A high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system and method

Abstract

The present invention belongs to the technical field of leaf spring performance analysis and evaluation, and discloses a high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system and method, including: collecting initial mechanical data of the leaf spring based on a multi-dimensional vehicle-mounted test experiment, performing integrated processing of heterogeneous load responses, and generating integrated mechanical response data of the leaf spring; performing dual stiffness gradient identification analysis to generate stiffness transition critical data; performing critical zone stress field deconstruction analysis to generate stress field distribution characteristic data; performing local microstructure change characterization to generate structural deformation data; constructing a multi-scale stress transfer link to generate critical zone stress transfer path data; performing fatigue damage accumulation mapping to generate cumulative characteristic data; constructing a fatigue life prediction model to generate life prediction data; performing quantitative evaluation of comprehensive performance indicators to generate performance evaluation results; executing leaf spring optimization and improvement plan generation and verification; and providing a scientific basis for performance optimization of the two-stage gradient stiffness leaf spring.

Description

Technical Field

[0001] The present invention relates to the technical field of leaf spring performance analysis and evaluation, and more particularly to a high-strength two-stage gradually changing 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. Leaf springs are commonly used elastic elements in commercial vehicles, and their performance directly affects the comfort, stability, and safety of the vehicle. Traditional leaf spring performance analysis methods mainly focus on the design of single stiffness characteristics. However, for the new high-strength two-stage gradient stiffness leaf spring, the existing analysis and evaluation methods have obvious shortcomings.

[0003] At present, the industry lacks an accurate quantitative analysis method for the stress concentration phenomenon at the critical point of the two-stage gradual stiffness transition. When the vehicle load changes from light load to heavy load, a complex stress distribution will be generated in the critical area where the leaf spring stiffness suddenly changes. Existing testing equipment and analysis algorithms cannot accurately capture the stress peak and fatigue cumulative effects in this area, resulting in unexpected fracture of the leaf spring before its expected service life in actual use, which seriously threatens driving safety. In the existing technology, although there are simulation methods based on finite element analysis, they cannot accurately simulate the material microstructure changes at the critical point under the complex load conditions of actual roads, resulting in significant deviations between the analysis results and the actual usage 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 the inability to capture the cumulative damage effects of high-frequency, small-amplitude load changes on the critical point area.

[0004] In view of this, the present invention proposes a high-strength two-stage gradient 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-mentioned objectives, the present invention provides the following technical solution: a method for analyzing and evaluating the performance of a high-strength, two-stage, gradually varying stiffness leaf spring, comprising:

[0006] Step S1: collecting initial mechanical data of the leaf spring based on a multi-dimensional vehicle-mounted test experiment to generate initial mechanical characteristic data of the leaf spring; performing heterogeneous load response integration processing on the initial mechanical characteristic data of the leaf spring to generate integrated mechanical response data of the leaf spring;

[0007] Step S2: performing dual stiffness gradient identification analysis based on the leaf spring integrated mechanical response data to generate stiffness transition critical data; performing critical region stress field deconstruction analysis based on the stiffness transition critical data to generate critical region stress field distribution characteristic data;

[0008] Step S3: Characterize local microstructure changes based on the critical region 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 region stress transfer path data;

[0009] Step S4: fatigue damage accumulation mapping is performed on the critical zone stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data; a fatigue life prediction model is constructed based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; a comprehensive performance index quantitative evaluation is performed based on 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; and a leaf spring optimization improvement plan is generated and verified based on the comprehensive performance evaluation result of the leaf spring.

[0010] Furthermore, step S1 includes the following steps:

[0011] Step S11: collecting initial mechanical data of the leaf spring based on a multi-dimensional vehicle-mounted test experiment to generate initial mechanical characteristic data of the leaf spring;

[0012] Step S12: performing load-displacement curve deconstruction processing based on the initial mechanical characteristic data of the leaf spring to generate nonlinear response characteristic deconstruction data;

[0013] Step S13: reconstructing multiple road load spectra based on the nonlinear response characteristic deconstruction data to generate simulated road load spectrum data;

[0014] Step S14: performing heterogeneous load response integration processing according to the simulated road load spectrum data to generate leaf spring integrated mechanical response data.

[0015] Furthermore, step S2 includes the following steps:

[0016] Step S21: Calculating the slope change rate of the stiffness curve based on the integrated mechanical response data of the leaf spring to generate stiffness gradient change data;

[0017] Step S22: accurately locate the stiffness mutation point based on the stiffness gradient change data, and generate stiffness transition critical area data;

[0018] Step S23: Using an ultra-high precision strain measurement device to perform stress field scanning on the stiffness transition critical region data to generate high-density stress distribution data in the critical region;

[0019] Step S24: Identify and quantify stress concentration areas based on the high-density stress distribution data in the critical area, and generate a critical area stress peak distribution map;

[0020] Step S25: performing a deconstruction analysis of the critical region stress field according to the critical region stress peak distribution map to generate critical region stress field distribution characteristic data.

[0021] Furthermore, step S24 includes the following steps:

[0022] Calculating the stress gradient tensor based on the high-density stress distribution data in the critical region to generate stress gradient tensor field data, wherein the stress gradient tensor field data includes principal stress distribution node data and stress intensity factor quantification data;

[0023] Perform cluster analysis of high stress areas based on principal stress distribution node data to generate stress cluster area data;

[0024] Identify potential crack sources based on stress clustering area data and stress intensity factor quantification data to generate potential fracture origin data;

[0025] The high-density stress distribution data in the critical area are quantitatively rated according to the potential fracture origin data to generate a peak stress distribution map of the critical area.

[0026] Furthermore, step S3 includes the following steps:

[0027] Step S31: characterizing local microstructure changes based on the critical region stress field distribution characteristic data to generate local microstructure deformation data;

[0028] Step S32: performing crystal dislocation density evolution analysis based on the local microstructure deformation data to generate micro dislocation evolution characteristic data;

[0029] Step S33: construct a multi-scale stress transfer link based on the microscopic dislocation evolution characteristic data to generate critical zone stress transfer path data.

