Article(id=1242140214571901312, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140195647201578, articleNumber=null, orderNo=11, doi=10.3981/j.issn.1000-7857.2022.24.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1650729600000, receivedDateStr=2022-04-24, revisedDate=1663603200000, revisedDateStr=2022-09-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1673426629370, onlineDateStr=2023-01-11, pubDate=1672156800000, pubDateStr=2022-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1673426629370, onlineIssueDateStr=2023-01-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078904999, creator=sys-migrate, updateTime=1774078904999, updator=sys-migrate, issue=Issue{id=1242140195647201578, tenantId=1146029695717560320, journalId=1146031591421210625, year='2022', volume='40', issue='24', pageStart='1', pageEnd='116', issueExtLink='null', onlineDate='null', pubDate='1672156800000', pubDateStr='2022-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=null, issueType=-1, specialIssue=null, createTime=1774078900489, creator='sys-migrate', updateTime=1774078900489, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=78, endPage=84, ext={EN=ArticleExt(id=1242140220070633878, articleId=1242140214571901312, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Research on topological characteristics of special equipment safety accidents time series, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=In order to reveal the nonlinear dynamic characteristics of the time series of special equipment safety accidents, taking the time series of different types of special equipment safety accidents in China from 2005 to 2020 as the research object, the visualization method was used to convert the time series of special equipment safety accidents into a topology network diagram to generate a topology network structural model; using topological network theory to analyze topological network characteristic parameters such as node degree, network density, weighted clustering coefficient, power law index, betweenness centrality, etc., the time series law of special equipment accidents was mined. The results show that: The topological networks of various special equipment safety accident time series have small-world characteristics and scale-free characteristics; the clustering coefficients of the topological networks are all large, and the community structure is obvious; the node with greater betweenness centrality corresponds to the corresponding year, the greater the probability of an accident. The topology network analysis method used in this paper can more concisely and intuitively display the topology network structure of the time series of special equipment safety accidents, and more comprehensively characterize the nonlinear dynamic characteristics of the time series of special equipment safety accidents, which can provide a theoretical basis for the prediction of special equipment safety accidents., authors=JIN Lianghai1,2,3 , XIA Lu1 , CHEN Shu1,2 , SHAO Bo1,2 , LIU Jia1 , FAN Ling1 , YAN Yuerong1 , authorsList=JIN Lianghai, XIA Lu, CHEN Shu, SHAO Bo, LIU Jia, FAN Ling, YAN Yuerong, authorCompany=1. College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China;1,2,3 , 夏露1 , 陈述1,2 , 邵波1,2 , 刘佳1 , 范玲1 , 闫月蓉1 , authorsList=晋良海, 夏露, 陈述, 邵波, 刘佳, 范玲, 闫月蓉, authorCompany=1. 三峡大学水利与环境学院, 宜昌 443002;
科技导报
| 论文 2022, 40(24): 78-84
特种设备安全事故时间序列拓扑网络特征研究
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晋良海1,2,3 , 夏露1 , 陈述1,2 , 邵波1,2 , 刘佳1 , 范玲1 , 闫月蓉1
作者信息
1. 三峡大学水利与环境学院, 宜昌 443002;
通讯作者:
陈述（通信作者），教授，研究方向为安全管理建设项目管理等，电子信箱：chenshu@ctgu.edu.cn
Research on topological characteristics of special equipment safety accidents time series
Affiliations
出版时间: 2022-12-28
doi: 10.3981/j.issn.1000-7857.2022.24.009
文章导航
为揭示特种设备安全事故时间序列的非线性动力特征，以2005—2020年中国不同类型特种设备安全事故时间序列为对象，采用可视图法将特种设备安全事故时间序列转化为拓扑网络图，生成拓扑网络结构模型；利用拓扑网络理论分析节点度、网络密度、加权聚类系数、幂律指数、介数中心性等拓扑网络特征参数，挖掘特种设备事故发生的时间序列规律。结果表明：各类特种设备安全事故时间序列的拓扑网络具有小世界特性和无标度特性；拓扑网络的聚类系数均较大，社团结构明显；介数中心性越大的节点，对应年份发生事故概率越大。采用的拓扑网络分析方法能更简洁、直观地展示特种设备安全事故时间序列的拓扑网络结构，更全面地表征特种设备安全事故时间序列的非线性动力特征，可为特种设备安全事故预测提供理论基础。
特种设备
/
安全事故
/
时间序列分析
/
可视图法
/
拓扑网络
In order to reveal the nonlinear dynamic characteristics of the time series of special equipment safety accidents, taking the time series of different types of special equipment safety accidents in China from 2005 to 2020 as the research object, the visualization method was used to convert the time series of special equipment safety accidents into a topology network diagram to generate a topology network structural model; using topological network theory to analyze topological network characteristic parameters such as node degree, network density, weighted clustering coefficient, power law index, betweenness centrality, etc., the time series law of special equipment accidents was mined. The results show that: The topological networks of various special equipment safety accident time series have small-world characteristics and scale-free characteristics; the clustering coefficients of the topological networks are all large, and the community structure is obvious; the node with greater betweenness centrality corresponds to the corresponding year, the greater the probability of an accident. The topology network analysis method used in this paper can more concisely and intuitively display the topology network structure of the time series of special equipment safety accidents, and more comprehensively characterize the nonlinear dynamic characteristics of the time series of special equipment safety accidents, which can provide a theoretical basis for the prediction of special equipment safety accidents.
special equipment
/
safety accidents
/
time series analysis
/
visualization method
/
topological characteristics
晋良海, 夏露, 陈述, 邵波, 刘佳, 范玲, 闫月蓉.
特种设备安全事故时间序列拓扑网络特征研究.
科技导报,
2022
, 40
(24)
: 78
-84
.
DOI: 10.3981/j.issn.1000-7857.2022.24.009
JIN Lianghai, XIA Lu, CHEN Shu, SHAO Bo, LIU Jia, FAN Ling, YAN Yuerong.
Research on topological characteristics of special equipment safety accidents time series[J].
Science & Technology Review ,
2022
, 40
(24)
: 78
-84
.
DOI: 10.3981/j.issn.1000-7857.2022.24.009
2022年第40卷第24期
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文章信息
doi: 10.3981/j.issn.1000-7857.2022.24.009
接收时间：2022-04-24
首发时间：2023-01-11
出版时间：2022-12-28
收稿日期：2022-04-24
修回日期：2022-09-20
通讯作者:
陈述（通信作者），教授，研究方向为安全管理建设项目管理等，电子信箱：chenshu@ctgu.edu.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2022.24.009
引用本文
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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