Metaverse data processing method and device

Abstract

The application discloses a meta-universe data processing method and device, relates to the technical field of meta-universe and artificial intelligence, and comprises the following steps: collecting a plurality of meta-universe data for a plurality of meta-universe data; based on the correlation between the pre-created meta-universe data types and data feature extraction methods, extracting and classifying the data features of each meta-universe data collected; according to the classification result, performing distributed storage on each meta-universe data and the corresponding data features in a parallel processing mode, and establishing an index file representing the meta-universe data, the corresponding data features and the corresponding storage location; based on a terminal meta-universe access request, calling the target meta-universe data and the corresponding data features corresponding to the request according to the index file; and performing real-time calculation and rendering display on the called data in an edge computing mode. The application is used to improve the processing efficiency, accuracy and timeliness of meta-universe data.

Description

Technical Field

[0001] This invention relates to the fields of metaverse and artificial intelligence technology, and in particular to metaverse data processing methods and apparatus. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] The metaverse, as a trend of merging the virtual and real worlds formed by the combination of technological and social development, has gradually evolved from the initial prototype exploration to a stage where it can be implemented in specific scenarios with certain technological support.

[0004] In the metaverse system, the management of various digital content is an important core capability and the foundation for the metaverse to continuously expand its applications to more areas of production and life.

[0005] In typical applications of the metaverse, the workflow mainly includes: source content acquisition, content processing and integration, persistence of associated content, request processing, content distribution, and terminal integration and display.

[0006] To enhance the immersive and integrated experience of the Metaverse, various requirements exist regarding the accuracy, comprehensiveness, and timeliness of data content on terminal displays, whether AR / VR devices or handheld devices such as mobile phones and tablets. In terms of content diversity, multiple types exist, primarily including multimedia content, text content, spatial information, and real-time events. The Metaverse application needs to support these diverse types, formats, and usage methods of digital content.

[0007] In practical applications, metaverse content is categorized into static and dynamic types from a temporal perspective. The combination of these two types is essential to fully represent or describe various entities within the metaverse, such as 3D models associated with physical locations, digital humans, and interactive menus. A lack of dynamic update capabilities, or a significant delay between update timeliness and actual needs, can negatively impact user experience and even hinder normal functionality, potentially leading to more severe production failures in scenarios like industrial manufacturing.

[0008] Because metaverse applications involve a wide variety of content types and often generate substantial amounts of data, which continues to increase with the activities of operators and participants, effectively and flexibly managing this massive amount of heterogeneous content is a crucial factor for the further expansion of metaverse applications.

[0009] Current metaverse processing solutions generally suffer from the following problems:

[0010] 1. For content of different types and access modes, a uniform processing scheme is often adopted for data collection, processing and storage, lacking a targeted dedicated management model;

[0011] 2. Regarding the updating of data content, it is difficult to achieve real-time or even near-real-time results in terms of timeliness;

[0012] 3. When the data volume of the content exceeds the capacity and processing capability of a single server, there is no clear, efficient and flexible management and processing method. Summary of the Invention

[0013] This invention provides a metaverse data processing method to improve the processing efficiency, accuracy, and timeliness of metaverse data, enhance its storage efficiency, and improve the efficiency of retrieving metaverse data features. The method includes:

[0014] For multiple types of metaverse data, based on the correlation between pre-created metaverse data access patterns and data acquisition methods, multiple types of metaverse data are collected;

[0015] Based on the correlation between the pre-created metaverse data type and the data feature extraction method, data features are extracted and classified for each metaverse data collected.

[0016] Based on the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established.

[0017] The terminal-based metaverse access request retrieves the target metaverse data and corresponding data features corresponding to the request based on the index file; and performs real-time calculation and rendering of the retrieved data in edge computing mode.

[0018] This invention also provides a metaverse data processing device to improve the processing efficiency, accuracy, and timeliness of metaverse data, improve the storage efficiency of metaverse data, and improve the retrieval efficiency of metaverse data features. The device includes:

[0019] The data acquisition module is used to collect various metaverse data based on the pre-created correlation between metaverse data access modes and data acquisition methods.

[0020] The feature extraction and classification module is used to extract and classify data features for each collected metaverse data based on the pre-created correlation between metaverse data types and data feature extraction methods.

[0021] The distributed storage module is used to perform distributed storage of each metaverse data and its corresponding data features in a parallel processing manner based on the classification results, and to establish an index file that represents the metaverse data, its corresponding data features, and its corresponding storage location.

[0022] The real-time computing and rendering module is used for terminal-based metaverse access requests. Based on the index file, it calls the target metaverse data and corresponding data features for the request; and performs real-time computing and rendering of the called data in edge computing mode.

[0023] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described metaverse data processing method.

[0024] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described metaverse data processing method.

[0025] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described metaverse data processing method.

