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基于大数据技术背景下环境影响评价的创新及应用 被引量:3

Innovation and application of environmental impact assessment based on big data technology
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摘要 进入信息时代,大数据技术已广泛应用于各个领域。大数据技术可以在环境评价中为项目提供基础数据、预测模型、计算资源等成果支持,为环境管理和决策提供非常重要的依据。本文简要分析了"新时代"和大数据制造的总体思路,以及环境影响评价中对大数据的新要求,并围绕环境影响评价大数据的创新及应用展开探讨。 Since the advent of the information age,big data technology has been widely used in various fields.Use big data technology in environmental assessment to provide basic data,prediction model,computing resources and other results for projects,and provide very important support for environmental management and decision-making.This paper briefly analyzes the general idea of "new era" and big data manufacturing,as well as the new requirements for big data in environmental impact assessment,and discusses the innovation and application of big data in environmental impact assessment.
作者 招文灿 Zhao Wencan(Guangdong Shuntian Environmental Protection Technology Co.,Ltd.Guangzhou 511450,China)
出处 《皮革制作与环保科技》 2021年第10期131-132,共2页 Leather Manufacture and Environmental Technology
关键词 大数据 环境影响评价 创新 应用 big data environmental impact assessment innovation application
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  • 1李国杰.大数据研究的科学价值[J].中国计算机学会通讯,2012,8(9):8-15.
  • 2李国杰,程学旗.赵国栋,等.2014中国大数据技术与产业发展报告[M].北京:机械工业出版社,2013:6-11.
  • 3周慧.国家发改委:资金支持大数据重大建设项目[EB/OL].2016-01-20[2016-04-08].http://news.hexun.com/2016-01-20/181906965.html.
  • 4Apache H. What is apache hadoop?[EB/OL]. 2013-08-26[2016-04-13]. http://hadoop.apache.org.
  • 5Dean J, Ghemawat S. MapReduce: Simplified data processing on large cluster[J]. Communications of the ACM, 2008, 51(1): 107-113.
  • 6Zaharia M, Chowdhury M, Das T, et al. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing[C]//Proceedings of the 9th USENIX Conference on Networked Systems Design and hnplementation. Berkeley, CA: USENIX Association, 2012: 141-146.
  • 7Lublinsky B, Smith K T, Yakubovich A. Professional hadoop solutions[M]. Birmingham: Wrox Press, 2013.
  • 8Gartner Research Report. Magic quadrant for data quality tools [EB/OL]. [2016-04-12]. http://useready.com/wp-contenffuploads/2013/07/Gartner-Data- Quality-2012.pdf.
  • 9Gonzalez J E, Low Y, Gu H, et al. Powergraph: Distributed graph-parallel computation on natural graphs[C]//Pmceedings of the 10th USENIX Sympo- sium on Operating Systems Design and Implementation. Berkeley, CA: USENIX Association, 2012: 17-30.
  • 10Engle C, Lupher A, Xin R, et al. Shark: Fast data analysis using coarse-grained distributed memory[C]//Proceedings of the 2012 ACM SIGMOD Interna- tional Conference on Management. New York: ACM, 2012.

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