Performance analysis and evaluation system and method for high-strength two-stage gradually-changing-stiffness plate spring

Abstract

The invention belongs to the technical field of plate spring performance analysis and evaluation, and discloses a high-strength two-stage gradual-change-stiffness plate spring performance analysis and evaluation system and method.The method comprises the steps that plate spring initial mechanical data collection and heterogeneous load response integrated processing are conducted on the basis of a multi-dimensional vehicle-mounted test experiment, and plate spring integrated mechanical response data are generated; performing double-rigidity gradient recognition analysis to generate rigidity transformation critical data; performing critical region stress field deconstruction analysis to generate stress field distribution characteristic data; carrying out local microstructure change characterization to generate structural deformation data; constructing a multi-scale stress transfer link, and generating critical region stress transfer path data; performing fatigue damage accumulative mapping to generate accumulative feature data; constructing a fatigue life prediction model, and generating life prediction data; carrying out comprehensive performance index quantitative evaluation to generate a performance evaluation result; executing plate spring optimization improvement scheme generation and verification; and a scientific basis is provided for performance optimization of the two-stage gradual-change-rigidity plate spring.

Description

Technical Field

[0001] The present invention relates to the technical field of leaf spring performance analysis and evaluation, and more specifically, to a high-strength two-stage variable stiffness leaf spring performance analysis and evaluation system and method. Background Art

[0002] With the continuous development of the automotive industry, the performance requirements for suspension systems are increasing day by day. As a commonly used elastic element in commercial vehicles, the performance of leaf springs directly affects the comfort, stability, and safety of vehicles. Traditional leaf spring performance analysis methods are mainly designed for single stiffness characteristics, while for new high-strength two-stage variable stiffness leaf springs, the existing analysis and evaluation methods have obvious deficiencies.

[0003] Currently, there is a lack of an accurate quantitative analysis method for the stress concentration phenomenon at the critical point of the two-stage variable stiffness transition in the industry. During the transition of vehicle load from light load to heavy load, a complex stress distribution will occur in the critical region where the leaf spring stiffness changes suddenly. Existing testing equipment and analysis algorithms cannot accurately capture the stress peak and fatigue accumulation effect in this region, resulting in unexpected fractures of leaf springs before their expected service life in actual use, seriously threatening driving safety; in the prior art, although there are simulation methods based on finite element analysis, due to the inability to accurately simulate the material microstructure changes at the critical point under complex actual road load conditions, there are significant deviations between the analysis results and the actual use conditions. At the same time, traditional bench test methods are also difficult to accurately reproduce the load conversion process of vehicles under complex road conditions, especially unable to capture the cumulative damage effect caused by high-frequency and small-amplitude load changes on the critical point region.

[0004] In view of this, the present invention proposes a high-strength two-stage variable stiffness leaf spring performance analysis and evaluation system and method to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A high-strength two-stage variable stiffness leaf spring performance analysis and evaluation method, comprising: Step S1: Collecting initial mechanical data of the leaf spring based on multi-dimensional on-vehicle test experiments to generate initial mechanical characteristic data of the leaf spring; performing heterogeneous load response integration processing according to the initial mechanical characteristic data of the leaf spring to generate integrated mechanical response data of the leaf spring; Step S2: Performing dual stiffness gradient identification analysis according to the integrated mechanical response data of the leaf spring to generate critical data for stiffness transition; performing critical region stress field deconstruction analysis according to the critical data for stiffness transition to generate distribution characteristic data of the critical region stress field; Step S3: Characterize the local microstructure changes based on the critical area stress field distribution characteristic data to generate local microstructure deformation data; construct a multi-scale stress transfer link based on the local microstructure deformation data to generate critical area stress transfer path data; Step S4: Map the fatigue damage accumulation under cyclic loading to the critical area stress transfer path data to generate fatigue damage accumulation characteristic data; construct a fatigue life prediction model based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; quantitatively evaluate the comprehensive performance index according to the fatigue life prediction data and the leaf spring integrated mechanical response data to generate the leaf spring comprehensive performance evaluation result; execute the generation and verification of the leaf spring optimization and improvement plan based on the leaf spring comprehensive performance evaluation result.

[0006] Further, step S1 includes the following steps: Step S11: Collect the initial mechanical data of the leaf spring based on multi-dimensional on-vehicle test experiments to generate the initial mechanical characteristic data of the leaf spring; Step S12: Decompose and process the load-displacement curve according to the initial mechanical characteristic data of the leaf spring to generate non-linear response characteristic decomposition data; Step S13: Reconstruct the multi-road condition load spectrum according to the non-linear response characteristic decomposition data to generate simulated road condition load spectrum data; Step S14: Integrate and process the heterogeneous load responses according to the simulated road condition load spectrum data to generate the leaf spring integrated mechanical response data.

[0007] Further, step S2 includes the following steps: Step S21: Calculate the slope change rate of the stiffness curve according to the leaf spring integrated mechanical response data to generate stiffness gradient change data; Step S22: Accurately locate the stiffness mutation point according to the stiffness gradient change data to generate stiffness transition critical region data; Step S23: Use an ultra-high-precision strain measurement device to scan the stress field of the stiffness transition critical region data to generate high-density stress distribution data of the critical area; Step S24: Identify and quantify the stress concentration region based on the high-density stress distribution data of the critical area to generate a critical area stress peak distribution map; Step S25: Conduct a deconstruction analysis of the critical area stress field according to the critical area stress peak distribution map to generate critical area stress field distribution characteristic data.

[0008] Further, step S24 includes the following steps: Calculate the stress gradient tensor according to the high-density stress distribution data of the critical area to generate stress gradient tensor field data, where the stress gradient tensor field data includes principal stress distribution node data and stress intensity factor quantization data; Perform clustering analysis on the high-stress area based on the principal stress distribution node data to generate stress clustering area data; Identify potential crack sources based on the stress clustering area data and the stress intensity factor quantification data to generate potential fracture origin data; Quantitatively rate the high-density stress distribution data in the critical area based on the potential fracture origin data to generate a stress peak distribution map of the critical area.

[0009] Further, step S3 includes the following steps: Step S31: Characterize the local microstructure changes based on the stress field distribution characteristic data in the critical area to generate local microstructure deformation data; Step S32: Analyze the evolution of crystal dislocation density based on the local microstructure deformation data to generate microscopic dislocation evolution characteristic data; Step S33: Construct a multi-scale stress transfer link based on the microscopic dislocation evolution characteristic data to generate critical area stress transfer path data.

