, and the seat adjustment method includes steps S10 to S40:

[0059] Step S10: controlling the wireless signal transmitting module to transmit a plurality of wireless detection signals to the cabin of the vehicle;

[0060] Step S20: generating a first target living body image including a target occupant based on the plurality of wireless detection signals, and extracting a first image feature of the first target living body image;

[0061] It should be noted that the wireless detection signal can specifically be a Wifi signal. In addition, the first target live image is image data containing the live detection target generated by the electronic device after detecting the presence of the live detection target in the vehicle cabin through the Wifi signal. In addition, the first image feature is an image feature contained in the first target live image that can reflect the position, sitting posture and other information of the detected live detection target. It can be understood that the live detection target can specifically be a target occupant detected to be inside the cabin or entering the cabin through the door.

[0062] In this embodiment, when the electronic device is running, it first calls the wireless signal transmission module configured by itself to continuously transmit multiple wireless detection signals to the vehicle cabin. After that, the electronic device controls the wireless signal transmission module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. The wireless signal transmission module then obtains multiple initial channel state information according to each wireless detection signal and each wireless reflection signal, and inputs the multiple initial channel state information into the data processing unit configured by itself. The data processing unit calls the preset convolutional neural network model to generate a first target living image including the target occupant in the vehicle cabin according to each received initial channel state information. The convolutional neural network model performs phase separation processing on the first target living image to obtain multiple sub-living images, and calls the encoder to encode the multiple sub-living images to identify the convolution feature weights corresponding to each of the multiple sub-living images. The convolutional neural network model then fuses the convolution feature weights to obtain posture fusion data, performs inference merging operation on the posture fusion data, and performs sampling processing on the inferenced and merged posture fusion data to extract the first image feature.

[0063] Exemplarily, for example, when the electronic device is running, it first controls the wireless transmission module configured by itself to control the TCAM in the wireless transmission module to start, so that the first Wifi Service in the TCAM is controlled to transmit multiple first Wifi signals to the vehicle cabin, and the first Wifi Service then receives the first reflection signals generated by the multiple first Wifi signals, thereby determining the initial CSI (Channel State Information) data corresponding to the multiple first Wifi signals according to the first reflection signals, and the TCAM then inputs the obtained initial CSI data into the DHU in the wireless transmission module, so that the initial CSI data enters the data processing unit Algorithm Service configured in the DHU; at the same time, the electronic device controls the DHU in the wireless transmission module to start, so that the second Wifi Service in the DHU transmits multiple second Wifi signals to the vehicle cabin, and the second Wifi Service then receives the second reflection signals generated by the multiple second Wifi signals, thereby determining the initial CSI data corresponding to the multiple second Wifi signals according to the second reflection signals, and the DHU then inputs the obtained initial CSI data into the data processing unit Algorithm Service configured in the DHU, and the Algorithm Service. After obtaining each initial CSI data, the Service inputs each CSI data into a preset RCNN (Regions with Convolutional Neural Networks). The RCNN generates first target image data including the target occupant in the cockpit based on each initial CSI data. The RCNN phase-separates the first target living image to obtain multiple sub-living images, and calls the encoder to encode the multiple sub-living images respectively to identify the convolution feature weights corresponding to each of the multiple sub-living images. The RCNN then fuses the obtained convolution feature weights to obtain preliminary posture fusion data. The RCNN performs inference operations through 8-bit 3-channel RGB reshape to merge the preliminary posture fusion data into 24-bit data. The RCNN then performs upsampling encoding operations and downsampling decoding operations on the 24-bit data in sequence to output a 3-channel image, and finally determines the 3-channel image as the image feature corresponding to the first target living image.

[0064] It should be noted that, in this embodiment and another embodiment, before controlling the wireless transmission module, the electronic device may also control the initialization of the QNX virtual machine (QNX Service) configured in the DHU to start the second Wifi Service and the Android subsystem (Android Service) in the DHU through the QNX virtual machine. At the same time, the electronic device also controls the Socket communication between the TCAM and the DHU to ensure that the TCAM can send the acquired initial CSI data to the Algorithm Service in the DHU.

[0065] In addition, in this embodiment and another embodiment, after controlling the TCAM to start, the electronic device also needs to first control the Core Service configured in the TCAM and the QNX virtual machine configured in the DHU to detect the communication status, so as to initiate a communication re-request in time when the heartbeat communication between the TCAM and the DHU is detected to be interrupted.

[0066] In this way, the vehicle can identify the target occupant in the vehicle cabin through the multi-channel wireless detection signals by calling the wireless transmission module to transmit multi-channel wireless detection signals to the cabin, and generate a first target live image of the target occupant to avoid interference from ambient light when detecting the target occupant. At the same time, the first image feature that can reflect the target occupant's position, sitting posture and other information is extracted through the first target live image, thereby accurately identifying the sitting posture of the target occupant on the target seat.

[0067] In a feasible implementation manner, the above step S20 of "generating a first target living body image including the target occupant based on the multiple wireless detection signals" may specifically include steps S201 to S202:

[0068] Step S201: Detecting initial channel state information matched by each of the plurality of wireless detection signals, and performing cleaning processing on each of the initial channel state information to obtain each complete channel state information;

[0069] Step S202: Screening each of the complete channel state information to determine target channel state information, and generating a first target living body image based on each of the target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal detecting a target occupant.

[0070] It should be noted that the initial channel state information is CSI data that has not been screened and cleaned. In addition, the complete channel state information is CSI data that has been cleaned and filtered and does not contain outliers. In addition, the target state channel information is the CSI data corresponding to the Wifi signal of the living detection target around the vehicle. It can be understood that the CSI data is a series of complex decimal sequences that can represent the signal wave ratio between the wireless detection signal and the corresponding wireless reception signal.

