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.
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
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