Real-time data platform based on KKS encoding rules and big data architecture

Abstract

The invention provides a real-time data platform based on KKS coding rule and big data structure, which uniformly codes all monitoring indexes through a coding management system, and provides a set ofuniversal codes for the whole platform, and each power plant uses the same codes, thereby avoiding the problem that data cannot be smoothly docked. Data monitoring and processing system can monitor many equipments in many power plants at the same time, and cache the monitoring data through the way of establishing database, so it can deal with massive data efficiently and in high real-time. Data output management system achieves the information output and release to different targets through the shared channel, which effectively improves the compatibility of the platform. Through the above design, the problems existing in the use of the traditional data monitoring system are effectively solved, so that the data monitoring system is more perfect and can meet more use needs.

Description

technical field [0001] The invention belongs to the technical field of power plant monitoring and management, and in particular relates to a real-time data platform based on KKS coding rules and big data architecture. Background technique [0002] The real-time data platform based on KKS coding and big data architecture, referred to as KDM, is a platform software system for real-time data aggregation, sharing and mining of power station equipment. The KDM system is based on the SOA architecture, which can be adapted to the deployment needs of power plants, secondary companies and groups. At the same time, the service components in the platform have high scalability, stability and reliability, and can adapt to power plants with different data volumes, Secondary company and group side. The KDM system supports multiple deployment schemes, modular services can be flexibly assembled, and functions can be continuously expanded and updated. Using the KDM platform can quickly buil...

AI Technical Summary

Problems solved by technology

[0004] 1. The codes of the various power plants under the group are inconsistent, resulting in various problems in data docking;

[0005] 2. In an enterprise, there are often more than one system, some of which generate data, others are responsible for consuming data, and there are often systems dedicated to storing data, collecting data, data storage, and data processing to form a data flow. Traditional Data flow solutions often encounter challenges such as data access exceeding processing capacity, system errors, and security;

[0007] 4. Lack of offline data acquisition function, resulting in some data cannot be obtained, such as on-site inspection data, regular offline test data, etc.;

[0008] 5. The data of the power industry has the characteristics of strong time series and large amount of real-time historical data. The existing data monitoring system cannot meet the needs of processing massive data;

[0009] 6. Different databases have different ways of adding, deleting, checking and modifying data, and the compatibility of existing data monitoring systems is insufficient

Images