Aircraft hovering waiting identification method based on real-time ADS-B data

Abstract

The invention discloses an aircraft hovering waiting identification method based on real-time ADS-B (Automatic Dependent Surveillance-Broadcast) data, which comprises the following steps of: 1, acquiring aircraft data information based on the ADS-B data to identify effective data of which the height is greater than 0 of an aircraft, and constructing an aircraft time continuous operation azimuth angle set by taking an azimuth angle function as a basis through the real-time ADS-B data; step 2, constructing a positive and negative tolerance calculation function of an azimuth angle and an inverse azimuth angle by taking an azimuth angle quadrant as a reference; and step 3, establishing a hovering login identification model, and judging whether the flight triggers hovering waiting or not through the hovering login identification model. Compared with the prior art, the hovering login identification model is provided and established according to the hovering waiting operation characteristics of the aircraft and based on the real-time ADS-B trajectory data, and the hovering waiting state of the aircraft and the real-time data in the hovering state can be accurately identified in real time based on the identification technology of the model, so that an air traffic control worker can make a decision in time.

Description

Technical Field

[0001] The present invention relates to the field of aviation, and in particular to a method for identifying the holding pattern of aircraft based on real-time ADS-B data. Background Art

[0002] The background significance of identifying the holding pattern is to improve the efficiency and safety of airport traffic control and avoid flight delays and safety accidents caused by air congestion. In busy airports, aircraft holding patterns have become a common phenomenon, especially during peak hours and under adverse weather conditions. Currently, the automated identification methods for aircraft holding pattern characteristics are still in their infancy, especially in the field of civil aviation trajectory data mining, and related research and applications are not yet fully mature. Traditional abnormal trajectory detection methods mainly focus on the following aspects: deviation from the route, sudden speed changes, abnormal altitude fluctuations, and flight into no-fly zones. Most of these methods are based on the time series and position change patterns of the trajectory and focus on the abnormal parts of the flight trajectory. Although the holding pattern has an obvious circular or curved trajectory in terms of characteristics, this trajectory itself is not necessarily abnormal. It is actually generated according to air traffic control instructions or the requirements of air traffic flow management. Currently, in China, there is no mature and stable technical system for automatically identifying the holding pattern characteristics in trajectories. In actual air traffic management, the trajectory identification of the holding pattern must have high real-time performance so that air traffic controllers can make decisions in a timely manner. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for identifying the holding pattern of aircraft based on real-time ADS-B data. According to the operating characteristics of the aircraft holding pattern, a holding landing identification model is proposed and established based on real-time ADS-B trajectory data. The identification technology based on this model can accurately identify the holding pattern state of the aircraft in real time, which is of great significance for the aviation industry and flight management.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is: a method for identifying the holding pattern of aircraft based on real-time ADS-B data. The method process is as follows.

[0005] Step 1: Based on ADS-B data, obtain aircraft data information to identify valid data with the aircraft altitude greater than 0. Based on real-time ADS-B data and taking the azimuth function as the basis, construct a set of azimuths for the continuous operation of the aircraft over time.

[0006] Step 2: Taking the azimuth quadrant as the reference, construct a calculation function for the positive and negative tolerances of 10° for the azimuth and the reverse azimuth.

[0007] Step 3: Analyze in real time the geometric configuration of the ADS-B trajectory data of flights with a continuous altitude greater than 0 within a certain period of time and the azimuth function. Based on the azimuth change curve of the azimuth function and combined with the azimuth change trend, establish a hovering landing recognition model, and determine whether a flight triggers hovering waiting through the hovering landing recognition model.

[0008] Preferably, in Step 1, monitor the aircraft altitude data based on ADS-B data, select the operating aircraft with an altitude greater than 0, and receive the ADS-B trajectory data of the aircraft in real time.

[0009] Preferably, the ADS-B trajectory data includes information such as flight number, longitude, latitude, altitude, heading, time, etc. Select the above time window data, and based on the ADS-B heading data, construct the aircraft operating azimuth curve function F 1 = f(t, c), where t is the aircraft time information and c is the aircraft heading information.

[0010] Preferably, in Step 1, when ADS-B real-time data cannot be obtained, monitor the aircraft altitude data based on ACARS message data, select the operating aircraft with an altitude greater than 0, and receive the ADS-B trajectory data of the aircraft in real time.

[0011] Preferably, in Step 2, the azimuth and reverse azimuth are corrected and calculated with a positive and negative tolerance of 10°.

