CN110930770A - Four-dimensional track prediction method based on control intention and airplane performance model - Google Patents
Four-dimensional track prediction method based on control intention and airplane performance model Download PDFInfo
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Abstract
The invention discloses a four-dimensional track prediction method based on a control intention and an airplane performance model, which can be used for establishing an airplane dynamics model by using initial plan information and combining empirical data which are obtained by mining historical data and reflect the control intention, such as the empirical height of a flight passing through each report point, and the like when the flight does not occur, and simultaneously predicting a four-dimensional track point sequence to be generated by the flight in advance by considering meteorological conditions, and dynamically correcting a flight 4D prediction result after receiving monitoring data. The method improves the flight 4D track prediction precision, is beneficial to carrying out flight conflict detection and flow management decision in advance, and relieves air traffic pressure.
Description
Technical Field
The invention belongs to the technical field of aircraft four-dimensional (4D) flight path prediction in an air traffic management system, and particularly relates to a four-dimensional flight path prediction method based on a control intention and an aircraft performance model.
Background
As an important industry and an advanced transportation mode for national economy and social development in China, the civil aviation transportation industry is also developed rapidly along with the continuous development of the whole national economy. By 2017, the total number of civil aviation airports in China has increased to 216 compared to 94 in 1990, with a total number of airlines of approximately 4000. The number of airplanes on the flight path is increased, the flight distance is reduced, and the air traffic safety situation is more severe; problems such as flight delay, airspace congestion and the like also frequently occur. Flight delay not only brings direct economic loss to passengers and airlines, but also influences national economic development. In recent years, researches on automatic and intelligent methods for empty pipe, such as aircraft collision detection and release, entering and leaving sequencing, track-based operation and the like, are increasingly started, and aims to improve air traffic efficiency, guarantee air traffic safety and reduce delay rate. And the rapid and accurate prediction of the aircraft track is the basis and guarantee for realizing the method.
At present, 4D dead reckoning methods for airplanes mainly include the following two types:
the method comprises the steps of integrating various information (type, position, altitude and speed) of a take-off airport, a landing airport and a report point in plan information to form a horizontal track, then generating an altitude profile and a speed profile which can be divided into a plurality of sections based on an airplane performance model, a standard flight program of an airplane, the cruising altitude and speed of the airplane, a conversion altitude and the like, and synthesizing and calculating a 4D track of the airplane by considering the influence of high-altitude wind in weather on the ground speed of the airplane and certain coupling after the three profiles are independently processed, so that information such as the position of a passing point, the speed of the passing point, the height of the passing point, the control sector and the like of a flight passing through each report point are obtained. For example: the four-dimensional flight path of the departure route and the approach route of a Beijing terminal area 36R runway is calculated by a method for establishing an aircraft model and performing flight simulation in 'calculation of four-dimensional flight path of the terminal area [ J ]. Chinese academy of civil aviation flight, 2008,19(4): 11-14' of Nippon Naqian et al; in the 4D track prediction method [ J ] based on the basic flight model, 2009,44(2):295 plus 300' of Wang super et al, based on the concept of the basic flight model, the basic flight model is used for constructing a horizontal track, a height profile and a speed profile according to the characteristics of flight stages, and the complete 4D track is generated by fitting; dolbin et al, a model and optimization of four-dimensional track based on a flight path object method [ J ] computer technology and development, 2012,22(8): 249-.
The other method is a track prediction method based on track data mining, and with the rise of big data technology, historical data mining and the like provide support for track prediction. Historical operating trajectory data is a reliable record of flight conditions that contains all possible factors that affect aircraft operation, such as changes in flight plans, regulatory intents, weather conditions, and the like. The information can be mined to analyze the track mode and use the track mode for track prediction, so that the prediction accuracy is improved. For example: in' traffic clustering and an application to airspace clustering [ J ]. Intelligent Transportation Systems,2011, 12(4): 1511-; an improved track clustering algorithm [ J ] based on density, such as Zhaowei et al, computer engineering, 2011,37(9), wherein a distance measurement combining weighted Manhattan distance and penalty coefficient is adopted in 270-plus 272', and an improved track clustering method based on density is provided for track prediction; kui et al, "BP neural network-based air target track prediction model [ J ]. command information system and technology, 2017,8(3): 54-58", propose an air target track prediction model based on back propagation neural network, adaptively cluster target track data, and thus extract the change rule of the activity area of a specific target.