[0030] Furthermore, step S32 includes the following steps:

[0031] Analyze the micromechanical deformation mechanism based on the local microstructural deformation data and generate microdeformation mechanism characteristic data;

[0032] Analyze the material strengthening effect based on the local microstructure deformation data to generate material strengthening characteristic data;

[0033] The crystal dislocation density distribution matrix is constructed based on the microscopic deformation mechanism characteristic data and the material strengthening characteristic data to generate the dislocation density evolution matrix;

[0034] The crystal dislocation density evolution analysis is performed based on the dislocation density evolution matrix to generate microscopic dislocation evolution characteristic data.

[0035] Furthermore, step S33 includes the following steps:

[0036] Conduct micro-macroscopic 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;

[0037] Multi-scale stress transfer links are constructed based on the cross-scale mechanical behavior linkage characteristic data to generate critical zone stress transfer path data.

[0038] Furthermore, step S4 includes the following steps:

[0039] Step S41: performing fatigue damage accumulation mapping on the critical region stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data;

[0040] Step S42: extracting high-frequency micro-amplitude load influence factors based on fatigue damage accumulation characteristic data, generating high-frequency load influence quantitative data, and establishing damage evolution prediction distribution nodes based on the high-frequency load influence quantitative data;

[0041] Step S43: constructing a progressive fatigue life prediction model based on the improved Miner nonlinear cumulative damage theory for the damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data, generating a fatigue life prediction model, and obtaining fatigue life prediction data;

[0042] Step S44: performing correlation analysis on the critical region stress field distribution characteristic data and the fatigue life prediction data to generate structure-performance correlation characteristic data;

[0043] Step S45: performing a quantitative evaluation of 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;

[0044] Step S46: Based on the comprehensive performance evaluation results of the leaf spring, perform leaf spring structure optimization design and parameter adjustment, generate a leaf spring optimization and improvement plan, and perform experimental verification of the leaf spring optimization and improvement plan.

[0045] Furthermore, step S43 includes the following steps:

[0046] Identify critical damage thresholds based on fatigue damage accumulation characteristic data and generate fatigue damage critical threshold data;

[0047] Based on the fatigue damage critical threshold data, a nonlinear cumulative damage evolution function is constructed to generate a nonlinear damage accumulation function;

[0048] The damage evolution prediction distribution node is modified according to the nonlinear damage accumulation function to generate a modified damage evolution prediction distribution node;

[0049] A progressive fatigue life prediction model is constructed based on the modified damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data to obtain the fatigue life prediction model.

[0050] A high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system, comprising:

[0051] Load response integration module: This module collects initial mechanical data of leaf springs based on multi-dimensional vehicle-mounted test experiments to generate initial mechanical characteristic data of leaf springs. It also integrates heterogeneous load responses based on the initial mechanical characteristic data of leaf springs to generate integrated mechanical response data of leaf springs.

[0052] Stiffness critical analysis module: performs dual stiffness gradient identification analysis based on the integrated mechanical response data of the leaf spring to generate stiffness transition critical data; performs critical zone stress field deconstruction analysis based on the stiffness transition critical data to generate critical zone stress field distribution characteristic data;

[0053] Micro-stress link module: Characterizes local microstructural changes based on the critical zone stress field distribution characteristic data and generates local microstructural deformation data; constructs multi-scale stress transfer links based on the local microstructural deformation data and generates critical zone stress transfer path data;

[0054] Fatigue life assessment module: Fatigue damage accumulation mapping is performed on the critical zone stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data; a fatigue life prediction model is constructed based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; a comprehensive performance index quantitative evaluation is performed based on the fatigue life prediction data and the integrated mechanical response data of the leaf spring to generate the comprehensive performance evaluation results of the leaf spring; and based on the comprehensive performance evaluation results of the leaf spring, the leaf spring optimization and improvement plan is generated and verified.

[0055] The technical effects and advantages of the high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system and method of the present invention are as follows:

[0056] The present invention achieves a comprehensive capture of the mechanical properties of leaf springs under complex working conditions through multi-dimensional on-vehicle test experimental data collection and integrated processing of heterogeneous load responses. It can integrate scattered test data into unified leaf spring integrated mechanical response data, thereby enhancing the integrity and accuracy of the analysis. Through dual stiffness gradient identification analysis and critical zone stress field deconstruction analysis, it can accurately locate the critical area of stiffness transition and generate high-density stress distribution data, achieving an in-depth analysis of the leaf spring stiffness mutation mechanism 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 area is revealed. The application of advanced electron microscopy and X-ray diffraction technology realizes a multi-scale correlation from macroscopic mechanical properties to microstructural changes, which improves the scientific nature and depth of the analysis. By constructing a multi-scale stress transfer link, the stress transfer path from macroscopic structure to microscopic material is accurately captured, and the key nodes and bottleneck areas of stress transfer are effectively identified, which greatly enhances the depth of understanding of the leaf spring failure mechanism. By mapping fatigue damage accumulation under cyclic loading and extracting high-frequency, low-amplitude load influencing factors, the authors comprehensively considered the impact of complex load spectra on leaf spring life under actual operating conditions. A progressive fatigue life prediction model, based on an improved Miner nonlinear cumulative damage theory, overcomes the limitations of traditional linear cumulative damage theory and significantly improves the accuracy of fatigue life prediction. Through structure-performance correlation analysis and quantitative evaluation of comprehensive performance indicators, the authors systematically quantified the static and dynamic performance of leaf springs, providing a precise basis for optimization and improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Schematic diagram of a performance analysis and evaluation method for a high-strength two-stage gradient stiffness leaf spring according to the present invention;

[0058] Figure 2 This is a schematic diagram of a high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] Example 1;

[0061] See also Figure 1 As shown, the performance analysis and evaluation method of a high-strength two-stage gradient stiffness leaf spring described in this embodiment includes:

