KR102616356B1 - Intelligent data management apparatus and intelligent data management method using the same - Google Patents

Abstract

This specification relates to intelligent data management and storage methods. The method includes collecting file information about a management target file; Analyzing and classifying the management target files into categories based on the collected file information; optimizing the classified files according to first preset criteria; and selecting and storing the optimized file in a data storage according to a second preset standard. This has the effect of optimizing and storing management target data at minimum cost.

Description

Intelligent data management and storage device and intelligent data management and storage method using the same {INTELLIGENT DATA MANAGEMENT APPARATUS AND INTELLIGENT DATA MANAGEMENT METHOD USING THE SAME}

This specification relates to an intelligent data management and storage device and an intelligent data management and storage method using the same.

Data storage technology is a technology that stores data in a data storage (storage medium) to ensure data stability and smooth business support.

Existing data storage technology reads the original data and stores a copy of the read data in the data storage, or compresses the read data to reduce data storage space and stores the compressed data in the data storage.

Existing data storage technology stores data without considering the type or characteristics of the data, so the management and storage costs of the original data are high, and the management and storage costs of the data stored in the data storage cannot be optimized. There was none. And existing data storage technology uses a network to store original data in a remote data storage or cloud data storage, but since the size of the original data affects performance, optimization of the original data is necessary.

In particular, as the 4th industry, such as artificial intelligence, big data, and cloud technology, develops, data management and storage technology that can accommodate massive amounts of data and changes in various types of data has become necessary.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

This specification presents an intelligent data management and storage method. The method includes collecting file information about a management target file; Analyzing and classifying the management target files into categories based on the collected file information; optimizing the classified files according to first preset criteria; and selecting and storing the optimized file in a data storage according to a second preset criterion.

The intelligent data management and storage method and other embodiments may include the following features.

According to an embodiment, the step of classifying the management target files into categories based on the collected file information includes classifying the management target files according to usage time based on usage time information of the collected files. , The step of optimizing the classified files according to a first preset standard may be a step of compressing files of a specific time period among the classified files.

According to an embodiment, the step of classifying the management target files into categories based on the collected file information includes classifying the management target files into file types based on file type information of the collected files, The step of optimizing the classified files according to the first preset standard may be a step of compressing the classified files using different compression methods for each file type.

According to an embodiment, the step of selecting and storing the optimized file in a data storage according to a second preset criterion may include analyzing a usage cost of each of a plurality of data storages; and storing the optimized file in a data storage with the lowest usage cost among the plurality of data storages.

Depending on the embodiment, the usage cost may be determined by at least one of the type of storage medium of the data storage, the installation location of the data storage, the owner of the data storage, and the purpose of the data storage.

According to the embodiment, the usage cost is determined by the 1 TB storage cost index (T), and the 1 TB storage cost index (T) is the price (S) of the data storage provider's 1 TB storage space provision service of 1. It is calculated as the result of dividing the storage cost per TB by the cost (C), and the storage cost per 1 TB (C) can be calculated as the result of dividing the cost of the total TB used by the total number of TB.

Depending on the embodiment, the categories are divided into classifications including file size, file characteristics, and file usage frequency, and the step of classifying the management target files into categories based on the collected file information includes: Based on the information, the management target files can be classified by file size range, file characteristics, or file usage frequency range.

Depending on the embodiment, the method may further include providing data information about the management target file by virtualizing the management target file.

Meanwhile, this specification presents a computer program. The computer program may be a computer program stored in a computer-readable recording medium in combination with computer hardware to execute each step included in the intelligent data management and storage method according to any one of the steps above.

On the other hand, this specification presents an intelligent data management and storage device. The device includes a memory storing one or more instructions; and a processor for executing the instruction, wherein when the instruction is executed, the processor collects file information about the file to be managed, and classifies the file to be managed into categories based on the collected file information. The classified files may be optimized according to first preset criteria, and the optimized files may be selected and stored in a data storage according to second preset criteria.

According to the embodiment disclosed in this specification, there is an effect of minimizing management costs and storage costs by optimizing data in consideration of the characteristics of the original data to be managed.

Meanwhile, the effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

The following drawings attached to this specification illustrate preferred embodiments of the present invention, and serve to further understand the technical idea of the present invention along with specific details for carrying out the invention. Therefore, the present invention is described in such drawings. It should not be interpreted as limited to the specific details.
1 shows an example of an intelligent data management and storage system including an intelligent data management and storage device according to an embodiment.
Figure 2 is a block diagram showing the configuration of an intelligent data management and storage device according to an embodiment.
3 is a diagram illustrating an intelligent data management and storage method using an intelligent data management and storage device according to an embodiment.
Figure 4 shows an example of visualizing data analysis results applied based on usage time among the time indexes.
Figure 5 shows an example of visualizing data analysis results according to file type.
Figure 6 is a diagram explaining the procedures of data analysis and data optimization according to time and file type.
Figure 7 is a diagram illustrating a system configuration that implements backup by an intelligent data management and storage method according to an embodiment.
Figure 8 is a diagram illustrating in detail the functions of data analysis, data management, and data storage performed by the intelligent data management and storage method according to the above-described embodiment in a table.
Figure 9 is a block diagram of an AI device according to an embodiment of the present invention.

