一项关于加强联合提取控制意图和导航信息的增强方法的研究,用于智能城市机场运营
Yi Yang1,2, Yaoyao Yu2, Xuan Wang2
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Scientific reports
|December 11, 2025
概括
本研究介绍了CII-BERT,这是一种用于空中交通控制 (ATC) 通信的新AI模型,通过减少指令错误和冲突概率,显著提高了飞行安全.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 航空安全航空安全
背景情况:
- 目前用于空中交通管制 (ATC) 通信的联合提取模型,如CII,在模拟双向依赖方面遇到了困难.
- 对空中管制指令的误解带来了重大航空风险,需要加强通信安全.
研究的目的:
- 开发一个先进的框架,CII-BERT,将BERT的上下文理解与深度神经网络 (DNN) 层集成在一起,以改进ATC通信分析.
- 通过准确识别意图并从ATC指令中提取信息来提高飞行安全,从而减少潜在的错误.
主要方法:
- 拟议的CII-BERT框架将BERT上下文嵌入与特定任务的DNN层相结合.
- 利用持续的预训练与特定领域的增强 (同义词替换,随机区块交换) 来进行稳健的ATC术语学习.
- 在7个操作场景中对6000多条ATC指令的数据集进行评估.
主要成果:
- CII-BERT实现了99.44%的意图识别精度和99.23%的信息提取精度,超过了CII模型.
- 与基线模型相比,显示高风险边缘病例减少了37%,例如模两可的高度命令.
- 与现有的CII模型相比,在意图识别方面提高了1.71%的精度,在信息提取方面提高了1.58%.
结论:
- CII-BERT有效地模拟了ATC通信中的双向依赖性,提高了安全性和准确性.
- 该框架显示了优化智能机场运营的潜力,包括控制器工作量和跑道效率.
- CII-BERT在减少与通信错误相关的航空风险方面取得了重大进展.
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