CN110209666B - data storage method and terminal equipment - Google Patents
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- CN110209666B CN110209666B CN201910269375.0A CN201910269375A CN110209666B CN 110209666 B CN110209666 B CN 110209666B CN 201910269375 A CN201910269375 A CN 201910269375A CN 110209666 B CN110209666 B CN 110209666B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2219—Large Object storage; Management thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6227—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
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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
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/2141—Access rights, e.g. capability lists, access control lists, access tables, access matrices
Abstract
The application provides data storage methods and terminal equipment, wherein the method comprises the steps of obtaining target data to be stored, determining storage nodes with the predicted online time matched with the predicted access time of the target data from a storage network database, storing the target data into the storage nodes, and storing storage information of the target data into each node of the storage network database.
Description
Technical Field
The application relates to the technical field of data storage, in particular to data storage method and terminal equipment.
Background
With the rapid development of internet technology and the arrival of the big data era, the importance of data is higher and higher, and correspondingly, the requirements on data storage technology are higher and higher, and especially, higher and higher requirements are provided for the authenticity, indelibility, non-tamper property and traceability of data storage.
At present, data storage mainly depends on a data server to realize, which is types of highly centralized storage modes, and after suffering from illegal events such as hacker intrusion, administrator tampering and the like, malicious tampering can be performed without being detected by a user, thereby causing serious loss to the user of data.
Moreover, with the rapid development of network storage technology, more and more nodes are in the network database, and a large number of non-dedicated nodes and multiple network nodes are added into the network database under the incentive of a reward mechanism, but these nodes have great instability and are easy to be offline, so that if the nodes are used to store data, the problem that the stored data cannot be read when needing to be accessed may be encountered, and the data access efficiency is affected.
Therefore, it is a problem to be solved urgently how to improve the security and convenience of the network data storage technology, so as to provide data storage schemes with higher security, traceability and higher access efficiency.
Disclosure of Invention
In view of the above, the present application provides data storage methods, apparatuses, terminal devices, and computer readable media.
An th aspect of the present application provides a data storage method, comprising:
acquiring target data to be stored;
determining a storage node with an expected online time matched with the expected access time of the target data from a storage network database;
storing the target data to the storage node;
and storing the storage information of the target data to each node of the storage network database.
In modified embodiments of the aspect of the present application, the determining a storage node from a storage network database whose projected online time matches the projected access time of the target data comprises:
determining a plurality of candidate nodes from a storage network database whose expected online times match the expected access times of the target data;
and selecting a storage node for storing the target data from the plurality of candidate nodes by adopting a consensus mechanism.
In modified embodiments of the aspect of the present application, the determining a plurality of candidate nodes from a storage network database whose projected online times match the projected access time of the target data comprises:
determining an expected access time of the target data;
determining the predicted online time of each node in the storage network database;
respectively calculating the matching degree of the predicted online time and the predicted access time of each node;
and selecting a plurality of nodes with the highest matching degree as alternative nodes.
In some of the modified embodiments of the aspect of the present application, the calculating a degree of matching between the estimated online time and the estimated access time of each of the nodes includes:
respectively calculating the matching degree of the predicted online time and the predicted access time of each node by adopting the following formula:
wherein the content of the first and second substances,
a＝x1-x0
in the above formula, σ represents a matching degree, m represents a correction coefficient, a represents a time length of the predicted access time, b represents a time length of coincidence of the predicted access time and the predicted online time, c represents an offset between a midpoint of the predicted access time and a midpoint of the predicted online time, and x represents0And x1A start time point and an end time point, y, respectively representing the expected access time0And y1Respectively representing the start and end points of the expected online time.
In modified embodiments of the present invention according to , the storing the storage information of the target data in each node of the storage network database includes:
generating storage information of the target data;
generating a data set according to the storage information;
and storing the data set to each node of the storage network database.
In modified embodiments of the aspect of the present application, the generating storage information of the target data includes:
generating fingerprint information of the target data;
and generating storage information of the target data according to the fingerprint information and the node identification of the storage node storing the target data.
In modified embodiments of the aspect of the present application, the generating fingerprint information of the target data includes:
acquiring a data identifier of the target data; and the number of the first and second groups,
generating abstract information of the target data by adopting a message abstract algorithm;
and generating fingerprint information of the target data according to the data identification and the abstract information.