[0071] In this embodiment, after controlling the wireless signal transmission module to transmit multiple wireless detection signals to the vehicle cabin, the electronic device further controls the wireless signal transmission module to receive wireless reflection signals generated by each of the multiple wireless detection signals, and controls the wireless signal transmission module to obtain initial channel state information that has not been cleaned and matches each of the multiple wireless detection signals according to each received wireless detection signal and each wireless reflection signal. The electronic device then controls the wireless signal transmission module to input each initial channel state information into a data processing unit configured in the electronic device, so that the data processing unit reads each initial amplitude parameter and each initial phase parameter contained in each initial channel state information, and the data processing unit performs a phase expansion operation on each initial amplitude parameter and each initial phase parameter. The data processing unit then performs a filtering and elimination operation on each initial amplitude parameter and each initial phase parameter after the phase expansion to eliminate the abnormal values ​​contained in each initial amplitude parameter and each initial phase parameter, and obtain each target amplitude parameter and each target phase. Parameters, the data processing unit fuses each target amplitude parameter with each matched target phase parameter, thereby obtaining each complete channel state information that does not contain abnormal values. After that, the data processing unit reads the storage module configured in the electronic device to obtain the standard channel state information corresponding to the wireless detection signal of the non-living detection target. The data processing unit compares each acquired complete channel state information with the standard channel state information respectively to obtain a plurality of comparison results. The data processing unit reads the plurality of comparison results to determine whether there is a target comparison result whose comparison result is that the complete channel state information and the standard channel state information are inconsistent. When the data processing unit determines that there is such a target comparison result, the complete channel state information corresponding to the target comparison result is determined as the target channel state information corresponding to the wireless detection signal of the target occupant. The data processing unit then calls the convolutional neural network to generate a first target living body image containing the target living body in the vehicle cabin based on each target channel state information.

[0072] Exemplarily, for example, after the electronic device controls the first Wifi Service configured in the TCAM to continuously transmit multiple first Wifi signals to the vehicle cabin, the electronic device controls the first Wifi Service to continuously receive the first reflected signals generated by the multiple first Wifi signals, and determines the initial CSI data that matches the multiple first Wifi signals based on the first reflected signals and the first Wifi signals. At the same time, after the electronic device controls the second Wifi Service configured in the DHU to continuously transmit multiple second Wifi signals to the vehicle cabin, the electronic device controls the second Wifi Service to continuously receive the second reflected signals generated by the multiple second Wifi signals, and determines the initial CSI data that matches the multiple second Wifi signals based on the second reflected signals and the second Wifi signals. The electronic device then controls the TCAM and the DHU to input the initial CSI data obtained respectively into the data processing unit Algorithm Service configured in the DHU. The Algorithm Service extracts the initial amplitude parameters and the initial phase parameters contained in each initial CSI data. The Algorithm Service performs phase unfolding processing on each initial amplitude parameter and each initial phase parameter, so that the unfolded amplitude curve and phase curve are restored to continuous curves. At the same time, the Algorithm Service performs median filtering and uniform filtering processing on each unfolded initial amplitude parameter and each initial phase parameter to eliminate the outliers of the unfolded initial amplitude parameter and initial phase parameter in the time and frequency domains, thereby obtaining the target amplitude parameter and target phase parameter with the target outliers eliminated. The Algorithm Service fuses each target amplitude parameter with each matching target phase parameter to obtain each complete CSI data that does not contain abnormal values. After that, the Algorithm Service reads the storage module configured in the electronic device to obtain the preset standard CSI data corresponding to when the Wifi signal detects a non-living detection target. The Algorithm Service compares each complete CSI data with the standard CSI data to obtain multiple comparison results. The Service determines a target comparison result, included in the multiple comparison results, in which the comparison result is that the complete CSI data and the standard CSI data are inconsistent, and screens the multiple Wifi signals based on the multiple target comparison results, thereby determining the Wifi signal corresponding to the target comparison result as a target Wifi signal that detects the target occupant, and determining the complete CSI data corresponding to the target Wifi signal as target CSI data that can generate and include a first target living body image of the target occupant.The Algorithm Service calls the RCNN configured by itself to generate a first target living body image including the target occupant in the vehicle cabin based on each target CSI data.

[0073] It should be noted that the initial CSI data has the problem of random drift and reversal of amplitude and phase. Therefore, if the initial CSI data is not denoised, the abnormal values ​​contained in the initial CSI data will have a certain impact on the final detection result. In addition, the amplitude and phase in the initial CSI data can be calculated, and in the process of calculating the amplitude and phase, the phase and amplitude that exceed the preset function range will be folded, resulting in discontinuity of phase and amplitude. In this case, it is necessary to perform a phase expansion operation to restore each phase and amplitude to a continuous state. It can be understood that the specific calculation process of the phase expansion operation is a prior art, so it will not be repeated here. In addition, the specific process of median filtering and uniform filtering of the expanded phase value and amplitude parameters by Algorithm Service is a prior art, so it will not be repeated here.

[0074] In this way, the vehicle can identify the target occupant in the vehicle cabin through the multi-channel wireless detection signals by calling the wireless transmission module to transmit multi-channel wireless detection signals to the cabin, and generate a first target live image of the target occupant to avoid interference from ambient light when detecting the target occupant. At the same time, the first image feature that can reflect the target occupant's position, sitting posture and other information is extracted through the first target live image, thereby accurately identifying the sitting posture of the target occupant on the target seat.

[0075] In a feasible implementation manner, the step of "detecting initial channel state information that matches each of the plurality of wireless detection signals" in the above step S201 may specifically include steps S2011 to S2013:

[0076] Step S2011: Detecting wireless receiving signals that match each of the plurality of wireless detection signals;

[0077] Step S2012: determining a signal change ratio between each of the plurality of wireless detection signals and a matching wireless reception signal;

[0078] Step S2013: determining initial channel state information that matches each of the plurality of wireless detection signals based on the signal change ratios.

[0079] In this embodiment, after controlling the wireless signal transmission module to continuously transmit multiple wireless detection signals to the vehicle cabin, the electronic device further controls the wireless signal transmission module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. Afterwards, the wireless signal transmission module compares the multiple wireless detection signals with each of the matching wireless reception signals, thereby determining the signal change ratio generated between each of the multiple wireless detection signals and the matching wireless reception signals. Finally, the wireless signal transmission module determines the signal change ratio corresponding to each of the multiple wireless detection signals as the initial channel state information matched by each of the multiple wireless detection signals, and inputs each initial channel state information into the above-mentioned data processing unit.

