Seat support adjusting method and seat

Abstract

The invention discloses a seat support adjusting method and a seat. The seat support adjusting method comprises the steps that the sitting posture of a user is recognized; according to a pre-constructed spine biomechanical model, obtaining target support parameters required by each segment of the spine of the user in the sitting posture; and adjusting the seat support according to the target support parameter. According to the embodiment of the invention, the corresponding target support parameter is obtained according to the sitting posture of the user through the spine biomechanical model, so that the chair can accurately support the spine of the human body in different sitting postures, and the comfort and health protection performance of the chair are improved.

Description

Technical Field

[0001] This application relates to the field of seating technology, and in particular to a method for adjusting seat support and a seat. Background Technology

[0002] When a person maintains a seated posture for extended periods, the spine, especially the lumbar region, bears significant pressure. Current chair designs often lack ergonomic principles in their support trajectory, and the movement paths of the support modules are relatively simple, failing to simulate the biomechanical changes of the spine under different sitting postures. Therefore, they cannot provide precise support, easily leading to problems such as lumbar fatigue and spinal deformities. For example, during prolonged desk work, the lack of sufficient lumbar support reduces lumbar lordosis and increases pressure on the intervertebral discs; while leaning back, the chair's support points cannot adjust to the changes in spinal shape, resulting in uneven stress on different areas of the spine. This not only affects user comfort but may also harm health in the long run. Summary of the Invention

[0003] Therefore, it is necessary to provide a seat support adjustment method and seat to address the above-mentioned technical problems. By using a spinal biomechanical model, the corresponding target support parameters are obtained according to the user's sitting posture, thereby achieving precise support of the human spine in different sitting postures and improving the comfort and health protection performance of the seat.

[0004] Firstly, a method for adjusting seat support is provided, including: Recognize the user's sitting posture; Based on the pre-constructed spinal biomechanical model, the target support parameters required for each segment of the user's spine in the sitting posture are obtained; Adjust the seat support according to the target support parameters.

[0005] Furthermore, the identification of the user's sitting posture includes: Obtain the pressure distribution of the seat back and seat cushion; Obtain changes in seat posture; Obtain the user's back muscle activity level; The user's sitting posture is identified based on the pressure distribution of the seat back and cushion, changes in seat posture, and the user's back muscle activity.

[0006] Furthermore, before obtaining the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model, the method further includes: Obtain human spinal data, wherein the human spinal data includes spinal data of multiple different ages, genders, body types and spinal health conditions. Biomechanical testing experiments were conducted on the spinal specimen to apply different axial pressures, different bending moments, and different torsional moments to the spinal specimen in a simulated human environment, so as to obtain the displacement, strain, and stress distribution parameters of each segment of the spine. Data on spinal deformation of multiple individuals in different sitting postures were obtained. Based on the spinal data of the human body, the displacement, strain, and stress distribution parameters of each segment of the spine, and the spinal deformation data, a biomechanical model of the spine is constructed.

[0007] Furthermore, it also includes: Obtain the user's back contour, height, spine length, and spinal curvature; The user's weight and hip pressure distribution were obtained; The model parameters of the spinal biomechanical model are fine-tuned based on the user's weight and hip pressure distribution, the user's back contour, height, spine length, and spine curvature.

[0008] Furthermore, the step of obtaining the target support parameters required for each segment of the user's spine in the sitting posture based on the pre-constructed spinal biomechanical model includes: Based on the spinal biomechanical model, the spinal morphology of the user corresponding to the sitting posture is obtained; Based on the user's spinal morphology, the target support parameters required for each segment of the user's spine are obtained.

[0009] Further, adjusting the seat support according to the target support parameters includes: Based on the pre-established mapping function between seat support motion and spinal biomechanical changes, the seat support adjustment parameters corresponding to the target support parameters are obtained; Adjust the seat support according to the seat support adjustment parameters.

[0010] Furthermore, before obtaining the seat support adjustment parameters corresponding to the target support parameters based on a pre-established mapping function between seat support motion and spinal biomechanical changes, the process further includes: In the aforementioned spinal biomechanical model, the stress distribution of the spine under different seat support adjustment parameters is simulated, and the maximum stress value of each segment of the spine is recorded. Based on the spinal stress distribution under different seat support adjustment parameters, the maximum stress value of each segment of the spine is recorded, and the mapping function with the support adjustment parameters as input and the goal of minimizing spinal stress is constructed.

