面向山岳风景区旅游用大型载人eVTOL的运行风险研究

doi:10.16265/j.cnki.issn1003-3033.2025.S1.0032

摘要/Abstract

摘要： 为提升山岳型风景区低空旅游场景中大型载人电动垂直起降航空器(eVTOL)的运行安全性,以广西贺州姑婆山为运行场景,以亿航EH216-S型无人驾驶航空器为例,基于风险管理理论并结合该型航空器的专用条件,系统识别与分析其运行风险。首先,运用文献研究法和类比推理法,从人为因素、设备因素、环境因素及管理因素4个方面识别并分析eVTOL的运行风险;然后,将模糊综合评价法和层次分析法引入贝叶斯网络(BN)模型,开展风险评价;最后,依据BN计算事故诱因后验概率。结果表明:在控制失效发生时,导航失效是首要致因,事件发生概率为79.41%,导致导航失效的主要原因是指挥控制链路故障,概率为97.02%;采用多重信道加密技术和复杂高速跳频技术,依托数据链干扰程度预测模型和抗干扰通信方案可降低eVTOL的运行风险。

关键词: 山岳风景区, 电动垂直起降航空器(eVTOL), 运行风险, 贝叶斯网络(BN), 低空旅游, 指挥控制链路

Abstract:

To enhance the operational safety of large manned eVTOL aircraft in low-altitude tourism scenarios in mountainous scenic areas, this study used Gupo Mountain in Hezhou, Guangxi Zhuang Autonomous Region as the operational context and the EHang EH216-S unmanned aerial vehicle as an example. Based on risk management theory and the specific conditions of this aircraft, the study identified and analyzed its operational risks. First, through literature review and analogical reasoning, the operational risks of the eVTOL aircraft were identified from four aspects: human factors, equipment factors, environmental factors, and management factors. Then, the fuzzy comprehensive evaluation method and analytic hierarchy process were integrated into the BN model to conduct risk assessment. Finally, the posterior probabilities of accident causation were calculated using the BN model. The results indicate that, in the event of a control failure, navigation failure is the primary cause, with an occurrence probability of 79.41%. A fault in the command and control link is the main reason for navigation failure, with a probability of 97.02%. The application of multiple-channel encryption technology and complex high-speed frequency hopping techniques, along with a data link interference prediction model and anti-jamming communication strategies, can reduce the operational risks associated with eVTOL.

Key words: mountainous scenic area, electric vertical take-off and landing (eVTOL) aircraft, operational risk, Bayesian network (BN), low-altitude tourism, command and control link

中图分类号:

X949

李佳音, 谭德强, 钱祎祎. 面向山岳风景区旅游用大型载人eVTOL的运行风险研究[J]. 中国安全科学学报, 2025, 35(S1): 210-216.

LI Jiayin, TAN Deqiang, QIAN Yiyi. Operational risks of large manned eVTOL for tourism in mountainous scenic areas[J]. China Safety Science Journal, 2025, 35(S1): 210-216.