[0062] Step S1: collecting initial mechanical data of the leaf spring based on a multi-dimensional vehicle-mounted test experiment to generate initial mechanical characteristic data of the leaf spring; performing heterogeneous load response integration processing on the initial mechanical characteristic data of the leaf spring to generate integrated mechanical response data of the leaf spring;

[0063] Step S2: performing dual stiffness gradient identification analysis based on the leaf spring integrated mechanical response data to generate stiffness transition critical data; performing critical region stress field deconstruction analysis based on the stiffness transition critical data to generate critical region stress field distribution characteristic data;

[0064] Step S3: Characterize local microstructure changes based on the critical region 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 region stress transfer path data;

[0065] Step S4: fatigue damage accumulation mapping is performed on the critical zone stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data; a fatigue life prediction model is constructed based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; a comprehensive performance index quantitative evaluation is performed based on 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; and a leaf spring optimization improvement plan is generated and verified based on the comprehensive performance evaluation result of the leaf spring.

[0066] Preferably, the process of executing step S1 may specifically include the following steps:

[0067] (1) Collect initial mechanical data of leaf springs based on multi-dimensional vehicle-mounted test experiments to generate initial mechanical characteristic data of leaf springs;

[0068] (2) Deconstructing the load-displacement curve based on the initial mechanical characteristic data of the leaf spring to generate nonlinear response characteristic deconstruction data;

[0069] (3) Reconstruct multiple road load spectra based on the nonlinear response characteristic deconstruction data to generate simulated road load spectrum data;

[0070] (4) Heterogeneous load response integration processing is performed based on the simulated road load spectrum data to generate leaf spring integrated mechanical response data.

[0071] Specifically, initial mechanical data for a high-strength, two-stage, gradient-stiffness leaf spring was collected through on-vehicle testing under various road conditions. The tests included driving on flat roads, driving on bumpy roads, and operating both fully loaded and unloaded. During testing, a multi-channel strain acquisition system and displacement sensors mounted on the leaf spring recorded real-time strain and displacement data in the vertical, horizontal, and torsional directions. The data acquisition frequency was set to 1000 Hz to capture the leaf spring's transient response under dynamic loads. Simultaneously, an accelerometer recorded vibration acceleration data from the vehicle's suspension system for subsequent analysis of the leaf spring's impact on vehicle dynamics. Through the comprehensive collection of these multi-dimensional test data, an initial leaf spring mechanical property dataset was generated. This dataset 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 curves of the initial leaf spring mechanical property data were deconstructed to identify the leaf spring's nonlinear mechanical response characteristics. First, the raw data was filtered and denoised using a wavelet transform 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, the stiffness value at different displacement points is calculated to identify the stiffness mutation point and nonlinear change law of the leaf spring, and generate nonlinear response characteristic deconstruction data. Based on the nonlinear response characteristic deconstruction data, multi-road condition load spectra are reconstructed. First, based on the measured road spectrum and international standard road spectrum (such as ISO8608), a typical excitation model of different grades of road surfaces is established. The road excitation function is expressed as the superposition of each harmonic component, including parameters such as amplitude, frequency and phase angle. The road excitation function can be expressed as:

[0072] ;in, For the The amplitude of the harmonic components, is the corresponding frequency, is the phase angle. Combined with the dynamic model of the vehicle suspension system, the road excitation is converted into dynamic loads on the leaf spring. A multi-dimensional load spectrum matrix is constructed for different road conditions and vehicle operating conditions. The elements in the load spectrum matrix represent different types of load parameters corresponding to different road conditions. This load spectrum matrix is used to generate simulated road load spectrum data, which comprehensively reflects the various load conditions that a leaf spring may experience during actual use. The simulated road load spectrum data is then integrated to process heterogeneous load responses. Heterogeneous loads include vertical compression, lateral shear, and torsional moments. These different load types are comprehensively processed using the superposition principle and weighted fusion method. Based on elastic mechanics theory, a response function for the leaf spring under multi-dimensional loads is established. This function integrates the leaf spring's responses to compression, bending, and torque using weighted coefficients. The weighting coefficients are optimized using experimental data to minimize the error between the model's predicted and measured values. This response function enables the integrated processing of the leaf spring's mechanical responses under heterogeneous load conditions, generating integrated leaf spring mechanical response data.

[0073] Preferably, the process of executing step S2 may specifically include the following steps:

[0074] (1) Calculate the slope change rate of the stiffness curve based on the integrated mechanical response data of the leaf spring to generate stiffness gradient change data;

[0075] (2) Accurately locate the stiffness mutation point based on the stiffness gradient change data and generate the stiffness transition critical region;

[0076] (3) Using an ultra-high-precision strain measurement device to scan the stress field in the critical region of stiffness transition, generating high-density stress distribution data in the critical region;

[0077] (4) Identify and quantify stress concentration areas based on high-density stress distribution data in the critical area, and generate a peak stress distribution map of the critical area;

[0078] (5) Deconstruction analysis of the critical zone stress field is performed based on the critical zone stress peak distribution map to generate critical zone stress field distribution characteristic data.