However, the existing 4D track prediction technique and the existing problems can be summarized as follows:
1. by adopting a track prediction algorithm based on a big data technology and based on a large amount of historical data, the historical flight characteristics of the flight are fully mined, but other factors influencing the flight, such as weather and the like, are not easy to be fused into the predicted flight path; on the other hand, the air transportation operation data volume is large, the data types are multiple, the data distribution characteristics are various, and for taking national flight path data, 5 hundred million effective data subjected to data cleaning are recorded in one day, so that the processing efficiency in the actual flight path prediction process is low, and the real-time requirement cannot be met; in addition, since the abnormal track has a large influence on the generation of the representative track, the prediction may be influenced.
2. The method based on the aircraft kinematics model is adopted, a large amount of flight performance data and dynamic parameters are used for calculation, the calculation efficiency is high, but the accuracy of track prediction is difficult to ensure under the condition that a meteorological model is not considered or the actual intention of the aircraft cannot be judged.
3. Other parameter-free estimation methods based on Kalman filtering or neural networks and the like are simple to realize, do not need a large amount of input parameters, but have small performance improvement space and larger errors.
Disclosure of Invention
In view of the above disadvantages of the prior art, the present invention provides a four-dimensional flight path prediction method based on a control intent and an aircraft performance model, so as to solve the problems of the existing air traffic volume continuously increasing, the area traffic efficiency waiting to be improved, the flight path prediction accuracy not being high, and the like; the method fully utilizes the superiority of the airplane performance model, considers the experience parameters (report point over-height) which reflect the actual control intention and are obtained from the historical track data, effectively couples the experience parameters and the report point over-height, and comprehensively improves the 4D track prediction precision by combining the meteorological high-altitude wind data under the condition of ensuring the calculation efficiency.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses a four-dimensional track prediction method based on a control intention and an airplane performance model, which comprises the following steps of:
1) preprocessing flight data;
2) extracting a control intention;
3) 4D track prediction modeling based on the airplane performance model and the control intention;
4) high-altitude wind correction modeling;
5) generating a static 4D track prediction result of the flight;
6) and (5) real-time radar data correction.
Further, the step 1) specifically includes: collecting radar track and AFTN data for a period of time from an air traffic management system; synchronously playing back the acquired radar track and AFTN data at the same time and the same speed; damaged data elimination and abnormal data cleaning are carried out on the dynamic empty pipe data; establishing a database based on AFTN telegraph and radar track data;
basic information data used by an ATC system is imported, and the basic data is updated and supplemented, wherein the basic data covers a navigation route, a key airport and sector division of a district management;
analyzing GRIB weather forecast data in the same period, and storing the data in a database;
analyzing and storing a BADA aircraft performance data file obtained from European control into a database;
and performing flight plan data processing on the historical AFTN message processing result, including performing air route decomposition and entering and leaving air route distribution based on a standard flight program.
