Load detection method and system for multi-terrain damping suspension system

Abstract

This invention relates to the field of dynamic load measurement technology, and particularly to a load detection method and system for multi-terrain damping suspension systems. The method includes: establishing a geometric correlation model; acquiring vehicle chassis attitude angle data and real-time suspension displacement data and inputting them into the geometric correlation model, and calculating a three-dimensional pose change vector; acquiring raw strain signals, performing dynamic signal separation by combining the three-dimensional pose change vector, and generating static load strain values; performing geometric correction on the real-time suspension displacement data based on the three-dimensional pose change vector, and generating displacement-characterized load values; detecting the current terrain environment type, weighting the displacement-characterized load values ​​and static load strain values ​​to obtain uncorrected real-time load values; and using the displacement-characterized load values ​​and static load strain values ​​as a benchmark, learning and generating nonlinear compensation parameters and outputting real-time compensated load values. This invention effectively solves the problem of dynamic load measurement distortion caused by the multi-degree-of-freedom motion of the suspension system under complex driving environments.

Description

Technical Field

[0001] This invention relates to the field of dynamic load measurement technology, and in particular to a load detection method and system for multi-terrain damping suspension systems. Background Technology

[0002] In the field of vehicle engineering, real-time and accurate measurement of dynamic loads is a core technical requirement for optimizing suspension control and improving driving safety. Especially for engineering vehicles, off-road equipment, and other multi-terrain operation machinery, the dynamic loads borne by their suspension systems directly affect structural fatigue assessment, center of gravity stability warning, and power distribution decisions.

[0003] Traditional load detection mainly relies on two technical approaches: one is the direct measurement method using physical sensors, which involves placing strain gauges or force sensors on suspension links or shock absorbers and directly outputting measured values ​​based on the calibrated voltage and load relationship; the other is the kinematic indirect deduction method, which uses vehicle attitude sensors or displacement gauges to obtain suspension geometric parameters and then uses a vehicle dynamics model to infer the load distribution. However, existing technologies often suffer from inherent defects in motion and load coupling distortion, exhibiting the following characteristics: the six-dimensional spatial motion of the suspension system under complex road conditions causes sensors to simultaneously output signals of real load strain and mechanical deformation interference. Traditional strain gauge methods cannot separate these two types of physical quantities, resulting in measured values ​​containing noise components not caused by the load; existing filtering algorithms, such as frequency domain bandpass filtering, may mistakenly filter out the real load signal on bumpy roads, while linear compensation models constructed under uniform speed conditions cannot respond to nonlinear distortions caused by random impacts; and statically calibrated sensors accumulate errors under continuous varying conditions due to factors such as temperature drift and mechanical friction. These defects collectively lead to a systematic deviation between dynamic load measurements and actual physical loads. Especially when driving at high speeds on unpaved roads, measurement distortion can cause significant engineering risks such as inaccurate vehicle safety assessments and delayed structural overload warnings.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a load detection method and system for multi-terrain shock absorption suspension systems, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A load detection method for a multi-terrain damping suspension system, the method comprising:

[0008] Establish a geometric correlation model describing the three-dimensional kinematic relationship of the vehicle suspension mechanism;

[0009] Acquire the chassis attitude angle data and real-time suspension displacement data of each suspension point of the vehicle, input the chassis attitude angle data and the real-time suspension displacement data into the geometric association model, and calculate the three-dimensional pose change vector of each suspension point relative to the frame.

[0010] The original strain signal of the shock absorber is collected, and dynamic signal separation is performed by combining the three-dimensional pose change vector of each suspension point to filter out the strain interference component caused by the movement of the vehicle suspension mechanism and generate static load strain value.

[0011] Geometric correction is performed on the real-time suspension displacement data based on the three-dimensional pose change vector of each suspension point to generate a displacement characterization load value.

[0012] The terrain environment type where the vehicle is currently driving is detected. The displacement characterization load value and the static load strain value are weighted based on the terrain environment type. The weighted results are then fused to calculate the uncorrected real-time load value of each suspension point.

[0013] When the vehicle is detected to be in a low-disturbance driving state where the vertical vibration frequency domain energy value of the center of mass is continuously lower than a preset threshold and exceeds a set time, nonlinear compensation parameters are learned and generated based on the displacement characterization load value and the static load strain value, and the uncorrected real-time load value is compensated in real time to output the corrected real-time load value of each suspension point.

[0014] Furthermore, the three-dimensional pose change vector of each suspension point relative to the vehicle frame is calculated, including:

[0015] The spatial constraints of the vehicle suspension mechanism are loaded into the geometric association model to construct the kinematic chain topology;

[0016] The real-time suspension displacement data is decomposed into suspension travel components and flexible deformation components, which are then input into the kinematic chain topology.

[0017] The chassis attitude angle data and the suspension travel component are subjected to a steering knuckle rotation transformation in the geometric association model to derive the wheel center position offset.

[0018] Based on the flexible deformation component, elastic correction compensation is performed on the wheel center position offset to generate hard point coordinate update values;

[0019] In the geometric association model, the transformation relationship between the vehicle coordinate system and the ground reference coordinate system is established and the coordinate transformation matrix is ​​calculated;

[0020] Based on the updated hard point coordinates, the spatial attitude quaternions of each suspension point are solved by inversely solving the coordinate transformation matrix, and the three-dimensional pose change vector is continuously generated by performing Lie group differential operations on the spatial attitude quaternions.

[0021] Furthermore, static load strain values ​​are generated, including:

[0022] Construct a mechanism motion coupling template signal based on the three-dimensional pose change vector of each suspension point;

[0023] A time-domain decoupling operation involving complex exponential modulation is performed on the original strain signal to separate the dynamic strain substrate related to the vehicle attitude.

[0024] The phase-synchronous interference wave is generated by cross-correlation interference calculation between the motion coupling template signal of the mechanism and the dynamic strain substrate.

[0025] Using the phase synchronization interference wave as a reference signal, coherent suppression is performed on the original strain signal to generate an initial value of the load strain.

[0026] The derivative terms of the cascaded three-dimensional pose change vector are used to dynamically compensate for leakage in the initial load strain value, and the static load strain value is output.

[0027] Further, the real-time suspension displacement data is geometrically corrected based on the three-dimensional pose change vector of each suspension point, including:

[0028] The three-dimensional pose change vector is analyzed into the spatial displacement projection components of the dynamic deformation of the vehicle suspension mechanism.

[0029] Based on the real-time constructed motion coordinate system transformation matrix, the real-time suspension displacement data is decomposed into load-sensitive vectors and geometric disturbance vectors.

[0030] The deformation disturbance amount corresponding to the spatial displacement projection component is separated from the geometric disturbance vector, and the deformation disturbance amount is eliminated in the load sensitive vector based on the dynamic inverse compensation mechanism to generate a geometrically corrected load sensitive vector.

[0031] The displacement characterization load value is generated by transforming the physical dimension of the geometrically corrected load sensitivity vector.

[0032] Furthermore, based on the real-time constructed motion coordinate system transformation matrix, the real-time suspension displacement data is decomposed into a load-sensitive vector and a geometric disturbance vector, including:

[0033] The displacement parameters of the instantaneous rotation center of the vehicle suspension mechanism are dynamically solved from the three-dimensional pose change vector and used as the reference origin of the coordinate system. At the same time, the orientation of three mutually orthogonal coordinate axes is determined according to the spatial constraints of the geometric association model.

[0034] A dynamic reference coordinate system is constructed using the origin of the coordinate system and the direction of the coordinate axes. The real-time suspension displacement data is then mapped to the orthogonal projection space of the dynamic reference coordinate system to generate original projection vector components.

