A performance data evaluation method for sole rubber material

Abstract

The present invention relates to the technical field of sole performance evaluation, and in particular to a performance data evaluation method for a sole rubber material. The method comprises the following steps: multi-parameter simulation of a sports scene to obtain a sole-road stress environment model; parameter mapping of the sole-road stress environment model to obtain a force diagram of the sole rubber material; real-time performance capture of the sole rubber material to obtain a sole original performance data set; X-ray diffraction analysis of the sole rubber material to obtain a microstructure change diagram of the sole material; material performance degradation modeling of the sole rubber material based on the microstructure change diagram of the sole material to obtain a sole micro-performance evolution model; acoustic emission feature extraction of the sole rubber material to obtain sole energy dissipation signal characteristics. Compared with traditional laboratory static testing, the present invention can provide performance data closer to actual sports conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of sole performance evaluation, and in particular to a performance data evaluation method for a sole rubber material. Background Art

[0002] In the field of modern sports shoes, the performance evaluation of rubber materials for soles is crucial because it is directly related to comfort, safety and durability during exercise. However, existing evaluation methods are often carried out in laboratory environments, and these environmental conditions are significantly different from actual use scenarios. For example, in high-intensity exercise, the rubber materials for soles need to withstand rapid impact and pressure changes. These dynamic changes place higher requirements on the elasticity and wear resistance of the material. Static tests in the laboratory often cannot simulate this dynamic environment, resulting in the evaluation results not accurately reflecting the performance of the soles in actual sports. Taking marathon running shoes as an example, during long-distance running, the rubber materials for the soles of runners will experience continuous friction and temperature rise, all of which will affect the wear resistance and elasticity of the rubber. However, existing evaluation methods rarely consider the impact of such long-term and high-intensity use on the performance of rubber materials. In actual competitions, runners will encounter various road surfaces, such as hard asphalt roads, slippery grass or uneven dirt roads. These different road conditions pose challenges to the adaptability of the rubber materials for soles. Existing evaluation methods often ignore this environmental adaptability and cannot fully evaluate the performance of the rubber materials for soles in different environments. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a performance data evaluation method for sole rubber material to solve at least one of the above technical problems.

[0004] To achieve the above object, a performance data evaluation method for a sole rubber material comprises the following steps:

[0005] Step S1: Perform multi-parameter simulation on the sports scene to obtain a sole-road stress environment model; perform parameter mapping on the sole-road stress environment model to obtain a force diagram of the sole rubber material; perform real-time performance capture on the sole rubber material to obtain an original sole performance data set;

[0006] Step S2: performing X-ray diffraction analysis on the sole rubber material to obtain a microstructure change diagram of the sole material; performing material performance degradation modeling on the sole rubber material based on the microstructure change diagram of the sole material to obtain a sole microperformance evolution model;

[0007] Step S3: extracting acoustic emission characteristics of the sole rubber material to obtain sole energy dissipation signal characteristics; performing thermodynamic mapping on the sole energy dissipation signal characteristics to obtain a sole material energy conversion efficiency diagram; quantitatively evaluating the energy characteristics of the sole rubber material according to the sole material energy conversion efficiency diagram to obtain a sole material energy efficiency evaluation result;

[0008] Step S4: Performing a degree of freedom stress response test on the sole rubber material to obtain extreme environment adaptability data of the sole; performing a stress-strain characteristic analysis on the sole rubber material based on the extreme environment adaptability data of the sole to obtain a sole performance stability evaluation report;

[0009] Step S5: Predict the performance evolution of the sole rubber material based on the original performance data set of the sole and the sole microscopic performance evolution model to obtain a semi-empirical prediction model for the sole material; perform probabilistic statistical inference on the semi-empirical prediction model for the sole material to obtain a performance attenuation trend diagram of the sole material; perform a comprehensive performance evaluation on the sole rubber material according to the sole material energy efficiency evaluation result, the sole performance stability evaluation report and the sole material performance attenuation trend diagram to obtain a final performance data evaluation report for the sole.

[0010] The present invention improves the accuracy of evaluation by simulating multiple parameters in actual sports scenes, and can provide performance data closer to actual sports conditions than traditional laboratory static tests. The present invention not only evaluates the macroscopic performance of the sole rubber material, but also goes deep into the changes in the microstructure of the material to conduct a comprehensive performance analysis, so as to fully understand the performance of the material under different conditions. By establishing a microscopic performance evolution model of the sole rubber material, the performance degradation trend of the material over time can be predicted. By quantitatively evaluating the energy conversion efficiency of the sole rubber material, it is helpful to identify and optimize the energy dissipation characteristics of the material. Through the degree of freedom stress response test, the adaptability of the sole rubber material in extreme environments can be evaluated to ensure the stability and durability of sports shoes under different road conditions. By comprehensively considering the energy efficiency evaluation results of the sole material, the performance stability evaluation report and the performance attenuation trend chart, a comprehensive sole rubber material performance evaluation report is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings:

[0012] Figure 1 A schematic flow chart of the steps of a method for evaluating performance data of a sole rubber material according to an embodiment is shown.

[0013] Figure 2 A detailed flow chart of step S16 of an embodiment is shown.

[0014] Figure 3 A detailed flow chart of step S26 of an embodiment is shown. DETAILED DESCRIPTION

[0015] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 3 The present invention provides a performance data evaluation method for a sole rubber material, comprising the following steps:

[0019] Step S1: Perform multi-parameter simulation on the sports scene to obtain a sole-road stress environment model; perform parameter mapping on the sole-road stress environment model to obtain a force diagram of the sole rubber material; perform real-time performance capture on the sole rubber material to obtain an original sole performance data set;

[0020] Step S2: performing X-ray diffraction analysis on the sole rubber material to obtain a microstructure change diagram of the sole material; performing material performance degradation modeling on the sole rubber material based on the microstructure change diagram of the sole material to obtain a sole microperformance evolution model;

[0021] Step S3: extracting acoustic emission characteristics of the sole rubber material to obtain sole energy dissipation signal characteristics; performing thermodynamic mapping on the sole energy dissipation signal characteristics to obtain a sole material energy conversion efficiency diagram; quantitatively evaluating the energy characteristics of the sole rubber material according to the sole material energy conversion efficiency diagram to obtain a sole material energy efficiency evaluation result;

[0022] Step S4: Performing a degree of freedom stress response test on the sole rubber material to obtain extreme environment adaptability data of the sole; performing a stress-strain characteristic analysis on the sole rubber material based on the extreme environment adaptability data of the sole to obtain a sole performance stability evaluation report;

[0023] Step S5: Predict the performance evolution of the sole rubber material based on the original performance data set of the sole and the sole microscopic performance evolution model to obtain a semi-empirical prediction model for the sole material; perform probabilistic statistical inference on the semi-empirical prediction model for the sole material to obtain a performance attenuation trend diagram of the sole material; perform a comprehensive performance evaluation on the sole rubber material according to the sole material energy efficiency evaluation result, the sole performance stability evaluation report and the sole material performance attenuation trend diagram to obtain a final performance data evaluation report for the sole.

[0024] In this embodiment, first, professional multi-body dynamics simulation software, such as RecurDyn, is used to perform multi-parameter simulation of the sports scene. In this process, the athlete's motion data, including gait, speed and acceleration, and the material properties of the road surface, such as hardness and friction coefficient, are input to build a sole-road stress environment model. Through simulation, the stress distribution of the sole in different sports scenes is obtained. Next, professional finite element analysis (FEA) software, such as ANSYS, is used to perform parameter mapping on the simulation results to generate a force diagram of the sole rubber material. These diagrams show the stress conditions of the sole in different areas, including pressure and shear force. A dynamic mechanical analyzer (DMA) is used to capture the real-time performance of the sole sample to obtain the original performance data set of the sole rubber material. DMA can provide mechanical performance data at different temperatures and frequencies, including storage modulus and loss modulus. An X-ray diffractometer (XRD) is used to perform microstructural analysis on the sole rubber material to obtain a microstructural change diagram of the sole material. XRD can reveal changes in the crystal structure inside the material. Based on XRD data, material science software such as MATLAB is used to model the material performance degradation of the sole rubber material and obtain the sole micro-performance evolution model. Acoustic emission (AE) technology is used to extract the acoustic emission characteristics of the sole rubber material and obtain the sole energy dissipation signal characteristics. AE sensors can capture the sound waves generated by microscopic events inside the material, which are closely related to the energy dissipation process of the material. Thermodynamic software such as ABAQUS is used to thermodynamically map the sole energy dissipation signal characteristics and obtain the sole material energy conversion efficiency diagram. This chart shows the energy conversion efficiency of the sole rubber material under different conditions. Based on the energy conversion efficiency diagram, the energy characteristics of the sole rubber material are quantitatively evaluated to obtain the sole material energy efficiency evaluation results. This includes calculating the energy return rate and energy loss rate. The sole sample is tested for the degree of freedom stress response using an environmental simulation box to obtain the sole extreme environmental adaptability data. The test includes mechanical performance testing under different temperature and humidity conditions. Based on these data, FEA software is used to perform stress-strain characteristic analysis to obtain a sole performance stability evaluation report. This report describes in detail the performance stability of the sole rubber material under extreme conditions. Finally, by combining the original performance data set of the sole and the sole micro-performance evolution model, statistical software such as SPSS is used to predict the performance evolution of the sole rubber material and obtain a semi-empirical prediction model for the sole material. Through probabilistic statistical inference, a performance attenuation trend chart of the sole material is obtained. Combining all the above data and analysis results, a final performance data evaluation report for the sole is compiled. This report includes the evaluation results of the sole material energy efficiency, the sole performance stability evaluation report, and the sole material performance attenuation trend chart.

[0025] Preferably, step S1 comprises the following steps:

[0026] Step S11: collecting application scenarios of the sole rubber material to obtain a sole application scenario set;

[0027] Specifically, a scene set can be set, including indoor basketball, outdoor running, and mountain hiking. Use a motion capture system to collect data for these scenes, such as the Vicon system, in which reflective marker balls are attached to key parts of the tester, including the feet. When the tester performs activities in a specific scene, the Vicon system tracks the position and movement of these marker balls, thereby recording the application of the rubber material of the sole in different sports scenes. For example, record the jumping and fast movement of basketball players on the indoor basketball court, the long-distance running of runners on the outdoor track, and the walking of hikers on mountain trails. With this data, a set of sole application scenarios can be obtained.