[0079] Specifically, the slope change rate of the stiffness curve is calculated based on the integrated mechanical response data of the leaf spring. First, the complete load-displacement curve is extracted from the mechanical response data, and the stiffness value at each point is calculated using numerical differentiation methods, that is, the rate of change of load with respect to displacement. Then, the slope change rate of the stiffness curve is calculated, that is, the rate of change of stiffness with respect to displacement. The slope change rate reflects the severity of the leaf spring stiffness change and is an important parameter for identifying stiffness mutation points. By analyzing the distribution characteristics of the slope change rate, stiffness gradient change data is generated, which intuitively demonstrates the nonlinear characteristics of the leaf spring stiffness change. Based on the stiffness gradient change data, a peak detection algorithm and threshold screening method are used to accurately locate the stiffness mutation point. When the slope change rate exceeds a preset threshold and reaches a maximum value in a local area, the point is identified as the stiffness mutation point. Based on the location of the identified mutation point, the critical region of stiffness transition is determined, which is the focus of leaf spring performance analysis. The stress field in the critical region of stiffness transition is scanned using an ultra-high-precision strain measurement device. High-precision digital image correlation (DIC) technology and optical fiber Bragg grating strain sensing system are used to perform high-density strain measurement in the critical area. 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 a small area. Through multi-angle and multi-position scanning measurement, the three-dimensional strain field data of the critical area is obtained. Combining Hooke's law and the material constitutive relationship, the strain field data is converted into stress field data to generate high-density stress distribution data in the critical area. This data describes the stress state of each point in the critical area in a gridded form. Based on the high-density stress distribution data in the critical area, stress concentration areas are identified and quantified. First, the stress gradient tensor is calculated based on the high-density stress distribution data in the critical area. The stress gradient tensor is an important parameter that describes the rate of change of stress in spatial distribution. 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 coordinate 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: ;in, For the Stress intensity factor of the quasi-mode, For the The stress corresponding to the class mode is, is the crack length, For the The collective correction factor corresponding to the class mode, the stress intensity factor includes three types of modes, namely the cracking mode (indicates the deformation mode in which the crack surfaces separate perpendicularly to each other), the slip mode (indicates the deformation mode in which the crack surfaces slide parallel to each other along the crack direction) and the tearing mode (indicates the deformation mode in which the crack surfaces slide parallel to each other perpendicular to the crack front direction). Through these calculations, the stress gradient tensor field data is generated. The high stress area clustering analysis is performed based on the principal stress distribution node data. The spatial clustering algorithm DBSCAN is used to cluster the nodes according to their spatial position and stress value. For each cluster, its characteristic parameters such as the center position, coverage area and average stress intensity are calculated to generate stress clustering area data. Potential crack sources are identified based on the stress clustering area data and the quantitative data of the stress intensity factor. Based on the fracture mechanics theory, the strain energy release rate of each cluster area is calculated. ;in 、 and represent the stress intensity factors in cracking mode, slip mode and tearing mode respectively, represents the elastic modulus, The shear modulus is represented by the strain energy release rate and compared with the preset critical strain energy release rate to determine whether the fracture condition is met. When the strain energy release rate is greater than or equal to the critical strain energy release rate, the region is identified as a potential crack source, and its hazard factor is calculated. The strain energy release rate is divided by the critical strain energy release rate. Potential crack sources are ranked according to the hazard factor to generate potential fracture origin data. Finally, the high-density stress distribution data in the critical region is quantitatively rated based on the potential fracture origin data. A risk matrix method is used to classify each region into a risk level using stress level and fracture probability as two dimensions. Based on the risk level distribution, a peak stress distribution map of the critical region is generated. This map visually displays the stress level and potential risk at each point in the critical region in the form of a heat map. Based on the peak stress distribution map of the critical region, a stress field deconstruction analysis of the critical region is performed. This stress field deconstruction analysis aims to reveal the inherent characteristics and patterns of the stress distribution. Principal component analysis is used to extract the main characteristic patterns of the stress field. Then, Fourier transform and wavelet analysis are used to perform spectral analysis of the stress field to identify periodic characteristics of stress fluctuations. Through these analysis methods, the multi-scale characteristics of the stress field are extracted and the critical zone stress field distribution characteristic data are generated. This data comprehensively describes the spatial distribution, spectral characteristics and intensity distribution law of the critical zone stress field.

[0080] Preferably, the process of executing step S3 may specifically include the following steps:

[0081] (1) Characterize the local microstructure changes based on the critical region stress field distribution characteristic data and generate local microstructure deformation data;

[0082] (2) Analyze the evolution of crystal dislocation density based on local microstructural deformation data to generate microscopic dislocation evolution characteristic data;

[0083] (3) Based on the microscopic dislocation evolution characteristic data, a multi-scale stress transfer link is constructed to generate critical zone stress transfer path data.

[0084] Specifically, based on the stress field distribution characteristic data of the critical region, advanced microstructure analysis technology is used to characterize the local microstructural changes of the material. First, a scanning electron microscope (SEM) is used to perform high-resolution imaging of the material surface in the critical region to observe the surface morphology and microcrack distribution. The SEM imaging resolution is set to 10nm to capture nanometer-level surface details. Next, a transmission electron microscope (TEM) is used to perform crystal structure analysis on thin-section samples in the critical region to observe lattice distortion and dislocation distribution. The spatial resolution of TEM analysis reaches 0.2nm, which can clearly display the atomic-level lattice structure. At the same time, the electron backscatter diffraction (EBSD) technology is used to analyze the grain orientation and grain boundary characteristics of the material. The EBSD scanning step size is set to 0.5μm to obtain sufficient grain statistical information. The residual stress and lattice constant changes of the material are measured by X-ray diffraction (XRD) technology. The XRD measurement uses a CuKα radiation source (wavelength of 1.5406 angstroms), with a scanning range of 30° to 90° and a step size of 0.02°. Based on the XRD patterns, the interplanar spacing and lattice strain—the relative change in interplanar spacing—are calculated. Furthermore, nanoindentation is used to measure the hardness and elastic modulus distribution of localized regions, with an indentation force of 10 mN and an indentation depth of less than 200 nm. Combining the results of these microanalysis techniques, the microstructural changes in the critical region are comprehensively characterized, generating localized microstructural deformation data.

