Image data processing and storage method based on meta-universe game

Abstract

The invention provides an image data processing and storing method based on a meta universe game, which comprises the following steps of: integrating and primarily processing image data to obtain an original image data set; a graphic database is designed based on a distributed storage system and by adopting a cold and hot data hierarchical storage strategy; processing the original image data set by utilizing a self-adaptive compression algorithm, a multi-thread asynchronous loading mode, GPU (Graphics Processing Unit) parallel computing, a progressive rendering technology, a physical basic rendering technology and illumination computing to obtain a standard image data set; a virtualization technology is utilized to simulate various hardware configurations, and a plurality of APIs are combined to carry out cross-platform compatible management of image data; and automatically updating and optimizing the content of the image data according to the monitoring result of the image data. According to the method, efficient storage and processing of the image data can be realized, the quality and compatibility of the image data are enhanced, the game performance and the user experience are remarkably improved, and development and popularization of the virtual world are promoted.

Description

Technical Field

[0001] The present invention relates to the field of metaverse game technology, and in particular to an image data processing and storage method based on metaverse games. Background Art

[0002] Metaverse games are interactive games played in a virtual world, usually combining elements such as augmented reality (AR), virtual reality (VR), blockchain technology, and social networks to create an immersive digital environment. Metaverse games are shared virtual spaces where players can create characters, interact, participate in game activities, trade virtual items, and socialize with other players. This virtual world is usually persistent, and players' behavior and creations will have an impact on the environment.

[0003] In metaverse games, image data usually refers to various digital information related to the visual performance of the game. These data constitute the visual elements and environment in the game, including but not limited to texture data, 3D models, environment maps, animation data, user interface elements, materials and shaders, special effects images, etc. These image data work together to create an immersive virtual world and provide a rich visual experience. The processing and storage methods of metaverse game image data directly affect the visual quality, performance and user experience of the game.

[0004] At present, the image data processing methods of Metaverse games include image compression, texture compression, lighting and shadow processing, etc. Due to the high resolution, dynamics, diversity, interactivity, and sociality of Metaverse game image data, when using existing methods and technologies to process image data, compression loss, loading delays, high storage costs, low compatibility and other problems often occur, which in turn affects the access speed of the game and user satisfaction, and reduces game performance and visual performance. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide an image data processing and storage method based on the metaverse game, which can realize efficient storage and processing of image data, enhance image data quality and compatibility, significantly improve game performance and user experience, and promote the development and popularization of the virtual world.

[0006] To achieve the above object, the present invention provides the following solution: a method for processing and storing image data based on a metaverse game, comprising the following steps:

[0007] Collect game image data, integrate and preliminarily process the image data to obtain the original image data set;

[0008] Based on a distributed storage system and using a hot and cold data tiered storage strategy, a graph database is designed, the original image data set is stored in the graph database, and the image data behavior is monitored in real time in combination with a machine learning prediction tool;

[0009] The original image data set is processed by using an adaptive compression algorithm, a multi-threaded asynchronous loading method and GPU parallel computing, and then the quality of the processed original image data set is controlled by using progressive rendering technology, physical based rendering technology and lighting calculation to obtain a standard image data set;

[0010] Use virtualization technology to simulate a variety of hardware configurations, and then combine multiple API interfaces to perform cross-platform compatible management of image data;

[0011] According to the monitoring results of the image data, the image data content is automatically updated and optimized.

[0012] Optionally, collect game image data, integrate and preliminarily process the image data to obtain an original image data set, including:

[0013] Collect all the image data of the game, convert the formats of all the image data into a unified format, develop an automated script for automatic classification and data labeling, use the automated script and a graphics processor to standardize the image data in the unified format, and then integrate the image data to obtain the original image data set;

[0014] A data compression strategy is designed according to the data type in the original image data set, and a distributed computing framework is used to process and merge the large-scale data in the original image data set to complete the preliminary processing of the original image data set.

