Metacosm data distributed storage method and device, electronic equipment and readable medium

Abstract

The embodiment of the invention discloses a meta universe data distributed storage method and device, electronic equipment and a readable medium. A specific embodiment of the method comprises the steps of collecting meta-universe data corresponding to a preset meta-universe platform; performing data preprocessing on the meta universe user data set; performing cold and hot data identification processing on the preprocessed meta-universe user data set to obtain a meta-universe user data set; storing the meta-universe user hot data in the meta-universe user data set into a main memory for a target user terminal to access; determining each meta-universe user cold data included in the meta-universe user data set as a meta-universe user cold data set; carrying out segmentation processing on the meta universe user cold data set; performing health detection processing on each preset storage node; and carrying out distributed storage on the meta universe user cold data blocks. According to the embodiment, the access response delay of the hot data included in the meta-universe data and the risk of loss of the meta-universe data are reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, electronic device, and readable medium for distributed storage of metaverse data. Background Art

[0002] Distributed Metaverse Data Storage is a technology for storing Metaverse data. Currently, the most common method for storing Metaverse data is to directly store it on each storage node.

[0003] However, when using the above method to store metaverse data, the following technical problems often arise: Directly storing Metaverse data on individual storage nodes without performing hot and cold data categorization, or storing the hot data included in the Metaverse data on storage media with faster read and write speeds (such as high-speed memory), results in high access response latency when target user terminals access frequently accessed data (i.e., hot data) included in the Metaverse data due to the relatively slow read and write speeds of the storage nodes. Furthermore, directly storing Metaverse data on individual storage nodes without performing health checks on these nodes may result in faulty nodes. Storing Metaverse data on faulty nodes can lead to data loss, increasing the risk of Metaverse data loss.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] Some embodiments of the present disclosure propose a method, apparatus, electronic device, and computer-readable medium for distributed storage of metaverse data to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide a method for distributed storage of metaverse data, the method comprising: collecting metaverse data corresponding to a preset metaverse platform, wherein the metaverse data comprises a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier; 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 hot and cold data identification processing on the preprocessed metaverse user data set to obtain a metaverse user data set, wherein each metaverse user data set in the metaverse user data set comprises cold metaverse user data and hot metaverse user data, and the metaverse The metaverse user data group has a corresponding metaverse user identifier; for each metaverse user data group in the above metaverse user data group set, the metaverse user hot data in the above metaverse user data group is stored in the main memory for access by the target user terminal, and the access permission configuration operation is performed on the above metaverse user hot data; the various metaverse user cold data included in the above metaverse user data group set are determined as the metaverse user cold data set; the above metaverse user cold data set is segmented to obtain a metaverse user cold data block set; a health check is 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 above metaverse user cold data block set, the above metaverse user cold data block is distributedly stored based on the above each node to be stored identifier.

