Three-dimensional element cosmic space optimization method based on edge calculation and electronic equipment

Abstract

The invention discloses a three-dimensional element cosmic space optimization method based on edge computing and electronic equipment, and belongs to the technical field of element cosmic and edge computing, and the method comprises the following steps: S1, deploying edge computing equipment at the edge of a network, and constructing an edge computing network; s2, collecting data of a rapid and complex three-dimensional space in the meta universe in real time; s3, preprocessing and analyzing the collected data by using computing resources on the edge computing device, and extracting key information; and S4, optimizing the three-dimensional space according to the extracted key information. According to the three-dimensional element cosmic space optimization method based on edge calculation and the electronic equipment, calculation is carried out at the edge of a network, data transmission delay is reduced, user experience and response speed are improved, and real-time optimization and efficient processing of a complex three-dimensional space are achieved through the calculation capacity of the edge calculation equipment; in the aspects of optimizing space rendering, object interaction and ray tracing, smoother and more real meta-universe experience is provided.

Description

Technical Field

[0001] The present invention relates to the field of metaverse and edge computing technology, and in particular to a three-dimensional metaverse space optimization method and electronic equipment based on edge computing. Background Art

[0002] With the rapid development of metaverse technology, three-dimensional metaverse space has become an important place for people's daily life and work. However, due to the large amount of data and high update frequency of three-dimensional metaverse space, the traditional cloud computing model has been difficult to meet its real-time needs. Edge computing technology can move functions such as computing and data storage to the edge of the network, achieving faster data processing and decision-making, and is suitable for large-scale, fast and complex metaverse environment management. Therefore, a three-dimensional metaverse space optimization method based on edge computing is urgently needed to solve the existing technical problems. Summary of the invention

[0003] The purpose of the present invention is to provide a three-dimensional metaverse space optimization method and electronic device based on edge computing to solve the problems existing in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides a three-dimensional metaverse space optimization method based on edge computing, comprising the following steps:

[0005] S1. Deploy edge computing devices at the edge of the network to build an edge computing network;

[0006] S2, real-time collection of fast and complex three-dimensional data in the metaverse;

[0007] S3, using the computing resources on the edge computing device to preprocess and analyze the data collected in step S2 to extract key information;

[0008] S4. Optimize the three-dimensional space according to the key information extracted in step S3.

[0009] Preferably, building the edge computing network in step S1 is specifically as follows:

[0010] S11. Plan and design the network; determine the layout of edge computing devices and edge nodes, and consider network topology, transmission protocol and security policy factors;

[0011] S12. Connect the edge computing device to the edge node or edge gateway; configure a unique identifier for each edge computing device and register it with the edge network to ensure smooth communication between edge computing devices;

[0012] S13, edge node deployment, deploying edge nodes at appropriate locations. Edge nodes have computing and storage capabilities, and are used to process data generated by edge computing devices and transmit them to the cloud or other edge computing devices;

[0013] S14. Edge computing resource management: manage edge computing resources, including edge computing devices, edge nodes, and edge gateways, monitor and control the usage of edge computing resources, and optimize resource allocation and scheduling.

[0014] Preferably, in step S2, data of fast and complex three-dimensional space in the metaverse is collected in real time, including user behavior data, environmental data, object attribute data, etc.

[0015] Preferably, when the data is user behavior data, it specifically includes:

[0016] Use the built-in motion tracking sensors of VR and AR devices to track the user's head and hand movements in real time; use optical tracking systems, laser scanning or electromagnetic tracking methods to accurately track the user's position and movement in three-dimensional space; through the user's interaction with elements in the metaverse, record behavior and convert it into data;

[0017] Process the collected real-time data and use the real-time data analysis platform or machine learning model to process and analyze the collected real-time data;

[0018] Store and query the processed data using NoSQL database or distributed database;

[0019] Analyze the collected user behavior data to obtain the user's behavior patterns, preferences, and needs in the metaverse.

[0020] Preferably, when the data is environmental data, it specifically includes:

[0021] Determine environmental data; environmental data includes lighting, texture, geometry, sound, temperature, etc.;

[0022] Based on the determined environmental data, use lidar, cameras, and sound sensors to collect data; use high-speed network connections and data transmission protocols to transmit the real-time collected data, clean, convert, and compress the data; store the data in distributed databases and time series databases;

[0023] Analyze and visualize the data, analyze the collected environmental data, obtain the characteristics and changes of the metaverse environment, and use visualization tools to visualize the environmental data by presenting the data as charts, images or animations.

