Method and device for distributed storage of metaverse data, electronic equipment and readable medium

Abstract

Embodiments of the present disclosure disclose a method and device for distributed storage of metaverse data, electronic equipment and readable medium. A specific embodiment of the method comprises: collecting metaverse data corresponding to a preset metaverse platform; performing data preprocessing on a metaverse user data set; performing cold and hot data identification processing on the preprocessed metaverse user data set to obtain a metaverse user data group set; storing metaverse user hot data in the metaverse user data group in a main memory for access by a target user terminal; determining each metaverse user cold data included in the metaverse user data group set as a metaverse user cold data set; performing segmentation processing on the metaverse user cold data set; performing health detection processing on each preset storage node; and performing distributed storage of metaverse user cold data blocks. This embodiment reduces the access response delay of hot data included in the metaverse data and the risk of loss of metaverse data.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of computer technology, and more specifically to a method, apparatus, electronic device, and readable medium for distributed storage of metaverse data. Background Technology

[0002] Metaverse data distributed storage is a technology for storing metaverse data. Currently, the common method for storing metaverse data is to directly store the metaverse data on various storage nodes.

[0003] However, when storing metaverse data using the above method, the following technical problems often arise:

[0004] Directly storing metaverse data on various storage nodes without classifying it as hot or cold data, and without storing the hot data (such as high-speed memory) on faster storage media, results in high latency when a user terminal accesses frequently accessed data (hot data) due to the relatively slow read / write speeds of the storage nodes. Furthermore, the lack of health checks on these nodes increases the risk of data loss, as faulty nodes may exist. Storing metaverse data on faulty nodes could lead to data loss.

[0005] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for distributed storage of metaverse data to address one or more of the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a distributed storage method for metaverse data. The method includes: collecting metaverse data corresponding to a preset metaverse platform, wherein the metaverse data includes a metaverse user dataset, and each metaverse user data in the metaverse user dataset corresponds to a metaverse user identifier; performing data preprocessing on the metaverse user dataset to obtain a preprocessed metaverse user dataset, wherein each preprocessed metaverse user data in the preprocessed metaverse user dataset has a corresponding metaverse user identifier; and performing cold and hot data identification processing on the preprocessed metaverse user dataset to obtain a metaverse user data set, wherein each metaverse user data set in the metaverse user data set includes metaverse user cold data and metaverse user hot data. Each metaverse user data group has a corresponding metaverse user identifier. For each metaverse user data group in the aforementioned metaverse user data group set, the metaverse user hot data in the aforementioned metaverse user data group is stored in the main memory for access by the target user terminal, and access permission configuration operations are performed on the aforementioned metaverse user hot data. Each metaverse user cold data included in the aforementioned metaverse user data group set is identified as a metaverse user cold dataset. The aforementioned metaverse user cold dataset is segmented to obtain a metaverse user cold data block set. Health checks are performed on each preset storage node to obtain the identifier of each node to be stored. For each metaverse user cold data block in the aforementioned metaverse user cold data block set, the aforementioned metaverse user cold data block is distributed and stored based on the aforementioned identifiers of each node to be stored.

[0009] Secondly, some embodiments of this disclosure provide a distributed storage device for metaverse data. The device includes: a collection unit configured to collect metaverse data corresponding to a preset metaverse platform, wherein the metaverse data includes a metaverse user dataset, and each metaverse user data in the metaverse user dataset corresponds to a metaverse user identifier; a data preprocessing unit configured to preprocess the metaverse user dataset to obtain a preprocessed metaverse user dataset, wherein each preprocessed metaverse user data in the preprocessed metaverse user dataset has a corresponding metaverse user identifier; and a hot / cold data identification and processing unit configured to perform hot / cold data identification and processing on the preprocessed metaverse user dataset to obtain a metaverse user data set, wherein each metaverse user data set includes metaverse user cold data and metaverse user hot data, and the metaverse user data... Each data set has a corresponding metaverse user identifier; a first storage unit is configured to store the metaverse user hot data in each metaverse user data set in the main memory, and to perform access permission configuration operations on the metaverse user hot data; a determination unit is configured to determine each metaverse user cold data included in the metaverse user data set as a metaverse user cold dataset; a segmentation processing unit is configured to segment the metaverse user cold dataset to obtain a metaverse user cold data block set; a health detection processing unit is configured to perform health detection processing on each preset storage node to obtain each node identifier to be stored; a second storage unit is configured to perform distributed storage on each metaverse user cold data block in the metaverse user cold data block set based on the node identifiers to be stored.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0012] The above-described embodiments of this disclosure have the following beneficial effects: The distributed storage method for metaverse data in some embodiments of this disclosure reduces the access response latency of hot data included in the metaverse data and reduces the risk of metaverse data loss. Specifically, the high access response latency and risk of metaverse data loss are caused by directly storing metaverse data on various storage nodes without classifying the metaverse data as hot or cold data, and without storing the hot data on storage media with high read / write speeds (such as high-speed memory). When a target user terminal accesses frequently accessed data (i.e., hot data) included in the metaverse data, the relatively slow read / write speed of the storage nodes leads to a high access response latency. Furthermore, directly storing metaverse data on various storage nodes without performing health checks on the storage nodes may result in faulty nodes. Storing metaverse data on faulty nodes may cause data loss, further increasing the risk of metaverse data loss. Based on this, the distributed storage method for metaverse data in some embodiments of this disclosure first collects metaverse data corresponding to a preset metaverse platform. This metaverse data includes a metaverse user dataset, where each metaverse user data in the metaverse user dataset corresponds to a metaverse user identifier. This allows the collection of metaverse data corresponding to the metaverse platform. Then, the metaverse user dataset is preprocessed to obtain a preprocessed metaverse user dataset, where each preprocessed metaverse user data in the preprocessed metaverse user dataset has a corresponding metaverse user identifier. This allows data preprocessing of the metaverse data, including the metaverse user dataset. Next, the preprocessed metaverse user dataset undergoes hot and cold data identification processing to obtain a metaverse user data set. Each metaverse user data set in the preprocessed metaverse user dataset includes cold metaverse user data and hot metaverse user data, and each hot metaverse user data set has a corresponding metaverse user identifier. This allows the preprocessed metaverse user dataset to be classified into hot and cold data categories, resulting in a metaverse user data set containing each metaverse user's cold and hot data. Subsequently, for each metaverse user data group in the aforementioned metaverse user data set, the metaverse user hot data within that group is stored in main memory for access by the target user terminal, and access permission configuration operations are performed on the aforementioned metaverse user hot data. This allows the various metaverse user hot data in the metaverse user data set to be stored in main memory, which has relatively fast read / write speeds, for access by the target user terminal. This reduces the access response latency of the hot data included in the metaverse data, and the access permission configuration operations improve data access security.Next, the individual Metaverse user cold data sets included in the aforementioned Metaverse user data set are identified as Metaverse user cold datasets. This yields the individual Metaverse user cold data sets included in the Metaverse data set. Then, these Metaverse user cold datasets are segmented to obtain Metaverse user cold data block sets. This allows for the segmentation of the Metaverse user cold dataset. Next, health checks are performed on each preset storage node to obtain the identifiers of each node to be stored. This yields the identifiers of each node to be stored that represents a fault-free storage node. Then, for each Metaverse user cold data block in the aforementioned Metaverse user cold data block set, distributed storage is performed based on the identifiers of each node to be stored. This distributed storage of the Metaverse user cold data block set, based on the identifiers of each fault-free storage node, reduces the possibility of storing Metaverse user cold data blocks on faulty storage nodes, thus reducing the risk of Metaverse data loss. Furthermore, by employing hot and cold data identification processing on the pre-processed metaverse user dataset and storing the identified hot data of each metaverse user in the relatively fast main memory for access by the target user terminal, the access response latency of the hot data included in the metaverse data is reduced. Simultaneously, health checks are performed on each preset storage node to obtain the identifier of each node to be stored. Based on the identifiers of each node to be stored on each non-faulty storage node, the set of cold data blocks of metaverse users is distributed for storage, reducing the possibility of storing cold data blocks of metaverse users on faulty storage nodes and reducing the risk of metaverse data loss. Attached Figure Description

