Sports building natural light environment optimization method and system based on visual work efficiency

Abstract

The invention discloses a sports building natural light environment optimization method and system based on visual work efficiency, and relates to the field of building visual work efficiency. The problem that in the prior art, a visual ergonomics method is applied, physiological and psychological indexes and cognitive tasks are combined to evaluate the light environment is solved. However, the visual ergonomics method is not suitable for sports buildings, and a complete visual ergonomics evaluation system needs to be established urgently. The method comprises the following steps: step 1, constructing a diversified top-side interface virtual gymnasium model; 2, preprocessing and analyzing the visual work efficiency data collected in the step 1, checking the preprocessed and analyzed data in combination with statistical analysis and an intermediary effect, and constructing a visual work efficiency evaluation factor prediction and evaluation model; and step 3, analyzing the simulation data by applying a genetic algorithm, obtaining an optimization target value of indoor environment performance, determining a top side interface virtual gymnasium model form design parameter, and screening and storing optimal design parameter information. The method is suitable for the field of sports building interface form optimization design based on visual work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of building visual ergonomics, and in particular to a method and system for optimizing the natural light environment of a sports building based on visual ergonomics. Background Art

[0002] With the rise of the national fitness craze in my country, the indoor environmental quality of sports venues has become increasingly critical, directly affecting the comfort and effectiveness of exercise. In the design of sports venues, the quality of natural lighting is restricted by multiple factors, such as the orientation of the venue, the interface form, the lighting method, the size and location of the lighting opening, and the material properties, which are determined in the early stage of design based on the type and scale of the building. Although the existing lighting specifications control the lighting of competition venues, there is still room for optimization according to the new human factors evaluation standards, and the evaluation system needs to be further integrated and constructed. Therefore, in-depth exploration of the human factors evaluation of sports venues is crucial to the sustainable development of sports buildings.

[0003] The study of natural light environment in gymnasiums often uses actual measurement, simulation and miniature models to explore the impact of variables on indoor performance. In the field of visual ergonomics, visual ergonomics methods are used to evaluate the light environment by combining physiological, psychological indicators and cognitive tasks. However, the existing technical methods are not suitable for sports buildings, and it is urgent to establish a complete visual ergonomics evaluation system.

[0004] In subjective evaluation tests, quantitative control of complex building light environments is quite challenging. In order to balance accuracy and diversity, researchers tend to use virtual scene experiments to accurately restore and control the light environment. In the virtual environment, the top and side interface forms are adjusted to change the light environment properties, and subjective feelings, objective reactions and attention levels are studied in depth. Visual ergonomics data is collected, and the building form is optimized with the visual ergonomics comprehensive evaluation factor (VEA) as the goal. On the Rhino platform, parametric VEA can be combined with a multi-objective genetic algorithm to construct a sports building simulation optimization method to help architects seek the interface form with the best visual ergonomics evaluation during the sports building design stage. Summary of the invention

[0005] The present invention aims to solve the problem that the visual ergonomics method is used in the field of visual ergonomics in the prior art to evaluate the light environment by combining physiological, psychological indicators and cognitive tasks. However, the visual ergonomics method is not suitable for sports buildings, and it is urgent to establish a complete visual ergonomics evaluation system.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] Solution 1: The present invention proposes a method for optimizing the natural light environment of a sports building based on visual ergonomics, the method comprising the following steps:

[0008] Step 1: Build a virtual gymnasium model with diverse top and side interfaces to collect visual ergonomics data in various environments;

[0009] Step 2: Preprocess and analyze the visual ergonomics data collected in various environments in step 1, test the preprocessed data by combining statistical analysis and mediation effect, and build a prediction and evaluation model of visual ergonomics evaluation factor VEA based on natural light;

[0010] Step 3: Based on the visual ergonomics evaluation factor VEA prediction and evaluation model described in step 2, a genetic algorithm is used to analyze the simulation data, obtain the optimization target value of the indoor environmental performance, establish the top and side interface virtual gymnasium model morphological design parameters, and screen and store the optimal design parameter information.

[0011] Furthermore, a preferred implementation manner is provided, in which a virtual gymnasium model with diversified top and side interfaces is constructed in step 1, and the method for collecting visual ergonomics data in various environments is as follows: the geometry of the virtual competition venue is created by Rhino software, an orthogonal working condition matrix based on various forms of the top and side interfaces is constructed, and the visual ergonomics data collection in the virtual reality environment is completed under different working conditions.

