A mass data management method, device, equipment and medium

Abstract

The application discloses a mass data management method, device and equipment and medium. The mass data management method comprises the following steps: acquiring object data to be managed, and determining object metadata of the object data to be managed; dividing the object metadata based on attention degree data of the object metadata, to obtain fragmented object data; creating a plurality of target storage nodes according to the fragmented object data, and constructing a target storage ring according to the plurality of target storage nodes. The technical scheme of the embodiment of the application can avoid a large amount of data migration, improve the reading speed of data, and avoid data loss caused by data migration.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, and medium for managing massive amounts of data. Background Technology

[0002] With the rapid development of the IT field and the ever-growing user base, the resulting data is increasing exponentially, leading to a rise in massive data processing scenarios. Data analysis presupposes having data, and data storage aims to support data analysis. How to store massive amounts of data is a key challenge facing companies conducting data analysis. Traditional data storage models have limitations in storage capacity or space. Designing a storage solution that can support large amounts of data is a primary prerequisite for conducting data analysis. Current solutions involve using distributed object management systems (DOS) for data storage. DOS promotes the use of object-oriented environments and interface operating systems or services on distributed computing platforms.

[0003] Currently, the main approach is to synchronize local data objects to the server, generate index files corresponding to the data objects, and then manage the data objects uniformly based on the index files, thereby solving the technical problem of chaotic management in existing distributed storage technologies.

[0004] However, after practical application by those skilled in the art, the above-mentioned methods for managing massive amounts of objects still have some drawbacks. The most obvious one is that when browsing hot object data, a large amount of data migration will occur, which can easily reduce the data reading speed and cause data loss due to the large amount of data migration. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for managing massive amounts of data, in order to solve the problems of data migration during browsing of hot object data in current massive data management, as well as the slow data reading speed and data loss caused by data migration.

[0006] According to one aspect of the present invention, a method for managing massive amounts of data is provided, comprising:

[0007] Obtain the data of the object to be managed, and determine the object metadata of the object to be managed;

[0008] Based on the attention level data of object metadata, the object metadata is divided to obtain fragmented object data;

[0009] Based on the sharded object data, create multiple target storage nodes, and construct a target storage chain based on the multiple target storage nodes.

[0010] According to another aspect of the present invention, a data management apparatus is provided, comprising:

[0011] The object metadata determination module is used to obtain the data of the object to be managed and determine the object metadata of the object to be managed.

[0012] The sharded object data determination module is used to divide the object metadata based on the attention level data of the object metadata to obtain sharded object data;

[0013] The target storage chain creation module is used to create multiple target storage nodes based on the sharded object data, and to construct a target storage chain based on the multiple target storage nodes.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the massive data management method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the massive data management method according to any embodiment of the present invention.

[0019] The technical solution of this invention acquires the data of the object to be managed and determines the object metadata of the object data. Based on the attention level data of the object metadata, the object metadata is divided into sharded object data. Then, multiple target storage nodes are created based on the sharded object data, and a target storage chain is constructed based on these multiple target storage nodes. In this solution, dividing the object metadata based on the attention level data can guide the access to hot data, avoid large-scale data migration, improve data reading speed, and prevent data loss caused by large-scale data migration. Furthermore, creating multiple target storage nodes based on the sharded object data and constructing a target storage chain based on these multiple target storage nodes allows for targeted reading of target storage nodes within the target storage chain, improving data reading speed. This solves the problems of data migration during browsing of hot object data in current massive data management, as well as the slow data reading speed and data loss caused by data migration. It avoids large-scale data migration, improves data reading speed, and prevents data loss due to data migration.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a massive data management method provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a massive data management method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a massive data management device provided in Embodiment 3 of the present invention;

[0025] Figure 4 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart illustrating a massive data management method according to Embodiment 1 of the present invention. This embodiment is applicable to reducing slow data reading speeds and data loss caused by browsing data of hot objects. The method can be executed by a massive data management device, which can be implemented in hardware and / or software and can be configured in an electronic device. The electronic device may include, but is not limited to, servers or computers. Figure 1 As shown, the method includes:

[0030] Step 110: Obtain the data of the object to be managed and determine the object metadata of the object to be managed.

[0031] The object data to be managed can be any object data that requires data management. The object data to be managed includes at least one object data. Object data can be used to represent review information in the programming domain and typically includes massive amounts of data. Object metadata can be the data name of the object data.

