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

An extraction method and aircraft technology, which are applied in database models, location-based services, and specific environment-based services, etc., can solve the problems of considerable impact on aircraft trajectory prediction, large flight trajectory prediction errors, and lack of operational data value, etc. Achieve the effect of improving the level of safety and operational service efficiency, reducing monotony, and improving service capabilities

An extraction method and aircraft technology, which are applied in database models, location-based services, and specific environment-based services, etc., can solve the problems of considerable impact on aircraft trajectory prediction, large flight trajectory prediction errors, and lack of operational data value, etc. Achieve the effect of improving the level of safety and operational service efficiency, reducing monotony, and improving service capabilities

CN109542876AActive Publication Date: 2019-03-29NANJING LES INFORMATION TECH
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  • A method for extracting key factors of aircraft empirical trajectory model base on Hadoop data mining
  • A method for extracting key factors of aircraft empirical trajectory model base on Hadoop data mining
  • A method for extracting key factors of aircraft empirical trajectory model base on Hadoop data mining

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[0042] Definitions of specific terms and common abbreviations used in this document:

[0043] ATC: Air traffic control air traffic control.

[0044] ATFM: Air traffic flow management air traffic flow management.

[0045] 4D track: The space-time four-dimensional coordinates, three-dimensional space position and corresponding passing time of each point experienced by the aircraft during the whole process from take-off to landing.

[0046] 4D track prediction: When a sortie flight has not occurred, predict and calculate the 4D track point series that will be generated by the sortie flight based on experience information and initial plan information.

[0047] Below, the present invention will be described in further detail in conjunction with the accompanying drawings.

[0048] The present invention is based on operational data such as radar track data and flight plans, adopts the big data Hadoop distributed computing architecture, and carries out flight experience route model ...

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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...

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Application Information

Patent Timeline
29 Mar 2019
Publication
CN109542876A
IPC
G06F16/21; G06F16/27; G06F16/28; G06F16/22; H04W4/02; H04W4/029; H04W4/42; G06Q10/04; G06Q50/30
CPC
H04W4/02; H04W4/029; H04W4/42; G06Q10/04; G06Q50/40; Y02D10/00
Inventors
丁一波; 庄青