Data management method, device and storage medium

Abstract

The embodiments of the present application disclose a data management method, device, and storage medium, which include: obtaining asset status information of a target data asset; determining data management indicators of the target data asset, wherein the data management indicators include multiple indicators; obtaining target status information that matches each of the data management indicators from the asset status information; grading the target data asset based on the target status information to obtain a grading result of the target data asset corresponding to each of the data management indicators; determining a target data management strategy based on the grading result of the target data asset corresponding to each of the data management indicators, and managing the target data asset based on the target data management strategy. The present application implements refined matching management of data, effectively improving the reliability of data management.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and in particular to a data management method, device and storage medium. Background Art

[0002] The increasing diversity of internet services, coupled with the continued growth in user numbers and online time, has resulted in a massive accumulation of data. Data is a core enterprise asset, offering irreplaceable and sustainable value in areas such as product functionality verification, driving business growth, refined operations, and personalized services. Therefore, reliable data management is crucial.

[0003] At present, data management is usually carried out by establishing unified standard data management strategies. During the data management process, core data loss, freezing and data security incidents are prone to occur, there is a waste of management resources, and it is difficult to match management data in a refined manner, which leads to low reliability of data management. Summary of the Invention

[0004] The embodiments of the present application provide a data management method and related devices, aiming to achieve refined matching management of data and effectively improve the reliability of data management.

[0005] To solve the above technical problems, the embodiments of the present application provide the following technical solutions:

[0006] According to one embodiment of the present application, a data management method includes: obtaining asset status information of a target data asset; determining data management indicators of the target data asset, wherein the data management indicators include multiple indicators; obtaining target status information matching each of the data management indicators from the asset status information; grading the target data asset based on the target status information to obtain a grading result of the target data asset corresponding to each of the data management indicators; determining a target data management strategy based on the grading result of the target data asset corresponding to each of the data management indicators, and managing the target data asset based on the target data management strategy.

[0007] According to one embodiment of the present application, a data management device includes: an acquisition module for acquiring asset status information of a target data asset; a determination module for determining data management indicators of the target data asset, wherein the data management indicators include multiple ones; a matching module for acquiring target status information matching each of the data management indicators from the asset status information; a grading module for grading the target data asset based on the target status information to obtain a grading result of the target data asset corresponding to each of the data management indicators; and a management module for determining a target data management strategy based on the grading result of the target data asset corresponding to each of the data management indicators, and managing the target data asset based on the target data management strategy.

[0008] In some embodiments of the present application, the asset status information includes attribute information corresponding to multiple attributes of the target data asset; the matching module includes: a label establishment unit, which is used to establish an attribute label corresponding to each attribute of the target data asset based on the attribute information corresponding to each attribute; and a label association unit, which is used to obtain the attribute label corresponding to each attribute matched by the data management indicator as the target status information matched by each data management indicator.

[0009] In some embodiments of the present application, the label establishment unit includes: a feature acquisition subunit, used to obtain information features of the attribute information corresponding to each of the attributes; a strategy matching subunit, used to determine the label establishment strategy corresponding to each of the attributes based on the information features of the attribute information corresponding to each of the attributes; and a label establishment subunit, used to establish the attribute label of the target data asset corresponding to each of the attributes based on the label establishment strategy corresponding to each of the attributes and using the attribute information corresponding to each of the attributes.

[0010] In some embodiments of the present application, the label association unit includes: a table acquisition subunit, used to obtain an attribute query table, wherein the attribute query table contains each of the data management indicators and the attributes that match each of the data management indicators; a query subunit, used to determine the target attribute that matches each of the data management indicators based on the attribute query table; and an attribute matching subunit, used to obtain the attribute label corresponding to the target attribute that matches each of the data management indicators as the target status information that matches each of the data management indicators.

[0011] In some embodiments of the present application, the grading module includes: a grading strategy determination unit, used to determine the grading strategy corresponding to each of the data management indicators; a strategy grading unit, used to grade the target data assets respectively according to the grading strategy corresponding to each of the data management indicators and using the target status information matched by each of the data management indicators, to obtain the grading results of the target data assets corresponding to each of the data management indicators.

[0012] In some embodiments of the present application, the grading strategy determination unit includes: an asset analysis sub-unit, used to determine the business scenario characteristics and data supervision requirements corresponding to the target data assets, the business scenario characteristics are the relevant characteristics of the business scenarios in which the target data assets are applied, and the data supervision requirements are the target requirements for managing the target data assets; a grading strategy determination sub-unit, used to determine the grading strategy corresponding to each of the data management indicators based on the business scenario characteristics and the data supervision requirements.

[0013] In some embodiments of the present application, the grading strategy corresponding to the first data management indicator includes multiple grading constraints, each of which corresponds to a level; the strategy grading unit includes: a constraint matching subunit, used to determine the grading constraints that the target state information matched by the first data management indicator complies with, and obtain the target grading constraints; a level matching subunit, used to obtain the level corresponding to the target grading constraints; and a result determination subunit, used to determine the level corresponding to the target grading constraints as the grading result of the target data asset corresponding to the first data management indicator.

[0014] In some embodiments of the present application, the first data management indicator includes an importance indicator; the multiple hierarchical constraints include a first hierarchical constraint, a second hierarchical constraint, and a third hierarchical constraint, the level corresponding to the first hierarchical constraint is a valid asset, the level corresponding to the second hierarchical constraint is a deactivated asset, and the level corresponding to the third hierarchical constraint is an asset to be observed; the constraint matching subunit is used to: determine in sequence whether the target state information matched by the importance indicator meets the first hierarchical constraint, the second hierarchical constraint, and the third hierarchical constraint; and determine the hierarchical constraint that the target state information matched by the importance indicator meets as the target hierarchical constraint.

[0015] In some embodiments of the present application, the grading strategy corresponding to the second data management indicator is a grading model; the strategy grading unit is used to: input the target state information matched by the second data management indicator into the grading model, and obtain the grading result of the target data asset corresponding to the second data management indicator output by the grading model.

[0016] In some embodiments of the present application, the second data management indicator includes a sensitivity indicator and a security indicator; the policy grading unit is used to: input the target state information matching the sensitivity indicator into a sensitivity grading model, and obtain the grading result of the target data asset corresponding to the sensitivity indicator output by the sensitivity grading model; input the target state information matching the security indicator into a security grading model, and obtain the grading result of the target data asset corresponding to the security indicator output by the security grading model.

[0017] In some embodiments of the present application, the management module includes: a template acquisition unit, used to acquire a policy template set, each policy template in the policy template set is marked with multiple level labels; a template matching unit, used to determine the level label matched by each grading result according to the grading result of the target data asset corresponding to each of the data management indicators, and obtain a target level label; a template determination unit, used to use the policy template marked with the target level label as the target data management policy.

[0018] In some embodiments of the present application, the management module includes: an analysis unit, used to input the grading results of the target data assets corresponding to each of the data management indicators into a policy decision model to obtain policy information output by the policy decision model; and a policy generation unit, used to generate the target data management policy based on the policy information.

[0019] In some embodiments of the present application, the data management device further includes: a metadata acquisition module for acquiring metadata corresponding to the target data asset; and a metadata association unit for associating the target data management policy with the metadata corresponding to the target data asset.

[0020] According to another embodiment of the present application, an electronic device may include: a memory storing computer-readable instructions; and a processor reading the computer-readable instructions stored in the memory to execute the method described in the embodiment of the present application.

[0021] According to another embodiment of the present application, a storage medium stores computer-readable instructions thereon. When the computer-readable instructions are executed by a processor of a computer, the computer executes the method described in the embodiment of the present application.

[0022] According to another embodiment of the present application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations described in the embodiments of the present application.

