Automobile seat flexible callback method and system based on human body characteristics
By acquiring passenger characteristics and real-time pressure values to calculate resistance values and target retraction speed, the seat's flexible retraction is controlled, solving the occupant safety problem during large-angle seat collisions and achieving a balance between safety and comfort.
Patent Information
- Application Number
- CN202511606234.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-05
- Publication Date
- 2026-01-09
AI Technical Summary
In the event of a collision with a car seat at a large tilt angle, the occupant is prone to excessive displacement, which leads to insufficient restraint of the seat belt, an increased risk of head collision with the vehicle interior structure, and the excessively fast rebound speed may cause secondary injury to the occupant.
By acquiring passengers' human body characteristic parameters and real-time pressure values, the resistance value and target retraction speed are calculated to control the seat's flexible retraction. Combining the resistance threshold and the baseline retraction speed, the retraction speed is ensured to adapt to different passenger characteristics and states, avoiding excessively fast retraction.
It enables dynamic adjustment of seat retraction speed based on passenger characteristics during a collision, balancing retraction efficiency and safety, and reducing the risk of occupant injury.
Smart Images
Figure CN121291236A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of automotive seat adjustment technology, specifically to a method and system for flexible adjustment of automotive seats based on human characteristics. Background Technology
[0002] With the development of the automotive industry, consumers' demand for car seat comfort is increasing, and the ability to adjust the seat back at a large angle has become a mainstream feature. Passengers can adjust the seat back angle to achieve a more comfortable seating posture, especially during long journeys or short rest periods, where large-angle seats are widely used. Currently, car seat angle adjustment is usually done by an electric adjustment mechanism, allowing passengers to adjust the seat to a larger angle according to their comfort needs. However, when the seat is at an excessively large angle, in the event of a collision, the occupant's body is prone to excessive forward or backward displacement, leading to insufficient seatbelt restraint and an increased risk of head impact with the vehicle's interior structure, significantly increasing the risk of neck, lumbar, and chest injuries.
[0003] Some existing seat systems have collision warning functions, which can quickly retract the seat to a safe position via motors. However, the retraction speed is usually too fast, which can easily cause secondary injuries to occupants. Therefore, there is an urgent need for a method that can dynamically retract the seat to meet the diverse collision safety needs of passengers of different body types. Summary of the Invention
[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method and system for flexible adjustment of automotive seats based on human body characteristics.
[0005] According to one aspect of this application, a method for flexible retraction of a car seat based on human characteristics is provided, comprising: acquiring human characteristic parameters of a passenger; wherein the human characteristic parameters include height; collecting real-time pressure values exerted by the passenger on the seat; if a vehicle collision warning signal is received and the tilt angle of the seat is greater than a preset tilt angle threshold, calculating a resistance value of the passenger's retraction of the seat based on the real-time pressure value; calculating a target retraction speed of the seat based on the resistance value, a preset resistance threshold, and a baseline retraction speed of the seat; wherein the baseline retraction speed is determined according to the human characteristic parameters of the passenger; controlling the seat to retract at the target retraction speed; and stopping the seat retraction if the seat meets a retraction termination condition.
[0006] In one embodiment, a plurality of flexible pressure sensors are arrayed on the seat; wherein, the collection of real-time pressure values exerted by the passenger on the seat includes: using the plurality of flexible pressure sensors to collect a plurality of the real-time pressure values exerted by the passenger on the seat.
[0007] In one embodiment, calculating the resistance value of the passenger's seat retraction based on the real-time pressure value includes: calculating the average of multiple real-time pressure values to obtain the resistance value.
[0008] In one embodiment, calculating the target return speed of the seat based on the resistance value, a preset resistance threshold, and a baseline return speed of the seat return includes: calculating a deceleration ratio based on the resistance value and the resistance threshold; and calculating the target return speed based on the deceleration ratio and the baseline return speed.
[0009] In one embodiment, calculating the deceleration ratio based on the resistance value and the resistance threshold includes: if the resistance value is less than or equal to the resistance threshold, the deceleration ratio is zero; if the resistance value is greater than the resistance threshold, the difference between the resistance value and the resistance threshold is calculated; and the ratio between the difference and the resistance threshold is calculated to obtain the deceleration ratio.
