期刊文献+

农业大数据与农产品监测预警 被引量:83

Agricultural Big Data and Monitoring and Early Warning of Agricultural Products
原文传递
导出
摘要 随着海量信息的爆发,农业跨步迈人大数据时代。在大数据的推动下,农业监测预警工作的思维方式和工作范式发生了根本性的变化,农产品监测预警的分析对象和研究内容更加细化、数据获取技术更加便捷、信息处理技术更加智能、信息表达和服务技术更加精准。伴随大数据技术在农产品监测预警领域的广泛应用,构建农业基准数据、开展农产品信息实时化采集技术研究、构建复杂智能模型分析系统、建立可视化的预警服务平台等将成为未来农产品监测预警发展的重要趋势。在大数据时代,农产品监测预警工作应该形成大思维,开展大合作,迎接大挑战。 With the outbreak of mass information,agriculture has stepped into an era of big data.Under the impetus of the big data,fundamental changes have taken place between the way of thinking and working paradigm about agricultural monitoring and early warning,the analytic target and research contents become more tessellate,data acquisition become more convenient,information processing technology become more intelligent,information expression and service technology are more accurate.With the wide utilization of big data technology in the field of monitoring and early warning for agricultural products,building agricultural benchmark data,studying agricultural product information real-time acquisition technology,constructing complex intelligent model analysis system,and establishing visual warning service platform,etc.become the important trend for agricultural product monitoring and early warning development in the future.In the era of big data,agricultural product monitoring and early warning work should form a big mind,carry out big cooperation and welcome big challenge.
作者 许世卫
出处 《中国农业科技导报》 CAS CSCD 北大核心 2014年第5期14-20,共7页 Journal of Agricultural Science and Technology
基金 农业部948项目(2014-S2) 农业部农业信息预警专项资助
关键词 农业大数据 农产品监测预警 变革 发展 agricultural big data monitoring and early warning of agricultural products reform development
  • 相关文献

参考文献15

  • 1汪洋谈大数据[EB/OL].http://miit.ccidnet.com/art/32661/20140114/5325641-1.html,2014-01-14.
  • 2阿尔文托夫勒.第三次浪潮[M].北京:新华出版社,2006.
  • 3Manyika J, Chui M, Brown B, et al.. Big data: The Next Frontier for Innovation, Competition, and Productivity [ R ]. Mckinsey & Co, 2011.
  • 4科学研究的第四范式[N/OL].http://eper.gmw.cn/zhdsb/html/2012-11/14/nw.D110000zhdsb_20121114-1-20.htm?div=1.2012-11-14.
  • 5维克托·迈尔-舍恩伯格.大数据时代:生活、工作与思维的大变革[M].杭州:浙江人民出版社,2012(12).
  • 6孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(1):146-169. 被引量:2372
  • 7王珊,王会举,覃雄派,周烜.架构大数据:挑战、现状与展望[J].计算机学报,2011,34(10):1741-1752. 被引量:615
  • 8中国互联网络发展状况统计报告[EB/OL].新华网.http://news.xinhuanet.com/tech/2014-01/16/c_126015636.htm.2014-1-16.
  • 9Ginsberg J, Mohebbi M H, Pate! R S, et al.. Detecting influenza epidemics using search engine query data[ J ] .Nature, 2009, 457 (7232) : 1012-1014.
  • 10张崇,吕本富,彭赓,刘颖.网络搜索数据与CPI的相关性研究[J].管理科学学报,2012,15(7):50-59. 被引量:99

二级参考文献283

  • 1陈云坪,赵春江,王秀,马金锋,田振坤.基于知识模型与WebGIS的精准农业处方智能生成系统研究[J].中国农业科学,2007,40(6):1190-1197. 被引量:29
  • 2刘金全,邵欣炜.流动性约束与消费行为关系的实证研究[J].管理科学学报,2004,7(4):90-94. 被引量:12
  • 3蒋雪松,王剑平,应义斌,李延斌.用于食品安全检测的生物传感器的研究进展[J].农业工程学报,2007,23(5):272-277. 被引量:55
  • 4[OL].<http://hadoop.apache.org.>.
  • 5WinterCorp: 2005 TopTen Program Summary. http:// www. wintercorp, com/WhitePapers/WC TopTenWP. pdf.
  • 6TDWI Checklist Report: Big Data Analytics. http://tdwi. org/research/2010/08/Big-Data-Analytics, aspx.
  • 7Chaudhuri S, Dayal U. An overview of data warehousing and OLAP technology. SIGMOD Rec, 1997,26(1): 65-74.
  • 8Madden S, DeWitt D J, Stonebraker M. Database parallelism choices greatly impact scalability. DatabaseColumn Blog. http://www, databasecolumn, com/2007/10/database-parallelism-choices, html.
  • 9Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters//Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI ' 04). San Francisco, California, USA, 2004: 137-150.
  • 10DeWitt D J, Gerber R H, Graefe G, Heytens M L, Kumar K B, Muralikrishna M. GAMMA--A high performance dataflow database machine//Proceedings of the 12th International Conference on Very Large Data Bases (VLDB' 86). Kyoto, Japan, 1986:228-237.

共引文献4314

同被引文献1012

引证文献83

二级引证文献739

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部