CN118229032A - Self-adaptive enterprise data management method and system based on business dynamic change - Google Patents

Abstract

The invention discloses a self-adaptive enterprise data management method and system based on service dynamic change, and relates to the field of data management. The method comprises the following steps: acquiring a target data management platform of a target enterprise; setting a first preset data constraint according to the first dynamic change information of the first edition; archiving and storing said first real-time collected data to a first database when said first predetermined data constraint is met; introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result; and generating a first confidential contour map of the first database in the first layout, and carrying out data management on the first layout. The technical problems that only data sharing processing is considered and future enterprise data addition and data dynamic change are not considered in the prior art are solved, the data are evaluated by introducing a data secret value evaluation function, and the technical effect of data management on enterprise data is achieved by generating a contour map.

Description

Self-adaptive enterprise data management method and system based on business dynamic change

Technical Field

The application relates to the technical field of data management, in particular to a self-adaptive enterprise data management method and system based on business dynamic change.

Background

In today's business environment, enterprise data is the core of enterprise operations and decisions. Enterprise data is continually growing and changing as business dynamics and market competition increases. To remain competitive, businesses need to be able to quickly and accurately acquire, process, and analyze data to support their decision making processes. Traditional enterprise data management methods are often based on static data models and fixed business processes, and are difficult to adapt to the requirements of dynamic changes of business. However, these methods tend to be frustrating when dealing with large data, real-time data, and frequently changing business data. Traditional data management methods are generally based on fixed data models and business processes, and are difficult to adapt to the dynamic change requirements of businesses. When a business changes, it often takes a lot of time and resources to update and adjust the data model and business processes. With the increasing growth of enterprise data, conventional data management systems often have difficulty supporting the storage and querying of large-scale data. Furthermore, when new data sources or data types need to be added, the system often requires complex integration and adaptation work.

In summary, in the prior art, only data sharing processing is considered, and technical problems of future enterprise data addition and data dynamic change are not considered.

Disclosure of Invention

Based on this, it is necessary to provide a method and a system for self-adaptive enterprise data management based on business dynamic change, which are capable of evaluating data by introducing a data secret value evaluation function and managing the enterprise data by generating a contour map.

In a first aspect, there is provided an adaptive enterprise data management method based on dynamic changes of a service, including: acquiring a target data management platform of a target enterprise, wherein the target data management platform comprises a plurality of target business blocks; setting a first preset data constraint according to the monitored first dynamic change information of a first plate in the plurality of target business plates; when the first real-time collected data of the first layout accords with the first preset data constraint, archiving and storing the first real-time collected data to a first database, wherein the first database is embedded in the first layout; introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result; generating a first confidential contour map of the first database in the first layout by combining the first evaluation result; and carrying out data management on the first layout based on the first confidential contour map.

In a second aspect, an adaptive enterprise data management system based on business dynamics is provided, comprising: the target business plate comprises a module, wherein the target business plate comprises a target data management platform for acquiring a target enterprise, and the target data management platform comprises a plurality of target business plates; the preset data constraint setting module is used for setting a first preset data constraint according to the monitored first dynamic change information of a first edition in the plurality of target business edition blocks; the real-time collection data archiving module is used for archiving and storing the first real-time collection data to a first database when the first real-time collection data of the first edition block accords with the first preset data constraint, and the first database is embedded in the first edition block; the evaluation analysis module is used for introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result; the confidential contour map generation module is used for generating a first confidential contour map of the first database in the first edition by combining the first evaluation result; and the data management module is used for carrying out data management on the first layout based on the first confidential contour map.

The self-adaptive enterprise data management method and system based on the business dynamic change solve the technical problems that only data sharing processing is considered in the prior art, future enterprise data addition and data dynamic change are not considered, the data are evaluated by introducing a data confidential value evaluation function, and the technical effect of data management on the enterprise data is achieved by generating a contour map.

The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.

Drawings

FIG. 1 is a flow diagram of a business dynamics-based adaptive enterprise data management method in one embodiment;

FIG. 2 is a schematic flow chart of establishing association connection of a business dynamic change-based adaptive enterprise data management method in one embodiment;

fig. 3 is a block diagram of an adaptive enterprise data management system based on business dynamics in one embodiment.

