Adjusting method and device for vehicle seat and vehicle

Abstract

The invention relates to the technical field of vehicles, in particular to a vehicle seat adjusting method and device and a vehicle. The method comprises the steps that the current use scene of the vehicle and human body characteristic parameters of a user are obtained; determining target arrangement parameters of the target seat according to the current use scene and the human body characteristic parameters; and controlling the current parameter of the target seat to be adjusted to the target arrangement parameter. Therefore, the problems that in the prior art, seat adjusting accuracy is low, user experience and comfort are affected, and body injuries are caused due to the fact that long-time keeping of the posture is not considered are solved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method and device for adjusting a vehicle seat and a vehicle. Background Art

[0002] With the development of intelligent connected cars, the comfort of the in-car environment has become one of the key concerns of users. Among them, the seat is the main component that directly contacts the human body, and its posture adjustment is crucial to improving the overall comfort of the driver and passengers.

[0003] The relevant technology is generally based on the visual recognition technology of the in-vehicle camera. By selecting users with different physical signs and adjusting them to a comfortable driving position, the correspondence between the body characteristics and the target seat position is fitted, and the seat is adjusted to the most suitable position and shape according to the correspondence, thereby improving the riding comfort and experience.

[0004] However, in the related technology, a large amount of data needs to be collected in advance to accurately recommend suitable seat settings for users. Due to large individual differences, the data may have discrete and non-convergent problems, resulting in the final seat adjustment result being not accurate enough, and failing to consider other physical injury risks caused by maintaining the posture for a long time. Summary of the invention

[0005] The present application provides a vehicle seat adjustment method, device and vehicle to solve the problems in the related art of low seat adjustment accuracy, which affects the user experience and comfort, and does not take into account the physical damage caused by maintaining a posture for a long time.

[0006] The first aspect of the present application provides a method for adjusting a vehicle seat, comprising the following steps: acquiring the current usage scenario of the vehicle and the body characteristic parameters of the user; determining target layout parameters of a target seat according to the current usage scenario and the body characteristic parameters; and controlling the current parameters of the target seat to be adjusted to the target layout parameters.

[0007] Optionally, the target layout parameters of the target seat are determined according to the current usage scenario and human body characteristic parameters, including: determining a target model according to the current usage scenario; inputting the human body characteristic parameters into the target model, and the target model outputting the target layout parameters of the target seat.

[0008] Optionally, before inputting the human body characteristic parameters into the target model, it also includes: acquiring the human body characteristic parameters in all usage scenarios; calibrating the target layout parameters of the seats corresponding to each human body characteristic parameter; and establishing the target model according to the human body characteristic parameters in all usage scenarios and the target layout parameters of the seats corresponding to each human body characteristic parameter.

[0009] Optionally, the current usage scenario includes first to fourth usage scenarios, and the target model includes first to fourth models, wherein the first to fourth usage scenarios correspond one-to-one to the first to fourth models, the second model includes: a first bone-muscle sub-model and a first body pressure sub-model, and the fourth model includes: a second bone-muscle sub-model and a second body pressure sub-model.

[0010] Optionally, if the target model is the first model, the target model is established based on the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying multiple joint control points in the human body characteristic parameters; calculating the first risk value and the second risk value of the multiple joint control points under different target layout parameters; and establishing the first model based on different target layout parameters, the first risk value and the second risk value.

[0011] Optionally, the establishment process of the first bone-muscle sub-model of the second model and the second bone-muscle sub-model of the fourth model are the same, and the establishment process of the first body pressure sub-model of the second model and the second body pressure sub-model of the fourth model are the same.

[0012] Optionally, if the target model is the second model or the fourth model, the target model is established according to the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying the electromyographic vibration data in the human body characteristic parameters; calculating the muscle activity and spinal joint force according to the electromyographic vibration data under different target layout parameters; establishing a bone muscle sub-model according to different target layout parameters, muscle activity, spinal joint force and a pre-established subjective evaluation table; and

[0013] Generate a corresponding human body geometry model according to the specified human body sign indicators; obtain the body pressure parameters related to the vehicle seat output by the human body geometry model under different target layout parameters; and establish a body pressure sub-model according to different target layout parameters, body pressure parameters related to the vehicle seat and a pre-established subjective rating table.

[0014] Optionally, if the target model is the third model, the target model is established based on the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying the dynamic response dynamic force and vibration excitation acceleration in the human body characteristic parameters; calculating the effective amplitude transfer rate of the seat based on the dynamic response dynamic force and vibration excitation acceleration under different target layout parameters; and establishing the third model based on different target layout parameters and the effective amplitude transfer rate of the seat.

[0015] The second aspect of the present application provides an adjustment device for a vehicle seat, including: a first acquisition module, used to obtain the current usage scenario of the vehicle and the user's human body characteristic parameters; a determination module, used to determine the target layout parameters of the target seat according to the current usage scenario and the human body characteristic parameters; an adjustment module, used to control the current parameters of the target seat to be adjusted to the target layout parameters.

[0016] Optionally, the determination module is further used to determine a target model according to a current usage scenario; human body feature parameters are input into the target model, and the target model outputs target layout parameters of a target seat.

