Three-dimensional simulation design method based on sports equipment structure

Abstract

The invention relates to the technical field of computers, and discloses a sports equipment structure-based three-dimensional simulation design method, which comprises the steps of determining a corresponding target sports equipment, acquiring 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 designing a three-dimensional model of the target sports equipment. Pre-collected human body motion capture data and the virtual human engineering module are called for simulation in the basic three-dimensional model; in the simulation process, the basic three-dimensional model is controlled to simulate use scenes of different user groups in a specific motion state, and behavior performance data of the basic three-dimensional model under different environment conditions are obtained; and collecting field use data corresponding to the target sports equipment, comparing the field use data with the behavior performance data to obtain performance evaluation data corresponding to the target sports equipment under various conditions, and optimizing the design parameters according to the performance evaluation data. The precision of three-dimensional simulation of the sports equipment can be improved.

Description

Technical Field

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

[0002] Traditional sports equipment design mainly relies on the designer's experience and limited physical model testing, but this method has the following disadvantages: from concept to finished product, multiple physical prototypes and tests are often required, which consumes a lot of time and resources; due to cost constraints, only some parameters can be tested, making it difficult to fully evaluate the performance of the equipment; most equipment is produced in a standardized manner and cannot meet the specific needs of different user groups.

[0003] With the development of computing technology, 3D simulation technology has begun to be applied to the design of sports equipment. 3D simulation can quickly generate multiple design schemes and test them under various conditions in a virtual environment, greatly shortening the design cycle. However, existing 3D simulation methods still have some problems with low simulation accuracy.

[0004] From the above, we can see that how to improve the accuracy of three-dimensional simulation of sports equipment still needs to be solved. Summary of the invention

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

[0006] In the first aspect, the present application provides a three-dimensional simulation design method based on sports equipment structure, which adopts the following technical solution:

[0007] A three-dimensional simulation design method based on sports equipment structure includes: determining a corresponding target sports equipment, obtaining corresponding basic structural parameters based on the target sports equipment, constructing a basic three-dimensional model corresponding to the target sports equipment based on the basic structural parameters, retrieving 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 process, inputting the human motion capture data into the virtual ergonomics module, controlling the basic three-dimensional model to simulate usage scenarios of different user groups in specific motion states, and obtaining behavioral performance data of the basic three-dimensional model under different environmental conditions based on a multi-physical field coupling algorithm; collecting field usage data corresponding to the target sports equipment in combination with an Internet of Things device, comparing the field usage data with the behavioral performance data, obtaining performance evaluation data corresponding to the target sports equipment under various conditions, and optimizing 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 ergonomics modules, combined with on-site usage data collected by IoT devices, to achieve comprehensive simulation and evaluation of the behavioral performance of the target sports equipment under different environmental conditions. It can optimize the design parameters according to the performance evaluation data, and can significantly improve the accuracy of the three-dimensional simulation of sports equipment, ensuring that the designed products not only meet the technical standards, but also better meet the actual needs of users.

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

[0010] By adopting the above technical solution, in the process of comparing field use data with behavioral performance data, data consistency is ensured through preprocessing and format conversion, key features (such as stress distribution, displacement, vibration frequency, temperature change) are extracted, the difference data between the two is calculated, and the corresponding performance evaluation data is determined using a relationship mapping table, and finally these differences and evaluation results are displayed through visualization. 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, in the process of optimizing the design parameters, the method also includes: obtaining corresponding historical data and real-time data, wherein the historical data includes previous design data, simulation results, and field usage data, and the real-time data is design data and environmental condition data collected in real time by Internet of Things devices; calling a prediction model trained based on the historical data, inputting the real-time data and historical data into the prediction model, and obtaining corresponding prediction results; determining 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 solutions, historical data (including previous design data, simulation results and field use data) and real-time data (design data and environmental condition data collected by IoT devices) are integrated, and prediction results are generated using a prediction model trained based on historical data, and the optimal design parameters are determined in combination with performance evaluation data, and finally these parameters are applied to the basic three-dimensional model. This process can realize data-driven intelligent design optimization, improve the accuracy and efficiency of design, and ensure that the performance of sports equipment under various conditions is optimal.

