CN110555138A - hybrid cloud storage method under cloud computing architecture - Google Patents
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- CN110555138A CN110555138A CN201910718220.0A CN201910718220A CN110555138A CN 110555138 A CN110555138 A CN 110555138A CN 201910718220 A CN201910718220 A CN 201910718220A CN 110555138 A CN110555138 A CN 110555138A
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Abstract
The invention discloses a hybrid cloud storage method under a cloud computing architecture, which specifically comprises the following steps: the method comprises the steps of S1, first classification processing of data, S2, recombination classification identification processing of the data, S3, data identification sorting and storage, S4, data extraction, S5 and database management. According to the hybrid cloud storage method under the cloud computing architecture, the stored data can be classified and expressed, the data can be rapidly screened, the purposes of saving data retrieval and extraction time by identifying the data storage tags are well achieved, the classification identification storage mode is very beneficial to the extraction of the data, a large amount of time is not needed to be spent for waiting when people extract the data in a database, the rapid and accurate extraction of the data is realized, and the cloud data extraction of people is greatly facilitated.
Description
Technical Field
The invention relates to the technical field of cloud computing data storage, in particular to a hybrid cloud storage method under a cloud computing architecture.
background
Cloud computing is a kind of distributed computing, which means that huge data computing processing programs are decomposed into countless small programs through a network cloud, and then the results are obtained by processing and analyzing the small programs through a system composed of a plurality of servers and returned to a user, and the cloud computing at the present stage is not only a distributed computing but also results which are evolved and leaped by mixing computer technologies such as distributed computing, utility computing, load balancing, parallel computing, network storage, hot backup redundancy, virtualization and the like through continuous progress, and is often confused with grid computing, utility computing and autonomous computing, and grid computing: one type of distributed computing, a super virtual computer consisting of a group of loosely coupled computers, is commonly used to perform a number of large tasks, utility computing: a packaging and charging mode of IT resources, such as respectively metering expenses according to calculation and storage, and autonomously calculating like traditional public facilities such as electric power and the like: in fact, many cloud computing deployments depend on a computer cluster (but are largely different from the composition, architecture, purpose and working mode of a grid), and also absorb the characteristics of autonomous computing and utility computing, and most cloud computing architectures serve data processing of a cloud database, wherein storage of cloud data is an important part of cloud data processing.
Most of the current data are directly stored in a cloud database, the storage is disordered and basically, the data are directly stored according to the time sequence of the data, the storage mode is not favorable for data extraction, people need to spend a large amount of time waiting when extracting the data in the database, the stored data cannot be classified and expressed, the data can be quickly screened, the purpose of saving data retrieval and extraction time by identifying data storage tags cannot be achieved, and great inconvenience is brought to people for extracting the cloud data.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a hybrid cloud storage method under a cloud computing architecture, which solves the problems that the existing storage mode is very unfavorable for data extraction, a great amount of time is needed for waiting when people extract data in a database, the stored data can not be classified and represented, the data can not be rapidly screened, and the purposes of saving data retrieval and extraction time by identifying data storage tags can not be achieved.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a hybrid cloud storage method under a cloud computing architecture specifically comprises the following steps:
S1, first classification processing of data: firstly, a user uses a user operation terminal to perform data interaction with the whole cloud system through a wireless communication module, data are imported into the system through a data import module, then a cloud computing server controls a classification algorithm entry module in a data classification unit to enter a classification algorithm into the system, then a data classification matching module performs matching classification on the imported data according to the classification algorithm, at the moment, a data classification label entry module enters a first classification type label node on an entered data address name, and then data integration processing is performed through a data integration module;
S2, data reorganization, classification and identification: then the cloud computing server controls the multiple data duplication module to conduct multiple classification processing on the classified data, the data are recombined through the data recombination classification unit, a recombination unzipping module in the data recombination classification unit can change a last input recombination data address, then a recombination constraint algorithm is poured into the data through the redo speech rate leading-in module, the recombined data are generated, at the moment, the recombination node inserting module inserts the recombined label nodes into the unzipped data addresses and generates new data addresses, and multiple label nodes can be generated on the data addresses through repeated operation;
S3, data identification sorting and storing: meanwhile, the cloud computing server controls a node recognition algorithm in the tag node arrangement module, identifies each tag node in the data, arranges the tag nodes according to the time sequence, and then guides the data into the hybrid storage database module for storage;
S4, data extraction: when data in the hybrid storage database needs to be extracted, the cloud computing server can control the storage data extraction unit to extract the data, people can input data tags of needed types into the system through a data tag input module in the storage data extraction unit, then the data tag node retrieval module quickly retrieves the corresponding data, and then the node data extraction integration module is used for integrating the extracted data;
s5, management of the database: people can manage the data in the mixed storage database module through the system management terminal, and meanwhile, the safety management module can identify and early warn dangerous data and programs in the whole storage system.
