中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (11): 67-74.doi: 10.16265/j.cnki.issn1003-3033.2023.11.1928

• 安全工程技术 • 上一篇    下一篇

通用航空森林消防救援数据适配性特征提取

潘卫军(), 覃莉茹, 骆晨, 黄园晶   

  1. 中国民用航空飞行学院 空中交通管理学院,四川 广汉 618307
  • 收稿日期:2023-05-12 修回日期:2023-08-15 出版日期:2023-11-28
  • 作者简介:

    潘卫军 (1968—),男,湖北黄冈人,博士,教授,主要从事空中交通管制、计算机仿真、航空运行安全等方面的研究。E-mail:

    骆晨 讲师

  • 基金资助:
    中央引导地方科技发展专项项目(2020ZYD094); 四川省科技厅项目(2021YFS0319); 四川省社科规划项目(SC21C088)

Adaptive feature extraction of general aviation forest fire rescue data

PAN Weijun(), QIN Liru, LUO Chen, HUANG Yuanjing   

  1. College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
  • Received:2023-05-12 Revised:2023-08-15 Published:2023-11-28

摘要:

通用航空森林消防实际救援行动中飞机的飞行受到气象、地形等因素影响,为确保救援效率和飞行安全,必须解决通航森林消防救援中火场客观数据与救援方案适配性较差的问题,结合通用航空森林消防的特点,分析多机型下的通航森林火场适配性影响因素;建立“距离-半径-速度”多火场多机型通用航空火场救援派遣数据提取模型,采用蒙特卡罗方法得到火场数据作为训练数据和测试数据输入模型;提出综合表征火场距离的方法,减少火场距离特征种类;基于朴素贝叶斯估计和高斯贝叶斯估计探究不同火场特征组合的特征提取效果。结果表明:火场距离参数计算方法能够有效表达多个距离特征,其参与构成的火场特征组合在数据集分类失衡情况下对结果影响较小;使用高斯朴素贝叶斯对3种火场特征组合进行特征提取,精度分别达到73.3%、70.2%以及72.4%,具有较好的效果,证明该方法具有一定的合理性与准确性。

关键词: 通用航空森林消防救援, 适配性, 特征提取, 多机型, 救援派遣决策, 贝叶斯估计

Abstract:

The flight of aircraft in general aviation forest fire rescue is affected by factors such as weather and terrain. In order to ensure rescue efficiency and flight safety, it is necessary to refer to objective data from fire scenes for rescue dispatch decisions. In order to address the problem of poor adaptability between objective data of fire scenes and rescue plans in general aviation forest fire rescue, the influence factors of the adaptability were analyzed based on the characteristics of general aviation forest fire rescue with multi-aircraft types. A "distance-radius-velocity" general aviation fire rescue dispatch data extraction model with multiple fire scenes and multi-aircraft types was established, the input of which was composed of training data and test data obtained by the Monte Carlo method. In order to reduce the number of distance features, a method for comprehensively characterizing the distance of the fire scenes was proposed. The feature extraction accuracy of combinations of different fire scenes was explored based on naive Bayes estimation and Gaussian Bayes estimation. The results show that the proposed calculation method of distance parameters can effectively express multiple distance features, the combinations of fire scene features composed of which have a small impact on the results under the unbalanced classification of the dataset. Gaussian Bayes method is used to extract features from 3 combinations of fire scene characteristics, and the prediction accuracy reaches 73.3%, 70.2%, and 72.4%, respectively, with good results.

Key words: general aviation forest fire rescue, adaptability, feature extraction, multi-aircraft types, rescue dispatch decision, Bayesian estimation