[0013] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0014] Figure 1 This is a flowchart of some embodiments of the metaverse data distributed storage method according to this disclosure;

[0015] Figure 2 This is a schematic diagram of the structure of some embodiments of the metaverse data distributed storage device according to the present disclosure;

[0016] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0020] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0021] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0022] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Figure 1 A flow 100 of some embodiments of the metaverse data distributed storage method according to this disclosure is shown. The metaverse data distributed storage method includes the following steps:

[0024] Step 101: Collect metaverse data corresponding to the preset metaverse platform.

[0025] In some embodiments, the execution entity of the metaverse data distributed storage method (e.g., a storage server) can collect metaverse data corresponding to a preset metaverse platform. This metaverse data includes a metaverse user dataset, where each metaverse user data item corresponds to a metaverse user identifier. The metaverse user data can include user behavior data, virtual value flow information, metaverse interaction data, and metaverse sensor data. The user behavior data can be data generated from various user actions on the metaverse platform (such as clicks, browsing, etc.). For example, the user behavior data could be "Browsing time: November 1, 2024, 10:05 AM, Browsed item: Virtual fashion". The virtual value flow information can represent information about the flow of virtual value corresponding to the metaverse user identifier. For example, the virtual value flow information could be "5 virtual coins flowed out on November 1, 2024, 10:06 AM". The metaverse interaction data can be data generated when users interact with other users or virtual environments on the metaverse platform, such as chat logs and social interaction data. The aforementioned Metaverse sensing data can be data collected by sensors on VR / AR devices or other smart devices when users access Metaverse, such as user motion data and location information collected by sensors. The aforementioned Metaverse user identifier can be the user's account on the Metaverse user platform.

[0026] In some optional implementations of certain embodiments, the aforementioned execution entity may collect metaverse data corresponding to the preset metaverse platform through the following steps:

[0027] The first step involves collecting metaverse user data from each metaverse user data log within the aforementioned preset metaverse platform. Each metaverse user data log corresponds to a metaverse user identifier. This metaverse user data includes user behavior data, virtual value flow information, metaverse interaction data, and metaverse sensor data. The metaverse user data log can record user behavior data, virtual value flow information, metaverse interaction data, and metaverse sensor data from the time a user enters the metaverse until they leave this virtual world.

[0028] The second step is to determine the collected metaverse user data corresponding to each metaverse user identifier as the metaverse user dataset.

[0029] The third step is to identify the aforementioned metaverse user dataset as the metaverse data corresponding to the preset metaverse platform.

[0030] Step 102: Perform data preprocessing on the Metaverse user dataset to obtain the preprocessed Metaverse user dataset.

[0031] In some embodiments, the aforementioned execution entity may perform data preprocessing on the aforementioned metaverse user dataset to obtain a preprocessed metaverse user dataset. Each preprocessed metaverse user data in the aforementioned preprocessed metaverse user dataset has a corresponding metaverse user identifier.

[0032] In some optional implementations of certain embodiments, the aforementioned execution entity may perform data preprocessing on the aforementioned metaverse user dataset through the following steps to obtain a preprocessed metaverse user dataset:

[0033] The first step is to denoise the aforementioned Metaverse user dataset to obtain a denoised Metaverse user dataset. Each Metaverse user data point in this dataset includes user behavior data, virtual value transfer information, Metaverse interaction data, and Metaverse sensor data. The user behavior data can include various user behavior sub-data. Each of these user behavior sub-data points can be data generated from a user's action on the Metaverse platform. In practice, for each user behavior sub-dataset included in the Metaverse User Dataset, the aforementioned execution entity can employ outlier detection technology to detect outliers in each user behavior sub-dataset, obtaining anomalous user behavior sub-datasets (for example, each user behavior sub-dataset could be "(2023-01-01, browsing the digital art section for 300 minutes), (2023-01-02, browsing the digital art section for 5 minutes), (2023-01-03, browsing the digital art section for 10 minutes)"). After processing these user behavior sub-datasets with outlier detection technology, the resulting anomalous user behavior sub-dataset could be "(2023-01-01, browsing the digital art section for 300 minutes)"). Subsequently, the aforementioned execution entity can delete these anomalous user behavior sub-datasets from the Metaverse User Dataset to update the Metaverse User Dataset. Finally, the aforementioned execution entity can determine the updated Metaverse User Dataset as the denoised Metaverse User Dataset.

