基于Web数据的农业网络信息自动采集与分类系统

发布时间：2018-10-17 14:30

【摘要】：为了快速、高效地获取农业Web信息,解决信息孤岛和信息不对称的问题,重点研究了农业Web数据自动采集与抽取、基于SVM(support vector machine)的文本分类、物联网异构数据采集等技术,并采用统一建模语言(unified modeling language,UML)描述了农业网络信息自动采集与分类系统。该系统实现了农业网站、物联网数据的自动抓取和共享,为用户提供农业资讯、农产品市场行情、供求信息在线查询,环境数据实时监测和个性化信息服务等功能。应用结果表明,该系统对样本集网站的信息抓取准确率为98.2%,资讯分类准确率为92.5%,具有数据采集实时性强、用户参与度好、通用性高等特点,该系统为农业信息整合和服务提供参考。
[Abstract]:In order to obtain agricultural Web information quickly and efficiently and solve the problems of information isolation and information asymmetry, this paper focuses on the techniques of automatic collection and extraction of agricultural Web data, text classification based on SVM (support vector machine), heterogeneous data acquisition in Internet of things, etc. The agricultural network information automatic collection and classification system is described by unified modeling language (unified modeling language,UML). The system realizes the functions of agricultural website, automatic capture and sharing of Internet of things data, providing users with agricultural information, agricultural product market price, online query of supply and demand information, real-time monitoring of environmental data and personalized information service, etc. The application results show that the information capture accuracy of the system is 98.2, the accuracy of information classification is 92.5, and the system has the characteristics of strong real-time data collection, good user participation, high universality, and so on. The system provides reference for agricultural information integration and service.
【作者单位】：中国农业大学信息与电气工程学院;北京市农业物联网工程技术研究中心;
【基金】：国家高技术研究发展计划(863计划)资助项目(2013AA102306) 山东省自主创新资助项目(2014XGA13054) 中央高校基本科研业务费专项资金资助项目(2015XD001)
【分类号】：TP274.2

【相似文献】