Extreme scooter structural design management method, system and extreme scooter

Abstract

The present application relates to the field of data processing technology, and discloses a method, system, and extreme scooter for structural design management of an extreme scooter. The method comprises: intelligently collecting and preprocessing multi-source heterogeneous data to obtain a multidimensional integrated data set; performing multi-level index construction and nonlinear calculation on the multidimensional integrated data set to obtain a quantitative index system for structural design of an extreme scooter; performing spatiotemporal multi-scale feature analysis on the quantitative index system for structural design of an extreme scooter to obtain a spatiotemporal distribution feature map; performing high-order correlation analysis on the spatiotemporal distribution feature map to obtain a multidimensional correlation intensity distribution matrix between the frame geometric parameters, wheel configuration, and shock absorption system, and performing key factor identification and sensitivity analysis, obtaining an importance ranking table of influencing factors, and performing parametric structural design scheme optimization to obtain a multi-objective structural design scheme combination and a performance evaluation index set. The present application improves the efficiency and accuracy of structural design management of extreme scooters.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a structural design management method and system for an extreme scooter, and an extreme scooter. Background Art

[0002] As an emerging extreme sports device, extreme scooters have become extremely popular among young people in recent years. Traditional extreme scooter structural design relies primarily on the designer's experience and simple mechanical analysis, typically using finite element analysis and static load testing to assess frame strength and stiffness. Wheel design primarily considers parameters such as wheel diameter, wheel width, and hardness, while the shock absorption system focuses on selecting spring stiffness and damping coefficients. These methods can meet basic performance requirements to a certain extent, but they struggle to cope with the complex and ever-changing nature of extreme sports.

[0003] However, with the continuous development of extreme sports, traditional design methods can no longer meet the demands of high performance and personalization. First, the existing design process lacks the comprehensive utilization of multi-source heterogeneous data, making it difficult to fully consider the complex relationship between frame stress distribution, wheel dynamic response, and shock absorption system performance. Second, the existing performance evaluation index system is relatively simple and cannot accurately reflect the dynamic performance of scooters under different road conditions. Furthermore, traditional methods often only consider a single objective or a limited number of objectives when performing structural optimization, making it difficult to achieve global optimization of multiple objectives. Finally, the existing design process lacks systematicity and intelligence, making it difficult to efficiently optimize parameters and iterate solutions. Summary of the Invention

[0004] The present application provides an extreme scooter structural design management method, system and extreme scooter, which are used to improve the efficiency and accuracy of extreme scooter structural design management.

[0005] In a first aspect, the present application provides a method for structural design and management of an extreme scooter, comprising: intelligently collecting and preprocessing multi-source heterogeneous data to obtain a multidimensional comprehensive data set including frame stress distribution data, wheel group dynamic response data, and shock absorber system performance; performing multi-level index construction and nonlinear calculation on the multidimensional comprehensive data set to obtain a quantitative index system for structural design of an extreme scooter including a body stiffness index, a steering sensitivity coefficient, and a vibration attenuation rate; performing spatiotemporal multi-scale feature analysis on the quantitative index system for structural design of an extreme scooter to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the body structure under different road conditions; performing high-order correlation analysis on the spatiotemporal distribution feature map to obtain a multidimensional correlation intensity distribution matrix between frame geometric parameters, wheel group configuration, and shock absorber system; performing key factor identification and sensitivity analysis on the multidimensional correlation intensity distribution matrix to obtain an importance ranking table of influencing factors including frame material elastic modulus, wheel group angular momentum, and shock absorber damping coefficient; and performing parametric structural design scheme optimization based on the importance ranking table of influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

[0006] In a second aspect, the present application provides an extreme scooter structure design and management system, the extreme scooter structure design and management system comprising:

[0007] The processing module is used to intelligently collect and pre-process multi-source heterogeneous data to obtain a multi-dimensional comprehensive data set including frame stress distribution data, wheel set dynamic response data, and shock absorption system performance;

[0008] a calculation module for performing multi-level index construction and nonlinear calculation on the multi-dimensional comprehensive data set to obtain a quantitative index system for the structural design of an extreme scooter, including a body stiffness index, a steering sensitivity coefficient, and a vibration attenuation rate;

[0009] A quantification module is used to perform spatiotemporal multi-scale feature analysis on the extreme scooter structural design quantitative index system to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions;

[0010] An analysis module, configured to perform a high-order correlation analysis on the spatiotemporal distribution characteristic map to obtain a multi-dimensional correlation intensity distribution matrix among the frame geometric parameters, wheel configuration, and shock absorption system;

[0011] an identification module for performing key factor identification and sensitivity analysis on the multidimensional correlation strength distribution matrix to obtain an importance ranking table of influencing factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber;

[0012] The optimization module is used to optimize the parametric structural design scheme according to the importance ranking table of the influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

[0013] A third aspect of the present application provides an extreme scooter, comprising: a frame having a structure optimized by a vehicle body stiffness index; a wheelset connected to the frame, wherein configuration parameters of the wheelset are optimized based on a steering sensitivity coefficient; a shock absorption system mounted on the frame, wherein the shock absorber damping coefficient of the shock absorption system is optimized through vibration attenuation rate analysis; a control system disposed on the frame for real-time monitoring and adjustment of the dynamic response of the vehicle body structure; a sensor network connected to the control system for collecting frame stress distribution, wheel set dynamic response, and shock absorption system performance data; and a human-computer interaction interface mounted on the frame for displaying vehicle performance parameters and receiving user input.

[0014] The technical solution provided in this application utilizes intelligent collection and preprocessing of multi-source heterogeneous data to generate a multidimensional, integrated dataset encompassing frame stress distribution, wheel dynamic response, and shock absorption system performance. This enables comprehensive collection and integration of scooter structural data, providing a rich data foundation for subsequent analysis. Multi-level index construction and nonlinear calculations are performed on this multidimensional, integrated dataset to develop a quantitative index system for the structural design of extreme scooters, including body stiffness index, steering sensitivity coefficient, and vibration attenuation rate. This establishes a comprehensive and scientific performance evaluation system that more accurately reflects the structural characteristics and dynamic performance of scooters. By performing a spatiotemporal multiscale feature analysis on this quantitative index system, a spatiotemporal distribution feature map reflecting the dynamic response of the body structure under different road conditions is generated. This process provides insights into the scooter's performance under various usage environments and provides an important basis for structural optimization. High-order correlation analysis of the spatiotemporal distribution feature map yields a multidimensional correlation intensity distribution matrix between frame geometry parameters, wheel configuration, and shock absorption system. This step reveals the complex interactions between these structural parameters, contributing to a comprehensive understanding of the system's overall performance. By identifying key factors and performing sensitivity analysis on the multidimensional correlation intensity distribution matrix, a ranking table of influencing factors, including the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient, was obtained. This process clarified the contribution of each design parameter to overall performance and provided a clear direction for design optimization. Finally, parametric structural design schemes were optimized based on the ranking table of influencing factors, resulting in a multi-objective structural design scheme combination and a set of performance evaluation indicators. This step achieved comprehensive optimization of the scooter structure, finding the optimal balance between multiple performance objectives. Overall, this method, through a systematic and intelligent data processing and analysis process, achieved comprehensive optimization of the extreme scooter structure, significantly improving the scooter's overall performance, including but not limited to increased body stiffness, improved steering sensitivity, and enhanced shock absorption. Furthermore, this method significantly improved design efficiency, shortened the product development cycle, and reduced design costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of an embodiment of the extreme scooter structure design and management method in the embodiment of the present application;

[0017] Figure 2This is a schematic diagram of an embodiment of the extreme scooter structure design management system in the embodiment of this application. DETAILED DESCRIPTION

[0018] The embodiments of the present application provide a method, system, and extreme scooter for structural design and management of an extreme scooter. The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the extreme scooter structure design management method in the embodiment of the present application includes:

[0020] Step S101: Intelligently collect and preprocess multi-source heterogeneous data to obtain a multi-dimensional comprehensive data set including frame stress distribution data, wheel set dynamic response data, and shock absorption system performance;

