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作 者:李艳丽[1] 毛天宇 王志勇[1] 李明昌[1] 曹宏梅[1] Li Yanli;Mao Tianyu;Wang Zhiyong;Li Mingchang;Cao Hongmei(Tianjin Research Institute for Water Transport Engineering,Laboratory of Environmental Protection in Water Transport Engineering,Tianjin 300456,China)
机构地区:[1]交通运输部天津水运工程科学研究所水路交通环境保护技术实验室,天津300456
出 处:《环境科学与管理》2018年第12期124-127,共4页Environmental Science and Management
摘 要:为了提高港口集装箱危险品泄露污染海洋环境监测能力,提出一种大数据融合和关联特征检测的港口集装箱危险品泄露污染海洋环境监测方法。首先设计港口集装箱危险品泄露污染海洋环境监测的数据采集系统,在数据采集的基础上,构建港口集装箱危险品泄露污染的大数据统计分析模型,采用分段样本采样和回归分析方法,进行污染环境信息的融合处理,结合关联特征提取方法进行能够污染信息的聚类分析和检测,实现环境污染的准确检测。采用该方法进行港口集装箱危险品泄露污染海洋环境监测的时效性较好,准确度较高。In order to improve the monitoring of marine environment pollution caused by container dangerous goods leakage,this paper presents a method of marine environmental monitoring based on big data fusion and correlation feature detection.Firstly,the paper designs a data acquisition system for monitoring the marine environment pollution caused by the leakage of dangerous goods in port containers.On the basis of the data collection,a statistical analysis model of big data for the leakage pollution of container dangerous goods in port is constructed.The method of segmented sample sampling and regression analysis is used to deal with the pollution information.The clustering analysis and detection of the pollution information are carried out by using the association feature extraction method to realize the accurate detection of environmental pollution.The simulation results show that the method is effective and accurate in monitoring the marine environment caused by the leakage of dangerous goods in port container.
分 类 号:X834[环境科学与工程—环境工程]
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