[0032] In this embodiment of the invention, it can be first determined that there is object data to be managed that needs data management, and then the object data to be managed can be parsed to obtain object metadata of the object data to be managed, which can be used for subsequent data search.

[0033] Optionally, object data can be understood as a database. Suppose the database includes product names, power, etc., and feature extraction is performed on the data in the database to use the product name or synonyms of the product name as object metadata.

[0034] For example, object data can be understood as a database describing the attributes of a refrigerator, and object metadata can be the refrigerator or a large appliance. This embodiment of the invention does not limit the type of database that matches the object data.

[0035] Step 120: Based on the attention level data of object metadata, divide the object metadata to obtain fragmented object data.

[0036] Among these, popularity data can be used to describe the popularity of object metadata. Sharded object data can be the result of partitioning object metadata based on popularity data.

[0037] In this embodiment of the invention, after obtaining the object metadata, the degree of attention to the object metadata can be further determined based on the user's operation behavior of the object metadata (such as searching, using, etc.). Then, based on the degree of attention data that matches the object metadata, the object metadata can be divided to obtain the corresponding fragmented object data of the object metadata.

[0038] Step 130: Based on the sharded object data, create multiple target storage nodes and construct a target storage chain based on the multiple target storage nodes.

[0039] The target storage node can be a node that stores the data after the object fragmentation data has been partitioned. The target storage chain can be a data storage chain generated based on the target storage node.

[0040] In this embodiment of the invention, fragmented object data can be divided and stored in multiple target storage nodes controlled by keys. Then, the multiple target storage nodes can be connected to form a target storage chain, which can not only ensure the integrity and security of the stored data, but also enable targeted searches of specific target storage nodes in the target storage chain, thereby improving the data reading speed.

[0041] The technical solution of this invention acquires the data of the object to be managed and determines the object metadata of the object data. Based on the attention level data of the object metadata, the object metadata is divided into sharded object data. Then, multiple target storage nodes are created based on the sharded object data, and a target storage chain is constructed based on these multiple target storage nodes. In this solution, dividing the object metadata based on the attention level data can guide the access to hot data, avoid large-scale data migration, improve data reading speed, and prevent data loss caused by large-scale data migration. Furthermore, creating multiple target storage nodes based on the sharded object data and constructing a target storage chain based on these multiple target storage nodes allows for targeted reading of target storage nodes within the target storage chain, improving data reading speed. This solves the problems of data migration during browsing of hot object data in current massive data management, as well as the slow data reading speed and data loss caused by data migration. It avoids large-scale data migration, improves data reading speed, and prevents data loss due to data migration.

[0042] Example 2

[0043] Figure 2 This is a flowchart of a massive data management method provided in Embodiment 2 of the present invention. This embodiment is based on the above embodiment and provides specific optional implementation methods for determining the object metadata of the object data to be managed. Figure 2 As shown, the method includes:

[0044] Step 210: Obtain the data of the object to be managed, and obtain the target product parameters of the object to be managed.

[0045] Among them, the target product parameters can be parameters that describe the product characteristics and performance extracted from the data of the objects to be managed.

[0046] In this embodiment of the invention, the data of the object to be managed can be parsed to determine the target product parameters corresponding to the data of the object to be managed.

[0047] Step 220: Determine the object metadata of the object to be managed based on the target product parameters.

[0048] In this embodiment of the invention, the data name of the object data in the object data to be managed can be determined based on the target product parameters, and then the object metadata of the corresponding object data can be determined based on the data name of the object data in the object data to be managed.

[0049] In an optional embodiment of the present invention, before dividing the object metadata based on the attention level data of the object metadata, the method may further include: obtaining the data operation frequency of the object metadata; and determining the attention level data of the object metadata based on the data operation frequency of the object metadata.

[0050] Among them, data usage frequency can be used to describe the frequency of data operation behavior.

[0051] In this embodiment of the invention, after obtaining the data of the object to be managed, the operation log of the data of the object to be managed can be further obtained, thereby determining the data operation frequency of the object metadata based on the operation log, and then using the data operation frequency of the object metadata as the attention level data of the object metadata.

[0052] Step 230: Based on the attention level data of object metadata, divide the object metadata to obtain fragmented object data.