The technology disclosed herein can be applied to data management and storage technology. However, the technology disclosed in this specification is not limited to this, and can be applied to all devices and methods to which the technical idea of the technology can be applied.

It should be noted that the technical terms used in this specification are only used to describe specific embodiments and are not intended to limit the spirit of the technology disclosed in this specification. In addition, the technical terms used in this specification, unless specifically defined in a different way in this specification, should be interpreted as meanings generally understood by those skilled in the art in the field to which the technology disclosed in this specification belongs. It should not be interpreted in a very comprehensive sense or in an excessively reduced sense. In addition, if the technical term used in this specification is an incorrect technical term that does not accurately express the idea of the technology disclosed in this specification, it is a technical term that can be correctly understood by a person with ordinary knowledge in the field to which the technology disclosed in this specification belongs. It should be understood and replaced with . Additionally, general terms used in this specification should be interpreted as defined in the dictionary or according to the context, and should not be interpreted in an excessively reduced sense.

Terms containing ordinal numbers, such as first, second, etc., used in this specification may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component without departing from the scope of the present invention.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings. However, identical or similar components will be assigned the same reference numerals regardless of the reference numerals, and duplicate descriptions thereof will be omitted.

Additionally, when describing the technology disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the technology disclosed in this specification, the detailed description will be omitted. In addition, it should be noted that the attached drawings are only intended to facilitate easy understanding of the spirit of the technology disclosed in this specification, and should not be construed as limiting the spirit of the technology by the attached drawings.

Hereinafter, we will look in detail at the method proposed in this specification.

Intelligent data management and storage technology refers to technology that sets analysis standards for data to be managed using specific metrics, analyzes the data according to these standards, and then optimizes the data. In general data management and storage technology, the original data is kept in the storage device as is, but the data optimization of this specification optimizes the storage space of the original data by performing optimization such as compression, deduplication, and virtualization of the original data itself. And when saving it to local storage, remote storage, or cloud storage, data storage space can be minimized and network usage can also be minimized.

To store data, it is necessary to define the data type, file type, type of database (DB) (hereinafter referred to as DB), and data storage.

The type of data can be divided into file-type data or DB-type data.

Types of files can be divided into structured and unstructured files, including text files, document files, image files, audio files, video files, and other multimedia data.

In addition, DB includes all types of existing commercially used DBs (Oracle, MSSQL, Informix, DB2, SYBASE, MySQL, and all other commercially used DBs), open source-based DBs, and domestically developed and commercialized DBs. Includes all DB types.

Types of data storage can be divided into local storage, remote storage, cloud storage, and other storage.

Below, an intelligent data management and storage device for intelligent data management and storage according to an embodiment of the present invention will be described.

1 shows an example of an intelligent data management and storage system including an intelligent data management and storage device according to an embodiment.

Referring to FIG. 1, the intelligent data management and storage system 10 includes an intelligent data management and storage device 100, a plurality of user terminals 200 that are management target computing devices, and a plurality of storage server servers 300. It can be configured.

The intelligent data management and storage device 100 is connected to a plurality of user terminals 200, collects and analyzes information about the data stored in the plurality of user terminals 200, and then collects and analyzes information on the data stored in the plurality of user terminals 200, and then collects and analyzes information on the data stored in the plurality of user terminals 200. It provides services that compress, move, store, and virtualize data of the user terminal 200.

The plurality of user terminals 200 are managed computing devices that receive data management services provided by the intelligent data management and storage device 100, and refer to PCs connected to the intelligent data management and storage device 100 through a network. can do. Additionally, these plurality of user terminals 200 may mean a local data server device in which a plurality of PCs are connected to store and share data. These plural user terminals 200 receive and install the client program provided by the intelligent data management and storage device 100, and through this, information and management rights (compression, movement, storage, copying) for the stored data are received and installed. , virtualization, etc.) are provided to the intelligent data management and storage device 100.

The plurality of storage device servers 300 may refer to remote storage and cloud storage excluding local storage provided in a plurality of user terminals 200 connected to the intelligent data management and storage device 100 through a network. The intelligent data management and storage device 100 reads data from a plurality of user terminals 200 and stores them in the plurality of storage servers 300, or user data stored in the plurality of storage servers 300. It is provided to a plurality of user terminals 200.

Figure 2 is a block diagram showing the configuration of an intelligent data management and storage device according to an embodiment.

Referring to FIG. 2, the intelligent data management and storage device 100 for implementing the method or function proposed in this specification includes a control unit 110, a storage unit 130, a bus (not shown) for data transmission, or an external It may include a communication unit 120, an output unit 140, and an input unit 150 for communicating with. The illustrated components are not essential, and the intelligent data management and storage device 100 may be implemented with more or fewer components. Additionally, these components may be implemented as hardware or software, or through a combination of hardware and software.

The control unit 110 may refer to all types of processing devices that can process data, such as a processor. The control unit 110 may be configured to execute processing logic to perform various operations and steps described herein. Here, 'processor' may mean, for example, a data processing device built into hardware that has a physically structured circuit to perform a function expressed by code or instructions included in a program. Examples of data processing devices built into hardware include a microprocessor, central processing unit (CPU), processor core, multiprocessor, and application-specific integrated (ASIC). circuit) and FPGA (field programmable gate array), etc., but the scope of the present invention is not limited thereto.