Further, the step 2) specifically includes:
21) performing fusion processing on the acquired aircraft track data, matching the cleaned data with corresponding flight plan data, and establishing an aircraft flight condition record table; each flight condition record consists of a machine type, a flight number, a route, planned take-off time, planned landing time, actual take-off time, actual landing time, a take-off airport, a landing airport, a track point, longitude and latitude, passing point time, passing point speed and passing point height; deleting the invalid record with the flight number being empty; adding a serial number field, and sequencing filling values according to the passing time in the process from the take-off time to the landing time of a flight; migrating flight records of ten dimensions, such as flight number, machine type, serial number, actual takeoff time, takeoff airport, landing airport, track point, longitude and latitude, passing point time and passing point height, to a distributed database HBase of a Hadoop cluster;
22) acquiring a course point experience course; constructing a mapping model of the data file by using flight record information of two similar flight track points in the same flight in sequence number sequencing; calculating the actual distances between flight track points, sequentially accumulating to obtain the actual range distance of the whole flight, and reserving the actual range distance as intermediate data;
23) acquiring actual passing point information of a report point; establishing a national airline model parameter library according to the airline starting time, wherein the national airline model parameter library comprises the following attributes: the method comprises the following steps of (1) carrying out route number, starting time, ending time, route report points and longitude and latitude; constructing a mapping model of a data file by using flight condition records (associated field routes) and a national route model parameter library (associated field route numbers), mapping original data stored in HBase into intermediate data only containing route report points and route points, obtaining report points and keeping the report points as result information when the route points are smaller than a certain threshold value (initially determined by 20km) by using a distance formula between two longitude and latitude points, replacing the report points with route points with minimum distances, and obtaining actual routes of the route report points by using the results in the step 22), wherein the over-point height of the route points with the minimum distances is the report point over-point height;
24) acquiring the height layer height of the report points and the height layer height of the track points; constructing a mapping model of a data file by using a national course model parameter library (associated field high level low degree and high level height), mapping the report points and the course point over-point high degree original data into the high level height, and storing the height as intermediate data;
25) using k-means (hard clustering algorithm) to obtain the actual voyage and the height of the height layer of the report points obtained in the steps 23) and 24) to obtain the empirical voyage and the height of the empirical height layer of each report point passing through the same take-off and landing airport and the same model under the same airline condition;
26) using the height of the track point height layer obtained in the step 24) and the track point passing time in the flight condition record as cruise height calculation process information, deleting information that the flight time is continuously lower than a certain range (temporarily set for 10min), and then obtaining the height with the longest duration from the information as the flight cruise height;
27) and establishing an airway experience data information table, and recording the data information obtained in the calculation process to obtain an airway experience data information table, wherein the data stored in the information table is the extracted control intention information.
Further, the air route experience data information table in the step 27) comprises machine types, flight numbers, take-off airports, landing airports, report point names, report point actual voyages, report point over-point heights, cruising heights, air route sections and the like.
Further, the step 3) specifically includes: receiving the created, modified or extracted flight plan processing information, replacing the report point height and the cruising height in the flight plan by using the report point experience passing point information in the air route experience data information table acquired in the step 27), and constructing a 4D flight path prediction model based on the control intention and the airplane performance model by combining the acquired actual radar flight path meteorological data information.
Further, the step 4) specifically includes: analyzing and interpolating GRIB format high-altitude wind/temperature data to obtain the high-altitude wind/temperature data in a uniformly divided single longitude and latitude height grid; calling high altitude wind data according to the predicted airway position and height information to obtain the calculated wind speed of the position of the 4D track point
The performance model calculates the vacuum speed of the point
Ground speed
The direction is the course of the current navigation section, vector operation is carried out on the three, the ground speed of the aircraft is obtained, and correction modeling of the high-altitude wind on the track prediction result is achieved.
Further, the step 5) specifically includes: and on the basis of the established 4D track prediction model of the coupling control intention and the airplane performance model, calculating and generating a static 4D track prediction result of the flight by combining a correction model of the high-altitude wind on the track prediction and utilizing the airspace basic parameters, flight planning route data and related parameters in a BADA performance database.
Further, the step 6) specifically includes: the flight real-time data detected by the radar is guided, when the aircraft receives actual radar data, the control intention at the position is considered to be clear, the actual track point time, the position, the speed and the course received by the current radar are used as virtual report points to be inserted into a planned route, an empirical route corresponding to the radar track point is obtained according to the position relation between the radar track point and the starting point of the route section according to the route section where the radar track point is located, the actual over-point height of the radar track point is used as the target height of the current position, and the prediction result of the subsequent stage is recalculated according to the step 5), so that the dynamic correction of the flight 4D track prediction result is realized.
The invention has the beneficial effects that:
(1) the invention relates to a control intention extraction method based on historical track data, which generates intention information data files of common over-point height, flight range and the like of each report point under the conditions of city pairs and model fields, and realizes the rapid programmed extraction of control intentions.
(2) The invention establishes the 4D flight path prediction mathematical physical model of the airplane coupling the performance model and the control intention. The prediction model considers various information such as weather, control intention, airplane performance and the like, can quickly and accurately realize 4D flight path prediction of the airplane, and improves control efficiency.
(3) The invention provides an airplane maneuvering mode using strategy, simulates the control instruction triggering time according to the position relation between a target point and a current calculation report point, and realizes the prediction calculation considering the control intention.
(4) According to the invention, the track points obtained by monitoring data in real time are used as definite control intention target points and added into the 4D prediction model, so that automatic optimization and reconstruction of a track prediction result are realized, and the track prediction accuracy is further improved.