[0012] Furthermore, a preferred embodiment is provided, in which the visual ergonomic data collection in a virtual reality environment under different working conditions is completed and presented through five SONNOC SNP-LW6500 laser projectors combined with a curved rear-projection screen with a resolution of 1280x2840 pixels.

[0013] Further, a preferred implementation is provided, in step 2, the method of combining statistical analysis and mediation effect to test and construct a prediction and evaluation model of the visual ergonomics evaluation factor VEA based on natural light is:

[0014] Step 2.1: Pre-process and analyze the visual ergonomics data collected in various environments based on the ErgoLab platform;

[0015] Step 2.2: Use the Scheirer-Ray-Hare test, Kruskal-Wallis test and Nemenyi test to evaluate the main effects of the two categorical factors of the top and side interfaces on visual perception and their interaction; to analyze the visual ergonomics comprehensive evaluation score VEA;

[0016] Step 2.3: Use principal component analysis (PCA) to condense information on different visual ergonomics evaluation factors and integrate the visual ergonomics comprehensive evaluation score (VEA);

[0017] Step 2.4: Use the mediation effect test to confirm the mediating effect of natural light on visual ergonomics, and use the polynomial approximation model to fit and confirm the visual ergonomics prediction model based on the natural light environment attributes.

[0018] Furthermore, a preferred implementation is provided, in which step 2.1 performs preprocessing analysis on the visual ergonomic data collected under various environments based on the ErgoLab platform, including time domain and frequency domain analysis, downsampling and filtering pre-implementation.

[0019] Further, a preferred implementation is provided, in which step 3 is specifically:

[0020] Step 3.1: Use a luminance meter and a photometric sensor to collect the illumination of the basketball court, and use a spectrophotometer to collect the interface material information to verify the accuracy and precision of the parametric simulation model and simulation platform;

[0021] Step 3.2: Based on the design parameters of the enclosure structure, the building lighting performance and the comprehensive evaluation of the visual ergonomics of the athlete are taken as the goal, and the building parameters are genetically optimized based on the Wallacei platform;

[0022] Step 3.3: According to the simulation results of the light environment simulation optimization experiment, select the optimal design parameter information of the natural light environment of the gymnasium.

[0023] Solution 2: A natural light environment optimization system for sports buildings based on visual ergonomics, the system comprising:

[0024] The visual ergonomics data collection module in the virtual reality environment is used to build a diverse top-side interface virtual gymnasium model to collect visual ergonomics data in various environments;

[0025] The visual ergonomics prediction model building module is used to pre-process and analyze the visual ergonomics data collected by the visual ergonomics data acquisition module in various environments, test the pre-processed data by combining statistical analysis and mediation effect, and build a visual ergonomics evaluation factor VEA prediction and evaluation model based on natural light;

[0026] The multi-objective optimization module is used to build the visual ergonomics prediction model based on the visual ergonomics evaluation factor VEA prediction and evaluation model described in the visual ergonomics prediction model construction module, use genetic algorithms to analyze simulation data, obtain the optimization target value of indoor environmental performance, establish the top and side interface virtual gymnasium model morphological design parameters, and screen and store the optimal design parameter information.

[0027] Solution three: A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes any one of the methods described in Solution one.

[0028] Solution 4: A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of Solution 1.

[0029] Solution 5: A computer program product is embedded with a computer program, and when the computer program is read, the method described in Solution 1 is implemented.

[0030] The present invention is beneficial in that:

[0031] The present invention is to realize the research of sports building interface morphology optimization design method based on visual ergonomics, and proposes a sports building natural light environment optimization method and system based on visual ergonomics. Subjective evaluation, physiological perception data and attention task efficiency data of experimenters are collected under virtual conditions of different interface attributes, and then a visual ergonomics prediction model is extracted. The performance genetic optimization of parameterized sports buildings is carried out in combination with the prediction model, which provides an important basis for sports buildings in the early design stage, thereby effectively improving the lighting performance and visual environment of the competition venue.