[0021] Step S102: constructing a multi-level index and performing nonlinear calculation on the multi-dimensional comprehensive data set to obtain a quantitative index system for the extreme scooter structural design, including a body stiffness index, a steering sensitivity coefficient, and a vibration attenuation rate;

[0022] Step S103: performing a spatiotemporal multi-scale feature analysis on the extreme scooter structural design quantitative index system to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions;

[0023] Step S104: performing a high-order correlation analysis on the spatiotemporal distribution characteristic map to obtain a multi-dimensional correlation intensity distribution matrix among the frame geometric parameters, wheel configuration, and shock absorption system;

[0024] Step S105: performing key factor identification and sensitivity analysis on the multi-dimensional correlation strength distribution matrix to obtain an importance ranking table of influencing factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber;

[0025] Step S106: Optimize the parametric structural design scheme according to the importance ranking table of influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

[0026] It is understandable that the execution subject of this application can be the extreme scooter structure design and management system, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0027] Specifically, multi-source heterogeneous data is intelligently collected and preprocessed to generate a multidimensional, integrated dataset containing frame stress distribution data, wheel dynamic response data, and shock absorption system performance data. In this step, a distributed sensor network is used to collect stress data from key points on the frame, accelerometers and gyroscopes are used to collect wheel dynamic response data, and displacement sensors and force sensors are used to collect shock absorption system performance data. The collected raw data undergoes outlier detection, denoising, and normalization to generate cleaned, multi-source, heterogeneous data. Subsequently, feature extraction and dimensionality reduction are performed on this data. Principal component analysis is used to reduce the data dimension, and finally, tensor decomposition is used to extract high-dimensional features, resulting in a multidimensional, integrated dataset. Multi-level indicators are constructed and nonlinear calculations are performed on this multidimensional, integrated dataset to obtain a quantitative indicator system for extreme scooter structural design, including body stiffness index, steering sensitivity coefficient, and vibration attenuation rate. This step first constructs the indicator hierarchy, followed by nonlinear mapping and fuzzy comprehensive evaluation. For the body stiffness index, finite element analysis is used to calculate the frame deformation under different loads, and the stiffness index is calculated based on material properties. The steering sensitivity coefficient is determined by establishing a steering dynamics model and analyzing the impact of wheel geometry and mass distribution on steering performance. The vibration attenuation rate is determined by spectral analysis of the shock absorption system and fitting the attenuation curve.

[0028] Subsequently, a spatiotemporal multiscale feature analysis was performed on the quantitative indicator system for extreme scooter structural design, generating a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle structure under different road conditions. This step first involved time series decomposition and spatial distribution analysis of the indicator system. Wavelet analysis and multiscale spatial autocorrelation analysis were then used to obtain the spatiotemporal multiscale features. Tensor fusion technology was used to combine the temporal and spatial features, and principal component analysis was used for dimensionality reduction to obtain a spatiotemporal feature matrix. Cluster analysis and support vector machines were then used to establish a road condition recognition model. Dynamic response data under different road conditions was classified and spatiotemporally interpolated. Finally, a contour plotting algorithm was used to visualize the spatiotemporal distribution feature map. High-order correlation analysis was performed on the spatiotemporal distribution feature map to obtain a multidimensional correlation intensity distribution matrix between the frame geometry, wheel configuration, and shock absorber system. This step first involved dimensionality reduction of the feature map, followed by a preliminary correlation matrix obtained through Pearson correlation and partial correlation analysis. Nonlinear correlation analysis and mutual information quantification were then performed to obtain a set of high-order correlation indicators. The correlation is structured through cluster analysis and hierarchical clustering algorithm, and finally the connectivity is analyzed by graph theory algorithm to obtain a multidimensional correlation intensity distribution matrix.

[0029] Next, key factors were identified and sensitivity analyzed on the multidimensional correlation intensity distribution matrix, resulting in an importance ranking of factors affecting the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient. Eigenvalue decomposition and principal component analysis were first performed to obtain a preliminary factor importance ranking. Variance analysis and global sensitivity analysis were then used to quantify the impact of each factor. Threshold screening and local sensitivity analysis were then performed to determine the key factor set. Finally, fuzzy comprehensive evaluation and multi-criteria decision-making methods were used to obtain balanced importance indices, which were then normalized and ranked to form the final factor importance ranking table. Based on this factor importance ranking table, parametric structural design schemes were optimized to obtain a multi-objective structural design scheme combination and a set of performance evaluation indicators. This step first involved parameter sensitivity analysis and orthogonal experimental design to determine the parameter optimization scheme. A multi-objective function was then constructed and solved using a genetic algorithm to obtain a set of candidate solutions. Pareto front analysis and the analytic hierarchy process were used to obtain a set of weighted non-inferior solutions. Cluster analysis and fuzzy comprehensive evaluation were performed on the schemes to obtain an evaluation score matrix. Finally, multi-criteria decision-making analysis and performance simulation were used to determine the optimal scheme combination and performance indicator set.

[0030] For example, during the design process of a certain extreme scooter, strain gauges were used to collect stress data at 20 key points on the frame. Accelerometers and gyroscopes collected dynamic response data for the wheels, and displacement sensors and force sensors collected performance data for the shock absorber system. After data cleaning and feature extraction, a multidimensional comprehensive dataset containing 100 features was generated. Using a multi-level index construction approach, the body stiffness index was calculated to be 85 MPa / mm, the steering sensitivity coefficient to be 0.8 rad / s / °, and the vibration attenuation rate to be 0.15 s-1. Spatiotemporal multiscale feature analysis revealed the dynamic response characteristics of the vehicle structure under five typical road conditions. High-order correlation analysis revealed a complex nonlinear relationship between the frame geometry, wheel configuration, and shock absorber system, with the maximum eigenvalue of the correlation coefficient matrix being 0.92. Sensitivity analysis revealed that the importance weights of the influencing factors for the frame material elastic modulus, wheel angular momentum, and shock absorber damping coefficient were 0.4, 0.35, and 0.25, respectively. The final optimized structural design scheme increased the body stiffness by 15%, improved the steering sensitivity by 10%, and enhanced the shock absorption effect by 20% while maintaining the original weight.

[0031] In this embodiment, intelligent collection and preprocessing of multi-source heterogeneous data yields a multidimensional, integrated dataset encompassing frame stress distribution, wheel dynamic response, and shock absorption system performance. This enables comprehensive collection and integration of scooter-related structural data, providing a rich data foundation for subsequent analysis. Multi-level index construction and nonlinear calculations are performed on this multidimensional, integrated dataset to develop a quantitative structural design index system for extreme scooters, including body stiffness index, steering sensitivity coefficient, and vibration attenuation rate. This establishes a comprehensive and scientific performance evaluation system that more accurately reflects the structural characteristics and dynamic performance of scooters. Multi-scale spatiotemporal analysis of this quantitative structural design index system yields a spatiotemporal distribution feature map reflecting the dynamic response of the body structure under different road conditions. This process provides insights into the scooter's performance under various usage environments, providing an important basis for structural optimization. High-order correlation analysis of this spatiotemporal distribution feature map yields a multidimensional correlation intensity distribution matrix between frame geometry, wheel configuration, and shock absorption system. This step reveals the complex interactions among these structural parameters, contributing to a comprehensive understanding of the system's overall performance. By identifying key factors and performing sensitivity analysis on the multidimensional correlation intensity distribution matrix, a ranking table of influencing factors, including the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient, was obtained. This process clarified the contribution of each design parameter to overall performance and provided a clear direction for design optimization. Finally, parametric structural design schemes were optimized based on the ranking table of influencing factors, resulting in a multi-objective structural design scheme combination and a set of performance evaluation indicators. This step achieved comprehensive optimization of the scooter structure, finding the optimal balance between multiple performance objectives. Overall, this method, through a systematic and intelligent data processing and analysis process, achieved comprehensive optimization of the extreme scooter structure, significantly improving the scooter's overall performance, including but not limited to increased body stiffness, improved steering sensitivity, and enhanced shock absorption. Furthermore, this method significantly improved design efficiency, shortened the product development cycle, and reduced design costs.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) Collect multi-source heterogeneous data to obtain the original data set, and perform outlier detection and denoising on the original data set to obtain cleaned multi-source heterogeneous data;

