A dynamic seat belt control method and system

Abstract

The application discloses a dynamic seat safety belt control method and system, and belongs to the technical field of programs and adaptive control. The method comprises the following steps: collecting the translational acceleration, angular velocity, Euler angle, passenger body weight and actual restraint force of the seat in real time; solving the dynamic differential equation m·dx / dt 2 x / dt 2 +c·dx / dt+F·cosγ=‑m·(a·e), calculating the predicted displacement of the passenger by adopting the fourth-order Runge-Kutta method; determining the target restraint force according to the relationship between the predicted displacement and a safety threshold, and performing individual calibration according to the body weight; and generating a control signal by a PID controller to drive the electric retractor to continuously adjust the safety belt restraint force, so that a closed-loop adaptive control is formed. The application solves the problems of lagging response of the safety belt, mismatch between the restraint force and dynamic demand, lack of closed-loop control and poor applicability to different body types in the prior art, and improves the safety and comfort of the dynamic seat.

Description

Technical Field

[0001] This invention belongs to the field of program and adaptive control technology, and particularly relates to a dynamic seat belt control method and system. Background Technology

[0002] Motion seats (including 4D motion seats, VR motion seats, etc.) are immersive experience devices widely used in the cultural tourism industry and large theme parks in recent years. Through multiple degrees of freedom (including translational, pitch, roll, yaw, and other rotational degrees of freedom), combined with film or VR content, they provide users with an immersive experience. In such scenarios of vigorous movement, the restraint performance of the seat belt is directly related to the user's personal safety.

[0003] Motion seats are typically installed in 4D cinemas, VR experience centers, or theme parks. When performing movie scenes, the seats need to generate large-scale, violent movements in multiple degrees of freedom, such as simulating the sudden drop of a roller coaster, the high-speed cornering of a race car, or the roll of an aircraft. Their instantaneous linear acceleration can reach 0.8g to 1.5g, and angular velocity can exceed 100° / s. However, the seatbelt systems used in most motion seats on the market today are directly adopted or simply modified from the fixed, manually adjustable seatbelts of ordinary amusement park rides. The restraint force of these seatbelts is manually set once when the occupant sits down and cannot be changed during operation; essentially, it is an "open-loop" passive restraint device. The underlying reasons for this deficiency include: First, the design concept of traditional seatbelts originates from the automotive industry. Their control strategies are mainly designed for short-term impact events such as emergency braking or collisions during vehicle operation. They rely on the locking mechanism of mechanical sensing components to achieve two discrete states of "locked" or "released" seatbelts, and do not require dynamic response to multi-degree-of-freedom, continuously changing motion postures. Secondly, the development of motion seat control systems has long focused on servo control of the motion platform and video synchronization, neglecting the necessity for seat belts, as the execution unit directly related to occupant safety, to be linked with posture in real time, and lacking predictive modeling of occupant inertial displacement. Furthermore, existing technologies generally lack sensor architectures and control algorithms capable of real-time acquisition of seat acceleration and posture angles, forming a closed-loop feedback with the seat belt retractor. This results in seat belts being unable to actively adjust restraint force according to the magnitude and direction of motion acceleration, leading to technical problems such as response lag, severe mismatch between restraint force and dynamic requirements, and a complete lack of continuous closed-loop adaptive capability. These issues significantly restrict the safety and comfort of motion seats in high-speed, high-acceleration scenarios.

[0004] In summary, existing seat belt technologies suffer from technical defects such as delayed restraint force adjustment response, inability to match the dynamic needs of seat movement posture, and lack of continuous closed-loop adaptive control. Summary of the Invention

[0005] This invention aims to provide a method and system for controlling seat belts in dynamic seating configurations. Based on the real-time motion posture of the seat, it utilizes a posture-displacement mapping model and a PID feedback control algorithm to achieve continuous, dynamic, and personalized adjustment of the seat belt restraint force. This addresses technical problems in existing technologies such as seat belt response lag, mismatch between restraint force and dynamic requirements, lack of closed-loop adaptive control, and poor applicability to different body types. The specific solution is as follows: A dynamic seat belt control method includes the following steps: 1) Real-time acquisition of seat posture parameters and occupant vital signs parameters; 2) Based on attitude parameters and occupant vital signs parameters, calculate the predicted displacement of the occupant relative to the seatbelt restraint point in the current seat posture; 3) Set the corresponding target restraint force based on whether the predicted displacement exceeds the safe displacement threshold; 4) Execute PID closed-loop control based on the error between the target binding force and the actual binding force.

[0006] Furthermore, step 5) is included between steps 3) and 4) to personalize the target restraint force based on the occupant's weight W.

[0007] Furthermore, the seat's attitude parameters include translational acceleration vector a(t), angular velocity vector ω(t), and Euler angles (φ(t), θ(t), ψ(t)); the occupant's vital signs parameters include occupant weight W and restraint force F(t) acting on the occupant's seatbelt restraint point.

[0008] Furthermore, the posture parameters of the seat are collected by an attitude sensor array, which includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The real-time posture parameters of the seat body in the global coordinate system are output through a complementary filtering data fusion algorithm. The restraint force F(t) of the seat belt acting on the occupant's seat belt restraint point is measured in real time by a tension sensor. The pressure distribution data of each area of ​​the seat cushion is collected by a distributed pressure sensor array, and the real-time estimated value of the occupant's weight W is obtained through weighted fusion calculation.

[0009] Further, step 2) includes: 2.1) The collected real-time translational acceleration a(t), seat belt direction angle γ(t), and occupant weight W (converted from occupant mass m=W / g, where g is the acceleration due to gravity, taken as 9.8 m / s²) 2 Substituting into the dynamic differential equation: m·(d 2 x(t) / dt 2 )+c·(dx(t) / dt)+F(t)·cosγ(t)=-m·(a(t)·e); 2.2) The fourth-order Runge-Kutta method is used to numerically integrate and solve the differential equation to obtain the predicted displacement x(t) of the occupant relative to the seat belt constraint point in the current seat posture; Where m is the mass of the crew, d 2 x(t) / dt 2 Let be the acceleration of the occupant relative to the seatbelt restraint point, c be the damping coefficient, dx(t) / dt be the velocity of the occupant relative to the seatbelt restraint point, c·(dx / dt) represent the damping force proportional to the velocity, F(t) be the actual restraining force of the seatbelt currently acting on the occupant's seatbelt restraint point, γ(t) be the angle between the seatbelt webbing direction and the direction of the human body's movement tendency, cosγ(t) be the cosine of this angle, F(t)·cosγ(t) be the effective component of the seatbelt restraining force in the direction of the human body's movement tendency, a(t) be the translational acceleration vector of the seat, and e be the unit vector in the direction of the human body's movement tendency.

[0010] Further, step 3) includes: 3.1) When x(t) ≤ x safe At that time, the target binding force F target Set as minimum holding force F min ; 3.2) When x(t) > x safe At that time, the target binding force is set to: F target (t)=min(F max ,[m·a eff (t)·x(t)] / [x max -x(t)]; Where x safe For the safe displacement threshold, a eff (t) represents the effective component of the seat translational acceleration in the direction of the human body's motion tendency, x max F is the maximum allowable displacement threshold. max This is the maximum permissible restraint force of the seatbelt.

[0011] Furthermore, the formula for personalized calibration based on the occupant's weight W in step 5) is: F target_W (t)=F target (t)·(α+β·(W / W ref )); where F target_W (t) represents the final target restraint force after individualized weight calibration, F target (t) represents the uncalibrated target restraint force calculated based on the predicted displacement, α is the basic weighting coefficient, and β is the weight adjustment coefficient α+β=1, W ref W is the reference weight, and W is the real-time estimated occupant weight.

[0012] Further, step 4) includes: calculating the error signal e(t) = F target_W (t)-F(t), which is input into the PID controller, and the PID controller operates according to the formula u(t)=K p e(t)+K i ∫e(τ)dτ+K d The control output u(t) is obtained by calculating de(t) / dt; where K p K i K d These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. τ is the integral variable from 0 to the current time t, and u(t) is the control signal u(t) of the seat belt retractor drive motor.

[0013] Furthermore, the u(t) signal output by the PID controller is converted into a corresponding PWM waveform by the motor driver, controlling the rotation direction, speed, and amount of rotation of the drive motor, causing the retractor to perform retraction or release operations on the seat belt webbing until the current actual restraint force F(t) matches the target restraint force F after individual weight calibration. target_W The error between (t) and (t) is within the allowable range.

