A data storage method and device based on service type

Abstract

The application discloses a data storage method and device based on service type. According to the data access frequency of a storage area, the capacity of a plurality of storage sub-areas is updated, and the data attributes of each storage sub-area are obtained; according to the access rule among each storage sub-area, all the storage sub-areas are classified into a plurality of storage sets, and the area attributes of each storage set are obtained; the data to be classified is read from a data stream, and the data to be classified is stored into the corresponding storage sub-area according to the data attributes of each storage sub-area and the area attributes of each storage set. Through the application, the data storage efficiency and safety in the data storage process can be improved.

Description

Technical Field

[0001] This invention relates to the field of data management, and in particular to a data storage method and apparatus based on business type. Background Technology

[0002] With the continuous development of various internet applications, the amount of internet data is growing rapidly, and the data structures and types that need to be processed are becoming increasingly diverse. Whether it is between departments within a company or between a company and external entities, a large amount of production data is generated during the process of exchanging and recording information through information technology. Some of this production data is extremely important and needs to be used frequently and stored for a long time, while some data is of a general type, used infrequently, and of low importance. This type of data only needs to be stored, and even if it is lost, it will not have a significant impact.

[0003] In the information age, the volume of data is enormous. If this production data is not categorized and stored, it will lead to chaotic data processing, heavy workload on storage systems, slow operation, and improper storage of important data, potentially resulting in its loss. Current technology often involves manually categorizing and storing this production data. However, when faced with large amounts of data storage, this method leads to low storage efficiency and is prone to classification errors, further resulting in incorrect selection of data storage areas and compromising data security. Summary of the Invention

[0004] This invention provides a data storage method and apparatus based on business type to solve the technical problems of low data storage efficiency and security when facing a large number of data storage services.

[0005] To address the aforementioned technical problems, in a first aspect, embodiments of the present invention provide a data storage method based on business type, comprising:

[0006] Based on the data access frequency of the storage area, update the capacity of several storage sub-regions and obtain the data attributes of each storage sub-region;

[0007] Based on the access patterns among the various storage sub-regions, all the storage sub-regions are classified into several storage sets, and the region attributes of each storage set are obtained;

[0008] Read the data to be classified from the data stream, and store the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set.

[0009] Compared with existing technologies, the above embodiments have the following beneficial effects: by updating the capacity of the storage sub-regions based on data access frequency, sufficient storage space is ensured when high-frequency access data is stored, thus improving the security of stored data; and multiple storage sub-regions are managed to achieve data classification storage, thereby improving data storage efficiency; furthermore, by using storage sets and storage sub-regions to perform two-level classification of the types of stored data, the classification efficiency when classifying data to be classified can be improved, further enhancing the accuracy and storage efficiency of storing data to be classified into the corresponding storage sub-regions.

[0010] In one embodiment of the first aspect, updating the capacity of several storage sub-regions according to the data access frequency of the storage regions and obtaining the data attributes of each of the storage sub-regions includes:

[0011] Get the data access frequency of different users to the data stored in each of the storage sub-areas within a preset time period;

[0012] Based on the data access frequency of the stored data, sort all the storage sub-regions in descending order;

[0013] Based on the descending sorting results, the capacity of the corresponding storage sub-regions is adjusted sequentially.

[0014] Compared with the prior art, the above embodiments have the following beneficial effects: Since the access frequency of data in different storage sub-regions is different, important data usually has a higher access frequency. In order to ensure that the corresponding storage sub-region has sufficient capacity when storing important data in the future, the access frequency of data in different storage sub-regions is statistically analyzed, and the capacity of the corresponding storage sub-region is adjusted according to the access frequency, thereby improving the security of data during data storage.

[0015] In one embodiment of the first aspect, classifying all the storage sub-regions into several storage sets according to the access patterns of each storage sub-region, and obtaining the region attributes of each storage set, includes:

[0016] Obtain the access patterns of different users to each of the storage sub-regions within a preset time period, and obtain the correlation between each of the storage sub-regions based on the access patterns;

[0017] Based on the correlation between the storage sub-regions, all the storage sub-regions are divided to obtain a storage set composed of several storage sub-regions.

[0018] Compared to existing technologies, the above embodiments have the following advantages: Due to the large amount of stored data, the number of storage sub-regions also increases accordingly. If the data is matched with the data attributes of each storage sub-region every time it is stored to determine which storage sub-region the data should be stored in, the data storage efficiency will be severely reduced. If the division of storage sub-regions is eliminated, the importance of the data cannot be clearly assessed, and the data storage will be relatively messy, which is not conducive to subsequent data retrieval. Therefore, by dividing the storage sub-regions and statistically analyzing the access patterns of the same user to the data in each storage sub-region, the correlation characteristics between the storage sub-regions can be obtained. Based on these correlation characteristics, a portion of the storage sub-spaces used to store the data to be classified can be quickly identified, thereby improving storage efficiency.

