CN114353666A - Analysis method for runway airplane running behavior state of airport runway - Google Patents
Analysis method for runway airplane running behavior state of airport runway Download PDFInfo
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Abstract
The invention relates to an analysis method for the runway airplane running behavior state of an airport runway, which comprises the following steps: 1) acquiring measurement distance data of monitoring equipment; 2) cleaning and screening effective data; 3) setting a time label for the screened measurement data and dividing airplane events; 4) for each airplane event, calculating the transverse offset of the airplane and counting the transverse offset distribution of the airplane; 5) calculating the wheel distance between a front wheel of an aircraft landing gear and a rear wheel of a main landing gear, and identifying the type of the aircraft; 6) judging the take-off and landing directions of the airplanes and calculating the average speed of the sliding for each airplane event; 7) and visualizing the state of the airplane running behavior. Compared with the prior art, the method provided by the invention considers the error of the monitoring equipment in the actual installation process, avoids the problem that the existing method only can provide single traffic load parameters, and realizes dynamic analysis and visualization of four traffic load parameters of the aircraft running transverse offset, the aircraft type, the average running speed and the take-off and landing direction in the aircraft running behavior process by means of machine learning.
Description
Technical Field
The invention relates to the field of airport engineering, in particular to an analysis method for the airplane run behavior state of an airport runway.
Background
By analyzing the runway airplane running behavior state, the information such as runway load parameters, load offset distribution and the like can be effectively obtained, traffic load parameters can be provided for subsequent runway structure character and operation character analysis and runway design, and the method has important significance for runway design, operation and maintenance work.
The existing airplane sliding behavior state analysis means mainly comprise a laser ranging method and an image identification method, the existing laser ranging method is mainly used for measuring the transverse distribution of the airplane and has no mature application for airplane type identification and airplane speed measurement; the image identification method mainly identifies the airplane type through the extraction and measurement of the overall outline of the airplane, and temporarily does not need the research on the wheel span measurement of the main landing gear of the airplane and the transverse distribution of the airplane. Therefore, in combination with the limitations of the current analysis means and the significance of the analysis of the runway behavior state, a complete runway behavior state analysis method is urgently needed to provide traffic load parameters in the processes of runway design and property analysis.
The analysis of the state of the airplane sliding behavior mainly comprises four parameters of transverse offset of the airplane, airplane type identification of the airplane, take-off and landing directions of the airplane and average sliding speed. The horizontal offset of the airplane refers to the distance of the center line of the airplane deviating from the center marking of the runway; the airplane type refers to a classification basis of the airplane which is classified due to different design and manufacturing types; the takeoff and landing direction of the airplane refers to the direction of judging whether the airplane is in takeoff or landing on a runway; the speed of an aircraft refers to the average speed of the aircraft as it slides on the runway.
Chinese patent No. CN103983978B discloses an airport wheel track test method, which is characterized in that when an airplane passes through a runway, the distances between the outer sides of the front and rear airplane wheels of the airplane and a distance measuring sensor are measured by a laser type distance measuring sensor and a laser wheel track distance measuring sensor; and judging the type of the airplane and the wheel track of the airplane according to the data of the ranging sensor. Although the technology obtains the distances from the sensors to the outer sides of the front wheels and the rear wheels of the airplane through the laser ranging sensors, no specific analysis scheme is provided for obtaining the transverse distribution and the airplane type according to ranging data, and how to calculate the average sliding speed and judge the taking-off and landing directions of the airplane.
Chinese patent application publication No. CN109444907A discloses a system and a method for testing the transverse distribution change rule of an airplane wheel track, and the technical scheme refers to that a laser distance measuring sensor mainly measures the distance between a near-side tire and a laser test unit when the airplane runs; the time synchronization and data processing unit is used for screening the test data of the laser ranging sensor and marking all screened effective data with uniform time parameters; the power supply unit provides power required by testing for the laser ranging sensor and the video unit; the data storage unit is used for storing the data processed by the time synchronization and data processing unit; the computer is used for performing later analysis processing on the data stored in the data storage unit at a specific time. Although the transverse offset and the speed of the airplane are obtained through the testing system in the technology, all the laser ranging sensors are arranged in a line along the direction parallel to the runway, airplane type information such as the wheel distance of the undercarriage cannot be obtained, and a data analysis scheme for identifying airplane types and obtaining the transverse distribution rule of the airplane in a computer is not specifically given. In addition, the technical scheme does not consider the problem of how to correct the ranging data when the ranging equipment has installation measurement errors.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an analysis method for the runway airplane running behavior state of an airport.
