A direct flight automatic identification method based on QAR data

Abstract

The application discloses a kind of direct flight automatic identification method based on QAR data, the present application is different by latitude and longitude, altitude track deviation distance man-machine interaction information in flight phase establishes t-SNE model perplexity, track deviation distance automatic identification algorithm is established in flight phase by t-SNE model perplexity, to utilize flight phase track deviation distance automatic identification algorithm to the position coordinate man-machine interaction information in flight phase is automatically identified, obtains the track deviation distance calibration time in safety limit interval data under the automatic driving state of flight phase, and the correction scheme of heading, flight speed, flight attitude is set by attitude stability judgment information, and aircraft safety control is carried out based on the correction scheme of heading, flight speed, flight attitude.The application improves the automatic identification precision of flight phase track deviation distance automatic identification.The method can efficiently process a large amount of flight data, improve the intelligent level of flight management, and reduce the operation complexity.

Description

Technical Field

[0001] The present invention relates to the field of automatic identification of direct flights of aircraft, and in particular to a method for automatic identification of direct flights based on QAR data. Background Art

[0002] A direct flight, in flight, refers to a situation where, while complying with route regulations, an aircraft is permitted to fly directly between waypoints due to control, airspace availability, favorable weather conditions, or other factors, skipping some of the originally planned waypoints, thereby shortening flight distance and time. Based on current airline fuel efficiency assessments, direct flights have attracted significant attention due to their ability to significantly reduce actual flight distance, thereby significantly reducing fuel consumption and achieving fuel conservation. Therefore, how to automatically identify and detect direct flights has become a key challenge for airlines.

[0003] Traditionally, there are two main methods for analyzing direct flights in the civil aviation sector. One relies on manual visual inspection, observing a flight's actual flight trajectory and comparing it to the planned flight trajectory. If deviations are detected, further observation is made to determine whether a flight is a direct flight. Furthermore, a single flight may experience multiple such deviations, requiring meticulous judgment and screening to identify actual direct flights and flight segments. This method is subject to significant human error and requires considerable experience to accurately determine its accuracy.

[0004] Another method involves asking pilots whether they have requested direct flights at certain locations, or determining where they have requested direct flights based on cockpit voice chatter. The pilots then contact the Air Traffic Control Bureau and other relevant departments via instant messaging, phone, or telegram to verify whether direct flights have actually taken place at those locations. This method is time-consuming and labor-intensive, prone to errors due to memory bias, and difficult to verify. Summary of the Invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a direct flight automatic identification method based on QAR data.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is a direct flight automatic identification method based on QAR data, comprising the following steps:

[0007] Establishing a QAR data integration center, using the QAR data integration center to set flight trajectory parameters, obtaining the QAR data integration center after the flight trajectory parameters are set, and using the QAR data integration center to control the position coordinate human-computer interaction information of the QAR integrated flight phase; the flight phase includes the takeoff phase, the climb phase, the cruise phase, and the descent phase;

[0008] The direct flight information database is used to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase. The t-SNE model perplexity is then used to establish an automatic recognition algorithm for the track deviation distance during the flight phase.

[0009] Automatically identify the human-machine interaction information of the position coordinates during the flight phase using the automatic identification algorithm for the flight phase track deviation distance, obtain the safety limit interval data within the calibration time of the track deviation distance during the flight phase in the automatic driving state, and judge the attitude stability based on the safety limit interval data within the calibration time of the track deviation distance during the flight phase in the automatic driving state to generate attitude stability judgment information;

[0010] The correction plan for heading, flight speed and flight attitude is set through attitude stability judgment information, and the aircraft is safely controlled based on the correction plan for heading, flight speed and flight attitude.

[0011] Furthermore, in this method, a QAR data integration center is established, and flight trajectory parameters are set for the QAR data integration center to obtain the QAR data integration center after the flight trajectory parameters are set, specifically including:

[0012] Establish a QAR data integration center and test it to obtain the ADS-B direct flight parameter jump interval characteristics at different latitudes, longitudes, and altitudes. Establish an ADS-B direct flight parameter boundary threshold evaluation model based on the track monitoring system.

[0013] Input the ADS-B direct flight parameter jump interval characteristics of different latitudes, longitudes and altitudes of the QAR data integration center into the ADS-B direct flight parameter boundary threshold evaluation model for training, and obtain the trained ADS-B direct flight parameter boundary threshold evaluation model;

[0014] Obtain the ADS-B direct flight parameter jump interval characteristics of the QAR data integration center within the preset time and input them into the trained ADS-B direct flight parameter boundary threshold evaluation model for evaluation, and obtain the ADS-B direct flight parameter boundary threshold of the QAR data integration center at a unit timestamp;

[0015] The real-time ADS-B direct flight parameter of the QAR data integration center is obtained. When the real-time ADS-B direct flight parameter is greater than the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp, the real-time ADS-B direct flight parameter is adjusted according to the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp to obtain the QAR data integration center after the flight trajectory parameters are set.

[0016] Furthermore, in this method, the direct flight information database is used to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase, and the t-SNE model perplexity is established based on the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase. The t-SNE model perplexity is used to establish an automatic recognition algorithm for the track deviation distance during the flight phase, specifically including:

[0017] The direct flight information database is used to integrate the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase. The track deviation distance types are classified using the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase. The human-computer interaction information of each track deviation distance type is obtained, and the t-SNE model perplexity of each track deviation distance type is established based on the human-computer interaction information of each track deviation distance type.

