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

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

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。

关键词: 数据融合, 危险识别, 马氏距离, 数值分析, 安全, 集成

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.

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.

Figures/Tables 7

Fig.1 Algorithm architecture

Fig.2 Sensor value of dangerous goods tank

Fig.3 DGT track

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 Hazard level description

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

Fig.4 Sensor weight, DGT weight and risky zones

Fig.5 Single sensor alarms(a) and sensor alarms when DGT passes(b)

Fig.6 Temporal distribution of risky-zone

References 30

1	孙爱军. 工业园区风险评价研究[D].天津：南开大学, 2011.
	SunA J. The study on accident risks in industrial park[D]. Tianjin：Nankai University, 2011.
2	VermaM, VerterV. A lead-time based approach for planning rail-truck intermodal transportation of dangerous goods[J]. European Journal of Operational Research, 2010, 202(3): 696-706.
3	KaraB Y, VerterV. Designing a road network for hazardous materials transportation[J]. Transportation Science, 2004, 38(2): 188-196.
4	PurdyG. Risk analysis of the transportation of dangerous goods by road and rail[J]. Journal of Hazardous Materials, 1993, 33(2): 229-259.
5	HanJ, PeiJ, YinY. Mining frequent patterns without candidate generation[C]//Proceedings of the 2000 ACM Sigmod International Conferenee on Management of Date. USA: ACM, 2000, 29(2): 1-12.
6	PlanasE, PastorE, PresuoF, et al. Results of the MITRA project: monitoring and intervention for the transportation of dangerous goods[J]. Journal of Hazardous Materials, 2008, 152(2): 516-526.
7	FabianoB, CurròF, ReverberiA P, et al. Dangerous good transportation by road: from risk analysis to emergency planning[J]. Journal of Loss Prevention in the Process Industries, 2005, 18(4/5/6): 403-413.
8	PurdyG. Risk analysis of the transportation of dangerous goods by road and rail[J]. Journal of Hazardous Materials, 1993, 33(2), 229-259.
9	GheorgheA V, BirchmeierJ, VamanuD, et al. Comprehensive risk assessment for rail transportation of dangerous goods: a validated platform for decision support[J]. Reliability Engineering & System Safety, 2005, 88(3): 247-272.
10	BubbicoR, CaveS D, MazzaroaB. Risk analysis for road and rail transport of hazardous materials: a simplified approach[J]. Journal of Loss Prevention in the Process Industries, 2004, 17(6): 477-482.
11	FabianoB, CurroF, ReverberiA P, et al. Dangerous good transportation by road: from risk analysis to emergency planning[J]. Journal of Loss Prevention in the Process Industries, 2005,18(4): 403-413.
12	WuZ, SunM. Statistic analysis and countermeasure study on 200 road transportation accidents of dangerous chemicals [J]. Journal of Safety Science and Technology, 2006, 2(2): 3-8.
13	AnscombeF J. Rejection of outliers[J]. Technometrics, 1960, 2(2): 123-146.
14	刘琦, 冯静, 周经伦. 基于专家信息的先验分布融合方法[J]. 中国空间科学技术, 2004, 24(3): 68-71.
	LiuQ, FengJ, ZhouJ L. Prior distribution fusion method based on expert information[J]. Chinese Space Science and Technology, 2004, 24(3): 68-71.
15	赵新勇. 基于多源异构数据的高速公路交通安全评估方法[D]. 哈尔滨: 哈尔滨工业大学,2013.
	ZhaoX Y. Highway traffic safety assessment method based on multi-source heterogeneous data[D]. Harbin :Harbin Institute of Technology, 2013.
16	AgrawalR, SrikantR. Fast algorithms for mining association rules[C]// Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 1994.
17	AgrawalR, MannilaH, SrikantR, et al. Fast discovery of association rules[J]. Advances in Knowledge Discovery and Data Mining, 1996, 12(1): 307-328.
18	LiH, WangY, ZhangD, et al. Pfp: parallel fp-growth for query recommendation[C]//Proceedings of the 2008 ACM Conference on Recommender Systems. ACM, 2008: 107-114.
19	BorgeltC. An Implementation of the FP-growth algorithm[C]//Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations. ACM, 2005: 1-5.
20	YuanN J, ZhengY, XieX. Segmentation of urban areas using road networks[R]. MSR-TR-2012-65, Tech. Rep., 2012.
21	ZhengY, LiuY, YuanJ, et al. Urban computing with taxicabs[C] //Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 2011: 89-98.
22	LiuW, ZhengY, ChawlaS, et al. Discovering spatio-temporal causal interactions in trafﬁc data streams[C]// Proceedings of 17th SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2011.
23	WangJ, ChenC, WuJ, et al. No longer sleeping with a bomb: a duet system for protecting urban safety from dangerous goods[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 1673-1681.
24	LeeR, SumiyaK. Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection[C]//Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks. ACM, 2010: 1-10.
25	SongX, ZhangQ, SekimotoY, et al. Modeling and probabilistic reasoning of population evacuation during large-scale disaster[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013: 1231-1239.
26	MahalanobisP C. On tests and measures of group divergence[J]. Journal of the Asiatic Society of Bengal, 1930, 26: 541-588.
27	De MaesschalckR, Jouan-RimbaudD, MassartD L. The mahalanobis distance[J]. Chemometrics and Intelligent Laboratory Systems, 2000, 50(1): 1-18.
28	叶冬芬, 叶桥龙, 罗玮琛. 基于高斯扩散模型的化工危险品泄露区域计算及其实现[J].计算机与应用化学, 2012, 29(2): 195-199.
	YeD F, YeQ L, LuoW S. A calculation approach and implementation of hazard chemical substance based on Gaussian diffusion model[J]. Computers and Applied Chemistry, 2012, 29(2): 195-199.
29	韩涵, 王厚军, 龙兵, 等. 基于改进马氏距离的模拟电路故障诊断方法[J]. 控制与决策, 2013, 28(11): 1713-1717+1722.
	HanH, WangH J, LongB, et al. Method for analog circuit fault diagnosis based on improved Mahalanobis distance[J]. Control and Decision, 2013, 28(11): 1713-1717+1722.
30	马贺贺, 胡益, 侍洪波. 基于马氏距离局部离群因子方法的复杂化工过程故障检测[J]. 化工学报, 2013, 64(5): 1674-1682.
	MaH H, HuY, ShiH B. Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor[J]. CIESC Journal, 2013, 64(5): 1674-1682.

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