基于N-K模型的危化品运输多因素耦合风险分析

doi:10.16265/j.cnki.issn1003-3033.2026.06.1194

摘要/Abstract

摘要：

为解决危险化学品道路运输多因素耦合风险动态演化机制不清、防控措施不精准的问题,基于N-K模型,收集并分析2019—2024年50起典型槽罐车运输事故案例,辨识人员、设备、危化品、环境及管理5类核心风险因素及其交互关系,构建多因素耦合风险量化评估模型,揭示不同风险耦合形态的动态演化特征,并建立分级防控技术体系。结果表明:五因素耦合风险值最高,达0.558,较单因素风险提升9.3倍;主观因素(人因与管理)对耦合风险的贡献率为67.3%;“人-管-环”为风险演化最高频的三因素耦合路径,风险值为0.105;据此构建的“本质安全-过程管控-应急响应”三级防控体系,涵盖智能罐体多参数监测、虚拟现实(VR)防御性驾驶培训、区块链培训与维保存证、数字孪生应急联动等关键技术,有助于推动风险管控由单因素静态评估向多因素耦合动态预警转变。

关键词: N-K模型, 危险化学品, 道路运输, 多因素耦合, 耦合风险, 防控体系

Abstract:

To address the unclear dynamic evolution mechanism of multi-factor coupling risks and the imprecision of prevention and control measures in hazardous chemical road transportation, an investigation was conducted based on the N-K model. Fifty typical tank truck transportation accidents occurring between 2019 and 2024 were collected and analyzed. Five core risk factors—personnel, equipment, hazardous chemicals, environment, and management—and their interaction relationships were identified. A quantitative assessment model for multi-factor coupling risk was constructed. The dynamic evolution characteristics of different risk coupling patterns were revealed, and a hierarchical prevention and control technical system was established. The results show that the five-factor coupling risk value is the highest, reaching 0.558, which is 9.3 times higher than that of a single factor. Subjective factors (human and management) are contributed 67.3% to the coupling risk, and the “personnel-management-environment” coupling is represented as the most frequent three-factor risk evolution path, with a risk value of 0.105. Based on these findings, a three-level prevention and control system of “inherent safety-process control-emergency response” is constructed, encompassing key technologies such as multi-parameter intelligent tank monitoring, virtual reality (VR) defensive driving training, blockchain-based training and maintenance evidence storage, and digital twin emergency coordination. The transformation of risk control from single-factor static assessment to multi-factor coupling dynamic early warning is promoted by this system.

Key words: N-K model, hazardous chemicals, road transportation, multi-factor coupling, coupling risk, prevention and control system

中图分类号:

X937

李京祥, 高学农, 景国勋. 基于N-K模型的危化品运输多因素耦合风险分析[J]. 中国安全科学学报, 2026, 36(6): 194-202.

Li Jingxiang, Gao Xuenong, Jing Guoxun. Multi-factor coupling risk analysis of hazardous chemicals transportation based on N-K model[J]. China Safety Science Journal, 2026, 36(6): 194-202.

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时间	地点	事故类型	触发类型	直接原因	后果	事故因素
2020-06-13	浙江温岭	液化石油气槽罐车爆炸	危化品车辆主责	弯道罐体碰撞护栏导致泄漏,遇火源爆炸	20人死亡,175人受伤;周边土壤、水体严重污染,大规模居民紧急疏散	人(疲劳驾驶)-车(罐体结构)-路(弯道)
2023-11-07	四川雅安	重烃泄漏爆燃	危化品车辆主责	罐体呼吸阀失效致泄漏;人员违规操作,泄漏物遇火花爆燃	2死2伤;重大经济损失;环境风险	人(违法、违规)、机(附件失效)、管(监控/培训缺位)
2023-02-13	广东惠州	汽油罐车泄漏爆炸	危化品车辆主责	阀门故障,处置中操作不当引发爆炸	驾驶员死亡;汽油挥发及燃烧产物造成大气污染,邻近区域环境监测超标	机(设备老化)-人(应急技能不足)

事故造成的人员伤亡	事故数	占比/%
无伤亡	42	84
死亡2人以上	1	2
死亡、受伤	4	8
受伤	3	6

环境后果类型	事故起数	占比/%
造成可观测环境污染	28	56
引发火灾/爆炸	14	28
需进行生态修复	9	18
无显著环境后果	22	44

单因素耦合	次数	n00000=0	n10000=21	n01000=6	n00100=2	n00010=7	n00001=3
单因素耦合	频率	P00000=0.000	P10000=0.420	P01000=0.120	P00100=0.040	P00010=0.140	P00001=0.060
双因素耦合	次数	n11000=8	n10100=2	n10010=4	n10001=2	n01100=3	n01010=1
	频率	P11000=0.160	P10100=0.040	P10010=0.080	P10001=0.040	P01100=0.060	P01010=0.020
	次数	n010001=6	n00110=2	n00101=1	n00011=3	—	—
	频率	P01000=0.120	P00110=0.040	P00101=0.002	P00011=0.060	—	—
多因素耦合	次数	n11100=9	n11010=2	n11001=3	n10110=2	n10101=4	n10011=1
	频率	P11100=0.180	P11010=0.040	P11001=0.060	P10110=0.040	P10101=0.080	P10011=0.020
	次数	n01110=1	n01101=3	n01011=4	n00111=2	n11110=3	n11101=2
	频率	P01110=0.02	P01101=0.060	P01011=0.080	P00111=0.040	P11110=0.060	P11101=0.040
	次数	n11011=1	n10111=0	n01111=0	n11111=0	—	—
	频率	P01000=0.020	P00110=0.000	P00101=0.000	P00011=0.000	—	—

P00...=0.039	P01...=0.140	P10...=0.481	P11...=0.068
P0.0..=0.368	P0.1..=0.081	P1.011=0.522	P1.1..=0.027
P0..0.=0.159	P0..1.=0.290	P1..0.=0.363	P1..1.=0.186
P0...0=0.436	P0...1=0.013	P1...0=0.509	P1...1=0.040
P.00..=0.722	P.01..=0.068	P.10..=0.168	P.11..=0.040
P.0.0.=0.345	P.0.1.=0.436	P.1.0.=0.168	P.1.1.=0.040
P.0..0=0.768	P.0..1=0.022	P.1..0=0.177	P.1..1=0.031
P..00.=0.436	P..01=0.454	P..10.=0.086	P..11.=0.022
P..0.0=0.850	P..0.1=0.040	P..1.0=0.095	P..1.1=0.013
P...00=0.472	P...01=0.050	P..10.=0.472	P...11=0.004