基于多源数据融合的化工园区危险态势感知

doi:10.11949/j.issn.0438-1157.20181363

Abstract

Abstract:

There are many safety threats in the chemical industry park, such as dangerous goods storage tanks and transport vehicles. The danger situation in the park need to be sensed in real time and potential safety threats must be discovered and eliminated in time. The traditional method relies on a single data source such as real-time monitoring of dangerous goods storage tanks for hazard identification, which is difficult to meet the current needs of the chemical park for safety status assessment. From the point of view of big data analysis, this paper integrates the data of dangerous goods storage tank sensors, dangerous goods transportation(DGT) and geographic information in the chemical park. Based on the characteristics of Gaussian diffusion of dangerous goods leakage, a multi-source heterogeneous data fusion method is proposed. The danger situation identification method realizes the dangerous situation awareness of the park and displays in real time the potential dangerous areas in the entire chemical park. Combined with the actual data of a chemical park, the effectiveness of the proposed method is verified.

Key words: data fusion, hazard identification, Mahalanobis distance, numerical analysis, safety, integration

CLC Number:

TP 391

Shan DOU, Guangyu ZHANG, Zhihua XIONG, Huangang WANG. Danger situation awareness of chemical industry park based on multiple source data fusion[J].CIESC Journal, 2019, 70(2): 460-466.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Table 1

Hazard level description"

Item	传感器报警	传感器不报警
D $<$ d	一	三
D $≥$ d	二	四

Table 1

Fig.4

Fig.5

Fig.6

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Danger situation awareness of chemical industry park based on multiple source data fusion

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