[0014] A dynamic seat belt control system includes: The first module collects the seat's posture parameters and the occupant's vital signs in real time. The second module calculates the predicted displacement of the occupant relative to the seat belt constraint point in the current seat posture, based on attitude parameters and occupant vital signs parameters. The third module sets the corresponding target restraint force based on whether the predicted displacement exceeds the safe displacement threshold. The fourth module executes PID closed-loop control based on the error between the target binding force and the actual binding force.

[0015] Compared with the prior art, the present invention has at least one of the following technical effects: 1. This invention achieves forward-looking estimation of occupant inertial displacement by calculating the predicted displacement of the occupant relative to the seatbelt restraint point in the current seat posture based on attitude parameters and occupant vital signs. Specifically, when the attitude acquisition unit detects seat acceleration, the control system immediately calculates the predicted displacement based on the magnitude and direction of the acceleration, rather than waiting for the occupant to actually displace before triggering the seatbelt locking. This attitude-based feedforward prediction mechanism allows the seatbelt to actively tighten the moment inertial force is generated, fundamentally overcoming the lag defect of traditional fixed seatbelts that "displace first, then lock." It solves the problem of delayed restraint force adjustment response in existing seatbelt technologies.

[0016] 2. This invention sets a corresponding target restraint force based on whether the predicted displacement exceeds a safe displacement threshold. Compared to existing technologies where seat belts can only provide a constant restraint force or only lock in a discrete state, this method achieves continuous dynamic matching between the restraint force and the seat's motion posture. This fundamentally solves the contradiction of fixed seat belts being either too tight and compressing the occupant, or too loose and causing slippage. While ensuring safety under high acceleration conditions, it avoids unnecessary compression under low acceleration conditions, significantly improving ride comfort. It also overcomes the deficiency of mismatch between restraint force and dynamic requirements in existing technologies.

[0017] 3. The PID closed-loop control of this invention transforms the seatbelt from an open-loop passive restraint device into an intelligent safety system capable of actively adjusting based on seat movement and real-time feedback. The integral term eliminates steady-state error, ensuring the actual restraint force accurately tracks the target value; the derivative term suppresses overshoot and oscillation, smoothing the restraint force changes. Compared to existing technologies that can only control the locked / released states, this method achieves continuous, smooth, and error-free closed-loop adjustment of the seatbelt restraint force, improving the response speed to the millisecond level, and automatically adapting to different motion modes (rapid acceleration, rollover, weightlessness, etc.). It overcomes the deficiency of existing technologies in lacking continuous closed-loop adaptive control.

[0018] 4. This invention's personalized calibration ensures that occupants of different body types (from teenagers weighing 50 kg to adults weighing over 90 kg) receive appropriate safety protection. Smaller occupants will not experience breathing difficulties or sternal pain due to excessive restraint force; larger occupants will not slip or displace excessively during strenuous activity due to insufficient restraint force. This achieves intelligent "one-person-one-policy" adaptation of seatbelt restraint force, improving the universality and user experience of the dynamic seat for different groups. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a dynamic seat belt control method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a dynamic seat belt control system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] See appendix Figure 1 This invention discloses a method for controlling the seat belt of a dynamic seat, comprising the following steps: S1. The attitude acquisition unit acquires the translational acceleration vector a(t), angular velocity vector ω(t), and Euler angles (φ(t), θ(t), ψ(t)) of the seat body in the global coordinate system at a sampling frequency of not less than 100 Hz. Simultaneously, the distributed pressure sensor array of the human body characteristic detection unit acquires the pressure distribution data of each area of ​​the seat cushion, and obtains the real-time estimate of the occupant's weight W through weighted fusion calculation; the tension sensor acquires the current actual restraint force F(t) in real time.

[0028] The attitude acquisition unit includes an attitude sensor array fixedly mounted on the seat body. This attitude sensor array consists of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, and outputs the real-time attitude parameters of the seat body in the global coordinate system through a complementary filtering data fusion algorithm. The real-time attitude parameters include the translational acceleration vector 'a', the angular velocity vector 'ω', and the Euler angles (φ(t), θ(t), ψ(t)). The sampling frequency of the attitude acquisition unit is no less than 100 Hz.

[0029] The human body characteristic detection unit includes a tension sensor mounted on the seatbelt webbing and a distributed pressure sensor array mounted inside the seat cushion. The tension sensor measures the restraint force F(t) exerted by the seatbelt on the occupant's chest and abdomen in real time. The distributed pressure sensor array is distributed in a matrix along the surface of the seat cushion and outputs a real-time estimate of the occupant's weight W for calibration of personalized restraint force thresholds.

[0030] S2. Combine the real-time translational acceleration a(t), seatbelt direction angle γ(t), and occupant weight W (converted from occupant mass m=W / g, where g is the acceleration due to gravity, taken as 9.8 m / s²) collected in step S1. 2 Substituting into the dynamic differential equation: m·(d 2 x(t) / dt 2 )+c·(dx(t) / dt)+F(t)·cosγ(t)=-m·(a(t)·e); The differential equation was solved by numerical integration using the fourth-order Runge-Kutta method to obtain the predicted displacement x(t) of the occupant relative to the seatbelt constraint point in the current seat posture.

[0031] The mathematical derivation of the dynamic differential equation is as follows: I. Establishing a coordinate system and defining variables Establish a global inertial coordinate system OXYZ (fixed to the ground). Define the following variables: r s (t): The position vector of the seat belt restraint point on the seat (e.g., the retractor exit position, i.e., the reference point where the seat belt exerts tension on the occupant).

[0032] rp (t): The position vector of the point of contact between the seat belt and the occupant's chest (usually the position where the seat belt contacts the occupant's body).

[0033] a(t): The translational acceleration vector of the seat, directly measured by the triaxial accelerometer in the attitude acquisition unit, a(t) = d 2 r s (t) / dt 2 .

[0034] e(t): A unit vector representing the direction of the human body's tendency to move. When the seat accelerates, the occupant will move in the opposite direction of the acceleration due to inertia relative to the seat. Therefore, e(t) = -a(t) / |a(t)| is defined. If |a| = 0 (uniform motion or stationary), then e is taken as the direction of the previous moment.

[0035] x(t): The scalar displacement (i.e., predicted displacement) of the occupant relative to the seatbelt restraint point along the e(t) direction, defined as x(t) = [r p (t) -r s [(t)]·e(t). At the initial moment (when the seat is stationary and the seat belt has no additional pretension), x(0) = 0.

[0036] II. Relationship between absolute acceleration and relative acceleration of occupants The absolute acceleration of the occupants, a body =d 2 r p (t) / dt 2 r p (t)=r s Substituting (t) + x(t)e(t) into the equation and taking the second derivative with respect to time: First, take the first derivative: dr p (t) / dt=dr s (t) / dt+(dx(t) / dt)e(t)+x(t)(de(t) / dt); then find the second derivative: d 2 r p (t) / dt 2 =d 2 r s (t) / dt 2 +(d 2 x(t) / dt 2 )e(t)+2(dx(t) / dt)(de(t) / dt)+x(t)(d 2 e(t) / dt 2 ). Where d 2 r s (t) / dt 2 =a(t) is the seat acceleration. In the above formula, 2(dx / dt)(de / dt) is the Coriolis acceleration term, x(d2 e / dt 2 ) represents the centripetal acceleration term (because de / dt is perpendicular to e, d 2 e / dt 2 (Includes the component opposite to e).

[0037] In practical applications of dynamic seating, the frequency of seat motion changes is typically below 5Hz, resulting in a small rate of change of e(t). Simultaneously, the occupant's velocity dx / dt relative to the seat is limited by the seatbelt and remains within a finite range (typically <2m / s). Therefore, the Coriolis and centripetal terms are relatively independent of the principal terms (a(t) and dt). 2 x / dt 2 The absolute acceleration can be ignored. Furthermore, we will only concern ourselves with the projection of the absolute acceleration onto the direction of e, while the Coriolis acceleration is perpendicular to e (because de / dt⊥e), and its dot product is zero; the projection of the centripetal acceleration onto e is -x|de / dt|. 2 This value is very small and can be ignored. Therefore, we have an approximate relationship: a body e(t) = a(t)·e(t) + d 2 x(t) / dt 2 ①. That is, the component of the occupant's absolute acceleration in the direction of the motion trend ≈ the component of the seat acceleration in that direction + the relative acceleration of the occupant.

