CN116612669A - Intelligent aviation real-time meteorological data analysis and early warning method and equipment - Google Patents

Intelligent aviation real-time meteorological data analysis and early warning method and equipment Download PDF

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CN116612669A
CN116612669A CN202310496343.0A CN202310496343A CN116612669A CN 116612669 A CN116612669 A CN 116612669A CN 202310496343 A CN202310496343 A CN 202310496343A CN 116612669 A CN116612669 A CN 116612669A
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aircraft
wind
early warning
monitoring
monitoring moment
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CN116612669B (en
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耿丹
刘新
焦圣明
倪童
鲍婷婷
陈景丽
牛霭琛
杜刚
王纪予
钱奇
周惠
魏晓奕
徐达
刘婷婷
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Jiangsu Provincial Meteorological Information Center (jiangsu Provincial Meteorological Archives)
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Jiangsu Provincial Meteorological Information Center (jiangsu Provincial Meteorological Archives)
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • Environmental & Geological Engineering (AREA)
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Abstract

The invention belongs to the technical field of aviation weather early warning, and particularly discloses a smart aviation real-time weather data analysis early warning method and device.

Description

Intelligent aviation real-time meteorological data analysis and early warning method and equipment
Technical Field
The invention belongs to the technical field of aviation weather early warning, and particularly relates to an intelligent aviation real-time weather data analysis early warning method and weather data analysis early warning equipment adopting the method.
Background
With the progress of aviation science and technology, the performance and the intelligent degree of modern aircraft are improved, the mechanical accidents of the aircraft are relatively reduced, and the flying accidents related to weather are continuously increased, so that it is seen that the weather factors become important factors affecting the flying safety.
The influence of wind on flight safety is outstanding in many meteorological factors, and the influence is manifested in influencing the accuracy and safety of the full-stage flight from take-off to landing of the aircraft, so that normal sailing activities are restricted. At present, the flight risk caused by wind mainly comprises yaw and wind shear, so that the yaw and wind shear existing in the aircraft can be found in time, the occurrence rate of accidents caused by the yaw and the wind shear is reduced to the maximum extent, and the yaw and wind shear is prejudged and early warned to be the key work of the current aircraft flight safety protection.
Because the influence of wind on the flight safety is caused by the fact that wind acts on an aircraft, the wind cannot be obtained according to the parameters of the wind alone, so that the pre-judgment on the yaw and the wind shear in the prior art is mostly based on whether the flight state of the aircraft changes, for example, the pre-judgment on whether the yaw exists in the aircraft is based on whether the flight position of the aircraft deviates from a route or not, the pre-judgment on whether the wind shear exists in the fan is based on whether the pitch angle of the aircraft changes or not, on one hand, the pre-judgment mode does not consider whether the acting force of the wind on the safety influence of the aircraft can be large or small, and when the acting force is small, the flight state of the aircraft cannot be detected, but the potential safety hazard exists in practice, and is not pre-judged under the condition of taking the flight state change of the aircraft as a reference, the pre-judgment lag is easy to be caused, the yaw and the wind shear of the aircraft are difficult to recognize in time, so that the pre-judgment result cannot meet the flight safety protection requirement of the aircraft under the influence of the wind force, and the flight safety protection of the aircraft cannot be guaranteed.
In the prior art, fixed early warning standards are generally adopted for convenient operation when early warning is carried out on wind shear, but in practice, the types of wind shear and the damage caused by the generated flight phase are different, for example, the wind shear generated in the climbing and descending phases is larger than the wind shear generated in the cruising phase, if the unified early warning standards are adopted, the problem of inaccurate early warning exists, even invalid early warning is caused, and timely and reliable reference basis is difficult to provide for the flight state adjustment of an airplane under the yaw and wind shear conditions, so that the available value of the early warning result is reduced.
Disclosure of Invention
Aiming at the problems, the technical task of the invention is to provide the intelligent aviation real-time meteorological data analysis and early warning method and equipment, which can effectively overcome the defects of the prior art on yaw and wind shear prejudgment and early warning.
The aim of the invention can be achieved by the following technical scheme: one aspect of the invention provides a smart aviation real-time meteorological data analysis and early warning method, which comprises the following steps: (1) flight route determination: and acquiring the starting place and the ending place of the current flight of the airplane and determining the navigation route of the current flight of the airplane.
(2) Wind power weather real-time detection: the method comprises the steps of positioning the flight position according to a set time interval when the aircraft starts taking off from the aircraft by using a positioning terminal in the navigation process according to the determined navigation route, and simultaneously detecting wind power weather information at the positioned flight position by using a weather detection device.
(3) And (3) aircraft navigation state indication acquisition: and acquiring navigation state indications of the aircraft by a navigation detection terminal arranged on the aircraft according to the set time interval.
(4) Yaw risk analysis; and analyzing yaw risk coefficients of the aircraft at each monitoring moment based on the wind meteorological information, the flight position and the navigation state indication at each monitoring moment.