[0035] In the orthogonal projection space, identify the vertical bearing axis that is sensitive to the suspended load, and obtain the projection vector component on the vertical bearing axis as the load-sensitive vector;

[0036] The remaining projection vector components in the orthogonal projection space, excluding those perpendicular to the bearing axis, are synthesized to form the geometric disturbance vector characterizing the deformation disturbance of the mechanism.

[0037] Furthermore, the displacement characterization load value and the static load strain value are weighted based on the terrain environment type, including:

[0038] Analyze the current characteristics of the terrain environment type to generate dynamic confidence levels for the displacement characterization load value and the static load strain value;

[0039] The contribution intensity ratio of the displacement characterization load value and the static load strain value is adjusted based on the dynamic confidence level.

[0040] The displacement-characterized load value is combined with the contribution intensity ratio to perform a displacement-weighted operation to obtain a spatial dimension-weighted load value; the static load strain value is combined with the contribution intensity ratio to perform a strain-weighted operation to obtain a time-dimensional-weighted strain value;

[0041] The spatially weighted load value and the time-weighted strain value are integrated to generate a synthetic intermediate load value;

[0042] The uncorrected real-time load value is generated by performing a load characterization transformation calculation on the intermediate value of the synthetic load.

[0043] Furthermore, nonlinear compensation parameters are learned and generated, including:

[0044] When the vehicle is determined to be in a low-disturbance driving state, the displacement characterization load value sequence and the static load strain value sequence are simultaneously collected to generate load characterization sample pairs;

[0045] For each suspension point, the difference values ​​of the load characterization sample pairs at the same time are calculated to generate a dynamic error sequence;

[0046] Based on the changing trend of the dynamic error sequence, a compensation factor increment distribution map is constructed, and the nonlinear changing law of displacement and strain coupling deviation is identified according to the compensation factor increment distribution map;

[0047] The compensation learning step value is dynamically updated based on the nonlinear change law, and the relative correction coefficient between the displacement characterization load value and the static load strain value is iteratively generated based on the compensation learning step value.

[0048] The relative correction coefficients are mapped to polynomial fitting parameters of the load transfer curve of the suspension system to form a nonlinear parameter table;

[0049] The nonlinear compensation parameters are output by integrating the nonlinear parameter table of all the suspension points at the current time.

[0050] Further, real-time compensation of the uncorrected real-time load value outputs the corrected real-time load value for each of the suspending points, including:

[0051] The nonlinear compensation parameters are called in combination with the uncorrected real-time load value at the current moment to perform nonlinear deviation compensation calculation, generating instantaneous correction amounts corresponding to each suspension point;

[0052] The instantaneous correction is superimposed on the uncorrected real-time load value to generate a compensated linear predicted load value;

[0053] The suspension motion compensation capability verification mechanism based on the geometric correlation model performs real-time inversion verification of the linear predicted load value, and adjusts the compensation residual error to output a stable load prediction sequence during the real-time inversion verification process.

[0054] The stable load prediction sequence is converted into a dimensionally normalized value consistent with the physical unit of vehicle load to generate a vehicle load numerical stream.

[0055] The vehicle load data stream is used to update the load status data of each suspension point in real time and output the corrected real-time load value of each suspension point.

[0056] A load detection system for a multi-terrain damping suspension system, the system comprising:

[0057] The motion association module establishes a geometric association model that describes the three-dimensional kinematic relationships of the vehicle's suspension mechanism;

[0058] The pose calculation module acquires the vehicle's chassis attitude angle data and real-time suspension displacement data of each suspension point. It inputs the chassis attitude angle data and real-time suspension displacement data into the geometric association model to calculate the three-dimensional pose change vector of each suspension point relative to the frame.

[0059] The signal separation module collects the original strain signal of the shock absorber, combines it with the three-dimensional pose change vector of each suspension point to perform dynamic signal separation, filters out strain interference components caused by the movement of the vehicle suspension mechanism, and generates static load strain values.

[0060] The geometric correction module performs geometric correction on the real-time suspension displacement data based on the three-dimensional pose change vector of each suspension point, and generates displacement characterization load values.

[0061] The terrain weighting module detects the terrain environment type in which the vehicle is currently driving, weights the displacement characterization load value and static load strain value based on the terrain environment type, and merges the weighting results to calculate the uncorrected real-time load value of each suspension point.

[0062] The parameter compensation module, when it detects that the vehicle is in a low-disturbance driving state where the vertical vibration frequency energy value of the center of gravity is continuously lower than the preset threshold and exceeds the set time, learns to generate nonlinear compensation parameters based on the displacement characterization load value and the static load strain value, and outputs the corrected real-time load value of each suspension point after real-time compensation of the uncorrected real-time load value.

[0063] Furthermore, the geometry correction module includes:

[0064] The displacement projection unit resolves the three-dimensional pose change vector into spatial displacement projection components of the dynamic deformation of the vehicle suspension mechanism.

[0065] The geometric decomposition unit decomposes real-time suspension displacement data into load-sensitive vectors and geometric disturbance vectors based on the real-time constructed motion coordinate system transformation matrix.

[0066] The load correction unit separates the deformation disturbance corresponding to the spatial displacement projection component from the geometric disturbance vector, and eliminates the deformation disturbance in the load sensitive vector based on the dynamic inverse compensation mechanism to generate a geometrically corrected load sensitive vector.

[0067] The load characterization unit converts the physical dimension of the geometrically corrected load sensitivity vector to generate displacement characterization load values.

[0068] The technical solution of this invention can achieve the following technical effects:

[0069] By establishing a three-dimensional dynamic model of the vehicle suspension mechanism's pose change, separating the motion interference components in the displacement and strain signals, and combining terrain-adaptive weighted fusion and a nonlinear real-time learning compensation mechanism under low-disturbance conditions, the coupling effect between mechanical motion and real load is decoupled. This solves the problem of dynamic load measurement distortion caused by the multi-degree-of-freedom motion of the suspension system in complex driving environments, and realizes high-precision continuous detection of suspension load in multi-terrain scenarios.

[0070] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 A flowchart illustrating the load detection method for multi-terrain damping suspension systems;

[0073] Figure 2 A flowchart illustrating the process of calculating the 3D pose change vector;

[0074] Figure 3 This is a flowchart illustrating the weighting based on terrain environment type.

[0075] Figure 4 A schematic diagram of the process for learning to generate nonlinear compensation coefficients. Detailed Implementation

[0076] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0077] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0078] Example 1;

[0079] like Figure 1 As shown, this application provides a load detection method for a multi-terrain damping suspension system, the method including:

[0080] Establish a geometric correlation model describing the three-dimensional kinematic relationship of the vehicle suspension mechanism;

[0081] Acquire the vehicle's chassis attitude angle data and real-time suspension displacement data of each suspension point, input the chassis attitude angle data and real-time suspension displacement data into the geometric association model, and calculate the three-dimensional pose change vector of each suspension point relative to the frame.

[0082] The original strain signal of the shock absorber is collected, and dynamic signal separation is performed by combining the three-dimensional pose change vector of each suspension point. The strain interference component caused by the movement of the vehicle suspension mechanism is filtered out, and the static load strain value is generated.

[0083] Geometric correction is performed on the real-time suspension displacement data based on the three-dimensional pose change vector of each suspension point to generate displacement characterization load values.

[0084] The system detects the terrain environment type in which the vehicle is currently driving, weights the displacement characterization load value and static load strain value based on the terrain environment type, and then merges the weighted results to calculate the uncorrected real-time load value of each suspension point.