[0032] The present invention completes visual ergonomics experiments in a virtual environment of a gymnasium with different top and side interface forms, and collects visual ergonomics data under different environments; analyzes and preprocesses the visual ergonomics data, and proposes a VEA prediction model for visual ergonomics evaluation factors based on light environment in combination with the intermediary effect test and polynomial approximation method; through genetic optimization with the lighting properties and visual ergonomics of sports buildings as the goals, the optimal sports building interface morphology design parameters are screened and saved, and finally the healthy building design goal is achieved.

[0033] The present invention is also applicable to the field of sports building interface morphology optimization design based on visual ergonomics. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a method for optimizing the natural light environment of a sports building based on visual ergonomics as described in Implementation Method 1. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0036] Embodiment 1: This embodiment provides a method for optimizing the natural light environment of a sports building based on visual ergonomics, the method comprising the following steps:

[0037] Step 1: Build a virtual gymnasium model with diverse top and side interfaces to collect visual ergonomics data in various environments;

[0038] Step 2: Preprocess and analyze the visual ergonomics data collected in various environments in step 1, test the preprocessed data by combining statistical analysis and mediation effect, and build a prediction and evaluation model of visual ergonomics evaluation factor VEA based on natural light;

[0039] Step 3: Based on the visual ergonomics evaluation factor VEA prediction and evaluation model described in step 2, a genetic algorithm is used to analyze the simulation data, obtain the optimization target value of the indoor environmental performance, establish the top and side interface virtual gymnasium model morphological design parameters, and screen and store the optimal design parameter information.

[0040] Implementation method 2. This implementation method is a further limitation of the method for optimizing the natural light environment of a sports building based on visual ergonomics described in implementation method 1. In step 1, a virtual gymnasium model with diverse top and side interfaces is constructed. The method for collecting visual ergonomics data in various environments is as follows: the geometry of a virtual competition venue is created through Rhino software, an orthogonal working condition matrix based on various forms of top and side interfaces is constructed, and visual ergonomics data collection in a virtual reality environment is completed under different working conditions.

[0041] Implementation method three. This implementation method is a further limitation of the method for optimizing the natural light environment of a sports building based on visual ergonomics described in implementation method two. The visual ergonomics data collection in a virtual reality environment under different working conditions is completed and presented through five SONNOC SNP-LW6500 laser projectors combined with a curved rear-projection screen with a resolution of 1280x2840 pixels.

[0042] Implementation method 4: This implementation method is a further limitation of the method for optimizing the natural light environment of a sports building based on visual ergonomics described in implementation method 1. In step 2, statistical analysis and mediation effect are combined to test and construct a prediction and evaluation model of the visual ergonomics evaluation factor VEA based on natural light as follows:

[0043] Step 2.1: Pre-process and analyze the visual ergonomics data collected in various environments based on the ErgoLab platform;

[0044] Step 2.2: Use the Scheirer-Ray-Hare test, Kruskal-Wallis test and Nemenyi test to evaluate the main effects of the two categorical factors of the top and side interfaces on visual perception and their interaction; to analyze the visual ergonomics comprehensive evaluation score VEA;

[0045] Step 2.3: Use principal component analysis (PCA) to condense information on different visual ergonomics evaluation factors and integrate the visual ergonomics comprehensive evaluation score (VEA);

[0046] Step 2.4: Use the mediation effect test to confirm the mediating effect of natural light on visual ergonomics, and use the polynomial approximation model to fit and confirm the visual ergonomics prediction model based on the natural light environment attributes.

[0047] The ErgoLab platform described in this embodiment is a human-machine environment synchronization cloud platform based on multi-modal data synchronous acquisition and quantitative analysis. Based on cloud architecture and synchronization technology, it can be used through smart wearable devices.

[0048] The Scheirer-Ray-Hare test is a rank-based test nonparametric statistical analysis method used to analyze research data with two or more factors.

[0049] The Kruskal-Wallis test is a nonparametric statistical test method used to compare groups of two or more continuous or discrete variables.

[0050] Nemenyi is a statistical method for pairwise comparison of multiple independent samples after the rank sum test.

[0051] Implementation method five: This implementation method is a further limitation of the method for optimizing the natural light environment of a sports building based on visual ergonomics described in implementation method one. Step 2.1 performs preprocessing and analysis of the visual ergonomics data collected under various environments based on the ErgoLab platform, including time domain and frequency domain analysis, downsampling, and pre-filtering.

