CN104008674B - March into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match - Google Patents
March into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match Download PDFInfo
- Publication number
- CN104008674B CN104008674B CN201410266912.3A CN201410266912A CN104008674B CN 104008674 B CN104008674 B CN 104008674B CN 201410266912 A CN201410266912 A CN 201410266912A CN 104008674 B CN104008674 B CN 104008674B
- Authority
- CN
- China
- Prior art keywords
- flight path
- flight
- path
- nominal
- termination environment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of termination environment based on pattern match to march into the arena flight time Forecasting Methodology, computer system comprises a termination environment operating in client and to march into the arena flight time predicting subsystem, and termination environment flight time predicting subsystem of marching into the arena comprises approach path segmentation module and approach path matching module; Approach path segmentation module carries out staging treating according to the history track data of each flight in termination environment to each bar track data, approach path matching module is the segmentation result based on history flight path, find the flight path section the most similar to current flight path, and realize the prediction to the current track flight time according to this history flight path segment information; Its advantage is, on the basis of airport track segmentation and track matching, can realize fast and effectively termination environment march into the arena the flight time prediction, the flight posture of aircraft thus the auxiliary scientific and rational assurance of related personnel is marched into the arena, take air traffic control measure timely and effectively, ensure safety, the Effec-tive Function of air traffic.
Description
Technical field
The present invention relates to flight track prediction field, to march into the arena flight time Forecasting Methodology in particular to a kind of termination environment based on pattern match, application this method realize termination environment flight march into the arena the flight time prediction, be beneficial to and take air traffic control measure timely and effectively, ensure termination environment traffic conevying efficiency and reduce airliner delay.
Background technology
Trajectory Prediction refers to before flight or in flight course, according to flight planning, radar asorbing paint data and FPL(FlightPlan flight planning) to aircraft, breath of notifying estimates that flight position, height and time are predicted.According to Trajectory Prediction information source used and predicted time, can be divided into and show flight path, flight planning flight path, real-time estimate flight path and control intention flight path the timetable.Trajectory Prediction is the important component part in air traffic control automation system and blank pipe aid decision-making system, is also one of hot issue of domestic and international blank pipe circle research.
Current flight track Forecasting Methodology mainly contains two large classes: one is the prediction based on basic flight model, and two is the predictions of excavating based on history track data.Wherein: basic flight model prediction needs to set up aircraft flight kinetic model and flight overall process aeroplane performance model, predicts accordingly to flight path information in flight course.As: according to Complex Adaptive Systems mechanism, flight overall process is divided into basic flight model module, sets up performance computation model respectively for basic flight model module, finally carry out composite tracking and obtain complete 4D flight track; Or obtain aircraft according to radar scanning and cross way point information, according to this information and aircraft flight speed, flight-path angle, infer by the principle that flies at a constant speed and obtain the time that next way point crossed by aircraft, the rest may be inferred can obtain continuous renewal and obtain follow-up way point information.Based on the prediction that history track data excavates, then according to history track data information, predict the flight time by regression analysis.As: set up corresponding regression model by analyzing the gentle impact resembling factor pair flight time accumulation of control, and obtain the prediction to flight position further by comparison; Or according to previous data and observation data in this flight course of aircraft, by Kalman filtering, neuron network simulation method establishment aircraft track Short-term Forecasting Model.
For the flight track of termination environment, due to aircraft configuration and parameter changeable, aircraft force analysis is very complicated, and adopt the actual very difficult accurate simulation airplane motion of simplified model, the computation model therefore adopting Aircraft Company to determine through taking a flight test within the scope of termination environment is the most accurate.But Aircraft Company generally can not directly provide corresponding computation model to airline, and also there is the impact of meteorology, control uncertain factor in aircraft flight.Therefore, adopt basic flight model Forecasting Methodology to be difficult to analyze these uncertain factors, need effective Forecasting Methodology to realize predicting termination environment flight track.
Consider the present situation that existing flight track is predicted, still lack a kind of can marching into the arena flight time Forecasting Methodology quick, effective, practical termination environment.