[0017] Optionally, the adjustment device for the vehicle seat also includes: a second acquisition module, used to acquire human body characteristic parameters in all usage scenarios before inputting the human body characteristic parameters into the target model; a calibration module, used to calibrate the target layout parameters of the seat corresponding to each human body characteristic parameter; and an establishment module, used to establish the target model based on the human body characteristic parameters in all usage scenarios and the target layout parameters of the seat corresponding to each human body characteristic parameter.

[0018] Optionally, the current usage scenario includes first to fourth usage scenarios, and the target model includes first to fourth models, wherein the first to fourth usage scenarios correspond one-to-one to the first to fourth models, the second model includes: a first bone-muscle sub-model and a first body pressure sub-model, and the fourth model includes: a second bone-muscle sub-model and a second body pressure sub-model.

[0019] Optionally, the establishment module is further used to: if the target model is the first model, then establish the target model according to the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying multiple joint control points in the human body characteristic parameters; calculating the first risk value and the second risk value of the multiple joint control points under different target layout parameters; and establishing the first model according to different target layout parameters, the first risk value and the second risk value.

[0020] Optionally, the establishment process of the first bone-muscle sub-model of the second model and the second bone-muscle sub-model of the fourth model are the same, and the establishment process of the first body pressure sub-model of the second model and the second body pressure sub-model of the fourth model are the same.

[0021] Optionally, the establishment module is further used to: identify electromyographic vibration data in human characteristic parameters; calculate muscle activity and spinal joint force based on the electromyographic vibration data under different target layout parameters; establish a bone-muscle sub-model based on different target layout parameters, muscle activity, spinal joint force and a pre-established subjective rating table; and generate a corresponding human body geometry model based on specified human body sign indicators; obtain body pressure parameters related to the vehicle seat output by the human body geometry model under different target layout parameters; establish a body pressure sub-model based on different target layout parameters, body pressure parameters related to the vehicle seat and a pre-established subjective rating table.

[0022] Optionally, the establishment module is further used for: if the target model is the third model, then establishing the target model according to the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying the dynamic response dynamic force and vibration excitation acceleration in the human body characteristic parameters; calculating the effective amplitude transfer rate of the seat according to the dynamic response dynamic force and vibration excitation acceleration under different target layout parameters; establishing the third model according to different target layout parameters and the effective amplitude transfer rate of the seat.

[0023] A third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a vehicle seat adjustment method as described in the above embodiment.

[0024] Therefore, this application has at least the following beneficial effects:

[0025] The embodiment of the present application can determine the optimal seat arrangement parameters in the current usage scenario in combination with human characteristic parameters, which not only effectively reduces muscle fatigue and injury risks caused by long-term driving, but also better adapts to the comfort needs of each user in different scenarios, ensuring that the user enjoys a comfortable riding experience immediately after entering the vehicle. In this way, the problem of low seat adjustment accuracy in the related art, which affects the user experience and comfort, and the problem of not considering the physical damage caused by maintaining the posture for a long time, is solved.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flowchart of a vehicle seat adjustment method provided according to an embodiment of the present application;

[0029] Figure 2 This is an example diagram of a target model in different usage modes provided according to an embodiment of the present application;

[0030] Figure 3 A flow chart of seat adjustment in a boarding welcome mode provided according to an embodiment of the present application;

[0031] Figure 4 A flow chart of seat adjustment in driving mode according to an embodiment of the present application;

[0032] Figure 5A flow chart of seat adjustment in zero gravity mode according to an embodiment of the present application;

[0033] Figure 6 A front view of a joint point identification position provided according to an embodiment of the present application;

[0034] Figure 7 A side view of a joint point identification position provided according to an embodiment of the present application;

[0035] Figure 8 This is an example diagram of seat arrangement parameters provided according to an embodiment of the present application;

[0036] Fig. 9 This is an example diagram of key characteristic parameters of sitting posture provided according to an embodiment of the present application;

[0037] Fig.10 is a block diagram of an adjustment device for a vehicle seat according to an embodiment of the present application;

[0038] Fig.11 It is a schematic diagram of the structure of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes the vehicle seat adjustment method, device and vehicle of the embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a vehicle seat adjustment method, in which the optimal seat layout parameters in the current usage scenario are determined in combination with human body characteristic parameters, which not only effectively reduces muscle fatigue and injury risks caused by long-term driving, but also better adapts to the comfort needs of each user in different scenarios, ensuring that the user enjoys a comfortable riding experience immediately after entering the vehicle. Thereby, the problem of low seat adjustment accuracy in the related technology, which affects the user experience and comfort, and the problem of not taking into account the physical damage caused by maintaining the posture for a long time, is solved.

[0041] Specifically, Figure 1 A schematic flow chart of a vehicle seat adjustment method provided in an embodiment of the present application.

[0042] like Figure 1 As shown, the vehicle seat adjustment method comprises the following steps:

[0043] In step S101, the current usage scenario of the vehicle and the user's body characteristic parameters are obtained.