[0013] Optionally, the method also includes: selecting a cloud platform for multiple designers to work collaboratively, setting access permissions for different designers on the cloud platform, and obtaining design file data jointly edited by different designers on the cloud platform during the simulation process; when a designer modifies the design file data, sending the modified design file data to other designers, and obtaining modification feedback data of other designers on the modified design file data; and 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 multiple designers to work together and setting access rights for different designers, multiple designers are allowed to edit design files together 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 these feedback data and the modified design file, multi-physics coupling simulation is run. This process realizes efficient multi-designer collaboration, ensures that design changes can be shared and synchronized in real time, improves the consistency and overall quality of the design, and verifies the effectiveness of the design changes through multi-physics coupling simulation, thereby accelerating design iteration and optimization.

[0015] Optionally, the method also includes: obtaining the user's historical usage data, collecting the user's corresponding preference data based on questionnaires, user interviews or user behavior analysis, wherein 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, and obtaining the user's corresponding personalized parameter data; sending the personalized parameter data to the user based on a user recommendation platform, and modifying the personalized parameter data based on user feedback.

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

[0017] Optionally, the method also includes: obtaining corresponding real-time monitoring data based on sensors installed at key parts of the target sports equipment, wherein the real-time monitoring data includes monitoring stress distribution, temperature changes, and vibration conditions; 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 has been monitored, and early warning data is generated and sent to relevant responsible persons.

[0018] By adopting the above technical solution, real-time monitoring data is obtained through sensors installed in key parts of the target sports equipment and compared with the pre-set monitoring threshold. If the real-time monitoring data exceeds the monitoring threshold, it is determined to be an abnormal situation, and early warning data is generated and sent to the relevant responsible persons. This process can achieve real-time monitoring and rapid response, timely discover and deal with 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 the second aspect, the present application provides a three-dimensional simulation design device based on the structure of sports equipment, which adopts the following technical solution:

[0020] A three-dimensional simulation design device based on sports equipment structure, comprising:

[0021] 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;

[0022] A behavior performance data acquisition module, during the simulation process, inputs the human motion capture data into the virtual ergonomics module, controls the basic three-dimensional model to simulate the 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;

[0023] 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.

[0024] In a third aspect, the present application provides a three-dimensional simulation design method based on the structure of sports equipment, which adopts the following technical solutions:

[0025] A method for designing a structure of sports equipment based on three-dimensional simulation includes a processor in which a program of any one of the above-mentioned methods for designing a structure of sports equipment based on three-dimensional simulation is run.

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

[0027] A storage medium stores a program according to any one of the above-mentioned three-dimensional simulation design methods for sports equipment structures.

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

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

[0030] Combining historical data with real-time data, the predictive model is used to generate the best design parameters, which are applied to the 3D model to achieve data-driven intelligent design optimization.

[0031] The cloud platform allows multiple designers to jointly edit design files, share and synchronize design changes in real time, and improve design consistency and overall quality.

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

[0033] Real-time monitoring data is obtained through sensors installed in key parts of the device, which are compared with preset thresholds to detect abnormal situations in a timely manner and generate early warnings to ensure the safety and reliability of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of a method for designing a sports equipment structure based on three-dimensional simulation according to an exemplary embodiment.

[0035] Figure 2It is a structural block diagram of a device for a three-dimensional simulation design method of a sports equipment structure according to an exemplary embodiment. DETAILED DESCRIPTION

[0036] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

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

[0038] The present application embodiment discloses a three-dimensional simulation design method based on sports equipment structure, referring to Figure 1 ,include:

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

[0040] Among them, according to project requirements and design goals, determine the specific sports equipment that needs to be three-dimensionally simulated, such as treadmills, bicycles, skis, etc., and obtain the basic information of the target sports equipment, including its functions, usage scenarios, user groups, etc.

