Flight bumping area prediction method and system based on air-ground data fusion

Abstract

The invention relates to the field of aviation safety, and provides a flight bumping area prediction method and system based on air-ground data fusion, and the method comprises the steps: obtaining a measurement area, and obtaining a barometric altitude through meteorological data; acquiring air detection data of the measurement aircraft so as to calculate a horizontal airspeed component; checking according to the horizontal airspeed component and the ground speed component to obtain a high-altitude wind speed component; generating an airborne data matrix, calculating the distance between a measurement point in the airborne data matrix and a grid point in a measurement area to obtain a latitude and longitude distance, and performing weight accumulation to obtain a grid point wind speed; wind field deformation is calculated, a jolting index is calculated according to the wind field deformation and normalized, a normalized jolting index is obtained, and a comprehensive jolting index is obtained through the normalized jolting index; and obtaining a target jolting index according to the ground measurement jolting index and the comprehensive jolting index to realize jolting prediction. According to the invention, accurate flight bumping prediction can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation safety, and particularly to a method and system for predicting flight turbulence areas based on air-ground data fusion. Background Art

[0002] Flight turbulence has a great impact on the aircraft structure, flight control, ride quality, and the personal safety of the crew and passengers. However, there is still a lack of accurate early warning means for current flight turbulence, especially clear-air turbulence. There are various reasons for aircraft turbulence during flight, and turbulence is the most common and frequent meteorological reason for flight turbulence. Atmospheric turbulence can be divided into convective turbulence and clear-air turbulence. Most of the wet turbulence can be detected by current airborne weather radars. Clear-air turbulence is a kind of turbulence that cannot be directly seen with the naked eye or optical instruments in clear skies, and is related to the enhanced wind shear and reduced stability near jet streams, tropopause, and upper-level fronts. It often appears in clear skies, sometimes also in stratus clouds, but does not appear in or near convective clouds, and is an important factor causing high-altitude flight turbulence.

[0003] Currently, turbulence prediction mainly uses upper-air winds for ground turbulence prediction. Ground turbulence prediction has the advantages of a wide range and a long forecast time for large-scale turbulence, but it cannot accurately predict small and medium-scale turbulence, and has a low update frequency. In addition to ground turbulence prediction, turbulence can also be identified through airborne flight parameters, but airborne turbulence detection can only identify turbulence in the small and medium-scale range and is easily affected by the aircraft's own flight. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the related art. For this purpose, the present invention provides a method and system for predicting flight turbulence areas based on air-ground data fusion to achieve the prediction of flight turbulence during flight.

[0005] The present invention provides a method for predicting flight turbulence areas based on air-ground data fusion, including: S1: Obtain meteorological data and divide to obtain a measurement area, and obtain the pressure altitude through the meteorological data; S2: Let a measurement aircraft fly in the measurement area, obtain the airborne detection data of the measurement aircraft, and calculate the horizontal airspeed component through the airborne detection data; S3: Obtain the ground speed component through the airborne detection data, and obtain the high-altitude wind speed component according to the horizontal airspeed component and the ground speed component; S4: Generate an airborne data matrix through the high-altitude wind speed component, calculate the distance between the measurement points in the airborne data matrix and the grid points in the measurement area to obtain the longitude and latitude distance, and perform weighted accumulation through the longitude and latitude distance to obtain the grid point wind speed; S5: Calculate the wind field deformation based on the grid point wind speed and barometric height, calculate the turbulence index according to the wind field deformation, normalize the turbulence index to obtain the normalized turbulence index, and obtain the comprehensive turbulence index through the normalized turbulence index; S6: Calculate the ground measured turbulence index, obtain the target turbulence index based on the ground measured turbulence index and the comprehensive turbulence index, and achieve turbulence prediction through the target turbulence index.

[0006] According to a flight turbulence area prediction method based on air-ground data fusion provided by the present invention, step S2 specifically includes: S21: Determine the measurement aircraft, make the measurement aircraft fly in the measurement area, and obtain air detection data including the measured true airspeed; S22: Conduct Mach number inspection on the measured true airspeed to obtain the first true airspeed error, conduct indicated airspeed inspection on the measured true airspeed to obtain the second true airspeed error, and calibrate the measured true airspeed according to the first true airspeed error and the second true airspeed error to obtain the true airspeed; S23: Obtain the climb angle of the measurement aircraft from the air detection data, decompose the true airspeed through the climb angle to obtain the horizontal true airspeed, and decompose the horizontal true airspeed to obtain horizontal airspeed components including the true airspeed zonal component and the true airspeed meridional component.