(5) The invention is beneficial to detecting the conflict between the flight tracks of different frames as early as possible and improving the air traffic safety.
(6) The invention is beneficial to generally grasping all the flight conditions before the current time point, smoothing the traffic flow, increasing the air traffic throughput and improving the air traffic efficiency.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention.
Fig. 2 is a schematic view of the manoeuvring mode 1.
Fig. 3 is a schematic view of maneuver mode 2.
Fig. 4 is a schematic view of the manoeuvring mode 3.
Fig. 5 is a velocity vector diagram.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Definitions of specific terms and common abbreviations used in the present invention:
referring to fig. 1, the four-dimensional track prediction method based on the regulatory intention and the aircraft performance model of the invention includes the following steps:
1. flight data preprocessing
Collecting radar track and AFTN data for a period of time from an air traffic management system; and synchronously playing back the collected dynamic empty pipe data at the same time and the same speed. Damaged data elimination and abnormal data cleaning are carried out on the dynamic empty management data for subsequent processing and warehousing; a set of database system which is based on AFTN telegraph and radar track data and can accurately describe all relevant operation dynamics of a track is established.
Basic information data (including airway, air route, airport and the like) used by the ATC system is imported, the basic data is updated and supplemented, contents such as main airway and air route, key airport, sector planning of district management and the like are covered, and data requirements of airway splitting are fully met.
And analyzing and warehousing the GRIB weather forecast data in the same period, and providing necessary weather forecast information for track prediction.
And analyzing and finishing data storage by using a BADA aircraft performance data file obtained from European control, and providing necessary aircraft performance information for track prediction.
And performing flight plan data processing on the historical AFTN message processing result, including performing air route decomposition, entering and leaving air route distribution and the like based on a standard flight program.
2. Regulatory intent extraction
21) Performing fusion processing on the acquired aircraft track data, matching the cleaned data with corresponding flight plan data, and establishing an aircraft flight condition record table; each flight condition record consists of attributes such as a machine type, a flight number, a route, planned take-off time, planned landing time, actual take-off time, actual landing time, a take-off airport, a landing airport, a track point, longitude and latitude, passing point time, passing point speed, passing point height and the like; deleting the invalid record with the flight number being empty; adding a serial number field, and sequencing filling values according to the passing time in the process from the take-off time to the landing time of a flight; migrating a flight record with ten dimensions of flight number, model, sequence number, actual take-off time, take-off airport, landing airport, track point, longitude and latitude, passing point time and passing point height to a distributed database HBase of a Hadoop cluster;
22) acquiring a course point experience course; constructing a mapping model of the data file by using flight record information of two similar flight track points in the same flight in sequence number sequencing; calculating the actual distances between flight track points, sequentially accumulating to obtain the actual range distance of the whole flight, and reserving the actual range distance as intermediate data;
23) acquiring actual passing point information of a report point; establishing a national airline model parameter library according to the airline starting time, wherein the national airline model parameter library comprises the following attributes: the method comprises the following steps of (1) carrying out route number, starting time, ending time, route report points and longitude and latitude; constructing a mapping model of a data file by using flight condition records (associated field routes) and a national route model parameter library (associated field route numbers), mapping original data stored in HBase into intermediate data only containing route report points and route points, obtaining report points and keeping the report points as result information when the route points are smaller than a certain threshold value (initially determined by 20km) by using a distance formula between two longitude and latitude points, replacing the report points with route points with minimum distances, and obtaining actual routes of the route report points by using the results in the step 22), wherein the over-point height of the route points with the minimum distances is the report point over-point height;
24) acquiring the height layer height of the report points and the height layer height of the track points; a height layer parameter library is established according to China RVSM airspace related standard specifications and mainly comprises 3 attributes, namely a height layer serial number, a height layer low degree and a height layer height. Utilizing a national route model parameter library (height level low and height level height of associated fields) to construct a mapping model of a data file, mapping the report point and route point over-point height original data into height level height, and storing the height level height as intermediate data;
25) using k-means (hard clustering algorithm) to obtain the actual voyage and the height of the height layer of the report points obtained in the steps 23) and 24) to obtain the empirical voyage and the height of the empirical height layer of each report point passing through the same take-off and landing airport and the same model under the same airline condition;
26) using the height of the track point height layer obtained in the step 24) and the track point passing time in the flight condition record as cruise height calculation process information, deleting information that the flight time is continuously lower than a certain range (temporarily set for 10min), and then obtaining the height with the longest duration from the information as the flight cruise height;
27) and establishing an airway experience data information table, and recording the data information obtained in the calculation process to obtain the airway experience data information table. The specific record information comprises a model, a flight number, a take-off airport, a landing airport, a report point name, a report point actual voyage, a report point over-point height, a cruising height, a route section and the like.