[0085] When the vehicle is detected to be in a low-disturbance driving state where the vertical vibration frequency energy value of the center of gravity is continuously lower than the preset threshold and exceeds the set time, the nonlinear compensation parameters are learned and generated based on the displacement characterization load value and the static load strain value, and the corrected real-time load value of each suspension point is output after real-time compensation of the uncorrected real-time load value.

[0086] Specifically, firstly, based on the specific type of vehicle suspension structure, such as double wishbone suspension, MacPherson strut suspension, or five-link suspension, a complete geometric model of the suspension mechanism is created. In the preferred case, coordinate transformation is used to accurately describe the kinematic characteristics of each component in the suspension mechanism, such as the control arms, links, and wheels. Specifically, the vehicle suspension system is simplified into a multi-link structure, where each joint connection is modeled as a hinge relationship, establishing the correlation matrix between each moving component. Secondly, for the suspension system, the chassis or body is selected as the spatial reference frame, and a fixed reference coordinate system is defined. For example, the longitudinal axis of the body is defined as the X-axis, the transverse axis as the Y-axis, and the vertical direction as the Z-axis. This is achieved by analyzing the relationship between the suspension points and the chassis. Based on the geometric constraints and using known parameters such as link length and motion limit angle, a mathematical model is established to describe the motion trajectory of the suspension points relative to the chassis in three-dimensional space. In the preferred embodiment, the linear and angular displacement characteristics of all suspension points in three-dimensional space can be refined based on the kinematic laws of wheel motion. Next, to construct the correlation model, the influence of wheel and suspension mechanism motion on vehicle attitude needs to be considered. In the preferred embodiment, high-precision displacement sensors and tension / compression sensors can be installed at the suspension points, combined with the inertial measurement unit (IMU) in the vehicle chassis to collect the three-dimensional attitude angle data of the chassis in real time, including pitch angle, roll angle, and yaw angle. This attitude angle data can dynamically reflect the attitude changes of the vehicle during driving. First, the model is used to correct the actual position of the suspension points in the three-dimensional space of the vehicle body. Then, the measured data from the sensors is input into the geometric association model. Using a calculation method based on coordinate transformation and rigid body kinematics, the three-dimensional pose change vector of each suspension point relative to the frame is calculated point by point, including the displacement and angle changes of the suspension points in three directions. Specifically, the three-dimensional kinematic equations can be solved by a step-by-step iterative convergence method. The optimal data solution scheme needs to be combined with the real-time data stream to ensure that the model solution results are consistent with the actual motion state of the vehicle. Second, in the dynamic signal separation step, the original strain signal needs to be collected by a high-precision strain sensor. This signal contains the strain caused by the movement of the suspension mechanism. To mitigate interference, the preferred implementation method can combine the three-dimensional pose change vector with a signal filtering algorithm for dynamic signal separation, refining the processing mechanism for signal and noise components to generate accurate static load strain values. The preferred implementation method may also include dynamically adjusting key parameters of the filtering and signal separation algorithms based on the vehicle's operating state, load conditions, and terrain complexity. Regarding the generation of displacement characterization load values, the real-time suspension displacement data is geometrically corrected based on the calculated three-dimensional pose change vector. This process preferably includes dynamically adjusting the weight parameters for displacement characterization load correction calculation by combining the suspension arm's geometric characteristics and the vehicle's mass distribution parameters, ultimately obtaining a high-precision load value that closely matches the vehicle's actual motion state.Furthermore, to enhance the adaptability of the load detection method, a terrain-based detection mechanism is preferred. This can be achieved through terrain sensors or terrain classification algorithms to perceive the terrain environment type in real time, such as flat roads, low-disturbance hilly terrain, or high-disturbance rugged mountainous terrain. Then, the displacement-characterized load value and static load strain value are weighted based on the terrain type. Preferably, a dynamic weight adjustment mechanism should be used to ensure that the weighting result accurately reflects the comprehensive impact of the terrain environment on the load data, thereby generating the uncorrected real-time load value for each suspension point. Finally, this method is preferably applied to low-disturbance driving conditions. The vehicle collects vertical vibration signals through an inertial sensor unit fixedly installed at the center of the vehicle structure. The original vibration signal undergoes digital filtering to retain a specific low-frequency band reflecting the inherent vibration of the suspension system. This band excludes high-frequency interference from the engine and high-frequency noise from the road surface. The filtered signal is divided into equal-duration analysis segments. Spectrum transformation is performed on each segment to extract the energy contribution value of all frequency components within the target frequency band. The vertical vibration value for that time period is generated through integration. The frequency domain energy quantization index has a preset energy judgment threshold that can be dynamically adjusted according to the actual load state of the vehicle. That is, when the vehicle's load weight increases or the rigidity of the suspension system increases, the judgment threshold increases accordingly; conversely, it decreases. The aforementioned frequency domain energy quantization index is continuously monitored. When multiple consecutive analysis segments of this index, with a total duration covering several natural vibration cycles of the vehicle's suspension system, are all below the current dynamic threshold, the vehicle is determined to be in a low-disturbance driving state. At this time, the vehicle typically travels on smooth paved roads such as highways or urban asphalt roads, maintains a constant speed or slow acceleration, or does not encounter sudden road undulations or obstacle impacts. After detecting this state, a deep learning model or nonlinear feedback control mechanism is established based on the displacement-characterized load value and the static load strain value to generate nonlinear compensation parameters for real-time compensation. Iterative algorithm optimization technology is preferably used to correct the parameter settings of the nonlinear compensation mechanism in real time, ensuring that the compensation effect has efficient adaptability to changes in vehicle suspension load under different terrain environments. Finally, the corrected real-time load values ​​of each suspension point are output.

[0087] The technical solution of this invention establishes a three-dimensional dynamic model of the vehicle suspension mechanism's pose change, separates the motion interference components in the displacement and strain signals, and combines terrain-adaptive weighted fusion and a nonlinear real-time learning compensation mechanism under low disturbance conditions to decouple the coupling effect between mechanical motion and real load. This solves the problem of dynamic load measurement distortion caused by the multi-degree-of-freedom motion of the suspension system in complex driving environments, and realizes high-precision continuous detection of suspension load in multi-terrain scenarios.

[0088] Furthermore, such as Figure 2 As shown, the calculation of the three-dimensional pose change vector of each suspension point relative to the vehicle frame includes:

[0089] The spatial constraints of the vehicle suspension mechanism are loaded into the geometric association model to construct the kinematic chain topology;

[0090] Real-time suspension displacement data is decomposed into suspension stroke components and flexible deformation components, which are then input into the kinematic chain topology.

[0091] The chassis attitude angle data and the suspension travel components are used to perform a steering knuckle rotation transformation in the geometric association model to derive the wheel center position offset.

[0092] Based on the flexible deformation component, elastic correction compensation is performed on the wheel center position offset to generate updated hard point coordinate values;

[0093] In the geometric association model, establish the transformation relationship between the vehicle coordinate system and the ground reference coordinate system and calculate the coordinate transformation matrix;

[0094] Based on the updated hard point coordinates, the spatial attitude quaternions of each suspension point are solved by inversely solving the coordinate transformation matrix, and the three-dimensional pose change vector is continuously generated by performing Lie group differential operations on the spatial attitude quaternions.