[0044] In the embodiment of the present application, the current usage scenario may include the first to fourth usage scenarios, wherein the first scenario may be a boarding welcoming mode, the second scenario may be a static driving mode, the third scenario may be a dynamic driving mode, and the fourth scenario may be a zero gravity mode. Human body characteristic parameters are key data for personalized adjustment of seats, including but not limited to the following items: gender, age, height, weight, BMI (Body Mass Index), seat height / height ratio, key joint measurement data (such as the height from the seat surface to the cervical vertebrae, the height from the seat surface to the eyes, the thickness of the thigh part, etc.) and body pressure distribution data (such as the maximum pressure of the cushion, the overall contact area of ​​the cushion area, the side wing support area of ​​the cushion area, and the average pressure of the side wing of the cushion area). In the actual implementation process, the embodiment of the present application can be collected through in-vehicle cameras, sensors and other equipment, and processed in combination with cloud databases or models to obtain human body characteristic parameters.

[0045] In step S102, target arrangement parameters of the target seat are determined according to the current usage scenario and human body characteristic parameters.

[0046] In one embodiment of the present application, target layout parameters of a target seat are determined based on a current usage scenario and human body characteristic parameters, including: determining a target model based on the current usage scenario; inputting the human body characteristic parameters into the target model, and the target model outputting the target layout parameters of the target seat.

[0047] The target arrangement parameters may include seat height, backrest angle, headrest position, lumbar support strength, etc. The embodiment of the present application can provide the user with the most suitable seat posture in different usage scenarios, thereby comprehensively improving the comfort and safety of the ride.

[0048] In the embodiments of the present application, Figure 2 As shown, the target model includes the first to fourth models, and the first to fourth usage scenarios correspond one-to-one to the first to fourth models, wherein the second model includes: a first bone-muscle sub-model and a first body pressure sub-model, and the fourth model includes: a second bone-muscle sub-model and a second body pressure sub-model.

[0049] Specifically, if Figure 3As shown, the first model can be a seat posture riding risk proxy model, which is suitable for the first usage scenario (boarding welcome mode). It collects human characteristic parameters through the visual recognition technology of the external camera, considers the differences in the seat layout space requirements of users with different physical signs, and inputs the human characteristic parameters into the seat posture riding risk proxy model in the cloud through the Internet of Vehicles; calculates the target layout parameters of the user's sitting process through the seat posture riding risk proxy model; activates the seat welcome function when the door is opened, and adjusts the seat back, seat cushion, headrest and other components to the set position.

[0050] like Figure 4 As shown, the second model is suitable for the second usage scenario (static driving mode), and may include a first bone muscle sub-model and a first body pressure sub-model, wherein the first bone muscle sub-model may be a driving posture seat bone muscle comfort proxy model for analyzing the comfort of the user's skeletal muscle system, and the first body pressure sub-model may be a driving posture seat body pressure comfort proxy model for analyzing the pressure distribution of the seat on the human body. In the embodiment of the present application, the identified human body feature parameters may be input into the driving posture seat bone muscle comfort proxy model and the driving posture seat body pressure comfort proxy model in the cloud through the Internet of Vehicles. The seat layout parameters with the highest subjective score for driving posture comfort are calculated through the proxy model. The seat control module automatically adjusts the seat to the most comfortable position for the posture.

[0051] The third model can be a multi-body dynamic comfort proxy model of the driving posture seat, which takes into account the dynamic changes of the vehicle during driving and is suitable for the third use scenario (dynamic driving mode). In the embodiment of the present application, the recognized human body feature parameters can be input into the multi-body dynamic comfort proxy model of the driving posture seat in the cloud through the Internet of Vehicles, and the seat layout parameters with the highest subjective score of driving posture comfort can be calculated through the proxy model, and the seat control module can automatically adjust the seat to the most comfortable position.

[0052] like Figure 5 As shown, the fourth model is applicable to the fourth usage scenario (zero gravity mode) and may include: a second bone muscle sub-model and a second body pressure sub-model. Among them, the second bone muscle sub-model is a zero-gravity posture seat bone muscle comfort proxy model, which is used to analyze the comfort of the user's skeletal muscle system in a zero-gravity state. The second body pressure sub-model is a zero-gravity posture seat body pressure comfort proxy model, which is used to analyze the pressure distribution of the seat on the human body in a zero-gravity state. In the embodiment of the present application, the identified human body feature parameters can be input into the zero-gravity posture seat bone muscle comfort proxy model and the zero-gravity posture seat body pressure comfort proxy model in the cloud through the Internet of Vehicles, and the seat layout parameters with the highest subjective score of driving posture comfort are calculated through the proxy model, and the seat control module automatically adjusts the seat to the most comfortable position for the posture.

[0053] In summary, the embodiment of the present application obtains human body shape data and calculates the matching seat posture parameters through the database / model in the cloud, thereby improving riding comfort. On this basis, the embodiment further subdivides the control process into boarding and welcoming mode, driving and riding mode (static), driving and riding mode (dynamic), and zero gravity mode according to the user's usage scenario. In the boarding and welcoming mode, a seat posture riding risk proxy model is established based on the rapid full-body assessment tool to obtain the riding path that causes the least damage to the user; in the static driving and riding mode and zero gravity mode, the seat bone muscle comfort proxy model and the seat body pressure comfort proxy model are used to obtain the most comfortable seat parameters for users in static environments; in the dynamic driving and riding mode, the seat multi-body dynamic comfort proxy model is used to obtain the most comfortable seat parameters for users in dynamic environments such as vehicle bumps. This case controls the characteristics of each scenario separately, and comprehensively improves the seat posture comfort of all scenarios.