[0018] The human-computer interaction information in the t-SNE model perplexity of each track deviation distance type is input into the safety limit interval clustering calculation model to obtain the safety limit interval within the track deviation distance calibration time in the human-computer interaction information per unit time, and determine whether the safety limit interval within the track deviation distance calibration time in the human-computer interaction information is equal to or exceeds the preset interval, and introduce the DE-9IM model;

[0019] When the safety limit interval within the track deviation distance calibration time present in the human-computer interaction information is equal to or exceeds the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type of the unit time human-computer interaction information is obtained as the relevant safety limit interval within the track deviation distance calibration time; when the safety limit interval within the track deviation distance calibration time present in the human-computer interaction information is less than the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type of the unit time human-computer interaction information is used as the relevant safety limit interval within the track deviation distance calibration time;

[0020] The relevant track deviation distance calibration time safety limit interval is input into the DE-9IM model, so that the input dimension is concentrated in the relevant track deviation distance calibration time safety limit interval, and the preset position node is obtained. Based on the route curvature, an automatic identification algorithm for track deviation distance during flight phase is established. The preset position node in the t-SNE model perplexity is input into the automatic identification algorithm for track deviation distance during flight phase to perform deviation warning.

[0021] The waypoints given by the computer flight plan trajectory are projected onto the map coordinate system. Circular buffers are drawn for all waypoints according to the maximum route offset distance specified by the Civil Aviation Administration. The actual QAR flight trajectory is projected onto the map coordinate system to form lines connecting the longitude and latitude trajectory points. A logical operation is performed using DE-9IM to determine whether the spatial relationship between the actual flight trajectory and the circular buffers of the waypoints intersects. The circular buffers of all waypoints are iteratively traversed to determine whether the flight trajectory does not pass through some waypoints, and the number of consecutive points not passed through is greater than or equal to 1, which is considered a flight trajectory deviation.

[0022] Extract the starting and ending waypoints of the flight plan from which the actual flight trajectory deviates, and find the nearest QAR actual flight trajectory point through Euclidean distance to intercept the actual flight trajectory;

[0023] By using the route curvature calculation method, the actual flight path distance is compared with the straight-line distance between the starting waypoint and the ending waypoint. If the curvature percentage is less than a given threshold, it is determined whether it is a direct flight. The threshold can be set by yourself. The saved distance is obtained by subtracting the flight distance of the actual flight path from the flight distance of the computer flight plan. Route curvature (%) = (actual flight path distance ÷

[0024] The straight-line distance from the starting waypoint to the ending waypoint) × 100%.

[0025] Furthermore, in this method, the position coordinate human-computer interaction information of the flight phase is automatically identified using the automatic identification algorithm of the flight phase track deviation distance, and the safe limited interval data of the flight phase track deviation distance within the calibration time in the automatic driving state is obtained. The attitude stability is judged by using the safe limited interval data of the flight phase track deviation distance within the calibration time in the automatic driving state, and the attitude stability judgment information is generated, which specifically includes:

[0026] Inputting the human-machine interaction information of the position coordinates of the flight phase into the automatic identification algorithm of the track deviation distance of the flight phase for automatic identification, obtaining the safe limit interval data of the track deviation distance of the flight phase in the automatic driving state within the calibration time, and presetting the attitude stability judgment angle standard;

[0027] The attitude stability judgment deflection standard is used to divide the attitude stability judgment deflection data of the safe limited interval within the track deviation distance calibration time during the flight phase in the automatic driving state, obtain the attitude stability judgment deflection angle of the safe limited interval within the track deviation distance calibration time during the flight phase in the automatic driving state, and integrate the navigation deviation information with different magnetic deflections during the flight phase;

[0028] The attitude stability judgment information is generated by judging the attitude stability declination angle of the flight phase in the automatic driving state within the safety limit range within the calibration time, the navigation deviation information with different magnetic declination angles in the flight phase, and the attitude stability judgment information is generated by judging the attitude stability of the flight phase in the automatic driving state within the safety limit range within the calibration time.

[0029] Furthermore, in this method, the correction scheme for heading, flight speed, and flight attitude is set based on attitude stability judgment information, specifically including:

[0030] The navigation deviation information of different magnetic declination angles in the flight phase is integrated through attitude stability judgment information, and a fixed magnetic declination influence position is established based on the navigation deviation information of different magnetic declination angles in the flight phase. Fixed-point correction is performed through the fixed magnetic declination influence position to integrate the track deviation of the flight phase;

[0031] The flight control system error is obtained through the track deviation during the flight phase, and a flight control system error risk coefficient is established. The flight control system error is input into the flight control system error risk coefficient for visualization to obtain risk assessment results of different levels;

[0032] Based on the risk assessment results of different levels, correction plans for heading, flight speed, and flight attitude during direct flight under different magnetic declination angles are set, and the correction plans for heading, flight speed, and flight attitude are output.

[0033] Furthermore, in this method, the aircraft safety control is performed based on the correction scheme of heading, flight speed, and flight attitude, specifically including:

[0034] Obtaining the distribution characteristic information of the turbulence frequency of different airflow types per unit time and the navigation deviation information with different magnetic declination angles in a specific area, and calculating the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase based on the distribution characteristic information of the turbulence frequency of different airflow types per unit time and the navigation deviation information with different magnetic declination angles in the flight phase;

[0035] A generative adversarial network (GAN) was introduced, and a weight matrix was set through the GAN. The Cronbach's alpha coefficient between the turbulence frequency areas of different airflow types and the navigation deviation information at different magnetic declination angles during flight phases was used to initialize the turbulence frequency data of different airflow types, and the dangerous calibration area of ​​turbulence frequency of each different airflow type was obtained.

[0036] Determine whether the Cronbach's alpha coefficient between the areas where the turbulence frequencies of different airflow types are located and the navigation deviation information at different magnetic declination angles during flight phases is greater than a preset Cronbach's alpha coefficient; if the Cronbach's alpha coefficient between the areas where the turbulence frequencies of different airflow types are located and the navigation deviation information at different magnetic declination angles during flight phases is not greater than the preset Cronbach's alpha coefficient, output a danger calibration area for each different turbulence frequency of different airflow types;

[0037] When the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase is greater than the preset Cronbach's alpha coefficient, the dangerous calibration areas with different turbulence frequencies of each airflow type will be screened in a focused manner until the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase is no greater than the preset Cronbach's alpha coefficient.