[0095] As a preferred embodiment of the above, the spatial constraints of the vehicle suspension mechanism must first be loaded into the geometric association model to construct the kinematic chain topology. A preferred implementation of this process requires first analyzing the structural characteristics of the vehicle suspension mechanism. Different suspension structures have spatial constraints including the motion limits and swing range of the suspension points, as well as the length and motion constraints of the suspension links. Specifically, based on the spatial hinge relationships in the suspension geometry, a kinematic chain topology can be established using nodes as the basis, constructing a topological framework to describe the association between each key motion point within the suspension mechanism. Next, the real-time suspension displacement data is decomposed. In a preferred embodiment, the real-time suspension displacement data can be decomposed... The displacement signal is further decomposed into suspension travel component and flexible deformation component. In actual scenarios, the suspension travel component is usually determined by the extension and contraction of the suspension spring and shock absorber, while the flexible deformation component mainly originates from the slight elastic deformation of the suspension arm or swing arm during vehicle movement. Ideally, data from multiple sensors should be combined for separation, and the two components should be accurately decomposed through filtering mechanisms and flexible mechanical analysis to ensure high precision and accuracy of the data input to the kinematic chain topology. In the geometric association model, the steering knuckle rotation transformation is preferably performed based on the suspension travel component and chassis attitude angle data to derive the wheel center position offset. In this process, the input chassis tilt is first... Pitch angle, roll angle, and yaw angle data are used to calculate the steering knuckle's motion angle. Combined with the suspension travel component, this forms the wheel center position offset trajectory. Preferably, dynamic coordinate transformation is used to dynamically predict the tire center and suspension geometry's position change relative to the chassis in three-dimensional space. Subsequently, elastic correction compensation is applied to the wheel center position offset based on the flexible deformation component to generate updated hard point coordinates. Preferably, a flexible mechanical model is used to analyze the dynamic wheel center position, compensating for the interference of suspension rod elastic deformation on the wheel center's spatial motion. In a preferred case, the vehicle's material properties, such as suspension arm material strength and elastic modulus, can be referenced to fit and calculate the flexible deformation. The actual wheel center offset is corrected to make the updated hard point coordinates more consistent with the actual vehicle motion structure. Subsequently, in order to establish the transformation relationship between the vehicle coordinate system and the ground reference coordinate system and calculate the coordinate transformation matrix, it is necessary to define a unified reference system transformation framework based on the motion characteristics of the chassis and body. In the preferred case, the vehicle coordinate system is defined as the central reference system of the suspension system, and the original ground coordinate system is used as the reference. The coordinate transformation rules are established through the relationship between position and angle. The chassis attitude angle data is used as input to calculate the position and attitude change trajectory of each suspension point in the ground reference system in real time. During the transformation process, the rotation angle in three-dimensional space is used to specify the change law and generate a dynamic coordinate matrix relationship that matches the vehicle motion.Finally, based on the updated hardpoint coordinates and the coordinate transformation matrix, the spatial attitude quaternions of each suspension point are obtained through inverse kinematics. In the preferred embodiment, to improve the accuracy of the quaternion calculation, compensation and correction are performed by collecting dynamic sequence data. Simultaneously, the effectiveness of the results is improved based on the joint constraints of the suspension mechanism. Based on the obtained spatial attitude quaternions, to maintain the continuity of attitude changes, Lie group differential operations are preferably performed. Lie groups describe the characteristics of continuous transformations. Through differential kinematics, the continuous pose change trajectory of the suspension point in three-dimensional space can be obtained. This preferred processing method ensures that the calculated three-dimensional pose change vector has temporal correlation and spatial linkage, providing necessary support for the dynamic real-time monitoring of the suspension system.

[0096] Furthermore, generating static load strain values ​​includes:

[0097] Construct the mechanism motion coupling template signal based on the three-dimensional pose change vector of each suspension point;

[0098] A time-domain decoupling operation involving complex exponential modulation is performed on the original strain signal to separate the dynamic strain substrate related to the vehicle attitude.

[0099] Phase-synchronized interference waves are generated by cross-correlation interference calculation between the motion coupling template signal of the mechanism and the dynamic strain substrate.

[0100] Using the phase synchronization interference wave as a reference signal, coherent suppression is performed on the original strain signal to generate the initial value of the load strain;

[0101] The derivative term of the cascaded three-dimensional pose change vector performs dynamic leakage compensation on the initial load strain value and outputs the static load strain value.

[0102] As a preferred embodiment of the above, firstly, a motion coupling template signal for the mechanism needs to be constructed based on the three-dimensional pose change vectors of each suspension point. In this preferred embodiment, the three-dimensional pose change vectors include the displacement and rotation information of the suspension points in each direction. Their dynamics directly reflect the motion characteristics of the suspension system. Based on these motion characteristics, the spatiotemporal distribution characteristics of key nodes are obtained through a geometric correlation model. Combined with the vehicle's suspension system architecture and road disturbance response, a coupling template signal for the overall motion trajectory of the suspension system is generated. The key point of the coupling template signal is to describe the overall motion state of the vehicle using the constraint relationships and correlation characteristics between suspension points, and to reduce discontinuities through high-precision interpolation methods to ensure... The template signal can dynamically reflect the synchronization relationship between the suspension mechanism and the actual vehicle movement in a time series manner. Next, a time-domain decoupling operation of complex exponential modulation is performed on the original strain signal to separate the dynamic strain basis related to the vehicle attitude from the strain signal. Preferably, the original strain signal is collected by a strain sensor mounted on the suspension mechanism. This signal contains composite features generated by the superposition of multiple factors such as external load, vehicle attitude changes, and road disturbances. To distinguish the dynamic strain component caused by vehicle attitude changes, complex exponential modulation is often used. Typically, the original strain signal is analyzed in the time domain based on the signal's time-domain pattern and frequency response relationship. A preferred implementation method is to combine real-time... Chassis attitude angle data, including synchronously adjusted parameters such as roll angle and pitch angle, are used to progressively decompose the signal through a dynamic filtering algorithm, ultimately retaining the dynamic strain base coupled with vehicle motion. Further, the motion coupling template signal of the mechanism is cross-correlated with the dynamic strain base to generate a phase-synchronized interference wave. In the preferred embodiment, the cross-correlation function is used to calculate the synchronization relationship between the two sets of signals. The phase difference characteristics of the signals reflect the degree of motion coupling. The cross-correlation results reflect the time and amplitude relationship between the vehicle attitude change vibration and the original strain response, which can effectively identify the anti-interference characteristics of the system and thus construct the wave profile of dynamic interference. Through this process, the system obtains... The obtained phase synchronization interference wave can be used as a reference signal for subsequent coherent suppression. Subsequently, based on the phase synchronization interference wave, coherent suppression is performed on the original strain signal to further generate the initial load strain value. In the preferred implementation, the coherent suppression process compares and analyzes the amplitude and phase of the original strain signal and the dynamic interference signal to remove the strain components related to the coupling of vehicle attitude and suspension motion, and only retains the strain part related to the external load. In order to improve the detection accuracy, this process preferably adopts the successive iterative filtering adjustment method to further reduce and suppress additional information such as noise components and frequency domain interference of the original signal, so that the generated initial load strain value is closer to the ideal state.Finally, through cascaded processing, dynamic leakage compensation is performed on the initial load strain value by combining the derivative term of the three-dimensional pose change vector, ultimately generating a static load strain value. This corrects the strain leakage error caused by the high-frequency dynamic characteristics of vehicle motion. In the preferred case, a typical dynamic leakage model is established by fitting and analyzing the time derivative characteristics of the pose change vector to correct the dynamic error component in the initial load strain value. Furthermore, multiple sets of compensation rules can be configured for the dynamic response parameters of different road conditions, such as rugged terrain and flat roads, continuously adjusting the dynamic compensation ratio of the strain signal to ensure that the output static load strain value can cover more complex application scenarios.

[0103] Furthermore, geometric corrections are performed on the real-time suspension displacement data based on the three-dimensional pose change vectors of each suspension point, including:

[0104] The three-dimensional pose change vector is analyzed into the spatial displacement projection components of the dynamic deformation of the vehicle suspension mechanism.