[0054] It should be noted that after automatically adjusting the seat to the most comfortable position based on one of the above models, the embodiment of the present application can detect whether the user manually adjusts the seat parameters. If so, the adjusted seat parameters and human body characteristic parameters are output to the cloud, thereby optimizing the corresponding model, improving the accuracy of prediction and adjustment, and providing better services to users.

[0055] In one embodiment of the present application, before inputting the human body characteristic parameters into the target model, it also includes: acquiring the human body characteristic parameters in all usage scenarios; calibrating the target layout parameters of the seats corresponding to each human body characteristic parameter; and establishing the target model based on the human body characteristic parameters in all usage scenarios and the target layout parameters of the seats corresponding to each human body characteristic parameter.

[0056] It is understandable that the embodiments of the present application can calibrate each human characteristic parameter through experiments and data analysis to determine the corresponding optimal seat layout parameters. For example: for different heights, calibrate the optimal seat height and backrest angle. For different weights, calibrate the optimal seat support strength and pressure distribution. For different BMIs, calibrate the optimal seat width and side support angle. The calibration process can be completed through laboratory testing, simulation calculations, and biomechanical analysis to ensure that each parameter can correspond to an optimized seat layout solution.

[0057] Furthermore, the embodiment of the present application can input the human body characteristic parameters and their corresponding seat layout parameters in all usage scenarios into the modeling system. Using machine learning algorithms or other statistical methods, a target model that can predict the optimal seat layout parameters in different usage scenarios is established. The details are as follows:

[0058] 1. If the target model is the first model, then the target model is established according to the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying multiple joint control points in the human body characteristic parameters; calculating the first risk value and the second risk value of the multiple joint control points under different target layout parameters; and establishing the first model according to different target layout parameters, the first risk value and the second risk value.

[0059] Specifically, the embodiment of the present application first uses REBA (Rapid Entire Body Assessment) to score the joints of the trunk, neck and legs (Group A) and upper arms, forearms and wrists (Group B) according to the angle; then the force scores are scored for Group A and Group B, and the joint scores and force scores are added to obtain the scores of Group A and Group B respectively; the score of Group C can be obtained according to the scores of Group A / B; finally, the human body movements are scored and added to the C score to obtain the final score of the whole body. The final score represents the risk of musculoskeletal diseases for people working in the current posture.

[0060] Scoring rules:

[0061] 1) The joint scores of Group A are shown in Tables 1 to 3 below, where Table 1 is the trunk scoring table, Table 2 is the neck scoring table, Table 3 is the leg scoring table, and the force scores of Group A are shown in Table 4.

[0062] Table 1

[0063] Torso posture Score Vertical angle close to 0° 1 Lean forward or backward within 20° 2 Leaning forward 20°-60° or leaning back more than 20° 3 Forward tilt more than 60° 4 If accompanied by tilt or rotation +1

[0064] Table 2

[0065] Neck posture Score Lower your head within 20° 1 Lowering the head more than 20 degrees or leaning back 2 If accompanied by tilt or head turning +1

[0066] Table 3

[0067] Leg position Score Average force on both legs (including walking and sitting) 1 Uneven force on the legs 2 If the knee is bent between 30°-60° +1 If the knee is bent more than 60° +2

[0068] Table 4

[0069]

[0070]

[0071] 2) The joint scores of Group B are shown in Tables 5 to 7 below, where Table 5 is the upper arm scoring table, Table 6 is the forearm scoring table, Table 7 is the wrist scoring table, and the force scores of Group B are shown in Table 8.

[0072] Table 5

[0073] Upper arm posture Score The forward or backward swing is within 20° 1 The front swing is between 20°-45° or the rear swing exceeds 20° 2 Front swing between 45°-90° 3 Front swing exceeds 90° 4 If accompanied by arm lifting or arm rotation +1 If accompanied by shoulder lift +1 If the arms are supported or the body is tilted -1

[0074] Table 6

[0075] Forearm posture Score Bend between 60°-100° 1 Bend less than 60° or more than 100° 2

[0076] Table 7

[0077] Wrist posture Score Pitch between 15° 1 More than 15° 2 If accompanied by lateral bending or wrist rotation +1

[0078] Table 8

[0079] Force Score The hand can be easily and comfortably grasped +0 Hand work is not in ideal condition but acceptable +1 Hand work status is unacceptable but achievable +2 Unacceptable hand working conditions can be dangerous +3

[0080] 3) The scores of Group C are shown in Tables 9 to 11 below, where Table 9 is the score table for Group A, Table 10 is the score table for Group B, Table 11 is the score table for Group C, and the human body movement score table is shown in Table 12.

[0081] Generate score A: Based on the scores of the torso, neck and legs, we get the joint score of Group A, and then add the force score of Group A to get the final score of Group A.

[0082] Generate score B: Based on the scores of the upper arm, forearm and wrist, obtain the joint score of Group B, and then add the force score of Group B to obtain the final score of Group B.

[0083] Generate score C: Based on the final score of group A / B, find the score of group C in table C.