[0008] In a second aspect, some embodiments of the present disclosure provide a metaverse data distributed storage device, the device comprising: a collection unit, configured to collect metaverse data corresponding to a preset metaverse platform, wherein the above-mentioned metaverse data includes a metaverse user data set, and each metaverse user data in the above-mentioned metaverse user data set corresponds to a metaverse user identifier; a data preprocessing unit, configured to perform data preprocessing on the above-mentioned metaverse user data set to obtain a preprocessed metaverse user data set, wherein each preprocessed metaverse user data in the above-mentioned preprocessed metaverse user data set has a corresponding metaverse user identifier; a hot and cold data identification processing unit, configured to perform hot and cold data identification processing on the above-mentioned preprocessed metaverse user data set to obtain a metaverse user data set, wherein each metaverse user data set in the above-mentioned metaverse user data set includes metaverse user cold data and metaverse user hot data, and the above-mentioned metaverse user data set has a corresponding metaverse user identifier. The data group has a corresponding metaverse user identifier; the first storage unit is configured to store the metaverse user hot data in the above metaverse user data group into the main memory for each metaverse user data group in the above metaverse user data group set, and perform access permission configuration operations on the above metaverse user hot data; the determination unit is configured to determine the various metaverse user cold data included in the above metaverse user data group set as the metaverse user cold data set; the segmentation processing unit is configured to segment the above metaverse user cold data set to obtain the 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 the identifier of each node to be stored; the second storage unit is configured to perform distributed storage on the above metaverse user cold data block in the above metaverse user cold data block set based on the above each node to be stored identifier.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0011] The above-described various embodiments of the present disclosure have the following beneficial effects: Through the distributed storage methods for metaverse data in some embodiments of the present disclosure, access response delays for hot data included in the metaverse data are reduced, as well as the risk of metaverse data loss. Specifically, the reasons for the access response delays for hot data included in the metaverse data and the higher risk of metaverse data loss are: directly storing the metaverse data in each storage node without performing hot and cold data classification, and not storing the hot data included in the metaverse data in a storage medium with faster read and write speeds (such as high-speed memory). When the target user terminal accesses the frequently accessed data (i.e., hot data) included in the metaverse data, the relatively slow read and write speeds of the storage nodes result in a higher access response delay. Furthermore, directly storing the metaverse data in each storage node without performing health checks on the storage nodes may result in faulty nodes among the storage nodes. Storing the metaverse data in faulty nodes may cause data loss, thereby increasing the risk of metaverse data loss. Based on this, some embodiments of the present disclosure provide a distributed storage method for metaverse data. First, metaverse data corresponding to a preset metaverse platform is collected. The metaverse data includes a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier. This allows the collection of metaverse data corresponding to the metaverse platform. Next, data preprocessing is performed on the metaverse user data set to obtain a preprocessed metaverse user data set. Each preprocessed metaverse user data in the preprocessed metaverse user data set corresponds to a metaverse user identifier. This allows the preprocessing of the metaverse data, including the metaverse user data set. Next, hot and cold data identification is performed on the preprocessed metaverse user data set to obtain a metaverse user data set. Each metaverse user data set in the preprocessed metaverse user data set includes cold metaverse user data and hot metaverse user data, each of which has a corresponding metaverse user identifier. This allows the preprocessed metaverse user data set to be classified into hot and cold data, resulting in a metaverse user data set containing each cold metaverse user data set and each hot metaverse user data set. Then, for each Metaverse user data group in the Metaverse user data group set, the Metaverse user hot data in the Metaverse user data group is stored in main memory for access by the target user terminal, and access permission configuration operations are performed on the Metaverse user hot data. Thus, each Metaverse user hot data in the Metaverse user data group set can be stored in main memory with relatively fast read and write speeds for access by the target user terminal, reducing access response latency for the hot data included in the Metaverse data. Furthermore, access permission configuration operations are performed on the Metaverse user hot data, improving data access security.Next, each Metaverse user cold data item included in the Metaverse user data set is identified as a Metaverse user cold data set. Thus, each Metaverse user cold data item included in the Metaverse data can be obtained. The Metaverse user cold data set is then segmented to obtain a Metaverse user cold data block set. Thus, the Metaverse user cold data set can be segmented. Then, a health check is performed on each preset storage node to obtain the identifiers of each node to be stored. Thus, the identifiers of each node to be stored, representing each non-faulty storage node, can be obtained. Next, for each Metaverse user cold data block in the Metaverse user cold data block set, distributed storage is performed based on the identifiers of each node to be stored. Thus, the Metaverse user cold data block set is distributed based on the identifiers of each non-faulty storage node, reducing the likelihood of storing a Metaverse user cold data block on a faulty storage node and mitigating the risk of Metaverse data loss. This also reduces access response delays for hot data contained in the Metaverse data by performing hot and cold data identification on the pre-processed Metaverse user dataset and storing each identified Metaverse user hot data in relatively fast main memory for access by the target user terminal. Simultaneously, health checks are performed on each pre-set storage node to obtain the identifiers of each node to be stored. Based on the identifiers of each non-faulty storage node, the Metaverse user cold data block set is distributed and stored. This reduces the likelihood of storing Metaverse user cold data blocks on faulty storage nodes, thereby reducing the risk of Metaverse data loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flowchart of some embodiments of the method for distributed storage of metaverse data according to the present disclosure; Figure 2 is a schematic structural diagram of some embodiments of the metaverse data distributed storage device according to the present disclosure; Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0014] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0015] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0018] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0019] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] Figure 1 A process 100 of some embodiments of the method for distributed storage of metaverse data according to the present disclosure is shown. The method for distributed storage of metaverse data includes the following steps: Step 101: Collect metaverse data corresponding to a preset metaverse platform.

[0021] In some embodiments, the execution entity (e.g., a storage server) of the metaverse data distributed storage method may collect metaverse data corresponding to a pre-set metaverse platform. This metaverse data includes a metaverse user dataset, where each metaverse user data item in the metaverse user dataset corresponds to a metaverse user identifier. This metaverse user data may include user behavior data, virtual value flow information, metaverse interaction data, and metaverse sensor data. This user behavior data may be data generated by user operations (such as clicks and browsing) on the metaverse platform. For example, this user behavior data may include "Browsing time: November 1, 2024, 10:05 AM, Browsed item: Virtual fashion." This virtual value flow information may represent information about the flow of virtual value corresponding to the metaverse user identifier. For example, this virtual value flow information may include "5 virtual gold coins were distributed at 10:06 AM on November 1, 2024." This metaverse interaction data may include 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 sensor data can be data collected by sensors when a user accesses the Metaverse through VR / AR devices or other smart devices, 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.

[0022] In some optional implementations of some embodiments, the execution entity may collect metaverse data corresponding to a preset metaverse platform through the following steps: The first step is to collect the metaverse user data from each metaverse user data log in the preset metaverse platform. Each metaverse user data log corresponds to a metaverse user identifier. The metaverse user data includes user behavior data, virtual value flow information, metaverse interaction data, and metaverse sensor data. The metaverse user data log may be a log that records 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 the virtual world.

[0023] In the second step, the collected metaverse user data corresponding to each metaverse user identifier is determined as a metaverse user data set.

[0024] The third step is to determine the above-mentioned metaverse user data set as the metaverse data corresponding to the preset metaverse platform.

[0025] Step 102: preprocess the metaverse user data set to obtain a preprocessed metaverse user data set.

[0026] In some embodiments, the execution entity may perform data preprocessing on the Metaverse user dataset to obtain a preprocessed Metaverse user dataset. Each preprocessed Metaverse user data in the preprocessed Metaverse user dataset has a corresponding Metaverse user identifier.

