CN113868498A - Data storage method, electronic device, device and readable storage medium - Google Patents
Data storage method, electronic device, device and readable storage medium Download PDFInfo
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Abstract
The application relates to a data storage method, an electronic device, a device and a readable storage medium, wherein the method comprises the following steps: receiving data to be stored, and classifying the data to be stored to obtain a data type corresponding to the data to be stored; matching a storage location corresponding to the data type; storing the data to be stored into the storage location. By classifying the data to be stored and respectively storing the data according to the classification, the data source can be determined, and the accuracy of the data analysis result can be improved on the basis.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a data storage method, an electronic device, an apparatus, and a readable storage medium.
Background
Heterogeneous data is used for analyzing data with large scale and different sources, searching patterns, correlations and other useful information in the process of researching a large amount of data, and can help enterprises to better adapt to changes and make more intelligent decisions. When processing heterogeneous data, data with different sources and types, such as relational data, flat data files and the like, need to be extracted, then cleaned, converted and integrated, and finally loaded into a data warehouse or a data mart to become the basis of online analysis processing and data mining. It should be noted that, although big data analysis has its advantages, it also has great limitations. Many times, the correlation generated by large data may be spurious, showing some regularity in the completely random data, because the amount of data is very large, and may generate various links radiating in various directions, thereby causing large bias in data analysis.
Disclosure of Invention
The application provides a data storage method, an electronic device, a device and a readable storage medium, and aims to solve the technical problem that in the prior art, when the data volume is large, the analysis of heterogeneous data has large deviation.
In order to solve the above technical problem or at least partially solve the above technical problem, the present application provides a data storage method, including the steps of:
receiving data to be stored, and classifying the data to be stored to obtain a data type corresponding to the data to be stored;
matching a storage location corresponding to the data type;
storing the data to be stored into the storage location.
Optionally, the storing the data to be stored into the storage location comprises:
judging whether the data to be stored comprises a unique identifier or not;
if the data to be stored does not comprise the unique identifier, performing characteristic marking operation on the data to be stored, and after the characteristic marking operation, executing the following steps: storing the data to be stored into the storage location; if the data to be stored comprises the unique identifier, executing the following steps: storing the data to be stored into the storage location.
Optionally, the performing a feature marking operation on the data to be stored includes:
acquiring subdata matched with a preset characteristic category in the data to be stored;
and associating the sub data serving as a unique identifier with the data to be stored.
Optionally, the storing the data to be stored into the storage location comprises:
converting the data to be stored in a key-value pair mode, wherein a key of the key-value pair is a unique identifier corresponding to the data to be stored, and a value of the key-value pair is the data to be stored;
and storing the converted data to be stored into the storage position.
Optionally, before the storing the data to be stored into the storage location, the method further comprises:
judging whether the data to be stored meet data storage conditions or not according to the data type;
if the data to be stored does not meet the data storage condition, deleting the data to be stored; if the data to be stored meets the data storage condition, executing the following steps: storing the data to be stored into the storage location.
Optionally, the determining, according to the data type, whether the data to be stored meets a data storage condition includes:
acquiring a data source of the data to be stored;
judging whether the data source is a preset unnecessary data source or not;
if the data source is a preset unnecessary data source, the data to be stored does not meet the data storage condition; and if the data source is not a preset unnecessary data source, the data to be stored meet the data storage condition.
Optionally, the classifying the data to be stored to obtain the data type corresponding to the data to be stored includes:
classifying the data to be stored by a decision tree classification method to obtain a classification result;
and determining the data type corresponding to the data to be stored according to the classification result.
In order to achieve the above object, the present invention also provides an electronic device, including:
the first receiving module is used for receiving data to be stored and classifying the data to be stored to obtain a data type corresponding to the data to be stored;
the first matching module is used for matching the storage position corresponding to the data type;
the first storage module is used for storing the data to be stored into the storage position.
