A method and device for incremental backup of data assets

Abstract

The present invention discloses a method and device for incremental backup of data assets, which can solve the technical problem that it is difficult to quickly locate and restore data assets when switching from a main server to a backup server. The method includes: obtaining a change sequence of data assets; the backup server obtains the usage frequency of each data asset in the changed data assets based on the change sequence of data assets, and determines the hot data assets from the changed data assets based on the usage frequency; obtains the associated data assets of the hot data assets; generates complete data of the hot data assets and the associated data assets of the hot data assets, and stores the generated complete data in a fixed area of ​​the backup server; a multi-tree and multiple linked list structures are set in an area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the multi-tree is used to store the contextual relationship of each data asset.

Description

Technical Field

[0001] The present invention relates to the field of data storage, and in particular to a method and device for incremental backup of data assets. Background Art

[0002] Data assets refer to data resources that are owned or controlled by individuals or enterprises and can bring economic benefits to the enterprise and are stored in physical or electronic form. With the development of computer technology, the data assets generated in the operation of companies or enterprises are increasing, which brings about the problem of massive data assets that need to be stored and backed up.

[0003] The backup method of the prior art usually uses two servers, one as the main server for storing data assets, and the other server as the backup server, which synchronously or asynchronously stores the same data assets as the main server. Common backup methods include full backup and incremental backup. When the main server fails, the main server switches to the backup server. However, for the backup method using incremental backup, the prior art has the problem that it is difficult to quickly locate and restore data assets when switching to the backup server.

[0004] Therefore, designing a method and device for incremental backup of data assets, using an incremental backup method so that data assets can be quickly located and restored when switching from a primary server to a backup server, is a technical problem that urgently needs to be solved. Summary of the invention

[0005] In view of this, the present invention provides a method and device for incremental backup of data assets, which can solve the technical problem of the prior art that it is difficult to quickly locate and restore data assets when switching from a primary server to a backup server.

[0006] In order to solve the above technical problems, the present invention is implemented as follows.

[0007] A method for incremental backup of data assets, comprising:

[0008] Step S1: Start the incremental backup of data assets, obtain the difference between the current data assets and the current data baseline, and determine the change sequence D of the data assets based on the difference. i ,…,d n}, the change sequence arranges the changes of each data asset in the data asset in chronological order; where 1≤i≤n, n is the number of data asset items that have changed compared with the current data baseline; d i ∈{ΔA i ,ΔD i ,ΔM i}, ΔA iis the increase in the i-th data asset that has changed relative to the current data baseline, ΔD i is the deletion item of the changed data asset relative to the current data baseline, ΔM i The modification item of the i-th data asset that has changed relative to the current data baseline;

[0009] Step S2: The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, based on the change sequence D of the data asset = {d1,…,d i ,…,d n}, obtain the usage frequency of each data asset in the changed data assets, and determine the hot data assets from the changed data assets based on the usage frequency; obtain the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets, and store the generated complete data in a fixed area of ​​the backup server;

[0010] Step S3: a multi-tree and a plurality of linked list structures are set in the area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the header of the linked list stores the attribute information of the data asset, the pointer of the linked list points to the change information recording node of the data asset, and the change information recording node of the data asset stores the change information and change time point of the data asset; the multi-tree is used to store the contextual relationship of each data asset;

[0011] The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, attach each element of the change sequence D of the data asset to the corresponding linked list; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list to store the data asset corresponding to the certain element of the change sequence D that is not in the backup server and update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

[0012] Preferably, the step S2, obtaining the usage frequency of each data asset among the changed data assets, and determining the hot data assets from the changed data assets based on the usage frequency, includes:

[0013] Obtain the usage frequency of each data asset among the changed data assets, and sort each data asset among the changed data assets in descending order based on the usage frequency; in the backup server, the data assets among the changed data assets that rank higher than a preset threshold are regarded as hot data assets.

[0014] Preferably, the step S2, obtaining the associated data assets of the hot data assets; generating complete data of the hot data assets and the associated data assets of the hot data assets, and storing the generated complete data in a fixed area of ​​the backup server, includes:

[0015] The attribute information of the hot data asset is used as input and input into the trained LSTM neural network model to obtain the associated data assets of the hot data asset;

[0016] The attribute information of the hot data asset includes the name, data type, usage time, and context information of the hot data asset. The context information of the usage refers to the name and usage time of the context of the hot data asset when the hot data asset is generated or used. The attribute information of the hot data asset is spliced ​​to construct a feature vector as the input data of the LSTM neural network model.

