182 results about "Vertical acceleration" patented technology

The invention discloses a bridge sensor self-optimization arrangement and state evaluation method based on a modal confidence criterion, and relates to the technical field of big data analysis and processing. The method comprises the following steps: step 1, acquiring a vertical acceleration time sequence at candidate installation coordinates of each sensor through preset vehicle excitation; 2, a physical information neural network is adopted to drive sparse damage reconstruction and sensor importance joint optimization algorithm to determine a bridge sensor self-optimization arrangement result; and step 3, installing a vertical acceleration sensor on the target bridge according to a bridge sensor self-optimization arrangement result, dividing bridge state grades according to a finite element unit damage estimation result, and outputting a bridge state evaluation result. The method can be continuously used in the bridge operation period, quantitative and traceable structure state evaluation results are provided, potential risks can be found in advance, and the reliability, practicability and intelligent level of bridge health monitoring are remarkably improved.

The invention discloses an automobile semi-active suspension PID parameter adaptive adjustment and vibration control method and system based on reinforcement learning, and the method mainly comprises the following working steps: 1, constructing a reinforcement learning environment comprising a suspension dynamics high-fidelity simulation model and a PID controller; 2, defining a state space including a vehicle body vertical acceleration error e (t), an error integral term summa e (t) dt, an error differential term de (t) / dt, a suspension dynamic stroke zdef (t), a driving vehicle speed v (t) and road surface unevenness estimation xi (t); and 3, defining an action space including a proportion coefficient increment delta Kp, an integral coefficient increment delta Ki, a differential coefficient increment delta Kd and an output compensation amount delta e (t) of the PID controller. According to the method, the problem that the suspension performance is reduced due to the fact that control parameters of a traditional PID control method are fixed under the non-linear and time-varying working conditions is solved, and the riding comfort and the handling stability of the vehicle are remarkably improved.

The invention belongs to the technical field of vehicles, and particularly relates to an ABS function trigger control method and device, a vehicle and a medium. The method comprises the following steps: calculating a wheel slip rate; determining driver intention according to the hydraulic sensor signal, the brake pedal sensor signal and the accelerator pedal sensor signal, wherein the driver intention comprises emergency braking, conventional braking, sliding and acceleration; the road bumping state is determined according to the wheel slip rate, the vehicle vertical acceleration and the suspension displacement; according to the intention of the driver and the road bumping state, the triggering condition of the ABS function is determined; and determining whether the triggering condition of the ABS function is met or not according to the wheel slip rate. By judging the intention of the driver and the road bumping state and adjusting the triggering condition of the ABS function in real time according to the intention of the driver and the road bumping state, false triggering of the ABS function when the vehicle passes through the bumping road is avoided.

The invention discloses a new energy automobile shock absorber state intelligent early warning method based on attitude monitoring, and relates to the field of state intelligent early warning, and the method comprises the steps: collecting multi-dimensional attitude data such as the vertical acceleration, the pitch angle speed and the roll angle speed of an automobile in real time, and carrying out the self-adaptive detection of an impact event through combining with a dynamic threshold algorithm; and normal driving vibration and abnormal impact working conditions can be accurately distinguished. In the feature extraction stage, multi-dimensional physical features such as a peak-to-peak value, a root-mean-square value, a damping ratio and the like are combined and coded, so that the dynamic attenuation characteristics of the shock absorber in an impact event can be comprehensively represented. By establishing a similar event feature database in a healthy state and performing multi-feature space deviation calculation by adopting a mahalanobis distance, the robustness of anomaly detection is remarkably improved. Therefore, the technical problems of poor environmental adaptability, low diagnosis precision and the like of the traditional method can be effectively solved.

