Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data

Conghui Qiu, Bo Zhao (), Suchun Liu, Weipeng Zhang, Liming Zhou, Yashuo Li and Ruoyu Guo
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Conghui Qiu: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Bo Zhao: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Suchun Liu: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Weipeng Zhang: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Liming Zhou: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Yashuo Li: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Ruoyu Guo: The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, China

Agriculture, 2022, vol. 13, issue 1, 1-11

Abstract: Because of the continuous improvement of technology, mechanization has emerged in various fields. Due to the different suitable seasons for the growth of agricultural plants, agricultural mechanization faces problems different from other industries. That is, agricultural machinery and equipment may be used frequently for a period of time, or may be idle for a long time. This leads to the aging of equipment no longer becoming regular, the maintenance time of spare parts is not fixed, the number of spare parts stored in the spare parts warehouse cannot be too large to occupy funds, and the number cannot be too small to meet the maintenance needs, so the prediction of agricultural machinery spare parts has become particularly important. Due to the lack of information, the difficulty of labeling, and the imbalance of positive and negative sample classification, this paper used a semi-supervised learning algorithm to solve the problem of agricultural machinery spare parts data classification. In order to forecast the demand for spare parts of agricultural machinery, this paper compared the IPSO-BP neural network algorithm and BP neural network algorithm. It was found that the IPSO-BP neural network was used to forecast the demand for spare parts of agricultural machinery, and the error between the predicted value and the actual value was small and met the accuracy requirements.

Keywords: semi-supervised; IPSO-BP; agricultural machinery spare parts (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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