[0012] Preferably, in Step 3, the hovering landing recognition model is based on the azimuth quadrant. According to the quadrant information where the heading is located, analyze the aircraft trajectory data in real time, obtain the aircraft heading and time in chronological order. Based on the azimuth change curve of the azimuth function and combined with the azimuth change trend, if the situation occurs, that is, it changes in the same azimuth and reverse azimuth directions and the same heading appears 2 times or more, it is judged as hovering waiting.

[0013] Preferably, the specific method for the hovering landing recognition model to identify and judge aircraft hovering waiting based on the azimuth quadrant is as follows,

[0014] First quadrant: That is, when the heading 0° ≤ α ≤ 90°,

[0015] If the azimuth value α - 10 > 0,

[0016] The effective range of the same azimuth β is β ≥ α - 10 ∩ β ≤ α + 10;

[0017] If the azimuth value α - 10 ≤ 0,

[0018] The effective range of the same azimuth β is {β ≥ 360 - 10 + α ∩ β ≤ 360} ∪ {β ≤ α + 10 ∩ β ≥ 0};

[0019] The effective range of the reverse azimuth angle θ is {θ≥α + 170 ∩ θ≤α + 190};

[0020] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, the change occurs along the same azimuth and reverse azimuth directions and the same course appears 2 times or more, it is judged as hovering and waiting;

[0021] Second quadrant: That is, when the course 90°≤α≤180°, the effective range of the same azimuth angle β is β≥α - 10 ∩ β≤α + 10,

[0022] If the azimuth angle value α + 190>360,

[0023] The effective range of the reverse azimuth angle θ is {θ≥α + 170 ∩ θ≤360} ∪ {θ≤α + 190 - 360 ∩ θ≥0};

[0024] If the azimuth angle value α + 190≤360,

[0025] The effective range of the reverse azimuth angle θ is {θ≥α + 170 ∩ θ≤α + 190};

[0026] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, the change occurs along the same azimuth and reverse azimuth directions and the same course appears 2 times or more, it is judged as hovering and waiting;

[0027] Third quadrant: That is, when the course 180°≤α≤270°, the effective range of the same azimuth angle β is β≥α - 10 ∩ β≤α + 10,

[0028] If the azimuth angle value α + 170>360,

[0029] The effective range of the reverse azimuth angle θ is {θ≤α + 190 - 360 ∩ θ≥0};

[0030] If the azimuth angle value α + 170≤360,

[0031] The effective range of the reverse azimuth angle θ is {θ≥α + 170 ∩ θ≤360} ∪ {θ≥0 ∩ θ≤α + 190 - 360};

[0032] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, the change occurs along the same azimuth and reverse azimuth directions and the same course appears 2 times or more, it is judged as hovering and waiting;

[0033] Fourth quadrant: That is, when the course 270°≤α≤360°,

[0034] If the azimuth angle value α + 10<360,

[0035] The effective range of the same azimuth angle β is β≥α - 10 ∩ β≤α + 10;

[0036] If the azimuth value α + 10 ≥ 360,

[0037] The valid range of the same azimuth β is {β ≥ α - 10 ∩ β ≤ 360} ∪ {β ≤ α + 10 - 360 ∩ β ≥ 0};

[0038] The valid range of the reverse azimuth θ is {θ ≥ α + 170 - 360 ∩ θ ≤ α + 190 - 360};

[0039] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, changing along the same azimuth and reverse azimuth directions and having the same course 2 times or more, then a is judged as hovering and waiting.

[0040] Compared with the prior art, the advantages of the present invention are as follows: According to the operating characteristics of the aircraft hovering and waiting, based on the real-time ADS-B trajectory data, changing along the same azimuth and reverse azimuth directions and having the same course 2 times or more, the present invention proposes and establishes a hovering landing recognition model. The recognition technology based on this model can accurately identify the hovering and waiting state of the aircraft in real time, as well as the real-time data in the hovering state, so that air traffic controllers can make decisions in a timely manner, avoid mutual influence and conflicts between flights, ensure the on-time takeoff and landing of flights, minimize the occurrence of delays and safety accidents, and improve the efficiency and safety of air transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of the method of the present invention;

[0042] Figure 2 It is a specific description diagram of the quadrant information where the present invention is located;

[0043] Figure 3 It is an operation example diagram of the hovering landing recognition model of the present invention based on the first quadrant;

[0044] Figure 4 It is an operation example diagram of the hovering landing recognition model of the present invention based on the second quadrant;

