CN116451988A - Landing drift runway risk real-time prediction method based on flight data - Google Patents
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Abstract
The invention discloses a landing drift runway risk real-time prediction method based on flight data, which comprises the following steps: acquiring flight data, constructing a threshold calculation model of the out-of-runway and out-of-runway based on the flight data, and acquiring risk influence factors of the out-of-runway of the aircraft; correcting the rushing-out runway threshold calculation model based on the risk influence factor data; obtaining optimal rushing-out and deviation runway threshold data based on the corrected model, wherein the optimal rushing-out and deviation runway threshold data comprises optimal rushing-out threshold data and optimal deviation threshold data; and obtaining actual measurement data, and judging the actual measurement data through optimal rushing out and deviation runway threshold value data to obtain risk prediction data. The technical scheme of the invention can effectively and accurately predict the risk of deviating from the runway in real time for Liu Chong, and effectively reduce the occurrence probability of drifting from the runway in the landing stage.
Description
Technical Field
The invention belongs to the field of civil aviation safety, and particularly relates to a landing drift runway risk real-time prediction method based on flight data.
Background
Statistics on unsafe aviation data indicate that off-runway is the second leading cause of near landing accidents, with 83% of off-runway occurring during the landing phase. At the global runway security seminar in 2011, the international civil aviation organization pointed out: the global economic loss due to off-track events is approximately $ 9 billion annually. Statistical analysis of commercial aviation accidents between 2001 and 2020 for air passengers shows that the runway-out accidents are main causes of aircraft body damage, and account for 36%. At present, a great deal of scholars and research institutions at home and abroad conduct a great deal of research on the risk of drifting out of a runway from different angles.
Currently, risk assessment for off-runway impact during landing stage is mainly focused on qualitative analysis of causative factors, but less on applying flight QAR data and human factor analysis to quantitative approval of off-runway risk. Therefore, the invention provides a real-time prediction method for the risk of the out-of-runway event based on the flight data based on the factor coupling effect, which predicts the risk of the out-of-runway of Liu Chong in real time, thereby reducing the occurrence probability of the out-of-runway in the landing stage.
Disclosure of Invention
The invention aims to provide a landing drift out runway risk real-time prediction method based on flight data, which is used for solving the problems of the prior art, and can effectively and accurately predict the risk of the drift out runway according to Liu Chong in real time so as to reduce the occurrence probability of the drift out runway in a landing stage.
In order to achieve the above purpose, the invention provides a landing drift out runway risk real-time prediction method based on flight data, comprising the following steps:
acquiring flight data, constructing a threshold calculation model of the out-of-runway and out-of-runway based on the flight data, and acquiring risk influence factors of the out-of-runway of the aircraft; correcting the rushing-out runway threshold calculation model based on the risk influence factor data; obtaining optimal rushing-out and deviation runway threshold data based on the corrected model, wherein the optimal rushing-out and deviation runway threshold data comprises optimal rushing-out threshold data and optimal deviation threshold data; and obtaining actual measurement data, and judging the actual measurement data through optimal rushing out and deviation runway threshold value data to obtain risk prediction data.
Optionally, the flight data includes: QAR data, weather data, aircraft performance data, personnel data.
Optionally, the rushing out and deviating out runway threshold calculation model includes a rushing out threshold calculation model and a deviating threshold calculation model.
Optionally, the process of generating the flushing threshold calculation model includes:
acquiring length data of an airport runway, acquiring landing taxi distance data of an airplane based on the length data, and analyzing and calculating the landing taxi distance data to acquire airplane grounding speed data, wherein the airplane grounding speed data is uncorrected optimal flushing threshold data;
optionally, the process of generating the offset threshold calculation model includes:
and acquiring width data of the airport runway, and analyzing and calculating the width data and the flight data to obtain yaw angle data, wherein the yaw angle data is uncorrected optimal offset threshold data.