[0084] Table 9

[0085]

[0086] Table 10

[0087]

[0088] Table 11

[0089]

[0090] Table 12 Human body action scoring

[0091] Human body movements Score If the movement requires the body to maintain a certain posture for more than 1 minute 1 If the small range of motion is repeated more than 4 times per minute 2 If the movement causes a significant change in body posture 3

[0092] Furthermore, the embodiment of the present application can add the C group score and the action score to obtain a REBA score, and find the corresponding conclusion in the REBA conclusion table according to the REBA score, as shown in Table 13. Among them, the RULA score in Table 13 can be obtained by evaluating the RULA (Rapid Upper Limb Assessment). For details, please refer to the calculation process of the REBA score. To avoid redundancy, it will not be elaborated here.

[0093] Table 13

[0094] Risk Level RULA score REBA score Action Level 0 1 No risk, negligible 1 1-2 2-3 Low risk, acceptable, no action required 2 3-4 4-7 Medium risk, requires investigation and may require action 3 5-6 8-10 High risk, need to investigate and take action as soon as possible 4 7 11-15 Very high risk, investigate immediately and take immediate action

[0095] In the embodiment of the present application, 23 key joint control points of the human skeleton are extracted through visual recognition technology, such as Figure 6 and Figure 7 As shown in the figure, the human posture recognition model outputs the spatial relative coordinates of the human skeleton joints. Two adjacent joints can form the corresponding limb structure, which is calculated using the spatial vector method. The joint angles of the head and neck, trunk, upper limbs, legs, etc. are measured.

[0096]

[0097] Where: θ is the joint angle; and are the lengths of adjacent joint vectors respectively.

[0098] The seat arrangement parameters of the embodiment of the present application are as follows: Figure 8 As shown, among them, A-front end height of seat cushion, B-backrest height, C-rear end height of seat cushion, D-length of seat cushion, E-seat cushion inclination, F-backrest and seat cushion angle, G-maximum width of seat cushion, H-seat cushion bonsai width, I-seat cushion side wing angle, J-maximum width of backrest, K-backrest bonsai width, L-backrest side wing angle, M-distance between seat cushion and pedal.

[0099] Measure the risk level and score of REBA and RULA during the user's seating process under different seat layout parameters, and perform correlation analysis to obtain seat layout parameters with high correlation. In the embodiment of the present application, a number of volunteers can be selected, and the seat parameters with high correlation can be adjusted. The REBA and RULA scores are recorded 100 times per second during the seating process, and the average and highest REBA and RULA scores from door opening to seating are recorded. Human body characteristic parameters and seat adjustment parameters are used as input variables, and the average and highest REBA and RULA scores are used as output. Latin square sampling is used to select the test matrix, obtain the input and output results, and construct the target model. The mathematical expression of the target model is:

[0100]

[0101] Where: Z(u) is the estimated value at position u; μ is the global mean; λ i is the Lagrange multiplier, obtained by minimizing the difference between the estimated value and the actual observed value; Z(u i ) is at a known position u i The observation at ; n is the number of neighboring observations used for regression.

[0102] Furthermore, the embodiment of the present application can calculate the optimal solution of seat parameters with the minimum REBA and RULA scores using the NSGA-II optimization algorithm based on the user's physical sign parameters collected by the in-vehicle camera, and output the optimal solution seat parameters to the seat control module to start the seat welcome mode.

[0103] 2. The establishment process of the first bone muscle sub-model of the second model and the second bone muscle sub-model of the fourth model is the same, and the establishment process of the first body pressure sub-model of the second model and the second body pressure sub-model of the fourth model is the same. If the target model is the second model or the fourth model, the target model is established according to the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying the electromyographic vibration data in the human body characteristic parameters; calculating the muscle activity and spinal joint force according to the electromyographic vibration data under different target layout parameters; establishing the bone muscle sub-model according to different target layout parameters, muscle activity, spinal joint force and a pre-established subjective rating table.

[0104] Specifically, the embodiment of the present application can obtain key characteristic parameters of sitting posture, such as Fig. 9 As shown, among them, 23-sit height, 24-sitting cervical point height, 25-sitting eye height, 26-sitting shoulder height, 27-sitting elbow height, 28-sitting thigh thickness, 29-sitting knee height, 30-sitting popliteal height, 31-sitting elbow width, 32-sitting hip width, 33-sitting hip-popliteal distance, 34-sitting hip-knee distance, 35 sitting lower limb length. According to different signs (5%, 50%, 95%), the seat comfort related sign table and subjective rating table are pre-established, as shown in Table 14 and Table 15 respectively.

[0105] Table 14

[0106]

[0107]

[0108] Table 15

[0109] Subjective description Corresponding score Subjective description Corresponding score No vibration feeling 10 points Quite uncomfortable 5 points There is a vibration feeling but it is acceptable 9 points Very uncomfortable 4 points Slight discomfort 8 points Severe discomfort 3 points A bit uncomfortable 7 points Very uncomfortable 2 points Discomfort 6 points

[0110] The embodiment of the present application can use the surface electromyography wireless acquisition system to measure the surface electromyography signals (SMEG) of the human surface muscle groups in a whole-body vibration environment. Among them, the electromyography signals recommend the selection of erector spinae, thigh muscles (such as quadriceps and biceps femoris), gluteus maximus and gluteus medius to paste electromyography sensors, use open source software to establish 5%, 50%, and 95% bone muscle models of driving posture and zero gravity posture respectively, use electromyography test results to calibrate the model, and input the seat posture adjustment parameters according to different signs (5%, 50%, 95%) and different postures (driving posture and zero gravity posture) of the bone muscle model, and output: subjective evaluation score, muscle activity and spinal joint force. According to the three basic bone muscle models of 5%, 50%, and 95%, the output results under different sitting posture human signs are obtained by intermediate interpolation.

