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研究了基于与QAR记录相匹配的飞行轨迹数据建立的BP神经网络油耗模型,利用飞行轨迹数据输入模型求得油耗估算值,通过与QAR真实燃油数据对比,进行模拟分析。以某些航班的雷达记录数据为例进行油耗计算,结果表明本实验模型在燃油方面的估算误差不超过2%,满足空管方面对燃油消耗的计算。研究结果可以用于定量分析空管运行对民航节能减排的影响,从而在确保飞行安全、管制容量的前提下更好地兼顾绿色运行的要求,提升空中交通运行质量。
The fuel consumption model of BP neural network based on the flight path data matching with QAR record is studied. The estimated fuel consumption is obtained by using the trajectory data input model, and compared with the real fuel data of QAR. Taking the data of radar records of some aircrafts as an example, the fuel consumption calculation is carried out. The results show that the fuel oil estimation error of the experimental model does not exceed 2%, which meets the calculation of fuel consumption by ATC. The results of the study can be used to quantitatively analyze the impact of ATC operation on energy saving and emission reduction of civil aviation, so as to better meet the requirements of green operation and improve the quality of air traffic operation under the premise of ensuring flight safety and regulatory capacity.