基于K-means聚类的通航协同空域规划算法设计和实现

随着通航产业的不断发展，通航协同空域的合理划设是其高效、安全运营的关键，但目前以人为认知和经验划设的协同空域在使用效率和便利性上并不能满足当前通航产业链快速发展的需求。针对这个问题，文章运用K-means算法对海量历史ADS-B监视数据进行聚类分析，计算得出使用率高、通用航空器数量密集的空域范围，可为低空运行中心空域规划提供辅助决策。

Design and Implementation of General Aviation Cooperative Airspace Planning Algorithm Based on K-means Clustering

With the continuous development of the general aviation industry, the reasonable allocation of the general aviation cooperative airspace is the key to its efficient and safe operation. However, the current collaborative airspace based on human cognition and experience cannot satisfy the rapid development of the current general aviation industry chain in terms of efficiency and convenience. In response to this problem, a K-means algorithm is proposed to use the cluster analysis of massive historical ADS-B surveillance data to calculate the airspace range with high utilization rate and intensive general aircraft, and provide assistance for the airspace planning of the low-altitude operation center, as well as decision making.