用AI从事故文本中自动识别飞行阶段，提升航空安全分析效率
Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences
- 用ResNet和sRNN两种深度学习模型处理未结构化事故文本
- 准确率超68%，显著高于随机猜测的14%（七分类）
- sRNN表现远超简化版ResNet，适合安全事件智能分析
航空运输系统高度重视安全，即使微小异常也可能导致严重后果。事故与事件报告在识别原因和提出安全建议中至关重要。然而，描述事故前事件的叙述性文本为非结构化形式，难以被计算机理解。基于这些文本对安全事件进行分类可支持行业决策。本研究采用自然语言处理（NLP）与人工智能（AI）模型，对来自美国国家运输安全委员会（NTSB）的27,000份安全事件报告中的文本进行分析，以识别其对应的飞行阶段。评估了两种深度学习模型——ResNet与sRNN的分类性能。结果显示，两种模型准确率均超过68%，远高于七类分类问题的随机猜测水平14%。模型还表现出高精确率、召回率与F1分数。其中，sRNN模型显著优于本文所用的简化版ResNet架构。结果表明，NLP与深度学习模型可从原始文本中有效推断飞行阶段，实现对安全事件的高效分析。
原文摘要 · Abstract (English)
The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured text that is not easily understood by computer systems. Classifying and categorizing safety occurrences based on these narratives can support informed decision-making by aviation industry stakeholders. In this study, researchers applied natural language processing (NLP) and artificial intelligence (AI) models to process text narratives to classify the flight phases of safety occurrences. The classification performance of two deep learning models, ResNet and sRNN was evaluated, using an initial dataset of 27,000 safety occurrence reports from the NTSB. The results demonstrated good performance, with both models achieving an accuracy exceeding 68%, well above the random guess rate of 14% for a seven-class classification problem. The models also exhibited high precision, recall, and F1 scores. The sRNN model greatly outperformed the simplified ResNet model architecture used in this study. These findings indicate that NLP and deep learning models can infer the flight phase from raw text narratives, enabling effective analysis of safety occurrences.
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