Summary of the invention
In view of the deficiency in prior art field, the object of the invention is to, a kind of new termination environment is provided to march into the arena flight time Forecasting Methodology, the method is studied termination environment based on the pattern match of flight track and to be marched into the arena flight time prediction, in the hope of predicting to march into the arena the time in termination environment fast and effectively, thus take air traffic control measure timely and effectively, ensure flight safety and improve conevying efficiency.
The present invention realizes like this, march into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match, realize by computer system is auxiliary, described computer system is formed primarily of client/server (C/S) pattern, computer system comprises spatial domain navigational route database, Simulation drive subsystem, termination environment structural modeling subsystem, flight planning subsystem and display and interactive subsystem, it is characterized in that, also comprise a termination environment operating in client in computer system to march into the arena flight time predicting subsystem, described termination environment flight time predicting subsystem of marching into the arena is used as termination environment and marches into the arena the implementation platform of flight time Forecasting Methodology,
Termination environment flight time predicting subsystem of marching into the arena comprises approach path segmentation module and approach path matching module; Wherein:
Described approach path segmentation module be history track data according to each flight in termination environment (
x i , Y i , Z i , V i , T i , H i ) staging treating is carried out to each bar track data,
i=1,2 ...,
p, wherein, (
x i , Y i , Z i ) be each track points three-dimensional coordinate,
v i for this ground velocity,
t i for spending the some moment,
h i for vector,
pfor track points number; Processing procedure comprises:
1) determine that certain flight path enters the sequence number of termination environment point, Initial Approach anchor point and Final Approach Fix, and set Initial Approach anchor point as
kindividual point;
2) on this flight path 1st o'clock to
kindividual, carry out sectional linear fitting according to horizontal slope, if track points be divided into after linear fit
mgroup, namely
mindividual straight-line segment;
3) to above-mentioned
mindividual flight path group, calculates it respectively and nominal flight path departs from situation, to each track points in each flight path group, according to
x i or
y i value carry out interpolation, to obtain on nominal flight path corresponding
or
value, calculates the departure degree that this track points and nominal flight path are put, namely respectively
or
, and this group track points average departure, maximum deviation and minimum deviation value;
4) depart from situation according to flight path, judge this section of flight path state, if maximum/minimum deviation value all within the specific limits, then think that this section of flight path is on nominal flight path, otherwise judge that it is in nominal flight path left/right; Then according to flight path deviation value situation of change, flight path variation tendency is judged;
5) adjacent flight path section identical for state of flight is merged, so-called to merge namely: according to aircraft left/right avertence is from nominal flight path, and aircraft is relative to the flight trend of nominal flight path, namely close, away from, parallel aircraft level flight condition is described, finally obtain this track segmentation, and the state of each section of flight path;
Described approach path matching module is the segmentation result based on history flight path, finds the flight path section the most similar to current flight path, and realizes the prediction to the current track flight time according to this history flight path segment information; Processing procedure comprises:
1) when aircraft enters termination environment, enter the position of termination environment point, flying height, speed and course according to aircraft, find the most similar flight path section and mate, using this flight path section flight time and state as initial predicted flight time and state of flight;
2) read track points data in first 1 minute every 1 minutes, total flight path is upgraded, and carries out sectional linear fitting, thus realize track segmentation;
3) comparing then according to prediction flight path state and actual flight path state, judge whether to need again to search coupling flight path; Track matching method is from the identical or close history flight path section of flight path state, find depart from current flight path position, highly, speed and the minimum flight path section of course drift, as coupling flight path section;
4) due to schedule flight markov property, namely subsequent flights is thought and is only depended on current state, and therefore according to coupling flight path section and coupling flight path follow-up, to marching into the arena, the flight time is predicted;
March into the arena flight time Forecasting Methodology in described a kind of termination environment based on pattern match, it is characterized in that, utilize described approach path segmentation module to realize the staging treating of history flight path, utilize described approach path matching module to realize current flight path with the step of mating of history flight path to be;
Step 1: entry terminal district route grid structured data in the structural modeling subsystem of termination environment, nominal air route course data, flight planning data are worked out in flight planning subsystem, simulation time is set, call Simulation drive subsystem, add up the actual flight path data of flight of respectively marching into the arena, add up departure degree and offset direction that actual flight path point and nominal flight path are put;
Step 2: march into the arena in termination environment in the approach path segmentation module of flight time predicting subsystem, based on the departure degree that actual flight path point and nominal flight path are put and offset direction, judge the state of flight of each flight path section of current flight path and history flight path: on nominal flight path; On the left of nominal flight path and away from nominal flight path; On the left of nominal flight path and near nominal flight path; On the left of nominal flight path and parallel nominal flight path; On the right side of nominal flight path and away from nominal flight path; On the right side of nominal flight path and near nominal flight path; On the right side of nominal flight path and parallel nominal flight path;
Step 3: march into the arena in termination environment in the approach path matching module of flight time predicting subsystem, the segmentation result of current flight path is mated with history track segmentation result, select the coupling flight path of the highest history flight path of similarity degree as current flight path, the flight time based on coupling flight path each flight path section was predicted the current flight path flight time of following 1 minute;
Step 4: termination environment march into the arena the flight time predict the outcome display with interactive subsystem in show.
