A 3D simulation design method based on the structure of sports equipment

Abstract

This application relates to the technical field of computers, and discloses a three-dimensional simulation design method based on the structure of sports equipment, including: determining the corresponding target sports equipment, obtaining basic structure parameters based on the target sports equipment, constructing a basic three-dimensional model of the target sports equipment based on the basic structure parameters, and retrieving pre-collected human motion capture data and a virtual human engineering module for simulation in the basic three-dimensional model; during the simulation process, controlling the basic three-dimensional model to simulate the usage scenarios of different user groups in specific motion states, and obtaining the behavioral performance data of the basic three-dimensional model under different environmental conditions; collecting the on-site usage data corresponding to the target sports equipment, comparing the on-site usage data with the behavioral performance data, obtaining the performance evaluation data corresponding to the target sports equipment under various conditions, and optimizing the design parameters according to the performance evaluation data. This application can improve the accuracy of three-dimensional simulation of sports equipment.

Description

Technical Field

[0001] This application relates to the technical field of computers, and particularly to a three-dimensional simulation design method based on the structure of sports equipment. Background Art

[0002] Traditional sports equipment design mainly relies on designers' experience and limited physical model testing. However, this method has the following disadvantages: from concept to finished product, multiple physical prototypes and tests are often required, consuming a large amount of time and resources; due to cost limitations, only some parameters can be tested, making it difficult to comprehensively evaluate the performance of the equipment; most equipment is mass-produced in a standardized way and cannot meet the specific needs of different user groups.

[0003] With the development of computing technology, three-dimensional simulation technology has begun to be applied to the design of sports equipment. Three-dimensional simulation can quickly generate multiple design schemes and conduct tests under various conditions in a virtual environment, greatly shortening the design cycle. However, existing three-dimensional simulation methods still have some problems with low simulation accuracy.

[0004] As can be seen from the above, how to improve the accuracy of three-dimensional simulation of sports equipment remains to be solved. Summary of the Invention

[0005] In order to improve the accuracy of three-dimensional simulation of sports equipment, this application provides a three-dimensional simulation design method based on the structure of sports equipment.

[0006] In a first aspect, this application provides a three-dimensional simulation design method based on the structure of sports equipment, adopting the following technical solutions:

[0007] A three-dimensional simulation design method based on the structure of sports equipment includes: determining the corresponding target sports equipment, obtaining the corresponding basic structure parameters based on the target sports equipment, constructing the corresponding basic three-dimensional model of the target sports equipment based on the basic structure parameters, retrieving the pre-collected human motion capture data and conducting simulation in the basic three-dimensional model with a virtual human engineering module, where the basic structure parameters include material type, geometric dimensions, connection method, and mechanical performance indicators; during the simulation process, inputting the human motion capture data into the virtual human engineering module, controlling the use scenarios of different user groups in specific motion states on the basic three-dimensional model, obtaining the behavior performance data of the basic three-dimensional model under different environmental conditions based on the multi-physics field coupling algorithm; combining the on-site use data collected by Internet of Things devices for the corresponding target sports equipment, comparing the on-site use data with the behavior performance data, obtaining the performance evaluation data of the target sports equipment under various conditions, and optimizing the design parameters according to the performance evaluation data.

[0008] By adopting the above technical solutions, the three-dimensional simulation design method based on the structure of sports equipment integrates multi-physics field coupling algorithms, human motion capture data, and virtual human engineering modules, combines the on-site usage data collected by Internet of Things devices, and realizes the comprehensive simulation and evaluation of the behavior performance of target sports equipment under different environmental conditions. It can optimize design parameters according to the performance evaluation data, significantly improve the accuracy of three-dimensional simulation of sports equipment, and ensure that the designed products not only meet technical standards but also better meet the actual needs of users.

[0009] Optionally, during the process of comparing the on-site usage data with the behavior performance data, the method further includes: preprocessing the behavior performance data, converting the format of the preprocessed behavior performance data to be consistent with the format of the on-site usage data, and extracting corresponding key features from the on-site usage data and the behavior performance data, where the key features include stress distribution, displacement, vibration frequency, and temperature change; obtaining the difference data between the key features of the on-site usage data and the key features of the behavior performance data, and retrieving the relationship mapping table between the difference data and the performance evaluation data; determining the corresponding performance evaluation data based on the difference data in the relationship mapping table, and visually displaying the difference data and the performance evaluation data.

[0010] By adopting the above technical solutions, during the process of comparing the on-site usage data with the behavior performance data, data consistency is ensured through preprocessing and format conversion, key features (such as stress distribution, displacement, vibration frequency, and temperature change) are extracted, the difference data between the two is calculated, and the corresponding performance evaluation data is determined using the relationship mapping table. Finally, these differences and evaluation results are visually displayed. This process can accurately identify performance deviations in actual use, provide a reliable basis for design optimization, and thus improve the design accuracy and performance of sports equipment.

[0011] Optionally, during the process of optimizing the design parameters, the method further includes: obtaining the corresponding historical data and real-time data, where the historical data includes previous design data, simulation results, and on-site usage data, and the real-time data is the design data and environmental condition data collected in real time by Internet of Things devices; retrieving the prediction model trained based on the historical data, inputting the real-time data and the historical data into the prediction model, and obtaining the corresponding prediction results; determining the corresponding optimal design parameters based on the prediction results and the performance evaluation data, and applying the optimal design parameters to the basic three-dimensional model.

[0012] By adopting the above technical solution, by integrating historical data (including past design data, simulation results, and on-site usage data) and real-time data (design data and environmental condition data collected by Internet of Things devices), using a prediction model trained based on historical data to generate prediction results, and combining performance evaluation data to determine the optimal design parameters, and finally applying these parameters to the basic 3D model. This process can achieve data-driven intelligent design optimization, improve the accuracy and efficiency of design, and ensure that the performance of sports equipment reaches the optimal level under various conditions.

