Green production cycle mining of mass production based on random forest algorithm

Tao Xiao, Tao Zhang and Ning Zhang

International Journal of Product Development, 2020, vol. 24, issue 2/3, 182-199

Abstract: In view of the problems of accuracy to be optimised in the research results of current production cycle mining for product, a method of green production cycle mining for mass production based on random forest algorithm is proposed. The environmental parameter information collection system of green production workshop for mass production is designed. The interest sensor collects the field information in real time, and transmits the information to the monitoring centre in the form of ZigBee and GPRS communication network. According to the collected data, the sample training model of random forest is constructed, and the decision tree is constructed with the trained data. According to the decision results, the time measurement results are dynamically combined to obtain the results of green production cycle mining for mass production. The simulation results show that this method has high accuracy and feasibility.

Keywords: random forest algorithm; product; green production cycle; mining. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=110269 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpdev:v:24:y:2020:i:2/3:p:182-199

Access Statistics for this article

More articles in International Journal of Product Development from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().