[0038] Beneficial effects:

[0039] The present invention establishes a QAR data integration center, sets flight trajectory parameters for the QAR data integration center, obtains the QAR data integration center after the flight trajectory parameters are set, uses the QAR data integration center to control the QAR to integrate the human-computer interaction information of the position coordinates of the flight phase, and then uses a direct flight information database to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes of the flight phase, and establishes a t-SNE model perplexity based on the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes of the flight phase, and establishes an automatic recognition algorithm for the track deviation distance of the flight phase based on the t-SNE model perplexity, thereby automatically recognizing the human-computer interaction information of the position coordinates of the flight phase using the automatic recognition algorithm for the track deviation distance of the flight phase, obtaining the track deviation distance safety limit interval data within the calibration time of the flight phase in the automatic driving state, and performing attitude stability judgment based on the track deviation distance safety limit interval data within the calibration time of the flight phase in the automatic driving state, generating attitude stability judgment information, and finally setting a correction scheme for the heading, flight speed, and flight attitude based on the attitude stability judgment information, and performing aircraft safety control based on the correction scheme for the heading, flight speed, and flight attitude. The present invention utilizes the DE-9IM model to process the target track deviation distance in the t-SNE model perplexity within the safe limit interval within the calibration time, so that the input dimension is concentrated in the target track deviation distance in the t-SNE model perplexity within the safe limit interval within the calibration time. This can suppress the interference of multi-scale features on the automatic identification algorithm of the track deviation distance during the flight phase, and can improve the automatic identification accuracy of the automatic identification of the track deviation distance during the flight phase. QAR data can provide accurate real-time track, speed, altitude and other multi-dimensional information of the aircraft during flight, providing reliable data support for the automatic identification of the direct flight path. By analyzing the track deviation in the QAR data, this method can monitor in real time whether the aircraft is flying according to the planned route, thereby achieving accurate assessment and correction of the track. This method has a high degree of automation, can reduce the risk of human intervention and pilot error, and improve flight safety. Compared with traditional manual judgment or data analysis based on manual records, the direct flight automatic identification method based on QAR data can accurately detect course deviation during flight in real time, adjust the track in time, and avoid the potential danger of deviation from the route. Furthermore, this method can efficiently process large amounts of flight data, improving the intelligence of flight management and reducing operational complexity. This data-driven approach not only optimizes flight efficiency and reduces fuel consumption, but also improves aircraft heading accuracy during flight, ensuring flight safety and stability. Therefore, the automatic direct flight identification method based on QAR data demonstrates significant advantages in improving flight safety, optimizing flight paths, and enhancing operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1The overall flow chart of the method of the present invention is shown. DETAILED DESCRIPTION

[0041] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0043] like Figure 1 As shown, the first aspect of the present invention provides a method for automatic identification of direct flights based on QAR data, comprising the following steps:

[0044] Step S101: Establish a QAR data integration center, set flight trajectory parameters for the QAR data integration center, obtain the QAR data integration center after the flight trajectory parameters are set, and use the QAR data integration center to control the QAR integration flight phase position coordinate human-computer interaction information, where the flight phase includes takeoff, climb, cruise, and descent.

[0045] Step S102: using the direct flight information database to integrate human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase, and establishing a t-SNE model perplexity based on the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase, and establishing an automatic recognition algorithm for track deviation distances during the flight phase based on the t-SNE model perplexity;

[0046] Step S103: Automatically identify the human-machine interaction information of the position coordinates during the flight phase using an automatic identification algorithm for the track deviation distance during the flight phase, obtain data on a safe limited interval within a calibrated time of the track deviation distance during the flight phase in the automatic driving state, and perform attitude stability judgment based on the data on the safe limited interval within a calibrated time of the track deviation distance during the flight phase in the automatic driving state, thereby generating attitude stability judgment information;

[0047] Step S104: setting a correction scheme for the heading, flight speed, and flight attitude based on the attitude stability judgment information, and performing aircraft safety control based on the correction scheme for the heading, flight speed, and flight attitude.

[0048] It should be noted that the present invention uses the DE-9IM model to process the target track deviation distance calibration time safety limit interval in the t-SNE model perplexity, so that the input dimension is concentrated in the target track deviation distance calibration time safety limit interval in the t-SNE model perplexity, which can suppress the interference of multi-scale features on the automatic recognition algorithm of the flight phase track deviation distance, and can improve the automatic recognition accuracy of the flight phase track deviation distance automatic recognition.

[0049] Furthermore, in this method, a QAR data integration center is established, and flight trajectory parameters are set for the QAR data integration center to obtain the QAR data integration center after the flight trajectory parameters are set, specifically including:

[0050] Establish a QAR data integration center and test it to obtain the ADS-B direct flight parameter jump interval characteristics at different latitudes, longitudes, and altitudes. Establish an ADS-B direct flight parameter boundary threshold evaluation model based on the track monitoring system.

[0051] Input the ADS-B direct flight parameter jump interval characteristics of different latitudes, longitudes and altitudes of the QAR data integration center into the ADS-B direct flight parameter boundary threshold evaluation model for training, and obtain the trained ADS-B direct flight parameter boundary threshold evaluation model;

[0052] Obtain the ADS-B direct flight parameter jump interval characteristics of the QAR data integration center within the preset time and input them into the trained ADS-B direct flight parameter boundary threshold evaluation model for evaluation, and obtain the ADS-B direct flight parameter boundary threshold of the QAR data integration center at a unit timestamp;

[0053] The real-time ADS-B direct flight parameter of the QAR data integration center is obtained. When the real-time ADS-B direct flight parameter is greater than the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp, the real-time ADS-B direct flight parameter is adjusted according to the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp to obtain the QAR data integration center after the flight trajectory parameters are set.