[0053] Step 240: Based on the sharded object data, create multiple target storage nodes and construct a target storage chain based on the multiple target storage nodes.

[0054] In an optional embodiment of the present invention, creating multiple target storage nodes based on sharded object data may include: creating a temporary database based on the sharded object data and determining the data key of the temporary database; generating multiple target storage nodes based on the temporary database and the data key.

[0055] The temporary storage database can be a database for temporarily storing data. The data key can be a key for encrypting or decrypting data.

[0056] In this embodiment of the invention, the fragmented object data can be stored to generate a temporary database, and then the data key of the temporary database can be determined. The data in the temporary database can then be further divided and stored to multiple target storage nodes controlled by the data key management.

[0057] In an optional embodiment of the present invention, constructing a target storage chain based on multiple target storage nodes may include: obtaining the link relationship of multiple target storage nodes; and connecting the multiple target storage nodes based on the link relationship to obtain the target storage chain.

[0058] The link relationships between target storage nodes can be determined by the logical relationships between stored data.

[0059] In this embodiment of the invention, the logical relationship between target storage nodes can be determined by the data stored in the target storage nodes, and then multiple target storage nodes can be connected according to the link relationship between multiple target storage nodes to obtain a target storage chain.

[0060] In an optional embodiment of the present invention, the data key may include: an encryption key and boot data.

[0061] The encryption key can be generated using random data. The boot data can be data associated with object metadata configured in the boot sector.

[0062] In this embodiment of the invention, an encryption key can be generated based on a random number generation method, and boot data can be obtained, thereby using the encryption key and boot data as a data key.

[0063] In an optional embodiment of the present invention, the number of target storage nodes included in the target storage chain may be no less than 5 and no more than 10, that is, 5 to 10 target storage nodes may be used to constitute a target storage node.

[0064] The technical solution of this invention acquires the data of the object to be managed and the target product parameters of the data. Based on the target product parameters, it determines the object metadata of the data. Then, based on the attention level data of the object metadata, it divides the object metadata into sharded object data. Further, based on the sharded object data, it creates multiple target storage nodes and constructs a target storage chain based on these nodes. In this solution, dividing the object metadata based on the attention level data can guide the access to hot data, avoiding large-scale data migration, improving data reading speed, and preventing data loss caused by large-scale data migration. Creating multiple target storage nodes based on the sharded object data and constructing a target storage chain based on these nodes allows for targeted reading of target storage nodes within the target storage chain, improving data reading speed. This solves the problems of data migration during browsing of hot object data in current massive data management, as well as the slow data reading speed and data loss caused by data migration. It avoids large-scale data migration, improves data reading speed, and prevents data loss due to data migration.

[0065] Example 3

[0066] Figure 3 This is a schematic diagram of a massive data management device provided in Embodiment 3 of the present invention.

[0067] like Figure 3 As shown, the device includes:

[0068] The object metadata determination module 310 is used to obtain the data of the object to be managed and determine the object metadata of the data of the object to be managed.

[0069] The sharded object data determination module 320 is used to divide the object metadata based on the attention level data of the object metadata to obtain sharded object data;

[0070] The target storage chain creation module 330 is used to create multiple target storage nodes based on the sharded object data, and to construct a target storage chain based on the multiple target storage nodes.

[0071] The technical solution of this invention acquires the data of the object to be managed and determines the object metadata of the object data. Based on the attention level data of the object metadata, the object metadata is divided into sharded object data. Then, multiple target storage nodes are created based on the sharded object data, and a target storage chain is constructed based on these multiple target storage nodes. In this solution, dividing the object metadata based on the attention level data can guide the access to hot data, avoid large-scale data migration, improve data reading speed, and prevent data loss caused by large-scale data migration. Furthermore, creating multiple target storage nodes based on the sharded object data and constructing a target storage chain based on these multiple target storage nodes allows for targeted reading of target storage nodes within the target storage chain, improving data reading speed. This solves the problems of data migration during browsing of hot object data in current massive data management, as well as the slow data reading speed and data loss caused by data migration. It avoids large-scale data migration, improves data reading speed, and prevents data loss due to data migration.

[0072] Optionally, the object metadata determination module 310 is specifically used to obtain the target product parameters of the object data to be managed; and determine the object metadata of the object data to be managed based on the target product parameters.