In modified embodiments of the aspect of the present application, the generating a data set from the stored information comprises:
searching a historical storage data set of the target data in the storage network database according to the data identification or the abstract information of the target data;
generating summary information of the historical storage data set;
and generating a storage data set according to the storage information and the summary information of the historical storage data set.
In modified embodiments of the aspect of the present application, the generating a data set from the stored information comprises:
searching a data set corresponding to the storage events of times in the storage network database;
generating summary information of the data set corresponding to the upper storage events;
and generating a storage data set according to the storage information and the summary information of the data set corresponding to the last storage events.
A second aspect of the present application provides a data storage device comprising:
the target data acquisition module is used for acquiring target data to be stored;
the storage node determining module is used for determining a storage node of which the expected online time is matched with the expected access time of the target data from a storage network database;
a target data storage module for storing the target data to the storage node;
and the storage information distribution module is used for storing the storage information of the target data to each node of the storage network database.
In modified embodiments of the second aspect of the present application, the storage node determination module includes:
the candidate node determining unit is used for determining a plurality of candidate nodes with the expected online time matched with the expected access time of the target data from a storage network database;
and the storage node determining unit is used for selecting a storage node for storing the target data from the plurality of candidate nodes by adopting a consensus mechanism.
In modified embodiments of the second aspect of the present application, the candidate node determination unit includes:
an access time determining subunit, configured to determine an expected access time of the target data;
the online time determining subunit is used for determining the predicted online time of each node in the storage network database;
the matching degree calculation operator unit is used for calculating the matching degree of the predicted online time and the predicted access time of each node respectively;
and the alternative node selection subunit is used for selecting a plurality of nodes with the highest matching degree as alternative nodes.
In modified embodiments of the second aspect of the present application, the matching degree calculation subunit calculates the degree of matching between the predicted online time and the predicted access time of each node, respectively, specifically using the following formula:
wherein the content of the first and second substances,
a＝x1-x0
in the above formula, σ represents a matching degree, m represents a correction coefficient, a represents a time length of the predicted access time, b represents a time length of coincidence of the predicted access time and the predicted online time, c represents an offset between a midpoint of the predicted access time and a midpoint of the predicted online time, and x represents0And x1A start time point and an end time point, y, respectively representing the expected access time0And y1Respectively representing the start and end points of the expected online time.
In modified embodiments of the second aspect of the present application, the stored information distribution module includes:
a storage information generating unit configured to generate storage information of the target data;
the data set generating unit is used for generating a data set according to the storage information;
and the data set storage unit is used for storing the data set to each node of the storage network database.
In modified embodiments of the second aspect of the present application, the stored information generating unit includes:
a fingerprint generation subunit, configured to generate fingerprint information of the target data;
and the storage information generating subunit is used for generating the storage information of the target data according to the fingerprint information and the node identification of the storage node for storing the target data.
In modified embodiments of the second aspect of the present application, the fingerprint generation subunit includes:
a data identifier obtaining subunit, configured to obtain a data identifier of the target data; and the number of the first and second groups,
the abstract information generating subunit is used for generating abstract information of the target data by adopting a message abstract algorithm;
and the fingerprint information generating subunit is used for generating the fingerprint information of the target data according to the data identifier and the abstract information.
In the modified embodiments of the second aspect of the present application, the data set generating unit includes:
a historical data set query subunit, configured to search, according to the data identifier or the summary information of the target data, a historical storage data set of the target data in the storage network database;
the historical data set summarization subunit is used for generating summary information of the historical storage data set;
data set generating subunit, configured to generate a storage data set according to the storage information and the summary information of the history storage data set.
In the modified embodiments of the second aspect of the present application, the data set generating unit includes:
the prior storage query subunit is used for searching a data set corresponding to the storage events in the storage network database;
the prior storage summary subunit is configured to generate summary information of the data set corresponding to the last storage events;
and the data set generating subunit is configured to generate a storage data set according to the storage information and the summary information of the data set corresponding to the last storage events.
A third aspect of the present application provides terminal devices, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to perform the method of the aspect of the present application.
A fourth aspect of the present application provides a computer readable medium having stored thereon computer readable instructions executable by a processor to perform the method of the aspect of the present application.
Compared with the prior art, the data storage method provided by the application only needs to store the target data on the storage nodes in the storage network database and then stores the storage information of the target data on the nodes in the storage network database, so that the nodes in the storage network database become the evidence nodes for the authenticity of the target data, and the traceability and authenticity of the target data can be ensured because the storage information cannot be easily deleted or tampered.