[0010] Further, step S32 includes the following steps: Analyze the microscopic mechanical deformation mechanism based on the local microstructure deformation data to generate microscopic deformation mechanism characteristic data; Analyze the material strengthening effect based on the local microstructure deformation data to generate material strengthening characteristic data; Construct a crystal dislocation density distribution matrix based on the microscopic deformation mechanism characteristic data and the material strengthening characteristic data to generate a dislocation density evolution matrix; Analyze the evolution of crystal dislocation density based on the dislocation density evolution matrix to generate microscopic dislocation evolution characteristic data.

[0011] Further, step S33 includes the following steps: Conduct a correlation analysis of microscopic-macroscopic mechanical behaviors under the influence of historical loads on the microscopic dislocation evolution characteristic data to generate cross-scale mechanical behavior linkage characteristic data; Construct a multi-scale stress transfer link based on the cross-scale mechanical behavior linkage characteristic data to generate critical area stress transfer path data.

[0012] Further, step S4 includes the following steps: Step S41: Map the fatigue damage accumulation under cyclic loads on the critical area stress transfer path data to generate fatigue damage accumulation characteristic data; Step S42: Extract the high-frequency micro-amplitude load influence factor based on the fatigue damage accumulation characteristic data to generate high-frequency load influence quantification data, and establish a damage evolution prediction distribution node through the high-frequency load influence quantification data; Step S43: Based on the improved Miner's non-linear cumulative damage theory, construct a progressive fatigue life prediction model for the damage evolution prediction distribution nodes and the fatigue damage accumulation characteristic data, generate a fatigue life prediction model, and obtain fatigue life prediction data; Step S44: Conduct a correlation analysis on the critical area stress field distribution characteristic data and the fatigue life prediction data to generate structure-performance correlation characteristic data; Step S45: Conduct a quantitative evaluation of the comprehensive performance indicators based on the structure-performance correlation characteristic data and the leaf spring integrated mechanical response data to generate a leaf spring comprehensive performance evaluation result; Step S46: Based on the leaf spring comprehensive performance evaluation result, conduct an optimized design and parameter adjustment of the leaf spring structure, generate an optimized improvement plan for the leaf spring, and perform an experimental verification of the optimized improvement plan for the leaf spring.

[0013] Furthermore, Step S43 includes the following steps: Identify the critical damage threshold based on the fatigue damage accumulation characteristic data to generate fatigue damage critical threshold data; Construct a non-linear cumulative damage evolution function based on the fatigue damage critical threshold data to generate a non-linear damage accumulation function; Modify the damage evolution prediction distribution nodes according to the non-linear damage accumulation function to generate modified damage evolution prediction distribution nodes; Based on the modified damage evolution prediction distribution nodes and the fatigue damage accumulation characteristic data, construct a progressive fatigue life prediction model to obtain a fatigue life prediction model.

[0014] A high-strength two-stage variable stiffness leaf spring performance analysis and evaluation system, comprising: Load response integration module: Collect the initial mechanical data of the leaf spring based on multi-dimensional vehicle-mounted test experiments to generate the initial mechanical characteristic data of the leaf spring; conduct heterogeneous load response integration processing according to the initial mechanical characteristic data of the leaf spring to generate the leaf spring integrated mechanical response data; Stiffness critical analysis module: Conduct a dual-stiffness gradient identification analysis according to the leaf spring integrated mechanical response data to generate stiffness transition critical data; conduct a critical area stress field deconstruction analysis according to the stiffness transition critical data to generate critical area stress field distribution characteristic data; Microscopic stress link module: Characterize the local microscopic structure changes according to the critical area stress field distribution characteristic data to generate local microscopic structure deformation data; construct a multi-scale stress transfer link based on the local microscopic structure deformation data to generate critical area stress transfer path data; Fatigue life assessment module: Map the fatigue damage accumulation of the stress transfer path data in the critical area under cyclic loading to generate fatigue damage accumulation characteristic data; construct a fatigue life prediction model based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; conduct a quantitative evaluation of comprehensive performance indicators based on the fatigue life prediction data and the integrated mechanical response data of the leaf spring to generate the comprehensive performance evaluation result of the leaf spring; execute the generation and verification of the optimization and improvement plan for the leaf spring based on the comprehensive performance evaluation result of the leaf spring.

[0015] Technical effects and advantages of a high-strength two-stage gradually variable stiffness leaf spring performance analysis and evaluation system and method of the present invention: Through the multi-dimensional vehicle-mounted test experiment data collection and heterogeneous load response integrated processing, the present invention realizes the comprehensive capture of the mechanical characteristics of the leaf spring under complex working conditions, and can integrate the scattered test data into unified integrated mechanical response data of the leaf spring, thereby enhancing the integrity and accuracy of the analysis. Through the dual stiffness gradient identification analysis and the critical area stress field deconstruction analysis, the critical region of stiffness transition can be accurately located and high-density stress distribution data can be generated, realizing an in-depth analysis of the stiffness mutation mechanism of the leaf spring and laying a solid foundation for subsequent microscopic analysis. Through the local microstructure change characterization and the crystal dislocation density evolution analysis, the microscopic deformation mechanism of the material in the stress concentration region is revealed. By applying advanced electron microscopy and X-ray diffraction techniques, the multi-scale correlation from macroscopic mechanical properties to microscopic structure changes is realized, improving the scientificity and depth of the analysis. By constructing a multi-scale stress transfer link, the stress transfer path from the macroscopic structure to the microscopic material is accurately captured, the key nodes and bottleneck regions of stress transfer are effectively identified, and the understanding depth of the failure mechanism of the leaf spring is greatly improved. Through the fatigue damage accumulation mapping under cyclic loading and the extraction of high-frequency micro-amplitude load influence factors, the influence of the complex load spectrum under actual working conditions on the life of the leaf spring is comprehensively considered. The progressive fatigue life prediction model constructed based on the improved Miner nonlinear cumulative damage theory breaks through the limitations of the traditional linear cumulative theory and significantly improves the accuracy of fatigue life prediction. Through the structure-performance correlation characteristic analysis and the quantitative evaluation of comprehensive performance indicators, the static and dynamic performance of the leaf spring is systematically quantified, providing an accurate basis for optimization and improvement. Description of the Drawings