[0026] In this embodiment of the invention, for multiple types of metaverse data, multiple types of metaverse data are collected based on the pre-created association between metaverse data access modes and data acquisition methods; based on the pre-created association between metaverse data types and data feature extraction methods, data features are extracted and classified for each collected metaverse data; according to the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established; based on the terminal's metaverse access request, the target metaverse data and its corresponding data features corresponding to the request are called according to the index file; and the called data is calculated and rendered in real time using edge computing mode, by utilizing different data... Based on the corresponding acquisition methods and data feature extraction methods, targeted acquisition of different types of metaverse data is achieved. By addressing the differences in metaverse data access modes, data classification and distributed storage are implemented, enabling targeted processing of metaverse data. This allows for the establishment of dedicated management and processing modes for different types of metaverse data, improving processing efficiency and accuracy. Based on the establishment of index files and the use of parallel processing and edge computing modes, metaverse data processing, integration, and rendering can be performed at a faster speed, improving the timeliness of metaverse data processing. Simultaneously, the distributed storage scheme also improves the storage efficiency of metaverse data, and the establishment of index files can handle metaverse access requests, improving the efficiency of calling metaverse data features. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0028] Figure 1 This is a flowchart illustrating a metaverse data processing method according to an embodiment of the present invention;

[0029] Figure 2 This is a specific example diagram of a metaverse data processing device in an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of the structure of a metaverse data processing device according to an embodiment of the present invention;

[0031] Figure 4 This is a specific example diagram of a metaverse data processing device in an embodiment of the present invention;

[0032] Figure 5This is a specific example diagram of a metaverse data processing device in an embodiment of the present invention;

[0033] Figure 6 This is a specific example diagram of a metaverse data processing device in an embodiment of the present invention;

[0034] Figure 7 This is a schematic diagram of a computer device used for metaverse data processing in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0036] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0037] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0038] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0039] The following terms are used in the embodiments of this invention and are explained below:

[0040] Metaverse: The metaverse is a new type of internet application and social form that integrates multiple new technologies and blends the virtual and the real. It provides immersive experiences based on virtual reality and augmented reality technologies, generates a mirror image of the real world based on digital twin technology, and builds an economic system based on blockchain technology, closely integrating the virtual world and the real world in terms of economic system, social system, and identity system.

[0041] AR: Augmented Reality. It is an interactive experience that combines the real world with computer-generated content. It uses image analysis technology, combining the position and angle of images acquired by sensors, to create a virtual world on a screen that can interact with and integrate with real-world scenes.

[0042] VR: Virtual Reality. It uses computers to simulate a three-dimensional virtual world, providing users with a simulation of their senses, such as vision, making them feel as if they are actually there, and allowing them to observe things in three-dimensional space in real time and without restrictions.

[0043] Digital twins are virtual models designed to accurately reflect physical objects, dynamically representing the past and present behavior or processes of a physical entity in digital form. Their true function lies in spanning the object's lifecycle and using real-time data from sensors on the object to simulate behavior and monitor operations, enabling a comprehensive real-time connection between the physical and digital worlds.

[0044] NLP (Natural Language Processing) is a machine learning technique that enables computers to interpret, process, and understand human language. It includes two core tasks: natural language understanding and natural language generation. Seq2seq is a natural language generation framework.

[0045] Edge computing is a distributed computing architecture that moves the computation of applications, data, and services from the central nodes of the network to logically located edge nodes. It's the process of bringing information storage and computing power closer to the devices that generate and retrieve that information, and to the users who use it. Various terminal devices can act as nodes in edge computing, executing computing tasks assigned by the system.

[0046] Digital humans: Digital avatars created using computer graphics technology that closely resemble human figures, possessing human appearance, sensory and interactive abilities, and expressive capabilities. They are divided into two categories: one relies on smart wearable devices to create a "digital clone" of a natural person in virtual space; the other uses artificial intelligence technology to generate digital virtual characters.

[0047] SLAM (Simultaneous localization and mapping): The computational problem of building or updating maps of unknown environments using spatial and visual information acquired by sensors, while simultaneously tracking the location of agents within them.

[0048] Real-time computing is an event-triggered computing model. Once new streaming data enters the real-time computing process, the computing task is immediately initiated and executed, aiming to provide the shortest possible latency from data visibility to usability. Flink is a real-time computing framework.

[0049] To enhance the immersive and integrated experience of the Metaverse, various requirements exist regarding the accuracy, comprehensiveness, and timeliness of data content on terminal displays, whether AR / VR devices or handheld devices such as mobile phones and tablets. In terms of content diversity, multiple types exist, primarily including multimedia content, text content, spatial information, and real-time events. The Metaverse application needs to support these diverse types, formats, and usage methods of digital content.

[0050] In practical applications, metaverse content is categorized into static and dynamic types from a temporal perspective. The combination of these two types is essential to fully represent or describe various entities within the metaverse, such as 3D models associated with physical locations, digital humans, and interactive menus. A lack of dynamic update capabilities, or a significant delay between update timeliness and actual needs, can negatively impact user experience and even hinder normal functionality, potentially leading to more severe production failures in scenarios like industrial manufacturing.

[0051] Because metaverse applications involve a wide variety of content types and often generate substantial amounts of data, which continues to increase with the activities of operators and participants, effectively and flexibly managing this massive amount of heterogeneous content is a crucial factor for the further expansion of metaverse applications.

[0052] The problems with existing technologies in managing metaverse content include:

[0053] There is a lack of dedicated management models for different types of content and different access patterns;

[0054] For data content updates, it is difficult to achieve real-time or even near-real-time results in terms of timeliness;

[0055] There is no clear, efficient, and flexible management and processing method when the data volume of the content exceeds the capacity and processing capacity of a single server.

[0056] To address the aforementioned problems, embodiments of the present invention provide a metaverse data processing method to improve the processing efficiency, accuracy, and timeliness of metaverse data, enhance its storage efficiency, and improve the efficiency of retrieving metaverse data features. See [link to relevant documentation]. Figure 1 The method may include:

[0057] Step 101: For multiple types of metaverse data, collect multiple types of metaverse data based on the pre-created correlation between metaverse data access patterns and data acquisition methods;

[0058] Step 102: Based on the correlation between the pre-created metaverse data type and the data feature extraction method, extract and classify the data features for each metaverse data collected;

[0059] Step 103: Based on the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established.