[0052] Implementation method 6: This implementation method further limits the method for optimizing the natural light environment of a sports building based on visual ergonomics described in implementation method 1. Step 3 is specifically as follows:

[0053] Step 3.1: Use a luminance meter and a photometric sensor to collect the illumination of the basketball court, and use a spectrophotometer to collect the interface material information to verify the accuracy and precision of the parametric simulation model and simulation platform;

[0054] Step 3.2: Based on the design parameters of the enclosure structure, the building lighting performance and the comprehensive evaluation of the visual ergonomics of the athlete are taken as the goal, and the building parameters are genetically optimized based on the Wallacei platform;

[0055] Step 3.3: According to the simulation results of the light environment simulation optimization experiment, select the optimal design parameter information of the natural light environment of the gymnasium.

[0056] The Wallacei described in this embodiment is a multi-objective optimization plug-in based on the Rhino-Grasshopper platform. It mainly adopts the NSGA-2 algorithm (Deb et.al., 2001) and uses the K-means method as a clustering algorithm to find the Pareto optimal solution set in the genetic iteration process. It has high optimization efficiency and good diversity.

[0057] Embodiment 7: This embodiment proposes a natural light environment optimization system for sports buildings based on visual ergonomics, the system comprising:

[0058] The visual ergonomics data collection module in the virtual reality environment is used to build a diverse top-side interface virtual gymnasium model to collect visual ergonomics data in various environments;

[0059] The visual ergonomics prediction model building module is used to pre-process and analyze the visual ergonomics data collected by the visual ergonomics data acquisition module in various environments, test the pre-processed data by combining statistical analysis and mediation effect, and build a visual ergonomics evaluation factor VEA prediction and evaluation model based on natural light;

[0060] The multi-objective optimization module is used to build the visual ergonomics prediction model based on the visual ergonomics evaluation factor VEA prediction and evaluation model described in the visual ergonomics prediction model construction module, use genetic algorithms to analyze simulation data, obtain the optimization target value of indoor environmental performance, establish the top and side interface virtual gymnasium model morphological design parameters, and screen and store the optimal design parameter information.

[0061] Embodiment 8: This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the method described in any one of embodiments 1 to 6.

[0062] Embodiment 9: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of embodiments 1 to 6 are implemented.

[0063] Embodiment 10: A computer program product is embedded with a computer program, and when the computer program is read, the method described in embodiment 1 is implemented.

[0064] Implementation eleven: This implementation proposes an example, which is used to explain the above-mentioned implementations one to ten. The specific example is as follows:

[0065] See also Figure 1To illustrate this embodiment, the present invention provides a method for optimizing the visual ergonomics of a sports building based on virtual reality, comprising the following steps:

[0066] Step 1: Build a 3D model of a virtual gymnasium based on different top and side interface forms, and collect visual ergonomics data of experimenters in different environments in the virtual environment.

[0067] The step 1 is specifically as follows:

[0068] The basic geometry of the virtual competition venue was created using Rhino software, and an orthogonal working condition matrix based on different forms of the top and side interfaces was constructed to complete the visual ergonomics data collection in the virtual environment under different working conditions. In the top interface design, in addition to retaining the X-shaped high side window foundation of the basic model, four different skylight forms were added: no new skylight (SL1), new point skylight (SL2), new strip skylight (SL3) and new ring skylight (SL4). In the side interface design, four typical working conditions were extracted, and the specific side window coverage ratios of these working conditions were 0.0 (SFCR1), 0.3 (SFCR2), 0.7 (SFCR3) and 1.0 (SFCR4). By combining different forms of the top and side interfaces, an orthogonal working condition matrix containing 16 working conditions was constructed.

[0069] The virtual reality environment is presented through a combination of five SONNOC SNP-LW6500 laser projectors and curved rear-projection screens with a resolution of 1280x2840 pixels, all of which are controlled by computers equipped with E5-2603CPU and GTX NVIDIAQuadroM4000.

[0070] The experimenters completed ten minutes of attention task training on a computer equipped with an i7-9750H CPU and NVIDIA GeForce GTX 1660Ti to ensure that the participants' response accuracy reached more than 90% and avoid experience differences.