[0015] Optionally, a graph database is designed based on a distributed storage system and adopts a hot and cold data tiered storage strategy, the original image data set is stored in the graph database, and the image data behavior is monitored in real time in combination with a machine learning prediction tool, including:

[0016] According to the data usage frequency and data priority in the original image data set, the original image data set is divided into two types: hot data and cold data, and hierarchically stored in the graphic database according to different data types;

[0017] In the graph database, image data and metadata are set as nodes and edges to obtain an image data relationship network, and then a query index is created according to the image data relationship network and different data types, and the query index is optimized using Cypher query language;

[0018] Constructing a version control system based on the graphic database, and using the version control system to record modifications to the image data relationship network;

[0019] Analyzing modification records in the version control system using a deep learning model, predicting the next access data of the graphic database according to the analysis results, and adjusting the image data relationship network in real time according to the prediction results;

[0020] Among them, the graphic database is set with hierarchical encryption strategy, end-to-end encryption transmission strategy, access control strategy and authority management.

[0021] Optionally, the query index is optimized using Cypher query language, including:

[0022] Using a MATCH statement without conditions in the query index and specifying a query path, analyzing the query path and the output of the query path, and performing an optimized query of the graph database;

[0023] Introducing an aggregate function into the query index and limiting the result set returned by the query;

[0024] The query index is checked and updated regularly, and parameterized queries are used to reduce parsing and compilation during the query process of the graph database.

[0025] Optionally, the original image data set is processed using an adaptive compression algorithm, a multi-threaded asynchronous loading method, and GPU parallel computing, including:

[0026] Monitor the resource usage and real-time workload of the game hardware in real time, adjust the compression level and format of the original image data set according to the monitoring results, and decompress and recompress the original image data set in real time to complete adaptive compression;

[0027] Use a multi-threaded task manager to allocate and schedule resource loading tasks, and use thread pool technology to manage resource requests through task priority queues, and then cut large resource files into multiple small resource blocks for time-sharing loading to complete multi-threaded asynchronous loading;

[0028] Use CUDA or OpenCL technology to transfer resource-intensive tasks to the graphics processor and develop custom shader programs to perform specific filter and reflection calculations as well as redundant calculations to automatically crop and reconstruct images and complete GPU parallel computing.

[0029] Optionally, using progressive rendering technology, physically based rendering technology and illumination calculation, quality control is performed on the processed standard image dataset to obtain a standard image dataset, including:

[0030] Based on the processed original image data set, the content in the user's sight is rendered first, and then the background image details are gradually loaded. According to the user data and the image data behavior predicted by monitoring, the user's sight direction is predicted to achieve progressive rendering;

[0031] Use PBR technology to simulate the interaction of real-world lighting and materials, and use ambient occlusion technology and normal mapping technology to improve the details of the simulation and complete physical-based rendering;

[0032] Dynamic light sources and shadow mapping technology are used to calculate lighting effects in real time, ray tracing technology is used to reflect and refract light, and lighting and scattering technology is used to propagate light to complete lighting calculations.

[0033] Optionally, virtualization technology can be used to simulate a variety of hardware configurations, combined with multiple API interfaces, to achieve cross-platform compatible management of image data, including:

[0034] Using virtualization technology to create a virtual rendering environment for simulating different hardware configurations, and developing an abstract graphics layer for encapsulating an underlying graphics API in the virtual rendering environment;

[0035] Unifying the rendering interface in the virtual rendering environment, utilizing the unified rendering interface and abstract graphics layer, encapsulating the underlying graphics API to shield the underlying hardware differences, and automatically managing the compatibility of different hardware;

[0036] Use CSS Grid and Flexbox technology for responsive UI design, use vector graphics and high-resolution icons to maintain the clarity of UI design, and then dynamically adjust the image layout of UI design according to the resolution and ratio of different hardware devices.

[0037] Optionally, based on the monitoring results of the image data, the image data content is automatically updated and optimized, including: based on the monitoring results of the image data, using the CI / CD pipeline to regularly update and optimize the image data, using performance monitoring tools to collect real-time image data and perform monitoring and analysis, and then combining user behavior data to adjust the image data processing process and user interaction method.

[0038] The present invention discloses the following technical effects by providing an image data processing and storage method based on a metaverse game:

[0039] Efficient storage: By designing a graph database, such as Neo4j, it can handle complex relationship queries and is very effective for complex scenes and resource relationships in games. At the same time, it adopts a hot and cold tiered storage strategy to distinguish between commonly used data and infrequently used data, implement efficient storage and transmission strategies, and significantly reduce bandwidth and storage costs.

