CN113407118B - Data storage device - Google Patents
Data storage device Download PDFInfo
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- CN113407118B CN113407118B CN202110702153.0A CN202110702153A CN113407118B CN 113407118 B CN113407118 B CN 113407118B CN 202110702153 A CN202110702153 A CN 202110702153A CN 113407118 B CN113407118 B CN 113407118B
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- 238000013500 data storage Methods 0.000 title claims abstract description 15
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 9
- 239000013598 vector Substances 0.000 claims description 38
- 238000000034 method Methods 0.000 claims description 14
- 238000010606 normalization Methods 0.000 claims description 5
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
- G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/061—Improving I/O performance
- G06F3/0611—Improving I/O performance in relation to response time
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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Abstract
The invention discloses a data storage device which comprises a shell, wherein a storage space, a data analyzer, a cloud interface and a data collator are arranged in the shell. The storage space is used for storing data; the data analyzer is used for classifying the data through an artificial intelligence algorithm to obtain local data and cloud data; the cloud interface is used for transmitting the cloud data to a cloud end; the data collator is used for storing the local data in the storage space and sending the cloud data into the cloud interface. According to the invention, data in the storage space is classified and processed, and the data which is not frequently used is stored to the cloud, so that the storage of the cloud and the local storage are combined, thus the storage space of the local hard disk of a user can be saved, the user does not need to frequently replace the local hard disk, the whole capacity can be enlarged, the idle time of the user using a computer can be automatically stored, and the time is saved compared with the manual automatic uploading of the user.
Description
Technical Field
The invention relates to the field of computer storage equipment, in particular to a data storage device.
Background
At present, data storage is mainly implemented by using a hard disk for data storage, and a local hard disk is a storage space of data, and under a general condition, the capacity of data which can be stored in the storage space is limited, so that the storage space cannot meet the use requirement of a user under the condition of long-time use by the user, and the user needs a local hard disk with a larger capacity to meet the requirement of data storage.
Disclosure of Invention
The present invention is directed to overcome the problems in the prior art, and provides a data storage device, which classifies data in a storage space, stores infrequent data in a cloud, and combines the cloud storage with local storage, so as to save the storage space of a local hard disk of a user, and enable the user to avoid frequent replacement of the local hard disk, and to expand the overall capacity.
Therefore, the invention provides a data storage device which comprises a shell, wherein a storage space, a data analyzer, a cloud interface and a data collator are arranged in the shell.
A storage space for storing data;
the data analyzer is used for classifying the data through an artificial intelligence algorithm to obtain local data and cloud data;
the cloud interface is used for transmitting the cloud data to a cloud end;
and the data collator is used for keeping the local data in the storage space and sending the cloud data into the cloud interface.
Further, the artificial intelligence algorithm comprises the following steps:
numbering the data according to their type;
clustering the data with the numbers by a Kmeans algorithm to obtain a clustering result;
and dividing the data into the local data and the cloud data according to the clustering result.
Still further, the method further comprises:
summarizing the local data and the cloud data, and counting the use frequency of each local data and the use frequency of each cloud data;
creating a local frequency vector according to the use frequency of the local data;
creating a cloud frequency vector according to the using frequency of the cloud data;
inputting the local frequency vector and the cloud frequency vector into a support vector machine as input, and outputting to obtain a result vector;
and judging whether the local data and the cloud data are numbered again according to the types of the local data and the cloud data according to the result vector.
Further, when determining the result vector, the method includes:
carrying out normalization processing on the result vector;
inputting the result vector after normalization processing into a Denes algorithm to obtain a one-dimensional numerical value;
and comparing the one-dimensional numerical value with a set range, if the one-dimensional numerical value is in the set range, the local data and the cloud data are not required to be numbered according to the types of the local data and the cloud data, and if the one-dimensional numerical value is not in the set range, the local data and the cloud data are considered to be numbered again according to the types of the local data and the cloud data.
Further, the storage space periodically cleans the data.
Further, when the cloud interface transmits cloud data to a cloud, the corresponding bandwidth size is taken up according to the size of the cloud data.
Furthermore, the size of the cloud data is in a set proportion to the bandwidth size.
The data storage device provided by the invention has the following beneficial effects:
1. according to the invention, data in the storage space is classified and processed, and the data which is not frequently used is stored to the cloud, so that the storage of the cloud and the local storage are combined, and thus, the storage space of a local hard disk of a user can be saved, the user does not need to frequently replace the local hard disk, and the whole capacity can be enlarged;
2. the data are classified in an artificial intelligence mode, so that the data can be stored in the cloud according to the using habits of the user, and compared with the method that the data are stored in the cloud manually by the user, the method and the system automatically store the idle time of the computer used by the user in a real-time mode, the time is saved compared with the manual and autonomous uploading of the user, and the user experience is improved.
Drawings
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a schematic block diagram illustrating a process flow for providing an artificial intelligence algorithm in accordance with the present invention;
FIG. 3 is a block diagram illustrating a process for determining a result vector according to the present invention.