[0041] Basic structural parameters include: Material type: determine the materials used in each part of the device, including metal, plastic, 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 device by measuring or consulting the design drawings; Connection method: record the connection method between the various parts of the device, such as welding, bolt connection, bonding, etc.; Mechanical performance indicators: obtain the mechanical performance indicators of key components, such as tensile strength, fatigue life, fracture toughness, etc. By providing the basic structural parameters required to build a 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 build a 3D model based on the collected basic structural parameters, add all necessary details such as hole positions, chamfers, surface treatment, etc. to the 3D model, and finally conduct preliminary inspection and verification to ensure that the 3D model is consistent with the actual device.

[0043] By retrieving the pre-collected human motion capture data, which can be obtained through an optical motion capture system or an inertial sensor, and inputting the motion capture data into a virtual ergonomics module, which usually contains a human biomechanical model, the body shape, muscle strength and other parameters of different user groups are set in the virtual ergonomics module to simulate their usage scenarios in specific motion states, start the simulation, observe and record the performance of the three-dimensional model in different user groups and motion states. The applicability and comfort of the equipment in different user groups and motion states are verified through simulation to ensure that the design meets ergonomic requirements.

[0044] S110, during the simulation process, 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 behavioral performance data of the basic three-dimensional model under different environmental conditions is obtained based on the multi-physics field coupling algorithm.

[0045] Among them, the human motion capture data is input into the virtual ergonomics module, and the corresponding parameters are set according to different user groups (such as different heights, weights, ages, athletic abilities, etc.), and different sports states need to be set (such as running, cycling, skiing, etc.), and then the simulation is started to observe and record the performance of the basic three-dimensional model in different user groups and sports states. Through the simulation of multiple user groups and multiple sports states, the applicability and comfort of the equipment are fully evaluated to ensure that the design can meet the needs of a wide range of users.

[0046] In addition, multi-physics is to define the multi-physics conditions in the simulation, such as temperature changes, humidity, wind speed, etc., and select the appropriate multi-physics coupling algorithm (such as finite element method, computational fluid dynamics, etc.), run multi-physics coupling simulation, obtain the basic three-dimensional model under different environmental conditions. Behavioral performance data such as stress distribution, temperature changes, vibration conditions, etc., and analyze the simulation results to extract key feature data.

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

[0048] S120, collect field usage data corresponding to the target sports equipment in combination with the Internet of Things device, compare the field usage data with the behavioral performance data, obtain 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, various sensors, such as accelerometers, pressure sensors, temperature sensors, etc., are installed at key parts of the target sports equipment, and the data collected by the sensors are transmitted to the cloud or local server in real time through wireless networks (such as Wi-Fi, Bluetooth or cellular networks), and the collected data is stored in a database to ensure the security and accessibility of the data.

[0050] In addition, the field use data and behavioral performance data are cleaned, normalized and time-synchronized, key features such as stress distribution, displacement, vibration frequency, temperature change, etc. are extracted from the two sets of data, and statistical methods (such as mean, variance, correlation coefficient, etc.) are used to compare the differences between the two sets of data. It is necessary to define a series of performance indicators (such as durability, comfort, safety, etc.), calculate the specific value of each indicator, and combine multiple performance indicators to comprehensively evaluate the overall performance of the device under various conditions.

[0051] Finally, based on the evaluation results, problems or deficiencies in the actual use of the device can be identified, and the causes of the problems can be analyzed, such as improper material selection, unreasonable structural design, and environmental factors. Based on the analysis results, the corresponding design parameters can be adjusted, such as material thickness, geometric dimensions, connection methods, etc. Based on the adjusted design parameters, the multi-physical field coupling simulation is re-performed to generate new behavioral performance data, and the new behavioral performance data is compared with the field use data again to verify the optimization effect. More data can also be collected, the model can be retrained, and the above steps can be repeated until satisfactory performance is achieved.