[0105] Based on the real-time construction of the motion coordinate system transformation matrix, the real-time suspension displacement data is decomposed into load-sensitive vectors and geometric disturbance vectors.

[0106] The deformation disturbance amount corresponding to the spatial displacement projection component is separated from the geometric disturbance vector, and the deformation disturbance amount is eliminated in the load-sensitive vector based on the dynamic inverse compensation mechanism to generate a geometrically corrected load-sensitive vector.

[0107] Transform the physical dimension of the geometrically corrected load-sensitive vector to generate displacement characterization load values.

[0108] As a preferred embodiment of the above, firstly, the spatial displacement projection components of the dynamic deformation of the vehicle suspension mechanism are analyzed using a three-dimensional pose change vector. In this preferred embodiment, the three-dimensional pose change vector includes linear and rotational displacement information of the suspension points. By analyzing this information, the motion trajectory of the suspension system in each direction in three-dimensional space, as well as the dynamic deformation of the suspension system relative to the vehicle frame, are determined. To achieve this analysis process, it is preferable to combine the geometric characteristics and dynamic response laws of the vehicle suspension system and use spatial vector decomposition technology to identify each deformation component under a specific motion coordinate system, thereby reflecting the spatial distribution characteristics of vehicle motion. Next, based on the real-time construction of the motion coordinate system transformation matrix, the real-time suspension displacement data is decomposed into load-sensitive vectors and geometric interference vectors. Specifically, in this preferred embodiment, the real-time suspension displacement data includes the spatial position and angle changes of each suspension point during vehicle motion. By constructing a coordinate system transformation matrix based on vehicle motion parameters in real time, the original suspension displacement data can be transformed and decomposed, eliminating non-load-related components in the suspension motion. The load-sensitive vector reflects the static and dynamic loads actually borne by the suspension points, while the geometric interference vector is mainly caused by instantaneous influencing factors such as irregular terrain and vehicle bumps. Then, from the geometric interference... The deformation interference corresponding to the spatial displacement projection component is separated from the vector, and these deformation interferences are eliminated in the load-sensitive vector based on a dynamic inverse compensation mechanism to generate a geometrically corrected load-sensitive vector. In this preferred implementation step, the dynamic behavior pattern of the interference source is determined by combining the suspension motion trajectory and the spatial displacement projection. The dynamic inverse compensation mechanism is used to perform real-time correction by solving the inverse problem model, iteratively reducing geometric interference to correct the load-sensitive vector. This process involves mathematical processing and algorithm optimization, aiming to ensure dynamic matching of displacement transformation relationships and improve the accuracy of load characterization. Finally, the physical dimension of the geometrically corrected load-sensitive vector is transformed to generate displacement characterization load values. Preferably, this step requires converting the geometrically corrected load-sensitive vector into a physical load signal dimension, such as force or pressure. For this purpose, the characteristic indicators of vehicle load and the material performance parameters of the suspension system can be combined, and the transformation can be performed by applying corresponding physical laws to ensure that the obtained load values ​​can reflect the load characteristics of the actual vehicle working state and the interaction with the ground. The physical dimension transformation process should especially consider the dynamic response of the vehicle under various working conditions to ensure the effective adaptability of the obtained load data under different operating conditions.

[0109] Furthermore, based on the real-time construction of the motion coordinate system transformation matrix, the real-time suspension displacement data is decomposed into load-sensitive vectors and geometric disturbance vectors, including:

[0110] The displacement parameters of the instantaneous rotation center of the vehicle suspension mechanism are dynamically solved from the three-dimensional pose change vector and used as the reference origin of the coordinate system. At the same time, the orientation of three mutually orthogonal coordinate axes is determined according to the spatial constraints pre-defined by the geometric association model.

[0111] A dynamic reference coordinate system is constructed using the origin of the coordinate system and the direction of the coordinate axes. Real-time suspension displacement data is mapped to the orthogonal projection space of the dynamic reference coordinate system to generate original projection vector components.

[0112] In the orthogonal projection space, identify the vertical bearing axis that is sensitive to the suspended load, and obtain the projection vector component on the vertical bearing axis as the load-sensitive vector.

[0113] The remaining projection vector components in the orthogonal projection space, excluding those perpendicular to the bearing axis, are synthesized to form a geometric disturbance vector characterizing the deformation disturbance of the mechanism.

[0114] As a preferred embodiment of the above, firstly, the displacement parameters of the instantaneous rotation center of the vehicle suspension mechanism need to be dynamically solved from the three-dimensional pose change vector, and used as the reference origin of the coordinate system. The definition of the instantaneous rotation center is based on the dynamic motion characteristics of the suspension system. For example, when the vehicle is driving on an irregular surface, the suspension point will rotate instantaneously relative to the ground. By calculating the displacement parameters of this rotation center, the spatial reference origin of the suspension system can be obtained. In a preferred embodiment, this position can be obtained by analyzing the dynamic response of the suspension arm and combining it with real-time dynamic data analysis. Next, the directions of three mutually orthogonal coordinate axes are determined according to the spatial constraints preset by the geometric association model. These coordinate axes are typically A dynamic reference coordinate system is established, encompassing the vehicle's front-to-back, left-to-right, and up-to-down directions. This system closely reflects the various dynamic changes in the vehicle's suspension during movement. Next, a dynamic reference coordinate system is constructed using the origin and axis directions. Real-time suspension displacement data is then mapped into the orthogonal projection space of this dynamic reference coordinate system to generate original projection vector components. In a preferred embodiment, the reference point and axial direction of the reference coordinate system are dynamically adjusted using real-time vehicle motion data. This ensures that the projection data obtained through coordinate system mapping accurately reflects the instantaneous state of the suspension system. The original projection vector components are generated through the coordinate system's projection mechanism. A three-dimensional spatial vector set is used to represent the motion trajectory of the suspension point in the reference space. In the orthogonal projection space, it is preferable to identify the vertical load-bearing axis that is sensitive to the suspension load, and obtain the projection vector component on the vertical load-bearing axis as the load-sensitive vector. The selection of the vertical load-bearing axis is based on the vehicle's load-bearing characteristics and the dynamic response of the suspension structure. It is usually perpendicular to the ground direction and connected to the load to achieve optimal identification. The most influential axis can be identified by combining terrain sensor data and mechanical characteristic data of the suspension system. This axis bears the load and directly affects the suspension motion. The vector component projected on it can most accurately reflect the real-time change characteristics of the load. Subsequently, the remaining projection vector components in the orthogonal projection space, excluding those perpendicular to the bearing axis, are synthesized to form a geometric interference vector characterizing the deformation interference of the mechanism. In the optimized implementation process, the geometric interference vector usually includes various projections of factors such as terrain complexity, vehicle dynamic motion, and suspension mechanism deformation. By calculating and synthesizing the remaining projection vectors in real time, a geometric interference vector representing the dynamic deformation influence of the suspension system can be formed. These interference factors are mainly non-inertial factors that cause deviations in load detection. In the optimized implementation, real-time correction is required through dynamic algorithms to ensure that the analysis of the interference vector can accurately reflect the vehicle motion and road conditions, providing necessary adjustment references for subsequent load calculations.

[0115] Furthermore, such as Figure 3 As shown, the displacement characterization load value and static load strain value are weighted based on the terrain environment type, including:

[0116] Analyze the current characteristics of terrain environment types to generate dynamic confidence levels for displacement characterization load values ​​and static load strain values;

[0117] The contribution intensity ratio of displacement characterization load value and static load strain value is adjusted based on the dynamic confidence level.

