CN115905167B - Intelligent data storage method and system capable of rapidly migrating data - Google Patents
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Abstract
The invention discloses an intelligent data storage method and system capable of rapidly migrating data, and belongs to the technical field of data storage management. In order to solve the problem of poor data migration efficiency of a storage system, a data evaluation module calculates results to perform data evaluation, determines a data migration mode according to the evaluation results, selects different migration paths, and directly migrates the data to a user terminal when the data is smaller and the evaluation results are basic results, otherwise, the data is transferred from a storage layer to a node, so that the node split migration of different data is realized, and the overall storage efficiency and migration efficiency of the storage system are improved. The data storage module processes data and realizes the data storage function of the cloud database, so that the data storage of multiple types and multiple nodes is realized, the storage is more efficient, the storage of the encrypted data and the non-encrypted data in the partial nodes can be realized, the storage in the partial areas can be realized, the safety of the data can be ensured, and the quick calling and searching of numbers can be realized.
Description
Technical Field
The invention relates to the technical field of data storage management, in particular to an intelligent data storage method and system capable of rapidly migrating data.
Background
When the data is stored in a system, related patents, for example, application number cn201610905587.X, disclose a method and a system for intelligently storing data, which are used for storing data information of each application program to be maintained on a first terminal in common use into a cloud storage account through setting the storage account on the cloud, and if the first terminal fails or loses, the data information of each application program can be reconfigured on a new second terminal to the second terminal by logging in the cloud storage account. The use of the original information is not affected. The data information on the commonly used first terminal is backed up to the mobile storage device through the cloud server or the first terminal, and the data information of the original application programs can be reconfigured on the second terminal by logging in the mobile storage account number on the new second terminal.
The above patent has the following problems in actual operation:
1. when migrating or uploading data in a storage system, a unified uploading or migration path is often adopted to work, however, when migrating larger data, the efficiency is often slower.
2. When different data are stored, such as public data and personal data, the data are often difficult to realize regional storage because of non-regional storage, so that the security of the data is low, and quick calling and searching of numbers are difficult to realize.
Disclosure of Invention
The invention aims to provide an intelligent data storage method and system capable of rapidly migrating data, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent data storage method capable of rapidly migrating data comprises the following steps:
step one: the data to be migrated or stored is acquired through the user terminal, and the data is retrieved and selected by the data retrieval module;
step two: the data calculation module and the data evaluation module calculate and evaluate the data to obtain an evaluation result, and the data migration module selects different migration paths according to the evaluation result;
step three: when the data is smaller and the evaluation result is a basic result, the data is directly transmitted to a cloud database for storage or is directly migrated to the user terminal by the cloud database;
step four: and when the data is larger and the evaluation result is an overmodular result, sending the data to the slave storage layer for node transfer, and transferring the data from the storage layer to a cloud database or migrating the data to a user terminal.
Further, an intelligent data storage system capable of rapidly migrating data comprises a slave storage layer, a data scheduling layer, a data storage layer and an implementation layer;
the secondary storage layer is used for carrying out staged transfer on data, and node transmission is carried out through the secondary storage layer when the data scheduling layer is large in data calling;
the data scheduling layer is used for scheduling data in the data source layer and the data storage layer, so that the data among the data source layer, the data storage layer and the implementation layer can be quickly migrated;
the data storage layer is used for carrying out cloud storage and data retrieval and transmission on the data;
the implementation layer is used for carrying the slave storage layer, the data scheduling layer and the data storage layer, controlling the storage system, sending and receiving migration data, and realizing signal interaction connection with the slave storage layer, the data scheduling layer and the data storage layer through a network.
Further, the secondary storage layer at least comprises two secondary storage nodes, and the secondary storage layer comprises a secondary storage data engine;
the slave storage node receives the information and initiates the slave database engine to identify the stored data.