[0017] Preferably, in the data asset change sequence D = {d1, ..., d i ,…,d n}When the amount of data exceeds the preset processing volume threshold of the backup server, the execution of step S2 is interrupted, that is, step S2 is skipped and step S3 is executed first, and after step S3 is completed, step S2 is executed again when the backup server is idle; the preset processing volume threshold of the backup server is determined by the system administrator based on the enterprise data usage.

[0018] The present invention provides a device for incremental backup of data assets, the device comprising:

[0019] The change sequence acquisition module is configured to start the incremental backup of data assets, obtain the difference between the current data assets and the current data baseline, and determine the change sequence D of the data assets based on the difference. i ,…,d n}, the change sequence arranges the changes of each data asset in the data asset in chronological order; where 1≤i≤n, n is the number of data asset items that have changed compared with the current data baseline; d i ∈{ΔA i ,ΔD i ,ΔM i}, ΔA iis the increase in the i-th data asset that has changed relative to the current data baseline, ΔD i is the deletion item of the changed data asset relative to the current data baseline, ΔM i The modification item of the i-th data asset that has changed relative to the current data baseline;

[0020] Complete data generation module: configured to obtain the change sequence D of the data asset from the backup server. i ,…,d n}, based on the change sequence D of the data asset = {d1,…,d i ,…,d n}, obtain the usage frequency of each data asset in the changed data assets, and determine the hot data assets from the changed data assets based on the usage frequency; obtain the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets, and store the generated complete data in a fixed area of ​​the backup server;

[0021] Full backup module: configured to set a multi-tree and multiple linked list structures in an area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the header of the linked list stores the attribute information of the data asset, the pointer of the linked list points to the change information recording node of the data asset, and the change information recording node of the data asset stores the change information and change time point of the data asset; the multi-tree is used to store the contextual relationship of each data asset;

[0022] The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, attach each element of the change sequence D of the data asset to the corresponding linked list; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list to store the data asset corresponding to the certain element of the change sequence D that is not in the backup server and update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

[0023] Preferably, obtaining the usage frequency of each data asset among the changed data assets, and determining the hot data assets from the changed data assets based on the usage frequency, includes:

[0024] Obtain the usage frequency of each data asset among the changed data assets, and sort each data asset among the changed data assets in descending order based on the usage frequency; in the backup server, the data assets among the changed data assets that rank higher than a preset threshold are regarded as hot data assets.

[0025] Preferably, the acquiring of the associated data assets of the hot data assets; generating complete data of the hot data assets and the associated data assets of the hot data assets, and storing the generated complete data in a fixed area of ​​the backup server includes:

[0026] The attribute information of the hot data asset is used as input and input into the trained LSTM neural network model to obtain the associated data assets of the hot data asset;

[0027] The attribute information of the hot data asset includes the name, data type, usage time, and context information of the hot data asset. The context information of the usage refers to the name and usage time of the context of the hot data asset when the hot data asset is generated or used. The attribute information of the hot data asset is spliced ​​to construct a feature vector as the input data of the LSTM neural network model.

[0028] Preferably, in the data asset change sequence D = {d1, ..., d i ,…,d n}When the amount of data exceeds the preset processing volume threshold of the backup server, the complete data generation module is closed, that is, the complete data generation module is closed to trigger the full backup module first, and after the full backup module is processed, the complete data generation module is opened and triggered when the backup server is idle; the preset processing volume threshold of the backup server is determined by the system administrator based on the enterprise data usage.

[0029] A data asset incremental backup system provided by the present invention includes:

[0030] A processor, which is used to execute multiple instructions;

[0031] A memory for storing a plurality of instructions;

[0032] The plurality of instructions are used to be stored by the memory and loaded and executed by the processor to implement the method as described above.

[0033] The present invention provides a computer-readable storage medium, wherein a plurality of instructions are stored in the storage medium; the plurality of instructions are used for a processor to load and execute the method as described above.

