|本期目录/Table of Contents|

[1]易高翔,潘长城,郭建中,等.基于多源数据融合的石油罐区安全监控模型[J].中国安全生产科学技术,2014,10(3):90-94.[doi:10.11731/j.issn.1673-193x.2014.03.015]
　YI Gao xiang,PAN Chang cheng,GUO Jian zhong,et al.Study on safety monitoring model of petroleum tank farm based on multisource data fusion[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(3):90-94.[doi:10.11731/j.issn.1673-193x.2014.03.015]
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基于多源数据融合的石油罐区安全监控模型
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年3期
页码:
90-94
栏目:
职业安全卫生管理与技术
出版日期:
2014-03-28

文章信息/Info

Title:
Study on safety monitoring model of petroleum tank farm based on multisource data fusion
作者:
易高翔1;  潘长城2;  郭建中2; 王时彬3;  王如君1; 康荣学1
（1.中国安全生产科学研究院, 北京100012；2.首都经济贸易大学 安全与环境工程学院, 北京100070； 3.昆明理工大学 国土资源工程学院, 云南昆明650093 ）
Author(s):
YI Gaoxiang1;  PAN Changcheng2;  GUO Jianzhong2;  WANG Shibin3;  WANG Rujun1;  KANG Rongxue1
（1.China Academy of Safety Science and Technology, Beijing 100029, China; 2.College of Safety and Environmental Engineering, Capital University of Economics and Business, Beijing 100070, China; 3. Faculty of Land Resource，Kunming University of Science and Technology，Kunming Yunnan 650093, China）
关键词:
石油罐区; 多源数据融合; BP神经网络; 最优加权融合
Keywords:
oil tank farm;  data fusion;  BP neural network;  optimal weighted fusion
分类号:
X924.3
DOI:
10.11731/j.issn.1673-193x.2014.03.015
文献标志码:
A
摘要:
由于单一传感器在石油罐区安全监控中容易受到外界因素影响从而产生误差，为提高传感器检测结果的可靠性和罐区安全监控预警的准确性，基于多源数据融合技术，建立罐区安全状态预警模型。首先，介绍了多源数据融合技术的3个层级: 数据级融合，特征级融合和决策级融合,以及目前各领域常见的数据融合方法；其次，建立了基于最优加权融合算法的一级融合模型和基于BP神经网络算法的二级融合模型；最后，得到石油罐区安全监控数据融合模型，并为进一步的实践应用打下了理论基础。
Abstract:
Due to the single sensor in the safety monitoring of oil tank farm is easily influenced by external factors and resultes in errors, in order to improve the reliability of sensor detection and the accuracy of tank farm safety monitoring, based on the multisource data fusion technology, an early-worning model of safety status in tank farm was established. Firstly the 3 levels of multisource data fusion technology were introduced including data level fusion, feature level fusion and decision level fusion, as well as the common data fusion methods. Secondly the 1st level fusion model based on optimal weighted fusion algorithm and 2nd level fusion model based on BP neural network algorithm were established. Ffinally safety monitoring data fusion model of oil tank farm was obtained, which provides the theory basis for further practice application.

参考文献/References:

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备注/Memo

备注/Memo:
“十二五”国家科技支撑计划项目(2012BAK03B03)
更新日期/Last Update: 2014-03-30