Further, the slave database engine retrieves data to be migrated and shunts and stores the data by the slave storage nodes, and the shunted and stored data is migrated and transmitted to the implementation layer through each slave storage node respectively.
Further, the data scheduling layer comprises a data retrieval module, a data calculation module, a data evaluation module and a data migration module;
the data retrieval module is used for retrieving and marking identification of data to be called and migrated in the data storage layer;
the data calculation module is used for calculating the data size of the data to be called and migrated, which is retrieved by the data retrieval module;
the data evaluation module is used for performing data evaluation on the result calculated by the data calculation module and determining a data migration mode according to the evaluation result;
the data migration module is used for carrying out data migration on the data to be called and migrated retrieved by the data retrieval module, and different migration paths are selected according to the evaluation result of the data evaluation module during data migration,
the migration paths include a base migration path and a slave storage migration path.
Further, the data evaluation module includes:
the determining module is used for acquiring an evaluation value corresponding to the evaluation index of the data calculation result to be migrated and determining the distribution characteristics of the evaluation index according to the evaluation value;
the fitting module is used for fitting the distribution characteristics according to the preset distribution characteristics in the database, eliminating performance evaluation indexes with correlation coefficients smaller than a preset threshold value, and obtaining target performance evaluation indexes;
the denoising module is used for denoising the related data and the calculation result obtained by the data calculation module to obtain a target value;
the evaluation module is used for inputting the target numerical value into a data evaluation model and acquiring a corresponding target parameter based on a target data evaluation index; carrying out load flow calculation on the target parameters and determining a matched evaluation result;
the evaluation result is divided into a basic result and an overmoded result.
Further, the data migration module adopts a basic migration path or a slave storage migration path based on the evaluation result;
when the evaluation result is a basic result, the data migration module adopts a basic migration path, and data to be migrated is transmitted in a direct transmission mode;
and when the evaluation result is an overmode result, the data migration module adopts a slave storage migration path, and data to be migrated is transmitted through nodes of the slave storage layer.
Further, the data storage layer comprises a cloud database, a data receiving module, a data storage module and a data transmission module;
the cloud database is used for carrying out cloud storage on data;
the data receiving module is used for receiving data sent to the cloud database;
the data storage module is used for processing the data received by the data receiving module and realizing a cloud database data storage function;
the data transmission module is used for carrying out data transmission on the data which are called in the cloud database.
Further, the data storage module includes:
building a self-identification multi-node data storage system; wherein,
the multi-node data storage system comprises a first distributed storage area, a second distributed storage area and a public cache identification area;
the first distributed storage area is used for storing unencrypted data; wherein,
the first distributed storage area is provided with a plurality of data storage nodes according to data types, and each data storage node is provided with a unique corresponding data type;
the second distributed storage area is used for storing encrypted data; wherein,
the second distributed storage area is provided with a private key matching mechanism for determining the encrypted data of each data storage node according to the private key matching parameters of the encrypted data;
the public buffer memory identification area is provided with a data identification mechanism, and the data is judged to be encrypted data or non-encrypted data;
the public buffer identification area comprises the following identification processes:
receiving data to be processed, analyzing the data to be processed, and generating analysis data; wherein,
the parsing data includes: data type, data formation and data content;
determining data identification parameters through data types;
determining a data composition morphology graph according to a data composition mode, and generating morphology parameters;
determining data weight according to the data content;
carrying out data identification matching through the data identification parameters, the morphological parameters and the data weights, and determining encrypted data and non-encrypted data; wherein,
the data identification matching comprises presetting a data entropy value library, and carrying out data identification matching through data entropy in the entropy value library.
Further, the implementation layer comprises a server and a plurality of user terminals which can interact through network communication;
the server comprises a processor, wherein the processor stores instructions for realizing functions of a slave storage layer, a data scheduling layer and a data storage layer;
the user terminal is used for logging in the equipment terminal by a user and realizing data migration between a plurality of user terminals and the cloud through the server.