Advantage of the present invention is, on the basis of airport track segmentation and track matching, can realize fast and effectively termination environment march into the arena the flight time prediction, the flight posture of aircraft thus the auxiliary scientific and rational assurance of related personnel is marched into the arena, and take air traffic control measure timely and effectively, ensure safety, the Effec-tive Function of air traffic.
Accompanying drawing explanation
Fig. 1, be termination environment of the present invention march into the arena the flight time prediction process flow diagram;
Fig. 2 is overall system topology diagram of the present invention.
In Fig. 2: 1, server; 2, Simulation drive server; 3, the first client; 4, the second client; 5, the 3rd client; 6, the 4th client.
Embodiment
Clearly understand to make object of the present invention, technical scheme and advantage, below in conjunction with accompanying drawing and example, the present invention is further elaborated, is to be understood that, instantiation described herein only in order to explain the present invention, is not intended to limit the present invention.
As shown in Figure 1, the segmentation result of current flight path with the flow process that history track segmentation result carries out mating is:
100: aircraft enters termination environment time t0;
101: initialization: ti-1=t0 rounds up IAF_STATUS=0;
102: read track points data in first 1 minute, be stored in traj_h; Total flight path traj_total=traj_h is set;
103: enter termination environment state according to aircraft, search coupling flight path, prediction flight time;
104:ti=ti-1+1 reads track points data in first 1 minute, is stored in traj_h;
105: whether aircraft arrives IAF point
Turn 106: intercept the track points before IAF point; IAF_STATUS=1;
No, turn 107: upgrade the total flight path traj_total of aircraft; Sectional linear fitting is carried out and by track segmentation according to total flight path;
108: the need of again searching for coupling flight path section
Turn 109: the most similar flight path section is searched in search again;
No, turn 110: upgrade Trajectory Prediction and export;
111：IAF_STATUS=1
Turn 112: terminate;
No, turn 104.
Now provide and realize termination environment and to march into the arena the example of flight time:
Suppose, with A airport and terminal area 3 history track data, as test data, to carry out flight time prediction.By carrying out Multi simulation running experiment in the flight path module of termination environment, obtaining 30 track data, adding up each track points data of each bar approach path, and then try to achieve departure degree and the offset direction of its track points corresponding to nominal flight path.
The segment information of wherein representational 3 track data is only listed below staging treating result to front 27 history flight paths, wherein flight path 2 is more common a kind of situations, namely aircraft is along Standard Approach programed flight to Initial Approach anchor point (IAF) XLN, then along the flight of standard approach procedure to Final Approach Fix (FAF).Flight path state divides using flight path 2 as nominal flight path, and these 3 track segmentation situations are as follows:
Flight path 1: be divided into 4 flight path sections, its state is respectively:
On the right side of nominal flight path, away from nominal flight path;
On the right side of nominal flight path, be parallel to nominal flight path;
On the right side of nominal flight path, near nominal flight path;
IAF-FAF enters nearly section.
Flight path 2: be divided into 2 flight path sections, its state is respectively:
Along nominal track flight;
IAF-FAF enters nearly section.
Flight path 3: be divided into 4 flight path sections, its state is respectively:
On the left of nominal flight path, near nominal flight path;
On the left of nominal flight path, away from nominal flight path;
On the left of nominal flight path, near nominal flight path;
IAF-FAF enters nearly section.