[0007] According to a flight turbulence area prediction method based on air-ground data fusion provided by the present invention, step S3 specifically includes: S31: Obtain the ground speed, course angle, and drift angle of the measurement aircraft from the air detection data, combine the course angle and the drift angle into the track angle, and decompose the ground speed through the track angle to obtain the ground speed components including the ground speed zonal component and the ground speed meridional component; S32: Obtain the high-altitude wind speed components including the high-altitude wind speed longitude component and the high-altitude wind speed latitude component through the ground speed components and the horizontal airspeed components, obtain the onboard recorded wind speed and decompose the onboard recorded wind speed to obtain the onboard recorded wind speed components, and verify the high-altitude wind speed components through the onboard recorded wind speed components.

[0008] According to a flight turbulence area prediction method based on air-ground data fusion provided by the present invention, step S4 specifically includes: S41: Generate an onboard data matrix including the high-altitude wind speed longitude component, the high-altitude wind speed latitude component, the aircraft longitude, the aircraft latitude, and the aircraft altitude through the high-altitude wind speed components; S42: Calculate the distance between the measurement point and the grid points in the measurement area through the aircraft longitude and the aircraft latitude to obtain the longitude and latitude distance; S43: Obtain the upper-air wind speed longitude component, upper-air wind speed latitude component, and longitude-latitude distance within the sampling range of the grid point, and obtain the weight coefficients of the upper-air wind speed longitude component and the upper-air wind speed latitude component based on the longitude-latitude distance; S44: Perform weighted accumulation on the upper-air wind speed longitude component and the upper-air wind speed latitude component respectively through the weight coefficients to obtain the grid point wind speed including the grid point longitude wind speed and the grid point latitude wind speed.

[0009] For a flight turbulence area prediction method based on air-ground data fusion provided by the present invention, step S5 specifically includes: S51: Calculate the wind field deformation including the horizontal shear of the horizontal wind field, the vertical shear of the horizontal wind field, and the total shear of the horizontal wind field through the grid point wind speed and the pressure altitude; S52: Calculate the first turbulence index according to the vertical shear of the horizontal wind field and the total shear of the horizontal wind field, calculate the second turbulence index according to the horizontal shear of the horizontal wind field and the vertical shear of the horizontal wind field, and use the first turbulence index and the second turbulence index as the turbulence index; S53: Determine the intensity level of the turbulence index, normalize the first turbulence index and the second turbulence index respectively through linear interpolation and the intensity level of the turbulence index to obtain the first normalized turbulence index and the second normalized turbulence index, and obtain the comprehensive turbulence index through the first normalized turbulence index and the second normalized turbulence index.

[0010] For a flight turbulence area prediction method based on air-ground data fusion provided by the present invention, step S6 specifically includes: S61: Calculate the ground measurement turbulence index, and obtain the average air-ground turbulence index deviation of the ground measurement turbulence index and the comprehensive turbulence index in the measurement area; S62: Obtain the target turbulence index through the air-ground turbulence index deviation mean, the ground measurement turbulence index, and the comprehensive turbulence index, and realize turbulence prediction through the target turbulence index.

[0011] For a flight turbulence area prediction method based on air-ground data fusion provided by the present invention, in step S6, when calculating the ground measurement turbulence index, select a ground measurement device, detect the measurement area through the ground measurement device to obtain a ground detection data matrix, and use the method of steps S4 to S5 through the ground detection data matrix to obtain the ground measurement turbulence index.

[0012] According to a method for predicting flight turbulence areas based on air-ground data fusion provided by the present invention, in step S6, when realizing turbulence prediction through the target turbulence index, a data conversion software is selected, the target turbulence index is input into the data conversion software to obtain a turbulence prediction contour map, and turbulence prediction is realized through the turbulence prediction contour map.

[0013] The present invention also proposes a system for predicting flight turbulence areas based on air-ground data fusion, including: Meteorological data processing module: used to obtain meteorological data and divide to obtain a measurement area, and obtain the pressure altitude through the meteorological data; Horizontal airspeed component module: used to make a measurement aircraft fly in the measurement area, obtain the aerial detection data of the measurement aircraft, and calculate the horizontal airspeed component through the aerial detection data; High-altitude wind speed component module: used to obtain the ground speed component through the aerial detection data, and obtain the high-altitude wind speed component according to the horizontal airspeed component and the ground speed component; Grid point wind speed module: used to generate an airborne data matrix through the high-altitude wind speed component, calculate the distance between the measurement points in the airborne data matrix and the grid points in the measurement area to obtain the longitude and latitude distance, and perform weighted accumulation through the longitude and latitude distance to obtain the grid point wind speed; Comprehensive turbulence index module: used to calculate the wind field deformation through the grid point wind speed and the pressure altitude, calculate the turbulence index according to the wind field deformation and normalize the turbulence index to obtain a normalized turbulence index, and obtain the comprehensive turbulence index through the normalized turbulence index; Turbulence prediction module: used to calculate the ground measurement turbulence index, obtain the target turbulence index according to the ground measurement turbulence index and the comprehensive turbulence index, and realize turbulence prediction through the target turbulence index.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method for predicting flight turbulence areas based on air-ground data fusion as described in any one of the above are realized.