Compared with the prior art, the invention has the beneficial effects that:
1. in the prior art, when data are migrated or uploaded in a storage system, a unified uploading or migration path is often adopted to work, however, when larger data are migrated, the efficiency is often slower, the data calculation module and the data evaluation module can calculate and evaluate the capacity of selected data, different migration paths are selected according to the evaluation result, when the data are smaller and the evaluation result is the basic result, the data are directly transmitted to a cloud database from a user terminal to be stored or are directly migrated to the user terminal from the cloud database, otherwise, the data are transmitted to a storage layer to be stored in a segmented mode, and the data are transferred to the cloud database from the storage layer to be migrated to the user terminal in a segmented mode, so that the segmented node split-flow migration of different data is realized, and the overall storage efficiency and the migration efficiency of the storage system are improved.
2. When different data are stored in the prior art, such as public data and personal data, the data are stored in different areas because the data are not stored in the nodes, so that the data are low in safety and the quick calling and searching of the data are difficult to realize. The premise of storing data by multiple nodes is that data identification is carried out, the data identification can realize the partial node storage of encrypted data and non-encrypted data, the public storage area can carry out efficient storage, under the matching condition, the fixed-point storage and the partial area storage of the data can be ensured, the safety of the data can be ensured, and the quick calling and searching of numbers can be realized.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions:
an intelligent data storage method capable of rapidly migrating data comprises the following steps:
step one: the data to be migrated or stored is acquired through the user terminal, and the data is retrieved and selected by the data retrieval module;
step two: the data calculation module and the data evaluation module calculate and evaluate the data to obtain an evaluation result, and the data migration module selects different migration paths according to the evaluation result;
step three: when the data is smaller and the evaluation result is a basic result, the data is directly transmitted to a cloud database for storage or is directly migrated to the user terminal by the cloud database;
step four: and when the data is larger and the evaluation result is an overmodular result, sending the data to the slave storage layer for node transfer, and transferring the data from the storage layer to a cloud database or migrating the data to a user terminal.
The intelligent data storage system capable of rapidly migrating data comprises a slave storage layer, a data scheduling layer, a data storage layer and an implementation layer;
the slave storage layer is used for carrying out staged transfer on the data, and the slave storage layer is used for carrying out node transmission when the data scheduling layer is large in data scheduling; the data scheduling layer is used for scheduling data in the data source layer and the data storage layer, so that the data among the data source layer, the data storage layer and the implementation layer can be quickly migrated; the data storage layer is used for carrying out cloud storage and data retrieval and transmission on the data; the implementation layer is used for carrying the slave storage layer, the data scheduling layer and the data storage layer, controlling the storage system, transmitting and receiving the migration data, and realizing signal interaction connection with the slave storage layer, the data scheduling layer and the data storage layer through a network.
Specifically, when data storage or migration is performed, the data retrieval module retrieves and selects the data, then the data calculation module and the data evaluation module in the system automatically calculate and evaluate the data to obtain evaluation results, the data migration module selects different migration paths according to the evaluation results, when the data is smaller and the evaluation results are basic results, the data is directly transmitted to the cloud database from the user terminal to be stored or directly migrated to the user terminal from the cloud database, otherwise, the data is sent to the slave storage layer to be stored in a segmented mode, and the data is transferred to the cloud database from the storage layer or migrated to the user terminal through the slave storage layer, so that the segmented node shunt migration of different data is realized, and the overall storage efficiency and migration efficiency of the storage system are improved.
The slave storage layer comprises at least two slave storage nodes, and the slave storage layer comprises a slave storage data engine; information is received from the storage node and the identification of the stored data from the database engine is initiated. The slave database engine retrieves the data to be migrated and performs split-storage through the slave storage nodes, and the split-storage data is respectively migrated and transmitted to the implementation layer through each slave storage node.
Specifically, when big data is migrated or stored, data information is dispersed to slave storage nodes in the slave storage layer, so that the data is utilized to perform node-division migration, and the overall storage efficiency and migration efficiency of the storage system are improved.
