Data storage method and device, computer equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of data storage, and particularly discloses a data storage method and device, computer equipment and a storage medium. According to the embodiment of the invention, in a storage learning stage, named storage habit data of a learning user is monitored, and a data named storage habit model is constructed; importing a data naming storage habit model, and outputting target naming storage information; performing pre-naming storage processing on the target data; generating a link management window; and performing batch naming storage processing on the multiple pieces of target naming storage information. The naming storage habit of a learning user can be monitored, a data naming storage habit model is constructed, pre-naming storage processing is performed on target data according to the data naming storage habit model, then a link management window is generated, batch processing information of the user is acquired, and batch naming storage processing is performed on multiple pieces of target naming storage information. Therefore, the user does not need to frequently manually name and store the data, and the working efficiency of the user is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data storage, and particularly relates to a data storage method, apparatus, computer device, and storage medium. Background Art

[0002] During the process of data creation and data editing on a computer device, users often perform different naming and storage processes according to the content of the data, the time of creation and editing, and the type of data. Especially on computer devices suitable for fixed work, users usually follow certain rules when naming and storing data. However, in the existing technology, it is usually impossible to automatically name and store data by using the rules of users' data naming and storage. As a result, after each data creation and data editing, users need to manually perform naming and storage processes. Especially when data creation and data editing are frequent, a lot of time is spent on manual naming and storage processes, resulting in low work efficiency. Summary of the Invention

[0003] The purpose of the embodiments of the present invention is to provide a data storage method, apparatus, computer device, and storage medium, aiming to solve the problems raised in the background art.

[0004] To achieve the above purpose, the embodiments of the present invention provide the following technical solutions: A data storage method, the method specifically includes the following steps: In the storage learning stage, monitor and learn the naming and storage habit data of the user, and construct a data naming and storage habit model according to the naming and storage habit data; Obtain target data, analyze the target data to obtain data analysis information, import the data analysis information into the data naming and storage habit model, and output target naming and storage information; Perform pre-naming and storage processing on the target data according to the target naming and storage information; Collect and statistically analyze multiple target naming and storage information processed in the pre-naming and storage process during a preset automatic processing period to generate a link management window; Obtain the batch processing information of the user in the link management window, and perform batch naming and storage processing on multiple target naming and storage information according to the batch processing information.

[0005] As a further limitation of the technical solution of the embodiments of the present invention, the step of monitoring and learning the naming and storage habit data of the user in the storage learning stage and constructing a data naming and storage habit model according to the naming and storage habit data specifically includes the following steps: In the storage learning stage, mark the learning data; Perform feature analysis on the learning data to generate data feature information; Monitor and learn the naming and storage habit data of users for learning data; Construct a data naming and storage habit model according to the naming and storage habit data.

[0006] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining target data, analyzing the target data to obtain data analysis information, importing the data analysis information into the data naming and storage habit model, and outputting target naming and storage information specifically include the following steps: Obtain target data; Perform feature analysis on the target data to generate data analysis information; Import the data analysis information into the data naming and storage habit model; Output target naming and storage information corresponding to the data analysis information.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the steps of pre-naming and storing the target data according to the target naming and storage information specifically include the following steps: Determine naming information and storage location according to the target naming and storage information; Pre-name the target data according to the naming information; Pre-store the target data according to the storage location.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the steps of centrally counting multiple target naming and storage information of the pre-naming and storing process in a preset automatic processing cycle to generate a link management window specifically include the following steps: Create target naming and storage information during the pre-naming and storing process; Centrally count multiple target naming outputs corresponding to the target data and the target naming and storage information in a preset automatic processing cycle; Generate multiple corresponding management links according to the multiple target naming and storage information; Generate a link management window according to the multiple management links.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining the batch processing information of the user in the link management window and performing batch naming and storage processing on multiple target naming and storage information according to the batch processing information specifically include the following steps: Obtain the batch processing information of the user in the link management window; Process and classify multiple target data according to the batch processing information to generate a processing classification result; Perform batch naming and storage processing on multiple corresponding target naming and storage information according to the processing classification result.

