Risk assessment of biological sample transport by UAVs based on Bayesian networks

doi:10.16265/j.cnki.issn1003-3033.2025.01.0441

Abstract

Abstract:

To quantify the transportation risks associated with biological samples using UAVs, this study first identified 32 risk factors across five dimensions-human, machine, environment, management, and hazard-based on national standards and relevant literature. A BN for risk assessment was constructed using Netica software, with prior probabilities determined through expert knowledge and fuzzy set quantitative analysis. The proposed risk assessment model was then used for bidirectional reasoning and scenario analysis. A case study of a UAV company in Shenzhen was presented to evaluate the transportation risks of biological samples and identify key influencing factors. The results indicate that the risk probability of biological sample transportation, as calculated through forward reasoning, is approximately 2.203×10^-5. The primary risk factors are related to hazardous materials, followed by equipment and facility-related issues. The core risk factors influencing biological sample transportation include the size, quantity and weight of hazardous material packages, the temperature control effectiveness of specialized cold chain logistics boxes, the integrity of emergency response plans, emergency handling capabilities, safety management and education, and the presence of obstacles.

Key words: Bayesian networks (BN), biological samples, unmanned aerial vehicle (UAV) transportation, risk assessment, emergency response

CLC Number:

X951交通运输安全

LIU Qing, SHEN Tian. Risk assessment of biological sample transport by UAVs based on Bayesian networks[J]. China Safety Science Journal, 2025, 35(1): 16-24.

Figures/Tables 8

Fig.1

Table 1

Influencing factors of UAV transportation of biological samples

目标层	因素层	风险层	风险解释
生物样本无人机运输风险水平X	设施设备因素X₁	无人机飞行控制系统X₁₁	无人机飞行控制系统保障飞行安全与稳定,发生故障或失灵可能导致无人机失控、坠毁或偏离预定航线
		无人机通信系统X₁₂	无人机与地面站之间的即时通信是保证飞行状态实时监控和操作指令传输的关键。一旦通信中断,可能导致无人机失联,增加样本丢失和安全事故风险
		起降场地X₁₃	无人机对起降场地要求严格,如果起降场地净空条件差或存在障碍物,容易发生事故
		专用冷链物流箱控温效果X₁₄	生物样本运输的存储温度需要满足规范要求,专用冷链物流箱的控温效果决定了样本运输质量。控温效果不佳可能影响样本活性,影响实验结果或样本使用
		无人机电池续航X₁₅	电池续航决定无人机性能,影响无人机运输距离、运输时间以及配送点的选择
		温度监控设备X₁₆	温度监控设备用于实时监控运输过程中生物样本的温度。如果设备失灵或数据不准确,温度异常造成生物样本失活,需要做出应急处置
		其他辅助设施X₁₇	生物样本无人机运输过程需要额外的辅助设施,如飞行数据记录器、样本容器、备用电池等
	人员因素X₂	人员专业业务水平X₂₁	无人机操作员和运输管理人员专业水平直接关系到整个运输过程的安全和效率。缺乏经验或技能不足的人员容易导致操作失误和意外事故
		职业道德水平X₂₂	如果运输管理人员存在失职或违规行为,可能导致生物样本损失或污染。例如:在托运物品中夹带危险品或者将危险品匿报、谎报为生物样本进行托运
		航线规划与控制X₂₃	运输管理人员和无人机操作员航线规划和设置不当,或空中超视距飞行过程中控制不力,可能导致无人机出现临时不可控情况,增加事故风险
		生物样本运输链条协调性X₂₄	承运人与托运人沟通不当,各部门未做好协调,可能导致生物样本运输过程中出现各种问题和风险。例如:托运人未能按时准备好样本,或承运人未能及时安排无人机和辅助设备,导致运输延误,影响生物样本的质量和有效性
		安全应急处置能力X₂₅	运输管理人员、承运人缺少应急预案、应急演练经验以及应急处置能力不足,可能导致未能按照应急程序处理突发事件,扩大事故影响
		特殊装备的包装检查X₂₆	安全监管人员、承运人在承担含有锂电池、加温装置等包装容器运输任务时,未检查外包装件,相关标记标签不明确或出现问题
		按标准规范的运输情况X₂₇	承运人在承担生物样本无人机实际运输过程中偏离标准规范的操作,带来潜在的安全隐患
		鸟类袭击X₃₁	在低空飞行或复杂环境中,鸟类袭击可能会增加无人机的飞行安全风险
		障碍物因素X₃₂	在飞行过程中可能遇到各种障碍物,如树木、高压电线、建筑物等,无人机需要避障,避免碰撞风险,防止造成机体损坏或货物掉落,进而引发安全事故
	环境因素X₃	恶劣天气X₃₃	低空突发不规则气流(如大风、风切变)、大雾、强降水等恶劣天气对低空航路和航线产生影响
		电磁环境干扰X₃₄	在飞行过程中受到来自各种电子设备或电磁场的干扰,如无线电信号、雷达波、高压线路等,导致无人机的控制系统失去稳定飞行能力
		空域管理X₃₅	需要事先提交飞行计划并获得批准。若飞行计划未获批准或临时变更未及时通知相关部门,可能导致空中交通管理混乱,增加碰撞风险
		与其他航空器相撞X₃₆	随着无人机数量的增加,与有人机、其他无人机之间的碰撞风险增加
	管理因素X₄	法律法规薄弱X₄₁	在低空运营管理和低空服务保障管理方面,现有法规和规范性文件存在不完善之处,企业对应运行手册也不完善,导致管理和监督不到位
		监测预警管控措施X₄₂	生物样本无人机运输监管部门和监管流程不完善
		应急预案完善情况X₄₃	生物样本无人机运输的应急处置流程、紧急事件报告程序不完善
		安全教育管理X₄₄	无人机操作人员、承运人缺乏系统的安全教育和培训可能导致操作失误、设备损坏
		事故调查与责任追溯机制X₄₅	完善的事故调查与责任追溯机制有助于快速查明事故原因,明确责任,规避类似事件重复发生
		地面站运保能力X₄₆	承运单位地面站运控能力决定了运输飞行过程中能否及时响应特情,影响飞行安全和生物样本运输效率
	危险品因素X₅	标本运输箱未消毒X₅₁	承运人未对标本箱内部及无人机表面进行喷洒擦拭消毒液,对运输质量有影响
		生物样品包装件存在泄漏或污染X₅₂	生物样本运输过程中存在包装件泄漏或污染,影响飞行安全
		生物样本的外部温度要求X₅₃	需要在特定的温度范围内运输,以确保其质量和有效性
		未贴有明显危险品标记标签X₅₄	生物样本属于危险品,承运人如未贴危险品标记标签,可能导致地面人员或其他飞行器误判,引发安全事故
		生物样品包装件的尺寸/数量/重量X₅₅	承运人应使用专用包装容器,确保其具有足够缓冲和保护性能
		生物样本种类X₅₆	不同种类的生物样本对运输条件要求各异,承运人需要根据样本特性制定相应的运输方案