The invention discloses a hovering accelerator estimation method and system for an electric power inspection unmanned aerial vehicle, equipment and a medium, and the method comprises the steps: taking a hovering accelerator of the unmanned aerial vehicle as an estimator, taking a vertical acceleration as a measurement quantity, and carrying out the real-time estimation of the hovering accelerator of the unmanned aerial vehicle through an extended Kalman filtering algorithm; the extended Kalman filtering algorithm is integrated into hovering control of the unmanned aerial vehicle for the first time, compared with an existing PID control algorithm, the control precision and the dynamic response capability of fixed-point hovering of the unmanned aerial vehicle are improved, better robustness is achieved, high-precision, real-time and dynamic stable hovering control can be achieved, the method can be suitable for the unmanned aerial vehicle with the weight changing in real time, and the hovering control accuracy of the unmanned aerial vehicle is improved. And the method has better generalization ability.

The invention provides a vehicle control method and a vehicle, and relates to the technical field of chassis control, and the method comprises the steps: obtaining the mass center vertical speed of the vehicle during the driving period of the vehicle; determining the wheel mass center vertical acceleration of each wheel according to the mass center vertical speed, the speed of the wheel moving in the vertical direction caused by the vehicle pitching motion corresponding to each wheel and the speed of the wheel moving in the vertical direction caused by the vehicle rolling motion; according to the mass center vertical speed and the wheel mass center vertical acceleration of each wheel, target action parameters of a suspension corresponding to each wheel are determined; and according to the target action parameters of the suspension corresponding to each wheel, the actual main power or the actual damping force output by the suspension corresponding to each wheel is controlled. According to the invention, the driving stability and riding comfort of the vehicle can be maintained during the driving period of the vehicle.

The invention relates to a vehicle jumping method, a storage medium, a program product, equipment, a system and a vehicle. The vehicle jumping method comprises the steps that in the vehicle jumping process, the tire pressure of a vehicle is adjusted so as to assist the vehicle in jumping. By adjusting the tire pressure of the vehicle, on one hand, the rigidity of the tire can be changed, and on the other hand, the vertical acceleration of the vehicle can be changed, so that the jumping capacity of the vehicle is optimized, for example, the take-off stroke is increased, the leap distance is increased, the oscillation curve is optimized, and landing buffering is increased, and more efficient and comfortable jumping is achieved.

The invention provides a vehicle control method, a vehicle control device and a vehicle, and relates to the technical field of vehicle control. The method comprises the steps that under the condition that a target instruction is received, the vehicle sinking depth of a vehicle is obtained, and the target instruction is used for indicating that the requirement for controlling the vehicle to be disengaged from the trapped state exists; based on the vehicle sinking depth, target output power of the suspension system is determined, and the target output power is used for controlling the suspension system to enable the vehicle to generate vertical acceleration; a suspension system is controlled based on the target output power, and a drive system is controlled to cause the vehicle to jump and drive away from the trapped state. According to the method, the vehicle can be controlled when the vehicle is in a trapped state, so that the success rate of vehicle escape is increased.

The invention belongs to the field of bridge detection and evaluation, and discloses a bridge line shape detection and damage prediction method based on a smart phone and a hybrid neural network, and the method comprises the steps: obtaining a bridge image through the smart phone by employing a distributed vision method, and obtaining vertical acceleration response data and environment temperature when a standard vehicle passes through a bridge through a mobile phone sensor; a YOLOv5s-CECA target detection model is used to obtain the pixel coordinates of the feature points; taking gyroscope attitude parameters during shooting as initial external parameters, converting positioning feature point pixel coordinates into a world coordinate system in combination with a pre-calibrated mobile phone internal parameter matrix, optimizing feature point coordinates by minimizing a re-projection error, drawing an actual bridge line shape, and obtaining a target line shape. And inputting the obtained bridge data into the trained AFTCNN-LSTM model to obtain a damage degree prediction result. The method can be rapidly deployed in a complex engineering field environment, improves the efficiency of bridge line shape detection and damage prediction, and reduces the bridge detection and evaluation cost.

