Data platform for integrated management of enterprise information

Abstract

The invention relates to a data medium station for integrated management of enterprise information, and the data medium station comprises a data integration module which employs a plug-in type collection architecture, achieves the unified access of multi-source heterogeneous data through a plurality of different data source adapters, and guarantees the real-time synchronization of the data; the data management module is used for full life cycle management of data, including metadata management, main data management, data modeling and data blood relationship analysis; the data service module is used for providing a unified API (Application Program Interface) management platform, realizing unified management, safety control and load balancing of services through an API gateway, providing a visual development tool and providing a built-in data processing component for a user to quickly construct data services; the data security module is used for guaranteeing data security and access control; and the system monitoring module is used for monitoring the running state of the system. Unified management and service of various data of an enterprise are realized, the utilization efficiency of data assets is remarkably improved, and data support is provided for decision making of the enterprise.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise information management, and in particular relates to a data center for integrated enterprise information management. Background Art

[0002] As enterprises’ digital transformation deepens, they face many challenges in data management and application. Existing enterprise data management solutions mainly have the following problems:

[0003] 1. The problem of data islands is serious:

[0004] Each business system of an enterprise is built independently, forming information islands; data is scattered in different systems and difficult to manage in a unified manner; cross-system data sharing is difficult, resulting in data duplication; data between systems are not interoperable, affecting business collaboration.

[0005] 2. Inadequate data quality management:

[0006] There is a lack of unified data standards and specifications; the quality of data entry varies; data updates are not timely and there are lags; and data consistency is difficult to ensure.

[0007] 3. Inefficient data services:

[0008] The data extraction process is complex and cumbersome; data interface standards are not unified; data service response speed is slow; and personalized needs are difficult to meet.

[0009] 4. The value of data assets is not fully utilized:

[0010] There is a lack of unified management of data assets; data usage efficiency is low; data analysis capabilities are insufficient; and data value is difficult to quantify. Summary of the invention

[0011] The purpose of this invention is to provide a data center for integrated enterprise information management, to achieve unified management, processing and service of multi-source heterogeneous data of enterprises, and to provide data support for the digital transformation of enterprises.

[0012] The present invention provides a data center for integrated enterprise information management, including:

[0013] The data integration module adopts a plug-in collection architecture to achieve unified access to multi-source heterogeneous data through a variety of different data source adapters and ensure real-time data synchronization; the sources of the multi-source heterogeneous data include: relational databases, big data components, message middleware, and application system interfaces;

[0014] Data management module, used for full life cycle management of data, including metadata management, master data management, data modeling and data lineage analysis;

[0015] The data service module is used to provide a unified API management platform, realize unified management, security control and load balancing of services through the API gateway, provide visual development tools, and provide built-in data processing components for users to quickly build data services;

[0016] Data security module, used to ensure data security and access control;

[0017] System monitoring module, used to monitor the system operation status.

[0018] Furthermore, the data integration module includes:

[0019] Data collection unit, used to provide batch, real-time and incremental collection methods;

[0020] Data conversion unit, used for data cleaning, conversion and standardization;

[0021] Data quality control unit, which is used for data quality inspection and processing, including data integrity check, consistency check, accuracy verification, and provides flexible quality control strategy configuration through the quality rule engine;

[0022] Data synchronization unit, which is used to provide multiple synchronization modes including full synchronization, incremental synchronization, and real-time synchronization, and automatically adjust the synchronization strategy according to the data volume and system load;

[0023] The data storage unit is used for data storage and supports multiple storage methods and data types.

[0024] Furthermore, the data management module includes:

[0025] The metadata management unit is used to build a unified metadata warehouse to collect and manage technical metadata, business metadata, and operation and maintenance metadata, realize unified management and retrieval of data assets, and provide data traceability and impact analysis;

[0026] The master data management unit is used to provide master data modeling tools for users to define master data models and standards, and ensure the consistency of enterprise core data through the master data synchronization mechanism;

[0027] Data modeling unit, which is used to provide visual modeling tools, support the design and conversion of conceptual models, logical models, and physical models, and ensure the rationality of the design through model verification;

[0028] The data lineage unit is used to automatically record and analyze the dependencies in the data flow process, and to display the data lineage in a visual way to help users understand the data flow path and facilitate problem location and impact analysis.