Reference numerals illustrate: the target business plate comprises a module 11, a preset data constraint setting module 12, a real-time collection data archiving module 13, an evaluation analysis module 14, a confidential contour map generating module 15 and a data management module 16.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

As shown in fig. 1, the present application provides an adaptive enterprise data management method based on service dynamic change, which includes:

And acquiring a target data management platform of a target enterprise, wherein the target data management platform comprises a plurality of target business blocks.

The business dynamic change refers to the change of business content, business mode, business flow and the like of an enterprise caused by the influence of various factors such as market environment, client demands, technical progress and the like in the operation process of the enterprise. Adaptive enterprise data management based on business dynamics is a flexible and proactive approach that allows enterprises to dynamically adjust their data management policies, flows, and tools according to business needs, market changes, and technological advances. The application provides a self-adaptive enterprise data management method based on service dynamic change, which can respond to market change, customer demand or technical innovation more rapidly and flexibly, ensures the accuracy and the integrity of data through a real-time data monitoring and quality control mechanism, is beneficial to improving service efficiency and meets the effect of overall data quality control.

The application relates to a target data management platform for acquiring target enterprises, wherein the target enterprises refer to enterprises which need to be researched and arbitrarily selected for research, and are recorded as target enterprises, the target data management platform refers to the current data management status of the target enterprises, and the target data management platform comprises aspects of used technology, tools, platforms, data quality, safety and the like. The target business block refers to business or functional areas inside the enterprise, such as sales, marketing, customer service, supply chain management, etc. Each layout may have its own specific data processing and management requirements, and in the present application, refers to a portion or module of the target data management platform that is specific to or serves a specific business segment. By the method, the data management platform with specific functions or purposes, which is used by the target enterprise, is found, and covers a plurality of parts or modules specially designed or served for the specific business field, so that a mat is provided for subsequent data management.

And setting a first preset data constraint according to the monitored first dynamic change information of a first plate in the plurality of target business plates.

Monitoring refers to continuously monitoring a plurality of target business blocks of a target enterprise to capture any possible data or business changes; the first board refers to any one of the plurality of target business boards, and is recorded as a first board, the first dynamic change information refers to changes or updates that occur with time, such as changes in business requirements, data structures, data flows and the like, certain data management rules are set or adjusted according to the monitored first dynamic change information of the first board, the data constraint is usually a rule or condition set for ensuring the integrity, accuracy, consistency and safety of data, and the first predetermined data constraint is also a constraint set for distinguishing different data constraints, particularly the constraint set for the first board, including the regulations in terms of data types, data ranges, data formats, data access rights and the like. By the method, when the dynamic change information of the first edition in a plurality of target business editions of the target enterprise is monitored, the data management constraint for the editions is set or adjusted according to the change information so as to ensure the accuracy and consistency of the data and meet new business requirements.

The first predetermined data constraint includes a first predetermined data format standard and a first predetermined data quality rule.

The first predetermined data constraint comprises a first predetermined data format standard and a first predetermined data quality rule, the data format standard refers to a unified specification followed for storage, transmission and use of data. This may include specific details of the encoding scheme, field definition, data length, decimal place, etc. in the present application, the first predetermined data format criteria is a specific data format requirement set for the first layout of the target enterprise. It is ensured that all data in the first layout follow a unified standard, including data values and units, the data values having a predetermined data range and the units having a predetermined unit, thereby improving the readability and interchangeability of the data. Data quality rules are provisions for ensuring accuracy, integrity, consistency, and reliability of data. The standard and flow to be followed in the process of data collection, processing, storage and use are defined, and the first predetermined data quality rule is a specific data quality requirement set for the first edition of the target enterprise, and may include a verification rule of data, an error processing mechanism, a data updating strategy and the like, for example, the fixed bit number of the data after the decimal point, whether the data is invalid data or not and the like. By following these rules, it is ensured that the quality of the data in the first layout reaches a predetermined criterion, thereby improving the reliability and decision support of the data. By the method, the first preset data constraint provides clear data management requirements and specifications for the first edition of the target enterprise by setting the first preset data format standard and the first preset data quality rule, so that accuracy and consistency of data are ensured, and value and utilization efficiency of the data are improved.

And when the first real-time collected data of the first layout accords with the first preset data constraint, archiving and storing the first real-time collected data to a first database, wherein the first database is embedded in the first layout.