(5) Yaw early warning judgment: judging whether yaw early warning is needed according to yaw risk coefficients of the aircraft at all monitoring moments.
(6) Yaw early warning implementation: and recording target monitoring time when the aircraft is judged to need yaw early warning, and carrying out yaw early warning by utilizing an early warning device built in the aircraft at the target monitoring time.
(7) Wind shear hidden danger analysis: and extracting the altitude from the navigation state indication, and comparing the wind meteorological information, the navigation altitude and the flight position at the adjacent monitoring moment, so as to analyze the wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment.
(8) Wind shear early warning judgment: judging whether wind shear early warning is needed according to the wind shear hidden danger coefficient of the aircraft at each monitoring moment.
(9) Wind shear early warning implementation: and recording the key monitoring moment when judging that the aircraft needs wind shear early warning, and carrying out wind shear early warning by utilizing an early warning device built in the aircraft at the key monitoring moment.
In an exemplary embodiment based on an intelligent aviation real-time weather data analysis and pre-warning method, the wind weather information includes wind direction and wind speed.
In an exemplary embodiment based on an intelligent aviation real-time weather data analysis pre-warning method, the navigational state indicators include navigational speed and navigational altitude.
In an exemplary embodiment of the method for analyzing and pre-warning real-time weather data based on intelligent aviation, the method for analyzing yaw risk coefficients of an aircraft at each monitoring moment based on wind-force weather information, flight position and navigation state indicators at each monitoring moment specifically comprises the following steps:
(41) And identifying the aircraft heading at each monitoring moment based on the flight position and the aircraft navigation route at each monitoring moment.
(42) And extracting wind direction from the wind power meteorological information, comparing the aircraft course at each monitoring moment with the wind direction, and obtaining an included angle between the aircraft course and the wind direction, and recording the included angle as a course-wind direction deviation angle.
(43) Extracting wind speed from wind power meteorological information, and substituting course-wind direction deviating angle and wind speed at each monitoring moment into a formulaCalculating to obtain the airplane yaw tendency index delta corresponding to each monitoring moment t ，sinθ t Heading-wind direction departure angle expressed as the monitoring time at the th monitoring time, t expressed as the monitoring time number, t=1, 2 Wind power t is the wind speed at the time of the t-th monitoring, v Wind 0 Denoted as reference wind speed.
(44) Extracting the sailing speed from the sailing state indication, and further importing the sailing speed at each monitoring moment into a formulaCalculating the sailing state anti-yaw index at each monitoring momentv Navigation system t is respectively expressed as the navigation speed at the t monitoring time, v Navigation 0 Expressed as critical wind force affects sailing speed, R is expressed as a set constant, and R>1。
(45) Comparing the flight position at each monitoring moment with the aircraft navigation route to obtain the distance between the flight position and the aircraft navigation route, and recording as d t 。
(46) Will delta t 、And d t Analysis model by yaw risk coefficient>Obtaining yaw risk coefficient Q of aircraft at each monitoring moment t ，d Allow for Expressed as an allowable separation distance, and e expressed as a natural constant.
In an exemplary embodiment of the intelligent aviation real-time meteorological data analysis and early warning method, the method for judging whether yaw early warning is needed is to compare the yaw risk coefficient of the aircraft at each monitoring moment with a preset safe yaw risk coefficient, if the yaw risk coefficient of the aircraft at a certain monitoring moment is larger than the preset safe yaw risk coefficient, the yaw early warning is judged to be needed, and the monitoring moment is marked as a target monitoring moment.
In an exemplary embodiment of the intelligent aviation real-time meteorological data analysis and early warning method, the analysis process corresponding to the wind shear hidden danger coefficient of the analysis aircraft at the adjacent monitoring moment is as follows: (71) Comparing the wind directions of the aircraft at adjacent monitoring moments to obtain a wind direction comparison angle beta of the aircraft at the adjacent monitoring moments t-1→t ，β t-1→t ∈[0,180°]。
(72) Comparing the wind speeds of the aircraft at adjacent monitoring moments to obtain a wind speed comparison value Deltav of the aircraft at the adjacent monitoring moments Wind t-1 → t 。
(73) Comparing the flight positions of the aircraft at adjacent monitoring moments to obtain the flight distance l of the aircraft at the adjacent monitoring moments t-1→t 。
(74) Comparing the flying heights of the aircraft at the adjacent monitoring moments to obtain the flying heights of the aircraft at the adjacent monitoring momentsDifference in degree Δh t-1→t 。
(75) Will l t-1→t And Δh t-1→t By the formulaCalculating the horizontal flight distance x of the aircraft at adjacent monitoring moments t-1→t 。
(76) Beta will be t-1→t 、Δv Wind t-1 → t 、Δh t-1→t And x t-1→t Induced wind shear hidden danger coefficient analysis model
Obtaining the wind shear hidden danger coefficient P of the aircraft at adjacent monitoring moments t-1→t 。
In an exemplary embodiment based on an intelligent aviation real-time meteorological data analysis and early warning method, the corresponding judging process for judging whether wind shear early warning is needed is as follows: (81) And comparing the flight heights of the aircraft at the adjacent monitoring moments, and identifying the flight phase of the aircraft at the adjacent monitoring moments.
