Data processing method, data processing system, data processing apparatus, computing device, computer readable storage medium, and computer program product

Abstract

Embodiments of the present disclosure provide a data processing method, a data processing system, a data processing apparatus, a computing device, a computer readable storage medium, and a computer program product. The data processing method comprises: acquiring operation information of a storage service, and determining at least one storage component type corresponding to the storage service, wherein the storage service has a storage component corresponding to the storage service; on the basis of the operation information, collecting statistics about access information corresponding to the at least one storage component type, and on the basis of the access information, generating analysis data corresponding to the storage component; and according to a preset data processing strategy, and on the basis of the analysis data, performing data migration on storage data in the storage component, so as to obtain a migrated storage component. The present disclosure achieves the objective of better performing data placement according to the data popularity, improves the storage service performance, and reduces the storage cost, and can also provide a better storage service for a user.

Description

[0001]Data Processing Method, Data Processing System, Data Processing Apparatus, Computing Device, Computer-Readable Storage Medium, and Computer Program Product. This disclosure claims priority to Chinese patent application number 202410303995.2, filed with the China Patent Office on March 15, 2024, and entitled "Data Processing Method, Data Processing System, Data Processing Apparatus, Computing Device, Computer-Readable Storage Medium, and Computer Program Product," the entire contents of which are incorporated herein by reference. Technical Field: Embodiments of the present disclosure relate to the field of computer technology, and more particularly to a data processing method, data processing system, and data processing apparatus. Background: With the continuous development of Internet technology, we have entered the era of big data. An increasing number of Internet applications are adopting cloud storage for their data storage. Due to factors such as the large number of users, high data access volume, and complex network environments, data storage systems face challenges in ensuring the quality of data storage services. In data storage systems, user demand, storage performance, and system resources all affect the quality of data storage services. Therefore, how to properly place and store data is an urgent issue that needs to be addressed. In view of this, embodiments of the present disclosure provide a data processing method. One or more embodiments of the present disclosure also relate to a data processing apparatus, a data processing system, a computing device, a computer-readable storage medium, and a computer program product to address technical deficiencies in the prior art. According to a first aspect of an embodiment of the present disclosure, a data processing method is provided, comprising: obtaining operational information of a storage service, determining at least one storage component class corresponding to the storage service, wherein the storage service has corresponding storage components; collecting access information corresponding to the at least one storage component class based on the operational information, and generating analysis data corresponding to the storage component based on the access information; and migrating storage data in the storage component based on the analysis data in accordance with a preset data processing strategy to obtain a migrated storage component.According to a second aspect of an embodiment of the present disclosure, a data processing system is provided, which includes a storage service end and an analysis service end, wherein the analysis service end is used to obtain operation information of a storage service for the storage service end, determine at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; based on the operation information, count access information corresponding to the at least one storage component class, and generate analysis data corresponding to the storage component according to the access information; the storage service end is used to migrate storage data in the storage component based on the analysis data in accordance with a preset data processing strategy to obtain a migrated storage component. According to a third aspect of an embodiment of the present disclosure, a data processing apparatus is provided, comprising: a determination module configured to obtain operational information of a storage service and determine at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; a statistics module configured to collect access information corresponding to the at least one storage component class based on the operational information and generate analysis data corresponding to the storage component based on the access information; and a migration module configured to migrate data stored in the storage component based on the analysis data according to a preset data processing strategy, thereby obtaining a migrated storage component. According to a fourth aspect of an embodiment of the present disclosure, a computing device is provided, comprising: a memory and a processor; the memory is configured to store computer-executable instructions, and the processor is configured to execute the computer-executable instructions, wherein the computer-executable instructions, when executed by the processor, implement the steps of the aforementioned data processing method. According to a fifth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-executable instructions are stored, wherein the computer-executable instructions, when executed by the processor, implement the steps of the aforementioned data processing method. According to a sixth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program or instructions. When executed by a processor, the computer program or instructions implement the steps of the aforementioned data processing method. The present disclosure provides a data processing method, comprising obtaining operational information of a storage service, determining at least one storage component class corresponding to the storage service, wherein the storage service has corresponding storage components; collecting access information corresponding to the at least one storage component class based on the operational information, and generating analysis data corresponding to the storage component based on the access information; and migrating storage data in the storage component based on the analysis data according to a preset data processing policy to obtain a migrated storage component.One embodiment of the present disclosure achieves this by acquiring and analyzing operational information of storage services, clustering storage components of the storage services based on the operational information, and identifying multiple different storage component classes. This facilitates subsequent access information statistics for different types of storage components. After access information for each storage component class is calculated based on the operational information, analytical data corresponding to the storage component can be generated based on the access information. Because analytical data is generated based on operational data combined with characteristics of the storage service type, the accuracy of the analytical data is improved. This allows for better data placement based on data popularity when migrating storage data based on the analytical data according to a preset data processing strategy, improving storage service performance and reducing storage costs. Furthermore, after data placement based on data popularity, better storage services can be provided to users, ensuring data access performance and enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS FIG1 is a schematic diagram of a data processing method provided by one embodiment of the present disclosure; FIG2 is a flow chart of a data processing method provided by one embodiment of the present disclosure; FIG3 is a flow chart of the processing process of a data processing method provided by one embodiment of the present disclosure; FIG4 is a schematic diagram of the structure of a data processing system provided by one embodiment of the present disclosure; FIG5 is a schematic diagram of the structure of a data processing apparatus provided by one embodiment of the present disclosure; and FIG6 is a block diagram of the structure of a computing device provided by one embodiment of the present disclosure. The following description sets forth numerous specific details to facilitate a thorough understanding of the present disclosure. However, the present disclosure can be implemented in many other ways than those described herein, and those skilled in the art may make similar generalizations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the specific implementations disclosed below. The terminology used in one or more embodiments of the present disclosure is for the purpose of describing specific embodiments only and is not intended to limit the present disclosure. As used in one or more embodiments of the present disclosure and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present disclosure refers to and encompasses any and all possible combinations of one or more associated listed items. It should be understood that while the terms "first," "second," and so on may be used to describe various information in one or more embodiments of the present disclosure, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, "first" could also be referred to as "second," and similarly, "second" could also be referred to as "first," without departing from the scope of one or more embodiments of the present disclosure.Depending on the context, the term "if" as used herein can be interpreted as "at the time of," "when," or "in response to a determination." Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data, etc.) involved in one or more embodiments of this disclosure are all authorized by the user or fully authorized by all parties. The collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or deny. First, the terms used in one or more embodiments of this disclosure are explained. Elastic Block Storage System: Elastic Block Storage (EBS) is an advanced storage solution for cloud computing environments, providing users with a scalable, highly available, and stable block-level storage service. Data Block: A data block can be understood as a storage unit in the Elastic Block Storage System. A storage volume is divided into multiple smaller, fixed-size logical units at the underlying layer. These units are data blocks. When applications read and write storage volumes, they actually operate on data blocks. Currently, in elastic block storage systems, due to the extremely limited semantic information of data blocks, it is difficult to predict data block access popularity, which significantly affects the efficiency of data placement strategies. Specifically, in heterogeneous distributed storage systems, the placement of data within a data block cannot be determined, which affects the data access performance of the entire system and increases system storage costs. Based on this, the present disclosure provides a data processing method. This disclosure also involves a data processing apparatus, a data processing system, a computing device, and a computer-readable storage medium, each of which is described in detail in the following embodiments. See Figure 1, which shows a scenario diagram of a data processing method according to one embodiment of the present disclosure. In a heterogeneous distributed storage system, multiple different storage services can be deployed on each storage node. Figure 1 only illustrates storage service A.During data migration, operational information corresponding to each storage service can be obtained. Based on this operational information, all storage services can be classified to obtain multiple storage component classes. Storage components within a storage component class are data blocks of the same type. Access information for each storage component class can then be calculated based on the operational information. Analytical data for the storage service can then be determined based on the access information for each storage component class. This analytical data includes popularity information for each storage component, i.e., each data block. This allows determination of which data blocks are frequently accessed by users. Data migration can then be performed based on the analytical data according to a preset data processing strategy. For example, in Figure 1 , data blocks a1, a2, and a3 of storage service A deployed on storage node 1 are migrated to storage node 2. This allows users to access each data block in storage service A through storage node 2, reducing access latency and improving access efficiency. Referring to Figure 2 , a flow chart of a data processing method according to an embodiment of the present disclosure is shown, specifically including the following steps: Step 202: Obtain operational information for a storage service and determine at least one storage component class corresponding to the storage service, wherein the storage service has corresponding storage components. In practice, cloud service providers offer users an online storage service, known as cloud disk storage. This service allows users to store user data, such as documents, images, videos, and audio files, on a remote server cluster via the network. When users store data online in cloud storage, they can set different service types for different cloud disks, such as storing log data on one cloud disk and system files on another. This results in multiple storage services with different service types. The business data corresponding to each storage service may be divided into several storage components, known as data blocks, for storage. The storage components of the same storage service may reside on the same storage node or on different storage nodes. A storage service, specifically a cloud disk storage service, can be multiple, each with different service types. Storage services can be categorized according to specific criteria. Operational information can be understood as data collected by cloud service providers in the process of providing cloud disk storage services, covering business, user, and other dimensions. Operational information may include user-specific information such as user-defined tag information, user business information, and user BPS (Bits Per Second) timing information. It may also include cloud disk-specific information such as cloud disk I / O Trace (input / output tracing) information, cloud disk size, and cloud disk creation time.By analyzing operational information, it is possible to subsequently implement coarse-grained user-based classification for different storage services. Classifying storage services by service type facilitates subsequent statistical analysis of access characteristics for different types of storage services. In one embodiment of the present disclosure, storage components of storage services can be clustered based on operational information to identify multiple storage component classes. A storage component can be understood as a data block corresponding to a cloud disk. Clustering data blocks corresponding to a cloud disk storage service based on operational information allows the multiple data blocks of the cloud disk storage service to be divided into multiple data block classes, facilitating subsequent statistical analysis of access information for data blocks of the same class. In practical applications, storage services can differentiate storage components based on their own needs. For example, storage components on storage nodes far from user addresses are grouped into one class. This disclosure uses the classification of storage components based on operational information as an example. In specific implementations, operational information can be obtained from the operations system, which is responsible for recording storage service-related data such as system logs, user information, cloud disk information, and load information. A collection module can collect and store this information or data from the operations system to facilitate subsequent processing and analysis of the collected operational information. When clustering storage components corresponding to storage services based on operational information, fine-grained clustering of storage services can be achieved using this operational information. Since each storage service corresponds to its own storage component, and the number of storage components is large, clustering storage services can be achieved from the perspective of storage services to reduce clustering costs and computational complexity. Specifically, fine-grained clustering of storage services can be performed using operational information to group data blocks belonging to the same clustered class into the same data block class. To ensure the proper placement of block storage data, the data processing method provided in this disclosure utilizes an operational information analysis approach to collect data block access popularity information. To facilitate statistical analysis, different storage services are categorized and data blocks for different types of storage services are clustered. Popularity statistics are then collected for data blocks of the same type, thereby improving statistical accuracy. Therefore, in a specific embodiment of this disclosure, operational information for all storage services in a cloud disk storage system is obtained. Storage components of these services are clustered based on this operational information, and multiple different storage component classes are identified. This achieves the goal of clustering data blocks based on operational analysis, facilitating subsequent popularity statistics for data blocks of the same type.Furthermore, since the operational information collected from the operation system may contain some noise data or information of little training value, preprocessing and data cleaning operations can be performed on the collected operational information to reduce subsequent computational complexity. Specifically, obtaining storage service operational information includes: collecting initial storage service operational information; filtering the initial operational information according to a preset data cleaning strategy to determine target operational information; and removing the target operational information from the initial operational information to obtain the operational information. In actual applications, the operation system records all information during the operation of the cloud disk storage service. After the operational information is collected from the operation system, some information may need to be filtered. Therefore, the collected information data needs to be preprocessed and cleaned to remove outliers and noise, and perform necessary feature selection and transformation to ensure the data quality and usability of the operational information. During actual operation, when the operation system records operational information, outliers and noise may be present in the collected operational information due to issues with the data printing itself or abnormalities in the data collection terminal or network. Therefore, this data needs to be removed. The collected operational information may contain some data that is not very meaningful for subsequent popularity statistics, such as user location information. This data is filtered. By selecting representative and important feature information from the raw data, the computational complexity of subsequent popularity statistics is reduced. Initial operational information can be understood as the raw data recorded in the operational information. When the collection module collects information from the operational information, it obtains the initial operational information. This initial operational information may contain outliers, noise, and unnecessary feature information, which requires removal and filtering. Therefore, the initial operation information can be screened according to the preset data cleaning strategy, that is, the data can be cleaned. The preset data cleaning strategy is a strategy for determining the data that needs to be eliminated from the initial operation information. The preset data cleaning strategy can include judgment conditions for noise and outlier data, and unnecessary feature identifiers. Therefore, by screening the initial operation information according to the preset data cleaning strategy, the target operation information can be determined. The target operation information is the data that needs to be eliminated, such as noise data and data with little training guidance significance. After eliminating these target operation information from the initial operation information, the preprocessed operation information can be obtained.In a specific embodiment of the present disclosure, a