The communication unit 120 may be composed of wired and/or wireless communication modules. For example, the communication unit 120 uses wireless fidelity (Wi-Fi), Bluetooth, Zigbee, near field communication (NFC), and wireless broadband Internet (Wibro). It may include a wireless communication module and a wired communication module such as a wired LAN such as Ethernet. The communication unit 120 can transmit and receive data by performing wired/wireless communication with other devices through a network.

The storage unit 130 may include magnetic storage media or flash storage media, but the scope of the present invention is not limited thereto.

The output unit 140 is intended to generate output related to vision, hearing, or tactile senses, and may include, for example, a display unit 145. The display unit 145 can implement a touch screen by forming a layered structure or being integrated with the touch sensor. Such a touch screen can function as a user input unit that provides an input interface between the device and the user, and at the same time, can provide an output interface between the device and the user.

The input unit 150 may be connected to the control unit 110 through an input/output interface connected to a bus. The input unit 150 may include a keyboard, pointing device, microphone, joystick, touchpad, scanner, etc. The input/output interface may include any one of a wide variety of interfaces, such as a serial port interface, PS/2 interface, parallel port interface, USB interface, and IEEE 1394 interface, or may logically represent a combination of other interfaces.

Meanwhile, the intelligent data management and storage device 100 according to the embodiment may be implemented as a separate device (device in FIG. 2) that operates by being connected to the user terminal 200 as shown in FIG. 1, and its functions It may be included in the user terminal 200 and implemented as computing elements of the user terminal 200.

The control unit 110 performs the functions of the intelligent data management and storage device 100, that is, the processes and procedures of the intelligent data management and storage method, which will be described later.

3 is a diagram illustrating an intelligent data management and storage method using an intelligent data management and storage device according to an embodiment.

Referring to FIG. 3, the intelligent data management and storage method by the intelligent data management and storage device 100 according to the embodiment reads the original data 210 from the user terminal 200 and stores copy data generated by the storage device server ( Before storing it in the data storage provided in 300), data analysis based on data categories is performed (S110), and the original data is optimized (S120) by compressing and deduplicating the original data according to the results of the analysis. The optimized data 220 is stored in a local storage (S130) or in a data storage provided in the storage device server 300 (S140). At this time, the intelligent data management and storage method supports virtualization of the original data (S150), so that after the original data is optimized and moved to the data storage, even if the original data (210) stored in the local storage is deleted, the virtualized original data Data information can be easily confirmed through the data 211.

When analysis data based on data categories is accumulated, the intelligent data management and storage device 100 uses this data to learn an artificial intelligence-based data analysis module to automatically analyze and optimize the data. At this time, the artificial intelligence-based data analysis module uses the original data and classified data categorized by the user as learning data to classify the data into categories and learn optimization patterns for each data category, and then automatically analyzes the data when new data is input. By classifying categories, optimization can be performed in an appropriate manner.

The intelligent data management and storage method according to the embodiment analyzes classification and data call patterns according to data characteristics of the original data to be managed, analyzes to minimize data storage costs, and automatically or manually compresses the original data based on this. Alternatively, data can be managed or moved and stored by removing redundant data or virtualizing it.

Intelligent data management and storage methods according to embodiments can be divided into data analysis, data management, and data storage based on data categories.

[1] Data analysis function

Below, a data analysis function using an intelligent data management and storage method according to an embodiment will be described.

The data analysis function means analyzing data or files based on categories, classifying the data according to type and characteristics, then reporting the classification results to the user and optimizing the data based on the results.

At this time, data optimization is performed based on categories, and the original data is optimized and stored in the following order: optimization for infrequently used data, optimization according to data type, reading/writing other data, or optimization according to other categories. Ledger information about the original data is provided to the user through virtualization of the original data. The categories corresponding to the data optimization criteria are (1) Time, (2) File Type, (3) File Size, (4) File Property, and (5) File It can be classified by frequency of use.

(1) Category classification according to time

Category classification analysis based on time divides data into infrequently used data and frequently used data based on the frequency of data use, optimizes and stores infrequently used data, and virtualizes the original data. Provides information about the original data to the user.

All data (files or DB) in the user terminal 200 can have three time indexes, and the three time indexes include the time the data was created (creation time) and the time the data was modified (modification time). time), and the time the data was called (access time) (or the time the data was read).

The intelligent data management and storage method according to the embodiment can analyze whether data is used based on three time indexes for data, including data creation time, data modification time, and data call time. The time index is divided into four levels based on usage time, for example, files used within 6 months based on the current time (level 4), files used between 6 and 12 months (level 3), 12 Data is divided into files that have not been used for more than a month (level 2) and unused free space (level 1). Intelligent data management and storage methods analyze the usage time of data, distinguish levels for optimization, and optimize the data included in that level and store them in the system or back them up in storage with low storage costs.

Roughly statistically, of the total amount of data in the system, only about 20% of the data belongs to level 4 and 80% of the data belongs to level 3 and later. For example, in the case of email, once an attachment is downloaded, it is rare to read the attachment again within the email. When optimizing data through compression, the compression rate may vary depending on the type of data, but statistically, the compression rate for data excluding video data or image data is 80% or higher on average, so the storage space for storing the original data must be at least 60%. It can be optimized by more than %. The free space corresponding to level 1, that is, the unused storage space, does not contain data due to the nature of the system, but has not been initialized. As a result, optimization may take a lot of time, so set the value of this space to '0' or Optimization performance time can also be reduced by performing optimization by initializing it to '1'.