[0038] III. The net force acting on the occupants in the direction of their tendency to move. In an inertial frame of reference, applying Newton's second law to the occupants along the e-direction: the component of the net force in the e-direction = occupant mass × the component of absolute acceleration in the e-direction, i.e., ∑F e =m·(a body ·e(t))②. The forces acting on the occupants along the direction of e (or the opposite direction) include the following: 1. The effective component of the seatbelt restraint force. The magnitude of the tension force exerted by the seatbelt on the occupant is F(t) (measured in real time by a tension sensor installed on the seatbelt webbing), and its direction is along the direction of the seatbelt webbing. The angle between this direction and the direction of the occupant's tendency to move, e(t), is γ(t) (calculated using the geometric parameters of the seatbelt anchor points and the real-time Euler angles of the seat). Therefore, the component of the seatbelt restraint force in the e(t) direction is -F(t)cosγ(t). The negative sign indicates that the direction of this force is opposite to that of e(t), i.e., it opposes the relative movement of the occupant.

[0039] 2. Damping Force. Sliding friction exists between the occupant and the seat contact surfaces (seat cushion, backrest). Simultaneously, internal friction occurs as the soft tissues of the chest and abdomen deform under the pressure of the seatbelt. These dissipation effects can be equivalently represented as a viscous damping force proportional to the relative velocity, with its direction opposite to the direction of relative motion, i.e., -c(dx(t) / dt), where c is the equivalent viscous damping coefficient (unit: kg / s). This damping force is not directly derived from Newton's laws but is an empirical term introduced based on experimental observations to describe energy dissipation, making the model more consistent with actual motion response. The value of c is determined through calibration experiments (standard dummy sinusoidal excitation, least squares fitting).

[0040] 3. The normal support force and other forces exerted by the seat on the occupant are perpendicular to the e-direction or their projection can be ignored. In addition, the component of gravity in the horizontal direction of motion (when the seat is tilted) can be compensated by the attitude angle, but in the high-speed motion scenario of the dynamic seat, inertial force dominates, and the influence of gravity can be incorporated into the initial conditions.

[0041] Therefore, the resultant force along the e direction is: ∑F e =-F(t)cosγ(t)-c(dx(t) / dt)③.

[0042] IV. Establishing Differential Equations Substituting equations (1) and (3) into Newton's second law equation (2), we get m·[a(t)·e(t)+d 2 x(t) / dt 2 ]=-F(t)cosγ(t)-c(dx(t) / dt), which contains d 2 x / dt 2 Move the term to the right side of the equation and the remaining terms to the left side, resulting in m·(d 2 x(t) / dt 2 )+c·(dx(t) / dt)+F(t)·cosγ(t)=-m·(a(t)·e)④.

[0043] The predicted displacement x(t) is defined as the linear displacement of the occupant's body (usually represented by the chest restraint point of the seatbelt) relative to its initial rest position, measured along the direction of the occupant's tendency to move (i.e., the direction indicated by the unit vector e). The unit is meters (m). x(t) is a time-varying scalar, with its positive direction aligned with the direction of the occupant's tendency to move e. x(t) reflects the degree to which the occupant attempts to leave the seat restraints under the influence of inertial forces. The predicted displacement x(t) obtained by solving this differential equation is the core basis for subsequent calculations of the target restraint force.

[0044] The seatbelt restraint point is the location where the seatbelt webbing makes direct contact with the occupant's body and applies restraining force. It is typically located at the seatbelt contact points in the occupant's chest and shoulder areas, as well as at the seatbelt's anchor points on the seat frame (including the retractor exit and buckle). In the dynamic differential equations, the predicted displacement x(t) is defined as the linear displacement of the occupant's body (specifically represented by the contact point between the seatbelt and the occupant's chest) relative to this restraint point in the direction of the body's tendency to move. Initially (when the seat is stationary and the seatbelt is at its minimum holding force), the occupant's chest and the seatbelt restraint point maintain a relatively static equilibrium position, which serves as the zero point of displacement x. When the seat accelerates, the occupant moves relative to this restraint point due to inertia, and x(t) describes the displacement of this relative motion. Therefore, the seatbelt restraint point is essentially a reference point used to quantify the degree of deviation of the occupant's body from the seatbelt restraint system.

[0045] Where m is the mass of the occupant, in kilograms (kg). This is calculated by dividing the occupant's weight W by the gravitational acceleration g (9.8 m / s²). 2 This can be converted to m = W / g. 2 x(t) / dt 2 : The acceleration of the occupant relative to the seatbelt restraint point, expressed in meters per second squared (m / s²). 2 Where x is the displacement (m) of the occupant relative to the seatbelt restraint point along the direction of human motion, and d 2 x(t) / dt 2 This is the second derivative of the displacement with respect to time t. m·(d 2 x(t) / dt 2 The product of the occupant's mass and their relative acceleration represents the inertial force that causes the occupant to "want" to continue moving due to inertia. This term is derived from Newton's second law, F=m·a.

[0046] c is the damping coefficient, measured in kilograms per second (kg / s). Its physical meaning is the equivalent viscous damping between the occupant and the seat contact surface, reflecting the frictional dissipation effect between the seat surface material and the occupant's clothing, as well as the internal friction characteristics of the human soft tissue during force application. This coefficient is determined through pre-calibration experiments (e.g., using a standard dummy for sinusoidal excitation testing, combined with least-squares fitting). The specific procedure is as follows: a standard mass dummy (e.g., a dummy conforming to the physical characteristics of a 50th percentile adult male, with a weight W=70kg, corresponding to a mass m=W / g≈71.4kg) is placed on the motion seat and fastened with a seatbelt. The motion control system applies a set of typical acceleration excitations (e.g., step or sinusoidal sweep signals with amplitudes of 0.5g, 1.0g, and 1.2g, covering a frequency range of 0.5Hz~5Hz), simultaneously acquiring the seat translational acceleration a(t), the actual restraint force F(t), and the measured displacement x of the occupant relative to the seatbelt restraint point. measured (t); Substitute the measured data into the dynamic differential equation. , with x measured (t) and the predicted displacement x obtained from the numerical solution of the equation simulated The objective is to minimize the root mean square error between (t) and (t), and the optimal damping coefficient c is obtained by back-reasoning using least squares or nonlinear optimization methods. opt After repeated experimental calibration, the typical c-value ranges from 150 to 350 kg / s (for a standard 70kg dummy and conventional seat contact surface materials). This value is stored as a configurable parameter in the actual control system, and a one-time calibration before leaving the factory can cover the same model of seat without the need for online adjustment.

[0047] dx(t) / dt: The velocity of the occupant relative to the seatbelt restraint point, in meters per second (m / s). It is the first derivative of displacement x with respect to time t. c·(dx / dt) represents the overall damping force proportional to the velocity, indicating the frictional resistance and soft tissue dissipation force experienced by the occupant during motion. This term originates from the viscous damping model in classical mechanics, and its function is to gradually dissipate the system's energy, preventing infinite oscillations in the displacement response.

[0048] F(t) represents the actual restraining force exerted by the seatbelt on the occupant's chest and abdomen, measured in Newtons (N). It is measured in real-time by a tension sensor installed on the seatbelt webbing. γ(t) represents the angle between the seatbelt webbing direction and the direction of the human body's movement tendency, measured in radians (rad). The direction of the human body's movement tendency is represented by the unit vector e, and γ is calculated using the geometric parameters of the seatbelt anchor points and the real-time Euler angles of the seat. cosγ(t) is the cosine of this angle, ranging from 0 to 1. When γ = 0° (the seatbelt direction is completely aligned with the direction of the human body's movement tendency), cosγ = 1, and the entire restraining force of the seatbelt is used to restrain the occupant; when γ = 90° (the two are perpendicular), cosγ = 0, and the component of the seatbelt restraining force in the direction of the movement tendency is zero and completely ineffective. F(t)·cosγ(t) represents the effective component of the seatbelt restraining force in the direction of the human body's movement tendency. It represents the actual restraining capacity of the seatbelt against occupant displacement.

[0049] The right-hand side 'm' is the same as the first term on the left, representing the occupant's mass (kg). 'a' is the translational acceleration vector of the seat, expressed in meters per second squared (m / s²). 2 The acceleration is measured in real time by the triaxial accelerometer in the attitude acquisition unit. e: Unit vector (dimensionless) representing the direction of the human body's motion tendency. Its direction is taken as the opposite direction to the seat acceleration vector a, i.e., e = -a / |a|, representing the direction of the occupant's motion tendency relative to the seat under inertia. a·e: Dot product of vector a and unit vector e, i.e., the projected component of the seat acceleration in the direction of the human body's motion tendency, in m / s². 2 Since the direction of e is opposite to that of a, the dot product is negative, and its absolute value is a. eff =|a·e| is the effective acceleration component. -m·(a(t)·e) represents the component of the inertial force exerted on the occupant by the seat motion in the direction of the occupant's tendency to move. The negative sign indicates that the direction of this inertial force is opposite to the direction of the occupant's tendency to move (i.e., the inertial force is the cause of the occupant's motion relative to the seat). This term is the "external excitation source" driving the entire system.