Preferably, in step S1, the user operation terminal is wirelessly and bidirectionally connected to the cloud computing server and the data importing module through the wireless communication module.
preferably, the data classification unit in step S1 includes a classification algorithm entry module, a data classification matching module, a data classification label entry module, and a data integration module, where an output end of the classification algorithm entry module is connected to an input end of the data classification matching module, an output end of the data classification matching module is connected to an input end of the data classification label entry module, and an output end of the data classification label entry module is connected to an input end of the data integration module.
Preferably, in step S2, the cloud computing server is bidirectionally connected to the multiple data duplication module, and the multiple data duplication module is bidirectionally connected to the data reassembly and classification unit.
Preferably, the data reassembly and sorting unit in step S2 includes a reassembly delinking module, a reassembly constraint introduction module, and a reassembly node insertion module, wherein an output of the reassembly delinking module is connected to an input of the reassembly constraint introduction module, and an output of the reassembly constraint introduction module is connected to an input of the reassembly node insertion module.
preferably, in step S3, the cloud computing server and the tag node arrangement module are in bidirectional connection, and the cloud computing server and the hybrid storage database module are in bidirectional connection.
Preferably, in step S4, the cloud computing server is bidirectionally connected to the storage data extraction unit, and the storage data extraction unit includes a data tag input module, a data tag node search module, and a node data extraction and integration module, an output end of the data tag input module is connected to an input end of the data tag node search module, and an output end of the data tag node search module is connected to an input end of the node data extraction and integration module.
Preferably, in step S5, the cloud computing server is respectively connected to the system management terminal and the security management module in a bidirectional manner.
(III) advantageous effects
The invention provides a hybrid cloud storage method under a cloud computing architecture. Compared with the prior art, the method has the following beneficial effects: the hybrid cloud storage method under the cloud computing architecture specifically comprises the following steps: s1, first classification processing of data: firstly, a user uses a user operation terminal to perform data interaction with the whole cloud system through a wireless communication module, data is imported into the system through a data import module, and S2 is used for data recombination, classification and identification processing: then the cloud computing server controls the multiple data re-engraving module to perform multiple classification processing on the classified data, the data are recombined through the data recombination classification unit, the recombination unzipping module in the data recombination classification unit can change the last input recombination data address, and S3, data identification sorting and storage are as follows: meanwhile, the cloud computing server controls a node recognition algorithm in the label node arrangement module, identifies each label node in the data, arranges the label nodes according to the time sequence, guides the data into the hybrid storage database module for storage, and extracts the data in S4: when data in the hybrid storage database needs to be extracted, the cloud computing server can control the storage data extraction unit to perform data extraction processing, people can input data tags of required types into the system through a data tag input module in the storage data extraction unit, and S5 manages the database: people's accessible system management terminal manages the data in the hybrid storage database module, safety management module can discern dangerous data and procedure in the whole storage system and early warning simultaneously, can realize carrying out the categorised processing that shows to the data of storage, realize screening data fast, fine reaching through discernment data storage label, the purpose of saving data retrieval and extraction time, such categorised sign storage mode very is favorable to the drawing of data, people need not to spend a large amount of time waiting when drawing the data in the database, realize that existing accuracy carries out the drawing of data fast, thereby people's the high in the clouds data that draws that has made things convenient for greatly.
