Abstract
ABSTRACTIn recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream Mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness.
Highlights
The development of information technology has triggered the explosive growth of the scale of information production and the speed of communication, making human social life and scientific research enter the era of big data
We propose a complex industrial automation data stream Mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness
The traditional data import and export method cannot meet the needs of enterprise data import and export, so this paper proposes an industrial data stream mining system based on IORT
Summary
The development of information technology has triggered the explosive growth of the scale of information production and the speed of communication, making human social life and scientific research enter the era of big data. Many systems in real life are too complicated, and there is no corresponding theoretical knowledge as research support Their characteristics and behaviours cannot be understood and mastered, and traditional methods cannot play a role. The emergence of large-scale databases, especially data warehouses, provides an excellent platform for the development and application of data mining technologies. Large-scale databases and data warehouses provide a material basis for the implementation and application of data mining technologies. In terms of data accumulation, building a big data platform can optimize the ability to collect, store, mine, and apply large amounts of data in the production process through clustering. Depending on the needs of the algorithm, it is sometimes necessary to standardize and discretize the data This part of the work is very important, it directly affects the quality of the data being mined. Before extending the model, the model should be evaluated more thoroughly, examining the steps it performs and be confident that it has achieved its goals correctly
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