[0050] The dynamic differential equation model is constructed based on a simplified model of human rigid body dynamics, abstracting the occupant's torso as a rigid body with mass *m* and rotational inertia, and treating the seatbelt constraint point as a fixed boundary condition. The equation also defines a unit vector *e* representing the direction of the occupant's motion tendency, which is opposite to the direction of the seat acceleration vector *a(t)*, i.e., *e = -a(t) / |a(t)|, representing the occupant's motion tendency direction relative to the seat under inertia. The angle γ(t) between the seatbelt and the occupant's motion tendency direction is calculated using the geometric relationship between the seatbelt fixing point, the tension sensor mounting point, and the occupant's chest position, combined with the real-time Euler angles of the seat. Specifically, the coordinates of the seatbelt fixing point and the occupant's chest constraint point are pre-calibrated in the seat's coordinate system. Based on the current Euler angles, these coordinates are transformed to the global coordinate system, and the angle between the seatbelt webbing direction vector and the unit vector *e* is calculated.

[0051] The differential equation of this dynamics (equation ④) can be traced back to the differential equation of forced vibration with a single degree of freedom in classical mechanics. Its standard form is m·x′′+c·x′+k·x=F ext (t). In this invention, the effective component of the seatbelt restraint force F·cosγ is analogous to the elastic restoring force k·x (where k is the equivalent stiffness, and F·cosγ varies nonlinearly with displacement x). The right-hand term -m·(a·e) is analogous to the external excitation force F. ext (t). The displacement x of the occupant relative to the seatbelt restraint point is considered as the displacement output of the vibration system. By solving this equation in real time, the occupant's displacement at future moments can be predicted based on the real-time acceleration of the seat, thus providing a dynamic target restraint force for the PID controller. The establishment of this mapping relationship allows the seatbelt to "predict" the possible displacement of the occupant and actively adjust the restraint force to suppress it, solving the problem of seatbelt response lag in the prior art.

[0052] The fourth-order Runge-Kutta method (RK4) is used to solve the dynamic differential equation m·d 2 x / dt 2 The detailed process of +c·dx / dt+F·cosγ=-m·(a·e) is as follows: Step 1: Reduce the second-order equation to a system of first-order equations.

[0053] Let the state variables be displacement x(t) and velocity v = dx / dt, then the original equation becomes: Let v = dx / dt, then dv / dt = [-c·vF·cosγ-m·(a·e)] / m, where m and c are constants, and F(t), γ(t), and a(t)·e are obtained by real-time measurement from the sensor.

[0054] Step 2: Set initial conditions and step size.

[0055] Take the initial time t0=0, the initial displacement x(0)=0 (the occupant has no offset relative to the seat belt constraint point), and the initial velocity v(0)=0. Choose a fixed step size h=Δt (e.g., 0.005 seconds or 0.01 seconds) to ensure that the sampling frequency satisfies the Nyquist condition and guarantees numerical stability.

[0056] Step 3: Perform RK4 iterations within each time step.

[0057] Assuming the current time t is known n state (x) n ,v n and the input value F at that moment. n γ n (a·e) n For from t n to t n+1 = t n The increment of +h is calculated using the following steps: 1) Calculate k1 (the slope at the starting point of this step): k1 x =v n ; k1 v =f(t n ,x n ,v n )=[-c·v n -F n ·cosγ n -m·(a·e) n ] / m.

[0058] 2) Calculate k2 (midpoint of half-step, using k1 to predict the state): x mid =x n +(h / 2)·k1 x ; v mid =v n +(h / 2)·k1 v ; Get t n The input value F at time +h / 2 mid γ mid (a·e) mid (This can be obtained through linear interpolation or zero-order preservation), then: k2 x =v mid ; k2 v =f(t n +h / 2,x mid ,v mid )=[-c·v mid -Fmid ·cosγ mid -m·(a·e) mid ] / m.

[0059] 3) Calculate k3 (the midpoint of another half-step, using k2 to predict the state) x mid2 =x n +(h / 2)·k2 x ; v mid2 =v n +(h / 2)·k2 v ; Using the same t n Input at time +h / 2 (F mid γ mid (a·e) mid Calculate: k3 x =v mid2 ; k3 v =f(t n +h / 2,x mid2 ,v mid2 )=[-c·v mid2 -F mid ·cosγ mid -m·(a·e) mid ] / m.

[0060] 4) Calculate k4 (the endpoint of the full step, using k3 to predict the state) x end =x n +h·k3 x ; v end =v n +h·k3 v ; Get t n+1 Input value F at time 1 n+1 γ n+1 (a·e) n+1 Calculate: k4 x =v end k4 v =f(t n +h,x end ,v end )=[-c·v end -F n+1 ·cosγ n+1 -m·(a·e) n+1 ] / m.

[0061] 5) Take the weighted average to obtain the state at the next time step. x n+1 =x n +(h / 6)·(k1 x +2k2 x +2k3 x +k4 x );v n+1 =v n +(h / 6)·(k1 v +2k2 v +2k3 v +k4 v ).

[0062] Step 4: Iterative progress.

[0063] The updated (x) n+1 , v n+1 Using this as the initial value for the next moment, repeat step three until all necessary time points have been calculated. The final x(t) sequence is the predicted displacement of the occupant relative to the seatbelt restraint point. The local truncation error of this method is O(h). 5 The overall accuracy is O(h) 4 In the dynamic seat control system, selecting h=0.005s~0.01s allows for real-time solution completion within an update cycle of over 100Hz, meeting the requirements of engineering applications.

[0064] Where x is the predicted displacement (m) of the occupant relative to the seatbelt restraint point, v = dx / dt is the relative velocity (m / s), and t n At the current discrete time, h = Δt is the fixed calculation step size (s), and k1 x k2 x k3 x k4 x These represent the slopes (m / s) estimated in the four substeps of the displacement differential equation, k1, and k2, respectively. v k2 v k3 v k4 v These are the slopes (m / s) estimated in the four substeps of the velocity differential equation. 2 ), x mid v mid x represents the estimated displacement and velocity at the midpoint of the half-step. end v end F is the estimated displacement and velocity at the end of the full step. n γ n (a·e) n They are the current time t. n The measured seat belt restraint force (N), the angle between the seat belt and the direction of motion (rad), and the projection of the seat acceleration in the direction of motion (m / s²) are all measured.2 ), F mid γ mid (a·e) mid For t n The corresponding input value at time +h / 2 (obtained through linear interpolation), F n+1 γ n+1 (a·e) n+1 For t n+1 The corresponding input values ​​at time t, where c is the equivalent viscous damping coefficient (kg / s) and m is the occupant mass (kg).

[0065] S3. Based on the predicted displacement x(t) obtained in step S2, determine the target restraint force F of the seat belt according to the following rules. target (t): 1) When x(t) ≤ x safe At that time, the target binding force F target Set as minimum holding force F min .

[0066] x safe The safety displacement threshold, ranging from 10mm to 20mm, means that when the predicted displacement does not exceed this value, the occupant's body will not come into contact with surrounding structures (such as armrests, seat back side wings, etc.) during seat movement, nor will it generate impact loads on the spine. Therefore, the seat belt only needs to provide the minimum restraint force to maintain the occupant's normal sitting posture. Minimum holding force F min The value range is 30N to 50N. This value is just enough to keep the seat belt webbing in contact with the human body without loosening, while avoiding a feeling of pressure. It is the optimal range determined after conducting a subjective comfort rating experiment on 30 test subjects of different body types.

[0067] 2) When x(t) > x safe At that time, the target binding force is set to: F target (t)=min(F max ,[m· a eff (t)·x(t)] / [x max -x(t)]).

[0068] In the formula, a eff (t) = |a(t)·e| is the effective component of the seat translational acceleration in the direction of the human body's motion tendency, or simply the effective acceleration (dimensions: m / s²). 2 The double vertical lines "| |" indicate taking the absolute value, that is, calculating the dot product of vector a(t) and unit vector e and taking its non-negative value. max This is the maximum allowable displacement threshold (range: 60mm~100mm). F max The maximum permissible restraint force of the seat belt (determined by the strength of the seat belt material and buckle structure, typically 1500N to 2500N).