Optionally, the electronic device further comprises:
the first judgment module is used for judging whether the data to be stored comprises a unique identifier or not;
a first execution module, configured to perform a feature marking operation on the to-be-stored data if the to-be-stored data does not include the unique identifier, and after the feature marking operation, execute the following steps: storing the data to be stored into the storage location; if the data to be stored comprises the unique identifier, executing the following steps: storing the data to be stored into the storage location.
Optionally, the first execution module includes:
the first obtaining unit is used for obtaining subdata matched with a preset characteristic category in the data to be stored;
and the first execution unit is used for associating the sub-data serving as a unique identifier with the data to be stored.
Optionally, the first storage module includes:
the first conversion unit is used for converting the data to be stored in a key-value pair form, wherein a key of the key-value pair is a unique identifier corresponding to the data to be stored, and a value of the key-value pair is the data to be stored;
and the first storage unit is used for storing the converted data to be stored into the storage position.
Optionally, the electronic device further comprises:
the second judgment module is used for judging whether the data to be stored meets the data storage condition or not according to the data type;
the second execution module is used for deleting the data to be stored if the data to be stored does not meet the data storage condition; if the data to be stored meets the data storage condition, executing the following steps: storing the data to be stored into the storage location.
Optionally, the second determining module includes:
the second acquisition unit is used for acquiring a data source of the data to be stored;
the first judging unit is used for judging whether the data source is a preset unnecessary data source or not;
the second execution unit is used for judging that the data to be stored does not meet the data storage condition if the data source is a preset unnecessary data source; and if the data source is not a preset unnecessary data source, the data to be stored meet the data storage condition.
Optionally, the first receiving module includes:
the first classification unit is used for classifying the data to be stored through a decision tree classification method to obtain a classification result;
and the first determining unit is used for determining the data type corresponding to the data to be stored according to the classification result.
To achieve the above object, the present invention also provides a data storage device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the data storage method as described above.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the data storage method as described above.
The invention provides a data storage method, an electronic device, a device and a readable storage medium, which are used for receiving data to be stored and classifying the data to be stored to obtain a data type corresponding to the data to be stored; matching a storage location corresponding to the data type; storing the data to be stored into the storage location. By classifying the data to be stored and respectively storing the data according to the classification, the data source can be determined, and the accuracy of the data analysis result can be improved on the basis.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a data storage method according to a first embodiment of the present invention;
FIG. 2 is a schematic overall flow chart of the data storage method of the present invention;
FIG. 3 is a block diagram of a data storage device according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application.
The invention provides a data storage method, referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the data storage method of the invention, and the method comprises the following steps:
step S10, receiving data to be stored, and classifying the data to be stored to obtain a data type corresponding to the data to be stored;
the data to be stored can be classified based on different levels, such as computer architecture, operating system, data format, data storage location, and logic mode of data storage; the classification based on the computer architecture means that data from computers of different architectures such as a large computer, a small computer, a workstation, a PC or an embedded system are classified according to the architecture of the computer from which the data originate; the classification based on the operating system means that the data in different operating systems such as Unix, Windows, Linux, OS/400 and the like are classified according to the operating systems of the sources; the classification based on the data format means that the data from a relational database system such as Oracle, SQL Server, DB2 and the like or the two-dimensional data of file lines such as txt, CSV, XLS and the like are classified according to the storage management mechanism of the source; the classification based on the data storage location means that the data from the storage locations arranged at different geographic locations are classified according to the geographic location of the source; the classification based on the logic mode of data storage refers to classifying data from different business logics according to the business logics of the sources, for example, codes of departments in an independent sales system and an independent purchasing system are inconsistent, so that the data with the same meaning have different expressions. It should be noted that the above classification basis is only used for illustration, and an appropriate classification basis may be set according to an actual application scenario and needs.
