A method for extracting key factors of aircraft empirical trajectory model base on Hadoop data mining

Abstract

A method for extracting key factors of an aircraft empirical trajectory model base on Hadoop data mining is disclosed, which is used for fast and accurate aircraft trajectory prediction to realize that foundation and guarantee base on trajectory operation. The invention is based on radar track data, flight plan and other operation data, adopts big data distributed operation framework, and conductsflight experience trajectory model research according to city pair, aircraft type and other characteristic attributes. The main key factors involved in the model are flight actual range, route reportpoint actual range, cruise altitude and speed, and the like.

Description

technical field [0001] This patent belongs to the invention patent of computer application program, which involves the field of aircraft flight trajectory prediction in civil aviation air traffic control and flow management. Background technique [0002] The concept of aircraft trajectory prediction can be traced back to a strategic control scheme proposed by Boeing in the 1970s. The basic idea is: to expand the range of traffic control of the aircraft, when it is still far away from the airport, or even just take off, specify a 4D flight plan for it, so that it can follow the optimized flight profile during the entire flight, according to the expected schedule Arriving at a waypoint or terminal airport, use the time interval to adjust its approach and landing sequence. In this way, the ATC personnel can start from the overall situation and dispatch the flight of the aircraft in a large range (or the entire airspace). The strategic control program quickly received strong s...

AI Technical Summary

Problems solved by technology

The volume of data is very large. For example, taking national flight track data as an example, there are 500 million pieces of valid data recorded in one day after data cleaning. It is very necessary to achieve efficient analysis and research; in addition, it includes airspace flow analysis, flight plan trajectory prediction and control. Most of the control intention sequence information, feature point altitude, speed and other information, experience routes and other information required by key businesses such as command plans still rely on manual experience, and lack the value of reflecting relevant operational data through analysis of historical information

[0005] Most of the control intention sequence information, feature point altitude, speed and other information, experience routes and other information required by key businesses such as airspace flow analysis, flight plan trajectory prediction, and control command plan still rely on manual experience, and lack of correlation through analysis of historical information. The value of operating data; and aircraft performance has a considerable impact on trajectory prediction. Different types of aircraft should have their own set of data models. Manual experience basically uses only one set of parameters, so the aircraft performance model used in trajectory prediction The attribute index does not distinguish the type of aircraft actually used. The use of such data has a large error in the prediction of the flight trajectory, the deviation of the prediction result is high, and the data availability is not strong

Images