[0010] Another object of an embodiment of the present invention is to provide a data storage device, which includes a habit model construction unit, a target data processing unit, a pre-named storage processing unit, a management window generation unit, and a target batch processing unit, wherein: The habit model construction unit is configured to monitor and learn the named storage habit data of a user during a storage learning stage, and construct a data named storage habit model according to the named storage habit data; The target data processing unit is configured to obtain target data, analyze the target data to obtain data analysis information, import the data analysis information into the data named storage habit model, and output target named storage information; The pre-named storage processing unit is configured to perform pre-named storage processing on the target data according to the target named storage information; The management window generation unit is configured to centrally count a plurality of target named storage information processed by pre-named storage during a preset automatic processing cycle, and generate a link management window; The target batch processing unit is configured to obtain batch processing information of a user in the link management window, and perform batch named storage processing on a plurality of target named storage information according to the batch processing information.

[0011] As a further limitation of the technical solution of an embodiment of the present invention, the habit model construction unit specifically includes: A data marking module, configured to mark learning data during a storage learning stage; A feature analysis module, configured to perform feature analysis on the learning data to generate data feature information; A monitoring and learning module, configured to monitor and learn the named storage habit data of a user for learning data; A model construction module, configured to construct a data named storage habit model according to the named storage habit data.

[0012] Another object of an embodiment of the present invention is to provide a computer device, including a memory and a processor, where a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the data storage method as described above.

[0013] Another object of an embodiment of the present invention is to provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor executes the steps of the data storage method as described above.

[0014] Compared with the prior art, the beneficial effects of the present invention are: In the storage learning stage of the embodiment of the present invention, the naming storage habit data of the learning user is monitored to construct a data naming storage habit model; the data naming storage habit model is imported to output the target naming storage information; pre-naming storage processing is performed on the target data; a link management window is generated; and batch naming storage processing is performed on multiple pieces of target naming storage information. It can monitor and learn the naming storage habits of users, construct a data naming storage habit model, perform pre-naming storage processing on target data according to the data naming storage habit model, and then generate a link management window, obtain the batch processing information of users, and perform batch naming storage processing on multiple pieces of target naming storage information, so that users do not need to perform frequent manual data naming and storage processing, improving the work efficiency of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0016] Figure 1 The flowchart of the method provided by the embodiment of the present invention is shown.

[0017] Figure 2 The flowchart of constructing a data naming storage habit model in the method provided by the embodiment of the present invention is shown.

[0018] Figure 3 The flowchart of outputting target naming storage information in the method provided by the embodiment of the present invention is shown.

[0019] Figure 4 The flowchart of pre-naming storage processing in the method provided by the embodiment of the present invention is shown.

[0020] Figure 5 The flowchart of generating a link management window in the method provided by the embodiment of the present invention is shown.

[0021] Figure 6 The flowchart of batch naming storage processing in the method provided by the embodiment of the present invention is shown.

[0022] Figure 7 The application architecture diagram of the device provided by the embodiment of the present invention is shown.

[0023] Figure 8 The structural block diagram of the habit model construction unit in the device provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] It can be understood that in the prior art, it is usually impossible to automatically name and store data by using the rules of data naming and storage by users. As a result, after each data creation and data editing by users, manual naming and storage processing are required. Especially when data creation and data editing are frequent, a lot of time needs to be spent on manual naming and storage processing, resulting in low work efficiency.

[0026] To solve the above problems, in the storage learning stage of the embodiment of the present invention, the naming and storage habit data of the user is monitored and learned to construct a data naming and storage habit model; the data naming and storage habit model is imported to output the target naming and storage information; pre-naming and storage processing is performed on the target data; a link management window is generated; and batch naming and storage processing is performed on multiple target naming and storage information. It is possible to monitor and learn the naming and storage habits of users, construct a data naming and storage habit model, perform pre-naming and storage processing on target data according to the data naming and storage habit model, and then generate a link management window, obtain the batch processing information of users, and perform batch naming and storage processing on multiple target naming and storage information, so that users do not need to perform frequent manual naming and storage processing of data, and the work efficiency of users is improved.

[0027] Figure 1 The flowchart of the method provided by the embodiment of the present invention is shown.