[0013] Optionally, the method further includes: selecting a cloud platform for multi-designer collaborative work, setting access permissions for different designers on the cloud platform, and during the simulation process, obtaining the design file data jointly edited by different designers on the cloud platform; when a designer modifies the design file data, sending the modified design file data to other designers, and obtaining the modification feedback data of other designers on the modified design file data; running a multi-physics field coupling simulation based on the modification feedback data and the modified design file data.

[0014] By adopting the above technical solution, by selecting a cloud platform for multi-designer collaborative work, setting access permissions for different designers, allowing multiple designers to jointly edit design files on the cloud platform. When a designer modifies a design file, the system will send the modified design file data to other designers and collect their modification feedback data. Based on this feedback data and the modified design file, a multi-physics field coupling simulation is run. This process realizes efficient multi-designer collaborative work, ensures that design changes can be shared and synchronized in real time, improves the consistency and overall quality of the design, and at the same time verifies the effectiveness of design changes through multi-physics field coupling simulation, thereby accelerating design iteration and optimization.

[0015] Optionally, the method further includes: obtaining the historical usage data of the user, collecting the corresponding preference data of the user based on questionnaire surveys, user interviews, or user behavior analysis, where the historical usage data includes the user's exercise habits, equipment usage frequency, performance feedback, and fault reports, and the preference data includes comfort, durability, and cost sensitivity; retrieving a pre-trained prediction model, inputting the historical usage data and the preference data into the prediction model to obtain the personalized parameter data corresponding to the user; sending the personalized parameter data to the user based on the user recommendation platform, and modifying the personalized parameter data based on the user feedback.

[0016] By adopting the above technical solutions, historical usage data of users (such as exercise habits, equipment usage frequency, performance feedback, fault reports) and preference data (such as comfort, durability, cost sensitivity) are collected, and personalized parameter data is generated by using a pre-trained prediction model. These parameter data are sent to the user through a user recommendation platform. Based on the feedback of the user, the personalized parameter data is further modified and improved. This process can provide customized design suggestions according to the actual needs and preferences of the user, improve the user experience and satisfaction of the product, and at the same time optimize the design through continuous user feedback to ensure that the final product better meets the specific needs of the user.

[0017] Optionally, the method further includes: obtaining corresponding real-time monitoring data based on sensors installed at key parts of the target sports equipment, where the real-time monitoring data includes monitored stress distribution, temperature change, vibration condition; retrieving a pre-set monitoring threshold, and comparing the real-time monitoring data with the monitoring threshold; if the real-time monitoring data is greater than the monitoring threshold, it is determined that an abnormal situation is detected, and warning data is generated and sent to relevant responsible persons.

[0018] By adopting the above technical solutions, real-time monitoring data is obtained through sensors installed at key parts of the target sports equipment and compared with a pre-set monitoring threshold. If the real-time monitoring data exceeds the monitoring threshold, it is determined as an abnormal situation, and warning data is generated and sent to relevant responsible persons. This process can achieve real-time monitoring and rapid response, timely discover and handle potential problems, ensure the safety and reliability of sports equipment, and thus improve the user experience and the overall performance of the equipment.

[0019] In a second aspect, the present application provides a three-dimensional simulation design device for the structure of sports equipment, adopting the following technical solutions:

[0020] A three-dimensional simulation design device for the structure of sports equipment includes:

[0021] A basic three-dimensional model construction module determines the corresponding target sports equipment, obtains corresponding basic structure parameters based on the target sports equipment, constructs a basic three-dimensional model corresponding to the target sports equipment based on the basic structure parameters, retrieves pre-collected human motion capture data and performs simulation in the basic three-dimensional model with a virtual human ergonomics module, where the basic structure parameters include material type, geometric dimensions, connection method, and mechanical property indexes;

[0022] A behavior performance data acquisition module, during the simulation process, inputs the human motion capture data into the virtual human ergonomics module, controls the use scenarios of different user groups in specific motion states on the basic three-dimensional model, and obtains the behavior performance data of the basic three-dimensional model under different environmental conditions based on a multi-physics field coupling algorithm;

[0023] The design parameter optimization module combines the on-site usage data collected by the Internet of Things devices for the target sports equipment, compares the on-site usage data with the performance data, obtains the performance evaluation data corresponding to the target sports equipment under various conditions, and uses the performance evaluation data to optimize the design parameters.

[0024] In a third aspect, the present application provides a three-dimensional simulation design method for the structure of sports equipment, adopting the following technical solution:

[0025] A three-dimensional simulation design method for the structure of sports equipment includes a processor, and a program of the three-dimensional simulation design method for the structure of sports equipment as described in any one of the above is run in the processor.

[0026] In a fourth aspect, the present application provides a storage medium, adopting the following technical solution:

[0027] A storage medium stores a program of the three-dimensional simulation design method for the structure of sports equipment as described in any one of the above.

[0028] In summary, the present application includes at least one of the following beneficial technical effects:

[0029] Through high-precision basic structure parameters, high-fidelity human motion capture data, and multi-physics field coupling algorithms, it is ensured that the simulation results are highly consistent with the actual usage situation.

[0030] Combining historical data and real-time data, using a prediction model to generate the best design parameters and applying them to the three-dimensional model to achieve data-driven intelligent design optimization.

[0031] Through the cloud platform, multiple designers are allowed to jointly edit the design files, and the design changes are shared and synchronized in real time, improving the design consistency and overall quality.

[0032] Collect the historical usage data and preference data of users, generate personalized parameter data, and continuously optimize the design through user feedback to improve the user experience and satisfaction.