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

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

[0054] S1201, preprocessing the behavior performance data, converting the format of the preprocessed behavior performance data into a format consistent with the field usage data, and extracting corresponding key features from the field usage data and the behavior performance data.

[0055] Among them, preprocessing includes data cleaning: removing noise, outliers and incomplete data records in behavioral performance data; data normalization: converting behavioral performance data into a unified format and unit for comparison with field usage data, for example, unifying stress units into MPa and temperature units into degrees Celsius; time synchronization: ensuring that the timestamp of the behavioral performance data is consistent with the timestamp of the field usage data for accurate comparison.

[0056] In addition, key features that need to be extracted are determined. These features usually include stress distribution, displacement, vibration frequency, temperature change, etc. These key features are extracted from field use data and behavioral performance data using data processing tools or programming languages ​​(such as Python, MATLAB), and the extracted key features are organized into a format suitable for further analysis, such as a table or array. By extracting key features, you can focus on the factors that have the greatest impact on design performance, allowing for more effective comparison and evaluation.

[0057] S1202, 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.

[0058] Among them, the key features of the field use data are compared one by one with the key features of the behavioral performance data, and the difference between each pair of features is calculated. For example, the difference in stress distribution can be expressed by calculating the root mean square error (RMSE) of the two stress distribution graphs, and the calculated difference data is organized into a structured data set. By calculating the difference data, the deviation between the actual usage 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 built in advance. The table defines the mapping relationship between different difference data ranges and 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, the corresponding performance evaluation data is found from the relationship mapping table. Through the relationship mapping table, the difference data can be quickly converted into specific performance evaluation data, which simplifies the evaluation process and improves the standardization of the evaluation.

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

[0061] Among them, according to the difference data, the corresponding performance evaluation data is found in the relationship mapping table. For example, if the difference data of stress distribution falls within a certain range, the corresponding performance evaluation data may be "good" or "needs improvement", and then the performance evaluation data found is organized 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, data visualization tools (such as Matplotlib, Tableau, Power BI, etc.) are used to display the difference data and performance evaluation data in the form of charts. Common chart types include line charts, bar charts, heat maps, etc., which can create an interactive visualization interface that allows users to dynamically view the difference data and performance evaluation data of different features. Through visualization, the difference data and performance evaluation data can be intuitively displayed, helping designers and decision makers to quickly understand the differences between simulation results and actual usage, thereby better guiding design optimization.

[0063] By executing the above steps S1201 to S1203, it is possible to systematically compare field usage data and behavioral performance data, extract key features, calculate difference data, determine performance evaluation data through a relationship mapping table, and finally display the results through visualization. This not only improves the accuracy and efficiency of data analysis, but 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 pointed out here that in the process of optimizing design parameters, the method also includes:

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

[0066] Among them, historical data collection: Design data: collect previous design documents, CAD models, material selection and other information; Simulation results: obtain previous simulation results, including stress distribution, temperature changes, vibration frequency, etc.; Field use data: collect data from actual use through IoT devices (such as sensors), including stress distribution, temperature changes, vibration conditions, user feedback, etc. Real-time data collection: Design data and environmental condition data: collect current design data (such as geometric dimensions, connection methods) and environmental condition data (such as temperature, humidity, wind speed, etc.) in real time through IoT devices (such as sensors) installed on target sports equipment; Data transmission: transmit real-time data to the cloud or local server through wireless networks (such as Wi-Fi, Bluetooth or cellular networks); Data storage: store the collected real-time data in the database to ensure data security and accessibility.

[0067] S122, calling a prediction model trained based on historical data, inputting real-time data and historical data into the prediction model, and obtaining corresponding prediction results.