The invention discloses a vehicle full-temporal cooperative control method and system based on multi-source sensing data fusion, and belongs to the field of vehicle control. Comprising the following steps that S1, vertical pressure signals, yaw velocity, vehicle three-axis acceleration, vertical acceleration generated by road excitation, front obstacle distance and relative velocity and front road images of all suspensions are synchronously collected, and multi-source sensing data are formed; s2, fusing the preprocessed multi-source sensing data of the same timestamp, determining the real-time three-dimensional gravity center, the vehicle body attitude angle and the real-time road surface state of the vehicle, and obtaining the predicted road surface state based on the historical multi-source sensing data; and S3, cooperatively controlling braking force distribution, steer-by-wire and an active suspension. According to the vehicle full-temporal cooperative control method and system based on multi-source sensing data fusion, through fusion of the multi-source sensing data, full-dimensional coverage of vehicle state-road environment-obstacle information is achieved, and it is ensured that the sensing result is stable and reliable.

The invention relates to a method and device for a biped robot to wear common sports shoes, and belongs to the technical field of robots. The method comprises the steps that a pre-planned gait track is initialized and generated; calculating the actual centroid position of the robot at each moment; a Kalman filtering algorithm is adopted to fuse multi-source data to update the normal stiffness and the friction coefficient of the sole in real time, and the rate is updated through adaptive gain adjustment parameters; adjusting a zero moment point constraint range, and generating a centroid reference position of a preset step number; establishing a sole characteristic database, training a machine learning model, inputting shoe parameters into the machine learning model, and outputting step length, step frequency and ankle joint rigidity by the machine learning model; an ankle joint target angle and a knee joint target angle are solved through inverse kinematics, and a joint motor is driven in combination with a PD control algorithm and a self-adaptive item; when the vertical acceleration of the IMU sensor is larger than a preset acceleration threshold value, the time period before grounding is judged, and a compliant mode is triggered to control the ankle joint rigidity and the torque borne by the joint.

The invention relates to the technical field of motorcycle lamp control, in particular to a self-adaptive control method and system for a motorcycle lamp, and the method comprises the steps: taking the ratio of the yaw velocity to the driving speed of a motorcycle as the steering intensity, and taking the change trend of the steering intensity in a preset time period as the steering trend; the fluctuation amplitude of the vertical acceleration in the preset time period serves as the jolting degree; correcting the vehicle body inclination angle based on the bumping degree and the steering trend to obtain a corrected inclination angle; and the corrected inclination angle is substituted into a preset mapping relation to output a basic steering angle, the vehicle lamp assembly is controlled to rotate to a target steering angle, and the target steering angle is equal to the basic steering angle. According to the technical scheme, the steering trend and the jolting degree in the driving process can be analyzed, and high-stability illumination control over the motorcycle lamp can be achieved.

The invention relates to the technical field of vehicle electric control suspensions, in particular to a chassis prediction control system and method based on binocular vision and multi-domain cooperation, and constructs the chassis prediction control system based on binocular vision and multi-domain cooperation based on an ADAS binocular camera. IMU data of a binocular camera is obtained by using a data acquisition module of the chassis prediction control system, visual observation data of a vehicle body is obtained by using a binocular visual processing module, and then the IMU data and the visual observation data are fused and converted into a three-dimensional image by using modules such as an extended Kalman filtering fusion module and a motion conversion module. The sprung mass vertical acceleration and sprung mass vertical speed of the vehicle four-corner suspension provide accurate suspension four-corner vertical motion state input for an electric control suspension controller, and the accuracy and real-time performance of the electric control suspension for vehicle body posture control are guaranteed. Meanwhile, the problems that a special IMU is difficult to arrange, high in cost and insufficient in precision and IMU resources of an ADAS system are wasted are solved.