[0033] Obtain real-time monitoring data through sensors installed at key parts of the equipment, compare it with the preset threshold, and promptly discover abnormal situations and generate warnings to ensure the safety and reliability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flowchart of a three-dimensional simulation design method for the structure of sports equipment shown according to an exemplary embodiment.

[0035] Figure 2It is a structural block diagram of a device for a three-dimensional simulation design method based on the structure of a sports apparatus shown according to an exemplary embodiment. Detailed implementation manners

[0036] The following details the implementation manners of the present application, and examples of the implementation manners are shown in the drawings.

[0037] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0038] An embodiment of the present application discloses a three-dimensional simulation design method based on the structure of a sports apparatus. Referring to Figure 1 , it includes:

[0039] S100, determine the corresponding target sports apparatus, obtain the corresponding basic structure parameters based on the target sports apparatus, construct a basic three-dimensional model corresponding to the target sports apparatus based on the basic structure parameters, and retrieve the pre-collected human motion capture data and the virtual human ergonomics module for simulation in the basic three-dimensional model.

[0040] Among them, according to the project requirements and design goals, determine the specific sports apparatus that needs to be designed by three-dimensional simulation, such as a treadmill, a bicycle, a snowboard, etc., and obtain the basic information of the target sports apparatus, including its functions, usage scenarios, user groups, etc.

[0041] The basic structure parameters include: material type: determine the materials used for each part of the apparatus, including metals, plastics, composite materials, etc., and obtain the physical properties of these materials (such as elastic modulus, density, thermal conductivity, etc.); geometric dimensions: obtain the precise geometric dimensions of each part of the apparatus by measurement or referring to the design drawings; connection method: record the connection methods between the components of the apparatus, such as welding, bolt connection, bonding, etc.; mechanical property indicators: obtain the mechanical property indicators of key components, such as tensile strength, fatigue life, fracture toughness, etc. By providing the basic structure parameters required for constructing the three-dimensional model, the accuracy and reliability of the model can be ensured.

[0042] Then, use CAD software (such as SolidWorks, CATIA, Fusion 360, etc.) to construct a 3D model based on the collected basic structural parameters. Add all necessary details to the 3D model, such as hole positions, chamfers, surface treatments, etc. Finally, through preliminary inspection and verification, ensure that the 3D model is consistent with the actual instrument.

[0043] By retrieving the pre-collected human motion capture data, which can be obtained through an optical motion capture system or inertial sensors, and input the motion capture data into the virtual human ergonomics module. This module usually contains a human biomechanics model. Among them, set parameters such as body shape and muscle strength for different user groups in the virtual human ergonomics module, simulate their usage scenarios in specific motion states, start the simulation, and observe and record the performance of the 3D model under different user groups and motion states. Verify the applicability and comfort of the instrument under different user groups and motion states through simulation to ensure that the design meets the ergonomic requirements.

[0044] S110, during the process of simulation, input the human motion capture data into the virtual human ergonomics module, control the usage scenarios of different user groups on the basic 3D model in specific motion states, and obtain the behavioral performance data of the basic 3D model under different environmental conditions based on the multi-physics field coupling algorithm.

[0045] Among them, input the human motion capture data into the virtual human ergonomics module, set corresponding parameters according to different user groups (such as different heights, weights, ages, motor abilities, etc.), and different motion states need to be set (such as running, cycling, skiing, etc.). Then start the simulation, observe and record the performance of the basic 3D model under different user groups and motion states. Through the simulation of multiple user groups and multiple motion states, comprehensively evaluate the applicability and comfort of the instrument to ensure that the design can meet the needs of a wide range of users.

[0046] In addition, the multi-physics field defines the multi-physics field conditions in the simulation, such as temperature change, humidity, wind speed, etc., and select a suitable multi-physics field coupling algorithm (such as the finite element method, computational fluid dynamics, etc.), run the multi-physics field coupling simulation, obtain the behavioral performance data such as stress distribution, temperature change, vibration conditions, etc. of the basic 3D model under different environmental conditions, and analyze the simulation results to extract key feature data.

[0047] Through the multi-physics field coupling simulation, comprehensively evaluate the performance of the instrument under different environmental conditions to ensure the reliability and safety of the design.

[0048] S120. Combine the on-site usage data collected by the Internet of Things devices for the target sports equipment, compare the on-site usage data with the behavioral performance data, obtain the performance evaluation data corresponding to the target sports equipment under various conditions, and optimize the design parameters according to the performance evaluation data.

[0049] Among them, install various sensors, such as accelerometers, pressure sensors, temperature sensors, etc., at the key parts of the target sports equipment, and transmit the data collected by the sensors to the cloud or local server in real time through a wireless network (such as Wi-Fi, Bluetooth or cellular network), and store the collected data in the database to ensure the security and accessibility of the data.

[0050] In addition, clean, normalize and synchronize the on-site usage data and the behavioral performance data in terms of time, extract key features from the two sets of data, such as stress distribution, displacement, vibration frequency, temperature change, etc., and use statistical methods (such as mean, variance, correlation coefficient, etc.) to compare the differences between the two sets of data. A series of performance indicators (such as durability, comfort, safety, etc.) need to be defined, and the specific values of each indicator are calculated. Combining multiple performance indicators, comprehensively evaluate the overall performance of the equipment under various conditions.

[0051] Finally, according to the evaluation results, identify the problems or deficiencies that occur in the actual use of the equipment. The reasons for the problems can be analyzed, such as improper material selection, unreasonable structural design, environmental factor influence, etc. Based on the obtained analysis results, adjust the corresponding design parameters, such as material thickness, geometric dimensions, connection methods, etc.; based on the adjusted design parameters, re-perform multi-physical field coupling simulation to generate new behavioral performance data, compare the new behavioral performance data with the on-site usage data again to verify the optimization effect, and more data can also be continuously collected, the model can be re-trained, and the above steps can be repeated until satisfactory performance is achieved.