[0028] Specifically, for the data storage method, the method specifically includes the following steps: Step S101, in the storage learning stage, monitor and learn the naming and storage habit data of the user, and construct a data naming and storage habit model according to the naming and storage habit data.

[0029] In the embodiment of the present invention, in the storage learning stage, the data created and edited by the user is identified and marked to obtain learning data. By analyzing the learning data, the data feature information of the learning data is obtained, and the naming and storage process of the user for the learning data is supervised and learned to generate naming and storage habit data corresponding to each learning data. Then, a part of the data feature information and naming and storage habit data corresponding to the learning data are randomly selected as the training set, and another part of the data feature information and naming and storage habit data corresponding to the learning data are selected as the test set for model training to construct a data naming and storage habit model.

[0030] It can be understood that the storage learning stage is a transitional period for the user's initial use. It can be bound to the user's account, and a certain time can be set as the storage learning stage, or it can be bound to the user's account, and the time corresponding to the user processing a certain amount of learning data can be set as the storage learning stage; the data feature information is the information obtained by statistically analyzing the features of the data after content analysis of the data, mainly including the content, format, type, etc. of the data.

[0031] Specifically, Figure 2 FIG. shows the flowchart of constructing a data naming and storage habit model in the method provided by an embodiment of the present invention.

[0032] Among them, in the preferred embodiment provided by the present invention, in the storage learning stage, monitoring the naming and storage habit data of the learning user, and constructing a data naming and storage habit model according to the naming and storage habit data specifically includes the following steps: Step S1011, in the storage learning stage, mark the learning data.

[0033] Step S1012, perform feature analysis on the learning data to generate data feature information.

[0034] Step S1013, monitor the naming and storage habit data of the learning user for the learning data.

[0035] Step S1014, construct a data naming and storage habit model according to the naming and storage habit data.

[0036] Furthermore, the data storage method further includes the following steps: Step S102, obtain target data, analyze the target data to obtain data analysis information, and import the data analysis information into the data naming and storage habit model to output target naming and storage information.

[0037] In the embodiment of the present invention, after the storage learning stage, the data created or edited by the user is marked as target data. By performing feature analysis on the target data, data analysis information is generated, and the data analysis information is imported into the data naming and storage habit model. The data naming and storage habit model automatically analyzes the data analysis information, matches the naming scheme and storage scheme of the target data, and outputs the target naming and storage information corresponding to the target data.

[0038] Specifically, Figure 3 FIG. shows the flowchart of outputting target naming and storage information in the method provided by an embodiment of the present invention.

[0039] Among them, in the preferred embodiment provided by the present invention, the steps of obtaining target data, analyzing the target data to obtain data analysis information, importing the data analysis information into a data naming and storage habit model, and outputting target naming and storage information specifically include the following steps: Step S1021, obtain target data.

[0040] Step S1022, perform feature analysis on the target data to generate data analysis information.

[0041] Step S1023, import the data analysis information into a data naming and storage habit model.

[0042] Step S1024, output target naming and storage information corresponding to the data analysis information.

[0043] Furthermore, the data storage method further includes the following steps: Step S103, perform pre-naming storage processing on the target data according to the target naming and storage information.

[0044] In the embodiment of the present invention, through the target naming and storage information, the naming information and storage location corresponding to the target data are determined, and pre-naming and pre-storage processing are performed on the target data according to the naming information and storage location.

[0045] It can be understood that performing pre-naming processing on the target data is a process of temporarily naming the target data according to the naming information, and performing pre-storage processing on the target data is a process of temporarily storing the target data according to the storage location. Although this pre-naming and pre-storage have achieved the naming and storage of the target data, this naming and storage are temporary and uncertain, and the user still needs to confirm the multiple target data of the pre-naming and pre-storage to ensure the accurate naming and storage of all data and avoid naming and storage errors.

[0046] Specifically, Figure 4 shows the flowchart of the pre-naming storage processing in the method provided by the embodiment of the present invention.

[0047] Among them, in the preferred embodiment provided by the present invention, the step of performing pre-naming storage processing on the target data according to the target naming and storage information specifically includes the following steps: Step S1031, determine the naming information and storage location according to the target naming and storage information.