(82) The wind shear tendency type of the aircraft at adjacent monitoring moments is identified based on the wind direction of the aircraft at each monitoring moment.
(83) And matching the flight phase and the wind shear trend type of the aircraft at the adjacent monitoring moment with the warning wind shear hidden danger coefficients of the aircraft stored in the early warning information base under various wind shear types at various flight phases, and matching the warning wind shear hidden danger coefficients of the aircraft at the adjacent monitoring moment.
(84) Comparing the wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment with the warning wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment, judging that wind shear early warning is needed if the wind shear hidden danger coefficient of the aircraft at a certain adjacent monitoring moment is larger than the warning wind shear hidden danger coefficient of the aircraft at a corresponding adjacent monitoring moment, and taking the latter monitoring moment of the adjacent monitoring moment as the key monitoring moment.
Based on intelligent aviation real-time meteorological dataIn an exemplary embodiment of the analysis and early warning method, the specific implementation process of identifying the flight phase of the aircraft at the adjacent monitoring time is as follows: (811) Passing the flying height of the aircraft at adjacent monitoring moments through an expressionCalculating to obtain flight height fluctuation degree +.>h t 、h t-1 Respectively expressed as the flying height of the aircraft at the t-th monitoring moment and the t-1 th monitoring moment.
(812) Comparing the flying height fluctuation degree of the aircraft at the adjacent monitoring moment with a preset limit flying height fluctuation degree interval, if the flying height fluctuation degree of the aircraft at a certain adjacent monitoring moment is within the preset limit flying height fluctuation degree interval, identifying the flying stage of the aircraft at the adjacent monitoring moment as a cruising stage, otherwise, passing the flying height fluctuation degree of the aircraft at the adjacent monitoring moment through an algorithmAnd obtaining the flight phase of the aircraft at the adjacent monitoring moment.
In an exemplary embodiment of the method for analyzing and pre-warning the real-time meteorological data based on intelligent aviation, the method for identifying the wind shear tendency type of the aircraft at the adjacent monitoring time specifically comprises the following steps: (821) Constructing a horizontal plane, constructing a wind direction characterization line according to the wind direction of the aircraft at each monitoring moment, and obtaining the included angle between the wind direction characterization line of the aircraft at each monitoring moment and the horizontal plane, which is marked as alpha t ，α t ∈[0,90°]。
(822) Will be alpha t Substituted wind type judgment expressionObtaining the wind type of the aircraft at each monitoring moment, wherein gamma 1 is expressed as a first limiting angle, and gamma 2 is expressed as a second limiting angleThe constraint of γ1 and γ2 is +.>
(823) Comparing wind types of the aircraft at adjacent monitoring moments, and identifying a model through wind shear trend types
Obtaining the wind shear trend type of the airplane at adjacent monitoring time, wherein D t-1 、D t Respectively expressed as wind types of the aircraft at the t-1 th and the t monitoring moments, U t-1→t Denoted as wind shear tendency type of the aircraft at the t-1 monitoring moment and the t monitoring moment.
Another aspect of the invention provides a smart aviation real-time weather data analysis and early warning device comprising a processor, a memory and a communication bus, wherein the memory stores a computer readable program executable by the processor.
The communication bus enables connection communication between the processor and the memory.
The processor executes the computer readable program to realize the intelligent aviation real-time meteorological data analysis and early warning method.
By combining all the technical schemes, the invention has the advantages and positive effects that:
1. according to the wind power weather information monitoring method, the wind power weather information of the flight position of the aircraft is monitored in real time in the aircraft navigation process, so that yaw risk and wind shear hidden danger analysis are dynamically carried out in combination with the navigation state of the aircraft, accurate, dynamic and advanced prejudgment of yaw and wind shear in the aircraft navigation process is realized, compared with the prejudgment carried out according to the change of the flight state of the aircraft alone, the prejudgment mode can take direct wind power parameters as a prejudgment analysis basis, further the influence of wind power on the flight state of a fan is intuitively quantified, the timeliness of identifying the yaw and the wind shear of the aircraft is greatly improved, the hysteresis of prejudgment of yaw and wind shear in the prior art is effectively avoided, the flight safety protection requirement of the aircraft under the influence of wind power is met to the greatest extent, and the flight safety of the aircraft is facilitated to be ensured.