[0010] In one embodiment, calculating the target callback speed based on the deceleration ratio and the baseline callback speed includes: obtaining the risk level of the seat during the callback process; determining a safety redundancy coefficient based on the risk level of the seat; and calculating the target callback speed based on the deceleration ratio, the safety redundancy coefficient, and the baseline callback speed.
[0011] In one embodiment, obtaining the risk level of the seat during the pullback process includes: collecting multiple injury indicators from the collision dummy during the collision test at the optimal tilt angle and the maximum tilt angle, respectively; calculating multiple risk enhancement rates of the seat at the maximum tilt angle based on the multiple injury indicators; and determining the risk level of the seat during the pullback process based on the multiple risk enhancement rates.
[0012] In one embodiment, the method for calculating the baseline callback speed includes: inputting the passenger's human body feature parameters into a neural network model to obtain the baseline callback speed; wherein the neural network model includes an input layer, a hidden layer, and an output layer.
[0013] In one embodiment, stopping the seat callback if the seat meets the callback termination condition includes stopping the seat callback if the seat tilt angle is adjusted to a safe angle or the vehicle collision warning signal is released.
[0014] According to another aspect of this application, a flexible retraction system for a car seat based on human characteristics is provided, comprising: a feature parameter acquisition module for acquiring human characteristic parameters of a passenger, wherein the human characteristic parameters include height; a real-time pressure acquisition module for acquiring real-time pressure values exerted by the passenger on the seat; a retraction resistance calculation module for calculating the resistance value of the passenger's retraction of the seat based on the real-time pressure value if a vehicle collision warning signal is received and the seat's tilt angle is greater than a preset tilt angle threshold; a retraction speed calculation module for calculating a target retraction speed of the seat based on the resistance value, a preset resistance threshold, and a reference retraction speed of the seat; wherein the reference retraction speed is determined according to the passenger's human characteristic parameters; a retraction execution control module for controlling the seat to retract at the target retraction speed; and a retraction operation stop module for stopping the seat retraction if the seat meets a retraction termination condition.
[0015] This application provides a method and system for flexible seat retraction in automobiles based on human characteristics. The method involves acquiring passenger human characteristic parameters, including height; collecting real-time pressure values exerted by the passenger on the seat; calculating the passenger's resistance to seat retraction based on the real-time pressure values if a vehicle collision warning signal is received and the seat tilt angle exceeds a preset tilt angle threshold; calculating a target retraction speed based on the resistance value, a preset resistance threshold, and a baseline retraction speed, where the baseline retraction speed is determined according to the passenger's human characteristic parameters; controlling the seat to retract at the target retraction speed; and stopping the seat retraction if the retraction termination condition is met. In essence, the method acquires the passenger's human characteristic parameters and determines the baseline retraction speed, collects real-time pressure values exerted by the passenger on the seat, calculates the passenger's resistance to seat retraction when a vehicle collision warning signal is received and the seat is at a large tilt angle, and calculates the target retraction speed by combining the resistance value, a preset resistance threshold, and the baseline retraction speed. This allows for the selection of the most suitable retraction speed based on the characteristics of different passengers and their states during the retraction process, balancing retraction efficiency and safety. Attached Figure Description
[0016] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.
[0017] Figure 1 This is a flowchart illustrating an exemplary embodiment of the flexible callback method for car seats based on human characteristics provided in this application.
[0018] Figure 2 This is a schematic diagram of the structure of a flexible car seat retraction system based on human characteristics provided in an exemplary embodiment of this application. Detailed Implementation
[0019] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.
[0020] Figure 1 This is a flowchart illustrating an exemplary embodiment of the car seat flexible retraction method based on human characteristics provided in this application. Figure 1 As shown, this human-feature-based car seat flexible callback method includes: Step 110: Obtain the passenger's human body characteristic parameters.