[0048] Step S1032, perform pre-naming on the target data according to the naming information.

[0049] Step S1033, perform pre-storage on the target data according to the storage location.

[0050] Further, the data storage method further includes the following steps: Step S104, centrally count a plurality of target named storage information pre-named and stored during a preset automatic processing cycle, and generate a link management window.

[0051] In an embodiment of the present invention, during the process of pre-naming and storing target data, target named storage information is created. By centrally counting a plurality of target named storage information during a preset automatic processing cycle, management links corresponding to each target data are generated according to the target named storage information. By centrally organizing a plurality of management links, a link management window is generated.

[0052] Specifically, Figure 5 The flowchart of generating a link management window in the method provided by the embodiment of the present invention is shown.

[0053] Among them, in the preferred embodiment provided by the present invention, the centrally counting a plurality of target named storage information pre-named and stored during a preset automatic processing cycle and generating a link management window specifically includes the following steps: Step S1041, during the pre-naming and storing process, create target named storage information.

[0054] Step S1042, centrally count a plurality of target named storage information during a preset automatic processing cycle.

[0055] Step S1043, generate a plurality of corresponding management links according to the plurality of target named storage information.

[0056] Step S1044, generate a link management window according to the plurality of management links.

[0057] Further, the data storage method further includes the following steps: Step S105, obtain the batch processing information of the user in the link management window, and perform batch naming and storing processing on a plurality of target named storage information according to the batch processing information.

[0058] In an embodiment of the present invention, a user can perform a batch confirmation operation on data in a link management window to generate batch processing information. By obtaining the batch processing information of the user in the link management window, multiple target data are processed and classified according to the batch processing information to generate a processing classification result. Then, according to the processing classification result, batch naming and storage processing is performed on multiple corresponding target naming storage information. Specifically, the processing classification result includes: unchanged, changed naming, changed storage, and changed simultaneously; the batch naming and storage processing includes: batch confirming the original naming and storage location, batch changing the original naming, batch changing the original storage location, and batch changing the original naming and storage location.

[0059] Specifically, Figure 6 FIG. shows a flowchart of batch naming and storage processing in the method provided by an embodiment of the present invention.

[0060] Among them, in a preferred embodiment provided by the present invention, the obtaining of the batch processing information of the user in the link management window and the batch naming and storage processing of multiple target naming storage information according to the batch processing information specifically include the following steps: Step S1051, obtaining the batch processing information of the user in the link management window.

[0061] Step S1052, processing and classifying multiple target data according to the batch processing information to generate a processing classification result.

[0062] Step S1053, performing batch naming and storage processing on multiple corresponding target naming storage information according to the processing classification result.

[0063] Furthermore, Figure 7 FIG. shows an application architecture diagram of the device provided by an embodiment of the present invention.

[0064] Among them, in another preferred embodiment provided by the present invention, a data storage device includes: A habit model construction unit 101, configured to monitor and learn the naming and storage habit data of a user during a storage learning stage, and construct a data naming and storage habit model according to the naming and storage habit data.

[0065] In an embodiment of the present invention, during the storage learning stage, the habit model construction unit 101 identifies and marks the data created and edited by the user to obtain learning data. By analyzing the learning data, the data feature information of the learning data is obtained, and the naming and storage processes of the user for the learning data are supervised and learned to generate naming storage habit data corresponding to each learning data. Furthermore, a part of the data feature information and naming storage habit data corresponding to the learning data are randomly selected as the training set, and another part of the data feature information and naming storage habit data corresponding to the learning data are selected as the test set for model training to construct a data naming storage habit model.

[0066] Specifically, Figure 8 FIG. shows a structural block diagram of the habit model construction unit 101 in the device provided by the embodiment of the present invention.

[0067] Among them, in the preferred embodiment provided by the present invention, the habit model construction unit 101 specifically includes: A data marking module 1011, configured to mark learning data during the storage learning stage.

[0068] A feature analysis module 1012, configured to perform feature analysis on the learning data to generate data feature information.

[0069] A monitoring and learning module 1013, configured to monitor and learn the naming storage habit data of the user for the learning data.

[0070] A model construction module 1014, configured to construct a data naming storage habit model according to the naming storage habit data.