[0034] The second step involves standardizing the data format of the aforementioned denoised metaverse user dataset to obtain a preprocessed metaverse user dataset. Each piece of preprocessed metaverse user data in this dataset includes preprocessed user behavior data, preprocessed virtual value transfer information, preprocessed metaverse interaction data, and preprocessed metaverse sensor data. In practice, the implementing entity can use data standardization techniques to standardize the data format of the aforementioned denoised metaverse user dataset to obtain the preprocessed metaverse user dataset.

[0035] Step 103: Perform hot and cold data identification processing on the preprocessed metaverse user dataset to obtain the metaverse user data set.

[0036] In some embodiments, the aforementioned execution entity may perform cold and hot data identification processing on the aforementioned preprocessed metaverse user dataset to obtain a metaverse user data set. Each metaverse user data set includes metaverse user cold data and metaverse user hot data, and each metaverse user data set has a corresponding metaverse user identifier.

[0037] In some optional implementations of certain embodiments, the aforementioned execution entity may perform hot and cold data identification processing on the preprocessed metaverse user dataset through the following steps to obtain a metaverse user data set:

[0038] The first step, for each preprocessed metaverse user data in the above preprocessed metaverse user dataset, is to perform the following identification steps:

[0039] The first sub-step is to obtain the current time. In practice, the aforementioned execution entity can obtain the system time as the current time.

[0040] The second sub-step involves determining the hot data time period based on the current time. In practice, the executing entity can define a preset time period prior to the current time as the hot data time period. The time interval between the start time of the preset time period and the current time is the preset duration. The end time of the preset time period can be the current time. For example, the current time could be 17:50 on January 3, 2025. With a preset duration of 24 hours, the hot data time period would be from 17:50 on January 2, 2025 to 17:50 on January 3, 2025.

[0041] The third sub-step involves identifying the preprocessed user behavior data included in the aforementioned preprocessed metaverse user data as the target user behavior data. This target user behavior data includes various target user behavior sub-data, each corresponding to a timestamp. The target virtual value flow information includes various target virtual value flow rotor information, each corresponding to a value flow timestamp. Each target user behavior sub-data can represent a user's operational behavior on the metaverse platform (such as clicking, browsing, etc.). Each target virtual value flow rotor information can represent the information about the flow of virtual value held by the user on the metaverse platform. Each value flow timestamp can represent the point in time when the virtual value held by the user flows on the metaverse platform.

[0042] The fourth sub-step involves identifying the preprocessed virtual value transfer information included in the aforementioned preprocessed metaverse user data as the target virtual value transfer information.

[0043] The fifth sub-step involves generating user behavior hot data, user behavior cold data, virtual value flow hot information, and virtual value flow cold information based on the aforementioned target user behavior data, target virtual value flow information, and hot data time periods.

[0044] The sixth sub-step involves identifying the preprocessed metaverse interaction data included in the aforementioned preprocessed metaverse user data as metaverse interaction heat data.

[0045] The seventh sub-step involves identifying the preprocessed metaverse sensing data included in the aforementioned preprocessed metaverse user data as metaverse sensing cold data.

[0046] The eighth sub-step involves identifying the aforementioned user behavior hot data, the aforementioned virtual value circulation hot information, and the aforementioned metaverse interaction hot data as metaverse user hot data.

[0047] The ninth sub-step involves identifying the aforementioned user behavior cold data, virtual value flow cold information, and metaverse sensing cold data as metaverse user cold data.

[0048] The tenth sub-step involves identifying the aforementioned metaverse user hot data and the aforementioned metaverse user cold data as the metaverse user data group.

[0049] The second step is to identify the various metaverse user data groups obtained as the metaverse user data group set.

[0050] In some optional implementations of certain embodiments, the aforementioned executing entity may generate user behavior hot data, user behavior cold data, virtual value flow hot information, and virtual value flow cold information based on the aforementioned target user behavior data, the aforementioned target virtual value flow information, and the aforementioned hot data time period through the following steps:

[0051] The first step is to determine, in response to the determination that there is a time point corresponding to a time point among the above timestamps that is within the above hot data time period, at least one timestamp among the above timestamps that is within the above hot data time period is determined as at least one target timestamp.

[0052] The second step is to identify at least one target user behavior sub-data that corresponds to at least one target timestamp from the aforementioned target user behavior sub-data as user behavior hot data.

[0053] The third step is to identify all target user behavior sub-data that are not included in the above-mentioned target user behavior sub-data as cold user behavior data.

[0054] The fourth step is to determine, in response to the determination that there is a time point corresponding to a value transfer timestamp among the above-mentioned value transfer timestamps that is within the above-mentioned hot data time period, at least one value transfer timestamp whose time point is within the above-mentioned hot data time period is determined as at least one target value transfer timestamp.

[0055] The fifth step is to identify at least one target virtual value flow rotor information that corresponds to at least one target value flow timestamp in each target virtual value flow rotor information as virtual value flow heat information.

[0056] The sixth step is to identify the target virtual value flow rotor information, excluding at least one of the target virtual value flow rotor information, as virtual value flow cold information.

[0057] Step 104: For each metaverse user data group in the metaverse user data group set, store the metaverse user hot data in the metaverse user data group into the main memory for access by the target user terminal, and perform access permission configuration operations on the metaverse user hot data.

[0058] In some embodiments, the executing entity may, for each metaverse user data group in the metaverse user data group set, store the metaverse user hot data in the main memory for access by a target user terminal, and perform access permission configuration operations on the metaverse user hot data. The target user terminal may be a device or system (e.g., a personal computer, smartphone, tablet, virtual reality (VR) headset, augmented reality (AR) glasses, etc.) that accesses or utilizes data stored in the main memory. In practice, the executing entity may perform access permission configuration operations on the metaverse user hot data through role-based access control (RBAC).