The purpose of the invention can be realized by the following technical scheme:
an analytical method for the runway airplane running behavior state of an airport comprises the following steps:
s1, acquiring measurement data of the monitoring equipment in real time, wherein the measurement data comprise a measurement distance from the monitoring equipment to the outer side of a nose wheel of the aircraft landing gear and a measurement distance from the monitoring equipment to the outer side of a rear wheel of the main landing gear;
s2, setting a screening range to clean and screen the measurement data of the monitoring equipment;
s3, setting time labels for the screened effective measurement data and dividing airplane events at set time intervals;
s4, for each airplane event, considering a measurement error caused by a deflection angle generated in the installation process of the monitoring equipment, and counting the transverse deflection distribution of the airplane according to the measurement data of the opposite monitoring equipment;
s5, for each airplane event, considering a measurement error caused by a deflection angle generated by monitoring equipment in the installation process, and identifying the airplane type in the running state according to the measurement data of the opposite monitoring equipment;
s6, judging the takeoff and landing directions of the airplanes according to the measurement data of the homodromous monitoring equipment and calculating the average speed of the rollout for each airplane event;
and S7, visualizing the sliding behavior state of the airplane and drawing a relation graph of the traffic load parameters and the time.
In the step S2, the screening range Limit specifically includes:
Limit∈[Length-{(Height-FH)÷i}，Length+{(Height-FH)÷i}]
wherein, Length is the distance between the COMS1 and COMS3 of the opposite monitoring devices and the center line of the runway, Height is the Height of the monitoring devices erected on the center line of the runway, FH is the minimum Height of the civil aviation aircraft engine, and i is the gradient of the runway cross slope.
In step S3, the initial monitoring time is used as the time stamp of the first piece of data, and the time stamp of each piece of data is the time of the last piece of data plus a measurement time interval.
The step S4 specifically includes the following steps:
s4.1, calculating the transverse offset of the airplane: and (3) correcting data by considering a measurement error, wherein the calculation formula of the transverse offset is as follows:
wherein D is1Distance, D, from the outside of the rear wheel of the main landing gear of the aircraft to the monitoring device COMS13The distance from the outer side of the rear wheel of the main landing gear of the airplane to the monitoring device COMS3, and theta is the deviation angle between the connecting line of the facing monitoring devices COMS1 and COMS3 and the horizontal line;
s4.2, counting the transverse offset distribution of the airplane: and drawing a histogram of the transverse offset of the airplane, fitting a probability density function of the transverse offset distribution by using a Gaussian kernel function, and checking and judging the type of the transverse distribution.
In step S4.2, the aircraft lateral offset distribution statistical method specifically includes:
recording the transverse track offset of each aircraft event to obtain the negative offset maximum value, the positive offset maximum value and the frequency of the track offset, representing the group distance by using the abscissa, and using the frequency/group distance as the ordinate, drawing a transverse track distribution diagram, fitting a probability density function curve, and testing whether the data conforms to normal distribution or not by Kolmogorov-Smirnov.
The step S5 specifically includes the following steps:
s5.1, calculating the wheel track D of the airplane, and correcting by considering the measurement error, wherein the calculation formula is as follows:
D＝{2×Length-(D1+D3)}×cosθ
and S5.2, establishing a machine learning KNN model recognition test data model according to the design data and the test data of the landing gear of the common aircraft model of civil aviation.