[0029] Furthermore, the data service module includes:

[0030] API service unit, used to provide standardized data access interface;

[0031] Data development unit, used to provide custom data processing logic;

[0032] Data analysis unit, used to provide data analysis and mining capabilities;

[0033] Data sharing unit, used to achieve secure data sharing;

[0034] The service monitoring unit is used to monitor the service call status.

[0035] Furthermore, the data security module includes:

[0036] Access control unit, used to manage data access permissions;

[0037] Data desensitization unit, used to protect sensitive data;

[0038] Audit log unit, used to record data operation logs;

[0039] Security policy unit, used to formulate data security policies;

[0040] The risk monitoring unit is used to monitor data security risks.

[0041] Furthermore, the system monitoring module includes:

[0042] Performance monitoring unit, used for resource monitoring, service monitoring, interface monitoring, task monitoring, and alarm setting;

[0043] Log management unit, used for log collection, log analysis, log retrieval, log archiving, and log auditing;

[0044] Operation and maintenance management unit, used for configuration management, deployment management, backup and recovery, capacity planning, and problem diagnosis.

[0045] Through the above solution, through the data middle platform used for integrated enterprise information management, a layered architecture design is adopted to achieve unified management and services of various types of enterprise data, significantly improve the utilization efficiency of data assets, and provide data support for enterprise decision-making.

[0046] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a diagram of the data center architecture for the integrated management of enterprise information in the present invention;

[0048] Figure 2 It is a structural schematic diagram of the data integration module of the present invention;

[0049] Figure 3 This is a functional structure diagram of the data management module of the present invention;

[0050] Figure 4 is a schematic diagram of a data service module of the present invention;

[0051] Figure 5 This is a diagram of the architecture of the data security module of the present invention;

[0052] Figure 6 It is a functional diagram of the system monitoring module of the present invention. DETAILED DESCRIPTION

[0053] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0054] This embodiment provides a data center for integrated enterprise information management, including:

[0055] The data integration module adopts a plug-in collection architecture to achieve unified access to multi-source heterogeneous data through a variety of different data source adapters and ensure real-time data synchronization; the sources of the multi-source heterogeneous data include: relational databases, big data components, message middleware, and application system interfaces;

[0056] Data management module, used for full life cycle management of data, including metadata management, master data management, data modeling and data lineage analysis;

[0057] The data service module is used to provide a unified API management platform, realize unified management, security control and load balancing of services through the API gateway, provide visual development tools, and provide built-in data processing components for users to quickly build data services;

[0058] Data security module, used to ensure data security and access control;

[0059] System monitoring module, used to monitor the system operation status.

[0060] Specifically, the data integration module includes:

[0061] Data collection unit, used to provide batch, real-time and incremental collection methods;

[0062] Data conversion unit, used for data cleaning, conversion and standardization;

[0063] Data quality control unit, which is used for data quality inspection and processing, including data integrity check, consistency check, accuracy verification, and provides flexible quality control strategy configuration through the quality rule engine;

[0064] The data synchronization unit is used to provide multiple synchronization modes, including full synchronization, incremental synchronization, and real-time synchronization, and automatically adjust the synchronization strategy according to the data volume and system load;

[0065] The data storage unit is used for data storage and supports multiple storage methods and data types.

[0066] Specifically, the data management module includes:

[0067] The metadata management unit is used to build a unified metadata warehouse to collect and manage technical metadata, business metadata, and operation and maintenance metadata, realize unified management and retrieval of data assets, and provide data traceability and impact analysis;

[0068] The master data management unit is used to provide master data modeling tools for users to define master data models and standards, and ensure the consistency of enterprise core data through the master data synchronization mechanism;

[0069] Data modeling unit, which is used to provide visual modeling tools, support the design and conversion of conceptual models, logical models, and physical models, and ensure the rationality of the design through model verification;

[0070] The data lineage unit is used to automatically record and analyze the dependencies in the data flow process, and to display the data lineage in a visual way to help users understand the data flow path and facilitate problem location and impact analysis.