The data storage apparatus provided by the second aspect of the present application, the terminal device provided by the third aspect of the present application, and the computer-readable medium provided by the fourth aspect of the present application have the same advantages and are based on the same inventive concept as the data storage method provided by the described above.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart illustrating data storage methods provided by embodiments of the present application;
FIG. 2 illustrates a schematic diagram of an data storage device provided by an embodiment of the present application;
fig. 3 shows a schematic diagram of terminal devices provided in the embodiment of the present application;
FIG. 4 shows a schematic diagram of computer-readable media provided by embodiments of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least embodiments of the application.
data storage method, device, terminal equipment and computer readable medium are provided in the embodiments of the present application.
Referring to fig. 1, it shows a flowchart of data storage methods provided in this application, where the data storage method includes the following steps:
step S101: and acquiring target data to be stored.
Step S102: and determining a storage node with the expected online time matched with the expected access time of the target data from a storage network database.
In the embodiment of the present application, in the storage network database, target data may be stored in any or more nodes of the storage network database, a node for storing the target data may be temporarily referred to as a storage node, and storage information of the target data may be stored in each node of the storage network database, which may not cause problems such as increase of network transmission overhead and increase of storage capacity requirements of each node, and provides data storage schemes with higher security and traceability.
In , the determining a storage node from the storage network database whose expected online time matches the expected access time of the target data may include:
determining a plurality of candidate nodes from a storage network database whose expected online times match the expected access times of the target data;
and selecting a storage node for storing the target data from the plurality of candidate nodes by adopting a consensus mechanism.
The consensus mechanism can be implemented by using any consensus algorithm in the prior art, and the application is not limited.
Through the implementation mode, the multiple candidate nodes with matched time can be selected firstly, and then the storage nodes are selected from the multiple candidate nodes by utilizing the consensus mechanism, so that the number of nodes participating in consensus can be reduced, and the efficiency is improved.
In , the determining a plurality of candidate nodes from the storage network database whose expected online times match the expected access times of the target data may include:
determining an expected access time of the target data;
determining the predicted online time of each node in the storage network database;
respectively calculating the matching degree of the predicted online time and the predicted access time of each node;
and selecting a plurality of nodes with the highest matching degree as alternative nodes.
The predicted access time of the target data may be determined according to the attribute information of the target data, the historical access data, and the like, for example, if the target data is office data of a certain company, the predicted access time is working time of a working day, and so on, which is not described in detail in in this embodiment.
The predicted online time of each node may be determined according to the attribute information, historical online data, usage plan, and the like of each node, for example, for a certain multiple network node (which may participate in nodes of multiple network databases, but needs to switch between the network databases as specified), the predicted online time of the stored network database may be determined according to a preset switching rule, switching time, and the like, for example, the online time of some nodes is already recorded in the attribute information of the node, and the predicted online time may be directly determined accordingly, for example, the predicted online time of the node may be determined statistically according to the historical online data of the node, which is not described in detail in this application, and which is all within the protection scope of this application, this embodiment is not repeated
In , the respectively calculating a matching degree between the expected online time and the expected access time of each node may include:
respectively calculating the matching degree of the predicted online time and the predicted access time of each node by adopting the following formula:
wherein the content of the first and second substances,
a＝x1-x0
in the above formula, σ represents a matching degree, m represents a correction coefficient, a represents a time length of the predicted access time, b represents a time length of coincidence of the predicted access time and the predicted online time, c represents an offset between a midpoint of the predicted access time and a midpoint of the predicted online time, and x represents0And x1A start time point and an end time point, y, respectively representing the expected access time0And y1Respectively representing the start and end points of the expected online time.
The selected node with the higher matching degree not only has higher coincidence between the predicted online time and the predicted access time of the target data, but also has smaller time difference between the predicted online time and the predicted access time, so that the node is not selected as a candidate node even if the online time is long, the problem that the time matching degree of the node with the long online time and all data is higher is solved, and further the probability that all data is possibly stored into the node with the long online precision is realized, and the data storage and differentiation are realized.
Step S103: and storing the target data to the storage node.
Step S104: and storing the storage information of the target data to each node of the storage network database.
In , the storing the storage information of the target data to each node of the storage network database may include:
generating storage information of the target data;
generating a data set according to the storage information;
and storing the data set to each node of the storage network database.