[0016] Figure 1 Schematic diagram of a high-strength two-stage gradually variable stiffness leaf spring performance analysis and evaluation method of the present invention; Figure 2 Schematic diagram of a high-strength two-stage gradually variable stiffness leaf spring performance analysis and evaluation system of the present invention. Detailed Embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1; Please refer to Figure 1 As shown, a method for analyzing and evaluating the performance of a high-strength two-stage variable stiffness leaf spring in this embodiment includes: Step S1: Collect the initial mechanical data of the leaf spring based on multi-dimensional vehicle-mounted test experiments to generate the initial mechanical characteristic data of the leaf spring; perform integrated processing of heterogeneous load responses according to the initial mechanical characteristic data of the leaf spring to generate the integrated mechanical response data of the leaf spring; Step S2: Perform double stiffness gradient identification and analysis according to the integrated mechanical response data of the leaf spring to generate critical data for stiffness transition; perform deconstruction analysis of the stress field in the critical area according to the critical data for stiffness transition to generate distribution characteristic data of the stress field in the critical area; Step S3: Characterize the local microstructure changes according to the distribution characteristic data of the stress field in the critical area to generate local microstructure deformation data; construct a multi-scale stress transfer link based on the local microstructure deformation data to generate stress transfer path data in the critical area; Step S4: Perform fatigue damage accumulation mapping under cyclic loading on the stress transfer path data in the critical area to generate fatigue damage accumulation characteristic data; construct a fatigue life prediction model based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; perform quantitative evaluation of comprehensive performance indicators according to the fatigue life prediction data and the integrated mechanical response data of the leaf spring to generate a comprehensive performance evaluation result of the leaf spring; execute the generation and verification of the leaf spring optimization and improvement plan based on the comprehensive performance evaluation result of the leaf spring.

[0019] Preferably, the process of executing Step S1 may specifically include the following steps: (1) Collect the initial mechanical data of the leaf spring based on multi-dimensional vehicle-mounted test experiments to generate the initial mechanical characteristic data of the leaf spring; (2) Perform deconstruction processing of the load-displacement curve according to the initial mechanical characteristic data of the leaf spring to generate deconstructed data of the nonlinear response characteristics; (3) Reconstruct the load spectrum for multiple road conditions according to the deconstructed data of the nonlinear response characteristics to generate simulated road condition load spectrum data; (4) Perform integrated processing of heterogeneous load responses according to the simulated road condition load spectrum data to generate the integrated mechanical response data of the leaf spring.

[0020] Specifically, through on-vehicle test experiments under different road conditions, the initial mechanical data of the high-strength two-stage variable stiffness leaf spring is collected. The test experiments include various conditions such as driving on flat roads, driving on potholed roads, full-load state, and no-load state. During the test process, through the multi-channel strain acquisition system and displacement sensors installed on the leaf spring, the strain and displacement data of the leaf spring in the vertical, horizontal, and torsional directions are recorded in real time. The data acquisition frequency is set to 1000 Hz to ensure capturing the transient response characteristics of the leaf spring under dynamic loads. At the same time, acceleration sensors are used to record the vibration acceleration data of the vehicle suspension system for subsequent analysis of the influence of the leaf spring on the vehicle's dynamic performance. Through the comprehensive acquisition of these multi-dimensional test data, an initial mechanical property dataset of the leaf spring is generated, which contains the force-displacement relationship, stress-strain relationship, and time-domain response characteristics of the leaf spring under multi-dimensional loading conditions. The load-displacement curve of the initial mechanical property data of the leaf spring is deconstructed to identify the non-linear mechanical response characteristics of the leaf spring. First, the original data is filtered and denoised, and the wavelet transform method is used to eliminate measurement noise and high-frequency interference. Then, the processed data is classified according to the load direction and magnitude to construct a complete load-displacement curve. The load-displacement curve is segmented and fitted to extract the slope change characteristics of the curve, that is, calculate the stiffness values at different displacement points, so as to identify the stiffness mutation points and non-linear variation rules of the leaf spring, and generate non-linear response characteristic deconstruction data. Based on the non-linear response characteristic deconstruction data, the multi-road-condition load spectrum is reconstructed. First, based on the measured road spectrum and international standard road spectrum (such as ISO8608), typical excitation models for different levels of road surfaces are established. The road surface excitation function is expressed as a superposition of harmonic components, including parameters such as amplitude, frequency, and phase angle. The road surface excitation function can be expressed as: ; where is the amplitude of the th harmonic component, is the corresponding frequency, Let it be the phase angle. Combining with the dynamic model of the vehicle suspension system, the road surface excitation is converted into the dynamic load on the leaf spring. For different road conditions and vehicle operating conditions, a multi-dimensional load spectrum matrix is constructed. The elements in the load spectrum matrix represent different categories of load parameters corresponding to different road conditions. Through the load spectrum matrix, simulated road condition load spectrum data is generated, which comprehensively reflects various load conditions that the leaf spring may experience during actual use. The simulated road condition load spectrum data is subjected to integrated processing of heterogeneous load responses. Heterogeneous loads include compressive loads in the vertical direction, lateral shear forces, and torsional moments, etc. For these different types of loads, the superposition principle and weighted fusion method are used for comprehensive processing. Based on the theory of elasticity, a response function of the leaf spring under multi-dimensional loads is established. This function synthesizes the responses of the leaf spring to compressive forces, bending moments, and torques through weighted coefficients. The weight coefficients are optimized and determined through experimental data to minimize the error between the model prediction value and the measured value. Through this response function, the integrated processing of the mechanical response of the leaf spring under heterogeneous load conditions is realized, and the integrated mechanical response data of the leaf spring is generated.

[0021] Preferably, the process of executing step S2 may specifically include the following steps: (1) Calculate the change rate of the stiffness curve slope according to the integrated mechanical response data of the leaf spring to generate stiffness gradient change data; (2) Accurately locate the stiffness mutation point according to the stiffness gradient change data to generate the critical region of stiffness transition; (3) Use a ultra-high-precision strain measurement device to scan the stress field in the critical region of stiffness transition to generate high-density stress distribution data in the critical region; (4) Identify and quantify the stress concentration region based on the high-density stress distribution data in the critical region to generate a stress peak distribution map in the critical region; (5) Conduct a deconstruction analysis of the stress field in the critical region according to the stress peak distribution map in the critical region to generate stress field distribution characteristic data in the critical region.