[0052] By performing the above process, the three-dimensional simulation accuracy of sports equipment can be significantly improved, ensuring that the designed products not only meet the technical standards but also better meet the actual needs of users.

[0053] In the solution of the embodiment of the present application, during the process of comparing the on-site usage data with the behavioral performance data, the method further includes:

[0054] S1201. Preprocess the behavioral performance data, convert the format of the preprocessed behavioral performance data to be consistent with the format of the on-site usage data, and extract the corresponding key features from the on-site usage data and the behavioral performance data.

[0055] Among them, the preprocessing includes data cleaning: removing noise, outliers, and incomplete data records from the behavioral performance data; data normalization: converting the behavioral performance data into a unified format and unit for comparison with the on-site usage data. For example, unifying the stress unit to MPa and the temperature unit to degrees Celsius; time synchronization: ensuring that the timestamps of the behavioral performance data are consistent with those of the on-site usage data for accurate comparison.

[0056] In addition, determine the key features to be extracted. These features usually include stress distribution, displacement, vibration frequency, temperature change, etc. Use data processing tools or programming languages (such as Python, MATLAB) to extract these key features from the on-site usage data and the behavioral performance data, and organize the extracted key features into a format suitable for further analysis, such as a table or an array. By extracting the key features, the factors that have the greatest impact on the design performance can be focused on, thus enabling more effective comparison and evaluation.

[0057] S1202, obtain the difference data between the key features of the on-site usage data and the key features of the behavioral performance data, and retrieve the relationship mapping table between the difference data and the performance evaluation data.

[0058] Among them, compare the key features of the on-site usage data with the key features of the behavioral performance data one by one, and calculate the difference between each pair of features. For example, the difference in stress distribution can be represented by calculating the root mean square error (RMSE) of two stress distribution maps. Organize the calculated difference data into a structured data set. By calculating the difference data, the deviation between the actual usage situation and the simulation results can be quantified, providing a basis for further analysis and optimization.

[0059] For the relationship mapping table, a relationship mapping table needs to be constructed in advance. This table defines the mapping relationship between different difference data ranges and the corresponding performance evaluation data. For example, a certain stress distribution difference range may correspond to a certain durability score; according to the calculated difference data, find the corresponding performance evaluation data from the relationship mapping table. Through the relationship mapping table, the difference data can be quickly converted into specific performance evaluation data, simplifying the evaluation process and improving the standardization degree of the evaluation.

[0060] S1203, based on the difference data, determine the corresponding performance evaluation data in the relationship mapping table, and visually display the difference data and the performance evaluation data.

[0061] Among them, according to the differential data, the corresponding performance evaluation data is searched in the relationship mapping table. For example, if the differential data of the stress distribution falls within a certain specific range, the corresponding performance evaluation data may be "good" or "needs improvement", and then the found performance evaluation data is sorted into a structured report or data set; by determining the performance evaluation data through the mapping table, the performance of the device under various conditions can be systematically evaluated, and clear guidance can be provided for subsequent design optimization.

[0062] In addition, use data visualization tools (such as Matplotlib, Tableau, Power BI, etc.) to display the differential data and performance evaluation data in the form of charts. Common chart types include line charts, bar charts, heat maps, etc. An interactive visualization interface can be created to allow users to dynamically view the differential data and performance evaluation data of different features. Through the visual display, the differential data and performance evaluation data can be intuitively shown, helping designers and decision-makers quickly understand the differences between the simulation results and the actual usage conditions, so as to better guide the design optimization.

[0063] By performing the above steps S1201 to S1203, the on-site usage data and behavioral performance data can be systematically compared, key features can be extracted, differential data can be calculated, and the performance evaluation data can be determined through the relationship mapping table. Finally, the results are presented through visualization means; not only does it improve the accuracy and efficiency of data analysis, but it also provides a clear and intuitive basis for design optimization, ensuring that the final product can better meet the actual needs of users.

[0064] It should be noted here that in the process of optimizing design parameters, the method also includes:

[0065] S121, obtain the corresponding historical data and real-time data.

[0066] Among them, historical data collection: Design data: Collect information such as previous design documents, CAD models, material selections, etc.; Simulation results: Obtain previous simulation results, including stress distribution, temperature changes, vibration frequencies, etc.; On-site usage data: Collect data in actual use through Internet of Things devices (such as sensors), including stress distribution, temperature changes, vibration conditions, user feedback, etc. Real-time data collection: Design data and environmental condition data: Real-time collect current design data (such as geometric dimensions, connection methods) and environmental condition data (such as temperature, humidity, wind speed, etc.) through Internet of Things devices (such as sensors) installed on the target sports equipment; Data transmission: Transmit the real-time data to the cloud or local server through a wireless network (such as Wi-Fi, Bluetooth or cellular network); Data storage: Store the collected real-time data in a database to ensure the security and accessibility of the data.

[0067] S122. Retrieve the prediction model trained based on historical data, input the real-time data and historical data into the prediction model, and obtain the corresponding prediction results.

[0068] Among them, for the prediction model, select appropriate machine learning algorithms, such as regression algorithms (linear regression, support vector machine), decision trees, random forests, neural networks, etc. Divide the historical data into a training set and a validation set, and usually use the cross-validation method to improve the generalization ability of the model; use the training set data to train the prediction model, and optimize the model performance by adjusting hyperparameters; use the validation set data to evaluate the performance of the model to ensure that the model has good generalization ability and prediction accuracy; save the trained prediction model for subsequent calls.

