Method and system for adjusting and analyzing 3D printing model of riding shoes

Abstract

The invention discloses a method and a system for adjusting and analyzing a 3D printing model of a riding shoe, and relates to the technical field of riding shoe design, the method comprises the following steps: acquiring initial three-dimensional graphic data and initial printing parameters of an initial 3D printing model designed for the riding shoe; wherein the initial three-dimensional graphic data comprises initial structure parameters, initial triangular patch data and initial texture information; the initial printing parameters comprise a printing material, a printing layer thickness, a printing temperature, a printing speed, a supporting structure and a printing path; according to the foot type data and the riding action mechanics parameters of the rider, the initial three-dimensional graph data and the initial printing parameters of the initial 3D printing model are analyzed, and an adaptability analysis result is obtained; adjusting the initial three-dimensional graphic data and the initial printing parameters according to the suitability analysis result; and outputting the adjusted 3D printing model of the riding shoe matched with the rider.

Description

Technical Field

[0001] The present application relates to the technical field of cycling shoe design, and in particular to a 3D printing model adjustment and analysis method and system for cycling shoes. Background Art

[0002] Cycling shoes, especially bicycle shoes, are specialized footwear designed for cycling. They usually have a hard sole so that the rider's pedaling force can be more effectively transferred to the pedals, thereby improving cycling efficiency. The design of cycling shoes needs to take into account comfort, breathability, support and durability to meet the needs of long-term riding. 3D printing technology, also known as additive manufacturing technology, is a process of building three-dimensional objects by adding materials layer by layer. In the field of cycling shoe design and manufacturing, 3D printing technology allows designers to create complex geometries, achieve personalized customization, and quickly iterate designs, thereby shortening product development cycles.

[0003] Although 3D printing technology has brought revolutionary changes to the design and manufacture of cycling shoes, in practical applications, the design of 3D printed models of cycling shoes still faces some challenges. The most important problem is experience dependence: the existing technology relies heavily on the designer's experience and intuition when designing 3D printed models of cycling shoes. This design method lacks systematic analysis and optimization, which may lead to low design efficiency and make it difficult to ensure the optimal design.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a 3D printing model adjustment and analysis method and system for cycling shoes to solve the above-mentioned technical problems.

[0006] The present application provides a method for adjusting and analyzing a 3D printing model of a cycling shoe, comprising: obtaining initial three-dimensional graphic data and initial printing parameters of an initial 3D printing model designed for the cycling shoe; wherein the initial three-dimensional graphic data comprises initial structural parameters, initial triangular facet data and initial texture information; the initial printing parameters comprises printing material, printing layer thickness, printing temperature, printing speed, supporting structure and printing path; analyzing the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the foot shape data and cycling dynamics parameters of the rider to obtain a compatibility analysis result; adjusting the initial three-dimensional graphic data and the initial printing parameters according to the compatibility analysis result; and outputting the adjusted 3D printing model of the cycling shoe matching the rider.

[0007] The present application provides a 3D printing model adjustment and analysis system for cycling shoes, comprising: an initial 3D printing information acquisition module, used to acquire initial three-dimensional graphic data and initial printing parameters of an initial 3D printing model designed for cycling shoes; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular facet data and initial texture information; the initial printing parameters include printing materials, printing layer thickness, printing temperature, printing speed, support structure and printing path; a rider adaptability analysis module, used to analyze the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the rider's foot shape data and cycling dynamics parameters to obtain adaptability analysis results; a printing information adjustment module, used to adjust the initial three-dimensional graphic data and the initial printing parameters according to the adaptability analysis results; and an adapted 3D printing model output module, used to output an adjusted 3D printing model of cycling shoes matching the rider.

[0008] Based on the embodiment provided by the present application, the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model designed for cycling shoes are obtained; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular facet data and initial texture information; the initial printing parameters include printing materials, printing layer thickness, printing temperature, printing speed, support structure and printing path; according to the rider's foot data and cycling dynamics parameters, the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis results; according to the adaptability analysis results, the initial three-dimensional graphic data and initial printing parameters are adjusted; and the adjusted 3D printing model of the cycling shoes matching the rider is output. Through the automated adjustment and analysis method to assist designers in adjusting and optimizing the model, it is achieved to reduce the dependence on personal experience, improve design efficiency and product quality, and make the 3D printing model design of cycling shoes more accurate and efficient; promote technological progress in the field of cycling shoe design and manufacturing, and provide riders with cycling shoes with better performance and more personalized needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0010] Figure 1 A flowchart of an optional 3D printing model adjustment and analysis method for cycling shoes according to an embodiment of the present application;

[0011] Figure 2 This is a structural diagram of an optional 3D printing model adjustment and analysis system for cycling shoes according to an embodiment of the present application.