[0023] The embodiment of the present application obtains the asset status information of the target data asset; determines the data management indicators of the target data asset, which include multiple data management indicators; then, obtains the target status information matching each data management indicator from the asset status information; grades the target data asset based on the target status information to obtain the grading results of the target data asset corresponding to each data management indicator; implements multi-dimensional grading of the target data asset according to the data asset's own status and multiple indicators; then, determines the target data management strategy based on the grading results of the target data asset corresponding to each data management indicator, implements the multi-dimensional grading results, and finely determines the target data management strategy that adapts to the target data asset's own status; and then manages the target data asset based on the target data management strategy, which can effectively avoid the loss, freezing and data security incidents of core data in the data management process, and avoid the problem of waste of management resources, achieve fine matching of management data, effectively improve the reliability of data management, and ensure management costs at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of a system to which embodiments of the present application can be applied is shown.

[0026] Figure 2 A flow chart of a data management method according to an embodiment of the present application is shown.

[0027] Figure 3 A flowchart of a method for obtaining target status information according to an embodiment of the present application is shown.

[0028] Figure 4 A flow chart of a classification method according to an embodiment of the present application is shown.

[0029] Figure 5A flowchart of a method for determining a target data management policy according to an embodiment of the present application is shown.

[0030] Figure 6 A flowchart of a method for determining a target data management policy according to an embodiment of the present application is shown.

[0031] Figure 7 A flowchart of data management in a scenario in which an embodiment of the present application is applied is shown.

[0032] Figure 8 A block diagram of a data management device according to an embodiment of the present application is shown.

[0033] Figure 9 A block diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0035] In the following description, the specific embodiments of the present application will be described with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and the computer execution referred to herein includes the operation of a computer processing unit by an electronic signal representing data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure in which the data is maintained is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

[0036] Figure 1 Schematic diagram of a system 100 to which embodiments of the present application can be applied is shown. Figure 1 As shown, the system 100 may include a server 101 and a terminal 102 . The server 101 may store target data assets, and the terminal 102 may manage the target data assets.

[0037] Server 101 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server 101 can execute background tasks and store data assets.

[0038] In one embodiment, server 101 can provide artificial intelligence cloud services, such as artificial intelligence cloud services for massively multiplayer online role-playing games (MMORPGs). The so-called artificial intelligence cloud services are generally also referred to as AIaaS (AIas a Service, Chinese for "AI as a Service"). This is a mainstream service mode of artificial intelligence platforms. Specifically, the AIaaS platform will split several common AI services and provide independent or packaged services in the cloud. This service model is similar to opening an AI theme mall: all developers can access and use one or more artificial intelligence services provided by the platform through API interfaces. Some senior developers can also use the AI ​​framework and AI infrastructure provided by the platform to deploy and operate their own exclusive cloud artificial intelligence services. For example, server 101 can provide data management based on artificial intelligence.

[0039] The terminal 102 may be an edge device, such as a smart phone, a computer, etc. The user may update, modify, delete, and perform other operations on the target data asset through the operation page on the terminal 102 .

[0040] The terminal 102 and the server 101 may be connected directly or indirectly via wireless communication, and this application does not impose any special restrictions thereon.

[0041] In one implementation of this example, the terminal 102 can obtain asset status information of the target data asset; determine the data management indicators of the target data asset, which include multiple data management indicators; obtain target status information matching each of the data management indicators from the asset status information; classify the target data asset based on the target status information to obtain a classification result of the target data asset corresponding to each of the data management indicators; determine a target data management strategy based on the classification result of the target data asset corresponding to each of the data management indicators, and manage the target data asset based on the target data management strategy.

[0042] Figure 2 The flowchart of the data management method according to one embodiment of the present application is schematically shown. The execution subject of the data management method may be an electronic device with computing and processing functions, such as Figure 1 The server 101 or terminal 102 shown in FIG.

[0043] like Figure 2 As shown, the data management method may include steps S210 to S250.

[0044] Step S210, obtaining asset status information of the target data asset;

[0045] Step S220, determining a data management indicator of the target data asset, wherein the data management indicator includes multiple indicators;

[0046] Step S230, obtaining target status information matching each data management indicator from the asset status information;

[0047] Step S240: classifying the target data assets based on the target status information to obtain a classification result of the target data assets corresponding to each data management indicator;

[0048] Step S250 , determining a target data management strategy based on the grading result of the target data asset corresponding to each data management indicator, and managing the target data asset based on the target data management strategy.

[0049] The following describes the specific process of each step in data management.

[0050] In step S210 , the asset status information of the target data asset is obtained.

[0051] In this example implementation, target data assets refer to data resources, recorded physically or electronically, owned or controlled by an individual or enterprise and capable of generating future economic benefits for the enterprise. For example, target data assets can be data tables in data storage types such as MySQL, KV, Hive, and TBase. They can also be resources such as data text files.

[0052] Asset status information is attribute information that describes multiple status attributes of data assets. The status attributes may include the target data asset's accessed status attributes, updated status attributes, business attributes, associated status attributes with other data assets, sensitive status attributes, and asset security impact status attributes.

[0053] The asset status information of the target data asset can be obtained by actively pulling or accepting push, such as periodically pulling the asset status information of the target data asset from the data asset storage system, or receiving the asset status information of the target data asset uploaded by the data asset storage system in real time.

[0054] In one embodiment, the target data asset is a data table, and obtaining the asset status information of the data table includes: establishing a blood relationship with the data table, obtaining the fan-in data and fan-out data of the data table based on the blood relationship, and obtaining relevant attribute information of the associated status attributes of the data table and other data assets; obtaining data operation records of the data storage system, and obtaining relevant attribute information of the update status attributes of the data table; obtaining access records of the data table in the data operation platform, and obtaining relevant attribute information of the accessed status attributes of the data table; obtaining user annotation data of the data table, and obtaining relevant attribute information of the business attributes, sensitive status attributes and asset security impact status attributes.

[0055] Among them, the blood relationship is a channel established to obtain the generation process and usage of data. By obtaining the number of upstream tasks and downstream tasks of the data table, the fan-in data and fan-out data of the data table can be obtained. The data storage system is a system that stores data tables. The data operation record of the data storage system records the update time of the data table and other information. The data operation platform is such as a big data management platform. The access record of the data table in the data operation platform records the access time of the data table and other information. User-annotated data is the information on the data table marked by the administrator of the data table, the description of the table fields, the table sensitivity level and the table security impact level.

[0056] In step S220 , a data management indicator of the target data asset is determined, and the data management indicator includes multiple indicators.

[0057] In this example implementation, data management indicators are defined data management standard indicators. The data management indicators may include multiple indicators, i.e., at least two indicators, such as a sensitivity indicator, a materiality indicator, and a security indicator. Each data management indicator corresponds to a data management standard in a specific dimension, enabling multi-dimensional, refined management of target data assets.

[0058] In one embodiment, the method for determining the data management indicators of the target data assets is: determining multiple data management indicators defined by the user for the target data assets, the user can select multiple data-related indicators of the current data management needs through a preset data management indicator table (the data management indicator table stores pre-defined data management indicator clusters), and can flexibly perform data management based on the selected data management indicators.

[0059] In one embodiment, the method for determining the data management indicators of the target data asset is as follows: determining the metadata of the target data asset, inputting the metadata into the indicator decision model, and obtaining multiple data management indicators output by the indicator decision model. The metadata describes the data of the target data asset, such as the table name, table description, table creation statement, table field, field type and field description, etc. The indicator decision model is a pre-trained machine learning model, which can be set up in a cloud server. The indicator decision model is trained in real time based on the metadata and corresponding data management indicators of the data asset samples collected in real time by the cloud server as training data. The data management indicators corresponding to the data asset samples are calibrated with corresponding scores based on the management effect of the data asset samples. The indicator decision model can be trained to determine multiple data-related indicators that are consistent with the status of the data asset itself and can bring good management effects.