[0024] Preferably, when the data is object attribute data, it specifically includes: first, using active technology and passive technology to collect object attribute data, the active technology is lidar or depth camera, the lidar or depth camera is used to emit laser or light waves to the object, and calculate the three-dimensional shape and position of the object by receiving the reflected signal, and at the same time, combined with the color information of the object, obtain point cloud data with rich attributes; then use the WebSocket protocol to transmit the collected data in real time, clean, convert and compress the data; store the data in a distributed database and a time series database; use data analysis tools to analyze the collected object attribute data in real time to obtain the behavior and status of the object in the metaverse; at the same time, use visualization tools to present the data as charts, images or three-dimensional models to more intuitively understand the data.

[0025] Preferably, in step S3, the data collected in step S2 is preprocessed and analyzed using computing resources on the edge computing device, and the data preprocessing includes:

[0026] On the edge computing device, the received raw data is preprocessed, including data cleaning, data compression, and data format conversion. For image or video data, this also includes image enhancement and using convolutional neural networks to extract key features of the image.

[0027] Data analysis includes: using the computing resources on the edge computing device to perform real-time analysis of pre-processed data, including statistical analysis, including calculation of average, maximum, minimum, etc., and also including machine learning or deep learning algorithms, including classification, clustering, prediction, etc.; according to the purpose and needs of the analysis, extract key information, including the object's motion trajectory, speed, acceleration and classification labels.

[0028] Preferably, key information is extracted in step S3, and the key information includes object attribute data, spatial layout data, lighting and shadow data, collision detection and physical simulation data, network performance data and user behavior data, etc.

[0029] Preferably, the optimization of the three-dimensional space in step S4 includes space rendering, object interaction and ray tracing;

[0030] Optimize the rendering of 3D space, specifically: optimize lighting and shadows, optimize materials and textures, optimize rendering algorithms, optimize space division and localization, optimize object levels, and optimize block loading and pre-rendering;

[0031] Optimize the interaction of objects in three-dimensional space, specifically: optimize the intuitiveness of interaction design, optimize performance, optimize real-time feedback, optimize multimodal interaction, optimize the accuracy of physical simulation, optimize cross-platform compatibility, and optimize accessibility and inclusiveness.

[0032] An electronic device, comprising:

[0033] A memory storing a plurality of programs;

[0034] A processor, provided with several processors, for executing the program in the memory; and implementing the steps of the method described in any one of claims 1 to 9.

[0035] Therefore, the present invention adopts the above-mentioned three-dimensional metaverse space optimization method and electronic device based on edge computing, which has the following beneficial effects:

[0036] (1) Low latency: By performing computing at the edge of the network, data transmission delay is reduced, improving user experience and response speed.

[0037] (2) High performance: Utilize the high-performance computing capabilities of edge computing devices to achieve real-time optimization and efficient processing of complex three-dimensional spaces.

[0038] (3) Scalability: Improve the scalability and stability of the system by locally processing large amounts of user data.

[0039] (4) Smooth experience: Optimize spatial rendering, object interaction, ray tracing, etc. to provide a smoother and more realistic metaverse experience.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a three-dimensional metaverse space optimization method based on edge computing of the present invention;

[0042] Figure 2 It is a structural schematic diagram of an electronic device embodiment of the present invention;

[0043] Reference numerals

[0044] 101. Processor; 102. Memory; 103. I / O interface; 104. Bus. DETAILED DESCRIPTION

[0045] Example

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0047] like Figure 1 As shown, a three-dimensional metaverse space optimization method and electronic device based on edge computing include the following steps:

[0048] S1. Deploy edge computing devices at the edge of the network to build an edge computing network. An edge computing network is a network architecture that processes and computes data close to the source of objects or data. It uses an open platform that integrates network, computing, storage, and application core capabilities to provide the nearest end service. Specifically, it includes:

[0049] S11. Plan and design the network; determine the layout of edge computing devices and edge nodes, and consider network topology, transmission protocol and security policy factors;

[0050] S12. Connect the edge computing device to the edge node or edge gateway; configure a unique identifier for each edge computing device and register it with the edge network to ensure smooth communication between edge computing devices;

[0051] S13, edge node deployment, deploying edge nodes at appropriate locations. Edge nodes have computing and storage capabilities, and are used to process data generated by edge computing devices and transmit them to the cloud or other edge computing devices;

[0052] S14. Edge computing resource management: manage edge computing resources, including edge computing devices, edge nodes, and edge gateways, monitor and control the usage of edge computing resources, and optimize resource allocation and scheduling.