[0045] Figure 5 It is an operation example diagram of the hovering landing recognition model of the present invention based on the third quadrant;

[0046] Figure 6 It is an operation example diagram of the hovering landing recognition model of the present invention based on the fourth quadrant;

[0047] Figure 7 , Figure 8 , Figure 9 It is an operation interface diagram of the hovering landing recognition model of the present invention;

[0048] Figure 10This is the display interface of the hovering state of multiple aircraft by the hovering landing recognition model of the present invention. Detailed implementation manners

[0049] The ADS-B system can provide high-precision real-time flight trajectory information by broadcasting data such as the real-time position information, speed, altitude, and heading of an aircraft. Therefore, using ADS-B data to automatically identify the hovering waiting trajectory can not only improve the timeliness of identification but also increase the accuracy of identification. Effective identification of hovering waiting can improve the efficiency and safety of airport traffic control, reduce the risk of flight delays and safety accidents, and improve the efficiency and safety of civil aviation transportation. Therefore, based on real-time ADS-B data, the present invention establishes a hovering landing recognition model, and through the design of the hovering landing recognition model, realizes the real-time determination of whether an aircraft is in a hovering waiting state.

[0050] The following will further illustrate the present invention. A method for identifying the hovering waiting of an aircraft based on real-time ADS-B data, the method flow is as follows.

[0051] Step 1: Based on ADS-B data, obtain the aircraft data information to identify the valid data with the aircraft altitude greater than 0. Based on the real-time ADS-B data and taking the azimuth function as the basis, construct a set of azimuths for the continuous operation of the aircraft over time.

[0052] Monitor the aircraft altitude data based on ADS-B data, select the operating aircraft with an altitude greater than 0, and receive the ADS-B trajectory data of the aircraft in real time. The ADS-B trajectory data includes information such as flight number, longitude, latitude, altitude, heading, and time. Select the above time window data, and based on the ADS-B heading data, construct the azimuth curve function F 1 = f(t, c), where t is the aircraft time information and c is the aircraft heading information.

[0053] As a preferred solution, when real-time ADS-B data cannot be obtained, monitor the aircraft altitude data based on ACARS message data, select the operating aircraft with an altitude greater than 0, and receive the ADS-B trajectory data of the aircraft in real time, and use ACARS message data to make up for the problem of incomplete ADS-B data signal coverage to ensure the real-time determination of the hovering waiting state of the aircraft.

[0054] Step 2: Considering that sometimes the ADS-B signal coverage is incomplete and the azimuth error situation, based on the azimuth quadrant, construct a function for calculating the positive and negative tolerances of 10° for the azimuth and reverse azimuth. According to the quadrant information where the heading is located, for specific descriptions, see Figure 2 :

[0055] Step 3: Analyze in real time the geometric configuration of the ADS-B trajectory data and azimuth function of flights with continuous altitude greater than 0 within a certain period of time, establish a circling and landing recognition model based on the azimuth change curve of the azimuth function and the azimuth change trend, and determine whether the flight triggers circling and waiting through the circling and landing recognition model.

[0056] The hovering landing recognition model uses the azimuth quadrant as a reference, analyzes the aircraft trajectory data in real time according to the quadrant information of the heading, obtains the aircraft heading and time in chronological order, and uses the azimuth change curve of the azimuth function and the azimuth change trend. If a situation occurs, the same heading changes along the same azimuth and reverse azimuth directions and occurs 2 or more times, it is judged as hovering waiting. The interface diagram of the hovering landing recognition model can be found in Figures 7 to 9 .

[0057] The hovering landing identification model uses the azimuth quadrant as a reference, and the specific method for performing the hovering waiting identification and judgment of the aircraft is as follows:

[0058] (1) First quadrant: when the heading is 0°≤α≤90°,

[0059] If the azimuth value α-10>0,

[0060] The effective range of the same azimuth angle β is β≥α-10∩β≤α+10;

[0061] If the azimuth value α-10≤0,

[0062] The effective range of the same azimuth angle β is {β≥360-10+α∩β≤360}∪{β≤α+10∩β≥0};

[0063] The effective range of the reverse azimuth angle θ is {θ≥α+170∩θ≤α+190};

[0064] If the situation (t1, α) → (t2, θ) → (t3, β) occurs, that is, the same heading changes along the same azimuth and counter-azimuth directions for 2 or more times, it is judged as circling and waiting. The operation example is shown in the figure below. Figure 3 As shown;

[0065] (2) Second quadrant: when the heading is 90°≤α≤180°, the effective range of the same azimuth angle β is β≥α-10∩β≤α+10.