[0060] In step S230, target status information matching each data management indicator is obtained from the asset status information.

[0061] In the implementation of this example, each data management indicator corresponds to a data management standard of a dimension, and the target status information matching each data management indicator is obtained, that is, the status information under the dimension corresponding to each data management indicator is obtained, and then in subsequent steps, the target data assets can be analyzed under each dimension respectively.

[0062] Among them, obtaining the target status information matching each data management indicator can be directly obtaining the attribute information corresponding to the attribute matching each data management indicator as the corresponding target status information. For example, for security indicators, directly obtaining the relevant information corresponding to the business attributes of the target data assets and the relevant information corresponding to the security impact status attributes marked by the user.

[0063] Obtain the target status information that matches each data management indicator, or obtain the attribute information corresponding to the attribute that matches each data management indicator, and then establish attribute labels based on the attribute information. For example, after obtaining the relevant information corresponding to the business attributes of the target data assets and the relevant information corresponding to the original security impact status attributes marked by the user for the security indicators, establish a security impact level label based on the obtained information. The security impact level label can be high, medium, or low level labels, or personalized labels such as the impact on storage location or the impact on the number of backups.

[0064] In one embodiment, see Figure 3 The asset status information includes attribute information corresponding to multiple attributes of the target data asset; in step S230, target status information matching each data management indicator is obtained from the asset status information, including:

[0065] Step S310: Based on the attribute information corresponding to each attribute, establish an attribute tag corresponding to each attribute of the target data asset;

[0066] Step S320 : Acquire the attribute label corresponding to the attribute matched by each data management indicator as the target state information matched by each data management indicator.

[0067] The multiple attributes may include the target data asset's access status attribute, update status attribute, business attribute, association status attribute with other data assets, sensitive status attribute, and asset security impact status attribute.

[0068] Each attribute has corresponding attribute information. For example, the attribute information corresponding to the accessed status attribute may include access user information, access time information, and access location information in the access record of the target data asset.

[0069] Establish attribute labels corresponding to each attribute of the target data asset, that is, establish significant attribute labels based on a large amount of attribute information. For example, the access status attribute can establish key labels: the last access time label and the access frequency label, and the update status attribute can establish the last update time label and the update frequency label.

[0070] The attribute labels corresponding to the attributes matched by each data management indicator are obtained as the target state information matched by each data management indicator. In subsequent steps, classification can be performed directly based on the significant attribute labels while ensuring classification efficiency and reliability.

[0071] In one embodiment, step S310, based on the attribute information corresponding to each attribute, establishes an attribute tag corresponding to each attribute of the target data asset, including:

[0072] Obtain information characteristics of attribute information corresponding to each attribute;

[0073] Determine the label establishment strategy corresponding to each attribute based on the information characteristics of the attribute information corresponding to each attribute;

[0074] Establish a strategy based on the label corresponding to each attribute, and use the attribute information corresponding to each attribute to establish the attribute label corresponding to each attribute of the target data asset.

[0075] The information characteristics of attribute information are the data characteristics of attribute information. For example, information characteristics may include data type (such as text type or numeric type), generation type (such as user-annotated data or operation record data) and information quantity.

[0076] The label establishment strategy is a way to establish labels. The label establishment strategy can include an establishment strategy based on a label establishment model (i.e., a machine learning model for establishing labels) and an establishment strategy based on a calculation function.

[0077] A mapping relationship between information characteristics and label creation strategies can be established in advance. Based on this mapping relationship and the information characteristics of the attribute information corresponding to each attribute, a label creation strategy can be determined for each attribute. Then, using the attribute information corresponding to each attribute, attribute labels corresponding to each attribute in the target data asset can be created. In this way, attribute labels can be accurately created based on the characteristics of the attribute information itself, for each attribute corresponding to the acquired attribute information.

[0078] Each label establishment strategy corresponds to a label establishment module. Determining the label establishment strategy corresponding to each attribute can be embodied as determining the label establishment module corresponding to each attribute.

[0079] In one embodiment, step S320, obtaining the attribute label corresponding to the attribute matched by each data management indicator as target state information matched by each data management indicator, includes:

[0080] Obtain an attribute query table, which contains each data management indicator and an attribute matching each data management indicator;

[0081] According to the attribute query table, determine the target attribute that each data management indicator matches;

[0082] The attribute label corresponding to the target attribute matched by each data management indicator is obtained as the target state information matched by each data management indicator.

[0083] The attribute query table contains each data management indicator and the attribute that matches each data management indicator. In other words, the attribute query table establishes a mapping relationship between data management indicators and attributes. Furthermore, based on the attribute query table, the target attribute that matches each data management indicator can be queried.

[0084] In step S240 , the target data assets are graded based on the target state information to obtain a grading result of the target data assets corresponding to each data management indicator.

[0085] In the implementation of this example, each data management indicator corresponds to matching target status information. Within the dimension where each data management indicator is located, the target data assets are graded using the target status information matching each data management indicator, thereby achieving multi-dimensional and refined grading of the target data assets based on the self-generated state of the target data assets, and obtaining the grading results of the target data assets corresponding to each data management indicator.

[0086] The grading results for importance indicators include five levels: core assets, backbone assets, ordinary assets, deactivated assets, and zombie assets (i.e., assets under observation); or three levels: active assets, deactivated assets, and zombie assets (i.e., assets under observation). The grading results for sensitivity indicators include five levels: top secret, confidential, highly sensitive, moderately sensitive, and low sensitive. The grading results for security indicators include five levels: level 5, level 4, level 3, level 2, and level 1.

[0087] In one embodiment, see Figure 4 , step S240, classifying the target data assets based on the target status information, and obtaining the classification results of the target data assets corresponding to each data management indicator:

[0088] Step S410, determining a grading strategy corresponding to each data management indicator;

[0089] Step S420 , according to the grading strategy corresponding to each data management indicator, and using the target state information matched by each data management indicator, the target data assets are graded respectively to obtain the grading results of the target data assets corresponding to each data management indicator.

[0090] A tiering strategy is a tiering method. These strategies can include those based on tiering models (i.e., tiered machine learning models) and those based on matching tiering constraints. Each tiering strategy corresponds to a tiering strategy module. Determining the tiering strategy for each data management indicator can be embodied by determining the tiering strategy module for each data management indicator.

[0091] Determining the grading strategy corresponding to each data management indicator may involve obtaining a pre-established mapping relationship between the data management indicator and the grading strategy, and directly obtaining the grading strategy corresponding to each data management indicator based on the mapping relationship.

[0092] In one embodiment, step S410, determining a grading strategy corresponding to each data management indicator, includes:

[0093] Determine the business scenario characteristics and data supervision requirements corresponding to the target data assets. Business scenario characteristics are the relevant characteristics of the business scenarios in which the target data assets are applied, and data supervision requirements are the target requirements for managing the target data assets.

[0094] Determine the grading strategy corresponding to each data management indicator based on business scenario characteristics and data supervision requirements.

[0095] Business scenario characteristics may include relevant characteristics of the business scenarios in which the target data assets are applied, such as the name of business scenarios such as payment, the population used in the scenarios, and other characteristics; data supervision requirements are the target requirements for managing target data assets, such as temporary supervision or regular supervision.

