Airspace situation assessment system

Abstract

The invention particularly relates to an airspace situation assessment system, which comprises a labeling module for extracting sector dynamic traffic data of a target airspace sector and labeling airspace operation complexity levels; a gridding module which is used for delimiting an external rectangle of a target airspace sector and carrying out gridding processing; an image construction module which is used for constructing multi-channel air traffic situation images and constructing an air traffic situation image library according to airspace operation complexity level marks; a model construction module which is used for constructing an airspace operation complexity hierarchical network model according to the multi-channel air traffic situation images; a model training module which is used for training the airspace operation complexity hierarchical network model; and an evaluation module which is used for carrying out airspace operation complexity evaluation according to the trainedairspace operation complexity hierarchical network model, so that the workload and the use threshold of airspace complexity evaluation are greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of airspace situation assessment of air traffic control, and in particular relates to an airspace situation assessment system. Background technique [0002] With the development of the air transport industry, the sharp increase in air traffic flow and limited airspace resources have brought huge workload and pressure to air traffic controllers. Airspace operational complexity is a key indicator for evaluating controller workload, and airspace operational complexity can also provide decision support for strategic and tactical air traffic management systems, so how to determine a scientific, accurate and reliable method to evaluate airspace Runtime complexity is one of the widely studied problems. In recent years, some scholars have adopted machine learning methods based on manual features to solve the problem of airspace operation complexity evaluation. Through machine learning algorithm training, the mappi...

AI Technical Summary

Problems solved by technology

[0004] Existing technologies rely on manually selected airspace operational complexity features. Although the machine learning algorithm model can learn the mapping relationship between different features and airspace operational complexity, the mapping relationship is not perfect due to possible defects in the feature set. , and the selection and use of feature sets are greatly affected by actual scenarios and professional knowledge

Images