[0071] Specifically, the data service module includes:

[0072] API service unit, used to provide standardized data access interface;

[0073] Data development unit, used to provide custom data processing logic;

[0074] Data analysis unit, used to provide data analysis and mining capabilities;

[0075] Data sharing unit, used to achieve secure data sharing;

[0076] The service monitoring unit is used to monitor the service call status.

[0077] Specifically, the data security module includes:

[0078] Access control unit, used to manage data access permissions;

[0079] Data desensitization unit, used to protect sensitive data;

[0080] Audit log unit, used to record data operation logs;

[0081] Security policy unit, used to formulate data security policies;

[0082] The risk monitoring unit is used to monitor data security risks.

[0083] Specifically, the system monitoring module includes:

[0084] Performance monitoring unit, used for resource monitoring, service monitoring, interface monitoring, task monitoring, and alarm setting;

[0085] Log management unit, used for log collection, log analysis, log retrieval, log archiving, and log auditing;

[0086] Operation and maintenance management unit, used for configuration management, deployment management, backup and recovery, capacity planning, and problem diagnosis.

[0087] The present invention is described in further detail below.

[0088] 1. System overall architecture

[0089] The data center of this embodiment adopts a layered design architecture to build a complete data management system. In terms of the overall framework, the system is divided into four layers: data access layer, data processing layer, data service layer, and application layer. Each layer has different functional responsibilities. Among them:

[0090] The data access layer is responsible for the unified access of multi-source heterogeneous data and supports the access adaptation of various data sources, including structured data, semi-structured data, and unstructured data. The data processing layer is responsible for data cleaning, conversion, and processing, and provides powerful data processing capabilities through a distributed computing framework.

[0091] The data service layer provides a unified data service interface and supports multiple service modes and protocols;

[0092] The application layer provides personalized data application support for specific business scenarios.

[0093] The system uses a unified data bus to realize data transfer between various levels, and uses technologies such as message queues and distributed cache to ensure the efficiency and reliability of data transmission. In terms of architectural design, the microservice architecture is adopted to divide the system functions into multiple independent service components. Each service component can be deployed and expanded independently, and unified management and calling of services are achieved through the service registration center and API gateway. The system's security framework runs through all levels to achieve unified user authentication, permission control and data security protection. At the same time, the system also provides comprehensive operation and maintenance monitoring functions, including performance monitoring, log management, alarm notification, etc., to ensure the stable operation of the system.

[0094] like Figure 1 As shown, in a specific implementation, the data center adopts a layered architecture design, which includes the data access layer, data processing layer, data service layer and application layer from the bottom to the top. The system adopts a containerized deployment method and performs resource scheduling and management based on Kubernetes.

[0095] In terms of hardware configuration, the recommended configuration is as follows:

[0096] Access layer server: CPU 16 cores, memory 64GB, storage 2TB

[0097] Processing layer server: CPU 32 cores, memory 128GB, storage 5TB

[0098] Service layer server: CPU 16 cores, memory 64GB, storage 1TB

[0099] Distributed storage cluster: total capacity not less than 50TB

[0100] The system adopts a microservice architecture, which mainly includes the following core services:

[0101] Data integration service: responsible for data collection and synchronization

[0102] Data processing services: responsible for data cleaning and conversion

[0103] Metadata service: responsible for metadata management

[0104] API Gateway Service: responsible for unified interface management

[0105] Task scheduling service: responsible for job scheduling Monitoring and alarm service: responsible for system monitoring.

[0106] 2. Data Integration Module

[0107] Through the data integration module, the present invention realizes powerful data integration capabilities and lays the foundation for the unified management of enterprise data. In terms of data source access, the system adopts a plug-in collection architecture, provides a rich data source adapter, supports relational databases (such as MySQL, Oracle, SQL Server, etc.), big data components (such as Hadoop, Hive, HBase, etc.), message middleware (such as Kafka, RabbitMQ, etc.), application system interfaces (such as REST API, WebServic e Access to various types of data sources such as .