[0034] (2) Feature extraction is performed on the cleaned multi-source heterogeneous data to obtain a preliminary feature set, and the preliminary feature set is subjected to dimensionality reduction processing using the principal component analysis algorithm to obtain a compressed feature representation;

[0035] (3) Perform spatial interpolation analysis on the frame stress distribution data to obtain a continuous stress field, and refine the continuous stress field using the finite element method to obtain the frame stress distribution data;

[0036] (4) Perform time-frequency domain analysis on the dynamic response data of the wheelset to obtain the response characteristic spectrum, and perform multi-scale decomposition of the response characteristic spectrum through wavelet transform to obtain the dynamic response data of the wheelset;

[0037] (5) Conduct multi-condition tests on the performance of the shock absorption system to obtain the original shock absorption performance, and then smooth the original shock absorption performance through the Kalman filter algorithm to obtain the shock absorption system performance;

[0038] (6) The compressed feature representation, frame stress distribution data, wheel dynamic response data and shock absorption system performance are fused to obtain a preliminary fused data set, and high-dimensional features are extracted from the preliminary fused data set through a tensor decomposition algorithm to obtain a multidimensional comprehensive data set.

[0039] Specifically, multi-source heterogeneous data is collected to generate a raw data set. This data, including information on frame stress distribution, wheel dynamic response, and damping system performance, is collected through a distributed sensor network. For example, strain gauges are used to collect stress data at key frame points, accelerometers and gyroscopes collect wheel dynamic response data, and displacement sensors and force sensors collect damping system performance data. Outlier detection is performed on the raw data set, using the 3σ criterion to identify outliers. Median filtering is then used to denoise the data, resulting in cleaned multi-source heterogeneous data. Feature extraction is performed on the cleaned multi-source heterogeneous data to generate a preliminary feature set. The feature extraction process involves calculating time-domain features (such as mean, variance, and peak) and frequency-domain features (such as power spectral density and dominant frequency). Dimensionality reduction is then performed on the preliminary feature set using the principal component analysis (PCA) algorithm. The PCA algorithm calculates eigenvectors and eigenvalues ​​and selects the eigenvectors corresponding to the k largest eigenvalues ​​as principal components, thereby generating a compressed feature representation. Spatial interpolation analysis is performed on the frame stress distribution data to generate a continuous stress field. Kriging interpolation is used here. This method takes into account the spatial correlation between data points and can more accurately estimate the value of unknown points. The continuous stress field is then refined using the finite element method. This discretizes the frame structure into small elements and, by solving the stress state of each element, obtains more detailed frame stress distribution data.

[0040] The wheel dynamic response data is analyzed in the time-frequency domain to obtain the response characteristic spectrum. Time-domain analysis involves calculating statistical quantities such as mean, standard deviation, and peak value. Frequency-domain analysis uses a fast Fourier transform (FFT) to convert the time-domain signal into the frequency domain. Subsequently, the response characteristic spectrum is decomposed at multiple scales using a wavelet transform. The wavelet transform can simultaneously locate the time and frequency characteristics of a signal on the time-frequency plane, making it suitable for analyzing non-stationary signals. Here, the Morlet wavelet is used to perform multi-scale decomposition of the response characteristic spectrum to obtain the time-frequency characteristics of the wheel dynamic response data. The damping system performance is tested under multiple operating conditions to obtain raw damping performance data. The test conditions include different road types, speeds, and loads. The raw damping performance data is smoothed using the Kalman filter algorithm. The Kalman filter is a recursive algorithm that estimates the state of a dynamic system in a noisy environment. It continuously optimizes the state estimate through two steps: prediction and update, resulting in smoothed damping system performance data.

[0041] Finally, data fusion is performed on the compressed feature representation, frame stress distribution data, wheel dynamic response data, and shock absorption system performance to generate a preliminary fused dataset. This data fusion utilizes the Dempster-Shafer evidence theory, which can handle data from different sources and with varying precision, and appropriately assigns confidence levels. High-dimensional features are then extracted from the preliminary fused dataset using a tensor decomposition algorithm. Decomposition is used here to decompose the high-dimensional data tensor into the product of a core tensor and a factor matrix, resulting in a multidimensional, integrated dataset. For example, during the design process of a certain extreme scooter, 20 strain gauges were used to collect frame stress data, four triaxial accelerometers and two gyroscopes to collect wheel dynamic response data, and four displacement sensors and two force sensors to collect shock absorption system performance data. The original dataset contained approximately one million data points. After outlier detection and denoising, 950,000 valid data points were retained. Feature extraction yielded 500 initial features, and dimensionality reduction using the PCA algorithm retained 50 principal components, explaining 95% of the variance. The frame stress distribution data was obtained using Kriging interpolation to create a 1000x1000 grid. Finite element analysis further refined the grid into a 10,000x10,000 high-precision stress distribution. The wheel dynamic response data was subjected to FFT and wavelet transforms to generate a 100x100 time-frequency feature matrix. The shock absorber system performance data was tested under 10 different operating conditions, with 1,000 data points collected for each condition. Kalman filtering and smoothing were used to generate continuous performance curves. Ultimately, through data fusion and tensor decomposition, a 50x100x100x10 four-dimensional tensor was generated. This multidimensional, integrated dataset provides comprehensive and accurate data support for subsequent structural design optimization.

[0042] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0043] (1) Perform hierarchical analysis on the multidimensional comprehensive data set to obtain the indicator hierarchy, and assign weights to the indicator hierarchy to obtain a preliminary indicator system;

[0044] (2) Perform nonlinear mapping on the preliminary indicator system to obtain a nonlinear indicator set, and fuse the nonlinear indicator set through the fuzzy comprehensive evaluation method to obtain a fusion indicator system;

[0045] (3) Perform stress and strain analysis on the vehicle body stiffness related data in the fusion index system to obtain the vehicle body stiffness distribution, and quantify the vehicle body stiffness distribution through finite element calculation to obtain the vehicle body stiffness index;

[0046] (4) Perform dynamic modeling on the steering system related data in the fusion index system to obtain the steering dynamics model, and extract the parameters of the steering dynamics model through sensitivity analysis to obtain the steering sensitivity coefficient;

[0047] (5) Perform spectrum analysis on the vibration-related data in the fusion index system to obtain the vibration spectrum, and extract the features of the vibration spectrum through attenuation curve fitting to obtain the vibration attenuation rate;

[0048] (6) A comprehensive evaluation of the body stiffness index, steering sensitivity coefficient and vibration attenuation rate is conducted to obtain comprehensive performance indicators. The comprehensive performance indicators are weighed through a multi-objective optimization algorithm to obtain a quantitative indicator system for the structural design of the extreme scooter.

[0049] Specifically, a hierarchical analysis was performed on a multidimensional comprehensive data set to obtain an indicator hierarchy. Using the Analytic Hierarchy Process (AHP), complex problems were decomposed into a target layer, a criterion layer, and an indicator layer. For extreme scooters, the target layer is overall performance; the criterion layer includes safety, handling, and comfort; and the indicator layer includes specific indicators such as body stiffness, steering sensitivity, and vibration attenuation. Weights were then assigned to the indicator hierarchy, and pairwise comparisons were performed using a 9-point scale. A judgment matrix A was constructed, and the eigenvector W and the maximum eigenvalue λmax were calculated to obtain the weights of each indicator. The weight calculation formula is as follows:

[0050]

[0051] Among them, A is the judgment matrix, W is the weight vector, is the maximum eigenvalue.

[0052] Next, the preliminary indicator system is nonlinearly mapped to obtain a nonlinear indicator set. Here, the sigmoid function is used for mapping, and the original indicator value is converted to Mapping to the interval [0,1]:

[0053]

[0054] in, is the index value after mapping, α is the slope parameter, and β is the midpoint parameter. By adjusting α and β, the degree of nonlinearity of the mapping can be controlled. Then, the fuzzy comprehensive evaluation method is used to fuse the nonlinear index set. First, the fuzzy relationship matrix R is established, and then combined with the weight vector W, the fuzzy comprehensive evaluation result B is calculated:

[0055]

[0056] in, Represents a fuzzy composition operation.