[0019] In one embodiment of the first aspect, obtaining the correlation degree between each of the storage sub-regions based on the access pattern includes:

[0020] Determine whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area;

[0021] If so, associate the first storage sub-region with the second storage sub-region;

[0022] If not, then check the next user's access or the next access of the current user, until the preset number of iterations is reached.

[0023] Compared with the prior art, the above embodiments have the following beneficial effects: based on the order in which the same user accesses the storage sub-regions, the correlation characteristics between the storage sub-regions can be quickly and accurately filtered out, thereby improving the accuracy of subsequent storage sub-region classification.

[0024] In one embodiment of the first aspect, storing the data to be classified into the corresponding storage sub-region based on the data attributes of each of the storage sub-regions and the region attributes of each of the storage sets includes:

[0025] After sequentially matching the data to be classified with the regional attributes of each of the storage sets, the data to be classified is assigned to the corresponding storage set;

[0026] The data to be classified and assigned to the corresponding storage set is matched with the data attributes of each storage sub-region in the storage set in turn, and then the data to be classified and assigned to the corresponding storage sub-region is stored.

[0027] Compared with the prior art, the above embodiments have the following beneficial effects: by matching the data to be classified with the regional attributes of the storage set and the data attributes of the storage sub-regions in the first layer of the two-layer classification rule, the category of the data to be classified can be quickly obtained, thereby quickly storing the data to be classified into the corresponding storage sub-space, which not only improves the accuracy and efficiency of data classification, but also improves the data storage efficiency.

[0028] Secondly, embodiments of the present invention also provide a data storage device based on business type, which includes: a storage sub-region capacity update module, a storage sub-region classification module, and a data storage module;

[0029] The storage sub-region capacity update module is used to update the capacity of several storage sub-regions according to the data access frequency of the storage region, and to obtain the data attributes of each storage sub-region.

[0030] The storage sub-region classification module is used to classify all the storage sub-regions into several storage sets according to the access rules between the storage sub-regions, and to obtain the region attributes of each storage set.

[0031] The data storage module is used to read the data to be classified from the data stream, and store the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set.

[0032] In one embodiment of the second aspect, the storage sub-region capacity update module is used to update the capacity of several storage sub-regions according to the data access frequency of the storage regions, and to obtain the data attributes of each storage sub-region, including:

[0033] Get the data access frequency of different users to the data stored in each of the storage sub-areas within a preset time period;

[0034] Based on the data access frequency of the stored data, sort all the storage sub-regions in descending order;

[0035] Based on the descending sorting results, the capacity of the corresponding storage sub-regions is adjusted sequentially.

[0036] In one embodiment of the second aspect, the storage sub-region classification module is used to classify all the storage sub-regions into several storage sets according to the access patterns among the storage sub-regions, and to obtain the region attributes of each storage set, including:

[0037] Obtain the access patterns of different users to each of the storage sub-regions within a preset time period, and obtain the correlation between each of the storage sub-regions based on the access patterns;

[0038] Based on the correlation between the storage sub-regions, all the storage sub-regions are divided to obtain a storage set composed of several storage sub-regions.

[0039] In one embodiment of the second aspect, obtaining the correlation degree between each of the storage sub-regions based on the access pattern includes:

[0040] Determine whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area;

[0041] If so, associate the first storage sub-region with the second storage sub-region;

[0042] If not, then check the next user's access or the next access of the current user, until the preset number of iterations is reached.

[0043] In one embodiment of the second aspect, the data storage module is configured to store the data to be classified into the corresponding storage sub-regions according to the data attributes of each of the storage sub-regions and the region attributes of each of the storage sets, including:

[0044] After sequentially matching the data to be classified with the regional attributes of each of the storage sets, the data to be classified is assigned to the corresponding storage set;

[0045] The data to be classified and assigned to the corresponding storage set is matched with the data attributes of each storage sub-region in the storage set in turn, and then the data to be classified and assigned to the corresponding storage sub-region is stored. Attached Figure Description

[0046] Figure 1 A flowchart illustrating a data storage method based on business type provided in an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of a storage space structure for storing data in a data storage method based on business type provided in an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of a data storage device based on business type, provided as an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0050] Example 1

[0051] Please refer to Figure 1 The present invention provides a data storage method based on business type, comprising steps S101 to S103, specifically:

[0052] S101: Update the capacity of several storage sub-regions according to the data access frequency of the storage region, and obtain the data attributes of each storage sub-region.

