An aircraft taxiing state recognition and aircraft type inversion method

Abstract

The present invention relates to a method for recognizing the taxiing state of an aircraft and inverting the aircraft type, belonging to the field of intelligent airport perception. Among them, the recognition of the aircraft taxiing state includes the following steps: collecting the original wheel track data of the aircraft taxiing by using a laser wheel tracker; extracting key information from the original wheel track data and performing data segmentation to form a wheel track data array; using a clustering algorithm to cluster the wheel track data array, and judging the aircraft taxiing state according to the wheel track data of the clustering center point. The aircraft type inversion calculates the distance between the main landing gears according to the clustering result and judges the aircraft type according to the distance between the main landing gears. Compared with the prior art, the present invention can realize accurate and effective recognition of the aircraft taxiing state and inversion of the aircraft type only by using one laser sensor.

Description

Technical Field

[0001] The present invention relates to the field of airport intelligent perception, and in particular to a method for identifying the taxiing state of an aircraft and inverting the aircraft type. Background Art

[0002] The lateral distribution of aircraft wheel tracks refers to the fact that when an aircraft taxis on a runway, the wheel tracks of the aircraft always sway left and right within a certain range near the center line of the cross section and are distributed on the runway cross section at a certain frequency. The standard deviation of the lateral distribution of wheel tracks is an important parameter for airport runway design. The study of the lateral distribution of wheel tracks is of great significance for the analysis of airport pavement structure response, pavement structure design and evaluation. Information such as the number of takeoff and landing operations, taxiing speed, and lateral position offset distance of different aircraft types is an important basis for airport operation and maintenance. Referring to the aircraft parameter table for pavement design in the "Code for Design of Cement Concrete Pavement of Civil Airports MHT5004-2010", the maximum takeoff weight of an aircraft is greater than the maximum landing weight. Therefore, the study of the taxiing state of takeoff aircraft is of more important significance for the operation and maintenance of airport runways.

[0003] At home and abroad, infrared, embedded sensors, video testing technology and laser sensing technology are mainly used to measure the taxiing trajectory. Among them, the infrared technology relies on infrared beams to detect the position of the wheels, but it is easily interfered by external heat sources and obstacles; the embedded vibration sensor is based on optical fiber technology and is suitable for point monitoring of large areas, but it needs to be buried in the road surface, which limits its application in service runways, and has high cost, short service life and low survival rate; the method of drawing scales on the runway and video imaging interferes with the normal operation of the aircraft.

[0004] Due to its advantages of strong anti-interference, high acquisition frequency and no impact on the normal operation of the airport, laser testing technology is more widely used in airport wheel track data acquisition. CN109444907A discloses a test system and method for the change law of the lateral distribution of aircraft wheel tracks, which arranges laser ranging sensors, a time synchronization and data processing unit, a power supply unit, a data storage unit and a computer on the runway. Among them: the laser ranging sensor mainly measures the distance between the near-side tire of the aircraft during taxiing and the laser test unit; the time synchronization and data processing unit is used to screen the test data of the laser ranging sensor and mark all the screened valid data with a unified time parameter; the power supply unit provides the power required for testing for the laser ranging sensor and the video unit; the data storage unit is used to store the data processed by the time synchronization and data processing unit; the computer is used to perform post-analysis processing on the data stored in the data storage unit at a specific time. However, this method requires the use of multiple laser ranging sensors to achieve, which not only increases the cost of sensor layout, but also puts forward more requirements for the layout position of the laser sensors. Summary of the Invention

[0005] The object of the present invention is to provide a method for identifying the taxiing state of an aircraft and inverting the aircraft type, which can accurately identify the taxiing state of the aircraft and invert the aircraft type only by using one laser sensor.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A method for identifying the taxiing state of an aircraft, comprising the following steps:

[0008] Collect the original wheel track data of the aircraft taxiing by using a laser wheel tracker;

[0009] Extract the key information in the original wheel track data and perform data segmentation to form a wheel track data array;

[0010] Use a clustering algorithm to cluster the wheel track data array, and judge the taxiing state of the aircraft according to the wheel track data of the clustering center point.

[0011] The laser wheel tracker is arranged at the cross-section position of the runway monitoring end.

[0012] The laser wheel tracker emits high-frequency laser signals. When the aircraft passes through the cross-section where the laser wheel tracker is located at high speed, the laser signals are cut. According to the laser signals, the distance from the laser wheel tracker to the landing gear wheel is calculated to obtain the original wheel track data.