The first real-time collection data refers to data collected in real-time in the first layout, which is a real-time data stream from various sources, such as sensors, user inputs, transaction records, etc., real-time collection means that the data is captured instantaneously without delay or processing; the first predetermined data constraint, the data management criteria and rules for the first layout, include a first predetermined data format criteria and a first predetermined data quality rule for ensuring accuracy and consistency of the data. Before archiving and storing the first real-time collected data, the system verifies that the data meets a first predetermined data constraint. This includes checking whether the format of the data meets the criteria and whether the data meets the quality rules, and if the first real-time collected data is validated, i.e., meets the first predetermined data constraint, then the data will be archived for storage. Archival storage means that the data will be securely saved for future retrieval, analysis, or other purposes. The archived data is typically structured by the enterprise for ease of management and querying. The first database is a database specially used for storing the first layout data, is embedded in the first layout, means tightly integrated with the first layout, and provides efficient and reliable data storage service for the first layout. The entire data management flow should be integrated and automated. This means that the data collection, verification, archiving and storage operations are all automated, without human intervention. This helps to improve the efficiency and accuracy of data management and reduces the risk of human error. By the method, the target enterprise can ensure that the data of the first edition is effectively stored and managed under the condition of conforming to the preset constraint, so that reliable data support is provided for subsequent data analysis and business decision.

And introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result.

The data confidentiality value evaluation function is a specific algorithm for evaluating the confidentiality or sensitivity of data, wherein the confidentiality value is usually related to risk or loss possibly caused by data leakage, and the evaluation function can be based on various factors such as the type of data, namely personal identity information, financial information, business confidentiality and the like; data use scenes, data access rights and the like; the data in the first database is evaluated piece by piece or in batches by using a data secret value evaluation function, the function can allocate a secret value or sensitivity level to each data item according to preset rules and standards, and the output result of the evaluation function is a first evaluation result, wherein the first evaluation result comprises the secret value or sensitivity level of each data item and possible risk rating or suggestion, and the first evaluation result is used for subsequent data management and security decision, such as data encryption, access control, data desensitization and the like. By the method, the data in the first database is evaluated and analyzed by introducing the data confidential value evaluation function, and enterprises can better know the confidentiality and sensitivity of the data, so that more effective data management and security strategies are formulated, and the core data assets of the enterprises are protected.

The expression of the data secret value evaluation function is as follows:

；

Wherein, Refers to a first index corresponding to first data in the first databaseIs used to determine the first secret value of (c),AndAndAndRespectively refer to the first indexSensitivity, importance, access frequency and business value,AndAndAndRespectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient; The first evaluation result is constructed based on the first secret value.

The expression of the data secret value evaluation function is used for calculating a first secret value of a first index corresponding to first data in a first database. Wherein,Refers to a first index corresponding to first data in the first databaseIs used to determine the first secret value of (c),AndAndAndRespectively refer to the first indexSensitivity is an index for measuring risk or influence possibly brought by data leakage, importance is an index for evaluating the key degree of the data for enterprises or businesses, access frequency is used for measuring the frequency of the data being accessed or used, and business value is an index for measuring the contribution degree of the data to business operation and decision,AndAndAndRespectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficientThis condition ensures that the sum of the weight coefficients is 1, so that the calculation of the secret value is based on the relative weights of the indices. And calculating a corresponding first secret value of each piece of data in the first database by using the function, and forming a first evaluation result by the calculated first secret value and other related information such as data identification, data type, source and the like. The first evaluation result can be used in subsequent security and management decisions such as data classification, encryption, access control, backup strategy and the like. By the method, a first evaluation result is obtained, and a bedding is provided for subsequent evaluation.

And generating a first confidential contour map of the first database in the first layout according to the first evaluation result.

Extracting relevant data in the first evaluation result, including a first confidential value of each data item, selecting a tool suitable for data visualization, determining the layout and style of the contour map, for example, using shades of color or different colors to represent the confidential value, setting the range of confidential values, and determining the color or legend identifier corresponding to each range. Mapping the first confidential value in the first evaluation result to the corresponding position of the contour map, relating to the spatial distribution of the data or the classification of the data, and particularly according to the definition of the data, and drawing the contour map according to the mapping result of the data in the visualization tool. The contour map will use different colors or lines to represent the high and low distribution of secret values. By generating the first confidential contour map, confidentiality distribution conditions of data in the first database can be intuitively displayed, and enterprises can be helped to better manage and protect data assets of the enterprises.

And carrying out data management on the first layout based on the first confidential contour map.