The invention belongs to the technical field of action recognition, and particularly relates to an electric power engineering construction safety early warning method and system, and the method comprises the steps: carrying out the threshold segmentation and Hough straight line detection of a monitoring image of an electric power engineering construction site, obtaining a rigid support structure set, obtaining a scene instantaneous motion potential index according to the optical flow vector of a visible key point, and carrying out the calculation of the scene instantaneous motion potential index. Constructing a human body and rigid structure interaction stability index in combination with the distance between a visible key point in the possible contact set and the rigid support structure, calculating a posture risk bias factor, complementing the key point by using a graph convolutional network, and correcting a preliminary complete posture estimation result according to the posture risk bias factor and a vector representing gravity influence, so as to obtain a posture estimation result. And determining the position of the human body centroid key point according to the correction result for calculating the vertical acceleration of the human body centroid key point and performing personnel falling risk judgment. According to the method, the problem that the graph convolutional network model generates the pseudo-true attitude under the shielding of the key points is solved.

The invention relates to the technical field of automatic control, and discloses a suction force self-adaptive adjusting method based on a suction cup base, comprising the following steps: collecting current attitude disturbance information of to-be-adsorbed objects including a lamp, glass, a steel plate, a plastic box body and a wood material plate, the attitude disturbance information comprises a pitch angle, a roll angle, an angular velocity and a vertical acceleration; estimating the offset of the gravity center of the to-be-adsorbed object relative to the center of the sucker array based on the attitude disturbance information; according to the offset, target adsorption force of the object to be adsorbed is determined, and distribution is carried out according to a set load distribution weight; the invention further discloses a suction force self-adaptive adjusting system based on the suction cup base. The system comprises a posture sensing module; an offset calculation module; an adsorption force distribution module; and an adsorption control module. According to the invention, dynamic coordination and accurate tracking control of multi-sucker adsorption force are realized by fusing attitude sensing, adsorption modeling and nonlinear predictive control.

The invention discloses a hydrofoil with an adjustable attack angle for an electric control type boat and a control system, relates to the technical field of attack angle control, is used for solving the hidden danger of grounding caused by insufficient underwater distance and limited lift force compensation during navigation in a shallow water area, and comprises a working condition judgment module, a backstepping fitting module, a risk grade judgment module and an attack angle execution module, the modules are in signal connection; the working condition judgment module obtains a water depth value, a draught value and a water leaving height value through a sensor, calculates a bottom gap and compares the bottom gap with a safe minimum gap, and judges shallow water working condition triggering; the backstepping fitting module calculates the actual lift force by combining the gravity, the buoyancy and the vertical acceleration of the boat body when the shallow water working condition is triggered, and fits the shallow water corrected lift force slope; the risk grade judgment module calculates the residual available lifting force and the lifting force required by lifting, and sets a risk flag bit through a dual-condition criterion; and the attack angle execution module adjusts the attack angle according to the risk flag bit to realize closed-loop control.

The invention relates to an expressway bridge collapse identification and early warning method based on vehicle dynamic monitoring, which belongs to the technical field of bridge safety monitoring, and comprises the following steps: if a vehicle falls into a bridge in a target bridge section, comparing with a bipartite model threshold based on a vertical acceleration, a vertical acceleration change rate and a three-axis resultant acceleration; whether the vehicle falls on the bridge or not is judged; under the condition that the vehicle falls to the bridge and falls to the water surface, the number of vehicles falling to the bridge, the first disaster correlation of the weather data associated with the vehicle falling to the bridge and the second disaster correlation of the social media data associated with the vehicle falling to the bridge are obtained; determining the credibility of the collapse state of the target bridge section based on the number of the vehicles falling into the bridge, the first disaster correlation of the vehicles falling into the bridge associated with the meteorological data and the second disaster correlation of the vehicles falling into the bridge associated with the social media data; and based on the credibility of the collapse state of the target bridge road section, issuing early warning information of the collapse state of the target bridge road section.