The above embodiments may be implemented by using a network data storage technology, for example, a data set is generated according to storage information, and the data set may be a data set of link information included in other data sets, so that each data set is linked to form a large data set of distributed storage, and the uncollapsibility and traceability of the storage information after storage are ensured.
In , the generating storage information of the target data may include:
generating fingerprint information of the target data;
and generating storage information of the target data according to the fingerprint information and the node identification of the storage node storing the target data.
In , in some embodiments, the generating fingerprint information of the target data may include:
acquiring a data identifier of the target data; and the number of the first and second groups,
generating abstract information of the target data by adopting a message abstract algorithm;
and generating fingerprint information of the target data according to the data identification and the abstract information.
In order to solve the above problems, in the embodiment of the present application, the target data is stored in at least storage nodes in the network database, and then the storage information of the target data is stored in a distributed manner, since the size of the fingerprint information may be much smaller than the size of the target data, the size of the storage information may ensure that the storage information is stored in a distributed manner, and since the storage information includes the fingerprint information of the target data, the storage information may be effectively used for identifying the authenticity of the target data, thereby ensuring the authenticity and traceability of the target data.
In summary, with the above embodiments, distributed storage and verification of authenticity of target data can be achieved with a smaller data volume by using the fingerprint information.
In , the generating the data set according to the stored information may include:
searching a historical storage data set of the target data in the storage network database according to the data identification or the abstract information of the target data;
generating summary information of the historical storage data set;
and generating a storage data set according to the storage information and the summary information of the historical storage data set.
Through the embodiment, when the storage data set is generated, the historical storage data set of the target data is firstly inquired, and then the storage data set is generated by using the storage information and the summary information of the historical storage data set, so that the current version and the historical version of the target data are associated to form a large data set for distributed storage, and the modification history of the target data is traced.
In , the generating the data set according to the stored information may include:
searching a data set corresponding to the storage events of times in the storage network database;
generating summary information of the data set corresponding to the upper storage events;
and generating a storage data set according to the storage information and the summary information of the data set corresponding to the last storage events.
Through the above embodiment, when generating a storage data set, firstly, querying a data set corresponding to the top storage events in the storage network database, and then generating a storage data set by using the storage information and the summary information of the data set corresponding to the top storage events, thereby associating the storage information of each storage event in the storage network database to form a large data set of distributed storage, thereby ensuring the non-tamper property and traceability of the storage information, and since the storage information is used for identifying or identifying target data, it is possible to further ensure the traceability of the target data at step.
Compared with the prior art, the data storage method provided by the application only needs to store the target data to the storage nodes in the storage network database, and then stores the storage information of the target data to each node of the storage network database, so that each node of the storage network database becomes a witness node for verifying the authenticity of the target data. In addition, the problem that the expected access time of the target data is matched with the expected online time of the storage node is fully considered when the storage node is selected, so that the condition that the data cannot be accessed due to offline of the storage node can be effectively avoided, and the data access efficiency is ensured. In summary, the data storage method provided by the embodiment of the application can effectively improve the security and traceability of data storage, and has higher access efficiency.
The present application also provides data storage devices, which can include systems (including distributed systems), software (applications), modules, components, servers, clients, quantum computers, etc. using the data storage methods described herein, in combination with necessary hardware-implemented devices, based on the same inventive concepts, the devices in embodiments provided herein are described in the following embodiments, since the implementation of the device solution to the problems is similar to the methods, the implementation of the specific devices of the present application can refer to the implementation of the aforementioned methods, and the repetition is not repeated.
Specifically, fig. 2 is a schematic block diagram of an data storage device according to an embodiment of the present invention, and as shown in fig. 2, the data storage device 10 may include:
a target data acquiring module 101, configured to acquire target data to be stored;
a storage node determination module 102, configured to determine, from a storage network database, a storage node whose expected online time matches an expected access time of the target data;
a target data storage module 103, configured to store the target data to the storage node;
a storage information distribution module 104, configured to store the storage information of the target data to each node of the storage network database.
In modifications of the embodiment of the present application, the storage node determining module 102 includes:
the candidate node determining unit is used for determining a plurality of candidate nodes with the expected online time matched with the expected access time of the target data from a storage network database;
and the storage node determining unit is used for selecting a storage node for storing the target data from the plurality of candidate nodes by adopting a consensus mechanism.