[0027] In some optional implementations of some embodiments, the execution entity may perform data preprocessing on the Metaverse user dataset through the following steps to obtain a preprocessed Metaverse user dataset: The first step is to perform denoising on the aforementioned Metaverse user dataset to obtain a denoised Metaverse user dataset. Each Metaverse user data in the Metaverse user dataset includes user behavior data, virtual value flow information, Metaverse interaction data, and Metaverse sensor data. The aforementioned user behavior data may include various user behavior sub-data. Each of these user behavior sub-data may be data generated by a user's operation on the Metaverse platform. In practice, the execution entity may use outlier detection technology to detect outliers on each user behavior sub-data included in each Metaverse user data in the Metaverse user data set, thereby obtaining abnormal user behavior sub-data (for example, each user behavior sub-data may be “(2023-01-01, browsed the digital artwork area for 300 minutes), (2023-01-02, browsed the digital artwork area for 5 minutes), (2023-01-03, browsed the digital artwork area for 10 minutes)”. After processing each of these user behavior sub-data using outlier detection technology, the abnormal user behavior sub-data obtained may be “(2023-01-01, browsed the digital artwork area for 300 minutes)”). The execution entity may then delete each abnormal user behavior sub-data from the Metaverse user data set to update the Metaverse user data set. Finally, the execution entity may determine the updated Metaverse user data set as the denoised Metaverse user data set.

[0028] The second step is to perform data format standardization on the denoised Metaverse user dataset to obtain a preprocessed Metaverse user dataset. Each preprocessed Metaverse user data in the preprocessed Metaverse user dataset includes preprocessed user behavior data, preprocessed virtual value flow information, preprocessed Metaverse interaction data, and preprocessed Metaverse sensor data. In practice, the execution entity can use data standardization technology to perform data format standardization on the denoised Metaverse user dataset to obtain the preprocessed Metaverse user dataset.

[0029] Step 103: perform hot and cold data identification processing on the pre-processed metaverse user data set to obtain a metaverse user data set.

[0030] In some embodiments, the execution entity may perform hot and cold data identification processing on the pre-processed metaverse user data set to obtain a metaverse user data set. Each metaverse user data set in the metaverse user data set includes cold metaverse user data and hot metaverse user data, and each metaverse user data set has a corresponding metaverse user identifier.

[0031] In some optional implementations of some embodiments, the execution entity may perform hot and cold data identification processing on the pre-processed Metaverse user data set to obtain a Metaverse user data set through the following steps: In the first step, for each pre-processed Metaverse user data in the above pre-processed Metaverse user data set, perform the following identification steps: The first sub-step is to obtain the current time. In practice, the execution subject may obtain the system time as the current time.

[0032] The second sub-step is to determine the hot data time period based on the above-mentioned current time. In practice, the above-mentioned execution entity may determine the preset time period before the current time as the hot data time period. The time interval between the starting time of the above-mentioned preset time period and the above-mentioned current time is the preset duration. The end time of the above-mentioned preset time period may be the above-mentioned current time. As an example, the current time may be 17:50 on January 3, 2025. If the preset duration is 24 hours, the hot data time period is 17:50 on January 2, 2025 to 17:50 on January 3, 2025.

[0033] The third sub-step is to determine the pre-processed user behavior data included in the pre-processed Metaverse user data as target user behavior data. The target user behavior data includes target user behavior sub-data, each of which corresponds to a timestamp. The target virtual value flow information includes target virtual value flow sub-information, each of which corresponds to a value flow timestamp. Each target user behavior sub-data in the target user behavior sub-data may represent a user's operational behavior (such as clicks, browsing, etc.) on the Metaverse platform. Each target virtual value flow sub-information in the target virtual value flow sub-information may represent information on the flow of the user's virtual value on the Metaverse platform. Each value flow timestamp in the value flow timestamp may indicate the time point at which the user's virtual value circulated on the Metaverse platform.

[0034] The fourth sub-step is to determine the pre-processed virtual value flow information included in the above-mentioned pre-processed metaverse user data as the target virtual value flow information.

[0035] The fifth sub-step is to generate user behavior hot data, user behavior cold data, virtual value flow hot information and virtual value flow cold information based on the above-mentioned target user behavior data, the above-mentioned target virtual value flow information and the above-mentioned hot data time period.

[0036] The sixth sub-step is to determine the pre-processed metaverse interaction data included in the above-mentioned pre-processed metaverse user data as metaverse interaction hot data.

[0037] The seventh sub-step is to determine the pre-processed Metaverse sensor data included in the above-mentioned pre-processed Metaverse user data as Metaverse sensor cold data.

[0038] The eighth sub-step is to determine the above-mentioned user behavior hot data, the above-mentioned virtual value flow hot information, and the above-mentioned metaverse interaction hot data as metaverse user hot data.

[0039] The ninth sub-step is to determine the above-mentioned user behavior cold data, virtual value flow cold information, and metaverse sensor cold data as metaverse user cold data.

[0040] The tenth sub-step is to determine the above-mentioned metaverse user hot data and the above-mentioned metaverse user cold data as a metaverse user data group.

[0041] The second step is to determine the obtained metaverse user data groups as a metaverse user data group set.

[0042] In some optional implementations of some embodiments, the execution 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 target user behavior data, the target virtual value flow information, and the hot data time period through the following steps: In the first step, in response to determining that a time point corresponding to a time stamp among the above timestamps is within the above hot data time period, at least one timestamp corresponding to a time point among the above timestamps within the above hot data time period is determined as at least one target timestamp.