Table 1

Fig.2

Fig.3

Table 2

Table 3

Table 4

Table 5

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语言评价	模糊数	语言评价	模糊数
非常低(VL)	(0, 0, 0.000 005, 0.000 01)	较高(RH)	(0.1, 0.2, 0.5, 0.65)
低(L)	(0.000 005, 0.000 01, 0.000 05, 0.000 1)	高(H)	(0.5, 0.65, 0.8, 0.9)
较低(RL)	(0.000 05, 0.000 1, 0.005, 0.01)	非常高(VH)	(0.8, 0.9, 1, 1)
中等(M)	(0.005, 0.01, 0.1, 0.2)	—	—

标准		级别	得分		标准		级别	得分
职称		正高级工程师/教授	10		教育程度		博士研究生	10
		高级工程师/副教授	8				硕士研究生	8
		工程师/讲师	6				学士	6
		助理工程师	4				大专	4
		技术员	2				中专	2
工作年限	>20			10	项目参与数	>15			10
	16~19			8		10~15			8
	10~15			6		6~9			6
	5~9			4		3~5			4
	<5			2		<3			2

专家编号	1	2	3	4	5
权重w_j	0.100	0.121	0.136	0.114	0.079
专家编号	6	7	8	9	10
权重w_j	0.093	0.129	0.050	0.079	0.100

影响因素	模糊可能性	先验概率	影响因素	模糊可能性	先验概率	影响因素	模糊可能性	先验概率
X₁₁	0.020	3.801×10^-9	X₂₅	0.292	8.105×10^-4	X₄₃	0.252	4.933×10^-4
X₁₂	0.068	3.114×10^-6	X₂₆	0.136	5.475×10^-5	X₄₄	0.192	1.928×10^-4
X₁₃	0.091	1.108×10^-5	X₂₇	0.021	5.231×10^-9	X₄₅	0.029	3.834×10^-8
X₁₄	0.290	7.920×10^-4	X₃₁	0.029	3.834×10^-8	X₄₆	0.070	3.550×10^-6
X₁₅	0.017	1.265×10^-9	X₃₂	0.261	5.556×10^-4	X₆₂	0.029	3.834×10^-8
X₁₆	0.135	5.324×10^-5	X₃₃	0.142	6.442×10^-5	X₅₂	0.129	4.477×10^-5
X₁₇	0.020	3.801×10^-9	X₃₄	0.043	3.370×10^-7	X₅₃	0.224	3.299×10^-4
X₂₁	0.150	7.902×10^-5	X₃₅	0.108	2.233×10^-5	X₅₄	0.060	1.747×10^-6
X₂₂	0.011	4.918×10^-11	X₃₆	0.026	1.999×10^-8	X₅₅	0.292	8.105×10^-4
X₂₃	0.030	4.672×10^-8	X₄₁	0.052	8.790×10^-7	X₅₆	0.024	1.222×10^-8
X₂₄	0.061	1.888×10^-6	X₄₂	0.064	2.360×10^-6	—	—	—

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