This invention belongs to the field of bridge quality inspection technology, specifically a method for evaluating the quality of box girders based on the MH wavelet average evolved power spectrum. The method includes: selecting a qualified standard beam, deploying vibration sensors to collect vertical acceleration time-history signals; preprocessing the signals by performing wavelet transform, calculating the squared modulus of the wavelet coefficients and normalizing them to obtain a three-dimensional evolved power spectrum, and then averaging the time dimension to obtain the average evolved power spectrum; extracting three characteristic parameters—frequency at maximum power, half-power bandwidth, and skewness—using the standard beam spectrum as a benchmark; repeating the above steps on the beam to be evaluated, comparing the deviations of the characteristic parameters, and combining a grading threshold and a differentiated review process to determine the quality, and statistically analyzing the batch data dispersion to provide feedback on the stability of the beam manufacturing process. This invention achieves non-destructive, batch, and accurate evaluation of box girder quality, and can effectively identify hidden defects such as insufficient elastic modulus of concrete and prestress loss.

The application relates to a bridge collapse identification method and system based on vehicle mobile terminal sensing data, and belongs to the technical field of bridge safety monitoring. The method comprises the following steps: in the case that a target vehicle is determined to be located on a bridge section based on relative positions, monitoring driving acceleration of the target vehicle according to IMU data of a vehicle mobile terminal; in the case that a mutation occurs in vertical acceleration in the driving acceleration of the target vehicle, judging whether the target vehicle appears a bridge falling state caused by bridge collapse based on the vertical acceleration and synthetic lateral acceleration; if the target vehicle appears the bridge falling state caused by bridge collapse, obtaining jerk of the vertical acceleration, and determining bridge falling time information of the target vehicle based on the jerk; and based on the bridge falling time information of the target vehicle and falling heights of multiple vehicles when falling off the bridge, adjusting the credibility of the bridge collapse based on the falling heights of the multiple vehicles when falling off the bridge, so as to improve the determination accuracy and credibility of the bridge collapse state.

The invention discloses a dynamic correction method, device and equipment for a contact network conductor height measurement error and a medium, and is applied to the field of precision measurement of geometric parameters of a contact network. According to the method, the three-dimensional distortion degree corresponding to the original pull-out value sequence is analyzed, the three-dimensional distortion degree, the vertical acceleration and the pitch angle rate jointly construct a height guide error-distortion degree time sequence correlation model related to the height guide error, then the original height guide sequence is compensated and corrected, and the correction effect is verified. The guide height error-distortion degree time sequence correlation model effectively utilizes the internal stability of a pull-out value, and accurate traceability and measurement of the guide height error are realized; the dynamic adaptive capacity of the model is enhanced through the three-dimensional distortion degree, so that the model can intelligently adapt to changes of complex working conditions, and the robustness and reliability under different line conditions are remarkably improved; according to the method provided by the invention, an acquisition-correction-correction verification closed loop is formed, and the credibility and the accuracy of the contact network detection system are improved.

The application discloses an elevator starting torque calculation method, comprising the following steps: estimating the load in the elevator car before the elevator car stops according to the torque current of the elevator hoist during the operation of the elevator car; obtaining the vertical acceleration data of the elevator car during the stopping of the elevator at the landing, extracting the characteristic value of the vertical acceleration data, and judging the number of passengers getting on and off the elevator car; estimating the load change value of the elevator car according to the number of passengers getting on and off the elevator car or the vertical acceleration; determining the load in the elevator car before the starting of the elevator car according to the load in the elevator car before the stopping and the load change value of the elevator car; and calculating the elevator starting torque according to the load in the elevator car before the starting of the elevator car. The application realizes the function of the weighing device in the elevator without the weighing device, optimizes the output of the starting torque, and enables the passengers to obtain a comfortable riding experience.