[0021] The human body characteristic parameters include height. The seat in this application includes a seat body, a backrest, and a tilt adjustment base. The backrest and seat body are connected by a hinge shaft. The tilt adjustment base contains a lead screw drive assembly to drive the backrest to rotate around the hinge shaft to achieve tilt adjustment. The seat body and backrest are filled with slow-rebound memory foam, which can buffer the instantaneous contact force between the occupant's body and the seat during the adjustment process, assisting in the flexible adjustment effect and reducing body pressure. This application uses pressure sensors, an infrared height measuring instrument, and an interactive display screen to collect human body characteristic parameters in real time (specifically including the pressure exerted by the passenger on the seat, the passenger's height, gender, age, etc.). The infrared height measuring instrument automatically triggers measurement, performs three laser scans, and adjusts the angle and retryes if the difference is >5mm. The measurement distance is 0.3-1.5m; if the distance is outside the range, a prompt to "maintain normal sitting posture" is displayed. The passenger enters their age and gender through the interactive display screen, needs to press for 2 seconds to confirm, and is prompted to re-enter invalid values. In addition, dry electrode EEG sensors, flexible ECG patches, and surface electromyography sensors can be used to collect multimodal physiological perception information of passengers, and cameras can be used to collect image information. The collected human feature parameters are then converted into electrical signals and transmitted to the control system.
[0022] Step 120: Collect the real-time pressure values exerted by the passenger on the seat.
[0023] This application uses a pressure sensor installed on the seat to collect the real-time pressure value exerted by the passenger on the seat, so as to know the reaction force of the seat on the passenger, thereby avoiding excessive reaction force that could cause injury to the passenger.
[0024] Step 130: If a vehicle collision warning signal is received and the seat tilt angle is greater than the preset tilt angle threshold, the resistance value of the passenger's seat retraction is calculated based on the real-time pressure value.
[0025] This application features a collision risk prediction interface that can communicate with the vehicle's ESP (Electronic Stability Program) and airbag controller. Upon receiving a vehicle collision warning signal, it automatically triggers the seat's flexible retraction function to prioritize occupant safety. If the seat's tilt angle exceeds a preset threshold, indicating a large tilt, the system retrieves the baseline retraction speed and resistance threshold for the corresponding occupant type from the database and calculates the passenger's resistance to seat retraction by receiving real-time pressure values.
[0026] Step 140: Calculate the target callback speed of the seat based on the resistance value, the preset resistance threshold, and the baseline callback speed of the seat.
[0027] The baseline retraction speed is determined based on the passenger's anthropometric parameters. This application calculates the target retraction speed of the seat based on the resistance value, a preset resistance threshold, and the baseline retraction speed, in order to avoid passenger injury due to excessively fast seat retraction.
[0028] Step 150: Control the seat to return to the target return speed.
[0029] The actuator of this application includes a servo motor and a reduction mechanism. The servo motor is connected to a lead screw drive assembly. After receiving control commands, the reduction mechanism adjusts the operating speed of the lead screw drive assembly to achieve dynamic control of the backrest retraction speed. When the resistance value is lower than the resistance threshold, the backrest retracts at a reference speed; when the resistance is higher than the resistance threshold, the speed is automatically reduced to a target retraction speed that matches the resistance value.
[0030] Step 160: If the seat meets the callback termination condition, then stop the seat callback.
[0031] The status of the seat and vehicle is monitored in real time during the callback process. If the status of the seat or vehicle meets the callback termination condition, the seat callback is stopped to ensure safety and comfort.
[0032] This application provides a method for flexible seat retraction in automobiles based on human characteristics. The method involves acquiring passenger human characteristic parameters, including height; collecting real-time pressure values exerted by the passenger on the seat; calculating the passenger's resistance to seat retraction based on the real-time pressure values if a vehicle collision warning signal is received and the seat tilt angle exceeds a preset tilt angle threshold; calculating a target retraction speed based on the resistance value, a preset resistance threshold, and a baseline retraction speed, where the baseline retraction speed is determined according to the passenger's human characteristic parameters; controlling the seat to retract at the target retraction speed; and stopping the seat retraction if the retraction termination condition is met. In essence, this method acquires the passenger's human characteristic parameters and determines the baseline retraction speed, collects real-time pressure values exerted by the passenger on the seat, calculates the passenger's resistance to seat retraction when a vehicle collision warning signal is received and the seat is at a large tilt angle, and calculates the target retraction speed by combining the resistance value, a preset resistance threshold, and the baseline retraction speed. This allows for the selection of the most suitable retraction speed based on the characteristics of different passengers and their states during the retraction process, balancing retraction efficiency and safety.