[0057] Perform stress and strain analysis on the body stiffness related data in the fusion index system to obtain the body stiffness distribution. This step uses the finite element analysis method to discretize the body structure into a finite number of elements, establish the stiffness matrix K and the load vector F, and solve the displacement vector :

[0058]

[0059] By calculating the stress and strain of each unit, the overall stiffness distribution is obtained. Then, the body stiffness distribution is quantified through integral calculation to obtain the body stiffness index SI:

[0060]

[0061] E(x) is the local stiffness at the spatial point x, and V is the volume of the entire vehicle body.

[0062] Dynamic modeling is performed on the steering system-related data in the fusion index system to obtain a steering dynamics model. A bicycle model is used here to establish the equilibrium equations for the center of mass lateral force, front and rear wheel lateral forces, and yaw moment. By solving the state equations, the vehicle's lateral velocity and yaw rate are obtained. Then, sensitivity analysis is performed to extract parameters from the steering dynamics model and determine the steering sensitivity coefficient SS:

[0063]

[0064] Where ψ is the yaw angular velocity, δ is the front wheel steering angle.

[0065] Perform spectral analysis on the vibration-related data in the fusion indicator system to obtain the vibration spectrum. This step uses the Fast Fourier Transform (FFT) to convert the time domain signal into the frequency domain. Then, extract the characteristics of the vibration spectrum through attenuation curve fitting to obtain the vibration attenuation rate DR. The exponential attenuation model is used:

[0066]

[0067] in, is the amplitude at time t, is the initial amplitude, is the vibration attenuation rate.

[0068] Finally, the vehicle body stiffness index, steering sensitivity coefficient, and vibration attenuation rate are comprehensively evaluated to obtain the comprehensive performance index CP:

[0069]

[0070] in, 、 、 is the weight of each indicator. The comprehensive performance indicators are weighed by a multi-objective optimization algorithm (such as NSGA-II) to obtain a quantitative index system for the extreme scooter structure design.

[0071] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0072] (1) The time series of the quantitative index system for the extreme scooter structural design is decomposed to obtain the time scale characteristics, and the time scale characteristics are processed by multi-resolution through wavelet analysis to obtain the time multi-scale feature set;

[0073] (2) The spatial distribution of the quantitative index system for the extreme scooter structural design is analyzed to obtain spatial scale characteristics, and the spatial scale characteristics are processed through multi-scale spatial autocorrelation analysis to obtain a spatial multi-scale feature set;

[0074] (3) Perform tensor fusion on the temporal multi-scale feature set and the spatial multi-scale feature set to obtain the spatiotemporal multi-scale feature tensor, and perform dimensionality reduction on the spatiotemporal multi-scale feature tensor through principal component analysis to obtain the spatiotemporal feature matrix after dimensionality reduction;

[0075] (4) Perform cluster analysis on the spatiotemporal feature matrix after dimensionality reduction to obtain the road condition classification, and then model the road condition classification using support vector machines to obtain a road condition recognition model;

[0076] (5) Collect the dynamic response data of the vehicle structure under different road conditions to obtain the original dynamic response data, and classify the original dynamic response data through the road condition recognition model to obtain the classified dynamic response data set;

[0077] (6) The classified dynamic response data set is subjected to spatiotemporal interpolation to obtain a continuous spatiotemporal distribution function, which is then visualized using a contour drawing algorithm to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle structure under different road conditions.

[0078] Specifically, the quantitative index system for the structural design of an extreme scooter is decomposed into a time series to obtain time-scale features. The empirical mode decomposition (EMD) method is used to decompose the original signal into a series of intrinsic mode functions (IMFs) and a residual term. The EMD method uses an iterative "screening" process to gradually extract high- to low-frequency components from the signal, forming IMFs at different time scales. Wavelet analysis then performs multi-resolution processing on the time-scale features to obtain a set of temporal multi-scale features. Wavelet analysis uses the discrete wavelet transform (DWT) and selects appropriate wavelet basis functions (such as the Daubechies wavelet) to perform multi-scale decomposition of the signal. The DWT decomposes the signal into approximate coefficients and detail coefficients, reflecting the signal's characteristics at different scales and frequencies. Spatial distribution analysis is performed on the quantitative index system for the structural design of an extreme scooter to obtain spatial scale features. This step uses spatial interpolation methods (such as Kriging interpolation) to expand the discrete measurement point data into a continuous space. The spatial scale features are then processed through multi-scale spatial autocorrelation analysis to obtain a set of spatial multi-scale features. Multiscale spatial autocorrelation analysis uses the Moran's I index to calculate autocorrelation at different spatial scales. The Moran's I index reflects the degree of similarity between spatial units. Values ​​closer to 1 indicate stronger positive correlations, values ​​closer to -1 indicate stronger negative correlations, and values ​​closer to 0 indicate random distribution.

[0079] Subsequently, tensor fusion is performed on the temporal and spatial multiscale feature sets to generate a spatiotemporal multiscale feature tensor. Tensor fusion employs the Tucker decomposition method, representing high-dimensional data as the product of a core tensor and a factor matrix. Tucker decomposition effectively captures the interrelationships between multidimensional data and preserves the data's structural information. Principal component analysis (PCA) is then used to reduce the dimensionality of the spatiotemporal multiscale feature tensor, resulting in a reduced spatiotemporal feature matrix. PCA calculates the eigenvalues ​​and eigenvectors of the covariance matrix to select the principal components, achieving data dimensionality reduction while preserving the primary data variation. Cluster analysis is performed on the reduced spatiotemporal feature matrix to generate a road condition classification. The K-means clustering algorithm is used to partition the data points into K clusters. The K-means algorithm iteratively optimizes cluster center locations to minimize the within-cluster sum of squares, thereby achieving natural data grouping. The road condition classification is then modeled using a support vector machine (SVM) to generate a road condition recognition model. SVM constructs the optimal separation hyperplane in high-dimensional feature space to achieve multi-category classification and has good generalization ability.

[0080] Subsequently, the vehicle structure dynamic response data under different road conditions is collected to obtain raw dynamic response data. Accelerometers, strain gauges, and other equipment are used to collect vibration, stress, and other data at key locations on the vehicle body. The raw dynamic response data is classified using a road condition recognition model to obtain a classified dynamic response dataset. This step uses a trained SVM model to assign the newly collected data to corresponding road condition categories, enabling intelligent classification of real-time data. The classified dynamic response dataset is then subjected to spatiotemporal interpolation to obtain a continuous spatiotemporal distribution function. This spatiotemporal interpolation utilizes the Kriging interpolation method, which accounts for the spatial and temporal correlation of the data and can estimate data values ​​at unsampled locations and time points. The continuous spatiotemporal distribution function is then visualized using a contour plotting algorithm to obtain a spatiotemporal distribution map reflecting the dynamic response of the vehicle structure under different road conditions. The contour plotting algorithm uses the Marching Squares algorithm to discretize the continuous function into a grid and interpolate contour lines within the grid cells, visually demonstrating the spatial distribution characteristics of the data.

[0081] For example, during the structural design process for a certain extreme scooter, metrics such as the body stiffness index, steering sensitivity coefficient, and vibration attenuation rate were continuously monitored for 30 days at a sampling frequency of 100 Hz. Using the EMD method, the time series was decomposed into five IMFs and one residual term, reflecting the time scale characteristics from high to low frequencies. A five-layer decomposition using the db4 wavelet yielded wavelet coefficients reflecting the different frequency characteristics. Spatially, measurements were taken at 100 key points on the vehicle body and expanded to a 1000x1000 grid using kriging interpolation. Moran's I was calculated at different scales (1 cm, 5 cm, 10 cm, and 50 cm), yielding values ​​of 0.85, 0.72, 0.61, and 0.45, respectively, reflecting the scale-dependent variation in spatial autocorrelation. Tensor fusion yielded a 100x100x5 third-order tensor. PCA dimensionality reduction was performed to retain the top 20 principal components that explained 95% of the variance. K-means clustering (K=5) categorized the data into five typical road conditions, and the SVM model achieved 93% classification accuracy in cross-validation. The resulting spatiotemporal distribution feature maps clearly demonstrate how stress distribution and vibration characteristics of various vehicle body parts change over time under different road conditions.