[0096] Based on the business scenario characteristics and data supervision requirements, the grading strategy corresponding to each data management indicator is determined. The mapping relationship between the business scenario characteristics and data supervision requirements and the grading strategy can be obtained (the mapping relationship can come from the data supervision agency or the data management enterprise). Based on the mapping relationship, the grading strategy corresponding to each data management indicator is determined (the grading strategy can be issued by the cloud server of the data supervision agency or the data management enterprise).

[0097] Each grading strategy corresponds to a grading strategy module, and determining the grading strategy corresponding to each data management indicator can be reflected in determining the grading strategy module corresponding to each data management indicator.

[0098] In one embodiment, the grading strategy corresponding to the first data management indicator includes multiple grading constraints, each grading constraint corresponding to a level. In step S420, according to the grading strategy corresponding to each data management indicator, the target data assets are graded using the target state information matched by each data management indicator, and the grading results of the target data assets corresponding to each data management indicator are obtained, including:

[0099] Determining the hierarchical constraint condition satisfied by the target state information matched by the first data management indicator, and obtaining the target hierarchical constraint condition;

[0100] Get the level corresponding to the target grading constraint;

[0101] The level corresponding to the target grading constraint condition is determined as the grading result of the target data asset corresponding to the first data management indicator.

[0102] Hierarchical constraints are constraints formed by combining target state information. For example, a hierarchical constraint could be "low fan-in" and ("updated within 30 days" or "low pageviews"). Furthermore, the hierarchical constraints that the target state information matching the first data management indicator meets can be determined. The first data management indicator can be specified based on actual circumstances. For example, the first data management indicator could be an importance indicator. It is understood that the first data management indicator could also be other indicators, such as a sensitivity indicator and a security indicator.

[0103] Each grading constraint corresponds to a level. For example, the grading constraint is "low fan-in" and ("updates within 30 days" or "low visit volume"), and the corresponding level is "ordinary assets". Then, the level corresponding to the target grading constraint can be obtained; the level corresponding to the target grading constraint is determined as the grading result of the target data asset corresponding to the first data management indicator.

[0104] In one embodiment, the first data management indicator includes an importance indicator; the multiple hierarchical constraints include a first hierarchical constraint, a second hierarchical constraint, and a third hierarchical constraint, the first hierarchical constraint corresponds to a level of valid assets, the second hierarchical constraint corresponds to a level of deactivated assets, and the third hierarchical constraint corresponds to a level of assets to be observed;

[0105] Determining the hierarchical constraint conditions that the target state information matched by the first data management indicator complies with, and obtaining the target hierarchical constraint conditions, including:

[0106] Determine in sequence whether the target state information matched by the importance index meets the first level constraint condition, the second level constraint condition, and the third level constraint condition;

[0107] The hierarchical constraint conditions met by the target state information matched by the importance index are determined as target hierarchical constraint conditions.

[0108] A hierarchical approach based on hierarchical constraints is interpretable and easily adjustable for oversight. The target state information matched by the importance indicators of data assets is typically generated by the assets themselves, such as "fan-in number," "update frequency," and "visits." A strategy based on hierarchical constraints effectively ensures the interpretability of the grading results corresponding to the importance indicators and facilitates oversight adjustments.

[0109] The target state information matched by the importance indicators is sequentially determined to see whether it meets the first, second, and third hierarchical constraints. Specifically, the target state information matched by the importance indicators is determined based on the asset importance order of active assets, deactivated assets, and assets to be observed. The first hierarchical constraint that is matched is determined as the hierarchical constraint that the target state information matched by the importance indicators meets. This prevents the target state information matched by the importance indicators from meeting multiple hierarchical constraints simultaneously.

[0110] Among them, valid assets include core tables, backbone tables, and ordinary tables; deactivated assets include deactivated tables; and assets to be observed include zombie tables.

[0111] In one embodiment, the grading strategy corresponding to the second data management indicator is a grading model; step S420, based on the grading strategy corresponding to each data management indicator, utilizes the target state information matched by each data management indicator to grade the target data assets respectively, and obtains the grading results of the target data assets corresponding to each data management indicator, including:

[0112] The target state information matched with the second data management indicator is input into the grading model to obtain a grading result of the target data asset output by the grading model corresponding to the second data management indicator.

[0113] The second data management indicator may be an importance indicator, or may be other indicators such as a sensitivity indicator and a security indicator.

[0114] The grading model is a machine learning model trained using target state information samples as input data and the grading results corresponding to the target state information samples as the desired output. The grading model analyzes the target state information that matches the second data management indicator and outputs the grading results for the target data asset corresponding to the second data management indicator. This ensures reliable grading results even when the target state information is complex.

[0115] In one embodiment, the second data management indicator includes a sensitivity indicator and a security indicator. Inputting target state information matching the second data management indicator into a grading model, and obtaining a grading result of the target data asset corresponding to the second data management indicator output by the grading model, includes:

[0116] Input the target state information that matches the sensitivity index into the sensitivity grading model, and obtain the grading result of the target data asset corresponding to the sensitivity index output by the sensitivity grading model;

[0117] The target state information that matches the security index is input into the security level grading model to obtain the grading result of the target data asset corresponding to the security index output by the security level grading model.

[0118] The sensitivity grading model is a machine learning model trained using target state information samples that match sensitivity indicators as input data and the hierarchical structure corresponding to the target state information samples as the expected output. Similarly, the security grading model is a machine learning model trained using target state information samples that match security indicators as input and the hierarchical structure corresponding to the target state information samples as the expected output.

[0119] The target state information matched by sensitivity indicators and safety indicators usually includes relatively complex information. The classification method based on machine learning models can ensure the reliability of the classification results.

[0120] It is understandable that when the second data management indicator includes other indicators, a grading model corresponding to the other indicators can also be provided to perform grading under the other indicators.

[0121] In step S250, a target data management strategy is determined according to the grading result of the target data asset corresponding to each data management indicator, and the target data asset is managed based on the target data management strategy.

[0122] In the implementation of this example, the target data management policy is a policy for managing target data assets. For example, it considers the target data assets from three perspectives: storage, computing, and security, and designs different levels of security management policies for each perspective.

[0123] Combined with the grading results of the target data assets corresponding to each data management indicator, a target data management strategy that integrates multi-dimensional data management standards can be determined. Based on this target data management strategy, the target data assets can be reliably managed. For example, the security, integrity and update frequency of high-level data assets can be prioritized.

[0124] In this way, based on steps S210-S250, by obtaining the asset status information of the target data asset; determining the data management indicators of the target data asset, which include multiple data management indicators; then, obtaining the target status information matching each data management indicator from the asset status information; grading the target data asset based on the target status information, and obtaining the grading results of the target data asset corresponding to each data management indicator; achieving multi-dimensional grading of the target data asset according to the data asset's own status and multiple indicators; then, determining the target data management strategy based on the grading results of the target data asset corresponding to each data management indicator, achieving a refined determination of the target data management strategy that adapts to the target data asset's own status based on the multi-dimensional grading results; and then managing the target data asset based on the target data management strategy, which can effectively avoid the loss, freezing and data security incidents of core data in the data management process, as well as the waste of management resources, achieve refined matching of management data, and effectively improve the reliability of data management.

[0125] In one embodiment, see Figure 5 Step S250, based on the grading results of the target data assets corresponding to each data management indicator, determines the target data management strategy, including:

[0126] Step S510: obtaining a policy template set, wherein each policy template in the policy template set is marked with multiple level labels;

[0127] Step S520: According to the grading results of the target data asset corresponding to each data management indicator, determine the grade label that matches each grading result to obtain the target grade label;

[0128] Step S530: Use the policy template marked with the target level label as the target data management policy.

[0129] A policy template set is a collection of policy templates for data management policies. Each policy template in the policy template set is labeled with multiple level labels. The level labels can be used to calibrate the applicability level of each policy template from different management perspectives. For example, the labels calibrated for a policy template include level labels from multiple perspectives such as data storage, data computing resources, data permissions, and data auditing, integrating multiple data management standards.