[0071] In each condition, after completing the virtual environment, the participants filled out subjective questionnaires, including comprehensive evaluation of the light environment, psychological perception, Igroup Presence Questionnaire (IPQ), and Virtual Reality Sickness Questionnaire (VRSQ). Finally, they took attention tests in the E-Prime program, including visual search, continuous operation test, cue switching task, and spatial cueing task.

[0072] Step 2: Analyze and preprocess the collected visual ergonomics data, combine statistical analysis and mediation effect test, and build a prediction and evaluation model of visual ergonomics assessment factor (VEA) based on natural light.

[0073] The step 2 is specifically as follows:

[0074] Data preprocessing is performed based on the Ergo Lab platform, including time domain and frequency domain analysis, downsampling, and pre-filtering to improve data quality and analysis efficiency, ensuring accurate extraction of key physiological features for visual ergonomics evaluation. The original physiological data of response or broken signals and the extreme values ​​of task efficiency data with reaction time exceeding three standard deviations are eliminated.

[0075] The Scheirer-Ray-Hare test, Kruskal-Wallis test, and Nemenyi test were used to evaluate the main effects of the two categorical factors of top and side interface forms on visual perception and their interaction, and simple effect analysis or post hoc analysis was provided in subsequent analyses.

[0076] The principal component analysis (PCA) method is used to condense the information of different visual ergonomics evaluation factors and determine the visual ergonomics comprehensive evaluation score (VEA) to provide integrated data for subsequent analysis and prediction.

[0077] The mediation effect test was used to confirm the mediation effect of natural light on visual ergonomics. The polynomial approximation model was used to fit and confirm the visual ergonomics prediction model based on the natural light environment attributes, providing a prediction method for subsequent genetic optimization.

[0078] Step 3: Conduct a multi-objective optimization-guided search for the visual ergonomics and lighting properties inside the sports building, use a genetic algorithm to analyze the simulation data, obtain the indoor environmental performance optimization target value, determine the sports building interface morphology design parameter values, and select and save the optimal morphology design parameter information.

[0079] The step 3 is specifically as follows:

[0080] Cooke-cal-SPOT brightness meter and Li-Cor photometric sensor were used to measure the illumination of the basketball court, and KonicaMinolta CM-600d spectrophotometer was used to collect interface material information. The measured data was used to verify the accuracy and precision of the parametric simulation model and simulation platform.

[0081] Based on the design parameters of the enclosure structure, the comprehensive evaluation of the building's lighting performance and the visual ergonomics of the athletes is taken as the goal, and the building parameters are genetically optimized based on the Wallacei platform.

[0082] According to the simulation results of the light environment simulation optimization experiment, the optimized design parameter information suitable for the natural light environment of the gymnasium is screened out.

[0083] The present invention completes visual ergonomics experiments in a virtual environment of a gymnasium with different top and side interface forms, and collects visual ergonomics data under different environments; analyzes and preprocesses the data, and proposes a visual ergonomics evaluation factor (VEA) prediction model based on the light environment in combination with the mediation effect test and the polynomial approximation method; through genetic optimization with the lighting properties and visual ergonomics of sports buildings as the goals, the optimal sports building interface morphology design parameters are screened and saved, and finally the people-oriented healthy building design goal is achieved.

[0084] The present embodiment describes a sports building visual ergonomics optimization system based on virtual reality, and the system includes three modules: a visual ergonomics data acquisition module in a virtual reality environment, a visual ergonomics prediction model construction module, and a multi-objective optimization module.

[0085] The visual ergonomics data acquisition module in the virtual reality environment performs three-dimensional modeling, simulates the top and side interface morphology control of the sports building, simulates the indoor natural light form, and completes the subjective evaluation collection, physiological data collection and attention measurement task efficiency collection;

[0086] The visual ergonomics prediction model building module visual ergonomics data processing and analysis module includes data preprocessing, statistical analysis and building a visual ergonomics polynomial approximation model fitting prediction model;

[0087] The multi-objective optimization module includes simulation platform verification, design parameter setting and genetic algorithm optimization.