[0082] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0083] (1) Extract features from the spatiotemporal distribution feature map to obtain a high-dimensional feature vector, and perform dimensionality reduction on the high-dimensional feature vector through principal component analysis to obtain a feature set after dimensionality reduction;

[0084] (2) Perform Pearson correlation analysis on the feature set after dimensionality reduction to obtain a preliminary correlation matrix, and then correct the preliminary correlation matrix through partial correlation analysis to obtain a corrected correlation matrix;

[0085] (3) Perform nonlinear correlation analysis on the corrected correlation matrix to obtain nonlinear correlation indices, and quantify the nonlinear correlation indices using the mutual information algorithm to obtain a set of high-order correlation indices;

[0086] (4) Cluster analysis is performed on the high-order correlation index set to obtain correlation clusters, and the correlation clusters are structured using a hierarchical clustering algorithm to obtain a correlation hierarchy tree;

[0087] (5) Pruning the correlation hierarchy tree to obtain an optimized correlation structure, and performing connectivity analysis on the optimized correlation structure using a graph theory algorithm to obtain a connection strength distribution;

[0088] (6) The connection strength distribution is normalized to obtain the standardized strength distribution, and the standardized strength distribution is reconstructed through matrix transformation to obtain the multidimensional correlation strength distribution matrix between the frame geometric parameters, wheel configuration and shock absorption system.

[0089] Specifically, feature extraction is performed on the spatiotemporal distribution feature map to obtain a high-dimensional feature vector. This step uses statistical moment and texture feature extraction methods, including calculating the mean, variance, skewness, kurtosis and other statistical quantities of the map, and extracting gray-level co-occurrence matrix (GLCM) features. Then, principal component analysis (PCA) is used to reduce the dimensionality of the high-dimensional feature vector to obtain a reduced feature set. The core of PCA is to solve the eigenvalue equation:

[0090]

[0091] Where C is the covariance matrix, λ is the eigenvalue, v is the eigenvector, and I is the identity matrix. Data dimensionality reduction is achieved by selecting the eigenvectors corresponding to the k largest eigenvalues. Next, a Pearson correlation analysis is performed on the reduced feature set to obtain a preliminary correlation matrix. This preliminary correlation matrix is ​​then modified using partial correlation analysis to obtain a revised correlation matrix. Partial correlation analysis eliminates the influence of other variables and reflects the direct correlation between two variables.

[0092] Subsequently, a nonlinear correlation analysis is performed on the corrected correlation matrix to obtain a nonlinear correlation index. The nonlinear correlation index is quantified using a mutual information algorithm to obtain a set of high-order correlation indices. Cluster analysis is performed on this set of high-order correlation indices to obtain correlation clusters. The K-means clustering algorithm is used here to partition data points by minimizing the within-cluster sum of squares. The correlation clusters are then structured using a hierarchical clustering algorithm to obtain a hierarchical correlation tree. Hierarchical clustering gradually merges clusters by calculating inter-cluster distances (such as Ward distance) to form a tree-like structure.

[0093] Prune the correlation hierarchy tree to obtain an optimized correlation structure. The pruning process removes redundant or insignificant branches based on a set threshold or inter-cluster distance. Then, a graph theory algorithm is used to analyze the connectivity of the optimized correlation structure to obtain a connection strength distribution. The PageRank algorithm is used here to calculate the importance of the node, and the formula is:

[0094]

[0095] in, For nodes The PageRank value, d is the damping coefficient, N is the total number of nodes, To point to The node set of for Finally, the connection strength distribution is normalized to obtain the standardized strength distribution. The standardized strength distribution is reconstructed through matrix transformation to obtain the multidimensional correlation strength distribution matrix between the frame geometric parameters, wheel configuration, and shock absorption system.

[0096] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0097] (1) Perform eigenvalue decomposition on the multidimensional correlation intensity distribution matrix to obtain a set of eigenvectors. Then, use principal component analysis to rank the importance of the eigenvectors and obtain a preliminary importance ranking of factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber.

[0098] (2) Perform variance analysis on the initial factor importance ranking to obtain the variance contribution rate of each factor, and quantify the variance contribution rate through global sensitivity analysis to obtain the global sensitivity index of the frame material elastic modulus, wheel angular momentum and shock absorber damping coefficient;

[0099] (3) Threshold screening of global sensitivity indicators is performed to obtain a set of key factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber. The key factor set is then refined through a local sensitivity analysis method to obtain local sensitivity indicators.

[0100] (4) Perform fuzzy comprehensive evaluation on local sensitivity indicators to obtain the comprehensive importance scores of the frame material elastic modulus, wheel angular momentum, and shock absorber damping coefficient. The comprehensive importance scores are weighed through a multi-criteria decision-making method to obtain the balanced importance index.

[0101] (5) The importance indicators after balancing are normalized to obtain the standardized importance indicators of the frame material elastic modulus, wheel set angular momentum and shock absorber damping coefficient. The standardized importance indicators are sorted by the sorting algorithm to obtain the importance ranking table of influencing factors.

[0102] Specifically, eigenvalue decomposition was performed on the multidimensional correlation intensity distribution matrix to obtain a set of eigenvectors. This step employed the singular value decomposition (SVD) method to decompose the matrix into the product of three matrices. Principal component analysis (PCA) was used to rank the eigenvectors by importance, yielding a preliminary factor importance ranking, including the frame material elastic modulus, wheel angular momentum, and shock absorber damping coefficient. PCA ranks factors based on eigenvalues; larger eigenvalues ​​indicate greater importance of the corresponding principal component. A variance analysis was then performed on this preliminary factor importance ranking to determine the variance contribution of each factor. The ANOVA used the F-test to calculate the F-value for each factor. A larger F-value indicates a more significant factor impact. Global sensitivity analysis was then used to quantify the variance contribution, yielding global sensitivity indices for the frame material elastic modulus, wheel angular momentum, and shock absorber damping coefficient. The Sobol's sensitivity analysis method was used to calculate the total effect index (TAI). The TPI reflects the overall impact of a factor and its interactions with other factors on the output. Threshold screening is performed on the global sensitivity index to obtain a set of key factors, including the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient. The threshold is selected based on the cumulative contribution rate, typically selecting factors with a cumulative contribution rate of 80% or 90%. Local sensitivity analysis is then used to refine the key factor set and obtain local sensitivity indices. Local sensitivity analysis uses the partial derivative method to calculate the partial derivatives of the output with respect to each input factor, reflecting the degree to which small changes in the input affect the output.

[0103] Next, a fuzzy comprehensive evaluation is performed on the local sensitivity indicators to obtain comprehensive importance scores for the frame material elastic modulus, wheel angular momentum, and shock absorber damping coefficient. This fuzzy comprehensive evaluation involves establishing a factor set and an evaluation set, determining a membership function and weight vector, and finally performing a fuzzy synthesis operation. The comprehensive importance scores are then weighed using a multi-criteria decision-making method to obtain a balanced importance index. The analytic hierarchy process (AHP) is employed here to construct a judgment matrix, calculate a weight vector, and ultimately determine the comprehensive weight of each factor. The balanced importance indexes are normalized to obtain standardized importance indexes for the frame material elastic modulus, wheel angular momentum, and shock absorber damping coefficient. This normalization uses the min-max normalization method to map the index values ​​to the [0, 1] interval. The standardized importance indexes are then ranked using a sorting algorithm to obtain a ranked importance table of influencing factors. Efficient algorithms such as quick sort or heap sort can be used for this sorting.