[0108] In terms of data synchronization, the system supports multiple synchronization methods such as full synchronization, incremental synchronization, and real-time synchronization, and provides intelligent scheduling strategies that can automatically adjust synchronization strategies based on factors such as data volume and system load. Real-time data synchronization: In distributed systems, data synchronization involves a trade-off between consistency and availability, which can be explained using the CAP theory. The following formula describes the relationship between the system's consistency C, availability A, and partition tolerance P:

[0109] C+A≤P

[0110] The above formula can illustrate how to make trade-offs based on actual scenarios during data synchronization.

[0111] During data transmission, a multi-threaded concurrent processing mechanism is adopted, and transmission efficiency is optimized through data compression, breakpoint resume and other technologies. In terms of data quality control, the system provides a complete quality monitoring mechanism, including data integrity check, consistency check, accuracy verification, etc., and supports flexible quality control strategy configuration through the quality rule engine. For quality problems found, the system provides two processing methods: automatic repair and manual intervention, and helps users to timely discover and solve data quality problems through quality analysis reports.

[0112] Data integrity check: Probabilistic statistical models can be used to measure data integrity and consistency. For example, the probability of data integrity is defined as P(Q) and the consistency is defined as P(C). The Bayesian formula is introduced to detect data anomalies, and P(A|B) is defined as the probability of event A occurring under condition B:

[0113]

[0114] This formula can be used to detect the degree of abnormality of new data based on the characteristics of historical data.

[0115] like Figure 2As shown, in a specific implementation, the data integration module adopts a distributed architecture to achieve unified access and processing of multi-source heterogeneous data. The source system enters the target system through data source management 201, data collection 202, data conversion 203, data quality control 204, and data loading 205 in sequence, wherein data source management 201 includes metadata management and connection configuration, data collection 202 includes real-time collection and batch collection, data conversion 203 includes format conversion and data mapping, data quality control 204 includes data verification and quality monitoring, and data loading 205 includes incremental loading and full loading.

[0116] The specific implementation mechanisms include the following:

[0117] 1) Data source access mechanism:

[0118] Support relational databases: MySQL, Oracle, SQL Server, etc.

[0119] Support big data components: Hive, HBase, Elasticsearch, etc.

[0120] Support file systems: local files, HDFS, object storage, etc.

[0121] Support message queues: Kafka, RabbitMQ, etc.

[0122] Support application systems: access through REST API, SDK, etc.

[0123] 2) Data synchronization mechanism:

[0124] Batch synchronization: supports both full and incremental modes

[0125] Real-time synchronization: millisecond-level latency based on log parsing

[0126] Scheduled synchronization: support cron expression configuration

[0127] Trigger synchronization: supports event triggering and API triggering.

[0128] 3) Data quality control:

[0129] Real-time verification: Real-time quality check of data

[0130] Rule engine: support custom quality rules

[0131] Problem handling: automatic repair or manual intervention

[0132] Quality Report: Generate data quality reports regularly.

[0133] 3. Data management module

[0134] This embodiment has established a complete data management system that can achieve full life cycle management of data. In terms of metadata management, the system builds a unified metadata warehouse to collect and manage technical metadata (including database table structure, field attributes, storage location, etc.), business metadata (including business description, indicator caliber, business rules, etc.), and operation and maintenance metadata (including task configuration, monitoring indicators, operation logs, etc.). Through metadata management, unified management and retrieval of data assets are achieved, and data traceability and impact analysis are supported. In terms of master data management, the system provides a master data modeling tool that supports users to define master data models and standards, and ensures the consistency of enterprise core data through a master data synchronization mechanism. In terms of data modeling, the system provides a visual modeling tool that supports the design and conversion of conceptual models, logical models, and physical models, and ensures the rationality of the design through model verification. In terms of data lineage analysis, the system automatically records and analyzes the dependencies in the data flow process, and displays the data lineage in a visual way to help users understand the data flow path, which is convenient for problem location and impact analysis.

[0135] like Figure 3 As shown, in a specific implementation, the data management module implements unified management of enterprise data assets, including metadata management 301, master data management 302, data modeling 303, and data lineage 304.