[0059] Step 105: Identify the individual Metaverse user cold data sets included in the Metaverse user data set as the Metaverse user cold dataset.

[0060] In some embodiments, the aforementioned executing entity may determine each metaverse user cold data included in the aforementioned metaverse user data set as a metaverse user cold dataset.

[0061] Step 106: Segment the Metaverse user cold dataset to obtain the Metaverse user cold data block set.

[0062] In some embodiments, the execution entity can segment the aforementioned metaverse user cold dataset to obtain a metaverse user cold data block set. In practice, firstly, the execution entity can identify each metaverse user cold data in the aforementioned metaverse user cold dataset as a metaverse user cold data block. Then, the execution entity can define the identified metaverse user cold data blocks as a metaverse user cold data block set.

[0063] Step 107: Perform health checks on each preset storage node to obtain the identifier of each node to be stored.

[0064] In some embodiments, the aforementioned execution entity may perform health checks on each preset storage node to obtain the identifier of each node to be stored.

[0065] In some optional implementations of certain embodiments, the aforementioned execution entity can perform health checks on each preset storage node through the following steps to obtain the identifier of each node to be stored:

[0066] The first step is to perform the following steps for each of the aforementioned preset storage nodes:

[0067] The first sub-step involves performing a liveness detection process on the aforementioned preset storage node to obtain liveness detection information. In practice, the executing entity can send a heartbeat signal to the preset storage node. Then, the executing entity can determine the time of sending the heartbeat signal as the start time. Afterward, the executing entity can determine a preset time period following the start time as the target time period. In response to receiving an acknowledgment response (e.g., an ACK acknowledgment character) from the preset storage node within the target time period, the executing entity can determine information indicating a normal liveness status as liveness detection information. This normal liveness status information can be represented by a status code or a Boolean value. For example, `true` can represent a normal liveness status.

[0068] The second sub-step involves performing storage overload and failure risk detection on the aforementioned preset storage nodes to obtain storage overload and failure risk detection information. In practice, the executing entity can use storage performance testing tools (e.g., ATT O-Disk) to perform storage overload and failure risk detection on the preset storage nodes and obtain storage overload and failure risk detection information. This storage overload and failure risk detection information indicates whether the preset storage nodes exhibit storage overload and failure risks.

[0069] The third sub-step is to determine, in response to the determination that the above-mentioned liveness status detection information indicates that the liveness status of the preset storage node is normal and the above-mentioned storage overload and fault risk detection information indicates that there is no overload and fault risk, the storage node identifier of the above-mentioned preset storage node is determined as a normal storage node identifier.

[0070] The second step is to identify each normal storage node as the identifier of each node to be stored.

[0071] Step 108: For each Metaverse User Cold Data Block in the Metaverse User Cold Data Block Set, perform distributed storage of the Metaverse User Cold Data Block based on the identifier of each node to be stored.

[0072] In some embodiments, the execution entity may perform distributed storage for each metaverse user cold data block in the metaverse user cold data block set based on the identifiers of the nodes to be stored.

[0073] In the process of adopting technical solutions to address the problems mentioned in the background section, the following issues often arise:

[0074] This method randomly selects a preset number of node identifiers from a pool of node identifiers to be stored, and stores the Metaverse user cold data blocks as replicas on the corresponding storage nodes. However, it doesn't consider network latency between the storage nodes storing the replicas and those storing the Metaverse user cold data blocks. This random storage of replicas might result in the replicas being stored on nodes with high network latency, leading to slow data retrieval when needed. Furthermore, storing replicas of Metaverse user cold data blocks on randomly selected nodes doesn't consider the storage space utilization of each node, resulting in some nodes nearing storage saturation while others have ample free space, leading to wasted storage resources.

[0075] Faced with the above-mentioned technical problems, the inventors decided to adopt the following solution:

[0076] In some optional implementations of certain embodiments, the aforementioned execution entity may perform distributed storage of the aforementioned metaverse user cold data blocks based on the identifiers of the respective nodes to be stored through the following steps:

[0077] The first step is to set the preset value as the number of data replicas. For example, the number of data replicas can be 3.

[0078] The second step is to perform a hash operation on the aforementioned metaverse user cold data block to obtain the hash value corresponding to the aforementioned metaverse user cold data block.

[0079] The third step is to sort the aforementioned node identifiers to be stored, obtaining a sequence of storage node identifiers. Each storage node identifier in this sequence has a corresponding sequence number. In practice, the executing entity can perform a sorting task corresponding to preset sorting information to sort the storage node identifiers, obtaining the storage node identifier sequence. The preset sorting information can be a custom sorting function. For example, the aforementioned storage node identifiers can be "Storage Node 3, Storage Node 1, Storage Node 2, Storage Node 4", and the storage node identifier sequence can be {Storage Node 1, Storage Node 2, Storage Node 3, Storage Node 4}. Storage Node 1 corresponds to sequence number 1. Storage Node 2 corresponds to sequence number 2.

[0080] The fourth step is to determine the number of storage node identifiers in the above storage node identifier sequence as the number of storage nodes.

[0081] The fifth step is to determine the sequence number to be assigned by dividing the hash value by the number of storage nodes.

[0082] The sixth step is to identify the storage node identifier in the storage node identifier sequence whose sequence number is the above-mentioned sequence number to be assigned as the first target storage node identifier.

[0083] Step 7: Identify each storage node identifier in the above storage node identifier sequence, except for the first target storage node identifier, as the storage node identifier of each replica to be screened.

[0084] Step 8: For each replica storage node identifier to be filtered among the above-mentioned identifiers, perform the following steps:

[0085] The first sub-step involves determining the network latency information between the storage node corresponding to the identifier of the replica storage node to be filtered and the storage node corresponding to the identifier of the first target storage node. In practice, the aforementioned execution entity's network probing tool (such as the ping command) determines the network latency information between the storage node corresponding to the identifier of the replica storage node to be filtered and the storage node corresponding to the identifier of the first target storage node. This network latency information can be the network latency time.