[0012] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0014] Alternatively, if Figure 1 As shown, the present application provides a 3D printing model adjustment and analysis method for cycling shoes, comprising:

[0015] S101, obtaining initial three-dimensional graphic data and initial printing parameters of an initial 3D printing model designed for a cycling shoe; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular face data and initial texture information; and the initial printing parameters include printing material, printing layer thickness, printing temperature, printing speed, support structure and printing path;

[0016] S102, analyzing the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the rider's foot shape data and cycling dynamics parameters to obtain a compatibility analysis result;

[0017] S103, adjusting the initial three-dimensional graphic data and initial printing parameters according to the adaptability analysis result;

[0018] S104: outputting the adjusted 3D printing model of the cycling shoes matching the rider.

[0019] Based on the embodiment provided by the present application, the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model designed for cycling shoes are obtained; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular facet data and initial texture information; the initial printing parameters include printing materials, printing layer thickness, printing temperature, printing speed, support structure and printing path; according to the rider's foot data and cycling dynamics parameters, the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis results; according to the adaptability analysis results, the initial three-dimensional graphic data and initial printing parameters are adjusted; and the adjusted 3D printing model of the cycling shoes matching the rider is output. Through the automated adjustment and analysis method to assist designers in adjusting and optimizing the model, it is achieved to reduce the dependence on personal experience, improve design efficiency and product quality, and make the 3D printing model design of cycling shoes more accurate and efficient; promote technological progress in the field of cycling shoe design and manufacturing, and provide riders with cycling shoes with better performance and more personalized needs.

[0020] According to the rider's foot shape data and riding dynamics parameters, the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis results, which are configured as follows:

[0021] Use 3D scanning technology to obtain the rider's foot shape data; the foot shape data includes foot length, width, instep height and arch height;

[0022] The cycling dynamics parameters of the cyclist are collected through a motion capture system; wherein the cycling dynamics parameters include pedaling force, frequency and foot position;

[0023] Extracting foot shape features from the rider's foot shape data, and establishing a first correlation relationship between the foot shape features and the initial structural parameters;

[0024] Extracting riding dynamics features from the riding dynamics parameters of the rider; establishing a second correlation relationship between the riding dynamics features and the initial structural parameters;

[0025] According to the first association relationship and the second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain a compatibility analysis result.

[0026] Further, according to the first association relationship and the second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain a compatibility analysis result, which is configured as follows:

[0027] Establish vector representation of foot shape characteristics and cycling dynamics characteristics in multi-dimensional space;

[0028] By using radial basis functions, the vector representation of foot shape features in multidimensional space and the vector representation of cycling dynamics features in multidimensional space are mapped to the extended dimensional space to obtain an extended foot shape vector and an extended cycling dynamics vector;

[0029] Construct a transaction dataset based on the extended foot shape vector and the extended cycling dynamics vector;

[0030] The Relim algorithm is used to mine frequent item sets from the transaction dataset; frequent item sets represent the combination patterns of foot shape features and cycling dynamics features;

[0031] For each frequent item set, a tree-based model is constructed using a gradient boosting tree; each tree-based model is used to predict the configuration of structural parameters based on foot shape characteristics and cycling dynamics characteristics;

[0032] Among them, the objective function of the gradient boosting tree Where n is the total number of samples; i is the cumulative index; ω i is the weight of the i-th sample; y iis the actual structural parameter of the i-th sample; is the predicted structural parameter of the i-th sample; γ is the regularization parameter; Indicates the complexity of the tree model; f t (x i ) represents the prediction function of the t-th tree for the i-th sample;

[0033] For each tree-based model, the correlation between its predicted structural parameters and the initial structural parameters was calculated;

[0034] Based on the correlation between the structural parameters predicted by each tree-based model and the initial structural parameters, the prediction results of all tree-based models are fused to obtain a fused prediction result;

[0035] The first association relationship is updated based on the fusion prediction result; the second association relationship is updated based on the fusion prediction result;

[0036] According to the updated first association relationship and the updated second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain a compatibility analysis result.