[0053] S2, real-time collection of fast and complex three-dimensional data in the metaverse, including user behavior data, environmental data, object attribute data, etc.

[0054] When the data is user behavior data, it specifically includes:

[0055] Use the built-in motion tracking sensors of VR and AR devices to track the user's head and hand movements in real time; use optical tracking systems, laser scanning or electromagnetic tracking methods to accurately track the user's position and movement in three-dimensional space; through the user's interaction with elements in the metaverse, record behavior and convert it into data;

[0056] Process the collected real-time data and use the real-time data analysis platform or machine learning model to process and analyze the collected real-time data;

[0057] Store and query the processed data using NoSQL database or distributed database;

[0058] Analyze the collected user behavior data to obtain the user's behavior patterns, preferences, and needs in the metaverse.

[0059] When the data is environmental data, it specifically includes:

[0060] Determine environmental data; environmental data includes lighting, texture, geometry, sound, temperature, etc.;

[0061] Based on the determined environmental data, use lidar, cameras, and sound sensors to collect data; use high-speed network connections and data transmission protocols to transmit the real-time collected data, clean, convert, and compress the data; store the data in distributed databases and time series databases;

[0062] Analyze and visualize the data, analyze the collected environmental data, obtain the characteristics and changes of the metaverse environment, and use visualization tools to visualize the environmental data by presenting the data as charts, images or animations.

[0063] When the data is object attribute data, it specifically includes: first, active technology and passive technology are used to collect object attribute data. The active technology is lidar or depth camera. Lidar or depth camera is used to emit laser or light waves to the object, and calculate the three-dimensional shape and position of the object by receiving the reflected signal. At the same time, combined with the color information of the object, point cloud data with rich attributes is obtained; then the WebSocket protocol is used to transmit the collected data in real time, and the data is cleaned, converted and compressed; the data is stored in a distributed database and a time series database; data analysis tools are used to analyze the collected object attribute data in real time to obtain the behavior and status of the object in the metaverse; at the same time, visualization tools are used to present the data as charts, images or three-dimensional models to more intuitively understand the data.

[0064] S3. Use the computing resources on the edge computing device to preprocess and analyze the data collected in step S2 to extract key information; data preprocessing includes:

[0065] On the edge computing device, the received raw data is preprocessed, including data cleaning, data compression, and data format conversion. For image or video data, it also includes image enhancement and the use of convolutional neural networks to extract key features of the image. Data analysis includes: using the computing resources on the edge computing device to perform real-time analysis of the preprocessed data, including statistical analysis, including calculation of average, maximum, minimum, etc., and also including machine learning or deep learning algorithms, including classification, clustering, prediction, etc.; according to the purpose and needs of the analysis, extract key information, including the object's motion trajectory, speed, acceleration and classification labels.

[0066] In step S3, key information is extracted, and the key information includes object attribute data, spatial layout data, lighting and shadow data, collision detection and physical simulation data, network performance data, user behavior data, etc.

[0067] S4, optimizing the three-dimensional space according to the key information extracted in step S3, including space rendering, object interaction and ray tracing;

[0068] Optimize the three-dimensional space rendering, specifically: optimize lighting and shadows, optimize materials and textures, optimize rendering algorithms, optimize space division and local optimization, optimize object levels, and optimize block loading and pre-rendering; Optimize the three-dimensional space through space rendering, starting from the following aspects:

[0069] Lighting and shadows: Proper lighting settings are an important step in optimizing spatial rendering. By adjusting the position, color, intensity, and type of light sources, you can create realistic light and shadow effects, making the three-dimensional space more vivid and realistic. Consider adding dynamic lighting to simulate light changes at different times and in different environments to enhance user experience and immersion. Implement real-time shadow rendering to ensure that objects produce natural shadow effects in the scene, further increasing the sense of reality.

[0070] Materials and Textures: Choose materials that match the scene and apply appropriate texture maps to increase the detail and realism of objects. Use high-quality textures and materials to ensure delicate visual effects during rendering. Consider using PBR (Physically Based Rendering) materials to more accurately simulate physical phenomena such as reflection, refraction, and scattering on the surface of objects.