[0136] The specific functions are as follows:

[0137] 1) Metadata management:

[0138] Technical metadata: table structure, field attributes, etc.

[0139] Business metadata: business description, indicator caliber, etc.

[0140] Operation and maintenance metadata: task configuration, monitoring indicators, etc.

[0141] Data standards: naming standards, coding standards, etc.

[0142] 2) Master Data Management:

[0143] Data model: unified master data model

[0144] Data standards: master data standards and specifications

[0145] Data synchronization: real-time synchronization of master data

[0146] Data quality: Master data quality control.

[0147] 3) Data Modeling:

[0148] Conceptual Model: Business Concepts and Relationships

[0149] Logical model: entity attributes and relationships

[0150] Physical Model: Storage Structure Design

[0151] Version management: Model version control.

[0152] 4) Data lineage:

[0153] Data Source

[0154] Data Flow

[0155] Data impact.

[0156] 4. Data service module

[0157] This embodiment provides rich data service capabilities and supports diverse data application scenarios. In terms of API services, the system provides a unified API management platform that supports multiple service protocols such as REST, GraphQL, WebSocket, and implements unified management, security control, and load balancing of services through the API gateway. In terms of service development, the system provides visual development tools that support drag-and-drop service orchestration, as well as a script development environment that supports multiple development languages ​​such as SQL and Python. The system has built-in commonly used data processing components, including data conversion, filtering, aggregation, etc., to facilitate users to quickly build data services.

[0158] In data analysis, K- means Clustering algorithm, K- means Clustering algorithm is an unsupervised machine learning algorithm used to divide a data set into several different clusters. The data points in each cluster have similar characteristics, while the data points between different clusters are quite different. In the data analysis module, the K-means algorithm is used to group the data and divide the data set into k clusters. The goal is to minimize the following objective function:

[0159]

[0160] in,

[0161] J represents the objective function value, which indicates the clustering cost (the smaller the better).

[0162] k represents the number of clusters.

[0163] C i Represents the i-th cluster, which contains the set of data points.

[0164] μ i is the centroid of cluster C_i.

[0165] ||x-μ i|| represents the Euclidean distance between the data point x and the centroid. Euclidean distance is a common way to measure the distance between data points and centroids in K-means clustering.

[0166] Data visualization. The system provides interactive analysis tools, supports ad hoc query and visual analysis, and provides efficient multi-dimensional analysis capabilities through the OLAP engine. Data visualization is an important means to support decision-making. Especially when the data dimension is high, direct visualization becomes very difficult.

[0167] In data visualization construction, principal component analysis (PCA) is used. PCA is mainly used in data analysis modules for dimensionality reduction, feature extraction, redundancy removal, visualization, and key indicator identification, so that the distribution and characteristics of complex data can be intuitively displayed on a two-dimensional or three-dimensional plane. This can help analysts better understand and assist in decision making.

[0168] The dimension of the data can be reduced by principal component analysis, the goal of which is to maximize the variance of the projected data, as follows:

[0169] Z = XW;

[0170] Among them, Z is the projected data, X is the original data matrix, and W is the eigenvector matrix.

[0171] In terms of data sharing, the system implements a data resource directory, provides unified display and management of data resources, supports data authorization and subscription, and promotes the effective circulation and reuse of data. At the same time, the system provides comprehensive service monitoring and management functions, including service call monitoring, performance analysis, capacity management, etc., to ensure the stability and reliability of data services.

[0172] like Figure 4 As shown, in a specific implementation, the data service module provides unified data service capabilities, including API service 401, data development 402, data analysis 403, and service monitoring 404. API service 401 includes interface management, service registration, and permission control; data development 402 includes development environment, task scheduling, and code management; data analysis 403 includes ad hoc query, visual analysis, and report generation; service monitoring 404 includes performance monitoring, alarm management, and log analysis.

[0173] The specific implementation is as follows:

[0174] 1) API service:

[0175] REST API: Standard RESTful interface

[0176] GraphQL: A Flexible Query Language

[0177] WebSocket: Real-time data push

[0178] RPC: High-performance remote call

[0179] Automatic document generation: Swagger documentation

[0180] 2) Data development:

[0181] Visual development: drag-and-drop development interface

[0182] Script development: SQL, Python, etc.

