Data storage method and device based on service types

Abstract

The invention discloses a data storage method and device based on service types. The method comprises the following steps: updating the capacity of a plurality of storage sub-regions according to the data access frequency of a storage region, and obtaining the data attribute of each storage sub-region; classifying all the storage sub-regions into a plurality of storage sets according to access rules among the storage sub-regions, and obtaining region attributes of the storage sets; and reading to-be-classified data from a data stream, and storing the to-be-classified data into the corresponding storage sub-regions according to the data attributes of the storage sub-regions and the region attributes of the storage sets. According to the invention, the data storage efficiency and security in the data storage process can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to a data storage method and device based on business type. Background Art

[0002] With the continuous deepening of various Internet applications, the amount of Internet data is growing rapidly, and the data structures and data types that need to be processed are becoming more and more diverse. Whether it is between departments within an enterprise or between the inside and outside of an enterprise, a large amount of production data will be generated in the process of communication and recording through information technology. Some of these production data are extremely important and need to be used frequently and stored for a long time. Some data are general types of data with low frequency of use and low importance. Such data only needs to be stored, and even if it is lost, it will not cause much impact.

[0003] The amount of data in the information age is huge. If these production data are not classified and stored, it will lead to data operation chaos, heavy workload of the storage system, slow operation, and improper preservation of important data, which is easy to cause the loss of important data. In the prior art, these production data are often classified and stored manually. When faced with a large amount of data storage, the above method will lead to low storage efficiency and easy classification errors, which will further cause errors in the selection of data storage areas, thereby affecting the security of data storage. Summary of the invention

[0004] The present invention provides a data storage method and device based on business type, so as to solve the technical problems of low data storage efficiency and low security when facing a large number of data storage businesses.

[0005] In order to solve the above technical problems, in a first aspect, an embodiment of the present invention provides a data storage method based on business type, including:

[0006] According to the data access frequency of the storage area, the capacity of several storage sub-areas is updated, and the data attribute of each storage sub-area is obtained;

[0007] According to the access rules between the storage sub-areas, all the storage sub-areas are classified into a plurality of storage sets, and the area attribute of each storage set is obtained;

[0008] The data to be classified is read from the data stream, and according to the data attribute of each storage sub-region and the region attribute of each storage set, the data to be classified is stored in the corresponding storage sub-region.

[0009] Compared with the prior art, the above-mentioned embodiment has the following beneficial effects: the capacity of the storage sub-area is updated according to the data access frequency to ensure that there is sufficient storage space when the data is accessed at a high frequency, thereby improving the security of the stored data; and multiple storage sub-areas are managed to realize data classification storage and improve the data storage efficiency; in addition, through the storage set and the storage sub-area, the type of stored data is classified in a double layer, which can improve the subsequent classification efficiency of the data to be classified, and further improve the accuracy and storage efficiency of the data to be classified stored in the corresponding storage sub-area.

[0010] In an embodiment of the first aspect, updating the capacity of a plurality of storage sub-regions according to the data access frequency of the storage region and acquiring the data attribute of each of the storage sub-regions includes:

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

[0012] sorting all the storage sub-areas in descending order according to the data access frequency of the stored data;

[0013] According to the descending sorting result, the capacities of the corresponding storage sub-areas are adjusted in sequence.

[0014] Compared with the prior art, the above embodiment has the following beneficial effects: since the access frequencies of data in different storage sub-areas are different, important data usually has a higher access frequency. In order to ensure that the corresponding storage sub-area has sufficient capacity when the subsequent important data is stored, the access frequencies of data in different storage sub-areas are counted, and the capacity of the corresponding storage sub-area is adjusted according to the size of the access frequency, thereby improving the security of data during data storage.

[0015] In an embodiment of the first aspect, classifying all the storage sub-areas into a plurality of storage sets according to access rules of the storage sub-areas, and acquiring a region attribute of each storage set includes:

[0016] Acquire the access rules of different users to the storage sub-areas within a preset time period, and acquire the correlation between the storage sub-areas according to the access rules;

[0017] According to the association degree between each of the storage sub-areas, all the storage sub-areas are divided to obtain a plurality of storage sets consisting of a plurality of the storage sub-areas.