[0069] Then clean, normalize, and synchronize the time of the real-time data to ensure the quality and consistency of the data. Merge the real-time data and historical data to form a complete data set. Extract key features from the merged data set, such as material type, geometric dimensions, connection method, environmental conditions, etc. Input the extracted key features into the trained prediction model, and finally run the prediction model to generate the corresponding prediction results of design parameters, such as expected stress distribution, temperature change, vibration frequency, etc.

[0070] By combining real-time data and historical data, use the prediction model to generate accurate prediction results of design parameters, providing a basis for subsequent optimization.

[0071] S123. Determine the corresponding optimal design parameters based on the prediction results and performance evaluation data, and apply the optimal design parameters to the basic 3D model.

[0072] Among them, compare the prediction results with the previously obtained performance evaluation data, identify the differences between the two, and set optimization goals according to design requirements and user needs, such as minimizing stress, maximizing durability, improving comfort, etc. It should be noted here that if there are multiple optimization goals, multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) can be used to find the optimal combination of design parameters; based on the prediction results and performance evaluation data, adjust the design parameters, such as material thickness, geometric dimensions, connection method, etc., to achieve the optimization goal, apply the adjusted design parameters to the 3D model, and re-run the simulation to verify the optimization effect. If the result is not satisfactory, continue to adjust the parameters and repeat the above steps until satisfactory performance is achieved.

[0073] Finally, apply the determined optimal design parameters to the basic 3D model, update the relevant parameters of the model, update the geometric dimensions, material properties, connection methods, etc. of the model in the 3D modeling software. Based on the updated 3D model, re-run the multi-physics field coupling simulation to verify whether the new design parameters achieve the expected effects; conduct a detailed analysis of the simulation results to ensure that all performance indicators meet the design requirements. If the simulation results meet the expectations, finally confirm the design scheme; otherwise, continue to adjust the design parameters and repeat the above steps.

[0074] Through the above steps, this method can systematically utilize historical data and real-time data, combine with prediction models, optimize design parameters, and apply them to the 3D model, which not only improves the design accuracy and efficiency, but also ensures that the final product can achieve the best performance under various conditions and better meet the actual needs of users.

[0075] It should be noted here that for some users, customized personalized sports equipment is required. In the solution of the embodiment of this application, the method further includes:

[0076] S124, obtain the historical usage data of the user, and collect the corresponding preference data of the user based on questionnaire surveys, user interviews or user behavior analysis.

[0077] Among them, the collection of historical usage data: Exercise habits: Collect the exercise data of the user through sensors, wearable devices or mobile applications, such as running speed, cycling distance, exercise frequency, etc.; Equipment usage frequency: Record the frequency and duration of the user's use of sports equipment, which can be achieved through Internet of Things devices (such as sensors) or user logs; Performance feedback: Collect the user's feedback on the performance of the equipment, such as comfort, durability, operation convenience, etc. It can be obtained through online questionnaires, user evaluation systems or customer service records; Fault reports: Record the fault situations encountered by the user during use, including fault types, occurrence times, solutions, etc. It can be obtained through the customer service system or maintenance records.

[0078] Questionnaire survey: Design questionnaire: Design a detailed questionnaire covering the user's preference information, such as comfort, durability, cost sensitivity, etc.; Distribute questionnaire: Distribute the questionnaire to the target users through email, social media, mobile applications, etc.; Collect feedback: Collect the questionnaire feedback of the users, and organize and analyze it.

[0079] User interview: Select a representative user sample for in-depth interviews, prepare an interview outline to ensure that all required preference information is covered, conduct face-to-face or remote interviews with the users, record the feedback and suggestions of the users, organize the interview records, and extract the key preference information.

[0080] User Behavior Analysis: Analyze user behavior data such as clickstream, purchase records, usage frequency, etc. from users on websites, mobile applications, or IoT devices using data analysis tools (such as Google Analytics, Tableau, etc.) to extract users' preference characteristics.

[0081] S125, Retrieve the pre-trained prediction model, input the historical usage data and preference data into the prediction model, and obtain the personalized parameter data corresponding to the user.

[0082] It should be noted here that the prediction model has been described above and will not be elaborated here. Preprocess the new historical usage data and preference data to ensure the quality and consistency of the data. Input the preprocessed data into the trained prediction model, then run the prediction model to generate the personalized parameter data corresponding to the user, such as the best material selection, geometric dimensions, connection methods, etc. Interpret the generated personalized parameter data to ensure it meets the user's needs and expectations.

[0083] By combining historical usage data and preference data, use the prediction model to generate accurate personalized parameter data and provide customized design suggestions for users.

[0084] S126, Send the personalized parameter data to the user based on the user recommendation platform and modify the personalized parameter data based on the user feedback.

[0085] Among them, select or develop a user recommendation platform, such as a mobile application, web page, or email system, integrate the generated personalized parameter data into an easy-to-understand form, such as charts, written descriptions, etc., and send the personalized parameter data to the user through the recommendation platform. It can be in the form of real-time push, regular report, or on-demand query, etc., and provide a friendly user interface to allow users to view and manage the personalized parameter data.

[0086] In addition, collect users' feedback opinions through the recommendation platform, such as satisfaction, improvement suggestions, etc., and analyze the user feedback to identify the specific needs and improvement points of the users. According to the user feedback, adjust the personalized parameter data. For example, if the user feedbacks that a certain component is not comfortable enough, the corresponding geometric dimensions or material selection can be adjusted, and the adjusted parameter data is re-input into the prediction model to generate new personalized parameter data. The above steps can also be continuously repeated until the user is satisfied.