[0085] The crystal dislocation density evolution analysis is performed based on the local microstructural deformation data. First, the dislocation density is estimated based on TEM images and XRD spectrum line broadening analysis. ;in, is the integrated width of the diffraction peak, is the Burgers vector; secondly, the micromechanical deformation mechanism is analyzed based on the local microstructural deformation data to identify the main deformation mechanisms, including slip, twinning, phase transformation, and dynamic recrystallization. For slip deformation, the main slip system and critical shear stress are determined. This stress is related to the friction stress and the inverse square root of the grain size. The specific calculation formula is: ;in, is the friction stress, is the material constant, is the grain size. By calculating the Schmidt factor of each slip system: ;in, is the angle between the normal line of the slip surface and the load direction, It is the angle between the slip direction and the load direction. When the Schmidt factor is greater than or equal to the critical shear stress, it means that the slip system is activated, and the slip system that is activated first is determined. For twin deformation, the twin shear strain and twin volume fraction are calculated. Through these analyses, the characteristic data of the microscopic deformation mechanism are generated. Then, the material strengthening effect is analyzed based on the local microstructural deformation data. Considering a variety of strengthening mechanisms, including solid solution strengthening, fine grain strengthening, dislocation strengthening and precipitation strengthening, material strengthening characteristic data are generated. Then, based on the microscopic deformation mechanism characteristic data and material strengthening characteristic data, a crystal dislocation density distribution matrix is constructed. The analysis area is discretized into a grid, and the dislocation density and dislocation type distribution are calculated for each grid point. Considering the interaction and movement law of dislocations, the dislocation density evolution equation is established:

[0086] ;in, represents the rate of change of 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 combined effects of dislocation generation, annihilation, and motion. By numerically solving this equation, a dislocation density evolution matrix is obtained. Finally, the dislocation density evolution matrix is used to analyze the crystal dislocation density evolution. Through time series analysis, the spatiotemporal evolution of the dislocation density is tracked, and key regions of dislocation accumulation and dissipation are identified. The spatial distribution of the dislocation density gradient and the dislocation density evolution rate are calculated, and the formation process of dislocation walls and subgrain boundaries is identified. Based on these analyses, microscopic dislocation evolution characteristic data are generated, which comprehensively describe the microscopic deformation mechanism and dislocation dynamics behavior of the material under load.

[0087] A multiscale stress transfer chain was constructed based on the characteristic data of microdislocation evolution. First, the micro-macroscopic mechanical behavior correlation analysis under the influence of historical loads was performed on the microdislocation evolution characteristic data. A mapping relationship was established between microstate 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 a weighted sum of macroscopic mechanical parameters and functions of the microstate variables. The parameters in this mapping relationship were determined through experimental data and theoretical analysis to achieve a quantitative correlation between microstructure and macroscopic properties. Considering the cumulative effect of historical loads on microstructural evolution, a history-dependent model of dislocation density evolution was established. This model represents the relationship between the temporal change of dislocation density and factors such as current dislocation density, strain, strain rate, and temperature. Using this model, the microstructural evolution paths under different loading histories were simulated to evaluate the influence of historical loads on the mechanical behavior of the material. Based on the correlation analysis of micro-macroscopic mechanical behavior, key linkage mechanisms of cross-scale mechanical behavior are identified, including the effect of grain boundaries on dislocation motion, the influence of dislocation structure on work hardening, and the contribution of microcracks to macroscopic damage. These analyses generate characteristic data of cross-scale mechanical behavior linkage. Then, based on this cross-scale mechanical behavior linkage characteristic data, a multiscale stress transfer chain is constructed. Using the representative volume element (RVE) approach, the material is divided into multiple scale levels, from the nanoscale (dislocations and point defects), to the microscale (grains and grain boundaries), to the millimeter scale (macrostructure). Appropriate mechanical models are established at each scale, such as molecular dynamics models at the atomic scale, crystal plasticity models at the grain scale, and continuum models at the macroscale. A multiscale computational framework is employed to facilitate information transfer and scale transition between models at different scales. For example, a homogenization method is used to transfer the microscopic stress-strain relationship to the macroscale, and the macroscopic average stress and strain are calculated as the volume average of the microscopic stress and strain over the representative volume element. Simultaneously, the macroscopic deformation is decomposed into the boundary conditions of the microscopic RVE by transferring boundary conditions. Using a multiscale computational framework, we simulate the stress transfer paths and distribution patterns across different scales, identifying key links and bottlenecks in stress transfer. Based on the simulation results, we construct a critical region stress transfer path diagram, which visually illustrates the stress transfer paths and key nodes from the macrostructure to the micromaterial. This analysis generates critical region stress transfer path data, providing a foundation for fatigue damage analysis and life prediction.

[0088] Preferably, the process of executing step S4 may specifically include the following steps:

[0089] (1) Perform fatigue damage accumulation mapping on the critical zone stress transfer path data under cyclic loading to generate fatigue damage accumulation characteristic data;

[0090] (2) Extract the high-frequency micro-amplitude load influence factor based on the fatigue damage accumulation characteristic data, generate the high-frequency load influence quantitative data, and establish the damage evolution prediction distribution node through the high-frequency load influence quantitative data;

[0091] (3) Based on the improved Miner nonlinear cumulative damage theory, a progressive fatigue life prediction model is constructed for the damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data to generate a fatigue life prediction model and obtain fatigue life prediction data;

[0092] (4) Perform correlation analysis on the critical area stress field distribution characteristic data and fatigue life prediction data to generate structure-performance correlation characteristic data;

[0093] (5) Quantitatively evaluate 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 results of the leaf spring;

[0094] (6) Based on the comprehensive performance evaluation results of the leaf spring, the leaf spring structure optimization design and parameter adjustment are carried out, the leaf spring optimization improvement plan is generated, and the leaf spring optimization improvement plan is experimentally verified.

[0095] Specifically, fatigue damage accumulation mapping under cyclic loading is performed on the critical region stress transfer path data. First, based on actual operating conditions and standard test methods, a cyclic load spectrum is designed, including various forms such as constant amplitude loading, variable amplitude loading, and random loading. Then, combined with the critical region stress transfer path data, the stress amplitude and stress ratio at each key node are calculated, which are half the difference between the maximum stress and the minimum stress, and the ratio of the minimum stress to the maximum stress, respectively. The fatigue life at each node is calculated based on the material's SN curve (stress-life curve) and the Basquin equation, which represents the power function relationship between stress amplitude and fatigue life cycles. For variable amplitude loading, the rain flow counting method is used to cyclically extract the load history, and the damage contribution of each cycle is calculated, that is, the ratio of the number of repetitions of the cycle to the fatigue life at that stress level. These damage contributions are accumulated to generate fatigue damage accumulation characteristic data, which reflects the damage evolution law of the material under cyclic loading.

