Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/1/49 |
| _version_ | 1797447330703605760 |
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| author | Conghui Qiu Bo Zhao Suchun Liu Weipeng Zhang Liming Zhou Yashuo Li Ruoyu Guo |
| author_facet | Conghui Qiu Bo Zhao Suchun Liu Weipeng Zhang Liming Zhou Yashuo Li Ruoyu Guo |
| author_sort | Conghui Qiu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:54:26Z |
| format | Article |
| id | doaj.art-c13f35f32fc34f3ba98f7cd5dc88ff46 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T13:54:26Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-c13f35f32fc34f3ba98f7cd5dc88ff462023-11-30T20:44:56ZengMDPI AGAgriculture2077-04722022-12-011314910.3390/agriculture13010049Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts DataConghui Qiu0Bo Zhao1Suchun Liu2Weipeng Zhang3Liming Zhou4Yashuo Li5Ruoyu Guo6The State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaThe State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaThe State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaThe State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaThe State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaThe State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaThe State Key Laboratory of Soil-Plant-Machinery System Technology, Chinese Academy of Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaBecause 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.https://www.mdpi.com/2077-0472/13/1/49semi-supervisedIPSO-BPagricultural machinery spare parts |
| spellingShingle | Conghui Qiu Bo Zhao Suchun Liu Weipeng Zhang Liming Zhou Yashuo Li Ruoyu Guo Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data Agriculture semi-supervised IPSO-BP agricultural machinery spare parts |
| title | Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data |
| title_full | Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data |
| title_fullStr | Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data |
| title_full_unstemmed | Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data |
| title_short | Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data |
| title_sort | data classification and demand prediction methods based on semi supervised agricultural machinery spare parts data |
| topic | semi-supervised IPSO-BP agricultural machinery spare parts |
| url | https://www.mdpi.com/2077-0472/13/1/49 |
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