[0018] Compared with the prior art, the above embodiment has the following beneficial effects: due to the huge amount of stored data, the number of storage sub-areas will also increase accordingly. If the data is matched with the data attributes of each storage sub-area each time data is stored, so as to determine the storage sub-area where the data needs to be stored, the data storage efficiency will be seriously reduced; if the division of the storage sub-area is cancelled, it will be impossible to clearly evaluate the importance of the data, and the data storage will be relatively messy, which is not conducive to the subsequent data query. Therefore, by dividing the storage sub-areas and counting the access rules of the same user to the data in each storage sub-area, the correlation characteristics between the storage sub-areas can be obtained. According to the correlation characteristics, part of the storage sub-space used to store the data to be classified can be quickly locked to improve the storage efficiency.

[0019] In an embodiment of the first aspect, acquiring the degree of association between the storage sub-areas according to the access rule includes:

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

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

[0022] If not, the next user's visit is judged or the next visit of the current user is judged until the preset number of iterative judgments is reached.

[0023] Compared with the prior art, the above embodiment has the following beneficial effects: according to the order in which the same user accesses the storage sub-regions, the associated features between the storage sub-regions can be quickly and accurately screened out, thereby improving the accuracy of subsequent storage sub-region classification.

[0024] In an embodiment of the first aspect, storing the to-be-classified data in the corresponding storage sub-region according to the data attributes of each storage sub-region and the region attributes of each storage set includes:

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

[0026] After sequentially matching the data to be classified that is divided into the corresponding storage set with the data attributes of each storage sub-region in the storage set, the data to be classified is stored in the corresponding storage sub-region.

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

[0028] In a second aspect, an embodiment of the present invention further provides a data storage device based on business type, which includes: a storage sub-area capacity update module, a storage sub-area 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 obtain the data attribute of each storage sub-region;

[0030] The storage sub-region classification module is used to classify all the storage sub-regions into a plurality of storage sets according to the access rules between the storage sub-regions, and obtain the region attribute 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 in the corresponding storage sub-area according to the data attributes of each storage sub-area and the area attributes of each storage set.

[0032] In an embodiment of the second aspect, the storage sub-region capacity updating module is used to update the capacity of a plurality of storage sub-regions according to the data access frequency of the storage region and obtain the data attribute of each of the storage sub-regions, including:

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

[0034] sorting all the storage sub-areas in descending order according to the data access frequency of the stored data;

[0035] According to the descending sorting result, the capacities of the corresponding storage sub-areas are adjusted in sequence.

[0036] In an embodiment of the second aspect, the storage sub-region classification module is used to classify all the storage sub-regions into a plurality of storage sets according to access rules between the storage sub-regions, and obtain a region attribute of each storage set, including:

[0037] Acquire the access rules of different users to the storage sub-areas within a preset time period, and acquire the correlation between the storage sub-areas according to the access rules;

[0038] According to the association degree between each of the storage sub-areas, all the storage sub-areas are divided to obtain a plurality of storage sets consisting of a plurality of the storage sub-areas.

[0039] In an embodiment of the second aspect, acquiring the degree of association between the storage sub-areas according to the access rule includes:

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

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

[0042] If not, the next user's visit is judged or the next visit of the current user is judged until the preset number of iterative judgments is reached.

[0043] In an embodiment of the second aspect, the data storage module is used to store the to-be-classified data in the corresponding storage sub-area according to the data attributes of each storage sub-area and the area attributes of each storage set, including:

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

[0045] After sequentially matching the data to be classified that is divided into the corresponding storage set with the data attributes of each storage sub-region in the storage set, the data to be classified is stored in the corresponding storage sub-region. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a flow chart of a data storage method based on business type provided by an embodiment of the present invention;

[0047] Figure 2 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 A schematic diagram of the structure of a data storage device based on business type provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Embodiment 1

[0051] Please refer to Figure 1 , a data storage method based on business type provided by an embodiment of the present invention, including S101 to S103, specifically:

[0052] S101: updating the capacity of a plurality of storage sub-regions according to the data access frequency of the storage region, and acquiring data attributes of each of the storage sub-regions.