3. 4D track prediction modeling based on the airplane performance model and the control intention;
receiving the created, modified or extracted flight plan processing information, replacing the height of the report point, the cruising height and the like in the flight plan by using the experience point information of the report point in the air route experience data information table acquired in the step 27), and constructing a 4D track prediction model based on the control intention and the airplane performance model by combining the acquired data information of the actual radar track weather and the like, thereby realizing the simulation prediction of the 4D track.
The flight performance model adopts a TEM (Total-EnergyModel) model in an aircraft performance database BADA of an European control test center. BADA (base of Aircraft date) is an Aircraft performance database developed by the centre of EEC (European Experimental centre) of France, stored in the form of American Standard Code for the exchange of information (ASCII) data, containing Aircraft performance parameters of different types and operating program parameters. The TEM model is modeled by considering the airplane as a mass point, and the work of the combined external force acting on the airplane is equal to the increment of kinetic energy and potential energy of the airplane.
Is represented as follows;
in the formula: m is the aircraft mass; vTasThe vacuum speed is set; t is thrust; d is resistance; g is the acceleration of gravity; γ is the climb/descent angle of the aircraft.
The ascending and descending rate is:
wherein, h is the height,
the energy distribution coefficient may be converted to a Mach number function f (M) representing the ratio of thrust for climb to thrust for acceleration while climbing at a selected speed. The method comprises the steps of calculating the thrust, the air resistance and the fuel flow of an engine by utilizing aircraft performance parameters in a BADA performance library, namely parameters related to the aerodynamic performance, the engine performance, the load and the range of the aircraft, including lift coefficient and drag coefficient of the aircraft in different postures, the fuel consumption rate of the engine under the conditions of different heights, different environmental temperatures and different thrust outputs, the weight, the economical cruising speed, the maximum range and the like of the aircraft, and then determining cruising, climbing and descending speeds according to the parameters to realize flight trajectory simulation dynamics modeling. According to the characteristics of the flight stage, the 4D flight path is divided into three sections, namely a horizontal flight path, a height section and a speed section, for modeling. Modeling thought: firstly, synthesizing a two-dimensional horizontal flight path from a starting point position and a heading to an end point position and a heading, and then researching a speed profile and a height profile along the known horizontal path, thereby realizing 4D flight path simulation calculation.
The coupling method of the regulatory intent and the aircraft performance model is described in detail below:
(1) horizontal section plane
And extracting the empirical voyage among the model, the voyage and the report points under the take-off and landing airport condition from the voyage empirical data obtained by data mining as the target voyage of each report point of the voyage needing to be calculated. And simultaneously calculating the target range of the range. For example, data mining yields a voyage S between report point A and report point BAAnd SBIf the target course of the flight segment is SAB＝SB-SA。
(2) Height and velocity profile calculation
In the process of building the aircraft flight model, under the condition of no special condition, the default of the route flight is to climb to the cruising altitude according to a standard flight procedure, then fly for a distance at the cruising altitude and descend to the landing airport according to a standard descent procedure. However, in the actual flight process, the aircraft needs to be commanded by different control sectors when passing through different control areas, and the aircraft needs to be commanded to enter a designated flight altitude layer and handed over to the next control sector through altitude adjustment on the basis of a sector handover protocol among the sectors. The height restriction requirements between different sectors will affect the aircraft's height to each reporting point. And when the track prediction is calculated, taking the experience over-height of the report point obtained from historical track mining as the target height of the report point. The aircraft is required to reach a specified altitude at a specified reporting point. On the basis of standard flight procedures, the flight of an aircraft from one reporting point 1 to another reporting point 2 is divided into three modes:
mode 1: the aircraft climbs/descends from the height of the report point 1 to the height of the report point 2, the climbing/descending posture is matched with the aircraft performance model, and after the aircraft reaches the height of the report point 2, the aircraft flies to the report point 2 horizontally, as shown in fig. 2;
mode 2: first, the aircraft flies a distance horizontally at the height of a report point 1, then the aircraft climbs/descends from the height of the report point 1 to the height of a report point 2, and the climbing/descending posture is matched with an aircraft performance model, as shown in fig. 3;
mode 3: the altitude at reporting point 1 climbs to the cruising altitude, then the cruising altitude climbs/descends to the altitude at reporting point 2, and the climbing/descending attitude matches the aircraft performance model, as shown in fig. 4.