[0015] One or more of the above technical solutions in the embodiments of the present invention have at least one of the following technical effects: A method and system for predicting flight turbulence areas based on air-ground data fusion provided by the present invention effectively remove invalid or potentially interfering data by combining the measurement by a measurement aircraft in the air and the measurement by ground measurement equipment on the ground, solve the problem of fewer measurement points covered in the air measurement, and also solve the problems that ground measurement cannot accurately predict small and medium-scale turbulence and has a low update frequency, and effectively combines the advantages of the two methods.

[0016] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of a method for predicting flight turbulence areas based on air-ground data fusion provided by the present invention.

[0019] Figure 2 It is an isogram of turbulence prediction for a method for predicting flight turbulence areas based on air-ground data fusion provided by the present invention.

[0020] Figure 3 It is a schematic structural diagram of a system for predicting flight turbulence areas based on air-ground data fusion provided by the present invention.

[0021] Figure 4 It is a schematic structural diagram of a device for predicting flight turbulence areas based on air-ground data fusion provided by the present invention.

[0022] Reference Signs: 100, meteorological data processing module; 200, horizontal airspeed component module; 300, high-altitude wind speed component module; 400, grid point wind speed module; 500, comprehensive turbulence index module; 600, turbulence prediction module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0024] In the description of the embodiments of the present invention, it should be noted that the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0025] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific situations.

[0026] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0027] The following combines Figures 1 to 4 to describe the specific implementation scheme of the present invention: Figure 1 It is a schematic flow chart of a method for predicting flight turbulence areas based on air-ground data fusion provided by the present invention, including first dividing the measurement area and obtaining the pressure altitude through meteorological data; then obtaining the air detection data of the measurement aircraft and calculating the horizontal airspeed component through the air detection data; subsequently obtaining the ground speed component and obtaining the high-altitude wind speed component based on the horizontal airspeed component and the ground speed component; then generating an airborne data matrix, obtaining the longitude and latitude distance, and performing weighted accumulation through the longitude and latitude distance to obtain the grid point wind speed; then calculating the turbulence index and normalizing it to obtain the normalized turbulence index, thereby obtaining the comprehensive turbulence index; finally obtaining the target turbulence index based on the ground turbulence index and the comprehensive turbulence index to achieve turbulence prediction.

[0028] The present invention provides a method for predicting flight turbulence areas based on air-ground data fusion, including: S1: Obtain meteorological data and divide to obtain the measurement area, and obtain the pressure altitude through the meteorological data; Further, the purpose of this stage is to divide the measurement area and obtain the barometric height from meteorological data. Specifically, first, meteorological data is obtained and the measurement area is divided. The process of obtaining the measurement area is to select an area at a fixed height in the air and determine its size. In the area, the north-south direction is taken as the longitude direction and several longitude lines are set, and the east-west direction is taken as the latitude direction and several latitude lines are set, so as to divide the area into several grids to obtain the measurement area. The meteorological data is GRIB2 format meteorological data, which includes the barometric pressure at the height where the measurement area is located. .

[0029] Subsequently, the barometric height can be obtained from the barometric pressure. When the barometric pressure is greater than 22632 Pa, the quasi-atmospheric temperature is first calculated. : Among them, is the standard atmospheric sea-level pressure, which is 101325 Pa; is the standard atmospheric sea-level temperature, which is 15 °C, that is, 288.15 K.

[0030] Subsequently, the barometric height is calculated. : Among them, is the standard atmospheric temperature lapse rate, which is 0.0065 °C / m.

[0031] When the barometric pressure is less than 22632 Pa, the barometric height is; S2: Let the measurement aircraft fly in the measurement area, obtain the airborne detection data of the measurement aircraft, and calculate the horizontal airspeed component through the airborne detection data; Further, the purpose of this stage is to calculate the horizontal airspeed component through the airborne detection data. Specifically, step S2 specifically includes: S21: Determine the measurement aircraft, let the measurement aircraft fly in the measurement area, and obtain the airborne detection data including the measured true airspeed; S22: Conduct a Mach number test on the measured true airspeed to obtain the first true airspeed error, conduct an indicated airspeed test on the measured true airspeed to obtain the second true airspeed error, and calibrate the measured true airspeed according to the first true airspeed error and the second true airspeed error to obtain the true airspeed; S23: Obtain the climb angle of the measurement aircraft from the airborne detection data, decompose the true airspeed through the climb angle to obtain the horizontal true airspeed, and decompose the horizontal true airspeed to obtain the horizontal airspeed component including the true airspeed zonal component and the true airspeed meridional component.