collection module collects initial operational information for each storage service from operational information. This initial operational information may include user identification information, user service information, cloud disk load characteristics, and other information. This initial operational information is preprocessed, including removing outliers and noise information, and filtering out unnecessary feature information, such as user geographic location information. Based on this, data cleaning and preprocessing of the operational information allows for the selection of representative and important features. This reduces the amount of statistical computation required for subsequent popularity statistics, improving the performance of the statistical model. Furthermore, since each cloud disk service corresponds to multiple data blocks, coarse-grained classification can be performed on the cloud disk services first, followed by fine-grained clustering of the data blocks for each type of cloud disk service to facilitate subsequent access popularity statistics. Specifically, determining at least one storage component class corresponding to the storage service includes: classifying the storage service based on the service information in the operation information to obtain at least one storage service class; determining a set of storage components corresponding to the at least one storage service class, and clustering each set of storage components based on the load information in the operation information to obtain at least one storage component class. In actual applications, the same cloud disk may have multiple data blocks, and data blocks of the same type may exhibit similar patterns in address and access popularity distribution. Therefore, coarse-grained classification can be performed on all cloud disk services first, followed by clustering of data blocks of the same type of cloud disk service. This facilitates statistical block access popularity and improves subsequent data placement efficiency. The business information within the operational information can be understood as user-level information about cloud disk services. This information can include user level, application type, cloud disk size, and cloud disk creation time. By classifying cloud disk storage services based on this business information, coarse-grained classification of cloud disks can be achieved from the user perspective. Since classification is based on user-related business information, the classification criteria are related to the business information at the user level. By classifying storage services at the user level, the classification of storage services is linked to user business usage, resulting in more accurate analysis results for subsequent storage services of the same type. In specific implementation, after classifying storage services based on business information, at least one storage service class is obtained. A storage service class is a class composed of storage services of the same type.After classifying storage services, multiple different types of storage service classes are obtained. Subsequently, data blocks need to be clustered for each type of storage service class. Further clustering is performed on each type of storage service class to group data blocks with similar characteristics. Therefore, a storage component set corresponding to each storage service class must be determined. A storage component set can be understood as the set of data blocks corresponding to each cloud disk storage service within that cloud disk storage service class. Since each cloud disk service has multiple data blocks, data blocks within the same cloud disk service class can form a storage component set. Data blocks within a storage component set exhibit similar distribution patterns in address and access popularity, facilitating subsequent popularity statistics. After determining the storage component sets, each storage component set can be clustered based on the load information in the operation information. The load information in the operation information can be understood as the load characteristics of the data blocks. Load characteristics can include BPS timing information, read-write ratio, I / O operations per second (I / O operations per second) timing information, etc. Storage components are clustered based on the load characteristics, grouping data blocks with the same load characteristics together to facilitate subsequent statistics on the popularity of each type of cloud disk data block. A storage component class is the cloud disk data block class obtained after clustering. Storage components, or data blocks, within the same storage component class share the same load characteristics, such as similar processing rates, read-write ratios, or response times. In a specific embodiment of the present disclosure, cloud disk storage services are classified based on service information in the operation information, such as user level and application type, to obtain multiple different types of storage service classes, including storage service classes A, B, and C. Storage component sets A1, B1, and C1 corresponding to each type of storage service class are then determined. Each storage component set is clustered based on load information in the operation information to obtain multiple storage component classes within each storage component set. For example, the storage component classes in storage component set A1 include a1 and a2, and the storage component classes in storage component set B1 include b1, b2, and b3. Based on this, by performing coarse-grained classification of cloud disk services based on the user dimension and fine-grained clustering of cloud disk services within a cloud disk service class based on the load dimension, data blocks under the same type of cloud disk services can be clustered together, facilitating statistical analysis of the access popularity of data blocks and improving statistical accuracy.Furthermore, to classify storage services, a classification standard needs to be determined based on the service information. Specifically, classifying the storage services based on the service information in the operation information to obtain at least one storage service class includes: determining at least one service classification identifier based on the service information in the operation information; and classifying the storage services based on the at least one service classification identifier to obtain at least one storage service class. The service classification identifier can be understood as a classification standard for classifying storage services. The service classification identifier can be determined based on the service information. For example, if the service information includes user level and cloud disk size, the determined service classification identifier can be user level and cloud disk size. Subsequent classification can be performed based on these two service classification identifiers. For example, users with a user level greater than 10 and a cloud disk size greater than 100 GB are classified into one class. In practical applications, after classifying storage services, a classification label can be determined for each storage service class obtained through classification. For example, a certain type of cloud disk storage service is a log data disk, while another type of cloud disk storage service is a system disk. In a specific embodiment of the present disclosure, a service classification identifier is determined based on service information in the operation information. The service classification identifiers are A and B. Multiple storage services are classified according to A and B to obtain multiple different types of storage service classes. Based on this, by determining the service classification identifier based on the service information in the operation information, storage services can be subsequently classified according to the service classification identifier, achieving coarse-grained classification of cloud disks from the user perspective. Furthermore, since each storage service class includes multiple storage services, and each storage service corresponds to multiple storage components, when determining the storage component set corresponding to each storage service class, it is necessary to first determine the storage components for each storage service. Specifically, determining the storage component set corresponding to at least one storage service class includes: determining the storage components corresponding to each storage service in the at least one storage service class; and combining the storage components corresponding to each storage service class to obtain the storage component set corresponding to the at least one storage service class based on the combination results. The storage components corresponding to each storage service can be understood as the storage components for each storage service in the same type of storage service class. For example, if storage service class A includes storage service a and storage service b, storage service a corresponds to storage components a1 and a2, and storage service b corresponds to storage components b1 and b2, then the storage component set corresponding to storage service class A includes storage components a1, a2, b1, and b2.In practical applications, to cluster the storage components of a storage service class, it is first necessary to determine the storage component set corresponding to the storage service class. Determining the storage component set requires first determining the storage components for each storage service within the storage service class. Once the storage components for each storage service have been determined, all storage components can be combined to obtain the storage component set for the storage service class. In specific implementations, after obtaining the storage component set for the storage service class, the storage services included in the storage service class can be clustered into multiple storage service subclasses. The storage components within the storage component set are then divided according to each storage service subclass, thereby obtaining the storage component classes corresponding to each storage service subclass. This method achieves the goal of clustering each storage component set based on load information within operational information. By determining the storage component set corresponding to each storage service class, clustering can be performed on storage components within the same storage service class, clustering data blocks with similar access patterns, facilitating subsequent popularity