[0054] It should be noted that, in reality, the ADS-B direct flight parameters of the QAR data integration center exist within a safety limit range due to the existence of a safety limit range for information transmission. Since the QAR data integration center uses multiple devices for control, the performance of the terminal devices will degrade after a certain period of use (such as a decrease in the amount of information transmitted per unit time), resulting in a safety limit range for the ADS-B direct flight parameters of the QAR data integration center. This method can adjust the real-time ADS-B direct flight parameters of the QAR data integration center using the ADS-B direct flight parameter boundary threshold of the QAR data integration center at a unit timestamp, thereby ensuring that the ADS-B direct flight parameters controlled by the human-machine QAR data integration center meet the predetermined requirements and ensure the stability of the QAR data integration center. A QAR (Quick Access Recorder) is an aircraft data recording device that is primarily used to collect, store, and provide various flight data generated during flight. The design purpose of the QAR is to facilitate airlines and related agencies to quickly access aircraft flight data for performance monitoring, fault diagnosis, maintenance, and accident investigation.

[0055] Furthermore, in this method, the direct flight information database is used to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase, and the t-SNE model perplexity is established based on the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase. The t-SNE model perplexity is used to establish an automatic recognition algorithm for the track deviation distance during the flight phase, specifically including:

[0056] The direct flight information database is used to integrate the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase. The track deviation distance types are classified using the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase. The human-computer interaction information of each track deviation distance type is obtained, and the t-SNE model perplexity of each track deviation distance type is established based on the human-computer interaction information of each track deviation distance type.

[0057] The human-computer interaction information in the t-SNE model perplexity of each track deviation distance type is input into the safety limit interval clustering calculation model to obtain the safety limit interval within the track deviation distance calibration time in the human-computer interaction information per unit time, and determine whether the safety limit interval within the track deviation distance calibration time in the human-computer interaction information is equal to or exceeds the preset interval, and introduce the DE-9IM model;

[0058] When the safety limit interval within the track deviation distance calibration time present in the human-computer interaction information is equal to or exceeds the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type of the unit time human-computer interaction information is obtained as the relevant safety limit interval within the track deviation distance calibration time; when the safety limit interval within the track deviation distance calibration time present in the human-computer interaction information is less than the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type of the unit time human-computer interaction information is used as the relevant safety limit interval within the track deviation distance calibration time;

[0059] The relevant track deviation distance safety limit interval within the calibration time is input into the DE-9IM model, so that the input dimension is concentrated in the relevant track deviation distance safety limit interval within the calibration time, and the preset position nodes are obtained. Based on the route curvature, an automatic identification algorithm for track deviation distance during flight phase is established, and the preset position nodes in the t-SNE model perplexity are input into the automatic identification algorithm for track deviation distance during flight phase to perform deviation warning.

[0060] The waypoints given by the computer flight plan trajectory are projected onto the map coordinate system. Circular buffers are drawn for all waypoints according to the maximum route offset distance specified by the Civil Aviation Administration. The actual QAR flight trajectory is projected onto the map coordinate system to form lines connecting the longitude and latitude trajectory points. A logical operation is performed using DE-9IM to determine whether the spatial relationship between the actual flight trajectory and the circular buffers of the waypoints intersects. The circular buffers of all waypoints are iteratively traversed to determine whether the flight trajectory does not pass through some waypoints, and the number of consecutive points not passed through is greater than or equal to 1, which is considered a flight trajectory deviation.

[0061] Extract the starting and ending waypoints of the flight plan from which the actual flight trajectory deviates, and find the nearest QAR actual flight trajectory point through Euclidean distance to intercept the actual flight trajectory;

[0062] By using the route curvature calculation method, the actual flight path distance is compared with the straight-line distance between the starting waypoint and the ending waypoint. If the curvature percentage is less than a given threshold, it is determined whether it is a direct flight. The threshold can be set by yourself. The saved distance is obtained by subtracting the flight distance of the actual flight path from the flight distance of the computer flight plan. Route curvature (%) = (actual flight path distance ÷

[0063] The straight-line distance from the starting waypoint to the ending waypoint) × 100%.

[0064] It should be noted that in the method, the position coordinate human-computer interaction information of the flight phase is automatically identified by the flight phase track deviation distance automatic identification algorithm, the safe limited interval data within the flight phase track deviation distance calibration time under the automatic driving state is obtained, and the attitude stability judgment is performed on the safe limited interval data within the flight phase track deviation distance calibration time under the automatic driving state, the attitude stability judgment information is generated, and specifically includes:

[0065] The position coordinate human-computer interaction information of the flight phase is input into the flight phase track deviation distance automatic identification algorithm for automatic identification, the safe limited interval data within the flight phase track deviation distance calibration time under the automatic driving state is obtained, and the attitude stability judgment angle standard is preset;

[0066] The safe limited interval data within the flight phase track deviation distance calibration time under the automatic driving state is divided into attitude stability judgment angles by the attitude stability judgment angle standard, the safe limited interval attitude stability judgment angle within the flight phase track deviation distance calibration time under the automatic driving state is obtained, and the navigation deviation information of the flight phase with different magnetic declination angles is integrated;

[0067] The attitude stability judgment is generated by the safe limited interval attitude stability judgment angle within the flight phase track deviation distance calibration time under the automatic driving state, the navigation deviation information of the flight phase with different magnetic declination angles, and the safe limited interval data within the flight phase track deviation distance calibration time under the automatic driving state, and the attitude stability judgment information is generated.

[0068] It should be noted that the standard of the attitude stability judgment angle can be set by the track deviation distance type and the size of the track deviation distance, wherein the attitude stability judgment angle includes low attitude stability judgment angle, medium attitude stability judgment angle, high attitude stability judgment angle, etc.

[0069] Further, in the method, the correction scheme of the heading, the flight speed and the flight attitude is set by the attitude stability judgment information, and specifically includes:

[0070] The navigation deviation information of the flight phase with different magnetic declination angles is integrated by the attitude stability judgment information, and the fixed-point magnetic declination influence position is established based on the navigation deviation information of the flight phase with different magnetic declination angles, the fixed-point correction is performed by the fixed-point magnetic declination influence position, and the flight phase track deviation is integrated.

[0071] The flight control system error is obtained by the flight phase track deviation, and the flight control system error risk coefficient is established, the flight control system error is input into the flight control system error risk coefficient for visualization, and the risk assessment results of different levels are obtained;

[0072] Based on the risk assessment results of different levels, correction plans for heading, flight speed, and flight attitude during direct flight under different magnetic declination angles are set, and the correction plans for heading, flight speed, and flight attitude are output.