The first confidential contour map is analyzed to identify areas or data items with higher confidential values. These areas generally represent potential high risk points, and require more stringent data management and protection measures, where data management refers to implementing more stringent access control policies for data areas with higher confidential values, including limiting access rights, using multi-factor authentication, periodically reviewing access logs, and so on, encrypting the data with higher confidential values to prevent the data from being acquired by unauthorized personnel during transmission or storage, and so on.

Extracting a target contour line in the first confidential contour map, wherein the target contour line is provided with an identifier of a target confidential value; generating a target access plan of the target contour line according to the target confidential value; and performing data access management on a plurality of target index data on the target contour line based on the target access plan.

In the first confidential contour map, a target contour line with a target confidential value identifier is first identified, and visual analysis is performed on the contour map, or the target contour line of the corresponding confidential value is extracted from a data source of the contour map in a programming mode. The target secret represented by the target contour is validated. This secret value generally reflects the sensitivity and importance of the data and is a key factor in determining the access rights of the data. Based on the target secret value, a target access plan is formulated, e.g., specifying which persons or systems have access to the data on the target contour, as well as access permission level, access time, access mode, etc. The protocol should also include handling measures for offending access, such as warnings, restricted access, journaling, etc. Based on the target access plan, data access management is performed on the plurality of target index data on the target contour, and access to the target data is restricted using appropriate technical means, such as a database management system, an access control list, and the like. Ensuring that only authorized personnel or systems can access the target data and access is strictly in accordance with the rights and modes specified by the protocol. By the method, effective data access management of a plurality of target index data on the target contour line can be ensured, and the safety, compliance and usability of the data are improved. At the same time, this also helps to reduce the risk of data leakage and abuse, protecting the enterprise's assets and reputation.

The target access plan includes at least one of a predetermined multi-level access authentication, wherein the predetermined multi-level access authentication includes knowledge authentication, ownership authentication, and biometric authentication.

The target access plan includes a predetermined multi-level access authentication, wherein the predetermined multi-level access authentication may include knowledge authentication, ownership authentication and biometric authentication, and the knowledge authentication refers to identity verification based on information known to a user, such as a password, an answer, and the like. In data access management, knowledge authentication may ensure that only users who know specific information, such as security passwords or answers to questions, can access the target data. Ownership authentication is the process of confirming that a person or entity has legal ownership of a certain asset or data. In data access management, ownership authentication may ensure that only the legitimate owner of the data or its authorized representative can access the data, involving review of related legal regulations, title certificates, contracts, or agreements, etc. to confirm ownership of the data, which in the present application refers to confirmation by the user's possession of an item, such as a cell phone, smart card, or hardware token. Biometric authentication is a means of authenticating an individual's identity by a computer using physical or behavioral characteristics inherent to the human body. This authentication method relies on unique biological characteristics of everyone, such as fingerprint, iris, facial features, voice, etc., and thus has high security and accuracy. In data access management, biometric authentication can be an additional security measure to ensure that only the principal can access his personal or sensitive data. By combining the three authentication modes, a powerful and multi-level access control system can be formed, so that the data security and the access control accuracy are improved. It should be noted that the specific authentication mode and implementation strategy should be customized and optimized according to the actual situation and requirements.

Obtaining target dynamic newly-added information of a target newly-added service block, wherein the target newly-added service block refers to a newly-added block of the target data management platform; analyzing the target dynamic newly-added information, setting target preset data constraint, and storing the target preset data constraint to the target newly-added business layout; and carrying out data management on the new business edition block of the target through the target data management platform.

The method comprises the steps of obtaining target dynamic newly-added information of a target newly-added service edition, determining specific content and range of the target newly-added service edition according to communication between a target data management platform and a service team or a project team, and obtaining dynamic newly-added information of the target newly-added service edition, wherein the dynamic newly-added information comprises description, data type, data format, data source, data use and the like of a new service, and the target dynamic newly-added information refers to requirements or standards related to data quality, data safety and data compliance in the newly-added information of the target newly-added service edition. The method comprises the steps of carrying out detailed analysis on acquired dynamic newly-added information of a target, understanding specific requirements and data characteristics of new services, evaluating possible influences of the new services on a data management platform, including storage requirements, calculation requirements, network bandwidth requirements and the like, identifying possible data risks such as data leakage, data loss, data tampering and the like, formulating corresponding precautions, setting target preset data constraints according to analysis on the dynamic newly-added information of the target, wherein the purpose of the data constraints is to ensure that data in a new service layout block meets preset standards and specifications, ensure the quality and safety of the data, and prevent invalid or non-compliant data from entering the system. Storing the set target preset data constraint into the target newly-added service edition, and carrying out data management on the target newly-added service edition by utilizing functions and tools provided by a target data management platform, wherein the links comprise data acquisition, cleaning, conversion, loading, storage, inquiry, analysis, report generation and the like. By the method, the data management is carried out on the target newly-added business layout, so that the technical effects of ensuring the data safety and compliance of the target newly-added business layout are achieved.