statistics. Step 204: Access information corresponding to the at least one storage component class is collected based on the operational information, and analytical data corresponding to the storage component is generated based on the access information. In practical applications, after each storage component class is determined, access information corresponding to each storage component class can be collected based on the operational information. Access information can be understood as the access frequency and probability corresponding to the storage component class. By collecting statistics on the access frequency and probability of each type of cloud disk data block within a certain time period, this serves as the primary indicator for measuring data block popularity. After determining the access information corresponding to each storage component class, analytical data corresponding to all storage components can be generated. The analytical data can be understood as data obtained by performing a heat analysis on the data blocks. Since a storage component class includes multiple different data blocks and storage component classes are clustered based on load characteristics, the storage components within a storage component class have similar access heat. Therefore, when collecting access information, the storage component class can be treated as an object for statistical analysis. After statistically determining the access information corresponding to each storage component class, that is, obtaining the access information for each storage component, analytical data for the storage component can be generated based on the access information. In a specific implementation, the analysis data may be an OAH (Operation-Analysis-Based Heatmap) heatmap.In the data structure of the heat table, each entry is represented by a two-tuple (Blockindex, HeatDegree), where Blockindex represents the data block number and HeatDegree represents the access heat of the data block. Because the heat table is obtained based on operational information analysis, it can better reflect the access status of each data block, facilitating subsequent data migration and placement based on the heat table. In other embodiments, the analysis data may also be other types of data files that store heat information, such as document-type data files or graph-type data files. Furthermore, to accurately count access information corresponding to each storage component class, it is first necessary to calculate the access frequency and probability corresponding to each storage component class. Specifically, counting access information corresponding to at least one storage component class based on the operational information includes: calculating access frequency information and access probability information corresponding to the at least one storage component class based on the operational information; and using the access frequency information and access probability information as the access information corresponding to the at least one storage component class. The access frequency information represents the access frequency of storage components in the storage component class within a certain time period, and the access probability information represents the access probability of storage components in the storage component class within a certain time period. By calculating access frequency and access probability information, these can be used as access information for the storage component class, subsequently facilitating the calculation of popularity information for the storage component class based on the access information. In specific implementations, access probability and frequency information can be statistically analyzed based on the I / O trace information of each storage component in the storage component class. Specifically, access information can be statistically analyzed based on the input and output trace information of each data block within a certain time period. In practical applications, statistical operations can be performed using a pre-trained statistical model to improve statistical efficiency. Specifically, each data block and its corresponding trace information are input into the statistical model, and the statistical model outputs access information for each data block. Based on this, the access frequency and access probability corresponding to each storage component class are calculated to determine access information for each storage component class. Subsequently, analytical data for the storage component class can be calculated based on the access information.Furthermore, in order to generate analytical data containing the heat information of each data block, the heat value of each data block must first be calculated. Specifically, generating analytical data corresponding to the storage component based on the access information includes: calculating a heat parameter corresponding to the storage component based on the access information corresponding to the at least one storage component class; obtaining an identification parameter of the storage component, associating the identification parameter with the heat parameter, and generating analytical data corresponding to the storage component based on the association result. The heat parameter can be understood as a heat value parameter of the storage component. Based on the access information of each storage component class, the heat parameter of each storage component included in each storage component class can be calculated, thereby determining the heat parameters of all storage components. The storage component identification parameter can be understood as a component identification parameter of each component. By associating each component identification parameter with its heat parameter, a tuple corresponding to each component is generated, and analytical data can be generated based on the tuple parameters of each component. In practical applications, the calculation of heat values ​​based on the access probability and frequency of storage components can be processed by a pre-trained model. By inputting the access information of each storage component into the pre-trained model, the heat value of each storage component output by the pre-trained model can be obtained. After obtaining the heat value of each storage component, an OAH heat table can be generated to facilitate subsequent data migration and placement based on the OAH heat table. Based on this, by calculating the heat parameters of each storage component based on the access information of the storage component class, the heat parameters of each storage component are associated with its corresponding component identifier, thereby generating analysis data containing the heat values ​​of all storage components, so as to facilitate the subsequent execution of the data placement strategy based on the analysis data. Step 206: Perform data migration on the storage data in the storage component based on the analysis data in accordance with the preset data processing strategy to obtain the migrated storage component. The preset data processing policy can be understood as a preset data migration strategy. By combining the preset processing policy with data analysis, data migration can be performed on the data stored in the current storage components. Specifically, the migration location of each storage component is determined, and the data stored in the storage component is migrated to obtain the migrated storage components. In practical applications, the purpose of using the preset data processing policy to migrate data based on the analyzed data is to place data blocks in appropriate locations, thereby improving cloud service performance and user experience while optimizing storage costs. In typical scenarios, analysis can be performed based on two dimensions: server location and configuration.Furthermore, the preset data processing strategy is a location processing strategy. Migrating the stored data in the storage component based on the analysis data according to the preset data processing strategy to obtain a migrated storage component includes: determining a target storage component in the storage component based on the analysis data according to the location processing strategy; determining a receiving storage node based on the target storage component, and migrating the target stored data in the target storage component to the receiving storage node to obtain the migrated target storage component. The location processing strategy can be understood as a server location partitioning strategy. Specifically, the location processing strategy places data blocks requiring migration in a storage area server that is closer to the user. The primary purpose of the location processing strategy is to reduce network transmission time and latency and improve user experience. The target storage component selected from all storage components based on the analysis data according to the location processing strategy is the component requiring data migration. The target storage component may be a relatively popular storage component. By migrating the target storage component to the receiving storage node, network transmission time and latency are reduced. In practical applications, a target storage component can be used to determine a receiving storage node. The target storage component represents the data block to be migrated and placed. The user who created or used the target storage component can be determined based on the target storage component. A storage node closest to the user is then selected as the receiving storage node. The target storage data of the target storage component is then transferred to the receiving storage node, completing the data migration process. In specific implementations, if the target storage component has multiple users, a suitable storage node can be selected as the receiving node based on the locations of the multiple users. Alternatively, the number of times or frequency of use of the data block by each user can be determined, and the first user can be identified for data migration based on the number of times or frequency of use. In a specific embodiment of the present disclosure, a target data block is selected from all data blocks based on analyzed data according to a location processing strategy. The target data block is data block A, and the user who uses data block A is determined to be user A. A target storage node is selected based on user A's IP address, and the data content of data block A is migrated to the receiving storage node. Thus, by migrating the storage component's stored data according to the location processing