Drawings
FIG. 1 is a schematic block diagram of the architecture of the system of the present invention;
FIG. 2 is a schematic block diagram of the structure of the data reorganization and classification unit of the present invention;
FIG. 3 is a schematic block diagram of the structure of the storage data extraction unit according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, an embodiment of the present invention provides a technical solution: a hybrid cloud storage method under a cloud computing architecture specifically comprises the following steps:
S1, first classification processing of data: firstly, a user uses a user operation terminal to perform data interaction with the whole cloud system through a wireless communication module, data are imported into the system through a data import module, then a cloud computing server controls a classification algorithm entry module in a data classification unit to enter a classification algorithm into the system, then a data classification matching module performs matching classification on the imported data according to the classification algorithm, at the moment, a data classification label entry module enters a first classification type label node on an entered data address name, and then data integration processing is performed through a data integration module;
S2, data reorganization, classification and identification: then the cloud computing server controls the multiple data duplication module to conduct multiple classification processing on the classified data, the data are recombined through the data recombination classification unit, a recombination unzipping module in the data recombination classification unit can change a last input recombination data address, then a recombination constraint algorithm is poured into the data through the redo speech rate leading-in module, the recombined data are generated, at the moment, the recombination node inserting module inserts the recombined label nodes into the unzipped data addresses and generates new data addresses, and multiple label nodes can be generated on the data addresses through repeated operation;
S3, data identification sorting and storing: meanwhile, the cloud computing server controls a node recognition algorithm in the tag node arrangement module, identifies each tag node in the data, arranges the tag nodes according to the time sequence, and then guides the data into the hybrid storage database module for storage;
S4, data extraction: when data in the hybrid storage database needs to be extracted, the cloud computing server can control the storage data extraction unit to extract the data, people can input data tags of needed types into the system through a data tag input module in the storage data extraction unit, then the data tag node retrieval module quickly retrieves the corresponding data, and then the node data extraction integration module is used for integrating the extracted data;
s5, management of the database: people can manage the data in the mixed storage database module through the system management terminal, and meanwhile, the safety management module can identify and early warn dangerous data and programs in the whole storage system.
In the invention, in step S1, the user operation terminal is respectively in wireless bidirectional connection with the cloud computing server and the data import module through the wireless communication module.
In the present invention, the data classification unit in step S1 includes a classification algorithm entry module, a data classification matching module, a data classification label entry module, and a data integration module, wherein an output end of the classification algorithm entry module is connected to an input end of the data classification matching module, an output end of the data classification matching module is connected to an input end of the data classification label entry module, and an output end of the data classification label entry module is connected to an input end of the data integration module.
In the invention, in step S2, the cloud computing server is bidirectionally connected to the multiple data re-engraving module, and the multiple data re-engraving module is bidirectionally connected to the data re-engraving classification unit.
In the present invention, the data reassembly and sorting unit in step S2 includes a reassembly and unzipping module, a reassembly and constraint import module, and a reassembly and node insertion module, wherein an output end of the reassembly and unzipping module is connected to an input end of the reassembly and constraint import module, and an output end of the reassembly and constraint import module is connected to an input end of the reassembly and node insertion module.
In the invention, in step S3, the cloud computing server and the label node arrangement module realize bidirectional connection, and the cloud computing server and the hybrid storage database module realize bidirectional connection.
In the invention, in step S4, the cloud computing server is bidirectionally connected to the storage data extraction unit, and the storage data extraction unit includes a data tag input module, a data tag node search module, and a node data extraction and integration module, an output end of the data tag input module is connected to an input end of the data tag node search module, and an output end of the data tag node search module is connected to an input end of the node data extraction and integration module.
In the invention, in the step S5, the cloud computing server is respectively connected with the system management terminal and the security management module in a bidirectional manner.