[0130] At the same time, the target data asset corresponds to multiple grading results (i.e., grading results corresponding to multiple data management indicators). By obtaining the mapping relationship between grading results and grade labels, the grade label matched by each grading result can be determined, and the target grade label (including multiple grade labels) can be obtained, forming a many-to-many matching relationship. Then, the policy template that is simultaneously labeled with multiple grade labels included in the target grade label is used as the target data management policy, and the target data management policy that matches the integrated multi-dimensional data management standards can be reliably obtained.

[0131] In one embodiment, see Figure 6 Step S250, based on the grading results of the target data assets corresponding to each data management indicator, determines the target data management strategy, including:

[0132] Step S610: Input the grading results of the target data assets corresponding to each data management indicator into the policy decision model to obtain policy information output by the policy decision model;

[0133] Step S620: Generate a target data management policy based on the policy information.

[0134] The policy decision model is a machine learning model trained using sample classification results as input data and the corresponding policy information labels as the desired output. Policy information can include data storage, data computing resources, data permissions, data auditing, and other aspects. For example, data storage can include information such as the number of backups and storage location levels. Based on this information, the policy decision model intelligently determines policy information that matches the target data assets and intelligently generates target data management policies.

[0135] In one embodiment, the data management method of the present application further includes:

[0136] Obtain metadata corresponding to the target data asset;

[0137] Associate the target data management policy with the metadata corresponding to the target data asset.

[0138] The metadata corresponding to the target data asset is data describing the target data asset, such as the table name, table description, table creation statement, table fields, field types, field descriptions, responsible person, creation time, modification history, and number of partitions. By obtaining metadata, data from different systems can be uniformly managed to form an asset management catalog. At the same time, associating the target data management policy with the metadata corresponding to the target data asset allows for reliable management of the data management policy and facilitates metadata-based retrieval.

[0139] Among them, the method of obtaining metadata can be to actively obtain metadata information of the target data asset in an active pull manner. For example, metadata information of various upstream data types (including MySQL, KV, Hive, TBase and other data storage types) can be obtained regularly, combined with the data in the current library (the current table that stores metadata information), by comparing the obtained metadata information with the data in the current library, and then performing metadata addition, modification, deletion and other operations based on the comparison results, updating the current table, and ensuring the ultimate consistency of the metadata information and the original table (target data asset) information. The metadata can be used as the asset status information of the obtained target data asset.

[0140] The method described in the above embodiment is further described in detail below with examples.

[0141] Figure 7 The flowchart of data management in a scenario where the embodiment of the present application is applied is shown. The target data asset in this scenario is a data table; the data management in this scenario can be based on the following Figure 7 Steps S710 to S740 are performed as shown.

[0142] Step S710: Obtain metadata corresponding to the data table.

[0143] Metadata includes information such as the table name, table description, table creation statement, table fields, field types, field descriptions, responsible person, creation time, modification records, and number of partitions.

[0144] Step S720: metadata tag system construction.

[0145] First, the asset status information of the data table is obtained, including: by establishing a blood relationship with the data table, obtaining the fan-in data and fan-out data of the data table based on the blood relationship, and obtaining the relevant attribute information of the associated status attributes of the data table and other data assets; obtaining the data operation records of the data storage system, and obtaining the relevant attribute information of the update status attributes of the data table; obtaining the access records of the data table in the data operation platform, and obtaining the relevant attribute information of the accessed status attributes of the data table; obtaining the user annotation data of the data table, and obtaining the relevant attribute information of the business attributes, sensitive status attributes and asset security impact status attributes.

[0146] Secondly, based on the attribute information corresponding to the multiple target attributes in the asset status information, an attribute label corresponding to each attribute in the data table is established, and the established attribute label also corresponds to the metadata corresponding to the data table.

[0147] The multiple target attributes may include the target data asset's access status attribute, update status attribute, business attribute, association status attribute with other data assets, sensitive status attribute, and asset security impact status attribute.

[0148] In step S730, metadata is graded in multiple dimensions.

[0149] First, determine the data management indicators of the data table. The data management indicators include multiple indicators, including at least importance indicators, security indicators and sensitivity indicators.

[0150] Secondly, target state information matching each data management indicator is obtained from the asset state information, specifically including obtaining the attribute label corresponding to the attribute matching each data management indicator as the target state information matching each data management indicator.

[0151] Among them, the attribute labels corresponding to the importance index include fan-in number, fan-out number, last access time label, access frequency label, last update time label and update frequency label; the attribute labels corresponding to the sensitivity index include sensitivity label and business label; the attribute labels corresponding to the security index include security impact degree label and business label.

[0152] Then, the target data assets are graded based on the target status information to obtain a grading result of the data table corresponding to each data management indicator, and the grading result also corresponds to the metadata of the data table.

[0153] Specifically, determine in turn whether the target state information matched by the importance index meets the first hierarchical constraint condition (fan-in > 0 (table is dependent on downstream tasks), and (data has been updated in the past 30 days, or no data has been accessed in the past 30 days)), the second hierarchical constraint condition (fan-in = 0 (table data is not dependent on downstream), and no data has been updated in the past 90 days, and no data has been accessed in the past 90 days), and the third hierarchical constraint condition (fan-in = 0 (table data is not dependent on downstream), and (no data has been updated in the past 90 days, or no data has been accessed in the past 90 days)).

[0154] The level corresponding to the first hierarchical constraint condition is the valid table, the level corresponding to the second hierarchical constraint condition is the disabled table, and the level corresponding to the third hierarchical constraint condition is the to-be-observed table.

[0155] Furthermore, valid tables can be further classified using the sub-classification constraints of the first classification constraint. For example, if the first sub-classification constraint is: high fan-in or high pageviews, the corresponding class is core table; if the second sub-classification constraint is: medium fan-in or medium pageviews, the corresponding class is backbone table; if the third sub-classification constraint is: low fan-in and (updates within 30 days or low pageviews), the corresponding class is ordinary table.

[0156] The target state information corresponding to the safety index and the sensitivity index is input into the corresponding grading model to obtain the grading results corresponding to the safety index and the sensitivity index.

[0157] The grading results for importance indicators include one of five levels: valid tables (valid tables include core tables, backbone tables, and ordinary tables), deactivated tables, and zombie tables (i.e., tables to be observed). Core and backbone tables are core assets, requiring high standards for data storage, computing, and security; deactivated tables are recyclable resources, and zombie tables are resources to be observed. The grading results for sensitivity indicators include one of five levels: top secret, confidential, highly sensitive, moderately sensitive, and low sensitive. The grading results for security indicators include one of five levels: level five, level four, level three, level two, and level one.

[0158] In step S740, data management policies are established and matched.

[0159] According to the grading result of the data table corresponding to each data management indicator, a target data management strategy is determined to manage the data table based on the target data management strategy.

[0160] Based on the grading results corresponding to the importance index, security index and sensitivity index respectively, the target data management strategy is comprehensively determined. The target data association strategy integrates the strategies from the three perspectives of storage, security and computing. The strategy from each perspective is matched with the grading results corresponding to the importance index, security index and sensitivity index respectively. Through this establishment process, the target data management strategy is also associated with the metadata of the data table.

[0161] For example, when the data table classification results include core tables, medium-sensitive tables, and security classification level 2, the target data management strategies include storage strategy: intranet, 4 backup storages, cluster physical isolation, computing strategy: shared cluster scheduling with high priority, 10 (or more) retry attempts upon task failure, and security strategy: table-level permission management, monthly validity period, and director approval.