[0068] Among them, for the prediction model, select appropriate machine learning algorithms, such as regression algorithms (linear regression, support vector machines), decision trees, random forests, neural networks, etc., divide historical data into training sets and validation sets, and usually use cross-validation methods 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 the 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] The real-time data is then cleaned, normalized, and time-synchronized to ensure data quality and consistency. The real-time data is merged with historical data to form a complete data set. Key features are extracted from the merged data set, such as material type, geometric dimensions, connection methods, environmental conditions, etc. The extracted key features are input into the trained prediction model. Finally, the prediction model is run to generate corresponding design parameter prediction results, such as expected stress distribution, temperature changes, vibration frequency, etc.

[0070] By combining real-time data and historical data, the predictive model is used to generate accurate design parameter prediction results, providing a basis for subsequent optimization.

[0071] S123, determining 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.

[0072] Among them, the prediction results are compared with the performance evaluation data obtained previously to identify the differences between the two, and the optimization goals are set according to the design requirements and user needs, such as minimizing stress, maximizing durability, and improving comfort. It should be pointed out here that if there are multiple optimization goals, a multi-objective optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) can be used to find the best design parameter combination; based on the prediction results and performance evaluation data, the design parameters such as material thickness, geometric dimensions, connection methods, etc. are adjusted to achieve the optimization goals, and the adjusted design parameters are applied to the three-dimensional model, and the simulation is re-run to verify the optimization effect. If the results are not ideal, 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 model's geometric dimensions, material properties, connection methods, etc. in the 3D modeling software, and rerun the multi-physics field coupling simulation based on the updated 3D model to verify whether the new design parameters have achieved the expected results; perform a detailed analysis of the simulation results to ensure that all performance indicators meet the design requirements. If the simulation results meet expectations, finally confirm the design plan; 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 predictive models, optimize design parameters, and apply them to three-dimensional models, which not only improves the accuracy and efficiency of design, but also ensures that the final product can achieve optimal performance under various conditions and better meet the actual needs of users.

[0075] It should be noted here that for some users, some personalized sports equipment needs to be customized. In the solution of the embodiment of the present application, the method also includes:

[0076] S124, obtaining the user's historical usage data, and collecting the user's corresponding preference data based on questionnaire surveys, user interviews or user behavior analysis.

[0077] Among them, historical usage data collection: Exercise habits: collect user exercise data such as running speed, cycling distance, exercise frequency, etc. through sensors, wearable devices or mobile applications; Equipment use frequency: record the frequency and duration of users using sports equipment, which can be achieved through IoT devices (such as sensors) or user logs; Performance feedback: collect user feedback on equipment performance, such as comfort, durability, ease of operation, etc. It can be obtained through online questionnaires, user evaluation systems or customer service records; Fault reports: record faults encountered by users during use, including fault type, occurrence time, solution, etc. It can be obtained through customer service systems or maintenance records.

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

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

[0080] User behavior analysis: Through the user's behavior data on websites, mobile applications or IoT devices, such as click streams, purchase records, usage frequency, etc., use data analysis tools (such as Google Analytics, Tableau, etc.) to analyze user behavior data and extract user preference characteristics.

[0081] S125, calling a pre-trained prediction model, inputting historical usage data and preference data into the prediction model, and obtaining personalized parameter data corresponding to the user.

[0082] It should be pointed out here that the prediction model has been explained above, so I will not repeat it here. The new historical usage data and preference data are preprocessed to ensure the quality and consistency of the data. The preprocessed data is input into the trained prediction model, and then the prediction model is run to generate personalized parameter data corresponding to the user, such as the best material selection, geometric dimensions, connection methods, etc. The generated personalized parameter data is interpreted to ensure that it meets the user's needs and expectations.

[0083] By combining historical usage data and preference data, predictive models are used to generate accurate personalized parameter data to provide users with customized design recommendations.