[0011] Furthermore, it also includes: Collect user feedback data on seat usage; The mapping function is optimized based on the user's seat usage feedback data.

[0012] Furthermore, after adjusting the seat support according to the target support parameters, the method further includes: Obtain the user's seat usage habits and the user's real-time status; The seat support is adaptively adjusted based on the user's seat usage habits and the user's real-time status.

[0013] Secondly, a seat is provided, comprising: The recognition module is used to identify the user's sitting posture; The acquisition module is used to obtain the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model. An adjustment module is used to adjust the seat support according to the target support parameters.

[0014] The embodiments of this application first identify the user's sitting posture; then, based on a pre-constructed spinal biomechanical model, obtain the target support parameters required for each segment of the user's spine in this sitting posture; finally, adjust the seat support according to the target support parameters. Thus, by using the spinal biomechanical model to obtain the corresponding target support parameters based on the user's sitting posture, the seat can achieve precise support for the human spine in different sitting postures, improving the comfort and health protection performance of the seat. Attached Figure Description

[0015] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A flowchart of a seat support adjustment method provided in an embodiment of this application; Figure 2 This is an overall structural diagram of the seat support adjustment method provided in the embodiments of this application; Figure 3 A structural block diagram of the seat provided in an embodiment of this application. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0017] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] The following describes in detail, with reference to the accompanying drawings, a seat support adjustment method, system, device, and medium according to embodiments of this application.

[0019] Figure 1 This is a flowchart of a seat support adjustment method according to an embodiment of this application. Figure 1 As shown, and in combination Figure 2 The seat support adjustment method according to the embodiments of this application includes the following steps: S101: Recognizes the user's sitting posture.

[0020] In one embodiment of this application, identifying the user's sitting posture includes: obtaining the pressure distribution of the seat back and seat cushion; obtaining changes in seat posture; obtaining the user's back muscle activity; and identifying the user's sitting posture based on the pressure distribution of the seat back and seat cushion, changes in seat posture, and the user's back muscle activity.

[0021] Specifically, establishing such Figure 2 The sensor system shown firstly includes a 20×20 pressure sensor array and a pressure-sensitive diaphragm on the seat back and cushion to detect the pressure distribution. The pressure sensors have a resolution of 1cm×1cm, a range of 0-100kPa, and an accuracy of ±2kPa. Secondly, three gyroscopes and three accelerometers are installed at the top of the backrest, the lumbar region, and the front of the cushion to measure changes in seat posture. The gyroscopes have a range of ±2000° / s and an accuracy of ±0.5° / s, while the accelerometers have a range of ±16g and an accuracy of ±0.01g. Thirdly, an electromyography (EMG) sensor is installed on the seat back below the scapula to monitor back muscle activity. The EMG sensor has a sampling frequency of 1kHz and a signal-to-noise ratio of 60dB.

[0022] Furthermore, the real-time sensor data is transmitted to the data processing and analysis unit via Bluetooth 5.0 at a transmission rate of 2Mbps and a latency of less than 10ms. The data processing and analysis unit first preprocesses the data, employing a filtering algorithm to fuse multi-sensor data, combined with noise reduction and calibration processing to improve the accuracy of posture recognition, thereby enabling user posture identification. For example, when the pressure sensor detects a decrease in lumbar pressure, the gyroscope detects a 15° forward tilt of the backrest, and the electromyography sensor detects an increase in back muscle activity, the system determines that the user is in a forward-leaning, hunched-over sitting posture. Testing shows that the posture classification accuracy of this method can reach over 98%.

[0023] S102: Based on the pre-constructed spinal biomechanical model, obtain the target support parameters required for each segment of the user's spine in the sitting posture.

[0024] In one embodiment of this application, before obtaining the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model, the method further includes: obtaining human spinal data, wherein the human spinal data includes spinal data from multiple individuals of different ages, genders, body types, and spinal health conditions; conducting biomechanical testing experiments on spinal specimens to apply different axial pressures, different bending moments, and different torsional moments to the spinal specimens in a simulated human environment to obtain displacement, strain, and stress distribution parameters of each segment of the spine; obtaining spinal deformation data of multiple individuals in different sitting postures; and constructing the spinal biomechanical model based on the human spinal data, the displacement, strain, and stress distribution parameters of each segment of the spine, and the spinal deformation data.