[0073] It should be noted that, since the frequency of turbulence caused by different types of airflow may be limited, this method can be used to formulate a more reasonable correction plan for heading, flight speed, and flight attitude.

[0074] Furthermore, in this method, the aircraft safety control is performed based on the correction scheme of heading, flight speed, and flight attitude, specifically including:

[0075] Obtaining the distribution characteristic information of the turbulence frequency of different airflow types per unit time and the navigation deviation information with different magnetic declination angles in a specific area, and calculating the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase based on the distribution characteristic information of the turbulence frequency of different airflow types per unit time and the navigation deviation information with different magnetic declination angles in the flight phase;

[0076] A generative adversarial network (GAN) was introduced, and a weight matrix was set through the GAN. The Cronbach's alpha coefficient between the turbulence frequency areas of different airflow types and the navigation deviation information at different magnetic declination angles during flight phases was used to initialize the turbulence frequency data of different airflow types, and the dangerous calibration area of ​​turbulence frequency of each different airflow type was obtained.

[0077] Determine whether the Cronbach's alpha coefficient between the areas where the turbulence frequencies of different airflow types are located and the navigation deviation information at different magnetic declination angles during flight phases is greater than a preset Cronbach's alpha coefficient; if the Cronbach's alpha coefficient between the areas where the turbulence frequencies of different airflow types are located and the navigation deviation information at different magnetic declination angles during flight phases is not greater than the preset Cronbach's alpha coefficient, output a danger calibration area for each different turbulence frequency of different airflow types;

[0078] When the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase is greater than the preset Cronbach's alpha coefficient, the dangerous calibration areas with different turbulence frequencies of each airflow type will be screened in a focused manner until the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase is no greater than the preset Cronbach's alpha coefficient.

[0079] It should be noted that this method can be used to configure the maintenance flight phase of direct flight under different magnetic declination angles, so that each protection area can respond quickly, and the safety control of aircraft with different airflow types and turbulence frequencies is more reasonable.

[0080] In addition, using the QAR data integration center to control the QAR integrated flight phase position coordinate human-machine interaction information can also include the following steps:

[0081] The QAR data integration center is used to obtain the flight route of the QAR per unit time, and the direct flight information database is used to obtain the position coordinate human-computer interaction feature data information under each magnetic declination affected position, and the position coordinate human-computer interaction feature data information under each magnetic declination affected position is input into the t-SNE model; the magnetic declination affected position is used as the first graph node of the t-SNE model, and the position coordinate human-computer interaction feature data information is used as the second graph node of the t-SNE model. An adjacency matrix is ​​established through the first graph node and the second graph node, and the adjacency matrix is ​​input into the knowledge graph for storage; the QAR within the preset time is obtained. The magnetic deviation of the AR's flight path is affected by the position information within a preset range, and the magnetic deviation of the QAR's flight path within the preset time is input into the knowledge graph to obtain the position coordinate human-computer interaction evaluation feature data information of each timestamp; if the position coordinate human-computer interaction evaluation feature data information is greater than the preset position coordinate human-computer interaction feature threshold, the corresponding time period is used as the danger calibration period of the QAR, and the corresponding time period is used as the danger calibration period of the QAR, and data is collected during the danger calibration period of the QAR to integrate the position coordinate human-computer interaction information of the flight stage.

[0082] In addition, the method may further include the following steps: using the direct flight information database to obtain the position coordinate human-computer interaction feature information of each sensor parameter set under each magnetic deflection influence position and the magnetic deflection influence position of the area where the QAR is located per unit time, and obtaining the position coordinate human-computer interaction feature information of the QAR of each sensor parameter through the position coordinate human-computer interaction feature information of each parameter set under each magnetic deflection influence position and the magnetic deflection influence position of the area where the QAR is located per unit time; obtaining the adjustable range of the sensor parameters of the sensor device of the QAR per unit time, and judging whether there is at least one sensor parameter among the sensor parameters that makes the position coordinate human-computer interaction feature information of the QAR greater than the preset position coordinate human-computer interaction feature threshold; if there is at least one sensor parameter among the sensor parameters When a sensor parameter makes the human-computer interaction feature information of the position coordinate of QAR greater than the data of the preset position coordinate human-computer interaction feature threshold, a sensor parameter will be randomly output to make the human-computer interaction feature information of the position coordinate of QAR greater than the data of the preset position coordinate human-computer interaction feature threshold and used as the sensor parameter of QAR; if there is no sensor parameter among the sensor parameters that makes the human-computer interaction feature information of the position coordinate of QAR greater than the data of the preset position coordinate human-computer interaction feature threshold, the data collection period of QAR, the flight route of QAR and the data collection point of QAR are adjusted until at least one sensor parameter among the sensor parameters makes the human-computer interaction feature information of the position coordinate of QAR greater than the data of the preset position coordinate human-computer interaction feature threshold.

[0083] It should be noted that due to the influence of the environment, no matter how the sensing parameters are adjusted, there is no parameter that makes the QAR position coordinate human-computer interaction feature information greater than the preset position coordinate human-computer interaction feature threshold data, so that the predetermined standard human-computer interaction cannot be obtained. The use of this method can further improve the rationality of data collection.

[0084] A second aspect of the present invention provides a direct flight automatic identification system based on QAR data. The system includes a QAR data calculation module and a flight trajectory information integrated monitoring module. The QAR data calculation module includes a method program for automatically identifying flight trajectory deviation distances during flight phases and determining attitude stability based on position coordinate monitoring. When the method program for automatically identifying flight trajectory deviation distances during flight phases and determining attitude stability based on position coordinate monitoring is executed by the flight trajectory information integrated monitoring module, the following steps are implemented:

[0085] Establish a QAR data integration center, use it to set flight trajectory parameters, obtain the QAR data integration center after flight trajectory parameters are set, and use the QAR data integration center to control the position coordinate human-computer interaction information of the QAR integration flight phase;

[0086] The direct flight information database is used to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase. The t-SNE model perplexity is then used to establish an automatic recognition algorithm for the track deviation distance during the flight phase.