[0104] For example, during the design process of a certain extreme scooter, SVD decomposition was first used to obtain 20 eigenvectors. Principal component analysis showed that the first five principal components cumulatively explained 90% of the variance. ANOVA results revealed that the F-values ​​of the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient were 15.3, 12.7, and 10.2, respectively, significantly higher than those of other factors. Sobol's global sensitivity analysis yielded total effect indices of 0.45, 0.38, and 0.32 for these three factors, respectively. After threshold screening, these three factors were identified as critical. Local sensitivity analysis showed that near the operating point, the frame material elastic modulus had the greatest impact on overall performance, with a 1% change resulting in a 0.8% change in performance. Fuzzy comprehensive evaluation yielded importance scores of 0.85, 0.75, and 0.70 for these three factors, respectively. AHP analysis, taking into account cost and feasibility, yielded balanced importance indices of 0.82, 0.78, and 0.73, respectively. After normalization, the final standardized importance indexes were 0.95, 0.90, and 0.85. The ranking results showed that the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient ranked first, second, and third, respectively.

[0105] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0106] (1) Perform parameter sensitivity analysis on the importance ranking table of influencing factors to obtain the parameter sensitivity matrix, and optimize the parameter sensitivity matrix through orthogonal experimental design to obtain the parameter optimization scheme;

[0107] (2) Construct a multi-objective function for the parameter optimization scheme to obtain an objective function set, and solve the objective function set through a genetic algorithm to obtain a candidate solution set;

[0108] (3) Perform Pareto frontier analysis on the candidate solution set to obtain the non-inferior solution set, and then assign weights to the non-inferior solution set through the hierarchical analysis method to obtain the weighted non-inferior solution set;

[0109] (4) Perform cluster analysis on the weighted non-inferior solution set to obtain a solution cluster, and evaluate the solution cluster using the fuzzy comprehensive evaluation method to obtain an evaluation score matrix;

[0110] (5) Perform multi-criteria decision analysis on the evaluation score matrix to obtain the optimal solution combination, and verify the optimal solution combination through performance simulation to obtain a performance indicator set;

[0111] (6) Comprehensively evaluate the performance index set to obtain a comprehensive performance score, and iteratively optimize the comprehensive performance score through a feedback optimization mechanism to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

[0112] Specifically, a parameter sensitivity analysis is performed on the importance-ranked table of influencing factors to obtain a parameter sensitivity matrix. Using a local sensitivity analysis method, the partial derivative of each parameter with respect to the objective function is calculated to determine the degree of influence of each parameter on system performance. The parameter sensitivity matrix is ​​optimized using an orthogonal experimental design to obtain a parameter optimization solution. Orthogonal experimental design uses an orthogonal table to arrange experiments, reducing the number of trials while ensuring representativeness and uniformity, effectively improving optimization efficiency. A multi-objective function is constructed for the parameter optimization solution to obtain a set of objective functions. These multi-objective functions typically include performance indicators (such as stiffness and stability) and cost indicators. These objective functions reflect the multiple aspects that need to be balanced in extreme scooter design, such as performance, weight, and cost. A genetic algorithm is used to solve the set of objective functions to obtain a set of candidate solutions. Genetic algorithms simulate natural selection and heredity processes, continuously optimizing a population of solutions through selection, crossover, and mutation operations. They are effective in handling complex multi-objective optimization problems.

[0113] Subsequently, a Pareto front analysis is performed on the candidate solution set to obtain a set of non-inferior solutions. The Pareto front includes solutions that cannot be improved on any objective without compromising other objectives, representing the optimal compromise solution for the multi-objective optimization problem. The non-inferior solution set is weighted using the Analytic Hierarchy Process (AHP) to obtain a weighted non-inferior solution set. The AHP constructs a judgment matrix and, by solving for eigenvalues ​​and eigenvectors, obtains the weight of each objective, reflecting the decision maker's preference for different objectives. Cluster analysis is performed on the weighted non-inferior solution set to obtain a cluster of solutions. The K-means clustering algorithm is used here to cluster similar solutions together to facilitate subsequent evaluation and selection. The cluster of solutions is then evaluated using a fuzzy comprehensive evaluation method to obtain an evaluation score matrix. Fuzzy comprehensive evaluation, based on fuzzy set theory, takes into account the uncertainty and ambiguity of the evaluation and can more objectively reflect the pros and cons of the solutions.

[0114] Perform multi-criteria decision analysis on the evaluation score matrix to obtain the optimal solution combination. Multi-criteria decision-making can use the TOPSIS method to calculate the distance between each solution and the ideal solution and the negative ideal solution to obtain the relative proximity, so as to select the optimal solution. The optimal solution combination is verified through performance simulation to obtain a set of performance indicators. Performance simulation can be performed using finite element analysis or multi-body dynamics simulation software to comprehensively evaluate the actual performance of the solution. A comprehensive evaluation is performed on the performance indicator set to obtain a comprehensive performance score. The comprehensive evaluation adopts the weighted summation method to weight each performance indicator according to its importance. The comprehensive performance score is iteratively optimized through the feedback optimization mechanism to obtain a multi-objective structural design solution combination and a set of performance evaluation indicators. Feedback optimization adopts the gradient descent method to continuously adjust parameters to improve the comprehensive performance score and achieve continuous improvement of the solution.

[0115] For example, in the design process of a certain extreme scooter, the first step is to conduct a parameter sensitivity analysis on the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber, and the sensitivities are 0.45, 0.35, and 0.20 respectively. Orthogonal array design experiments were conducted to determine the optimal parameter combination. The multi-objective function included maximizing the stiffness index and minimizing the weight. 100 candidate solutions were obtained using the NSGA-II algorithm. Pareto front analysis screened out 20 non-inferior solutions, and the hierarchical analysis method gave stiffness and weight weights of 0.6 and 0.4, respectively. K-means clustering (K=5) divided the non-inferior solutions into 5 clusters, and fuzzy comprehensive evaluation was used to obtain the score of each cluster. The TOPSIS method selected the solution with the highest relative closeness, where the elastic modulus of the frame material is 210GPa and the angular momentum of the wheel group is The shock absorber damping coefficient is 1500 N·s / m. Finite element simulations verified the performance of this solution under various operating conditions. The final overall performance score was 0.92. After five rounds of iterative optimization, the score was improved to 0.95, forming a complete set of extreme scooter structural design solutions and performance evaluation indicators.

[0116] The above describes the extreme scooter structure design management method in the embodiment of the present application. The following describes the extreme scooter structure design management system in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the extreme scooter structure design management system includes:

[0117] Processing module 201 is used to intelligently collect and pre-process multi-source heterogeneous data to obtain a multi-dimensional comprehensive data set including frame stress distribution data, wheel group dynamic response data and shock absorption system performance;

[0118] A calculation module 202 is configured to perform multi-level index construction and nonlinear calculation on the multi-dimensional integrated data set to obtain a quantitative index system for the extreme scooter structural design, including a body stiffness index, a steering sensitivity coefficient, and a vibration attenuation rate;

[0119] The quantification module 203 is used to perform spatiotemporal multi-scale feature analysis on the extreme scooter structural design quantitative index system to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions;

[0120] An analysis module 204 is configured to perform a high-order correlation analysis on the spatiotemporal distribution characteristic map to obtain a multi-dimensional correlation intensity distribution matrix among the frame geometric parameters, wheel configuration, and shock absorption system;

[0121] Identification module 205, configured to perform key factor identification and sensitivity analysis on the multi-dimensional correlation strength distribution matrix to obtain an importance ranking table of influencing factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber;

[0122] The optimization module 206 is used to optimize the parameterized structural design scheme according to the importance ranking table of the influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

