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

Authors: Tao Xiao; Tao Zhang; Ning Zhang

Addresses: College of Science, Hebei Agricultural University, Baoding 071001, Hebei, China ' Youth League Committee, Hebei Agricultural University, Baoding 071001, Hebei, China ' Department of Computer Science and Technology, Renai College of Tianjin University, Tianjin 301636, Jinghai, China

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.

DOI: 10.1504/IJPD.2020.110269

International Journal of Product Development, 2020 Vol.24 No.2/3, pp.182 - 199

Received: 15 Mar 2020
Accepted: 15 Jun 2020
Published online: 12 Oct 2020 *

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