[0013] The calculation method of the distance from the laser wheel tracker to the landing gear wheel is as follows:

[0014] ,

[0015] ,

[0016] Wherein, is the time elapsed from the emission to the return of the laser signal, is the time when the laser signal reaches the landing gear wheel; v is the laser propagation speed; is the distance between the landing gear wheel and the laser wheel tracker, that is, the original wheel track data.

[0017] The acquisition method of the wheel track data array is as follows: extract the three decimal places after the identifier in the original wheel track data output by the laser wheel tracker as the effective original wheel track data; subtract the distance from the laser wheel tracker to the runway edge from the effective original wheel track data to obtain the wheel track data; extract the time data in the brackets in the original wheel track data output by the laser wheel tracker, and save the time data corresponding to the wheel track data, wherein, one time data corresponds to multiple wheel track data; according to the time data, perform data segmentation on the saved data at a preset time interval, divide the data within one preset time interval into the wheel track data of one flight, and the wheel track data of each flight constitutes a wheel track data array.

[0018] The landing gear of the aircraft is of the nose-gear-three type. The left and right wheels of the main landing gear are symmetrically arranged on both sides with a certain distance behind the center of mass of the aircraft, and the nose gear is arranged below the head of the aircraft. During the takeoff phase, when the aircraft passes through the section where the laser wheel track instrument is located, both the nose gear and the main landing gear are in the grounded state, and their lateral positions are successively captured by the laser wheel track instrument. That is, three types of data, namely the nose gear, the left wheel of the main landing gear, and the right wheel of the main landing gear, of the takeoff aircraft are captured. During the landing phase, the aircraft has a certain elevation angle, and the main landing gear touches the ground before the nose gear. That is, two types of data, namely the left wheel of the main landing gear and the right wheel of the main landing gear, of the landing aircraft are captured.

[0019] The clustering algorithm is used to cluster the array of wheel track data, and the taxiing state of the aircraft is judged according to the wheel track data of the clustering center points. Specifically:

[0020] The array of wheel track data is input into the K-means clustering algorithm, and each array of wheel track data is clustered into three categories to obtain three clustering center points. The wheel track data of the three clustering center points are sequentially recorded as the first wheel track data, the second wheel track data, and the third wheel track data from small to large. It is judged whether the wheel track data of the three clustering center points meet the first condition. If they meet, it is judged that the taxiing state of the aircraft in this flight is the takeoff state; if they do not meet, it is judged that the taxiing state of the aircraft in this flight is the landing state. Among them, the first condition is that the difference between the second wheel track data and the first wheel track data is greater than the preset value and the difference between the third wheel track data and the second wheel track data is also greater than the preset value.

[0021] When the first condition is not met, it is further judged whether the wheel track data of the three clustering center points meet the second condition. If the second condition is met, each array of wheel track data is clustered into two categories to obtain two clustering center points. The wheel track data of the two clustering center points are sequentially recorded as the fourth wheel track data and the fifth wheel track data from small to large. If the second condition is not met, the array of wheel track data is deleted. Among them, the second condition is that the difference between the second wheel track data and the first wheel track data is greater than the preset value or the difference between the third wheel track data and the second wheel track data is greater than the preset value.

[0022] An aircraft model inversion method includes the following steps:

[0023] Cluster the array of wheel track data by the above method;

[0024] Calculate the distance between the main landing gears based on the wheel track data of the clustering center points;

[0025] Judge the aircraft model based on the distance between the main landing gears.

[0026] The distance between the main landing gears is the difference between the third wheel track data and the first wheel track data; when the second condition is met, the distance between the main landing gears is the difference between the fifth wheel track data and the fourth wheel track data.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) Low cost: The present invention can collect data only by using one laser sensor (laser wheel path instrument). By analyzing the laser signal, it can realize the recognition of the aircraft taxiing state and the inversion of the aircraft type. Not only does it compress the number of required laser sensors to one, greatly reducing the required cost, but also the requirements for the installation position of the laser sensor are low. It only needs to set the laser wheel path instrument at the monitoring end section of the runway, without being limited by the requirements of different airport designs or the location of the power supply, and there is no need to consider the influence of the relative positions of multiple laser wheel path instruments on the recognition accuracy.