[0087] Through the steps S124 to S126 above, historical usage data and preference data of users can be systematically collected, personalized parameter data can be generated using a prediction model, and this data can be sent to users through a user recommendation platform. At the same time, the personalized parameter data is continuously adjusted and optimized based on user feedback to ensure that the final product can better meet the actual needs of users and improve user experience and satisfaction.

[0088] For the design process, it may involve multiple designers working together, so the method also includes:

[0089] S127, select a cloud platform for multi-designer collaborative work, set access permissions for different designers on the cloud platform, and during the simulation process, obtain the design file data jointly edited by different designers on the cloud platform.

[0090] Among them, select a cloud platform suitable for multi-designer collaborative work, such as Onshape, Fusion360, Autodesk Collaborative Design, PTC Onshape, etc., then register accounts for each designer and set corresponding permissions and roles. For example, the project manager can have all permissions, while ordinary designers can only edit specific parts, and the project leader or administrator creates a new design project on the cloud platform and invites other designers to join.

[0091] Then, according to the roles and responsibilities of the designers, set different access permissions. For example, some designers may only have view permissions, while other designers have edit permissions. Through the management interface of the cloud platform, the permissions of designers can be dynamically adjusted. For example, when a designer completes a certain task, their permission can be downgraded from edit to view to ensure the security and privacy of sensitive data and prevent unauthorized access and modification.

[0092] Designers upload existing design files (such as CAD models, simulation setup files, etc.) to the cloud platform. All designers participating in the project can access these design files and perform real-time editing. The cloud platform automatically processes concurrent conflicts to ensure data consistency; and the cloud platform automatically saves each design version, and designers can go back to previous versions at any time to ensure the traceability of the design process.

[0093] By allowing multiple designers to edit the same design file simultaneously, the design efficiency is improved, and it is ensured that all designers can access the latest design data.

[0094] S128, when a designer modifies the design file data, send the modified design file data to other designers and obtain the modification feedback data from other designers for the modified design file data.

[0095] Among them, the designer modifies the design file on the cloud platform, such as adjusting geometric dimensions, material properties, connection methods, etc. The system automatically detects the modification of the design file and notifies other relevant designers through the built-in notification system (such as email, in-app message), and the system records the content, time, and modifier of each modification for subsequent tracking and review.

[0096] At the same time, the system automatically synchronizes the data of the modified design file to the working environments of other designers. Other designers can view the modification content and provide feedback. The feedback can be carried out through the built-in chat tool, comment function, or dedicated feedback form; designers can conduct real-time discussions on the cloud platform to negotiate the best design solution, and the system supports multiple communication methods such as text chat, voice call, and video conferencing.

[0097] S129, perform multi-physics field coupling simulation based on the modification feedback data and the data of the modified design file.

[0098] Among them, integrate the feedback opinions of all designers into the design file, make necessary adjustments, and based on the updated design file, reset the parameters of the multi-physics field coupling simulation, such as boundary conditions, loads, material properties, etc. Then start the multi-physics field coupling simulation, calculate and generate new behavior performance data, such as stress distribution, temperature change, vibration frequency, etc. The simulation results can be analyzed in detail to evaluate the effect of the design change. If further optimization is needed, continue to adjust the design parameters and repeat the above steps.

[0099] Through the above steps, it is possible to achieve efficient collaborative work of multiple designers on the cloud platform, ensure real-time sharing and synchronization of design files, promote effective communication and feedback among designers, and finally verify the effect of design changes through multi-physics field coupling simulation; not only improve the efficiency and quality of design, but also ensure the performance and reliability of the final product in actual use.

[0100] Finally, for the sports equipment designed and produced according to the solution of this application, the method further includes:

[0101] S131, obtain corresponding real-time monitoring data based on the sensors installed at key parts of the target sports equipment.

[0102] Among them, sensor deployment: Install various sensors such as strain gauges, temperature sensors, accelerometers, etc. at key parts of the target sports equipment. These sensors are used to monitor stress distribution, temperature changes, and vibration conditions; collect data in real time through sensors. For example, strain gauges can measure stress distribution, temperature sensors can measure temperature changes, and accelerometers can measure vibration conditions; transmit the collected data to the cloud or local server in real time through a wireless network (such as Wi-Fi, Bluetooth, or cellular network); store the collected data in a database to ensure data security and accessibility.

[0103] S132, retrieve the pre-set monitoring thresholds and compare the real-time monitoring data with the monitoring thresholds.

[0104] Among them, according to design specifications, historical data, and experience, pre-set the normal ranges and abnormal thresholds of each monitoring parameter. For example, the maximum allowable value of stress, the normal range of temperature, the upper limit of vibration frequency, etc.; and configure and manage these thresholds in the system to ensure that they can be adjusted according to the actual situation. They can be set and updated through the management interface of the cloud platform.

[0105] Preprocess the real-time monitoring data, including data cleaning, normalization, and time synchronization, to ensure data quality and consistency. Compare the preprocessed real-time monitoring data with the pre-set monitoring thresholds. For example, check whether the current stress value exceeds the maximum allowable value, whether the temperature exceeds the normal range, and whether the vibration frequency exceeds the upper limit. If the real-time value of a certain monitoring parameter exceeds the set threshold, it is marked as abnormal.

[0106] S133, if the real-time monitoring data is greater than the monitoring threshold, determine that an abnormal situation has been detected and generate warning data to send to the relevant responsible persons.

[0107] Among them, when the real-time value of a certain monitoring parameter exceeds the set threshold, the system will automatically confirm it as an abnormal situation, record the time when the abnormality occurred, the specific monitoring parameter values, and relevant information for subsequent analysis and processing. The system automatically generates warning information, including specific descriptions of the abnormality, occurrence time, affected components, etc. Set different warning levels (such as low, medium, high) according to the severity of the abnormality so that relevant personnel can respond quickly. For example, minor abnormalities may only require low-level warnings, while severe abnormalities require high-level emergency notifications.