Referring to fig. 2, in the present embodiment, data types such as transaction data, mobile communication data, artificial data, machine and sensor data, and internet open data are mainly set according to the source of the data to be stored; the transaction data mainly comprises POS machine data, credit card swiping data, electronic commerce data, internet clicking data, Enterprise Resource Planning (ERP) system data, sales system data, Customer Relationship Management (CRM) system data, production data, inventory data, order data, supply chain data and the like of a company; the mobile communication data refers to data recorded by mobile equipment, and the existing mobile equipment capable of surfing the internet is more and more common. The amount of data recorded by a mobile communication device and the stereo integrity of the data are often superior to data mastered by various internet companies. Software on the mobile device can track and communicate numerous events, from transaction data stored using the software, such as recorded events for searching products, to personal information data or status reporting events, such as a place change, i.e., reporting a new geocode, etc.; the artificial data mainly comprises e-mails, documents, pictures, audios, videos and data streams generated by social media, and most of the data are non-structural data and need to be analyzed by a text analysis function; machine and sensor data primarily includes data from sensors, gauges, and other facilities, positioning or GPS system data, etc., including data created or generated by functional devices, such as smart temperature controllers, smart meters, factory machines, and internet-connected home appliances, as well as data from the emerging internet of things IoT, which can be used to build analytical models, perform continuous monitoring and predictive behaviors, such as recognizing when a sensor value indicates a problem, and provide prescribed instructions, such as alerting technicians to check equipment before a real problem occurs, etc.; the internet open data mainly comprises data provided by government agencies, non-profit organizations and enterprises for free. Wherein: transaction data and mobile communication data can be obtained from corresponding databases, machine and sensor data can be obtained from a data structure server, artificial data can be directly obtained from a front section interface, and internet open data can be obtained from a corresponding remote server.
Step S20, matching the storage position corresponding to the data type;
dividing a storage space into a plurality of storage positions corresponding to the data types in advance according to different data types, and associating the storage positions with the corresponding data types; and when the data type corresponding to the data to be stored is obtained, determining the storage position corresponding to the data type according to the incidence relation between the data type and the storage position. It should be noted that different storage locations may be different databases, or may be different storage spaces divided in the same database.
And step S30, storing the data to be stored in the storage position.
And after the storage position corresponding to the data type is acquired, storing the data to be stored in the storage position.
According to the data processing method and device, the data to be stored are classified and stored respectively according to the classification, so that the data source can be made clear, and the accuracy of the data analysis result can be improved on the basis.
Further, in the second embodiment of the data storage method according to the present invention proposed based on the first embodiment of the present invention, the step S30 includes the steps of:
step S31, judging whether the data to be stored includes a unique identifier;
step S32, if the data to be stored does not include the unique identifier, the data to be stored is subjected to the characteristic marking operation, and after the characteristic marking operation, the step S30 is executed; if the data to be stored includes the unique identifier, step S30 is executed.
The unique identifier refers to an identifier for characterizing the uniqueness of the data, such as an order number or a serial number. When the data to be stored comprises the unique identifier, after storage, the corresponding data can be directly determined through the unique identifier, so that the data to be stored can be directly stored to a storage position; when the data to be stored does not include the unique identifier, the data cannot be quickly and conveniently found after storage, so that the data to be stored needs to be subjected to feature marking to obtain data capable of representing the uniqueness of the data to be stored, and further the efficiency of subsequent data searching can be improved.
The step S50 includes the steps of:
step S51, obtaining subdata matched with preset characteristic categories in the data to be stored;
and step S52, associating the sub data as a unique identifier with the data to be stored.
The preset characteristic category comprises but is not limited to a user name, a timestamp, a unique device identifier, a data number or a combination thereof and the like; the subdata matched with the preset characteristic categories in the data to be stored is used as the unique identifier of the data to be stored, so that the uniqueness of the stored data can be represented under the condition that the data to be stored does not have the existing unique identifier, and the searching efficiency of the data to be stored can be improved.