[0060] Step 104: Based on the terminal's metaverse access request, according to the index file, call the target metaverse data and corresponding data features corresponding to the request; and in edge computing mode, perform real-time calculation and rendering display on the called data.

[0061] In this embodiment of the invention, for multiple types of metaverse data, multiple types of metaverse data are collected based on the pre-created association between metaverse data access modes and data acquisition methods; based on the pre-created association between metaverse data types and data feature extraction methods, data features are extracted and classified for each collected metaverse data; according to the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established; based on the terminal's metaverse access request, the target metaverse data and its corresponding data features corresponding to the request are called according to the index file; and the called data is calculated and rendered in real time using edge computing mode, by utilizing different data... Based on the corresponding acquisition methods and data feature extraction methods, targeted acquisition of different types of metaverse data is achieved. By addressing the differences in metaverse data access modes, data classification and distributed storage are implemented, enabling targeted processing of metaverse data. This allows for the establishment of dedicated management and processing modes for different types of metaverse data, improving processing efficiency and accuracy. Based on the establishment of index files and the use of parallel processing and edge computing modes, metaverse data processing, integration, and rendering can be performed at a faster speed, improving the timeliness of metaverse data processing. Simultaneously, the distributed storage scheme also improves the storage efficiency of metaverse data, and the establishment of index files can handle metaverse access requests, improving the efficiency of calling metaverse data features.

[0062] In practice, the process begins by collecting various types of metaverse data based on the pre-created association between metaverse data access modes and data collection methods.

[0063] In one embodiment, the method further includes: creating an association between the metaverse data access mode and the data acquisition method in the following manner:

[0064] Establish a connection between the metaverse space scene construction data and digital human image generation data in the metaverse data and the data collection method reported by the stateless interface;

[0065] Establish a connection between event data in the metaverse data and data collection methods using message queues;

[0066] Establish a connection between digital twin data in the metaverse data and data acquisition methods using long connections.

[0067] In this embodiment, by creating a correlation between metaverse data access modes and data acquisition methods, based on the analysis of the use of metaverse-related content, it is proposed to classify the content at the entry stage of the data receiving process and divide the dedicated processing methods in subsequent processing, and further realize the systematization of content management and processing according to the differences in access modes.

[0068] In one embodiment, for multiple types of metaverse data, based on the association between a pre-created metaverse data access pattern and a data acquisition method, multiple types of metaverse data are collected, including:

[0069] For each piece of metaverse data, based on the pre-created association between metaverse data access mode and data acquisition method, determine the data acquisition method corresponding to the data access mode of that metaverse data;

[0070] Collect the metaverse data according to the data acquisition method corresponding to the data access mode of the metaverse data.

[0071] In the above embodiments, real-time reception of metaverse space scene construction data, digital human image generation data, digital twin data, and event data can be achieved. For different types of content or different access modes, stateless interface reporting, message queues, and stateful socket long-lived connection reuse can all be employed. Stateless interface reporting is generally used for the initialization of metaverse space, digital humans, etc., where the data scale is large and there are no or low timeliness requirements. Message queues are mainly used for real-time reception of event data. Stateful socket long-lived connections can be used for digital twin data acquisition.

[0072] In practice, for multiple types of metaverse data, based on the pre-created correlation between metaverse data access mode and data acquisition method, after collecting multiple types of metaverse data, based on the pre-created correlation between metaverse data type and data feature extraction method, data features are extracted and classified for each collected metaverse data.

[0073] In this embodiment, the metaverse data after initial classification can be processed using multimedia feature extraction, NLP, and spatial computing. For multimedia content such as video, images, and audio, feature extraction is performed to generate corresponding feature files; for text content, NLP processing is used to generate corresponding text features and models; for spatially related data, a correspondence between spatial locations and associated content is generated. Furthermore, the extracted features can also be classified.

[0074] In one embodiment, it also includes:

[0075] The collected metaverse data undergoes data normalization processing; the data normalization processing includes one or any combination of constant value processing, deduplication and integration processing of data from multiple sources with the same information, unique identifier creation processing, and category coding processing.

[0076] Based on the correlation between pre-created metaverse data types and data feature extraction methods, data features are extracted and classified for each collected metaverse data set, including:

[0077] Based on the correlation between the pre-created metaverse data type and the data feature extraction method, data features are extracted and classified for each metaverse data after data normalization.

[0078] In this embodiment, the input data content is standardized to facilitate further processing. The main tasks include: handling outliers, deduplication and integration of data from multiple sources with the same information, creating unique identifiers, category coding, and generating content summaries.

[0079] In one embodiment, the method further includes: creating an association between the metaverse data type and the data feature extraction method in the following manner:

[0080] Establish a correlation between the multimedia content types in the metaverse data type and the multimedia data feature extraction method;

[0081] Establish a connection between the text content type in the metaverse data type and the feature extraction method of NLP processing;

[0082] Establish a correlation between the spatial content types in the metaverse data type and the metaverse spatial feature extraction method;

[0083] Establish a connection between the event content type in the metaverse data type and the event feature extraction method.

[0084] In the embodiments, in the metaverse data of multimedia content types, visual data and auditory data, as the media closest to human senses, are essential basic experience forms in the metaverse and also constitute the largest proportion of data among many content categories.

[0085] In this embodiment, for text content data, NLP technologies can be used, including semantic analysis, multilingual translation, and text generation. Through text content processing, massive amounts of unrelated text information and related content are linked within a metaverse, enriching the input and output formats of the metaverse. Text generation can employ a seq2seq encoding / decoding structure.