[0040] Efficient processing: 1) By compressing and optimizing image data, the image quality and loading speed can be adaptively adjusted according to hardware performance and current workload; 2) By processing image data in parallel and asynchronously, multi-threaded data loading and processing can be achieved, reducing the workload of the main thread and CPU load, and improving frame rate stability.

[0041] High-quality images: 1) Through progressive rendering and physically based rendering technology, priority rendering can be achieved in the user's focused area, ensuring that the material performs consistently under different lighting conditions; 2) Through lighting calculation, the lighting effects are calculated to achieve more realistic reflection and refraction effects, and enhance the propagation of light in the air.

[0042] High compatibility: By virtualizing the rendering environment and developing an abstract graphics layer, it is possible to simulate different hardware configurations and automatically manage compatibility issues. At the same time, it is able to encapsulate the differences in the underlying graphics APIs and unify the interface. The game engine can run seamlessly on different platforms, ensuring that the game can be displayed at the highest level on a variety of devices.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention;

[0046] Figure 2 A schematic flow chart of a method for storing image data provided by an embodiment of the present invention;

[0047] Figure 3 A schematic flow chart of a method for image data processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1-3 As shown, the present invention provides an image data processing and storage method based on a metaverse game, comprising the following steps:

[0051] 1. Collect game image data, integrate and preliminarily process the image data to obtain the original image data set. Including:

[0052] 1.1 Collect all the image data of the game and use open source tools (such as Open Asset Import Library) to convert various data formats into a unified format (such as USD or glTF). At the same time, a compatible library that supports multiple formats can be developed to achieve instant interoperability.

[0053] 1.2 Develop an automated script for automatic classification and data labeling, and use the automated script and graphics processing unit (GPU) to standardize the image data in a unified format (such as unified scale and coordinate system) to ensure consistent performance on different platforms. Then integrate the image data to obtain the original image data set.

[0054] 1.3 According to the data type in the original image data set, a data compression strategy is designed, and the compression format and degree (such as ASTC for texture and vertex simplification for 3D model) are intelligently selected according to the data type (such as image and model). Among them, ASTC (Adaptive Scalable Texture Compression) is a texture compression technology developed by ARM. The vertex simplification technology for 3D model mainly includes two methods: vertex clustering and vertex downsampling.

[0055] 1.4 Use a distributed computing framework (such as Apache Hadoop or Spark) to process and merge the large-scale data in the original image dataset to improve the processing speed and complete the preliminary processing of the original image dataset.

[0056] 2. Based on the distributed storage system and using the hot and cold data tiered storage strategy, a graphic database is designed to store the original image data set in the graphic database, and the image data behavior is monitored in real time in combination with machine learning prediction tools. Including:

[0057] 2.1 According to the data usage frequency and data priority in the original image data set, the original image data set is divided into two types: hot data and cold data, and hierarchical storage is performed in the graphic database according to different data types.

[0058] Hot data, such as dynamic UI elements and textures required for the current scene, is stored on fast-access NVMe SSDs.

[0059] Cold data, such as infrequently used textures or historical versions, is stored on more economical HDDs or using cloud storage.

[0060] 2.2 Use graph databases such as Neo4j, which can handle complex relationship queries and are very effective for complex scenes and resource relationships in games.

[0061] In the graph database, image data and metadata (such as type, size, access frequency, and usage scenarios) are set as nodes and edges to obtain an efficient image data relationship network (i.e., resource relationship network). A query index is then created based on the image data relationship network and different data types to accelerate the query speed, and the query index is optimized using the Cypher query language to quickly obtain associated resource information, such as quickly finding a texture and its associated 3D model.

[0062] Cypher query language is the query language used by Neo4j graph database, which supports efficient query of graph data. MATCH statement is a core component of Cypher query language, which is used to find and match nodes and the relationships between them in Neo4j graph database. It is similar to SELECT statement in SQL, but is specifically used to process graph data structure. In Cypher, indexes can be created for nodes and relationships to speed up query. Indexes can significantly increase the speed of finding specific nodes or relationships.

[0063] The process of optimizing the query index using the Cypher query language includes:

[0064] 1) Use a MATCH statement without conditions in the query index, as this will result in a full-graph scan. Explicitly specifying the query path in the query can help the database optimize the query, analyze the query path and the output of the query path, identify potential performance bottlenecks, and perform optimized queries on the graph database.