2. According to the method, when the wind shear occurring in the navigation process of the aircraft is pre-warned, the flight stage and the wind shear trend type of the aircraft are identified in real time based on the flight height comparison and the wind direction comparison of the aircraft, so that different pre-warning standards are set according to the flight stage and the wind shear trend type of the aircraft, the pertinence and flexibility pre-warning of the wind shear are realized, the accuracy of the wind shear pre-warning is improved to a certain extent, the incidence rate of ineffective pre-warning is reduced to the greatest extent, a timely and reliable reference basis can be provided for the flight state adjustment of the aircraft under the yaw and wind shear conditions, and the method is beneficial to improving the available value of the pre-warning result.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of the steps of the method of the present invention.
FIG. 2 is a schematic diagram of aircraft heading identification in accordance with the present invention.
Fig. 3 is a schematic diagram showing the distance between the flight position and the navigation route of the aircraft according to the present invention.
Fig. 4 is a schematic view showing wind direction versus angle of an aircraft of the present invention at adjacent monitoring moments.
Fig. 5 is a schematic view of the horizontal flight distance of an aircraft of the present invention at adjacent monitoring moments.
Fig. 6 is a schematic diagram showing the angle between the wind direction characterization line and the horizontal plane of the wind direction construction of the aircraft at each monitoring moment.
Reference numerals: a1 or A-navigation route, A2-repeated navigation route, B-flight position, F t-1 -wind direction of aircraft at t-1 monitoring time, F t -wind direction of the aircraft at the time of the t monitoring, B t-1 -flight position of aircraft at t-1 monitoring moment, B t -fly to the flyFlight position of machine at t monitoring moment, S-ground, M t -wind direction characterization line, G, of the aircraft at the time of the t-th monitoring, the horizontal plane of the structure.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the invention provides a smart aviation real-time meteorological data analysis and early warning method, which comprises the following steps: (1) flight route determination: and acquiring the starting place and the ending place of the current flight of the airplane and determining the navigation route of the current flight of the airplane.
(2) Wind power weather real-time detection: and positioning the flight position according to a set time interval when the aircraft starts taking off from the aircraft by utilizing the positioning terminal in the navigation process of the aircraft according to the determined navigation route to obtain the flight position of the aircraft at each monitoring moment, and simultaneously utilizing the weather detection equipment to detect wind power weather information at the positioned flight position, wherein the wind power weather information comprises wind direction and wind speed.
(3) And (3) aircraft navigation state indication acquisition: and acquiring navigation state indicators of the airplane by a navigation detection terminal arranged on the airplane according to a set time interval, wherein the navigation speed and the navigation height.
In the above preferred embodiment, the voyage detection terminal is mainly a flight meter, wherein the voyage speed is detected by an airspeed meter in the flight meter, and the voyage altitude is detected by an altimeter in the flight meter.
(4) Yaw risk analysis; based on wind meteorological information, flight position and navigation state indication at each monitoring moment, the yaw risk coefficient of the aircraft at each monitoring moment is analyzed, and the method specifically comprises the following steps: (41) Referring to fig. 2, the aircraft heading at each monitoring time is identified based on the flight position and the aircraft navigational route at each monitoring time.
Based on the scheme, the specific recognition mode of the airplane heading is that whether the flight position falls on a navigation route is judged firstly based on the flight position of each monitoring moment, if the flight position of a certain monitoring moment does not fall on the navigation route, the flight position of the airplane at the monitoring moment is taken as a base point to re-nick the navigation route, the re-nick navigation route of the airplane at the monitoring moment is obtained, the flight position of the airplane at the monitoring moment is taken as a tangent line on the re-nick navigation route, the tangent line direction is the airplane navigation at the monitoring moment, if the flight position of the certain monitoring moment falls on the navigation route, the flight position of the airplane at the monitoring moment is taken as a tangent line on the navigation route, and the tangent line direction is the airplane navigation at the monitoring moment.
In the above, the aircraft flight position is identified based on whether the aircraft flight position falls on the flight path, and the reason for this is that considering a rough flight track of the flight path, the aircraft does not fly according to the flight path precisely due to the influence of the flight environment factors during the flight, and the situation that the aircraft position does not fall on the flight path exists, but the aircraft flight position is considered to be safe as long as the aircraft flight position falls within the allowable ranges on both sides of the flight path.
(42) And extracting wind direction from the wind power meteorological information, comparing the aircraft course at each monitoring moment with the wind direction, and obtaining an included angle between the aircraft course and the wind direction, and recording the included angle as a course-wind direction deviation angle.
(43) Extracting wind speed from wind power meteorological information, and substituting course-wind direction deviating angle and wind speed at each monitoring moment into a formulaθ t ∈[0,180°]Calculating to obtain the airplane yaw tendency index delta corresponding to each monitoring moment t ，sinθ t Heading-wind direction departure angle expressed as the monitoring time at the th monitoring time, t expressed as the monitoring time number, t=1, 2 Wind power t is the wind speed at the time of the t-th monitoring, v Wind 0 Expressed as reference wind speed, wherein course-wind direction off angleThe larger the wind speed, the larger the yaw tendency index of the aircraft, which indicates that the yaw tendency degree of the aircraft under the action of wind force is higher.