[0162] In this way, compared with the unified standard management of data assets and the hierarchical management of data assets in a single dimension, this application, based on the multi-dimensional grading results, finely determines the target data management strategy that adapts to the data table's own state; and then manages the data table based on the target data management strategy, which can effectively avoid the loss, freezing and data security incidents of core data in the data management process, as well as avoid the waste of management resources, realize the refined matching management of data, and effectively improve the reliability of data management. On the basis of ensuring the value output and security of data, the cost of data management is greatly reduced.

[0163] Data assets are managed using unified standards. For example, data assets involving user account information are uniformly classified as top secret and divided into fixed and unified levels, so that the storage, calculation, and security policies of all data assets follow unified standards. There are either problems with "conservative" standards, such as all data being backed up three times in two locations (three copies of data are stored in computer rooms in two cities), resulting in high solution management costs, or there are problems with "loose" standards, such as three copies of core data in a single computer room, resulting in high solution management risks. It is difficult to solve both the "conservative" and "loose" standard problems at the same time, making it difficult to balance cost and reliability.

[0164] Single-dimensional asset classification makes it impossible to fine-tune data management strategies, making it difficult to effectively manage cost risks. For example, if the storage and access authentication schemes for highly sensitive table A are set based solely on sensitivity level, it is possible that highly sensitive table A will remain inaccessible and unupdated for extended periods, potentially leading to freezing or even deletion.

[0165] To facilitate better implementation of the data management method provided in the embodiment of the present application, the embodiment of the present application also provides a data management device based on the above data management method. The meanings of the terms herein are the same as those in the above data management method, and the specific implementation details can be referred to the description in the method embodiment. Figure 8 A block diagram of a data management device according to an embodiment of the present application is shown.

[0166] like Figure 8 As shown, the data management device 800 may include an acquisition module 810 , a determination module 820 , a matching module 830 , a grading module 840 and a management module 850 .

[0167] The acquisition module 810 can be used to obtain the asset status information of the target data asset; the determination module 820 can be used to determine the data management indicators of the target data asset, and the data management indicators include multiple ones; the matching module 830 can be used to obtain the target status information that matches each of the data management indicators from the asset status information; the grading module 840 can be used to grade the target data asset based on the target status information, and obtain the grading result of the target data asset corresponding to each of the data management indicators; the management module 850 can be used to determine the target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators, and manage the target data asset based on the target data management strategy.

[0168] In some embodiments of the present application, the asset status information includes attribute information corresponding to multiple attributes of the target data asset; the matching module includes: a label establishment unit, which is used to establish an attribute label corresponding to each attribute of the target data asset based on the attribute information corresponding to each attribute; and a label association unit, which is used to obtain the attribute label corresponding to each attribute matched by the data management indicator as the target status information matched by each data management indicator.

[0169] In some embodiments of the present application, the label establishment unit includes: a feature acquisition subunit, used to obtain information features of the attribute information corresponding to each of the attributes; a strategy matching subunit, used to determine the label establishment strategy corresponding to each of the attributes based on the information features of the attribute information corresponding to each of the attributes; and a label establishment subunit, used to establish the attribute label of the target data asset corresponding to each of the attributes based on the label establishment strategy corresponding to each of the attributes and using the attribute information corresponding to each of the attributes.

[0170] In some embodiments of the present application, the label association unit includes: a table acquisition subunit, used to obtain an attribute query table, wherein the attribute query table contains each of the data management indicators and the attributes that match each of the data management indicators; a query subunit, used to determine the target attribute that matches each of the data management indicators based on the attribute query table; and an attribute matching subunit, used to obtain the attribute label corresponding to the target attribute that matches each of the data management indicators as the target status information that matches each of the data management indicators.

[0171] In some embodiments of the present application, the grading module includes: a grading strategy determination unit, used to determine the grading strategy corresponding to each of the data management indicators; a strategy grading unit, used to grade the target data assets respectively according to the grading strategy corresponding to each of the data management indicators and using the target status information matched by each of the data management indicators, to obtain the grading results of the target data assets corresponding to each of the data management indicators.

[0172] In some embodiments of the present application, the grading strategy determination unit includes: an asset analysis sub-unit, used to determine the business scenario characteristics and data supervision requirements corresponding to the target data assets, the business scenario characteristics are the relevant characteristics of the business scenarios in which the target data assets are applied, and the data supervision requirements are the target requirements for managing the target data assets; a grading strategy determination sub-unit, used to determine the grading strategy corresponding to each of the data management indicators based on the business scenario characteristics and the data supervision requirements.

[0173] In some embodiments of the present application, the grading strategy corresponding to the first data management indicator includes multiple grading constraints, each of which corresponds to a level; the strategy grading unit includes: a constraint matching subunit, used to determine the grading constraints that the target state information matched by the first data management indicator complies with, and obtain the target grading constraints; a level matching subunit, used to obtain the level corresponding to the target grading constraints; and a result determination subunit, used to determine the level corresponding to the target grading constraints as the grading result of the target data asset corresponding to the first data management indicator.

[0174] In some embodiments of the present application, the first data management indicator includes an importance indicator; the multiple hierarchical constraints include a first hierarchical constraint, a second hierarchical constraint, and a third hierarchical constraint, the level corresponding to the first hierarchical constraint is a valid asset, the level corresponding to the second hierarchical constraint is a deactivated asset, and the level corresponding to the third hierarchical constraint is an asset to be observed; the constraint matching subunit is used to: determine in sequence whether the target state information matched by the importance indicator meets the first hierarchical constraint, the second hierarchical constraint, and the third hierarchical constraint; and determine the hierarchical constraint that the target state information matched by the importance indicator meets as the target hierarchical constraint.

[0175] In some embodiments of the present application, the grading strategy corresponding to the second data management indicator is a grading model; the strategy grading unit is used to: input the target state information matched by the second data management indicator into the grading model, and obtain the grading result of the target data asset corresponding to the second data management indicator output by the grading model.

[0176] In some embodiments of the present application, the second data management indicator includes a sensitivity indicator and a security indicator; the policy grading unit is used to: input the target state information matching the sensitivity indicator into a sensitivity grading model, and obtain the grading result of the target data asset corresponding to the sensitivity indicator output by the sensitivity grading model; input the target state information matching the security indicator into a security grading model, and obtain the grading result of the target data asset corresponding to the security indicator output by the security grading model.

[0177] In some embodiments of the present application, the management module includes: a template acquisition unit, used to acquire a policy template set, each policy template in the policy template set is marked with multiple level labels; a template matching unit, used to determine the level label matched by each grading result according to the grading result of the target data asset corresponding to each of the data management indicators, and obtain a target level label; a template determination unit, used to use the policy template marked with the target level label as the target data management policy.

[0178] In some embodiments of the present application, the management module includes: an analysis unit, used to input the grading results of the target data assets corresponding to each of the data management indicators into a policy decision model to obtain policy information output by the policy decision model; and a policy generation unit, used to generate the target data management policy based on the policy information.

[0179] In some embodiments of the present application, the data management device further includes: a metadata acquisition module for acquiring metadata corresponding to the target data asset; and a metadata association unit for associating the target data management policy with the metadata corresponding to the target data asset.

[0180] In this way, based on the data management device 800, by obtaining the asset status information of the target data asset; determining the data management indicators of the target data asset, which include multiple data management indicators; then, from the asset status information, obtaining the target status information that matches each data management indicator; based on the target status information, grading the target data asset to obtain the grading results of the target data asset corresponding to each data management indicator; realizing multi-dimensional grading of the target data asset according to the data asset's own status and multiple indicators; then, determining the target data management strategy based on the grading results of the target data asset corresponding to each data management indicator, realizing the multi-dimensional grading results, and finely determining the target data management strategy that adapts to the target data asset's own status; and then managing the target data asset based on the target data management strategy, which can effectively avoid the loss, freezing and data security incidents of core data in the data management process, and avoid the problem of waste of management resources, realize fine matching of management data, effectively improve the reliability of data management, and ensure management costs at the same time.