[0123] Through the collaborative efforts of these components and the intelligent collection and preprocessing of multi-source heterogeneous data, a multidimensional, integrated dataset encompassing frame stress distribution, wheel dynamic response, and shock absorption system performance is generated. This comprehensive collection and integration of scooter structural data is achieved, providing a rich data foundation for subsequent analysis. Multi-level index construction and nonlinear calculations are performed on this multidimensional, integrated dataset to develop a quantitative structural design index system for extreme scooters, including body stiffness index, steering sensitivity coefficient, and vibration attenuation rate. This system more accurately reflects the structural characteristics and dynamic performance of scooters. A multi-scale spatial and temporal analysis of this quantitative structural design index system reveals spatiotemporal distribution patterns of the structural dynamic response under different road conditions. This process provides insights into the scooter's performance under various usage environments and provides an important basis for structural optimization. High-order correlation analysis of this spatiotemporal distribution pattern reveals a multidimensional correlation intensity distribution matrix between frame geometry, wheel configuration, and shock absorption system. This reveals the complex interactions among these structural parameters and facilitates a comprehensive understanding of the overall system performance. By identifying key factors and conducting sensitivity analysis on the multidimensional correlation intensity distribution matrix, a ranking table of influencing factors, including the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient, was obtained. This clearly defined the contribution of each design parameter to overall performance and provided a clear direction for design optimization. Finally, parametric structural design schemes were optimized based on the ranking table of influencing factors, resulting in a multi-objective structural design scheme combination and a set of performance evaluation indicators. This step achieved comprehensive optimization of the scooter structure, finding the optimal balance between multiple performance objectives. Overall, this method, through a systematic and intelligent data processing and analysis process, achieved comprehensive optimization of the extreme scooter structure, significantly improving the scooter's overall performance, including but not limited to increased body stiffness, improved steering sensitivity, and enhanced shock absorption. Furthermore, this method significantly improved design efficiency, shortened the product development cycle, and reduced design costs.

[0124] Based on the same technical concept, an embodiment of the present application also provides an extreme scooter, characterized in that it includes: a frame, the frame having a structure optimized by the body stiffness index; a wheel set connected to the frame, the configuration parameters of the wheel set being optimized based on the steering sensitivity coefficient; a shock absorption system installed on the frame, the shock absorber damping coefficient of the shock absorption system being optimized through vibration attenuation rate analysis; a control system arranged on the frame, for real-time monitoring and adjusting the dynamic response of the body structure; a sensor network connected to the control system, for collecting frame stress distribution, wheel set dynamic response and shock absorption system performance data; a human-computer interaction interface installed on the frame, for displaying vehicle performance parameters and receiving user input.

[0125] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for designing and managing the structure of an extreme scooter, characterized in that: The extreme scooter structure design management method includes: Intelligently collect and preprocess multi-source heterogeneous data to obtain a multi-dimensional comprehensive data set including frame stress distribution data, wheel dynamic response data, and shock absorption system performance; The multi-dimensional comprehensive data set is subjected to multi-level index construction and nonlinear calculation to obtain a quantitative index system for the structural design of an extreme scooter including a body stiffness index, a steering sensitivity coefficient and a vibration attenuation rate, including: performing hierarchical analysis on the multi-dimensional comprehensive data set to obtain an index hierarchy structure, and performing weight distribution on the index hierarchy structure to obtain a preliminary index system; performing nonlinear mapping on the preliminary index system to obtain a nonlinear index set, and fusing the nonlinear index set through a fuzzy comprehensive evaluation method to obtain a fused index system; performing stress and strain analysis on the body stiffness related data in the fused index system to obtain a body stiffness distribution, and performing finite element calculation on the body stiffness. The degree distribution is quantified to obtain the body stiffness index; dynamic modeling is performed on the steering system-related data in the fusion index system to obtain a steering dynamics model, and parameters of the steering dynamics model are extracted through sensitivity analysis to obtain a steering sensitivity coefficient; spectrum analysis is performed on the vibration-related data in the fusion index system to obtain a vibration spectrum, and features of the vibration spectrum are extracted through attenuation curve fitting to obtain a vibration attenuation rate; the body stiffness index, steering sensitivity coefficient and vibration attenuation rate are comprehensively evaluated to obtain a comprehensive performance index, and the comprehensive performance index is weighed through a multi-objective optimization algorithm to obtain a quantitative index system for the extreme scooter structure design; The time-space multi-scale feature analysis of the quantitative index system of the extreme scooter structure design is performed to obtain a time-space distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions, including: performing time series decomposition on the quantitative index system of the extreme scooter structure design to obtain time scale features, and performing multi-resolution processing on the time scale features through wavelet analysis to obtain a time multi-scale feature set; performing spatial distribution analysis on the quantitative index system of the extreme scooter structure design to obtain spatial scale features, and processing the spatial scale features through multi-scale spatial autocorrelation analysis to obtain a spatial multi-scale feature set; performing tensor fusion on the time multi-scale feature set and the spatial multi-scale feature set to obtain a time-space multi-scale feature tensor, and performing principal component analysis on the time multi-scale feature set and the spatial multi-scale feature set. The spatiotemporal multi-scale feature tensor is subjected to dimensionality reduction to obtain a spatiotemporal feature matrix after dimensionality reduction; cluster analysis is performed on the spatiotemporal feature matrix after dimensionality reduction to obtain a road condition type classification, and the road condition type classification is modeled by a support vector machine to obtain a road condition recognition model; dynamic response data of the vehicle body structure under different road conditions are collected to obtain original dynamic response data, and the original dynamic response data are classified and processed by the road condition recognition model to obtain a classified dynamic response data set; spatiotemporal interpolation is performed on the classified dynamic response data set to obtain a continuous spatiotemporal distribution function, and the continuous spatiotemporal distribution function is visualized by an isoline drawing algorithm to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions; Performing a high-order correlation analysis on the spatiotemporal distribution characteristic map to obtain a multi-dimensional correlation intensity distribution matrix among the frame geometric parameters, wheel configuration, and shock absorption system; Performing key factor identification and sensitivity analysis on the multi-dimensional correlation intensity distribution matrix to obtain an importance ranking table of influencing factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber; The parametric structural design scheme is optimized according to the importance ranking table of the influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

2. The extreme scooter structure design management method according to claim 1, characterized in that: The intelligent collection and preprocessing of multi-source heterogeneous data obtains a multi-dimensional comprehensive data set containing frame stress distribution data, wheel set dynamic response data, and shock absorption system performance, including: Collecting multi-source heterogeneous data to obtain an original data set, and performing outlier detection and denoising on the original data set to obtain cleaned multi-source heterogeneous data; Performing feature extraction on the cleaned multi-source heterogeneous data to obtain a preliminary feature set, and performing dimensionality reduction processing on the preliminary feature set using a principal component analysis algorithm to obtain a compressed feature representation; Performing spatial interpolation analysis on the frame stress distribution data to obtain a continuous stress field, and refining the continuous stress field using a finite element method to obtain the frame stress distribution data; Performing time-frequency domain analysis on the dynamic response data of the wheelset to obtain a response characteristic spectrum, and performing multi-scale decomposition on the response characteristic spectrum through wavelet transform to obtain the dynamic response data of the wheelset; Performing multi-operating condition tests on the performance of the damping system to obtain original damping performance, and smoothing the original damping performance using a Kalman filter algorithm to obtain the performance of the damping system; The compressed feature representation, frame stress distribution data, wheel set dynamic response data and shock absorption system performance are fused to obtain a preliminary fused data set, and high-dimensional features are extracted from the preliminary fused data set using a tensor decomposition algorithm to obtain a multidimensional comprehensive data set.

3. The extreme scooter structure design management method according to claim 1, characterized in that: The high-order correlation analysis is performed on the spatiotemporal distribution characteristic map to obtain a multi-dimensional correlation intensity distribution matrix between the frame geometric parameters, wheel configuration and shock absorption system, including: Extracting features from the spatiotemporal distribution feature map to obtain a high-dimensional feature vector, and performing dimensionality reduction processing on the high-dimensional feature vector through principal component analysis to obtain a feature set after dimensionality reduction; Performing Pearson correlation analysis on the feature set after dimensionality reduction to obtain a preliminary correlation matrix, and correcting the preliminary correlation matrix through partial correlation analysis to obtain a corrected correlation matrix; Performing nonlinear correlation analysis on the corrected correlation matrix to obtain a nonlinear correlation index, and quantifying the nonlinear correlation index using a mutual information algorithm to obtain a high-order correlation index set; Performing cluster analysis on the high-order correlation indicator set to obtain correlation clusters, and performing structured processing on the correlation clusters using a hierarchical clustering algorithm to obtain a correlation hierarchy tree; Performing a pruning operation on the correlation hierarchy tree to obtain an optimized correlation structure, and performing a connectivity analysis on the optimized correlation structure using a graph theory algorithm to obtain a connection strength distribution; The connection strength distribution is normalized to obtain a standardized strength distribution, and the standardized strength distribution is reconstructed through matrix transformation to obtain a multidimensional correlation strength distribution matrix among the frame geometric parameters, wheel set configuration and shock absorption system.

