Complex industrial automation data stream mining algorithm based on random Internet of robotic things
| Autor: | Lianhe Cui |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Automatika, Vol 60, Iss 5, Pp 570-579 (2019) |
| Druh dokumentu: | article |
| ISSN: | 0005-1144 1848-3380 00051144 |
| DOI: | 10.1080/00051144.2019.1683287 |
| Popis: | In 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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