[0088] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0089] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the natural light environment of a sports building based on visual ergonomics, characterized in that: The method comprises the following steps: Step 1: Build a virtual gymnasium model with diverse top and side interfaces to collect visual ergonomics data in various environments; Step 2: Preprocess and analyze the visual ergonomics data collected in various environments in step 1, test the preprocessed data by combining statistical analysis and mediation effect, and build a prediction and evaluation model of visual ergonomics evaluation factor VEA based on natural light; Step 3: Based on the visual ergonomics evaluation factor VEA prediction and evaluation model described in step 2, a genetic algorithm is used to analyze the simulation data, obtain the optimization target value of the indoor environmental performance, establish the top and side interface virtual gymnasium model morphological design parameters, and screen and store the optimal design parameter information.

2. The method for optimizing the natural light environment of a sports building based on visual ergonomics according to claim 1 is characterized in that: In step 1, a virtual gymnasium model with various top and side interfaces is constructed to collect visual ergonomics data in various environments. The method is as follows: the geometry of the virtual competition venue is created by Rhino software, and an orthogonal working condition matrix based on various forms of top and side interfaces is constructed to complete the visual ergonomics data collection in the virtual reality environment under different working conditions.

3. The method for optimizing the natural light environment of a sports building based on visual ergonomics according to claim 2 is characterized in that: The visual ergonomics data collection in a virtual reality environment under different working conditions is presented through five SONNOC SNP-LW6500 laser projectors combined with a curved rear-projection screen with a resolution of 1280x2840 pixels.

4. The method for optimizing the natural light environment of a sports building based on visual ergonomics according to claim 1 is characterized in that: In step 2, statistical analysis and mediation effect tests are combined to construct a prediction and evaluation model for the visual ergonomics evaluation factor VEA based on natural light: Step 2.1: Pre-process and analyze the visual ergonomics data collected in various environments based on the ErgoLab platform; Step 2.2: The Scheirer-Ray-Hare test, Kruskal-Wallis test and Nemenyi test were used to evaluate the main effects of the two categorical factors of the top and side interfaces on visual perception and their interaction, which was used to analyze the visual ergonomics comprehensive evaluation score VEA; Step 2.3: Use principal component analysis (PCA) to condense information on different visual ergonomics evaluation factors and integrate the visual ergonomics comprehensive evaluation score (VEA); Step 2.4: Use the mediation effect test to confirm the mediating effect of natural light on visual ergonomics, and use the polynomial approximation model to fit and confirm the visual ergonomics prediction model based on the natural light environment attributes.

5. The method for optimizing the natural light environment of a sports building based on visual ergonomics according to claim 1 is characterized in that: Step 2.1 Based on the ErgoLab platform, pre-process and analyze the visual ergonomic data collected in various environments, including time domain and frequency domain analysis, downsampling and filtering pre-implementation.

6. The method for optimizing the natural light environment of a sports building based on visual ergonomics according to claim 1 is characterized in that: Step 3 is as follows: Step 3.1: Use a luminance meter and a photometric sensor to collect the illumination of the basketball court, and use a spectrophotometer to collect the interface material information to verify the accuracy and precision of the parametric simulation model and simulation platform; Step 3.2: Based on the design parameters of the enclosure structure, the building lighting performance and the comprehensive evaluation of the visual ergonomics of the athlete are taken as the goal, and the building parameters are genetically optimized based on the Wallacei platform; Step 3.3: According to the simulation results of the light environment simulation optimization experiment, select the optimal design parameter information of the natural light environment of the gymnasium.

7. A natural light environment optimization system for sports buildings based on visual ergonomics, characterized in that: The system comprises: The visual ergonomics data collection module in the virtual reality environment is used to build a diverse top-side interface virtual gymnasium model to collect visual ergonomics data in various environments; The visual ergonomics prediction model building module is used to pre-process and analyze the visual ergonomics data collected by the visual ergonomics data acquisition module in various environments, test the pre-processed data by combining statistical analysis and mediation effect, and build a visual ergonomics evaluation factor VEA prediction and evaluation model based on natural light; The multi-objective optimization module is used to build the visual ergonomics prediction model based on the visual ergonomics evaluation factor VEA prediction and evaluation model described in the visual ergonomics prediction model construction module, use genetic algorithms to analyze simulation data, obtain the optimization target value of indoor environmental performance, establish the top and side interface virtual gymnasium model morphological design parameters, and screen and store the optimal design parameter information.

8. A computer device comprising a memory and a processor, characterized in that A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, in which a computer program is embedded, characterized in that When the computer program is read, the method described in claim 1 is implemented.

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