[0037] Alternatively, if Figure 2 As shown, the present application provides a 3D printing model adjustment and analysis system for cycling shoes, comprising:

[0038] The initial 3D printing information acquisition module 201 is used to acquire initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model designed for the cycling shoe; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular face data and initial texture information; the initial printing parameters include printing material, printing layer thickness, printing temperature, printing speed, support structure and printing path;

[0039] A rider adaptability analysis module 202 is used to analyze the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the rider's foot shape data and cycling dynamics parameters to obtain a adaptability analysis result;

[0040] The printing information adjustment module 203 is used to adjust the initial three-dimensional graphic data and initial printing parameters according to the adaptability analysis result;

[0041] The adapted 3D printing model output module 204 is used to output the adjusted 3D printing model of the cycling shoe that matches the rider.

[0042] Furthermore, the rider adaptability analysis module analyzes the initial three-dimensional graphic data and initial printing parameters of the initial 3D printed model according to the rider's foot shape data and riding dynamics parameters to obtain adaptability analysis results, and is configured as follows:

[0043] Use 3D scanning technology to obtain the rider's foot shape data; the foot shape data includes foot length, width, instep height and arch height;

[0044] The cycling dynamics parameters of the cyclist are collected through a motion capture system; wherein the cycling dynamics parameters include pedaling force, frequency and foot position;

[0045] Extracting foot shape features from the rider's foot shape data, and establishing a first correlation relationship between the foot shape features and the initial structural parameters;

[0046] Extracting riding dynamics features from the riding dynamics parameters of the rider; establishing a second correlation relationship between the riding dynamics features and the initial structural parameters;

[0047] According to the first association relationship and the second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain a compatibility analysis result.

[0048] Further, according to the first association relationship and the second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain a compatibility analysis result, which is configured as follows:

[0049] Establish vector representation of foot shape characteristics and cycling dynamics characteristics in multi-dimensional space;

[0050] By using radial basis functions, the vector representation of foot shape features in multidimensional space and the vector representation of cycling dynamics features in multidimensional space are mapped to the extended dimensional space to obtain an extended foot shape vector and an extended cycling dynamics vector;

[0051] Construct a transaction dataset based on the extended foot shape vector and the extended cycling dynamics vector;

[0052] The Relim algorithm is used to mine frequent item sets from the transaction dataset; frequent item sets represent the combination patterns of foot shape features and cycling dynamics features;

[0053] For each frequent item set, a tree-based model is constructed using a gradient boosting tree; each tree-based model is used to predict the configuration of structural parameters based on foot shape characteristics and cycling dynamics characteristics;

[0054] Among them, the objective function of the gradient boosting tree Where n is the total number of samples; i is the cumulative index; ω i is the weight of the i-th sample; y i is the actual structural parameter of the i-th sample; is the predicted structural parameter of the i-th sample; γ is the regularization parameter; Indicates the complexity of the tree model; f t (x i ) represents the prediction function of the t-th tree for the i-th sample;

[0055] For each tree-based model, the correlation between its predicted structural parameters and the initial structural parameters was calculated;

[0056] Based on the correlation between the structural parameters predicted by each tree-based model and the initial structural parameters, the prediction results of all tree-based models are fused to obtain a fused prediction result;

[0057] The first association relationship is updated based on the fusion prediction result; the second association relationship is updated based on the fusion prediction result;

[0058] According to the updated first association relationship and the updated second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain a compatibility analysis result.

[0059] Further, the initial structural parameters include design parameters of the initial cleat structure, design parameters of the initial partition reinforcement belt, design parameters of the initial asymmetric heel cup, design parameters of the initial carbon fiber outsole, design parameters of the initial arch support plate, and design parameters of the initial metatarsal pad; the initial triangular patch data includes vertex data and edge data; the initial texture information includes UV coordinate information and material information; according to the updated first association relationship and the updated second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis result, which is configured as:

[0060] Define the initial structural parameter vector P = {p cleat ,p reinforce ,p heel ,p sole ,p arch ,p ball}; where p cleat represents the design parameters of the initial locking plate structure; p reinforce represents the design parameters of the initial partition reinforcement zone; p heel represents the design parameters of the initial asymmetric heel cup; p sole represents the design parameters of the initial carbon fiber outsole; p arch represents the design parameters of the initial arch support; p ball represents the design parameters of the initial metatarsal spacer;

