Airspace network capacity prediction method in severe weather

Abstract

The invention relates to the technical field of air traffic management, and provides an airspace network capacity prediction method in severe weather. The method comprises the following steps: dividing an airport terminal area into a plurality of sub-airspaces according to a historical track, constructing a space-time diagram convolutional neural network diagram structure data set based on an attention mechanism according to a severe weather time list and a set number of node features, and designing and training the space-time diagram convolutional neural network based on the attention mechanism, the method comprises the following steps: acquiring a neural network parameter mapped by a weather-to-capacity influence mechanism, inputting graph structure feature data by using a trained time-space graph convolutional neural network based on an attention mechanism, and carrying out capacity prediction on each sub-airspace of an airspace network. According to the method, the airspace network capacity is predicted more accurately, the decision quality and the refinement degree are improved, meanwhile, the interpretability of the model is improved, the model is closer to the manual control judgment effect of a controller, and the control pressure of an air traffic control department is effectively relieved.

Description

technical field [0001] The invention relates to the technical field of air traffic management, in particular to a method for predicting airspace network capacity under bad weather. Background technique [0002] In all kinds of flight abnormalities, severe weather is one of the main factors that prevent the normal operation of the flight, and its impact on the normal operation of the flight is reflected in the fact that the affected sectors and airport capacity are limited by the control department under severe weather, resulting in Affected by the flow control, flights were delayed or cancelled on a large scale, which eventually resulted in a large number of passengers stranded at the airport. Civil aviation takes passenger experience as the core. In order to avoid the occurrence of passengers stranded at the airport, it is necessary to predict the airspace capacity affected by bad weather in advance, so as to plan a new flight plan or cancel the flight, and release the info...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides an airspace network capacity prediction method under bad weather to solve the problem that it is difficult to adapt to complex air traffic operation scenarios in the prior art, the mechanism of capacity impact is not well grasped, and each airspace node in the airspace network is in bad weather. Weather conditions will interact spatially and create interrelated problems in the temporal dimension

Images