基于文本挖掘的民航事件风险评估

Civil Aviation Incident Risk Assessment Based on Text Mining

为实现“安全第一、预防为主、综合治理”的民航安全管理目标,建立了从报告中学习并评估风险等级的深度学习模型.首先采集航空安全报告系统中10年报告,根据严重度建立事件后果的量化指标,确定5个风险等级:高、中高、中、中低和低风险,并消除事件结果分布不平衡和结果多样性的影响.然后应用卷积神经网络(Convolutional Neural Network,CNN)探索非结构化的事件概要与风险等级之间的关系,通过该模型对事件进行分类,确定风险等级.风险评估模型与不同量化指标和不同方法对比,其分类准确率可达96%,优于其他指标和方法.最后应用该模型对非结构化的事件概要挖掘,对2020年事件进行快速的风险评估,预测准确率可达80%.基于CNN的民航风险评估模型可以对文本格式的事件概要充分挖掘,快速评估与主动感知风险,对支持安全预警具有重要意义.

In order to achieve the civil aviation safety management goal of‘safety first,prevention first and comprehensive management’,a deep learning model is established to learn from reports and assess the risk level. Based on the 10-year incident reports available in the Aviation Safety Reporting System,we first establish quantitative indicators of incident consequences and classify all incidents into 5 levels according to their severity:high,moderately high,moderate,moderately low and low risk,which helps to eliminate the impact of unbalanced and intricate event consequences. Then,the relationship between the unstructured incident synopsis and the risk level is explored by convolutional neural network(CNN),and the events are classified by the model to determine the risk level. The classification model proves its superiority by comparing it with different quantitative indicators and methods,with an accuracy of 96%,which is better than the compared models. Finally,the 2020’s incident reports are predicted by this model,which enables rapid risk assessment of the synopsis of the incident,with an accuracy rate of 80%. The CNNbased civil aviation risk assessment model can fully mine the text-formatted incident synopsis,and quickly assess and actively perceive the risk level,which helps support the early warning of civil aviation safety.