Choose 3 history track data next, as test data, carry out flight time prediction.
Flight path 28:
| Estimate to arrive IAF point required time (minute) | Estimate to arrive FAF point required time (minute) | Actual arrival IAF point required time (minute) | Actual arrival FAF point required time (minute) |
| 24.08 | 34.06 | 29.48 | 34.33 |
| 23.09 | 33.07 | 28.55 | 33.40 |
| 25.58 | 29.46 | 27.48 | 32.33 |
| 24.65 | 28.53 | 26.48 | 31.33 |
| 23.67 | 27.56 | 25.48 | 30.33 |
| 22.72 | 26.61 | 24.48 | 29.33 |
| 21.73 | 25.61 | 23.48 | 28.33 |
| 20.79 | 24.67 | 22.48 | 27.33 |
| 21.94 | 24.52 | 21.55 | 26.40 |
| 14.50 | 19.02 | 20.48 | 25.33 |
| 15.18 | 20.53 | 19.48 | 24.33 |
| 13.99 | 19.74 | 18.50 | 23.35 |
| 14.13 | 19.13 | 17.50 | 22.35 |
| 17.28 | 19.86 | 16.48 | 21.33 |
| 12.36 | 17.36 | 15.48 | 20.33 |
| 11.50 | 16.50 | 14.53 | 19.38 |
| 10.60 | 15.60 | 13.53 | 18.38 |
| 9.78 | 14.78 | 12.50 | 17.35 |
| 9.15 | 14.15 | 11.75 | 16.60 |
| 8.91 | 13.91 | 10.48 | 15.33 |
| 6.22 | 11.97 | 9.53 | 14.38 |
| 7.77 | 9.52 | 8.53 | 13.38 |
| 5.23 | 9.96 | 7.50 | 12.35 |
| 4.79 | 12.79 | 6.47 | 11.32 |
| 4.12 | 12.12 | 5.50 | 10.35 |
| 3.45 | 11.45 | 4.53 | 9.38 |
| 2.51 | 10.51 | 3.45 | 8.30 |
| 2.09 | 7.35 | 2.47 | 7.32 |
| 1.24 | 6.51 | 1.50 | 6.35 |
| 0.37 | 5.63 | 0.50 | 5.35 |
| 0.09 | 5.36 | 0.00 | 4.85 |
IAF AME-2.1513 mean square deviation 1.7275 Error Absolute Value mean value 2.2335, maximal value 5.9822,
FAF AME-1.6997 mean square deviation 2.0042, Error Absolute Value mean value 2.2484, maximal value 6.3155.
Flight path 29:
| Estimate to arrive IAF point required time | Estimate to arrive FAF point required time | Actual arrival IAF point required time | Actual arrival FAF point required time |
| 25.55 | 29.08 | 27.07 | 28.62 |
| 28.05 | 31.93 | 25.93 | 27.48 |
| 24.38 | 28.26 | 24.98 | 26.53 |
| 22.90 | 25.03 | 23.93 | 25.48 |
| 21.95 | 24.08 | 22.93 | 24.48 |
| 21.46 | 23.60 | 22.47 | 24.02 |
| 19.86 | 21.99 | 20.93 | 22.48 |
| 17.21 | 21.55 | 19.92 | 21.47 |
| 16.20 | 20.54 | 18.93 | 20.48 |
| 15.79 | 17.54 | 17.93 | 19.48 |
| 14.80 | 16.55 | 16.92 | 18.47 |
| 13.77 | 15.52 | 15.93 | 17.48 |
| 11.96 | 16.30 | 15.00 | 16.55 |
| 11.97 | 15.86 | 13.92 | 15.47 |
| 13.41 | 15.16 | 12.93 | 14.48 |
| 12.26 | 14.01 | 11.92 | 13.47 |
| 11.10 | 12.85 | 10.92 | 12.47 |
| 9.95 | 11.70 | 9.92 | 11.47 |
| 8.85 | 10.60 | 8.92 | 10.47 |
| 5.90 | 9.79 | 7.92 | 9.47 |
| 4.89 | 8.77 | 6.92 | 8.47 |
| 5.84 | 7.59 | 5.92 | 7.47 |
| 4.07 | 6.20 | 4.92 | 6.47 |
| 3.91 | 5.70 | 3.92 | 5.47 |
| 2.89 | 4.67 | 2.92 | 4.47 |
| 1.86 | 3.65 | 1.92 | 3.47 |
| 1.20 | 2.95 | 1.00 | 2.55 |
| 0.19 | 1.94 | 0.00 | 1.55 |
IAF AME-0.8799 mean square deviation 1.2252 Error Absolute Value mean value 1.1326, maximal value 3.0359,
FAF AME 0.1136 mean square deviation 1.1649, Error Absolute Value mean value 0.6927, maximal value 4.4490.