[0086] The second sub-step involves determining the storage space utilization rate of the storage node corresponding to the aforementioned replica storage node identifier to be filtered. In practice, the executing entity can obtain the storage space utilization rate of the storage node corresponding to the replica storage node identifier to be filtered by remotely executing commands (such as SSH commands).

[0087] The third sub-step involves generating storage node adaptation value information corresponding to the identifier of the replica storage node to be filtered, based on the aforementioned network latency information and storage space utilization rate. In practice, the executing entity can determine a first value by multiplying a first preset weight value by the aforementioned storage space utilization rate. Then, the executing entity can determine a second value by multiplying a second preset weight value by the latency duration represented by the aforementioned network latency information. Then, the executing entity can determine a target value by summing the first value and the second value. Finally, the executing entity can determine the reciprocal of the target value as the storage node adaptation value information corresponding to the identifier of the replica storage node to be filtered.

[0088] The ninth step is to sort the obtained storage node adaptation value information from largest to smallest according to the adaptation value represented by the storage node adaptation value information, so as to obtain the storage node adaptation value information sequence.

[0089] Step 10: Determine the number of data replicas and the number of replica storage node identifiers to be filtered from the storage node adaptation value information sequence of the first number of data replicas.

[0090] The eleventh step involves deduplicating the data in the aforementioned metaverse user cold data block to obtain a deduplicated metaverse user cold data block. In practice, the aforementioned executing entity can use data deduplication technology to deduplicate the data in the aforementioned metaverse user cold data block to obtain a deduplicated metaverse user cold data block.

[0091] The twelfth step is to compress the deduplicated metaverse user cold data blocks to obtain compressed metaverse user cold data blocks. In practice, the aforementioned execution entity can use the Snappy algorithm to compress the deduplicated metaverse user cold data blocks to obtain compressed metaverse user cold data blocks.

[0092] Step 13 involves storing the aforementioned compressed metaverse user cold data blocks to the respective preset storage nodes corresponding to the first target storage node identifier and each of the aforementioned replica storage node identifiers, and configuring access permissions for the aforementioned compressed metaverse user cold data blocks. In practice, role-based access control (RBAC) can be used to configure access permissions for the compressed metaverse user cold data blocks.

[0093] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the technical problem of "slow acquisition speed of replica data and waste of storage resources." Factors leading to slow acquisition speed of replica data and waste of storage resources are often as follows: Randomly selecting a preset number of node identifiers from various node identifiers to be stored, and storing the Metaverse user cold data block as replica data in the storage nodes corresponding to the preset number of node identifiers, does not consider the network latency between the storage nodes storing replica data and the storage nodes storing Metaverse user cold data blocks. When randomly storing replica data, it may be stored on nodes with high network latency, resulting in slow acquisition speed of replica data when it needs to be accessed. Simultaneously, storing the replica data of Metaverse user cold data blocks in various storage nodes corresponding to randomly selected preset number of node identifiers does not consider the storage space utilization rate of each storage node storing the replica data, leading to a situation where some storage nodes are close to saturation while others still have a large amount of remaining space, resulting in waste of storage resources. Solving these factors can improve the acquisition speed of replica data and reduce the waste of storage resources. To achieve this effect, firstly, the preset value is determined as the number of data replicas. This determines the number of data replicas storing the replica data of the Metaverse user's cold data block. Then, a hash operation is performed on the aforementioned Metaverse user's cold data block to obtain a hash value corresponding to it. This yields a hash value used to determine the first target storage node identifier. Next, the aforementioned node identifiers to be stored are sorted to obtain a storage node identifier sequence. Each storage node identifier in the above storage node identifier sequence has a corresponding sequence number. This yields a storage node identifier sequence used to determine the first target storage node identifier and each replica storage node identifier to be filtered. Then, the number of storage node identifiers in the above storage node identifier sequence is determined as the number of storage nodes. This yields the number of storage nodes used to generate the sequence numbers to be assigned. Next, the remainder when the hash value is divided by the number of storage nodes is determined as the sequence number to be assigned. Then, the storage node identifier in the storage node identifier sequence whose sequence number is the sequence number to be assigned is determined as the first target storage node identifier. This yields the first target storage node identifier used to store the Metaverse user's cold data block. Then, each storage node identifier in the above storage node identifier sequence, excluding the first target storage node identifier, is determined as a separate replica storage node identifier to be filtered. Next, for each replica storage node identifier to be filtered, the following steps are performed: First, determine the network latency information between the storage node corresponding to the replica storage node identifier to be filtered and the storage node corresponding to the first target storage node identifier.Therefore, the network latency information between the storage node corresponding to the storage node identifier of the replica to be screened and the storage node corresponding to the first target storage node identifier can be obtained. The second step is to determine the storage space utilization rate of the storage node corresponding to the aforementioned replica to be screened storage node identifier. This yields the storage space utilization rate used to determine the storage node adaptation value information for the replica to be screened storage node identifier. The third step is to generate storage node adaptation value information corresponding to the aforementioned replica to be screened storage node identifier based on the aforementioned network latency information and the aforementioned storage space utilization rate. This yields an indicator (i.e., storage node adaptation value information) that measures whether the storage node corresponding to the replica to be screened storage node identifier is suitable for storing replica data, considering both network latency and storage space utilization. The obtained storage node adaptation value information is sorted from largest to smallest according to the adaptation value represented by the storage node adaptation value information, resulting in a sequence of storage node adaptation value information. This sequence can be used to determine the storage node adaptation value information sequence for each replica storage node identifier. Then, the number of replica to be screened storage node identifiers corresponding to the first number of data replicas in the above storage node adaptation value information sequence are determined as the replica storage node identifiers. Therefore, the identifiers of each replica storage node with low network latency and low storage space utilization can be determined. Next, the data in the aforementioned metaverse user cold data block is deduplicated to obtain a deduplicated metaverse user cold data block. Then, the deduplicated metaverse user cold data block is compressed to obtain a compressed metaverse user cold data block. This compression further reduces the data size of the metaverse user cold data block. The compressed metaverse user cold data block is stored in the respective preset storage nodes corresponding to the first target storage node identifier and each replica storage node identifier, and access permissions are configured for the compressed metaverse user cold data block. This allows the compressed metaverse user cold data block to be stored in the respective preset storage nodes with low network latency and low storage space utilization between the first target storage node identifier and the storage node corresponding to the first target storage node identifier. Considering the network latency between the storage node storing replica data and the storage node storing metaverse user cold data blocks, the probability of storing replicas on nodes with high network latency is reduced, improving the data retrieval speed when accessing replica data.Because the storage space utilization of each storage node is considered before storing the replica data of the Metaverse user cold data blocks, the deduplicated and compressed Metaverse user cold data blocks are stored in each preset storage node corresponding to the replica storage node identifier with low network space latency and low storage space utilization. This balances the storage resources of each storage node, reduces the situation where some storage nodes are close to saturation while others still have a lot of spare space, and reduces the waste of storage resources of storage nodes.