In the step S5.2, establishing the machine learning KNN model and testing the classified model specifically includes the following steps:
s5.2.1, standardizing data, and processing the data into the same format;
s5.2.2, selecting K value according to the number of the airplane types to be classified;
s5.2.3, calculating Euclidean distances from the wheel track test data points to all the training wheel track data points;
s5.2.4, sorting the distances from big to small, and selecting the top k nearest categories;
s5.2.5, counting the K categories according to the categories to which the K categories belong, wherein the category with the largest number is the category of the model to which the current wheel track sample belongs;
s5.2.6, repeating the above S5.2.3-S5.2.5 steps for other data that need to be classified.
In the step S6, the average speed V of running12The calculation formula of (A) is as follows:
V1＝V2＝V12＝D12/(t1-t2)
wherein, V1For the instantaneous speed, V, of the aircraft at the monitoring device COMS12For the instantaneous speed, V, of the aircraft at the monitoring device COMS212Average speed for the aircraft between the monitoring devices COMS1 to COMS2, i.e. average speed for rollout, D12To monitor the distance, t, between the devices COMS1 and COMS21Time, t, for aircraft landing gear nose wheel passing through COMS12The time for the nose wheel of the landing gear of the aircraft to pass through the COMS 2.
In step S3, the time interval for dividing the airplane event is set to 2S.
The ranging system for realizing the analysis method comprises three monitoring devices COMS1, COMS2 and COMS3, wherein the two monitoring devices COMS1 and COMS3 are opposite monitoring devices symmetrically arranged along the two sides of the center line of the runway, the other monitoring device COMS2 and COMS1 are arranged at intervals in the same direction and form a monitoring device in the same direction with COMS1, and the monitoring devices adopt a laser ranging or infrared ranging mode.
Compared with the prior art, the invention has the following advantages:
the method for analyzing the airplane run behavior state of the airport runway considers the angle error in the installation process of the monitoring equipment, avoids the problem that the prior method can only provide a part of parameters under an ideal condition, corrects the measurement error by data, obtains four analysis parameters of the airplane run behavior state, and provides a statistical method of the parameter distribution rule.
The analysis parameters of the airplane running behavior state adopted by the invention are scientific and reasonable, and the use is efficient and convenient, wherein the four analysis parameters (transverse offset, airplane type, take-off and landing direction and average speed of running) have the following characteristics: the method is related to airport runway structure design, structural mechanical response inversion and runway surface character evaluation, and can provide load parameters for finite element simulation in the design, operation and maintenance processes so as to scientifically design the runway structure and evaluate the runway structure and operation characters in real time.
Drawings
Fig. 1 is a schematic diagram of an actual layout situation of monitoring equipment of an airplane run behavior analysis system.
Fig. 2 is a schematic view of the distance measurement in example 1.
Fig. 3 is a schematic view of the distribution of lateral shift amounts in example 1.
Fig. 4 is a diagram showing the test results of the KNN model in example 1.
Fig. 5 is a schematic view of a visual airplane run behavior event in embodiment 1.
Fig. 6 is a flow chart of the method for analyzing the runway airplane run behavior state of the airport runway.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
As shown in fig. 6, the present invention provides an analysis method for the airplane run behavior state of an airport runway, which comprehensively considers the measurement error in the installation and burying processes of actual monitoring equipment, and realizes the analysis of the airplane run behavior state of a scientific system through airplane lateral offset calculation, model identification, take-off and landing direction judgment and airplane run speed calculation, and comprises the following steps:
s1, obtaining the measurement distances from the monitoring equipment to the outer side of a nose wheel of an aircraft landing gear and the outer side of a rear wheel of a main landing gear;
s2, cleaning and screening effective data, wherein the method for calculating the screening range Limit is as follows:
Limit∈[Length-{(Height-FH)÷i}，Length+{(Height-FH)÷i}]
wherein, Length is the distance from the COMS1 and COMS3 of the opposite monitoring devices to the central line of the runway, and the unit is m; height is the Height of the monitoring equipment erected on the central line of the runway, and the unit is m; FH is the minimum height of the civil aviation aircraft engine, generally 0.46 m; i is the slope of the cross slope of the runway, and the unit is%;
s3, setting time labels for the screened measurement data and dividing airplane events at intervals of 2S;
s4, for each airplane event, under the condition that the measurement error caused by the deflection angle generated in the installation process of the monitoring equipment is considered, utilizing COMS1 and COMS3 data of the opposite monitoring equipment to count the transverse deflection distribution of the airplane, and the method specifically comprises the following steps:
s4.1, calculating the transverse offset of the airplane, taking measurement errors into consideration, and correcting data, wherein the transverse offset delta L is calculated by the following method:
note in particular that to distinguish whether the aircraft footprint is left-handed or right-handed, it is negative when left-handed, i.e., if | Length-D in equation case one1|＞|Length-D3If yes, calculating the result to be negative; and vice versa.