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

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

[0086] In addition, the recommendation platform collects user feedback, such as satisfaction, improvement suggestions, etc., and analyzes user feedback to identify users' specific needs and improvement points. According to user feedback, the personalized parameter data is adjusted. 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 can be re-entered into the prediction model to generate new personalized parameter data. The above steps can also be repeated until the user is satisfied.

[0087] Through the above steps S124 to S126, the user's historical usage data and preference data can be systematically collected, and personalized parameter data can be generated using the prediction model, and these data can be sent to the user through the 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 user's actual needs and improve user experience and satisfaction.

[0088] The design process may involve multiple designers, so the method also includes:

[0089] S127, selecting a cloud platform for multiple designers to work collaboratively, setting access rights for different designers on the cloud platform, and obtaining design file data jointly edited by different designers on the cloud platform during the simulation process.

[0090] Among them, choose a cloud platform suitable for collaborative work among multiple designers, such as Onshape, Fusion360, Autodesk Collaborative Design, PTC Onshape, etc., then register an account 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. The project leader or administrator creates a new design project on the cloud platform and invites other designers to join.

[0091] Different access permissions are then set based on the designer’s roles and responsibilities. For example, some designers may only have viewing permissions, while other designers have editing permissions. Designer permissions can be dynamically adjusted through the cloud platform’s management interface. For example, when a designer completes a task, his or her permissions can be downgraded from editing to viewing, thereby ensuring the security and privacy of sensitive data and preventing unauthorized access and modification.

[0092] Designers upload existing design files (such as CAD models, simulation setting files, etc.) to the cloud platform. All designers involved in the project can access these design files and edit them in real time. The cloud platform will automatically handle concurrency conflicts to ensure data consistency. The cloud platform automatically saves each design version, and designers can go back to the previous version at any time to ensure the traceability of the design process.

[0093] Improve design efficiency by allowing multiple designers to edit the same design file at the same time and ensure that all designers have access to the latest design data.

[0094] S128, 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.

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

[0096] At the same time, the system automatically synchronizes the modified design file data to the work environment of other designers. Other designers can view the modified content and provide feedback through built-in chat tools, comment functions or special feedback forms. Designers can have real-time discussions on the cloud platform and negotiate the best design plan. The system supports multiple communication methods such as text chat, voice calls and video conferencing.

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

[0098] Among them, all the feedback from designers is integrated into the design file, necessary adjustments are made, and based on the updated design file, the parameters of the multi-physics field coupling simulation are reset, such as boundary conditions, loads, material properties, etc. Then the multi-physics field coupling simulation is started to 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 changes. If further optimization is required, the design parameters are adjusted and the above steps are repeated.

[0099] Through the above steps, efficient collaborative work among multiple designers on the cloud platform can be achieved, ensuring real-time sharing and synchronization of design files, promoting effective communication and feedback among designers, and ultimately verifying the effects of design changes through multi-physics field coupling simulation; this not only improves the efficiency and quality of the design, but also ensures the performance and reliability of the final product in actual use.

[0100] Finally, in the sports equipment designed and produced according to the scheme of the present application, the method also includes:

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

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

[0103] S132, retrieve a preset monitoring threshold, and compare the real-time monitoring data with the monitoring threshold.

[0104] Among them, according to design specifications, historical data and experience, the normal range and abnormal threshold of each monitoring parameter are pre-set, such as the maximum allowable value of stress, the normal range of temperature, the upper limit of vibration frequency, etc., and these thresholds are configured and managed in the system to ensure that they can be adjusted according to actual conditions. 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 the quality and consistency of the data. 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 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, it is determined that an abnormal situation has been monitored, and warning data is generated and sent to the relevant responsible persons.

[0107] Among them, when the real-time value of a 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 value and related information, and facilitate subsequent analysis and processing. The system automatically generates warning information, including a specific description of the abnormality, the time of occurrence, the affected components, etc., and sets different warning levels (such as low, medium, and high) according to the severity of the abnormality, so that relevant personnel can respond quickly. For example, a minor abnormality may only require a low-level warning, while a serious abnormality requires a high-level emergency notification.