Meanwhile, the intelligent data management and storage method according to the embodiment can provide data analysis results by dividing the entire data into four levels based on usage time and visualizing it in a graph or table.

Figure 4 shows an example of visualizing data analysis results applied based on usage time among the time indexes.

Referring to FIG. 4, the intelligent data management and storage method according to the embodiment maintains 24GB of recently used data corresponding to level 4 out of the total 200GB of storage capacity provided in the user terminal 200 and maintains 24GB of recently used data corresponding to levels 1 to 3. The remaining 176GB is selected as the optimization target 410, and the selected optimization target data is optimized to 80%, thereby saving about 140GB of storage space of the user terminal 200. At this time, the intelligent data management and storage method according to the embodiment performs data compression, redundant data removal, and virtualization on 176GB of optimization target data and stores it in a local storage within the user terminal 200 or in another storage to store it locally. Optimize storage.

(2) Category classification according to file type

The local storage of the user terminal 200 includes an OS file area and a user data area used by the operating system (OS). User data used by users is stored separately in the form of files or DB. Intelligent data management and storage methods according to embodiments include text files, document files, image files, video files, audio files, and other types (external). You can classify file types, calculate the file name and data size for each type, generate statistics, and visualize the analysis results in the form of a graph or table. Through the visualization results, users can easily find out what types of files are most used in a specific system (for example, the analysis results show that systems used in hospitals have a lot of video data or image data). You can find out the size of the OS area, the contents and size of the file area, and the contents and size of the DB area. The user can selectively apply optimization to data for each file type through the intelligent data management and storage method provided by the intelligent data management and storage device 100 to optimize the data type. In the case of optimization, when using compression, compression techniques for compressing general file types and video data are very different, so optimal results can be obtained by selectively using a compression technique that can be optimized for the type of data.

Figure 5 shows an example of visualizing data analysis results according to file type.

Referring to FIG. 5, since image data and video data 510 mainly occupy a large proportion of the total data storage space of 400 GB of a user terminal 200, when applying a compression method for optimization, image data is compressed rather than uniform data compression. Data compression can be optimized by using selective compression techniques that provide the highest compression ratio for data and video data. At this time, for data other than image data and video data, a compression method optimized for each data type is used. Meanwhile, for data optimized through compression, data information in its original state can be provided to users through data virtualization.

(3) Category classification according to file size

Category classification by file size can be divided into large files, medium files, and small files. For example, the sizes of all files are collected, the average value is calculated, and files whose size is close to the average value are classified as medium files. In addition, by classifying files below the average value as small files and files above the average value as large files, the data to be optimized can be classified according to file size and classification result information can be provided to the user.

(4) Category classification according to file properties (File Property)

Category classification by file characteristics can be broadly divided into four categories: system files, private files, business files, and public files. Classification by file characteristics means analyzing the number of files of each of the above four types and the size of the files.

System files refer to files or program codes stored in the area (OS area) used by the OS (operating system) in the local storage space of the user terminal 200. The intelligent data management and storage method according to the embodiment calculates the size of the storage space used in the OS area and the ratio of the space used in the OS area to the size of the total storage space.

Personal files refer to files that have personal privacy. In consideration of individual privacy, these data or files are managed so that no one with any authority can read, write, or even delete them unless encryption is used when backing up or duplicating the individual or the individual is authenticated. The intelligent data management and storage method according to the embodiment calculates the size of the storage space used by the personal file and the ratio of the space used to store the personal file to the size of the total storage space.

Business files refer to files used while doing company work. Files used for business purposes must be shared using the concept of permission by using a shared folder concept, and must be directly linked to data storage and backed up or copied to protect data information. The intelligent data management and storage method according to the embodiment calculates the size of the storage space used by business files and the ratio used to store business files compared to the size of the total storage space.

Public files refer to files created through searches or downloads from the Internet. Since many of these data are duplicated files or data created between multiple people within a group, duplicate files are removed to maintain consistency as a single file or data, and the rest are connected using links. The intelligent data management and storage method according to the embodiment calculates the size of the storage space used by public files and the ratio used to store public files compared to the size of the total storage space.

(5) Category classification according to frequency of file use

Meanwhile, categorization based on the frequency of file use analyzes how much a file is being used by users or the system. It creates a history of the most frequently used files, analyzes information on recently used files, and provides information to users. Manage. For example, among files used during a preset period, files can be classified according to the preset frequency of file use, and files in that classification can be optimized according to preset criteria.

Below, as an example of data optimization according to category classification, data category classification according to time and file type and data optimization according to the classification result will be described with reference to Figure 6 attached.

Figure 6 is a diagram explaining the procedures of data analysis and data optimization according to time and file type.