[0071] Furthermore, the data storage device further includes: A target data processing unit 102, configured to obtain target data, analyze the target data to obtain data analysis information, import the data analysis information into the data naming storage habit model, and output target naming storage information.

[0072] In an embodiment of the present invention, after the storage learning stage, the target data processing unit 102 marks the data created or edited by the user as target data. By performing feature analysis on the target data, data analysis information is generated, and the data analysis information is imported into the data naming storage habit model. The data naming storage habit model automatically analyzes the data analysis information to match the naming scheme and storage scheme of the target data, and outputs target naming storage information corresponding to the target data.

[0073] A pre-naming storage processing unit 103, configured to perform pre-naming storage processing on the target data according to the target naming storage information.

[0074] In an embodiment of the present invention, the pre - naming storage processing unit 103 determines the naming information and storage location corresponding to the target data by storing information with the target name, and performs pre - naming and pre - storage processing on the target data according to the naming information and storage location.

[0075] The management window generation unit 104 is used to centrally count multiple target naming storage information of the pre - naming storage processing in a preset automatic processing cycle, and generate a link management window.

[0076] In an embodiment of the present invention, during the process of pre - naming and storing the target data, the management window generation unit 104 creates target naming storage information, centrally counts multiple target naming storage information in a preset automatic processing cycle, generates a management link corresponding to each target data according to the target naming storage information, and generates a link management window by centrally organizing multiple management links.

[0077] The target batch processing unit 105 is used to obtain the batch processing information of the user in the link management window, and perform batch naming and storage processing on multiple target naming storage information according to the batch processing information.

[0078] In an embodiment of the present invention, the user can perform a confirmation batch operation on the data in the link management window to generate batch processing information. The target batch processing unit 105 obtains the batch processing information of the user in the link management window, classifies the processing of multiple target data according to the batch processing information to generate a processing classification result, and then performs batch naming and storage processing on multiple corresponding target naming storage information according to the processing classification result. Specifically, the processing classification results include: unchanged, changed naming, changed storage, and changed simultaneously; the batch naming and storage processing includes: batch - confirming the original naming and storage location, batch - changing the original naming, batch - changing the original storage location, and batch - changing the original naming and storage location.

[0079] In another embodiment, a computer device is proposed. The computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: In the storage learning stage, monitor and learn the naming storage habit data of the user, and construct a data naming storage habit model according to the naming storage habit data; Obtain the target data, analyze the target data to obtain data analysis information, import the data analysis information into the data naming storage habit model, and output target naming storage information; Perform pre - naming and storage processing on the target data according to the target naming storage information; Collectively count multiple target named storage information that has been pre-named and stored during a preset automatic processing cycle, and generate a link management window; Obtain the batch processing information of the user in the link management window, and perform batch named storage processing on the multiple target named storage information according to the batch processing information.

[0080] In another embodiment, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the processor is caused to perform the following steps: During the storage learning stage, monitor and learn the named storage habit data of the user, and construct a data named storage habit model according to the named storage habit data; Obtain target data, analyze the target data to obtain data analysis information, import the data analysis information into the data named storage habit model, and output target named storage information; Perform pre-named storage processing on the target data according to the target named storage information; Collectively count multiple target named storage information that has been pre-named and stored during a preset automatic processing cycle, and generate a link management window; Obtain the batch processing information of the user in the link management window, and perform batch named storage processing on the multiple target named storage information according to the batch processing information.

[0081] In summary, in the embodiment of the present invention, during the storage learning stage, the named storage habit data of the user is monitored and learned, and a data named storage habit model is constructed according to the named storage habit data; target data is obtained, the target data is analyzed to obtain data analysis information, the data analysis information is imported into the data named storage habit model, and target named storage information is output; pre-named storage processing is performed on the target data according to the target named storage information; multiple target named storage information that has been pre-named and stored during a preset automatic processing cycle is collectively counted, and a link management window is generated; the batch processing information of the user in the link management window is obtained, and batch named storage processing is performed on the multiple target named storage information according to the batch processing information. It is possible to monitor and learn the named storage habits of the user, construct a data named storage habit model, perform pre-named storage processing on the target data according to the data named storage habit model, and then generate a link management window, obtain the batch processing information of the user, and perform batch named storage processing on the multiple target named storage information, so that the user does not need to perform frequent manual data naming and storage processing, improving the work efficiency of the user.