[0025] like Figure 2 As shown in the spinal biomechanical model construction module, firstly, spinal biomechanical data from multiple scenarios are collected to obtain human spinal data for different ages, genders, body types, and spinal health conditions. Among them, the age range is 20-70 years old, with each 5-year interval set; body type is assessed by BMI, with a BMI range of 18.5-30 and above; spinal health conditions include normal spine, mild scoliosis, and lumbar disc herniation, etc.

[0026] Next, three-dimensional morphological images of the spine were acquired using high-precision CT and MRI equipment to accurately capture details of structures such as vertebrae, intervertebral discs, and ligaments, with an image resolution of 0.5mm×0.5mm×0.5mm. A biomechanical testing platform was used to conduct mechanical experiments on the spinal specimens. Specifically, in a simulated human body environment with a temperature of 37℃ and humidity of 50%, axial pressure of 0-500N, bending torque of 0-10N•m, and torsional torque of 0-5N•m were applied to measure parameters such as displacement, strain, and stress distribution of each segment of the spine, with a sampling frequency of 1kHz. Simultaneously, a motion capture system was used to record spinal deformation data from 1000 volunteers in 10 common sitting postures (such as upright, 15° forward tilt, 30° forward tilt, 10° backward tilt, 20° backward tilt, 10° left lateral bend, and 10° right lateral bend). Each sitting posture was maintained for 30 seconds, with a sampling interval of 0.1 seconds.

[0027] Furthermore, the collected data underwent preprocessing, including: using a Gaussian filtering algorithm to denoise the image data with a standard deviation of 1.0; using the U-Net deep learning model for segmentation and 3D reconstruction, achieving a segmentation accuracy of over 95%; using a Butterworth low-pass filter to filter the motion data with a cutoff frequency of 10Hz; removing outliers based on the 3σ criterion; and performing spatiotemporal alignment and standardization of the motion capture data.

[0028] Secondly, a high-precision model was constructed by combining anatomical and biomechanical principles. Specifically, based on preprocessed data, a spinal biomechanical model was established using the finite element analysis software ANSYS. The model includes 7 cervical vertebrae, 12 thoracic vertebrae, 5 lumbar vertebrae, 1 sacrum, and corresponding intervertebral discs, facet joints, ligaments, and other structures. The vertebral bodies were constructed using solid elements (SOLID186), the nucleus pulposus and annulus fibrosus of the intervertebral discs were constructed using solid elements with different properties, and the ligaments were constructed using spring elements (COMBIN14). The material properties were set as follows: vertebral cortical bone elastic modulus of 12 GPa, Poisson's ratio of 0.3; cancellous bone elastic modulus of 0.5 GPa, Poisson's ratio of 0.2; intervertebral disc nucleus pulposus elastic modulus of 0.1 GPa, Poisson's ratio of 0.45; annulus fibrosus elastic modulus of 1.5 GPa, Poisson's ratio of 0.3; and ligament elastic modulus of 0.05 GPa, Poisson's ratio of 0.4. Next, the model was calibrated using experimental data, and the material properties and boundary conditions were adjusted. Tests showed that the model's mechanical response (such as displacement and stress) under the same load had an error of less than 5% compared to the experimental data.

[0029] In one embodiment of this application, the method further includes: obtaining the user's back contour, height, spine length, and spinal curvature; obtaining the user's weight and hip pressure distribution; and fine-tuning the model parameters of the spinal biomechanical model based on the user's weight and hip pressure distribution, the user's back contour, height, spine length, and spinal curvature. The spinal curvature is obtained by fitting a spinal curve using the least squares method and calculating the radius of curvature.

