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）是指利用空中交通流量管理措施来调整每个交通流量中的交通需求，从而使飞机在随后的空中交通管制过程中能够安全地分开。另一方面，空中交通管制员（atco）给不同的飞机航向、速度和飞行高度改变指令，使它们在飞行中分开。在过去的几十年里，ATFM和ATC问题一直是研究的主题，然而，所有以前的工作都是独立解决ATFM和ATC问题。本项目旨在开发一个基于先进的人工智能强化学习方法、持续重新评估和动态更新的HyperSolver，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（动态容量平衡时间框架内的潜在冲突）、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.