[0028] Step S12: collecting kinematic trajectory data of the sole rubber material according to the sole application scenario set to obtain a sole motion trajectory basic data set;

[0029] Specifically, taking indoor basketball as an example, the Vicon system can be used to record the footsteps of basketball players during the game. The specific operation is as follows: Arrange the cameras of the Vicon system around the basketball court to ensure that the entire venue is under monitoring. Let the athletes wearing special basketball shoes equipped with reflective marker balls perform a series of basketball-related actions in the venue, such as running, jumping, sudden stops and changes of direction. The Vicon system will record the movement trajectory of these marker balls in real time, including key kinematic parameters such as speed, acceleration and angle changes. These data will be stored in the computer to form a basic data set of the sole movement trajectory.

[0030] Step S13: parameterizing the material characteristics of the road surface material based on the basic data set of the sole motion trajectory to obtain a road surface material parameter library;

[0031] Specifically, it can be assumed that the pavement materials of interest include concrete, asphalt, and grass. First, use physical testing instruments, such as a universal material testing machine, to measure the mechanical properties of these pavement materials, including elastic modulus, Poisson's ratio, hardness, and friction coefficient. For example, for a concrete pavement, the elastic modulus is measured to be 30 GPa, the Poisson's ratio is 0.2, the hardness is 55 MPa, and the friction coefficient is 0.7. These data will be entered into a database to form a pavement material parameter library.

[0032] Step S14: simulating the pavement-sole interface interaction based on the pavement material parameter library to obtain a discretized model of pavement material properties;

[0033] Specifically, we can assume that the performance of a rubber sole on a concrete pavement is simulated. Use finite element analysis (FEA) software, such as ANSYS or ABAQUS, to perform this simulation. First, create a geometric model of the sole and the pavement in the software, and assign material properties to these models based on the data in the parameter library. For example, assign a material property of an elastic modulus of 1 MPa and a Poisson's ratio of 0.45 to the rubber sole, and assign previously measured parameters to the concrete pavement. Set boundary conditions and loads to simulate the pressure and friction on the sole on the pavement. By running the simulation, you can get data such as stress distribution, deformation, and friction response in the contact area between the sole and the pavement. These data will help understand the performance of the sole under different pavement conditions and generate a discretized pavement material property model.

[0034] Step S15: performing stress simulation on the sole according to the discretized model of road surface material properties to obtain a sole-road surface stress simulation unit set;

[0035] Specifically, we can assume that the stress on a sports shoe sole on an asphalt road is simulated. The finite element analysis software ANSYS is used to perform this simulation. First, create detailed geometric models of the sole and asphalt road in ANSYS. The sole model is based on the size and shape of the actual sports shoe sole, while the asphalt road model is simulated as a plane. According to the pavement material parameter library, assign material properties to the asphalt road model, such as an elastic modulus of 2 GPa and a Poisson's ratio of 0.35. For the rubber material of the sole, assign a material property of an elastic modulus of 0.1 GPa and a Poisson's ratio of 0.499, which are specific values ​​obtained from experiments. Set the boundary conditions of the simulation, such as fixing the edge of the sole to simulate the situation where the sole is pressed on the road surface. Apply a vertical downward load to the area where the sole contacts the road surface to simulate the pressure on the sole of the athlete when running. The size of the load can be determined based on the athlete's weight and the impact force during exercise. For example, a load value of 700 N is selected to simulate the force applied to the sole by an athlete weighing 70 kg when running. Next, run the simulation, and ANSYS software will calculate and display the stress distribution of the sole on the asphalt road. The simulation results will include the stress values ​​of each part of the sole, such as the maximum stress, minimum stress, and stress concentration area. These data are organized into a set of stress simulation units, each unit represents a specific area on the sole and contains the stress information of that area.

[0036] Step S16: performing stress simulation on the sole rubber material based on the sole-pavement stress simulation unit set to obtain a sole-pavement stress environment model, and performing parameter mapping on the sole-pavement stress environment model to obtain a force diagram of the sole rubber material;

[0037] Specifically, please refer to the sub-steps of step S16 for the detailed implementation process of this embodiment.

[0038] Step S17: Capture the real-time performance of the sole rubber material to obtain an original performance data set of the sole.

[0039] Specifically, a specific experimental scenario can be selected, for example, an athlete is allowed to run on a treadmill while a series of sensors are used to capture the performance data of the sole. Pressure sensors and accelerometers are used to monitor the pressure distribution and impact absorption of the sole during running. These sensors are precisely placed in key areas of the sole, such as the forefoot, heel, and arch areas. FlexiForce pressure sensors (model A201) are used, which can measure up to 200 pounds of pressure and have an accuracy of 0.1 pounds. These sensors are attached to the inside of the sole with conductive glue, and a triaxial accelerometer (model ADXL345) is used, which can measure the acceleration changes of the sole in three directions. Before starting, a data acquisition system (such as National Instruments' NI cDAQ-9174) is used to set the sampling frequency and data recording parameters of the sensor. A sampling frequency of 1000 Hz is selected. After the experiment begins, the athlete runs at a constant speed on the treadmill, and the data acquisition system records data from the pressure sensor and accelerometer in real time. After the experiment, the collected data is analyzed by NI's LabVIEW software. LabVIEW software can convert pressure and acceleration data into graphs and numerical reports. Key performance indicators of interest include maximum pressure value, average pressure value, impact peak value, and energy absorption efficiency, ultimately obtaining a raw data set that contains the performance of the sole rubber material under actual use conditions.

[0040] The present invention can simulate the actual use environment more accurately and obtain more accurate performance evaluation data by collecting the application scenarios of the sole rubber material. By capturing the basic data set of the sole motion trajectory, it is helpful to deeply understand the dynamic behavior of the sole in actual motion and provide input parameters for stress simulation. The influence of different road surface materials on the performance of the sole rubber material can be analyzed more deeply through material property parameterization. The complex mechanical relationship between the sole and the road surface is revealed through interface interaction simulation. By establishing a sole-road surface stress simulation unit set, a refined simulation of the stress condition of the sole rubber material under different road conditions is achieved. The force diagram obtained by parameter mapping provides an intuitive visual reference for the sole design, showing the stress condition of the sole in different areas. The actual performance data of the sole in use can be collected through real-time performance capture, which enhances the real-time and dynamic nature of the evaluation result.

[0041] Preferably, step S16 comprises the following steps:

[0042] Step S161: performing stress characteristic analysis on the sole-pavement stress simulation unit set to obtain sole-pavement stress distribution characteristic data;

[0043] Specifically, ANSYS post-processing tools, such as ANSYS Workbench, can be used to extract key data from the sole-road stress simulation unit set. Focus on the maximum principal stress value, the minimum principal stress value, and the stress concentration area. For example, it is found that the maximum principal stress value is 15 MPa in the forefoot area of ​​the sole, while the minimum principal stress value is 5 MPa in the heel area. Stress concentration areas, such as the edge of the sole, with stress values ​​as high as 20 MPa, can also be identified. Use the Python programming language and the NumPy library to process this data. Write a script to import the data exported by ANSYS (such as CSV or TXT format) into Python, and then use NumPy to perform mathematical operations, such as calculating stress gradients and standard deviations of stress distribution. These calculation results help understand how stress is distributed in different areas of the sole and which areas are more prone to wear or damage. Finally, these stress distribution feature data are organized into reports, including stress distribution graphs, stress values ​​in key areas, and statistical data.

[0044] Step S162: performing molecular structure dynamic coupling on the sole rubber material based on the sole-road stress distribution characteristic data to obtain a sole molecular structure dynamic response model;

[0045] Specifically, molecular dynamics simulation software, such as LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator), can be used to perform dynamic coupling of molecular structures. In LAMMPS, a molecular model of the sole rubber material is constructed, which can be based on the known rubber polymer chain structure, such as polystyrene-butadiene-rubber (SBR). The stress distribution characteristic data is used as input conditions to simulate the molecular structure response of the sole rubber material in actual use. For example, the maximum principal stress value of 15 MPa in the forefoot area is used as a boundary condition to simulate the deformation and rearrangement of the rubber molecular chain in this area. Temperature and time parameters can also be set to simulate the effect of long-term exercise on the rubber molecular structure. During the simulation, LAMMPS will calculate the interaction forces between molecules, such as van der Waals forces and the stretching and compression of covalent bonds. Through these calculations, a dynamic response model of the molecular structure of the sole rubber material under stress can be obtained, including data such as displacement, rotation and deformation of the molecular chain.

[0046] Step S163: performing cross-scale stress simulation on the sole rubber material based on the sole-road stress simulation unit set and the sole molecular structure dynamic response model to obtain a sole-road stress environment model;

[0047] Specifically, multiscale simulation software, such as Multiscale Simulation Framework (MSF), can be used to integrate the sole-pavement stress simulation unit set and the sole molecular structure dynamic response model. The sole-pavement stress simulation unit set is imported into MSF as the input of the macroscale. The sole molecular structure dynamic response model is used as the input of the microscale. MSF can combine the molecular behavior at the microscale with the stress distribution at the macroscale to perform cross-scale simulation. In MSF, set the simulation parameters, such as the temperature is room temperature 25°C, and the simulation time is the typical time period of the sole in contact with the road surface, such as 0.05 seconds. Select a specific sole area, such as the forefoot area, and match the macro stress value of the area with the micro molecular response data. Through the algorithm of MSF, simulate the changes in the molecular structure of the sole rubber material under the action of macro stress, and how these changes affect the macro performance of the material. After the simulation is completed, the sole-pavement stress environment model is obtained.

[0048] Step S164: Parameter mapping is performed on the sole-road stress environment model to obtain a force diagram of the sole rubber material.

[0049] Specifically, you can use graphics processing and data visualization software, such as MATLAB or Python's matplotlib library, to perform parameter mapping and drawing. Import the data of the sole-pavement stress environment model into these software. These data include stress values, strain values, and other relevant physical parameters of each unit of the sole. In MATLAB, use interpolation functions, such as interp2, to process these discrete data points to create a continuous stress distribution surface. Use the contourf function to generate a stress distribution map of the sole, where different colors represent different stress levels. You can also add a legend to facilitate identification of the stress value range corresponding to each color, and finally obtain the force diagram of the sole rubber material, which clearly shows the stress distribution of the sole when it is in contact with the road surface.

[0050] The present invention can obtain stress distribution characteristic data by analyzing the stress characteristics of the sole-pavement stress simulation unit set, so as to more accurately understand and predict the stress conditions of the sole in actual use. By combining the stress distribution characteristic data, the molecular structure of the sole rubber material is dynamically coupled, and a sole molecular structure dynamic response model is constructed to deeply understand the microscopic behavior of the material when it is stressed. Through the implementation of cross-scale stress simulation, the stress response of the sole rubber material is comprehensively evaluated from micro to macro, thereby improving the comprehensiveness and accuracy of the simulation. The sole rubber material stress diagram obtained by parameter mapping intuitively shows the stress conditions of the sole in different areas. The molecular structure dynamic response model can also predict the performance changes of the sole rubber material during long-term use.