In order to solve the technical problems that in the prior art, when data is migrated or uploaded in a storage system, a unified uploading or migration path is often adopted for working, but when larger data is migrated, the efficiency is often slower, the invention provides the following technical scheme:
the data scheduling layer comprises a data retrieval module, a data calculation module, a data evaluation module and a data migration module;
the data retrieval module is used for retrieving and marking identification of data to be retrieved and migrated in the data storage layer; the data calculation module is used for calculating the data size of the data to be called and migrated, which is searched by the data search module; the data evaluation module is used for performing data evaluation on the result calculated by the data calculation module and determining a data migration mode according to the evaluation result; the data migration module is used for carrying out data migration on the data to be called and migrated retrieved by the data retrieval module, and different migration paths are selected according to the evaluation result of the data evaluation module during data migration,
the migration paths include a base migration path and a slave storage migration path.
The data evaluation module comprises:
the determining module is used for acquiring an evaluation value corresponding to the evaluation index of the data calculation result to be migrated and determining the distribution characteristics of the evaluation index according to the evaluation value;
the fitting module is used for fitting the distribution characteristics according to the preset distribution characteristics in the database, eliminating performance evaluation indexes with correlation coefficients smaller than a preset threshold value, and obtaining target performance evaluation indexes;
the denoising module is used for denoising the related data and the calculation result obtained by the data calculation module to obtain a target value;
the evaluation module is used for inputting the target numerical value into a data evaluation model and acquiring a corresponding target parameter based on a target data evaluation index; carrying out load flow calculation on the target parameters and determining a matched evaluation result; the evaluation results are divided into basic results and overmoded results.
The data migration module adopts a basic migration path or a slave storage migration path based on the evaluation result;
when the evaluation result is a basic result, the data migration module adopts a basic migration path, and data to be migrated is transmitted in a direct transmission mode; when the evaluation result is an overmodular result, the data migration module adopts a slave storage migration path, and data to be migrated is transmitted through nodes from a storage layer.
Specifically, the data calculation module and the data evaluation module can calculate and evaluate the capacity of the selected data, select different migration paths according to the evaluation result, and when the data is smaller and the evaluation result is the basic result, directly transfer the data from the user terminal to the cloud database for storage or directly transfer the data from the cloud database to the user terminal, otherwise, send the data to the storage layer for node transfer, and transfer the data from the storage layer to the cloud database or transfer the data to the user terminal, thereby realizing node transfer of different data, and improving the overall storage efficiency and the overall migration efficiency of the storage system.
In order to solve the technical problems that when different data are stored, such as public data and personal data, the data are difficult to realize regional storage because the data are not stored in a node, so that the security of the data is low and the quick calling and searching of the data are difficult to realize, the invention provides the following technical scheme:
the data storage layer comprises a cloud database, a data receiving module, a data storage module and a data transmission module;
the cloud database is used for carrying out cloud storage on the data; the data receiving module is used for receiving data sent to the cloud database; the data storage module is used for processing the data received by the data receiving module and realizing the data storage function of the cloud database; the data transmission module is used for carrying out data transmission on the data which are called in the cloud database.
The data storage module includes:
building a self-identification multi-node data storage system; wherein,
the multi-node data storage system comprises a first distributed storage area, a second distributed storage area and a public cache identification area;
the first distributed storage area is used for storing unencrypted data; wherein,
the first distributed storage area is provided with a plurality of data storage nodes according to data types, and each data storage node is provided with a unique corresponding data type;
the second distributed storage area is used for storing encrypted data; wherein,
the second distributed storage area is provided with a private key matching mechanism for determining the encrypted data of each data storage node according to the private key matching parameters of the encrypted data;
the public buffer memory identification area is provided with a data identification mechanism, and the data is judged to be encrypted data or non-encrypted data;
the public buffer identification area comprises the following identification processes:
receiving data to be processed, analyzing the data to be processed, and generating analysis data; wherein,
the parsing data includes: data type, data formation and data content;
determining data identification parameters through data types;
determining a data composition morphology graph according to a data composition mode, and generating morphology parameters;
determining data weight according to the data content;
carrying out data identification matching through the data identification parameters, the morphological parameters and the data weights, and determining encrypted data and non-encrypted data; wherein,
the data identification matching comprises presetting a data entropy value library, and carrying out data identification matching through data entropy in the entropy value library.