[0032] For the above steps, the specific implementation in this embodiment is as follows: First, determine the measurement aircraft. Here, the measurement aircraft can be a drone specifically for measurement purposes, or other aircraft cruising in the area to perform other tasks. Let the measurement aircraft fly in the measurement area, and obtain the air detection data including the measured true airspeed through the pitot tube and on-board sensors. The sampling frequency is once per second in this embodiment. Each time a sample is taken, the position of the measurement aircraft during sampling in the measurement area can be used as a measurement point. Subsequently, perform Mach number verification on the measured true airspeed: First, obtain the static temperature T from the air detection data and calculate the speed of sound : : Among them, is the adiabatic index, which is taken as 1.4 here, is the gas constant, which is taken as 287.05 J / kg / K here. Subsequently, calculate the first measured true airspeed through the Mach number M in the air detection data: Subsequently, obtain the difference between the first measured true airspeed and the measured true airspeed to get the first true airspeed error , thus completing the Mach number verification: Next, perform indicated airspeed verification on the measured true airspeed. First, obtain the indicated airspeed of the measurement aircraft, the static temperature T and static pressure of the outside world from the air detection data, so as to obtain the second measured true airspeed : Among them, is the standard atmospheric sea-level speed of sound, which is 340 m / s. Subsequently, obtain the difference between the second measured true airspeed and the measured true airspeed to get the second true airspeed error , thus completing the indicated airspeed verification: Next, verify the measured true airspeed according to the first true airspeed error and the second true airspeed error, that is, judge whether the first true airspeed error and the second true airspeed error are greater than the threshold set according to experience. If they are greater, it is considered that the measured true airspeed is unreliable and needs to be excluded. Otherwise, the measured true airspeed is used as the true airspeed.

[0033] Subsequently, obtain the climb angle θ of the measurement aircraft from the air detection data, and decompose the true airspeed through the climb angle to obtain the horizontal true airspeed in the horizontal direction : Obtain the heading angle of the measurement aircraft from the aerial detection data , where it is defined that the angle of the heading angle is 0 when the heading is due north, so as to decompose the horizontal true airspeed and obtain the horizontal airspeed components including the true airspeed zonal component and the true airspeed meridional component : S3: Obtain the ground speed components from the aerial detection data, and obtain the high-altitude wind speed components based on the horizontal airspeed components and the ground speed components; Furthermore, the purpose of this stage is to obtain the ground speed components and perform verification, and obtain the high-altitude wind speed components. Specifically, step S3 specifically includes: S31: Obtain the ground speed, heading angle and drift angle of the measurement aircraft from the aerial detection data, combine the heading angle and the drift angle into the track angle, and decompose the ground speed through the track angle to obtain the ground speed components including the ground speed zonal component and the ground speed meridional component;

[0034] For the above steps, the specific implementation in this embodiment is as follows: First, obtain the ground speed of the measurement aircraft from the aerial detection data , heading angle and drift angle β. Here, the drift angle is the angle between the aircraft's movement direction and the fuselage, and combine the heading angle and the drift angle into the track angle : The track angle is the angle of the measurement aircraft's track relative to the due north direction in the measurement area. Then, decompose the ground speed through the track angle to obtain the ground speed components including the ground speed zonal component and the ground speed meridional component : Here, since the unit of the ground speed is knot, it is necessary to multiply it by 0.5144 to convert it to km / h for unified unit calculation. Then, the high-altitude wind speed components including the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed can be obtained from the ground speed component and the horizontal airspeed component: Obtain the onboard recorded wind speed from the airborne detection data and decompose the onboard recorded wind speed to obtain the components of the onboard recorded wind speed including the meridional component of the onboard recorded wind speed and the zonal component of the onboard recorded wind speed : Here, is the angle between the wind direction and the due north direction. Since the positive speed direction of the onboard recorded wind speed is opposite to the positive direction of the high-altitude wind speed component, it is necessary to reverse it with a negative sign. Subsequently, calculate the wind speed error and verify the high-altitude wind speed component: If the wind speed error exceeds the wind speed error threshold set according to experience, the high-altitude wind speed component is considered unreliable and should be discarded; otherwise, it is retained.

[0035] S4: Generate an onboard data matrix from the high-altitude wind speed components, calculate the distance between the measurement points in the onboard data matrix and the grid points in the measurement area to obtain the longitude-latitude distance, and perform weighted accumulation through the longitude-latitude distance to obtain the grid point wind speed; Furthermore, the purpose of this stage is to calculate the distance between the measurement points in the onboard data matrix and the grid points in the measurement area, so as to perform weighted accumulation to obtain the grid point wind speed. Specifically, step S4 specifically includes: S41: Generate an onboard data matrix including the longitude component of the high-altitude wind speed, the latitude component of the high-altitude wind speed, the longitude of the aircraft, the latitude of the aircraft, and the altitude of the aircraft from the high-altitude wind speed components; S42: Calculate the distance between the measurement points and the grid points in the measurement area through the longitude of the aircraft and the latitude of the aircraft to obtain the longitude-latitude distance; S43: Obtain the longitude component of the high-altitude wind speed, the latitude component of the high-altitude wind speed, and the longitude-latitude distance within the sampling range of the grid points, and obtain the weight coefficients of the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed through the longitude-latitude distance; S44: Perform weighted accumulation on the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed respectively through the weight coefficients to obtain the grid point wind speed including the grid point longitude wind speed and the grid point latitude wind speed.