strategy, network transmission time and latency are reduced, improving the user experience.Furthermore, the preset data processing strategy is a configuration processing strategy; wherein migrating the stored data in the storage component based on the analysis data according to the preset data processing strategy to obtain a migrated storage component includes: determining a first storage component and a second storage component in the storage component based on the analysis data according to the location processing strategy, wherein the first storage component and the second storage component have different access information; determining a first preset storage node and a second preset storage node, wherein the first preset storage node and the second preset storage node have different storage configurations; migrating the first stored data in the first storage component to the first preset storage node to obtain the migrated first storage component, and migrating the second stored data in the second storage component to the second preset storage node to obtain the migrated second storage component. In actual applications, in addition to partitioning based on server location, partitioning based on hardware configuration can also be used. That is, when the preset data processing strategy is a configuration processing strategy, the configuration processing strategy can be understood as a strategy for partitioning based on hardware configuration. The configuration processing strategy divides data blocks into two categories: cold data and hot data, and then places them in storage areas with different performance. Since cold data is typically rarely accessed, it can be stored in low-cost storage areas. Hot data, on the other hand, is frequently accessed and should be stored in high-cost storage areas with faster transmission. The purpose of configuring a processing strategy is to reduce costs and improve efficiency. If the preset data processing strategy is a location processing strategy, the location processing strategy can be used based on the analyzed data to determine, from the first and second storage components, different access information corresponding to the two storage components. This means that the first storage component may be a hot data block, while the second storage component may be a cold data block. Alternatively, the first storage component may be a cold data block, while the second storage component may be a hot data block. This disclosure uses the example of a hot data block in the first storage component and a cold data block in the second storage component. In actual applications, according to the configuration processing strategy, cold data needs to be placed in a low-cost storage area, and hot data needs to be placed in a high-cost storage area. Therefore, when the preset data processing strategy is the location processing strategy, two storage areas can be first determined, namely, a first preset storage node and a second preset storage node. The storage configurations of these two storage nodes are different, namely, one is a high-cost storage area and the other is a low-cost storage area.After selecting a first storage node and a second storage node from the egress storage nodes, the first storage node can be migrated to the first preset storage node, and the second storage node can be migrated to the second preset storage node, respectively. This allows hot data blocks to be migrated to a high-cost storage area, while cold data blocks to be migrated to a low-cost storage area. By migrating the storage component's stored data according to the configuration processing policy, storage costs can be reduced and storage transmission efficiency can be improved. In specific implementations, in addition to using the location processing policy and the configuration processing policy separately for data migration, a multi-dimensional and multi-level approach can be implemented to optimize the overall performance and cost of the block storage service by combining location and configuration. A specific comprehensive processing strategy can be to select an appropriate storage node for data migration based on the weights of the two. Furthermore, since the cloud storage service is continuously operating, operational information changes during operation, potentially requiring data migration at regular intervals. The method further includes: generating data migration instructions at preset time intervals; and obtaining operational information of the storage service in response to the data migration instructions. The preset time interval can be understood as a pre-set interval for data migration, such as performing data migration every day. The data migration instruction can be understood as an instruction that triggers data migration. The data migration instruction can be received by the analysis system. The collection module in the analysis system collects operational information from the operation system over the past period and analyzes it to generate analytical data. The execution module then executes the corresponding data processing strategy based on the analytical data to complete the data migration. Based on this, by performing regular data migration, the cloud storage service can continuously optimize storage service performance and reduce storage costs without interrupting operations. Furthermore, when storing business data of a storage service, the data's required storage location can be determined based on the analytical data, thereby ensuring appropriate data placement during the data storage phase, reducing subsequent data migration operations and lowering storage costs. Specifically, the method further includes: determining, in response to the data storage instruction, data to be stored and a target storage service corresponding to the data to be stored, wherein the target storage service has the same business information as the storage service; selecting a target storage component from the storage components corresponding to the target storage service based on the analytical data; and storing the data to be stored in the target storage component. In actual applications, if a storage service has other storage services with similar or identical services, data placement can be performed based on the analysis data of the other storage services.If a user already uses a system cloud disk and now creates a new system cloud disk using the cloud disk service, the business data of the new system cloud disk can be stored based on the popularity analysis of the previous system cloud disk. The data storage instruction can be understood as an instruction to store data. After receiving the data storage instruction, the system can determine the data to be stored and the target storage service corresponding to the data to be stored. The business information of the target storage service is the same as that of the storage service, which can be understood as indicating that the target storage service and the storage service are similar or identical, such as both storing system disk data or logs. If the business information of the two storage services is the same, it indicates that the popularity analysis data of the two storage services will be similar over a period of time. Therefore, the analysis data of the storage service can be used as the analysis data of the target storage service for data storage. This avoids the need to re-migrate the stored data after obtaining historical popularity analysis data, as the target storage service does not have historical popularity analysis data. This reduces the pressure on the storage system and reduces storage costs. In specific implementations, when placing business data for a target storage service, a determination can be made as to whether a storage service with identical business information exists. If so, a target storage component can be selected from the storage components corresponding to the target storage service based on the storage service's analysis data. The target storage component is then the storage component suitable for storing the business data. If no storage service with identical business information exists, this indicates that the target storage service lacks reference analysis data. In this case, data can be stored according to a default storage policy, which can be determined based on actual circumstances. The present disclosure provides a data processing method, comprising obtaining operational information of a storage service, determining at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; collecting access information corresponding to the at least one storage component class based on the operational information, and generating analysis data corresponding to the storage component based on the access information; and migrating the storage data in the storage component based on the analysis data according to a preset data processing policy to obtain a migrated storage component.By acquiring and analyzing operational information of storage services, storage components of storage services are clustered based on this operational information to identify multiple different storage component classes. This facilitates subsequent access information statistics for different types of storage components. After access information for each storage component class is calculated based on the operational information, analytical data corresponding to the storage component can be generated based on the access information. Because analytical data is generated based on operational data combined with characteristics of the storage service type, the accuracy of the analytical data is improved. This allows for better data placement based on data popularity when migrating storage data according to a preset data processing strategy based on the analytical data, improving storage service performance and reducing storage costs. Furthermore, by placing storage data based on data popularity, better storage services can be provided to users, ensuring data access performance and enhancing the user experience. The data processing method provided by the present disclosure will be further described below, using the application of the data processing method provided by the present disclosure in a cloud disk service as an example, with reference to FIG3 . FIG3 shows a process flow chart of a data processing method provided by one embodiment of the present disclosure, specifically comprising the following steps. Step 302: Initial operational information of the storage service is collected and preprocessed to obtain operational information. In one achievable implementation, data migration instructions are generated at preset