Referring to FIG. 6, the file classification module 610 of the intelligent data management and storage device 100 receives storage directory information 601, file access time information 602, and file information about the file to be managed from the user terminal 200. After receiving the type information (603), files are collected based on time based on the received information (S601), and the files are classified based on time (S603). Next, the file classification module 610 collects files based on file type based on the received information (S605) and classifies the files based on file type (S607). The classification results classified by time standard and type are stored in the intelligent data management and storage device 100 (S609), and each classification result (621, 622) goes through a visualization process (S611) in the file classification module 610. After being output to the user, data optimization is performed manually or automatically according to the user's selection (S613). Visualization is output in the form of a table or graph so that users can easily understand the analysis results at a glance.

(6) Data optimization based on analysis of number of files

Meanwhile, the intelligent data management and storage method according to the embodiment can analyze the number of files constituting management target data and optimize data according to the analysis results.

Analysis by the number of files refers to the total number of files the user has on the user terminal 200, and is classified according to the number of files. For example, if the number of files is less than 100,000, the data of the user terminal 200 is classified as Small, if the number is between 100,000 and 1,000,000, it is Normal, and if it is more than 1,000,000, it is classified as Large. means that In data management, the larger the number of files, the lower the processing or transmission speed. Therefore, when the number of files is large, volume processing or archive methods are used to process or transmit files. This allows you to optimize data management.

(7) Data optimization based on storage cost analysis

Meanwhile, the intelligent data management and storage method according to the embodiment can analyze data storage costs and optimize data according to the analysis results.

The cost of storing data varies depending on the type of storage medium (disk, virtual tape library (VTL), solid-state drive (SSD), memory, tape, USB, etc.). Storage costs vary depending on whether it is for personal or business use. Additionally, storage costs vary depending on whether the data is stored in local storage, remote storage, or the cloud. It may vary depending on whether the data is stored in a personal storage location, a corporate storage location, or a data center, and may also vary depending on the region (city) where the data center being used is located.

The intelligent data management and storage method according to the embodiment calculates the storage cost using a formula by using factors that can vary the storage cost as variables, converts the storage cost into absolute and relative figures, finds a storage where the storage cost is minimized, and stores the data. You can move.

The storage cost cost (C) of 1 TB can be calculated using the following <Formula 1>.

<Formula 1> 1 TB storage cost cost (C) = (cost of total TB used by the system)/total number of TB

Therefore, if the price of the 1 TB storage space service provided by the Cloud Service Provider (CSP) is S, the storage cost index (T) of 1 TB can be calculated using the following <Equation 2>.

<Formula 2> 1 TB storage cost index (T) = S/C

The smaller T, which represents the 1 TB storage cost index, minimizes the storage cost, so the intelligent data management and storage method according to the embodiment minimizes the storage cost by utilizing the storage cost index when selecting a storage for storing data. The intelligent data management and storage method according to the embodiment can calculate the storage cost index even if there is already stored data, and then move the data to a data storage with the minimum storage cost index as a policy.

(8) Data optimization based on storage performance

Meanwhile, the intelligent data management and storage method according to the embodiment collects and analyzes data storage or reading speed data, visualizes the analysis results, and provides storage performance figures for all storage media to the user. It provides data movement and data optimization so that the most used data can be stored in this good storage.

[2] Data management function

Below, the data management function using the intelligent data management and storage method according to the embodiment will be described.

The data management function refers to the data storage management function that manages the data storage and data storage media in which data is stored. The data storage management function includes management of designated multi-data storage, local storage, remote storage, and cloud storage, as well as storage media such as disk (DISK), virtual tape library (VTL), and physical tape library ( It has management functions for Physical Tape Library (PTL), Storage Area Network (SAN), and Network Attached Storage (NAS), and a monitoring function that provides detailed information about running processes and termination. This refers to a reporting function that provides detailed information about the process.

The intelligent data management and storage method according to the embodiment transfers or backs up the management target data of the user terminal 200 to a storage selected from among data storages connected to a network based on the results of storage cost analysis according to the storage medium described above. , optimized data can be stored.

[3] Data storage function

Below, a data storage function using an intelligent data management and storage method according to an embodiment will be described.

The intelligent data management and storage method according to the embodiment includes a data storage function, which includes a backup function that reads the original data once and stores it based on a point in time, a replication function that reflects and saves the original data in real time whenever it is changed, and backup and It provides a hybrid function that combines the replication function.

Figure 7 is a diagram illustrating a system configuration that implements backup by an intelligent data management and storage method according to an embodiment.

Referring to Figure 7, the intelligent data management and storage method includes a backup UI module (Backup UI module: 710), a backup master module (720), a backup client module (Backup Client module: 730), and a backup server module ( Data backup is implemented with a configuration that includes the Backup Server module: 740).

The backup UI module 710 is a software module installed in the intelligent data management and storage device 100 or the user terminal 200 that performs the functions of the intelligent data management and storage device 100 and manages all backup and recovery. Provides a user UI (User Interface) that can be used. Through this user UI, users can set backup policies, schedule backups, register clients to receive backups, and register backup servers. They can also manage data recovery and check the results of backup and recovery. The user interface screen provided by the backup UI module divides the data storage areas into local storage with the original data and remote storage or cloud storage, allowing users to transfer data from local storage to local storage with just a click of the mouse on the screen. You can easily save data from remote storage, from local storage to cloud storage, and from remote storage to local storage, from cloud storage to local storage, and from cloud storage to remote storage.