[0043] In the second step, at least one target user behavior sub-data corresponding to the at least one target timestamp among the target user behavior sub-data is determined as user behavior hot data.

[0044] In a third step, each target user behavior sub-data except for the at least one target user behavior sub-data in the target user behavior sub-data is determined as user behavior cold data.

[0045] In the fourth step, in response to determining that a time point corresponding to a value flow timestamp among the above-mentioned value flow timestamps is within the above-mentioned hot data time period, at least one value flow timestamp whose corresponding time point among the above-mentioned value flow timestamps is within the above-mentioned hot data time period is determined as at least one target value flow timestamp.

[0046] In the fifth step, at least one target virtual value flow sub-information corresponding to the at least one target value flow timestamp in each target virtual value flow sub-information is determined as virtual value flow hot information.

[0047] In the sixth step, each target virtual value flow sub-information except for the at least one target virtual value flow sub-information is determined as virtual value flow cold information.

[0048] Step 104: For each metaverse user data group in the metaverse user data group set, the metaverse user hot data in the 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 metaverse user hot data.

[0049] In some embodiments, the execution entity may, 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 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 computer, virtual reality (VR) helmet, augmented reality (AR) glasses, etc.) that accesses or utilizes the data stored in the main memory. In practice, the execution entity may perform access permission configuration operations on the Metaverse user hot data through role-based access control (RBAC).

[0050] Step 105: determine each metaverse user cold data included in the metaverse user data set as a metaverse user cold data set.

[0051] In some embodiments, the execution entity may determine each metaverse user cold data included in the metaverse user data set as a metaverse user cold data set.

[0052] Step 106: segment the Metaverse user cold data set to obtain a Metaverse user cold data block set.

[0053] In some embodiments, the execution entity may segment the Metaverse user cold data set to obtain a Metaverse user cold data block set. In practice, the execution entity may first determine each Metaverse user cold data block in the Metaverse user cold data set as a Metaverse user cold data block. Then, the execution entity may determine each determined Metaverse user cold data block as a Metaverse user cold data block set.

[0054] Step 107: Perform health check on each preset storage node to obtain an identifier of each node to be stored.

[0055] In some embodiments, the execution entity may perform health detection on each preset storage node to obtain an identifier of each node to be stored.

[0056] In some optional implementations of some embodiments, the execution entity may perform health detection on each preset storage node through the following steps to obtain the identifier of each node to be stored: In the first step, for each of the above preset storage nodes, perform the following steps: The first sub-step is to perform survival status detection processing on the above-mentioned preset storage node to obtain survival status detection information. In practice, the above-mentioned execution entity can send a heartbeat signal to the above-mentioned preset storage node. Then, the above-mentioned execution entity can determine the time of sending the heartbeat signal as the starting time. Afterwards, the above-mentioned execution entity can determine the preset time period after the above-mentioned starting time as the target time period. In response to receiving the confirmation response information (for example, ACK confirmation character) corresponding to the above-mentioned heartbeat signal sent by the preset storage node within the above-mentioned target time period, the above-mentioned execution entity can determine the information indicating that the survival status is normal as the survival status detection information. The above-mentioned information that the survival status is normal can be represented by a status code or a Boolean value. For example, true is used to represent information that the survival status is normal.

[0057] The second sub-step involves performing a storage overload and failure risk test on the preset storage node to obtain storage overload and failure risk test information. In practice, the execution entity may use a storage performance testing tool (e.g., ATTO-Disk) to perform a storage overload and failure risk test on the preset storage node to obtain the storage overload and failure risk test information. The storage overload and failure risk test information may indicate whether the preset storage node is at risk of storage overload and failure.

[0058] The third sub-step is, in response to determining that the survival status detection information indicates that the survival status of the preset storage node is normal and the storage overload and failure risk detection information indicates that there is no overload and failure risk, determining the storage node identifier of the preset storage node as a normal storage node identifier.

[0059] In the second step, each normal storage node identifier is determined as each to-be-stored node identifier.

[0060] Step 108 : For each Metaverse user cold data block in the Metaverse user cold data block set, the Metaverse user cold data block is distributedly stored based on the identifiers of the nodes to be stored.

[0061] In some embodiments, the execution entity may perform distributed storage on each Metaverse user cold data block in the Metaverse user cold data block set based on the identifiers of the nodes to be stored.

[0062] In the process of adopting technical solutions to solve the problems mentioned in the background technology, the following problems often arise: A preset number of node identifiers to be stored are randomly selected from the various node identifiers to be stored, and the Metaverse user cold data blocks are stored as replica data in the storage nodes corresponding to the preset number of node identifiers to be stored. This does not take into account the network latency between the storage nodes storing the replica data and the storage nodes storing the Metaverse user cold data blocks. When randomly storing replica data, the replicas may be stored on nodes with higher network latency, resulting in slower retrieval of the replica data when it is needed. Furthermore, the replica data of the Metaverse user cold data blocks is stored in the randomly selected storage nodes corresponding to the preset number of node identifiers to be stored, without considering the storage space utilization of each storage node storing the replica data. This can result in some storage nodes having nearly saturated storage space while others still have a large amount of remaining space, resulting in a waste of storage resources.