[0086] In this embodiment, the spatial content types in the metaverse data type can be associated with the metaverse spatial feature extraction method. It is worth noting that spatial perception and association are crucial components of metaverse applications and are essential for establishing the integration of the virtual and real worlds. Spatial content processing primarily targets content related to spatial positioning and representation within the metaverse. The processed content includes, but is not limited to, latitude and longitude, three-dimensional absolute coordinates, relative coordinates, and multi-dimensional rotation angles.

[0087] In this embodiment, based on the event content type, processing can be performed using real-time computing technology. The event content mainly targets various application events generated in real time, such as IoT sensor monitoring events, user operation behavior events, and device displacement events. It features small data volume per event, high concurrency, and strong continuity, and there are combinations of various operations between events, including filtering, extraction, augmentation, time-series correlation, and aggregation. The Flink real-time computing framework can be used to process the event content.

[0088] In practice, based on the association between the pre-created metaverse data type and the data feature extraction method, after extracting and classifying the data features of each collected metaverse data, according to the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing, and an index file representing the metaverse data, its corresponding data features and its corresponding storage location is established.

[0089] In this embodiment, creating an index file allows a unified scheduling module to handle distributed storage and manage the mapping between related content and its actual storage location, addressing the issue of content data growth exceeding the processing capacity of a single server.

[0090] In one embodiment, it also includes:

[0091] Based on the data corresponding to the index file and the data access and update frequency, the created index file is stored in a hierarchical isolation manner.

[0092] In this embodiment, considering the relatively high frequency of index content access and updates, the management resources for index content are hierarchically isolated to ensure the access efficiency of each level of index.

[0093] In one embodiment, based on the classification results, each metaverse data point and its corresponding data features are distributed and stored in a parallel processing manner, including:

[0094] Based on the classification results, each metaverse data and its corresponding data features are distributed and stored using object storage and hierarchical storage methods in a parallel processing manner.

[0095] In this embodiment, object storage can be used and hierarchical management can be supported. This takes into account the difference in access frequency between hot and cold content, and provides a balance between capacity and performance. This solves the problem that there is no clear, efficient and flexible management and processing solution when the data scale of the content exceeds the capacity and processing capacity of a single server.

[0096] In one embodiment, it also includes:

[0097] The metaverse data and corresponding index files, which are stored in a distributed manner, are updated in real time using the metaverse data collected and updated in real time.

[0098] In this embodiment, dynamic content can be processed, integrated, and displayed at a faster speed by introducing real-time computing.

[0099] In the above embodiments, a highly efficient and flexible management and processing solution can also be provided by combining hierarchical multi-level storage and separating hot and cold content.

[0100] In practice, based on the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode. After establishing an index file that represents the metaverse data, its corresponding data features, and its corresponding storage location, the target metaverse data and its corresponding data features are called according to the index file based on the terminal's metaverse access request. The called data is then calculated and rendered in real time using edge computing mode.

[0101] In one embodiment, edge computing is used to perform real-time computation and rendering of the invoked data, including:

[0102] Perform real-time calculations on the retrieved data;

[0103] Based on the SDK driven by the terminal display device, the data called at the real-time computing point is rendered and displayed.

[0104] In this embodiment, the real-time calculation and rendering results obtained by the above method, which serve as the interface for the interactive experience of the metaverse application, can run on terminal devices such as AR and VR. By adopting the edge computing mode, the computing resources of the terminal device can be utilized to integrate with real-time information such as local sensors to generate complete output content and output the final metaverse display effect.

[0105] In one embodiment, it also includes:

[0106] The terminal sends data to the terminal based on the feedback of the metaverse access request; the terminal is used to: combine the local data stored in the terminal and the data called to execute edge computing tasks; the edge computing tasks include 3D model generation, spatial positioning, depth occlusion calculation and AI model driving.

[0107] In this embodiment, the terminal can utilize the computing resources in the terminal device to integrate with real-time information from local sensors and other sources to generate complete output content and output the final metaverse display effect.

[0108] In this embodiment of the invention, for multiple types of metaverse data, multiple types of metaverse data are collected based on the pre-created association between metaverse data access modes and data acquisition methods; based on the pre-created association between metaverse data types and data feature extraction methods, data features are extracted and classified for each collected metaverse data; according to the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established; based on the terminal's metaverse access request, the target metaverse data and its corresponding data features corresponding to the request are called according to the index file; and the called data is calculated and rendered in real time using edge computing mode, by utilizing different data... Based on the corresponding acquisition methods and data feature extraction methods, targeted acquisition of different types of metaverse data is achieved. By addressing the differences in metaverse data access modes, data classification and distributed storage are implemented, enabling targeted processing of metaverse data. This allows for the establishment of dedicated management and processing modes for different types of metaverse data, improving processing efficiency and accuracy. Based on the establishment of index files and the use of parallel processing and edge computing modes, metaverse data processing, integration, and rendering can be performed at a faster speed, improving the timeliness of metaverse data processing. Simultaneously, the distributed storage scheme also improves the storage efficiency of metaverse data, and the establishment of index files can handle metaverse access requests, improving the efficiency of calling metaverse data features.

[0109] This invention also provides a metaverse data processing device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the metaverse data processing method, the implementation of this device can refer to the implementation of the metaverse data processing method; repeated details will not be elaborated further.