As shown in fig. 2, a second layout in the plurality of target business layouts is extracted, wherein the second layout is provided with a second preset data constraint, and the second preset data constraint refers to a constraint on data information of each index in a second index set; performing index relevance analysis on the second index set and a target index set in the target preset data constraint to obtain a relevance result; screening indexes meeting a preset association index threshold value in the second index set by combining the association result to form an association index set; and establishing an association relation link between the association index set and the target index set.

And identifying and extracting a second plate from the plurality of target service plates, wherein the second plate refers to any plate selected from the plurality of target service plates except the first plate and is marked as a second plate, a second preset data constraint is determined in the second plate, the acquisition method is the same as the first preset data constraint, the requirements of the aspects of data type, data format, data range, data integrity, data accuracy and the like are met, and the second index set and the target index set in the target preset data constraint are subjected to relevance analysis, so that obvious relevance relations exist between indexes and the target index set are determined. And screening indexes meeting a preset association index threshold value in the second index set according to the result of the association analysis, wherein the preset association index threshold value is a preset value for determining which association degrees are considered to be obvious, and the screened indexes form the association index set. The establishment of the association relation link is realized by establishing a corresponding data model, a relation table or a mapping file in the data management platform, so that the association relation link is helpful for quickly identifying and utilizing the association in the subsequent data analysis, data monitoring or data management process, the data island is prevented from being lack of association between the data, the data island is the phenomenon that the databases are incompatible with each other, the main reasons formed include that each department can generate service data, and the requirements for data storage and use are possibly different, so that the data between the departments cannot be communicated, and the like.

As shown in fig. 3, an embodiment of the present application includes an adaptive enterprise data management system based on business dynamics, comprising:

The target business plate comprises a module 11, wherein the target business plate comprises a target data management platform for acquiring a target enterprise, and the target data management platform comprises a plurality of target business plates;

A predetermined data constraint setting module 12, where the predetermined data constraint setting module 12 is configured to set a first predetermined data constraint according to the monitored first dynamic change information of a first layout in the plurality of target service layouts;

The real-time collection data archiving module 13 is configured to archive and store the first real-time collection data into a first database when the first real-time collection data of the first layout conforms to the first predetermined data constraint, where the first database is embedded in the first layout;

The evaluation analysis module 14 is used for introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result;

the confidential contour map generating module 15 is configured to generate a first confidential contour map of the first database in the first layout in combination with the first evaluation result;

The data management module 16, the data management module 16 is configured to perform data management on the first layout based on the first confidential contour map.

Further, the embodiment of the application further comprises:

The system comprises a target dynamic newly-added information acquisition module, a target dynamic newly-added information generation module and a target data management platform, wherein the target dynamic newly-added information acquisition module is used for acquiring target dynamic newly-added information of a target newly-added business layout, and the target newly-added business layout refers to the newly-added layout of the target data management platform;

The target preset data constraint setting module is used for analyzing the target dynamic newly-added information, setting target preset data constraint and storing the target preset data constraint into the target newly-added business layout;

And the data management module is used for carrying out data management on the new business edition block of the target through the target data management platform.

Further, the embodiment of the application further comprises:

The plate extraction module is used for extracting a second plate in the plurality of target business plates, and the second plate is provided with a second preset data constraint, wherein the second preset data constraint refers to constraint on data information of each index in a second index set;

The relevance result obtaining module is used for carrying out index relevance analysis on the second index set and a target index set in the target preset data constraint to obtain a relevance result;

The relevance index set composition module is used for screening indexes meeting a preset relevance index threshold value in the second index set by combining the relevance result to form a relevance index set;

and the incidence relation link module is used for establishing incidence relation links between the incidence index set and the target index set.

Further, the embodiment of the application further comprises:

a predetermined data constraint comprising module for the first predetermined data constraint comprising a first predetermined data format standard and a first predetermined data quality rule.