Wherein D is1The distance from the outer side of the rear wheel of the main landing gear of the airplane to COMS1 is expressed in m; d3The distance from the outer side of the rear wheel of the main landing gear of the airplane to COMS3 is expressed in m; theta is the deviation angle between the connecting line of COMS1 and COMS3 and the horizontal line, and the unit is DEG;
s4.2, counting the transverse offset distribution of the airplane: drawing a histogram of the transverse offset of the airplane, fitting a probability density function of the transverse offset distribution in a Python seatron library by using a Gaussian kernel function, and judging the type of the transverse distribution by adopting Kolmogorov-Smirnov test; the form of the gaussian kernel is as follows:
wherein,
is the squared euclidean distance between the two eigenvectors; x' is the kernel function center; sigma is a width parameter of the function, and the radial action range of the function is controlled;
s5, for each airplane event, under the condition that a measurement error caused by a deflection angle generated in the installation process of the monitoring equipment is considered, recognizing the airplane model in the running state by utilizing the COMS1 and COMS3 data of the opposite direction monitoring equipment, and specifically comprising the following steps:
s5.1, calculating the wheel track D of the airplane, and correcting by considering the measurement error, wherein the calculation method comprises the following steps:
D＝{2×Length-(D1+D3)}×cosθ
s5.2, establishing a machine learning KNN model identification test data model by utilizing the design data and the test data of the landing gear of the civil aviation common airplane model;
s6, for each airplane event, judging the takeoff and landing directions of the airplane by utilizing the data of the monitoring devices COMS1 and COMS2 in the same direction, and calculating the average speed V of the sliding12The calculation method is as follows:
V1＝V2＝V12＝D12/(t1-t2)
wherein, V1Is the instantaneous speed of the aircraft at COMS1, in m/s; stand is the instantaneous velocity of the aircraft at COMS2 in m/s; v12Average speed for the aircraft between COMS1 and COMS2, i.e. average speed for rollout, in m/s; d12Is the distance between COMS1 and COMS2 in m; t is t1Time in units of s for the nose wheel of the landing gear of the aircraft to pass through COMS 1; t is t2The time in s for the nose wheel of the landing gear of the aircraft to pass through COMS 2.
S7, visualizing the sliding behavior state of the airplane and drawing a relation graph of four traffic load parameters and time.
In the embodiment of the invention, in the step S1, a distance measuring system is arranged on the runway, wherein the distance measuring system consists of three monitoring devices, two opposite monitoring devices COMS1 and COMS3 are symmetrically arranged along the centerline of the runway, and the distance from the centerline of the runway is set to be 50-80 m; the other monitoring device COMS2 is arranged in the same direction as COMS1, the distance between the two devices is set to be 60-80m, and the monitoring device can adopt a laser ranging mode or an infrared ranging mode according to the situation of a specific airport runway, the measuring frequency is generally 1000Hz, and one piece of data is acquired in 0.001 s.
In step S2, the initial monitoring time is used as the time label of the first piece of data, and the time label of each piece of data thereafter is the time of the last piece of data plus 0.001S.
In step S4, the aircraft lateral distribution statistical method is to record the track lateral offset of each aircraft event, obtain the negative bias maximum, the positive bias maximum, and the frequency of the track offset, express the group spacing with the abscissa, and the ordinate is the frequency/group spacing, draw the track lateral distribution diagram, fit the probability density function curve with the seborn library in python, and check whether the data conforms to the normal distribution through Kolmogorov-Smirnov.