[0096] Based on the cumulative characteristic data of fatigue damage, the influencing factors of high-frequency micro-amplitude loads are extracted. 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, the load spectrum is decomposed into different frequency components through spectral analysis, that is, the time domain load is subjected to Fourier transform. Then, the high-frequency components (usually greater than 50Hz) are identified and their amplitude distribution is evaluated. An equivalent damage model for high-frequency micro-amplitude loads is established. This model shows that the equivalent damage is related to factors such as high-frequency load amplitude, threshold amplitude, and frequency. Through this model, the contribution of high-frequency micro-amplitude loads to fatigue damage is quantified, and quantitative data on the impact of high-frequency loads is generated.

[0097] Based on quantified data on the effects of high-frequency loads, nodes are distributed in key damage regions to predict damage evolution. These nodes form the foundation of the prediction network, each containing information such as the local stress state, cumulative damage value, and estimated remaining life. The node distribution density is correlated with the local stress gradient, with higher node density in areas of stress concentration to ensure prediction accuracy.

[0098] Based on the improved Miner nonlinear cumulative damage theory, a progressive fatigue life prediction model is constructed for the damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data. First, the critical damage threshold is identified based on the fatigue damage accumulation characteristic data. The traditional Miner linear cumulative damage theory assumes that the critical damage threshold is constant at 1, but in fact the critical damage threshold varies under the influence of material properties, load history and environmental conditions. By analyzing the existing fatigue test data, a prediction model for the critical damage threshold is established. The model indicates that the critical damage threshold is related to factors such as average 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 threshold under different working conditions is predicted, and fatigue damage critical threshold data is generated. Then, a nonlinear cumulative damage evolution function is constructed based on the fatigue damage critical threshold data. Considering the nonlinear cumulative characteristics of fatigue damage, a damage evolution equation is established: Establish a damage evolution equation: ;in, 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 nonlinear damage accumulation function can be expressed as:

[0099] ;in, and is the material parameter, is the stress state correction function, represents the critical damage threshold. The damage evolution equation is solved through numerical integration to obtain a damage accumulation curve under any load history, generating a nonlinear damage accumulation function. Next, the damage evolution prediction distribution nodes are corrected based on the nonlinear damage accumulation function. For each prediction node, the nonlinear damage accumulation function is applied to update the prediction parameters based on its local stress state and accumulated damage. A damage field evolution model is established, taking into account inter-node interactions and damage propagation. This model represents the spatiotemporal evolution of damage, which is related to damage diffusion and damage sources. This model simulates the spatial expansion and concentration of damage, resulting in a revised damage evolution prediction distribution node. Finally, a progressive fatigue life prediction model is constructed based on the revised damage evolution prediction distribution node and fatigue damage accumulation characteristic data. A Bayesian network or deep learning method is used to establish a mapping from the current damage state to the remaining life. Training data includes historical load spectra, accumulated damage values, and observed failure times. This model enables real-time prediction and updating of the remaining life of leaf springs, resulting in a fatigue life prediction model. This model is adaptive and interpretable, enabling continuous optimization of prediction results based on real-time monitoring data. Through this model, the fatigue life of leaf springs under different working conditions is predicted and fatigue life prediction data is generated.

[0100] Correlation analysis was performed between the stress field distribution characteristic data in the critical region and fatigue life prediction data. Through correlation analysis and causal inference, a quantitative relationship between stress field characteristics and fatigue life was established. The correlation coefficient between each stress characteristic parameter and fatigue life was calculated, which is the ratio of the covariance of the two variables multiplied by their standard deviations. Based on the correlation analysis results, the stress field characteristics with the most significant impact on fatigue life were identified, and a regression model was established to represent the linear combination relationship between the logarithm of fatigue life and each stress field characteristic parameter. This model maps stress field characteristics to fatigue life prediction, generating structure-performance correlation characteristic data. Based on this structure-performance correlation characteristic data and the integrated mechanical response data of the leaf spring, a comprehensive performance index quantitative evaluation was performed. The evaluation indexes include static performance indicators (such as stiffness, strength, and linearity) and dynamic performance indicators (such as fatigue life, comfort, and stability). Appropriate weights were assigned to each indicator to construct a comprehensive evaluation function, expressed as the weighted sum of the standardized scores of each indicator. This evaluation function was used to quantitatively evaluate the comprehensive performance of the leaf spring, generating a comprehensive performance evaluation result.

[0101] Based on the comprehensive performance evaluation results of the leaf spring, the leaf spring structure is optimized and its parameters are adjusted. Parametric design methods and optimization algorithms, such as genetic algorithms, particle swarm optimization, or response surface methodology, are used to search for the optimal combination of design parameters. The optimization goal is to maximize the comprehensive performance evaluation function while satisfying the constraints. Design parameters include the geometric dimensions, material composition, heat treatment process, and surface treatment of the leaf spring. Through optimization calculations, the optimal design scheme is obtained, and its performance is verified through finite element analysis. Finally, based on the optimization results, an optimization and improvement scheme for the leaf spring is generated, and its effectiveness is verified through experiments, including bench tests and actual vehicle road tests. Experimental verification indicators include static load-displacement curves, dynamic response characteristics, and durability test results. By comparing the performance indicators before and after optimization, the effect and value of the optimization are evaluated.