[0053] Furthermore, the step of updating the capacity of several storage sub-regions based on the data access frequency of the storage regions and obtaining the data attributes of each storage sub-region includes: obtaining the data access frequency of different users to the data already stored in each storage sub-region within a preset time period; sorting all the storage sub-regions in descending order based on the data access frequency of the stored data; and adjusting the capacity of the corresponding storage sub-regions in sequence according to the descending order sorting result.

[0054] For example, refer to Figure 2 The storage area includes, but is not limited to, sub-areas for storing plant data, line data, meter readings, voltage data, power curve data, harmonic curve data, daily frozen meter readings, daily maximum demand data, power consumption data, line loss data, terminal alarm data, master station alarm data, data acquisition task message data, alarm event message data, system parameter data, rule threshold data, etc.

[0055] Preferably, the above embodiments can assign weights to the storage capacity of each storage sub-region based on the frequency of each storage sub-region, which is used to characterize the amount and frequency of data access. It is understood that the sum of the capacities of each storage sub-region is equal to the total capacity of the storage region.

[0056] As can be seen from the above embodiments, due to the large amount of stored data, the access frequency of data in different storage sub-regions is not the same. For important data, there is usually a higher access frequency. In order to ensure that the corresponding storage sub-region has sufficient capacity when storing important data in the future, the access frequency of data in different storage sub-regions is statistically analyzed, and the capacity of the corresponding storage sub-region is adjusted according to the access frequency, thereby improving the security of data during data storage.

[0057] S102: Based on the access rules between the storage sub-regions, classify all the storage sub-regions into several storage sets, and obtain the region attributes of each storage set.

[0058] Furthermore, the step of classifying all the storage sub-regions into several storage sets according to the access patterns of each storage sub-region, and obtaining the regional attributes of each storage set, includes: obtaining the access patterns of different users to each storage sub-region within a preset time period; obtaining the correlation between each storage sub-region according to the access patterns; dividing all the storage sub-regions according to the correlation between each storage sub-region, and obtaining several storage sets composed of several storage sub-regions.

[0059] Due to the massive amount of data to be stored, the number of storage sub-regions also increases accordingly. If each time data is stored, the data attributes are matched with those of each storage sub-region to determine which sub-region to store the data in, storage efficiency will be severely reduced. Conversely, if the sub-region division is eliminated, the importance of the data cannot be clearly assessed, and the data storage will be disorganized, hindering subsequent data retrieval. Therefore, by dividing the storage into sub-regions and analyzing the access patterns of the same user across these sub-regions, the correlation characteristics between them can be obtained. Based on these correlation characteristics, specific storage sub-spaces can be quickly identified for storing data to be classified, thus improving storage efficiency.

[0060] Furthermore, obtaining the correlation between each of the storage sub-regions based on the access pattern includes: determining whether the same user accesses the second storage sub-region within a preset time after accessing the first storage sub-region; if yes, associating the first storage sub-region and the second storage sub-region; if no, judging the access of the next user or the next access of the current user, until a preset number of iterations is reached.

[0061] Preferably, a preset time, such as 1 minute, can be set. That is, if the user visits the next storage sub-region within 1 minute after accessing the current storage sub-region, the two storage sub-regions are associated as a storage set. If the user accesses two storage sub-regions within 1 minute and then accesses a third storage sub-region within the following 1 minute, the three storage sub-regions are associated, and the process continues to determine if there are other storage sub-regions that can be associated, until a preset number of iterations is reached. All associated storage sub-regions in this iteration are then generated into a storage set. If the user does not make any further accesses within 1 minute, the process waits for the next access to begin before restarting the iteration process.

[0062] Based on the order in which the same user accesses storage sub-regions within a certain period of time, the correlation characteristics between storage sub-regions can be quickly and accurately identified. After several iterations of discrimination, the regional attributes of the storage set tend to stabilize, which can significantly improve the accuracy of subsequent storage sub-region classification.

[0063] Preferably, association can also be achieved through the data attributes of storage sub-regions, see reference. Figure 2 Plant data, line data, transformer data, and metering equipment data are all associated with an archive-type data storage set. Meter data, voltage data, current data, and power data are all associated with a minute-level data storage set, and so on. Therefore, in this application, the number of storage sets is not limited; they can be associated based on the initial data attributes or based on user access frequency and the degree of association.

[0064] S103: Read the data to be classified from the data stream, and store the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set.