[0069] The physical meaning of the formula for the target restraint force is: the restraint force required by the seat belt is related to the occupant's mass m and effective acceleration a. eff The displacement x(t) is proportional to the current predicted displacement x(t), and as the displacement x(t) approaches the maximum allowable displacement x... max And tends to infinity (in practical systems, it is affected by F) max (Limited width), thereby simulating the "locking" hard constraint characteristics of seat belt webbing at the tensile limit.

[0070] According to the law of conservation of energy: the occupants under effective acceleration a eff The kinetic energy accumulated when the object moves to displacement x under the action of (t) is E. k =m·a eff ·x, while the seat belt needs to be in place for the remaining braking distance (x) max The kinetic energy is completely absorbed within the -x) region to prevent the occupants from continuing to move, according to the functional principle F. avg ·(x max -x)=E k The required average binding force F can be obtained. avg =m·a eff ·x / (x max -x), therefore the core expression of the instantaneous target binding force is naturally related to m and a. eff The product of (t) and x is directly proportional.

[0071] When the predicted displacement x(t) exceeds the safety threshold x safe At this point, the seatbelt needs to provide additional restraint force to limit further displacement of the occupant, and the required restraint force increases non-linearly with increasing displacement. When the displacement approaches the maximum permissible displacement x... max At that time, the binding force approaches infinity (in a real system, it is subject to F). max (Limited), which simulates the "hard constraint" characteristic of a seatbelt, meaning that the seatbelt webbing cannot be stretched further after reaching its limit length, and the restraining force rises rapidly to prevent further movement of the occupant. This function form originates from the potential energy relationship of an elastic constraint system and can be understood as the restoring force provided by the seatbelt webbing when stretched exhibiting a non-linear growth characteristic with increasing stretch, where x(t) / (x max The nonlinear term -x(t) ensures that the restraint force increases sharply when the displacement approaches the limit, which is consistent with the physical characteristic of the seat belt webbing "locking" near the tensile limit.

[0072] In addition, the target restraint force needs to be individually calibrated based on the occupant's weight W: F target_W (t)=F target (t)·(α+β·(W / W ref )). F target_W(t) represents the final target binding force (in N) after individualized calibration based on body weight. F target (t) represents the uncalibrated target restraint force (in N) calculated based on the predicted displacement. α is the basic weighting coefficient (dimensionless), representing the basic proportion of restraint force independent of body weight; a value of 0.4 is recommended. β is the body weight adjustment coefficient (dimensionless), representing the weight of body weight on the restraint force; a value of 0.6 is recommended, and α + β = 1 is satisfied. W is the real-time estimated occupant weight (in kg), measured by a distributed pressure sensor array. ref For reference weight (unit: kg), 70 kg is used, representing the standard adult weight. (W / W) ref (α+β·(W / W)) represents the relative body weight ratio (dimensionless). The ratio is 1 when the occupant's weight equals the reference weight, greater than 1 when the weight is greater than the reference weight, and less than 1 when the weight is less than the reference weight. ref )) is the weight calibration factor (dimensionless). When W=W ref At that time, the calibration factor = 1, F target_W =F target When W>W ref When the calibration factor is greater than 1, the binding force increases; when W <W ref When the calibration factor is less than 1, the restraint force is reduced. The physical meaning of this formula is that the occupant's inertial force is proportional to their mass; the greater the weight, the more significant the inertial effect, thus requiring a higher restraint force to provide an equivalent restraint effect. This calibration method dynamically adjusts the restraint force setting value by estimating the occupant's weight in real time, thereby ensuring that occupants of different body types receive appropriate safety protection.

[0073] The personalized calibration formula is based on Newtonian mechanics, where inertial force is proportional to occupant mass: the magnitude of the inertial force experienced by an occupant under acceleration *a* is *m*·*a*, where *m* = *W* / *g*. Therefore, the required restraint force should theoretically be linearly related to the occupant's weight *W*. By fitting experimental data on the comfort and safety of the dynamic seat for occupants of different body types (typical values ​​are 50kg for lightweight, 70kg for standard, and 90kg for heavyweight), it was found that when the weight deviates from the reference weight *W*... ref When the target binding force is 70kg, it needs to be adjusted proportionally to achieve an equivalent constraint effect. To achieve simple and continuous calibration in engineering, a linear interpolation model is used: Let the calibration factor = A + B * (W / W) ref And apply boundary conditions: when W=0 (extreme case), the factor should be the basic safety factor α to avoid zero binding force; when W=W ref When the factor is 1, that is, F target_W =F target Therefore, we can solve for A = α and B = 1 - α. Let β = 1 - α, then we get the calibration factor = α + β·(W / W) refThis form conforms to physical linear relationships and is also convenient for real-time embedded system calculations.

[0074] S4. Calculate the error signal e(t) = F target_W (t)-F(t), which is input into the PID controller, and the PID controller operates according to the formula u(t)=K p e(t)+K i ∫e(τ)dτ+K d The control output u(t) is obtained by calculating de(t) / dt.

[0075] The PID controller is the core execution module of the closed-loop control system of this invention. Its input is the target restraint force F after being individually calibrated based on body weight. target_W The error signal e(t) between the current actual restraint force F(t) and the current restraint force F(t) is output as the control signal u(t) for the seat belt retractor drive motor.

[0076] In the formula, K p K i K d These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. The units of these three coefficients are all related to the electromechanical characteristics of the specific actuator and are determined through system identification and parameter tuning. τ is the integral variable, representing the virtual time independent variable in the time integration process from 0 to the current time t. e(t)=F target_W F(t) - F(t) represents the restraint force error signal. The PID controller performs calculations at a refresh rate of 100Hz, and the output signal is converted into a PWM waveform by the motor driver to drive the retractor motor to achieve continuous adjustment of the seat belt restraint force. This formula originates from the proportional-integral-derivative control law in classical control theory. Its applicability lies in the fact that the seat belt restraint force control system is essentially a typical closed-loop position / force control system—the current actual restraint force F(t) can be measured in real time by a tension sensor and compared with the set target value F. target_W (t) After comparison, an error signal is generated. After PID calculation, the motor is driven to adjust the rotation angle of the retractor, thereby changing the tightening amount of the seat belt webbing to adjust the restraint force. The proportional term provides the basic control quantity for immediate response, the integral term eliminates steady-state error, and the derivative term suppresses overshoot and oscillation. The three work together to achieve fast, accurate, and smooth adjustment of the seat belt restraint force.

[0077] S5. The u(t) signal output by the PID controller is converted into a corresponding PWM waveform by the motor driver, which controls the rotation direction, speed, and amount of rotation of the drive motor, causing the retractor to perform retraction or release operations on the seat belt webbing until the current actual restraint force F(t) matches the target restraint force F after personalized weight calibration. target_W The error between (t) is within the allowable range (typically within ±5%).

[0078] The seatbelt actuator of this invention includes a reversible electric retractor (a seatbelt retractor mechanism with bidirectional driving capability, capable of both tightening and releasing the webbing), a stepper or servo drive motor, and a matching motor driver and transmission mechanism. This actuator receives a control signal u(t) output from a PID controller, which is converted into rotational torque of the retractor shaft via the motor driver and transmission mechanism, causing the seatbelt webbing to be tightened or released, thereby achieving continuous adjustment of the restraint force F(t) exerted on the occupant.

[0079] S6. Repeat steps S1 to S5 to achieve real-time, closed-loop, adaptive control of the seat belt restraint force.

[0080] This invention achieves forward-looking estimation of occupant inertial displacement by calculating the predicted displacement of the occupant relative to the seatbelt restraint point in the current seat posture based on attitude parameters and occupant vital signs. Specifically, when the attitude acquisition unit detects seat acceleration, the control system immediately calculates the predicted displacement based on the magnitude and direction of the acceleration, rather than waiting for the occupant to actually displace before triggering the seatbelt locking. This attitude-based feedforward prediction mechanism enables the seatbelt to actively tighten the moment inertial force is generated, overcoming the lag defect of traditional fixed seatbelts that "displace first, then lock."