Further, referring again to fig. 2, in the third embodiment of the data storage method of the present invention proposed based on the first embodiment of the present invention, the step S30 includes the steps of:
step S33, converting the data to be stored in a form of key value pairs, wherein the key of the key value pair is a unique identifier corresponding to the data to be stored, and the value of the key value pair is the data to be stored;
and step S34, storing the converted data to be stored into the storage position.
The key-value pair is < key-value >, where key is a key and value is a value.
In the embodiment, the key, that is, the unique identifier corresponding to the data to be stored, may be placed in the linked list type heterogeneous data system as needed, and when the data needs to be read, the key is first searched in the linked list type heterogeneous data system, and then the corresponding data is matched according to the key.
According to the embodiment, the data to be stored is converted into the key value pair for storage, so that the subsequent data searching step can be optimized, and the data searching efficiency is improved.
Further, in a fourth embodiment of the data storage method according to the present invention proposed based on the first embodiment of the present invention, before the step S30, the method includes the steps of:
step S35, judging whether the data to be stored meets the data storage condition according to the data type;
step S36, if the data to be stored does not meet the data storage condition, deleting the data to be stored; if the data to be stored meets the data storage condition, executing step S30.
In the practical application process, a large amount of useless invalid data can be generated, and in order to avoid that the data occupies too much storage space, the data is judged whether to meet the data storage condition before storage so as to distinguish the invalid data from the valid data and only store the valid data.
The step S33 includes the steps of:
step S331, acquiring a data source of the data to be stored;
step S332, judging whether the data source is a preset unnecessary data source;
step S333, if the data source is a preset unnecessary data source, the data to be stored does not meet the data storage condition; and if the data source is not a preset unnecessary data source, the data to be stored meet the data storage condition.
Judging whether the data acquired from each data source is valid data or not based on the characteristics of different data sources, if the data is artificial data, a large amount of invalid data exists, so that the artificial data can be set as a preset unnecessary data source, and when the data source of the acquired data to be stored is data, the data to be stored is not stored; further, when the related data needs to be saved in the artificial data, a data embedding point can be set, the data obtained based on the data embedding point in the artificial data is stored, and the data obtained in the general flow is deleted, so that the needed data can be stored on the basis of not wasting the storage space.
Further, in a fifth embodiment of the data storage method of the present invention proposed based on the first embodiment of the present invention, the step S10 includes the steps of:
step S11, classifying the data to be stored through a decision tree classification method to obtain a classification result;
and step S12, determining the data type corresponding to the data to be stored according to the classification result.
The decision tree classification method is supervised learning, namely, given a stack of samples, each sample has a group of attributes and a class, and the classes are determined in advance, so that a classifier is obtained through learning, and the classifier can give correct classification to newly appeared objects. The data type for the data pair to be stored can be accurately acquired by the decision tree classification method.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
The present application further provides an electronic device for implementing the data storage method, where the electronic device includes:
the first receiving module is used for receiving data to be stored and classifying the data to be stored to obtain a data type corresponding to the data to be stored;
the first matching module is used for matching the storage position corresponding to the data type;
the first storage module is used for storing the data to be stored into the storage position.
The electronic device classifies the data to be stored and stores the data according to the classification, so that the data source can be determined, and the accuracy of the data analysis result can be improved on the basis.
It should be noted that the first receiving module in this embodiment may be configured to execute step S10 in this embodiment, the first matching module in this embodiment may be configured to execute step S20 in this embodiment, and the first storing module in this embodiment may be configured to execute step S30 in this embodiment.
Further, the electronic device further includes:
the first judgment module is used for judging whether the data to be stored comprises a unique identifier or not;
a first execution module, configured to perform a feature marking operation on the to-be-stored data if the to-be-stored data does not include the unique identifier, and after the feature marking operation, execute the following steps: storing the data to be stored into the storage location; if the data to be stored comprises the unique identifier, executing the following steps: storing the data to be stored into the storage location.