Optionally, the process of correcting the punching threshold calculation model includes:
v in a calculation model of a hedging threshold based on risk impact factor data B Correcting to obtain the sliding distance S 'of the transition section' T ：
Wherein C is V For V in actual operation B Is used for the correction of the coefficient of (c),
corrected landing distance S 'of aircraft' L The method comprises the following steps:
S′ L ＝S A +S′ T +S B
corrected landing distance S 'based on aircraft' L The optimal landing speed threshold V is obtained by reverse push jd '：
Wherein H is LK Is the vertical height of the aircraft as it passes the runway threshold; p is the engine thrust; f (F) N Is a normal acceleration force; d is aerodynamic resistance; g is gravity acceleration;is the slope of the runway; μ is the runway friction coefficient; l is lift force; ρ is the atmospheric density; v is the relative velocity of the air flow; s is S W Is the area of the wing; c (C) L Is the lift coefficient; c (C) D Is the aerodynamic drag coefficient; s is S A The horizontal distance of the sliding-down leveling section; w is the weight of the aircraft; n is an overload factor; k is a unit coefficient of speed; v (V) B The speed for taking all the deceleration measures; v (V) W Is the wind speed component along the runway centerline; Δt is the time of the transition; mu (mu) B Is the friction coefficient of the brake; wherein the optimal landing speed threshold V jd ' is the corrected optimum flush threshold data.
Optionally, the process of correcting the offset threshold calculation model includes:
correcting the yaw angle alpha in the offset threshold calculation model based on the risk influence factor data to obtain optimal yaw angle threshold data alpha':
wherein C is w The yaw angle correction coefficient is obtained by comprehensively analyzing flight data and weather variation probability and fitting. W is main landing gearSpacing; w (W) 2 To the distance from the main landing gear to the centre of rotation in the x-direction, F 1 、F 2 The tires on the left side and the right side are respectively subjected to ground vertical counterforces; mu (mu) 1 、μ 2 Friction factors at the left side and the right side of the runway respectively; t is the taxiing time of the aircraft; i t Is the moment of inertia of the machine body at the moment t.
Optionally, the process of acquiring risk prediction data includes:
and respectively judging actual measurement speed data and offset data in the actual measurement data through the optimal punching threshold data and the optimal offset threshold data, generating punching risk data and offset risk data according to the judging results, and integrating the punching risk data and the offset risk data to obtain risk prediction data.
The invention has the technical effects that: the method calculates and verifies risk influence factors and occurrence probability of the civil aviation drift out-of-runway event, and better ensures reliability and safety of the aircraft landing and sliding process. The risk real-time prediction method can determine the grounding speed and the grounding angle threshold according to the actual situation, once the external environment suddenly changes in the landing process, the environment data can be transmitted back to the risk prediction system in real time, the system can timely and accurately provide an aircraft landing control strategy, real-time effective and accurate prediction can be carried out on the risk of deviating from the runway according to Liu Chong, and the occurrence probability of the event of deviating from the runway is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flow chart of a method for predicting risk of a drift out runway event based on flight data in real time in an embodiment of the invention;
FIG. 2 is a flow chart of a risk analysis data of a drift out runway event in an embodiment of the invention;
FIG. 3 is a schematic view of an aircraft landing stage according to an embodiment of the present invention;
FIG. 4 is a schematic view of a two-dimensional motion stress of an aircraft in an embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
As shown in fig. 1-4, in this embodiment, a method for predicting risk of landing drift out of a runway based on flight data in real time is provided, including:
acquiring flight data, constructing a threshold calculation model of the out-of-runway and out-of-runway based on the flight data, and acquiring risk influence factors of the out-of-runway of the aircraft; correcting the rushing-out runway threshold calculation model based on the risk influence factor data; obtaining optimal rushing-out and deviation runway threshold data based on the corrected model, wherein the optimal rushing-out and deviation runway threshold data comprises optimal rushing-out threshold data and optimal deviation threshold data; and obtaining actual measurement data, and judging the actual measurement data through optimal rushing out and deviation runway threshold value data to obtain risk prediction data.
Aiming at the real-time prediction method of the out-of-runway event, the invention establishes an analysis model from the two aspects of the out-of-runway and the out-of-runway, and a specific analysis flow is shown in figure 1.
The method model mainly comprises four plates: data acquisition, data analysis, risk assessment and early warning feedback. The data acquisition part comprises QAR data, meteorological data, aircraft performance data, personnel data and the like; analyzing the acquired data, specifically referring to fig. 2, establishing a schematic diagram (referring to fig. 3) and a stress analysis diagram (referring to fig. 4) of an airplane taxiing stage, and calculating a landing speed threshold value and a yaw angle threshold value; the relevant causative factors are analyzed by the analysis evaluation plate and are shown in the table 1 and the table 2, wherein the table 1 is the summary of flight data related to the risk of drifting out of the runway; table 2 is a summary of impact factors on the risk of drift out of the runway in an embodiment of the invention.