The use strategy is as follows: and the mobile selection uses the predicted flight mode according to the position situation of the current calculation point and the next calculation point in the total air route and the position situation between the two points.
First, assume the current calculation point voyage S0Estimating the required range S for the current calculation point to climb up/down to the next calculation point according to the airplane performance model and the standard flight program1The total target course of the route is StotalFlight distance S required for the takeoff airport to climb to cruising altitudeclimbThe required range S of the cruise altitude descending to the destination airportdescend。
Secondly, when the current calculation point height is less than the cruising height and the certain flight range threshold value Sm of the flying is met (when the current calculation point height is less than the cruising height)Stotal>＝n1*Sclimb+n2*SdescendTaking Sm as n 1SclimbWhen S istotal>＝n1*Sclimb+n2*SdescendTaking Sm 0.5S0N1 and n2 are adjustable coefficients, n1 and n2 are more than 1), and the possibility of entering the cruise altitude is still considered. This time is: when the next calculation point with the target height is the destination airport, selecting a mode 3; if the descending voyage threshold n 2S is not the destination airport when the next calculation point with the target altitude is not the destination airportdescend>Voyage S of next point0+S1>Climb voyage threshold n 1SclimbThen mode 1 is selected; otherwise, mode 3 is selected.
And thirdly, the condition of the step two is not met, if the height of the current point position is higher than the cruising height or flies out for a long distance, the possibility of climbing to the cruising height is considered to be no longer available, and at the moment: when the next calculation point with the target height is the destination airport, selecting a mode 2; when the distance between the current calculation point and the next calculation point is < n3 × s1, selecting a mode 1, wherein n3 is an adjustable coefficient; when the distance between the current calculation point and the next calculation point is n4 × s1, mode 2 is selected, and n4 is an adjustable coefficient.
(3) Section coupling
And calculating a planned voyage between the report points according to the longitude and latitude information of the report points.
Setting the longitude and latitude (B1, L1) of a first point A and the longitude and latitude (B2, L2) of a second point B; longitude difference a and latitude difference B between points A and B:
a＝B1-B2
b＝L1-L2
planned voyage S 'between points A and B'AB：
And calculating the segment to which the 4D track point belongs according to the report point target course and the 4D track point course, and solving a planned course from the 4D track point to the starting point of the corresponding segment according to equal proportion of the segment planned course and the segment target course. For example: suppose there are 4D trace points N, SC>4D track point calculation course SN>SBIf the track point is in the navigation segment BC, the planned navigation route of the navigation segment BC and the target navigation route obtained by historical mining are respectively SBCAnd S'BCThe planned voyage from the equal proportion relation to N to B should be:
from S'BNAnd S'BCThe position coordinates of the track point N can be obtained.
And calculating to obtain the position information corresponding to the 4D track points according to the planned voyage and the initial report point position information of the affiliated voyage section, thereby obtaining the predicted positions, time, heights and speed results of all the 4D track points and the predicted passing point time, passing point speed and passing point height of the report points.
4. High altitude wind correction
Referring to fig. 5, the GRIB format high altitude wind/temperature data is analyzed and interpolated to obtain high altitude wind/temperature data in a single latitudinal altitude grid which is uniformly divided. Calling high altitude wind data according to the predicted airway position and height information to obtain the calculated wind speed of the position of the 4D track point
The performance model calculates the vacuum speed of the point
Ground speed
The direction is the course of the current navigation section, vector operation is carried out on the three, the ground speed of the aircraft is obtained, and correction calculation of 4D track prediction by high-altitude wind is achieved.