[0034] Beneficial effects:

[0035] Compared with the prior art, the method and device for incremental backup of data assets proposed in the present invention determine hot data assets and the data assets associated with the hot data assets; generate complete data of the hot data assets and the data assets associated with the hot data assets, and store the generated complete data in a fixed area of ​​the backup server, so as to facilitate the rapid acquisition of hot data assets that may need to be used and other related data assets associated with the hot data assets when the main server fails and switches to the backup server. The backup server also performs a full backup of the incremental data, so as to facilitate the backup of global data assets. That is, two backup modes are provided in the backup server to facilitate the acquisition of related data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of a method for incremental backup of data assets;

[0037] Figure 2 A block diagram of the device structure for incremental backup of data assets. DETAILED DESCRIPTION

[0038] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0039] The present invention proposes a method for incremental backup of data assets. The method obtains differential data of data assets in the form of incremental backup. The backup server obtains the change sequence of the data assets, determines the hot data assets and the associated data assets of the hot data assets; generates complete data of the hot data assets and the associated data assets of the hot data assets, and stores the generated complete data in a fixed area of ​​the backup server, so that when the main server fails and switches to the backup server, the hot data assets that may need to be used and other related data assets associated with the hot data assets can be quickly obtained. The backup server also performs a full backup of the incremental data, so as to facilitate the backup of global data assets.

[0040] like Figure 1 As shown, the method for incremental backup of data assets provided by the present invention comprises the following steps:

[0041] Step S1: Start the incremental backup of data assets, obtain the difference between the current data assets and the current data baseline, and determine the change sequence D of the data assets based on the difference. i ,…,d n}, the change sequence arranges the changes of each data asset in the data asset in chronological order; where 1≤i≤n, n is the number of data asset items that have changed compared with the current data baseline; d i ∈{ΔA i ,ΔD i,ΔM i}, ΔA i is the increase in the i-th data asset that has changed relative to the current data baseline, ΔD i is the deletion item of the changed data asset relative to the current data baseline, ΔM i The modification item of the i-th data asset that has changed relative to the current data baseline;

[0042] Step S2: The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, based on the change sequence D of the data asset = {d1,…,d i ,…,d n}, obtain the usage frequency of each data asset in the changed data assets, and determine the hot data assets from the changed data assets based on the usage frequency; obtain the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets, and store the generated complete data in a fixed area of ​​the backup server;

[0043] Step S3: a multi-tree and a plurality of linked list structures are set in the area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the header of the linked list stores the attribute information of the data asset, the pointer of the linked list points to the change information recording node of the data asset, and the change information recording node of the data asset stores the change information and change time point of the data asset; the multi-tree is used to store the contextual relationship of each data asset;

[0044] The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, attach each element of the change sequence D of the data asset to the corresponding linked list; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list to store the data asset corresponding to the certain element of the change sequence D that is not in the backup server and update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

[0045] The step S1 starts the incremental backup of data assets by obtaining an instruction to start the incremental backup of data assets, or reaching the time point of the incremental backup of data assets in the working mode of performing incremental backup of data assets on a scheduled basis. The current data assets are all the data assets stored on the master server at the current moment. The differential result between the current data assets and the current data baseline is obtained. For the first backup, the current data baseline is empty. The current data baseline is the data baseline stored on the backup server during the last incremental backup. The data baseline can be generated based on other data baselines that are earlier than the current baseline, and the differential result combination between the other data baselines and the current data assets. Based on the differential result, the change sequence D of the data asset is determined = {d1,…,d i ,…,d n The change sequence arranges the changes of each data asset in the data asset in chronological order, which means obtaining the change time points of each data asset, and sorting the change information of each data asset according to the order of the change time points, and obtaining the data asset change sequence D = {d1, ..., d i ,…,d n}.

[0046] The step S2, obtaining the usage frequency of each data asset among the changed data assets, and determining the hot data assets from the changed data assets based on the usage frequency, includes:

[0047] Obtain the usage frequency of each data asset in the changed data assets, and sort each data asset in the changed data assets in descending order based on the usage frequency; in the backup server, the data assets ranked higher than the preset threshold among the changed data assets are regarded as hot data assets. In this embodiment, the usage frequency of each data asset in the changed data assets is obtained based on the server log, and the server can be a server independent of the main server and the backup server, or it can be the main server. The preset threshold can be taken as one-half or one-third, or any specified value.

[0048] In this embodiment, the usage frequency of each data asset among the changed data assets is determined based on the usage of each data asset by the enterprise's internal servers. For example, taking three days as a statistical period, the usage frequency of each data asset within the statistical period is determined based on the log information of the internal server.

[0049] The step of acquiring the associated data assets of the hot data assets; generating complete data of the hot data assets and the associated data assets of the hot data assets, and storing the generated complete data in a fixed area of ​​the backup server includes:

[0050] The attribute information of the hot data asset is used as input and input into the trained LSTM neural network model to obtain the associated data assets of the hot data asset. The attribute information of the hot data asset includes the name, data type, usage time, and context information of the hot data asset. The context information of the usage refers to the name and usage time of the context of the hot data asset when the hot data asset is generated or used. The attribute information of the hot data asset is spliced ​​to construct a feature vector as the input data of the LSTM neural network model.