time intervals, and operational information of the cloud disk service is obtained in response to the data migration instructions. Step 304: Storage services are classified based on the service information in the operational information to obtain at least one storage service class. In one achievable implementation, a service classification identifier is determined based on the service information in the operational information. The service classification identifier is composed of user level and cloud disk creation time. Cloud disk services with a user level greater than 10 and a creation time greater than three months ago are classified into one class, and the remaining services are classified into another class, ultimately obtaining cloud disk service class A and cloud disk service class B. Step 306: Storage component sets corresponding to at least one storage service class are determined, and each storage component set is clustered based on the load information in the operational information to obtain at least one storage component class.In one achievable implementation, the data blocks corresponding to each cloud disk service in cloud disk service class A are determined to form a data block set a corresponding to cloud disk service class A, and the data blocks corresponding to each cloud disk service in cloud disk service class B are formed into a data block set b0 corresponding to cloud disk service class B. Clustering data block set a is specifically implemented by clustering each cloud disk service in cloud disk service class A based on load information to obtain cloud disk service subclasses "a1, a2, ..., an." The data blocks in data block set a are then divided based on the clustering results for each cloud disk service subclass, thereby obtaining data block subsets corresponding to each cloud disk service subclass. The data block subsets corresponding to each cloud disk service subclass are then used as data block classes. Accordingly, clustering data block set b in the same manner as described above can obtain the data block classes corresponding to storage service class B after clustering. Step 308: Access information corresponding to at least one storage component class is collected based on operational information. In one achievable implementation, the access frequency and access probability of each data block class are calculated based on the operational information, and the access probability and access frequency are used as access information. Step 310: Calculate the heat parameter corresponding to the storage component based on the access information corresponding to at least one storage component class. In one achievable method, calculate the heat value of each data block based on the access information corresponding to each data block class. Step 312: Obtain the identification parameter of the storage component, associate the identification parameter with the heat parameter, and generate analysis data corresponding to the storage component based on the association result. In one achievable method, determine the data block number identifier of each data block class, associate the data block number identifier of each data block class with its corresponding heat value, and generate a heat table based on the association result. Step 314: Migrate the stored data in the storage component based on the analysis data according to a preset data processing strategy to obtain a migrated storage component. In one achievable method, determine the target data block in the data block based on the analysis data according to a location processing strategy, and determine the to-be-received storage node corresponding to the target data block. The to-be-received storage node is a storage node that is closer to the user, and migrate the stored data corresponding to the target data block to the to-be-received storage node. In one achievable manner, a first data block and a second data block are determined in the data blocks based on the analysis data according to the location processing strategy, the first data block is a hot data block, and the second data block is a cold data block. The first data block is migrated to a first preset storage node, and the second data block is migrated to a second preset storage node. The first preset storage node is a high-cost storage area, and the second preset storage node is a low-cost storage area.The present disclosure provides a data processing method that obtains and analyzes operational information of cloud disk services. Based on this operational information, the storage components of the cloud disk services are clustered to identify multiple different data block classes. This facilitates subsequent access information statistics for different types of data blocks. After access information for each data block class is calculated based on the operational information, analytical data corresponding to the data block is generated based on the access information. Because analytical data is generated based on operational data combined with characteristics of the cloud disk service type, the accuracy of the analytical data is improved. This allows for data migration based on the analytical data according to a preset data processing strategy, enabling better data placement based on data popularity, improving storage service performance and reducing storage costs. Furthermore, by placing stored data based on data popularity, better storage services can be provided to users, ensuring data access performance and enhancing the user experience. Referring to Figure 4, a schematic diagram of the structure of a data processing system provided according to an embodiment of the present disclosure is shown. The system includes a storage service client 402 and an analysis server 404. Specifically, the analysis server 404 is configured to obtain operational information for the storage service client's storage service, determine at least one storage component class corresponding to the storage service, and determine the storage component with which the storage service has a corresponding relationship; collect access information corresponding to the at least one storage component class based on the operational information, and generate analysis data corresponding to the storage component based on the access information; and the storage service client 402 is configured to migrate the storage data in the storage component based on the analysis data according to a preset data processing strategy, thereby obtaining the migrated storage component. In actual applications, the analysis server is deployed with a collection module, a data module, and an analysis module, and the storage service client may be deployed with an execution module. The collection module is configured to collect operational data from the storage service client's operational system. This module may obtain data through an API interface, log files, or other means, and send the data to the data module. The data module stores operational data sent by the acquisition module for use by the analysis module. It also stores analysis results for execution by the execution module. The analysis module analyzes the operational data in the storage module. This module uses various machine learning algorithms, statistical models, and other analytical techniques to determine user types, load categories, hot and cold data blocks, and then sends the analysis results to the storage module. The execution module retrieves the analysis results from the storage module and performs data scheduling based on them.The present disclosure provides a data processing system comprising a storage service client and an analysis service client. The analysis service client is configured to obtain operational information of a storage service for the storage service client, determine at least one storage component class corresponding to the storage service, and generate analysis data corresponding to the storage component based on the access information. The analysis service client is configured to collect access information corresponding to the at least one storage component class based on the operational information, and generate analysis data corresponding to the storage component based on the access information. The storage service client is configured to migrate storage data in the storage component based on the analysis data according to a preset data processing strategy, thereby obtaining the migrated storage component. The system obtains and analyzes the operational information of the storage service, clusters the storage components of the storage service based on the operational information, and determines multiple different storage component classes. This facilitates subsequent access information statistics for different types of storage components. After access information for each storage component class is collected based on the operational information, analysis data corresponding to the storage component is generated based on the access information. Because the analysis data is generated based on the operational data combined with characteristics of the storage service type, the accuracy of the analysis data is improved. When migrating stored data based on the analyzed data according to a preset data processing strategy, data placement can be better performed based on data popularity, improving storage service performance and reducing storage costs. Furthermore, after the stored data is placed based on data popularity, better storage services can be provided to users, ensuring user data access performance and improving the user experience. Corresponding to the above-mentioned method embodiments, the present disclosure also provides a data processing device embodiment. FIG5 shows a schematic structural diagram of a data processing device provided by one embodiment of the present disclosure. As shown in FIG5 , the device includes: a determination module 502 configured to obtain operational information of a storage service and determine at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; a statistics module 504 configured to collect access information corresponding to the at least one storage component class based on the operational information and generate analysis data corresponding to the storage component based on the access information; and a migration module 506 configured to migrate the stored data in the storage component based on the analysis data according to the preset data processing strategy to obtain a migrated storage component. Optionally, the determining module 502 is further configured to collect initial operation information of the storage service; and perform data preprocessing