[0022] Specifically, based on the integrated mechanical response data of the leaf spring, the slope change rate of the stiffness curve is calculated. First, the complete load-displacement curve is extracted from the mechanical response data, and the stiffness value at each point is calculated by numerical differentiation, that is, the change rate of the load relative to the displacement is calculated. Then, the slope change rate of the stiffness curve is calculated, that is, the change rate of the stiffness value relative to the displacement. The slope change rate reflects the severity of the leaf spring stiffness change and is an important parameter for identifying the stiffness mutation point. By analyzing the distribution characteristics of the slope change rate, the stiffness gradient change data is generated, which intuitively shows the non-linear characteristics of the leaf spring stiffness change. According to the stiffness gradient change data, the peak detection algorithm and the threshold screening method are used to accurately locate the stiffness mutation point. When the slope change rate exceeds the preset threshold and reaches the maximum value in the local area, this point is identified as the stiffness mutation point. According to the identified mutation point position, the critical region of stiffness transition is determined, and this region is the key object of concern in the leaf spring performance analysis. The ultra-high-precision strain measurement device is used to scan the stress field in the critical region of stiffness transition. The high-precision digital image correlation (DIC) technology and the optical fiber grating strain sensing system are used to perform high-density strain measurement on the critical region. The spatial resolution of the DIC system reaches 10μm, and the strain resolution is 0.01%, which can capture the details of the strain distribution in the micro-region. Through multi-angle and multi-position scanning measurements, the three-dimensional strain field data of the critical region is obtained. Combining Hooke's law and the material constitutive relationship, the strain field data is converted into stress field data to generate the high-density stress distribution data of the critical region, which describes the stress state of each point in the critical region in a grid form. Based on the high-density stress distribution data of the critical region, the stress concentration region is identified and quantified. First, the stress gradient tensor is calculated according to the high-density stress distribution data of the critical region. The stress gradient tensor is an important parameter for describing the change rate of the stress distribution in space. The stress gradient tensor at each grid point is calculated by the finite difference method, that is, the partial derivative of the stress tensor with respect to the spatial coordinates is calculated. According to the stress gradient tensor, the principal stress distribution node data is extracted, and the stress intensity factor at each node is calculated. The calculation formula of the stress intensity factor is: ; where is the stress intensity factor of the th mode, is the stress corresponding to the th mode, is the crack length, is the The set correction factor corresponding to the class mode. The stress intensity factor includes three classes of modes, namely the opening mode (representing the deformation mode in which the crack surfaces are perpendicularly separated from each other), the sliding mode (representing the deformation mode in which the crack surfaces slide parallel to each other along the crack direction), and the tearing mode (representing the deformation mode in which the crack surfaces slide parallel to each other perpendicular to the crack front direction). Through these calculations, stress gradient tensor field data is generated. High-stress area clustering analysis is performed based on the principal stress distribution node data. The spatial clustering algorithm DBSCAN is used to perform clustering according to the spatial positions and stress values of the nodes. For each cluster, characteristic parameters such as its central position, coverage area, and average stress intensity are calculated to generate stress cluster area data. Potential crack source identification is performed based on the stress cluster area data and the stress intensity factor quantization data. Based on the fracture mechanics theory, the strain energy release rate of each cluster area is calculated ; where , and represent the stress intensity factors in the opening mode, sliding mode, and tearing mode respectively, represents the elastic modulus, represents the shear modulus, and is compared with a preset critical strain energy release rate to determine whether the fracture condition is satisfied. When the strain energy release rate is greater than or equal to the critical strain energy release rate, this area is identified as a potential crack source, and its risk coefficient is calculated by dividing the strain energy release rate by the critical strain energy release rate. The potential crack sources are ranked according to the magnitude of the risk coefficient to generate potential fracture origin data. Finally, quantitative rating processing is performed on the high-density stress distribution data in the critical area based on the potential fracture origin data. Using the risk matrix method, the stress level and fracture possibility are used as two dimensions to divide the risk levels of each area. According to the risk level distribution, a stress peak distribution map of the critical area is generated. This map intuitively shows the stress levels and potential risks of each point in the critical area in the form of a heat map. According to the stress peak distribution map of the critical area, stress field deconstruction analysis of the critical area is performed. The stress field deconstruction analysis aims to reveal the internal characteristics and laws of the stress distribution. The principal component analysis method is used to extract the main characteristic modes of the stress field. Then, Fourier transform and wavelet analysis are used to perform spectral analysis on the stress field to identify the periodic characteristics of the stress fluctuations. Through these analysis methods, multi-scale characteristics of the stress field are extracted to generate stress field distribution characteristic data of the critical area, which comprehensively describes the spatial distribution, spectral characteristics, and intensity distribution law of the stress field in the critical area

[0023] Preferably, the process of executing step S3 may specifically include the following steps: (1) Characterize local microstructural changes based on the stress field distribution characteristic data of the critical area to generate local microstructural deformation data; (2) Analyze the evolution of crystal dislocation density based on the local microstructural deformation data to generate microdislocation evolution characteristic data; (3) Construct a multi-scale stress transfer link based on the micro dislocation evolution characteristic data to generate critical zone stress transfer path data.

[0024] Specifically, based on the critical zone stress field distribution characteristic data, use advanced microstructure analysis techniques to characterize the local microstructure changes of the material. First, use a scanning electron microscope (SEM) to perform high-resolution imaging on the material surface in the critical area to observe the surface topography and microcrack distribution. The SEM imaging resolution is set to 10 nm to capture surface details at the nanoscale. Then, use a transmission electron microscope (TEM) to analyze the crystal structure of the thin slice sample in the critical area to observe lattice distortion and dislocation distribution. The spatial resolution of the TEM analysis reaches 0.2 nm, which can clearly show the lattice structure at the atomic level. At the same time, use the electron backscatter diffraction (EBSD) technique to analyze the grain orientation and grain boundary characteristics of the material. The EBSD scanning step is set to 0.5 μm to obtain sufficient grain statistical information. Measure the residual stress and lattice constant changes of the material by X-ray diffraction (XRD) technique. The XRD measurement uses a CuKα radiation source (wavelength of 1.5406 Å), the scanning range is from 30° to 90°, and the step is 0.02°. According to the XRD spectrum, calculate the interplanar spacing and lattice strain, that is, the relative change of the interplanar spacing. In addition, use nanoindentation technology to measure the hardness and elastic modulus distribution in the local area. The indentation force is set to 10 mN, and the indentation depth is controlled within 200 nm. Combine the results of these microscopic analysis techniques to comprehensively characterize the microstructure changes of the critical zone material and generate local microstructure deformation data.

