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Received:May 27, 2024 Revised:June 26, 2024
Received:May 27, 2024 Revised:June 26, 2024
中文摘要: 航段和航路点对网络正常运行具有重要意义, 正确识别关键航段和关键航路点, 并分析各个指标对航段或航路点的重要程度的关联程度有利于针对性地提升航路网络的抗打击能力. 为改善航路网络对各种突发情况的弱“抵抗力”情况, 从静态指标和动态指标两个方面入手, 采用熵权法从数据本身的波动程度出发确定静态指标和动态指标的权重, 并采用优劣解距离法通过计算连边的最优和最劣解, 得到各个航段和航路点的综合得分. 并继续分析各个指标之间及指标与航段或航路点综合得分之间的关联度, 结果表明各个指标之间都相对独立, 但各个指标与航段或航路点得分的关联度较高, 该结论为航路网络结构优化提出改进依据.
中文关键词: 航路网络 节点识别 连边识别 TOPSIS-EWM 关联度
Abstract:Flight segments and waypoints are crucial for the normal operation of a route network. A network’s resistance to disruptions can be enhanced by correctly identifying key flight segments and waypoints and analyzing the correlation between various indicators and the importance of these segments or waypoints. To address the weak resistance of the route network to unexpected situations, both static and dynamic indicators are considered in this study. Using the entropy weight method, the weights of these indicators are determined based on their intrinsic fluctuations. Then, the technique for order preference by similarity to ideal solution is applied to calculate the optimal and worst solutions for the edges, so as to obtain comprehensive scores for each flight segment and waypoint. Further, analysis is conducted on the correlation among indicators, as well as the correlation between indicators and the comprehensive scores of flight segments or waypoints. The results show that while the indicators are independent of each other, their correlation with the scores of the flight segments or waypoints is high. This conclusion provides a basis for improving the route network structure.
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基金项目:江苏省青年基金(BK20230892)
引用文本:
孟贤,田勇,李江晨.基于多指标关联的航路网络节点和连边识别.计算机系统应用,2024,33(12):256-263
MENG Xian,TIAN Yong,LI Jiang-Chen.Identification of Route Network Nodes and Edges Based on Multi-indicator Correlation.COMPUTER SYSTEMS APPLICATIONS,2024,33(12):256-263
孟贤,田勇,李江晨.基于多指标关联的航路网络节点和连边识别.计算机系统应用,2024,33(12):256-263
MENG Xian,TIAN Yong,LI Jiang-Chen.Identification of Route Network Nodes and Edges Based on Multi-indicator Correlation.COMPUTER SYSTEMS APPLICATIONS,2024,33(12):256-263