[0029] (2) High recognition accuracy: By performing data segmentation and clustering processing on the collected aircraft wheel path data, the present invention can effectively obtain the lateral position of the aircraft wheel path and its distribution law, as well as the takeoff and landing states and the aircraft type of the aircraft, and can infer the taxiing speed therefrom, providing a scientific theoretical support for the design of the pavement structure thickness by airport design units. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is the flowchart of the method of the present invention;

[0031] Figure 2 is the schematic diagram of the test principle of the laser wheel path instrument;

[0032] Figure 3 is the schematic diagram of the landing gear of a civil aircraft;

[0033] Figure 4 is the schematic diagram of the original wheel path data collected by the laser wheel path instrument;

[0034] Figure 5 is the schematic diagram of the process of extracting and segmenting the original data of the laser wheel path instrument of the present invention;

[0035] Figure 6 is the flowchart of the recognition of the aircraft taxiing state of the present invention;

[0036] Figure 7 is the flowchart of the inversion of the aircraft type of the present invention;

[0037] Figure 8 is the lateral distribution diagram of the wheel paths of the takeoff flights in an embodiment;

[0038] Figure 9 is the lateral distribution diagram of the wheel paths of the landing flights in an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives the detailed implementation manner and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0040] Embodiment 1

[0041] This embodiment provides a method for identifying the taxiing state of an aircraft. A laser sensor is used to test the taxiing state of the aircraft. The reason is that there is a deviation angle between the aircraft during taxiing and the runway center line, which makes it possible to carry out the test using a single laser sensor. Specifically, as Figure 1 shown in the upper part of, this method includes the following steps:

[0042] S1, use a laser track gauge to collect the original track data of the aircraft taxiing.

[0043] In this embodiment, a non-contact laser sensing test device (referred to as a laser track gauge) is installed on one side of the runway monitoring end section position to collect track data. The laser track gauge belongs to a type of laser sensor. By emitting high-frequency laser signals, when the aircraft passes through the section where the laser track gauge is located at high speed, the laser signals are cut. According to the laser signals, the distance from the laser track gauge to the landing gear wheel can be calculated to obtain the original track data. Its principle is as Figure 2 shown, and the calculation method of the distance from the laser track gauge to the landing gear wheel is:

[0044] ,

[0045] ,

[0046] Among them, is the time elapsed from the emission to the return of the laser signal, is the time when the laser signal reaches the landing gear wheel; v is the laser propagation speed; is the distance between the landing gear wheel and the laser track gauge, that is, the original track data.

[0047] Modern civil aircraft landing gears are mainly of the nose-wheel type. The main landing gear left and right wheels are arranged symmetrically at a certain distance on both sides and slightly behind the center of mass of the aircraft, and the nose wheel is arranged below the head of the aircraft, as Figure 3 shown in (3a) of. Since the laser track gauge section is at the runway end, during the takeoff phase, when the aircraft passes through the laser track gauge section, both the nose wheel and the main landing gear are in the grounded state, and their lateral positions will be successively captured by the laser track gauge, that is, three types of data, namely the nose wheel, the main landing gear left wheel, and the main landing gear right wheel, will be captured for the takeoff aircraft; while during the landing phase, the aircraft has a certain elevation angle, and the main landing gear will touch the ground before the nose wheel, that is, two types of data, namely the main landing gear left wheel and the main landing gear right wheel, will be captured for the landing aircraft, as Figure 3 shown in (3b) of.

[0048] S2. Extract the key information from the original wheel track data and perform data segmentation to form a wheel track data array.

[0049] The original wheel track data collected by the laser wheel tracker is as Figure 4 shown. As Figure 5 shown, the formation process of the wheel track data numbers is as follows: The three decimal places after the identifier "$BM," are valid data. Extract this part of the data as the valid original wheel track data d. Subtract the distance from the laser wheel tracker to the runway edge from the valid original wheel track data d to obtain the wheel track data x; extract the time data t within the square brackets [] from the original wheel track data output by the laser wheel tracker, and save the corresponding time data t and the wheel track data x, where one time data corresponds to multiple wheel track data. According to the time data t, perform data segmentation on the saved data at a preset time interval (3 s), divide the data within one preset time interval into the wheel track data of one flight, and the wheel track data of each flight constitutes a wheel track data array [t, x].

[0050] S3. Use a clustering algorithm to cluster the wheel track data array, and judge the aircraft taxiing state according to the wheel track data of the clustering center point.