[0033] In one embodiment, multiple flexible pressure sensors are arrayed on the seat; wherein, the specific implementation of step 120 above may be: using multiple flexible pressure sensors to collect multiple real-time pressure values exerted by the passenger on the seat.
[0034] This application installs a flexible pressure sensing array and a torque sensor of a lead screw drive assembly on the inside of the backrest. The flexible pressure sensing array detects the contact resistance between the passenger's back and the backrest in real time, and the torque sensor collects the load torque of the drive assembly, indirectly reflecting the resistance of the passenger's body to the seat's retraction, and transmits the resistance data to the control system in real time.
[0035] In one embodiment, step 130 can be implemented by calculating the average of multiple real-time pressure values to obtain the resistance value.
[0036] This application utilizes a pressure sensor to collect body weight data, with a sampling time of 1 second. Three samples are taken, and the average value is calculated after removing extreme values. If the sample exceeds the range (0-200kg) or the difference is greater than 5kg, the sample is retried twice. If the result is still abnormal, the default value (60kg) is used, and the user is prompted to "adjust sitting posture and retry". A flexible pressure sensor array and a torque sensor continuously collect resistance data, calculate the average resistance value in real time, and perform filtering to obtain the resistance value.
[0037] In one embodiment, step 140 can be implemented as follows: calculate the deceleration ratio based on the resistance value and the resistance threshold; calculate the target callback speed based on the deceleration ratio and the baseline callback speed.
[0038] This application calculates the deceleration ratio based on the real-time resistance value and resistance threshold, and calculates the target reversal speed based on the deceleration ratio and the baseline reversal speed. That is, during the reversal process, the target reversal speed is adjusted according to the real-time resistance value, so as to ensure that the resistance value (the force exerted by the seat on the passenger) is maintained within a reasonable range to avoid injury to the passenger.
[0039] In one embodiment, step 140 can be implemented as follows: if the resistance value is less than or equal to the resistance threshold, the deceleration ratio is zero; if the resistance value is greater than the resistance threshold, the difference between the resistance value and the resistance threshold is calculated; the ratio between the difference and the resistance threshold is calculated to obtain the deceleration ratio.
[0040] If the resistance value is less than or equal to the resistance threshold, it means that the force between the passenger and the seat is not large. In this case, there is no need to reduce speed; the seat can be retracted directly using the baseline retraction speed. If the resistance value is greater than the resistance threshold, it means that the force between the passenger and the seat is large. In this case, it is necessary to reduce speed to reduce the impact on the passenger during seat retraction. In this case, the difference between the resistance value and the resistance threshold can be calculated, and the ratio between this difference and the resistance threshold can be calculated to obtain the speed reduction ratio.
[0041] In one embodiment, step 140 can be implemented as follows: obtaining the risk level of the seat during the callback process; determining the safety redundancy coefficient based on the risk level of the seat; and calculating the target callback speed based on the deceleration ratio, the safety redundancy coefficient, and the baseline callback speed.
[0042] If the ratio between the difference and the resistance threshold suddenly increases (e.g., the occupant suddenly leans forward), the target pullback speed will drop sharply, causing a sudden change in the support force of the backrest on the body, resulting in discomfort. Furthermore, the resistance sensor also has measurement errors; small errors can be amplified into frequent speed fluctuations, affecting system stability. Therefore, this application introduces a safety redundancy coefficient to correct the speed reduction ratio. Specifically, this application determines the corresponding safety redundancy coefficient based on the risk level; for example, the safety redundancy coefficient for low risk is 0.2-0.3, for medium risk it is 0.5-0.6, and for high risk it is 0.8-1.0. The formula for calculating the target pullback speed after introducing the safety redundancy coefficient is: V = V 0×(1- a ×( FF 0) / F 0); where, V For the target callback speed, V 0 is the baseline callback speed. a For safety redundancy coefficient, F This is the resistance value. F 0 represents the resistance threshold.