[0183] Debugging tools: breakpoint debugging, log viewing

[0184] Version management: code version control

[0185] Release deployment: Automated release process

[0186] 3) Data analysis:

[0187] Ad hoc query: interactive query analysis

[0188] Report development: visual report design

[0189] Data export: export in multiple formats

[0190] Permission control: fine-grained access control.

[0191] 5. Data security module

[0192] like Figure 5 As shown, in a specific implementation, the data security module implements a comprehensive data security protection mechanism, including access control 501, data desensitization 502, security audit 503, and risk monitoring 504; access control 501 includes identity authentication, authority management, and access policy; data desensitization 502 includes sensitive data identification, desensitization rules, and desensitization processing; security audit 503 includes operation logs, access records, and compliance checks; risk monitoring 504 includes anomaly detection, risk assessment, and early warning response.

[0193] The specific implementation includes:

[0194] 1) Access control implementation:

[0195] Unified authentication: support multiple authentication methods (LDAP, OAuth2.0, etc.)

[0196] Permission management: Permission control based on RBAC model

[0197] Data permissions: Support fine-grained control at the row and column level

[0198] Access audit: record all access operations

[0199] Dynamic authorization: supports temporary permission granting.

[0200] 2) Data desensitization implementation:

[0201] Static desensitization: desensitization during storage

[0202] Dynamic desensitization: desensitization is performed during query

[0203] Desensitization rules: support multiple desensitization algorithms

[0204] Desensitization strategy: Dynamic desensitization based on roles

[0205] Key Management: Secure key storage and management.

[0206] 3) Security monitoring implementation:

[0207] Real-time monitoring: monitoring abnormal access behavior

[0208] Risk Analysis: Rule-based risk assessment

[0209] Alarm notification: multi-channel alarm push

[0210] Security Report: Generate security reports regularly

[0211] Emergency response: Quickly handle security incidents.

[0212] 6. System monitoring module

[0213] like Figure 6 As shown, in a specific implementation, the system monitoring module realizes comprehensive operation and maintenance monitoring capabilities, including performance monitoring 601, log management 602, operation and maintenance management 603, and alarm center 604; performance monitoring 601 includes resource monitoring, performance indicators, and load balancing, and monitoring indicators include CPU usage, memory usage, network traffic, and response time; log management 602 includes log collection, log analysis, and log storage, and log types include system logs, application logs, security logs, and operation logs; operation and maintenance management 603 includes task scheduling, configuration management, and system maintenance, and maintenance tasks include backup and recovery, version updates, and fault handling; the alarm center 604 includes alarm rules, alarm triggering, and alarm processing, and alarm levels include general alarms, important alarms, and emergency alarms.

[0214] Specifically include:

[0215] 1) Performance monitoring:

[0216] Resource monitoring: CPU, memory, disk, etc.

[0217] Service monitoring: service status and performance

[0218] Interface monitoring: API call status

[0219] Task monitoring: data processing tasks

[0220] Alarm settings: Threshold alarm configuration.

[0221] 2) Log management:

[0222] Log collection: unified collection of multi-source logs

[0223] Log analysis: real-time log analysis

[0224] Log retrieval: full-text retrieval capability

[0225] Log archive: Historical log archive

[0226] Log audit: operation log audit.

[0227] 3) Operation and maintenance management:

[0228] Configuration Management: Unified Configuration Center

[0229] Deployment management: automated deployment

[0230] Backup and recovery: regular data backup

[0231] Capacity planning: resource capacity forecasting

[0232] Problem diagnosis: Quickly locate faults.

[0233] The present invention has the following significant advantages and beneficial effects:

[0234] 1) Significantly improve data management efficiency:

[0235] Achieve unified management of enterprise data and eliminate data silos; standardize data processing procedures to improve data processing efficiency; automate data synchronization mechanisms to reduce manual intervention; unify metadata management to improve data asset management efficiency; and use visual management tools to reduce management difficulty.