[0036] For the above steps, the specific implementation in this embodiment is as follows: First, perform operations on the aerial detection data of multiple measurement points, so as to generate an airborne data matrix M including the high-altitude wind speed longitude component, high-altitude wind speed latitude component, aircraft longitude, aircraft latitude, and aircraft altitude through its high-altitude wind speed component: Among them, is the aircraft longitude vector, including the aircraft longitude of the position of each measurement point, is the aircraft latitude vector, including the aircraft latitude of the position of each measurement point, is the aircraft altitude vector, including the aircraft altitude of the position of each measurement point, and the altitude, longitude, and latitude data can be obtained from the GPS data of the measuring aircraft; is the high-altitude wind speed longitude vector, including the high-altitude wind speed longitude component of each measurement point, is the high-altitude wind speed latitude vector, including the high-altitude wind speed latitude component of each measurement point.

[0037] Subsequently, judge the validity of the high-altitude wind speed longitude component and high-altitude wind speed latitude component through the barometric altitude: Among them, is the aircraft altitude of the i-th measurement point, is the altitude difference threshold determined according to experience. If the altitude difference is too large, it means that the deviation between the measurement point and the barometric altitude of the sampling area is too large, then the values of the high-altitude wind speed longitude component and high-altitude wind speed latitude component of this measurement point are considered invalid and should be excluded. Subsequently, determine a grid point in the measurement area, and calculate the distance between the measurement points within a certain range around the grid point and the grid point to obtain the n-th longitude and latitude distance , in this embodiment, the selected measurement points are the measurement points located within a circle with the grid point as the center and a radius of 1 / 2 of the grid length, and this circle is used as the sampling range of the grid point.

[0038] Among them, is the longitude of the grid point, is the measurement point longitude of the n-th measurement point, is the latitude of the grid point, the measurement point latitude of the n-th measurement point.

[0039] Subsequently, obtain the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed of the measurement points within the sampling range from the airborne data matrix, and obtain their longitude and latitude distances. Since the data of the measurement points closer to the grid points can also reflect the state of the grid points, higher weights can be assigned to the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed of the measurement points close to the grid points. Therefore, the square of the reciprocal of the longitude and latitude distance of the nth measurement point is used as the weight coefficient for the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed of the nth measurement point.

[0040] Finally, perform weighted accumulation on the longitude component of the high-altitude wind speed and the latitude component of the high-altitude wind speed in the sampling range respectively through the weight coefficient, and the grid point wind speed including the longitude wind speed of the grid point and the latitude wind speed of the grid point can be obtained: wherein, is the longitude component of the high-altitude wind speed of the nth measurement point, is the latitude component of the high-altitude wind speed of the nth measurement point.

[0041] S5: Calculate the wind field deformation through the grid point wind speed and the pressure altitude, calculate the turbulence index according to the wind field deformation and normalize the turbulence index to obtain a normalized turbulence index, and obtain a comprehensive turbulence index through the normalized turbulence index; Furthermore, the purpose of this stage is to calculate the turbulence index according to the wind field deformation and normalize the turbulence index to obtain a normalized turbulence index, so as to obtain a comprehensive turbulence index. Specifically, step S5 specifically includes: S51: Calculate the wind field deformation including the horizontal shear of the horizontal wind field, the vertical shear of the horizontal wind field, and the total shear of the horizontal wind field through the grid point wind speed and the pressure altitude; S52: Calculate the first turbulence index according to the vertical shear of the horizontal wind field and the total shear of the horizontal wind field, calculate the second turbulence index according to the horizontal shear of the horizontal wind field and the vertical shear of the horizontal wind field, and use the first turbulence index and the second turbulence index as the turbulence index; S53: Determine the intensity level of the turbulence index, normalize the first turbulence index and the second turbulence index respectively through linear interpolation and the intensity level of the turbulence index to obtain a first normalized turbulence index and a second normalized turbulence index, and obtain a comprehensive turbulence index through the first normalized turbulence index and the second normalized turbulence index.