[0080] Exemplarily, for example, after controlling the first Wifi Service configured by the TCAM to transmit multiple first Wifi signals to the vehicle cabin, the electronic device further controls the TCAM to receive first Wifi reflected signals formed by each of the first Wifi signals after being reflected by an obstacle. Then, the TCAM compares each of the first Wifi signals with the first Wifi reflected signals that match them, thereby determining a first signal wave ratio formed between each of the first Wifi signals and the matched first Wifi reflected signals. Finally, the TCAM determines the first signal wave ratio corresponding to each of the first Wifi signals as initial CSI data corresponding to each of the first Wifi signals.

[0081] Similarly, after controlling the second Wifi Service configured by the DHU to transmit multiple second Wifi signals into the vehicle cabin, the electronic device further controls the DHU to receive second Wifi reflected signals formed by each second Wifi signal after being reflected by an obstacle. Afterwards, the DHU compares each second Wifi signal with its matching second Wifi reflected signal, thereby determining a second signal wave ratio formed between each second Wifi signal and its matching second Wifi reflected signal. Finally, the TCAM determines the second signal wave ratio corresponding to each second Wifi signal as the second initial CSI data corresponding to each second Wifi signal. The electronic device controls the TCAM and the DHU to input the collected initial CSI data into the data processing unit Algorithm Service configured in the DHU.

[0082] In this way, the electronic device can obtain the initial channel state information generated by each of the multiple wireless detection signals by calling the wireless transmission module to transmit the multiple wireless detection signals to the vehicle cabin.

[0083] In a feasible implementation manner, the step of “generating a first target living body image based on each target channel state information” in the above step S202 may specifically include steps S2021 to S2022:

[0084] Step S2021: extracting one-dimensional vector features contained in each of the target channel state information;

[0085] Step S2022: fusing the one-dimensional vector features to obtain a two-dimensional vector feature, and generating a first target living body image based on the two-dimensional vector feature.

[0086] It should be noted that the one-dimensional vector feature is the latent spatial feature contained in the target channel state information. The one-dimensional feature vector specifically includes an amplitude tensor and a phase tensor, wherein the size of each tensor is 150×3×3 (5 consecutive samples, 30 frequencies, 3 transmitters and 3 receivers). In this way, RCNN (Regions with Convolutional Neural Networks) can extract the spatial features contained in the one-dimensional vector feature through the last two dimensions of each tensor.

[0087] In this embodiment, after determining multiple target channel state information, the data processing unit first inputs the multiple target channel state information into the convolutional neural network model configured by itself, so that the convolutional neural network model calls multiple encoders to extract the one-dimensional feature vectors contained in each of the multiple target channel state information. After that, the data processing unit fuses the one-dimensional feature vectors to obtain a two-dimensional fused feature, and converts the two-dimensional fused feature into a feature map in the spatial domain, and then upsamples the converted two-dimensional feature map to obtain a first target living image containing the target occupant.

[0088] For example, see Figure 3 , Figure 3Schematic diagram of a first target living body image involved in an embodiment of the seat adjustment method of the present application, Algorithm After determining multiple target CSI data, Service first inputs the multiple target CSI data into the RCNN configured by itself, so that RCNN calls two different encoders to process each target CSI data to extract the amplitude tensor and phase tensor of size 150×3×3 corresponding to each of the multiple target CSI data. RCNN then determines the amplitude tensor and phase tensor corresponding to each target CSI data as 1D features. After that, RCNN extracts the spatial information contained in each 1D feature based on the last two dimensions (3 transmitters and 3 receivers) of each amplitude tensor and each phase tensor, and fuses each 1D feature according to each spatial information to obtain a fused 1D feature. RCNN then reshapes the fused 1D feature into an initial 2D feature map of 24×24, and extracts the spatial information contained in the initial 2D feature map through the two convolution blocks configured by itself to obtain a 2D feature vector with a spatial dimension of 6×6. Finally, RCNN performs an upsampling operation on the 2D feature vector with a spatial dimension of 6×6 to adjust the 2D feature vector to the following: Figure 3 The size of 3×720×1280 is shown, containing the first target living body image in the image field of the target occupant.

[0089] In this way, the electronic device can construct a target living body image including the target occupant based on the filtered target channel state information.

[0090] Step S30: determining a first sitting posture type of the target occupant according to the first image feature, and determining a target seat adjustment parameter according to the first sitting posture type;

[0091] Step S40: adjusting the target seat where the target occupant is located according to the target seat adjustment parameters, so that the target seat enters a target seat state that matches the first sitting posture type.

[0092] In this embodiment, after the electronic device extracts the first image feature through the convolutional neural network model, it may also process the first image feature to determine the first sitting posture type of the target occupant on the target seat according to the first image feature, and determine the matching target seat adjustment parameters according to the first sitting posture type. Finally, the electronic device inputs the target seat adjustment parameters into the seat control module configured by itself, and the seat control module adjusts the target seat according to the target seat adjustment parameters to switch the target seat from the preset first seat state to the target seat state matching the first sitting posture type.

[0093] Exemplarily, for example, after extracting the first image feature through RCNN, Algorithm Service may further identify the first image feature to determine the first sitting posture type adopted by the target occupant when sitting in the target seat. If Algorithm Service detects that the first sitting posture type is a leg opening and closing type, Algorithm Service determines that the target seat state matching the leg opening and closing type is a leg rest opening state. Algorithm Service determines the target seat adjustment parameter that can control the leg rest on the target seat according to the target seat state. Finally, Algorithm Service converts the target seat adjustment parameter into a CAN signal through the MCU connected to the DHU, and sends the CAN signal to the bidirectional motor configured in the vehicle for controlling the target seat through the BUS bus in the vehicle, so that the bidirectional motor controls the target seat according to the target seat adjustment parameter, thereby making the leg rest on the target seat enter the opening state.

[0094] Similarly, if the Algorithm Service detects that the first sitting posture type is a torso tilt type according to the first image feature, the Algorithm Service determines that the target seat state matching the torso tilt type is a backrest tilt state, and the Algorithm Service determines the target seat adjustment parameters that can control the backrest of the target seat according to the target seat state. Finally, the Algorithm Service converts the target seat adjustment parameters into CAN signals through the MCU connected to the DHU, and sends the CAN signals to the bidirectional motor configured in the vehicle for controlling the target seat through the BUS in the vehicle, so that the bidirectional motor controls the target seat according to the target seat adjustment parameters, thereby making the backrest on the target seat enter a tilted state.