[0181] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0182] In addition, an embodiment of the present application further provides an electronic device, which may be a terminal or a server, such as Figure 9 , which shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0183] The electronic device may include one or more processing core processors 901, one or more computer-readable storage media memories 902, a power supply 903, an input unit 904 and other components. Those skilled in the art will understand that Figure 9 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0184] Processor 901 is the control center of the electronic device, connecting the various components of the entire computer device using various interfaces and lines. By running or executing software programs and / or modules stored in memory 902 and accessing data stored in memory 902, it performs various functions of the computer device and processes data, thereby providing overall management of the electronic device. Optionally, processor 901 may include one or more processing cores; preferably, processor 901 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, user interfaces, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 901.

[0185] The memory 902 can be used to store software programs and modules. The processor 901 executes various functional applications and data processing by running the software programs and modules stored in the memory 902. The memory 902 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 902 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 902 may also include a memory controller to provide the processor 901 with access to the memory 902.

[0186] The electronic device also includes a power supply 903 for supplying power to various components. Preferably, the power supply 903 can be logically connected to the processor 901 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 903 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0187] The electronic device may further include an input unit 904, which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0188] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 901 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 902 according to the following instructions, and the processor 901 will run the application programs stored in the memory 902 to implement various functions as follows:

[0189] Obtain asset status information of the target data asset;

[0190] Determining a data management indicator of the target data asset, wherein the data management indicator includes a plurality of indicators;

[0191] Acquiring target status information matching each of the data management indicators from the asset status information;

[0192] Classifying the target data assets based on the target status information to obtain a classification result of the target data assets corresponding to each of the data management indicators;

[0193] A target data management strategy is determined according to the grading result of the target data asset corresponding to each of the data management indicators, and the target data asset is managed based on the target data management strategy.

[0194] In one embodiment, the asset status information includes attribute information corresponding to multiple attributes of the target data asset; and obtaining target status information matching each of the data management indicators from the asset status information includes:

[0195] Based on the attribute information corresponding to each of the attributes, establishing an attribute tag of the target data asset corresponding to each of the attributes;

[0196] The attribute label corresponding to the attribute matched by each data management indicator is obtained as the target state information matched by each data management indicator.

[0197] In one embodiment, establishing an attribute tag corresponding to each attribute of the target data asset based on the attribute information corresponding to each attribute includes:

[0198] Obtaining information characteristics of attribute information corresponding to each of the attributes;

[0199] Determining a label establishment strategy corresponding to each attribute according to information characteristics of the attribute information corresponding to each attribute;

[0200] According to a label establishment strategy corresponding to each of the attributes, attribute information corresponding to each of the attributes is used to establish an attribute label of the target data asset corresponding to each of the attributes.

[0201] In one embodiment, obtaining the attribute label corresponding to the attribute matched by each data management indicator as the target state information matched by each data management indicator includes:

[0202] Obtaining an attribute query table, wherein the attribute query table includes each of the data management indicators and an attribute matching each of the data management indicators;

[0203] Determining target attributes that match each of the data management indicators based on the attribute query table;

[0204] The attribute label corresponding to the target attribute matched by each of the data management indicators is obtained as the target state information matched by each of the data management indicators.

[0205] In one embodiment, grading the target data assets based on the target status information to obtain a grading result of the target data assets corresponding to each of the data management indicators includes:

[0206] Determining a grading strategy corresponding to each of the data management indicators;

[0207] According to the grading strategy corresponding to each data management indicator, the target data assets are graded respectively using the target state information matched by each data management indicator to obtain the grading result of the target data assets corresponding to each data management indicator.

[0208] In one embodiment, determining the grading strategy corresponding to each of the data management indicators includes:

[0209] Determine the business scenario characteristics and data supervision requirements corresponding to the target data asset, wherein the business scenario characteristics are related characteristics of the business scenario in which the target data asset is applied, and the data supervision requirements are target requirements for managing the target data asset;

[0210] Determine a grading strategy corresponding to each of the data management indicators based on the business scenario characteristics and the data supervision requirements.

[0211] In one embodiment, the grading strategy corresponding to the first data management indicator includes a plurality of grading constraints, each of which corresponds to a grade;

[0212] The target data assets are graded according to the grading strategy corresponding to each data management indicator and using the target state information matched by each data management indicator to obtain the grading result of the target data asset corresponding to each data management indicator, including:

[0213] Determining the hierarchical constraint conditions satisfied by the target state information matched by the first data management indicator, and obtaining target hierarchical constraint conditions;

[0214] Obtaining the level corresponding to the target grading constraint condition;

[0215] The level corresponding to the target grading constraint condition is determined as the grading result of the target data asset corresponding to the first data management indicator.

[0216] In one embodiment, the first data management indicator includes an importance indicator; the multiple hierarchical constraints include a first hierarchical constraint, a second hierarchical constraint, and a third hierarchical constraint; the first hierarchical constraint corresponds to a level of active assets, the second hierarchical constraint corresponds to a level of inactive assets, and the third hierarchical constraint corresponds to a level of assets to be observed;

[0217] The step of determining the hierarchical constraint condition satisfied by the target state information matched by the first data management indicator to obtain the target hierarchical constraint condition includes:

[0218] determining in sequence whether the target state information matched by the importance index meets the first grading constraint condition, the second grading constraint condition, and the third grading constraint condition;

[0219] The hierarchical constraint condition satisfied by the target state information matched by the importance index is determined as the target hierarchical constraint condition.

[0220] In one embodiment, the grading strategy corresponding to the second data management indicator is a grading model;

[0221] The target data assets are graded according to the grading strategy corresponding to each data management indicator and using the target state information matched by each data management indicator to obtain the grading result of the target data asset corresponding to each data management indicator, including:

[0222] The target state information matched by the second data management indicator is input into the grading model to obtain a grading result of the target data asset corresponding to the second data management indicator output by the grading model.

[0223] In one embodiment, the second data management indicator includes a sensitivity indicator and a security indicator;

[0224] Inputting the target state information matching the second data management indicator into the grading model to obtain a grading result of the target data asset corresponding to the second data management indicator output by the grading model includes:

[0225] Inputting the target state information matched with the sensitivity index into a sensitivity grading model, and obtaining a grading result of the target data asset corresponding to the sensitivity index output by the sensitivity grading model;

[0226] The target state information that matches the security indicator is input into a security level grading model to obtain a grading result of the target data asset corresponding to the security indicator output by the security level grading model.

[0227] In one embodiment, determining the target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators includes:

[0228] Acquire a policy template set, wherein each policy template in the policy template set is marked with multiple level labels;

[0229] According to the grading result of the target data asset corresponding to each of the data management indicators, determining the grade label matched by each of the grading results to obtain a target grade label;

[0230] The policy template marked with the target level label is used as the target data management policy.

[0231] In one embodiment, determining the target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators includes:

[0232] Inputting the grading result of the target data asset corresponding to each of the data management indicators into a policy decision model to obtain policy information output by the policy decision model;

[0233] The target data management policy is generated according to the policy information.

[0234] In one embodiment, it further includes:

[0235] Obtain metadata corresponding to the target data asset;

[0236] The target data management policy is associated with metadata corresponding to the target data asset.

[0237] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by a computer program, or by controlling related hardware through a computer program. The computer program may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0238] To this end, an embodiment of the present application further provides a storage medium storing a computer program, which can be loaded by a processor to execute the steps of any method provided in the embodiment of the present application.