Detailed Description
An embodiment of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the embodiment.
In the present application, the type and structure of components that are not specified are all the prior art known to those skilled in the art, and those skilled in the art can set the components according to the needs of the actual situation, and the embodiments of the present application are not specifically limited.
Specifically, as shown in fig. 1 to 3, an embodiment of the present invention provides a data storage device, which includes a housing, and a storage space, a data analyzer, a cloud interface, and a data organizer are disposed in the housing.
A storage space for storing data; the storage space is a storage area of a hard disk and is used for storing data, and the data does not distinguish local data from cloud data.
The data analyzer is used for classifying the data through an artificial intelligence algorithm to obtain local data and cloud data; the data analyzer divides data into two parts, namely local data and cloud data, and classifies the data according to a certain mode during classification, and for the classification mode, the data can be classified according to the use frequency of the data and the type of the data.
The cloud interface is used for transmitting the cloud data to a cloud end; the cloud interface is an execution terminal which can cache cloud data on one hand and execute a process of transmitting the cloud data to the cloud on the other hand, and the process is also used for establishing an interface channel between the local and the cloud. In subsequent citations, the cloud interface is an interface providing bidirectional data transmission, and can be uploaded or downloaded.
And the data collator is used for keeping the local data in the storage space and sending the cloud data into the cloud interface. The data collator is a process of collating the distinguished data, and the collated data is classified and sent to a corresponding space.
Among the above-mentioned technical scheme, will distinguish at the data of storage space, just so can obtain local data and high in the clouds data, later use the data collator to continue to put back local data to storage space, the high in the clouds data is sent into the operation interface, just so can deposit the operation data in the high in the clouds.
When the invention is used, the data is separated and stored by utilizing leisure time due to the real-time property of the inside of the invention for data processing, so that the data transmitted each time is not large, the bandwidth is not excessively occupied, and the use rate of the computer is not slowed down. On the basis, the local data and the cloud data can be acted together to establish a larger storage space.
In this embodiment, the artificial intelligence algorithm includes the following steps:
numbering the data according to the type of the data;
(II) clustering the data with the numbers through a Kmeans algorithm to obtain a clustering result;
and thirdly, dividing the data into the local data and the cloud data according to the clustering result.
In the steps (a) to (b), the data are numbered in the step (a), so that the data are more convenient and faster when the Kmeans algorithm clustering is performed, a basis is provided for the application of the Kmeans algorithm in the step (b), the Kmeans algorithm is used in the step (b) to obtain a clustering result, and the clustering result of the Kmeans algorithm is divided in the step (b) to obtain the local data and the cloud data.
In the above technical solution, when the Kmeans algorithm is used, when the condition is entered, the entered condition is user-defined, for example, if the user considers that the data should be classified according to the type, the entered condition is the type, and if the user considers that the data should be classified according to the use frequency of the data, the entered condition is the frequency, that is, the entered condition is the classification standard.
In the embodiment provided by the present invention, the use frequency of data is used as a criterion for classification.
Meanwhile, in this embodiment, when the use frequency of the data is used as the standard of the classification, the process of numbering the data includes the following steps:
(1) Summarizing the local data and the cloud data, and counting the use frequency of each local data and the use frequency of each cloud data;
(2) Creating a local frequency vector according to the use frequency of the local data;
(3) Creating a cloud frequency vector according to the use frequency of the cloud data;
(4) Inputting the local frequency vector and the cloud frequency vector into a support vector machine as input, and outputting to obtain a result vector;
(5) And judging whether the local data and the cloud data are numbered again according to the types of the local data and the cloud data according to the result vector.
In the steps (1) to (5), the step (1) is a statistical process, that is, the frequency used by each data is obtained, the steps (2) and (3) are vector establishing processes, that is, the frequencies of all the data are combined into a sequence according to a specific sequence, and finally a vector is obtained, so that a local frequency vector and a cloud frequency vector are obtained, after the local frequency vector and the cloud frequency vector are obtained, the step (4) is executed, the step is the use of a learning model, the output can obtain a result vector, that is, the frequency corresponding to each data is regulated, in the step (5), whether the requirement is met or not can be judged according to the data corresponding to each data output by the result vector, and whether the number is numbered or not can be judged according to the result.
Meanwhile, in this embodiment, when determining the result vector, the method includes:
normalizing the result vector;
inputting the result vector after normalization processing into a Denes algorithm to obtain a one-dimensional numerical value;
and comparing the one-dimensional numerical value with a set range, if the one-dimensional numerical value is in the set range, the local data and the cloud data are not required to be numbered according to the types of the local data and the cloud data, and if the one-dimensional numerical value is not in the set range, the local data and the cloud data are not required to be numbered again according to the types of the local data and the cloud data.
In the technical scheme, the vectors are converted into numerical values by using the Denes algorithm, so that the processing of the CPU can greatly reduce the operation amplitude when judging, and the operation amount can be greatly reduced compared with the calculation vectors due to the comparison of the calculation numerical values, so that the operation efficiency can be improved and the power consumption can be reduced when the system operates.