[0095] It should be noted that, in this embodiment and another embodiment, if the data processing unit detects that the target occupant has adopted multiple first sitting posture types (for example, in the leg opening and closing state and the torso tilting type at the same time), the data processing unit can determine the target seat adjustment parameters for adjusting the leg rest and backrest on the target seat at the same time, and send the target seat adjustment parameters to the seat control module, which adjusts the target seat according to the target seat adjustment parameters to make the backrest of the target seat enter a tilted state and the leg rest of the target seat enter an open state. It can be understood that the present application has no restriction on the number of target seat modules that can be adjusted by the generated target seat adjustment parameters.

[0096] In this way, the electronic device can identify the sitting posture type adopted by the user on the target seat by processing the first image feature, and can determine the target seat state that best matches the sitting posture type, thereby generating seat adjustment parameters according to the target seat state, and then adjusting the target seat according to the seat adjustment parameters to switch the target seat to the target seat state.

[0097] In a feasible implementation manner, the step of “determining the target seat adjustment parameter according to the first sitting posture type” in the above step S30 may specifically include steps S301 to S302:

[0098] Step S301: determining a target seat corresponding to the target occupant, and detecting a current seat state of the target seat;

[0099] Step S302: determining a target seat state according to the first sitting posture type, and determining a target seat adjustment parameter based on the current seat state and the target seat state.

[0100] In this embodiment, after extracting the first image feature, the data processing unit may also first read the seat pressure sensors configured on each seat in the vehicle, and determine the target seat where the target occupant is located in each seat based on the seat sensors. At the same time, the data processing unit detects the target seat to determine the current seat state of the target seat. Thereafter, the data processing unit queries a preset seat adjustment table, and determines the target seat state that matches the first sitting posture type based on the seat adjustment table. The data processing unit then determines the target seat adjustment parameters that can switch the target seat from the current seat state to the target seat state based on the target seat state and the current seat state.

[0101] Exemplarily, for example, after extracting the first image feature, the Algorithm Service may first detect each seat configured in the vehicle, thereby determining the target seat where the target occupant is located in each seat through the seat pressure sensor configured for each sitting posture, and detecting that the current seat state of the target seat is the leg rest retracted state. Thereafter, the Algorithm Service determines that when the target occupant sits in the target seat, the first sitting posture type adopted is the leg opening and closing type according to the first image feature. The Algorithm Service reads the above-mentioned storage module to obtain a seat adjustment table including multiple preset sitting posture types and preset seat states that match each of the multiple preset sitting posture types. The Algorithm Service queries the seat adjustment table based on the leg opening and closing type, thereby comparing the leg opening and closing type with the multiple preset sitting posture types respectively, so as to determine the target preset sitting posture type that matches the leg opening and closing type among the multiple preset sitting posture types, and determine the preset seat state that matches the target preset sitting posture type as the target seat state that matches the leg opening and closing type. Finally, the Algorithm Service When detecting that the target seat state is the leg rest open state, Service determines the target seat adjustment parameter that can switch the leg rest on the target seat from the leg rest folded state to the leg rest open state. AlgorithmService converts the target seat adjustment into a CAN signal through the MCU connected to the DHU, and sends the CAN signal to the bidirectional motor configured in the vehicle for controlling the target seat through the BUS in the vehicle, so that the bidirectional motor controls the target seat according to the target seat adjustment parameter, thereby making the leg rest on the target seat enter the leg rest open state;

[0102] Similarly, when the Algorithm Service determines, based on the first image feature, that the target occupant is sitting in the target seat and the first sitting posture type adopted is the trunk tilt type, the Algorithm Service queries the above-mentioned seat adjustment table based on the trunk tilt type to compare the trunk tilt type with multiple preset sitting posture types respectively, thereby determining the target preset sitting posture type that matches the trunk tilt type among the multiple preset sitting posture types, and determining the preset seat state that matches the target preset sitting posture type as the target seat state that matches the trunk tilt type. Finally, when the Algorithm Service detects that the target seat state is the backrest tilt state, it determines the target seat adjustment parameters that can adjust the backrest of the target seat to the backrest tilt state. The Algorithm Service converts the target seat adjustment into a CAN signal through the MCU connected to the DHU, and sends the CAN signal to the bidirectional motor configured in the vehicle for controlling the target seat through the BUS bus in the vehicle, so that the bidirectional motor controls the target seat according to the target seat adjustment parameters, thereby making the backrest of the target seat enter the backrest tilt state.

[0103] In addition, in the present embodiment and another embodiment, after the Algorithm Service extracts the first image feature and identifies the first sitting posture type according to the first image feature, if it is detected that the first sitting posture type is a leg opening and closing type, the Algorithm Service may also identify the leg opening parameters corresponding to the target occupant according to the first image feature, and the Algorithm Service further queries the leg rest adjustment table including multiple preset leg opening parameters and preset leg rest angle parameters that match each of the multiple preset leg opening parameters based on the leg opening and closing parameters, thereby determining the target preset leg rest angle parameters that match the leg opening parameters, and the Algorithm Service generates the target seat adjustment parameters based on the initial leg rest angle parameters and the target preset leg rest angle parameters contained in the current seat state, and sends the target seat adjustment parameters to the bidirectional motor, which adjusts the target leg rest to the target leg rest angle according to the target seat adjustment parameters, so that the legs of the target occupant are placed on the leg rest at a more comfortable angle, thereby further improving the riding experience of the occupant.

[0104] Similarly, if the Algorithm Service detects that the first sitting posture type is a torso tilt type, it can also identify the torso tilt angle corresponding to the target occupant based on the first image feature, and then determine the matching target seat back angle based on the torso tilt angle. The Algorithm Service identifies the initial seat back angle of the target seat, and generates target seat adjustment parameters based on the initial seat back angle and the target seat back angle, and then sends the target seat adjustment to the bidirectional motor, which adjusts the seat back to the target seat back angle according to the target seat adjustment parameters, so that the torso of the target occupant can lean on the seat back in a more comfortable posture, further improving the occupant's riding experience.