In particular, the invention is a multi-node data storage system, each node can store different data, and can realize the high-efficiency storage of data. The premise of storing data by multiple nodes is that data identification is carried out, the data identification can realize the partial node storage of encrypted data and non-encrypted data, the public storage area can carry out efficient storage, under the matching condition, the fixed-point storage and the partial area storage of the data can be ensured, the safety of the data can be ensured, and the quick calling and searching of numbers can be realized.
The implementation layer comprises a server and a plurality of user terminals which can interact through network communication;
the server comprises a processor, wherein the processor stores instructions for realizing functions of a slave storage layer, a data scheduling layer and a data storage layer; the user terminal is used for logging in the equipment terminal by the user and realizing data migration between the plurality of user terminals and the cloud through the server.
Specifically, the server works to drive the storage system to integrally operate, so that the data between different user terminals can be interactively uploaded or migrated, and the data is stored in the cloud or downloaded from the cloud.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (5)
1. An intelligent data storage method capable of rapidly migrating data is characterized in that: the method comprises the following steps:
step one: the data to be migrated or stored is acquired through the user terminal, and the data is retrieved and selected by the data retrieval module;
step two: the data calculation module and the data evaluation module calculate and evaluate the data to obtain an evaluation result, and the data migration module selects different migration paths according to the evaluation result;
step three: when the data is smaller and the evaluation result is a basic result, the data is directly transmitted to a cloud database for storage or is directly migrated to the user terminal by the cloud database;
step four: when the data is large and the evaluation result is an overmould result, the data is sent to a slave storage layer for node transfer, and the data is transferred to a cloud database or a user terminal through transfer from the storage layer;
the system also comprises a slave storage layer, a data scheduling layer, a data storage layer and an implementation layer;
the secondary storage layer is used for carrying out staged transfer on data, and node transmission is carried out through the secondary storage layer when the data scheduling layer is large in data calling;
the data scheduling layer is used for scheduling data in the data source layer and the data storage layer, so that the data among the data source layer, the data storage layer and the implementation layer can be quickly migrated;
the data storage layer is used for carrying out cloud storage and data retrieval and transmission on the data;
the implementation layer is used for carrying the slave storage layer, the data scheduling layer and the data storage layer, controlling the storage system, transmitting and receiving migration data, and realizing signal interaction connection with the slave storage layer, the data scheduling layer and the data storage layer through a network;
the slave storage layer comprises at least two slave storage nodes, and the slave storage layer comprises a slave storage data engine;
the slave storage node receives information and starts a slave database engine to identify storage data;
the data evaluation module comprises:
the determining module is used for acquiring an evaluation value corresponding to the evaluation index of the data calculation result to be migrated and determining the distribution characteristics of the evaluation index according to the evaluation value;
the fitting module is used for fitting the distribution characteristics according to the preset distribution characteristics in the database, eliminating performance evaluation indexes with correlation coefficients smaller than a preset threshold value, and obtaining target performance evaluation indexes;
the denoising module is used for denoising the related data and the calculation result obtained by the data calculation module to obtain a target value;
the evaluation module is used for inputting the target numerical value into a data evaluation model and acquiring a corresponding target parameter based on a target data evaluation index; carrying out load flow calculation on the target parameters and determining a matched evaluation result;
the evaluation result is divided into a basic result and an overmoded result;
the data migration module adopts a basic migration path or a slave storage migration path based on the evaluation result;
when the evaluation result is a basic result, the data migration module adopts a basic migration path, and data to be migrated is transmitted in a direct transmission mode;
when the evaluation result is an overmodular result, the data migration module adopts a slave storage migration path, and data to be migrated is transmitted through nodes of a slave storage layer;