[0087] Automatically identify the human-machine interaction information of the position coordinates during the flight phase using the automatic identification algorithm for the flight phase track deviation distance, obtain the safety limit interval data within the calibration time of the track deviation distance during the flight phase in the automatic driving state, and judge the attitude stability based on the safety limit interval data within the calibration time of the track deviation distance during the flight phase in the automatic driving state to generate attitude stability judgment information;

[0088] The correction plan for heading, flight speed and flight attitude is set through attitude stability judgment information, and the aircraft is safely controlled based on the correction plan for heading, flight speed and flight attitude.

[0089] Furthermore, in this system, a QAR data integration center is established, and flight trajectory parameters are set for the QAR data integration center to obtain the QAR data integration center after the flight trajectory parameters are set, specifically including:

[0090] Establish a QAR data integration center and test it to obtain the ADS-B direct flight parameter jump interval characteristics at different latitudes, longitudes, and altitudes. Establish an ADS-B direct flight parameter boundary threshold evaluation model based on the track monitoring system.

[0091] Input the ADS-B direct flight parameter jump interval characteristics of different latitudes, longitudes and altitudes of the QAR data integration center into the ADS-B direct flight parameter boundary threshold evaluation model for training, and obtain the trained ADS-B direct flight parameter boundary threshold evaluation model;

[0092] Obtain the ADS-B direct flight parameter jump interval characteristics of the QAR data integration center within the preset time and input them into the trained ADS-B direct flight parameter boundary threshold evaluation model for evaluation, and obtain the ADS-B direct flight parameter boundary threshold of the QAR data integration center at a unit timestamp;

[0093] The real-time ADS-B direct flight parameter of the QAR data integration center is obtained. When the real-time ADS-B direct flight parameter is greater than the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp, the real-time ADS-B direct flight parameter is adjusted according to the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp to obtain the QAR data integration center after the flight trajectory parameters are set.

[0094] Furthermore, in this system, the direct flight information database is used to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase, and the t-SNE model perplexity is established based on the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase. The automatic recognition algorithm of the track deviation distance during the flight phase is established based on the t-SNE model perplexity, which specifically includes:

[0095] The direct flight information database is used to integrate the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase. The track deviation distance types are classified using the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase. The human-computer interaction information of each track deviation distance type is obtained, and the t-SNE model perplexity of each track deviation distance type is established based on the human-computer interaction information of each track deviation distance type.

[0096] The human-computer interaction information in the t-SNE model perplexity of each track deviation distance type is input into the safety limit interval clustering calculation model to obtain the safety limit interval within the track deviation distance calibration time in the human-computer interaction information per unit time, and determine whether the safety limit interval within the track deviation distance calibration time in the human-computer interaction information is equal to or exceeds the preset interval, and introduce the DE-9IM model;

[0097] When the safety limit interval within the track deviation distance calibration time present in the human-computer interaction information is equal to or exceeds the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type of the unit time human-computer interaction information is obtained as the relevant safety limit interval within the track deviation distance calibration time; when the safety limit interval within the track deviation distance calibration time present in the human-computer interaction information is less than the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type of the unit time human-computer interaction information is used as the relevant safety limit interval within the track deviation distance calibration time;

[0098] The relevant track deviation distance safety limit interval within the calibration time is input into the DE-9IM model, so that the input dimension is concentrated in the relevant track deviation distance safety limit interval within the calibration time, and the preset position nodes are obtained. Based on the route curvature, an automatic identification algorithm for track deviation distance during flight phase is established, and the preset position nodes in the t-SNE model perplexity are input into the automatic identification algorithm for track deviation distance during flight phase to perform deviation warning.

[0099] Furthermore, in this system, an automatic identification algorithm for the flight phase track deviation distance is used to automatically identify the human-computer interaction information of the position coordinates during the flight phase, and the track deviation distance safety limit interval data within the calibration time during the flight phase in the automatic driving state is obtained. The attitude stability is judged based on the track deviation distance safety limit interval data during the flight phase in the automatic driving state, and attitude stability judgment information is generated, specifically including:

[0100] Inputting the human-machine interaction information of the position coordinates of the flight phase into the automatic identification algorithm of the track deviation distance of the flight phase for automatic identification, obtaining the safe limit interval data of the track deviation distance of the flight phase in the automatic driving state within the calibration time, and presetting the attitude stability judgment angle standard;

[0101] The attitude stability judgment deflection standard is used to divide the attitude stability judgment deflection data of the safe limited interval within the track deviation distance calibration time during the flight phase in the automatic driving state, obtain the attitude stability judgment deflection angle of the safe limited interval within the track deviation distance calibration time during the flight phase in the automatic driving state, and integrate the navigation deviation information with different magnetic deflections during the flight phase;

[0102] The attitude stability judgment information is generated by judging the attitude stability declination angle of the flight phase in the automatic driving state within the safety limit range within the calibration time, the navigation deviation information with different magnetic declination angles in the flight phase, and the attitude stability judgment information is generated by judging the attitude stability of the flight phase in the automatic driving state within the safety limit range within the calibration time.

[0103] Furthermore, in this system, the correction scheme for heading, flight speed, and flight attitude is set based on attitude stability judgment information, specifically including:

[0104] The navigation deviation information of different magnetic declination angles in the flight phase is integrated through attitude stability judgment information, and a fixed magnetic declination influence position is established based on the navigation deviation information of different magnetic declination angles in the flight phase. Fixed-point correction is performed through the fixed magnetic declination influence position to integrate the track deviation of the flight phase;

[0105] The flight control system error is obtained through the track deviation during the flight phase, and a flight control system error risk coefficient is established. The flight control system error is input into the flight control system error risk coefficient for visualization to obtain risk assessment results of different levels;

[0106] Based on the risk assessment results of different levels, correction plans for heading, flight speed, and flight attitude during direct flight under different magnetic declination angles are set, and the correction plans for heading, flight speed, and flight attitude are output.