In modified embodiments of the present embodiment, the candidate node determination unit includes:
an access time determining subunit, configured to determine an expected access time of the target data;
the online time determining subunit is used for determining the predicted online time of each node in the storage network database;
the matching degree calculation operator unit is used for calculating the matching degree of the predicted online time and the predicted access time of each node respectively;
and the alternative node selection subunit is used for selecting a plurality of nodes with the highest matching degree as alternative nodes.
In modifications of the embodiment of the present application, the matching degree calculation subunit calculates the matching degree between the predicted online time and the predicted access time of each node by using the following formula:
wherein the content of the first and second substances,
a＝x1-x0
in the above formula, σ represents a matching degree, m represents a correction coefficient, a represents a time length of the predicted access time, b represents a time length of coincidence of the predicted access time and the predicted online time, c represents an offset between a midpoint of the predicted access time and a midpoint of the predicted online time, and x represents0And x1A start time point and an end time point, y, respectively representing the expected access time0And y1Respectively representing the start and end points of the expected online time.
In modifications of the embodiment of the present application, the stored information distribution module 104 includes:
a storage information generating unit configured to generate storage information of the target data;
the data set generating unit is used for generating a data set according to the storage information;
and the data set storage unit is used for storing the data set to each node of the storage network database.
In modifications of the embodiment of the present application, the stored information generating means includes:
a fingerprint generation subunit, configured to generate fingerprint information of the target data;
and the storage information generating subunit is used for generating the storage information of the target data according to the fingerprint information and the node identification of the storage node for storing the target data.
In modifications of the embodiment of the present application, the fingerprint generation subunit includes:
a data identifier obtaining subunit, configured to obtain a data identifier of the target data; and the number of the first and second groups,
the abstract information generating subunit is used for generating abstract information of the target data by adopting a message abstract algorithm;
and the fingerprint information generating subunit is used for generating the fingerprint information of the target data according to the data identifier and the abstract information.
In modifications of the embodiment of the present application, the data set generating means includes:
a historical data set query subunit, configured to search, according to the data identifier or the summary information of the target data, a historical storage data set of the target data in the storage network database;
the historical data set summarization subunit is used for generating summary information of the historical storage data set;
data set generating subunit, configured to generate a storage data set according to the storage information and the summary information of the history storage data set.
In modifications of the embodiment of the present application, the data set generating means includes:
the prior storage query subunit is used for searching a data set corresponding to the storage events in the storage network database;
the prior storage summary subunit is configured to generate summary information of the data set corresponding to the last storage events;
and the data set generating subunit is configured to generate a storage data set according to the storage information and the summary information of the data set corresponding to the last storage events.
In modifications of the embodiment of the present application, the target data storage module 102 includes:
and the multi-node storage unit is used for storing the target data to a plurality of storage nodes in a storage network database.
The data storage device 10 provided in the embodiment of the present application has the same beneficial effects as the data storage method provided in the foregoing embodiment of the present application.
In the foregoing embodiment, data storage methods and apparatuses are provided, and accordingly, the present application also provides terminal devices, where the terminal devices may be any electronic devices having a storage function and may be used as nodes in a storage network database, such as a server, a server cluster, a desktop computer, a laptop computer, a smartphone, and the like, please refer to fig. 3, and fig. 3 is a schematic diagram of terminal devices provided in this embodiment of the present application, as shown in fig. 3, the terminal device 20 includes a processor 200, a memory 201, a bus 202, and a communication interface 203, where the processor 200, the communication interface 203, and the memory 201 are connected by the bus 202, and a computer program operable on the processor 200 is stored in the memory 201, and when the processor 200 executes the computer program, the data storage method provided in the present application is executed.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least disk memories, and the communication connection between the network element of the system and at least other network elements is realized through at least communication interfaces 203 (which may be wired or wireless), and the internet, domain network, local network, metropolitan area network, and the like may be used.
The bus 202 may be an ISA bus, a PCI bus, an EISA bus, etc., wherein the bus may be divided into an address bus, a data bus, a control bus, etc., wherein the memory 201 is used for storing programs, and the processor 200 executes the programs after receiving execution instructions, and the data storage method disclosed in any embodiment of the present application may be applied to the processor 200, or may be implemented by the processor 200.
The Processor 200 may be an type integrated circuit chip having signal Processing capability, and in the implementation process, the steps of the method may be implemented by instructions in the form of hardware integrated logic or software in the Processor 200, the Processor 200 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc., a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable array (FPGA) or other programmable logic device, discrete or transistor logic, discrete hardware components, and the methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed.
