A diagnosis optimization system for grain processing based on multiple data analysis algorithms

• Main Authors: Haocheng Fang, Kang Zhou, Yilin Zou, Weilin Deng, Xin He, Jiapeng Zhou
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-11-01
• Series: Systems Science & Control Engineering
• Subjects: Grain processing; data analysis; database; information management system; decision support system
• Online Access: http://dx.doi.org/10.1080/21642583.2019.1666318

Currently, the grain processing industry excessively pursues the goal of fine processing, resulting in serious waste of grain processing enterprises, increasing raw material cost and low machining efficiency, etc. To solve problems, this paper builds a food processing and data analysis system which can perform various data analysis. The system has following functions: Grain processing database is able to store and call consumer market surveys and data of grain processing enterprises, visualizing the national diet and grain processing process. Data analysis can carry out statistical analysis of data sets extracted according to required indicators, diagnostic optimization based on Data Envelopment analysis, prediction analysis based on curve fitting, classification analysis based on clustering algorithm, etc. It can obtain conclusions of relationships between the output rate, loss rate and unit power consumption of each link, and influencing factors of grain processing links like rice milling and polishing on products. The diagnostic evaluation system can evaluate consumption of raw materials and power for different product enterprises, and improve the scheme to avoid loss caused by the mismatch between raw materials and process in production process. The production practice shows that the grain processing data analysis system designed in this paper is effective.