[0051] Preferably, step S2 comprises the following steps:

[0052] Step S21: Scan the sole rubber material sample at nanometer scale to obtain original material structure positioning mapping data;

[0053] Specifically, an atomic force microscope (AFM) can be used for nanoscale scanning. A newly produced sole rubber sample is selected and cut into small pieces of 1 cm square. Before scanning, the sample surface is cleaned with acetone to remove any impurities or oil stains that affect the scanning results. The sample is mounted on the sample stage of the AFM and the distance between its surface and the scanning probe is ensured to be appropriate. A Bruker Dimension Icon AFM is used, which is equipped with a probe with a low spring constant to adapt to the softness of the rubber material. The scanning parameters are set, including a scanning range of 50 microns × 50 microns, a scanning resolution of 512 pixels × 512 pixels, and a scanning speed of 1 Hz. After the scan is completed, a three-dimensional image containing the height and roughness information of the sole rubber sample surface is obtained. The AFM software is used to convert these three-dimensional image data into the original material structure positioning mapping data.

[0054] Step S22: performing micro-area X-ray diffraction on the sole rubber material sample based on a preset synchrotron radiation beam intensity parameter, and performing nanoscale scanning on the sole rubber material sample after diffraction to obtain diffraction material structure positioning mapping data;

[0055] Specifically, the wavelength of the X-ray beam can be set to 1.54 angstroms, which is achieved by selecting an appropriate monochromator and beam line. The focus of the X-ray beam is adjusted to the micron level so that a specific area of ​​the sole rubber sample can be diffracted. A specific area is selected, such as the wear area of ​​the sole, and an XRD scan is performed on the area with a scanning range of 10 microns × 10 microns and a scanning speed of 1 degree / minute. The diffraction data is recorded to obtain a diffraction pattern of the sole rubber material, which contains information about the internal structure of the material, such as the crystal structure and molecular arrangement. After the diffraction is completed, the diffracted sole rubber sample is scanned at the nanoscale again using an atomic force microscope (AFM) to obtain the positioning mapping data of the diffraction material structure. The parameters of this AFM scan are similar to the settings in step S21.

[0056] Step S23: performing three-dimensional structure capture on the diffracted sole rubber material sample to obtain a diffraction material microstructure model;

[0057] Specifically, the sole rubber sample can be scanned using a SkyScan 1272 microfocus X-ray CT scanner. Before scanning, the sample is placed on the rotating stage of the scanner and the center of the sample is aligned with the axis of the X-ray source and detector. The scanning parameters are set, including a voltage of 80 kV and a current of 100 microamperes, to obtain sufficient penetration and image quality. The rotation step is set to 0.5 degrees and the total rotation angle is 180 degrees. After the scan is completed, a series of two-dimensional X-ray images are obtained, which are converted into a three-dimensional structural model through a computer reconstruction algorithm. Image reconstruction is performed using NRecon software, and reconstruction parameters are set, such as a ring width of 1 and a smoothness of 3, to optimize the reconstruction results, and finally a three-dimensional microstructure model of the sole rubber material is obtained, which clearly shows the pore structure, fiber distribution, and micromorphology of the rubber matrix inside the material.

[0058] Step S24: performing coloring and projection on the diffraction material microstructure model according to the original material structure positioning mapping data and the diffraction material structure positioning mapping data to obtain a projected diffraction material microstructure model;

[0059] Specifically, Avizo software can be used for coloring and projection processing. Import the original material structure positioning mapping data and the nanoscale scanning data after diffraction into Avizo, and align these data with the diffraction material microstructure model. Ensure that the spatial resolution and coordinate system of all data sets are consistent. According to the surface height and roughness information in the original material structure positioning mapping data, and the crystal structure and molecular arrangement information in the diffraction data, different colors are assigned to different areas in the three-dimensional structure model. For example, the high elasticity area is colored red, the high hardness area is colored blue, and the pore area is colored yellow. Finally, use the projection function of Avizo to project these coloring information onto the three-dimensional structure model to generate a projected diffraction material microstructure model.

[0060] Step S25: extracting the material microstructure change area of ​​the sole rubber material based on the projection diffraction material microstructure model to obtain a microstructure change map of the sole material;

[0061] Specifically, the projection diffraction material microstructure model can be processed using ImageJ software. The projection diffraction material microstructure model is exported as three-dimensional image data. In ImageJ, the three-dimensional image is first preprocessed, including denoising and contrast enhancement. Use the "Sharpen" and "Enhance Contrast" functions under the "Filter" menu, set the sharpening radius to 1 pixel, and the contrast enhancement factor to 1.5. Use the "3D Object Manager" plug-in of ImageJ to identify and segment the microstructure change area. Set thresholds to distinguish different material properties, for example, set the thresholds of elastic and inelastic areas to 0.7 and 0.3, respectively. In this way, different areas of the sole rubber material can be segmented and the change areas can be extracted separately. The extracted areas are measured and analyzed, including parameters such as volume, surface area, and shape descriptors. For example, it is found that the volume of a certain area is reduced from 100 cubic millimeters in the original state to 80 cubic millimeters, which indicates the wear of the material. These analysis results are output in the form of graphics to generate a microstructure change map of the sole material. This image clearly shows the microstructural changes in the rubber material of the sole, including worn areas, cracks, and deformations.

[0062] Step S26: Based on the original material structure positioning mapping data, the diffraction material structure positioning mapping data and the diffraction material microstructure model, material performance degradation modeling is performed on the sole rubber material to obtain a sole micro-performance evolution model.

[0063] Specifically, please refer to the sub-steps of step S26 for the detailed implementation process of this embodiment.

[0064] The present invention achieves accurate data acquisition of sole rubber material samples through nanoscale material structure analysis. By using synchrotron radiation beams for micro-area X-ray diffraction and combining it with nanoscale scanning, the internal structure of the material is further revealed, and detailed mapping data of the material structure after diffraction is obtained. Through three-dimensional structure capture, a three-dimensional model of the material microstructure is constructed. Through colored projection, the change area of ​​the diffraction material microstructure model is intuitively displayed, which enhances the ability to identify material changes. By combining the original and diffracted material structure mapping data and the three-dimensional microstructure model, accurate modeling of material performance degradation is achieved, and the long-term performance changes of the material are predicted.

[0065] Preferably, step S26 comprises the following steps:

[0066] Step S261: performing cross-scale structure-performance correlation based on the diffraction material structure positioning mapping data to obtain a sole structure-performance mapping matrix;

[0067] Specifically, the following diffraction material structure positioning mapping data can be extracted from the database: Grain size: average value is 10 nanometers, standard deviation is 2 nanometers. Porosity: average value is 12%, standard deviation is 3%. Molecular chain arrangement angle: average value is 45 degrees, standard deviation is 5 degrees. Next, the macroscopic performance data of the sole rubber material is obtained from the laboratory test report, including: Elastic modulus: average value is 2 GPa, standard deviation is 0.2 GPa. Tensile strength: average value is 15 MPa, standard deviation is 1.5 MPa. Wear resistance: average value is 200 thousand cycles, standard deviation is 20 thousand cycles. MATLAB software is used for data analysis. The above data are organized into a matrix form, in which each row represents a microstructure feature and each column represents a macroscopic performance index. For example, create a 3x3 matrix, the first row is the grain size data, the second row is the porosity data, and the third row is the molecular chain arrangement angle data; the first column is the elastic modulus data, the second column is the tensile strength data, and the third column is the wear resistance data. In MATLAB, a multivariate linear regression analysis method is used to establish a relationship model between microstructural features and macroscopic performance indicators. For each pair of microstructural features and macroscopic performance indicators, a regression model is established. For example, for the relationship between grain size and elastic modulus, the corresponding data is input, the multivariate linear regression analysis tool is selected, and the model parameters are set, such as selecting ridge regression to avoid overfitting, and the regularization parameter is set to 0.1. In this way, a structure-performance mapping matrix containing all regression models can be obtained, and the model in each cell can describe the relationship between the corresponding microstructural features and macroscopic performance indicators.

[0068] Step S262: identifying materials that affect performance on the diffraction material microstructure model to obtain key factors affecting sole performance;

[0069] Specifically, the random forest algorithm can be applied to identify the key microstructural features that affect the performance of the sole rubber material. Use the RandomForestRegressor or RandomForestClassifier class in Python's scikit-learn library to implement this algorithm. Prepare a dataset that contains two sets of data: one set is the microstructural feature data obtained from the diffraction material microstructure model, and the other set is the performance data obtained from the macroscopic performance test. The microstructural feature data includes parameters such as grain size, porosity, and molecular chain arrangement, while the macroscopic performance data includes indicators such as wear resistance, elastic modulus, and tensile strength. Use the random forest algorithm in the scikit-learn library to train the model. Divide the dataset into feature sets (X) and labels (y). The feature set X contains the microstructural feature data, and the label y contains the corresponding macroscopic performance data. Divide the dataset into training sets and test sets, and split them according to the ratio of 80% training set and 20% test set. Set the parameters of the random forest. For example, select the number of trees (n_estimators) as 100, which means that the random forest will build 100 decision trees. Set the maximum depth (max_depth) to 10 to prevent the decision tree from being too complex and causing overfitting. The random forest algorithm will automatically select the most representative features during the training process and evaluate the impact of each feature on the performance. After the training is completed, view the feature importance ranking of the random forest model. This ranking shows the impact of each microstructural feature on the macroscopic performance. For example, it was found that grain size and crosslink density are key factors affecting the wear resistance of the sole, and they are ranked at the top of the feature importance ranking. In this way, the key factors affecting the performance of the sole rubber material can be identified.

[0070] Step S263: weighting the key influencing factors of the sole performance based on the original material structure positioning mapping data and the diffraction material structure positioning mapping data to obtain weight data of the influencing factors of the sole performance;

[0071] Specifically, the key factors affecting the performance of the sole include grain size, cross-link density, and molecular chain arrangement. The analytic hierarchy process (AHP) can be used to assign weights to these factors. A panel of experts, consisting of material scientists, engineers, and designers, is formed to compare each pair of influencing factors based on the performance requirements of the sole rubber material and the expertise of the experts, and give a score of relative importance. For example, it is believed that the importance of grain size to wear resistance is 1.5 times that of cross-link density, while the importance of cross-link density to elastic modulus is 2 times that of molecular chain arrangement. Using an AHP software tool, such as Expert Choice, input the scores of these pairwise comparisons and calculate the weight of each factor. The software will ensure the rationality of the score through consistency checks and output the relative weight of each factor. For example, the weight of grain size is 0.4, the weight of cross-link density is 0.3, and the weight of molecular chain arrangement is 0.3. These weights will be used to quantify the contribution of each factor to the performance of the sole, thereby obtaining the weight data of the factors affecting the performance of the sole.