[0030] Specifically, the seat's built-in 3D scanner scans the user's back contour upon first use, extracting parameters such as height, spine length, and spinal curvature. The scanner's accuracy is 0.1mm, with a height error of ±1cm and a spine length error of ±0.5cm. Spinal curvature is calculated by fitting a spinal curve using the least squares method. Simultaneously, a pressure sensor array measures the user's weight and hip pressure distribution, with a user weight error of ±0.5kg. These parameters are input into a model parameter mapping function trained on data from 1000 volunteers. This function adjusts parameters such as vertebral body size, intervertebral disc thickness, and ligament stiffness based on user characteristics, matching the model to individual features to obtain an optimized biomechanical analysis model. For example, for taller users, the vertebral body and intervertebral disc dimensions are proportionally enlarged; for heavier users, the elastic modulus of the vertebral body and intervertebral disc is appropriately increased. Tests show that fine-tuning the model parameters can improve the model's matching accuracy to individual characteristics by more than 30%.

[0031] By fine-tuning the model parameters, the model can be optimized according to the physical characteristics of different users, enabling the chair to adapt to people of various body types and spinal characteristics, thus improving its applicability.

[0032] In one embodiment of this application, obtaining the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model includes: obtaining the spinal morphology of the user corresponding to the sitting posture based on the spinal biomechanical model; and obtaining the target support parameters required for each segment of the user's spine based on the spinal morphology of the user.

[0033] Specifically, combined Figure 2 As shown in the data processing and analysis unit, the optimized biomechanical model obtained in the above steps yields the spinal morphology corresponding to the user's sitting posture, and then the target support parameters required for each segment of the spine, i.e., spinal state information, are obtained. This information is then output to the support path planning module for seat support adjustment.

[0034] By simulating a biomechanical model of the spine, the movement path of the support module can be precisely planned, so that the seat can provide support for the spine that conforms to its physiological structure and mechanical characteristics under different sitting postures.

[0035] S103: Adjust the seat support according to the target support parameters.

[0036] In one embodiment of this application, adjusting the seat support according to the target support parameter includes: obtaining a seat support adjustment parameter corresponding to the target support parameter based on a pre-established mapping function between seat support movement and spinal biomechanical changes; and adjusting the seat support according to the seat support adjustment parameter.

[0037] Specifically, combined Figure 2As shown in the support path planning module, based on the spinal state information input in the above steps, and with ergonomics as the goal while considering the motion constraints of the support modules, the seat support surface is divided into 5 independent controllable areas: upper back (corresponding to thoracic vertebrae 1 to 6), middle back (corresponding to thoracic vertebrae 7 to 12), lower back (corresponding to lumbar vertebrae 1 to 2), lumbar region (corresponding to lumbar vertebrae 3 to 5), and sacrococcygeal region. The support modules for each area adopt a modular design. The support modules for the upper and middle back areas are 10cm × 15cm in size; the support modules for the lower back and lumbar regions are 15cm × 15cm in size; and the support modules for the sacrococcygeal region are 20cm × 15cm in size. The drive unit uses a servo motor with a rated power of 20W and a speed of 3000rpm. It works in conjunction with a ball screw mechanism to enable the support module to move in three directions: X (forward / backward), Y (up / down), and Z (perpendicular to the support surface), with strokes of 0-50mm, 0-30mm, and 0-20mm respectively, and a positioning accuracy of ±0.1mm. Each module is also equipped with a servo motor with a torque of 2kg•cm, allowing for ±15° angle adjustment with a response time of less than 0.1 seconds. The drive unit is controlled by a motor controller, which is an STM32H743 microprocessor with a main frequency of 400MHz, supporting pulse width modulation speed regulation and closed-loop position control.

[0038] In one embodiment of this application, before obtaining the seat support adjustment parameters corresponding to the target support parameters based on a pre-established mapping function between seat support motion and spinal biomechanical changes, the method further includes: simulating the spinal stress distribution under different seat support adjustment parameters in the spinal biomechanical model, and recording the maximum stress value of each segment of the spine; based on the spinal stress distribution under different seat support adjustment parameters, recording the maximum stress value of each segment of the spine, and constructing the mapping function with the support adjustment parameters as input and the goal of minimizing spinal stress.