[0118] The displacement-characterized load value is combined with the contribution intensity ratio to perform a displacement-weighted operation to obtain the spatial dimension-weighted load value; the static load strain value is combined with the contribution intensity ratio to perform a strain-weighted operation to obtain the time dimension-weighted strain value.

[0119] Integrating spatially weighted load values ​​and time-weighted strain values ​​generates a synthetic intermediate load value;

[0120] Perform load characterization transformation calculations on the intermediate value of the synthetic load to generate an uncorrected real-time load value.

[0121] As a preferred embodiment of the above, firstly, the current characteristics of the terrain environment are analyzed to generate dynamic confidence levels for displacement characterization load values ​​and static load strain values. Preferably, the terrain environment characteristics can be acquired in real time through terrain classification sensors or terrain recognition algorithms, such as distinguishing whether the vehicle is traveling on a flat road, rough ground, or a mountain path. These terrain types directly affect the vehicle's suspension dynamics and strain characteristics. Based on this information, a dynamic confidence model can be constructed, thereby assigning different confidence levels to different parameter output results to ensure more reliable detection results. Next, the contribution intensity ratio of displacement characterization load values ​​and static load strain values ​​is adjusted based on the dynamic confidence level. To ensure the accuracy and balance of the results during implementation, a weighting mechanism can be established. Based on the actual terrain complexity and dynamic response, the contribution ratio of load and strain values ​​in the comprehensive detection can be dynamically adjusted. For example, if the terrain is complex and unstable, the weight of static load and strain values ​​should be increased to reduce errors caused by terrain disturbances. Through reasonable weighting adjustments, the true load-bearing state of the vehicle can be better reflected. Subsequently, the displacement-characterized load values ​​need to be weighted according to the contribution intensity ratio to obtain a spatially weighted load value. In the preferred implementation, the displacement-characterized load values ​​are weighted to give them a higher spatial detection weight in specific terrain environments, combined with the contribution intensity... The ratio directly correlates the dynamic response of the terrain environment with the spatial displacement changes of the suspension system to output the most representative spatial load value. This process ensures that the direct impact of terrain on suspension load changes is always accurately reflected. Simultaneously, the static load strain value is combined with the contribution intensity ratio for strain weighting to obtain a time-weighted strain value. More stable terrain should emphasize time-weighting because stable terrain reduces dynamic disturbances, allowing the strain value to more accurately reflect the load impact. Ideally, dynamic strain weighting filters out short-term local disturbances, enabling the strain signal to represent the long-term load effect. Furthermore, the spatial-weighted load value and the time-weighted strain value are integrated... The intermediate value of the composite load is generated by weighting the strain values ​​of different dimensions. In the preferred implementation, this integrated processing integrates the feature data of spatial and temporal dynamic response by fusing feature data of different loading dimensions and combining them with comprehensive weights of the input signal to provide an accurate overall load profile. Finally, the intermediate value of the composite load is converted into an uncorrected real-time load value. In the preferred implementation, this conversion calculation involves further standardization of the integrated result to make it usable for multi-dimensional load detection. By correcting and converting the composite load in real time, the output uncorrected real-time load value can intuitively reflect the comprehensive load status of the vehicle suspension system in the current terrain environment.

[0122] Furthermore, such as Figure 4 As shown, the nonlinear compensation parameters are learned and generated, including:

[0123] When the vehicle is determined to be in a low-disturbance driving state, the displacement characterization load value sequence and the static load strain value sequence are collected simultaneously to generate load characterization sample pairs.

[0124] For each suspension point, calculate the difference values ​​of load characterization sample pairs at the same time to generate a dynamic error sequence;

[0125] Based on the changing trend of the dynamic error sequence, an incremental distribution map of the compensation factor is constructed, and the nonlinear variation law of the displacement and strain coupling deviation is identified according to the incremental distribution map of the compensation factor.

[0126] The compensation learning step value is dynamically updated based on the nonlinear variation law, and the relative correction coefficient between the displacement characterization load value and the static load strain value is iteratively generated based on the compensation learning step value.

[0127] The relative correction coefficients are mapped to polynomial fitting parameters of the load transfer curve of the suspension system to form a nonlinear parameter table;

[0128] The nonlinear compensation parameters are output by integrating the nonlinear parameter table of all suspension points at the current moment.

[0129] As a preferred embodiment of the above, firstly, when the vehicle is determined to be in a low-disturbance driving state, it is necessary to simultaneously collect the displacement characterization load value sequence and the static load strain value sequence to generate a load characterization sample pair. In the preferred implementation process, the vehicle's state on a smooth road or in a similar low-disturbance driving environment is determined through environmental monitoring and vehicle dynamic judgment. This state identification is based on a comprehensive judgment of the suspension dynamic change amplitude and road surface information to ensure that disturbance factors are minimized during sample collection. The collected displacement characterization load value sequence reflects the vehicle suspension displacement change, while the static load strain value sequence shows the strain borne by the frame. The combination of the two constitutes a complete load characterization sample pair; Next... For each suspension point, the difference values ​​of these load characterization sample pairs at the same time are calculated to generate a dynamic error sequence. This step involves comparative analysis of the sample data. By calculating the instantaneous difference between displacement and strain, the dynamic deviation between load and strain characterization is identified. Ideally, a precise time synchronization algorithm should be used to ensure that the generated dynamic error sequence can truly reflect the impact of the current environment on the changes in suspension characteristics. The difference values ​​should be able to show the micro-error characteristics of different suspension points under low-disturbance conditions. Then, a compensation factor increment distribution map is constructed based on the changing trend of the dynamic error sequence. The nonlinear changing law of displacement and strain coupling deviation is identified through the information in the map. In implementation, dynamic error sequence analysis and node trend extraction are combined to enable the increment of the compensation factor to reflect complex coupling deviations, such as how terrain characteristics cause static and dynamic deviations in displacement and strain at certain times. However, the coupling changes caused by different types of terrain need to be achieved through curve fitting and adjustment to achieve the best compensation effect. Then, the compensation learning step value is dynamically updated based on the above-mentioned nonlinear change law. The relative correction coefficient between the displacement characterization load value and the static load strain value is generated iteratively through the compensation learning step value. In the preferred case, adaptive learning is used to respond to the dynamic strain influence of terrain changes in a timely manner, so that the step value can achieve flexibility in different states. The adjustment involves iteratively calculating the relative correction coefficient, with each iteration aimed at reducing error deviations to ensure a more accurate reflection between load and strain values. Subsequently, the relative correction coefficient is mapped to polynomial fitting parameters of the suspension system load transfer curve, forming a nonlinear parameter table. In the preferred embodiment, this table represents the load transfer relationship between suspension points in polynomial form, including important geometric and mechanical properties. The polynomial fitting parameters ensure accurate prediction of suspension system behavior under different load and strain conditions. The relative correction coefficient is integrated into this table as a key characteristic parameter, ensuring that the suspension dynamic response can be predicted and adjusted within the nonlinear range.Finally, the nonlinear parameter tables of all suspension points at the current moment are integrated, and the nonlinear compensation parameters are output. In the preferred embodiment, by comprehensively analyzing the load transfer characteristics of the suspension system, the compensation parameter table provides a basis for vehicle control and suspension tuning. The nonlinear compensation information of each suspension point is integrated to form an overall compensation strategy, ensuring that the suspension system load detection adapts to various dynamic strain influences in different environments.