4. The extreme scooter structure design and management method according to claim 1, characterized in that: The key factor identification and sensitivity analysis of the multi-dimensional correlation intensity distribution matrix are performed to obtain an importance ranking table of influencing factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber, including: Performing eigenvalue decomposition on the multidimensional correlation intensity distribution matrix to obtain an eigenvector set, and ranking the eigenvector set by importance through principal component analysis to obtain a preliminary importance ranking of factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber; Performing variance analysis on the preliminary factor importance ranking to obtain the variance contribution rate of each factor, and quantifying the variance contribution rate through a global sensitivity analysis method to obtain global sensitivity indices of the frame material elastic modulus, wheelset angular momentum, and shock absorber damping coefficient; Performing threshold screening on the global sensitivity index to obtain a set of key factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber, and performing a refined analysis on the key factor set using a local sensitivity analysis method to obtain a local sensitivity index; Performing a fuzzy comprehensive evaluation on the local sensitivity index to obtain a comprehensive importance score of the frame material elastic modulus, the wheelset angular momentum, and the shock absorber damping coefficient, and weighing the comprehensive importance score using a multi-criteria decision-making method to obtain a balanced importance index; The balanced importance indices are normalized to obtain standardized importance indices of the frame material elastic modulus, the wheelset angular momentum, and the shock absorber damping coefficient. The standardized importance indices are sorted using a sorting algorithm to obtain an importance ranking table of influencing factors.

5. The extreme scooter structure design and management method according to claim 1, characterized in that: The parametric structural design scheme optimization is performed according to the importance ranking table of the influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set, including: Perform parameter sensitivity analysis on the importance ranking table of influencing factors to obtain a parameter sensitivity matrix, and optimize the parameter sensitivity matrix through orthogonal experimental design to obtain a parameter optimization scheme; Performing multi-objective function construction on the parameter optimization scheme to obtain an objective function set, and solving the objective function set by a genetic algorithm to obtain a candidate solution set; Performing Pareto frontier analysis on the candidate solution set to obtain a non-inferior solution set, and assigning weights to the non-inferior solution set using a hierarchical analysis method to obtain a weighted non-inferior solution set; Performing cluster analysis on the weighted non-inferior solution set to obtain a solution cluster, and evaluating the solution cluster using a fuzzy comprehensive evaluation method to obtain an evaluation score matrix; Performing a multi-criteria decision analysis on the evaluation score matrix to obtain an optimal solution combination, and verifying the optimal solution combination through performance simulation to obtain a performance indicator set; The performance indicator set is comprehensively evaluated to obtain a comprehensive performance score, and the comprehensive performance score is iteratively optimized through a feedback optimization mechanism to obtain a multi-objective structural design scheme combination and a performance evaluation indicator set.

6. An extreme scooter structure design management system, used to implement the extreme scooter structure design management method according to any one of claims 1 to 5, characterized in that: The extreme scooter structure design management system includes: The processing module is used to intelligently collect and preprocess multi-source heterogeneous data to obtain a multi-dimensional comprehensive data set including frame stress distribution data, wheel set dynamic response data, and shock absorption system performance; The calculation module is used to perform multi-level index construction and nonlinear calculation on the multidimensional comprehensive data set to obtain a quantitative index system for the structural design of an extreme scooter including a body stiffness index, a steering sensitivity coefficient and a vibration attenuation rate, including: performing hierarchical analysis on the multidimensional comprehensive data set to obtain an index hierarchy structure, and performing weight distribution on the index hierarchy structure to obtain a preliminary index system; performing nonlinear mapping on the preliminary index system to obtain a nonlinear index set, and fusing the nonlinear index set through a fuzzy comprehensive evaluation method to obtain a fused index system; performing stress and strain analysis on the body stiffness related data in the fused index system to obtain a body stiffness distribution, and performing finite element calculation on the body stiffness related data in the fused index system to obtain a body stiffness distribution. The vehicle body stiffness distribution is quantified to obtain a vehicle body stiffness index; dynamic modeling is performed on the steering system-related data in the fusion index system to obtain a steering dynamics model, and parameters of the steering dynamics model are extracted through sensitivity analysis to obtain a steering sensitivity coefficient; spectral analysis is performed on the vibration-related data in the fusion index system to obtain a vibration spectrum, and features of the vibration spectrum are extracted through attenuation curve fitting to obtain a vibration attenuation rate; the vehicle body stiffness index, steering sensitivity coefficient, and vibration attenuation rate are comprehensively evaluated to obtain a comprehensive performance index, and the comprehensive performance index is weighed through a multi-objective optimization algorithm to obtain a quantitative index system for the extreme scooter structural design; The quantification module is used to perform spatiotemporal multi-scale feature analysis on the quantitative index system of the extreme scooter structure design to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions, including: performing time series decomposition on the quantitative index system of the extreme scooter structure design to obtain time scale features, and performing multi-resolution processing on the time scale features through wavelet analysis to obtain a time multi-scale feature set; performing spatial distribution analysis on the quantitative index system of the extreme scooter structure design to obtain spatial scale features, and processing the spatial scale features through multi-scale spatial autocorrelation analysis to obtain a spatial multi-scale feature set; performing tensor fusion on the time multi-scale feature set and the spatial multi-scale feature set to obtain a spatiotemporal multi-scale feature tensor, and performing principal component analysis on the time multi-scale feature set and the spatial multi-scale feature set to obtain a spatiotemporal multi-scale feature tensor. Analyze and reduce the dimension of the spatiotemporal multi-scale feature tensor to obtain a spatiotemporal feature matrix after dimensionality reduction; perform cluster analysis on the spatiotemporal feature matrix after dimensionality reduction to obtain a road condition type classification, and model the road condition type classification through a support vector machine to obtain a road condition recognition model; collect dynamic response data of the vehicle body structure under different road conditions to obtain original dynamic response data, and classify the original dynamic response data through the road condition recognition model to obtain a classified dynamic response data set; perform spatiotemporal interpolation on the classified dynamic response data set to obtain a continuous spatiotemporal distribution function, and visualize the continuous spatiotemporal distribution function through a contour drawing algorithm to obtain a spatiotemporal distribution feature map reflecting the dynamic response of the vehicle body structure under different road conditions; An analysis module, configured to perform a high-order correlation analysis on the spatiotemporal distribution characteristic map to obtain a multi-dimensional correlation intensity distribution matrix among the frame geometric parameters, wheel configuration, and shock absorption system; an identification module for performing key factor identification and sensitivity analysis on the multidimensional correlation strength distribution matrix to obtain an importance ranking table of influencing factors including the elastic modulus of the frame material, the angular momentum of the wheelset, and the damping coefficient of the shock absorber; The optimization module is used to optimize the parametric structural design scheme according to the importance ranking table of the influencing factors to obtain a multi-objective structural design scheme combination and a performance evaluation index set.

7. An extreme scooter, characterized in that: include: A frame designed by the extreme scooter structural design management method according to any one of claims 1 to 5, wherein the frame has a structure optimized by the body stiffness index; A wheelset connected to the frame, wherein configuration parameters of the wheelset are optimized based on a steering sensitivity coefficient; A shock absorption system mounted on the vehicle frame, wherein the damping coefficient of the shock absorber of the shock absorption system is optimized through vibration attenuation rate analysis; A control system provided on the vehicle frame is used for real-time monitoring and adjustment of the dynamic response of the vehicle body structure; a sensor network connected to the control system is used for collecting data on the stress distribution of the vehicle frame, the dynamic response of the wheelset, and the performance of the shock absorption system; and a human-computer interaction interface installed on the vehicle frame is used for displaying vehicle performance parameters and receiving user input.

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