[0111] Furthermore, the embodiment of the present application can establish a target model based on Latin square sampling, with human body characteristic parameters and seat layout parameters as input and subjective evaluation scores, muscle activity and spinal joint force as output, and calculate the optimal solution of the target layout parameters with the minimum subjective evaluation score, muscle activity and spinal joint force using the NSGA-II optimization algorithm based on the user's physical sign parameters collected by the in-vehicle camera. The seat controller controls the seat posture according to the optimal solution of the target layout parameters.

[0112] In addition, the embodiments of the present application can also generate a corresponding human body geometry model based on specified human body sign indicators; obtain body pressure parameters related to the vehicle seat output by the human body geometry model under different target layout parameters; and establish a body pressure sub-model based on different target layout parameters, body pressure parameters related to the vehicle seat and a pre-established subjective evaluation table.

[0113] Specifically, the embodiment of the present application can specify corresponding human body sign indicators (5 / 50 / 95 percentile adult males under the national standard), including gender, age, BMI, height and sitting height / height ratio, and use the specified human body sign indicators and the corresponding human body sign database to generate the corresponding specified body sign human body geometric model. Based on the generated human body sign geometric model, the human body finite element model mesh transformation technology is used to transform the human body finite element model into a human body finite element model of the specified body sign.

[0114] By conducting static seat stiffness tests and human-seat static body pressure distribution tests, the human-seat body pressure finite element models with different postures and different signs are calibrated. According to the human-seat body pressure model, the influence of seat foam thickness, density, suspension spring stiffness and other parameters on body pressure analysis is analyzed. Body pressure parameters include maximum cushion pressure, overall cushion area contact area, cushion area side wing support area, cushion area side wing average pressure, maximum thigh front support pressure, average thigh front support pressure, shoulder support contact area, shoulder support average pressure, lumbar support contact area, and lumbar support maximum pressure.

[0115] Furthermore, the embodiment of the present application can establish a target model based on Latin square sampling, with human body characteristic parameters and seat layout parameters as input and subjective evaluation scores and body pressure parameters as output. According to the user's physical sign parameters collected by the in-car camera, the NSGA-II optimization algorithm is used to calculate the optimal solution of the subjective evaluation score and body pressure parameters, and the seat controller controls the seat posture according to the optimal solution of the seat parameters.

[0116] In one embodiment of the present application, if the target model is the third model, the target model is established based on the human body characteristic parameters in all usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying the dynamic response dynamic force and vibration excitation acceleration in the human body characteristic parameters; calculating the effective amplitude transfer rate of the seat based on the dynamic response dynamic force and vibration excitation acceleration under different target layout parameters; and establishing the third model based on different target layout parameters and the effective amplitude transfer rate of the seat.

[0117] Specifically, the embodiment of the present application can select 5%, 50%, and 95% of test personnel for human vibration response testing, and use a three-dimensional force plate (Kistler) and a seat accelerometer (Sit-pad) to obtain the dynamic force of human vibration response and vibration excitation acceleration. In the test, it is necessary to use a three-dimensional seat accelerometer (Sit-pad) to measure the vibration acceleration response of the vibration table, seat cushion, and backrest, and use a three-dimensional PCB accelerometer to test the vibration response of positions such as seat rails and headrests. In matlab, the modeling program of the 5% and 50% two-sign human explicit mass prediction model under front-back, lateral and vertical vibration excitation is completed, and parameter identification is performed based on relevant data. The human-seat modeling program is written, and after obtaining the calibrated human body model, the transfer function prediction modeling of the human-seat coupling system can be carried out.

[0118] Furthermore, the embodiment of the present application can perform correlation analysis based on the human body-seat coupling model and the subjective evaluation results. The effective amplitude transfer rate of the seat can be used as a more intuitive and simple method to characterize and evaluate the dynamic objective comfort and vibration isolation effect of the seat. The calculation method is as follows:

[0119]

[0120] Among them, G oo and G ii are the autopower spectral densities of the seat, seat cushion or backrest vibration output and floor vibration input, respectively, W i (f) is the frequency weighting factor for the seat's position and direction of interest, and the numerator and denominator are the same. The seat effective amplitude transmissibility value can be considered as the weighted RMS value of the acceleration at the seat's position and direction divided by the weighted RMS value of the acceleration in the same direction on the floor, and the frequency weighting factor at the floor needs to be consistent with the seat's position and direction of interest.