[0065] Furthermore, the step of storing the data to be classified into the corresponding storage sub-region based on the data attributes of each storage sub-region and the region attributes of each storage set includes: sequentially matching the data to be classified with the region attributes of each storage set, and then dividing the data to be classified into the corresponding storage set; sequentially matching the data to be classified into the corresponding storage set with the data attributes of each storage sub-region in the storage set, and then storing the data to be classified into the corresponding storage sub-region.

[0066] By matching the data to be classified sequentially with the regional attributes of the storage set and the data attributes of the storage sub-regions in the first layer of the two-level classification rule, the category of the data to be classified can be quickly obtained, thereby quickly storing the data to be classified into the corresponding storage sub-space. This not only improves the accuracy and efficiency of data classification, but also improves the data storage efficiency.

[0067] In summary, the data method based on business type provided by this invention has the following advantages over existing technologies: It updates the capacity of storage sub-regions based on data access frequency, ensuring sufficient storage space for high-frequency data access and improving data security; it manages multiple storage sub-regions to achieve data classification and storage, improving data storage efficiency; furthermore, it performs two-level classification of stored data types through storage sets and storage sub-regions, improving the classification efficiency of subsequent classification of data to be classified, and further enhancing the accuracy and efficiency of storing massive amounts of data to be classified into corresponding storage sub-regions.

[0068] Example 2

[0069] refer to Figure 3This invention also provides a data storage device based on business type, including: a storage sub-region capacity update module 11, a storage sub-region classification module 12, and a data storage module 13; wherein, the storage sub-region capacity update module 11 is used to update the capacity of several storage sub-regions according to the data access frequency of the storage regions, and obtain the data attributes of each storage sub-region; the storage sub-region classification module 12 is used to classify all the storage sub-regions into several storage sets according to the access rules between the storage sub-regions, and obtain the region attributes of each storage set; the data storage module 13 is used to read the data to be classified from the data stream, and store the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set.

[0070] Furthermore, the storage sub-region capacity update module 11 is used to update the capacity of several storage sub-regions according to the data access frequency of the storage regions, and to obtain the data attributes of each storage sub-region, including: obtaining the data access frequency of different users to the data already stored in each storage sub-region within a preset time period; sorting all the storage sub-regions in descending order according to the data access frequency of the stored data; and adjusting the capacity of the corresponding storage sub-regions in sequence according to the descending order sorting result.

[0071] Furthermore, the storage sub-region classification module 12 is used to classify all the storage sub-regions into several storage sets according to the access patterns between the storage sub-regions, and to obtain the regional attributes of each storage set, including: obtaining the access patterns of different users to the storage sub-regions within a preset time period, obtaining the correlation between the storage sub-regions according to the access patterns; dividing all the storage sub-regions according to the correlation between the storage sub-regions, and obtaining several storage sets composed of several storage sub-regions.

[0072] Furthermore, obtaining the correlation between each of the storage sub-regions based on the access pattern includes: determining whether the same user accesses the second storage sub-region within a preset time after accessing the first storage sub-region; if yes, associating the first storage sub-region and the second storage sub-region; if no, judging the access of the next user or the next access of the current user, until a preset number of iterations is reached.

[0073] Furthermore, the data storage module 13 is used to store the data to be classified into the corresponding storage sub-regions according to the data attributes of each storage sub-region and the region attributes of each storage set, including: sequentially matching the data to be classified with the region attributes of each storage set, and then dividing the data to be classified into the corresponding storage set; sequentially matching the data to be classified into the corresponding storage set with the data attributes of each storage sub-region in the storage set, and then storing the data to be classified into the corresponding storage sub-region.

[0074] In summary, the data storage device based on business type provided by this invention has the following advantages compared to existing technologies: Due to the large volume of stored data, the number of storage sub-regions also increases accordingly. If data is matched with the data attributes of each storage sub-region each time it is stored to determine which sub-region the data should be stored in, storage efficiency will be severely reduced. If the division of storage sub-regions is eliminated, the importance of data cannot be clearly assessed, and data storage will be disorganized, hindering subsequent data retrieval. Therefore, by dividing storage sub-regions and statistically analyzing the access patterns of the same user to data in each sub-region, the correlation characteristics between storage sub-regions can be obtained. Based on these correlation characteristics, a portion of the storage sub-space used to store data to be classified can be quickly identified, improving storage efficiency.

[0075] Example 3

[0076] Based on the above-described embodiments of the data storage method based on business type, another embodiment of the present invention provides a data storage terminal device based on business type. The data storage terminal device based on business type includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the data storage method based on business type of any embodiment of the present invention.

[0077] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the business type-based data storage device.