[0043] Optionally, this application calculates the rate of change and acceleration of the seat tilt angle in real time during the callback process. If the rate of change of the seat tilt angle is greater than a preset rate of change threshold (e.g., ...), the application will determine whether to proceed. If the passenger's body is deemed unbalanced, the baseline return speed is reduced in advance; if the acceleration due to the change in seat tilt angle exceeds a preset acceleration threshold (e.g., ...), then the passenger's body is deemed unbalanced. If the condition is met, the emergency buffer mode is triggered, and the safety redundancy factor is increased to 1.5.
[0044] In one embodiment, step 140 can be implemented by: collecting multiple injury indicators of the collision dummy during the collision test at the optimal tilt angle and the maximum tilt angle; calculating multiple risk enhancement rates of the seat at the maximum tilt angle based on the multiple injury indicators; and determining the risk level of the seat during the recovery process based on the multiple risk enhancement rates.
[0045] Specifically, this application first divides Chinese adults into 8 groups based on their anthropometric dimensions, recruiting 5 qualified participants in each group. Random sampling and matched completion are used, with the anthropometric dimensions corresponding to the 5th, 25th, 50th, 75th, and 95th percentiles, covering typical participants in the driver's and passenger's seats. The specific anthropometric parameters of the participants are shown in Table 1. Table 1. Human body size parameters of different groups of test subjects
[0046] Each participant was seated in the experimental chair, with the tilt angle gradually increased from 80° in increments of 5°, for 3 minutes. Objective and subjective data were then collected. The objective data are detailed in Table 2. Table 2 Objective characteristic parameters of the test subjects
[0047] Supervisory data was obtained by participants filling out the "Real-time Comfort Rating Form," which was broken down into 5 dimensions (each item scored from 1 to 10 points, with a maximum score of 50 points). The "Real-time Comfort Rating Form" is shown in Table 3. Table 3 Real-time comfort rating table for test subjects
[0048] The maximum tilt angle that meets the criteria of "total score ≥ 40 points, lumbar support and hip pressure dimensions both ≥ 7 points, and all physiological indicators within the normal range" is taken as the "individual optimal adaptation angle" for the test subject. Normally, the alpha wave frequency is 8-13 Hz, the beta wave frequency is 14-30 Hz, the heart rate is 60-100 beats / min, the muscle frequency is 3-5 Hz, and the wrist temperature is 34-36℃. During periods of tension, the beta wave frequency increases, the heart rate rises, muscle frequency becomes abnormal, and facial expressions such as frowning and lip biting appear, along with increased skin temperature. If multiple tilt angles meet the criteria (e.g., 44 points for a total score at 100°, 42 points for a total score at 105°), the larger tilt angle is chosen (balancing comfort and the scenario of a large tilt angle).
[0049] The optimal adaptation angles for each of the five participants in each group were determined, and the minimum and maximum values were taken as the optimal adaptation angle range for that group. Based on the optimal adaptation angle range and body size for each group, a crash dummy corresponding to the percentile was selected to ensure that the crash dummy parameters were highly consistent with the group population. The crash dummy corresponding to the body size of each participant was fixed to the seat (with seatbelts fastened to simulate a real sitting posture), and the crash dummy sensor thresholds were set. The seat was then reset to the median of the optimal adaptation angle range for each group (e.g., 110° for Group 1, which is 100-120°) or the large tilt angle control value (130°). The crash simulation platform was started, simulating three crash speeds: 30 km / h, 40 km / h, and 50 km / h. Each speed was repeated three times, and the crash dummy's fixation status (seatbelt tension, sensor connection) was checked before each repetition to ensure consistency. During the crash, data such as the head performance index (HPC) and chest performance index (ThCC) of the crash dummy were collected simultaneously, and the injury index values at different tilt angles were recorded.