[0130] Furthermore, the real-time compensation outputs the corrected real-time load values ​​for each suspending point, including:

[0131] The nonlinear compensation parameters are called in combination with the current uncorrected real-time load value to perform nonlinear deviation compensation calculation, generating the instantaneous correction amount corresponding to each suspension point;

[0132] The instantaneous correction is superimposed on the uncorrected real-time load value to generate a compensated linear predicted load value;

[0133] The suspension motion compensation capability verification mechanism based on the geometric correlation model performs real-time inversion verification of the linear predicted load value, and adjusts the compensation residual error to output a stable load prediction sequence during the real-time inversion verification process.

[0134] The stable load prediction sequence is converted into a dimensionally normalized value consistent with the physical unit of vehicle load to generate a vehicle load numerical stream.

[0135] The application uses vehicle load data streams to update the load status data of each suspension point in real time and outputs the corrected real-time load values ​​of each suspension point.

[0136] As a preferred embodiment of the above, firstly, when performing nonlinear deviation compensation calculation, it is necessary to call the generated nonlinear compensation parameters. These parameters are derived from the previously constructed nonlinear parameter table and combined with the uncorrected real-time load value measured at the current moment. The purpose of this step is to identify and adjust the inherent deviations in the load calculation of each suspension point, and generate a corresponding instantaneous correction amount for each suspension point through compensation calculation. In the preferred implementation process, it is necessary to quickly respond to various terrain and suspension dynamic changes to ensure accurate real-time correction. Next, the above instantaneous correction amount is superimposed on the uncorrected real-time load value to generate the compensated linear predicted load value. The compensated load value reflects a more accurate vehicle load state after compensation correction. It is required that the superposition calculation can be performed quickly under dynamic conditions. In the preferred case, the influence of suspension dynamics and nonlinear characteristics needs to be considered during the calculation process to ensure that the corrected load value can maintain consistency under different loads. Subsequently, the linear predicted load is verified through the suspension motion compensation capability verification mechanism of the geometric correlation model. The system performs real-time inversion verification. During this verification process, the model is used for real-time feedback adjustments to determine if there are any uncompensated residual errors. If so, adjustments are made to output a stable load prediction sequence. This process does not rely solely on a single compensation result but dynamically adjusts the feedback mechanism to ensure that errors are minimized, achieving stability and accuracy of the load values. Furthermore, the stable load prediction sequence is converted into a dimensionally normalized value consistent with the physical units of vehicle load, generating a vehicle load numerical stream. This conversion process, through multi-dimensional data constraints and physical unit consistency correction, ensures that the transmitted load data stream is comparable and stable over long periods and under different environments, making the obtained load information easy to analyze and apply subsequently. Finally, the vehicle load numerical stream is used to update the load status data of each suspension point in real time, outputting the corrected real-time load values ​​for each suspension point. This update mechanism continuously receives real-time data to adjust the current load status of the suspension points, ensuring that the load value output at any time during dynamic operation has high accuracy and field adaptability.

[0137] Example 2;

[0138] Based on the same inventive concept as the load detection method for the multi-terrain damping suspension system in the foregoing embodiments, the present invention also provides a load detection system for a multi-terrain damping suspension system, the system comprising:

[0139] The motion association module establishes a geometric association model that describes the three-dimensional kinematic relationships of the vehicle's suspension mechanism;

[0140] The pose calculation module acquires the vehicle's chassis attitude angle data and real-time suspension displacement data of each suspension point. It inputs the chassis attitude angle data and real-time suspension displacement data into the geometric association model to calculate the three-dimensional pose change vector of each suspension point relative to the frame.

[0141] The signal separation module collects the original strain signal of the shock absorber, combines it with the three-dimensional pose change vector of each suspension point to perform dynamic signal separation, filters out strain interference components caused by the movement of the vehicle suspension mechanism, and generates static load strain values.

[0142] The geometric correction module performs geometric correction on the real-time suspension displacement data based on the three-dimensional pose change vector of each suspension point, and generates displacement characterization load values.

[0143] The terrain weighting module detects the terrain environment type in which the vehicle is currently driving, weights the displacement characterization load value and static load strain value based on the terrain environment type, and merges the weighting results to calculate the uncorrected real-time load value of each suspension point.

[0144] The parameter compensation module, when it detects that the vehicle is in a low-disturbance driving state where the vertical vibration frequency energy value of the center of gravity is continuously lower than the preset threshold and exceeds the set time, learns to generate nonlinear compensation parameters based on the displacement characterization load value and the static load strain value, and outputs the corrected real-time load value of each suspension point after real-time compensation of the uncorrected real-time load value.

[0145] The adjustment system described above in this invention can effectively realize the load detection method of multi-terrain shock absorption suspension system, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0146] Furthermore, the geometry correction module includes:

[0147] The displacement projection unit resolves the three-dimensional pose change vector into spatial displacement projection components of the dynamic deformation of the vehicle suspension mechanism.

[0148] The geometric decomposition unit decomposes real-time suspension displacement data into load-sensitive vectors and geometric disturbance vectors based on the real-time constructed motion coordinate system transformation matrix.

[0149] The load correction unit separates the deformation disturbance corresponding to the spatial displacement projection component from the geometric disturbance vector, and eliminates the deformation disturbance in the load sensitive vector based on the dynamic inverse compensation mechanism to generate a geometrically corrected load sensitive vector.

[0150] The load characterization unit converts the physical dimension of the geometrically corrected load sensitivity vector to generate displacement characterization load values.

[0151] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0152] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method of load detection for a multi-terrain shock absorbing suspension system, characterized by, The method comprises: establishing a geometric correlation model describing the three-dimensional kinematic relationship of the vehicle suspension mechanism; obtaining chassis attitude angle data and real-time suspension displacement data of each suspension point, inputting the chassis attitude angle data and the real-time suspension displacement data into the geometric correlation model, and solving a three-dimensional pose change vector of each suspension point relative to the vehicle frame; collecting an original strain signal of the shock absorber, combining the three-dimensional pose change vector of each suspension point to perform dynamic signal separation, filtering out the strain interference components caused by the movement of the vehicle suspension mechanism, and generating a static load strain value; performing geometric correction on the real-time suspension displacement data according to the three-dimensional pose change vector of each suspension point to generate a displacement representation load value; detecting the terrain environment type in which the vehicle is currently driving, weighting the displacement representation load value and the static load strain value based on the terrain environment type, and fusing and calculating the weighted results to obtain an uncorrected real-time load value of each suspension point; when it is detected that the vehicle is in a low disturbance driving state in which the energy value of the mass center vertical vibration frequency band is continuously lower than a preset threshold value and exceeds a set time length, taking the displacement representation load value and the static load strain value as the reference, learning to generate a nonlinear compensation parameter and real-time compensate the uncorrected real-time load value to output the real-time load value of each corrected suspension point; solving the three-dimensional pose change vector of each suspension point relative to the vehicle frame, comprising: loading the spatial constraint conditions of the vehicle suspension mechanism into the geometric correlation model to construct a motion chain topological relationship; decomposing the real-time suspension displacement data into suspension stroke components and flexible deformation components and inputting them into the motion chain topological relationship; performing a knuckle rotation transformation in the geometric correlation model to derive a wheel center position offset based on the chassis attitude angle data and the suspension stroke components; performing elastic correction compensation on the wheel center position offset based on the flexible deformation components to generate a hard point coordinate update value; establishing a conversion relationship between the vehicle body coordinate system and the ground reference coordinate system in the geometric correlation model and calculating a coordinate transformation matrix; based on the hard point coordinate update value, inversely solving the spatial attitude quaternion of each suspension point based on the coordinate transformation matrix, and performing Lie group differential operation on the spatial attitude quaternion to continuously derive and evolve the three-dimensional pose change vector.