[0072] Step S264: simulating the long-term performance evolution of the sole rubber material based on the diffraction material microstructure model according to the weight data of the sole performance influencing factors to obtain the sole performance degradation trend mapping data;

[0073] Specifically, finite element analysis (FEA) software, such as ANSYS, can be used to simulate the long-term performance evolution. According to the weight data of the factors affecting the performance of the sole, the microstructural parameters of the sole rubber material are adjusted. For example, if the weight of the grain size is 0.4, set the parameters in the simulation that the grain size has a greater impact on the material performance. Set the boundary conditions and loads of the simulation to simulate the repeated stress and strain of the sole rubber material during long-term use. Select a typical usage scenario, such as the use of running shoes in a marathon, and set the corresponding repeated stress cycle and duration. In ANSYS, run the simulation and record the changes in the performance parameters of the sole rubber material over time. For example, record the changes in parameters such as elastic modulus, tensile strength, and wear resistance. Through these data, it is possible to observe how the performance of the sole rubber material degrades over time, identify the key stages of performance degradation, and finally obtain the sole performance degradation trend mapping data, which includes the performance change curve of the sole rubber material during long-term use and the predicted service life.

[0074] Step S265: reconstructing the limit performance boundary of the diffraction material microstructure model according to the sole structure-performance mapping matrix and the sole performance degradation trend mapping data to obtain a sole microperformance evolution model.

[0075] Specifically, key information can be extracted from the sole structure-performance mapping matrix, which contains the relationship between microstructural features (such as grain size, porosity, and molecular chain arrangement) and macroscopic performance indicators (such as elastic modulus, tensile strength, and wear resistance). For example, it was found that there is a significant linear relationship between grain size and wear resistance, which can be expressed by the following equation: Next, the sole performance degradation trend mapping data was used to describe the changing trend of the performance parameters of the sole rubber material during long-term use. For example, it was found that the degradation of wear resistance over time can be described by an exponential decay model: ,in is the time (in months), is the base of the natural logarithm. MATLAB software was used for data processing and model building. The structure-performance mapping matrix and performance degradation trend mapping data were input, and then the Curve Fitting Toolbox of MATLAB was used to fit the data and build a micro-performance evolution model of the sole rubber material. For example, a nonlinear regression model was used to fit the data of grain size changes over time and predict its effect on wear resistance. During the model building process, model parameters were set, such as selecting a suitable attenuation coefficient to describe the degradation of wear resistance over time. The best model was selected by comparing the goodness of fit of different models (such as R-squared value), and finally a sole micro-performance evolution model was obtained, which can describe the long-term performance changes of the sole rubber material under different use conditions. For example, it is possible to predict how changes in grain size will affect wear resistance within a year, and adjust the formula or structure of the sole rubber accordingly to extend its service life.

[0076] The present invention realizes cross-scale association from microstructure to macroscopic performance by establishing a sole structure-performance mapping matrix. By identifying the key influencing factors of sole performance, resources and attention can be focused on the material properties that have the greatest impact on performance, thereby improving the efficiency and effect of material design. By weighting these key influencing factors, their specific impact on sole performance is quantified. By simulating the long-term performance evolution of the sole rubber material, the durability and service life of the material are predicted, providing a scientific basis for the long-term reliability of the product.

[0077] Preferably, step S3 comprises the following steps:

[0078] Step S31: performing acoustic emission sensor preprocessing on the sole rubber material to obtain acoustic detection basic configuration data;

[0079] Specifically, you can choose to use the AEYE-2000 acoustic emission sensor from Physical Acoustics, install the sensor on the surface of the sole rubber sample, use a special coupling agent to ensure good acoustic contact, use an acoustic emission preamplifier to enhance the signal received from the sensor, and set the amplifier gain to 40dB to adapt to the wide range of acoustic signals generated by the rubber material. Use an acoustic emission analyzer (AE Analyzer) to calibrate the sensor to ensure that its frequency response range covers the frequency band of acoustic signals emitted by the rubber material, set to 100kHz to 1MHz. Preprocess the sensor, including removing background noise and adjusting the sensitivity threshold. Finally, record the configuration parameters of the sensor, including gain, threshold, filter settings, etc. These data constitute the basic configuration data for acoustic detection.

[0080] Step S32: formulating an acoustic signal collection scheme for the sole rubber material according to the acoustic detection basic configuration data to obtain a sole acoustic signal collection scheme;

[0081] Specifically, the parameter settings for acoustic signal acquisition can be determined based on the basic configuration data. For example, the sampling rate is set to 2MHz, and the National Instruments data acquisition card (DAQ) is used to collect acoustic signals. According to the characteristics of the sole rubber material and the test purpose, a suitable acoustic signal acquisition scheme is selected. For example, if the focus is on the acoustic characteristics of the sole during walking, multiple sensors will be arranged at different parts of the sole to capture acoustic activities in different areas. The threshold of each sensor is set to -40dB. In the experiment, the tester is asked to walk on a treadmill wearing a sole sample equipped with an acoustic emission sensor, while recording the acoustic signal in real time through the DAQ card. Set up data acquisition software (such as LabVIEW) to monitor the signal in real time and automatically save the data when a signal exceeding the threshold is detected. In this way, a sole acoustic signal acquisition scheme can be obtained, which describes in detail the acquisition parameters, arrangement method and data processing flow of the acoustic signal.

[0082] Step S33: performing an omnidirectional acoustic feature scan on the sole rubber material according to the sole acoustic signal collection scheme to obtain an original sole acoustic emission signal data set;

[0083] Specifically, a set of acoustic emission (AE) sensors placed at different areas of the sole can be used for scanning. These sensors are connected to a multi-channel acoustic emission data acquisition system, such as the one provided by MISTRAS Group. The acquisition parameters of each sensor are set, including a gain of 40dB and a threshold of -40dB. In the laboratory, the sole rubber sample is placed in a controlled environment to simulate actual use conditions. A dynamic mechanical analyzer (DMA) is used to apply periodic loads to the sole sample to simulate the force conditions during walking or running. The DMA is set to apply a sinusoidal waveform load with a frequency of 5Hz and an amplitude of 2mm. During the loading process, the AE sensor captures the acoustic signals generated by the microscopic events caused by stress inside the sole rubber material. These signals are transmitted to the data acquisition system in real time and recorded to form the original sole acoustic emission signal data set. This data set contains information such as the amplitude, duration, and signal energy of the acoustic signals captured by each sensor at different times and different locations.

[0084] Step S34: constructing an energy dissipation spectrum of the sole rubber material based on the original sole acoustic emission signal data set to obtain sole energy dissipation spectrum mapping data;

[0085] Specifically, signal processing software, such as Sound and Vibration Toolkit in LABVIEW, can be used to process the raw acoustic emission signal data, and a bandpass filter is selected with a cutoff frequency set to 100kHz to 500kHz to match the main frequency range of the acoustic signal of the rubber material. Perform a fast Fourier transform (FFT) on the signal to convert it from the time domain to the frequency domain to obtain the frequency distribution of the signal. The sampling frequency of the FFT is set to 10MHz, and the power spectral density (PSD) of each frequency component is calculated, which will help identify the main frequency area of ​​energy dissipation. Finally, the energy dissipation spectrum mapping data of the sole rubber material is constructed, which includes the energy dissipation level at different frequencies. For example, it is found that at a frequency of 200kHz, the energy dissipation increases significantly, indicating that the microstructure inside the material is changing.

[0086] Step S35: extracting energy dissipation signal features from the sole energy dissipation spectrum mapping data to obtain sole energy dissipation signal features;

[0087] Specifically, signal processing software, such as MATLAB, can be used to process the energy dissipation spectrum mapping data of the sole. Import the energy dissipation spectrum mapping data of the sole, and use the functions in the signal processing toolbox of MATLAB to identify the significant features of energy dissipation. Pay special attention to the peak frequency and bandwidth of energy dissipation, which can indicate the activity of the internal dissipation mechanism of the material. Apply a peak detection algorithm to locate the peak in the spectrum, such as using the findpeaks function, and set the minimum peak height and minimum peak spacing parameters to avoid detecting false peaks caused by noise. For example, set the minimum peak height to three times the average value of the PSD data and the minimum peak spacing to 100Hz. In this way, the energy dissipation peak of the sole rubber material at a specific frequency can be identified. Calculate the integrated energy of each peak, which represents the amount of energy dissipation at that frequency. The total energy of the spectrum can also be calculated by integrating the PSD data over a certain frequency range. These calculation results provide the characteristics of the energy dissipation signal of the sole, including parameters such as peak frequency, bandwidth, and total energy dissipation.

[0088] Step S36: performing thermodynamic mapping on the energy dissipation signal characteristics of the sole to obtain an energy conversion efficiency diagram of the sole material;

[0089] Specifically, thermodynamic simulation software, such as ABAQUS, can be used to simulate the thermodynamic behavior of the sole rubber material under stress. According to the characteristics of the sole energy dissipation signal, the internal dissipation parameters of the material, such as the loss factor, are set, which is related to the viscoelastic properties of the material. The range of the loss factor is set according to the peak frequency and bandwidth. For example, if the peak frequency is high, a larger loss factor is selected. The finite element model of the sole rubber material is set in ABAQUS, and boundary conditions and loads similar to actual use conditions are applied. Run the simulation and record the temperature changes and energy conversion inside the material. Through simulation, the energy conversion efficiency of the sole rubber material under stress can be obtained, which includes the energy absorbed by the material, the energy dissipated, and the stored energy. Finally, the energy conversion efficiency diagram of the sole material is generated, which includes the energy conversion efficiency distribution of the sole rubber material under different conditions.

[0090] Step S37: quantitatively evaluate the energy characteristics of the sole rubber material according to the sole material energy conversion efficiency diagram to obtain the sole material energy efficiency evaluation result.

[0091] Specifically, please refer to the sub-steps of step S37 for the detailed implementation process of this embodiment.

[0092] The present invention improves the accuracy of acoustic detection through acoustic emission sensor preprocessing, ensuring the accuracy of acoustic detection basic configuration data. The customized acoustic signal acquisition scheme makes data acquisition more in line with the actual characteristics of the sole rubber material, improving the relevance and practicality of the data. Comprehensive acoustic emission signal data is collected through omnidirectional acoustic feature scanning. The energy dissipation characteristics of the sole rubber material are analyzed in detail through the constructed energy dissipation spectrum mapping data. By extracting features from the energy dissipation spectrum mapping data, the energy dissipation signal characteristics of the sole rubber material are accurately obtained. The sole material energy conversion efficiency diagram obtained by thermodynamic mapping provides a perspective for in-depth understanding of the efficiency and performance of the sole rubber material in the energy conversion process.