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参考文献 19

[1]	王立硕, 刘正航, 毕晟. 山岳型风景名胜区低空旅游的发展建议:以武夷山风景区为例[J]. 空运商务, 2023, 23(5): 8-13.
[2]	丁水汀, 丁硕, 孙爽, 等. 复合翼eVTOL电池需求及对动力总成安全性的影响[J]. 推进技术, 2024, 45(3):212-226.
	DING Shuiting, DING Shuo, SUN Shuang, et al. Compound wing eVTOL battery requirements and implications for powertrain safety[J]. Journal of Propulsion Technology, 2024, 45(3):212-226.
[3]	王运盛, 祁辛天, 汪鹏辉. eVTOL飞机级安全性减缓措施和效果分析[J]. 民用飞机设计与研究, 2024, 39(1):114-120.
	WANG Yunsheng, QI Xintian, WANG Penghui. eVTOL aircraft level safety mitigation measures and efficacy analysis[J]. Civil Aircraft Design & Research, 2024, 39(1):114-120.
[4]	袁乐平, 谷泽坤, 李东琪. 复杂网络城市空中交通生态下eVTOL运行风险演化分析[J]. 中国安全科学学报, 2024, 34(3):192-199. doi: 10.16265/j.cnki.issn1003-3033.2024.03.0266
	YUAN Leping, GU Zekun, LI Dongqi. Risk evolution analysis of eVTOL operation in UAM ecosystem based on complex networks[J]. China Safety Science Journal, 2024, 34(3):192-199. doi: 10.16265/j.cnki.issn1003-3033.2024.03.0266
[5]	ZHAO Chunrong, MAZO J R, VERSTRAETE D. Optimisation of a liquid cooling system for eVTOL aircraft: impact of sizing mission and battery size[J]. Applied Thermal Engineering, 2024, 246:DOI:10.1016/j.applthermaleng.2024.122988.
[6]	YE Mian, ZHAO Jinchen, GUAN Quanli, et al. Research on eVTOL air route network planning based on improved A^* algorithm[J]. Sustainability, 2024, 16(2):DOI:10.3390/su16020561.
[7]	罗天啸, 陆施毅, 林寿珣, 等. 广西典型公益林区野生动物资源监测分析:以广西姑婆山自治区级自然保护区为例[J]. 湖南林业科技, 2021, 48(5):58-64, 77.
	LUO Tianxiao, LU Shiyi, LIN Shouxun, et al. Monitoring and analysis of wildlife resources in typical public welfare forest areas in Guangxi taking Guposhan nature reserve of Guangxi as an example[J]. Huanan Forestry Science & Technology, 2021, 48(5):58-64, 77.
[8]	刘丹, 罗颜声, 李诗轩, 等. 面向飞行全过程的民航事故致因关联网络分析[J]. 中国安全科学学报, 2024, 34(3):84-92. doi: 10.16265/j.cnki.issn1003-3033.2024.03.0679
	LIU Dan, LUO Yansheng, LI Shixuan, et al. Causes and correlation network analysis of civil aviation accidents for whole flight phases[J]. China Safety Science Journal, 2024, 34(3):84-92. doi: 10.16265/j.cnki.issn1003-3033.2024.03.0679
[9]	韩鹏, 王梦琦, 赵嶷飞. 基于贝叶斯网络的物流无人机失效风险评估[J]. 中国安全生产科学技术, 2020, 16(11):178-183.
	HAN Peng, WANG Mengqi, ZHAO Yifei. Failure risk assessment of logistics UAV based on Bayesian network[J]. Journal of Safety Science and Technology, 2020, 16(11):178-183.
[10]	张晓全, 马晗. 基于贝叶斯网络的电动垂直起降航空器运行风险研究[J]. 科学技术与工程, 2022, 22(36):16 269-16 276.
	ZHANG Xiaoquan, MA Han. Operation risk of electric vertical takeoff and landing aircraft based on Bayesian network[J]. Science Technology and Engineering, 2022, 22(36):16 269-16 276.
[11]	丁水汀, 鲍梦瑶, 杜发荣. 无人机系统适航与安全性分析方法[J]. 航空动力学报, 2012, 27(1):233-240.
	DING Shuiting, BAO Mengyao, DU Farong. Safety research on unmanned aircraft system for airworthiness[J]. Journal of Aerospace Power, 2012, 27(1):233-240.
[12]	王妍蒙, 胡长明, 王旭东, 等. 基于模糊贝叶斯网络的钢管桩围堰安全评价研究[J]. 工业安全与环保, 2024, 50(3):18-24, 91.
	WANG Yanmeng, HU Changming, WANG Xudong, et al. Study on safety evaluation of steel pipe pile cofferdam based on fuzzy Bayesian network[J]. Industrial Safety and Environmental Protection, 2024, 50(3):18-24, 91.
[13]	陈晓伟. 基于贝叶斯理论的既有铁路桥梁可靠度综合评估方法的研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
	CHEN Xiaowei. Research on comprehensive evaluation method of reliability of existing railway bridge based on Bayes theory[D]. Harbin: Harbin Institute of Technology, 2021.
[14]	陈义友, 张建平, 邹翔, 等. 民用无人机交通管理体系架构及关键技术[J]. 科学技术与工程, 2021, 21(31):13 221-13 237.
	CHEN Yiyou, ZHANG Jianping, ZOU Xiang, et al. System framework and key technologies of civil unmanned aircraft system traffic management[J]. Science Technology and Engineering, 2021, 21(31):13 221-13 237.
[15]	王依恺, 张卓, 王宝宝. 无人机卫星数据链抗干扰能力发展探析[J]. 通讯世界, 2023, 30(5):13-15.
[16]	张薇玮, 丁文锐, 刘春辉. 复杂环境中无人机数据链干扰效果预测方法[J]. 系统工程与电子技术, 2016, 38(4):760-766.
	ZHANG Weiwei, DING Wenrui, LIU Chunhui. Prediction of interference effect on UAV data link in complex environment[J]. Systems Engineering and Electronics, 2016, 38(4):760-766. doi: 10.3969/j.issn.1001-506X.2016.04.06
[17]	唐韬, 赵润晖, 冯学炜, 等. 基于离线学习的无人机网络抗干扰通信方案[J]. 通信技术, 2024, 57(5):495-499.
	TANG Tao, ZHAO Runhui, FENG Xuewei, et al. An offline learning-based anti-interference communication scheme for UAV networks[J]. Communications Technology, 2024, 57(5):495-499.
[18]	王永刚, 王海玥, 张迪. 基于不安全信息与危险源关联的维修单位风险评价[J]. 中国安全科学学报, 2023, 33(12): 31-37. doi: 10.16265/j.cnki.issn1003-3033.2023.12.1576
	WANG Yonggang, WANG Haiyue, ZHANG Di. Risk assessment model of maintenance organization based on correlation between unsafe information and hazards[J]. China Safety Science Journal, 2023, 33(12):31-37. doi: 10.16265/j.cnki.issn1003-3033.2023.12.1576
[19]	刘林, 吴金南, 常志朋. 安全违规行为的人际传染效应研究[J]. 中国安全科学学报, 2021, 31(8):22-29. doi: 10.16265/j.cnki.issn1003-3033.2021.08.004
	LIU Lin, WU Jinnan, CHANG Zhipeng. Study on interpersonal contagion effect of safety violation behaviors[J]. China Safety Science Journal, 2021, 31(8):22-29. doi: 10.16265/j.cnki.issn1003-3033.2021.08.004