[0051] In this embodiment, the LSTM neural network model is a mature LSTM neural network model in the art.

[0052] As an alternative implementation, the attribute information of the hot data asset is obtained, the similarity of the hot data asset and the semantics of the data asset are used as influencing factors, and the associated data assets of the hot data asset are determined and obtained in the data assets.

[0053] Obtain the latest version data of the hot data asset and the associated data asset of the hot data asset from the backup server; from the change sequence D of the data asset = {d1, ..., d i ,…,d n} Obtain changes in the hot data assets and the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets based on the current latest version data of the hot data assets and the associated data assets of the hot data assets, and changes in the hot data assets and the associated data assets of the hot data assets; store the generated complete data in a fixed area of ​​the backup server.

[0054] Specifically, in this embodiment, based on d i ∈{ΔA i ,ΔD i ,ΔM i}, according to d i The specific value of d in the backup server is obtained i The latest version of the corresponding data asset is superimposed on this basis. i The specific value of generates the complete data of the data asset.

[0055] In this embodiment, the fixed area of ​​the backup server may be an area such as a cache that can quickly acquire data.

[0056] By storing complete data of hot data assets and their associated data assets in a fixed area of ​​the backup server, it is possible to quickly obtain the hot data assets that may need to be used, as well as other related data assets associated with the hot data assets, when the main server fails and switches to the backup server.

[0057] In step S3, the linked list can be implemented in a variety of ways. For example, the linked list can be a single linked list, and the remaining nodes in the linked list except the header are all change information recording nodes. Each change information recording node stores a change information and the change time point corresponding to the change information. Multiple change information recording nodes corresponding to the same linked list are arranged in chronological order. In addition, the linked list can also be a bidirectional linked list. The structure of the bidirectional linked list facilitates version rollback of data assets.

[0058] The multi-branch tree is used to store the contextual relationship of each data asset. The root node of the multi-branch tree is empty, and each of the remaining nodes corresponds to a data asset.

[0059] In this embodiment, the purpose of setting a multi-tree and multiple linked lists to store the changes of data assets is to back up the global data assets.

[0060] The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, attach each element of the change sequence D of the data asset to the corresponding linked list, for example, add a change information recording node in the corresponding linked list, store the change information and the change time point corresponding to the change information in the newly added change information recording node; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list for storing the data asset corresponding to the certain element of the change sequence D that is not in the backup server; if a data asset is deleted, delete the corresponding linked list; update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

[0061] Furthermore, in this embodiment, blockchain is used to store incremental data to improve the reliability and tamper-proofing of backup data.

[0062] Furthermore, according to the change sequence D of the data assets obtained by the backup server, i ,…,d n}, in order to balance the backup speed and data processing volume, step S2 is made an interruptible step. For example, in the data asset change sequence D = {d1,…,d i ,…,d n} When the amount of data in the backup server exceeds the preset processing volume threshold of the backup server, the execution of step S2 is interrupted, that is, step S2 is skipped and step S3 is executed first, and after step S3 is completed, step S2 is executed again when the backup server is idle. The preset processing volume threshold of the backup server is determined by the system administrator based on the enterprise data usage.

[0063] The present invention also provides a device for incremental backup of data assets, such as Figure 2 As shown, the device comprises:

[0064] The change sequence acquisition module is configured to start the incremental backup of data assets, obtain the difference between the current data assets and the current data baseline, and determine the change sequence D of the data assets based on the difference. i ,…,d n}, the change sequence arranges the changes of each data asset in the data asset in chronological order; where 1≤i≤n, n is the number of data asset items that have changed compared with the current data baseline; d i ∈{ΔA i ,ΔD i ,ΔM i}, ΔA i is the increase in the i-th data asset that has changed relative to the current data baseline, ΔD i is the deletion item of the changed data asset relative to the current data baseline, ΔM i The modification item of the i-th data asset that has changed relative to the current data baseline;

[0065] Complete data generation module: configured to obtain the change sequence D of the data asset from the backup server. i ,…,d n}, based on the change sequence D of the data asset = {d1,…,d i ,…,d n}, obtain the usage frequency of each data asset in the changed data assets, and determine the hot data assets from the changed data assets based on the usage frequency; obtain the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets, and store the generated complete data in a fixed area of ​​the backup server;