TABLE 1
TABLE 2
Correcting the proposed threshold calculation model; and finally, early warning feedback of the event risk of the offset runway is realized through development of the real-time prediction platform. The specific analysis is as follows:
(1) Aiming at the runway-out event, analyzing the influence of external influence factors and internal operation factors on the sliding distance according to the risk influence factors of the runway-out event; and determining the landing sliding distance by taking the length of the airfield runway as a reference, and analyzing to obtain the grounding speed threshold value of the aircraft.
Risk analysis for drift out of runway
The aircraft landing taxi phase rushing out runway generally has two conditions, rushing out runway (Over run) and rushing out runway (Veer-off), so the rushing out runway risk is that the two are combined:
R RE ＝R OR +R VO (1)
in the formula (1), R RE To offset runway risk, R OR To rush out of runway risk, R VO For off-track risk. In the invention, a landing speed threshold value is determined based on the runway length, R OR Is considered as landing speed V B Greater than threshold V BT Risk of (2); at the same time, the yaw distance D is determined based on the landing taxiing distance and the yaw angle VO The deflection distance is greater than the distance D between the center line and the edge of the runway W I.e. is considered to deviate from the runway. Thus, the first and second substrates are bonded together,
R OR ＝R(V B >V BT ) (2)
R VO ＝R(D VO >D W ) (3)
data flow analysis
For out-of-runway events, the data sources involved mainly include: flight QAR data, meteorological data, airport data, aircraft performance data, etc., wherein portions of the meteorological data, such as wind speed, wind direction, etc., are also included in the QAR data. QAR data and meteorological data are stored in a ground data base station, compiled by a decoding server and then enter a database; the risk real-time prediction platform reads data from the database, combines the airport database (including runway conditions, ground meteorological conditions and the like) based on the ultra-limit value and the measured value to obtain the critical values of ground speed and yaw angle, and realizes real-time risk analysis of the rushing runway and the deviation runway sub-module. The ground operating parameters are referenced to threshold values and field real-time data, uploaded to a monitoring terminal server and provided to airports, pilots and management departments as original data of operating characteristic analysis, and basis is provided for providing out-of-runway event risk reduction measures. And a ground-air data chain is gradually built, so that a reference threshold value is uploaded to an aircraft control system in real time, and theoretical support is provided for landing and approaching operations. The data flow diagram is shown in fig. 2.
Aircraft landing speed threshold analysis
Aircraft landing refers to the process of the aircraft beginning to descend, land, roll, and eventually stop on the runway from the airport entrance at 50ft altitude. The landing phase is generally divided into three phases: lower slide leveling segment S A Transition section S T And a deceleration running section S B As shown in fig. 3, the landing distance of the aircraft
S L ＝S A +S T +S B (4)
Wherein:
wherein H is LK For vertical altitude of aircraft as it passes the runway thresholdThe method comprises the steps of carrying out a first treatment on the surface of the P is the engine thrust; f (F) N Is a normal acceleration force; d is aerodynamic resistance; g is gravity acceleration;is the slope of the runway; μ is the runway friction coefficient; l is lift force; ρ is the atmospheric density; v is the relative velocity of the air flow; s is S W Is the area of the wing; c (C) L Is the lift coefficient; c (C) D Is the aerodynamic drag coefficient; s is S A The horizontal distance of the sliding-down leveling section; w is the weight of the aircraft; n is an overload factor, the size of which is determined by the aerodynamic characteristics of the aircraft, and is generally 1.2; k is a unit coefficient of speed, the conversion constant is 3.849 in English units, and the international unit is 0.07716; v (V) jd Landing speed; v (V) B The speed for taking all the deceleration measures; v (V) W Is the wind speed component along the runway centerline (positive upwind and negative downwind); Δt is the time of the transition; mu (mu) B Is the friction coefficient of the brake.
Wherein, for formula (6), V B To take the speed of all deceleration measures, all deceleration measures generally include thrust reversers, spoilers, wheel brakes, etc., and V B Closely related to Δt, for autopilots, the existing model considers Δt to take a value of 0.54s. However, in the actual braking process, all the deceleration measures are not necessarily adopted at the same time and are influenced by pilot operation, so the invention aims at providing a brake system based on flight data B Correcting to obtain the sliding distance of the transition section as
Wherein C is V For V in actual operation B And (3) the correction coefficient of the pilot can be obtained by fitting the flight data measured values, and meanwhile, different correction coefficients can be obtained aiming at the operation characteristics due to the difference of the operation characteristics of the pilot.