[0050] The median absolute deviation (MAD) was used to screen the data from each repeated trial. The calculation formula is as follows: ; Where median represents the median, x i This represents the i-th data value in each set of experimental data. When a certain indicator (such as HPC) deviates from the median by more than one value, it is judged as an outlier, removed, and the test is repeated once. If it is still outlier after the retest, the equipment (such as dummy sensors) is checked and calibrated before testing. The "mean ± standard deviation" is calculated for the valid data as a representative indicator for that set, speed, and tilt angle, as shown in Table 4. Table 4 Representative Indicators of the Collision Test
[0051] For each group, compare the injury indicators between the optimal adaptation angle and a large tilt angle of 130° at the same speed, and calculate the risk increase rate:
[0052] For example: Group 1 (males aged 18-25) in a crash test at a speed of 50 km / h: , , Based on the risk enhancement rate, the risks of large tilt angles are divided into three levels, as shown in Table 5: Table 5 Risk Level Table for Large Inclination Angles
[0053] Optionally, this application can also calculate the maximum tolerable resistance (resistance threshold) of the human body to the rebound based on the back contact pressure and lumbar spine force of the collision dummy at the optimal angle:
[0054] For example: the peak back contact pressure of the Group 1 dummy is 80 kPa, the backrest contact area is 0.06 m², and the safety factor is 0.8. ; For men aged 18-25, with a height of 157-189cm and a weight of 45-101kg, the resistance threshold for seat retraction should be 384N under high-risk conditions.
[0055] In one embodiment, the above-mentioned baseline callback speed can be calculated by inputting the passenger's human body feature parameters into a neural network model to obtain the baseline callback speed; wherein, the neural network model includes an input layer, a hidden layer and an output layer.
[0056] This application uses a three-layer BP neural network to establish an "input feature-baseline callback speed" model. The nonlinear mapping model is as follows: Input layer: 5 neurons (corresponding to) Specifically, it includes: Posture risk characteristics Fusion , , ; Physiological tension characteristics Fusion Frequency ratio, heart rate, muscle frequency, wrist temperature; Discomfort in facial expressions The probability of uncomfortable facial expressions appearing; Contact pressure characteristics Peak pressure; Basic human characteristics Standardized values for height, weight, and age.
[0057] Hidden layer: The formula is as follows: ; in, j Represents the number of hidden layers. For the first j The output of each hidden layer For activation function, The weights from the input layer to the hidden layer. This is the bias term for the hidden layer.
[0058] It's necessary to compare model performance (e.g., evaluation accuracy, computation time) under different numbers of neurons. It's crucial to avoid having too many or too few neurons. Too few neurons will fail to cover the complex features of the data, leading to insufficient feature extraction and low model evaluation accuracy; too many neurons will cause overfitting and computational redundancy, and increase the computational burden on the device. The number of hidden layers should be determined based on a comparison of model performance, finding a balance between accuracy and efficiency. An example is provided here. j =12.
[0059] Output layer: 1 neuron (baseline callback speed) The formula is as follows: ; in; The weights from the hidden layer to the output layer. For the bias term of the output layer, The activation function for the output layer is the Sigmoid function.
[0060] In real-time calculations, the system only needs to input... Automatically output through a pre-trained network .
[0061] In one embodiment, step 160 can be implemented as follows: if the seat tilt angle is adjusted back to a safe angle or the vehicle collision warning signal is released, then the seat adjustment is stopped.
[0062] When the backrest tilt angle is adjusted to a safe angle (preset to 100°±0.5°) or the vehicle collision warning signal is released, the adjustment operation stops. During the seat adjustment process, the seat tilt angle is continuously monitored. When the tilt angle reaches a safe angle, the system automatically stops the adjustment and informs the passenger that the adjustment is complete through sound feedback or light prompts. At the same time, the parameters of this adjustment process are recorded for subsequent safety analysis.
[0063] Figure 2 This is a schematic diagram of the structure of a flexible car seat adjustment system based on human characteristics provided in an exemplary embodiment of this application. Figure 2As shown, the human body feature-based flexible seat retraction system 20 includes: a feature parameter acquisition module 21 for acquiring the human body feature parameters of the passenger, including height; a real-time pressure acquisition module 22 for acquiring the real-time pressure value exerted by the passenger on the seat; a retraction resistance calculation module 23 for calculating the resistance value of the passenger's seat retraction based on the real-time pressure value if a vehicle collision warning signal is received and the seat tilt angle is greater than a preset tilt angle threshold; a retraction speed calculation module 24 for calculating the target retraction speed of the seat retraction based on the resistance value, a preset resistance threshold, and a baseline retraction speed of the seat retraction, wherein the baseline retraction speed is determined according to the passenger's human body feature parameters; a retraction execution control module 25 for controlling the seat to retract at the target retraction speed; and a retraction operation stop module 26 for stopping the seat retraction if the seat meets the retraction termination condition.