Flight path 30:
| Estimate to arrive IAF point required time | Estimate to arrive FAF point required time | Actual arrival IAF point required time | Actual arrival FAF point required time |
| 23.75 | 27.22 | 25.83 | 30.25 |
| 22.32 | 25.78 | 24.38 | 28.80 |
| 21.26 | 24.73 | 23.30 | 27.72 |
| 20.28 | 23.75 | 22.30 | 26.72 |
| 19.38 | 22.85 | 21.32 | 25.73 |
| 20.85 | 23.11 | 20.32 | 24.73 |
| 20.14 | 25.89 | 19.32 | 23.73 |
| 21.32 | 23.07 | 18.30 | 22.72 |
| 20.32 | 22.07 | 17.30 | 21.72 |
| 19.37 | 21.12 | 16.32 | 20.73 |
| 15.91 | 20.97 | 15.32 | 19.73 |
| 14.93 | 19.99 | 14.30 | 18.72 |
| 14.00 | 19.06 | 13.37 | 17.78 |
| 15.19 | 16.94 | 12.32 | 16.73 |
| 14.09 | 15.84 | 11.30 | 15.72 |
| 13.06 | 14.81 | 10.30 | 14.72 |
| 12.00 | 13.75 | 9.32 | 13.73 |
| 10.91 | 12.66 | 8.30 | 12.72 |
| 7.80 | 12.54 | 7.33 | 11.75 |
| 6.74 | 11.47 | 6.32 | 10.73 |
| 5.57 | 10.31 | 5.32 | 9.73 |
| 5.14 | 9.87 | 4.90 | 9.32 |
| 3.43 | 8.16 | 3.30 | 7.72 |
| 2.32 | 7.05 | 2.30 | 6.72 |
| 1.28 | 6.01 | 1.30 | 5.72 |
| 0.39 | 5.12 | 0.38 | 4.80 |
| 0.01 | 4.75 | 0.00 | 4.42 |
IAF AME 0.6438 mean square deviation 1.7225 Error Absolute Value mean value 1.3961, maximal value 3.0485
FAF AME-0.1747 mean square deviation 1.4992, Error Absolute Value mean value 1.0523, maximal value 3.0333
Predicted the outcome by above-mentioned, the march into the arena error of flight time prediction of the termination environment can finding out based on pattern match is less, accuracy is higher, blank pipe personnel can be assisted to strengthen marching into the arena to A airport and terminal area the supervision of flight posture of aircraft future time period, to take measure of control in time.
Fig. 2 is overall system topology diagram of the present invention: described computer system is formed primarily of client/server (C/S) pattern, and computer system comprises:
A server 1, installs spatial domain navigational route database in the hard disk of server 1, for providing airport terminal plot structure, course line, flight road, restricted area, explosive area, forbidden zone, barrier, navigation data service to client.
A Simulation drive server 2, installing simulation driving data (storehouse) in the hard disk of Simulation drive server 2, for providing flying power model and the service of sporting flying model data to client.
First client 3, airport and terminal area modeling subsystem is installed in the hard disk of the first client 3, for setting up airport and terminal area structural model, comprise airport, guidance station, way point, barrier, course line, air route, sector, regulatory area, restricted area, explosive area, forbidden zone.
Flight planning subsystem is installed, for generation of the flight planning meeting certain regularity of distribution in the hard disk of the second client 4, second client 4.