[0063] Faced with the above technical problems, the inventors decided to adopt the following solutions: In some optional implementations of some embodiments, the execution entity may perform distributed storage of the metaverse user cold data blocks based on the identifiers of the nodes to be stored by performing the following steps: In the first step, a preset value is determined as the number of data copies. For example, the number of data copies may be 3.

[0064] The second step is to perform a hash operation on the above-mentioned Metaverse user cold data block to obtain a hash value corresponding to the above-mentioned Metaverse user cold data block.

[0065] The third step is to sort the above-mentioned node identifiers to be stored to obtain a storage node identifier sequence. Each storage node identifier in the above-mentioned storage node identifier sequence has a corresponding serial number. In practice, the above-mentioned execution subject can execute the sorting task corresponding to the preset sorting information to sort the various storage node identifiers to obtain a storage node identifier sequence. The above-mentioned preset sorting information can be a custom sorting function. As an example, the above-mentioned storage node identifiers can be "storage node 3, storage node 1, storage node 2, storage node 4" and the above-mentioned storage node identifier sequence can be {storage node 1, storage node 2, storage node 3, storage node 4}. The corresponding serial number of storage node 1 is 1. The corresponding serial number of storage node 2 is 2.

[0066] In the fourth step, the number of storage node identifiers in the storage node identifier sequence is determined as the number of storage nodes.

[0067] In the fifth step, the remainder of dividing the above hash value by the number of storage nodes is determined as the sequence number to be allocated.

[0068] In the sixth step, the storage node identifier with the sequence number being the sequence number to be allocated in the storage node identifier sequence is determined as the first target storage node identifier.

[0069] In the seventh step, each storage node identifier except the first target storage node identifier in the above storage node identifier sequence is determined as each replica storage node identifier to be screened.

[0070] In step 8, for each of the aforementioned replica storage node identifiers to be screened, perform the following steps: The first sub-step is to determine the network delay 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. In practice, the execution subject network detection tool (such as the ping command) determines the network delay 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. The network delay information may be the network delay time.

[0071] The second sub-step is to determine the storage space usage rate of the storage node corresponding to the storage node identifier of the replica to be screened. In practice, the execution subject can obtain the storage space usage rate of the storage node corresponding to the storage node identifier of the replica to be screened by remotely executing a command (such as an SSH command).

[0072] The third sub-step is to generate the storage node adaptation value information corresponding to the storage node identifier of the replica to be screened based on the above-mentioned network delay information and the above-mentioned storage space utilization rate. In practice, the above-mentioned execution entity may determine the product of the first preset weight value and the above-mentioned storage space utilization rate as the first value. Then, the above-mentioned execution entity may determine the product of the second preset weight value and the delay duration represented by the above-mentioned network delay information as the second value. Then, the above-mentioned execution entity may determine the sum of the above-mentioned first value and the above-mentioned second value as the target value. Next, the above-mentioned execution entity may determine the reciprocal of the above-mentioned target value as the storage node adaptation value information corresponding to the storage node identifier of the replica to be screened.

[0073] In the ninth step, the obtained storage node adaptation value information is sorted from large to small according to the adaptation value represented by the storage node adaptation value information to obtain a storage node adaptation value information sequence.

[0074] In the tenth step, the identifiers of the replica storage nodes to be screened, which correspond to the first number of data replicas of the storage node adaptation value information in the above storage node adaptation value information sequence, are determined as identifiers of the respective replica storage nodes.

[0075] In the eleventh step, the data in the Metaverse user cold data block is deduplicated to obtain a deduplicated Metaverse user cold data block. In practice, the execution entity may use data deduplication technology to deduplicate the data in the Metaverse user cold data block to obtain a deduplicated Metaverse user cold data block.

[0076] Step 12: Compress the deduplicated Metaverse user cold data blocks to obtain compressed Metaverse user cold data blocks. In practice, the above execution entity can compress the deduplicated Metaverse user cold data blocks using the Snappy algorithm to obtain compressed Metaverse user cold data blocks.

[0077] Step 13: Store the compressed Metaverse user cold data block in the preset storage nodes corresponding to the first target storage node identifier and the replica storage node identifiers, and configure access permissions for the compressed Metaverse user cold data block. In practice, access permissions for the compressed Metaverse user cold data block can be configured using role-based access control (RBAC).