Thus, the landing distance of the aircraft is:
S' L ＝S A +S' T +S B (9)
for a runway-out event, the runway length is taken as a landing distance threshold S' L Based on formulas (4) - (9), the optimal landing speed threshold V is obtained by reverse-pushing jd ' as
And (5) influence factor analysis.
The parameters of formulas (4) - (10) related to the influence of landing distance include aircraft weight W, and the meteorological factors include wind velocity component V along the runway centerline W Aircraft performance influencing factors include the coefficient of friction of the brakes, runway conditions include the runway coefficient of friction μ, and the like. And (3) analyzing the influence of the external influence factors and the human operation factors on the landing process (parameters of other risk influence factors), uploading data in real time, and flushing out of the runway event risk real-time prediction platform to obtain a landing speed threshold value as theoretical guidance of landing operation.
(2) Aiming at the off-runway event caused by the overlarge yaw angle after the aircraft landes, an aircraft stress model in a landing sliding stage is established, a yaw angle threshold value is obtained based on flight data and based on the runway width, and the influence factors and the coupling action of pilot operating characteristics are analyzed.
And analyzing the landing sliding yaw angle of the aircraft, and obtaining the yaw angle of the aircraft when the friction resistances of the two sides of the road are unbalanced through stress analysis according to the existing sliding theoretical model. And in the landing and running stage of the aircraft, the rotation center of the aircraft is taken as an origin O, the sliding advancing direction of the aircraft is taken as an x-axis, the advancing direction is vertically taken as a y-axis, and a coordinate system xOy is established. The two-dimensional plane motion stress condition of the machine body is shown in fig. 4, in which: alpha is an angle deviating from the central line of the runway when the aircraft lands and slides; w is the main landing gear spacing; w (W) 1 The distance of the nose landing gear from the center of rotation, in the x-direction; w (W) 2 The distance of the main landing gear from the centre of rotation is in the x-direction.
Calculation of yaw angle alpha during landing taxiing of aircraft (11)
Wherein F is 1 、F 2 The tires on the left side and the right side are respectively subjected to ground vertical counterforces; mu (mu) 1 、μ 2 Friction factors at the left side and the right side of the runway respectively; t is the taxiing time of the aircraft; i t Is the moment of inertia of the machine body at the moment t.
Since the crosswind has a large influence during the actual sliding process, the formula (11) only considers the situation of unbalance friction at the two sides of the runway, and therefore, the yaw angle of the formula (11) needs to be corrected to obtain the optimal yaw angle threshold value alpha':
wherein C is w In order to consider the correction coefficient of yaw angle when the influence of other factors such as crosswind and the like is considered, the yaw angle correction coefficient is obtained by comprehensively analyzing and fitting according to flight data, combined with weather change probability and the like.
Thus, the deflection distance is
D VO ＝S' L ×tanα' (13)
When deflection distance D VO Greater than the distance D between the central line and the edge of the runway W When the runway is considered to be deviated.
W, W when determining the model 2 、I t The fixed values are obtained from a model manual or an airport manual. Thus, from equation (11), it can be seen that the aircraft taxiing yaw angle α and the coefficient of friction μ on both sides of the runway centerline 1 、μ 2 And the taxiing speed, i.e. the coefficient of friction is the main factor affecting the aircraft's running phase. Meanwhile, model change, weather change and personnel operation characteristics also have a certain influence on the probability of the airplane deviating from a runway, and the yaw angle correction coefficient C is obtained through analysis and calculation of historical data w 。
(3) And developing a real-time prediction system for the risk of the drift out runway event, and obtaining the optimal grounding speed, the grounding angle and the airplane control scheme after inputting related parameters, so as to reduce the occurrence probability of the drift out runway event.
The method comprises the steps of predicting platform development in real time, embedding contents such as model data, airport data and personnel operating characteristics into the platform in advance, timely inputting changed parameters (including meteorological parameters including wind speed, wind direction, rain, snow and ice, airport runway data including runway length, friction coefficient and the like) according to the change of external factors in the flight process, and calculating a landing speed threshold value to obtain an optimal grounding speed, a grounding angle and an airplane operating scheme as theoretical support of landing operation.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The landing drift runway risk real-time prediction method based on flight data is characterized by comprising the following steps of: acquiring flight data, constructing a threshold calculation model of the out-of-runway and out-of-runway based on the flight data, and acquiring risk influence factors of the out-of-runway of the aircraft; correcting the rushing-out runway threshold calculation model based on the risk influence factor data; obtaining optimal rushing-out and deviation runway threshold data based on the corrected model, wherein the optimal rushing-out and deviation runway threshold data comprises optimal rushing-out threshold data and optimal deviation threshold data; and obtaining actual measurement data, and judging the actual measurement data through optimal rushing out and deviation runway threshold value data to obtain risk prediction data.