[0110] This invention provides a metaverse data processing device to improve the processing efficiency, accuracy, and timeliness of metaverse data, enhance its storage efficiency, and improve the efficiency of retrieving metaverse data features. Figure 3 As shown, the device includes:

[0111] Data acquisition module 301 is used to acquire multiple metaverse data based on the pre-created association between metaverse data access mode and data acquisition method.

[0112] The feature extraction and classification module 302 is used to extract and classify data features for each collected metaverse data based on the association between the pre-created metaverse data type and the data feature extraction method.

[0113] The distributed storage module 303 is used to perform distributed storage of each metaverse data and its corresponding data features in a parallel processing manner based on the classification results, and to establish an index file that represents the metaverse data, its corresponding data features, and its corresponding storage location.

[0114] The real-time computing and rendering module 304 is used for terminal-based metaverse access requests. Based on the index file, it calls the target metaverse data and corresponding data features corresponding to the request; and performs real-time computing and rendering of the called data in edge computing mode.

[0115] In one embodiment, such as Figure 4 As shown, it also includes:

[0116] The first association establishment module 401 is used for:

[0117] Create the association between the metaverse data access mode and the data acquisition method as follows:

[0118] Establish a connection between the metaverse space scene construction data and digital human image generation data in the metaverse data and the data collection method reported by the stateless interface;

[0119] Establish a connection between event data in the metaverse data and data collection methods using message queues;

[0120] Establish a connection between digital twin data in the metaverse data and data acquisition methods using long connections.

[0121] In one embodiment, the data acquisition module is specifically used for:

[0122] For each piece of metaverse data, based on the pre-created association between metaverse data access mode and data acquisition method, determine the data acquisition method corresponding to the data access mode of that metaverse data;

[0123] Collect the metaverse data according to the data acquisition method corresponding to the data access mode of the metaverse data.

[0124] In one embodiment, such as Figure 5 As shown, it also includes:

[0125] Data normalization processing module 501 is used for:

[0126] The collected metaverse data undergoes data normalization processing; the data normalization processing includes one or any combination of constant value processing, deduplication and integration processing of data from multiple sources with the same information, unique identifier creation processing, and category coding processing.

[0127] The feature extraction and classification module is specifically used for:

[0128] Based on the correlation between the pre-created metaverse data type and the data feature extraction method, data features are extracted and classified for each metaverse data after data normalization.

[0129] In one embodiment, such as Figure 6 As shown, it also includes:

[0130] The second association establishment module 601 is used for:

[0131] Create the association between the metaverse data type and the data feature extraction method as follows:

[0132] Establish a correlation between the multimedia content types in the metaverse data type and the multimedia data feature extraction method;

[0133] Establish a connection between the text content type in the metaverse data type and the feature extraction method of NLP processing;

[0134] Establish a correlation between the spatial content types in the metaverse data type and the metaverse spatial feature extraction method;

[0135] Establish a connection between the event content type in the metaverse data type and the event feature extraction method.

[0136] In one embodiment, it also includes:

[0137] The index file storage module is used for:

[0138] Based on the data corresponding to the index file and the data access and update frequency, the created index file is stored in a hierarchical isolation manner.

[0139] In one embodiment, the distributed storage module is specifically used for:

[0140] Based on the classification results, each metaverse data and its corresponding data features are distributed and stored using object storage and hierarchical storage methods in a parallel processing manner.

[0141] In one embodiment, it also includes:

[0142] The data update module is used for:

[0143] The metaverse data and corresponding index files, which are stored in a distributed manner, are updated in real time using the metaverse data collected and updated in real time.

[0144] In one embodiment, the real-time calculation and rendering module is specifically used for:

[0145] Perform real-time calculations on the retrieved data;

[0146] Based on the SDK driven by the terminal display device, the data called at the real-time computing point is rendered and displayed.

[0147] In one embodiment, it also includes:

[0148] The data sending module is used to send data to the terminal based on the feedback of the metaverse access request; the terminal is used to combine the local data stored in the terminal and the called data to execute edge computing tasks; the edge computing tasks include 3D model generation, spatial positioning, depth occlusion calculation and AI model driving.

[0149] The following is a specific embodiment to illustrate the specific application of the device of the present invention. In this embodiment, as shown in the example... Figure 2 As shown, it can include the following modules:

[0150] A. Data Acquisition Module (i.e., the data collection module mentioned above)

[0151] B. Classification and Content Distribution Module (i.e., the feature extraction and classification module and the distributed storage module mentioned above)

[0152] C. Integration and Display (i.e., the real-time calculation and rendering display module mentioned above)

[0153] The following is a detailed description of each module in the specific embodiment:

[0154] A. Data Acquisition Module (i.e., the data collection module mentioned above)

[0155] This module includes a sensor acquisition module, a batch import module, a data cleaning and standardization module, and a content classification module.

[0156] A-1. Real-time Acquisition Module: This module is used for the real-time reception of metaverse space scene construction, digital human image generation, digital twin data acquisition, and event data reception. For different types of content, it employs stateless interface reporting, message queues, and stateful socket long-connection reuse. Stateless interface reporting is generally used for the initialization of metaverse space and digital humans, where the data scale is large and there are no or low timeliness requirements. Message queues are mainly used for real-time reception of event data. Stateful socket long-connections can be used for digital twin data acquisition.

[0157] A-2, Batch Import Module

[0158] This module is used for large-scale, batch content import, particularly when reusing existing content. It can accelerate the construction and updating of the metaverse space, entities, and related content.

[0159] A-3, Data Normalization Module

[0160] This module refers to the standardization operation based on the data input in modules A-1 and A-2, which facilitates further processing. The main tasks include: outlier handling, deduplication and integration of data from multiple sources with the same information, creation of unique identifiers, category coding, and generation of content summaries.