[0066] Full backup module: configured to set a multi-tree and multiple linked list structures in an area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the header of the linked list stores the attribute information of the data asset, the pointer of the linked list points to the change information recording node of the data asset, and the change information recording node of the data asset stores the change information and change time point of the data asset; the multi-tree is used to store the contextual relationship of each data asset;

[0067] The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n}, attach each element of the change sequence D of the data asset to the corresponding linked list; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list to store the data asset corresponding to the certain element of the change sequence D that is not in the backup server and update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

[0068] The embodiment of the present invention further provides a system for incremental backup of data assets, including:

[0069] A processor, which is used to execute multiple instructions;

[0070] A memory for storing a plurality of instructions;

[0071] The plurality of instructions are used to be stored by the memory, and loaded and executed by the processor to implement the aforementioned method for incremental backup of data assets.

[0072] The embodiment of the present invention further provides a computer-readable storage medium, in which a plurality of instructions are stored; the plurality of instructions are used by a processor to load and execute the aforementioned method for incremental backup of data assets.

[0073] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0076] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a physical server, or a network cloud server, etc., which needs to be installed with Windows or Windows Server operating system) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (RandomAccess Memory, RAM), disk or optical disk and other media that can store program codes.

[0077] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for incremental backup of data assets, characterized in that: include: Step S1: Start the incremental backup of data assets, obtain the difference between the current data assets and the current data baseline, the current data assets are all the data assets stored on the primary server at the current moment, the current data baseline is the data baseline stored on the backup server at the last incremental backup, and determine the change sequence D of the data assets based on the difference result = {d1, ..., d i ,…,d n }, the change sequence arranges the changes of each data asset in the data asset in chronological order; where 1≤i≤n, n is the number of data asset items that have changed compared with the current data baseline; d i ∈{ΔA i ,ΔD i ,ΔM i }, ΔA i is the increase in the i-th data asset that has changed relative to the current data baseline, ΔD i is the deletion item of the changed data asset relative to the current data baseline, ΔM i The modification item of the i-th data asset that has changed relative to the current data baseline; Step S2: The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n }, based on the change sequence D of the data asset = {d1,…,d i ,…,d n }, obtain the usage frequency of each data asset in the changed data assets, and determine the hot data assets from the changed data assets based on the usage frequency; obtain the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets, and store the generated complete data in the fixed area of ​​the backup server; wherein, the attribute information of the hot data assets is used as input, and the trained LSTM neural network model is input to obtain the associated data assets of the hot data assets; based on d i ∈{ΔA i ,ΔD i ,ΔM i }, according to d i Get the specific value of the backup server i The latest version of the corresponding data asset, superimposed with d i The specific value of generates the complete data of the data asset; Step S3: a multi-tree and a plurality of linked list structures are set in the area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the header of the linked list stores the attribute information of the data asset, the pointer of the linked list points to the change information recording node of the data asset, and the change information recording node of the data asset stores the change information and change time point of the data asset; the multi-tree is used to store the contextual relationship of each data asset; The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n }, attach each element of the change sequence D of the data asset to the corresponding linked list; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list to store the data asset corresponding to the certain element of the change sequence D that is not in the backup server and update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

2. The method according to claim 1, characterized in that The step S2, obtaining the usage frequency of each data asset among the changed data assets, and determining the hot data assets from the changed data assets based on the usage frequency, includes: Obtain the usage frequency of each data asset among the changed data assets, and sort each data asset among the changed data assets in descending order based on the usage frequency; in the backup server, the data assets among the changed data assets that rank higher than a preset threshold are regarded as hot data assets.

3. The method according to claim 2, characterized in that The step S2, obtaining the associated data assets of the hot data assets; generating complete data of the hot data assets and the associated data assets of the hot data assets, and storing the generated complete data in a fixed area of ​​the backup server, specifically includes: The attribute information of the hot data asset is used as input and input into the trained LSTM neural network model to obtain the associated data assets of the hot data asset; the attribute information of the hot data asset includes the name, data type, usage time and context information of the hot data asset, and the context information of the usage refers to the name and usage time of the context of the hot data asset when the hot data asset is generated or used; the attribute information of the hot data asset is spliced ​​to construct a feature vector as the input data of the LSTM neural network model.

4. The method according to claim 3, characterized in that In the data asset change sequence D = {d1,…,d i ,…,d n }When the amount of data exceeds the preset processing volume threshold of the backup server, the execution of step S2 is interrupted, that is, step S2 is skipped and step S3 is executed first, and after step S3 is completed, step S2 is executed again when the backup server is idle; the preset processing volume threshold of the backup server is determined by the system administrator based on the enterprise data usage.

