Part of the source of industrial big data is the data in the field of production and operation, and a large part is the machine data generated in the operation process of production equipment and high-end products and equipment produced.
And the real big data is not the data, the data after the access to save can be done, the real thing is intelligent analysis and intelligent decision, through the integration of the two on the basis of the intelligent analysis optimization system "industrial brain" to carry out the corresponding intelligent decision.
These intelligent analysis and decision-making cannot be separated from the support of the original information system and automation system, but also cannot be separated from the physical equipment and equipment that produce these data. Based on the environment data where the data is integrated, a big data system with intelligent analysis and optimization ability is built on the basis of the information management system and automation system to achieve the purpose of improving quality, increasing efficiency, reducing consumption and controlling risks.
Industrial big data can be divided into three categories. One part is industrial Internet of Things data, such as the data generated by production equipment, intelligent products and complex equipment 24 hours a day. Part of the enterprise informatization data, and an important part of the data is the external data across the industrial chain, including the environmental data of the equipment in the operation process, such as meteorological data, geographical data, and corresponding environmental data. Only when these three kinds of data are combined can they be called industrial big data.
How to use data to drive. The first is to look at what kind of data we have now, where they come from, how to collect them if we don't have them, what are the characteristics of these data, such as time series data, time space data, data generated by intelligent products and data generated by production equipment, and how much data is in the end. The second is to understand the data, the data how to save, management, use, another is more important is how to ensure the quality of the data. The third is to use what kind of system, what kind of tools to ensure data storage, data management, data processing? At the same time, how to integrate and associate these data is not only to analyze and manage the data generated by the equipment, but also to associate the surrounding environmental data, geographic data and other cross-border data in the process of analysis.
