基于Hadoop的产品制造过程的碳足迹核算研究
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摘要
为准确核算产品制造过程中的碳足迹,首先采用Hadoop分布式存储技术存储产品加工数据,将碳足迹核算所需的产品实时加工数据进行预处理后,按照规定存储策略存储到分布式文件系统中,解决了碳足迹核算基础数据的来源问题。然后采用MapReduce分布式计算作为碳足迹核算的具体核算方式,根据产品各加工阶段的碳排放特性建立准确的碳排放核算模型,并将模型转化成MapReduce函数,快速获取制造过程的碳排放量。最后将所提理论应用到阶梯轴粗车阶段的碳足迹核算,结果表明所提理论可行。
In order to calculate the carbon footprint of the product manufacturing process accurately,the Hadoop distributed storage technology was firstly applied to store product processing data.After preprocessing of product real-time processing data required by carbon footprint accounting,the data were stored in the distributed file system according to the required storage strategy,which solved the basic data source problem of carbon footprint accounting.Then,MapReduce distributed computing was used as a method of calculating carbon footprint.Accurate carbon emission accounting model was established according to the carbon emission characteristics of each product processing stage,and then the model was converted to MapReduce function to rapidly get carbon emission of manufacturing process.Finally,the theory was applied to calculate carbon footprint in the rough turning stage of stepped shaft,which verified feasibility of the proposed method.
In order to calculate the carbon footprint of the product manufacturing process accurately,the Hadoop distributed storage technology was firstly applied to store product processing data.After preprocessing of product real-time processing data required by carbon footprint accounting,the data were stored in the distributed file system according to the required storage strategy,which solved the basic data source problem of carbon footprint accounting.Then,MapReduce distributed computing was used as a method of calculating carbon footprint.Accurate carbon emission accounting model was established according to the carbon emission characteristics of each product processing stage,and then the model was converted to MapReduce function to rapidly get carbon emission of manufacturing process.Finally,the theory was applied to calculate carbon footprint in the rough turning stage of stepped shaft,which verified feasibility of the proposed method.
引文
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[2]孙良峰,裘乐淼,张树有,等.面向低碳化设计的复杂装备碳排放分层递阶模型[J].计算机集成制造系统,2012,18(11):2381-2390.
[3]刘琼,田友全,周迎东,等.产品制造过程碳足迹核算及其优化问题[J].中国机械工程,2015,26(17):2336-2343.
[4]Fang K,Uhan N,Zhao F,et al.A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction[J].Journal of Manufacturing Systems,2012,30(4):234-240.
[5]张翠侠,刘从虎,张明玉,等.机械加工车间碳足迹监测与预警方法及关键技术[J].计算机集成制造系统,2015,21(10):2677-2686.
[6]刘莉,徐玉生,马志新.数据挖掘中数据预处理技术综述[J].甘肃科学学报,2003,15(41):117-119.
[7]栾景超,马志强,李昊甦,等.Hadoop分布式文件系统资源管理器的设计与实现[J].科研信息化技术与应用,2014,5(1):41-52.
[8]杨震宇.基于MapReduce框架下的数据挖掘方法研究[J].中国高新技术企业,2017(4):8-10.
[9]Spatari S,Betz M,Florin H,et al.Using GaBi 3to perform life cycle assessment and life cycle engineering[J].The International Journal of Life Cycle Assessment,2001,6(2):81-84.
[10]张肖,向晓东,刘汉杰,等.钢铁行业碳排放量核算方法的实证性研究[J].工业安全与环保,2012,38(6):86-88.
[11]梁国熙,梁耀彰,陈海寿,等.香港中小企业碳审计工具箱[EB/OL].(2010-02-01)[2017-08-18].http://www6.cityu.edu.hk/aerc/sme/images/sme_chi.pdf.
[12]刘飞,刘霜.机床服役过程机电主传动系统的时段能量模型[J].机械工程学报,2012,48(21):132-140.