(44) Extracting the sailing speed from the sailing state indication, and further importing the sailing speed at each monitoring moment into a formulaCalculating the sailing state anti-yaw index at each monitoring momentv Navigation system t is respectively expressed as the navigation speed at the t monitoring time, v Navigation 0 Expressed as critical wind force affects sailing speed, R is expressed as a set constant, and R>1。
The method is characterized in that when the sailing speed of the airplane is based on the sailing state anti-yaw index analysis, the effect of wind power on the yaw of the airplane is smaller, namely the anti-yaw performance of the airplane is higher, the difference between the speed which can be achieved by the wind power and the sailing speed of the airplane is not large when the sailing speed of the airplane is smaller than or equal to the critical wind power influence sailing speed, the wind power can have a certain effect on the yaw of the airplane, and when the sailing speed of the airplane is greater than the critical wind power influence sailing speed, the speed which can be achieved by the wind power can not be compared with the sailing speed of the airplane, the wind power almost has no effect on the yaw of the airplane, and the anti-yaw performance of the airplane is highest.
(45) Referring to FIG. 3, the flight position at each monitoring time is compared with the aircraft route to obtain the distance between the flight position and the aircraft route, denoted as d t The specific acquisition mode is to make the flight position of each monitoring moment into a perpendicular line to the aircraft navigation route, and the perpendicular line distance is the distance between the flight position and the aircraft navigation route.
(46) Will delta t 、And d t By yaw risk factor analysis modelObtaining yaw risk coefficient Q of aircraft at each monitoring moment t ，d Allow for Expressed as allowable stand-off distance, e is expressed as a natural constant, wherein the influence of the aircraft yaw tendency index, the stand-off distance of the flight position and the aircraft voyage on the yaw risk coefficient is positive and the influence of the voyage state anti-yaw index on the yaw risk coefficient is negative.
(5) Yaw early warning judgment: judging whether yaw early warning is needed according to yaw risk coefficients of the aircraft at all monitoring moments, wherein the specific judging mode is to compare the yaw risk coefficients of the aircraft at all monitoring moments with preset safe yaw risk coefficients, judging whether yaw early warning is needed if the yaw risk coefficients of the aircraft at certain monitoring moments are larger than the preset safe yaw risk coefficients, and recording the monitoring moments as target monitoring moments.
(6) Yaw early warning implementation: and recording target monitoring time when the aircraft is judged to need yaw early warning, and carrying out yaw early warning by utilizing an early warning device built in the aircraft at the target monitoring time.
(7) Wind shear hidden danger analysis: extracting the altitude from the navigation state indication, and comparing the wind meteorological information, the navigation altitude and the flight position at the adjacent monitoring moment, so as to analyze the wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment, wherein the specific analysis process is as follows:
(71) Comparing the wind directions of the aircraft at adjacent monitoring moments to obtain a wind direction comparison angle beta of the aircraft at the adjacent monitoring moments t-1→t ，β t-1→t ∈[0,180°]See fig. 4.
(72) Comparing the wind speeds of the aircraft at adjacent monitoring moments to obtain a wind speed comparison value Deltav of the aircraft at the adjacent monitoring moments Wind t-1 → t 。
(73) Comparing the flight positions of the aircraft at adjacent monitoring moments to obtain the flight distance l of the aircraft at the adjacent monitoring moments t-1→t 。
(74) Comparing the flight heights of the aircraft at adjacent monitoring moments to obtain the aircraft at the same timeFlight level difference delta h at adjacent monitoring moments t-1→t 。
(75) Constructing a right triangle according to the flight position of the aircraft at adjacent monitoring time, as shown in fig. 5, and using the Pythagorean theorem to construct l t-1→t And Δh t-1→t By the formulaCalculating the horizontal flight distance x of the aircraft at adjacent monitoring moments t-1→t 。
(76) Beta will be t-1→t 、Δv Wind t-1 → t 、Δh t-1→t And x t-1→t Induced wind shear hidden danger coefficient analysis model
Obtaining the wind shear hidden danger coefficient P of the aircraft at adjacent monitoring moments t-1→t The larger the wind direction contrast angle, the larger the wind speed contrast value, the larger the flying height difference and the horizontal flying distance of the airplane at the adjacent monitoring moment are, the larger the wind shear hidden danger coefficient of the airplane at the adjacent monitoring moment is, which represents the larger the probability of wind shear of the airplane at the adjacent monitoring moment is.
According to the wind power weather information monitoring method, the wind power weather information of the flight position of the aircraft is monitored in real time in the aircraft navigation process, so that yaw risk and wind shear hidden danger analysis are dynamically carried out in combination with the navigation state of the aircraft, accurate, dynamic and advanced prejudgment of yaw and wind shear in the aircraft navigation process is realized, compared with the prejudgment carried out according to the change of the flight state of the aircraft alone, the prejudgment mode can take direct wind power parameters as a prejudgment analysis basis, further the influence of wind power on the flight state of a fan is intuitively quantified, the timeliness of identifying the yaw and the wind shear of the aircraft is greatly improved, the hysteresis of prejudgment of yaw and wind shear in the prior art is effectively avoided, the flight safety protection requirement of the aircraft under the influence of wind power is met to the greatest extent, and the flight safety of the aircraft is facilitated to be ensured.