the data storage module includes:
building a self-identification multi-node data storage system; wherein,
the multi-node data storage system comprises a first distributed storage area, a second distributed storage area and a public cache identification area;
the first distributed storage area is used for storing unencrypted data; wherein,
the first distributed storage area is provided with a plurality of data storage nodes according to data types, and each data storage node is provided with a unique corresponding data type;
the second distributed storage area is used for storing encrypted data; wherein,
the second distributed storage area is provided with a private key matching mechanism for determining the encrypted data of each data storage node according to the private key matching parameters of the encrypted data;
the public buffer memory identification area is provided with a data identification mechanism, and the data is judged to be encrypted data or non-encrypted data;
the public buffer identification area comprises the following identification processes:
receiving data to be processed, analyzing the data to be processed, and generating analysis data; wherein,
the parsing data includes: data type, data formation and data content;
determining data identification parameters through data types;
determining a data composition morphology graph according to a data composition mode, and generating morphology parameters;
determining data weight according to the data content;
carrying out data identification matching through the data identification parameters, the morphological parameters and the data weights, and determining encrypted data and non-encrypted data; wherein,
the data identification matching comprises presetting a data entropy value library, and carrying out data identification matching through data entropy in the entropy value library.
2. An intelligent data storage method capable of rapidly migrating data according to claim 1, wherein: the slave database engine retrieves the data to be migrated and performs split-storage through the slave storage nodes, and the split-storage data is respectively migrated and transmitted to the implementation layer through each slave storage node.
3. An intelligent data storage method capable of rapidly migrating data according to claim 2, wherein: the data scheduling layer comprises a data retrieval module, a data calculation module, a data evaluation module and a data migration module;
the data retrieval module is used for retrieving and marking identification of data to be called and migrated in the data storage layer;
the data calculation module is used for calculating the data size of the data to be called and migrated, which is retrieved by the data retrieval module;
the data evaluation module is used for performing data evaluation on the result calculated by the data calculation module and determining a data migration mode according to the evaluation result;
the data migration module is used for carrying out data migration on the data to be called and migrated retrieved by the data retrieval module, and different migration paths are selected according to the evaluation result of the data evaluation module during data migration,
the migration paths include a base migration path and a slave storage migration path.
4. An intelligent data storage method capable of rapidly migrating data according to claim 2, wherein: the data storage layer comprises a cloud database, a data receiving module, a data storage module and a data transmission module;
the cloud database is used for carrying out cloud storage on data;
the data receiving module is used for receiving data sent to the cloud database;
the data storage module is used for processing the data received by the data receiving module and realizing a cloud database data storage function;
the data transmission module is used for carrying out data transmission on the data which are called in the cloud database.
5. An intelligent data storage method capable of rapidly migrating data according to claim 2, wherein: the implementation layer comprises a server and a plurality of user terminals which can interact through network communication;
the server comprises a processor, wherein the processor stores instructions for realizing functions of a slave storage layer, a data scheduling layer and a data storage layer;
the user terminal is used for logging in the equipment terminal by a user and realizing data migration between a plurality of user terminals and the cloud through the server.
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| CN107391629A (en) * | 2017-06-30 | 2017-11-24 | 北京奇虎科技有限公司 | Data migration method, system, server and computer-readable storage medium between cluster |
| CN108881042A (en) * | 2018-04-25 | 2018-11-23 | 郑州易通众联电子科技有限公司 | Data transmission method and data transmission device |
| CN113923235A (en) * | 2021-10-21 | 2022-01-11 | 上海威固信息技术股份有限公司 | Data distributed storage system based on cloud computing platform |
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