[0061] Initialize the hidden variable probability distribution; wherein the hidden variable probability distribution represents the potential categories of the cyclist's foot type and cycling dynamics parameters;

[0062] Given an initial structural parameter vector P, calculate the posterior probability distribution of foot shape characteristics and riding dynamics characteristics for each design parameter;

[0063] According to the calculated posterior probability distribution, the initial structural parameter vector P is updated to maximize the log-likelihood function of the observed data H; wherein the observed data H includes foot shape data and riding dynamics parameters;

[0064] Iteratively calculate the posterior probability distribution and update the initial structure parameter vector P until the initial structure parameter vector P converges to obtain a reference structure parameter vector;

[0065] Among them, P (k+1) = argmax P G(Z|H,P (k) )×logG(H,Z|P (k) ), where P (k+1) is the initial structural parameter vector P updated in the k+1th iteration; P (k) is the initial structural parameter vector P updated in the kth iteration; G(Z|H,P (k) ) is the parameter estimate P for the given observation data H and the kth iteration (k) Under the condition of , the posterior probability of the hidden variable Z; logG(H,Z|P (k) ) is the log-likelihood function, which is used to calculate the logarithmic value of the joint probability of the observed data H and the latent class Z under the given parameter P;

[0066] Based on the reference structure parameter vector, the fitness analysis results are determined.

[0067] Furthermore, given the initial structural parameter vector P, the posterior probability distribution of the foot shape characteristics and riding dynamics characteristics for each design parameter is calculated based on the following formula;

[0068]

[0069] Where G(Z|H,P) is the posterior probability of the hidden variable Z given the observed data H and the current parameter estimate P. Z represents the latent class of the rider's foot shape and dynamics, representing different combinations of foot shape and cycling dynamics. G(H,Z|P) represents the joint probability of the observed data H and the latent class Z given the parameter P. k G(H,Z k |P) represents all potential classes Z k The joint probability sum is used to normalize the posterior probability.

[0070] Furthermore, the 3D printing model adjustment and analysis system for cycling shoes also includes:

[0071] The user interface is used to receive the foot shape data and cycling dynamics parameters input by the rider, and to visually display the fitness analysis results and the adjusted 3D printed model.

[0072] Furthermore, the 3D printing model adjustment and analysis system for cycling shoes also includes:

[0073] The intelligent recommendation module is used to determine target cycling shoes and customization options from a set of candidate cycling shoes according to the cyclist's foot shape data and cycling dynamics parameters.

[0074] It should be noted that in the present application, the embodiments implemented by the 3D printing model adjustment and analysis system side of the cycling shoe can be referenced with the embodiments implemented by the 3D printing model adjustment and analysis method side of the cycling shoe, and this application will not go into details one by one.

[0075] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A 3D printing model adjustment and analysis method for cycling shoes, characterized in that: include: Acquire initial three-dimensional graphic data and initial printing parameters of an initial 3D printing model designed for a cycling shoe; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular face data and initial texture information; the initial printing parameters include printing material, printing layer thickness, printing temperature, printing speed, support structure and printing path; Analyzing the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the rider's foot shape data and cycling dynamics parameters to obtain a compatibility analysis result; According to the adaptability analysis result, adjusting the initial three-dimensional graphic data and the initial printing parameters; Outputting the adjusted 3D printing model of the cycling shoe matching the cyclist.

2. The 3D printing model adjustment and analysis method for cycling shoes according to claim 1, characterized in that: The initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model are analyzed according to the rider's foot shape data and the riding dynamics parameters to obtain the adaptability analysis result, and is configured as follows: Using 3D scanning technology to obtain the rider's foot shape data; wherein the foot shape data includes foot length, width, instep height and arch height; Collecting the cycling dynamics parameters of the rider through a motion capture system; wherein the cycling dynamics parameters include pedaling force, frequency and foot position; Extracting foot shape features from the foot shape data of the rider, and establishing a first association relationship between the foot shape features and initial structural parameters; Extracting riding dynamics features from the riding dynamics parameters of the rider; and establishing a second correlation relationship between the riding dynamics features and the initial structural parameters; According to the first association relationship and the second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis result.