[0094] The above-described embodiments of this disclosure have the following beneficial effects: The distributed storage method for metaverse data in some embodiments of this disclosure reduces the access response latency of hot data included in the metaverse data and reduces the risk of metaverse data loss. Specifically, the high access response latency and risk of metaverse data loss are caused by directly storing metaverse data on various storage nodes without classifying the metaverse data as hot or cold data, and without storing the hot data on storage media with high read / write speeds (such as high-speed memory). When a target user terminal accesses frequently accessed data (i.e., hot data) included in the metaverse data, the relatively slow read / write speed of the storage nodes leads to a high access response latency. Furthermore, directly storing metaverse data on various storage nodes without performing health checks on the storage nodes may result in faulty nodes. Storing metaverse data on faulty nodes may cause data loss, further increasing the risk of metaverse data loss. Based on this, the distributed storage method for metaverse data in some embodiments of this disclosure first collects metaverse data corresponding to a preset metaverse platform. This metaverse data includes a metaverse user dataset, where each metaverse user data in the metaverse user dataset corresponds to a metaverse user identifier. This allows the collection of metaverse data corresponding to the metaverse platform. Then, the metaverse user dataset is preprocessed to obtain a preprocessed metaverse user dataset, where each preprocessed metaverse user data in the preprocessed metaverse user dataset has a corresponding metaverse user identifier. This allows data preprocessing of the metaverse data, including the metaverse user dataset. Next, the preprocessed metaverse user dataset undergoes hot and cold data identification processing to obtain a metaverse user data set. Each metaverse user data set in the preprocessed metaverse user dataset includes cold metaverse user data and hot metaverse user data, and each hot metaverse user data set has a corresponding metaverse user identifier. This allows the preprocessed metaverse user dataset to be classified into hot and cold data categories, resulting in a metaverse user data set containing each metaverse user's cold and hot data. Subsequently, for each metaverse user data group in the aforementioned metaverse user data set, the metaverse user hot data within that group is stored in main memory for access by the target user terminal, and access permission configuration operations are performed on the aforementioned metaverse user hot data. This allows the various metaverse user hot data in the metaverse user data set to be stored in main memory, which has relatively fast read / write speeds, for access by the target user terminal. This reduces the access response latency of the hot data included in the metaverse data, and the access permission configuration operations improve data access security.Next, the individual Metaverse user cold data sets included in the aforementioned Metaverse user data set are identified as Metaverse user cold datasets. This yields the individual Metaverse user cold data sets included in the Metaverse data set. Then, these Metaverse user cold datasets are segmented to obtain Metaverse user cold data block sets. This allows for the segmentation of the Metaverse user cold dataset. Next, health checks are performed on each preset storage node to obtain the identifiers of each node to be stored. This yields the identifiers of each node to be stored that represents a fault-free storage node. Then, for each Metaverse user cold data block in the aforementioned Metaverse user cold data block set, distributed storage is performed based on the identifiers of each node to be stored. This distributed storage of the Metaverse user cold data block set, based on the identifiers of each fault-free storage node, reduces the possibility of storing Metaverse user cold data blocks on faulty storage nodes, thus reducing the risk of Metaverse data loss. Furthermore, by employing hot and cold data identification processing on the pre-processed metaverse user dataset and storing the identified hot data of each metaverse user in the relatively fast main memory for access by the target user terminal, the access response latency of the hot data included in the metaverse data is reduced. Simultaneously, health checks are performed on each preset storage node to obtain the identifier of each node to be stored. Based on the identifiers of each node to be stored on each non-faulty storage node, the set of cold data blocks of metaverse users is distributed for storage, reducing the possibility of storing cold data blocks of metaverse users on faulty storage nodes and reducing the risk of metaverse data loss.

[0095] Further reference Figure 2 As an implementation of the methods shown in the figures, this disclosure provides some embodiments of a metaverse data distributed storage device, which are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0096] like Figure 2As shown, the metaverse data distributed storage device 200 in some embodiments includes: a data acquisition unit 201, a data preprocessing unit 202, a hot and cold data identification and processing unit 203, a first storage unit 204, a determination unit 205, a segmentation processing unit 206, a health detection processing unit 207, and a second storage unit 208. The acquisition unit 201 is configured to acquire metaverse data corresponding to a preset metaverse platform. This metaverse data includes a metaverse user dataset, where each metaverse user data item corresponds to a metaverse user identifier. The data preprocessing unit 202 is configured to preprocess the metaverse user dataset to obtain a preprocessed metaverse user dataset, where each preprocessed metaverse user data item has a corresponding metaverse user identifier. The hot / cold data identification processing unit 203 is configured to perform hot / cold data identification processing on the preprocessed metaverse user dataset to obtain a metaverse user data set, where each metaverse user data set includes both cold and hot metaverse user data, and each metaverse user data set has a corresponding metaverse user identifier. The first storage unit 204 is configured to... The system is configured to: store hot data of metaverse users in each metaverse user data group in the aforementioned metaverse user data group set into main memory for access by target user terminals; configure access permissions for the hot data of metaverse users; determine unit 205 is configured to determine each metaverse user cold data included in the aforementioned metaverse user data group set as a metaverse user cold dataset; segmentation processing unit 206 is configured to segment the aforementioned metaverse user cold dataset to obtain a set of metaverse user cold data blocks; health detection processing unit 207 is configured to perform health detection processing on each preset storage node to obtain the identifier of each node to be stored; and second storage unit 208 is configured to perform distributed storage of each metaverse user cold data block in the aforementioned metaverse user cold data block set based on the identifier of each node to be stored.