The application provides a speed bump energy absorption working condition recognition method and system, and relates to the technical field of intelligent transportation systems.The method collects vehicle weight, speed and vertical acceleration data in real time through a laser radar and a load cell, constructs a full-quantity feature data set, first calculates kinetic energy change and vertical impact energy, and after the cumulative energy reaches a threshold, stores the speed and acceleration data before and after the speed bump according to vehicle weight, performs Fourier transform on the acceleration data, extracts the low-frequency energy proportion, and constructs a quadratic regression model combined with the speed before and after the speed bump to predict the speed after passing through.The energy proportion abnormal threshold is set by statistical stratification of the mean and standard deviation of the energy proportion database, and when the low-frequency energy proportion of a new vehicle is lower than the energy proportion abnormal threshold and the speed prediction error is greater than or equal to 15%, it is determined that the energy absorption structure fails, and the dynamic monitoring and accurate diagnosis of the health state of the speed bump are realized.

The application belongs to the field of bridge detection and evaluation, and discloses a bridge linear detection and damage prediction method based on a smart phone and a hybrid neural network, which obtains bridge pictures by using a distributed vision method of the smart phone, obtains vertical acceleration response data and environmental temperature of a standard vehicle when passing through the bridge by using a sensor of the smart phone, obtains pixel coordinates of feature points by using a YOLOv5s-CECA target detection model, converts the pixel coordinates of the positioning feature points to a world coordinate system by taking the gyroscope attitude parameters when shooting as initial external parameters and combining a pre-calibrated internal parameter matrix of the smart phone, optimizes the feature point coordinates by minimizing the re-projection error, draws the actual linear of the bridge, and inputs the obtained bridge data into a trained AFTCNN-LSTM model to obtain a damage degree prediction result. The application can be quickly deployed in a complex engineering site environment, improves the efficiency of bridge linear detection and damage prediction, and reduces the cost of bridge detection and evaluation.

The invention relates to a method for quickly acquiring ramp information and a vehicle running state, which is based on a first acceleration sensor parallel to the vehicle running direction and a second acceleration sensor perpendicular to the first acceleration sensor. Synchronously measuring to obtain a first acceleration component along the slope surface direction and a second acceleration component vertical to the slope surface direction; according to the projection relation between the second acceleration component and the gravitational acceleration in the direction perpendicular to the slope surface, the slope angle and the running acceleration of the current ramp are analyzed; and torque compensation control is conducted on a vehicle driving motor through the slope angle and the running acceleration. Aiming at the limitation of ramp detection of the existing GPS and gyroscope, a ramp information and vehicle running state acquisition system which is low in cost, high in real-time performance and adaptive to a whole scene is constructed by taking double vertical acceleration sensors as a basic core; the problems of slow ramp detection response, high cost, insufficient precision and load interference of the industrial vehicle in a closed and complex scene are effectively solved.

The invention provides an asphalt mixture pavement compactness prediction model construction method and a real-time prediction system, and the method comprises the steps: regarding a vibration compaction wheel as a dynamic coupling system interacting with a pavement material, and employing a Burgers creep model to simulate the deformation of an asphalt mixture; a physical mapping relation among the bituminous mixture pavement compactness, the bituminous mixture creep parameter and the vertical acceleration of the compaction wheel is established, a creep parameter value is obtained by combining a dynamic modulus test, and then a compactness prediction model is obtained. According to the method, the randomness and instability of a traditional empirical model are overcome, real-time monitoring of the pavement compaction quality in the whole compaction process can be achieved through vertical acceleration signals, frequent coring detection is not needed, detection is more timely, and the construction efficiency and uniformity can be remarkably improved; the prediction model has good environmental adaptability and robustness, prediction can be carried out under different construction temperatures and vibration compaction work conditions, the pavement compaction degree can be predicted in real time, and the prediction result is reliable.