[0081] This invention sets a corresponding target restraint force based on whether the predicted displacement exceeds a safe displacement threshold. When the predicted displacement is small, a lower minimum holding force is set to avoid over-restraint; when the predicted displacement exceeds the safe threshold, a higher target restraint force is set, and the restraint force increases non-linearly with the increase of the predicted displacement. This method enables real-time matching of the target restraint force with the dynamic requirements of the seat's motion posture: the greater the acceleration and the greater the predicted displacement, the higher the target restraint force is automatically adjusted; when the acceleration decreases, the target restraint force automatically decreases. Compared to the discrete state where seat belts in the prior art can only provide a constant restraint force or can only lock, this method achieves continuous dynamic matching between the restraint force and the seat's motion posture, solving the contradiction of fixed seat belts being "either too tight and compressing the occupant, or too loose and causing slippage." While ensuring safety under high acceleration conditions, it avoids unnecessary compression under low acceleration conditions, thus improving ride comfort. This invention employs PID closed-loop control based on the error between the target restraint force and the actual restraint force. The actual restraint force is fed back in real time by a tension sensor, and the error signal is obtained by comparing it with the target restraint force. This error signal is then generated by a PID controller (which performs proportional, integral, and derivative operations) to drive an electric retractor to continuously adjust the tightening or loosening of the seatbelt webbing. All steps are executed cyclically, forming a complete closed-loop control chain. This closed-loop control transforms the seatbelt from an open-loop passive restraint device into an intelligent safety system that actively adjusts based on the seat's movement and real-time feedback. The integral term eliminates steady-state errors, ensuring the actual restraint force accurately tracks the target value; the derivative term suppresses overshoot and oscillation, resulting in smooth changes in restraint force. Compared to existing technologies that can only control the locked / released states, this method achieves continuous, smooth, and error-free closed-loop adjustment of the seatbelt restraint force, improving the response speed to the millisecond level, and automatically adapting to different motion modes (rapid acceleration, rollover, weightlessness, etc.).

[0082] In step 1) of this invention, occupant vital signs parameters are collected, and in step 2), predicted displacement is calculated based on these parameters. Specifically, the occupant vital signs parameters include the occupant's weight W, estimated in real-time by a distributed pressure sensor array. This weight parameter is used for personalized calibration of the target restraint force, allowing the restraint force to adjust linearly with the occupant's weight: the greater the weight, the proportionally higher the target restraint force; the smaller the weight, the proportionally lower the target restraint force. This personalized calibration ensures that occupants of different body types (from teenagers weighing 50 kg to adults weighing over 90 kg) receive appropriate safety protection. Smaller occupants will not experience breathing difficulties or sternal tenderness due to excessive restraint force; larger occupants will not slip or experience excessive displacement during strenuous activity due to insufficient restraint force. This achieves intelligent adaptation of seatbelt restraint force to a "one person, one policy" approach, improving the universality and user experience of the dynamic seat for different groups.

[0083] In the above-described scheme of the present invention, the angle γ(t) between the seat belt and the direction of the occupant's movement tendency is calculated in the following way: the initial positions of the seat belt fixing point coordinates (retractor exit point A and buckle insertion point B) and the occupant's chest restraint point C are pre-calibrated in the seat body coordinate system (based on standard sitting dummy calibration). Then, the above coordinates are transformed to the global coordinate system according to the current Euler angles (φ,θ,ψ), and the angle γ(t) between the seat belt webbing direction vector (the composite direction from the occupant's chest point C to the seat belt fixing point A or B) and the unit vector e of the human body's movement tendency direction is calculated.

[0084] However, during actual vigorous activity, the occupant's upper body will experience pitching (rotation around the Y-axis) and torsion (rotation around the Z-axis) relative to the seat, causing the actual contact point of the seatbelt to slip along the webbing, thus reducing the actual restraint point C of the occupant's chest. actual (t) and the pre-calibrated standard position C calibAn offset is generated. This offset consists of two parts: (1) Translation offset Δr(t) = C actual (t)-C calib (2) Local directional changes caused by posture shift. When the occupant leans forward or to the side, the contact point between the seat belt webbing and the body may slip from the center of the chest to the shoulder or armpit, causing the actual angle γ to change. actual (t) and calculated value γ calc The deviation (t) will increase the calculation error of the effective binding force component F·cosγ, which is particularly prominent in high-frequency, multi-degree-of-freedom compound motion (such as helical tumbling), and thus affects the accuracy of the predicted displacement x(t) and the response effect of the PID controller.

[0085] To address the aforementioned issues, this invention adds occupant upper body posture tracking and dynamic constraint point correction functions to the above solution. The specific solution is as follows: Occupant upper body posture tracking unit A six-axis inertial sensor (three-axis accelerometer + three-axis gyroscope) should be installed at a key location on the occupant's upper body (ideally near the chest restraint point of the seatbelt, such as a miniature patch attached to the surface of clothing or a miniaturized inertial measurement unit integrated into the seatbelt webbing), labeled IMU. body . The IMU body Real-time acquisition of local acceleration a of the occupant's chest body_local (t) and angular velocity ω body_local (t), and outputs the relative attitude angles of the occupant's upper body relative to the seat body through attitude calculation algorithms (such as quaternion-based complementary filtering or extended Kalman filtering), including the relative pitch angle Δθ(t) and the relative roll angle Δφ(t). This IMU body It connects to the main control chip of the seat wirelessly (such as Bluetooth 5.0) or via wire, with a sampling frequency of not less than 100Hz, and is synchronized with the attitude acquisition unit.

[0086] The process of calculating the relative attitude angle of the occupant's upper body with respect to the seat body is as follows: First, a six-axis inertial sensor (IMU_body) installed near the occupant's chest outputs the raw triaxial acceleration value a at a sampling frequency of not less than 100 Hz. body_raw =[a x ,a y ,a z ] T and the original value of the triaxial angular velocity ω body_raw =[ω x ,ω y ,ω z ] ᵀ Both of these vectors are defined in the IMU bodyIn its own local coordinate system. Meanwhile, the Euler angles (φ,θ,ψ) of the seat body output by the seat posture acquisition unit, after coordinate transformation, provide the posture quaternion q of the seat body in the global coordinate system. seat To obtain the relative posture of the occupant's upper body with respect to the seat body, a quaternion-based extended Kalman filter (EKF) is used for data fusion: the state variable is taken as the relative quaternion q of the occupant's upper body with respect to the seat body. seat =[q0,q1,q2,q3] ᵀ (Saving the unit module constraint), the corresponding relative pitch angle Δθ and relative roll angle Δφ can be obtained using the quaternion to Euler angle conversion formula. In the prediction stage, the angular velocity measurement value ω is used. body_raw Construct the state transition matrix for q rel A one-step prediction is performed, while online estimation is performed considering the gyroscope's zero-bias drift as an extended state. In the update phase, the acceleration measurement value a is... body_raw The projection of gravitational acceleration in the IMU local coordinate system (via q) rel and q seat The calculated values ​​are compared to the observed residuals, and the Kalman gain is calculated to correct the state estimate. The filtered relative quaternion q is then used. rel After conversion, we obtain the stable relative pitch angle Δθ = arcsin(2(q1·q3-q0·q2)) and the relative roll angle Δφ = arctan2(2(q0·q1+q2·q3),1-2(q1·q2)). 2 +q2 2 )), where q rel =[q0,q1,q2,q3] ᵀ This algorithm effectively integrates the low-frequency attitude reference characteristics of the accelerometer with the high-frequency dynamic response characteristics of the gyroscope, suppressing noise and drift from a single sensor, even during intense motion in a motion seat (acceleration rate can reach 50 m / s²). 3 Even under harsh conditions, it can still output a stable and reliable relative attitude angle.

[0087] Among them, a body_raw =[a x ,a y ,a z ] ᵀ A six-axis inertial measurement unit (IMU) for the occupant's chest. body The original triaxial acceleration values ​​output in its own local coordinate system, where a x a y a z The acceleration components corresponding to the X, Y, and Z axes of the sensor (unit: m / s²) 2 ); ω body_raw =[ω x ,ωy ,ω z ] ᵀ These are the raw triaxial angular velocities output from the same sensor, where ω x ω y ω z These correspond to the angular velocity components (unit: rad / s) of rotation around the X, Y, and Z axes of the sensor, respectively; (φ, θ, ψ) are the Euler angles of the seat body in the global coordinate system, corresponding to the roll, pitch, and yaw angles (unit: rad), respectively, obtained by data fusion from the nine-axis attitude sensor in the seat attitude acquisition unit; q seat Let be the attitude quaternion of the seat body in the global coordinate system, which is an equivalent representation of Euler angles (φ,θ,ψ) and satisfies q seat =[cos(ψ / 2)cos(θ / 2)cos(φ / 2)+sin(ψ / 2)sin(θ / 2)sin(φ / 2),sin(ψ / 2)cos(θ / 2)cos(φ / 2)-cos(ψ / 2)sin(θ / 2)sin(φ / 2) ,cos(ψ / 2)sin(θ / 2)cos(φ / 2)+sin(ψ / 2)cos(θ / 2)sin(φ / 2),cos(ψ / 2)cos(θ / 2)sin(φ / 2)-sin(ψ / 2)sin(θ / 2)cos(φ / 2)] T q is used to describe the rotational relationship of the seat body from the global coordinate system to the seat body coordinate system; rel =[q0,q1,q2,q3] ᵀ Let q0 be the relative quaternion of the occupant's upper body with respect to the seat body. 2 +q1 2 +q2 2 +q3 2 =1, where q0 is the scalar part, [q1,q2,q3] ᵀ The vector part; the state transition matrix in the Extended Kalman Filter (EKF) is used to predict the current state based on the state at the previous time step, and its specific form is based on the angular velocity measurement ω. body_raw The constructed antisymmetric matrix of angular velocity; the projection of gravitational acceleration in the local coordinate system of the IMU refers to the projection of the gravity vector g in the global coordinate system. global =[0,0,-9.8] ᵀ (Unit: m / s) 2 ) via q seat and q rel Composite rotational conversion to IMU body The vector after the local coordinate system is used to correlate with the accelerometer measurement value a. body_rawThe comparison is used to correct the relative attitude estimate; the observation residual is the difference between the acceleration measurement and the gravity projection vector, reflecting the degree of error in the current attitude estimate; the Kalman gain is the coefficient matrix in EKF used to weight the predicted and observed values ​​to obtain the optimal estimate, and is calculated in real time based on the state covariance and the observation noise covariance.