[0064] This application provides a flexible car seat adjustment system based on human characteristics. The system acquires passenger human characteristic parameters, including height, through a feature parameter acquisition module 21. A real-time pressure acquisition module 22 collects the real-time pressure value exerted by the passenger on the seat. If a vehicle collision warning signal is received and the seat tilt angle is greater than a preset tilt angle threshold, a adjustment resistance calculation module 23 calculates the passenger's resistance to seat adjustment based on the real-time pressure value. A adjustment speed calculation module 24 calculates the target adjustment speed of the seat based on the resistance value, a preset resistance threshold, and a baseline adjustment speed. The baseline adjustment speed is determined according to the passenger's human characteristic parameters. The number is determined; the callback execution control module 25 controls the seat to callback at the target callback speed; if the seat meets the callback termination condition, the callback operation stop module 26 stops the seat callback; that is, the human body characteristic parameters of the passenger on the seat are obtained and the baseline callback speed is determined, the pressure value of the passenger acting on the seat is collected in real time, and the resistance value of the passenger to the seat callback is calculated when a vehicle collision warning signal is received and the seat is in a large tilt state. The target callback speed of the seat callback is calculated by combining the resistance value, the preset resistance threshold, and the baseline callback speed of the seat callback. Thus, the most suitable callback speed is adopted for different passenger characteristics and passenger status during the callback process, so as to balance the callback efficiency and safety.
[0065] In one embodiment, a plurality of flexible pressure sensors are arrayed on the seat; wherein, the aforementioned real-time pressure acquisition module 22 may be further configured to: use multiple flexible pressure sensors to acquire multiple real-time pressure values exerted by the passenger on the seat.
[0066] In one embodiment, the above-mentioned callback resistance calculation module 23 can be further configured to: calculate the average value of multiple real-time pressure values to obtain a resistance value.
[0067] In one embodiment, the callback speed calculation module 24 can be further configured to: calculate the deceleration ratio based on the resistance value and the resistance threshold; and calculate the target callback speed based on the deceleration ratio and the baseline callback speed.
[0068] In one embodiment, the above-mentioned callback speed calculation module 24 can be further configured as follows: if the resistance value is less than or equal to the resistance threshold, the speed reduction ratio is zero; if the resistance value is greater than the resistance threshold, the difference between the resistance value and the resistance threshold is calculated; the ratio between the difference and the resistance threshold is calculated to obtain the speed reduction ratio.
[0069] In one embodiment, the callback speed calculation module 24 can be further configured to: obtain the risk level of the seat during the callback process; determine the safety redundancy coefficient based on the risk level of the seat; and calculate the target callback speed based on the deceleration ratio, the safety redundancy coefficient, and the baseline callback speed.
[0070] In one embodiment, the callback speed calculation module 24 can be further configured to: collect multiple injury indicators of the collision dummy during the collision test at the optimal tilt angle and the maximum tilt angle respectively; calculate multiple risk enhancement rates of the seat at the maximum tilt angle based on the multiple injury indicators; and determine the risk level of the seat during the callback process based on the multiple risk enhancement rates.
[0071] In one embodiment, the above-mentioned flexible callback system 20 for automobile seats based on human characteristics can be further configured to: input the human characteristic parameters of the passenger into a neural network model to obtain a baseline callback speed; wherein the neural network model includes an input layer, a hidden layer and an output layer.
[0072] In one embodiment, the above-mentioned callback operation stop module 26 can be further configured to stop the seat callback if the seat tilt angle is returned to a safe angle or the vehicle collision warning signal is released.
[0073] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.
[0074] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.
[0075] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.
[0076] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.
[0077] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.