[0161] A-4, Content Classification Module

[0162] This module refers to the data normalized by the A-3 module, which is then connected to the corresponding classification processing module according to the category code.

[0163] B. Classification and Content Distribution Module (i.e., the feature extraction and classification module and the distributed storage module mentioned above)

[0164] This module includes a classification processing module, a content distribution control module, and a content update synchronization module. It primarily processes the content classified in stage A-4 using multimedia feature extraction, NLP, and spatial computation. For multimedia content such as video, images, and audio, feature extraction is performed to generate corresponding feature files. For text content, NLP processing is used to generate corresponding text features and models. For spatially related data, the correspondence between spatial locations and associated content is generated. The processed content is then distributed and stored according to access patterns and updated and synchronized with the display module.

[0165] B-1, Classification Processing Module

[0166] This module is the core module of the present invention, including a multimedia content processing module, a text content processing module, a spatial content processing module, and an event content processing module.

[0167] B-1-1, Multimedia Content Processing Module

[0168] This module processes multimedia content within the metaverse. Visual and auditory senses, as media closest to human senses, are essential basic experience forms in the metaverse and also constitute the largest portion of data across various content categories. The module supports key functions such as image / speech recognition, image / speech analysis, and image / graphics / speech synthesis.

[0169] B-1-2, Text Content Processing Module

[0170] This module processes text-related content within the metaverse, based on NLP techniques, including semantic analysis, multilingual translation, and text generation. Through text content processing, it links massive amounts of unrelated text information and related content within the metaverse, enriching the input and output formats of the metaverse. Text generation can employ a seq2seq encoding / decoding structure. Alternatively, text generation within the text content processing module can utilize a Conditional Variational Autoencoder (CVAE), suitable for one-to-many problems.

[0171] B-1-3, Spatial Content Processing Module

[0172] This module specifically addresses the processing of spatial content in the construction and application of the metaverse. Spatial perception and association are crucial components of metaverse applications, essential for establishing the fusion of the virtual and real worlds. Spatial content processing primarily focuses on elements related to spatial positioning and representation within the metaverse. Processed content includes, but is not limited to: latitude and longitude, absolute three-dimensional spatial coordinates, relative coordinates, and multi-dimensional rotation angles.

[0173] B-1-4, Event Content Processing Module

[0174] This module handles event-based content processing using real-time computing technology. Event-based content primarily targets various application events generated in real-time, such as IoT sensor monitoring events, user operation events, and device displacement events. These events are characterized by small individual event data volumes, high concurrency, and strong continuity, and involve combinations of operations such as filtering, extraction, augmentation, time-series correlation, and aggregation. The Flink real-time computing framework can be used for event processing. While Spark Streaming can also be used for real-time processing within this module, its design principle of simulating real-time processing in batches results in lower latency compared to Flink.

[0175] B-2, Content Distribution Control Module

[0176] Based on how content is used in the metaverse application, this module divides it into two parts: indexed content and related content. To address the issue of content data growth exceeding the processing capacity of a single server, a unified scheduling module is responsible for distributed storage and managing the mapping between related content and its actual storage location.

[0177] B-2-1, Index Content Management Module

[0178] This module organizes and manages the indexed content, using different indexing techniques based on the content's type and attributes. The goal is to enable immediate execution and return when related content needs to be retrieved in the metaverse application. Considering the relatively high frequency of indexed content access and updates, the management resources for the indexed content are hierarchically isolated to ensure efficient access at each level of the index.

[0179] B-2-2, Related Content Management Module

[0180] This module manages the organization of content, including metadata recording, access control, and access statistics. It also provides scheduling input parameters for the distributed scheduling module.

[0181] B-2-3, Distributed Scheduling Module

[0182] This module maintains the distributed storage of indexed and related content, addressing the challenge of single-machine servers struggling to handle massive amounts of data, while also providing high availability. Based on the heterogeneous diversity of metaverse application content, it employs object storage and supports hierarchical management, accommodating the differences in access frequency between hot and cold content, thus providing a balance between capacity and performance.

[0183] B-3, Content Update Module

[0184] This module takes as input the distribution information data and update operation records of various types of content generated by the content distribution control module. Based on terminal functions and business configurations, it sends the updated metaverse content to the terminal. For content related to the display, a data prefetching and asynchronous sending method is used, and for frequently accessed content, a distributed cache is deployed to accelerate the content update speed. Simultaneously, it actively maintains the synchronization status between the cache and the updated content.

[0185] C. Integration and Display (i.e., the real-time calculation and rendering display module mentioned above)

[0186] This module serves as the interface for the Metaverse application's interactive experience, running on AR, VR, and other terminal devices using an edge computing model. Based on content and command interaction with Module B, it utilizes the terminal device's computing resources and integrates with real-time information from local sensors to generate complete output content, ultimately showcasing the Metaverse display effect.

[0187] C-1, Content Receiving Module

[0188] This module receives content update data from the B-3 content update module. During the receiving process, it is responsible for content integrity verification, storage allocation, and release.

[0189] C-2, Content Integration Module

[0190] This module takes as input the multimedia content, text content, spatial content, and event content received from module C-1, and combines this with real-time content stored locally on the terminal to perform edge computing tasks. These tasks include 3D model generation, spatial localization, depth occlusion calculation, and AI model-driven processing.

[0191] C-3, Content Display Module

[0192] This module takes as input the complete content generated by module C-2, calls the display device driver SDK, and performs the final graphical rendering of the content.