In step S5, statistics of the design data of the nose wheel track and the main landing gear track of the landing gear of the civil aviation common model are shown in the following table 1, and the following statistical results may have a certain measurement error.
TABLE 1 main landing gear track for common aircraft models
| Airplane type | Main landing gear wheel track | Front wheel of landing gear | Class of belonging |
| A300-6 | 10.89 | 0.98 | Class D |
| A319 | 8.93 | 0.72 | Class C |
| A320-2 | 8.95 | 0.96 | Class C |
| A330-2 | 12.61 | 1.11 | Class E |
| A333 | 12.61 | 1.11 | Class E |
| B737-7 | 7.00 | 0.66 | Class C |
| B738 | 7.00 | 0.66 | Class C |
| B747-3 | 12.55 | 1.34 | Class E |
| B757-2 | 8.59 | 0.94 | Class D |
| B767-3 | 10.90 | 1.00 | Class D |
| B777-3 | 12.93 | 1.23 | Class E |
In step S5, the method of building a machine learning KNN model and testing the classified model is as follows:
s5.2.1, standardizing data, and processing the data into the same format;
s5.2.2, selecting a K value, and selecting according to the number of the airplane types to be classified;
s5.2.3, calculating Euclidean distance from the wheel track test data point to all the training wheel track data points, wherein the distance formula of the Euclidean distance m in space is
S5.2.4, sorting the distances from big to small, and selecting the top k nearest categories;
s5.2.5, counting the K categories according to the categories to which the K categories belong, wherein the category with the largest number is the category of the model to which the current wheel track sample belongs;
s5.2.6, repeating the above S5.2.3-S5.2.5 steps for other data that need to be classified.
In step S6, the determination of the takeoff and landing direction of the airplane is related to the runway construction direction, and if the runway construction position is north-south, the takeoff and landing direction is north-south or north-south.
Example 1
As shown in fig. 1, the system for analyzing the state of the airplane running behavior on the land area of the runway of a certain international airport is provided with three monitoring devices, the gradient of the runway cross slope is 1.4%, the erection height of the monitoring devices at the center line of the runway is 0.35m, the monitoring devices COMS1 are 659m away from the south end of the runway, the COMS1 and the COMS2 are in the same direction, and the distance between the monitoring devices and the COMS2 is 60 m; COMS1 and COMS3 are opposite, COMS3 is apart from the south end 660m of the runway, and the slant distance between the two is 110 m. Due to measurement errors during the embedding installation, the line between COMS1 and COMS3 is not strictly perpendicular to the runway axis, and has an angle θ of 0 ° 31' 15 "with the cross section of COMS 1.
In order to provide traffic load parameters for the subsequent analysis of runway structure properties and operation properties, the analysis of the airplane sliding behavior is urgently needed, real-time measurement data is corrected under the condition of considering measurement errors, and four load parameters of transverse offset of the airplane, machine type identification, takeoff and landing direction judgment and average sliding speed calculation are further calculated.
The process of acquiring the runway airplane run behavior state and acquiring the traffic load parameters by using the analysis method of the invention is as follows (the data processing process is carried out in Python):
(1) obtaining the measurement distance from the outer side of the tire of the main landing gear of the airplane to the monitoring equipment;
(2) cleaning and screening original data, extracting effective data in accordance with the measuring range, wherein the screening range calculation formula is as follows:
Limit∈[Length-{(Height-FH)÷i}，Length+{(Height-FH)÷i}]
wherein Length is 55m, Height is 0.46-0.35 is 0.11m, i is 1.4%, the data screening range is [47m, 63m ], the closed interval is taken, and the screened data is shown in fig. 2;
(3) and dividing the data generated in the same 2s into an airplane event according to the time label of each piece of effective data.
(4) The lateral offset, nose gear wheel track, and main gear rear wheel track are calculated for each single aircraft event using the measurement data for COMS1 and COMS 3.