[0108] For the three-dimensional simulation design scheme, monitor the sports equipment produced subsequently. In the case of multiple abnormalities in the sports equipment, it indicates that the possible reasons for the abnormalities are related to the initial design. Thus, establish a feedback mechanism to ensure that designers can respond in a timely manner after receiving warnings and record the processing process and results for optimizing design parameters.

[0109] An embodiment of the present application discloses a three-dimensional simulation design device for a sports equipment structure. Referring to Figure 2 , including but not limited to:

[0110] A basic three-dimensional model construction module 200 determines a corresponding target sports equipment, obtains corresponding basic structure parameters based on the target sports equipment, constructs a basic three-dimensional model corresponding to the target sports equipment based on the basic structure parameters, and retrieves pre-collected human motion capture data and the virtual human engineering module for simulation in the basic three-dimensional model. Among them, the basic structure parameters include material type, geometric dimensions, connection methods, and mechanical performance indicators;

[0111] A behavior performance data acquisition module 210, during the simulation process, inputs the human motion capture data into the virtual human engineering module, controls the use scenarios of different user groups on the basic three-dimensional model under specific motion states, and uses the multi-physics field coupling algorithm to obtain the behavior performance data of the basic three-dimensional model under different environmental conditions;

[0112] A design parameter optimization module 220 combines the on-site use data collected by the Internet of Things devices for the target sports equipment, compares the on-site use data with the behavior performance data, obtains the performance evaluation data corresponding to the target sports equipment under various conditions, and optimizes the design parameters according to the performance evaluation data.

[0113] Furthermore, the device includes but not limited to:

[0114] A key feature extraction module preprocesses the behavior performance data, converts the format of the preprocessed behavior performance data to be consistent with the format of the on-site use data, and extracts corresponding key features from the on-site use data and the behavior performance data. Among them, the key features include stress distribution, displacement, vibration frequency, and temperature change;

[0115] A difference data acquisition module is used to obtain the difference data between the key features of the on-site use data and the key features of the behavior performance data, and retrieves the relationship mapping table between the difference data and the performance evaluation data;

[0116] A performance evaluation data determination module determines the corresponding performance evaluation data based on the difference data in the relationship mapping table, and visualizes the difference data and the performance evaluation data.

[0117] Furthermore, the device includes but not limited to:

[0118] A historical data acquisition module is used to obtain corresponding historical data and real-time data. Among them, the historical data includes previous design data, simulation results, and on-site use data, and the real-time data is the design data and environmental condition data collected in real time through the Internet of Things devices;

[0119] A prediction model retrieval module, which is used to retrieve a prediction model trained based on historical data, input real-time data and historical data into the prediction model, and obtain corresponding prediction results;

[0120] An optimal design parameter determination module, which is used to determine corresponding optimal design parameters based on the prediction results and performance evaluation data, and apply the optimal design parameters to the basic 3D model.

[0121] Furthermore, the device includes but is not limited to:

[0122] A design file data acquisition module, which selects a cloud platform for multi-designer collaborative work, sets access permissions for different designers on the cloud platform, and is used to acquire design file data jointly edited by different designers on the cloud platform during the simulation process;

[0123] A modified feedback data acquisition module, when a designer modifies the design file data and sends the modified design file data to other designers, is used to acquire modified feedback data of other designers on the modified design file data;

[0124] A simulation operation module, which is used to run multi-physics field coupling simulation based on the modified feedback data and the modified design file data.

[0125] Furthermore, the device includes but is not limited to:

[0126] A preference data collection module, which acquires the user's historical usage data and is used to collect the user's corresponding preference data based on questionnaire surveys, user interviews or user behavior analysis. Among them, the historical usage data includes the user's exercise habits, equipment usage frequency, performance feedback, and fault reports, and the preference data includes comfort, durability, and cost sensitivity;

[0127] A personalized parameter data acquisition module, which retrieves a pre-trained prediction model, inputs the historical usage data and preference data into the prediction model, and is used to acquire the user's corresponding personalized parameter data;

[0128] A personalized parameter data modification module, which sends the personalized parameter data to the user based on the user recommendation platform and modifies the personalized parameter data based on the user feedback.

[0129] Furthermore, the device includes but is not limited to:

[0130] A real-time monitoring data acquisition module, which is used to acquire corresponding real-time monitoring data based on sensors installed at key parts of the target sports equipment. Among them, the real-time monitoring data includes monitored stress distribution, temperature change, and vibration condition;

[0131] A monitoring threshold retrieval module, configured to retrieve a pre-set monitoring threshold and compare the real-time monitoring data with the monitoring threshold;

[0132] An early warning data generation module, if the real-time monitoring data is greater than the monitoring threshold, determines that an abnormal situation is detected, and is configured to generate early warning data and send it to relevant responsible persons.

[0133] An embodiment of the present application also discloses a three-dimensional simulation design method for a sports equipment structure, including a processor, and a program of the three-dimensional simulation design method for a sports equipment structure described in any one of the above is run in the processor.

[0134] An embodiment of the present application also discloses a storage medium storing a program of the three-dimensional simulation design method for a sports equipment structure described in any one of the above.