The terminal device provided by the embodiment of the application and the data storage method provided by the embodiment of the application have the same beneficial effects from the same inventive concept.
An embodiment of the present application further provides computer-readable media corresponding to the data storage method, please refer to fig. 4, which illustrates a computer-readable storage medium, which is an optical disc 30, and a computer program (i.e., a program product) is stored on the optical disc, and when the computer program is executed by a processor, the computer program will execute the data storage method provided in any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail in herein.
The computer-readable storage medium provided by the embodiment of the application and the data storage provided by the embodiment of the application have the same inventive concept and the same beneficial effects.
It should also be noted that in some alternative implementations, each block of the flowchart and/or block diagram illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units into logical functional divisions may be realized in other ways, and for example, multiple units or components may be combined or integrated into another systems, or features may be omitted or not executed, and at the other point, the shown or discussed coupling or direct coupling or communication connection between each other may be through communication interfaces, indirect coupling or communication connection between the units or devices may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in places, or may also be distributed on multiple network units.
In addition, functional units in the embodiments of the present application may be integrated into processing units, or each unit may exist alone physically, or two or more units are integrated into units.
Based on the understanding, the technical solution of the present application, which is essentially or partially contributed to by the prior art, or the technical solution may be embodied in the form of a software product, which is stored in storage media and includes instructions for causing computer devices (which may be personal computers, servers, or network devices) to execute all or part of the steps of the methods described in the embodiments of the present application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.
Claims (7)
1, a data storage method, comprising:
acquiring target data to be stored;
determining an expected access time for the target data;
determining the predicted online time of each node in a storage network database;
respectively calculating the matching degree of the predicted online time and the predicted access time of each node;
selecting a plurality of nodes with the highest matching degree as alternative nodes;
selecting a storage node for storing the target data from the alternative nodes by adopting a consensus mechanism;
storing the storage information of the target data to each node of the storage network database;
wherein the calculating the matching degree of the estimated online time and the estimated access time of each node respectively comprises:
respectively calculating the matching degree of the predicted online time and the predicted access time of each node by adopting the following formula:
wherein the content of the first and second substances,
a＝x1-x0
in the above formula, σ represents a matching degree, m represents a correction coefficient, a represents a time length of the predicted access time, b represents a time length of coincidence of the predicted access time and the predicted online time, c represents an offset between a midpoint of the predicted access time and a midpoint of the predicted online time, and x represents0And x1A start time point and an end time point, y, respectively representing the expected access time0And y1Respectively representing the start and end points of the expected online time.
2. The method of claim 1, wherein storing the storage information of the target data to each node of the storage network database comprises:
generating storage information of the target data;
generating a data set according to the storage information;
and storing the data set to each node of the storage network database.
3. The method of claim 2, wherein the generating the storage information of the target data comprises:
generating fingerprint information of the target data;
and generating storage information of the target data according to the fingerprint information and the node identification of the storage node storing the target data.
4. The method of claim 3, wherein the generating fingerprint information for the target data comprises:
acquiring a data identifier of the target data; and the number of the first and second groups,
generating abstract information of the target data by adopting a message abstract algorithm;
and generating fingerprint information of the target data according to the data identification and the abstract information.
5. The method of claim 2, wherein generating the data set from the stored information comprises:
searching a historical storage data set of the target data in the storage network database according to the data identification or the abstract information of the target data;
generating summary information of the historical storage data set;
and generating a storage data set according to the storage information and the summary information of the historical storage data set.
6. The method of claim 2, wherein generating the data set from the stored information comprises:
searching a data set corresponding to the storage events of times in the storage network database;
generating summary information of the data set corresponding to the upper storage events;
and generating a storage data set according to the storage information and the summary information of the data set corresponding to the last storage events.
7, terminal device comprising a memory, a processor and a computer program stored on said memory and executable on said processor, characterized in that said processor, when executing said computer program, performs the method of any of claims 1 to 6 to .
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| CN102075582A (en) * | 2011-01-26 | 2011-05-25 | 南京财经大学 | Storage method for data storage model of credible cloud storage system |
| CN103020255A (en) * | 2012-12-21 | 2013-04-03 | 华为技术有限公司 | Hierarchical storage method and hierarchical storage device |
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