[0105] It is understandable that the Algorithm Service can also integrate the acquired target backrest adjustment parameters with the above-mentioned target seat adjustment parameters to obtain the target seat adjustment parameters, so that the bidirectional motor can adjust the seat back and leg rest at the same time based on the target seat adjustment parameters. In addition, the Algorithm Service can also calculate other adjustment parameters such as armrests, seat height, position, etc. based on the first image feature, and integrate the various adjustment parameters to adjust the seat more comprehensively.

[0106] In this embodiment, when the electronic device is running, it first calls the wireless signal transmission module configured by itself to continuously transmit multiple wireless detection signals to the vehicle cabin. After that, the electronic device controls the wireless signal transmission module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. The wireless signal transmission module then obtains multiple initial channel state information according to each wireless detection signal and each wireless reflection signal, and inputs the multiple initial channel state information into the data processing unit configured by itself. The data processing unit calls the preset convolutional neural network model to generate a first target live image containing the target occupant in the vehicle cabin according to each received initial channel state information. The convolutional neural network model performs phase separation processing on the first target live image to obtain multiple sub-live images, and calls the encoder to encode the multiple sub-live images. Processing is performed to identify the convolution feature weights corresponding to each of the multiple sub-living images, the convolution neural network model then fuses the convolution feature weights to obtain posture fusion data, performs inference merging operations on the posture fusion data, and performs sampling processing on the inferenced and merged posture fusion data to extract a first image feature. Thereafter, the electronic device processes the first image feature to determine a first sitting posture type of the target occupant on the target seat according to the first image feature, and determines a matching target seat adjustment parameter according to the first sitting posture type. Finally, the electronic device inputs the target seat adjustment parameter into a seat control module configured by itself, and the seat control module adjusts the target seat according to the target seat adjustment parameter to switch the target seat from a preset first seat state to a target seat state matching the first sitting posture type.

[0107] In this way, the present application solves the technical problem in the related art that it is difficult for a vehicle to accurately identify the sitting posture of an occupant. That is, the present application utilizes the characteristic that a wireless detection signal will generate different channel state information when it comes into contact with a living body, and can accurately identify the sitting posture type of a target occupant in a vehicle cabin, thereby determining seat adjustment parameters according to the sitting posture type, and adjusting the target seat to a target seat state that matches the sitting posture type according to the seat adjustment parameters, thereby avoiding the situation where the camera device is unable to accurately identify the sitting posture of the occupant due to the influence of ambient light, and achieving the technical effect of enabling the electronic device to accurately adjust the seat based on the sitting posture of the occupant.

[0108] Based on the first embodiment of the present application, a second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to the above description and will not be described in detail later. On this basis, after the step of "determining the target seat corresponding to the target occupant" in the above step S301, the seat adjustment method of the present application can also include steps A10 to A20:

[0109] Step A10: detecting a door of the vehicle, and determining a first channel size corresponding to the target seat when detecting that the door is in an open state;

[0110] Step A20: Determine a first seat position adjustment parameter based on the first channel size and a preset target channel size, and adjust the target seat according to the first seat position adjustment parameter so that the channel size corresponding to the target seat is switched from the first channel size to the target channel size.

[0111] It should be noted that the first channel size is the width of the channel formed between the front edge of the seat and the obstacle in front of the target seat when the target seat is in the initial position. The obstacle in front of the target seat can be the back edge of the backrest of the front seat corresponding to the target seat, or the obstacle can be the back edge of the front console corresponding to the target seat. In addition, the target channel size is a preset channel size that allows the target occupant to pass smoothly. It is understandable that the specific value of the target channel size can be set by the technician according to the needs, and this application does not limit this.

[0112] In this embodiment, after identifying the target seat where the target occupant is located, the data processing unit may further detect the door, so as to determine the first channel size corresponding to the aisle where the target seat is located when it is detected that the door is in an open state. Thereafter, the data processing unit reads the above-mentioned storage module to obtain the preset target channel size, and determines a first seat position adjustment parameter for adjusting the seat position according to the first channel size and the target channel size. The data processing unit then transmits the first seat position adjustment parameter to the seat control module, and the seat control module adjusts the position of the seat according to the first seat position adjustment parameter, so that the channel size of the channel corresponding to the seat is switched from the first channel size to the target channel size.

[0113] Exemplarily, for example, after identifying the target seat where the target occupant is located, the Algorithm Service may also detect the vehicle door, so that when it is detected that the vehicle door is in the door-open state, the Algorithm Service reads the above-mentioned storage module to obtain the initial position information of the target seat, and determines the first channel size of the aisle in front of the target seat according to the initial position information. Thereafter, the Algorithm Service reads the above-mentioned storage module to obtain the preset target channel size for enabling the target occupant to pass through the aisle smoothly, and determines the first seat position adjustment parameter corresponding to the target seat according to the first channel size and the target channel size. The Algorithm Service converts the first seat position adjustment parameter into a CAN signal through the MCU connected to the DHU, and sends the CAN signal to a bidirectional motor configured in the vehicle for controlling the target seat through the BUS bus in the vehicle, so that the bidirectional motor controls the target seat according to the first seat position adjustment parameter, so that the target seat moves to the rear of the vehicle until the size of the aisle in front of the target seat reaches the target channel size, so that the target occupant can smoothly enter the vehicle and reach the target seat;

[0114] It should be noted that, in this embodiment and another embodiment, after controlling the target seat to move backward, the Algorithm Service may further detect the vehicle door, so as to generate a second seat position adjustment parameter for moving the seat forward to the initial position based on the first seat position adjustment parameter when detecting that the vehicle door is in the closed state, and convert the second seat position adjustment parameter into a CAN signal through the MCU connected to the DHU, and send the CAN signal to a bidirectional motor configured in the vehicle for controlling the target seat through the BUS bus in the vehicle, so that the bidirectional motor controls the target seat according to the second seat position adjustment parameter, so as to move the target seat to the front of the vehicle, thereby restoring the channel size of the aisle in front of the target seat from the target channel size to the first channel size, so that the seat moves to the original position after the target occupant sits down.

[0115] In this way, when the door is open, the electronic equipment can control the seat to move backward to create enough space for the target passenger to enter the cabin, and then control the seat to move forward to restore the aisle to its original aisle size after detecting that the door is closed, further enhancing the passenger's riding experience.