[0071] Optimize rendering algorithms: Select rendering algorithms suitable for the project, such as ray tracing, global illumination, etc., to improve rendering quality and efficiency. Use LOD (Level of Detail) technology to dynamically adjust the rendering accuracy and level of detail according to the distance between the object and the viewpoint to reduce unnecessary rendering calculations. Introduce optimization technologies such as deferred rendering or forward rendering to improve rendering speed and efficiency.

[0072] Spatial division and local optimization: Use spatial division techniques, such as octrees and kd trees, to divide the 3D scene into multiple regions for local optimization. Different rendering strategies and optimization methods are used according to the characteristics and needs of different regions to improve the overall rendering performance.

[0073] Object-level optimization: Specific optimization strategies are used for different types of objects. For example, pre-calculated lighting information is used for static objects, while trajectory prediction and other methods are used for dynamic objects. According to the importance of the object in the scene, the rendering priority and visibility judgment are reasonably set to reduce unnecessary rendering calculations.

[0074] Block loading and pre-rendering: For extremely large scenes with large amounts of data, block loading technology is used to dynamically load and unload data based on the user's viewpoint to reduce memory usage and increase loading speed. Pre-rendering technology is used to generate some images or textures at fixed viewing angles to reduce the burden of real-time rendering.

[0075] Hardware and software support: Choose a high-performance graphics processor (GPU) and sufficient memory to ensure the smooth rendering process. Use professional 3D modeling and rendering software such as Maya, 3ds Max, Blender, etc., which provide rich rendering functions and optimization options.

[0076] Test and Iterate:

[0077] Frequent testing and iterations are performed during the development process, and rendering effects and performance are continuously optimized based on user feedback and data analysis. Performance analysis tools are used to monitor and analyze the rendering process, identify performance bottlenecks, and perform targeted optimizations.

[0078] By combining the above methods and strategies, spatial rendering can be effectively used to optimize three-dimensional space and improve visual effects, interactive experience and performance.

[0079] Optimize the interaction of objects in three-dimensional space, specifically: optimize the intuitiveness of interactive design, optimize performance, optimize real-time feedback, optimize multimodal interaction, optimize the accuracy of physical simulation, optimize cross-platform compatibility, optimize accessibility and inclusiveness. Optimize the interaction of objects in three-dimensional space, which can be done from the following aspects:

[0080] Intuitiveness of interaction design: Ensure that users can intuitively understand how to interact with objects. Design interactive interfaces and actions that are easy to understand and operate to reduce users' learning costs. Use common interaction modes such as click, drag, rotate, etc., as well as intuitive gesture and action recognition to improve the naturalness of interaction.

[0081] Performance optimization: Optimize the rendering performance of object interactions to ensure a smooth frame rate during the interaction. Use appropriate LOD (Level of Detail) technology to dynamically adjust the rendering accuracy of objects based on the distance between the user and the object to reduce unnecessary calculation burden. For complex object interactions, use block loading and pre-rendering technology to improve performance.

[0082] Real-time feedback: Provide immediate visual and auditory feedback so that users can immediately perceive the impact of their interactive behavior on the three-dimensional space. The feedback should be accurate, clear, and match the user's interactive behavior.

[0083] Multimodal interaction: Support multiple interaction methods, such as mouse, keyboard, touch screen, gesture recognition, voice control, etc., to meet the needs and preferences of different users. Provide appropriate interaction methods and interface designs according to different devices and platforms.

[0084] User customization: Allow users to customize the interaction methods and effects of objects to meet their individual needs. Provide configurable interaction options and parameters so that users can adjust them according to their preferences.

[0085] Accuracy of physical simulation: Whenever possible, introduce physical simulation to enhance the realism of object interactions. Consider the physical properties of objects such as mass, friction, collision, etc. to provide a more accurate interactive experience.

[0086] Error handling and fault tolerance: Design good error handling and fault tolerance mechanisms to deal with possible user errors or abnormal situations.

[0087] Provide clear error prompts and solutions to help users quickly return to the normal interaction process.

[0088] Cross-platform compatibility: Ensure that object interactions work properly on different platforms and devices. Perform necessary adaptation and optimization based on the characteristics and limitations of different platforms.

[0089] Accessibility and Inclusivity: Consider the needs and abilities of different users and ensure that object interactions are accessible and inclusive for everyone.