[0093] Preferably, step S37 includes the following steps:

[0094] Step S371: performing feature analysis on the energy conversion efficiency diagram of the sole material to obtain a key feature vector of sole energy conversion;

[0095] Specifically, data analysis software, such as Python with NumPy and SciPy libraries, can be used to analyze the energy conversion efficiency diagram of the sole material, and the data of the energy conversion efficiency diagram can be imported, including the temperature change, energy absorption rate and energy dissipation rate of the sole rubber material under different loading conditions. The principal component analysis (PCA) method is applied to parse these data, and the PCA class in the scikit-learn library of Python is used to implement this analysis. For example, the PCA model is set to retain 95% of the variance to determine how many principal components are needed to represent the main changes in the data set. Through PCA analysis, several key principal components are obtained, each of which is a linear combination of the variables in the original data set and represents a key feature in the energy conversion efficiency diagram. For example, the first principal component represents the energy absorption and dissipation efficiency of the sole rubber material under high load, while the second principal component represents the energy conversion characteristics under low load. These principal components constitute the key feature vectors of sole energy conversion.

[0096] Step S372: Mapping and reducing the dimension of the key feature vector of the sole energy conversion to obtain preliminary mapping data of energy efficiency;

[0097] Specifically, the PCA class in Python's scikit-learn library can be used for mapping and dimensionality reduction. The key feature vector of sole energy conversion is mapped to a low-dimensional space, and two principal components are selected to be retained, which can explain most of the data variance. The key feature vector of sole energy conversion is projected onto these two principal components using the PCA model to generate a new reduced-dimensionality data set. Each data point in this data set is the coordinate of the original data point on these two principal components, representing the value of the sole rubber material on these two key features. For example, a data point is obtained, whose value on the first principal component is 0.8 and whose value on the second principal component is 0.3, which means that the sole rubber material corresponding to the data point has higher energy absorption and dissipation efficiency under high load, while the energy conversion characteristics under low load are relatively low. These reduced-dimensional data constitute the preliminary mapping data of energy efficiency.

[0098] Step S373: tracing the energy conversion path of the preliminary energy efficiency mapping data to obtain the energy conversion path of the sole material;

[0099] Specifically, data analysis software, such as MATLAB, can be used to process preliminary energy efficiency mapping data, and the preliminary energy efficiency mapping data can be imported into MATLAB. These data contain the energy conversion efficiency of the sole rubber material under different conditions. Time series analysis methods are applied to track the energy conversion path. For example, wavelet transform is used to analyze the change of energy conversion efficiency over time. This method can reveal the characteristics of the signal at different times and frequencies. Select a suitable wavelet function, such as Daubechies wavelet, and set the number of wavelet transform layers to 5 to obtain the details of energy conversion efficiency at different frequencies. Through wavelet transform, the change of energy conversion efficiency of the sole rubber material at different loading stages can be identified, thereby tracking the energy conversion path. For example, it is found that in the early stage of loading, the sole rubber material mainly exhibits elastic energy storage, while in the later stage of loading, energy dissipation becomes dominant. This information constitutes the energy conversion path of the sole material, including the key stages of energy absorption, storage and dissipation.

[0100] Step S374: performing microscopic energy level mapping on the sole rubber material according to the sole material energy conversion path to obtain an energy state distribution diagram of the sole material;

[0101] Specifically, molecular dynamics simulation software, such as LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator), can be used to simulate the microscopic energy level state of the sole rubber material. The initial conditions of the LAMMPS simulation are set according to the key parameters in the energy conversion path of the sole material, such as the energy absorption rate and dissipation rate. In LAMMPS, a molecular model of the sole rubber material is constructed, and an appropriate potential energy function is assigned according to the chemical structure and physical properties of the material. The simulation is run to record the dynamic behavior of the molecules under different energy states, including the vibration, rotation and translation of the molecular chain. Through the simulation, the microscopic energy level state data of the sole rubber material is obtained. Data processing software, such as Python with NumPy and Matplotlib libraries, is used to analyze these data and generate energy state distribution diagrams of the sole material. For example, it is found that in the low energy state, the molecular chain mainly exists with a small vibration amplitude, while in the high energy state, the vibration amplitude of the molecular chain increases significantly. These energy state distribution diagrams reveal the microscopic behavior of the sole rubber material under different energy states.

[0102] Step S375: performing conversion correlation evaluation on the energy state distribution diagram of the sole material to obtain material energy conversion synergy evaluation data;

[0103] Specifically, statistical analysis software, such as SPSS, can be used to process the energy state distribution diagram of the sole material, and the data of the energy state distribution diagram can be imported into SPSS. These data include the molecular vibration frequency, energy absorption rate and energy dissipation rate of the sole rubber material in different energy states. The correlation analysis method is applied to evaluate the correlation between different energy states. For example, the Pearson correlation coefficient is used to quantify the strength and direction of the linear relationship between two variables. The correlation coefficient between the energy absorption rate and the energy dissipation rate is calculated, and the significance level is set to 0.05 to determine whether the correlation is statistically significant. Through correlation analysis, the energy conversion synergy evaluation data of the sole rubber material under different energy states are obtained. For example, it is found that there is a strong positive correlation between the energy absorption rate and the energy dissipation rate in the high energy state, which indicates that the energy conversion efficiency of the sole rubber material is high under these states.

[0104] Step S376: Perform multivariate statistics on the material energy conversion synergy evaluation data to obtain the sole material energy efficiency evaluation results.

[0105] Specifically, the energy conversion synergy evaluation data of the sole rubber material include energy absorption rate (e.g., mean value is 0.8, standard deviation is 0.1), energy dissipation rate (e.g., mean value is 0.3, standard deviation is 0.05) and molecular vibration frequency (e.g., mean value is 100 Hz, standard deviation is 10 Hz) under different test conditions. Use SPSS software to import these data and establish a multiple regression model. In this model, the energy absorption rate is set as X1, the energy dissipation rate is X2, the molecular vibration frequency is X3, and the energy efficiency of the sole rubber material is the dependent variable Y. Select "Enter method", which means that all selected independent variables will be included in the model at the same time, rather than entering the model step by step or hierarchically. In SPSS, run the multiple regression analysis and get the output results of the model. Focus on several key statistical indicators: R-squared value, which indicates the amount of variation explained by the model; standardized regression coefficient of each independent variable, which indicates the relative importance of the independent variable on the dependent variable after controlling other variables; and the significance level (p-value) of each independent variable, which indicates the statistical significance of the independent variable on the dependent variable. The results of the hypothesis analysis show that the R-squared value is 0.85, indicating that the model explains 85% of the variation of the dependent variable. The standardized regression coefficients of energy absorption rate (X1) and energy dissipation rate (X2) are 0.6 and 0.5, respectively, and the p-values ​​of both are less than 0.05, indicating that they have a significant effect on energy efficiency. The standardized regression coefficient of molecular vibration frequency (X3) is 0.2, and the p-value is greater than 0.05, indicating that its effect on energy efficiency is not significant. Finally, the evaluation results of the energy efficiency of the sole material are obtained. These results show that energy absorption rate and energy dissipation rate are the key factors affecting the energy efficiency of the sole rubber material.

[0106] The present invention identifies the key feature vectors of the sole material in the energy conversion process through feature analysis, providing key information for understanding the energy conversion mechanism of the material. By utilizing the mapping dimension reduction technology, the complex energy conversion feature vectors are converted into preliminary mapping data, simplifying the data processing process and improving the analysis efficiency. The specific path of energy conversion in the sole rubber material is revealed by the clear tracking of the energy conversion path. The detailed mapping of the microscopic energy level state provides an intuitive view of the microscopic energy conversion characteristics of the material through the energy state distribution diagram. The evaluation of the energy conversion synergy reveals the synergistic effect of energy conversion inside the material, and the application of multivariate statistical analysis enhances the comprehensive evaluation capability of the material energy efficiency.

[0107] Preferably, step S4 comprises the following steps:

[0108] Step S41: performing extreme working condition simulation on the sole application scenario set to obtain a sole composite stress boundary condition set;

[0109] Specifically, finite element analysis (FEA) software, such as ANSYS, can be used to perform extreme working condition simulations, create a three-dimensional geometric model of the sole, and assign corresponding material parameters according to material properties, such as the elastic modulus and Poisson's ratio of rubber. Set the boundary conditions of the simulation, including maximum and minimum pressure values, such as from 0.5 MPa to 2 MPa, to simulate the pressure on the sole by users of different weights. It is also necessary to set a temperature range, such as from -20°C to 50°C, to simulate the performance of the sole under extreme temperatures. During the simulation, different load combinations are applied, such as impact forces during walking, running, and jumping, as well as the effects of different road conditions, such as hard and soft roads. Run the simulation and record the stress distribution and deformation of the sole under various working conditions. Through these simulations, a set of composite stress boundary conditions for the sole is finally obtained, including various stress combinations and boundary conditions encountered by the sole under extreme working conditions.

[0110] Step S42: constructing a degree-of-freedom dynamic response system based on the sole composite stress boundary condition set to obtain a sole stress coupling analysis platform;

[0111] Specifically, you can use multibody dynamics simulation software, such as RecurDyn, to build a dynamic response system, import the geometric model and material parameters of the sole, and set the initial conditions of the system according to the sole composite stress boundary condition set. Create an interactive model of the sole in contact with the ground, and define the contact force and friction coefficient. Set the kinematic parameters of the sole, such as mass, inertia, and joint freedom, to simulate the dynamic behavior of the sole in motion. Define the connection method between the sole and the shoe body, such as rigid connection or elastic connection, and set the corresponding connection parameters. Run dynamic simulation to simulate the stress response of the sole in movements such as walking, running, and jumping. Through simulation, a sole stress coupling analysis platform is obtained, which can simulate the stress and deformation of the sole under various sports and road conditions.

[0112] Step S43: fine-tuning the load application on the sole rubber material according to the sole stress coupling analysis platform to obtain a sole material stress application unit;

[0113] Specifically, the material testing system MTS (Material Testing System) can be used to fine-tune the load application, install the sole rubber sample in the fixture of the MTS machine, and ensure that the contact between the sample and the fixture is uniform. Set the control software of the MTS machine and input the data obtained from the stress coupling analysis platform, including the magnitude, direction and rate of the force applied to the sole. For example, simulate the cyclic load applied to the sole when a person walks. According to the data of the analysis platform, set the MTS machine to apply a cyclic load with a maximum value of 1.5kN (simulating the gait of a person weighing 75 kg) and a frequency of 1Hz (simulating a normal walking speed). Adjust the parameters of the MTS machine to ensure that the applied load is consistent with the results simulated by the analysis platform. Through the MTS machine, the actual load is applied to the sole rubber sample, and the deformation and stress response of the sample are recorded at the same time, and finally the sole material stress application unit is obtained, which can accurately simulate the stress conditions of the sole in actual use.