[0193] In this specific embodiment, in response to the problem that existing metaverse content management lacks a targeted dedicated management mode for different types and access modes, this invention sets up a dedicated management and processing mode for metaverse content of different types and access modes. Based on the analysis of the use of metaverse-related content, it proposes to classify the content at the entry stage of the processing flow and divide it into dedicated processing modules in subsequent processing. Furthermore, based on the differences in access modes, it realizes the systematization of content management and processing.

[0194] Furthermore, for updates to metaverse application content, in addition to categorized processing, each type supports parallel processing and introduces a real-time computing mode to ensure timeliness. This invention focuses on the timeliness of content updates. By introducing real-time computing, dynamic content can be processed, integrated, and displayed at a faster speed.

[0195] Furthermore, when the data scale of the content exceeds the capacity and processing capability of a single server, there is no clear, efficient, and flexible management and processing method. This invention provides an efficient and flexible management and processing method when the data scale of the metaverse application content exceeds the capacity and processing capability of a single server, through a content distribution management module combined with hierarchical multi-level storage and separation of hot and cold content.

[0196] This invention provides an embodiment of a computer device for implementing all or part of the above-described metaverse data processing method. The computer device specifically includes the following components:

[0197] The computer device comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between related devices; the computer device can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the computer device can be implemented with reference to the embodiments for implementing the metaverse data processing method and the embodiments for implementing the metaverse data processing device, the contents of which are incorporated herein by reference, and repeated details will not be described again.

[0198] Figure 7 This is a schematic block diagram illustrating the system configuration of the computer device 1000 according to an embodiment of this application. Figure 7 As shown, the computer device 1000 may include a central processing unit 1001 and a memory 1002; the memory 1002 is coupled to the central processing unit 1001. It is worth noting that... Figure 7 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0199] In one embodiment, the metaverse data processing function can be integrated into the central processing unit 1001. The central processing unit 1001 can be configured to perform the following control:

[0200] For multiple types of metaverse data, based on the correlation between pre-created metaverse data access patterns and data acquisition methods, multiple types of metaverse data are collected;

[0201] Based on the correlation between the pre-created metaverse data type and the data feature extraction method, data features are extracted and classified for each metaverse data collected.

[0202] Based on the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established.

[0203] Based on the terminal's metaverse access request, the system retrieves the target metaverse data and corresponding data features corresponding to the request according to the index file; and performs real-time calculation and rendering of the retrieved data in an edge computing mode.

[0204] In another embodiment, the metaverse data processing device can be configured separately from the central processing unit 1001. For example, the metaverse data processing device can be configured as a chip connected to the central processing unit 1001, and the metaverse data processing function can be implemented through the control of the central processing unit.

[0205] like Figure 7 As shown, the computer device 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, and a power supply 1007. It is worth noting that the computer device 1000 does not necessarily need to include... Figure 7 All components shown; in addition, the computer device 1000 may also include Figure 7 For components not shown, please refer to existing technologies.

[0206] like Figure 7 As shown, the central processing unit 1001, sometimes also referred to as a controller or operation control, may include a microprocessor or other processor device and / or logic device. The central processing unit 1001 receives input and controls the operation of various components of the computer device 1000.

[0207] The memory 1002 may be, for example, one or more of a cache, flash memory, hard drive, removable medium, volatile memory, non-volatile memory, or other suitable device. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 1001 may execute the program stored in the memory 1002 to perform information storage or processing, etc.

[0208] Input unit 1004 provides input to central processing unit 1001. This input unit 1004 may be, for example, a keypad or touch input device. Power supply 1007 provides power to computer device 1000. Display 1006 displays images, text, and other display objects. This display may be, for example, an LCD display, but is not limited to this.

[0209] The memory 1002 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs, etc. The memory 1002 can also be some other type of device. The memory 1002 includes a buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application / function storage unit 1022 for storing application programs and function programs or processes for executing operations of the computer device 1000 via the central processing unit 1001.

[0210] The memory 1002 may also include a data storage unit 1023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 1024 of the memory 1002 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0211] The communication module 1003 is a transmitter / receiver 1003 that transmits and receives signals via the antenna 1008. The communication module (transmitter / receiver) 1003 is coupled to the central processing unit 1001 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0212] Based on different communication technologies, multiple communication modules 1003 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010, thereby realizing typical telecommunications functions. The audio processor 1005 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 1005 is also coupled to a central processing unit 1001, enabling on-device recording via the microphone 1010 and on-device playback of stored sound via the speaker 1009.

[0213] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described metaverse data processing method.

[0214] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described metaverse data processing method.

[0215] In this embodiment of the invention, for multiple types of metaverse data, multiple types of metaverse data are collected based on the pre-created association between metaverse data access modes and data acquisition methods; based on the pre-created association between metaverse data types and data feature extraction methods, data features are extracted and classified for each collected metaverse data; according to the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established; based on the terminal's metaverse access request, the target metaverse data and its corresponding data features corresponding to the request are called according to the index file; and the called data is calculated and rendered in real time using edge computing mode, by utilizing different data... Based on the corresponding acquisition methods and data feature extraction methods, targeted acquisition of different types of metaverse data is achieved. By addressing the differences in metaverse data access modes, data classification and distributed storage are implemented, enabling targeted processing of metaverse data. This allows for the establishment of dedicated management and processing modes for different types of metaverse data, improving processing efficiency and accuracy. Based on the establishment of index files and the use of parallel processing and edge computing modes, metaverse data processing, integration, and rendering can be performed at a faster speed, improving the timeliness of metaverse data processing. Simultaneously, the distributed storage scheme also improves the storage efficiency of metaverse data, and the establishment of index files can handle metaverse access requests, improving the efficiency of calling metaverse data features.