[0078] The above technical solution and its related contents, as an inventive feature of an embodiment of the present disclosure, solve the technical problem of "slow replica data acquisition speed and waste of storage resources." Factors that lead to slow replica data acquisition speed and waste of storage resources are often as follows: a preset number of to-be-stored node identifiers are randomly selected from each to-be-stored node identifier, and the Metaverse user cold data blocks are stored as replica data in the storage nodes corresponding to the preset number of to-be-stored node identifiers. This does not take into account the network latency between the storage nodes storing the replica data and the storage nodes storing the Metaverse user cold data blocks. When randomly storing replica data, the replicas may be stored on nodes with higher network latency, resulting in slower replica data acquisition speed when the replica data is needed. Furthermore, the storage of replica data of the Metaverse user cold data blocks in each of the randomly selected storage nodes corresponding to the preset number of to-be-stored node identifiers is also not considered. This results in some storage nodes being nearly saturated while others still have a large amount of remaining space, resulting in a waste of storage resources. If the above factors are resolved, the effect of improving replica data acquisition speed and reducing storage resource waste can be achieved. To achieve this effect, first, a preset value is determined as the number of data replicas. This determines the number of data replicas storing the Metaverse user cold data block. Then, a hash operation is performed on the Metaverse user cold data block to obtain a hash value corresponding to the Metaverse user cold data block. This yields a hash value for determining the first target storage node identifier. Subsequently, the node identifiers to be stored are sorted to obtain a storage node identifier sequence. Each storage node identifier in the storage node identifier sequence has a corresponding sequence number. This yields a storage node identifier sequence for determining the first target storage node identifier and each replica storage node identifier to be screened. The number of storage node identifiers in the storage node identifier sequence is then determined as the number of storage nodes. This yields the number of storage nodes used to generate the sequence number to be assigned. Next, the remainder of dividing the hash value by the number of storage nodes is determined as the sequence number to be assigned. Subsequently, 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 for storing the Metaverse user cold data block. Then, each storage node identifier in the above storage node identifier sequence, except for the first target storage node identifier, is determined as each replica storage node identifier to be screened. Then, for each replica storage node identifier to be screened in the above replica storage node identifiers to be screened, the following steps are performed: Step 1: Determine the network delay information between the storage node corresponding to the above replica storage node identifier to be screened and the storage node corresponding to the above first target storage node identifier.Thus, network delay information between the storage node corresponding to the replica storage node identifier to be screened and the storage node corresponding to the first target storage node identifier can be obtained. Second, the storage space utilization rate of the storage node corresponding to the replica storage node identifier to be screened is determined. Thus, the storage space utilization rate used to determine the storage node adaptation value information for the replica storage node identifier to be screened is obtained. Third, based on the network delay information and the storage space utilization rate, storage node adaptation value information corresponding to the replica storage node identifier to be screened is generated. Thus, an indicator (i.e., storage node adaptation value information) is obtained that measures the suitability of the storage node corresponding to the replica storage node identifier to be screened for storing replica data based on both network delay 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 to obtain a storage node adaptation value information sequence. This can be used to determine the storage node adaptation value information sequence for each replica storage node identifier. Then, the number of data replicas corresponding to the number of data replicas in the storage node adaptation value information sequence is determined as each replica storage node identifier. Thus, the number of data replicas can be determined, and each replica storage node identifier corresponding to the first target storage node identifier has a low network latency and low storage space utilization. Next, the data in the Metaverse user cold data block is deduplicated to obtain a deduplicated Metaverse user cold data block. The deduplicated Metaverse user cold data block is then compressed to obtain a compressed Metaverse user cold data block. Thus, the deduplicated Metaverse user cold data block can be compressed, further reducing the data volume of the Metaverse user cold data block. The compressed Metaverse user cold data block is stored in the preset storage nodes corresponding to the first target storage node identifier and each replica storage node identifier, and access rights are configured for the compressed Metaverse user cold data block. Thus, the compressed Metaverse user cold data block can be stored in the preset storage nodes between the first target storage node identifier and the storage node corresponding to the first target storage node identifier, and has a low network latency and low storage space utilization. This takes into account the network latency between the storage node storing the replica data and the storage node storing the Metaverse user cold data block, reducing the probability of storing the replica on a node with high network latency and improving the speed of data acquisition when accessing the replica data.Also, before storing the replica data of the Metaverse user cold data blocks, the storage space utilization rate of each storage node is taken into consideration, and the deduplicated and compressed Metaverse user cold data blocks are stored in the preset storage nodes corresponding to the replica storage node identifiers with low network space latency and low storage space utilization rate. This balances the storage resources of each storage node, reduces the situation where the storage space of some storage nodes is close to saturation, while other storage nodes still have a large amount of remaining space, and reduces the waste of storage resources of storage nodes.