[0236] 2) Comprehensively ensure data quality:

[0237] The whole process quality control mechanism ensures data accuracy; real-time quality monitoring detects problems in a timely manner; automated quality repair improves processing efficiency; a complete quality assessment system quantifies quality levels; and traceable data lineage facilitates problem location.

[0238] 3) Provide flexible data services:

[0239] Unified service interface simplifies development and integration; rich service types meet diverse needs; visual development tools improve development efficiency; sophisticated permission control ensures data security; and a complete monitoring mechanism guarantees service quality.

[0240] 4) Enhanced data security protection:

[0241] Unified security framework, all-round protection; fine-grained permission control to prevent data leakage; complete audit mechanism to track data usage; flexible desensitization strategy to protect sensitive data; multiple backup mechanisms to ensure data security.

[0242] 5) Support business innovation and development:

[0243] Provide data support to assist decision-making analysis; promote data sharing and drive business collaboration; accelerate data circulation and improve response speed; tap into data value and drive business innovation; accumulate data assets and enhance corporate competitiveness.

[0244] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A data center for integrated enterprise information management, characterized in that: include: The data integration module adopts a plug-in collection architecture, realizes unified access to multi-source heterogeneous data through a variety of different data source adapters, and ensures real-time data synchronization; The sources of the multi-source heterogeneous data include: relational database, big data component, message middleware, and application system interface; Data management module, used for full life cycle management of data, including metadata management, master data management, data modeling and data lineage analysis; The data service module is used to provide a unified API management platform, realize unified management, security control and load balancing of services through the API gateway, provide visual development tools, and provide built-in data processing components for users to quickly build data services; Data security module, used to ensure data security and access control; System monitoring module, used to monitor the system operation status.

2. The data center for integrated enterprise information management according to claim 1, characterized in that: The data integration module includes: Data collection unit, used to provide batch, real-time and incremental collection methods; Data conversion unit, used for data cleaning, conversion and standardization; Data quality control unit, which is used for data quality inspection and processing, including data integrity check, consistency check, accuracy verification, and provides flexible quality control strategy configuration through the quality rule engine; The data synchronization unit is used to provide multiple synchronization modes, including full synchronization, incremental synchronization, and real-time synchronization, and automatically adjust the synchronization strategy according to the data volume and system load; The data storage unit is used for data storage and supports multiple storage methods and data types.

3. The data center for integrated enterprise information management according to claim 2, characterized in that: The data management module comprises: The metadata management unit is used to build a unified metadata warehouse to collect and manage technical metadata, business metadata, and operation and maintenance metadata, realize unified management and retrieval of data assets, and provide data traceability and impact analysis; The master data management unit is used to provide master data modeling tools for users to define master data models and standards, and ensure the consistency of enterprise core data through the master data synchronization mechanism; Data modeling unit, which is used to provide visual modeling tools, support the design and conversion of conceptual models, logical models, and physical models, and ensure the rationality of the design through model verification; The data lineage unit is used to automatically record and analyze the dependencies in the data flow process, and to display the data lineage in a visual way to help users understand the data flow path and facilitate problem location and impact analysis.

4. The data middle platform for integrated enterprise information management according to claim 3, characterized in that: The data service module includes: API service unit, used to provide standardized data access interface; Data development unit, used to provide custom data processing logic; Data analysis unit, used to provide data analysis and mining capabilities; Data sharing unit, used to achieve secure data sharing; The service monitoring unit is used to monitor the service call status.

5. The data middle platform for integrated enterprise information management according to claim 4, characterized in that: The data security module comprises: Access control unit, used to manage data access permissions; Data desensitization unit, used to protect sensitive data; Audit log unit, used to record data operation logs; Security policy unit, used to formulate data security policies; The risk monitoring unit is used to monitor data security risks.

6. The data middle platform for integrated enterprise information management according to claim 5, characterized in that: The system monitoring module includes: Performance monitoring unit, used for resource monitoring, service monitoring, interface monitoring, task monitoring, and alarm setting; Log management unit, used for log collection, log analysis, log retrieval, log archiving, and log auditing; Operation and maintenance management unit, used for configuration management, deployment management, backup and recovery, capacity planning, and problem diagnosis.

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