In the hard disk of the 3rd client the 5, three client 5, installing terminal district marches into the arena the prediction of flight time predicting subsystem, schedule flight time of marching into the arena for termination environment.
3rd client 6, in the hard disk of the 3rd client 6, display and interactive subsystem are installed, for air route, the termination environment environment generated in airport and terminal area modeling subsystem, and the display of schedule flight time of marching into the arena that flight time predicting subsystem generates is marched into the arena with mutual in termination environment.
According to the above description, the solution of the present invention can be realized in conjunction with art technology.
Claims (2)
1. march into the arena flight time Forecasting Methodology in the termination environment based on pattern match, realize by computer system is auxiliary, described computer system is formed primarily of client/server C/S model, computer system comprises spatial domain navigational route database, Simulation drive subsystem, termination environment structural modeling subsystem, flight planning subsystem and display and interactive subsystem, it is characterized in that, also comprise a termination environment operating in client in computer system to march into the arena flight time predicting subsystem, described termination environment flight time predicting subsystem of marching into the arena is used as termination environment and marches into the arena the implementation platform of flight time Forecasting Methodology,
Termination environment flight time predicting subsystem of marching into the arena comprises approach path segmentation module and approach path matching module; Wherein:
Described approach path segmentation module is history the track data { (X according to each flight in termination environment
i, Y
i, Z
i, V
i, T
i, H
i) staging treating is carried out, i=1,2 to each bar track data ..., p, wherein, (X
i, Y
i, Z
i) be each track points three-dimensional coordinate, V
ifor this ground velocity, T
ifor spending the some moment, H
ifor vector, p is track points number; Processing procedure comprises:
1) determine that certain flight path enters the sequence number of termination environment point, Initial Approach anchor point and Final Approach Fix, and set Initial Approach anchor point as a kth point;
2) to a kth point, according to horizontal slope, sectional linear fitting is carried out to the 1st point on this flight path, if track points is divided into m group after linear fit, i.e. m straight-line segment;
3) to an above-mentioned m straight-line segment, it is calculated respectively and nominal flight path departs from situation to each track points in each flight path group, according to X
ior Y
ivalue carry out interpolation, to obtain on nominal flight path corresponding X '
i0or Y '
i0value, calculates the departure degree that this track points and nominal flight path are put, i.e. Δ d=X respectively
i-X '
i0or Δ d=Y
i-Y '
i0, and this group track points average departure, maximum deviation and minimum deviation value;
4) depart from situation according to flight path, judge this section of flight path state, if maximum/minimum deviation value all within the specific limits, then think that this section of flight path is on nominal flight path, otherwise judge that it is in nominal flight path left/right; Then according to flight path deviation value situation of change, flight path variation tendency is judged;
5) adjacent flight path section identical for state of flight is merged, so-called to merge namely: according to aircraft left/right avertence is from nominal flight path, and aircraft is relative to the flight trend of nominal flight path, namely near away from or parallel aircraft level flight condition is described, finally obtain this track segmentation, and the state of each section of flight path;
Described approach path matching module is the segmentation result based on history flight path, finds the flight path section the most similar to current flight path, and realizes the prediction to the current track flight time according to this history flight path segment information; Processing procedure comprises:
1) when aircraft enters termination environment, enter the position of termination environment point, flying height, speed and course according to aircraft, find the most similar flight path section and mate, using this flight path section flight time and state as initial predicted flight time and state of flight;
2) read track points data in first 1 minute every 1 minutes, total flight path is upgraded, and carries out sectional linear fitting, thus realize track segmentation;
3) comparing then according to prediction flight path state and actual flight path state, judge whether to need again to search coupling flight path; Track matching method is from the identical or close history flight path section of flight path state, find depart from current flight path position, highly, speed and the minimum flight path section of course drift, as coupling flight path section;
4) due to schedule flight markov property, namely subsequent flights is thought and is only depended on current state, and therefore according to coupling flight path section and coupling flight path follow-up, to marching into the arena, the flight time is predicted.