(8) Wind shear early warning judgment: judging according to wind shear hidden danger coefficients of aircraft at each monitoring momentWhether wind shear early warning is needed or not is judged specifically as follows: (81) Comparing the flight heights of the aircraft at adjacent monitoring moments, and identifying the flight phase of the aircraft at the adjacent monitoring moments, wherein the specific implementation process is as follows: (811) Passing the flying height of the aircraft at adjacent monitoring moments through an expressionCalculating to obtain flight height fluctuation degree +.>h t 、h t-1 Respectively expressed as the flying height of the aircraft at the t-th monitoring moment and the t-1 th monitoring moment.
(812) Comparing the flying height fluctuation degree of the aircraft at the adjacent monitoring moment with a preset limit flying height fluctuation degree interval, if the flying height fluctuation degree of the aircraft at a certain adjacent monitoring moment is within the preset limit flying height fluctuation degree interval, identifying the flying stage of the aircraft at the adjacent monitoring moment as a cruising stage, otherwise, passing the flying height fluctuation degree of the aircraft at the adjacent monitoring moment through an algorithmAnd obtaining the flight phase of the aircraft at the adjacent monitoring moment.
(82) Based on the wind direction of the aircraft at each monitoring moment, the wind shear tendency type of the aircraft at the adjacent monitoring moment is identified, and the method specifically comprises the following identification steps: (821) Constructing a horizontal plane, wherein the horizontal plane is parallel to the ground, and constructing a wind direction characterization line according to the wind direction of the aircraft at each monitoring moment, thereby obtaining the included angle between the wind direction characterization line of the aircraft at each monitoring moment and the horizontal plane, and marking the included angle as alpha t ，α t ∈[0,90°]See fig. 6.
(822) Will be alpha t Substituted wind type judgment expressionObtaining wind type of the aircraft at each monitoring moment, whereinγ1 is denoted as a first limiting angle, γ2 is denoted as a second limiting angle, and the constraints of γ1 and γ2 are +.>
In the above analysis of the wind types of the aircraft at each monitoring time based on the angle between the wind direction characterization line and the horizontal plane, the wind shear is generally considered to occur in the case of horizontal wind and vertical wind when the wind types of the aircraft at each monitoring time are constructed based on the wind direction of the aircraft at each monitoring time, so that only the horizontal wind and the vertical wind are selected when the angle between the wind direction characterization line and the horizontal plane is selected, and other angles are not considered, and γ1=10°, γ2=80°.
(823) Comparing wind types of the aircraft at adjacent monitoring moments, and identifying a model through wind shear trend types
Obtaining the wind shear trend type of the airplane at adjacent monitoring time, wherein D t-1 、D t Respectively expressed as wind types of the aircraft at the t-1 th and the t monitoring moments, U t-1→t Denoted as wind shear tendency type of the aircraft at the t-1 monitoring moment and the t monitoring moment.
(83) And matching the flight phase and the wind shear trend type of the aircraft at the adjacent monitoring moment with the warning wind shear hidden danger coefficients of the aircraft stored in the early warning information base under various wind shear types at various flight phases, and matching the warning wind shear hidden danger coefficients of the aircraft at the adjacent monitoring moment.
(84) Comparing the wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment with the warning wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment, judging that wind shear early warning is needed if the wind shear hidden danger coefficient of the aircraft at a certain adjacent monitoring moment is larger than the warning wind shear hidden danger coefficient of the aircraft at a corresponding adjacent monitoring moment, and taking the latter monitoring moment of the adjacent monitoring moment as the key monitoring moment.
(9) Wind shear early warning implementation: and recording the key monitoring moment when judging that the aircraft needs wind shear early warning, and carrying out wind shear early warning by utilizing an early warning device built in the aircraft at the key monitoring moment.
According to the method, when the wind shear occurring in the navigation process of the aircraft is pre-warned, the flight stage and the wind shear trend type of the aircraft are identified in real time based on the flight height comparison and the wind direction comparison of the aircraft, so that different pre-warning standards are set according to the flight stage and the wind shear trend type of the aircraft, the pertinence and flexibility pre-warning of the wind shear are realized, the accuracy of the wind shear pre-warning is improved to a certain extent, the incidence rate of ineffective pre-warning is reduced to the greatest extent, a timely and reliable reference basis can be provided for the flight state adjustment of the aircraft under the yaw and wind shear conditions, and the method is beneficial to improving the available value of the pre-warning result.
The invention also uses an early warning information base in the specific implementation process, and is used for storing warning wind shear hidden danger coefficients of the aircraft under various wind shear types in various flight phases.