[0116] Based on the first embodiment and / or the second embodiment of the present application, a third embodiment of the present application is proposed. In the third embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to the above description and will not be described in detail later. On this basis, after the above step S40, the seat adjustment method of the present application can also include steps B10 to B30:

[0117] Step B10: generating a second target living body image including the target occupant based on the plurality of wireless detection signals, and extracting a second image feature of the second target living body image;

[0118] Step B20: determining a second sitting posture type corresponding to the target occupant according to the second image feature, and determining a function control instruction corresponding to the second sitting posture type, wherein the function control instruction is a control instruction for controlling a target function module configured in the vehicle and required to be adjusted by the target occupant;

[0119] Step B30: Control the target functional module according to the functional control instruction, so that the target functional module enters a functional operation state matching the functional control instruction.

[0120] In this embodiment, after the seat control module completes the adjustment of the target seat, the electronic device can also call the above-mentioned wireless signal transmission module to continuously transmit multiple wireless detection signals into the vehicle cabin, and determine multiple initial channel state information through the multiple wireless detection signals and the corresponding wireless reflection signals. The electronic device inputs the multiple initial channel state information into the data processing unit, and the data processing unit calls the convolutional neural network model to generate a second target living image containing the target occupant based on the multiple initial channel state information, and processes the second target living image to extract the second image features contained in the second target living image. After that, the data processing unit determines the second sitting posture type adopted by the target occupant on the target seat according to the second image features, and determines the functional control instructions that match the second sitting posture type and are used to control the target functional module that needs to be adjusted by the target occupant. Finally, the electronic device controls the target functional module according to the functional control instructions to make the target functional module enter an operating state that matches the functional control instructions.

[0121] Exemplarily, for example, after adjusting the target seat to the target seat state, the electronic device may continue to control the above-mentioned wireless transmission module to control the TCAM and DHU in the wireless transmission module to transmit multiple wireless wifi signals into the cabin, and receive the respective reflected signals of the multiple wireless wifi signals, and then determine each second initial CSI data according to each wireless wifi signal and each reflected signal, and the electronic device inputs each second initial CSI data into the data processing unit Algorithm Service, and the Algorithm Service inputs each second initial CSI data into the RCNN configured by the electronic device, and the RCNN generates a second target living image including the target occupant sitting on the target seat based on each second initial CSI data, and the RCNN further performs feature processing operations on the second target living image to extract the second image features, and then the Algorithm Service determines the second sitting posture type emitted by the target occupant on the target seat based on the second image, and determines that the target function module that the target occupant needs to adjust is the speaker / air conditioning / seat heating function according to the second sitting posture type, and finally, the Algorithm Service determines the second sitting posture type emitted by the target occupant on the target seat based on the second image, and determines that the target function module that the target occupant needs to adjust is the speaker / air conditioning / seat heating function according to the second sitting posture type, and finally, the Algorithm Service determines the second sitting posture type emitted by the target occupant on the target seat based on the second image. Service converts the function control instruction into a CAN signal through the MCU connected to the DHU, and sends the CAN signal to the function control module configured in the vehicle through the BUS in the vehicle. The function control module adjusts the speaker / air conditioning / seat heating functions in the cabin according to the function control instruction, so that the speaker / air conditioning / seat heating functions enter an operating mode that matches the function control instruction.

[0122] In this way, the electronic device can determine the functional control instructions that the target occupant needs to execute by detecting the target occupant's sitting posture type, and then directly control the corresponding target functional modules without the target occupant performing additional operations, thereby further improving the occupant's riding experience.

[0123] In a feasible implementation manner, the step of "determining the function control instruction corresponding to the second sitting posture type" in the above step B20 may specifically include steps B201 to B203:

[0124] Step B201: determining a preset sitting posture type that matches the second sitting posture type;

[0125] Step B202: Determine the preset function instruction that matches the preset sitting posture type as the function control instruction corresponding to the second sitting posture type.

[0126] In this embodiment, after the data processing unit identifies the second sitting posture type according to the second image feature, it can also first read the above-mentioned storage module to obtain multiple preset sitting posture types. The data processing unit screens the multiple preset sitting postures based on the second sitting posture type to determine the preset sitting posture type that matches the second sitting posture type. Finally, the data processing unit determines the preset function instructions corresponding to the preset sitting posture type that matches the second sitting posture type as the function control instructions that the target occupant needs to adopt.

[0127] Exemplarily, for example, after the Algorithm Service recognizes that the second sitting posture type of the target occupant in the target seat is actions such as raising one hand / raising both hands based on the second image feature, it can also first read the above-mentioned storage module to obtain an instruction mapping table containing multiple preset sitting posture types and preset function instructions that match each of the multiple preset sitting posture types. The Algorithm Service queries the instruction mapping table based on the second sitting posture type to respectively compare the second sitting posture type with the multiple preset sitting posture types contained in the instruction mapping table, thereby determining the preset sitting posture type that matches the second sitting posture type among the multiple preset sitting posture types. Finally, if the Algorithm Service determines that the preset sitting posture type that matches the second sitting posture type has a preset function instruction that matches the seat heating instruction in the instruction mapping table, the Algorithm Service determines the seat heating instruction as the function control instruction that the target occupant needs to adopt.

[0128] Similarly, if the Algorithm Service determines that the preset function instruction matched by the second sitting posture type in the instruction mapping table is an air conditioning heating instruction, the Algorithm Service determines the air conditioning heating instruction as the function control instruction that the target occupant needs to adopt. It can be understood that the mapping relationship between multiple preset sitting posture types and their respective matching preset function instructions can be set by the technician according to actual needs, and this application does not limit this.

[0129] In this way, the electronic device can determine the functional control instructions that the target occupant needs to execute by detecting the target occupant's sitting posture type, and then directly control the corresponding functional modules without the target occupant performing additional operations, thereby further improving the occupant's riding experience.

[0130] For example, in order to help understand the implementation process of the seat adjustment method obtained by combining this embodiment with the above embodiments, please refer to Figure 4 , Figure 4 This is a brief flowchart of the seat adjustment method of this application, specifically:

[0131] In this embodiment, when the electronic device is running, it first starts the TCAM and DHU configured by itself, and the Core Service in the TCAM detects whether the TCAM and the DHU can communicate normally. When the Core Service detects that the TCAM and the DHU communicate normally, the electronic device controls the first Wifi Service in the TCAM to transmit multiple first Wifi signals into the vehicle cabin, and controls the first Wifi Service to receive the first Wifi reflection signals generated by each of the first Wifi signals. The first Wifi Service generates multiple first initial CSI data according to each of the first Wifi signals and each of the first Wifi reflection signals, and the TCAM then sends the generated first initial CSI data to the data processing unit in the DHU.