[0025] Perform crystal dislocation density evolution analysis based on the local microstructure deformation data. First, estimate the dislocation density based on TEM images and XRD line broadening analysis ; where is the integral width of the diffraction peak, is the Burgers vector; secondly, based on the local microstructure deformation data, perform a microscopic mechanical deformation mechanism analysis to identify the main deformation mechanisms, including slip, twinning, phase transformation, and dynamic recrystallization, etc. For slip deformation, determine the main slip system and critical resolved shear stress, which is related to the friction stress and the inverse square root of the grain size. The specific calculation formula is: ; where is the friction stress, is the material constant, is the grain size. By calculating the Schmid factor of each slip system: ; where is the angle between the slip plane normal and the load direction, is the angle between the slip direction and the load direction. When the Schmid factor is greater than or equal to the critical resolved shear stress, it represents the activation of the slip system and determines the preferentially activated slip system. For twinning deformation, the twinning shear strain and the twinning volume fraction are calculated. Through these analyses, the characteristic data of the microscopic deformation mechanism are generated. Then, the material strengthening effect analysis is carried out based on the local microscopic structure deformation data. Considering various strengthening mechanisms, including solid solution strengthening, grain refinement strengthening, dislocation strengthening, and precipitation strengthening, etc., the material strengthening characteristic data are generated. Next, based on the characteristic data of the microscopic deformation mechanism and the material strengthening characteristic data, a crystal dislocation density distribution matrix is constructed. The analysis area is discretized into grids, and the dislocation density and the distribution of dislocation types are calculated for each grid point. Considering the interaction and movement law of dislocations, a dislocation density evolution equation is established: ; where, represents the rate of change of the dislocation density at the grid point with time, represents the dislocation generation rate, represents the dislocation annihilation rate, represents the divergence operation, represents the dislocation velocity vector, represents the dislocation density; this equation considers the comprehensive effects of dislocation generation, annihilation, and movement. By numerically solving this equation, a dislocation density evolution matrix is obtained. Finally, the crystal dislocation density evolution analysis is carried out based on the dislocation density evolution matrix. Through time series analysis, the spatio-temporal evolution law of the dislocation density is traced, and the key regions of dislocation accumulation and dissipation are identified. The spatial distributions of the dislocation density gradient and the dislocation density evolution rate are calculated to identify the formation processes of dislocation walls and subgrain boundaries. Based on these analyses, the microscopic dislocation evolution characteristic data are generated, which comprehensively describe the microscopic deformation mechanism and the dislocation dynamics behavior of the material under load.

[0026] Construct a multi-scale stress transfer link based on the microscopic dislocation evolution characteristic data. First, conduct a microscopic-macroscopic mechanical behavior correlation analysis on the microscopic dislocation evolution characteristic data under the influence of historical loads. Establish the mapping relationship between microscopic state variables (such as dislocation density, subgrain size, and texture parameters) and macroscopic mechanical responses (such as yield strength, work hardening rate, and elastic modulus). This relationship can be expressed as the weighted sum of macroscopic mechanical parameters and functions of microscopic state variables. Determine the parameters in the mapping relationship through experimental data and theoretical analysis to achieve the quantitative correlation between microscopic structure and macroscopic properties. Consider the cumulative effect of historical loads on the evolution of the microscopic structure and establish a history-dependent model for the evolution of dislocation density. This model indicates that the change in dislocation density over time is related to factors such as the current dislocation density, strain, strain rate, and temperature. Through this model, simulate the evolution paths of the microscopic structure under different loading histories and evaluate the influence of historical loads on the mechanical behavior of materials. Based on the correlation analysis of microscopic-macroscopic mechanical behavior, identify the key coupling mechanisms of cross-scale mechanical behavior, including the hindrance effect of grain boundaries on dislocation motion, the influence of dislocation structure on work hardening, and the contribution of microcracks to macroscopic damage, etc. Through these analyses, generate cross-scale mechanical behavior coupling characteristic data. Then, construct a multi-scale stress transfer link according to the cross-scale mechanical behavior coupling characteristic data. Adopt the representative volume element (RVE) method to divide the material into multiple scale levels, from the nanoscale (dislocations and point defects), the microscale (grains and grain boundaries) to the millimeter scale (macroscopic structure). Establish appropriate mechanical models at each scale, such as the molecular dynamics model at the atomic scale, the crystal plasticity model at the grain scale, and the continuum model at the macroscopic scale. Adopt a multi-scale calculation framework to achieve information transfer and scale transition between different scale models. For example, use the homogenization method to upload the microscopic stress-strain relationship to the macroscopic scale and calculate the macroscopic average stress and strain as the volume average of the microscopic stress and strain over the representative volume element. At the same time, decompose the macroscopic deformation into the boundary conditions of the microscopic RVE through the downward transmission of boundary conditions. Through the multi-scale calculation framework, simulate the stress transfer path and distribution law between different scales, identify the key links and bottleneck regions of stress transfer. Based on the simulation results, construct a stress transfer path diagram of the critical region, which visually shows the stress transfer link and key nodes from the macroscopic structure to the microscopic material. Through this analysis, generate stress transfer path data of the critical region, providing a basis for fatigue damage analysis and life prediction.

[0027] Preferably, the process of executing step S4 may specifically include the following steps: (1) Conduct a fatigue damage accumulation mapping on the stress transfer path data of the critical region under cyclic loading to generate fatigue damage accumulation characteristic data; (2) Extract the influence factors of high-frequency micro-amplitude loads based on the fatigue damage accumulation characteristic data, generate the quantified data of the influence of high-frequency loads, and establish the damage evolution prediction distribution nodes through the quantified data of the influence of high-frequency loads; (3) Construct a progressive fatigue life prediction model for the damage evolution prediction distribution nodes and the fatigue damage accumulation characteristic data based on the improved Miner's non-linear cumulative damage theory, generate the fatigue life prediction model, and obtain the fatigue life prediction data; (4) Conduct a correlation analysis between the stress field distribution characteristic data in the critical area and the fatigue life prediction data to generate the structure-performance correlation characteristic data; (5) Conduct a quantitative evaluation of the comprehensive performance indicators based on the structure-performance correlation characteristic data and the integrated mechanical response data of the leaf spring to generate the comprehensive performance evaluation result of the leaf spring; (6) Based on the comprehensive performance evaluation result of the leaf spring, conduct an optimized design of the leaf spring structure and parameter adjustment, generate the optimized improvement plan for the leaf spring, and perform the experimental verification of the optimized improvement plan for the leaf spring.

[0028] Specifically, perform a fatigue damage accumulation mapping on the stress transfer path data in the critical area under cyclic loading. First, based on the actual usage conditions and standard test methods, design a cyclic load spectrum, including various forms such as constant amplitude load, variable amplitude load, and random load. Then, combined with the stress transfer path data in the critical area, calculate the stress amplitude and stress ratio at each key node, which are respectively half of the difference between the maximum stress and the minimum stress, and the ratio of the minimum stress to the maximum stress. According to the S-N curve (stress-life curve) and Basquin equation of the material, calculate the fatigue life at each node, and this equation represents the power function relationship between the stress amplitude and the number of fatigue life cycles. For variable amplitude loads, use the rain flow counting method to cyclically extract the load history and calculate the damage contribution of each cycle, that is, the ratio of the number of repetitions of this cycle to the fatigue life at this stress level. Accumulate these damage contributions to generate the fatigue damage accumulation characteristic data, which reflects the damage evolution law of the material under cyclic loading.