The backup master module 720 is a software module installed in the intelligent data management and storage device 100 or the user terminal 200 that performs the functions of the intelligent data management and storage device 100, and includes a backup client 730 and a backup server. It communicates with (740) to manage and control the overall backup target, and stores all data related to backup in DB (MySQL/postgres). The intelligent data management and storage device 100 may be a Linux server device, etc. In this case, the user controls the backup master module 720 through the backup UI module 710 installed on a terminal such as a Windows PC.

The backup client module 730 is a software module installed in a server device (user terminals 200 in FIG. 1) that stores data to be backed up, such as a Windows system, Linux server, or Unix server, and reads and backs up the data to be backed up. It is transmitted to the data storage provided in the server device 740.

The backup server module 740 is a software module installed in a server device (storage server 300 in FIG. 1) with backup storage, such as a Windows system, Linux server, or Unix server, and includes the backup master module 720 and the backup client. It communicates with the module 730, sets up a storage to store data, and serves as a storage that reads data from the backup client 730 and stores the data.

Figure 8 is a diagram illustrating in detail the functions of data analysis, data management, and data storage performed by the intelligent data management and storage method according to the above-described embodiment in a table.

Hereinafter, the apparatus and method proposed in this specification, particularly the artificial intelligence processing module applicable to the above-mentioned artificial intelligence-based data analysis module, and its application will be examined with reference to FIG. 9. That is, it is clear that the content described later, that is, the content related to FIG. 9, can be applied to implement the method proposed in this specification.

Artificial Intelligence (AI) can use numerous analyzes to determine how a complex task is performed. In other words, AI can increase efficiency and reduce processing delays. Time-consuming tasks, such as analyzing large amounts of data, can be performed instantly by using AI.

Below, we will look at machine learning, a type of AI, in more detail.

Machine learning refers to a series of operations that train machines to create machines that can perform tasks that are difficult or difficult for humans to perform. Machine learning requires data and a learning model. In machine learning, data learning methods can be broadly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

Neural network learning is intended to minimize errors in output. Neural network learning repeatedly inputs learning data into the neural network, calculates the output of the neural network and the error of the target for the learning data, and backpropagates the error of the neural network from the output layer of the neural network to the input layer to reduce the error. ) is the process of updating the weight of each node in the neural network.

Supervised learning uses training data in which the correct answer is labeled, while unsupervised learning may not have the correct answer labeled in the training data. That is, for example, in the case of supervised learning on data classification, the learning data may be data in which each training data is labeled with a category. Labeled learning data is input to a neural network, and error can be calculated by comparing the output (category) of the neural network and the label of the learning data. The calculated error is back-propagated in the neural network in the reverse direction (i.e., from the output layer to the input layer), and the connection weight of each node in each layer of the neural network can be updated according to back-propagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to ensure that the neural network quickly achieves a certain level of performance to increase efficiency, and in the later stages of training, a low learning rate can be used to increase accuracy.

Learning methods may vary depending on the characteristics of the data. For example, in a communication system, when the goal is to accurately predict data transmitted from a transmitter at a receiver, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

The learning model corresponds to the human brain, and can be considered the most basic linear model. However, the paradigm of machine learning that uses a highly complex neural network structure such as artificial neural networks (ANN) as a learning model is called deep learning ( It is called deep learning.

Neural network cores used as learning methods are broadly divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent neural networks (RNN). there is.

An artificial neural network is an example of connecting multiple perceptrons.

Figure 9 is a block diagram of an AI device according to an embodiment of the present invention.

The AI device 900 may include an electronic device including an AI module capable of performing AI processing or a server including the AI module. Additionally, the AI device 900 may be included as at least a part of the intelligent data management and storage device 100 according to an embodiment and may be equipped to perform at least part of AI processing together.

The AI device 900 may include an AI processor 901, a memory 905, and/or a communication unit 907.

The AI device 900 is a computing device capable of learning a neural network, and may be implemented in various electronic devices such as servers, desktop PCs, laptop PCs, and tablet PCs.

The AI processor 901 can learn a neural network using a program stored in the memory 905. In particular, the AI processor 901 can learn a neural network for recognizing image-related data. Here, a neural network for recognizing image-related data may be designed to simulate the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. Multiple network modes can exchange data according to each connection relationship to simulate the synaptic activity of neurons sending and receiving signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In a deep learning model, multiple network nodes are located in different layers and can exchange data according to convolutional connection relationships. Examples of neural network models include deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), and deep trust. It includes various deep learning techniques such as deep belief networks (DBN) and Deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and voice/signal processing.

Meanwhile, the processor that performs the above-described functions may be a general-purpose processor (e.g., CPU), or may be an AI-specific processor (e.g., GPU) for artificial intelligence learning.

The memory 905 can store various programs and data necessary for the operation of the AI device 900. The memory 905 can be implemented as non-volatile memory, volatile memory, flash-memory, hard disk drive (HDD), or solid state drive (SDD). The memory 905 is accessed by the AI processor 901, and reading/writing/modifying/deleting/updating data by the AI processor 901 can be performed. Additionally, the memory 905 may store a neural network model (eg, deep learning model 906) generated through a learning algorithm for data classification/recognition according to an embodiment of the present invention.