[0102] This embodiment comprehensively captures the mechanical properties of leaf springs under complex operating conditions through multi-dimensional on-vehicle test data collection and integrated processing of heterogeneous load responses. This allows for the integration of dispersed test data into unified leaf spring integrated mechanical response data, thereby enhancing the completeness and accuracy of the analysis. Through dual stiffness gradient identification analysis and critical region stress field deconstruction analysis, critical regions of stiffness transition can be precisely located and high-density stress distribution data generated, enabling in-depth analysis of the leaf spring stiffness mutation mechanism and laying a solid foundation for subsequent microscopic analysis. By characterizing local microstructural changes and analyzing the evolution of crystal dislocation density, the microscopic deformation mechanism of the material in stress concentration areas is revealed. Advanced electron microscopy and X-ray diffraction techniques are applied to achieve multi-scale correlations from macroscopic mechanical properties to microstructural changes, enhancing the scientific nature and depth of the analysis. By constructing a multi-scale stress transfer link, the stress transfer path from the macrostructure to the micromaterial is precisely captured, effectively identifying key nodes and bottleneck areas of stress transfer, significantly enhancing the understanding of leaf spring failure mechanisms. By mapping fatigue damage accumulation under cyclic loading and extracting high-frequency, low-amplitude load influencing factors, the authors comprehensively considered the impact of complex load spectra on leaf spring life under actual operating conditions. A progressive fatigue life prediction model, based on an improved Miner nonlinear cumulative damage theory, overcomes the limitations of traditional linear cumulative damage theory and significantly improves the accuracy of fatigue life prediction. Through structure-performance correlation analysis and quantitative evaluation of comprehensive performance indicators, the authors systematically quantified the static and dynamic performance of leaf springs, providing a precise basis for optimization and improvement.

[0103] Example 2;

[0104] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of embodiment 1. A high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system is provided, which includes:

[0105] Load response integration module: This module collects initial mechanical data of leaf springs based on multi-dimensional vehicle-mounted test experiments to generate initial mechanical characteristic data of leaf springs. It also integrates heterogeneous load responses based on the initial mechanical characteristic data of leaf springs to generate integrated mechanical response data of leaf springs.

[0106] Stiffness critical analysis module: performs dual stiffness gradient identification analysis based on the integrated mechanical response data of the leaf spring to generate stiffness transition critical data; performs critical zone stress field deconstruction analysis based on the stiffness transition critical data to generate critical zone stress field distribution characteristic data;

[0107] Micro-stress link module: Characterizes local microstructural changes based on the critical zone stress field distribution characteristic data and generates local microstructural deformation data; constructs multi-scale stress transfer links based on the local microstructural deformation data and generates critical zone stress transfer path data;

[0108] Fatigue life assessment module: Fatigue damage accumulation mapping is performed on the critical zone stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data; a high-frequency micro-amplitude load impact assessment model is constructed based on the fatigue damage accumulation characteristic data to generate fatigue life prediction data; a comprehensive performance index quantitative evaluation is performed based on 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; based on the comprehensive performance evaluation result of the leaf spring, an optimization and improvement plan for the leaf spring is generated and verified; each module is connected via wired and / or wireless means to realize data transmission between modules.

[0109] Example 3;

[0110] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation method is implemented.

[0111] Since the electronic device described in this embodiment is used to implement the method for analyzing and evaluating the performance of a high-strength, two-stage, gradually varying stiffness leaf spring in the embodiment of this application, those skilled in the art will be able to understand the specific implementation and various variations of the electronic device in this embodiment based on the method described in the embodiment of this application. Therefore, how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as the electronic device used by those skilled in the art to implement the method for analyzing and evaluating the performance of a high-strength, two-stage, gradually varying stiffness leaf spring in the embodiment of this application falls within the scope of protection of this application.

[0112] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0113] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for analyzing and evaluating the performance of a high-strength, two-stage, gradient-stiffness leaf spring, characterized in that: include: Step S1: collecting initial mechanical data of the leaf spring based on a multi-dimensional vehicle-mounted test experiment to generate initial mechanical characteristic data of the leaf spring; Based on the initial mechanical characteristic data of the leaf spring, the responses to heterogeneous loads are integrated to generate the integrated mechanical response data of the leaf spring. Heterogeneous loads include vertical compression loads, lateral shear forces, and torsional moments. For these different types of loads, the superposition principle and weighted fusion method are used for comprehensive processing. Based on the theory of elastic mechanics, a response function of the leaf spring under multi-dimensional loads is established. This function integrates the responses of the leaf spring to compression, bending moment, and torque using weighted coefficients. The weighted coefficients are determined by optimizing experimental data to minimize the error between the model prediction value and the measured value. Through this response function, the mechanical response of the leaf spring under heterogeneous load conditions is integrated to generate the integrated mechanical response data of the leaf spring. Step S2: performing dual stiffness gradient identification analysis based on the leaf spring integrated mechanical response data to generate stiffness transition critical data; performing critical region stress field deconstruction analysis based on the stiffness transition critical data to generate critical region stress field distribution characteristic data; Step S3: Characterize the local microstructure changes based on the critical region stress field distribution characteristic data to generate local microstructure deformation data; Based on the local microstructure deformation data, multi-scale stress transfer links are constructed to generate critical zone stress transfer path data; Step S4: performing fatigue damage accumulation mapping on the critical zone stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data; Based on the fatigue damage accumulation characteristic data, a fatigue life prediction model is constructed to generate fatigue life prediction data; based on the fatigue life prediction data and the integrated mechanical response data of the leaf spring, a comprehensive performance index quantitative evaluation is performed to generate the comprehensive performance evaluation results of the leaf spring; Generate and verify leaf spring optimization and improvement plans based on comprehensive leaf spring performance evaluation results; Step S3 includes the following steps: Step S31: characterizing local microstructure changes based on the critical region stress field distribution characteristic data to generate local microstructure deformation data; Step S32: performing crystal dislocation density evolution analysis based on the local microstructure deformation data to generate micro dislocation evolution characteristic data; Step S33: constructing a multi-scale stress transfer link based on the microscopic dislocation evolution characteristic data to generate critical zone stress transfer path data; Step S32 includes the following steps: Perform micromechanical deformation mechanism analysis based on local microstructural 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; The crystal dislocation density distribution matrix is constructed based on the microscopic deformation mechanism characteristic data and the material strengthening characteristic data to generate the dislocation density evolution matrix; Perform crystal dislocation density evolution analysis based on the dislocation density evolution matrix to generate microscopic dislocation evolution characteristic data; Step S33 includes the following steps: Conduct micro-macroscopic 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; Multi-scale stress transfer links are constructed based on the cross-scale mechanical behavior linkage characteristic data to generate critical zone stress transfer path data.