The application belongs to the technical field of aircrafts, and specifically discloses a vertical take-off and landing aircraft landing system, a control method and an aircraft. The vertical take-off and landing aircraft landing system comprises a landing mechanism, an energy absorption mechanism and a braking mechanism. The landing mechanism comprises a frame, a support frame and a wheel arranged at the lower end of the support frame. The energy absorption mechanism is arranged on the frame and is used for absorbing kinetic energy when the support frame is turned outwards. The braking mechanism is used for adjusting the braking force of the wheel to adjust the turning-out amplitude of the support frame. When the aircraft lands, the braking mechanism actively adjusts the friction coefficient between the landing gear and the ground to adjust the size of the friction work of the landing gear and the work of the energy absorption system of the landing gear, thereby reducing the vertical acceleration of the fuselage of the aircraft, further reducing the fuselage load, reducing the risk of plastic deformation and damage of the fuselage and airborne equipment, reducing the load transmitted to the passengers, improving the safety and reliability of the whole machine, ensuring the safe landing of the aircraft, and reducing the risk of damage of the aircraft due to hard landing.

The present application relates to a kind of theoretical detection methods for determining local void of monolithic track bed, comprising the steps of: obtaining train-monolithic track bed-lining-ground coupling vibration equation according to the control equation of rail, monolithic track bed and lining and the dynamic balance equation of train, the vertical acceleration of car body is calculated using Newmark algorithm;The Hilbert-Huang transform spectrum of the vertical acceleration of car body is obtained;According to the energy change of the Hilbert-Huang transform spectrum of the imf2 decomposition curve under the void state, the position of the local void area of monolithic track bed is judged.The beneficial effects of the present application are: the position of the local void area of monolithic track bed can be judged according to the energy value change of the Hilbert-Huang transform spectrum of the vertical acceleration of car body imf2;Whether there is track bed void and the starting position of void can be judged, and the severity of void length can be qualitatively estimated according to the length of energy value change, which helps to reduce the operation and maintenance cost of subway track.

The application discloses a multi-module fusion vehicle state estimation model and a control method thereof, relates to the technical field of vehicle engineering and intelligent vehicle control, and comprises a tire force estimation module, a vehicle pose estimation module, an adhesion coefficient calculation module, a slip rate calculation module, a road surface friction coefficient identification module and a yaw angular acceleration calculation module; the tire force estimation module acquires four-wheel speeds, four-wheel driving torques, four-wheel braking torques, a vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw angular velocity, pitch angular velocity and roll angular velocity, performs tire rotation dynamics calculation and axle load transfer calculation, and obtains longitudinal forces and vertical forces of each wheel. The application adopts a layered module architecture and multi-parameter fusion calculation, realizes accurate estimation of vehicle states, optimizes calculation logic and constraint mechanisms in different scenes through a working condition self-adaptive strategy, and improves overall estimation accuracy, system stability and engineering adaptation capability.

This application discloses a method and apparatus for acquiring wheel-center vertical input force under transient durability conditions of suspension. The method includes: constructing a suspension transient input force prediction model and a full-condition error compensation parameter library; acquiring wheel-end vertical acceleration parameters, suspension state parameters, and vehicle condition identification parameters; classifying conditions according to the wheel-end vertical acceleration parameters, suspension state parameters, and vehicle condition identification parameters to obtain the current transient condition; obtaining the current compensation parameters according to the full-condition error compensation parameter library; and obtaining the wheel-center vertical input force corresponding to the current transient condition through the suspension transient input force prediction model, based on the current transient condition, wheel-end vertical acceleration parameters, suspension state parameters, and current compensation parameters. This application can improve the accuracy of wheel-center vertical input force acquisition and can cover all high-frequency transient high-damage conditions, and can be widely used in the field of automotive testing technology.

A passenger restraint system for roller coasters designed to maintain a passenger in an essentially seated or lying posture, with suspended feet. The passenger restraint system includes a fixed column and a seat column designed to support and hold the passenger, which seat column is coupled to the fixed column so as to be displaceable with respect to the fixed column during a roller coaster ride. The seat column is coupled to the fixed column by an articulated linkage, and the passenger restraint system further includes a balancing device ensuring a balancing of the seat column, which balancing device is fixed, at a first end, to the fixed column and, at a second end, to the seat column. The balancing device can advantageously be configured to ensure a vertical travel of the seat column during the roller coaster ride in dependence of a vertical acceleration exerted on the passenger.