基于K-means算法的民航事故结构化分析
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1.南京航空航天大学民航学院;2.上海航空工业集团有限公司;3.南京航空航天大学通用航空与飞行学院

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V328.2

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南京航空航天大学研究生科研与实践创新计划项目(xcxjh20220732),国家自然科学基金项目(面上项目,重点项目,重大项目)


Structured Analysis of Civil Aviation Accidents Based on K-means Algorithm
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1.College of Civil Aviation,Nanjing University of Aeronautics and Astronautics;2.COMAC shanghai Aviation IndsutrialGroupCo,Ltd,Shanghai

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    摘要:

    为深层次剖析民航事故诱因预防事故的发生,针对当前事故数据统计的不完整性并使事故致因分析具有可信度和深度,以2000年全球范围内的民航事故为样本采集事故报告数据并进行结构化处理,将事故分阶段类型等字段进行统计,并基于K-means算法从多方面进行民航安全风险要素聚类分析,对比了结构化与非结构化处理结果的差异,综合考虑多种风险要素对事故的共同影响,根据词云图中的关键致险因素和LDA主题建模识别文本数据中潜在主题的分布,从多个方面为以后的民航安全提供了有价值的参考信息和安全建议。

    Abstract:

    To conduct an in-depth analysis of the causes of civil aviation accidents and prevent accidents, given the incompleteness of current accident data statistics and the need to make accident causation analysis credible and profound, accident report data were collected as samples from global civil aviation accidents in the year 2000. These data were subjected to structured processing, including the categorization of accidents into stages and types. Using the K-means algorithm, a cluster analysis of civil aviation safety risk factors was performed from multiple perspectives. A comparison was made between the results of structured and unstructured data processing. Various risk factors" combined impact on accidents was considered. Key risk factors identified from word clouds and latent topic distribution in text data using LDA topic modeling were analyzed. This comprehensive approach provides valuable reference information and safety recommendations for future civil aviation safety.

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刘旭,张艳,邓少阁,等. 基于K-means算法的民航事故结构化分析[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-09-18
  • 最后修改日期:2024-03-26
  • 录用日期:2024-03-28
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