Artificial Intelligence controller able to manage Air traffic Control (ATC) and Air Traffic Flow Management (ATFM) within a single framework

空中交通流量管理(ATFM)是指使用空中交通流量管理(ATFM)措施来调整每个交通量中的交通需求，以便在随后的空中交通管制(ATC)过程中能够安全地分离飞机。另一方面，空管人员(ATCO)发出不同的飞机航向、速度和飞行级别改变指令，以区分飞行中的不同飞机。几十年来，ATFM和ATC问题一直是研究的对象，然而，以前的工作都是独立地研究ATFM和ATC问题。该项目旨在开发一个基于先进的人工智能强化学习方法的超级求解器，不断重新评估和动态更新，即从端到端的整体求解器，涵盖整个管理过程、飞机密度、轨迹复杂性、轨迹相互作用(动态能力平衡时间框架中的潜在冲突)、中期轨迹冲突和短期轨迹冲突。

Air Traffic Flow Management (ATFM) is the problem of adjusting the traffic demand in each traffic volume using ATFM measures so that aircraft can be safely separated during the subsequent Air Traffic Control (ATC) process. On the other hand, ATC officers (ATCOs) give different aircraft heading, speed, and flight level change instructions to separate them in flight. Both ATFM and ATC problems have been subject of research during decades, however, all previous works addressed the ATFM and ATC problems independently. The project aims to develop an HyperSolver based on advanced Artificial Intelligent Reinforcement Learning method with continuous reassessment and dynamic updates, i.e. an holistic solver from end-to-end, covering the whole process to manage, density of aircraft, complexity of trajectories, interactions (potential conflict in Dynamic Capacity Balancing timeframe) of trajectories, conflict of trajectories at medium term and conflict of trajectories at short-term.