[0073] Optionally, the massive data management device further includes a attention level data determination module, used to obtain the data operation frequency of the object metadata; and determine the attention level data of the object metadata based on the data operation frequency of the object metadata.

[0074] Optionally, the target storage chain creation module 330 includes a target storage node creation unit and a target storage chain creation unit. The target storage node creation unit is used to create a temporary database based on the sharded object data and determine the data key of the temporary database; and to generate multiple target storage nodes based on the temporary database and the data key.

[0075] Optionally, the target storage chain creation unit is used to obtain the link relationship of multiple target storage nodes; and based on the link relationship of multiple target storage nodes, connect multiple target storage nodes to obtain the target storage chain.

[0076] Optionally, the number of target storage nodes included in the target storage chain is not less than 5 and not more than 10.

[0077] The massive data management device provided in the embodiments of the present invention can execute the massive data management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0078] Example 4

[0079] Figure 4 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0080] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0081] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0082] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as massive data management methods.

[0083] This massive data management method includes: acquiring the data of the objects to be managed and determining the object metadata of the data; then, based on the popularity data of the object metadata, dividing the object metadata into sharded object data; subsequently, creating multiple target storage nodes based on the sharded object data; and constructing a target storage chain based on these multiple target storage nodes. In this solution, dividing the object metadata based on the popularity data can guide the access to hot data, avoiding large-scale data migration, improving data reading speed, and preventing data loss caused by large-scale data migration. Furthermore, creating multiple target storage nodes based on the sharded object data and constructing a target storage chain based on these nodes allows for targeted reading of target storage nodes within the target storage chain, improving data reading speed. This solves the problems of data migration during browsing of hot object data in current massive data management, as well as the slow data reading speed and data loss caused by data migration. It avoids large-scale data migration, improves data reading speed, and prevents data loss due to data migration.

[0084] In some embodiments, the massive data management method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the massive data management method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the massive data management method by any other suitable means (e.g., by means of firmware).

[0085] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0086] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0087] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0088] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0089] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0090] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0091] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0092] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for managing massive amounts of data, characterized in that, include: Obtain the data of the object to be managed, and determine the object metadata of the data of the object to be managed; The object data to be managed includes at least one object data, and the object metadata is the data name of the object data; Based on the attention level data of the object metadata, the object metadata is divided to obtain fragmented object data; Based on the sharded object data, multiple target storage nodes are created, and a target storage chain is constructed based on the multiple target storage nodes; Prior to segmenting the object metadata based on the attention level data of the object metadata, the process also includes: The frequency of data operations used to obtain the object's metadata; Based on the frequency of data operations on the object's metadata, determine the level of attention given to the object's metadata; The step of constructing a target storage chain based on multiple target storage nodes includes: Obtain the link relationships between multiple target storage nodes; Based on the link relationship of multiple target storage nodes, the multiple target storage nodes are connected to obtain the target storage chain.

2. The method according to claim 1, characterized in that, The object metadata for determining the data of the object to be managed includes: Obtain the target product parameters of the object to be managed; Based on the target product parameters, determine the object metadata of the object data to be managed.

3. The method according to claim 1, characterized in that, The step of creating multiple target storage nodes based on the sharded object data includes: Based on the fragmented object data, a temporary database is created, and the data key of the temporary database is determined; Based on the temporary database and the data key, multiple target storage nodes are generated.

4. The method according to claim 3, characterized in that, The data key includes: an encryption key and boot data.

5. The method according to claim 1, characterized in that, The number of target storage nodes included in the target storage chain is no less than 5 and no more than 10.

6. A massive data management device, characterized in that, include: The object metadata determination module is used to acquire the data of the object to be managed and determine the object metadata of the data of the object to be managed. The attention level data determination module is used to obtain the data operation frequency of the object's metadata. Based on the frequency of data operations on the object's metadata, determine the level of attention given to the object's metadata; The sharded object data determination module is used to divide the object metadata based on the attention level data of the object metadata to obtain sharded object data; The target storage chain creation module is used to create multiple target storage nodes based on the shard object data, and to construct a target storage chain based on the multiple target storage nodes; The target storage chain creation module includes a target storage chain creation unit; The target storage chain creation unit is used to obtain the link relationship of multiple target storage nodes; based on the link relationship of multiple target storage nodes, the multiple target storage nodes are connected to obtain the target storage chain.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the massive data management method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the massive data management method according to any one of claims 1-5.

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