Example 2
The invention provides intelligent aviation real-time meteorological data analysis and early warning equipment, which comprises a processor, a memory and a communication bus, wherein a computer readable program which can be executed by the processor is stored in the memory.
The communication bus enables connection communication between the processor and the memory.
The processor executes the computer readable program to realize the intelligent aviation real-time meteorological data analysis and early warning method.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art of describing particular embodiments without departing from the structures of the invention or exceeding the scope of the invention as defined by the claims.

Claims (10)

1. An intelligent aviation real-time meteorological data analysis and early warning method is characterized by comprising the following steps of:
(1) Flight route determination: acquiring the starting place and the ending place of the current flight of the airplane and determining the navigation route of the current flight of the airplane;
(2) Wind power weather real-time detection: positioning the flight position according to a set time interval when the aircraft starts taking off from the aircraft by utilizing a positioning terminal in the navigation process of the aircraft according to the determined navigation route, and simultaneously utilizing a weather detection device to detect wind weather information at the positioned flight position;
(3) And (3) aircraft navigation state indication acquisition: acquiring navigation state indications of the aircraft by a navigation detection terminal arranged on the aircraft according to a set time interval;
(4) Yaw risk analysis; analyzing yaw risk coefficients of the aircraft at each monitoring moment based on wind meteorological information, flight positions and navigation state indexes at each monitoring moment;
(5) Yaw early warning judgment: judging whether yaw early warning is needed according to yaw risk coefficients of the aircraft at each monitoring moment;
(6) Yaw early warning implementation: recording target monitoring time when the aircraft is judged to need yaw early warning, and carrying out yaw early warning by utilizing an early warning device built in the aircraft at the target monitoring time;
(7) Wind shear hidden danger analysis: extracting the altitude from the navigation state indication, and comparing the wind meteorological information, the navigation altitude and the flight position at the adjacent monitoring moment, so as to analyze the wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment;
(8) Wind shear early warning judgment: judging whether wind shear early warning is needed according to the wind shear hidden danger coefficient of the aircraft at each monitoring moment;
(9) Wind shear early warning implementation: and recording the key monitoring moment when judging that the aircraft needs wind shear early warning, and carrying out wind shear early warning by utilizing an early warning device built in the aircraft at the key monitoring moment.
2. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 1, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the wind meteorological information includes wind direction and wind speed.
3. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 2, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the navigational state indicators include navigational speed and navigational altitude.
4. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 3, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the method for analyzing the yaw risk coefficient of the aircraft at each monitoring moment based on the wind meteorological information, the flight position and the navigation state indication at each monitoring moment specifically comprises the following steps:
(41) Identifying the aircraft heading at each monitoring moment based on the flight position and the aircraft navigational route at each monitoring moment;
(42) Extracting wind direction from wind power meteorological information, comparing the aircraft course at each monitoring moment with the wind direction, and obtaining an included angle between the aircraft course and the wind direction, and recording the included angle as a course-wind direction deviation angle;
(43) Extracting wind speed from wind power meteorological information, and substituting course-wind direction deviating angle and wind speed at each monitoring moment into a formulaCalculating to obtain the airplane yaw tendency index delta corresponding to each monitoring moment t ，θ t Heading-wind direction departure angle expressed as the monitoring time at the th monitoring time, t expressed as the monitoring time number, t=1, 2 Wind power t is the wind speed at the time of the t-th monitoring, v Wind 0 Expressed as a reference wind speed;
(44) Extracting the sailing speed from the sailing state indication, and further importing the sailing speed at each monitoring moment into a formulaCalculating the sailing state anti-yaw index at each monitoring momentv Navigation system t is respectively expressed as the navigation speed at the t monitoring time,v Navigation 0 Expressed as critical wind force affects sailing speed, R is expressed as a set constant, and R>1；
(45) Comparing the flight position at each monitoring moment with the aircraft navigation route to obtain the distance between the flight position and the aircraft navigation route, and recording as d t ；
(46) Will delta t 、And d t Analysis model by yaw risk coefficient>Obtaining yaw risk coefficient Q of aircraft at each monitoring moment t ，d Allow for Expressed as an allowable separation distance, and e expressed as a natural constant.
5. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 1, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the judging mode of judging whether yaw early warning is needed is that the yaw risk coefficient of the aircraft at each monitoring moment is compared with a preset safe yaw risk coefficient, if the yaw risk coefficient of the aircraft at a certain monitoring moment is larger than the preset safe yaw risk coefficient, the yaw early warning is judged to be needed, and the monitoring moment is recorded as a target monitoring moment.
6. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 3, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the resolving process corresponding to the wind shear hidden danger coefficient of the resolving aircraft at the adjacent monitoring moment is as follows:
(71) Comparing the wind directions of the aircraft at adjacent monitoring moments to obtain a wind direction comparison angle beta of the aircraft at the adjacent monitoring moments t-1→t ，β t-1→t ∈[0,180°]；
(72) Comparing the wind speeds of the aircraft at adjacent monitoring moments to obtain a wind speed comparison value Deltav of the aircraft at the adjacent monitoring moments Wind t-1 → t ；
(73) The aircraft is positioned atComparing the flight positions of the adjacent monitoring moments to obtain the flight distance l of the aircraft at the adjacent monitoring moments t-1→t ；
(74) Comparing the flying heights of the aircraft at the adjacent monitoring moments to obtain the flying height difference delta h of the aircraft at the adjacent monitoring moments t-1→t ；
(75) Will l t-1→t And Δh t-1→t By the formulaCalculating the horizontal flight distance x of the aircraft at adjacent monitoring moments t-1→t ；
(76) Beta will be t-1→t 、Δv Wind t-1 → t 、Δh t-1→t And x t-1→t Induced wind shear hidden danger coefficient analysis modelObtaining the wind shear hidden danger coefficient P of the aircraft at adjacent monitoring moments t-1→t 。
7. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 6, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the corresponding judging process for judging whether wind shear early warning is needed is as follows:
(81) Comparing the flight heights of the aircraft at adjacent monitoring moments, and identifying the flight phase of the aircraft at the adjacent monitoring moments;
(82) Identifying the wind shear tendency type of the aircraft at adjacent monitoring moments based on the wind direction of the aircraft at each monitoring moment;
(83) Matching the flight phase and the wind shear trend type of the aircraft at the adjacent monitoring moment with the warning wind shear hidden danger coefficients of the aircraft stored in the early warning information base under various wind shear types at various flight phases, and matching the warning wind shear hidden danger coefficients of the aircraft at the adjacent monitoring moment;
(84) Comparing the wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment with the warning wind shear hidden danger coefficient of the aircraft at the adjacent monitoring moment, judging that wind shear early warning is needed if the wind shear hidden danger coefficient of the aircraft at a certain adjacent monitoring moment is larger than the warning wind shear hidden danger coefficient of the aircraft at a corresponding adjacent monitoring moment, and taking the latter monitoring moment of the adjacent monitoring moment as the key monitoring moment.
8. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 7, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the specific implementation process of the identifying airplane in the flight phase of the adjacent monitoring moment is as follows:
(811) Passing the flying height of the aircraft at adjacent monitoring moments through an expressionCalculating to obtain flight height fluctuation degree +.>h t 、h t-1 The flight heights of the aircraft at the t monitoring moment and the t-1 monitoring moment are respectively expressed;
(812) Comparing the flying height fluctuation degree of the aircraft at the adjacent monitoring moment with a preset limit flying height fluctuation degree interval, if the flying height fluctuation degree of the aircraft at a certain adjacent monitoring moment is within the preset limit flying height fluctuation degree interval, identifying the flying stage of the aircraft at the adjacent monitoring moment as a cruising stage, otherwise, passing the flying height fluctuation degree of the aircraft at the adjacent monitoring moment through an algorithmAnd obtaining the flight phase of the aircraft at the adjacent monitoring moment.
9. The intelligent aviation real-time meteorological data analysis and early warning method according to claim 7, wherein the intelligent aviation real-time meteorological data analysis and early warning method is characterized in that: the method for identifying the wind shear tendency type of the aircraft at the adjacent monitoring moment specifically comprises the following steps:
(821) A horizontal plane is constructed and,constructing a wind direction characterization line according to the wind direction of the aircraft at each monitoring moment, thereby obtaining the included angle between the wind direction characterization line of the aircraft at each monitoring moment and the horizontal plane, and marking the included angle as alpha t ，α t ∈[0,90°]；
(822) Will be alpha t Substituted wind type judgment expressionObtaining the wind type of the aircraft at each monitoring moment, wherein gamma 1 is expressed as a first limiting angle, gamma 2 is expressed as a second limiting angle, and the constraint conditions of gamma 1 and gamma 2 are->
(823) Comparing wind types of the aircraft at adjacent monitoring moments, and identifying a model through wind shear trend typesObtaining the wind shear trend type of the airplane at adjacent monitoring time, wherein D t-1 、D t Respectively expressed as wind types of the aircraft at the t-1 th and the t monitoring moments, U t-1→t Denoted as wind shear tendency type of the aircraft at the t-1 monitoring moment and the t monitoring moment.
10. An intelligent aviation real-time meteorological data analysis and early warning device is characterized by comprising a processor, a memory and a communication bus, wherein a computer readable program which can be executed by the processor is stored in the memory;
the communication bus realizes the connection communication between the processor and the memory;
the steps in an intelligent aviation real-time meteorological data analysis and early warning method according to any one of claims 1-9 are realized when the processor executes the computer readable program.
CN202310496343.0A 2023-05-05 Intelligent aviation real-time meteorological data analysis and early warning method and equipment Active CN116612669B (en)

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