Complex industrial automation data stream mining algorithm based on random Internet of robotic things

Abstract

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.

Application of Data Mining Technology in Enterprise Digital Human Resource Management

“…e strategic planning level mainly includes corporate leadership team development strategic plan, human resource team development strategic plan, and corporate organizational capacity development strategic plan [12]. Alloghani et al recommend that we not only focus on preemployment education, postemployment continuing education, and vocational continuing education, but also organize the on-thejob vocational training of trainees in several stages and at several levels to improve the work skills of trainees, vocational training assessment work skills, and the quality level of on-the-job training [13][14][15]. ese studies have incomplete experimental data, and the conclusions have yet to be investigated, so they are not suitable for popularization and promotion.…”

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

“…The kernelization transformed the expressions of membership, distance, and objective function to get a kernelized version of fuzzy c-means [34]- [36]. Relevant scholars have proposed the realization of three different sampling techniques, which are based on non-iterative extended sampling [37]- [39]. Relevant scholars have performed parallelization of the k-Medoids clustering algorithm based on the Map Reduce model, allowing the algorithm to effectively use the Hadoop cluster, and greatly increasing the scale of the algorithm's processing data [40], [41].…”