[0042] For the above steps, the specific implementation methods in this embodiment are as follows: First, calculate the horizontal shear of the horizontal wind field including , the vertical shear of the horizontal wind field and the total shear of the horizontal wind field through the grid point wind speed Wind field deformation of grid points: Among them, represents differentiation, and then the first bump index is calculated according to the vertical shear of the horizontal wind field and the total shear of the horizontal wind field : The second bump index is calculated according to the horizontal shear of the horizontal wind field and the vertical shear of the horizontal wind field : The first bump index and the second bump index are used as the bump index. Then, the intensity level of the bump index is determined. In this embodiment, the intensity level of the bump index includes extremely heavy, heavy, medium, light, and none, and the intensity parameter corresponding to extremely heavy is 1, the intensity parameter corresponding to heavy is 0.75, the intensity parameter corresponding to medium is 0.5, the intensity parameter corresponding to light is 0.25, and the intensity parameter corresponding to none is 0. Then, the first bump index and the second bump index are normalized respectively by linear interpolation and the bump index intensity level. In this embodiment, the method of normalizing by linear interpolation is as follows: First, determine the corresponding relationship between the bump index intensity level and the first bump index and the second bump index, as shown in Table 1: Table 1 Corresponding relationship table between bump index intensity level and the first bump index and the second bump index

[0043] When the first bump index is less than 1.5 or the second bump index is less than 20, the first normalized bump index and the second normalized bump index are both taken as 0. When the first bump index is greater than 12 or the second bump index is greater than 60, the first normalized bump index and the second normalized bump index are both taken as 1. When the first bump index or the second bump index is the parameter in Table 1, then directly take the intensity parameters of the bump index intensity level corresponding to the first bump index or the second bump index as the first normalized bump index and the second normalized bump index respectively. When the first bump index or the second bump index is between two parameters in Table 1, then through linear interpolation, the first normalized bump index or the second normalized bump index is respectively the value between the intensity parameters of the bump index intensity levels corresponding to the two parameters. For example, when the first bump index is 4.5 and the second bump index is 45, then through linear interpolation, the first normalized bump index is 0.375 and the second normalized bump index is 0.625.

[0044] Finally, through the first normalized bump index and the second normalized bump index to obtain a comprehensive bump index , and use the method of steps S42 to S53 to obtain the comprehensive bump index of the grid points with measurement points within all sampling ranges: S6: Calculate the ground measurement bump index, obtain the target bump index based on the ground measurement bump index and the comprehensive bump index, and realize bump prediction through the target bump index.

[0045] Furthermore, the purpose of this stage is to obtain the ground measurement bump index and the target bump index, so as to realize bump prediction. Specifically, step S6 specifically includes: S61: Calculate the ground measurement bump index, and obtain the mean deviation of the air-ground bump index between the ground measurement bump index and the comprehensive bump index in the measurement area; S62: Obtain the target bump index through the mean deviation of the air-ground bump index, the ground measurement bump index and the comprehensive bump index, and realize bump prediction through the target bump index.

[0046] In step S6, when calculating the ground measurement bump index, select a ground measurement device, and detect the measurement area through the ground measurement device to obtain a ground detection data matrix. Through the ground detection data matrix and using the method of steps S4 to S5, obtain the ground measurement bump index.

[0047] In step S6, when realizing bump prediction through the target bump index, select data conversion software, input the target bump index into the data conversion software to obtain a bump prediction contour map, and realize bump prediction through the bump prediction contour map.

[0048] For the above steps, the specific implementation method in this embodiment is as follows: First, calculate the ground measurement bump index. When calculating the ground measurement bump index, first select a ground measurement device, such as a weather radar, etc., detect the measurement area through the ground measurement device to obtain a ground detection data matrix with the same data content as the airborne data matrix, so as to obtain the ground measurement bump index through the ground detection data matrix and using the method of steps S4 to S5. Subsequently, obtain the mean deviation of the air-ground bump index between the ground measurement bump index and the comprehensive bump index in the measurement area: First, obtain the comprehensive bump index of the j-th grid point in the measurement area and the ground measurement bump index of the j-th grid point , so as to obtain the air-ground bump index deviation of the j-th grid point : Among them, when there is no comprehensive turbulence index at a grid point due to reasons such as the measurement aircraft not passing by, its value is 0. Then, the mean value of the air-ground turbulence index deviation of all grid points is obtained to get the mean value of the air-ground turbulence index deviation. .

[0049] Then, the target turbulence index of the j-th grid point is obtained through the mean value of the air-ground turbulence index deviation, the ground measurement turbulence index, and the comprehensive turbulence index. : Since the comprehensive turbulence index is relatively more accurate, the comprehensive turbulence index is preferentially used when it exists. Subsequently, data conversion software is selected. In this embodiment, the data conversion software is MATLAB. The target turbulence index is input into the data conversion software, and then the turbulence prediction contour map of the measurement area can be generated. The turbulence prediction contour map is as Figure 2 shown. Turbulence prediction can be achieved through the turbulence prediction contour map.

[0050] Next, a flight turbulence area prediction device based on air-ground data fusion provided by the present invention is described. The flight turbulence area prediction device described below and the flight turbulence area prediction method based on air-ground data fusion described above can be referred to each other correspondingly.