[0114] Step S44: performing response calibration on the sole material stress applying unit to obtain sole material stress applying accuracy mapping data;

[0115] Specifically, force sensors and displacement sensors can be used to monitor the load applied by the MTS machine and the response of the sole rubber sample. For example, a Kistler force sensor is used to measure the force applied to the sole rubber sample with an accuracy of 0.01N. At the same time, a Micro-Epsilon displacement sensor is used to measure the deformation of the sample after the force is applied with an accuracy of 0.01mm. While the MTS machine applies the load, the data of the force sensor and displacement sensor are recorded and compared with the simulation results of the stress coupling analysis platform. LabVIEW software is used to process these data and perform response calibration. For example, if the simulation results show that under a load of 1.5kN, the sole rubber sample should have a deformation of 2mm, and the actual measurement result is 1.8mm, the parameters of the MTS machine will be adjusted until the actual measurement result is consistent with the simulation result. Through this process, the stress application accuracy mapping data of the sole material can be obtained, which includes the applied force, the actual deformation, and the deviation from the simulation result.

[0116] Step S45: constructing a sensor array network for the sole rubber material based on the sole material stress application accuracy mapping data to obtain a sole material dynamic data acquisition network;

[0117] Specifically, a multi-channel data acquisition system, such as the NIcDAQ-9174 provided by National Instruments, can be used to construct a sensor array network, and multiple strain gauges and pressure sensors can be installed in different key areas of the sole rubber sample (such as the forefoot, heel, and arch) to capture the stress distribution of the sole when it is under force. The selection of these sensors includes using Vishay strain gauges (model Vishay 350Ω), setting the connection parameters of each sensor, such as setting the gain of the strain gauge to 100, connecting all sensors to the NI cDAQ-9174 data acquisition system, and configuring them using NI LabVIEW software. Set the sampling frequency of data acquisition to 1000 Hz. Through LabVIEW, create a user interface to monitor the output signals of each sensor in real time and record the data. By applying a known load, ensure that the sensor can accurately reflect the response of the sole rubber material under different stress conditions, and finally obtain a sole material dynamic data acquisition network, which can monitor and record the stress and deformation of the sole rubber material under various use conditions in real time.

[0118] Step S46: Based on the sole material dynamic data acquisition network, a degree of freedom stress response test is performed on the sole rubber material to obtain the sole extreme environment adaptability data, and based on the sole extreme environment adaptability data, a stress-strain characteristic analysis is performed on the sole rubber material to obtain a sole performance stability evaluation report.

[0119] Specifically, please refer to the sub-steps of step S46 for the detailed implementation process of this embodiment.

[0120] The present invention improves the ability to predict the performance of the sole under extreme conditions through extreme working condition simulation. By constructing a sole stress coupling analysis platform, the behavior of the sole rubber material under complex stress conditions is accurately simulated and analyzed. The accuracy and reliability of the test are improved by fine-tuning the load application of the sole rubber material. The accuracy of the experimental data is further improved by the response calibration of the stress application unit. Through the sensor array network established based on the stress application precision mapping data, real-time monitoring of the dynamic response of the sole rubber material is achieved. The adaptability data of the sole under extreme conditions obtained by the degree of freedom stress response test provides a basis for evaluating the performance stability of the sole. The stress-strain characteristic analysis based on these data understands the behavior of the sole material under different stress conditions, and the sole performance stability evaluation report finally generated comprehensively evaluates the performance stability of the sole rubber material.

[0121] Preferably, step S46 includes the following steps:

[0122] Step S461: Simulating an extreme temperature environment on the sole material dynamic data acquisition network to obtain a sole material temperature gradient control module;

[0123] Specifically, an environmental test chamber, such as the Binder brand KS 240 model, can be used to simulate an extreme temperature environment, and the sole material dynamic data acquisition network, including the previously installed strain gauges and pressure sensors, is placed in the test chamber. Set the temperature range of the test chamber, for example, from -40°C to 80°C, to cover the lowest and highest temperature conditions that the sole will encounter. Use LabVIEW software to communicate with the controller of the test chamber and set the temperature change program, including the temperature gradient and holding time. For example, set a temperature cycle, starting from room temperature, gradually decreasing to -40°C, holding for 1 hour, and then gradually increasing to 80°C, and holding for another hour. During the whole process, LabVIEW software records the data collected by the sensor in real time, including the stress and strain response of the sole material at different temperatures. Through this simulation process, a sole material temperature gradient control module is obtained.

[0124] Step S462: constructing a composite corrosion environment simulation unit based on the sole material temperature gradient control module to obtain a sole material composite environment stress simulator;

[0125] Specifically, a composite environmental test chamber, such as Weiss Technik's WTS 2000 model, can be used to build a composite corrosion environment simulation unit, integrate the sole material temperature gradient control module into the test chamber, and set the temperature gradient control module to communicate with the controller of the test chamber. Add humidity and corrosive medium control functions. For example, set the relative humidity range to 10% to 90%, and select a suitable corrosive medium, such as salt spray, to simulate the impact of the coastal environment on the sole material. Use the built-in spray system of the test chamber to apply salt spray, and set the cycle and intensity of the spray. Run the composite environmental stress simulation, and use a dynamic data acquisition network to record the stress and strain response of the sole material in the composite environment. For example, it was found that in a high temperature and high humidity environment, the deformation of the sole material increased, while in a salt spray environment, the strength of the material decreased. Through this simulation process, a composite environmental stress simulator for the sole material can be obtained.

[0126] Step S463: performing a multi-parameter coupling test on the sole rubber material according to the sole material composite environmental stress simulator to obtain a sole extreme environment simulation data set;

[0127] Specifically, a composite environmental stress simulator, such as Weiss Technik's WTS 2000 model, can be set up to simulate specific extreme environmental conditions. Select a test scenario, such as chemical medium erosion in a high temperature and high humidity environment, set the temperature to 60°C, the relative humidity to 80%, and select 3% salt water as the chemical medium. During the test, a dynamic data acquisition system, such as National Instruments' NI cDAQ-9174, is used to monitor the stress and strain response of the sole rubber material in real time. Install strain gauges and pressure sensors in key areas of the sole and connect them to the data acquisition system. Set the sampling frequency to 10Hz. Run the test to simulate the use of the sole rubber material in the above extreme environment for 48 hours. During the entire test, the data acquisition system records the stress and strain data of the sole rubber material at different time points. These data include key performance indicators such as deformation, strength change and durability of the sole rubber material under different environmental conditions. After the test, the extreme environment simulation data set of the sole is obtained.

[0128] Step S464: performing characteristic spectrum clustering on the sole extreme environment simulation data set to obtain a characteristic spectrum of the sole material limit response;

[0129] Specifically, data analysis software, such as Python with the scikit-learn library, can be used to process the extreme environment simulation data set of the sole, import the time series data in the simulation data set into Python, and use Fourier transform to convert the time series data into frequency domain data to identify the frequency characteristics of the response of the sole rubber material. Apply clustering algorithms, such as K-means clustering, to identify patterns and trends in the data. Select the K-means algorithm and set the number of clusters to 3. Through K-means clustering, the extreme response characteristic map of the sole rubber material under extreme environments is obtained. For example, it is found that under high temperature and high humidity conditions, the deformation response of the sole rubber material is mainly concentrated in the low-frequency area, while under chemical medium erosion, the response is mainly concentrated in the high-frequency area.

[0130] Step S465: identifying the critical point of failure of the extreme response characteristic spectrum of the sole material to obtain extreme environment adaptability data of the sole;

[0131] Specifically, data analysis software, such as MATLAB, can be used to analyze the extreme response characteristic spectrum of the sole material, import the extreme response characteristic spectrum data into MATLAB, and apply statistical analysis methods to identify outliers and mutation points in the data, which are signs of critical failure points. Use the peak detection algorithm in the signal processing toolbox of MATLAB, such as the findpeaks function, to identify the peaks in the spectrum, which represent the sharp changes in the performance of the sole rubber material. Set the minimum height and minimum distance parameters for peak detection, such as the minimum height is twice the average response intensity, and the minimum distance is 10 data points. Through analysis, it is identified that under specific environmental conditions, such as high temperature and high humidity, the deformation response of the sole rubber material has a significant peak, which indicates that the material is close to its performance limit. Record the position and intensity of these peaks, and combine the physical properties of the material, such as tensile strength and elongation at break, to determine the critical failure point, and finally obtain the extreme environmental adaptability data of the sole, which includes the performance limit and critical failure point of the sole rubber material under different extreme environments.

[0132] Step S466: mapping the extreme working conditions of the sole rubber material based on the extreme environment adaptability data of the sole to obtain a sole material extreme adaptability prediction model;

[0133] Specifically, predictive analysis software, such as SAS, can be used to process the extreme environmental adaptability data of the sole, import the performance limit and failure critical point data of the sole rubber material under different extreme environments into SAS, and apply the survival analysis method to establish the extreme adaptability prediction model. The Cox proportional hazard model is selected as the basic model for survival analysis, which can process time and event data and evaluate the impact of multiple risk factors on the life of the sole rubber material. The parameters of the model are set, including environmental factors such as temperature, humidity and concentration of chemical media, as well as performance parameters of the sole rubber material, such as tensile strength and elongation at break. Through survival analysis, a sole material extreme adaptability prediction model is established, which can predict the expected life and failure risk of the sole rubber material under given extreme working conditions. For example, it is found that the life of the sole rubber material is significantly shortened under high temperature and high humidity environment, while the life is extended after adding certain anti-aging agents, and finally the sole material extreme adaptability prediction model is obtained.

[0134] Step S467: performing stress decoupling clustering on the sole material limit adaptability prediction model to obtain sole material stress decomposition mapping data, and performing damage accumulation tracking on the sole rubber material according to the sole material stress decomposition mapping data to obtain sole material microscopic damage evolution data;

[0135] Specifically, data analysis software, such as Python with the scikit-learn library, can be used to perform stress decoupling clustering, and the data in the sole material limit adaptability prediction model can be imported into Python. These data include the performance parameters of the sole rubber material under different environmental and stress conditions, such as stress, strain, and elongation at break. The K-means clustering algorithm is applied to perform stress decoupling and divide the data into several different stress state categories. For example, the data is divided into three categories: "low stress", "medium stress", and "high stress", each of which represents a specific stress state. Set the number of clusters to 3, select a suitable initialization center point, and then run the clustering algorithm. After clustering is completed, the stress decomposition mapping data of the sole material is obtained, which associates the performance parameters of the sole rubber material with a specific stress state. Use these data to track the damage accumulation of the sole rubber material. Analyze the damage evolution of the sole rubber material under each stress state. For example, it is found that the damage accumulation rate of the sole rubber material is the fastest under the "high stress" state. Through this process, the microscopic damage evolution data of the sole material can be obtained, which describes in detail the damage development process of the sole rubber material under different stress conditions.