2. The method for predicting risk of landing drift out of runway based on flight data as set forth in claim 1, wherein,
the flight data includes: QAR data, weather data, aircraft performance data, personnel data.
3. The method for predicting risk of landing drift out of runway based on flight data as set forth in claim 1, wherein,
the rushing out and deviating out runway threshold calculation model comprises a rushing out threshold calculation model and a deviating threshold calculation model.
4. A method for real-time prediction of landing drift out runway risk based on flight data as defined in claim 3,
the process of generating the shoot threshold calculation model includes:
acquiring length data of an airport runway, acquiring landing taxi distance data of an airplane based on the length data, and analyzing and calculating the landing taxi distance data to acquire airplane grounding speed data, wherein the airplane grounding speed data is uncorrected optimal flushing threshold data.
5. A method for real-time prediction of landing drift out runway risk based on flight data as defined in claim 3,
the process of generating the offset threshold calculation model includes:
and acquiring width data of the airport runway, and analyzing and calculating the width data and the flight data to obtain yaw angle data, wherein the yaw angle data is uncorrected optimal offset threshold data.
6. The method for predicting risk of landing drift out of runway based on flight data as set forth in claim 4, wherein,
the process for correcting the punching threshold calculation model comprises the following steps:
v in a calculation model of a hedging threshold based on risk impact factor data B Correcting to obtain the sliding distance S 'of the transition section' T ：
Wherein C is V For V in actual operation B Is used for the correction of the coefficient of (c),
corrected landing distance S 'of aircraft' L The method comprises the following steps:
S’ L ＝S A +S’ T +S B
corrected landing distance S 'based on aircraft' L The optimal landing speed threshold V is obtained by reverse push jd '：
Wherein H is LK Is the vertical height of the aircraft as it passes the runway threshold; p is the engine thrust; f (F) N Is a normal acceleration force; d is aerodynamic resistance; g is gravity acceleration;is the slope of the runway; μ is the runway friction coefficient; l is lift force; ρ is the atmospheric density; v is the relative velocity of the air flow; s is S W Is the area of the wing; c (C) L Is the lift coefficient; c (C) D Is the aerodynamic drag coefficient; s is S A The horizontal distance of the sliding-down leveling section; w is the weight of the aircraft; n is an overload factor; k is a unit coefficient of speed; v (V) B The speed for taking all the deceleration measures; v (V) W Is the wind speed component along the runway centerline; Δt is the time of the transition; mu (mu) B Is the friction coefficient of the brake; wherein the optimal landing speed threshold V jd ' is the corrected optimum flush threshold data.
7. The method for predicting risk of landing drift out of runway based on flight data as set forth in claim 5, wherein,
the process of correcting the offset threshold calculation model comprises the following steps:
correcting the yaw angle alpha in the offset threshold calculation model based on the risk influence factor data to obtain optimal yaw angle threshold data alpha':
wherein C is w The correction coefficient of the yaw angle is obtained by comprehensively analyzing flight data and weather variation probability and fitting; w is the main landing gear spacing; w (W) 2 To the distance from the main landing gear to the centre of rotation in the x-direction, F 1 、F 2 The tires on the left side and the right side are respectively subjected to ground vertical counterforces; mu (mu) 1 、μ 2 Friction factors at the left side and the right side of the runway respectively; t is the taxiing time of the aircraft; i t Is the moment of inertia of the machine body at the moment t.
8. The method for predicting risk of landing drift out of runway based on flight data as set forth in claim 1, wherein,
the process of acquiring risk prediction data includes:
and respectively judging actual measurement speed data and offset data in the actual measurement data through the optimal punching threshold data and the optimal offset threshold data, generating punching risk data and offset risk data according to the judging results, and integrating the punching risk data and the offset risk data to obtain risk prediction data.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117541046A (en) * | 2023-10-26 | 2024-02-09 | 中国民航科学技术研究院 | Non-warning monitoring item, security event and single-body risk measurement method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541046A (en) * | 2023-10-26 | 2024-02-09 | 中国民航科学技术研究院 | Non-warning monitoring item, security event and single-body risk measurement method |
| CN117541046B (en) * | 2023-10-26 | 2024-10-18 | 中国民航科学技术研究院 | Non-warning monitoring item, security event and single-body risk measurement method |
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