Further, the first execution module includes:
the first obtaining unit is used for obtaining subdata matched with a preset characteristic category in the data to be stored;
and the first execution unit is used for associating the sub-data serving as a unique identifier with the data to be stored.
Further, the first storage module includes:
the first conversion unit is used for converting the data to be stored in a key-value pair form, wherein a key of the key-value pair is a unique identifier corresponding to the data to be stored, and a value of the key-value pair is the data to be stored;
and the first storage unit is used for storing the converted data to be stored into the storage position.
Further, the electronic device further includes:
the second judgment module is used for judging whether the data to be stored meets the data storage condition or not according to the data type;
the second execution module is used for deleting the data to be stored if the data to be stored does not meet the data storage condition; if the data to be stored meets the data storage condition, executing the following steps: storing the data to be stored into the storage location.
Further, the second determination module includes:
the second acquisition unit is used for acquiring a data source of the data to be stored;
the first judging unit is used for judging whether the data source is a preset unnecessary data source or not;
the second execution unit is used for judging that the data to be stored does not meet the data storage condition if the data source is a preset unnecessary data source; and if the data source is not a preset unnecessary data source, the data to be stored meet the data storage condition.
Further, the first receiving module comprises:
the first classification unit is used for classifying the data to be stored through a decision tree classification method to obtain a classification result;
and the first determining unit is used for determining the data type corresponding to the data to be stored according to the classification result.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. The modules may be implemented by software as part of the apparatus, or may be implemented by hardware, where the hardware environment includes a network environment.
Referring to fig. 3, the data storage device may include components such as a communication module 10, a memory 20, and a processor 30 in a hardware structure. In the data storage device, the processor 30 is connected to the memory 20 and the communication module 10, respectively, the memory 20 stores thereon a computer program, which is executed by the processor 30 at the same time, and when executed, implements the steps of the above-mentioned method embodiments.
The communication module 10 may be connected to an external communication device through a network. The communication module 10 may receive a request from an external communication device, and may also send a request, an instruction, and information to the external communication device, where the external communication device may be another data storage device, a server, or an internet of things device, such as a television.
The memory 20 may be used to store software programs as well as various data. The memory 20 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as storing the data to be stored in the storage location), and the like; the storage data area may include a database, and the storage data area may store data or information created according to use of the system, or the like. Further, the memory 20 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 30, which is a control center of the data storage device, connects various parts of the entire data storage device using various interfaces and lines, performs various functions of the data storage device and processes data by operating or executing software programs and/or modules stored in the memory 20 and calling data stored in the memory 20, thereby performing overall monitoring of the data storage device. Processor 30 may include one or more processing units; alternatively, the processor 30 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 30.
Although not shown in fig. 3, the data storage device may further include a circuit control module for connecting with a power supply to ensure the normal operation of other components. Those skilled in the art will appreciate that the data storage device configuration shown in FIG. 3 does not constitute a limitation of data storage devices, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
The invention also proposes a computer-readable storage medium on which a computer program is stored. The computer-readable storage medium may be the Memory 20 in the data storage device of fig. 3, and may also be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, and an optical disk, and the computer-readable storage medium includes instructions for enabling a terminal device (which may be a television, an automobile, a mobile phone, a computer, a server, a terminal, or a network device) having a processor to execute the method according to the embodiments of the present invention.
In the present invention, the terms "first", "second", "third", "fourth" and "fifth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance, and those skilled in the art can understand the specific meanings of the above terms in the present invention according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although the embodiment of the present invention has been shown and described, the scope of the present invention is not limited thereto, it should be understood that the above embodiment is illustrative and not to be construed as limiting the present invention, and that those skilled in the art can make changes, modifications and substitutions to the above embodiment within the scope of the present invention, and that these changes, modifications and substitutions should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method of data storage, the method comprising:
receiving data to be stored, and classifying the data to be stored to obtain a data type corresponding to the data to be stored;
matching a storage location corresponding to the data type;
storing the data to be stored into the storage location.