[0053] Furthermore, the capacity of several storage sub-areas is updated according to the data access frequency of the storage area, and the data attributes of each storage sub-area are obtained, including: obtaining the data access frequency of different users to the stored data in each storage sub-area within a preset time period; sorting all the storage sub-areas in descending order according to the data access frequency of the stored data; and adjusting the capacity of the corresponding storage sub-areas in turn according to the descending sorting result.

[0054] Exemplary, reference Figure 2 The storage area includes but is not limited to plant data, line data, meter data, voltage data, power curve data, harmonic curve data, daily frozen meter data, daily maximum demand data, power data, line loss data, terminal alarm data, master station alarm data, collection task message data, alarm event message data, system parameter data, rule threshold data and other storage sub-areas.

[0055] Preferably, the above embodiment can assign weights to the storage capacity of the storage sub-regions according to the frequencies of the storage sub-regions to characterize the access volume and frequency of the data, and it can be understood that the sum of the capacities of the storage sub-regions is equal to the total capacity of the storage region.

[0056] It can be seen from the above embodiments that due to the huge amount of stored data, the access frequency of data in different storage sub-areas is not the same. Important data usually has a higher access frequency. In order to ensure that the corresponding storage sub-area has sufficient capacity when subsequent important data is stored, the access frequency of data in different storage sub-areas is counted, and the capacity of the corresponding storage sub-area is adjusted according to the size of the access frequency, thereby improving the security of data during data storage.

[0057] S102: Classifying all the storage sub-areas into a number of storage sets according to access rules between the storage sub-areas, and obtaining a region attribute of each storage set.

[0058] Furthermore, the method of classifying all the storage sub-areas into a number of storage sets according to the access rules of each storage sub-area and obtaining the area attribute of each storage set includes: obtaining the access rules between different users to the storage sub-areas within a preset time period, and obtaining the correlation between the storage sub-areas according to the access rules; and dividing all the storage sub-areas according to the correlation between the storage sub-areas to obtain a number of the storage sets consisting of the storage sub-areas.

[0059] Due to the huge amount of stored data, the number of storage sub-areas will also increase accordingly. If the data is matched with the data attributes of each storage sub-area every time data is stored to determine the storage sub-area where the data needs to be stored, the data storage efficiency will be seriously reduced; if the division of storage sub-areas is cancelled, it will be impossible to clearly evaluate the importance of the data, and the data storage will be more messy, which is not conducive to subsequent data queries. Therefore, by dividing the storage sub-areas and counting the access rules of the same user to the data in each storage sub-area, the correlation characteristics between the storage sub-areas can be obtained. According to the correlation characteristics, part of the storage sub-space used to store the data to be classified can be quickly locked to improve the storage efficiency.

[0060] Furthermore, obtaining the degree of association between the storage sub-areas according to the access rule includes: determining whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area; if so, associating the first storage sub-area with the second storage sub-area; if not, determining the access of the next user or determining the next access of the current user until a preset number of iterative determinations is reached.

[0061] Preferably, a preset time, such as 1 minute, can be set. That is, if the user accesses the next storage sub-area within 1 minute after the user accesses the current storage sub-area, the above two storage sub-areas are associated as a storage set; if the user accesses two storage sub-areas within 1 minute and then accesses the third storage sub-area within the next 1 minute, the above three storage sub-areas are associated, and it is continuously determined whether there are other storage sub-areas that can be further associated, until the preset judgment iteration number is reached, and a storage set is generated for all associated storage sub-areas in this judgment iteration process; and if the user does not make further access within 1 minute, the judgment iteration process is restarted after the next access begins.

[0062] According to the order in which the same user accesses the storage sub-areas within a certain period of time, the associated features between the storage sub-areas can be quickly and accurately screened out, and after several iterative judgments, the regional attributes of the storage set have tended to be stable, which can significantly improve the accuracy of subsequent storage sub-area classification.

[0063] Preferably, the association can also be performed by storing data attributes of the sub-areas, see Figure 2 , plant data, line data, household data, and metering equipment data are all associated in the archive data storage set. Meter code data, voltage data, current data, and power data are all associated in the minute-level data storage set. Therefore, in this application, there is no limit on the number of storage sets, and they can be associated according to the initial data attribute association method or by the user's access frequency and association degree.

[0064] S103: Reading the data to be classified from the data stream, and storing the data to be classified into the corresponding storage sub-area according to the data attribute of each storage sub-area and the area attribute of each storage set.