[0051] Figure 3 It is a schematic structural diagram of a flight turbulence area prediction system based on air-ground data fusion, as Figure 3 shown, and is used to execute a flight turbulence area prediction method based on air-ground data fusion as described above, including: Meteorological data processing module 100: used to obtain meteorological data and divide to obtain a measurement area, and obtain the pressure altitude through the meteorological data; Horizontal airspeed component module 200: used to make a measurement aircraft fly in the measurement area, obtain the air detection data of the measurement aircraft, and calculate the horizontal airspeed component through the air detection data; High-altitude wind speed component module 300: used to obtain the ground speed component through the air detection data, and obtain the high-altitude wind speed component according to the horizontal airspeed component and the ground speed component; Grid point wind speed module 400: used to generate an airborne data matrix through the high-altitude wind speed component, calculate the distance between the measurement points in the airborne data matrix and the grid points in the measurement area to obtain the longitude and latitude distance, and perform weighted accumulation through the longitude and latitude distance to obtain the grid point wind speed; Integrated Turbulence Index Module 500: It is used to calculate the deformation of the wind field through the grid point wind speed and barometric height, calculate the turbulence index according to the deformation of the wind field and normalize the turbulence index to obtain the normalized turbulence index, and obtain the integrated turbulence index through the normalized turbulence index; Turbulence Prediction Module 600: It is used to calculate the ground measured turbulence index, obtain the target turbulence index according to the ground measured turbulence index and the integrated turbulence index, and realize turbulence prediction through the target turbulence index.

[0052] On the other hand, Figure 4 An example of the physical structure diagram of an electronic device is shown in Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 complete mutual communication through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute a method for predicting flight turbulence areas based on air-ground data fusion. The method includes: S1: Obtain meteorological data and divide to obtain a measurement area, and obtain the barometric height through the meteorological data; S2: Let the measurement aircraft fly in the measurement area, obtain the air detection data of the measurement aircraft, and calculate the horizontal airspeed component through the air detection data; S3: Obtain the ground speed component through the air detection data, and obtain the high-altitude wind speed component according to the horizontal airspeed component and the ground speed component; S4: Generate an airborne data matrix through the high-altitude wind speed component, calculate the distance between the measurement points in the airborne data matrix and the grid points in the measurement area to obtain the longitude and latitude distance, and perform weighted accumulation through the longitude and latitude distance to obtain the grid point wind speed; S5: Calculate the deformation of the wind field through the grid point wind speed and barometric height, calculate the turbulence index according to the deformation of the wind field and normalize the turbulence index to obtain the normalized turbulence index, and obtain the integrated turbulence index through the normalized turbulence index; S6: Calculate the ground measured turbulence index, obtain the target turbulence index according to the ground measured turbulence index and the integrated turbulence index, and realize turbulence prediction through the target turbulence index.

[0053] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0054] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting flight turbulence areas based on air-ground data fusion, characterized in that Including: S1: Obtain meteorological data and divide to obtain a measurement area, and obtain the pressure altitude from the meteorological data; S2: Make a measurement aircraft fly in the measurement area, obtain the airborne detection data of the measurement aircraft, and calculate the horizontal airspeed component from the airborne detection data; S3: Obtain the ground speed component from the airborne detection data, and obtain the high-altitude wind speed component based on the horizontal airspeed component and the ground speed component; S4: Generate an airborne data matrix from the high-altitude wind speed component, calculate the distance between the measurement points in the airborne data matrix and the grid points in the measurement area to obtain the longitude and latitude distance, and perform weighted accumulation through the longitude and latitude distance to obtain the grid point wind speed; S5: Calculate the wind field deformation from the grid point wind speed and the pressure altitude, calculate the bump index based on the wind field deformation and normalize the bump index to obtain the normalized bump index, and obtain the comprehensive bump index from the normalized bump index; S6: Calculate the ground measurement bump index, obtain the target bump index based on the ground measurement bump index and the comprehensive bump index, and achieve bump prediction through the target bump index.

2. The flight bump area prediction method based on air-ground data fusion according to claim 1, wherein Step S2 specifically includes: S21: Determine the measurement aircraft, make the measurement aircraft fly in the measurement area, and obtain the airborne detection data including the measured true airspeed; S22: Conduct a Mach number test on the measured true airspeed to obtain the first true airspeed error, conduct an indicated airspeed test on the measured true airspeed to obtain the second true airspeed error, and calibrate the measured true airspeed based on the first true airspeed error and the second true airspeed error to obtain the true airspeed; S23: Obtain the climb angle of the measurement aircraft from the airborne detection data, decompose the true airspeed through the climb angle to obtain the horizontal true airspeed, and decompose the horizontal true airspeed to obtain the horizontal airspeed component including the true airspeed zonal component and the true airspeed meridional component.

3. A method for predicting flight turbulence areas based on air-ground data fusion according to claim 1, characterized in that, Step S3 specifically includes: S31: Obtain the ground speed, course angle, and drift angle of the measurement aircraft from the airborne detection data, combine the course angle and the drift angle into the track angle, and decompose the ground speed through the track angle to obtain the ground speed component including the ground speed zonal component and the ground speed meridional component; S32: Obtain the high-altitude wind speed component including the high-altitude wind speed longitude component and the high-altitude wind speed latitude component from the ground speed component and the horizontal airspeed component, obtain the airborne recorded wind speed and decompose the airborne recorded wind speed to obtain the airborne recorded wind speed component, and verify the high-altitude wind speed component through the airborne recorded wind speed component.