[0039] Specifically, through extensive simulation experiments, a mapping relationship between the movement of the support module and changes in spinal biomechanics was established. In the spinal biomechanical model, the stress distribution of the spine under different displacements and angles of the support module was simulated. The displacement range in the X direction was 0-50 mm with a step size of 5 mm; the displacement range in the Y direction was 0-30 mm with a step size of 3 mm; the displacement range in the Z direction was 0-20 mm with a step size of 2 mm; and the angle range was ±15° with a step size of 3°. The maximum stress values ​​of each spinal segment were recorded. Next, a multiple linear regression analysis method was used to construct a mapping function with support adjustment parameters as input and minimizing spinal stress as the objective. The coefficient of determination R0 of the mapping function between seat support movement and changes in spinal biomechanics proposed in this application was tested. 2It can reach over 0.9. Then, the optimal motion path is planned. For example, when the model calculates that the stress in the third segment of the lumbar spine is 1.2MPa (exceeding the comfort threshold of 1.0MPa), the mapping function of seat support motion and spinal biomechanical changes outputs that the lumbar support module needs to move forward 10mm, upward 5mm, and adjust the angle by 5° to reduce the stress in this segment to 0.8MPa.

[0040] After generating motion path instructions according to the above steps, combined with Figure 2 As shown, a support module drive system is constructed to receive path instructions and drive the corresponding support module to move, such as inflating or deflating the airbag or extending or retracting the electric push rod, thereby completing the adjustment of the seat support path.

[0041] In one embodiment of this application, the method further includes: collecting user seat usage feedback data; and optimizing the mapping function based on the user seat usage feedback data.

[0042] Specifically, usage data from 1000 users was collected as feedback data, totaling 10,000 hours. The Deep Reinforcement Learning (DQN) algorithm was used to optimize the mapping function between seat support motion and spinal biomechanical changes obtained in the above process. A state space, action space, and reward function were constructed. The state space includes the current sitting posture, spinal stress distribution, and support module position; the action space includes the adjustment of motion parameters of the support module; and the reward function uses minimizing maximum spinal stress, maximizing pressure distribution uniformity, and user feedback comfort scores as indicators.

[0043] Using the above feedback data as training samples, the model was trained using a GPU cluster and iterated 1 million times, which improved the average reward value of the algorithm by 40%. The optimized algorithm can adjust the support strategy according to the user's real-time status. For example, the user's fatigue level is judged by the root mean square value of the electromyography signal, with a threshold set to 0.1mV. When user fatigue is detected, the adjustment frequency and amplitude of the support module are appropriately increased.

[0044] In one embodiment of this application, after adjusting the seat support according to the target support parameters, the method further includes: obtaining the user's seat usage habits and the user's real-time status; and adaptively adjusting the seat support according to the user's seat usage habits and the user's real-time status.

[0045] Specifically, a multi-level adaptive adjustment strategy is designed according to different time scales, including short-term, medium-term, and long-term adjustments. The short-term adjustment time scale is 1-5 minutes, based on pressure sensor data. For example, when the pressure in a certain area exceeds 30 kPa for 1 minute, the position of the support module in that area is finely adjusted (±3mm in the X / Y direction, ±2mm in the Z direction) to reduce the pressure by 10%-15%. The medium-term adjustment time scale is 10-30 minutes, based on the frequency of posture changes. For example, when the user changes posture less than 3 times within 10 minutes, five preset support modes are automatically triggered, each lasting 5 minutes. These modes include gradually increasing lumbar support intensity and slightly shifting the upper back support position to prevent muscle stiffness. The long-term adjustment timescale is 1-7 days, based on user usage habit data, such as daily usage time, common sitting posture, and average usage time. It also uses clustering algorithms to identify typical user scenarios and pre-generates the optimal support path for each scenario. For example, based on time and initial sitting posture, when a user enters a scenario, the pre-stored path is quickly called to adjust the seat support, with a response time of less than 0.5 seconds.

[0046] According to the seat support adjustment method of this application embodiment, the user's sitting posture is first identified; then, based on a pre-constructed spinal biomechanical model, the target support parameters required for each segment of the user's spine under this sitting posture are obtained; finally, the seat support is adjusted according to the target support parameters. Thus, by using the spinal biomechanical model to obtain the corresponding target support parameters based on the user's sitting posture, the seat achieves precise support for the human spine under different sitting postures, improving the comfort and health protection performance of the seat.

[0047] Figure 3 This is a structural block diagram of a seat according to one embodiment of this application. Figure 3 As shown, a seat according to one embodiment of this application includes: a recognition module 310, an acquisition module 320, and an adjustment module 330, wherein: The recognition module 310 is used to recognize the user's sitting posture; The acquisition module 320 is used to obtain the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model. The adjustment module 330 is used to adjust the seat support according to the target support parameters.