2. The method for analyzing and evaluating the performance of a high-strength, two-stage, gradient-stiffness leaf spring according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting initial mechanical data of the leaf spring based on a multi-dimensional vehicle-mounted test experiment to generate initial mechanical characteristic data of the leaf spring; Step S12: performing load-displacement curve deconstruction processing based on the initial mechanical characteristic data of the leaf spring to generate nonlinear response characteristic deconstruction data; Step S13: reconstructing multiple road load spectra based on the nonlinear response characteristic deconstruction data to generate simulated road load spectrum data; Step S14: performing heterogeneous load response integration processing according to the simulated road load spectrum data to generate leaf spring integrated mechanical response data.

3. The method for analyzing and evaluating the performance of a high-strength, two-stage, gradient-stiffness leaf spring according to claim 1, wherein: Step S2 includes the following steps: Step S21: Calculating the slope change rate of the stiffness curve based on the leaf spring integrated mechanical response data to generate stiffness gradient change data; Step S22: accurately locate the stiffness mutation point based on the stiffness gradient change data, and generate stiffness transition critical area data; Step S23: Using an ultra-high precision strain measurement device to perform stress field scanning on the stiffness transition critical region data to generate high-density stress distribution data in the critical region; Step S24: Identify and quantify stress concentration areas based on the high-density stress distribution data of the critical area, and generate a critical area stress peak distribution map; Step S25: performing a deconstruction analysis of the critical region stress field according to the critical region stress peak distribution map to generate critical region stress field distribution characteristic data.

4. The method for analyzing and evaluating the performance of a high-strength, two-stage, gradient-stiffness leaf spring according to claim 3, wherein: Step S24 includes the following steps: Calculating the stress gradient tensor based on the high-density stress distribution data in the critical region to generate stress gradient tensor field data, wherein the stress gradient tensor field data includes principal stress distribution node data and stress intensity factor quantification data; Perform cluster analysis of high stress areas based on principal stress distribution node data to generate stress cluster area data; Identify potential crack sources based on stress clustering area data and stress intensity factor quantification data to generate potential fracture origin data; The high-density stress distribution data in the critical area are quantitatively rated according to the potential fracture origin data to generate a peak stress distribution map of the critical area.

5. The method for analyzing and evaluating the performance of a high-strength, two-stage, gradient-stiffness leaf spring according to claim 1, wherein: Step S4 includes the following steps: Step S41: performing fatigue damage accumulation mapping on the critical region stress transfer path data under cyclic load to generate fatigue damage accumulation characteristic data; Step S42: extracting high-frequency micro-amplitude load influence factors based on fatigue damage accumulation characteristic data, generating high-frequency load influence quantitative data, and establishing damage evolution prediction distribution nodes based on the high-frequency load influence quantitative data; Step S43: constructing a progressive fatigue life prediction model based on the improved Miner nonlinear cumulative damage theory for the damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data, generating a fatigue life prediction model, and obtaining fatigue life prediction data; Step S44: performing correlation analysis on the critical region stress field distribution characteristic data and the fatigue life prediction data to generate structure-performance correlation characteristic data; Step S45: performing a quantitative evaluation of 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 comprehensive performance evaluation results of the leaf spring, perform leaf spring structure optimization design and parameter adjustment, generate a leaf spring optimization and improvement plan, and perform experimental verification of the leaf spring optimization and improvement plan.

6. The method for analyzing and evaluating the performance of a high-strength, two-stage, gradient-stiffness leaf spring according to claim 5, characterized in that: Step S43 includes the following steps: Identify critical damage thresholds based on fatigue damage accumulation characteristic data and generate fatigue damage critical threshold data; Based on the fatigue damage critical threshold data, a nonlinear cumulative damage evolution function is constructed to generate a nonlinear damage accumulation function; The damage evolution prediction distribution node is modified according to the nonlinear damage accumulation function to generate a modified damage evolution prediction distribution node; A progressive fatigue life prediction model is constructed based on the modified damage evolution prediction distribution nodes and fatigue damage accumulation characteristic data to obtain the fatigue life prediction model.

7. A high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation system, which is used to implement the high-strength two-stage gradient stiffness leaf spring performance analysis and evaluation method described in claim 1, characterized in that: include: Load response integration module: collects initial mechanical data of leaf springs based on multi-dimensional vehicle-mounted test experiments and generates initial mechanical characteristic data of leaf springs; Perform heterogeneous load response integration processing based on the initial mechanical characteristic data of the leaf spring to generate integrated mechanical response data of the leaf spring; Stiffness critical analysis module: performs dual stiffness gradient identification analysis based on the integrated mechanical response data of the leaf spring to generate stiffness transition critical data; performs critical zone stress field deconstruction analysis based on the stiffness transition critical data to generate critical zone stress field distribution characteristic data; Micro-stress link module: Characterizes local microstructure changes based on the critical zone stress field distribution characteristic data and generates local microstructure deformation data; Based on the local microstructure deformation data, multi-scale stress transfer links are constructed to generate critical zone stress transfer path data; Fatigue life assessment module: performs fatigue damage accumulation mapping on critical zone stress transfer path data under cyclic loading to generate fatigue damage accumulation characteristic data; Based on the fatigue damage accumulation characteristic data, a fatigue life prediction model is constructed to generate fatigue life prediction data; based on the fatigue life prediction data and the integrated mechanical response data of the leaf spring, a comprehensive performance index quantitative evaluation is performed to generate the comprehensive performance evaluation results of the leaf spring; Based on the comprehensive performance evaluation results of leaf springs, leaf spring optimization and improvement plans are generated and verified.

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