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

DOU Shan; ZHANG Guangyu; XIONG Zhihua; WANG Huangang

doi:10.11949/j.issn.0438-1157.20181363

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

Process system engineering | 更新时间：2026-07-24

- Danger situation awareness of chemical industry park based on multiple source data fusion
- CIESC Journal Vol. 70, Issue 2, Pages: 460-466(2019)
- 作者机构：
  
  1.清华大学自动化系，北京 100084
  2.浙江航天恒嘉数据科技有限公司，浙江嘉兴 314201
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181363
  CLC： TP 391
- Received：28 November 2018，
  
  Revised：2018-12-04，
  
  Online First：16 June 2020，
  
  Published：05 February 2019
- 稿件说明：
移动端阅览
窦珊, 张广宇, 熊智华, 王焕钢. 基于多源数据融合的化工园区危险态势感知[J]. 化工学报, 2019, 70(2): 460-466

DOU Shan, ZHANG Guangyu, XIONG Zhihua, WANG Huangang. Danger situation awareness of chemical industry park based on multiple source data fusion[J]. CIESC Journal, 2019, 70(2): 460-466
窦珊, 张广宇, 熊智华, 王焕钢. 基于多源数据融合的化工园区危险态势感知[J]. 化工学报, 2019, 70(2): 460-466 DOI： 10.11949/j.issn.0438-1157.20181363.

DOU Shan, ZHANG Guangyu, XIONG Zhihua, WANG Huangang. Danger situation awareness of chemical industry park based on multiple source data fusion[J]. CIESC Journal, 2019, 70(2): 460-466 DOI： 10.11949/j.issn.0438-1157.20181363.

摘要

化工园区存在危险品储罐、运输车等众多安全威胁，需要实时感知园区的危险态势，及时发现并排除潜在的安全威胁。传统方法是依靠危险品储罐实时监测等单一数据源进行危险识别，难以满足目前化工园区对安全状态评估的需求。从大数据分析的角度出发，整合化工园区内危险品储罐监测传感器、危险品运输车、地理信息等数据，基于危险品泄漏呈高斯扩散的特点，提出了一种多源异构数据融合的危险识别方法，实现园区的危险态势感知，并实时展示整个化工园区内的潜在危险区域。结合某化工园区的实际数据，验证了所提方法的有效性。

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.

关键词

Keywords

references

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