[0065] Furthermore, storing the to-be-classified data in the corresponding storage sub-area according to the data attributes of each storage sub-area and the area attributes of each storage set includes: matching the to-be-classified data with the area attributes of each storage set in turn, and then dividing the to-be-classified data into corresponding storage sets; matching the to-be-classified data divided into the corresponding storage set with the data attributes of each storage sub-area in the storage set in turn, and then storing the to-be-classified data in the corresponding storage sub-area.

[0066] By matching the data to be classified with the region attributes of the storage set in the first layer of the two-layer classification rule and the data attributes of the storage sub-region in turn, the category of the data to be classified can be quickly obtained, so that the data to be classified can be quickly stored in the corresponding storage subspace, which not only improves the accuracy and efficiency of data classification, but also improves the storage efficiency of data.

[0067] From the above, it can be seen that the data method based on business type provided by an embodiment of the present invention has the following beneficial effects compared with the prior art: the capacity of the storage sub-area is updated according to the data access frequency to ensure that there is sufficient storage space when high-frequency data access is performed, thereby improving the security of stored data; and multiple storage sub-areas are managed to realize data classification storage and improve data storage efficiency; in addition, through storage sets and storage sub-areas, the types of stored data are classified in a double layer, which can improve the classification efficiency of subsequent classification of the data to be classified, and further improve the accuracy and storage efficiency of storing massive data to be classified in the corresponding storage sub-areas.

[0068] Embodiment 2

[0069] refer to Figure 3The embodiment of the present invention also provides a data storage device based on business type, including: a storage sub-area capacity update module 11, a storage sub-area classification module 12 and a data storage module 13; wherein the storage sub-area capacity update module 11 is used to update the capacity of several storage sub-areas according to the data access frequency of the storage area, and obtain the data attributes of each of the storage sub-areas; the storage sub-area classification module 12 is used to classify all the storage sub-areas into several storage sets according to the access rules between the storage sub-areas, and obtain the area attributes of each of the storage sets; 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 in the corresponding storage sub-area according to the data attributes of each of the storage sub-areas and the area attributes of each of the storage sets.

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

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

[0072] Furthermore, obtaining the degree of association between the storage sub-areas according to the access rule includes: determining whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area; if so, associating the first storage sub-area with the second storage sub-area; if not, determining the access of the next user or determining the next access of the current user until a preset number of iterative determinations is reached.

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

[0074] From the above, it can be seen that the data storage device based on business type provided by the embodiment of the present invention has the following beneficial effects compared with the prior art: due to the huge amount of stored data, the number of storage sub-areas will also increase accordingly. If the data is matched with the data attributes of each storage sub-area each time data is stored, so as to determine the storage sub-area where the data needs to be stored, the data storage efficiency will be seriously reduced; if the division of storage sub-areas is cancelled, it will be impossible to clearly evaluate the importance of the data, and the data storage will be relatively messy, which is not conducive to the query of subsequent data. Therefore, by dividing the storage sub-areas and by counting the access rules of the same user to the data in each storage sub-area, the correlation characteristics between the storage sub-areas can be obtained. According to the correlation characteristics, part of the storage sub-space used to store the data to be classified can be quickly locked to improve the storage efficiency.

[0075] Embodiment 3

[0076] Based on the above-mentioned embodiment 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, the data storage method based on business type of any embodiment of the present invention is implemented.

[0077] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the data storage device based on the business type.

[0078] The data storage device based on the business type may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The data storage terminal device based on the business type may include, but is not limited to, a processor and a memory.

[0079] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the data storage device based on the business type, and uses various interfaces and lines to connect the various parts of the entire data storage device based on the business type. The memory may be used to store the computer program and / or module, and the processor implements various functions of the data storage device based on the business type by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0080] Embodiment 4

[0081] Based on the above-mentioned embodiment of the data storage method based on business type, another embodiment of the present invention provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the data storage method based on business type according to 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 may be in source code form, object code form, executable file or some intermediate form, etc. 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), electric 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 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 electric carrier signals and telecommunication signals.