[0065] 2) Introduce aggregate functions (such as COUNT, SUM, etc.) in the query index to ensure that the index is used in the appropriate place to improve performance. Use LIMIT to limit the size of the result set returned by the query, which can reduce the amount of data processed and improve the query speed.

[0066] 3) Regularly check and update the query index, and use parameterized queries to reduce parsing and compilation during the graph database query process.

[0067] 2.3 Build a version control system based on the graphic database, use the version control system to record the modification of the image data relationship network, and support the gradual evolution and rollback of resources.

[0068] 2.4 Use a deep learning model to analyze the modification records in the version control system, and predict the next access data of the graphic database based on the analysis results, that is, predict which data resource will be accessed next time, and use the prediction to load data into the cache in advance to reduce real-time loading delays, and adjust the image data relationship network in real time based on the prediction results.

[0069] The graphic database is provided with hierarchical encryption strategy, end-to-end encryption transmission strategy, access control strategy and authority management. For example:

[0070] Hierarchical encryption strategy: Apply AES-256 or ChaCha20 encryption standards to sensitive image data (such as user-generated content and key assets). Select appropriate encryption algorithms at different levels (block level, file level, container level) to balance the relationship between security and performance.

[0071] End-to-end encrypted transmission: TLS1.3 is implemented for encryption during data transmission to prevent man-in-the-middle attacks and ensure the security of data during transmission.

[0072] Role-based access control (RBAC): Implement strict access control policies to control access to different datasets and resources based on user roles or departments.

[0073] Fine-grained permission management: Use protocols such as OAuth 2.0 and OpenID Connect to provide refined permission management and authentication to ensure that only authorized users can access specific data sets.

[0074] 3. The original image data set is processed using an adaptive compression algorithm, a multi-threaded asynchronous loading method, and GPU parallel computing. The quality of the processed original image data set is then controlled using progressive rendering technology, physically based rendering technology, and lighting calculation to obtain a standard image data set.

[0075] Specifically include:

[0076] 3.1 Data Compression and Optimization

[0077] Monitor the resource usage of the game hardware (such as the GPU and CPU load) and the real-time workload in real time, adjust the compression level and format of the original image data set according to the monitoring results, and use an efficient format such as ASTC. Decompress and recompress the original image data set in real time to complete adaptive compression, so that the system can adjust the display quality in real time when the load changes. For example, under high load conditions, use a slightly higher compression ratio to reduce the burden; under low load conditions, improve the quality.

[0078] 3.2 Parallel and Asynchronous Processing

[0079] Use the multi-threaded task manager to allocate and schedule resource loading tasks, and use thread pool technology to manage resource requests through task priority queues. The main thread is mainly responsible for user input and core game logic, and the secondary thread loads asset loading and decompression.

[0080] Then divide the large resource file into multiple small resource blocks for time-sharing loading, complete multi-threaded asynchronous loading, improve frame rate stability and avoid lag.

[0081] 3.3 GPU Parallel Computing

[0082] Using CUDA or OpenCL technology, resource-intensive tasks (such as texture mapping and lighting calculations) are transferred to the graphics processor, and custom shader programs are developed (automatically identifying redundant calculations such as repeated texture sampling and other invalid operations, and performing automatic cropping and reconstruction) to perform specific filter and reflection calculations and redundant calculations, freeing up the CPU load and completing GPU parallel computing, ensuring performance optimization without sacrificing quality.

[0083] Among them, CUDA and OpenCL are both frameworks for GPU parallel computing.

[0084] 3.4 Progressive Rendering

[0085] Based on the processed original image data set, the content in the user's field of view is rendered first, and then the background image details are gradually loaded. In addition, based on the user data and the image data behavior predicted by monitoring, the user's future field of view direction is predicted, so as to prioritize loading of resources that may be needed and achieve progressive rendering.

[0086] 3.5 Physically Based Rendering (PBR) Technology

[0087] PBR technology is used to simulate the interaction between lighting and materials in the real world. PBR is based on the laws of physics and ensures that materials perform consistently under different lighting conditions. Ambient occlusion technology and normal mapping technology are used to improve the details of the simulation and complete physical-based rendering.