[0135] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A three-dimensional simulation design method based on sports equipment structure, characterized in that: include: Determine a corresponding target sports equipment, obtain corresponding basic structural parameters based on the target sports equipment, construct a basic three-dimensional model corresponding to the target sports equipment based on the basic structural parameters, retrieve pre-collected human motion capture data and a virtual ergonomics module to perform simulation in the basic three-dimensional model, wherein the basic structural parameters include material type, geometric dimensions, connection method, and mechanical performance indicators; During the simulation, the human motion capture data is input into the virtual ergonomics module, the basic three-dimensional model is controlled to simulate the usage scenarios of different user groups in specific motion states, and the behavior performance data of the basic three-dimensional model under different environmental conditions is obtained based on the multi-physics field coupling algorithm; In conjunction with the Internet of Things device, field usage data corresponding to the target sports equipment is collected, the field usage data is compared with the behavioral performance data, performance evaluation data corresponding to the target sports equipment under various conditions is obtained, and design parameters are optimized according to the performance evaluation data.

2. The three-dimensional simulation design method based on sports equipment structure according to claim 1 is characterized in that: In the process of comparing the on-site usage data with the behavioral performance data, the method further includes: Preprocessing the behavior performance data, converting the format of the preprocessed behavior performance data into a format consistent with the field use data, and extracting corresponding key features from the field use data and the behavior performance data, wherein the key features include stress distribution, displacement, vibration frequency, and temperature change; Obtaining difference data between key features of on-site usage data and key features of behavioral performance data, and retrieving a relationship mapping table between the difference data and performance evaluation data; Corresponding performance evaluation data is determined in the relationship mapping table based on the difference data, and the difference data and the performance evaluation data are displayed visually.

3. The three-dimensional simulation design method based on sports equipment structure according to claim 2 is characterized in that: In the process of optimizing the design parameters, the method further comprises: Obtain corresponding historical data and real-time data, where historical data includes previous design data, simulation results, and field usage data, and real-time data is design data and environmental condition data collected in real time through IoT devices; Retrieving a prediction model trained based on the historical data, inputting the real-time data and the historical data into the prediction model, and obtaining corresponding prediction results; Corresponding optimal design parameters are determined based on the prediction results and the performance evaluation data, and the optimal design parameters are applied to the basic three-dimensional model.

4. The three-dimensional simulation design method based on sports equipment structure according to claim 1 is characterized in that: The method also includes: Select a cloud platform where multiple designers can work together, set access permissions for different designers on the cloud platform, and obtain the design file data jointly edited by different designers on the cloud platform during the simulation process; When a designer modifies the design file data, the modified design file data is sent to other designers, and modification feedback data of the modified design file data from other designers is obtained; A multi-physics coupling simulation is run based on the modified feedback data and the modified design file data.

5. The three-dimensional simulation design method based on sports equipment structure according to claim 1 is characterized in that: The method also includes: Obtain users' historical usage data and collect users' corresponding preference data based on questionnaires, user interviews or user behavior analysis. The historical usage data includes users' exercise habits, frequency of equipment use, performance feedback, and fault reports. The preference data includes comfort, durability, and cost sensitivity. Retrieving a pre-trained prediction model, inputting the historical usage data and the preference data into the prediction model, and obtaining personalized parameter data corresponding to the user; The personalized parameter data is sent to users based on the user recommendation platform, and the personalized parameter data is modified based on user feedback.

6. The three-dimensional simulation design method based on sports equipment structure according to claim 1 is characterized in that: The method also includes: Acquire corresponding real-time monitoring data based on sensors installed at key parts of target sports equipment, where the real-time monitoring data includes monitoring stress distribution, temperature changes, and vibration conditions; Retrieving a preset monitoring threshold, and comparing the real-time monitoring data with the monitoring threshold; If the real-time monitoring data is greater than the monitoring threshold, it is determined that an abnormal situation has been detected, and early warning data is generated and sent to the relevant responsible persons.

7. A three-dimensional simulation design device based on sports equipment structure, characterized in that: include: A basic three-dimensional model construction module determines a corresponding target sports equipment, obtains corresponding basic structural parameters based on the target sports equipment, uses the basic structural parameters to construct a basic three-dimensional model corresponding to the target sports equipment, retrieves pre-collected human motion capture data and a virtual ergonomics module to perform simulation in the basic three-dimensional model, wherein the basic structural parameters include material type, geometric size, connection method and mechanical performance index; A behavior performance data acquisition module, during the simulation process, inputs the human motion capture data into a virtual ergonomics module, controls the basic three-dimensional model to simulate usage scenarios of different user groups in specific motion states, and is used to acquire the behavior performance data of the basic three-dimensional model under different environmental conditions based on a multi-physics field coupling algorithm; The design parameter optimization module combines with the Internet of Things device to collect the field usage data corresponding to the target sports equipment, compares the field usage data with the behavioral performance data, obtains the performance evaluation data corresponding to the target sports equipment under various conditions, and uses the performance evaluation data to optimize the design parameters.

8. The three-dimensional simulation design device based on sports equipment structure according to claim 7 is characterized in that: include: A key feature extraction module preprocesses the behavior performance data, converts the format of the preprocessed behavior performance data into a format consistent with the field use data, and extracts corresponding key features from the field use data and the behavior performance data, wherein the key features include stress distribution, displacement, vibration frequency, and temperature change; A difference data acquisition module is used to acquire difference data between key features of field use data and key features of behavioral performance data, and retrieve a relationship mapping table between the difference data and performance evaluation data; The performance evaluation data determination module is used to determine corresponding performance evaluation data based on the difference data in the relationship mapping table, and visually displays the difference data and the performance evaluation data.

9. A three-dimensional simulation design method based on sports equipment structure, characterized in that: It comprises a processor, in which runs a program based on the three-dimensional simulation design method of sports equipment structure as described in any one of claims 1 to 6.

10. A storage medium, characterized in that: A program based on a three-dimensional simulation design method for a sports equipment structure as described in any one of claims 1 to 6 is stored.

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