[0097] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the method described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0098] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0099] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0100] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0101] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0102] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0103] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0104] A computer-readable medium may be contained within an electronic device or may exist independently, not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: collect metaverse data corresponding to a preset metaverse platform, wherein the metaverse data includes a metaverse user dataset, and each metaverse user data in the metaverse user dataset corresponds to a metaverse user identifier; perform data preprocessing on the metaverse user dataset to obtain a preprocessed metaverse user dataset, wherein each preprocessed metaverse user data in the preprocessed metaverse user dataset has a corresponding metaverse user identifier; and perform cold and hot data identification processing on the preprocessed metaverse user dataset to obtain a metaverse user data set, wherein each metaverse user data set includes metaverse user cold data and metaverse user hot data. The aforementioned metaverse user data groups have corresponding metaverse user identifiers. For each metaverse user data group in the aforementioned metaverse user data group set, the metaverse user hot data in the aforementioned metaverse user data group is stored in the main memory for access by the target user terminal, and access permission configuration operations are performed on the aforementioned metaverse user hot data. Each metaverse user cold data included in the aforementioned metaverse user data group set is identified as a metaverse user cold dataset. The aforementioned metaverse user cold dataset is segmented to obtain a metaverse user cold data block set. Health checks are performed on each preset storage node to obtain the identifiers of each node to be stored. For each metaverse user cold data block in the aforementioned metaverse user cold data block set, the aforementioned metaverse user cold data block is distributed and stored based on the aforementioned identifiers of each node to be stored.

[0105] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0107] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a data preprocessing unit, a hot / cold data identification and processing unit, a first storage unit, a determination unit, a segmentation processing unit, a health detection processing unit, and a second storage unit. The names of these units do not necessarily limit the specific unit; for example, the acquisition unit may also be described as "a unit that acquires metaverse data corresponding to a preset metaverse platform."

[0108] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0109] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of technical features, but should also cover other technical solutions formed by arbitrary combinations of technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for distributed storage of metaverse data, comprising: collecting metaverse data corresponding to a preset metaverse platform, wherein the metaverse data comprises a metaverse user data set, each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier, the metaverse user data comprises user behavior data, virtual value flow information, metaverse interaction data, and metaverse sensing data, the user behavior data is data generated by each operation behavior of a user on the metaverse platform, the virtual value flow information represents information of virtual value flow corresponding to the metaverse user identifier, the metaverse interaction data is data generated when the user interacts with other users or a virtual environment in the metaverse platform, and the metaverse sensing data is data collected by sensors of VR / AR devices or other smart devices when the user accesses the metaverse through the devices; performing data preprocessing on the metaverse user data set to obtain a preprocessed metaverse user data set, wherein each preprocessed metaverse user data in the preprocessed metaverse user data set has a corresponding metaverse user identifier; performing cold and hot data identification processing on the preprocessed metaverse user data set to obtain a metaverse user data group set, wherein each metaverse user data group in the metaverse user data group set comprises metaverse user cold data and metaverse user hot data, and the metaverse user data group has a corresponding metaverse user identifier; for each metaverse user data group in the metaverse user data group set, storing metaverse user hot data in the metaverse user data group in a main memory for access by a target user terminal, and performing access permission configuration operation on the metaverse user hot data; determining each metaverse user cold data included in the metaverse user data group set as a metaverse user cold data set; performing segmentation processing on the metaverse user cold data set to obtain a metaverse user cold data block set; performing health detection processing on each preset storage node to obtain each to-be-stored node identifier; for each metaverse user cold data block in the metaverse user cold data block set, performing distributed storage of the metaverse user cold data block based on the to-be-stored node identifiers; wherein the cold and hot data identification processing on the preprocessed metaverse user data set to obtain the metaverse user data group set comprises: for each preprocessed metaverse user data in the preprocessed metaverse user data set, performing the following identification steps: obtaining a current time; determining a hot data time period based on the current time, wherein a preset time period before the current time is determined as the hot data time period; determining preprocessed user behavior data included in the preprocessed metaverse user data as target user behavior data; determining preprocessed virtual value flow information included in the preprocessed metaverse user data as target virtual value flow information; generate user behavior hot data, user behavior cold data, virtual value flow hot information, and virtual value flow cold information based on the target user behavior data, the target virtual value flow information, and the hot data time period; determine the pre-processed meta-universe interaction data included in the pre-processed meta-universe user data as meta-universe interaction hot data; determine the pre-processed meta-universe sensing data included in the pre-processed meta-universe user data as meta-universe sensing cold data; determine the user behavior hot data, the virtual value flow hot information, and the meta-universe interaction hot data as meta-universe user hot data; determine the user behavior cold data, the virtual value flow cold information, and the meta-universe sensing cold data as meta-universe user cold data; determine the meta-universe user hot data and the meta-universe user cold data as a meta-universe user data group; determine the obtained meta-universe user data groups as a meta-universe user data group set.

2. The method of claim 1, wherein, The meta-universe data corresponding to the preset meta-universe platform includes: For each meta-universe user data log in each meta-universe user data log of the preset meta-universe platform, collect the meta-universe user data in the meta-universe user data log, wherein the meta-universe user data log corresponds to a meta-universe user identifier, and wherein determine the collected meta-universe user data corresponding to each meta-universe user identifier as a meta-universe user data set; determine the meta-universe user data set as meta-universe data corresponding to the preset meta-universe platform.