[0029] According to the fatigue damage accumulation characteristic data, extract the influence factors of high-frequency micro-amplitude loads. High-frequency micro-amplitude loads are generally considered to have a significant impact on fatigue life, but are often ignored in traditional fatigue analysis methods. First, decompose the load spectrum into different frequency components through spectrum analysis, that is, perform a Fourier transform on the time-domain load. Then, identify the high-frequency components (usually greater than 50 Hz) and evaluate their amplitude distribution. Establish an equivalent damage model for high-frequency micro-amplitude loads, and this model represents that the equivalent damage is related to factors such as the high-frequency load amplitude, threshold amplitude, and frequency. Through this model, quantify the contribution of high-frequency micro-amplitude loads to fatigue damage and generate the quantified data of the influence of high-frequency loads.

[0030] Based on the quantified data of high-frequency load effects, damage evolution prediction distribution nodes are established in the critical damage areas. These nodes form the basis of the prediction network, and each node contains information such as local stress state, cumulative damage value, and predicted remaining life. The node distribution density is related to the local stress gradient, and the node density is higher in the stress concentration areas to ensure the prediction accuracy.

[0031] Based on the improved Miner's non-linear cumulative damage theory, a progressive fatigue life prediction model is constructed for the damage evolution prediction distribution nodes and the fatigue damage cumulative characteristic data. First, the critical damage threshold is identified according to the fatigue damage cumulative characteristic data. The traditional Miner's linear cumulative damage theory assumes that the critical damage threshold is constant at 1, but in fact, the critical damage threshold varies with material properties, load history, and environmental conditions. By analyzing the existing fatigue test data, a prediction model of the critical damage threshold is established, which shows that the critical damage threshold is related to factors such as mean stress, stress ratio, load frequency, and temperature. Data mining and machine learning methods, such as support vector machines or random forests, are used to train the critical damage threshold prediction model, and the model performance is evaluated through cross-validation. Through this model, the critical damage thresholds under different working conditions are predicted, and the fatigue damage critical threshold data are generated. Then, a non-linear cumulative damage evolution function is constructed based on the fatigue damage critical threshold data. Considering the non-linear cumulative characteristics of fatigue damage, a damage evolution equation is established: ; where is the damage increment rate, is the current damage value, is the number of cycles, is the stress amplitude, is the mean stress, is the stress ratio. Specifically, the non-linear damage cumulative function can be expressed as: ; where and are material parameters, is the stress state correction function, Represents the critical damage threshold. By numerically integrating the damage evolution equation, the damage accumulation curve under any load history is obtained, and a nonlinear damage accumulation function is generated. Then, the damage evolution prediction distribution nodes are corrected according to the nonlinear damage accumulation function. For each prediction node, the prediction parameters are updated by applying the nonlinear damage accumulation function based on its local stress state and the accumulated damage. Considering the interaction between nodes and damage propagation, a damage field evolution model is established, which represents the spatio-temporal evolution of damage related to damage diffusion and damage sources. Through this model, the process of damage propagation and concentration in space is simulated, and the corrected damage evolution prediction distribution nodes are obtained. Finally, a progressive fatigue life prediction model is constructed based on the corrected damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data. Using Bayesian network or deep learning method, a mapping relationship from the current damage state to the remaining life is established. The training data includes historical load spectra, accumulated damage values, and observed failure times. Through this model, the real-time prediction and update of the remaining life of the leaf spring are realized, and a fatigue life prediction model is obtained. This model has self-adaptability and interpretability, and can continuously optimize the prediction results according to real-time monitoring data. Through this model, the fatigue life of the leaf spring under different working conditions is predicted, and fatigue life prediction data are generated.

[0032] Perform a correlation analysis on the stress field distribution characteristic data in the critical area and the fatigue life prediction data. Through correlation analysis and causal inference, a quantitative relationship between the stress field characteristics and the fatigue life is established. Calculate the correlation coefficient between each stress characteristic parameter and the fatigue life, which is the ratio of the covariance of the two variables to the product of their standard deviations. According to the results of the correlation analysis, identify the stress field characteristics that have the most significant impact on the fatigue life, and establish a regression model representing the linear combination relationship between the logarithm of the fatigue life and each stress field characteristic parameter. Through this model, the prediction mapping from the stress field characteristics to the fatigue life is realized, and the structure-performance correlation characteristic data are generated. According to the structure-performance correlation characteristic data and the integrated mechanical response data of the leaf spring, a quantitative evaluation of the comprehensive performance index is carried out. The evaluation indicators include static performance indicators (such as stiffness, strength, and linearity) and dynamic performance indicators (such as fatigue life, comfort, and stability). For each indicator, an appropriate weight is set, and a comprehensive evaluation function is constructed, which is expressed as the weighted sum of the standardized scores of each indicator. Through this evaluation function, the comprehensive performance of the leaf spring is quantitatively evaluated, and the comprehensive performance evaluation result of the leaf spring is generated.

[0033] Based on the comprehensive performance evaluation results of the leaf spring, carry out the structural optimization design and parameter adjustment of the leaf spring. Adopt parametric design methods and optimization algorithms, such as genetic algorithms, particle swarm optimization, or response surface methods, to search for the optimal combination of design parameters. The optimization goal is to maximize the comprehensive performance evaluation function while meeting the constraint conditions. The design parameters include the geometric dimensions of the leaf spring, material composition, heat treatment process, and surface treatment, etc. Through optimization calculations, obtain the optimal design scheme, and verify its performance through finite element analysis. Finally, generate an optimized improvement plan for the leaf spring according to the optimization results, and verify its effectiveness through experiments, including bench tests and on-road vehicle tests, etc. The indicators for experimental verification include static load-displacement curves, dynamic response characteristics, and durability test results, etc. Evaluate the effect and value of the optimization by comparing the performance indicators before and after optimization.

