基于贝叶斯网络的物流无人机碰撞风险评估

为降低城市物流无人机（unmanned aerial vehicle，UAV）碰撞风险，识别关键风险因素，构建贝叶斯网络模型进行风险评估。在收集7个真实无人机碰撞事故案例和文献调研的基础上，提炼出15个主要风险因素，并通过问卷调查将风险集划分为低、中、高三个等级对风险发生概率数据进行收集与评估，随后运用解释结构模型（interpretative structural modeling，ISM）对风险因素进行层级划分构建贝叶斯网络模型。根据所构建的贝叶斯网络，将收集数据导入GeNIe，对贝叶斯网络模型进行参数学习，得到无人机碰撞风险不同等级的概率分布。最后对贝叶斯网络模型进行逆向推理、敏感性分析和影响强度分析，明确导致无人机发生碰撞事故的关键因素和敏感因素，依据分析结果提出风险防控建议。

Collision risk assessment of logistics UAV based on Bayesian network

In order to reduce the collision risk of urban logistics unmanned aerial vehicle(UAV) and identify the key risk factors, a Bayesian network model is constructed for risk assessment. On the basis of collecting 7 real UAV collision accident cases and literature research, 15 main risk factors are extracted, and the risk set is divided into three levels: low, medium and high through questionnaire survey to collect and evaluate the risk probability data. Then the risk factors are hierarchically divided by using the interpretative structural modeling(ISM) to construct the Bayesian network model. According to the constructed Bayesian network, the collected data is imported into GeNIe, and the parameter learning of Bayesian network model is carried out to obtain the probability distribution of different levels of UAV collision risk. Finally, reverse reasoning, sensitivity analysis and impact intensity analysis are carried out on the Bayesian network model to clarify the key and sensitive factors that lead to UAV collision accidents, and propose risk prevention and control suggestions based on the analysis results.