To sum up the above
The invention can realize the classified representation processing of the stored data to realize the rapid screening of the data, and well achieves the purpose of saving the data retrieval and extraction time by identifying the data storage label.
it is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. a hybrid cloud storage method under a cloud computing architecture is characterized in that: the method specifically comprises the following steps:
s1, first classification processing of data: firstly, a user uses a user operation terminal to perform data interaction with the whole cloud system through a wireless communication module, data are imported into the system through a data import module, then a cloud computing server controls a classification algorithm entry module in a data classification unit to enter a classification algorithm into the system, then a data classification matching module performs matching classification on the imported data according to the classification algorithm, at the moment, a data classification label entry module enters a first classification type label node on an entered data address name, and then data integration processing is performed through a data integration module;
S2, data reorganization, classification and identification: then the cloud computing server controls the multiple data duplication module to conduct multiple classification processing on the classified data, the data are recombined through the data recombination classification unit, a recombination unzipping module in the data recombination classification unit can change a last input recombination data address, then a recombination constraint algorithm is poured into the data through the redo speech rate leading-in module, the recombined data are generated, at the moment, the recombination node inserting module inserts the recombined label nodes into the unzipped data addresses and generates new data addresses, and multiple label nodes can be generated on the data addresses through repeated operation;
S3, data identification sorting and storing: meanwhile, the cloud computing server controls a node recognition algorithm in the tag node arrangement module, identifies each tag node in the data, arranges the tag nodes according to the time sequence, and then guides the data into the hybrid storage database module for storage;
S4, data extraction: when data in the hybrid storage database needs to be extracted, the cloud computing server can control the storage data extraction unit to extract the data, people can input data tags of needed types into the system through a data tag input module in the storage data extraction unit, then the data tag node retrieval module quickly retrieves the corresponding data, and then the node data extraction integration module is used for integrating the extracted data;
S5, management of the database: people can manage the data in the mixed storage database module through the system management terminal, and meanwhile, the safety management module can identify and early warn dangerous data and programs in the whole storage system.
2. the hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: in the step S1, the user operation terminal is wirelessly and bidirectionally connected to the cloud computing server and the data import module through the wireless communication module.
3. The hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: the data classification unit in the step S1 includes a classification algorithm entry module, a data classification matching module, a data classification label entry module, and a data integration module, wherein an output end of the classification algorithm entry module is connected to an input end of the data classification matching module, an output end of the data classification matching module is connected to an input end of the data classification label entry module, and an output end of the data classification label entry module is connected to an input end of the data integration module.
4. The hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: in the step S2, the cloud computing server is bidirectionally connected to the multiple data re-engraving module, and the multiple data re-engraving module is bidirectionally connected to the data reassembly and sorting unit.
5. The hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: the data recombination and classification unit in the step S2 includes a recombination unzipping module, a recombination constraint importing module, and a recombination node inserting module, wherein an output end of the recombination unzipping module is connected to an input end of the recombination constraint importing module, and an output end of the recombination constraint importing module is connected to an input end of the recombination node inserting module.
6. The hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: in the step S3, the cloud computing server and the tag node arrangement module are connected in a bidirectional manner, and the cloud computing server and the hybrid storage database module are connected in a bidirectional manner.
7. The hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: in step S4, the cloud computing server is bidirectionally connected to the stored data extracting unit, and the stored data extracting unit includes a data tag input module, a data tag node retrieving module, and a node data extracting and integrating module, an output end of the data tag input module is connected to an input end of the data tag node retrieving module, and an output end of the data tag node retrieving module is connected to an input end of the node data extracting and integrating module.
8. the hybrid cloud storage method under the cloud computing architecture according to claim 1, wherein: in the step S5, the cloud computing server is respectively connected to the system management terminal and the security management module in a bidirectional manner.
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| CN113449036A (en) * | 2021-06-29 | 2021-09-28 | 未鲲(上海)科技服务有限公司 | Intelligent data storage method and device, computer equipment and storage medium |
| CN114625320A (en) * | 2022-03-15 | 2022-06-14 | 江苏太湖慧云数据系统有限公司 | Hybrid cloud platform data management system based on characteristics |
| CN114625320B (en) * | 2022-03-15 | 2024-01-02 | 江苏太湖慧云数据系统有限公司 | Hybrid cloud platform data management system based on characteristics |
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Denomination of invention: A Cloud computing#Hybrid cloud storage method under cloud computing architecture Effective date of registration: 20230705 Granted publication date: 20220913 Pledgee: Agricultural Bank of China Limited Shanghai Songjiang Sub-branch Pledgor: HUIRONG ELECTRONIC SYSTEM ENGINEERING LTD. Registration number: Y2023980047238 |