5. A device for incremental backup of data assets, characterized in that: The device comprises: The change sequence acquisition module is configured to start the incremental backup of data assets, obtain the difference between the current data assets and the current data baseline, where the current data assets are all the data assets stored on the primary server at the current moment, and the current data baseline is the data baseline stored on the backup server at the last incremental backup, and determine the change sequence D of the data assets based on the difference. i ,…,d n }, the change sequence arranges the changes of each data asset in the data asset in chronological order; where 1≤i≤n, n is the number of data asset items that have changed compared with the current data baseline; d i ∈{ΔA i ,ΔD i ,ΔM i }, ΔA i is the increase in the i-th data asset that has changed relative to the current data baseline, ΔD i is the deletion item of the changed data asset relative to the current data baseline, ΔM i The modification item of the i-th data asset that has changed relative to the current data baseline; Complete data generation module: configured to obtain the change sequence D of the data asset from the backup server. i ,…,d n }, based on the change sequence D of the data asset = {d1,…,d i ,…,d n }, obtain the usage frequency of each data asset in the changed data assets, and determine the hot data assets from the changed data assets based on the usage frequency; obtain the associated data assets of the hot data assets; generate complete data of the hot data assets and the associated data assets of the hot data assets, and store the generated complete data in the fixed area of ​​the backup server; wherein, the attribute information of the hot data assets is used as input, and the trained LSTM neural network model is input to obtain the associated data assets of the hot data assets; based on d i ∈{ΔA i ,ΔD i ,ΔM i }, according to d i Get the specific value of the backup server i The latest version of the corresponding data asset, superimposed with d i The specific value of generates the complete data of the data asset; Full backup module: configured to set a multi-tree and multiple linked list structures in an area outside the fixed area of ​​the backup server, each linked list corresponds to a data asset; the header of the linked list stores the attribute information of the data asset, the pointer of the linked list points to the change information recording node of the data asset, and the change information recording node of the data asset stores the change information and change time point of the data asset; the multi-tree is used to store the contextual relationship of each data asset; The backup server obtains the change sequence D of the data asset = {d1, ..., d i ,…,d n }, attach each element of the change sequence D of the data asset to the corresponding linked list; if there is no data asset corresponding to a certain element of the change sequence D in the backup server, create a new linked list to store the data asset corresponding to the certain element of the change sequence D that is not in the backup server and update the multi-branch tree; based on the multi-branch tree and the linked list structure in the backup server, generate a data baseline, and use the data baseline as the current data baseline.

6. The device according to claim 5, characterized in that The obtaining the usage frequency of each data asset among the changed data assets, and determining the hot data assets from the changed data assets based on the usage frequency, includes: Obtain the usage frequency of each data asset among the changed data assets, and sort each data asset among the changed data assets in descending order based on the usage frequency; in the backup server, the data assets among the changed data assets that rank higher than a preset threshold are regarded as hot data assets.

7. The device according to claim 6, characterized in that The obtaining of the associated data assets of the hot data assets; generating complete data of the hot data assets and the associated data assets of the hot data assets, and storing the generated complete data in a fixed area of ​​the backup server specifically includes: The attribute information of the hot data asset is used as input and input into the trained LSTM neural network model to obtain the associated data assets of the hot data asset; the attribute information of the hot data asset includes the name, data type, usage time and context information of the hot data asset, and the context information of the usage refers to the name and usage time of the context of the hot data asset when the hot data asset is generated or used; the attribute information of the hot data asset is spliced ​​to construct a feature vector as the input data of the LSTM neural network model.

8. The device according to claim 7, characterized in that In the data asset change sequence D = {d1,…,d i ,…,d n }When the amount of data exceeds the preset processing volume threshold of the backup server, the complete data generation module is closed, that is, the complete data generation module is closed to trigger the full backup module first, and after the full backup module is processed, the complete data generation module is opened and triggered when the backup server is idle; the preset processing volume threshold of the backup server is determined by the system administrator based on the enterprise data usage.

9. A system for incremental backup of data assets, characterized in that: include: A processor, which is used to execute multiple instructions; A memory for storing a plurality of instructions; The plurality of instructions are used to be stored by the memory, and loaded and executed by the processor to implement the method for incremental backup of data assets as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The storage medium stores a plurality of instructions; the plurality of instructions are used by the processor to load and execute the method for incremental backup of data assets as described in any one of claims 1-4.

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