[0078] The data storage device based on business type can be a desktop computer, laptop, handheld computer, or cloud server, etc. The data storage terminal device based on business type may include, but is not limited to, processors and memory.

[0079] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the service-type-based data storage device, connecting various parts of the device via various interfaces and lines. The memory can be used to store the computer programs and / or modules. The processor implements various functions of the service-type-based data storage device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart memory card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0080] Example 4

[0081] Based on the above embodiments of the data storage method based on business type, another embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the data storage method based on business type of any embodiment of the present invention.

[0082] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A data storage method based on business type, characterized in that, include: Based on the data access frequency of the storage area, update the capacity of several storage sub-regions and obtain the data attributes of each storage sub-region; Based on the access patterns among the various storage sub-regions, all the storage sub-regions are classified into several storage sets, and the region attributes of each storage set are obtained; Read the data to be classified from the data stream, and store the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set; The step of classifying all the storage sub-regions into several storage sets according to the access patterns of each storage sub-region, and obtaining the region attributes of each storage set, includes: Obtain the access patterns of different users to each of the storage sub-regions within a preset time period, and obtain the correlation between each of the storage sub-regions based on the access patterns; Based on the correlation between each of the storage sub-regions, all the storage sub-regions are divided to obtain a storage set composed of several of the storage sub-regions; The step of obtaining the correlation degree between each of the storage sub-regions based on the access pattern includes: Determine whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area; If so, associate the first storage sub-region with the second storage sub-region; If not, then check the next user's access or the next access of the current user, until the preset number of iterations is reached.

2. The data storage method based on business type as described in claim 1, characterized in that, The step of updating the capacity of several storage sub-regions based on the data access frequency of the storage region and obtaining the data attributes of each of the storage sub-regions includes: Get the data access frequency of different users to the data stored in each of the storage sub-areas within a preset time period; Based on the data access frequency of the stored data, sort all the storage sub-regions in descending order; Based on the descending sorting results, the capacity of the corresponding storage sub-regions is adjusted sequentially.

3. The data storage method based on business type as described in claim 1, characterized in that, The step of storing the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set includes: After sequentially matching the data to be classified with the regional attributes of each of the storage sets, the data to be classified is assigned to the corresponding storage set; The data to be classified and assigned to the corresponding storage set is matched with the data attributes of each storage sub-region in the storage set in turn, and then the data to be classified and assigned to the corresponding storage sub-region is stored.

4. A data storage device based on business type, characterized in that, include: Storage sub-area capacity update module, storage sub-area classification module, and data storage module; The storage sub-region capacity update module is used to update the capacity of several storage sub-regions according to the data access frequency of the storage region, and to obtain the data attributes of each storage sub-region. The storage sub-region classification module is used to classify all the storage sub-regions into several storage sets according to the access rules between the storage sub-regions, and to obtain the region attributes of each storage set. The data storage module is used to read the data to be classified from the data stream, and store the data to be classified into the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set; The storage sub-region classification module is used to classify all the storage sub-regions into several storage sets according to the access patterns among the storage sub-regions, and to obtain the region attributes of each storage set, including: Obtain the access patterns of different users to each of the storage sub-regions within a preset time period, and obtain the correlation between each of the storage sub-regions based on the access patterns; Based on the correlation between each of the storage sub-regions, all the storage sub-regions are divided to obtain a storage set composed of several of the storage sub-regions; The step of obtaining the correlation degree between each of the storage sub-regions based on the access pattern includes: Determine whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area; If so, associate the first storage sub-region with the second storage sub-region; If not, then check the next user's access or the next access of the current user, until the preset number of iterations is reached.

5. A data storage device based on business type as described in claim 4, characterized in that, The storage sub-region capacity update module is used to update the capacity of several storage sub-regions according to the data access frequency of the storage regions, and to obtain the data attributes of each storage sub-region, including: Get the data access frequency of different users to the data stored in each of the storage sub-areas within a preset time period; Based on the data access frequency of the stored data, sort all the storage sub-regions in descending order; Based on the descending sorting results, the capacity of the corresponding storage sub-regions is adjusted sequentially.

6. A data storage device based on business type as described in claim 4, characterized in that, The data storage module is configured to store the data to be classified into the corresponding storage sub-regions according to the data attributes of each storage sub-region and the region attributes of each storage set, including: After sequentially matching the data to be classified with the regional attributes of each of the storage sets, the data to be classified is assigned to the corresponding storage set; The data to be classified and assigned to the corresponding storage set is matched with the data attributes of each storage sub-region in the storage set in turn, and then the data to be classified and assigned to the corresponding storage sub-region is stored.

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