2. The data storage method of claim 1, wherein said storing the data to be stored into the storage location comprises:
judging whether the data to be stored comprises a unique identifier or not;
if the data to be stored does not comprise the unique identifier, performing characteristic marking operation on the data to be stored, and after the characteristic marking operation, executing the following steps: storing the data to be stored into the storage location; if the data to be stored comprises the unique identifier, executing the following steps: storing the data to be stored into the storage location.
3. The data storage method of claim 2, wherein said performing a feature tag operation on said data to be stored comprises:
acquiring subdata matched with a preset characteristic category in the data to be stored;
and associating the sub data serving as a unique identifier with the data to be stored.
4. The data storage method of claim 1, wherein said storing the data to be stored into the storage location comprises:
converting the data to be stored in a key-value pair mode, wherein a key of the key-value pair is a unique identifier corresponding to the data to be stored, and a value of the key-value pair is the data to be stored;
and storing the converted data to be stored into the storage position.
5. The data storage method of claim 1, wherein prior to said storing the data to be stored in the storage location, comprising:
judging whether the data to be stored meet data storage conditions or not according to the data type;
if the data to be stored does not meet the data storage condition, deleting the data to be stored; if the data to be stored meets the data storage condition, executing the following steps: storing the data to be stored into the storage location.
6. The data storage method of claim 5, wherein the determining whether the data to be stored satisfies a data storage condition according to the data type comprises:
acquiring a data source of the data to be stored;
judging whether the data source is a preset unnecessary data source or not;
if the data source is a preset unnecessary data source, the data to be stored does not meet the data storage condition; and if the data source is not a preset unnecessary data source, the data to be stored meet the data storage condition.
7. The data storage method of claim 1, wherein the classifying the data to be stored to obtain the data type corresponding to the data to be stored comprises:
classifying the data to be stored by a decision tree classification method to obtain a classification result;
and determining the data type corresponding to the data to be stored according to the classification result.
8. An electronic device, comprising:
the first receiving module is used for receiving data to be stored and classifying the data to be stored to obtain a data type corresponding to the data to be stored;
the first matching module is used for matching the storage position corresponding to the data type;
the first storage module is used for storing the data to be stored into the storage position.
9. Data storage means, characterized in that the data storage means comprise a memory, a processor and a computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the data storage method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the data storage method according to any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114564739A (en) * | 2022-02-14 | 2022-05-31 | 浙江惠瀜网络科技有限公司 | Method and device for preventing illegal acquisition of index source code of coded file |
| CN114726880A (en) * | 2022-04-12 | 2022-07-08 | 铜陵久装网络科技有限公司 | Information storage method based on cloud computing |
| CN116302712A (en) * | 2023-05-23 | 2023-06-23 | 北京国科恒通科技股份有限公司 | Power grid data backup method, device and system |
| CN116340975A (en) * | 2023-03-16 | 2023-06-27 | 江苏骏安信息测评认证有限公司 | Cache data safety protection system based on cloud computing |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114564739A (en) * | 2022-02-14 | 2022-05-31 | 浙江惠瀜网络科技有限公司 | Method and device for preventing illegal acquisition of index source code of coded file |
| CN114726880A (en) * | 2022-04-12 | 2022-07-08 | 铜陵久装网络科技有限公司 | Information storage method based on cloud computing |
| CN114726880B (en) * | 2022-04-12 | 2024-04-26 | 于成龙 | Information storage method based on cloud computing |
| CN116340975A (en) * | 2023-03-16 | 2023-06-27 | 江苏骏安信息测评认证有限公司 | Cache data safety protection system based on cloud computing |
| CN116302712A (en) * | 2023-05-23 | 2023-06-23 | 北京国科恒通科技股份有限公司 | Power grid data backup method, device and system |
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