on the initial operation information to obtain operation information.Optionally, the determination module 502 is further configured to classify the storage services based on the service information in the operation information to obtain at least one storage service class; determine a set of storage components corresponding to the at least one storage service class, and cluster each set of storage components based on the load information in the operation information to obtain at least one storage component class. Optionally, the determination module 502 is further configured to determine at least one service classification identifier based on the service information in the operation information; classify the storage services according to the at least one service classification identifier to obtain at least one storage service class. Optionally, the determination module 502 is further configured to determine a storage component corresponding to each storage service in the at least one storage service class; combine the storage components corresponding to each storage service class, and obtain a storage component set corresponding to the at least one storage service class based on the combination result. Optionally, the statistics module 504 is further configured to calculate access frequency information and access probability information corresponding to the at least one storage component class based on the operation information; and use the access frequency information and access probability information as access information corresponding to the at least one storage component class. Optionally, the statistics module 504 is further configured to calculate a popularity parameter corresponding to the storage component based on the access information corresponding to the at least one storage component class; obtain an identification parameter of the storage component, associate the identification parameter with the popularity parameter, and generate analysis data corresponding to the storage component based on the association result. Optionally, the migration module 506 is further configured to determine a target storage component in the storage component based on the analysis data in accordance with the location processing policy; determine a receiving storage node based on the target storage component; and migrate the target storage data in the target storage component to the receiving storage node to obtain the migrated target storage component. Optionally, the migration module 506 is further configured to determine a first storage component and a second storage component in the storage component based on the analysis data in accordance with the location processing strategy, wherein the access information corresponding to the first storage component and the second storage component is different; determine a first preset storage node and a second preset storage node, wherein the storage configurations corresponding to the first preset storage node and the second preset storage node are different; migrate the first storage data in the first storage component to the first preset storage node to obtain the migrated first storage component, and migrate the second storage data in the second storage component to the second preset storage node to obtain the migrated second storage component.Optionally, the device further includes a response module configured to generate a data migration instruction at a preset time interval; obtain operational information of the storage service in response to the data migration instruction. Optionally, the device further includes a storage module configured to determine, in response to the data storage instruction, data to be stored and a target storage service corresponding to the data to be stored, wherein the target storage service has the same service information as the storage service; select a target storage component from the storage components corresponding to the target storage service based on the analysis data; and store the data to be stored in the target storage component. The present disclosure provides a data processing device comprising: a determination module configured to obtain operational information of a storage service, determine at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; a statistics module configured to collect access information corresponding to the at least one storage component class based on the operation information, and generate analysis data corresponding to the storage component based on the access information; and a migration module configured to migrate the stored data in the storage component based on the analysis data in accordance with a preset data processing policy, thereby obtaining a migrated storage component. By acquiring and analyzing operational information of storage services, storage components of storage services are clustered based on this operational information to identify multiple different storage component classes. This facilitates subsequent access information statistics for different types of storage components. After access information for each storage component class is calculated based on the operational information, analytical data corresponding to the storage component can be generated based on the access information. Because analytical data is generated based on operational data combined with characteristics of the storage service type, the accuracy of the analytical data is improved. This allows for better data placement based on data popularity when migrating storage data according to a preset data processing strategy based on the analytical data, improving storage service performance and reducing storage costs. Furthermore, by placing storage data based on data popularity, better storage services can be provided to users, ensuring data access performance and improving the user experience. The above is a schematic diagram of a data processing device according to this embodiment. It should be noted that the technical solutions of this data processing device and the technical solutions of the aforementioned data processing method share the same concept. For details not described in detail in the technical solution of the data processing device, reference can be made to the description of the technical solution of the aforementioned data processing method. Figure 6 shows a block diagram of a computing device 600 according to one embodiment of the present disclosure.The components of the computing device 600 include, but are not limited to, a memory 610 and a processor 620. The processor 620 and the memory 610 are connected via a bus 630. A database 650 is used to store data. The computing device 600 also includes an access device 640 that enables the computing device 600 to communicate via one or more networks 660. Examples of such networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 640 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface. In one embodiment of the present disclosure, the aforementioned components of the computing device 600 and other components not shown in FIG. 6 may also be connected to each other, for example, via a bus. It should be understood that the computing device structure block diagram shown in FIG. 6 is for illustrative purposes only and does not limit the scope of the present disclosure. Those skilled in the art may add or replace other components as needed. Computing device 600 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, personal digital assistant, laptop computer, notebook computer, netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or personal computer (PC). Computing device 600 can also be a mobile or stationary server.The processor 620 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the aforementioned data processing method. The above is a schematic diagram of a computing device according to this embodiment. It should be noted that the technical solution of the computing device and the technical solution of the aforementioned data processing method are based on the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the aforementioned data processing method. An embodiment of the present disclosure also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by the processor, implement the steps of the aforementioned data processing method. The above is a schematic diagram of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the aforementioned data processing method are based on the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the aforementioned data processing method. An embodiment of the present disclosure also provides a computer program product, including a computer program or instructions, which, when executed by the processor, implements the steps of the aforementioned data processing method. The above is a schematic diagram of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the aforementioned data processing method share the same concept. For details not described in detail in the technical solution of the computer program product, reference should be made to the description of the technical solution of the aforementioned data processing method. The above description describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous. The computer instructions comprise computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased based on the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. It should be noted that for ease of description, the aforementioned method embodiments are described as a series of combined actions. However, those skilled in the art should understand that the embodiments of the present disclosure are not limited to the described order of actions, as certain steps may be performed in a different order or simultaneously according to the embodiments of the present disclosure. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for the embodiments of the present disclosure. In the above embodiments, the description of each embodiment has its own emphasis. For portions not described in detail in a particular embodiment, reference should be made to the relevant descriptions of other embodiments. The preferred embodiments of the present disclosure disclosed above are merely intended to help illustrate the present disclosure. The optional embodiments do not describe all details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and variations are possible based on the content of the embodiments of the present disclosure. These embodiments are selected and described in detail in this disclosure to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize the disclosure. This disclosure is limited only by the claims and their full scope and equivalents.