Taking a single airplane roll event as an example, the measurement data for COMS1 and COMS3 are shown in table 2 below (there is a certain fluctuation in the distance measurement process of the monitoring device, and in this case, the peak value is generally taken), and the distance is given by m:
TABLE 2 COMS1 and COMS3 measurement data
| COMS1 measurement data | COMS3 measurement data | Measuring time of day |
| 55.026 | 0 | 06：23：57.419 |
| 0 | 54.1544 | 06：23：57.446 |
| 49.921 | 0 | 06：23：57.582 |
| 50.2307 | 0 | 06：23：57.603 |
| 0 | 49.522 | 06：23：57.633 |
The lateral offset of the nose wheel of the landing gear is then:
the lateral offset of the rear wheel of the main landing gear is as follows:
the wheel track of the front wheel of the landing gear is as follows:
D1＝{2×55-(55.026+54.1544)}×cos(0°31′15″)＝0.711m
the rear wheel track of the main landing gear is as follows:
D2＝{2×55-(50.2307+49.522)}×cos(0°31′15″)＝8.889m
(5) an aircraft model is identified. The statistical front wheel track of the common aircraft landing gear, the rear wheel track of the main landing gear and the model thereof are utilized to construct a sample set together with data obtained by testing, a data set with a set type label is used as a training set, a data set without the label is used as a testing set, a KNN model is adopted in Python for training and classification, and the k value is 12 because the aircraft category in the common model library is 12.
The training model divides the sample into 12 types of models, the model is A319 by taking the wheel track of the front wheel of the landing gear of the test data as 0.711m and the wheel track of the rear wheel as 8.889m as examples for input.
(6) For each single aircraft event, the following table 3 shows the measured data of the COMS1 and the COMS2 in the same direction, the distance unit is m, the average sliding speed of the aircraft is calculated to be 80.214m/s, and the takeoff and landing direction can be determined to be north-south because the COMS1 obtains the data first.
TABLE 3 COMS1 and COMS2 measurement data
(7) The aircraft events are visualized, and a relation graph of four traffic load parameters and time is drawn as shown in fig. 3, so that reference is made by subsequent runway operation and maintenance managers.
Example 2
The system for analyzing the state of the airplane run behavior on the land area of the runway of an international airport obtains 1164 groups of ranging data through one month of data accumulation, and can obtain the ranging data through calculation in the same steps (1) to (4) of the embodiment 1, wherein the transverse offset of the various airplane runs, the wheel track of the front wheel of the landing gear and the wheel track of the rear wheel of the main landing gear on the runway are shown in table 4 (space limit, only partial data are displayed), and the distance unit is m.
TABLE 4 aircraft lateral offset, undercarriage nose wheel track, and main undercarriage rear wheel track calculation results
| Transverse offset | Front wheel track of undercarriage | Rear wheel track of main landing gear |
| 1.44 | 0.74 | 8.74 |
| 0.24 | 1.3 | 12.88 |
| 0.46 | 0.78 | 7 |
| 0.32 | 1.24 | 12.62 |
| 0.5 | 0.78 | 7 |
| 0.57 | 0.76 | 6.38 |
| -0.43 | 0.76 | 6.38 |
| 0.66 | 0.74 | 8.74 |
| 0.48 | 0.98 | 8.56 |
| 0.58 | 1.3 | 12.88 |
| -2.34 | 0.74 | 8.74 |
| -3.84 | 0.78 | 7 |
(5) And (5) counting a transverse distribution rule. Accumulating the transverse offset of the wheel track of each aircraft event to obtain a negative offset maximum value and a positive offset maximum value of the wheel track offset, drawing a probability density function curve which is drawn and fitted by a wheel track transverse distribution diagram and is represented by a Kolmogorov-Smirnov test (K-S test) if the transverse offset is represented by an abscissa when the group pitch is 30 and the ordinate is frequency/group pitch, and testing whether the transverse offset accords with the normal distribution or not by using the Kolmogorov-Smirnov test (K-S test) to obtain a test result P-value which is 0.0001 and less than 0.05, so that the transverse offset of the wheel track accords with the offset distribution more, and the middle part of the wheel track is steeper and the tail part of the wheel track is thicker than the normal distribution.