[0121] Since the frequency weighting coefficient is used in the calculation of the effective amplitude transfer rate value of the seat, it can be used to evaluate the subjective vibration isolation effect of the seat. When the effective amplitude transfer rate value of the seat is 100%, from the perspective of acceleration vibration transfer rate, the seat amplifies vibration at low frequencies and attenuates vibration at high frequencies, but overall the seat does not improve or deteriorate in riding comfort. This means that when the effective amplitude transfer rate value of the seat is greater than 100%, the seat amplifies the discomfort of the vibration, and vice versa. The effective amplitude transfer rate value of the seat depends not only on the spectral characteristics of the vibration input and the vibration transfer characteristics of the seat, but also on the sensitivity of the human body to different vibration frequencies and amplitudes.

[0122] Furthermore, the embodiment of the present application can establish a target model based on Latin square sampling, with human body feature parameters and seat layout parameters as input and subjective evaluation scores and seat effective amplitude transfer rate as output. The subjective evaluation score and the optimal solution of the seat effective amplitude transfer rate parameters are calculated using the NSGA-II optimization algorithm based on the user's vital sign parameters collected by the in-vehicle camera, and the seat controller controls the seat posture according to the optimal solution of the seat parameters.

[0123] In step S103 , the current parameters of the target seat are controlled to be adjusted to the target arrangement parameters.

[0124] According to the vehicle seat adjustment method proposed in the embodiment of the present application, the optimal seat arrangement parameters in the current use scenario are determined in combination with human characteristic parameters, which not only effectively reduces muscle fatigue and injury risks caused by long-term driving, but also better adapts to the comfort needs of each user in different scenarios, ensuring that the user enjoys a comfortable riding experience immediately after entering the vehicle. In this way, the problem of low seat adjustment accuracy in the related art, which affects the user experience and comfort, and the problem of not considering the physical damage caused by maintaining the posture for a long time, is solved.

[0125] Next, the adjustment device for a vehicle seat according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0126] Fig.10 It is a block diagram of an adjustment device for a vehicle seat according to an embodiment of the present application.

[0127] like Fig.10 As shown, the vehicle seat adjustment device 100 includes: a first acquisition module 101 , a determination module 102 and an adjustment module 103 .

[0128] Among them, the first acquisition module 101 is used to obtain the current usage scenario of the vehicle and the user's human body characteristic parameters; the determination module 102 is used to determine the target layout parameters of the target seat according to the current usage scenario and the human body characteristic parameters; the adjustment module 103 is used to control the current parameters of the target seat to be adjusted to the target layout parameters.

[0129] In one embodiment of the present application, the determination module 102 is further used to determine a target model according to the current usage scenario; human body feature parameters are input into the target model, and the target model outputs target layout parameters of the target seat.

[0130] In one embodiment of the present application, the vehicle seat adjustment device 100 also includes: a second acquisition module, used to acquire human body characteristic parameters in all usage scenarios before inputting the human body characteristic parameters into the target model; a calibration module, used to calibrate the target layout parameters of the seat corresponding to each human body characteristic parameter; and an establishment module, used to establish the target model based on the human body characteristic parameters in all usage scenarios and the target layout parameters of the seat corresponding to each human body characteristic parameter.

[0131] In one embodiment of the present application, the current usage scenario includes first to fourth usage scenarios, and the target model includes first to fourth models, wherein the first to fourth usage scenarios correspond one-to-one to the first to fourth models, the second model includes: a first bone-muscle sub-model and a first body pressure sub-model, and the fourth model includes: a second bone-muscle sub-model and a second body pressure sub-model.

[0132] In one embodiment of the present application, the establishment module is further used to: if the target model is the first model, then establish the target model according to the human body characteristic parameters in all usage scenarios, and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying multiple joint control points in the human body characteristic parameters; calculating the first risk value and the second risk value of the multiple joint control points under different target layout parameters; and establishing the first model according to different target layout parameters, the first risk value and the second risk value.

[0133] In one embodiment of the present application, the establishment process of the first bone-muscle sub-model of the second model and the second bone-muscle sub-model of the fourth model are the same, and the establishment process of the first body pressure sub-model of the second model and the second body pressure sub-model of the fourth model are the same.

[0134] In one embodiment of the present application, the establishment module is further used to: identify electromyographic vibration data in human characteristic parameters; calculate muscle activity and spinal joint force based on the electromyographic vibration data under different target layout parameters; establish a bone-muscle sub-model based on different target layout parameters, muscle activity, spinal joint force and a pre-established subjective rating table; and generate a corresponding human body geometry model based on specified human body sign indicators; obtain body pressure parameters related to the vehicle seat output by the human body geometry model under different target layout parameters; establish a body pressure sub-model based on different target layout parameters, body pressure parameters related to the vehicle seat and a pre-established subjective rating table.

[0135] In one embodiment of the present application, the establishment module is further used to: if the target model is the third model, then establish the target model according to the human body characteristic parameters in all usage scenarios, and the target parameters of the seat corresponding to each human body characteristic parameter, including: identifying the dynamic response dynamic force and vibration excitation acceleration in the human body characteristic parameters; calculating the effective amplitude transfer rate of the seat according to the dynamic response dynamic force and vibration excitation acceleration under different target layout parameters; and establishing the third model according to different target layout parameters and the effective amplitude transfer rate of the seat.

[0136] It should be noted that the above explanation of the embodiment of the vehicle seat adjustment method is also applicable to the vehicle seat adjustment device of this embodiment, which will not be repeated here.