[0107] Furthermore, in this system, aircraft safety control is performed based on the correction scheme of heading, flight speed, and flight attitude, specifically including:

[0108] Obtaining the distribution characteristic information of the turbulence frequency of different airflow types per unit time and the navigation deviation information with different magnetic declination angles in a specific area, and calculating the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase based on the distribution characteristic information of the turbulence frequency of different airflow types per unit time and the navigation deviation information with different magnetic declination angles in the flight phase;

[0109] A generative adversarial network (GAN) was introduced, and a weight matrix was set through the GAN. The Cronbach's alpha coefficient between the turbulence frequency areas of different airflow types and the navigation deviation information at different magnetic declination angles during flight phases was used to initialize the turbulence frequency data of different airflow types, and the dangerous calibration area of ​​turbulence frequency of each different airflow type was obtained.

[0110] Determine whether the Cronbach's alpha coefficient between the areas where the turbulence frequencies of different airflow types are located and the navigation deviation information at different magnetic declination angles during flight phases is greater than a preset Cronbach's alpha coefficient; if the Cronbach's alpha coefficient between the areas where the turbulence frequencies of different airflow types are located and the navigation deviation information at different magnetic declination angles during flight phases is not greater than the preset Cronbach's alpha coefficient, output a danger calibration area for each different turbulence frequency of different airflow types;

[0111] When the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase is greater than the preset Cronbach's alpha coefficient, the dangerous calibration areas with different turbulence frequencies of each airflow type will be screened in a focused manner until the Cronbach's alpha coefficient between the areas where the turbulence frequency of different airflow types is located and the navigation deviation information with different magnetic declination angles in the flight phase is no greater than the preset Cronbach's alpha coefficient.

[0112] The third aspect of the present invention provides a computer-readable storage medium, and the computer-readable QAR data calculation module medium includes a method program for automatically identifying the track deviation distance and judging the attitude stability of the flight phase based on position coordinate monitoring. When the method program for automatically identifying the track deviation distance and judging the attitude stability of the flight phase based on position coordinate monitoring is executed by the flight trajectory information comprehensive supervision module, any step of the method for automatically identifying the track deviation distance and judging the attitude stability of the flight phase based on position coordinate monitoring is implemented.

[0113] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A direct flight automatic identification method based on QAR data, characterized in that: The following steps are involved: Step S101: Establish a QAR data integration center, set flight trajectory parameters for the QAR data integration center, obtain the QAR data integration center after the flight trajectory parameters are set, and use the QAR data integration center to control the QAR integration flight phase position coordinate human-computer interaction information, where the flight phase includes takeoff, climb, cruise, and descent. Step S102: using the direct flight information database to integrate human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase, and establishing a t-SNE model perplexity based on the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase, and establishing an automatic recognition algorithm for track deviation distances during the flight phase based on the t-SNE model perplexity; Step S103: Automatically identify the position coordinate human-machine interaction information of the flight phase using the flight phase track deviation distance automatic identification algorithm, obtain the track deviation distance safety limit interval data within the calibration time of the flight phase in the automatic driving state, and perform attitude stability judgment based on the track deviation distance safety limit interval data within the calibration time of the flight phase in the automatic driving state to generate attitude stability judgment information; Step S104: setting a correction scheme for heading, flight speed, and flight attitude based on the attitude stability judgment information, and performing aircraft safety control based on the correction scheme for heading, flight speed, and flight attitude. The method includes: Obtaining distribution characteristic information of turbulence frequencies of different airflow types per unit time and navigation deviation information with different magnetic declination angles in a specific area, and calculating the Cronbach's alpha coefficient between the navigation deviation information with different magnetic declination angles in the area where the turbulence frequencies of different airflow types are located and in the flight stage based on the distribution characteristic information of turbulence frequencies of different airflow types per unit time and the navigation deviation information with different magnetic declination angles; A generative adversarial network is introduced, a weight matrix is ​​set using the generative adversarial network, and turbulence frequency data of different airflow types is initialized using the Cronbach's alpha coefficient between navigation deviation information of different magnetic declination angles in the regions where the turbulence frequencies of different airflow types are located during the flight phase to obtain a dangerous calibration area for each different turbulence frequency of airflow types. determining whether the Cronbach's alpha coefficient between the navigation deviation information at different magnetic declinations in the flight phase in the region where the turbulence frequencies of different airflow types are located is greater than a preset Cronbach's alpha coefficient; if the Cronbach's alpha coefficient between the navigation deviation information at different magnetic declinations in the flight phase in the region where the turbulence frequencies of different airflow types are located is not greater than the preset Cronbach's alpha coefficient, outputting a danger calibration region for each different turbulence frequency of different airflow types; If the Cronbach's alpha coefficient between the navigation deviation information with different magnetic declinations during the flight phase in the area where the turbulence frequencies of different airflow types are located is greater than the preset Cronbach's alpha coefficient, then the dangerous calibration area with each different airflow type turbulence frequency is screened in a focused manner until the Cronbach's alpha coefficient between the navigation deviation information with different magnetic declinations during the flight phase in the area where the turbulence frequencies of different airflow types are located is no greater than the preset Cronbach's alpha coefficient.