3. The 3D printing model adjustment and analysis method of cycling shoes according to claim 2, characterized in that: The analyzing the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model according to the first association relationship and the second association relationship to obtain the adaptability analysis result is configured as follows: Establishing a vector representation of the foot shape feature and the riding dynamics feature in a multi-dimensional space; Mapping the vector representation of the foot shape feature in the multidimensional space and the vector representation of the riding dynamics feature in the multidimensional space to an extended dimensional space through a radial basis function to obtain an extended foot shape vector and an extended riding dynamics vector; constructing a transaction data set based on the extended foot shape vector and the extended riding dynamics vector; Using the Relim algorithm to mine frequent item sets from the transaction data set; wherein the frequent item sets represent the combination pattern of the foot shape feature and the riding dynamics feature; For each frequent item set, a tree-based model is constructed using a gradient boosting tree; wherein each tree-based model is used to predict the configuration of the structural parameters according to the foot shape characteristics and the riding dynamics characteristics; Among them, the objective function of the gradient boosting tree is Where n is the total number of samples; i is the cumulative index; ω i is the weight of the i-th sample; y i is the actual structural parameter of the i-th sample; is the predicted structural parameter of the i-th sample; γ is the regularization parameter; Indicates the complexity of the tree model; f t (x i ) represents the prediction function of the t-th tree for the i-th sample; For each tree-based model, calculating the correlation between its predicted structural parameters and the initial structural parameters; Based on the correlation between the structural parameters predicted by each tree-based model and the initial structural parameters, the prediction results of all tree-based models are integrated to obtain a fused prediction result; The first association relationship is updated based on the fusion prediction result; the second association relationship is updated based on the fusion prediction result; According to the updated first association relationship and the updated second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis result.

4. A 3D printing model adjustment and analysis system for cycling shoes, the system implementing the method according to claim 1, characterized in that: include: An initial 3D printing information acquisition module is used to acquire initial three-dimensional graphic data and initial printing parameters of an initial 3D printing model designed for cycling shoes; wherein the initial three-dimensional graphic data includes initial structural parameters, initial triangular face data and initial texture information; the initial printing parameters include printing material, printing layer thickness, printing temperature, printing speed, support structure and printing path; A rider adaptability analysis module, used to analyze the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the rider's foot shape data and riding dynamics parameters, to obtain a adaptability analysis result; A printing information adjustment module, used for adjusting the initial three-dimensional graphic data and the initial printing parameters according to the adaptability analysis result; The 3D printing model output module is adapted to output the adjusted 3D printing model of the cycling shoes that matches the rider.

5. The 3D printing model adjustment and analysis system for cycling shoes according to claim 4, characterized in that The rider adaptability analysis module analyzes the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model according to the rider's foot shape data and riding dynamics parameters to obtain the adaptability analysis result, and is configured as follows: Using 3D scanning technology to obtain the rider's foot shape data; wherein the foot shape data includes foot length, width, instep height and arch height; Collecting the cycling dynamics parameters of the rider through a motion capture system; wherein the cycling dynamics parameters include pedaling force, frequency and foot position; Extracting foot shape features from the foot shape data of the rider, and establishing a first association relationship between the foot shape features and initial structural parameters; Extracting riding dynamics features from the riding dynamics parameters of the rider; and establishing a second correlation relationship between the riding dynamics features and the initial structural parameters; According to the first association relationship and the second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis result.

6. The 3D printing model adjustment and analysis system for cycling shoes according to claim 5, characterized in that: The analyzing the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model according to the first association relationship and the second association relationship to obtain the adaptability analysis result is configured as follows: Establishing a vector representation of the foot shape feature and the riding dynamics feature in a multi-dimensional space; Mapping the vector representation of the foot shape feature in the multidimensional space and the vector representation of the riding dynamics feature in the multidimensional space to an extended dimensional space through a radial basis function to obtain an extended foot shape vector and an extended riding dynamics vector; constructing a transaction data set based on the extended foot shape vector and the extended riding dynamics vector; Using the Relim algorithm to mine frequent item sets from the transaction data set; wherein the frequent item sets represent the combination pattern of the foot shape feature and the riding dynamics feature; For each frequent item set, a tree-based model is constructed using a gradient boosting tree; wherein each tree-based model is used to predict the configuration of the structural parameters according to the foot shape characteristics and the riding dynamics characteristics; Among them, the objective function of the gradient boosting tree is Where n is the total number of samples; i is the cumulative index; ω i is the weight of the i-th sample; y i is the actual structural parameter of the i-th sample; is the predicted structural parameter of the i-th sample; γ is the regularization parameter; Indicates the complexity of the tree model; f t (x i ) represents the prediction function of the t-th tree for the i-th sample; For each tree-based model, calculating the correlation between its predicted structural parameters and the initial structural parameters; Based on the correlation between the structural parameters predicted by each tree-based model and the initial structural parameters, the prediction results of all tree-based models are integrated to obtain a fused prediction result; The first association relationship is updated based on the fusion prediction result; the second association relationship is updated based on the fusion prediction result; According to the updated first association relationship and the updated second association relationship, the initial three-dimensional graphic data and the initial printing parameters of the initial 3D printing model are analyzed to obtain the adaptability analysis result.

