Data Center
Integrated Big Data Platform
Data assets have gradually become one of the core assets of power generation enterprises. The power plant is facing the problems of various information systems and data volume multiplying, Data redundancy, data coding inconsistency, data dispersion, data idleness, etc. The huge data of the power plant has not been effectively used. In order to efficiently manage and use these data, an integrated Big data platform built according to the characteristics of power generation enterprises came into being. The platform will use data as a valuable asset in core application scenarios such as equipment monitoring and early warning, equipment diagnosis, unit operation optimization, and enterprise business decision-making, which can bring great value to the work of power generation enterprises in the fields of operation, production, operation, safety, and environmental protection.
  • Data Governance
    Data governance refers to a series of concrete work carried out by treating data as the core asset of power plants, which is the full lifecycle management of enterprise data. Data governance is the basis for building a Big data platform. Its goal is to achieve a unified standard for data within the enterprise, improve the quality of data, ensure the safety, integrity and availability of data, and promote the integration, docking and sharing of data resources
  • Data Acquisition
    Solve the problem of data access, receive the data transmitted from the software system or hardware edge, and collect the data to the Big data platform. To achieve the collection of heterogeneous data from multiple sources, it is necessary to support capabilities such as multi-protocol access, data preprocessing, real-time rules, and secure encrypted transmission.
  • Storage
    The data collected from power plant DCS, SIS, and MIS systems is generally divided into three forms: sensing timing data (such as external environmental information, temperature and humidity data, pressure and flow data, vibration data, etc.), relational data (such as equipment KKS code, equipment maintenance ledger, etc.), and file data (such as stored videos, word documents, images, etc.). The Big data storage platform can realize the centralized storage of the above data, and should have the characteristics of distributed deployment and horizontal expansion.
  • Data Analysis
    The data analysis layer provides offline and Real-time computing data calculation capabilities, supports index calculation capabilities, artificial intelligence analysis and calculation capabilities, and multi-dimensional statistical analysis capabilities, and can provide methods and tools for in-depth analysis of Big data. By implementing deep analysis and mining of data in the data analysis layer, analytical models are provided for business analysis, equipment defect diagnosis, and unit operation optimization.
  • Data Collaboration
    Data collaboration provides Big data service interfaces, multi-dimensional visualization services and other capabilities. It supports the sharing of the original data in the Big data platform and the data after analysis and calculation in the form of services to external applications. At the same time, it realizes centralized management and control of services, reduces repetitive development work, and improves the reuse rate of data and models.
  • Centralized Resource Monitoring
    Through a centralized resource monitoring platform, all IT infrastructure, including hard resources such as hosts, storage, routers Different types of network devices such as switches, firewalls, and load balancing, as well as basic software platforms such as operating systems and data Fully unify according to the middle platform and middleware, as well as application resources (such as business applications, network applications, and communication applications) Monitoring and visual management enable users to understand and master the current health status of network hardware and software in real-time, ensuring the use of Reliable operation of household business and meeting various assessment indicators, predicting potential faults, and providing early warning.
  • Unified authentication management
    Unified authentication provides a centralized access point for enterprise applications, achieving high-strength multi factor authentication technology to ensure application security Certification security. Establish a centralized and high-strength security certification center to safeguard users with a unified security certification strategy and technology Certification security; By defining the access standards and standards for various modules and business applications in the data center, the current mainstream authentication is achieved Protocol and authentication technology, supporting heterogeneous application integration; By using the above techniques, information such as personnel, institutions, and permissions can be Unified management to solve unified login, authentication, and information authentication security issues for both new and old systems.
  • Business application layer
    The Big data platform can effectively support the construction of smart power plant applications in various fields, including but not limited to: business decision analysis, production operation management, equipment condition maintenance system, unit operation optimization system, equipment maintenance management system, etc. The application system realizes efficient reuse of resources and reduces system construction time and cost by reusing relevant capabilities already built in the Big data platform.