[0034] In this embodiment, through the acquisition of multi-dimensional vehicle-mounted test experimental data and the integrated processing of heterogeneous load responses, the comprehensive capture of the mechanical characteristics of the leaf spring under complex working conditions is realized. It can integrate scattered test data into unified integrated mechanical response data of the leaf spring, thus enhancing the integrity and accuracy of the analysis. Through the dual stiffness gradient identification analysis and the critical area stress field deconstruction analysis, the critical region of stiffness transition can be accurately located and high-density stress distribution data can be generated, realizing an in-depth analysis of the mechanism of stiffness mutation of the leaf spring and laying a solid foundation for subsequent microscopic analysis. Through the characterization of local microstructural changes and the analysis of crystal dislocation density evolution, the microscopic deformation mechanism of the material in the stress concentration region is revealed. Applying advanced electron microscopy and X-ray diffraction techniques, the multi-scale correlation from macroscopic mechanical properties to microscopic structural changes is realized, improving the scientific nature and depth of the analysis. By constructing a multi-scale stress transfer link, the stress transfer path from the macroscopic structure to the microscopic material is accurately captured, the key nodes and bottleneck regions of stress transfer are effectively identified, and the understanding depth of the failure mechanism of the leaf spring is greatly improved. Through the mapping of fatigue damage accumulation under cyclic loading and the extraction of influence factors of high-frequency micro-amplitude loading, the influence of complex load spectra under actual working conditions on the life of the leaf spring is comprehensively considered. Based on the improved Miner's non-linear cumulative damage theory, a progressive fatigue life prediction model is constructed, breaking through the limitations of the traditional linear cumulative theory and significantly improving the accuracy of fatigue life prediction. Through the analysis of the structure-performance correlation characteristics and the quantitative evaluation of comprehensive performance indicators, the static and dynamic performance of the leaf spring is systematically quantified, providing an accurate basis for optimization and improvement.

[0035] Embodiment 2; Please refer to Figure 2 As shown, for the parts not described in detail in this embodiment, refer to the description content of Embodiment 1. Provide a high-strength two-stage variable stiffness leaf spring performance analysis and evaluation system, including: Load response integration module: Collect the initial mechanical data of leaf springs based on multi-dimensional vehicle test experiments to generate the initial mechanical characteristic data of leaf springs; perform heterogeneous load response integration processing according to the initial mechanical characteristic data of leaf springs to generate the integrated mechanical response data of leaf springs; Stiffness critical analysis module: Perform dual stiffness gradient identification and analysis according to the integrated mechanical response data of leaf springs to generate critical data for stiffness transition; perform deconstruction analysis of the stress field in the critical area according to the critical data for stiffness transition to generate data on the distribution characteristics of the stress field in the critical area; Microscopic stress link module: Characterize local microscopic structure changes according to the data on the distribution characteristics of the stress field in the critical area to generate local microscopic structure deformation data; construct a multi-scale stress transfer link based on the local microscopic structure deformation data to generate data on the stress transfer path in the critical area; Fatigue life assessment module: Map the fatigue damage accumulation under cyclic load on the data of the stress transfer path in the critical area to generate data on the characteristics of fatigue damage accumulation; construct an evaluation model for the influence of high-frequency micro-amplitude loads based on the data on the characteristics of fatigue damage accumulation to generate data on predicted fatigue life; perform a quantitative evaluation of comprehensive performance indicators according to the data on predicted fatigue life and the integrated mechanical response data of leaf springs to generate the comprehensive performance evaluation result of leaf springs; execute the generation and verification of the optimization and improvement plan for leaf springs based on the comprehensive performance evaluation result of leaf springs; Each module is connected by wired and / or wireless means to achieve data transmission between modules.

[0036] Example 3; This embodiment publicly provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the above-provided method for analyzing and evaluating the performance of a high-strength two-stage gradually changing stiffness leaf spring.

[0037] Since the electronic device introduced in this embodiment is the electronic device used to implement the method for analyzing and evaluating the performance of a high-strength two-stage gradually changing stiffness leaf spring in the embodiments of the present application, based on the method for analyzing and evaluating the performance of a high-strength two-stage gradually changing stiffness leaf spring introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manner and various forms of change of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the method for analyzing and evaluating the performance of a high-strength two-stage gradually changing stiffness leaf spring in the embodiments of the present application, it falls within the scope of protection of the present application.

[0038] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulations to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0039] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. Any technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A performance analysis and evaluation method for a high-strength two-stage gradually variable stiffness leaf spring, characterized in that Including: Step S1: Based on multi-dimensional vehicle-mounted test experiments, collect the initial mechanical data of the leaf spring to generate the initial mechanical characteristic data of the leaf spring; Perform heterogeneous load response integration processing according to the initial mechanical characteristic data of the leaf spring to generate the integrated mechanical response data of the leaf spring; Step S2: Conduct dual-stiffness gradient identification and analysis according to the integrated mechanical response data of the leaf spring to generate critical data for stiffness transition; conduct stress field deconstruction analysis in the critical area according to the critical data for stiffness transition to generate data on the distribution characteristics of the stress field in the critical area; Step S3: Characterize the local microstructure changes according to the data on the distribution characteristics of the stress field in the critical area to generate data on local microstructure deformation; Construct a multi-scale stress transfer link based on the data on local microstructure deformation to generate data on the stress transfer path in the critical area; Step S4: Perform fatigue damage accumulation mapping under cyclic loading on the data on the stress transfer path in the critical area to generate data on fatigue damage accumulation characteristics; Construct a fatigue life prediction model based on the data on fatigue damage accumulation characteristics to generate fatigue life prediction data; conduct a quantitative evaluation of comprehensive performance indicators according to the fatigue life prediction data and the integrated mechanical response data of the leaf spring to generate the comprehensive performance evaluation result of the leaf spring; Execute the generation and verification of the optimization and improvement plan for the leaf spring based on the comprehensive performance evaluation result of the leaf spring.

2. The performance analysis and evaluation method of the high-strength two-stage variable stiffness leaf spring according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Based on multi-dimensional vehicle-mounted test experiments, collect the initial mechanical data of the leaf spring to generate the initial mechanical characteristic data of the leaf spring; Step S12: Decompose and process the load-displacement curve according to the initial mechanical characteristic data of the leaf spring to generate data on the decomposition of non-linear response characteristics; Step S13: Reconstruct the load spectrum for multiple road conditions according to the data on the decomposition of non-linear response characteristics to generate data on the simulated road condition load spectrum; Step S14: Perform heterogeneous load response integration processing according to the data on the simulated road condition load spectrum to generate the integrated mechanical response data of the leaf spring.