[0051] As Figure 6 shown, S3 specifically includes the following steps:

[0052] Input the wheel track data array [t, x] into the K-means clustering algorithm, cluster each wheel track data array into three categories, and obtain three clustering center points (t1, x1), (t2, x2), (t3, x3). In this embodiment, it is assumed that x1 < x2 < x3. Judge whether x2 - x1 > 2 and x3 - x2 > 2 are satisfied. If satisfied, judge that the taxiing state of the aircraft in this flight is the takeoff state; if not satisfied, judge that the taxiing state of the aircraft in this flight is the landing state. Further judge whether the wheel track data of the three clustering center points satisfies x2 - x1 > 2 or x3 - x2 > 2. If satisfied, cluster each wheel track data array into two categories to obtain two clustering center points (t4, x4), (t5, x5). In this embodiment, it is assumed that x4 < x5; if not satisfied, delete this wheel track data array.

[0053] This embodiment also provides an aircraft model inversion method, as shown in the lower half of Figure 1 , including the following steps:

[0054] A1. Use the above method to cluster the wheel track data array;

[0055] A2. Calculate the main landing gear spacing based on the wheel track data of the clustering center point.

[0056] A3 Judging the aircraft type based on the distance between the main landing gears.

[0057] As Figure 7 shown, when x2 - x1 > 2 and x3 - x2 > 2 are satisfied, the distance between the main landing gears ∆x = x3 - x1, and the time ∆t = t of this flight passing through the laser track gauge max - t min ; when x2 - x1 > 2 and x3 - x2 > 2 are not satisfied, but x2 - x1 > 2 or x3 - x2 > 2 is satisfied, the distance between the main landing gears ∆x = x5 - x4.

[0058] After processing the data of each flight, the lateral position distribution law of the aircraft wheel tracks on the airport pavement can be analyzed. Referring to the aircraft parameter table for pavement design in the "Code for Design of Cement Concrete Pavement of Civil Airports MHT5004 - 2010", according to the calculated distance between the main landing gears ∆x, the aircraft type of each flight can be obtained.

[0059] In one embodiment, the distance l from the nose wheel of the aircraft to the main landing gear can also be obtained according to the aircraft type information. Combining the time ∆t = t of the aircraft flight passing through the laser track gauge calculated above max - t min , the takeoff speed v of the aircraft is calculated as v = l / ∆t.

[0060] Embodiment 2

[0061] Taking the taxiing state and aircraft type identification of a certain actual aircraft during takeoff as an example.

[0062] Input the original track data (t, x) of a certain actual aircraft collected by the laser track gauge, where the time data is t and the track data is x. Using the K - means algorithm, the original track data is clustered into three categories, and three cluster centers (t1, x1), (t2, x2), (t3, x3) (x1 < x2 < x3) are obtained. For this flight, x2 - x1 > 2 and x3 - x2 > 2, it is judged that this flight is an aircraft in the takeoff state, and the three categories of data are the nose wheel, the left main landing gear, and the right main landing gear of the aircraft. The time ∆t = t of this flight passing through the laser track gauge max - t min , and the distance between the main landing gears ∆x = x3 - x1. The lateral distribution diagram of the track of this takeoff flight is as Figure 8 shown. For this flight, the distance between the main landing gears ∆x = 5.8m ≈ 5.72m, ∆t = 0.38s, the aircraft type may be B737 - 800 / B737 - 700, and the distance from the nose wheel to the main landing gear, that is, the wheelbase l is 17.17m, then the takeoff speed v = l / ∆t = 45.18m / s.

[0063] Embodiment 3

[0064] Take the taxiing state and aircraft type identification during the landing process of a certain actual aircraft as an example.

[0065] Input the original wheel track data (t, x) of a certain actual aircraft collected by a laser wheel tracker, where the time data is t and the wheel track data is x. Using the K-means algorithm, cluster the original wheel track data into three categories to obtain three cluster center points (t1, x1), (t2, x2), (t3, x3), (x1 < x2 < x3). If this flight does not satisfy x2 - x1 > 2 and x3 - x2 > 2, then determine that this flight is an aircraft in the landing state. If it satisfies x2 - x1 > 2 or x3 - x2 > 2, use the K-means algorithm to cluster the original wheel track data into two categories to obtain two cluster center points (t4, x4), (t5, x5), (x4 < x5). The two categories of data are respectively the left main landing gear wheel and the right main landing gear wheel of the aircraft, and the distance between the main landing gears of this flight is ∆x = x5 - x4. The lateral distribution diagram of the wheel tracks of this landing flight is as Figure 9 shown. The distance between the main landing gears of this flight is ∆x = 9.6m ≈ 9.8m, and the aircraft type may be B787-9.

[0066] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art should fall within the protection scope determined by the claims.

