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基于Hadoop的云环境下作物生长模型算法的实现与测试
  • ISSN号:1002-6819
  • 期刊名称:《农业工程学报》
  • 时间:0
  • 分类:S126[农业科学—农业基础科学]
  • 作者机构:[1]南京农业大学信息科技学院,南京210095, [2]南京农业大学/国家信息农业工程技术中心,南京210095
  • 相关基金:国家自然科学基金(30971697); 国家863计划(2013AA100404); 国家科技支撑计划(2011BAD21B03); 江苏高校优势学科建设工程资助项目(PAPD)
中文摘要:

为了提高作物生长模型的计算速度,该文提出了云环境下作物生长模型算法的实现方案。综合分析了作物生长模型和子模型之间的数据依赖关系,以及不同并行计算方法的特点。以云计算基础架构开源软件Hadoop为基础,设计云环境下作物生长模型处理方案。以小麦生长模型WheatGrow为测试对象,在真实云环境下,验证了该方案的有效性。研究表明,在处理作物生长模型这类具有复杂数据依赖关系问题时,当区域数据点较多,需采用数据并行计算方法;且区域数据点越多,加入计算的计算结点越多,越能体现出MapReduce在并行计算上具有的可扩展性。研究可为促进作物生长模型和数字农业的发展提供参考。

英文摘要:

As the inputs of the crop growth model increased, based on data of multiple sites, weather, and soil, and especially when dealing with massive regional data, the response time of the model gets longer. After a parallel computation scheme of cloud computing was selected in this paper, considering the large amount of weather data, an algorithm of crop growth model based on Cloud Computing was proposed to improve parallel computation speed and response time of the crop growth model. First, the authors analyzed the Crop growth model and data dependence relationships among sub-models, and then summarized different parallel computation schemes. From a system constitution perspective, the crop growth model included model description, model structure, model algorithm, and forcing data. Complex data dependence relations between sub-models and among computing units in the sub-models comprised independency, synchronous dependency, self-reliance, and interdependency. Parallel computation was grouped into data-intensive computing and computing-intensive computing, according to characteristics of the calculation. The former was suitable for computation tasks with large amount of data and simple computing relations, while the latter was suitable for computation tasks with little amount of data and complex computing relations. Second, a scheme of crop growth model based on Cloud Computing was designed on the basis of Hadoop, which is an open-source software of Cloud Computing infrastructure. The MapReduce parallel computation scheme of Crop growth model assumption was that computing tasks of all sub-models in a regional point of the same crop were viewed as a computing job, and a number of computing nodes completed crop growth process computing of multiple regional points. Hence, the granularity of MapReduce parallel computation was a regional point crop, and a computing task of crop growth model could be broken down into multiple sub-computing tasks that executed on different nodes in parallel. The object-oriented approach was

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期刊信息
  • 《农业工程学报》
  • 北大核心期刊(2011版)
  • 主管单位:中国科学技术协会
  • 主办单位:中国农业工程学会
  • 主编:朱明
  • 地址:北京朝阳区麦子店街41号
  • 邮编:100125
  • 邮箱:tcsae@tcsae.org
  • 电话:010-59197076 59197077 59197078
  • 国际标准刊号:ISSN:1002-6819
  • 国内统一刊号:ISSN:11-2047/S
  • 邮发代号:18-57
  • 获奖情况:
  • 百种中国杰出学术期刊,中国精品科技期刊,中国科协精品科技期刊工程项目期刊,RCCSE中国权威学术期刊
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国化学文摘(网络版),英国农业与生物科学研究中心文摘,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊(2004版),中国北大核心期刊(2008版),中国北大核心期刊(2011版),中国北大核心期刊(2014版),英国食品科技文摘,中国北大核心期刊(2000版)
  • 被引量:93231