段青玲,刘怡然,张璐,李道亮.水产养殖大数据技术研究进展与发展趋势分析[J].农业机械学报,2018,49(6):1-16.
DUAN Qingling,LIU Yiran,ZHANG Lu,LI Daoliang.State-of-the-art Review for Application of Big Data Technology in Aquaculture[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(6):1-16.
摘要点击次数: 2585
全文下载次数: 2198
水产养殖大数据技术研究进展与发展趋势分析   [下载全文]
State-of-the-art Review for Application of Big Data Technology in Aquaculture   [Download Pdf][in English]
投稿时间:2018-05-03  
DOI:10.6041/j.issn.1000-1298.2018.06.001
中文关键词:  水产养殖  大数据技术  数据分析与挖掘
基金项目:北京市科技计划项目(Z171100001517016)和公益性行业(农业)科研专项(201203017)
作者单位
段青玲 中国农业大学 
刘怡然 中国农业大学 
张璐 中国农业大学 
李道亮 中国农业大学 
中文摘要:水产养殖对象特殊、环境复杂、影响因素众多,精准地监测、检测和优化控制极其困难。大数据技术结合数学模型,把水产养殖产生的大量数据加以处理和分析,并将有用的结果以直观的形式呈现给生产者与决策者,是解决上述难题的根本途径。本文主要对水产养殖大数据技术研究进展与发展趋势进行了深入剖析,提出了水产养殖业大数据技术的总体架构;分析了水产养殖大数据的来源和获取手段,重点总结了几种水产养殖大数据分析技术的研究进展和现有水产养殖大数据平台及其提供的应用服务;最后针对水产养殖与大数据技术结合过程所面临的困难与挑战,从实现全面感知、全产业链数据智能分析与自动决策、水产养殖大数据标准体系建设等方面提出水产养殖大数据技术的发展方向。数据是根本,分析是核心,利用大数据技术提高水产养殖综合生产力和效益是最终目的,应深度挖掘现实需求,整合水产养殖全产业链数据,加强基础理论和核心关键技术研究,从而推进大数据技术与水产养殖产业的深度融合,支撑我国水产养殖业彻底转型升级。
DUAN Qingling  LIU Yiran  ZHANG Lu  LI Daoliang
China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:aquaculture  big data technology  data analysis and mining
Abstract:It has many difficulties in monitoring and detection accurately and optimal control in aquaculture because the targets are so special and environment is so sophisticated that contributes too many impact factors. Big data technology, as well as mathematical models are used to process and analyze the large scale of data producing in aquaculture industry and the useful results are presented to producers and decision makers in intuitive form, which is the fundamental way to solve the above problems. The research progress and development trend of the applications of big data technology in aquaculture were deeply discussed. Firstly, the overall architecture of applying big data technology in aquaculture was proposed and the data sources and data acquisition tools were listed. Then, several kinds of analysis techniques, which had been well applied to deal with the existing problems in aquaculture, were mainly summarized and the several current big data platforms and the services they provided for aquaculture were introduced. Finally, in view of solving the difficulties and challenges faced in the process of applying big data technologies in aquaculture, the research future in this field was proposed form the aspects of comprehensive awareness, intelligent analysis, automatic decision-making, and big data standard system construction of aquaculture. In the applications of big data technology in aquaculture, data is the basis and analysis is the core. The ultimate goal is to take advantage of big data technology to improve the comprehensive productivity and efficiency of aquaculture. In order to achieve it, the actual demands in aquaculture should be greatly concerned. In addition, data of the whole industry chain in aquaculture should be integrated and the basic theories and core key technologies should be studied intensively and thoroughly. In this way, the application of big data technology in aquaculture will be deeper and the integration of the two will be closer, which will support the complete transformation and upgrading of China aquaculture industry.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

   下载PDF阅读器