Claims

1. A method for identifying an aircraft taxiing state, characterized in that: The following steps are involved: A laser wheel track meter is used to collect the original wheel track data of the aircraft taxiing. The landing gear of the aircraft is a front three-point type. The left and right wheels of the main landing gear are arranged symmetrically behind the center of mass of the aircraft with a certain distance, and the nose wheel is arranged below the head of the aircraft. Extract key information from the original wheel track data and perform data segmentation to form a wheel track data array, wherein the wheel track data of each flight constitutes a wheel track data array; The wheel track data array is clustered using a clustering algorithm, and the aircraft taxiing state is determined based on the wheel track data at the cluster center point, specifically: The wheel track data array is input into the K-means clustering algorithm, and each wheel track data array is clustered into three categories to obtain three cluster center points. The wheel track data of the three cluster center points are recorded in order from small to large as the first wheel track data, the second wheel track data and the third wheel track data, and it is determined whether the wheel track data of the three cluster center points meet the first condition. If so, it is determined that the taxiing state of the aircraft is the take-off state; if not, it is determined that the taxiing state of the aircraft is the landing state, wherein the first condition is: the difference between the second wheel track data and the first wheel track data is greater than a preset value and the difference between the third wheel track data and the second wheel track data is also greater than the preset value.

2. The method for identifying an aircraft taxiing state according to claim 1, characterized in that: The laser wheel track meter is arranged at the monitoring end section position of the runway.

3. The method for identifying an aircraft taxiing state according to claim 2, characterized in that: The laser wheel track meter emits a high-frequency laser signal. When the aircraft passes through the section where the laser wheel track meter is located at a high speed, the laser signal is cut. The distance from the laser wheel track meter to the aircraft wheel is calculated based on the laser signal to obtain the original wheel track data.

4. The method for identifying an aircraft taxiing state according to claim 3, characterized in that: The calculation method of the distance from the laser wheel tracker to the wheel is: , , in, is the time from when the laser signal is sent out to when it returns. is the time it takes for the laser signal to reach the wheel; v is the laser propagation speed; is the distance between the wheel and the laser wheel track meter, that is, the original wheel track data.

5. The method for identifying an aircraft taxiing state according to claim 1, characterized in that: The method for acquiring the wheel track data array is as follows: extracting three decimal places after the identifier in the original wheel track data output by the laser wheel track meter as valid original wheel track data; subtracting the distance from the laser wheel track meter to the edge of the runway from the valid original wheel track data to obtain the wheel track data; extracting the time data in the brackets in the original wheel track data output by the laser wheel track meter, and saving the time data after matching the time data with the wheel track data, wherein one time data corresponds to a plurality of wheel track data; and segmenting the saved data according to the time data at a preset time interval, dividing the data within a preset time interval into wheel track data of one flight, and each wheel track data of one flight constitutes a wheel track data array.

6. The method for identifying an aircraft taxiing state according to claim 1, characterized in that: During the take-off phase, when the aircraft passes through the section where the laser wheel track meter is located, both the nose wheel and the main landing gear are in a grounded state, and their lateral positions are captured successively by the laser wheel track meter, that is, three types of data of the nose wheel, the left main landing gear wheel and the right main landing gear wheel are captured for the taking-off aircraft; during the landing phase, the aircraft has a certain pitch angle, and the main landing gear touches the ground before the nose wheel, that is, two types of data of the left main landing gear wheel and the right main landing gear wheel are captured for the landing aircraft.

7. The method for identifying an aircraft taxiing state according to claim 1, characterized in that: When the first condition is not met, it is further determined whether the wheel track data of the three cluster center points meet the second condition. If the second condition is met, each wheel track data array is clustered into two categories to obtain two cluster center points, and the wheel track data of the two cluster center points are recorded as the fourth wheel track data and the fifth wheel track data from small to large. If the second condition is not met, the wheel track data array is deleted, wherein the second condition is that the difference between the second wheel track data and the first wheel track data is greater than a preset value or the difference between the third wheel track data and the second wheel track data is greater than a preset value.

8. An aircraft model inversion method, characterized in that: The following steps are involved: Clustering the wheel track data array using the method described in any one of claims 1 to 7; Calculate the main landing gear spacing based on the wheel track data of the cluster center point; Determine the aircraft model based on the main landing gear spacing.

9. The aircraft model inversion method according to claim 8, characterized in that: When the first condition is met, the main landing gear spacing is the difference between the third wheel track data and the first wheel track data; when the second condition is met, the main landing gear spacing is the difference between the fifth wheel track data and the fourth wheel track data.

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