[0088] 3.6 Lighting calculation

[0089] Use dynamic light sources and shadow mapping technology to calculate lighting effects in real time, use ray tracing technology to achieve more realistic light reflection and refraction effects, and then use volumetric lighting and scattering technology to propagate light and enhance the propagation effect of light in the air to complete the lighting calculation.

[0090] 4. Use virtualization technology to simulate a variety of hardware configurations, and then combine multiple API interfaces to perform cross-platform compatible management of image data. Including:

[0091] 4.1 Use virtualization technology (such as Docker or VMware) to create a virtual rendering environment for simulating different hardware configurations, simulate different hardware configurations, and automatically manage compatibility issues. And develop an abstract graphics layer in the virtual rendering environment to encapsulate the underlying graphics API;

[0092] 4.2 Unify the rendering interface in the virtual rendering environment, utilize the unified rendering interface and abstract graphics layer, encapsulate the underlying graphics API to shield the underlying hardware differences, automatically manage the compatibility of different hardware, and ensure that the game can be displayed at the highest level on various devices.

[0093] 4.3 Use CSS Grid and Flexbox technology for responsive UI design, dynamically adjust the layout according to the device resolution and ratio, use vector graphics (such as SVG) and high-resolution icons to maintain the clarity of the UI design, and dynamically adjust the image layout of the UI design according to the resolution and ratio of different hardware devices.

[0094] Among them, CSS Grid and Flexbox are technologies provided by CSS for creating responsive and flexible layouts, but they differ in layout dimensions, usage scenarios, and functional features. SVG is a two-dimensional vector graphics standard based on XML.

[0095] 5. Automatically update and optimize image data content based on the monitoring results of image data. This includes: based on the monitoring results of image data, use CI / CD pipelines (such as Jenkins) to regularly update and optimize image data, use performance monitoring tools to collect real-time image data and conduct monitoring and analysis, and then combine user behavior data to adjust the image data processing process and user interaction methods.

[0096] Among them, the CI / CD pipeline refers to connecting the code integration, testing, construction, deployment and other links in the software development process through automated tools and processes to form a continuous automated process. Jenkins is an open source continuous integration tool that is mainly used to automate the software development process. It supports a variety of triggers, such as scheduled tasks, code submission, etc., and can perform tasks such as compilation, testing, and deployment. Jenkins extends its functions through a plug-in system, supports integration with tools such as Git and Docker, and provides powerful customization and extensibility. Jenkins' ease of use and flexibility make it the tool of choice for many teams.

[0097] Therefore, the present invention provides an image data processing and storage method based on the metaverse game, which can achieve efficient storage and processing of image data, enhance image data quality and compatibility, significantly improve game performance and user experience, and promote the development and popularization of the virtual world.

[0098] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0099] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for processing and storing image data based on a metaverse game, characterized in that: The following steps are involved: Collect game image data, integrate and preliminarily process the image data to obtain the original image data set; Based on a distributed storage system and using a hot and cold data tiered storage strategy, a graph database is designed, the original image data set is stored in the graph database, and the image data behavior is monitored in real time in combination with a machine learning prediction tool; The original image data set is processed by using an adaptive compression algorithm, a multi-threaded asynchronous loading method and GPU parallel computing, and then the quality of the processed original image data set is controlled by using progressive rendering technology, physical based rendering technology and lighting calculation to obtain a standard image data set; Use virtualization technology to simulate a variety of hardware configurations, and then combine multiple API interfaces to perform cross-platform compatible management of image data; According to the monitoring results of the image data, the image data content is automatically updated and optimized.

2. The method for processing and storing image data based on a metaverse game according to claim 1, characterized in that: Collect game image data, integrate and preliminarily process the image data to obtain the original image data set, including: Collect all the image data of the game, convert the formats of all the image data into a unified format, develop an automated script for automatic classification and data labeling, use the automated script and a graphics processor to standardize the image data in the unified format, and then integrate the image data to obtain the original image data set; A data compression strategy is designed according to the data type in the original image data set, and a distributed computing framework is used to process and merge the large-scale data in the original image data set to complete the preliminary processing of the original image data set.