[0088] 2) Dynamic constraint point correction A dynamic constraint point correction module is deployed in the main control chip. Its inputs are the relative posture angles of the occupant's upper body (Δθ(t), Δφ(t)) and the pre-calibrated coordinates of the standard chest constraint point C. calib The output is the actual constraint point position C at the current moment. actual (t). The modified model is based on the rigid body kinematics of the upper human body: it assumes that the occupant's torso above the thoracic vertebrae is approximately a rigid body, with the center of rotation O located near the intersection of the seat back and the seat cushion (roughly corresponding to the 5th lumbar vertebra). torso (Pre-calibrated), the positional change of the occupant's chest restraint point is described by the rotational motion around that center.

[0089] The position correction formula is: C actual (t)=C calib +R(Δθ,Δφ)·(C calib -O torso )-(C calib -O torso Where R(Δθ,Δφ) is the rotation matrix corresponding to the relative attitude angle. For small angle approximations (Δθ,Δφ<30°), a linearized form can be used: ΔC x ≈0, ΔC y ≈-(C z -O z )·Δθ (the y-direction is the fore-and-aft direction of the seat; pitch causes fore-and-aft offset), ΔC z ≈(C y -O y )·Δθ (z direction is up and down), ΔC x_roll ≈(C y -O y )·Δφ (lateral displacement caused by roll).

[0090] Where C calib The initial constraint point coordinates of the occupant's standard chest, pre-calibrated using a standard seated dummy (in the seat body coordinate system); rotation center O torsoThe approximate center point coordinates for the occupant's torso rotation are given (usually the position of the 5th lumbar vertebra at the junction of the seat back and seat cushion, pre-calibrated); Δθ(t) and Δφ(t) are the relative pitch angle (around the Y-axis) and relative roll angle (around the X-axis) of the occupant's upper body relative to the seat body, respectively, calculated in real time by the six-axis inertial sensor in the occupant's upper body attitude tracking unit; R(Δθ,Δφ) is the three-dimensional rotation matrix (3×3 matrix) corresponding to the relative attitude angles, used to convert the relative rotation into coordinate transformation; ΔC x ΔC y ΔC z These represent the positional corrections for the occupant's chest restraint points in the X (usually lateral), Y (forward / backward), and Z (vertical) directions of the seat coordinate system. ΔC x_roll This represents the positional correction of the chest restraint point in the X-direction of the seat coordinate system (usually the lateral direction of the seat, i.e., the left-right direction of the occupant) caused by the lateral movement of the occupant's upper body (rotation around the X-axis, i.e., tilting left and right). When the occupant's upper body tilts to the left or right, the chest restraint point will undergo an arc-shaped displacement along the lateral direction (X-direction) with the torso rotation center as the fulcrum. The amount of this lateral displacement is approximately equal to the rotation radius (C). y -O y Multiplying this by the roll angle Δφ (a small angle approximation), this correction is used to dynamically update the actual constraint point C at the occupant's chest. actual The X-coordinate is used to eliminate the influence of seat belt contact point offset caused by occupant tilt on the accuracy of angle γ(t) calculation. y C z C calib The Y and Z coordinate components; O y O z For O torso The Y and Z coordinate components; d belt The equivalent resultant force direction vector (unnormalized) exerted by the seatbelt webbing on the occupant's chest; d belt_unit γ is the normalized unit vector representing the effective constraint direction of the seatbelt; corrected (t) is the angle (rad) between the corrected direction of the seat belt and the direction of the human body's movement tendency, used to replace γ(t) in the original dynamic differential equation.

[0091] In practical implementation, since the contact point between the seatbelt webbing and the body will slide within a certain range, a weighted filter can be used to correct the C. actual (t) is smoothed to avoid abrupt changes in the position of constraint points due to attitude jitter.

[0092] 3) Corrected included angle γ corrected (t) Calculation method Obtain the actual occupant chest restraint point C actual After (t), the seat belt webbing direction vector is recalculated: Let the coordinates of the seat belt retractor exit point A and the buckle insertion point B in the seat body coordinate system be known fixed values ​​(obtained from mechanical design drawings). After transformation to the global coordinate system using the current Euler angles (φ,θ,ψ), let them be denoted as A. global (t) and B global (t). Actual restraint point C on the occupant's chest. actual (t) has also been expressed in the global coordinate system.

[0093] The direction of the force exerted by the seatbelt webbing on the occupant is not simply determined by a single point, but rather by the resultant force of the two-point constraint. More precisely, the seatbelt forms a V-shaped constraint at the occupant's chest (the shoulder strap portion extends from the retractor A through the chest to the buckle B), and its effective constraint direction vector can be approximated as originating from C. actual (t) points to the midpoint between A and B, or a weighted average is used: d belt =(A global -C actual ) / |A global -C actual |+(B global -C actual ) / |B global -C actual )|;d belt_unit =d belt / |d belt The angle γ between the corrected direction of the seatbelt and the direction of human movement is then determined. corrected (t) is: γ corrected (t)=arccos(d belt_unit ·e(t)). Where e(t) = -a(t) / |a(t)| is the unit vector of the direction of the human motion tendency (opposite to the direction of the seat acceleration). Let γ corrected Substituting (t) into the cosγ term in the original dynamic differential equation yields the corrected predicted displacement model.

[0094] Among them, A global (t) and B global (t) represents the real-time position vectors of the seatbelt retractor exit point and the buckle engagement point in the global coordinate system (O-XYZ coordinate system fixed to the ground), respectively, derived from the fixed coordinates A in the pre-calibrated seat body coordinate system. body B body C is obtained by rotation and translation transformation of the Euler angles (φ,θ,ψ) at the current moment; actual (t) represents the position vector of the actual constraint point of the occupant's chest in the global coordinate system at the current moment, output by the dynamic constraint point correction module; d belt The equivalent resultant force direction vector (unnormalized) of the seat belt on the occupant's chest is calculated from C. actual (t) points to Aglobal The unit vector of (t) and from C actual (t) points to B global The sum of unit vectors (t) is used to approximate the average constraint direction of the V-shaped seatbelt webbing; |A global -C actual | represents vector A global -C actual The modulus (Euclidean norm) is the straight-line distance from the retractor exit point to the occupant's chest restraint point; d belt_unit The normalized unit vector representing the effective constraint direction of the seatbelt, i.e., d belt Divide by its own modulus |d belt |; e(t) is the unit vector representing the direction of the human body's motion tendency, taken as e(t) = -a(t) / |a(t)|, where a(t) is the translational acceleration vector of the seat; arccos(·) is the inverse cosine function, used to calculate the angle between two unit vectors based on the dot product value; γ corrected γ(t) is the angle between the corrected direction of the seat belt and the direction of the human body's movement, in radians (rad), used to replace γ(t) in the original dynamic differential equation.

[0095] In summary, this solution tracks the relative attitude angles Δθ(t) and Δφ(t) of the upper body in real time by adding a six-axis inertial sensor near the occupant's chest, and establishes a rotation center O around the torso. torso Based on the dynamic constraint point correction model, the pre-calibrated standard chest constraint point C is used. calib Corrected to the current actual position C actual (t). Corrected C actual (t) Combined with the take-up outlet point A global (t) and the latching contact point B global (t), recalculate the equivalent resultant force direction vector d of the seat belt. belt_unit Thus, the corrected included angle γ is obtained. corrected (t)=arccos(d belt_unit •e). This correction eliminates the constraint point offset caused by occupant pitch and roll and the angle calculation error caused by webbing slippage, reducing the error of the effective restraint force component F·cosγ, improving the solution accuracy of predicted displacement x(t) and the PID control response effect in high dynamic scenarios.