[0079] The above-described various embodiments of the present disclosure have the following beneficial effects: Through the distributed storage methods for metaverse data in some embodiments of the present disclosure, access response delays for hot data included in the metaverse data are reduced, as well as the risk of metaverse data loss. Specifically, the reasons for the access response delays for hot data included in the metaverse data and the higher risk of metaverse data loss are: directly storing the metaverse data in each storage node without performing hot and cold data classification, and not storing the hot data included in the metaverse data in a storage medium with faster read and write speeds (such as high-speed memory). When the target user terminal accesses the frequently accessed data (i.e., hot data) included in the metaverse data, the relatively slow read and write speeds of the storage nodes result in a higher access response delay. Furthermore, directly storing the metaverse data in each storage node without performing health checks on the storage nodes may result in faulty nodes among the storage nodes. Storing the metaverse data in faulty nodes may cause data loss, thereby increasing the risk of metaverse data loss. Based on this, some embodiments of the present disclosure provide a distributed storage method for metaverse data. First, metaverse data corresponding to a preset metaverse platform is collected. The metaverse data includes a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier. This allows the collection of metaverse data corresponding to the metaverse platform. Next, data preprocessing is performed on the metaverse user data set to obtain a preprocessed metaverse user data set. Each preprocessed metaverse user data in the preprocessed metaverse user data set corresponds to a metaverse user identifier. This allows the preprocessing of the metaverse data, including the metaverse user data set. Next, hot and cold data identification is performed on the preprocessed metaverse user data set to obtain a metaverse user data set. Each metaverse user data set in the preprocessed metaverse user data set includes cold metaverse user data and hot metaverse user data, each of which has a corresponding metaverse user identifier. This allows the preprocessed metaverse user data set to be classified into hot and cold data, resulting in a metaverse user data set containing each cold metaverse user data set and each hot metaverse user data set. Then, for each Metaverse user data group in the Metaverse user data group set, the Metaverse user hot data in the Metaverse user data group is stored in main memory for access by the target user terminal, and access permission configuration operations are performed on the Metaverse user hot data. Thus, each Metaverse user hot data in the Metaverse user data group set can be stored in main memory with relatively fast read and write speeds for access by the target user terminal, reducing access response latency for the hot data included in the Metaverse data. Furthermore, access permission configuration operations are performed on the Metaverse user hot data, improving data access security.Next, each Metaverse user cold data item included in the Metaverse user data set is identified as a Metaverse user cold data set. Thus, each Metaverse user cold data item included in the Metaverse data can be obtained. The Metaverse user cold data set is then segmented to obtain a Metaverse user cold data block set. Thus, the Metaverse user cold data set can be segmented. Then, a health check is performed on each preset storage node to obtain the identifiers of each node to be stored. Thus, the identifiers of each node to be stored, representing each non-faulty storage node, can be obtained. Next, for each Metaverse user cold data block in the Metaverse user cold data block set, distributed storage is performed based on the identifiers of each node to be stored. Thus, the Metaverse user cold data block set is distributed based on the identifiers of each non-faulty storage node, reducing the likelihood of storing a Metaverse user cold data block on a faulty storage node and mitigating the risk of Metaverse data loss. This also reduces access response delays for hot data contained in the Metaverse data by performing hot and cold data identification on the pre-processed Metaverse user dataset and storing each identified Metaverse user hot data in relatively fast main memory for access by the target user terminal. Simultaneously, health checks are performed on each pre-set storage node to obtain the identifiers of each node to be stored. Based on the identifiers of each non-faulty storage node, the Metaverse user cold data block set is distributed and stored. This reduces the likelihood of storing Metaverse user cold data blocks on faulty storage nodes, thereby reducing the risk of Metaverse data loss.

[0080] Further references Figure 2 As an implementation of the methods shown in the figures, the present disclosure provides some embodiments of a distributed storage device for metaverse data. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0081] like Figure 2As shown, the metaverse data distributed storage device 200 of some embodiments includes: an acquisition unit 201, a data preprocessing unit 202, a hot and cold data identification 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 collection unit 201 is configured to collect metaverse data corresponding to a preset metaverse platform, wherein the metaverse data includes a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier; the data preprocessing unit 202 is configured to perform 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; the hot and cold data identification processing unit 203 is configured to perform hot and cold data identification processing on the preprocessed metaverse user data set 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, and the metaverse user data set has a corresponding metaverse user identifier; the first storage unit 204 is configured to It is configured to store the metaverse user hot data in the above-mentioned metaverse user data group into the main memory for access by the target user terminal, and perform access permission configuration operation on the above-mentioned metaverse user hot data for each metaverse user data group in the above-mentioned metaverse user data group set; the determination unit 205 is configured to determine the respective metaverse user cold data included in the above-mentioned metaverse user data group set as the metaverse user cold data set; the segmentation processing unit 206 is configured to segment the above-mentioned metaverse user cold data set to obtain the metaverse user cold data block set; the health detection processing unit 207 is configured to perform health detection processing on each preset storage node to obtain the identification of each node to be stored; the second storage unit 208 is configured to perform distributed storage on each metaverse user cold data block in the above-mentioned metaverse user cold data block set based on the above-mentioned each node to be stored identification.

[0082] It is understood that the units described in the device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units included therein, and will not be repeated here.

[0083] Reference below Figure 3 , which shows a structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0084] like Figure 3 As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0085] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but 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 instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0086] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the methods of some embodiments of the present disclosure are performed.

[0087] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, 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, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present 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. Furthermore, in some embodiments of the present 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 a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0088] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0089] The computer-readable medium may be contained in an electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When one or more programs are executed by the electronic device, the electronic device: collects metaverse data corresponding to a preset metaverse platform, wherein the metaverse data includes a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier; performs 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; performs hot and cold data identification processing on the preprocessed metaverse user data set to obtain a metaverse user data set, wherein each metaverse user data set in the metaverse user data set includes cold metaverse user data and hot metaverse user data. The above-mentioned metaverse user data group has a corresponding metaverse user identifier; for each metaverse user data group in the above-mentioned metaverse user data group set, the metaverse user hot data in the above-mentioned metaverse user data group is stored in the main memory for access by the target user terminal, and the access permission configuration operation is performed on the above-mentioned metaverse user hot data; the various metaverse user cold data included in the above-mentioned metaverse user data group set are determined as the metaverse user cold data set; the above-mentioned metaverse user cold data set is segmented to obtain a metaverse user cold data block set; a health check is 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 above-mentioned metaverse user cold data block set, the above-mentioned metaverse user cold data block is distributedly stored based on the above-mentioned each node to be stored identifier.

[0090] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone 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 a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0092] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor including an acquisition unit, a data preprocessing unit, a hot and cold data identification 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, in some cases, constitute a limitation on the units themselves. For example, the acquisition unit may also be described as a "unit for acquiring metaverse data corresponding to a preset metaverse platform."

