Industrial operation system data lake construction method based on data warehouse

Abstract

The invention discloses an industrial operating system data lake construction method based on a data warehouse, which belongs to the technical field of industrial enterprise management, and comprises the following steps: respectively constructing a data storage layer, a data management layer and a data analysis layer in a data lake architecture, the data analysis layer is divided into a source pasting layer, a uniform data warehouse layer and an application data layer, and the data analysis layer is constructed by adopting a real-time analysis component, an offline analysis component and an inner layer analysis component. According to the industrial operation system data lake construction method based on the data warehouse, equipment state data, operation data and equipment asset data are acquired through an equipment access tool, production data, processing data and supply and demand data of a production and manufacturing system are acquired by adopting a system intervention tool, and files and model data are acquired by applying an intelligent filling tool. Unified storage, analysis and management are carried out on the data, and a data basis is provided for rapid intervention of industrial resources and agile development of applications.

Description

technical field [0001] The invention belongs to the technical field of industrial enterprise management, and specifically relates to a data warehouse-based industrial operating system data lake construction method. Background technique [0002] With the transformation of the industrial economy into a digital economy, industrial enterprises urgently need to improve asset transparency and improve the efficiency of manufacturing lifecycle management. Aiming at the challenges of incomplete user data sources, insufficient correlation between data, and low-quality data analysis, it is urgent to provide high-efficiency, low-latency data asset management services to realize seamless storage, archiving, integrated extraction, and transmission of industrial data load. Meet cross-application and cross-platform data sharing requirements, support users to query asset data in real time, monitor equipment status, and realize data-driven equipment preventive maintenance and asset managemen...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, the big data architecture is directly applied in the industrial field for data collection, data storage, data analysis, and data query, but no special technical system for data analysis has been established, resulting in a high time complexity for data analysis. Timely response to user analysis needs

In response to this problem, a data warehouse-based industrial operating system data lake construction method was invented, which decomposes complex data analysis tasks into multiple layers to complete, standardizes data layering, and reduces extremely repeated calculations through intermediate layer data. Raw data, solving data anomalies and data sensitivity issues

Images