[0108] For the 3D simulation design solution, the sports equipment produced subsequently is monitored. If the sports equipment exhibits abnormalities multiple times, it indicates that the cause of the abnormality may be related to the initial design, thereby establishing a feedback mechanism to ensure that the designer can respond in a timely manner after receiving the early warning, and record the processing process and results to optimize the design parameters.

[0109] The present application embodiment discloses a three-dimensional simulation design device based on the structure of sports equipment, referring to Figure 2 , including but not limited to:

[0110] The basic three-dimensional model construction module 200 determines the corresponding target sports equipment, obtains the corresponding basic structural parameters based on the target sports equipment, uses the basic structural parameters to construct the basic three-dimensional model corresponding to the target sports equipment, retrieves the pre-collected human motion capture data and simulates it in the basic three-dimensional model with the virtual ergonomics module, wherein the basic structural parameters include material type, geometric size, connection method and mechanical performance index;

[0111] The behavior performance data acquisition module 210 inputs the human motion capture data into the virtual ergonomics module during the simulation process, controls the basic three-dimensional model to simulate the 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 the multi-physics field coupling algorithm;

[0112] The design parameter optimization module 220 collects field usage data corresponding to the target sports equipment in conjunction with the Internet of Things device, compares the field usage data with the behavioral performance data, obtains performance evaluation data corresponding to the target sports equipment under various conditions, and uses the performance evaluation data to optimize the design parameters.

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

[0114] A key feature extraction module pre-processes the behavior performance data, converts the format of the pre-processed 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;

[0115] A difference data acquisition module, 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;

[0116] The performance evaluation data determination module is used to determine 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 is not limited to:

[0118] A historical data acquisition module is used to acquire corresponding historical data and real-time data, wherein the historical data includes previous design data, simulation results, and field use data, and the real-time data is the design data and environmental condition data collected in real time through IoT devices;

[0119] The prediction model retrieval module is used to 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;

[0120] The optimal design parameter determination module is used to determine the corresponding optimal design parameters based on the prediction results and the performance evaluation data, and apply the optimal design parameters to the basic three-dimensional model.

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

[0122] The design file data acquisition module selects a cloud platform where multiple designers work together, sets access rights for different designers on the cloud platform, and is used to obtain the design file data jointly edited by different designers on the cloud platform during the simulation process;

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

[0124] The simulation running module 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] The preference data collection module obtains 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. The historical usage data includes the user's exercise habits, equipment usage frequency, performance feedback, and fault reports. The preference data includes comfort, durability, and cost sensitivity.

[0127] The personalized parameter data acquisition module calls the pre-trained prediction model and inputs the historical usage data and preference data into the prediction model to obtain the personalized parameter data corresponding to the user;

[0128] The personalized parameter data modification module sends the personalized parameter data to the user based on the user recommendation platform and is used to modify the personalized parameter data based on user feedback.

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

[0130] A real-time monitoring data acquisition module is used to acquire corresponding real-time monitoring data based on sensors installed at key parts of the target sports equipment, wherein the real-time monitoring data includes monitoring stress distribution, temperature changes, and vibration conditions;

[0131] A monitoring threshold retrieval module is used to retrieve a preset monitoring threshold and compare the real-time monitoring data with the monitoring threshold;

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

[0133] An embodiment of the present application also discloses a method for designing a sports equipment structure based on three-dimensional simulation, including a processor in which a program of any one of the above-mentioned methods for designing a sports equipment structure based on three-dimensional simulation is run.

[0134] The embodiment of the present application further discloses a storage medium storing a program of any one of the above-mentioned three-dimensional simulation design methods based on the structure of sports equipment.

[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 cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify 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 a corresponding prediction result; 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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