2. The method for automatic direct flight identification based on QAR data according to claim 1, characterized in that: The direct flight information database is used to integrate the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase, and the t-SNE model perplexity is established based on the human-computer interaction information of the track deviation distance at different latitudes, longitudes, and altitudes during the flight phase. The automatic recognition algorithm for the track deviation distance during the flight phase is established based on the t-SNE model perplexity, specifically including: Using the direct flight information database to integrate human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase, classifying the human-computer interaction information of track deviation distances at different latitudes, longitudes, and altitudes during the flight phase into track deviation distance types, obtaining human-computer interaction information of each track deviation distance type, and establishing the t-SNE model perplexity of each track deviation distance type based on the human-computer interaction information of each track deviation distance type; Input the human-computer interaction information in the t-SNE model perplexity of each track deviation distance type into the safety limit interval clustering calculation model, obtain the safety limit interval within the track deviation distance calibration time in the human-computer interaction information per unit time, and determine whether the safety limit interval within the track deviation distance calibration time in the human-computer interaction information is equal to or exceeds the preset interval, and introduce the DE-9IM model; If the safety limit interval within the track deviation distance calibration time in the human-computer interaction information is smaller than the preset interval, the safety limit interval within the track deviation distance calibration time corresponding to the safety limit interval information of the track deviation distance type per unit time human-computer interaction information is used as the relevant safety limit interval within the track deviation distance calibration time; Inputting the relevant track deviation distance within the safety limit interval within the calibration time into the DE-9IM model, so that the input dimension is concentrated within the safety limit interval within the relevant track deviation distance calibration time, obtaining a preset position node, establishing an automatic identification algorithm for track deviation distance during flight phase based on the route curvature, and inputting the preset position node into the automatic identification algorithm for track deviation distance during flight phase to provide deviation warning; The waypoints given by the computer flight plan trajectory are projected onto the map coordinate system. Circular buffers are drawn for all waypoints according to the given maximum offset distance of the route. The actual QAR flight trajectory is projected onto the map coordinate system and a line connecting the longitude and latitude trajectory points is formed. A logical operation is performed through the DE-9IM to determine whether the spatial relationship between the actual flight trajectory and the circular buffer of the waypoints intersects. The circular buffers of all waypoints are iteratively traversed to determine whether the flight trajectory does not pass through some waypoints, and the number of consecutive points not passed through is greater than or equal to 1, which is considered a flight trajectory deviation. Extract the starting and ending waypoints of the flight plan from which the actual flight trajectory deviates, and find the nearest QAR actual flight trajectory point through Euclidean distance to intercept the actual flight trajectory; Direct flight is determined by calculating the curvature of the route. The actual flight distance is compared with the straight-line distance from the starting waypoint to the ending waypoint to see if the curvature percentage is less than a given threshold. The saved distance is then determined by subtracting the actual flight distance from the planned flight distance. Curvature of the route = (actual flight distance ÷ straight-line distance from the starting waypoint to the ending waypoint) × 100%.

3. The method for automatic direct flight identification based on QAR data according to claim 1, characterized in that: Establishing a QAR data integration center, and using the QAR data integration center to set flight trajectory parameters, and obtaining the QAR data integration center after the flight trajectory parameters are set, specifically including: Establish a QAR data integration center and test the QAR data integration center to obtain the ADS-B direct flight parameter jump interval characteristics at different latitudes, longitudes, and altitudes of the QAR data integration center, and establish an ADS-B direct flight parameter boundary threshold evaluation model based on the track monitoring system; Inputting the ADS-B direct flight parameter jump interval characteristics of different latitudes, longitudes and altitudes of the QAR data integration center into the ADS-B direct flight parameter boundary threshold evaluation model for training, thereby obtaining a trained ADS-B direct flight parameter boundary threshold evaluation model; Obtaining the ADS-B direct flight parameter jump interval characteristics of the QAR data integration center within a preset time and inputting them into the trained ADS-B direct flight parameter boundary threshold evaluation model for evaluation, thereby obtaining the ADS-B direct flight parameter boundary threshold of the QAR data integration center at a unit timestamp; Obtain the real-time ADS-B direct flight parameter of the QAR data integration center. If the real-time ADS-B direct flight parameter is greater than the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp, adjust the real-time ADS-B direct flight parameter according to the ADS-B direct flight parameter boundary threshold of the QAR data integration center at the unit timestamp to obtain the QAR data integration center after the flight trajectory parameters are set.

4. The method for automatic direct flight identification based on QAR data according to claim 1, characterized in that: Automatically identifying the position coordinate human-computer interaction information of the flight phase using the flight phase track deviation distance automatic identification algorithm, obtaining the track deviation distance safety limit interval data within the calibration time of the flight phase in the automatic driving state, and performing attitude stability judgment based on the track deviation distance safety limit interval data within the calibration time of the flight phase in the automatic driving state, and generating attitude stability judgment information, specifically including: Inputting the position coordinate human-machine interaction information of the flight phase into the flight phase track deviation distance automatic identification algorithm for automatic identification, obtaining the safety limit interval data of the track deviation distance calibration time of the flight phase in the automatic driving state, and presetting the attitude stability judgment deflection angle standard; The attitude stability judgment deflection angle standard is used to divide the data of the safe limited interval within the track deviation distance calibration time during the flight phase in the automatic driving state into attitude stability judgment deflection angles, obtain the attitude stability judgment deflection angle within the safe limited interval within the track deviation distance calibration time during the flight phase in the automatic driving state, and integrate the navigation deviation information with different magnetic deflections during the flight phase; Attitude stability judgment is performed using the declination information for attitude stability judgment of the safe limited interval within the calibration time of the track deviation distance during the flight phase in the automatic driving state, the navigation deviation information with different magnetic declination angles during the flight phase, and the data of the safe limited interval within the calibration time of the track deviation distance during the flight phase in the automatic driving state to generate attitude stability judgment information.

5. The method for automatic direct flight identification based on QAR data according to claim 1, characterized in that: The correction scheme for heading, flight speed, and flight attitude is set based on the attitude stability judgment information, specifically including: integrating navigation deviation information of different magnetic declination angles during flight phases through the attitude stability judgment information, establishing a fixed-point magnetic declination influence position based on the navigation deviation information of different magnetic declination angles during flight phases, performing fixed-point correction through the fixed-point magnetic declination influence position, and integrating the track deviation during the flight phase; Obtaining a flight control system error through the track deviation of the flight phase, establishing a flight control system error risk coefficient, inputting the flight control system error into the flight control system error risk coefficient for visualization, and obtaining risk assessment results of different levels; Correction schemes for the heading, flight speed, and flight attitude of direct flights under different magnetic declination angles are set according to the risk assessment results of the different levels, and the correction schemes for the heading, flight speed, and flight attitude are output.

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