[0216] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0218] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0220] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for processing metaverse data, characterized in that, include: For multiple types of metaverse data, based on the correlation between pre-created metaverse data access patterns and data acquisition methods, multiple types of metaverse data are collected; Based on the correlation between the pre-created metaverse data type and the data feature extraction method, data features are extracted and classified for each metaverse data collected. Based on the classification results, each metaverse data and its corresponding data features are distributed and stored in parallel processing mode, and an index file representing the metaverse data, its corresponding data features, and its corresponding storage location is established. Based on the terminal's metaverse access request, the system retrieves the target metaverse data and corresponding data features corresponding to the request according to the index file; and performs real-time calculation and rendering of the retrieved data in an edge computing mode. For various types of metaverse data, based on the pre-created association between metaverse data access modes and data acquisition methods, multiple types of metaverse data are collected. This includes: for each type of metaverse data, determining the data acquisition method corresponding to the data access mode based on the pre-created association between metaverse data access modes and data acquisition methods; collecting the metaverse data according to the data acquisition method corresponding to the data access mode of the metaverse data; real-time reception of metaverse space scene construction data, digital human image generation data, digital twin data, and event data; and multiplexing of stateless interface reporting, message queues, and stateful socket long connections for different types or access modes of content. Stateless interface reporting is used for metaverse space and digital human initialization; message queues are mainly used for real-time reception of event data; and stateful socket long connections are used for digital twin data acquisition.

2. The method as described in claim 1, characterized in that, For various types of metaverse data, based on the pre-created correlation between metaverse data access patterns and data acquisition methods, various types of metaverse data are collected, including: For each type of metaverse data, based on the pre-created association between metaverse data access mode and data acquisition method, determine the data acquisition method corresponding to the data access mode of the metaverse data; Collect the metaverse data according to the data acquisition method corresponding to the data access mode of the metaverse data.

3. The method as described in claim 1, characterized in that, Also includes: The collected metaverse data undergoes data normalization processing; the data normalization processing includes one or any combination of constant value processing, deduplication and integration processing of data from multiple sources with the same information, unique identifier creation processing, and category coding processing. Based on the correlation between pre-created metaverse data types and data feature extraction methods, data features are extracted and classified for each collected metaverse data set, including: Based on the correlation between the pre-created metaverse data type and the data feature extraction method, data features are extracted and classified for each metaverse data after data normalization.

4. The method as described in claim 1, characterized in that, This also includes: creating the association between metaverse data types and data feature extraction methods in the following manner: Establish a correlation between the multimedia content types in the metaverse data type and the multimedia data feature extraction method; Establish a connection between the text content type in the metaverse data type and the feature extraction method of NLP processing; Establish a correlation between the spatial content types in the metaverse data type and the metaverse spatial feature extraction method; Establish a connection between the event content type in the metaverse data type and the event feature extraction method.

5. The method as described in claim 1, characterized in that, Also includes: Based on the data corresponding to the index file and the data access and update frequency, the created index file is stored in a hierarchical isolation manner.

6. The method as described in claim 1, characterized in that, Based on the classification results, each element of the universe data and its corresponding data features are distributed and stored in a parallel processing manner, including: Based on the classification results, each metaverse data and its corresponding data features are distributed and stored using object storage and hierarchical storage methods in a parallel processing manner.

7. The method as described in claim 1, characterized in that, Also includes: The metaverse data and corresponding index files, which are stored in a distributed manner, are updated in real time using the metaverse data collected and updated in real time.

8. The method as described in claim 1, characterized in that, Using edge computing, the called data is processed and rendered in real time, including: Perform real-time calculations on the retrieved data; Based on the SDK driven by the terminal display device, the data called at the real-time computing point is rendered and displayed.

9. The method as described in claim 1, characterized in that, Also includes: The terminal sends data to the terminal based on the feedback of the metaverse access request; the terminal is used to: combine the local data stored in the terminal and the data called to execute edge computing tasks; the edge computing tasks include 3D model generation, spatial positioning, depth occlusion calculation and AI model driving.

10. A metaverse data processing device, characterized in that, include: The data acquisition module is used to collect various metaverse data based on the pre-created correlation between metaverse data access modes and data acquisition methods. The feature extraction and classification module is used to extract and classify data features for each collected metaverse data based on the pre-created correlation between metaverse data types and data feature extraction methods. The distributed storage module is used to perform distributed storage of each metaverse data and its corresponding data features in a parallel processing manner based on the classification results, and to establish an index file that represents the metaverse data, its corresponding data features, and its corresponding storage location. The real-time computing and rendering module is used for terminal-based metaverse access requests. Based on the index file, it calls the target metaverse data and corresponding data features for the request; and performs real-time computing and rendering of the called data in edge computing mode. The feature extraction and classification module is specifically used to: for each metaverse data, determine the data acquisition method corresponding to the data access mode of the metaverse data based on the pre-created association between the metaverse data access mode and the data acquisition method; Based on the data acquisition method corresponding to the data access mode of the metaverse data, the metaverse data is collected; among them, metaverse space scene construction data, digital human image generation data, digital twin data, and event data are received in real time; for different types or access modes of content, stateless interface reporting, message queues, and stateful socket long connection reuse methods are adopted; among them, stateless interface reporting is used for metaverse space and digital human initialization; message queues are mainly used for real-time reception of event data; and stateful socket long connections are used for digital twin data acquisition.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 9.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 9.

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