[0082] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0083] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0084] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0085] The above-described embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0086] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data storage method, characterized in that: The method specifically comprises the following steps: In the storage learning phase, the naming and storage habit data of the learning user is monitored, and a data naming and storage habit model is constructed according to the naming and storage habit data; Acquire target data, analyze the target data to obtain data analysis information, import the data analysis information into a data naming and storage habit model, and output target naming and storage information; According to the target naming storage information, pre-naming and storing the target data; Centrally count the multiple target named storage information pre-named and stored in the preset automatic processing cycle, and generate a link management window; The batch processing information of the user in the link management window is obtained, and batch naming and storage processing is performed on multiple target naming and storage information according to the batch processing information.

2. The data storage method according to claim 1, characterized in that: In the storage learning stage, monitoring and learning the naming and storage habit data of the user, and constructing a data naming and storage habit model according to the naming and storage habit data specifically include the following steps: In the storage learning phase, the learning data is marked; Performing feature analysis on the learning data to generate data feature information; Monitor learning users’ naming and storage habits of learning data; According to the naming and storage habit data, a data naming and storage habit model is constructed.

3. The data storage method according to claim 1, characterized in that: The acquiring of target data, analyzing the target data to obtain data analysis information, importing the data analysis information into a data naming and storage habit model, and outputting the target naming and storage information specifically comprises the following steps: Get target data; Performing feature analysis on the target data to generate data analysis information; Importing the data analysis information into a data naming and storage custom model; Output the target naming storage information corresponding to the data analysis information.

4. The data storage method according to claim 1, characterized in that: The pre-naming and storing processing of the target data according to the target naming storage information specifically comprises the following steps: Determining the naming information and the storage location according to the target naming storage information; Pre-naming the target data according to the naming information; The target data is pre-stored according to the storage location.

5. The data storage method according to claim 1, characterized in that: The centralized statistics of multiple target named storage information pre-named and stored in a preset automatic processing cycle to generate a link management window specifically includes the following steps: During the pre-named storage process, target named storage information is created; Centrally count the named storage information of multiple targets in the preset automatic processing cycle; Naming the storage information according to the plurality of targets, and generating a plurality of corresponding management links; A link management window is generated according to the plurality of management links.

6. The data storage method according to claim 1, characterized in that: The step of obtaining the batch processing information of the user in the link management window and performing batch naming and storage processing on the plurality of target naming and storage information according to the batch processing information specifically comprises the following steps: Obtaining batch processing information of the user in the link management window; According to the batch processing information, multiple target data are processed and classified to generate processing and classification results; According to the processing classification result, batch naming and storage processing is performed on multiple corresponding target naming and storage information.

7. A data storage device, characterized in that The device comprises a habit model building unit, a target data processing unit, a pre-named storage processing unit, a management window generating unit and a target batch processing unit, wherein: A habit model building unit, used to monitor the naming and storage habit data of the learning user during the storage learning phase, and build a data naming and storage habit model according to the naming and storage habit data; A target data processing unit, used to obtain target data, analyze the target data, obtain data analysis information, import the data analysis information into a data naming and storage habit model, and output target naming and storage information; A pre-naming storage processing unit, used for performing pre-naming storage processing on the target data according to the target naming storage information; A management window generating unit, used for centrally counting the multiple target named storage information pre-named and stored in a preset automatic processing cycle, and generating a link management window; The target batch processing unit is used to obtain the batch processing information of the user in the link management window, and perform batch naming and storage processing on multiple target naming and storage information according to the batch processing information.

8. The data storage device according to claim 7, characterized in that: The habit model building unit specifically includes: A data labeling module is used to label learning data during the storage learning phase; A feature analysis module, used to perform feature analysis on the learning data and generate data feature information; A monitoring learning module is used to monitor the naming and storage habits of learning data by learning users; The model building module is used to build a data naming and storage habit model according to the naming and storage habit data.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the data storage method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the data storage method according to any one of claims 1 to 6.

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