7. The 3D printing model adjustment and analysis system for cycling shoes according to claim 6, characterized in that: The initial structural parameters include design parameters of the initial locking plate structure, design parameters of the initial partition reinforcement belt, design parameters of the initial asymmetric heel cup, design parameters of the initial carbon fiber outsole, design parameters of the initial arch support plate, and design parameters of the initial metatarsal pad; the initial three-dimensional graphic data and initial printing parameters of the initial 3D printing model are analyzed according to the updated first association relationship and the updated second association relationship to obtain the adaptability analysis result, which is configured as follows: Define the initial structural parameter vector P = {p cleat ,p reinforce ,p heel ,p sole ,p arch ,p ball }; where p cleat represents the design parameters of the initial locking plate structure; P reinforce represents the design parameters of the initial partition reinforcement belt; p heel represents the design parameters of the initial asymmetric heel cup; p sole represents the design parameters of the initial carbon fiber outsole; p arch represents the design parameters of the initial arch support sheet; p ball represents the design parameters of the initial metatarsal spacer; Initializing a hidden variable probability distribution; wherein the hidden variable probability distribution represents the potential categories of the rider's foot type and cycling dynamics parameters; Under a given initial structural parameter vector P, calculating the posterior probability distribution of the foot shape characteristics and the riding dynamics characteristics for each design parameter; According to the calculated posterior probability distribution, the initial structural parameter vector P is updated to maximize the log-likelihood function of the observed data H; wherein the observed data H includes the foot shape data and the riding dynamics parameters; Iteratively calculate the posterior probability distribution and update the initial structure parameter vector P until the initial structure parameter vector P converges to obtain a reference structure parameter vector; Among them, P (k+1) = argmax P G(Z|H,P (k) )×logG(H,Z|P (k) ), where P (k+1) is the initial structural parameter vector P updated in the k+1th iteration; P (k) is the initial structural parameter vector P updated in the kth iteration; G(Z|H,P (k) ) is the parameter estimate P for the given observation data H and the kth iteration (k) Under the condition of , the posterior probability of the hidden variable Z; logG(H,Z|P (k) ) is the log-likelihood function, which is used to calculate the logarithmic value of the joint probability of the observed data H and the latent class Z under a given parameter P; Based on the reference structure parameter vector, the adaptability analysis result is determined.

8. The 3D printing model adjustment and analysis system for cycling shoes according to claim 7, characterized in that: Given an initial structural parameter vector P, the posterior probability distribution of the foot shape feature and the riding dynamics feature for each design parameter is calculated based on the following formula: Where G(Z|H,P) is the posterior probability of the hidden variable Z given the observed data H and the current parameter estimate P. Z represents the latent class of the rider's foot shape and dynamics, representing different combinations of foot shape and cycling dynamics. G(H,Z|P) represents the joint probability of the observed data H and the latent class Z given the parameter P. k G(H,Z k |P) represents all potential classes Z k The joint probability sum is used to normalize the posterior probability.

9. The 3D printing model adjustment and analysis system for cycling shoes according to claim 4, characterized in that: The 3D printing model adjustment and analysis system for cycling shoes also includes: The user interface is used to receive the foot shape data and cycling dynamics parameters input by the rider, and to display the adaptability analysis results and the adjusted 3D printing model in a visual manner.

10. The 3D printing model adjustment and analysis system for cycling shoes according to claim 4, characterized in that: The 3D printing model adjustment and analysis system for cycling shoes also includes: The intelligent recommendation module is used to determine target cycling shoes and customization options in a set of candidate cycling shoes according to the foot shape data and cycling dynamics parameters of the rider.

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