要素	传统直升机	民用无人机	eVTOL
动力来源	传统化石燃料	燃料或电池或太阳能	电池
系统组成	升力系统、动力系统、传动系统及机载飞行设备等	飞控系统、动力系统、机载链路系统等	能源系统、动力系统、飞控系统、导航系统、通信系统等
飞行原理	利用桨叶攻角改变实现垂直起降,通过桨盘倾斜实现前后左右飞行,利用尾桨推力变化实现航向控制	依靠电机旋转和输出功率的增减实现升降和水平方向运动	依靠电机旋转实现垂直起降,通过俯仰轴上总推力差实现水平方向运动
适航管理	基于中国民用航空规章进行审定	基于运行风险的适航审定	中国民航局将其适航审定纳入基于运行风险的无人驾驶航空器审查体系
特点	用途广泛、噪声较大、使用成本较高、安全性较差	用途广泛、载物、灵活、经济、安全性较差	用途不一、载人、绿色智能、噪声极低、运行成本低、可靠性较高

根节点X_i	三角模糊数/10^-1	中心值
X₁	{6.2,7.2,8.2}	0.72
X₂	{0.4,1.3,2.2}	0.13
X₃	{0.7,0.8,0.9}	0.08
X₄	{2.4,3.4,4.4}	0.34
X₅	{0.8,1.8,2.8}	0.18
X₆	{3.4,4.4,5.4}	0.44
X₇	{1.5,2.5,3.5}	0.25
X₈	{4.9,5.9,6.9}	0.59
X₉	{2.7,3.7,4.7}	0.37
X₁₀	{5.8,6.8,7.8}	0.68
X₁₁	{1.1,2.1,3.1}	0.21
X₁₂	{2.0,3.0,4.0}	0.30

后验概率	控制失效
后验概率	导航失效	航空器失去结构完整性	远程机组反应不足	飞行系统故障
风险事件发生	79.41	52.01	57.50	61.09

后验概率	导航失效		航空器失去结构完整性					远程机组反应不足		飞控系统故障
后验概率	指挥控制链路故障	人机交互界面崩溃	设计制造缺陷	野生动物侵扰	零部件失效	地面维修人员检修失误	安检失误	管理缺陷	机载传感器或挂载设备故障	动力系统失效	飞控系统软件故障	机载传感器反馈失效
风险事件发生	97.02	52.99	52.82	63.46	54.68	71.04	58.01	85.31	64.70	86.88	54.88	58.25