[0136] Step S468: Perform performance stability evaluation on the microscopic damage evolution data of the sole material to obtain a sole performance stability evaluation report.

[0137] Specifically, statistical analysis software, such as SPSS, can be used to process the microscopic damage evolution data of the sole material, and the damage evolution data can be imported into SPSS. These data include the damage accumulation rate and damage degree of the sole rubber material under different stress states. Reliability analysis methods, such as Weibull analysis, are applied to evaluate the performance stability of the sole rubber material, which can determine the failure mode and life distribution of the sole rubber material. The parameters of the Weibull distribution, such as shape parameter and scale parameter, are set, and the maximum likelihood estimation (MLE) method is used to fit the data. Through Weibull analysis, the performance stability evaluation results of the sole rubber material are obtained, including the expected life and failure risk of the material. For example, it is found that under specific conditions of use, the average life of the sole rubber material is 1000 cycles, and the failure risk increases significantly at the end of the life. Finally, a sole performance stability evaluation report is obtained, which describes in detail the performance stability and life prediction of the sole rubber material under different conditions of use.

[0138] The present invention enhances the ability to control the temperature gradient of the sole material by simulating extreme temperature environments. The composite environmental stress simulator for sole materials constructed can simulate the stress response of the sole under various environmental factors, thereby improving the accuracy of the material durability assessment. The performance of the material under extreme environments is comprehensively evaluated through multi-parameter coupling testing. The extreme response characteristics of the sole material are identified through characteristic spectrum clustering, which deepens the understanding of the behavior of the material under extreme conditions. The identification of critical failure points provides key information for material design and improvement, which helps prevent material failure and extend service life. The damage accumulation of the sole rubber material is tracked through stress decoupling clustering, which provides an understanding of the evolution of material damage.

[0139] Preferably, step S5 comprises the following steps:

[0140] Step S51: performing feature deconstruction on the original performance data set of the sole to obtain the sole material performance mapping data;

[0141] Specifically, data analysis software, such as Python with pandas and scikit-learn libraries, can be used to process the original sole performance data set, and the original performance data set can be imported into Python. These data include multiple performance indicators such as tensile strength, wear resistance, and elastic modulus of the sole rubber material. The PCA class in the scikit-learn library is used to implement feature deconstruction, and the proportion of retaining 95% variance is set to determine how many principal components are needed to represent the main changes in the data set. Through PCA analysis, several key principal components are obtained, each of which is a linear combination of the variables in the original data set, representing a key performance feature of the sole rubber material. For example, the first principal component represents the comprehensive performance of the wear resistance and tensile strength of the sole rubber material, while the second principal component represents the influence of the elastic modulus. These principal components constitute the sole material performance mapping data.

[0142] Step S52: performing microstructure dispersion evaluation on the sole rubber material based on the sole material performance mapping data to obtain a sole material performance dispersion evaluation index;

[0143] Specifically, statistical analysis software, such as R language, can be used to analyze the performance mapping data of the sole material. The performance mapping data is imported into R language, and the coefficient of variation (CV) is applied to evaluate the microstructure dispersion. The coefficient of variation is the ratio of the standard deviation to the mean, which can describe the degree of dispersion of the data. The coefficient of variation of each principal component is calculated. For example, for the first principal component (the comprehensive performance of wear resistance and tensile strength), its standard deviation and mean are calculated to obtain the coefficient of variation. A higher coefficient of variation indicates that the performance characteristics have greater fluctuations in different samples or under different conditions, while a lower coefficient of variation indicates that the performance characteristics are more consistent. Through this analysis, the evaluation indicators of the dispersion of sole material performance can be obtained, which include the coefficient of variation of each principal component.

[0144] Step S53: tracking the co-evolution of the sole rubber material according to the sole material performance dispersion evaluation index to obtain an initial feature set of the material micro-performance evolution;

[0145] Specifically, material testing equipment, such as a dynamic mechanical analyzer (DMA), can be used to perform cyclic loading tests on the sole rubber material. DMA can provide a dynamic loading environment with controllable temperature and frequency, and can simulate the stress conditions of the sole rubber material in actual use. Set the test parameters of DMA, such as applying a sinusoidal waveform dynamic load at a frequency of 1Hz in the temperature range of -20°C to 60°C. During the DMA test, the changes in the storage modulus and loss modulus of the sole rubber material over time and temperature are recorded. Use Python with NumPy and SciPy libraries to process these data, and track the evolution of the microscopic properties of the sole rubber material by analyzing the changes in the storage modulus and loss modulus. Calculate the mean and standard deviation of the storage modulus and loss modulus of each test point. These statistical data constitute the evaluation index of the discreteness of the sole material performance. Use these indicators to identify the changes in the microscopic properties of the sole rubber material under different conditions, and obtain the initial feature set of the evolution of the material's microscopic properties.

[0146] Step S54: constructing a mapping conversion network based on the initial feature set of the material microscopic performance evolution to obtain a sole material performance evolution association network;

[0147] Specifically, an artificial neural network (ANN) can be used to construct a mapping conversion network. The Keras library in the TensorFlow framework is selected to implement this network, and the initial feature set of the micro-performance evolution of the sole rubber material is used as input data, including the change data of the storage modulus and the loss modulus. Design an ANN model containing an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is determined according to the number of features in the initial feature set. For example, if there are 5 features, the input layer has 5 nodes. Select two hidden layers, each containing 10 nodes, and use the ReLU activation function. The number of nodes in the output layer is determined according to the number of macro-performance indicators to be predicted. For example, if you want to predict the wear resistance and elastic modulus of the sole rubber material, the output layer has 2 nodes. Use the mean square error (MSE) as the loss function and train the ANN model through the back propagation algorithm. Set the training cycle (epochs) to 100, and use the Adam optimizer to adjust the network weights to minimize the loss function. After the training is completed, the sole material performance evolution association network is obtained.

[0148] Step S55: inferring the critical point of transformation of the sole material performance evolution correlation network to obtain the sole material performance evolution critical point prediction data;

[0149] Specifically, data analysis software, such as MATLAB, can be used to analyze the data of the sole material performance evolution association network, the historical performance data in the association network is imported into MATLAB, and the time series analysis method is applied to identify the trend and pattern of performance changes. The findpeaks function in MATLAB is used to identify the local maximum in the performance data, which represents the degradation or transition point of performance. The parameters of findpeaks are set, such as the minimum peak height is 1.2 times the average performance value, and the minimum peak spacing is 10 data points. In addition, threshold analysis is applied to set the threshold of performance degradation, such as when the performance value drops to 80% of the initial performance, it is considered that the critical point of performance evolution has been reached. Through this method, the critical point of the sole rubber material performance evolution can be inferred, and future performance changes can be predicted, and finally the sole material performance evolution critical point prediction data is obtained, which includes the predicted critical point time, the amplitude of performance degradation and the failure mode.

[0150] Step S56: based on the sole micro-performance evolution model, the sole material micro-structure is dynamically dissipated and tracked according to the sole material performance evolution critical point prediction data to obtain a semi-empirical prediction model for the sole material;

[0151] Specifically, finite element analysis (FEA) software, such as ANSYS, can be used to simulate the microstructural changes of the sole rubber material, and the initial and boundary conditions of the simulation can be set according to the prediction data of the critical point of performance evolution. For example, it is predicted that after 1000 cycles of loading, a certain part of the sole rubber material will experience performance degradation, and this condition is used as the starting point of the simulation. In ANSYS, a microstructural model of the sole rubber material is constructed, and the viscoelastic properties of the material are applied. Run the simulation to track the microstructural changes of the sole rubber material under cyclic loading, such as the breaking and rearrangement of molecular chains. Through simulation, the dynamic dissipation characteristic data of the sole rubber material are obtained, which include the distribution of stress and strain inside the material, as well as the mode of energy dissipation. Combining experimental data and simulation results, a semi-empirical prediction model for sole materials is established. For example, it was found that under specific loading conditions, the dissipated energy of the sole rubber material increases with the number of cycles. This trend is consistent with the prediction of performance degradation. Ultimately, a semi-empirical prediction model for the sole material is obtained, which can predict the microstructural changes and performance degradation of the sole rubber material during long-term use.

[0152] Step S57: performing probability statistical inference on the semi-empirical prediction model of the sole material to obtain a sole material performance attenuation trend diagram;

[0153] Specifically, statistical analysis software, such as R language, can be used to process the data of the semi-empirical prediction model, import the performance data predicted by the model, such as wear resistance, elastic modulus and tensile strength, into R language, and use time series analysis methods to analyze these data. A linear regression model is applied to fit the relationship between performance data and time to determine the rate of performance decay. For example, it is found that the logarithmic decay model of wear resistance over time is: ,in and are model parameters, is the time variable. Use the lm function in R language to estimate these parameters and calculate the R-squared value of the model to evaluate the goodness of fit. Use the ggplot2 package in R language to plot the performance decay trend of the sole material. For example, plot the wear resistance curve over time and add confidence intervals to indicate the uncertainty of the prediction. These charts intuitively show the decay trend of the sole rubber material performance over time, and finally obtain the sole material performance decay trend chart.

[0154] Step S58: Perform a comprehensive performance evaluation on the sole rubber material according to the sole material energy efficiency evaluation result, the sole performance stability evaluation report and the sole material performance attenuation trend chart to obtain a final sole performance data evaluation report.

[0155] Specifically, report writing software, such as Microsoft Word, can be used in combination with data analysis software, such as Excel, to organize and analyze the above data, summarize the data of the sole material energy efficiency evaluation results, the sole performance stability evaluation report and the sole material performance attenuation trend chart in Excel, and conduct comparative analysis. In Excel, use chart tools to create multiple charts, such as bar charts and line charts, to intuitively show the performance of the sole rubber material under different performance indicators. Analyze these charts to identify the advantages and disadvantages of the sole rubber material in terms of energy efficiency, stability and durability. Write a final sole performance data evaluation report in Microsoft Word, which includes a detailed analysis and conclusion of the comprehensive performance of the sole rubber material. For example, it is concluded that although the sole rubber material performs well in terms of energy efficiency, its stability in high temperature environments has decreased, and its wear resistance has significantly decayed over time.