5. Static 4D track prediction results
The method comprises the steps of utilizing mastered basic airspace data (including position point basic information, airplane performance data, entering and leaving programs, air route data, map data and the like) and meteorological GRIB message information, combining related data in an air route experience air route information table obtained by data mining, adopting an established airplane 4D air route prediction model based on combination of control intention and airplane performance, considering the influence of high-altitude wind on ground speed, and calculating to obtain an aircraft static 4D track prediction result.
6. Real-time radar data correction
The method comprises the steps of leading in flight real-time data detected by a radar, including time, position, speed, course and the like, when an aircraft receives actual radar data, considering that the control intention at the position is clear, inserting the actual track point time, position, speed and course received by the current radar into a planned route as virtual report points, obtaining an experience route corresponding to the radar track point according to the position relation between the radar track and the starting point of the route section according to the route section where the radar track point is located, using the actual over-point height of the radar track point as the target height of the current position, recalculating the prediction result of the subsequent stage according to the strategy, and realizing the dynamic correction of track prediction.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (7)
1. A four-dimensional track prediction method based on control intention and an airplane performance model is characterized by comprising the following steps:
1) preprocessing flight data;
2) extracting a control intention;
3) 4D track prediction modeling based on the airplane performance model and the control intention;
4) high-altitude wind correction modeling;
5) generating a static 4D track prediction result of the flight;
6) and (5) real-time radar data correction.
2. The four-dimensional track prediction method based on the regulatory intention and the aircraft performance model according to claim 1, wherein the step 1) specifically comprises: collecting radar track and AFTN data for a period of time from an air traffic management system; synchronously playing back the acquired radar track and AFTN data at the same time and the same speed; damaged data elimination and abnormal data cleaning are carried out on the dynamic empty pipe data; establishing a database based on AFTN telegraph and radar track data;
basic information data used by an ATC system is imported, and the basic data is updated and supplemented, wherein the basic data covers a route, a key airport and sector planning of district management;
analyzing GRIB weather forecast data in the same period, and storing the data in a database;
analyzing and storing a BADA aircraft performance data file obtained from European control into a database;
and performing flight plan data processing on the historical AFTN message processing result, including performing airway decomposition and entering and leaving airway distribution based on a standard flight program.
3. The four-dimensional track prediction method based on the regulatory intention and the aircraft performance model according to claim 1, wherein the step 2) specifically comprises:
21) performing fusion processing on the acquired aircraft track data, matching the cleaned data with corresponding flight plan data, and establishing an aircraft flight condition record table;
22) acquiring a course point experience course;
23) acquiring actual passing point information of a report point; establishing a national route model parameter library according to the starting time of the route, and obtaining the actual route of the route report point by using the result in the step 22), wherein the over-point height of the route point with the minimum distance is the report point over-point height;
24) acquiring the height layer height of the report points and the height layer height of the track points;
25) obtaining the actual voyage and the height layer height of the report points obtained in the steps 23) and 24) by using a hard clustering algorithm to obtain the empirical voyage and the height layer height of the empirical voyage passing through each report point under the condition of the same take-off and landing airport and the same model and the same route;
26) using the height of the track point height layer obtained in the step 24) and the track point passing time in the flight condition record as cruise height calculation process information, deleting information that the flight time is continuously lower than a certain range, and obtaining the height with the longest duration as the flight cruise height;
27) and establishing an airway experience data information table, and recording the data information obtained in the calculation process to obtain the airway experience data information table.
4. The four-dimensional track prediction method based on the regulatory intention and the aircraft performance model according to claim 3, wherein the step 3) specifically comprises: receiving the created, modified or extracted flight plan processing information, replacing the height of the report point and the cruising height in the flight plan by using the experience point information of the report point in the air route experience data information table acquired in the step 27), and constructing a 4D flight path prediction model based on the control intention and the airplane performance model by combining the collected actual radar flight path meteorological data information.
5. The four-dimensional track prediction method based on regulatory intention and aircraft performance model according to claim 4, wherein the step 4) specifically comprises: analyzing and interpolating GRIB format high-altitude wind/temperature data to obtain the high-altitude wind/temperature data in a uniformly divided single longitude and latitude height grid; calling high altitude wind data according to the predicted airway position and height information to obtain the calculated wind speed of the position of the 4D track point
The performance model calculates the vacuum speed of the point
Ground speed
The direction is the course of the current navigation road section, the three are subjected to vector operation,and acquiring the ground speed of the aircraft, and realizing the correction modeling of the high-altitude wind on the track prediction result.