[0048] According to the embodiments of this application, the seat first identifies the user's sitting posture; then, based on a pre-constructed spinal biomechanical model, it obtains the target support parameters required for each segment of the user's spine in this sitting posture; finally, it adjusts the seat support according to the target support parameters. Thus, by using the spinal biomechanical model to obtain the corresponding target support parameters based on the user's sitting posture, the seat achieves precise support for the human spine in different sitting postures, improving the seat's comfort and health protection performance.

[0049] For specific limitations regarding the seat, please refer to the limitations of the seat support adjustment method described above, which will not be repeated here. The various modules of the aforementioned seat can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0050] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0051] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0052] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for adjusting seat support, characterized in that, include: Recognize the user's sitting posture; Based on the pre-constructed spinal biomechanical model, the target support parameters required for each segment of the user's spine in the sitting posture are obtained; Adjust the seat support according to the target support parameters.

2. The seat support adjustment method according to claim 1, characterized in that, The method of recognizing the user's sitting posture includes: Obtain the pressure distribution of the seat back and seat cushion; Obtain changes in seat posture; Obtain the user's back muscle activity level; The user's sitting posture is identified based on the pressure distribution of the seat back and cushion, changes in seat posture, and the user's back muscle activity.

3. The seat support adjustment method according to claim 1, characterized in that, Before obtaining the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model, the process also includes: Obtain human spinal data, wherein the human spinal data includes spinal data of multiple different ages, genders, body types and spinal health conditions. Biomechanical testing experiments were conducted on the spinal specimen to apply different axial pressures, different bending moments, and different torsional moments to the spinal specimen in a simulated human environment, so as to obtain the displacement, strain, and stress distribution parameters of each segment of the spine. Data on spinal deformation of multiple individuals in different sitting postures were obtained. Based on the spinal data of the human body, the displacement, strain, and stress distribution parameters of each segment of the spine, and the spinal deformation data, a biomechanical model of the spine is constructed.

4. The seat support adjustment method according to claim 3, characterized in that, Also includes: Obtain the user's back contour, height, spine length, and spinal curvature; The user's weight and hip pressure distribution were obtained; The model parameters of the spinal biomechanical model are fine-tuned based on the user's weight and hip pressure distribution, the user's back contour, height, spine length, and spine curvature.

5. The seat support adjustment method according to any one of claims 1-4, characterized in that, The step of obtaining the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model includes: Based on the spinal biomechanical model, the spinal morphology of the user corresponding to the sitting posture is obtained; Based on the user's spinal morphology, the target support parameters required for each segment of the user's spine are obtained.

6. The seat support adjustment method according to claim 5, characterized in that, The step of adjusting the seat support according to the target support parameters includes: Based on the pre-established mapping function between seat support motion and spinal biomechanical changes, the seat support adjustment parameters corresponding to the target support parameters are obtained; Adjust the seat support according to the seat support adjustment parameters.

7. The seat support adjustment method according to claim 6, characterized in that, Before obtaining the seat support adjustment parameters corresponding to the target support parameters based on a pre-established mapping function between seat support motion and spinal biomechanical changes, the process further includes: In the aforementioned spinal biomechanical model, the stress distribution of the spine under different seat support adjustment parameters is simulated, and the maximum stress value of each segment of the spine is recorded. Based on the spinal stress distribution under different seat support adjustment parameters, the maximum stress value of each segment of the spine is recorded, and the mapping function with the support adjustment parameters as input and the goal of minimizing spinal stress is constructed.

8. The seat support adjustment method according to claim 7, characterized in that, Also includes: Collect user feedback data on seat usage; The mapping function is optimized based on the user's seat usage feedback data.

9. The seat support adjustment method according to claim 1, characterized in that, After adjusting the seat support according to the target support parameters, the process also includes: Obtain the user's seat usage habits and the user's real-time status; The seat support is adaptively adjusted based on the user's seat usage habits and the user's real-time status.

10. A type of seat, characterized in that, include: The recognition module is used to identify the user's sitting posture; The acquisition module is used to obtain the target support parameters required for each segment of the user's spine in the sitting posture based on a pre-constructed spinal biomechanical model. An adjustment module is used to adjust the seat support according to the target support parameters.

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