Meanwhile, the AI processor 901 may include a data learning unit 902 that learns a neural network for data classification/recognition. The data learning unit 902 can learn standards regarding which learning data to use to determine data classification/recognition and how to classify and recognize data using the learning data. The data learning unit 902 can learn a deep learning model by acquiring training data to be used for learning and applying the acquired training data to the deep learning model.

The data learning unit 902 may be manufactured in the form of at least one hardware chip and mounted on the AI device 900. For example, the data learning unit 902 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or may be manufactured as part of a general-purpose processor (CPU) or graphics processor (GPU) to be used in the AI device 900. It may be mounted. Additionally, the data learning unit 902 may be implemented as a software module. When implemented as a software module (or a program module including instructions), the software module may be stored in a non-transitory computer readable recording medium that can be read by a computer. In this case, at least one software module may be provided by an operating system (OS) or an application.

The data learning unit 902 may include a learning data acquisition unit 903 and a model learning unit 904.

The learning data acquisition unit 903 may acquire learning data required for a neural network model for classifying and recognizing data. For example, the learning data acquisition unit 903 may acquire data and/or sample data to be input to a neural network model as learning data.

The model learning unit 904 can use the acquired training data to train the neural network model to have a judgment standard on how to classify certain data. At this time, the model learning unit 904 can learn a neural network model through supervised learning that uses at least some of the learning data as a judgment standard. Alternatively, the model learning unit 904 can learn a neural network model through unsupervised learning, which discovers a judgment standard by learning on its own using training data without guidance. In addition, the model learning unit 904 can learn a neural network model through reinforcement learning using feedback on whether the result of situational judgment based on learning is correct. Additionally, the model learning unit 904 may learn a neural network model using a learning algorithm including error back-propagation or gradient descent.

When the neural network model is learned, the model learning unit 904 may store the learned neural network model in memory. The model learning unit 904 may store the learned neural network model in the memory of a server connected to the AI device 900 through a wired or wireless network.

The data learning unit 902 further includes a learning data pre-processing unit (not shown) and a learning data selection unit (not shown) to improve the analysis results of the recognition model or save the resources or time required for generating the recognition model. You may.

The learning data preprocessor may preprocess the acquired data so that the acquired data can be used for learning to determine the situation. For example, the learning data preprocessor may process the acquired data into a preset format so that the model learning unit 904 can use the acquired learning data for training for image recognition.

Additionally, the learning data selection unit may select data required for learning among the learning data acquired by the learning data acquisition unit 903 or the learning data pre-processed by the pre-processing unit. The selected learning data may be provided to the model learning unit 904. For example, the learning data selection unit may detect a specific area among the acquired images and select only data for objects included in the specific area as the learning data.

Additionally, the data learning unit 902 may further include a model evaluation unit (not shown) to improve the analysis results of the neural network model.

The model evaluation unit inputs evaluation data into the neural network model, and when the analysis result output from the evaluation data does not meet a predetermined standard, the model learning unit 902 can learn again. In this case, the evaluation data may be predefined data for evaluating the recognition model. As an example, the model evaluation unit may evaluate the evaluation data as not meeting a predetermined standard if the number or ratio of inaccurate evaluation data exceeds a preset threshold among the analysis results of the learned recognition model for the evaluation data. there is.

The communication unit 907 can transmit the results of AI processing by the AI processor 901 to an external electronic device.

Here, the external electronic device is a device that can communicate wired/wireless with the AI device and may include a broadcasting device and a viewer device, which will be described later. In this case, the AI device may be implemented in a manager device.

Meanwhile, the AI device 900 shown in FIG. 9 has been described as functionally divided into an AI processor 901, a memory 905, and a communication unit 907, but the above-described components are integrated into one module to form an AI module. Please note that it may also be referred to as .

In the foregoing description, the steps or operations may be further divided into additional steps, procedures or operations, or may be combined into fewer steps, procedures or operations, depending on the implementation of the invention. Additionally, some steps, processes, or operations may be omitted as needed, and the order between steps, processes, or operations may be switched. In addition, each step or operation included in the intelligent data management and storage method using the above-described intelligent data management and storage device can be implemented as a computer program and stored in a computer-readable recording medium, and each step can be performed by a computer device. , a process or operation may be executed.

The embodiments described above combine the components and features of the present invention in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, it is possible to configure an embodiment of the present invention by combining some components and/or features. The order of operations described in embodiments of the present invention may be changed. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments. It is obvious that claims that do not have an explicit reference relationship in the patent claims can be combined to form an embodiment or included as a new claim through amendment after filing.

Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, an embodiment of the present invention includes one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), and FPGAs ( Field Programmable Gate Arrays), processor, controller, microcontroller, microprocessor, etc.

In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above. Software code can be stored in memory and run by a processor. The memory is located inside or outside the processor and can exchange data with the processor through various known means.

The term “unit” (e.g., control unit, etc.) used herein may mean, for example, a unit including one or a combination of two or more of hardware, software, or firmware. “Part” may be used interchangeably with terms such as unit, logic, logical block, component, or circuit, for example. A “part” may be the minimum unit of an integrated part or a part thereof. “Part” may be the minimum unit or part of one or more functions. The “part” may be implemented mechanically or electronically. For example, a “part” may be an Application-Specific Integrated Circuit (ASIC) chip, Field-Programmable Gate Arrays (FPGAs), or programmable-logic device, known or to be developed in the future, that performs certain operations. It can contain at least one.