[0132] At the same time, the electronic device controls the second Wifi Service in the DHU to transmit multiple second Wifi signals into the vehicle cabin, and controls the second Wifi Service to receive the second Wifi reflected signals generated by each of the second Wifi signals, so that the second Wifi Service generates multiple second initial CSI data according to each of the second Wifi signals and each of the second Wifi reflected signals, and the DHU further sends the generated second CSI data to the data processing unit;

[0133] Afterwards, the data processing unit performs noise reduction processing on each of the first initial CSI data and each of the second initial CSI data to obtain a plurality of complete CSI data. At this time, the data processing unit reads a storage module in the electronic device to obtain preset CSI data corresponding to the Wifi signal when a non-living detection target is detected, and compares each of the complete CSI data with each of the preset CSI data to obtain a plurality of comparison results. The data processing unit thereby determines the target CSI data corresponding to the Wifi signal that detects the living detection target in each of the complete CSI data based on the plurality of comparison results.

[0134] Afterwards, the data processing unit inputs each target CSI data into a preset convolutional neural network model, which extracts a one-dimensional feature vector contained in each target CSI data, and fuses each one-dimensional feature vector to generate a first target living image containing the target occupant in the cockpit. The convolutional neural network model then extracts a first image feature of the first target living image, and uploads the first image feature to the data processing unit, which matches the first image feature with a preset human feature, and further identifies the seat pressure sensors configured on each seat in the vehicle when it is determined that the image feature matches the human feature, so as to obtain the first target living image of the target occupant through each seat. The seat pressure sensor determines the target seat where the target occupant is located and the current seat state of the target seat. At the same time, the data processing unit determines the first sitting posture type adopted by the target occupant on the target seat according to the first image feature, and queries the preset seat adjustment table to determine the target seat state that matches the first sitting posture type. The data processing unit then generates target seat adjustment parameters based on the current seat state and the target seat state. Then, the data processing unit sends the target seat adjustment parameters to the seat control module configured in the vehicle, and the seat control module adjusts the target seat according to the target seat adjustment parameters to switch the target seat from the current seat state to the target seat state.

[0135] After detecting that the target seat switches from the current seat state to the target seat state, the electronic device further calls the wireless signal transmission module to continuously transmit multiple wireless detection signals into the vehicle cabin, and generates a second target live image containing the target occupant through the multiple wireless detection signals and the corresponding wireless reflection signals. The data processing unit extracts the second target live image to obtain the second image feature, and determines the second sitting posture type adopted by the target occupant on the target seat according to the second image feature. Finally, the data processing unit queries the instruction mapping table based on the second sitting posture type to determine the functional control instruction that matches the second sitting posture type. Finally, the electronic device controls the target functional module that needs to be adjusted by the target occupant according to the functional control instruction, so that the target functional module enters an operating state that matches the functional control instruction.

[0136] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the seat adjustment method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0137] This application also provides a seat adjustment device, please refer to Figure 5 The seat adjustment device is applied to an electronic device, the electronic device includes a wireless signal transmission module, and the device includes:

[0138] A signal detection module 10, used to control the wireless signal transmission module to transmit a plurality of wireless detection signals to the cabin of the vehicle;

[0139] A feature extraction module 20, configured to generate a first target living body image including a target occupant based on the plurality of wireless detection signals, and extract a first image feature of the first target living body image;

[0140] a parameter identification module 30, configured to determine a first sitting posture type of the target occupant according to the first image feature, and determine a target seat adjustment parameter according to the first sitting posture type;

[0141] The seat adjustment module 40 is used to adjust the target seat where the target occupant is located according to the target seat adjustment parameters, so that the target seat enters a target seat state that matches the first sitting posture type.

[0142] In a feasible implementation manner, the parameter identification module 30 is further used for:

[0143] Determining a target seat corresponding to the target occupant, and detecting a current seat state of the target seat;

[0144] A target seat state is determined according to the first sitting posture type, and a target seat adjustment parameter is determined based on the current seat state and the target seat state.

[0145] In a feasible implementation manner, the parameter identification module 30 is further used for:

[0146] Detecting a door of the vehicle, and determining a first channel size corresponding to the target seat when detecting that the door is in an open state;

[0147] A first seat position adjustment parameter is determined according to the first channel size and a preset target channel size, and the target seat is adjusted according to the first seat position adjustment parameter so that the channel size corresponding to the target seat is switched from the first channel size to the target channel size.

[0148] In a feasible implementation manner, the seat adjustment module 40 is further used for:

[0149] generating a second target living body image including the target occupant based on the plurality of wireless detection signals, and extracting a second image feature of the second target living body image;

[0150] Determining a second sitting posture type corresponding to the target occupant according to the second image feature, and determining a function control instruction corresponding to the second sitting posture type, wherein the function control instruction is a control instruction for controlling a target function module configured in the vehicle and required to be adjusted by the target occupant;

[0151] The target functional module is controlled according to the functional control instruction so that the target functional module enters a functional operation state matching the functional control instruction.

[0152] In a feasible implementation manner, the seat adjustment module 40 is further used for:

[0153] determining a preset sitting posture type that matches the second sitting posture type;

[0154] The preset function instruction matching the preset sitting posture type is determined as the function control instruction corresponding to the second sitting posture type.

[0155] In a feasible implementation manner, the feature extraction module 20 is further used for:

[0156] Detecting initial channel state information respectively matched by the plurality of wireless detection signals, and performing cleaning processing on each of the initial channel state information to obtain each complete channel state information;

[0157] Each of the complete channel state information is screened to determine target channel state information, and a first target living body image is generated based on each of the target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal detecting a target occupant.

[0158] In a feasible implementation manner, the feature extraction module 20 is further used for:

[0159] Detecting wireless receiving signals that are matched to each of the plurality of wireless detection signals;

[0160] Determining a signal change ratio between each of the plurality of wireless detection signals and a matching wireless reception signal;

[0161] Based on each of the signal change ratios, initial channel state information that matches each of the plurality of wireless detection signals is determined.

