Abstract: A method for collaborative and optimal allocation of a route time slot and a flight level comprises: acquiring planned route information and alternative route information in a traffic limited airspace, available time slot information of a downstream airspace unit of the planned route, and flight operation information first, then formulating an algorithm for the collaborative and optimal allocation of the route time slot and the flight level, establishing an objective function and a constraint condition which meet effectiveness by taking a minimum total ground delay loss of all flights, a minimum change number of flight levels of all flights and a minimum total fuel consumption of all flights as objectives, further establishing a multi-objective optimization model for the collaborative allocation of the route time slot and the flight level, and forming a strategy for the collaborative and optimal allocation of the route time slot and the flight level.

Abstract: A flight trajectory multi-objective dynamic planning method, which belongs to the field of air traffic management, comprises: acquiring a route network structure, a flight segment length, flight level configurations of each flight segment and flow control information in an available airspace of a flight first, establishing a flight trajectory multi-objective dynamic planning model by taking a minimum fuel consumption, a shortest flight time and a minimum number of flight level changes as objectives, further designing a solving algorithm of the flight trajectory multi-objective dynamic planning model, and finally solving the model to form a plurality of strategies of flight trajectory multi-stage decision.

Abstract: A flight trajectory multi-objective dynamic planning method, which belongs to the field of air traffic management, comprises: acquiring a route network structure, a flight segment length, flight level configurations of each flight segment and flow control information in an available airspace of a flight first, establishing a flight trajectory multi-objective dynamic planning model by taking a minimum fuel consumption, a shortest flight time and a minimum number of flight level changes as objectives, further designing a solving algorithm of the flight trajectory multi-objective dynamic planning model, and finally solving the model to form a plurality of strategies of flight trajectory multi-stage decision.

Abstract: A method for collaborative and optimal allocation of a route time slot and a flight level comprises: acquiring planned route information and alternative route information in a traffic limited airspace, available time slot information of a downstream airspace unit of the planned route, and flight operation information first, then formulating an algorithm for the collaborative and optimal allocation of the route time slot and the flight level, establishing an objective function and a constraint condition which meet effectiveness by taking a minimum total ground delay loss of all flights, a minimum change number of flight levels of all flights and a minimum total fuel consumption of all flights as objectives, further establishing a multi-objective optimization model for the collaborative allocation of the route time slot and the flight level, and forming a strategy for the collaborative and optimal allocation of the route time slot and the flight level.

Abstract: A method accurately evaluates a corresponding operation capacity for an operating characteristic of a to-be-evaluated object in a to-be-evaluated time period in combination with already-operated historical data of the to-be-evaluated object, which specifically includes: for a capacity influence factor in an operating process of an airspace unit, constructing a capacity similarity characteristic model to form a capacity similarity characteristic index set; acquiring historical data of an evaluation object, on the basis of the capacity similarity characteristic index set, classifying historical data samples of different time periods by a clustering algorithm, and generating a capacity similarity time period sample set to which an evaluation time period of the current evaluation object belongs; and classifying historical capacity values of the capacity similarity time period sample set by a density clustering algorithm, and calculating a capacity reference value on the basis of a maximum class cluster.