2. march into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match as claimed in claim 1, it is characterized in that, utilize described approach path segmentation module to realize the staging treating of history flight path, utilize described approach path matching module to realize current flight path with the step of mating of history flight path to be;
Step 1: entry terminal district route grid structured data in the structural modeling subsystem of termination environment, nominal air route course data, flight planning data are worked out in flight planning subsystem, simulation time is set, call Simulation drive subsystem, add up the actual flight path data of flight of respectively marching into the arena, add up departure degree and offset direction that actual flight path point and nominal flight path are put;
Step 2: march into the arena in termination environment in the approach path segmentation module of flight time predicting subsystem, based on the departure degree that actual flight path point and nominal flight path are put and offset direction, judge the state of flight of each flight path section of current flight path and history flight path: on nominal flight path; On the left of nominal flight path and away from nominal flight path; On the left of nominal flight path and near nominal flight path; On the left of nominal flight path and parallel nominal flight path; On the right side of nominal flight path and away from nominal flight path; On the right side of nominal flight path and near nominal flight path; On the right side of nominal flight path and parallel nominal flight path;
Step 3: march into the arena in termination environment in the approach path matching module of flight time predicting subsystem, the segmentation result of current flight path is mated with history track segmentation result, select the coupling flight path of the highest history flight path of similarity degree as current flight path, the flight time based on coupling flight path each flight path section was predicted the current flight path flight time of following 1 minute;
Step 4: termination environment march into the arena the flight time predict the outcome display with interactive subsystem in show.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410266912.3A CN104008674B (en) | 2014-06-16 | 2014-06-16 | March into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410266912.3A CN104008674B (en) | 2014-06-16 | 2014-06-16 | March into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104008674A CN104008674A (en) | 2014-08-27 |
| CN104008674B true CN104008674B (en) | 2016-01-20 |
Family
ID=51369309
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410266912.3A Expired - Fee Related CN104008674B (en) | 2014-06-16 | 2014-06-16 | March into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104008674B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303896B (en) * | 2015-09-07 | 2018-08-24 | 飞友科技有限公司 | Flight E.T.A accurate predictor method |
| CN105427675B (en) * | 2015-12-30 | 2018-05-01 | 北京招通致晟科技有限公司 | Prediction method and device for landing time of aircraft |
| CN105427676B (en) * | 2015-12-30 | 2017-12-08 | 北京招通致晟科技有限公司 | Method and device for determining time for aircraft to pass through departure point |
| CN108615411B (en) * | 2016-12-13 | 2021-03-05 | 北京航管科技有限公司 | Method and device for processing flight information |
| CN108230757B (en) * | 2018-01-29 | 2020-12-15 | 上海空地信息技术有限公司 | Terminal area approach efficiency measuring method based on horizontal track |
| CN109493644B (en) * | 2018-11-16 | 2020-08-14 | 四川大学 | Four-dimensional track conjecture method based on historical track data mining |
| CN111899571B (en) * | 2020-07-29 | 2021-11-02 | 成都民航空管科技发展有限公司 | Method and system for automatically adjusting flight approach procedure based on AMAN system |
| CN111968414B (en) * | 2020-08-26 | 2022-08-05 | 成都民航空管科技发展有限公司 | 4D trajectory prediction method and device based on big data and AI and electronic equipment |
| CN112164247A (en) * | 2020-09-03 | 2021-01-01 | 重庆大学 | Ship route prediction method based on ship track clustering |
| CN112529248B (en) * | 2020-11-09 | 2024-06-04 | 北京宇航系统工程研究所 | Data-driven intelligent flying space mirror image system of carrier rocket |
| CN112862171B (en) * | 2021-01-29 | 2022-05-27 | 北京航空航天大学 | Flight arrival time prediction method based on space-time neural network |
| CN113838313B (en) * | 2021-11-29 | 2022-02-18 | 中国民用航空总局第二研究所 | Obstacle identification method for course beacon channel clearance jitter |