[0093] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0094] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of technical features, but also encompasses other technical solutions formed by any combination of technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing a feature with (but not limited to) a technical feature having similar functions disclosed in the embodiments of the present 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 includes a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier; 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 hot and cold data identification processing on the pre-processed metaverse user data set to obtain a metaverse user data set, wherein each metaverse user data set in the metaverse user data set includes cold metaverse user data and hot metaverse user data, and the metaverse user data set has a corresponding metaverse user identifier; For each Metaverse user data group in the Metaverse user data group set, storing the Metaverse user hot data in the Metaverse user data group into a main memory for access by a target user terminal, and performing an access permission configuration operation on the Metaverse user hot data; Determining each metaverse user cold data included in the metaverse user data set as a metaverse user cold data set; Segmenting the Metaverse user cold data set to obtain a Metaverse user cold data block set; Perform health checks on each preset storage node to obtain the identifier of each node to be stored; For each Metaverse user cold data block in the Metaverse user cold data block set, distributed storage is performed on the Metaverse user cold data block based on the identifiers of the nodes to be stored.

2. The method according to claim 1, wherein The collecting of metaverse data corresponding to the preset metaverse platform includes: For each Metaverse user data log in each Metaverse user data log of the preset Metaverse platform, collect Metaverse user data in the Metaverse user data log, wherein the Metaverse user data log corresponds to a Metaverse user identifier, wherein the Metaverse user data includes user behavior data, virtual value flow information, Metaverse interaction data, and Metaverse sensor data; Determine the collected metaverse user data corresponding to each metaverse user identifier as a metaverse user data set; The metaverse user data set is determined to be the metaverse data corresponding to the preset metaverse platform.

3. The method according to claim 1, wherein The performing data preprocessing on the Metaverse user dataset to obtain a preprocessed Metaverse user dataset includes: Performing denoising on the metaverse user dataset to obtain a denoised metaverse user dataset; The denoised metaverse user data set is subjected to data format standardization processing to obtain a preprocessed metaverse user data set, wherein each preprocessed metaverse user data in the preprocessed metaverse user data set includes preprocessed user behavior data, preprocessed virtual value flow information, preprocessed metaverse interaction data, and preprocessed metaverse sensor data.

4. The method according to claim 1, wherein The health check process is performed on each preset storage node to obtain the identifier of each node to be stored, including: For each of the preset storage nodes, perform the following steps: Performing a survival status detection process on the preset storage node to obtain survival status 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 status detection information indicates that the survival status of the preset storage node is normal and the storage overload and failure risk detection information indicates that there is no overload and failure risk, determining the storage node identifier of the preset storage node as a normal storage node identifier; The determined normal storage node identifiers are determined as the identifiers of the nodes to be stored.

5. The method according to claim 1, wherein The step of performing hot and cold data identification processing on the pre-processed Metaverse user data set to obtain a Metaverse user data set includes: For each pre-processed Metaverse user data in the pre-processed Metaverse user data set, the following identification steps are performed: Get the current time; Determining a hot data time period based on the current time; Determining the pre-processed user behavior data included in the pre-processed metaverse user data as target user behavior data; Determining the pre-processed virtual value flow information included in the pre-processed 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; Determining the pre-processed metaverse interaction data included in the pre-processed metaverse user data as metaverse interaction hot data; Determining the pre-processed metaverse sensor data included in the pre-processed metaverse user data as metaverse sensor 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 sensor 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; The obtained metaverse user data groups are determined as a metaverse user data group set.

6. The method according to claim 5, wherein: The target user behavior data includes target user behavior sub-data, each target user behavior sub-data corresponds to a timestamp, and the target virtual value flow information includes target virtual value flow sub-information, each target virtual value flow sub-information corresponds to a 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 a time point corresponding to a time stamp among the various timestamps is within the hot data time period, determining at least one timestamp corresponding to a time point among the various timestamps within the hot data time period 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 except the at least one target user behavior sub-data in the target user behavior sub-data as user behavior cold data; In response to determining that a time point corresponding to a value flow timestamp among the value flow timestamps is within the hot data time period, determining at least one value flow timestamp corresponding to a time point within the hot data time period among the 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 target virtual value flow sub-information as virtual value flow hot information; Each target virtual value stream rotor information among the target virtual value stream rotor information except the at least one target virtual value stream rotor information is determined as virtual value stream cold information.

7. A distributed storage device for metaverse data, comprising: a collection unit configured to collect metaverse data corresponding to a preset metaverse platform, wherein the metaverse data includes a metaverse user data set, and each metaverse user data in the metaverse user data set corresponds to a metaverse user identifier; a data preprocessing unit configured to perform 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; a cold and hot data identification processing unit configured to perform cold and hot data identification processing on the pre-processed metaverse user data set 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, and the metaverse user data set has a corresponding metaverse user identifier; a first storage unit configured to, 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 a main memory for access by a target user terminal, and perform an access permission configuration operation on the Metaverse user hot data; a determining unit configured to determine each metaverse user cold data included in the metaverse user data set as a metaverse user cold data set; a segmentation processing unit configured to perform segmentation processing on the Metaverse user cold data set 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 an identifier of each node to be stored; The 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 identifiers of the nodes to be stored.

8. An electronic device comprising: one or more processors; a storage device 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 the method according to any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images