【摘要】 为进一步妥善处理航空运行中发生的不正常事件，以2022年空管安全信息系统（ASIS）收集到的10995件不正常事件数据为研究对象，首先通过海恩法则统计得到不同严重程度的不正常事件比例，然后利用Apriori算法和关联规则对所发生的不正常事件进行关联性分析，确定致因并构建不同性质的事件分类。最后使用SPSS Modeler对统计的所有不正常事件数据进行建模，构建布尔矩阵并产生关联规则，分析出不正常事件致因和事件发生之间的关系。依据事件发生概率降序排列，逐一进行排查预警，提出相应的安全建议，降低不正常事件发生频率，对国内空管高质量运行与发展有着重要意义。更多还原

【Abstract】 In order to further properly deal with abnormal events in aviation operations, taking the data of 10995 abnormal events collected by the Air Traffic Control Safety Information System(ASIS)in 2022 as the research object, the proportion of abnormal events of different severity was first calculated through Hayne’s law, and then the Apriori algorithm and association rules were used to analyze the correlation of abnormal events to determine the causes and construct event classifications of different natures. Finally, use SPSS Modeler to model all abnormal event data collected, construct a Boolean matrix, and generate association rules to analyze the relationship between the cause of abnormal events and their occurrence. Arranging events in descending order of occurrence probability, conducting troubleshooting and early warning one by one, proposing corresponding safety recommendations, and reducing the frequency of abnormal events are of great significance for the high-quality operation and development of domestic air traffic control.更多还原