Claims
1. A method for flexible adjustment of car seats based on human body characteristics, characterized in that, include: Obtain the passenger's human body feature parameters; wherein, the human body feature parameters include height; Collect the real-time pressure values exerted by the passenger on the seat; If a vehicle collision warning signal is received and the seat tilt angle is greater than a preset tilt angle threshold, the resistance value of the passenger to the seat recoil is calculated based on the real-time pressure value. Based on the resistance value and the preset resistance threshold, and the baseline return speed of the seat, the target return speed of the seat is calculated; wherein, the baseline return speed is determined according to the passenger's human body characteristic parameters; Control the seat to return at the target return speed; If the seat meets the callback termination condition, then the seat callback is stopped.
2. The automotive seat flexible adjustment method based on human characteristics according to claim 1, characterized in that, The seat is arrayed with multiple flexible pressure sensors; wherein, the collection of real-time pressure values exerted by the passenger on the seat includes: The multiple flexible pressure sensors are used to collect multiple real-time pressure values exerted by the passenger on the seat.
3. The automotive seat flexible adjustment method based on human characteristics according to claim 2, characterized in that, The calculation of the passenger's resistance to the seat recoil based on the real-time pressure value includes: The resistance value is obtained by calculating the average of the multiple real-time pressure values.
4. The automotive seat flexible adjustment method based on human body characteristics according to claim 1, characterized in that, The calculation of the target callback speed of the seat, based on the resistance value, a preset resistance threshold, and the baseline callback speed of the seat callback, includes: Based on the resistance value and the resistance threshold, the deceleration ratio is calculated; The target callback speed is calculated based on the reduction ratio and the baseline callback speed.
5. The automotive seat flexible adjustment method based on human characteristics according to claim 4, characterized in that, The calculation of the deceleration ratio based on the resistance value and the resistance threshold includes: If the resistance value is less than or equal to the resistance threshold, then the deceleration ratio is zero; If the resistance value is greater than the resistance threshold, then the difference between the resistance value and the resistance threshold is calculated; The ratio between the difference and the resistance threshold is calculated to obtain the deceleration ratio.
6. The automotive seat flexible adjustment method based on human characteristics according to claim 4, characterized in that, The calculation of the target callback speed based on the reduction ratio and the baseline callback speed includes: Obtain the risk level of the seat during the callback process; Based on the risk level of the seats, a safety redundancy coefficient is determined; The target callback speed is calculated based on the reduction ratio, the safety redundancy coefficient, and the baseline callback speed.
7. The automotive seat flexible adjustment method based on human characteristics according to claim 6, characterized in that, The risk level of the seat during the callback process includes: Multiple injury indicators were collected from the collision dummies during the collision experiments at the optimal tilt angle and the maximum tilt angle, respectively. Based on the aforementioned multiple injury indicators, the risk enhancement rates of the seat at the maximum tilt angle are calculated. Based on the multiple risk enhancement rates, the risk level of the seat during the callback process is determined.
8. The automotive seat flexible adjustment method based on human body characteristics according to claim 1, characterized in that, The method for calculating the baseline callback speed includes: The passenger's human feature parameters are input into a neural network model to obtain the baseline callback speed; wherein, the neural network model includes an input layer, a hidden layer, and an output layer.
9. The automotive seat flexible adjustment method based on human characteristics according to claim 1, characterized in that, The step of stopping the seat callback if the seat meets the callback termination condition includes: If the seat tilt angle is adjusted to a safe angle or the vehicle collision warning signal is released, the seat adjustment will stop.
10. A flexible adjustment system for car seats based on human body characteristics, characterized in that, include: The feature parameter acquisition module is used to acquire the human body feature parameters of passengers; wherein, the human body feature parameters include height; The real-time pressure acquisition module is used to acquire the real-time pressure value exerted by the passenger on the seat; The back-off resistance calculation module is used to calculate the resistance value of the passenger to the back-off of the seat based on the real-time pressure value if a vehicle collision warning signal is received and the tilt angle of the seat is greater than a preset tilt angle threshold. The retraction speed calculation module is used to calculate the target retraction speed of the seat retraction based on the resistance value, a preset resistance threshold, and the baseline retraction speed of the seat retraction; wherein the baseline retraction speed is determined according to the passenger's human body characteristic parameters; The callback execution control module is used to control the seat to callback according to the target callback speed; The callback operation stop module is used to stop the seat callback if the seat meets the callback termination condition.