Dynamic monitoring method of flight chain operation status

doi:10.11772/j.issn.1001-9081.2023121758

Abstract

Abstract:

In order to grasp the overall status of flight operation more accurately， a dynamic monitoring method of flight chain operation status was proposed. Firstly， from the perspective of the whole flight chain， a flight chain data processing method was designed on the basis of the flight chain operation business process and data characteristics， and the operation status characteristics of relevant flights and airports in all life cycle of the flight chain were integrated. Then， a dynamic monitoring function model of flight chain operation status including flight chain delay prediction module， error compensation module based on historical data， and flight chain status monitoring module was constructed. Finally， a dynamic update strategy of the model was designed on the basis of incremental learning in order to improve the model robustness. Through simulation experiments in laboratory environment， it can be seen that the proposed method achieves excellent results in terms of computational efficiency and accuracy， and the accuracy reaches 92.07%. Therefore， the proposed method can monitor the operation status of flight chain effectively， help to achieve precise control of the flight operation situation， and improve operational control efficiency.

Key words: flight chain operation status, dynamic monitoring, flight delay, error compensation, incremental learning

摘要：

为了更准确地把握航班运行的整体状态，提出一种航班链运行状态动态监控方法。首先，从航班链整体的角度出发，根据航班链运行业务流程和数据特点设计航班链数据处理方法，并整合航班链全生命周期内相关航班和机场的运行状态特征；其次，构建包含航班链延误预测模块、基于历史数据的误差补偿模块和航班链状态监控模块在内的航班链运行状态动态监控功能模型；最后，基于增量学习设计了模型的动态更新策略，从而提高模型的鲁棒性。通过在实验室环境下进行的模拟实验可知，所提方法在运算效率和准确度上均取得了优异结果，其中准确率达到92.07%。因此，所提方法能有效监控航班链运行状态，有助于实现对航班运行态势的精准把控并提高运控效能。

关键词: 航班链运行状态, 动态监控, 航班延误, 误差补偿, 增量学习

CLC Number:

TP182

Jianli DING, Hui HUANG, Weidong CAO. Dynamic monitoring method of flight chain operation status[J]. Journal of Computer Applications, 2024, 44(12): 3941-3948.

丁建立, 黄辉, 曹卫东. 航班链运行状态动态监控方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3941-3948.

Figures/Tables 13

Tab. 1 Missing rates and handling methods of some features

特征	特征含义	缺失率/%	处理方式
company	航空公司	0.06	标志位填充
pta	计划到达时间	0.67	直接删除
delayreason	延误原因	93.13	直接删除
windspeed	风速	9.36	0填充
flightlong	飞行距离	15.71	均值填充
cloud	云层状态	23.72	上一时刻值填充
longitude	经度	3.30	前后两处观测点取均值

Fig.1 Schematic diagram of hybrid coding

Tab.2 Data characteristics of flight chain

航班链元素	特征来源	特征维度	包含特征
航班元素	航班计划数据	32	航班号、飞机尾号、计划起飞时间、到达机场……
航班元素	气象数据	12	时间戳、机场、温度、风速……
机场元素	机场状态数据	9	机场经度、机场海拔、机场等级、繁忙程度……
机场元素	气象数据	12	云层状态、湿度、风向、能见度……
当前运行航班元素	航班运行数据	12	维度、速度、方向角、高度……

Fig.2 Dynamic monitoring model of flight chain operation status

Fig.3 Flight chain delay prediction model

Tab.3 Classes of flight chain operation status

$S C$ /min	航班链运行状态类别
$[0,15]$	良好
$(15,60]$	轻度延误
$(60,180]$	中度延误
$(180, + ∞)$	重度延误

Tab.3 Classes of flight chain operation status

$S C$ /min	航班链运行状态类别
$[0,15]$	良好
$(15,60]$	轻度延误
$(60,180]$	中度延误
$(180, + ∞)$	重度延误

Tab.4 Trends of change in flight chain operation status

$S C$ （当前）	$Δ S C$	$S C$ （未来）	航班链运行状态变化趋势
$[0,15]$	$(- 15,15)$	$[0,15]$	状态正常
$[0,15]$	$(0, + ∞)$	$(15, + ∞)$	发生延误
$(15, + ∞)$	$(10, + ∞)$	$(15, + ∞)$	延误加重
$(15, + ∞)$	$[- 10,10]$	$(15, + ∞)$	延误持续
$(15, + ∞)$	$(- ∞, - 10)$	$(15, + ∞)$	延误减轻
$(15, + ∞)$	$(- ∞, 0)$	$[0,15]$	恢复正常

Tab.4 Trends of change in flight chain operation status

$S C$ （当前）	$Δ S C$	$S C$ （未来）	航班链运行状态变化趋势
$[0,15]$	$(- 15,15)$	$[0,15]$	状态正常
$[0,15]$	$(0, + ∞)$	$(15, + ∞)$	发生延误
$(15, + ∞)$	$(10, + ∞)$	$(15, + ∞)$	延误加重
$(15, + ∞)$	$[- 10,10]$	$(15, + ∞)$	延误持续
$(15, + ∞)$	$(- ∞, - 10)$	$(15, + ∞)$	延误减轻
$(15, + ∞)$	$(- ∞, 0)$	$[0,15]$	恢复正常

Fig.4 Model update strategy based on incremental learning

Tab.5 Model operation effect

批次大小	平均耗时/ms	准确率
100	76.00	0.892 0
200	58.66	0.920 7
300	92.94	0.913 4
400	81.06	0.905 0
500	102.10	0.876 0

Fig.5 Average time taken by each module of model

Tab.6 Experimental results of comparison

实验设置	准确率	精确率	召回率	F1分数
使用误差补偿模块	0.920 7	0.919 6	0.915 6	0.917 6
未使用误差补偿模块	0.853 0	0.834 8	0.840 1	0.837 4

Fig. 6 Confusion matrix

Fig. 7 Simulated operation results

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