6. The four-dimensional track prediction method based on the regulatory intention and the aircraft performance model according to claim 5, wherein the step 5) specifically comprises: and on the basis of the established 4D track prediction model of the coupling control intention and the airplane performance model, calculating and generating a static 4D track prediction result of the flight by combining a correction model of the high-altitude wind on track prediction and utilizing the airspace basic parameters, flight planning route data and related parameters in a BADA performance database.
7. The four-dimensional track prediction method based on regulatory intention and aircraft performance model according to claim 6, wherein the step 6) specifically comprises: the flight real-time data detected by the radar is guided, when the aircraft receives actual radar data, the control intention at the position is considered to be clear, the actual track point time, the position, the speed and the course received by the current radar are used as virtual report points to be inserted into a planned route, according to the route section where the radar track point is located, an empirical route corresponding to the radar track point is obtained according to the position relation between the radar track and the route section initial point, the actual over-point height of the radar track point is used as the target height of the current position, and the prediction result of the subsequent stage is recalculated according to the step 5), so that the dynamic correction of the flight 4D track prediction result is realized.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101201978A (en) * | 2007-12-20 | 2008-06-18 | 四川川大智胜软件股份有限公司 | Method for predicting short-run air traffic flux based on real time radar and flight information |
| CN101692315A (en) * | 2009-09-25 | 2010-04-07 | 民航总局空管局技术中心 | Method for analyzing high precision 4D flight trajectory of airplane based on real-time radar data |
| CN102496313A (en) * | 2011-12-31 | 2012-06-13 | 南京莱斯信息技术股份有限公司 | Correction method of aircraft plan prediction locus by using supervision data |
| US20140277853A1 (en) * | 2013-03-13 | 2014-09-18 | General Electric Company | System and method for determining aircraft operational parameters and enhancing aircraft operation |
| CN104240541A (en) * | 2014-09-09 | 2014-12-24 | 中国电子科技集团公司第二十八研究所 | 4D track generating method |
| CN106846919A (en) * | 2017-01-16 | 2017-06-13 | 南京航空航天大学 | A kind of four-dimensional dynamic track prediction method based on ADS B information updatings |
| CN109542876A (en) * | 2018-11-20 | 2019-03-29 | 南京莱斯信息技术股份有限公司 | Extracting method based on Hadoop data mining aircraft experience locus model key factor |
-
2019
- 2019-11-06 CN CN201911076298.3A patent/CN110930770B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101201978A (en) * | 2007-12-20 | 2008-06-18 | 四川川大智胜软件股份有限公司 | Method for predicting short-run air traffic flux based on real time radar and flight information |
| CN101692315A (en) * | 2009-09-25 | 2010-04-07 | 民航总局空管局技术中心 | Method for analyzing high precision 4D flight trajectory of airplane based on real-time radar data |
| CN102496313A (en) * | 2011-12-31 | 2012-06-13 | 南京莱斯信息技术股份有限公司 | Correction method of aircraft plan prediction locus by using supervision data |
| US20140277853A1 (en) * | 2013-03-13 | 2014-09-18 | General Electric Company | System and method for determining aircraft operational parameters and enhancing aircraft operation |
| CN104240541A (en) * | 2014-09-09 | 2014-12-24 | 中国电子科技集团公司第二十八研究所 | 4D track generating method |
| CN106846919A (en) * | 2017-01-16 | 2017-06-13 | 南京航空航天大学 | A kind of four-dimensional dynamic track prediction method based on ADS B information updatings |
| CN109542876A (en) * | 2018-11-20 | 2019-03-29 | 南京莱斯信息技术股份有限公司 | Extracting method based on Hadoop data mining aircraft experience locus model key factor |
Non-Patent Citations (3)
| Title |
|---|
| 宋雷亮: "基于历史轨迹集的四维轨迹预测方法", 《计算机技术与发展》 * |
| 张佳静 等: "基于4D轨迹预测的航空气象辅助决策技术", 《江苏科技信息》 * |
| 靳学梅 等: "4D轨迹预测应用系统关键技术研究_靳学梅", 《实践与应用》 * |
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