[0162] In a feasible implementation manner, the feature extraction module 20 is further used for:

[0163] Extracting one-dimensional vector features contained in each of the target channel state information;

[0164] The one-dimensional vector features are fused to obtain two-dimensional vector features, and a first target living body image is generated based on the two-dimensional vector features.

[0165] The seat adjustment device provided by the present application adopts the seat adjustment method in the above embodiment, which can solve the technical problem in the related art that it is difficult for the vehicle to accurately identify the sitting posture of the occupant. Compared with the prior art, the beneficial effects of the seat adjustment device provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above embodiment, and the other technical features of the seat adjustment device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0166] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the seat adjustment method in the above-mentioned embodiment one.

[0167] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, an electronic device with a wireless signal transmission module configured therein, or a mobile terminal, a data storage control terminal, a PC, or other terminals connected to an electronic control unit supporting the electronic device. Figure 6 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0168] like Figure 6As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0169] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0170] The electronic device provided by the present application adopts the seat adjustment method in the above embodiment, which can solve the technical problem in the related art that it is difficult for the vehicle to accurately identify the sitting posture of the occupant. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0171] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0172] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0173] The present application provides a vehicle having the electronic device as described above, and the electronic device is used to execute the seat adjustment method in the above embodiment.

[0174] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the seat adjustment method in the above-mentioned embodiment.

[0175] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0176] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0177] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: controls the wireless signal transmission module to transmit multiple wireless detection signals to the vehicle cabin; generates a first target living image containing a target occupant based on the multiple wireless detection signals, and extracts a first image feature of the first target living image; determines a first sitting posture type of the target occupant according to the first image feature, and determines a target seat adjustment parameter according to the first sitting posture type; adjusts the target seat where the target occupant is located according to the target seat adjustment parameter, so that the target seat enters a target seat state that matches the first sitting posture type.

[0178] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0179] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0180] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0181] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned seat adjustment method, and can solve the technical problem in the related art that it is difficult for a vehicle to accurately identify the sitting posture of an occupant. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above-mentioned embodiment, and will not be elaborated here.

[0182] The present application also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the seat adjustment method as described above are implemented.

[0183] The computer program product provided by the present application can solve the technical problem in the related art that it is difficult for a vehicle to accurately identify the sitting posture of an occupant. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above embodiment, and will not be elaborated here.

[0184] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A seat adjustment method, characterized in that: The seat adjustment method is applied to an electronic device, the electronic device includes a wireless signal transmission module, and the seat adjustment method includes: Controlling the wireless signal transmitting module to transmit a plurality of wireless detection signals to the cabin of the vehicle; generating a first target living body image including a target occupant based on the plurality of wireless detection signals, and extracting a first image feature of the first target living body image; determining a first sitting posture type of the target occupant according to the first image feature, and determining a target seat adjustment parameter according to the first sitting posture type; The target seat in which the target occupant is located is adjusted according to the target seat adjustment parameters so that the target seat enters a target seat state that matches the first sitting posture type.

2. The seat adjustment method according to claim 1, characterized in that: The step of determining the target seat adjustment parameter according to the first sitting posture type comprises: Determining a target seat corresponding to the target occupant, and detecting a current seat state of the target seat; A target seat state is determined according to the first sitting posture type, and a target seat adjustment parameter is determined based on the current seat state and the target seat state.

3. The seat adjustment method according to claim 2, characterized in that: After the step of determining the target seat corresponding to the target occupant, the method further includes: Detecting a door of the vehicle, and determining a first channel size corresponding to the target seat when detecting that the door is in an open state; A first seat position adjustment parameter is determined according to the first channel size and a preset target channel size, and the target seat is adjusted according to the first seat position adjustment parameter so that the channel size corresponding to the target seat is switched from the first channel size to the target channel size.

4. The seat adjustment method according to claim 1, characterized in that: After the step of adjusting the target seat where the target occupant is located according to the target seat adjustment parameter, the method further includes: generating a second target living body image including the target occupant based on the plurality of wireless detection signals, and extracting a second image feature of the second target living body image; Determining a second sitting posture type corresponding to the target occupant according to the second image feature, and determining a function control instruction corresponding to the second sitting posture type, wherein the function control instruction is a control instruction for controlling a target function module configured in the vehicle and required to be adjusted by the target occupant; The target functional module is controlled according to the functional control instruction so that the target functional module enters a functional operation state matching the functional control instruction.

5. The seat adjustment method according to claim 4, characterized in that: The step of determining the function control instruction corresponding to the second sitting posture type includes: determining a preset sitting posture type that matches the second sitting posture type; The preset function instruction matching the preset sitting posture type is determined as the function control instruction corresponding to the second sitting posture type.

6. The seat adjustment method according to claim 1, characterized in that: The step of generating a first target living body image including a target occupant based on the plurality of wireless detection signals comprises: Detecting initial channel state information respectively matched by the plurality of wireless detection signals, and performing cleaning processing on each of the initial channel state information to obtain each complete channel state information; Each of the complete channel state information is screened to determine target channel state information, and a first target living body image is generated based on each of the target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal detecting a target occupant.

7. The seat adjustment method according to claim 6, characterized in that: The step of detecting initial channel state information that matches each of the plurality of wireless detection signals comprises: Detecting wireless receiving signals that are matched to each of the plurality of wireless detection signals; determining a signal change ratio between each of the plurality of wireless detection signals and a matching wireless reception signal; Based on each of the signal change ratios, initial channel state information that matches each of the plurality of wireless detection signals is determined.

8. The seat adjustment method according to claim 6, characterized in that: The step of generating a first target living body image based on each target channel state information comprises: Extracting one-dimensional vector features contained in each of the target channel state information; The one-dimensional vector features are fused to obtain two-dimensional vector features, and a first target living body image is generated based on the two-dimensional vector features.

9. An electronic device, characterized in that: The device comprises: a wireless signal transmission module, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the seat adjustment method according to any one of claims 1 to 8.

10. A vehicle, characterized in that: The vehicle comprises the electronic device as claimed in claim 9.

11. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the seat adjustment method according to any one of claims 1 to 8 are implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, which implements the steps of the seat adjustment method according to any one of claims 1 to 8 when executed by a processor.

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