[0239] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0240] Since the computer program stored in the storage medium can execute the steps of any method provided in the embodiments of the present application, the beneficial effects that can be achieved by the method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0241] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the above-described embodiments of the present application.

[0242] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0243] It should be understood that the present application is not limited to the embodiments that have been described above and shown in the accompanying drawings, but various modifications and changes may be made without departing from the scope thereof.

Claims

1. A data management method, characterized in that: include: Obtain asset status information of the target data asset; Determining a data management indicator of the target data asset, wherein the data management indicator includes a plurality of indicators; Acquiring target status information matching each of the data management indicators from the asset status information, the data management indicators including at least two of a sensitivity indicator, an importance indicator, and a security indicator; Classifying the target data assets based on the target status information to obtain a classification result of the target data assets corresponding to each of the data management indicators; determining a target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators, and managing the target data asset based on the target data management strategy; Determining the data management indicators of the target data asset includes: determining metadata of the target data asset, inputting the metadata into an indicator decision model, and obtaining a plurality of data management indicators output by the indicator decision model; The asset status information includes attribute information corresponding to multiple attributes of the target data asset; and obtaining target status information matching each of the data management indicators from the asset status information includes: Based on the attribute information corresponding to each of the attributes, establishing a significant attribute label corresponding to each of the attributes of the target data asset; The attribute label corresponding to the attribute matched by each of the data management indicators is obtained as the target state information matched by each of the data management indicators.

2. The data management method according to claim 1, wherein: The step of establishing a significant attribute label corresponding to each attribute of the target data asset based on the attribute information corresponding to each attribute includes: Obtaining information characteristics of attribute information corresponding to each of the attributes; Determining a label establishment strategy corresponding to each attribute based on information characteristics of the attribute information corresponding to each attribute; According to a label establishment strategy corresponding to each of the attributes, attribute information corresponding to each of the attributes is used to establish an attribute label of the target data asset corresponding to each of the attributes.

3. The data management method according to claim 1, wherein: The acquiring of the attribute label corresponding to the attribute matched by each data management indicator as target state information matched by each data management indicator includes: Obtaining an attribute query table, wherein the attribute query table includes each of the data management indicators and an attribute matching each of the data management indicators; Determining target attributes that match each of the data management indicators based on the attribute query table; The attribute label corresponding to the target attribute matched by each of the data management indicators is obtained as the target state information matched by each of the data management indicators.

4. The data management method according to claim 1, wherein: The grading of the target data assets based on the target status information to obtain a grading result of the target data assets corresponding to each of the data management indicators includes: Determining a grading strategy corresponding to each of the data management indicators; According to the grading strategy corresponding to each data management indicator, the target data assets are graded respectively using the target state information matched by each data management indicator to obtain the grading result of the target data assets corresponding to each data management indicator.

5. The data management method according to claim 4, characterized in that: Determining the grading strategy corresponding to each of the data management indicators includes: Determine the business scenario characteristics and data supervision requirements corresponding to the target data asset, wherein the business scenario characteristics are related characteristics of the business scenario in which the target data asset is applied, and the data supervision requirements are target requirements for managing the target data asset; Determine a grading strategy corresponding to each of the data management indicators based on the business scenario characteristics and the data supervision requirements.

6. The data management method according to claim 4, characterized in that: The grading strategy corresponding to the first data management indicator includes a plurality of grading constraints, each of which corresponds to a grade; The target data assets are graded according to the grading strategy corresponding to each data management indicator and using the target state information matched by each data management indicator to obtain the grading result of the target data asset corresponding to each data management indicator, including: Determining the hierarchical constraint conditions satisfied by the target state information matched by the first data management indicator, and obtaining target hierarchical constraint conditions; Obtaining the level corresponding to the target grading constraint condition; The level corresponding to the target grading constraint condition is determined as the grading result of the target data asset corresponding to the first data management indicator.

7. The data management method according to claim 6, characterized in that: The first data management indicator includes an importance indicator; the multiple hierarchical constraints include a first hierarchical constraint, a second hierarchical constraint, and a third hierarchical constraint, the first hierarchical constraint corresponds to a level of valid assets, the second hierarchical constraint corresponds to a level of deactivated assets, and the third hierarchical constraint corresponds to a level of assets under observation; The step of determining the hierarchical constraint condition satisfied by the target state information matched by the first data management indicator to obtain the target hierarchical constraint condition includes: determining in sequence whether the target state information matched by the importance index meets the first grading constraint condition, the second grading constraint condition, and the third grading constraint condition; The hierarchical constraint condition satisfied by the target state information matched by the importance index is determined as the target hierarchical constraint condition.

8. The data management method according to claim 4, characterized in that: The grading strategy corresponding to the second data management indicator is a grading model; The target data assets are graded according to the grading strategy corresponding to each data management indicator and using the target state information matched by each data management indicator to obtain the grading result of the target data asset corresponding to each data management indicator, including: The target state information matched by the second data management indicator is input into the grading model to obtain a grading result of the target data asset corresponding to the second data management indicator output by the grading model.

9. The data management method according to claim 8, characterized in that: The second data management index includes a sensitivity index and a security index; Inputting the target state information matching the second data management indicator into the grading model to obtain a grading result of the target data asset corresponding to the second data management indicator output by the grading model includes: Inputting the target state information matched with the sensitivity index into a sensitivity grading model, and obtaining a grading result of the target data asset corresponding to the sensitivity index output by the sensitivity grading model; The target state information that matches the security indicator is input into a security level grading model to obtain a grading result of the target data asset corresponding to the security indicator output by the security level grading model.

10. The data management method according to claim 1, wherein: Determining a target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators includes: Acquire a policy template set, wherein each policy template in the policy template set is marked with multiple level labels; According to the grading result of the target data asset corresponding to each of the data management indicators, determining the grade label matched by each of the grading results to obtain a target grade label; The policy template marked with the target level label is used as the target data management policy.

11. The data management method according to claim 1, wherein: Determining a target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators includes: Inputting the grading result of the target data asset corresponding to each of the data management indicators into a policy decision model to obtain policy information output by the policy decision model; The target data management policy is generated according to the policy information.

12. The data management method according to any one of claims 1 to 11, characterized in that: The data management method further includes: Obtain metadata corresponding to the target data asset; The target data management policy is associated with metadata corresponding to the target data asset.

13. A data management device, characterized in that: include: An acquisition module is used to obtain the asset status information of the target data asset; a determination module, configured to determine data management indicators of the target data asset, wherein the data management indicators include a plurality of data management indicators, including at least two data management indicators selected from the group consisting of a sensitivity indicator, an importance indicator, and a security indicator; a matching module, configured to obtain target status information matching each of the data management indicators from the asset status information; The determination module is used to: determine the metadata of the target data asset, input the metadata into the indicator decision model, and obtain multiple data management indicators output by the indicator decision model; The asset status information includes attribute information corresponding to multiple attributes of the target data asset; the matching module is used to: establish an attribute tag corresponding to each attribute of the target data asset based on the attribute information corresponding to each attribute; Obtaining an attribute label corresponding to an attribute matched by each of the data management indicators as target state information matched by each of the data management indicators; a grading module, configured to grade the target data asset based on the target status information, and obtain a grading result of the target data asset corresponding to each of the data management indicators; The management module is used to determine a target data management strategy according to the grading result of the target data asset corresponding to each of the data management indicators, and manage the target data asset based on the target data management strategy.

14. A storage medium having computer-readable instructions stored thereon, which, when executed by a processor of a computer, causes the computer to execute the method according to any one of claims 1 to 12.

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