基于飞行参数的全面飞行性能评估模型,与主观和人工智能评估进行比较
Hao Jiang1, Xing Peng1, Xueying Luo1
1College of Flight Technique, Civil Aviation Flight University of China, Guanghan, Sichuan, China.
Ergonomics
|December 7, 2025
概括
开发了一种新的飞行性能评估模型,并使用模拟器数据进行验证. 该模型显示正常和紧急飞行条件的可靠得分,与教练和AI评估保持一致.
科学领域:
- 航空心理学 航空心理学
- 飞行模拟飞行模拟器
- 绩效指标 绩效指标 是一个指标.
背景情况:
- 开发强大的飞行性能评估模型对于有效的飞行员培训至关重要.
- 现有的模型可能无法充分覆盖各种飞行条件,包括紧急情况.
- 客观和一致的绩效评估仍然是航空培训的一个挑战.
研究的目的:
- 使用飞行参数创建一个全面的飞行性能评估模型.
- 确保模型在正常和异常飞行场景中的适用性.
- 根据专家和基于AI的评估来验证模型的可靠性.
主要方法:
- 使用Cessna-172模拟器进行37名飞行员的正常和单发动机故障紧急飞行.
- 将飞行分成不同的阶段 (起飞到降落,包括紧急情况).
- 采用分析层次流程来权衡飞行阶段和指标;纳入教练和ChatGPT评估.
主要成果:
- 在模型,飞行教练和ChatGPT生成的分数之间表现出良好的一致性.
- 在所有评估来源中发现了显著的相关性,验证了模型的可靠性.
- 该模型有效地评估了不同飞行阶段和条件的性能.
结论:
- 开发的飞行性能评估模型是可靠和准确的.
- 该模型显示了在现实世界飞行训练和操作中实际应用的潜力.
- 像ChatGPT这样的AI工具可以作为航空培训中的补充评估来源.
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