[0066] If the azimuth angle value α+190>360,

[0067] The effective range of the back azimuth angle θ is {θ≥α+170∩θ≤360}∪{θ≤α+190-360∩θ≥0};

[0068] If the azimuth angle α+190≤360,

[0069] The effective range of the reverse azimuth angle θ is {θ≥α + 170 ∩ θ≤α + 190};

[0070] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, it changes along the same azimuth and reverse azimuth directions and the same course appears 2 times or more, it is judged as hovering and waiting. The operation example diagram is as Figure 4 shown;

[0071] (3) Third quadrant: That is, when the course 180°≤α≤270°, the effective range of the same azimuth angle β is β≥α - 10 ∩ β≤α + 10,

[0072] If the azimuth angle value α + 170>360,

[0073] The effective range of the reverse azimuth angle θ is {θ≤α + 190 - 360 ∩ θ≥0};

[0074] If the azimuth angle value α + 170≤360,

[0075] The effective range of the reverse azimuth angle θ is {θ≥α + 170 ∩ θ≤360} ∪ {θ≥0 ∩ θ≤α + 190 - 360};

[0076] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, it changes along the same azimuth and reverse azimuth directions and the same course appears 2 times or more, it is judged as hovering and waiting. The operation example diagram is as Figure 5 shown;

[0077] (4) Fourth quadrant: That is, when the course 270°≤α≤360°,

[0078] If the azimuth angle value α + 10<360,

[0079] The effective range of the same azimuth angle β is β≥α - 10 ∩ β≤α + 10;

[0080] If the azimuth angle value α + 10≥360,

[0081] The effective range of the same azimuth angle β is {β≥α - 10 ∩ β≤360} ∪ {β≤α + 10 - 360 ∩ β≥0};

[0082] The effective range of the reverse azimuth angle θ is {θ≥α + 170 - 360 ∩ θ≤α + 190 - 360};

[0083] If the situation of (t1, α) → (t2, θ) → (t3, β) occurs, that is, it changes along the same azimuth and reverse azimuth directions and the same course appears 2 times or more, it is judged as hovering and waiting. The operation example diagram is as Figure 6 shown.

[0084] Based on the characteristics of aircraft holding operations and real-time ADS-B trajectory data, the present invention proposes and establishes a holding landing recognition model by varying along the same azimuth and reverse azimuth directions and having the same heading two or more times. The recognition technology based on this model can accurately identify the aircraft holding state and real-time data during the holding state in real time. See Figure 10 , and its relevant data includes flight information such as the number of holding circles, holding position, holding time, etc., so that air traffic controllers can make decisions in a timely manner.

[0085] The real-time recognition technology for the aircraft holding state of the present invention not only helps to improve the decision-making ability of airport traffic control. After air traffic controllers learn the aircraft holding state in a timely manner, they can make decisions in a timely manner to avoid mutual influence and conflicts between flights, ensure the on-time takeoff and landing of flights, minimize delays and safety accidents, and improve the efficiency and safety of air transportation. Since the real-time recognition technology for the aircraft holding state can clearly know the real-time state of the flight at this time, it can also assist airports and airlines to more accurately predict and plan flights, optimize operations and resource allocation, and improve operational efficiency and passenger satisfaction.

[0086] The above has introduced in detail a method for recognizing aircraft holding based on real-time ADS-B data provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. It is possible to make changes and improvements to the present invention without exceeding the concept and scope defined by the appended claims. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for identifying aircraft circling and waiting based on real-time ADS-B data, characterized by: The method flow is as follows: Step 1: Obtain aircraft data information based on ADS-B data to identify valid data with aircraft altitude greater than 0, and construct a time-continuous aircraft azimuth set based on the azimuth function using real-time ADS-B data; Step 2: Based on the azimuth quadrant, a calculation function for the positive and negative tolerances of the azimuth and the reverse azimuth is constructed; Step 3: Analyze in real time the geometric configuration of the ADS-B trajectory data and azimuth function of flights with continuous altitude greater than 0 within a certain period of time, establish a circling and landing recognition model based on the azimuth change curve of the azimuth function and the azimuth change trend, and determine whether the flight triggers circling and waiting through the circling and landing recognition model.