2. The method of load detection for a multi-terrain shock absorbing suspension system of claim 1, wherein, generating a static load strain value, comprising: constructing an institution motion coupling template signal according to the three-dimensional pose change vector of each suspension point; performing complex exponential modulation time domain decoupling operation on the original strain signal to separate out a dynamic strain base related to the vehicle attitude; performing mutual correlation interference calculation on the institution motion coupling template signal and the dynamic strain base to generate a phase synchronous interference wave; performing coherent suppression on the original strain signal with the phase synchronous interference wave as a reference signal to generate a load strain initial value; concatenating the derivative term of the three-dimensional pose change vector to perform dynamic leakage compensation on the load strain initial value to output the static load strain value.

3. The method of load detection for a multi-terrain shock absorbing suspension system of claim 1, wherein, performing geometric correction on the real-time suspension displacement data according to the three-dimensional pose change vector of each suspension point, comprising: analyzing the three-dimensional pose change vector to obtain a spatial displacement projection component of dynamic deformation of the vehicle suspension mechanism; decomposing the real-time suspension displacement data into a load-sensitive vector and a geometric interference vector according to a real-time motion coordinate system conversion matrix; separating a deformation interference quantity corresponding to the spatial displacement projection component from the geometric interference vector, and eliminating the deformation interference quantity in the load-sensitive vector to generate a geometric correction load-sensitive vector based on a dynamic inverse compensation mechanism; converting the physical dimension of the geometric correction load-sensitive vector to generate the displacement representation load value.

4. The method of load detection for a multi-terrain shock absorbing suspension system of claim 3, wherein, The method for decomposing the real-time suspension displacement data into a load-sensitive vector and a geometric interference vector according to a real-time motion coordinate system conversion matrix comprises: dynamically solving a displacement parameter of an instantaneous rotation center of the vehicle suspension mechanism from the three-dimensional pose change vector and taking the displacement parameter as a coordinate system reference origin, and determining directions of three mutually orthogonal coordinate axes according to a spatial constraint condition of the geometric correlation model; constructing a dynamic reference coordinate system with the coordinate system reference origin and the directions of the coordinate axes, mapping the real-time suspension displacement data into an orthogonal projection space of the dynamic reference coordinate system to generate an original projection vector component; identifying a vertical load-bearing axis sensitive to a suspension load in the orthogonal projection space, and obtaining a projection vector component on the vertical load-bearing axis as the load-sensitive vector; synthesizing the remaining projection vector components in the orthogonal projection space except the vertical load-bearing axis to constitute the geometric interference vector representing mechanism deformation interference.

5. The method of load detection for a multi-terrain shock absorbing suspension system of claim 1, wherein, The method for weighting the displacement representation load value and the static load strain value based on the terrain environment type comprises: analyzing current characteristics of the terrain environment type to generate a dynamic credibility level of the displacement representation load value and the static load strain value; adjusting contribution intensity proportions of the displacement representation load value and the static load strain value based on the dynamic credibility level; performing displacement weighting operation on the displacement representation load value combined with the contribution intensity proportions to obtain a spatial dimension weighted load value, and performing strain weighting operation on the static load strain value combined with the contribution intensity proportions to obtain a time dimension weighted strain value; integrating the spatial dimension weighted load value and the time dimension weighted strain value to generate a synthesized load intermediate value; performing load representation conversion calculation on the synthesized load intermediate value to generate the uncorrected real-time load value.

6. The method of load detection for a multi-terrain shock absorbing suspension system of claim 1, wherein, The method for learning to generate nonlinear compensation parameters comprises: synchronously collecting a load representation sample pair of the displacement representation load value sequence and the static load strain value sequence when the vehicle is determined to be in a low disturbance driving state; calculating difference values of the load representation sample pair at the same time for each suspension point to generate a dynamic error sequence; constructing a compensation factor increment distribution map based on a change trend of the dynamic error sequence, and identifying a nonlinear variation law of displacement and strain coupling deviation according to the compensation factor increment distribution map; dynamically updating a compensation learning step value combined with the nonlinear variation law, and iteratively generating a relative correction coefficient between the displacement representation load value and the static load strain value based on the compensation learning step value. mapping the relative correction coefficients as polynomial fitting parameters of a load transfer curve of the suspension system forms a nonlinear parameter table; integrating the nonlinear parameter tables of all the suspension points at the current time outputs the nonlinear compensation parameters.

7. The method of load detection for a multi-terrain shock absorbing suspension system of claim 6, wherein, real-time compensation of the uncorrected real-time load values outputs corrected real-time load values of each of the suspension points, including: calling the nonlinear compensation parameters to perform nonlinear deviation compensation calculation in combination with the uncorrected real-time load values at the current time, to generate instantaneous correction amounts corresponding to each of the suspension points; superimposing the instantaneous correction amounts on the uncorrected real-time load values to generate compensated linear predicted load values; based on a suspension motion compensation capability verification mechanism of the geometric correlation model, performing real-time inversion checking on the linear predicted load values, adjusting compensation residual errors in the real-time inversion checking process, and outputting a stable load prediction sequence; converting the stable load prediction sequence into dimensionally normalized values consistent with physical units of vehicle load to generate a vehicle load numerical stream; applying the vehicle load numerical stream to real-time update of load state data of each of the suspension points, and outputting the corrected real-time load values of each of the suspension points.

8. A load detection system for a multi-terrain shock absorbing suspension system, characterized by, The load detection method of the multi-terrain shock-absorbing suspension system according to claim 1, wherein the system comprises: a motion correlation module that establishes a geometric correlation model describing three-dimensional kinematic relationships of a vehicle suspension mechanism; a pose solving module that acquires chassis attitude angle data of the vehicle and real-time suspension displacement data of each of the suspension points, inputs the chassis attitude angle data and the real-time suspension displacement data into the geometric correlation model, and solves three-dimensional pose change vectors of each of the suspension points relative to the vehicle frame; a signal separation module that acquires original strain signals of the shock absorber, performs dynamic signal separation in combination with the three-dimensional pose change vectors of each of the suspension points, filters out strain interference components caused by motion of the vehicle suspension mechanism, and generates static load strain values; a geometric correction module that performs geometric correction on the real-time suspension displacement data according to the three-dimensional pose change vectors of each of the suspension points, and generates displacement-represented load values; a terrain weighting module that detects a terrain environment type in which the vehicle is currently driving, weights the displacement-represented load values and the static load strain values based on the terrain environment type, and fuses and calculates the weighting results to obtain uncorrected real-time load values of each of the suspension points; a parameter compensation module that, when detecting that the vehicle is in a low disturbance driving state in which a mass center vertical vibration frequency energy value continuously falls below a preset threshold value and exceeds a set time length, learns to generate nonlinear compensation parameters based on the displacement-represented load values and the static load strain values, and real-time compensates the uncorrected real-time load values to output corrected real-time load values of each of the suspension points.

9. The load detection system of a multi-terrain shock absorbing suspension system of claim 8, wherein, The geometric correction module comprises: a displacement projection unit that analyzes the three-dimensional pose change vectors into spatial displacement projection components of dynamic deformation of the vehicle suspension mechanism; a geometric decomposition unit that decomposes the real-time suspension displacement data into a load-sensitive vector and a geometric interference vector according to a real-time motion coordinate system conversion matrix. The load correction unit separates a deformation disturbance quantity corresponding to a spatial displacement projection component from the geometric disturbance vector, and eliminates the deformation disturbance quantity in the load sensitive vector based on a dynamic inverse compensation mechanism to generate a geometric correction load sensitive vector; The load representation unit converts the physical dimension of the geometric correction load sensitive vector to generate a displacement representation load value.

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