3. The method for processing and storing image data based on a metaverse game according to claim 2, characterized in that: Based on the distributed storage system and adopting the hot and cold data tiered storage strategy, a graph database is designed, the original image data set is stored in the graph database, and the image data behavior is monitored in real time in combination with a machine learning prediction tool, including: According to the data usage frequency and data priority in the original image data set, the original image data set is divided into two types: hot data and cold data, and hierarchically stored in the graphic database according to different data types; In the graph database, image data and metadata are set as nodes and edges to obtain an image data relationship network, and then a query index is created according to the image data relationship network and different data types, and the query index is optimized using Cypher query language; Constructing a version control system based on the graphic database, and using the version control system to record modifications to the image data relationship network; Analyzing modification records in the version control system using a deep learning model, predicting the next access data of the graphic database according to the analysis results, and adjusting the image data relationship network in real time according to the prediction results; Among them, the graphic database is set with hierarchical encryption strategy, end-to-end encryption transmission strategy, access control strategy and authority management.

4. The method for processing and storing image data based on a metaverse game according to claim 3, characterized in that: The query index is optimized using the Cypher query language, including: Using a MATCH statement without conditions in the query index and specifying a query path, analyzing the query path and the output of the query path, and performing an optimized query of the graph database; Introducing an aggregate function into the query index and limiting the result set returned by the query; The query index is checked and updated regularly, and parameterized queries are used to reduce parsing and compilation during the query process of the graph database.

5. The method for processing and storing image data based on a metaverse game according to claim 4, characterized in that: The original image data set is processed by using an adaptive compression algorithm, a multi-threaded asynchronous loading method and GPU parallel computing, including: Monitor the resource usage and real-time workload of the game hardware in real time, adjust the compression level and format of the original image data set according to the monitoring results, and decompress and recompress the original image data set in real time to complete adaptive compression; Use a multi-threaded task manager to allocate and schedule resource loading tasks, and use thread pool technology to manage resource requests through task priority queues, and then cut large resource files into multiple small resource blocks for time-sharing loading to complete multi-threaded asynchronous loading; Use CUDA or OpenCL technology to transfer resource-intensive tasks to the graphics processor and develop custom shader programs to perform specific filter and reflection calculations as well as redundant calculations to automatically crop and reconstruct images and complete GPU parallel computing.

6. The method for processing and storing image data based on a metaverse game according to claim 5, characterized in that: The quality of the processed standard image data set is controlled by using progressive rendering technology, physical based rendering technology and illumination calculation to obtain a standard image data set, including: Based on the processed original image data set, the content in the user's sight is rendered first, and then the background image details are gradually loaded. According to the user data and the image data behavior predicted by monitoring, the user's sight direction is predicted to achieve progressive rendering; Use PBR technology to simulate the interaction of real-world lighting and materials, and use ambient occlusion technology and normal mapping technology to improve the details of the simulation and complete physical-based rendering; Dynamic light sources and shadow mapping technology are used to calculate lighting effects in real time, ray tracing technology is used to reflect and refract light, and lighting and scattering technology is used to propagate light to complete lighting calculations.

7. The method for processing and storing image data based on a metaverse game according to claim 6, characterized in that: Use virtualization technology to simulate a variety of hardware configurations, and combine multiple API interfaces to manage image data across platforms, including: Using virtualization technology to create a virtual rendering environment for simulating different hardware configurations, and developing an abstract graphics layer for encapsulating an underlying graphics API in the virtual rendering environment; Unifying the rendering interface in the virtual rendering environment, utilizing the unified rendering interface and abstract graphics layer, encapsulating the underlying graphics API to shield the underlying hardware differences, and automatically managing the compatibility of different hardware; Use CSS Grid and Flexbox technology for responsive UI design, use vector graphics and high-resolution icons to maintain the clarity of UI design, and then dynamically adjust the image layout of UI design according to the resolution and ratio of different hardware devices.

8. The method for processing and storing image data based on a metaverse game according to claim 7, characterized in that: Based on the monitoring results of image data, the image data content is automatically updated and optimized, including: based on the monitoring results of image data, using the CI / CD pipeline to regularly update and optimize image data, using performance monitoring tools to collect real-time image data and perform monitoring and analysis, and then combining user behavior data to adjust the image data processing process and user interaction method.

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