[0090] Provide accessibility features and options to help users with visual, hearing, or other impairments interact.

[0091] Testing and iteration: Conduct frequent testing and iteration during the development process, and continuously optimize the design and implementation of object interactions based on user feedback and data analysis. Use performance analysis tools to monitor and analyze the interaction process, identify performance bottlenecks, and perform targeted optimizations.

[0092] In this embodiment, edge computing can also process large amounts of user data more efficiently, thereby improving the scalability and responsiveness of the Metaverse. Specifically, edge computing plays a key role in processing large amounts of user data and improving the scalability and responsiveness of the Metaverse. The following are explanations of several major aspects:

[0093] Reduce latency: In the metaverse, the interaction between users and the virtual environment needs to be responded to in real time and quickly. Edge computing can significantly reduce the latency of data transmission by moving data processing and analysis capabilities to the edge of the network (that is, close to the user). This means that user actions and inputs can be responded to more quickly, thereby improving the user experience.

[0094] Improve scalability: As the number of users in the Metaverse increases, data traffic will also grow rapidly. Traditional cloud computing architectures may face scalability issues due to network congestion and data center processing capacity limitations. Edge computing can effectively alleviate the load on the central server and improve the scalability of the entire system by allocating part of the data processing tasks to edge nodes close to users.

[0095] Optimize bandwidth usage: In the Metaverse, a large amount of 3D graphics, audio, and video data needs to be transmitted in real time. This data usually requires a lot of bandwidth resources. By preprocessing and compressing data on edge nodes, the amount of data that needs to be transmitted to the central server can be reduced, thereby optimizing bandwidth usage and reducing network costs.

[0096] Enhanced data security: Since edge computing moves data processing and analysis tasks closer to users, it can reduce the risk of data being intercepted or tampered with during transmission. In addition, edge computing can also use distributed storage and encryption technology to protect the security of user data.

[0097] Support real-time analysis and decision-making: In the metaverse, real-time analysis and decision-making are essential to optimizing user experience and ensuring stable system operation. Edge computing can collect and analyze user data in real time, providing timely feedback and decision-making support for the system.

[0098] In short, edge computing has significant advantages in processing large amounts of user data and improving the scalability and response speed of the Metaverse. With the continuous development of technology and the expansion of application scenarios, edge computing will play an increasingly important role in the construction of the Metaverse.

[0099] like Figure 2 As shown, based on the same concept, this embodiment also provides an electronic device, including:

[0100] One or more processors 101, memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement any optimization method in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, and are configured to implement information exchange between the processor and the memory.

[0101] Among them, the processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU), etc.; the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus), etc.

[0102] The processor 101 , the memory 102 , and the I / O interface 103 are connected to each other via a bus 104 , and further connected to other components of the computing device.

[0103] The one or more processors 101 include a field programmable gate array.

[0104] A computer-readable medium is also provided. The computer-readable medium stores a computer program, wherein when the program is executed by a processor, the steps in any optimization method in the above embodiments are implemented.

[0105] Therefore, the present invention adopts the above-mentioned three-dimensional metaverse space optimization method and electronic device based on edge computing, performs calculations at the edge of the network, reduces data transmission delays, improves user experience and response speed, and utilizes the computing power of edge computing devices to achieve real-time optimization and efficient processing of complex three-dimensional space; in terms of optimizing space rendering, object interaction, and ray tracing, it provides a smoother and more realistic metaverse experience.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A three-dimensional metaverse space optimization method based on edge computing, characterized in that: The following steps are involved: S1. Deploy edge computing devices at the edge of the network to build an edge computing network; S2, real-time collection of fast and complex three-dimensional data in the metaverse; S3, using the computing resources on the edge computing device to preprocess and analyze the data collected in step S2 to extract key information; S4. Optimize the three-dimensional space according to the key information extracted in step S3.

2. The three-dimensional metaverse space optimization method based on edge computing according to claim 1 is characterized in that: The construction of the edge computing network in step S1 is specifically as follows: S11. Plan and design the network; determine the layout of edge computing devices and edge nodes; S12. Connect the edge computing device to the edge node or edge gateway; configure a unique identifier for each edge computing device and register it with the edge network to ensure smooth communication between edge computing devices; S13, edge node deployment, edge nodes have computing and storage capabilities, and are used to process data generated by edge computing devices and transmit them to the cloud or other edge computing devices; S14, edge computing resource management; Manage edge computing resources, including edge computing devices, edge nodes, and edge gateways, monitor and control the usage of edge computing resources, and optimize resource allocation and scheduling.