Further, the embodiment of the application further comprises:

a data secret value evaluation function module, wherein the expression of the data secret value evaluation function module is as follows:

；

Wherein, Refers to a first index corresponding to first data in the first databaseIs used to determine the first secret value of (c),AndAndAndRespectively refer to the first indexSensitivity, importance, access frequency and business value,AndAndAndRespectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient；

And the evaluation result constructing module is used for constructing the first evaluation result based on the first confidential value.

Further, the embodiment of the application further comprises:

The target contour extraction module is used for extracting a target contour line in the first confidential contour map, and the target contour line is provided with a mark of a target confidential value;

The target access plan generation module is used for generating a target access plan of the target contour line according to the target confidential value;

And the data access management proceeding module is used for proceeding data access management to a plurality of target index data on the target contour line based on the target access plan.

Further, the embodiment of the application further comprises:

A predetermined multi-level access authentication inclusion module for the target access plan to include at least one of a predetermined multi-level access authentication, wherein the predetermined multi-level access authentication includes a knowledge authentication, an ownership authentication, and a biometric authentication.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application.

Claims (8)

1. The self-adaptive enterprise data management method based on the dynamic change of the business is characterized by comprising the following steps:

acquiring a target data management platform of a target enterprise, wherein the target data management platform comprises a plurality of target business blocks;

Setting a first preset data constraint according to the monitored first dynamic change information of a first plate in the plurality of target business plates;

When the first real-time collected data of the first layout accords with the first preset data constraint, archiving and storing the first real-time collected data to a first database, wherein the first database is embedded in the first layout;

introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result;

Generating a first confidential contour map of the first database in the first layout by combining the first evaluation result;

and carrying out data management on the first layout based on the first confidential contour map.

2. The method according to claim 1, characterized in that it comprises:

Obtaining target dynamic newly-added information of a target newly-added service block, wherein the target newly-added service block refers to a newly-added block of the target data management platform;

analyzing the target dynamic newly-added information, setting target preset data constraint, and storing the target preset data constraint to the target newly-added business layout;

and carrying out data management on the new business edition block of the target through the target data management platform.

3. The method of claim 2, wherein the data management of the target newly added service section by the target data management platform further comprises:

Extracting a second layout in the plurality of target business layouts, wherein the second layout is provided with a second preset data constraint, and the second preset data constraint refers to constraint on data information of each index in a second index set;

performing index relevance analysis on the second index set and a target index set in the target preset data constraint to obtain a relevance result;

Screening indexes meeting a preset association index threshold value in the second index set by combining the association result to form an association index set;

And establishing an association relation link between the association index set and the target index set.

4. The method of claim 1, wherein the first predetermined data constraint comprises a first predetermined data format standard and a first predetermined data quality rule.

5. The method of claim 1, wherein the expression of the data secret value evaluation function is as follows:

；

Wherein, Refers to a first index/>, corresponding to first data, in the first databaseFirst secret value,/>AndAnd/>And/>Respectively the first index/>Sensitivity, importance, access frequency and business value,/>And/>And/>And/>Respectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient；

The first evaluation result is constructed based on the first secret value.

6. The method of claim 1, wherein data managing the first layout based on the first confidential contour map comprises:

extracting a target contour line in the first confidential contour map, wherein the target contour line is provided with an identifier of a target confidential value;

generating a target access plan of the target contour line according to the target confidential value;

And performing data access management on a plurality of target index data on the target contour line based on the target access plan.

7. The method of claim 6, wherein the target access plan comprises at least one of a predetermined multi-level access authentication, wherein the predetermined multi-level access authentication comprises a knowledge authentication, an ownership authentication, and a biometric authentication.

8. An adaptive enterprise data management system based on business dynamics, comprising:

the target business plate comprises a module, wherein the target business plate comprises a target data management platform for acquiring a target enterprise, and the target data management platform comprises a plurality of target business plates;

The preset data constraint setting module is used for setting a first preset data constraint according to the monitored first dynamic change information of a first edition in the plurality of target business edition blocks;

The real-time collection data archiving module is used for archiving and storing the first real-time collection data to a first database when the first real-time collection data of the first edition block accords with the first preset data constraint, and the first database is embedded in the first edition block;

The evaluation analysis module is used for introducing a data confidential value evaluation function to evaluate and analyze the data in the first database to obtain a first evaluation result;

the confidential contour map generation module is used for generating a first confidential contour map of the first database in the first edition by combining the first evaluation result;

And the data management module is used for carrying out data management on the first layout based on the first confidential contour map.