In this embodiment, the storage space periodically cleans the data. Therefore, most of garbage data can be deleted by data cleaning before the data are processed, so that the operation amount is reduced when the system runs, the operation speed is increased, and the storage requirement of the storage space is reduced.
In this embodiment, when the cloud interface transmits cloud data to a cloud, the cloud interface seizes a corresponding bandwidth according to the size of the cloud data. Therefore, the appropriate bandwidth can be selected according to the adaptive data volume to transmit the data, so that the transmission speed is ensured during transmission.
Meanwhile, in this embodiment, the size of the cloud data and the bandwidth size are in a set proportion. The technical scheme is the limitation on the bandwidth, in the invention, when the data to be transmitted is more, the bandwidth is larger, and when the data to be transmitted is less, the bandwidth is smaller, so that the transmission speed is ensured to be moderate and maintain a uniform speed in the data transmission process. Thereby improving the user experience of the user using the computer.
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (4)
1. A data storage device is characterized by comprising a shell, wherein a storage space, a data analyzer, a cloud interface and a data collator are arranged in the shell;
a storage space for storing data;
the data analyzer is used for classifying the data through an artificial intelligence algorithm to obtain local data and cloud data;
the cloud interface is used for transmitting the cloud data to a cloud end;
the data collator is used for keeping the local data in the storage space and sending the cloud data into the cloud interface;
the artificial intelligence algorithm comprises the following steps:
numbering the data according to their type;
clustering the data with the numbers by a Kmeans algorithm to obtain a clustering result;
dividing the data into the local data and the cloud data according to a clustering result;
further comprising:
summarizing the local data and the cloud data, and counting the use frequency of each local data and the use frequency of each cloud data;
creating a local frequency vector according to the use frequency of the local data;
creating a cloud frequency vector according to the using frequency of the cloud data;
inputting the local frequency vector and the cloud frequency vector into a support vector machine as input, and outputting to obtain a result vector;
judging whether the local data and the cloud data are numbered again according to the types of the local data and the cloud data according to the result vector;
when judging the result vector, the method comprises the following steps:
carrying out normalization processing on the result vector;
inputting the result vector after normalization processing into a Denes algorithm to obtain a one-dimensional numerical value;
and comparing the one-dimensional numerical value with a set range, if the one-dimensional numerical value is in the set range, the local data and the cloud data are not required to be numbered according to the types of the local data and the cloud data, and if the one-dimensional numerical value is not in the set range, the local data and the cloud data are not required to be numbered again according to the types of the local data and the cloud data.
2. A data storage device as claimed in claim 1 wherein said storage space is periodically purged of said data.
3. The data storage device as claimed in claim 1, wherein the cloud interface is configured to, when transmitting cloud data to a cloud, seize a corresponding bandwidth size according to a size of the cloud data.
4. A data storage device as claimed in claim 3, wherein the size of the cloud data is a set proportion of the bandwidth size.
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| CN105956702A (en) * | 2016-04-29 | 2016-09-21 | 中国农业大学 | Aquaculture water quality dissolved oxygen prediction method based on data fusion |
| CN110059781A (en) * | 2019-04-11 | 2019-07-26 | 北京信诺必成科技有限公司 | Cow digitalization Activity recognition device |
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| CN104834713A (en) * | 2015-05-08 | 2015-08-12 | 武汉网幂科技有限公司 | Method and system for storing and transmitting image data of terminal equipment |
| CN106445994A (en) * | 2016-07-13 | 2017-02-22 | 广州精点计算机科技有限公司 | Mixed algorithm-based web page classification method and apparatus |
| US20190149777A1 (en) * | 2017-11-16 | 2019-05-16 | Jungo Connectivity Ltd. | System for recording a scene based on scene content |
| CN109656696B (en) * | 2018-12-03 | 2020-10-16 | 华南师范大学 | Processing method for efficient calling of data API |
| CN109947373B (en) * | 2019-03-28 | 2022-05-13 | 北京大道云行科技有限公司 | Data processing method and device |
| CN112380399A (en) * | 2020-11-18 | 2021-02-19 | 上海科技网络通信有限公司 | Cloud platform-based power consumption big data processing system and processing method thereof |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102194127A (en) * | 2011-05-13 | 2011-09-21 | 中国科学院遥感应用研究所 | Multi-frequency synthetic aperture radar (SAR) data crop sensing classification method |
| CN105956702A (en) * | 2016-04-29 | 2016-09-21 | 中国农业大学 | Aquaculture water quality dissolved oxygen prediction method based on data fusion |
| CN110059781A (en) * | 2019-04-11 | 2019-07-26 | 北京信诺必成科技有限公司 | Cow digitalization Activity recognition device |
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Application publication date: 20210917 Assignee: Gongqingcheng Gangcheng Intelligent Technology Co.,Ltd. Assignor: JIUJIANG VOCATIONAL AND TECHNICAL College Contract record no.: X2023980053504 Denomination of invention: A data storage device Granted publication date: 20221101 License type: Common License Record date: 20231221 |