| 基于北斗的农机作业大数据系统构建 |
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| 引用本文： | 吴才聪,陈瑛,杨卫中,杨丽丽,乔鹏,马钦,翟卫欣,李冬,张晓强,万传峰,李光远,黄嘉华,田伟泽,范雪枫,谈陆军,苏春华.基于北斗的农机作业大数据系统构建[J].农业工程学报,2022,38(5):1-8. |
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| 作者姓名： | 吴才聪 陈瑛 杨卫中 杨丽丽 乔鹏 马钦 翟卫欣 李冬 张晓强 万传峰 李光远 黄嘉华 田伟泽 范雪枫 谈陆军 苏春华 |
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| 作者单位： | 中国农业大学信息与电气工程学院,北京 100083;农业农村部农机作业监测与大数据应用重点实验室,北京 100083,中国农业大学信息与电气工程学院,北京 100083;农业农村部农机作业监测与大数据应用重点实验室,北京 100083;北京大学工学院,北京 100871,农业农村部农业机械化总站,北京 100122 |
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| 基金项目： | 国家精准农业应用项目（No. JZNYYY001） |
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| 摘 要： | 针对全国范围农机作业动态监测和量化统计的应用需求，该研究通过在农业机械上安装北斗终端，制订数据传输规范，完成了基于北斗的农机作业大数据系统建设。该系统由农业机械及北斗终端、农机制造企业物联网平台和农机作业大数据管理服务平台3部分组成。系统共接入农机290 153辆。经数据清洗、轨迹分割和参数提取3个数据处理步骤，可获得农机的工作时长、行驶里程和作业面积等基本统计量。以2021年夏小麦机械化收割为例，利用该系统进行数据获取、处理和统计分析，输出收割机分布热力图和作业重心转移图，进行了收割时长、收割效率与收割面积等统计，分析了小麦主产区对跨区作业的依赖程度。麦收期间在线收割机累计35 243辆，日均18 568辆，收割时长中位数均值为8.3 h/d，收割面积中位数均值为5.5 hm2/d，约75%的小麦收割机进行了跨区作业，跨区距离中位数约为597 km。应用结果表明，农机作业大数据系统可准确开展数据处理和作业统计，可以向农业农村部门、农机制造企业、农机合作社和农机手提供作业动态监测和数据分析服务。
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| 关 键 词： | 农业机械 大数据 北斗 跨区作业 |
| 收稿时间： | 2021/12/1 0:00:00 |
| 修稿时间： | 2022/1/6 0:00:00 |
Construction of big data system of agricultural machinery based on BeiDou |
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| Wu Caicong,Chen Ying,Yang Weizhong,Yang Lili,Qiao Peng,Ma Qin,Zhai Weixin,Li Dong,Zhang Xiaoqiang,Wan Chuanfeng,Li Guangyuan,Huang Jiahu,Tian Weize,Fan Xuefeng,Tan Lujun,Su Chunhua.Construction of big data system of agricultural machinery based on BeiDou[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(5):1-8. |
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| Authors: | Wu Caicong Chen Ying Yang Weizhong Yang Lili Qiao Peng Ma Qin Zhai Weixin Li Dong Zhang Xiaoqiang Wan Chuanfeng Li Guangyuan Huang Jiahu Tian Weize Fan Xuefeng Tan Lujun Su Chunhua |
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| Institution: | 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China;;1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China; 3. College of Engineering, Peking University, Beijing 100871, China;; 4. Agricultural Mechanization General Station, Ministry of Agriculture and Rural Affairs, Beijing 100122, China; |
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| Abstract: | Big data analysis has offered much greater statistical power around the world in recent years, particularly with the ever-increasing information technology, such as positioning, sensing, and mobile communication. In the field of agriculture, big data has also presented the promising potential to promote the terminals of the BeiDou Navigation Satellite System on agricultural machinery in China. However, the current management can be decentralized fail to form the nationwide operation of agricultural machinery using the big data system and service scheme, due mainly to the rapid increase of the data. In this study, a new big data system of BeiDou terminals was developed to compile, the data transmission specifications of platform-to-platform for the nationwide dynamic monitoring and quantitative statistical analysis of agricultural machinery operations in China. Three parts of the big data system were also installed in the agricultural machinery of the manufacturers. The first part was involved the agricultural machinery and the BeiDou/GNSS terminals. The second part was the Internet of Things (IoTs) platform for agricultural machinery for manufacturers. The third part was the big data management and service platform for the agricultural machinery operation by the BeiDou Team of China Agricultural University. Specifically, the BeiDou terminals were used to collect the position and working condition data of agricultural machinery. The gathered information was forwarded in real time to the manufacturers, then to the big data management and service platform. There were 290 153 sets of agricultural machinery in the big data system. Data mining and statistical analysis were performed on the big data management and service platform. The information products were generated to process the operation data on the big data management and service platform using three major procedures, including data cleaning, trajectory segmentation, and parameter extraction. In addition, the basic statistics were achieved, such as the working hours, driving mileage, and working area. Taking the trajectory data generated by the wheat harvester in North China from May 31 to June 26, 2021, as an example, a further examination was conducted to verify the applicability and efficiency of the big data system. The heat map and operation center of gravity transfer diagram in the harvesters were established to extract the harvesting parameters of time, efficiency, and harvesting area. The dependence on cross-regional harvesters was then determined in the main wheat-producing areas. It was found that there were 35 243 sets of online wheat harvesters during harvesting, with a daily average of 18 568 sets. The average daily harvesting duration was approximately 8.3 h, and the harvesting area was roughly 5.5 hm2/d. Besides, around 75% of the wheat harvesters were accomplished the cross-zone mechanized harvest, 69% of which presented a cross-zone distance (Euclidean distance) of more than 300 km, and an average of 597 km. More than 50% of the fields were harvested by the harvesters in Hubei and Hebei Province. The big data system was implemented the full connection with the 290 153 sets of agricultural machinery at the end of November, 2021. As such, the location and working condition data were integrated from the major manufacturers of agricultural machinery. The big data system can be widely expected to accurately evaluate the data processing and operation. The finding can also provide for the dynamic and real-time monitoring of agricultural machinery operation and data analysis services in modern agriculture. |
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| Keywords: | agricultural machinery big data BeiDou cross-zone mechanized harvesting |
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