| CN114495582B (en) * | 2021-12-08 | 2023-03-31 | 中国电子科技集团公司第二十八研究所 | Four-dimensional track speed dynamic segmented correction method based on real-time radar data |
| CN114595518B (en) * | 2022-03-09 | 2022-09-27 | 中国民航大学 | Commercial space flight and civil aviation operation cooperative system based on digital twinborn |
| CN117272236B (en) * | 2023-11-23 | 2024-03-12 | 青岛民航凯亚系统集成有限公司 | Multi-source arrival time fusion method and system based on flight time sequence |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5786773A (en) * | 1996-10-02 | 1998-07-28 | The Boeing Company | Local-area augmentation system for satellite navigation precision-approach system |
| CN101344995A (en) * | 2008-08-18 | 2009-01-14 | 中国民航大学 | Cooperated discharging system for air station departure flight |
| CN101527087A (en) * | 2009-04-24 | 2009-09-09 | 中国民航大学 | Method for implementing flight transmit interval |
| CN101527086A (en) * | 2009-04-24 | 2009-09-09 | 中国民航大学 | Method for implementing flight time slot allocation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2953627B1 (en) * | 2009-12-04 | 2012-07-13 | Thales Sa | METHOD FOR HELPING THE JOINING OF A VERTICAL DOWNWARD TRAJECTORY AND ASSOCIATED DEVICE |
-
2014
- 2014-06-16 CN CN201410266912.3A patent/CN104008674B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5786773A (en) * | 1996-10-02 | 1998-07-28 | The Boeing Company | Local-area augmentation system for satellite navigation precision-approach system |
| CN101344995A (en) * | 2008-08-18 | 2009-01-14 | 中国民航大学 | Cooperated discharging system for air station departure flight |
| CN101527087A (en) * | 2009-04-24 | 2009-09-09 | 中国民航大学 | Method for implementing flight transmit interval |
| CN101527086A (en) * | 2009-04-24 | 2009-09-09 | 中国民航大学 | Method for implementing flight time slot allocation |
Non-Patent Citations (1)
| Title |
|---|
| 飞行间隔标准的安全评估研究;张晓燕;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20070815(第02期);C031-34：正文全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104008674A (en) | 2014-08-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104008674B (en) | March into the arena flight time Forecasting Methodology in a kind of termination environment based on pattern match | |
| CN102496313B (en) | Correction method of aircraft plan prediction locus by using supervision data | |
| US8825366B2 (en) | Method and device for determining an optimal flight trajectory followed by an aircraft | |
| CN104077928B (en) | Be applicable to the navigation of irregular flight outside air route dot position information update method | |
| CN104246641A (en) | Safe emergency landing of a UAV | |
| CN101694752A (en) | System and method for automatically detecting and reconciling conflicts in airspace operation simulation | |
| CN101807081A (en) | Autonomous navigation guidance method used for pilotless plane | |
| CN102436764A (en) | Method for mining flight number regulatory factors through historical data | |
| Chen et al. | Cooperative area reconnaissance for multi-UAV in dynamic environment | |
| CN109191921A (en) | A kind of blank pipe monitoring data simulation method based on 4D trajectory predictions | |
| CN103413011A (en) | Airspace sector dividing method based on computation geometry and simulated annealing | |
| US10497269B2 (en) | Integrated management for airport terminal airspace | |
| Murrieta-Mendoza et al. | Commercial aircraft lateral flight reference trajectory optimization | |
| Cobano et al. | 4D trajectory planning in ATM with an anytime stochastic approach | |
| Ma et al. | Volcanic Ash Region Path Planning Based on Improved A‐Star Algorithm | |
| Vormer et al. | Optimization of flexible approach trajectories using a genetic algorithm | |
| Lee et al. | Real‐time collision‐free landing path planning for drone deliveries in urban environments | |
| Chen et al. | A Two‐Stage Method for UCAV TF/TA Path Planning Based on Approximate Dynamic Programming | |
| Zheng et al. | Time-of-arrival taxi conformance monitoring for surface operations | |
| Tang et al. | 4D trajectory prediction of aircraft taxiing based on fitting velocity profile | |
| Fügenschuh et al. | Flight Planning for Unmanned Aerial Vehicles | |
| Gardi et al. | 4-dimensional trajectory generation algorithms for RPAS Mission Management Systems | |
| Ren et al. | An en route capacity optimization model based on air traffic control process | |
| Di Vito et al. | An overview on systems and algorithms for on-board 3D/4D trajectory management | |
| Alam et al. | Real time prediction of worst case air traffic sector collision risk using evolutionary optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160120 Termination date: 20200616 |