Claims

Claims 1. A data processing method, comprising: Obtain operational information of a storage business and determine at least one storage component class corresponding to the storage business, wherein the storage business has a corresponding storage component; collect access information corresponding to the at least one storage component class based on the operational information, and generate analysis data corresponding to the storage component based on the access information; perform data migration on the storage data in the storage component based on the analysis data in accordance with a preset data processing strategy to obtain a migrated storage component.

2. The method according to claim 1, wherein obtaining storage service operation information comprises: Initial operational information of Miji's storage business; Screening the initial operation information according to a preset data cleaning strategy to determine target operation information; The target operation information is removed from the initial operation information to obtain operation information.

3. The method according to claim 1 or 2, wherein determining at least one storage component class corresponding to the storage service comprises: Classifying the storage service according to the service information in the operation information to obtain at least one storage service class; A storage component set corresponding to the at least one storage service class is determined, and each storage component set is clustered according to load information in the operation information to obtain at least one storage component class.

4. The method according to claim 3, wherein classifying the storage service according to the service information in the operation information to obtain at least one storage service class comprises: Determine at least one business classification identifier based on the business information in the operation information; The storage service is classified according to the at least one service classification identifier to obtain at least one storage service class.

5. The method according to claim 3 or 4, wherein determining the storage component set corresponding to the at least one storage service class comprises: Determining a storage component corresponding to each storage service in the at least one storage service class; The storage components corresponding to each storage service class are combined, and a storage component set corresponding to the at least one storage service class is obtained according to the combination result.

6. The method according to any one of claims 1 to 5, wherein collecting statistics on access information corresponding to the at least one storage component class based on the operation information comprises: Calculate access frequency information and access probability information corresponding to the at least one storage component class based on the operation information; The access frequency information and the access probability information are used as access information corresponding to the at least one storage component class.

7. The method according to any one of claims 1 to 6, wherein generating analysis data corresponding to the storage component according to the access information comprises: Calculating a heat parameter corresponding to the storage component based on access information corresponding to the at least one storage component class; Acquire an identification parameter of the storage component, associate the identification parameter with the popularity parameter, and generate analysis data corresponding to the storage component according to the association result.

8. The method according to any one of claims 1 to 7, wherein the preset data processing strategy is a location processing strategy; Performing data migration on the storage data in the storage component based on the analysis data according to a preset data processing strategy to obtain a migrated storage component, including: determining a target storage component in the storage component based on the analysis data according to the location processing strategy; determining a to-be-received storage node based on the target storage component, and migrating the target storage data in the target storage component to the to-be-received storage node to obtain the migrated target storage component.

9. The method according to any one of claims 1 to 7, wherein the preset data processing strategy is a configuration processing strategy; Performing data migration on the storage data in the storage component based on the analysis data according to a preset data processing strategy to obtain a migrated storage component, including: determining a first storage component and a second storage component in the storage component based on the analysis data according to the location processing strategy, wherein the first storage component and the second storage component respectively correspond to different access information; determining a first preset storage node and a second preset storage node, wherein the first preset storage node and the second preset storage node respectively correspond to different storage configurations; migrating the first storage data in the first storage component to the first preset storage node to obtain the migrated first storage component, and migrating the second storage data in the second storage component to the second preset storage node to obtain the migrated second storage component.

10. The method according to any one of claims 1 to 9, further comprising: Generate data migration instructions according to preset time intervals; Operation information of the storage service is obtained in response to the data migration instruction.

11. The method according to any one of claims 1 to 0, further comprising: In response to a data storage instruction, determine data to be stored and a target storage service corresponding to the data to be stored, wherein the target storage service has the same service information as the storage service; select a target storage component from the storage components corresponding to the target storage service according to the analysis data; and store the data to be stored in the target storage component.

12. A data processing system, comprising a storage service end and an analysis service end, wherein: The analysis server is used to obtain operational information of the storage service for the storage service, determine at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; based on the operational information, count the access information corresponding to the at least one storage component class, and generate analysis data corresponding to the storage component according to the access information; the storage service is used to migrate the storage data in the storage component based on the analysis data in accordance with a preset data processing strategy to obtain the migrated storage component.

13. A data processing device, comprising: a determination module configured to obtain operation information of a storage service and determine at least one storage component class corresponding to the storage service, wherein the storage service has a corresponding storage component; and a statistics module configured to count access statistics corresponding to the at least one storage component class based on the operation information. information, and generates analysis data corresponding to the storage component according to the access information; a migration module configured to perform data migration on the storage data in the storage component based on the analysis data in accordance with a preset data processing strategy to obtain a migrated storage component.

14. A computing device, comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 11.

16. A computer program product comprising a computer program or instructions, which implement the steps of the method according to any one of claims 1 to 11 when executed by a processor.

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