3. The high-strength two-stage variable stiffness leaf spring performance analysis and evaluation method according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Calculate the change rate of the slope of the stiffness curve according to the integrated mechanical response data of the leaf spring to generate data on the change of the stiffness gradient; Step S22: Accurately locate the stiffness mutation point according to the data on the change of the stiffness gradient to generate data on the critical area for stiffness transition; Step S23: Use an ultra-high-precision strain measurement device to scan the stress field of the data on the critical area for stiffness transition to generate data on the high-density stress distribution in the critical area; Step S24: Identify and quantify the stress concentration area based on the data on the high-density stress distribution in the critical area to generate a map of the stress peak distribution in the critical area; Step S25: Conduct stress field deconstruction analysis in the critical area according to the map of the stress peak distribution in the critical area to generate data on the distribution characteristics of the stress field in the critical area.

4. The high-strength two-stage variable stiffness leaf spring performance analysis and evaluation method according to claim 3, characterized in that Step S24 includes the following steps: Calculate the stress gradient tensor according to the data on the high-density stress distribution in the critical area to generate data on the stress gradient tensor field, where the data on the stress gradient tensor field includes data on the main stress distribution nodes and data on the quantification of the stress intensity factor; Conduct clustering analysis of the high-stress area according to the data on the main stress distribution nodes to generate data on the stress clustering area; Identify potential crack sources according to the data on the stress clustering area and the data on the quantification of the stress intensity factor to generate data on potential fracture origins; Quantitatively rate the high-density stress distribution data in the critical area according to the potential fracture origin data to generate a stress peak distribution map of the critical area.

5. The high-strength two-stage variable stiffness leaf spring performance analysis and evaluation method according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Characterize the local microstructure changes according to the stress field distribution characteristic data in the critical area to generate local microstructure deformation data; Step S32: Analyze the crystal dislocation density evolution based on the local microstructure deformation data to generate microscopic dislocation evolution characteristic data; Step S33: Construct a multi-scale stress transfer link according to the microscopic dislocation evolution characteristic data to generate stress transfer path data in the critical area.

6. The performance analysis and evaluation method of the high-strength two-stage gradually variable stiffness leaf spring according to claim 5, characterized in that, Step S32 includes the following steps: Analyze the micro-mechanical deformation mechanism based on the local microstructure deformation data to generate micro-deformation mechanism characteristic data; Analyze the material strengthening effect based on the local microstructure deformation data to generate material strengthening characteristic data; Construct a crystal dislocation density distribution matrix according to the micro-deformation mechanism characteristic data and the material strengthening characteristic data to generate a dislocation density evolution matrix; Analyze the crystal dislocation density evolution based on the dislocation density evolution matrix to generate microscopic dislocation evolution characteristic data.

7. The performance analysis and evaluation method of the high-strength two-stage variable stiffness leaf spring according to claim 5, characterized in that Step S33 includes the following steps: Conduct a micro-macro mechanical behavior correlation analysis on the microscopic dislocation evolution characteristic data under the influence of historical loads to generate cross-scale mechanical behavior linkage characteristic data; Construct a multi-scale stress transfer link according to the cross-scale mechanical behavior linkage characteristic data to generate stress transfer path data in the critical area.

8. The performance analysis and evaluation method of the high-strength two-stage variable stiffness leaf spring according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Map the fatigue damage accumulation under cyclic loads to the stress transfer path data in the critical area to generate fatigue damage accumulation characteristic data; Step S42: Extract the influence factor of high-frequency micro-amplitude loads based on the fatigue damage accumulation characteristic data to generate high-frequency load influence quantification data, and establish damage evolution prediction distribution nodes through the high-frequency load influence quantification data; Step S43: Construct a progressive fatigue life prediction model for the damage evolution prediction distribution nodes and the fatigue damage accumulation characteristic data based on the improved Miner's non-linear cumulative damage theory to generate a fatigue life prediction model and obtain fatigue life prediction data; Step S44: Conduct a correlation analysis between the stress field distribution characteristic data in the critical area and the fatigue life prediction data to generate structure-performance correlation characteristic data; Step S45: Conduct a quantitative evaluation of the comprehensive performance index according to the structure-performance correlation characteristic data and the integrated mechanical response data of the leaf spring to generate a comprehensive performance evaluation result of the leaf spring; Step S46: Based on the comprehensive performance evaluation result of the leaf spring, optimize the design and adjust the parameters of the leaf spring structure to generate an optimized improvement plan for the leaf spring, and perform experimental verification of the optimized improvement plan for the leaf spring.

9. The performance analysis and evaluation method of the high-strength two-stage variable stiffness leaf spring according to claim 8, characterized in that, Step S43 includes the following steps: Identify the critical damage threshold according to the fatigue damage accumulation characteristic data to generate fatigue damage critical threshold data; Construct a non-linear cumulative damage evolution function based on the fatigue damage critical threshold data to generate a non-linear damage accumulation function; Correct the damage evolution prediction distribution nodes according to the non-linear damage accumulation function to generate corrected damage evolution prediction distribution nodes; Construct a progressive fatigue life prediction model based on the corrected damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data to obtain a fatigue life prediction model.

10. A high-strength two-stage variable stiffness leaf spring performance analysis and evaluation system, which is used to implement the high-strength two-stage variable stiffness leaf spring performance analysis and evaluation method described in claims 1-9, and is characterized in that, It includes: Load response integration module: Collect the initial mechanical data of the leaf spring based on multi-dimensional vehicle-borne test experiments to generate the initial mechanical characteristic data of the leaf spring; Perform heterogeneous load response integration processing according to the initial mechanical characteristic data of the leaf spring to generate the integrated mechanical response data of the leaf spring; Stiffness critical analysis module: Conduct dual stiffness gradient identification analysis based on the integrated mechanical response data of the leaf spring to generate critical data for stiffness transition; Conduct critical area stress field deconstruction analysis based on the critical data for stiffness transition to generate the distribution characteristic data of the critical area stress field; Microscopic stress link module: Characterize the local microscopic structure changes based on the distribution characteristic data of the critical area stress field to generate local microscopic structure deformation data; Construct a multi-scale stress transfer link based on the local microscopic structure deformation data to generate the critical area stress transfer path data; Fatigue life evaluation module: Map the fatigue damage accumulation under cyclic loads for the critical area stress transfer path data to generate fatigue damage accumulation characteristic data; Construct a fatigue life prediction model based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; Conduct a quantitative evaluation of the comprehensive performance indicators based on the fatigue life prediction data and the integrated mechanical response data of the leaf spring to generate the comprehensive performance evaluation result of the leaf spring; Execute the generation and verification of the leaf spring optimization and improvement plan based on the comprehensive performance evaluation result of the leaf spring.

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