[0137] According to the vehicle seat adjustment device proposed in the embodiment of the present application, the optimal seat layout parameters in the current usage scenario are determined in combination with the human body characteristic parameters, which not only effectively reduces the muscle fatigue and injury risks caused by long-term driving, but also better adapts to the comfort needs of each user in different scenarios, ensuring that users enjoy a comfortable riding experience immediately after entering the vehicle.

[0138] Fig.11 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0139] A memory 1101 , a processor 1102 , and a computer program stored in the memory 1101 and executable on the processor 1102 .

[0140] When the processor 1102 executes the program, the vehicle seat adjustment method provided in the above embodiment is implemented.

[0141] Furthermore, the vehicle also includes:

[0142] The communication interface 1103 is used for communication between the memory 1101 and the processor 1102 .

[0143] The memory 1101 is used to store computer programs that can be executed on the processor 1102 .

[0144] The memory 1101 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0145] If the memory 1101, the processor 1102 and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101 and the processor 1102 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.11 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0146] Optionally, in a specific implementation, if the memory 1101, the processor 1102 and the communication interface 1103 are integrated on a chip, the memory 1101, the processor 1102 and the communication interface 1103 can communicate with each other through an internal interface.

[0147] The processor 1102 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0148] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0149] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0150] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0151] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0152] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0153] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for adjusting a vehicle seat, characterized in that: The following steps are involved: Obtain the current usage scenario of the vehicle and the user's human body characteristic parameters; Determining target arrangement parameters of a target seat according to the current usage scenario and the human body characteristic parameters; The current parameters of the target seat are controlled to be adjusted to the target arrangement parameters.

2. The vehicle seat adjustment method according to claim 1, characterized in that: The step of determining the target arrangement parameters of the target seat according to the current usage scenario and the human body characteristic parameters includes: Determine a target model according to the current usage scenario; The human body feature parameters are input into the target model, and the target model outputs the target arrangement parameters of the target seat.

3. The vehicle seat adjustment method according to claim 2, characterized in that: Before inputting the human body feature parameters into the target model, the method further comprises: Obtain human body characteristic parameters in all usage scenarios; Calibrate the target arrangement parameters of the seats corresponding to each human body characteristic parameter; The target model is established according to the human body characteristic parameters in all the usage scenarios and the target arrangement parameters of the seats corresponding to each human body characteristic parameter.

4. The vehicle seat adjustment method according to claim 3, characterized in that: The current usage scenario includes the first to fourth usage scenarios, and the target model includes the first to fourth models, wherein the first to fourth usage scenarios correspond one-to-one to the first to fourth models, the second model includes: a first bone-muscle sub-model and a first body pressure sub-model, and the fourth model includes: a second bone-muscle sub-model and a second body pressure sub-model.

5. The vehicle seat adjustment method according to claim 4, characterized in that: If the target model is the first model, the target model is established according to the human body characteristic parameters in all the usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: Identifying multiple joint control points in the human body feature parameters; Calculating first risk values ​​and second risk values ​​of the plurality of joint control points under different target arrangement parameters; The first model is established according to the different target arrangement parameters, the first risk value and the second risk value.

6. The vehicle seat adjustment method according to claim 4, characterized in that: The first bone-muscle sub-model of the second model and the second bone-muscle sub-model of the fourth model are established in the same process, and the first body pressure sub-model of the second model and the second body pressure sub-model of the fourth model are established in the same process.

7. The vehicle seat adjustment method according to claim 6, characterized in that: If the target model is the second model or the fourth model, the target model is established according to the human body characteristic parameters in all the usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: Identifying electromyographic vibration data in the human body characteristic parameters; Calculating muscle activity and spinal joint force based on the myoelectric vibration data under different target arrangement parameters; establishing a bone-muscle sub-model according to the different target placement parameters, the muscle activity, the spinal joint force and a pre-established subjective evaluation table; and Generate a corresponding human body geometry model according to the specified human body signs indicators; acquiring body pressure parameters related to the vehicle seat output by the human body geometric model under different target arrangement parameters; A body pressure sub-model is established based on the different target arrangement parameters, the vehicle seat-related body pressure parameters, and a pre-established subjective rating table.

8. The vehicle seat adjustment method according to claim 4, characterized in that: If the target model is the third model, the target model is established according to the human body characteristic parameters in all the usage scenarios and the target parameters of the seat corresponding to each human body characteristic parameter, including: Identifying the dynamic response dynamic force and vibration excitation acceleration in the human body characteristic parameters; Calculating the effective amplitude transfer rate of the seat according to the dynamic response dynamic force and the vibration excitation acceleration under different target arrangement parameters; The third model is established according to the different target arrangement parameters and the seat effective amplitude transfer rate.

9. A vehicle seat adjustment device, characterized in that: include: A first acquisition module is used to acquire the current usage scenario of the vehicle and the user's body characteristic parameters; A determination module, configured to determine target arrangement parameters of a target seat according to the current usage scenario and the human body characteristic parameters; The adjustment module is used to control the current parameters of the target seat to be adjusted to the target arrangement parameters.

10. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle seat adjustment method according to any one of claims 1 to 8.

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