[0156] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0157] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A performance data evaluation method for a sole rubber material, characterized in that: The following steps are involved: Step S1: Perform multi-parameter simulation on the sports scene to obtain a sole-road stress environment model; perform parameter mapping on the sole-road stress environment model to obtain a force diagram of the sole rubber material; perform real-time performance capture on the sole rubber material to obtain an original sole performance data set; Step S2: performing X-ray diffraction analysis on the sole rubber material to obtain a microstructure change diagram of the sole material; performing material performance degradation modeling on the sole rubber material based on the microstructure change diagram of the sole material to obtain a sole microperformance evolution model; Step S3: extracting acoustic emission characteristics of the sole rubber material to obtain sole energy dissipation signal characteristics; performing thermodynamic mapping on the sole energy dissipation signal characteristics to obtain a sole material energy conversion efficiency diagram; quantitatively evaluating the energy characteristics of the sole rubber material according to the sole material energy conversion efficiency diagram to obtain a sole material energy efficiency evaluation result; Step S4: Performing a degree of freedom stress response test on the sole rubber material to obtain extreme environment adaptability data of the sole; performing a stress-strain characteristic analysis on the sole rubber material based on the extreme environment adaptability data of the sole to obtain a sole performance stability evaluation report; Step S5: Predict the performance evolution of the sole rubber material based on the original performance data set of the sole and the sole microscopic performance evolution model to obtain a semi-empirical prediction model for the sole material; perform probabilistic statistical inference on the semi-empirical prediction model for the sole material to obtain a performance attenuation trend diagram of the sole material; perform a comprehensive performance evaluation on the sole rubber material according to the sole material energy efficiency evaluation result, the sole performance stability evaluation report and the sole material performance attenuation trend diagram to obtain a final performance data evaluation report for the sole.

2. The performance data evaluation method of the sole rubber material according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting application scenarios of the sole rubber material to obtain a sole application scenario set; Step S12: collecting kinematic trajectory data of the sole rubber material according to the sole application scenario set to obtain a sole motion trajectory basic data set; Step S13: parameterizing the material characteristics of the road surface material based on the basic data set of the sole motion trajectory to obtain a road surface material parameter library; Step S14: simulating the pavement-sole interface interaction based on the pavement material parameter library to obtain a discretized model of pavement material properties; Step S15: performing stress simulation on the sole according to the discretized model of road surface material properties to obtain a sole-road surface stress simulation unit set; Step S16: performing stress simulation on the sole rubber material based on the sole-pavement stress simulation unit set to obtain a sole-pavement stress environment model, and performing parameter mapping on the sole-pavement stress environment model to obtain a force diagram of the sole rubber material; Step S17: Capture the real-time performance of the sole rubber material to obtain an original performance data set of the sole.

3. The performance data evaluation method of the sole rubber material according to claim 2, characterized in that: Step S16 includes the following steps: Step S161: performing stress characteristic analysis on the sole-pavement stress simulation unit set to obtain sole-pavement stress distribution characteristic data; Step S162: performing molecular structure dynamic coupling on the sole rubber material based on the sole-road stress distribution characteristic data to obtain a sole molecular structure dynamic response model; Step S163: performing cross-scale stress simulation on the sole rubber material based on the sole-road stress simulation unit set and the sole molecular structure dynamic response model to obtain a sole-road stress environment model; Step S164: Parameter mapping is performed on the sole-road stress environment model to obtain a force diagram of the sole rubber material.

4. The performance data evaluation method of the sole rubber material according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Scan the sole rubber material sample at nanometer scale to obtain original material structure positioning mapping data; Step S22: performing micro-area X-ray diffraction on the sole rubber material sample based on a preset synchrotron radiation beam intensity parameter, and performing nanoscale scanning on the sole rubber material sample after diffraction to obtain diffraction material structure positioning mapping data; Step S23: performing three-dimensional structure capture on the diffracted sole rubber material sample to obtain a diffraction material microstructure model; Step S24: performing coloring and projection on the diffraction material microstructure model according to the original material structure positioning mapping data and the diffraction material structure positioning mapping data to obtain a projected diffraction material microstructure model; Step S25: extracting the material microstructure change area of ​​the sole rubber material based on the projection diffraction material microstructure model to obtain a microstructure change map of the sole material; Step S26: Based on the original material structure positioning mapping data, the diffraction material structure positioning mapping data and the diffraction material microstructure model, material performance degradation modeling is performed on the sole rubber material to obtain a sole micro-performance evolution model.

5. The performance data evaluation method of the sole rubber material according to claim 4, characterized in that: Step S26 includes the following steps: Step S261: performing cross-scale structure-performance association based on the diffraction material structure positioning mapping data to obtain a sole structure-performance mapping matrix; Step S262: identifying materials that affect performance on the diffraction material microstructure model to obtain key factors affecting sole performance; Step S263: weighting the key influencing factors of the sole performance based on the original material structure positioning mapping data and the diffraction material structure positioning mapping data to obtain weight data of the influencing factors of the sole performance; Step S264: simulating the long-term performance evolution of the sole rubber material based on the diffraction material microstructure model according to the weight data of the sole performance influencing factors to obtain the sole performance degradation trend mapping data; Step S265: reconstructing the limit performance boundary of the diffraction material microstructure model according to the sole structure-performance mapping matrix and the sole performance degradation trend mapping data to obtain a sole microperformance evolution model.

6. The performance data evaluation method of the sole rubber material according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing acoustic emission sensor preprocessing on the sole rubber material to obtain acoustic detection basic configuration data; Step S32: formulating an acoustic signal collection scheme for the sole rubber material according to the acoustic detection basic configuration data to obtain a sole acoustic signal collection scheme; Step S33: performing an omnidirectional acoustic feature scan on the sole rubber material according to the sole acoustic signal collection scheme to obtain an original sole acoustic emission signal data set; Step S34: constructing an energy dissipation spectrum of the sole rubber material based on the original sole acoustic emission signal data set to obtain sole energy dissipation spectrum mapping data; Step S35: extracting energy dissipation signal features from the sole energy dissipation spectrum mapping data to obtain sole energy dissipation signal features; Step S36: performing thermodynamic mapping on the energy dissipation signal characteristics of the sole to obtain an energy conversion efficiency diagram of the sole material; Step S37: quantitatively evaluate the energy characteristics of the sole rubber material according to the sole material energy conversion efficiency diagram to obtain the sole material energy efficiency evaluation result.

7. The performance data evaluation method of the sole rubber material according to claim 6, characterized in that: Step S37 includes the following steps: Step S371: performing feature analysis on the energy conversion efficiency diagram of the sole material to obtain a key feature vector of sole energy conversion; Step S372: Mapping and reducing the dimension of the key feature vector of the sole energy conversion to obtain preliminary mapping data of energy efficiency; Step S373: tracing the energy conversion path of the preliminary energy efficiency mapping data to obtain the energy conversion path of the sole material; Step S374: performing microscopic energy level mapping on the sole rubber material according to the sole material energy conversion path to obtain an energy state distribution diagram of the sole material; Step S375: performing conversion correlation evaluation on the energy state distribution diagram of the sole material to obtain material energy conversion synergy evaluation data; Step S376: Perform multivariate statistics on the material energy conversion synergy evaluation data to obtain the sole material energy efficiency evaluation results.

8. The performance data evaluation method of the sole rubber material according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing extreme working condition simulation on the sole application scenario set to obtain a sole composite stress boundary condition set; Step S42: constructing a degree-of-freedom dynamic response system based on the sole composite stress boundary condition set to obtain a sole stress coupling analysis platform; Step S43: fine-tuning the load application on the sole rubber material according to the sole stress coupling analysis platform to obtain a sole material stress application unit; Step S44: performing response calibration on the sole material stress applying unit to obtain sole material stress applying accuracy mapping data; Step S45: constructing a sensor array network for the sole rubber material based on the sole material stress application accuracy mapping data to obtain a sole material dynamic data acquisition network; Step S46: Based on the sole material dynamic data acquisition network, a degree of freedom stress response test is performed on the sole rubber material to obtain the sole extreme environment adaptability data, and based on the sole extreme environment adaptability data, a stress-strain characteristic analysis is performed on the sole rubber material to obtain a sole performance stability evaluation report.

9. The performance data evaluation method of the sole rubber material according to claim 8, characterized in that: Step S46 includes the following steps: Step S461: Simulating an extreme temperature environment on the sole material dynamic data acquisition network to obtain a sole material temperature gradient control module; Step S462: constructing a composite corrosion environment simulation unit based on the sole material temperature gradient control module to obtain a sole material composite environment stress simulator; Step S463: performing a multi-parameter coupling test on the sole rubber material according to the sole material composite environmental stress simulator to obtain a sole extreme environment simulation data set; Step S464: performing characteristic spectrum clustering on the sole extreme environment simulation data set to obtain a characteristic spectrum of the sole material limit response; Step S465: identifying the critical point of failure of the extreme response characteristic spectrum of the sole material to obtain extreme environment adaptability data of the sole; Step S466: mapping the extreme working conditions of the sole rubber material based on the extreme environment adaptability data of the sole to obtain a sole material extreme adaptability prediction model; Step S467: performing stress decoupling clustering on the sole material limit adaptability prediction model to obtain sole material stress decomposition mapping data, and performing damage accumulation tracking on the sole rubber material according to the sole material stress decomposition mapping data to obtain sole material microscopic damage evolution data; Step S468: Perform performance stability evaluation on the microscopic damage evolution data of the sole material to obtain a sole performance stability evaluation report.

10. The performance data evaluation method of the sole rubber material according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing feature deconstruction on the original performance data set of the sole to obtain the sole material performance mapping data; Step S52: performing microstructure dispersion evaluation on the sole rubber material based on the sole material performance mapping data to obtain a sole material performance dispersion evaluation index; Step S53: tracking the co-evolution of the sole rubber material according to the sole material performance dispersion evaluation index to obtain an initial feature set of the material microscopic performance evolution; Step S54: constructing a mapping conversion network based on the initial feature set of the material microscopic performance evolution to obtain a sole material performance evolution association network; Step S55: inferring the critical point of transformation of the sole material performance evolution correlation network to obtain the sole material performance evolution critical point prediction data; Step S56: based on the sole micro-performance evolution model, the sole material micro-structure is dynamically dissipated and tracked according to the sole material performance evolution critical point prediction data to obtain a semi-empirical prediction model for the sole material; Step S57: performing probability statistical inference on the semi-empirical prediction model of the sole material to obtain a sole material performance attenuation trend diagram; Step S58: Perform a comprehensive performance evaluation on the sole rubber material according to the sole material energy efficiency evaluation result, the sole performance stability evaluation report and the sole material performance attenuation trend chart to obtain a final sole performance data evaluation report.

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