At least a portion of the device (e.g., modules or functions thereof) or method (e.g., operations) according to various embodiments may be stored in a computer-readable storage media, e.g., in the form of a program module. It can be implemented with instructions stored in . When the instruction is executed by a processor, the one or more processors may perform the function corresponding to the instruction. A computer-readable storage medium may be, for example, memory.

As used herein, the term “one” is defined as one or more than one. Additionally, the use of introductory phrases such as “at least one” and “one or more” in a claim may mean that the same claim contains introductory phrases such as “at least one” and “one or more” and ambiguous phrases such as “an.” The introduction of another claim element by the ambiguous phrase "a", if any, shall be construed to mean that any particular claim containing the claim element so introduced is limited to an invention containing only one such element. It shouldn't be.

Additionally, those skilled in the art will recognize that the boundaries between the functionality of the foregoing operations are illustrative only. Multiple operations may be combined into a single operation, a single operation may be distributed into additional operations, and the operations may be executed at least partially overlapping in time. Additionally, alternative embodiments may include multiple instances for a particular operation, and the order of the operations may vary in various other embodiments. However, other modifications, variations and alternatives are also possible. Accordingly, the detailed description and drawings are to be regarded in an illustrative rather than a restrictive sense.

The phrase “may be X” indicates that condition X may be met. This phrase also indicates that condition X may not be met. For example, a reference to a system containing a specific component should also include scenarios in which the system does not contain the specific component. For example, a reference to a method that includes a specific behavior should also include scenarios in which the method does not include that specific component. However, as another example, a reference to a system configured to perform a specific action should also include scenarios in which the system is not configured to perform a specific task.

The terms “comprising,” “having,” “consisting of,” “consisting of,” and “consisting essentially of” are used interchangeably. For example, any method may include at least the operations included in the drawings and/or the specification, or may include only the operations included in the drawings and/or the specification. Alternatively, the word “comprising” does not exclude the presence of elements or acts listed in a claim.

It is obvious to those skilled in the art that the present invention can be embodied in other specific forms without departing from the essential features of the present invention. Accordingly, the above detailed description should not be construed as restrictive in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

10: Intelligent data management and storage system
100: Intelligent data management and storage
110: control unit
120: Department of Communications
130: storage unit
140: output unit
200: user terminal
300: storage server

Claims (10)

In an intelligent data management and storage method,
Collecting file information about management target files;
Based on the collected file information, the management target files are analyzed and classified by category, and the management target files are classified according to usage time based on the usage time information of the files among the collected file information. Classifying the management target files according to file type based on file type information among file information;
The classified files are optimized according to a first preset standard, but the files classified according to usage time are compressed and optimized for a specific time period, and the files classified according to file type are compressed using a different compression method for each file type. optimizing; and
Comprising: selecting and storing the optimized file in at least one of a plurality of network-connected data stores according to a second preset standard;
method. delete delete According to claim 1,
The step of selecting and storing the optimized file in at least one of a plurality of network-connected data storages according to a second preset standard,
analyzing usage costs of each of the plurality of data stores; and
Comprising the step of storing the optimized file in a data storage with the lowest usage cost among the plurality of data storages.
A method characterized by: The method of claim 4, wherein the usage cost is,
determined by at least one of the following: the type of storage medium for the data store, the installation location of the data store, the owner of the data store, and the purpose of the data store.
A method characterized by: According to clause 5,
The usage cost is determined by the 1 TB storage cost index (T),
The 1 TB storage cost index (T) is calculated as the result of dividing the price of the data storage provider's 1 TB storage space service (S) by the storage cost per 1 TB (C),
The storage cost per 1 TB (C) is calculated as the result of dividing the cost of the total TB used by the total number of TB.
A method characterized by: In an intelligent data management and storage method,
Collecting file information about management target files;
Based on the collected file information, the management target files are analyzed and classified by category, and the categories are divided into classifications including file size, file characteristics, and file use frequency,
Classifying the management target files by file size range, file characteristics, or file usage frequency range based on the collected file information;
Compressing and optimizing the classified files according to a first preset standard; and
Comprising: selecting and storing the optimized file in at least one of a plurality of network-connected data stores according to a second preset standard;
A method characterized by: According to claim 1,
Further comprising providing data information about the management target file by virtualizing the management target file.
method. Combined with computer hardware,
A computer program stored in a computer-readable recording medium to execute each step included in the intelligent data management and storage method according to any one of claims 1 and 4 to 8. A memory that stores one or more instructions; and
Includes a processor for executing the instructions,
When the instruction is executed, the processor:
Collects file information about managed files,
Classify the files subject to management by category based on the collected file information, classify the files subject to management according to usage time based on usage time information of the files among the collected file information, and classify the files subject to management according to usage time. Classifying the management target files according to file type based on the file type information,
The classified files are compressed and optimized according to a first preset standard, but among the files classified according to usage time, files in a specific time period are compressed and optimized, and the files classified according to file type are compressed and optimized by different compression methods for each file type. Compress and optimize, and
An intelligent data management and storage device that selects and stores the optimized file in at least one of a plurality of network-connected data stores according to a second preset standard.

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