[0096] Corrected γ corrected (t) can accurately reflect the effective constraint direction of the seat belt, reduce the error of the cosγ term in the dynamic differential equation, and thus improve the calculation accuracy of the predicted displacement x(t), providing a more accurate target value for the PID controller.

[0097] In complex motion scenarios such as spiraling rolls and rapid dives, the occupant's upper body posture changes drastically, and traditional fixed constraint point models will severely underestimate or overestimate the effective restraint force. This solution dynamically adjusts the restraint points by tracking the occupant's posture in real time, ensuring that the seat belt restraint force always matches the occupant's actual force state, avoiding seat belts that are too tight or too loose due to incorrect angle calculations.

[0098] By analyzing the relative posture changes of different occupants under the same motion excitation (e.g., the upper body sway is smaller for those with greater weight), the personalized value of the damping coefficient c can be optimized in reverse to form a more complete occupant model.

[0099] The mass production cost of miniature six-axis IMU modules has dropped to below 20 yuan per unit. The wireless transmission solution is mature. For high-end dynamic seats (single seat price of tens of thousands of yuan), the marginal cost of adding this function is low, while the resulting improvement in safety and comfort has high economic value.

[0100] See appendix Figure 2 This invention also provides a dynamic seat belt control system, the system specifically including: The first module collects the seat's posture parameters and the occupant's vital signs in real time. The second module calculates the predicted displacement of the occupant relative to the seat belt constraint point in the current seat posture, based on attitude parameters and occupant vital signs parameters. The third module sets the corresponding target restraint force based on whether the predicted displacement exceeds the safe displacement threshold. The fourth module executes PID closed-loop control based on the error between the target binding force and the actual binding force.

[0101] See attached document Figure 3 The present invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory, wherein when the computer program is executed on the processor, it implements a dynamic seat belt control method as described in any of the above methods.

[0102] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0103] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0104] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0105] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a dynamic seat belt control method as described in any of the above methods.

[0106] In this embodiment, if the integrated control unit is implemented as a software functional unit and sold or used as an independent product for motion seat belt control, it can be stored in a computer-readable storage medium specifically designed for motion seat belt control. Based on this understanding, all or part of the processes in the above-described embodiments can be implemented by a specific computer program instructing related hardware for motion seat belt control. This computer program can be stored in a dedicated computer-readable storage medium for motion seat belt control. When executed by a processor, this computer program can implement the application steps of the various method embodiments described above in motion seat belt control. The computer program includes computer program code for motion seat belt control, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium may include at least: any entity or device capable of carrying computer program code for a motion seat belt control to a motion seat belt control device, a recording medium, a machine tool computer memory, a read-only memory (ROM), a random access memory (RAM), and other media suitable for distributing motion seat belt control software, such as a dedicated motion seat belt control card, a data storage card, etc.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for controlling the seat belt of a dynamic seat, characterized in that, Includes the following steps: 1) Real-time collection of seat posture parameters and occupant vital signs parameters; 2) Based on attitude parameters and occupant vital signs parameters, calculate the predicted displacement of the occupant relative to the seatbelt restraint point in the current seat posture; 3) Set the corresponding target restraint force based on whether the predicted displacement exceeds the safe displacement threshold; 4) Execute PID closed-loop control based on the error between the target binding force and the actual binding force.

2. The method for controlling a dynamic seat belt as described in claim 1, characterized in that, Step 5) is included between steps 3) and 4) to perform personalized calibration of the target restraint force based on the occupant's weight W.

3. The method for controlling a dynamic seat belt as described in claim 1, characterized in that, The seat's attitude parameters include translational acceleration vector a(t), angular velocity vector ω(t), and Euler angles (φ(t), θ(t), ψ(t)); the occupant's vital signs parameters include occupant weight W and restraint force F(t) acting on the occupant's seatbelt restraint point.

4. The method for controlling a dynamic seat belt as described in claim 3, characterized in that, The seat's attitude parameters are collected by an attitude sensor array, which includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The real-time attitude parameters of the seat body in the global coordinate system are output through a complementary filtering data fusion algorithm. The restraint force F(t) of the seat belt acting on the occupant's seat belt restraint point is measured in real time by a tension sensor. The pressure distribution data of each area of ​​the seat cushion is collected by a distributed pressure sensor array, and the real-time estimated value of the occupant's weight W is obtained through weighted fusion calculation.

5. The method for controlling a dynamic seat belt as described in claim 2, characterized in that, Step 2) includes: 2.1) The collected real-time translational acceleration a(t), seat belt direction angle γ(t), and occupant weight W (converted from occupant mass m=W / g, where g is the acceleration due to gravity, taken as 9.8 m / s²) 2 Substituting into the dynamic differential equation: m·(d 2 x(t) / dt 2 )+c·(dx(t) / dt)+F(t)·cosγ(t)=-m·(a(t)·e); 2.2) The fourth-order Runge-Kutta method is used to numerically integrate and solve the differential equation to obtain the predicted displacement x(t) of the occupant relative to the seat belt constraint point in the current seat posture; Where m is the mass of the crew, d 2 x(t) / dt 2 Let be the acceleration of the occupant relative to the seatbelt restraint point, c be the damping coefficient, dx(t) / dt be the velocity of the occupant relative to the seatbelt restraint point, c·(dx / dt) represent the damping force proportional to the velocity, F(t) be the actual restraining force of the seatbelt currently acting on the occupant's seatbelt restraint point, γ(t) be the angle between the seatbelt webbing direction and the direction of the human body's movement tendency, cosγ(t) be the cosine of this angle, F(t)·cosγ(t) be the effective component of the seatbelt restraining force in the direction of the human body's movement tendency, a(t) be the translational acceleration vector of the seat, and e be the unit vector in the direction of the human body's movement tendency.

6. The method for controlling a dynamic seat belt as described in claim 2, characterized in that, Step 3) includes: 3.1) When x(t) ≤ x safe At that time, the target binding force F target Set as minimum holding force F min ; 3.2) When x(t) > x safe At that time, the target binding force is set to: F target (t)=min(F max ,[m·a eff (t)·x(t)] / [x max -x(t)]; Where x safe For the safe displacement threshold, a eff (t) represents the effective component of the seat translational acceleration in the direction of the human body's motion tendency, x max F is the maximum allowable displacement threshold. max This is the maximum permissible restraint force of the seatbelt.

7. The method for controlling a dynamic seat belt as described in claim 2, characterized in that, The formula for personalized calibration based on the occupant's weight W in step 5) is: F target_W (t)=F target (t)·(α+β·(W / W ref )); where F target_W (t) represents the final target restraint force after individualized weight calibration, F target (t) represents the uncalibrated target restraint force calculated based on the predicted displacement, α is the basic weighting coefficient, and β is the weight adjustment coefficient α+β=1, W ref W is the reference weight, and W is the real-time estimated occupant weight.

8. The method for controlling a dynamic seat belt as described in claim 7, characterized in that, Step 4) specifically includes: calculating the error signal e(t) = F target_W (t)-F(t), which is input into the PID controller, and the PID controller operates according to the formula u(t)=K p e(t)+K i ∫e(τ)dτ+K d The control output u(t) is obtained by calculating de(t) / dt; where K p K i K d These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. τ is the integral variable from 0 to the current time t, and u(t) is the control signal u(t) of the seat belt retractor drive motor.

9. A method for controlling a dynamic seat belt as described in claim 8, characterized in that, The u(t) signal output by the PID controller is converted into a corresponding PWM waveform by the motor driver, which controls the rotation direction, speed, and amount of rotation of the drive motor, causing the retractor to perform retraction or release operations on the seat belt webbing until the current actual restraint force F(t) matches the target restraint force F after individual weight calibration. target_W The error between (t) and (t) is within the allowable range.

10. A dynamic seat belt control system, characterized in that, include: The first module collects the seat's posture parameters and the occupant's vital signs in real time. The second module calculates the predicted displacement of the occupant relative to the seat belt constraint point in the current seat posture, based on attitude parameters and occupant vital signs parameters. The third module sets the corresponding target restraint force based on whether the predicted displacement exceeds the safe displacement threshold. The fourth module executes PID closed-loop control based on the error between the target binding force and the actual binding force.

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