3. The method of claim 1, wherein, The data preprocessing of the meta-universe user data set obtains a pre-processed meta-universe user data set, including: performing denoising processing on the meta-universe user data set to obtain a denoised meta-universe user data set; performing data format standardization processing on the denoised meta-universe user data set to obtain a pre-processed meta-universe user data set, wherein each pre-processed meta-universe user data in the pre-processed meta-universe user data set includes pre-processed user behavior data, pre-processed virtual value flow information, pre-processed meta-universe interaction data, and pre-processed meta-universe sensing data.

4. The method of claim 1, wherein, The health detection processing of each preset storage node obtains each to-be-stored node identifier, including: For each preset storage node in the plurality of preset storage nodes, the following steps are performed: performing survival state detection processing on the preset storage node to obtain survival state detection information; performing storage overload and failure risk detection on the preset storage node to obtain storage overload and failure risk detection information; in response to determining that the survival state detection information represents that the survival state of the preset storage node is normal and the storage overload and failure risk detection information represents that there is no overload and failure risk, determining the storage node identifier of the preset storage node as a normal storage node identifier; determine the determined each normal storage node identifier as each to-be-stored node identifier.

5. The method of claim 4, wherein, The target user behavior data includes respective target user behavior sub-data, each of which corresponds to a respective timestamp, and the target virtual value flow information includes respective target virtual value flow sub-information, each of which corresponds to a respective value flow timestamp; and generating user behavior hot data, user behavior cold data, virtual value flow hot information, and virtual value flow cold information based on the target user behavior data, the target virtual value flow information, and the hot data time period, includes: in response to determining that there is a time point corresponding to a timestamp in the hot data time period among the respective timestamps, determining at least one timestamp corresponding to a time point in the hot data time period among the respective timestamps as at least one target timestamp; determining at least one target user behavior sub-data corresponding to the at least one target timestamp among the respective target user behavior sub-data as user behavior hot data; determining each target user behavior sub-data other than the at least one target user behavior sub-data among the respective target user behavior sub-data as user behavior cold data; in response to determining that there is a time point corresponding to a value flow timestamp in the hot data time period among the respective value flow timestamps, determining at least one value flow timestamp corresponding to a time point in the hot data time period among the respective value flow timestamps as at least one target value flow timestamp; determining at least one target virtual value flow sub-information corresponding to the at least one target value flow timestamp among the respective target virtual value flow sub-information as virtual value flow hot information; determining each target virtual value flow sub-information other than the at least one target virtual value flow sub-information among the respective target virtual value flow sub-information as virtual value flow cold information.

6. A meta-universe data distributed storage device, comprising: a collection unit configured to collect meta-universe data corresponding to a preset meta-universe platform, wherein the meta-universe data includes a meta-universe user data set, each meta-universe user data in the meta-universe user data set corresponding to a meta-universe user identifier, the meta-universe user data including user behavior data, virtual value flow information, meta-universe interaction data, and meta-universe sensing data, the user behavior data being data generated by each operation behavior of a user on the meta-universe platform, the virtual value flow information representing information of virtual value flow corresponding to the meta-universe user identifier, the meta-universe interaction data being data generated when the user interacts with other users or a virtual environment in the meta-universe platform, and the meta-universe sensing data being data collected by sensors of VR / AR devices or other smart devices when the user accesses the meta-universe through these devices; a data preprocessing unit configured to perform data preprocessing on the meta-universe user data set to obtain a preprocessed meta-universe user data set, wherein each preprocessed meta-universe user data in the preprocessed meta-universe user data set has a corresponding meta-universe user identifier; The cold and hot data identification processing unit is configured to perform cold and hot data identification processing on the preprocessed metaverse user data set to obtain a metaverse user data group set, wherein each metaverse user data group in the metaverse user data group set comprises metaverse user cold data and metaverse user hot data, and the metaverse user data group has a corresponding metaverse user identifier; The first storage unit is configured to, for each metaverse user data group in the metaverse user data group set, store metaverse user hot data in the metaverse user data group into a main storage for access by a target user terminal, and perform access permission configuration operation on the metaverse user hot data; The determination unit is configured to determine each metaverse user cold data included in the metaverse user data group set as a metaverse user cold data set; The segmentation processing unit is configured to perform segmentation processing on the metaverse user cold data set to obtain a metaverse user cold data block set; The health detection processing unit is configured to perform health detection processing on each preset storage node to obtain each to-be-stored node identifier; The second storage unit is configured to, for each metaverse user cold data block in the metaverse user cold data block set, perform distributed storage on the metaverse user cold data block based on the to-be-stored node identifiers; The cold and hot data identification processing unit is further configured to: For each preprocessed metaverse user data in the preprocessed metaverse user data set, the following identification steps are performed: obtain a current time; determine a hot data time period based on the current time, wherein a preset time period before the current time is determined as the hot data time period; determine preprocessed user behavior data included in the preprocessed metaverse user data as target user behavior data; determine preprocessed virtual value flow information included in the preprocessed metaverse user data as target virtual value flow information; generate user behavior hot data, user behavior cold data, virtual value flow hot information, and virtual value flow cold information based on the target user behavior data, the target virtual value flow information, and the hot data time period; determine preprocessed metaverse interaction data included in the preprocessed metaverse user data as metaverse interaction hot data; determine preprocessed metaverse sensing data included in the preprocessed metaverse user data as metaverse sensing cold data; determine the user behavior hot data, the virtual value flow hot information, and the metaverse interaction hot data as metaverse user hot data; determine the user behavior cold data, the virtual value flow cold information, and the metaverse sensing cold data as metaverse user cold data; determine the metaverse user hot data and the metaverse user cold data as a metaverse user data group; determine each obtained metaverse user data group as a metaverse user data group set.

7. An electronic device, comprising: one or more processors; storage having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement a method as claimed in any of claims 1 to 5.

8. A computer readable medium having stored thereon a computer program, wherein, The program, which when executed by a processor, implements a method as claimed in any of claims 1 to 5.

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