[0083] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data storage method based on business type, characterized in that: include: According to the data access frequency of the storage area, the capacity of several storage sub-areas is updated, and the data attribute of each storage sub-area is obtained; According to the access rules between the storage sub-areas, all the storage sub-areas are classified into a plurality of storage sets, and the area attribute of each storage set is obtained; The data to be classified is read from the data stream, and according to the data attribute of each storage sub-region and the region attribute of each storage set, the data to be classified is stored in the corresponding storage sub-region.

2. A data storage method based on business type as claimed in claim 1, characterized in that: The updating of the capacity of a plurality of storage sub-regions according to the data access frequency of the storage region and obtaining the data attribute of each storage sub-region includes: Obtaining the data access frequency of different users to the data stored in each storage sub-area within a preset time period; sorting all the storage sub-areas in descending order according to the data access frequency of the stored data; According to the descending sorting result, the capacities of the corresponding storage sub-areas are adjusted in sequence.

3. A data storage method based on business type as claimed in claim 1, characterized in that: The step of classifying all the storage sub-areas into a plurality of storage sets according to the access rules of the storage sub-areas and obtaining the area attribute of each storage set includes: Acquire the access rules of different users to the storage sub-areas within a preset time period, and acquire the correlation between the storage sub-areas according to the access rules; According to the association degree between each of the storage sub-areas, all the storage sub-areas are divided to obtain a plurality of storage sets consisting of a plurality of the storage sub-areas.

4. A data storage method based on business type as claimed in claim 3, characterized in that: The obtaining the degree of association between the storage sub-areas according to the access rule includes: Determining whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area; If so, associating the first storage sub-region with the second storage sub-region; If not, the next user's visit is judged or the next visit of the current user is judged until the preset number of iterative judgments is reached.

5. A data storage method based on business type as claimed in claim 1, characterized in that: According to the data attribute of each storage sub-region and the region attribute of each storage set, storing the to-be-classified data in the corresponding storage sub-region includes: After sequentially matching the data to be classified with the regional attributes of each storage set, the data to be classified is divided into corresponding storage sets; After sequentially matching the data to be classified that is divided into the corresponding storage set with the data attributes of each storage sub-region in the storage set, the data to be classified is stored in the corresponding storage sub-region.

6. A data storage device based on business type, characterized in that: include: A storage sub-area capacity update module, a storage sub-area classification module, and a 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 obtain the data attribute of each storage sub-region; The storage sub-region classification module is used to classify all the storage sub-regions into a plurality of storage sets according to the access rules between the storage sub-regions, and obtain the region attribute 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 in the corresponding storage sub-area according to the data attributes of each storage sub-area and the area attributes of each storage set.

7. A data storage device based on business type as claimed in claim 6, 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 region and obtain the data attribute of each storage sub-region, including: Obtaining the data access frequency of different users to the data stored in each storage sub-area within a preset time period; sorting all the storage sub-areas in descending order according to the data access frequency of the stored data; According to the descending sorting result, the capacities of the corresponding storage sub-areas are adjusted in sequence.

8. A data storage device based on business type as claimed in claim 6, characterized in that: The storage sub-region classification module is used to classify all the storage sub-regions into a plurality of storage sets according to the access rules between the storage sub-regions, and obtain the region attribute of each storage set, including: Acquire the access rules of different users to the storage sub-areas within a preset time period, and acquire the correlation between the storage sub-areas according to the access rules; According to the association degree between each of the storage sub-areas, all the storage sub-areas are divided to obtain a plurality of storage sets consisting of a plurality of the storage sub-areas.

9. A data storage device based on business type as claimed in claim 8, characterized in that: The obtaining the degree of association between the storage sub-areas according to the access rule includes: Determining whether the same user accesses the second storage sub-area within a preset time after accessing the first storage sub-area; If so, associating the first storage sub-region with the second storage sub-region; If not, the next user's visit is judged or the next visit of the current user is judged until the preset number of iterative judgments is reached.

10. A data storage device based on business type as claimed in claim 6, characterized in that: The data storage module is used to store the to-be-classified data in the corresponding storage sub-area according to the data attribute of each storage sub-area and the area attribute of each storage set, including: After sequentially matching the data to be classified with the regional attributes of each storage set, the data to be classified is divided into corresponding storage sets; After sequentially matching the data to be classified that is divided into the corresponding storage set with the data attributes of each storage sub-region in the storage set, the data to be classified is stored in the corresponding storage sub-region.

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