4. A method for predicting flight bump areas based on air-ground data fusion according to claim 1, characterized in that, Step S4 specifically includes: S41: Generate an airborne data matrix including the high-altitude wind speed longitude component, the high-altitude wind speed latitude component, the aircraft longitude, the aircraft latitude, and the aircraft altitude from the high-altitude wind speed component; S42: Calculate the distance between the measurement point and the grid points in the measurement area through the aircraft longitude and the aircraft latitude to obtain the longitude and latitude distance; S43: Obtain the upper-air wind speed longitude component, upper-air wind speed latitude component, and longitude-latitude distance within the sampling range of the grid point, and obtain the weight coefficients of the upper-air wind speed longitude component and the upper-air wind speed latitude component based on the longitude-latitude distance; S44: Perform weighted accumulation on the upper-air wind speed longitude component and the upper-air wind speed latitude component respectively using the weight coefficients to obtain the grid point wind speed including the grid point longitude wind speed and the grid point latitude wind speed.

5. A method for predicting flight turbulence areas based on air-ground data fusion according to claim 1, characterized in that Step S5 specifically includes: S51: Calculate the wind field deformation including the horizontal shear of the horizontal wind field, the vertical shear of the horizontal wind field, and the total shear of the horizontal wind field based on the grid point wind speed and the pressure altitude; S52: Calculate the first turbulence index based on the vertical shear of the horizontal wind field and the total shear of the horizontal wind field, calculate the second turbulence index based on the horizontal shear of the horizontal wind field and the vertical shear of the horizontal wind field, and use the first turbulence index and the second turbulence index as the turbulence index; S53: Determine the intensity level of the turbulence index, normalize the first turbulence index and the second turbulence index respectively through linear interpolation and the intensity level of the turbulence index to obtain the first normalized turbulence index and the second normalized turbulence index, and obtain the comprehensive turbulence index through the first normalized turbulence index and the second normalized turbulence index.

6. A method for predicting flight turbulence areas based on air-ground data fusion according to claim 1, characterized in that Step S6 specifically includes: S61: Calculate the ground measurement turbulence index, and obtain the mean deviation of the air-ground turbulence index between the ground measurement turbulence index and the comprehensive turbulence index in the measurement area; S62: Obtain the target turbulence index through the mean deviation of the air-ground turbulence index, the ground measurement turbulence index, and the comprehensive turbulence index, and realize turbulence prediction through the target turbulence index.

7. A method for predicting flight turbulence areas based on air-ground data fusion according to claim 1, characterized in that, In step S6, when calculating the ground measurement turbulence index, select ground measurement equipment, detect the measurement area through the ground measurement equipment to obtain a ground detection data matrix, and use the methods of steps S4 to S5 through the ground detection data matrix to obtain the ground measurement turbulence index.

8. A method for predicting flight turbulence areas based on air-ground data fusion according to claim 1, characterized in that In step S6, when realizing turbulence prediction through the target turbulence index, select data conversion software, input the target turbulence index into the data conversion software to obtain a turbulence prediction contour map, and realize turbulence prediction through the turbulence prediction contour map.

9. A flight turbulence area prediction system based on air-ground data fusion, which is used to execute a flight turbulence area prediction method based on air-ground data fusion according to any one of claims 1 to 8, characterized in that, Including: Meteorological data processing module: used to obtain meteorological data and divide to obtain the measurement area, and obtain the pressure altitude through the meteorological data; Horizontal airspeed component module: used to make a measurement aircraft fly in the measurement area, obtain the airborne detection data of the measurement aircraft, and calculate the horizontal airspeed component through the airborne detection data; Upper-air wind speed component module: used to obtain the ground speed component through the airborne detection data, and obtain the upper-air wind speed component based on the horizontal airspeed component and the ground speed component; Grid point wind speed module: used to generate an airborne data matrix through the upper-air wind speed component, calculate the distance between the measurement points in the airborne data matrix and the grid points in the measurement area to obtain the longitude-latitude distance, and perform weighted accumulation through the longitude-latitude distance to obtain the grid point wind speed; Comprehensive Turbulence Index Module: It is used to calculate the deformation of the wind field through the grid point wind speed and barometric altitude, calculate the turbulence index according to the wind field deformation, normalize the turbulence index to obtain the normalized turbulence index, and obtain the comprehensive turbulence index through the normalized turbulence index; Turbulence Prediction Module: It is used to calculate the ground measured turbulence index, obtain the target turbulence index according to the ground measured turbulence index and the comprehensive turbulence index, and realize turbulence prediction through the target turbulence index.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for predicting flight turbulence areas based on air-ground data fusion according to any one of claims 1 to 8.

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