3. The three-dimensional metaverse space optimization method based on edge computing according to claim 1, characterized in that: In step S2, data of fast and complex three-dimensional space in the metaverse is collected in real time, including user behavior data, environmental data, and object attribute data.

4. The three-dimensional metaverse space optimization method based on edge computing according to claim 3 is characterized in that: When the data is user behavior data, it specifically includes: Use the built-in motion tracking sensors of VR and AR devices to track the user's head and hand movements in real time; use optical tracking systems, laser scanning or electromagnetic tracking methods to accurately track the user's position and movement in three-dimensional space; through the user's interaction with elements in the metaverse, record behavior and convert it into data; Process the collected real-time data and use the real-time data analysis platform or machine learning model to process and analyze the collected real-time data; Store and query the processed data using NoSQL database or distributed database; Analyze the collected user behavior data to obtain the user's behavior patterns, preferences, and needs in the metaverse.

5. The three-dimensional metaverse space optimization method based on edge computing according to claim 3 is characterized in that: When the data is environmental data, it specifically includes: Determine environmental data; environmental data includes lighting, texture, geometry, sound, and temperature; Based on the determined environmental data, use lidar, cameras, and sound sensors to collect data; use high-speed network connections and data transmission protocols to transmit the real-time collected data, clean, convert, and compress the data; store the data in distributed databases and time series databases; Analyze and visualize the data, analyze the collected environmental data, obtain the characteristics and changes of the metaverse environment, and use visualization tools to visualize the environmental data by presenting the data as charts, images or animations.

6. The three-dimensional metaverse space optimization method based on edge computing according to claim 3 is characterized in that: When the data is object attribute data, it specifically includes: first, active technology and passive technology are used to collect object attribute data. The active technology is lidar or depth camera. Lidar or depth camera is used to emit laser or light waves to the object, and calculate the three-dimensional shape and position of the object by receiving the reflected signal. At the same time, combined with the color information of the object, point cloud data with rich attributes is obtained; then the WebSocket protocol is used to transmit the collected data in real time, and the data is cleaned, converted and compressed; the data is stored in a distributed database and a time series database; data analysis tools are used to analyze the collected object attribute data in real time to obtain the behavior and status of the object in the metaverse; at the same time, visualization tools are used to present the data as charts, images or three-dimensional models.

7. The three-dimensional metaverse space optimization method based on edge computing according to claim 1, characterized in that: In step S3, the data collected in step S2 is preprocessed and analyzed using computing resources on the edge computing device. The data preprocessing includes: On the edge computing device, the received raw data is preprocessed, including data cleaning, data compression, and data format conversion. For image or video data, this also includes image enhancement and using convolutional neural networks to extract key features of the image. Data analysis includes: using the computing resources on edge computing devices to perform real-time analysis of pre-processed data, including statistical analysis, including calculation of average, maximum, and minimum values, and machine learning or deep learning algorithms, including classification, clustering, and prediction; according to the purpose and needs of the analysis, extract key information, including the object's motion trajectory, speed, acceleration, and classification labels.

8. The three-dimensional metaverse space optimization method based on edge computing according to claim 1, characterized in that: In step S3, key information is extracted, and the key information includes object attribute data, spatial layout data, lighting and shadow data, collision detection and physical simulation data, network performance data and user behavior data.

9. The three-dimensional metaverse space optimization method based on edge computing according to claim 1, characterized in that: Step S4 optimizes the three-dimensional space, including space rendering, object interaction, and ray tracing; Optimize the rendering of 3D space, specifically: optimize lighting and shadows, optimize materials and textures, optimize rendering algorithms, optimize space division and localization, optimize object levels, and optimize block loading and pre-rendering; Optimize the interaction of objects in three-dimensional space, specifically: optimize the intuitiveness of interaction design, optimize performance, optimize real-time feedback, optimize multimodal interaction, optimize the accuracy of physical simulation, optimize cross-platform compatibility, and optimize accessibility and inclusiveness.

10. An electronic device, characterized in that: include: A memory storing a plurality of programs; A processor, provided with a plurality of them, for executing a program in the memory; Implement the steps of the method described in any one of claims 1 to 9.

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