水产养殖物联网的应用评价方法  

Evaluation method for application of internet of things for aquaculture

作　　者：袁晓庆[1] 孔箐锌[2] 李奇峰[3] 李琳[1] 李道亮[1] 

机构地区：[1]中国农业大学信息与电气工程学院,北京100083 [2]农业部规划设计研究院,北京100026 [3]北京农业信息技术研究中心,北京100097

出　　处：《农业工程学报》2015年第4期258-265,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家公益性行业(农业)科研专项(200903009);"十二五"国家科技支撑计划资助项目(2011BAD21B01)

摘　　要：构建科学合理的水产养殖物联网应用评价指标体系和评价方法,是保证水产养殖物联网系统发挥最大能效的基础。为解决指标设置随意、冗余、交叉及技术指标过剩的问题,该文构建了指标筛选模型,将水产养殖物联网应用评价指标体系从40个优化到14个,用35%的指标表达了88.45%的信息,保证了指标体系的完备性和简洁性。同时,基于模糊评价法构建了水产养殖物联网应用评价模型,可对水产养殖物联网应用水平进行总体评价以及功能、性能、效益方面的评价。最后,以江苏宜兴河蟹养殖物联网和广东湛江南美白对虾养殖物联网为实例进行了验证,宜兴物联网的评价结果为优,而湛江物联网的评价结果为良,与实际情况相符,表明该研究构建的指标体系科学合理,评价方法可行,可为水产养殖物联网应用评价提供参考。Internet of things for Aquaculture is an integrated modern system based on computing and communications technology like smart sensor technology, reliable telecommunication, intelligent information processing, which can collect data and images, transmit and process data intelligently, forecast future trend and early-warning for decision support. First of all, it is a key issue to establish scientific and rational index system and evaluation method for internet of things for aquaculture to guarantee its effectiveness. With the rapid development of information technology in China, the internet of things for aquaculture has been promoted and applied in Jiangsu, Shandong, Hunan, Hubei, Zhejiang and Guangdong. However, the internet of things for aquaculture in China is at an early stage and there are some problems, which cause negative impact on the promotion and application of the system, for examples, redundant functions, high cost, unstable performances, and so on. Secondly, in order to assess internet of things for aquaculture system, index system to assess internet of things for aquaculture was built in this paper by indicator optimization model to solve randomicity, redundancy, cross-connection and overlap caused subjective selection. Three steps composed of the selection process： 1） first round selection, three categories indices of function indicator, performance indicator and effectiveness indicator, targeted to 40 indicators were selected; 2） second round selection, 40 indicators representing perception layer, transmission layer and application layer, were optimized to 26 by method proposed by Dale and Beyeler, in which the standard conformity degree of each indicator was checked one by one and indicators need to meet at least 5 standards, otherwise they will be eliminated; 3） indicator screening model, by which 26 indicators were reduced to 14, with only 35% of total indicators representing 88.45% of total information, capturing the requirements of completeness and simplicity. Thirdly, fuzzy comprehe

关 键 词：水产养殖 指标 优化 物联网 模糊评价 

分 类 号：S96[农业科学—水产养殖]