2. The method for identifying an aircraft circling and waiting based on real-time ADS-B data according to claim 1, characterized in that: In step 1, aircraft altitude data is monitored based on ADS-B data, operating aircraft with altitude greater than 0 are selected, and the ADS-B trajectory data of the aircraft is received in real time.

3. The method for identifying an aircraft circling and waiting based on real-time ADS-B data according to claim 2, characterized in that: The ADS-B trajectory data includes flight number, longitude, latitude, altitude, heading, and time information. The above time window data is selected, and based on the ADS-B heading data, an aircraft operation azimuth curve function F1=f(t,c) is constructed, where t is the aircraft time information and c is the aircraft heading information.

4. The method for identifying an aircraft circling and waiting based on real-time ADS-B data according to claim 2, characterized in that: In step 1, when ADS-B real-time data cannot be obtained, aircraft altitude data is monitored based on ACARS message data, and operating aircraft with altitude greater than 0 are selected, and the ADS-B trajectory data of the aircraft is received in real time.

5. The method for identifying an aircraft circling and waiting based on real-time ADS-B data according to claim 1, characterized in that: In step 2, the azimuth angle and the counter-azimuth angle are corrected and calculated with a positive and negative tolerance of 10°.

6. The method for identifying aircraft circling and waiting based on real-time ADS-B data according to claim 1, characterized in that: In step three, the hovering landing recognition model uses the azimuth quadrant as a reference, analyzes the aircraft trajectory data in real time according to the quadrant information of the heading, obtains the aircraft heading and time in chronological order, and uses the azimuth change curve of the azimuth function in combination with the azimuth change trend. If a situation occurs, where the aircraft changes along the same azimuth and reverse azimuth directions and the same heading appears 2 or more times, it is judged as hovering waiting.

7. The method for identifying aircraft circling and waiting based on real-time ADS-B data according to claim 6, characterized in that: The hovering landing identification model uses the azimuth quadrant as a reference, and the specific method for performing the hovering waiting identification and judgment of the aircraft is as follows: The first quadrant: when the heading is 0°≤α≤90°, If the azimuth value α-10>0, The effective range of the same azimuth angle β is β≥α-10∩β≤α+10; If the azimuth value α-10≤0, The effective range of the same azimuth angle β is {β≥360-10+α∩β≤360}∪{β≤α+10∩β≥0}; The effective range of the reverse azimuth angle θ is {θ≥α+170∩θ≤α+190}; If the situation (t1, α) → (t2, θ) → (t3, β) occurs, that is, the same heading changes along the same azimuth and counter-azimuth directions for 2 or more times, it is judged as circling and waiting; Second quadrant: when the heading is 90°≤α≤180°, the effective range of the same azimuth angle β is β≥α-10∩β≤α+10. If the azimuth angle value α+190>360, The effective range of the back azimuth angle θ is {θ≥α+170∩θ≤360}∪{θ≤α+190-360∩θ≥0}; If the azimuth angle α+190≤360, The effective range of the reverse azimuth angle θ is {θ≥α+170∩θ≤α+190}; If the situation (t1, α) → (t2, θ) → (t3, β) occurs, i.e., the aircraft changes along the same azimuth and counter-azimuth directions and the same heading occurs 2 or more times, it is considered to be circling and waiting; The third quadrant: when the heading is 180°≤α≤270°, the effective range of the same azimuth angle β is β≥α-10∩β≤α+10. If the azimuth angle value α+170>360, The effective range of the back azimuth angle θ is {θ≤α+190-360∩θ≥0}; If the azimuth angle α+170≤360, The effective range of the back azimuth angle θ is {θ≥α+170∩θ≤360}∪{θ≥0∩θ≤α+190-360}; If the situation (t1, α) → (t2, θ) → (t3, β) occurs, i.e., the aircraft changes along the same azimuth and counter-azimuth directions and the same heading occurs 2 or more times, it is considered to be circling and waiting; The fourth quadrant: when the heading is 270°≤α≤360°, If the azimuth value α+10<360, The effective range of the same azimuth angle β is β≥α-10∩β≤α+10; If the azimuth value α+10 ≥ 360, The effective range of the same azimuth angle β is {β≥α-10∩β≤360}∪{β≤α+10-360∩β≥0}; The effective range of the back azimuth angle θ is {θ≥α+170-360∩θ≤α+190-360}; If the situation (t1, α) → (t2, θ) → (t3, β) occurs, that is, the aircraft changes along the same azimuth and counter-azimuth directions and the same heading appears 2 or more times, a is judged as circling and waiting.

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