(6) An aircraft model is identified. And (3) a data co-building sample set is obtained by utilizing the counted front wheel track of the common aircraft landing gear in the airport, the rear wheel track of the main landing gear and the corresponding aircraft model with the test. The data of the known airplane model is marked with classification labels, unknown data is used as a test set, a python sklern library is used for training a KNN model, the batch airplane model identification of the test set can be realized, and the identification condition is shown in fig. 5.
In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. An analysis method for the runway airplane running behavior state of an airport is characterized by comprising the following steps:
s1, acquiring measurement data of the monitoring equipment in real time, wherein the measurement data comprise a measurement distance from the monitoring equipment to the outer side of a nose wheel of the aircraft landing gear and a measurement distance from the monitoring equipment to the outer side of a rear wheel of the main landing gear;
s2, setting a screening range to clean and screen the measurement data of the monitoring equipment;
s3, setting time labels for the screened effective measurement data and dividing airplane events at set time intervals;
s4, for each airplane event, considering a measurement error caused by a deflection angle generated in the installation process of the monitoring equipment, and counting the transverse deflection distribution of the airplane according to the measurement data of the opposite monitoring equipment;
s5, for each airplane event, considering a measurement error caused by a deflection angle generated by monitoring equipment in the installation process, and identifying the airplane type in the running state according to the measurement data of the opposite monitoring equipment;
s6, judging the takeoff and landing directions of the airplanes according to the measurement data of the homodromous monitoring equipment and calculating the average speed of the rollout for each airplane event;
and S7, visualizing the sliding behavior state of the airplane and drawing a relation graph of the traffic load parameters and the time.
2. The method for analyzing the runway airplane running behavior according to claim 1, wherein in the step S2, the screening range Limit is specifically:
Limit∈[Length-{(Height-FH)÷i}，Length+{(Height-FH)÷i}]
wherein, Length is the distance between the COMS1 and COMS3 of the opposite monitoring devices and the center line of the runway, Height is the Height of the monitoring devices erected on the center line of the runway, FH is the minimum Height of the civil aviation aircraft engine, and i is the gradient of the runway cross slope.
3. The method as claimed in claim 1, wherein the step S3 is performed by using the initial monitoring time as the time stamp of the first data, and adding a measurement time interval to the time stamp of the previous data for each subsequent data.
4. The method for analyzing the runway airplane run behavior according to claim 2, wherein the step S4 specifically comprises the following steps:
s4.1, calculating the transverse offset of the airplane: and (3) correcting data by considering a measurement error, wherein the calculation formula of the transverse offset is as follows:
wherein D is1Distance, D, from the outside of the rear wheel of the main landing gear of the aircraft to the monitoring device COMS13The distance from the outer side of the rear wheel of the main landing gear of the airplane to the monitoring device COMS3, and theta is the deviation angle between the connecting line of the facing monitoring devices COMS1 and COMS3 and the horizontal line;
s4.2, counting the transverse offset distribution of the airplane: and drawing a histogram of the transverse offset of the airplane, fitting a probability density function of the transverse offset distribution by using a Gaussian kernel function, and checking and judging the type of the transverse distribution.
5. The method for analyzing the runway airplane run-off behavior state of the airport runway according to claim 4, wherein in the step S4.2, the statistical method of the airplane lateral offset distribution specifically comprises the following steps:
recording the transverse track offset of each aircraft event to obtain the negative offset maximum value, the positive offset maximum value and the frequency of the track offset, representing the group distance by using the abscissa, and using the frequency/group distance as the ordinate, drawing a transverse track distribution diagram, fitting a probability density function curve, and testing whether the data conforms to normal distribution or not by Kolmogorov-Smirnov.
6. The method for analyzing the runway airplane run behavior according to claim 4, wherein the step S5 specifically comprises the following steps:
s5.1, calculating the wheel track D of the airplane, and correcting by considering the measurement error, wherein the calculation formula is as follows:
D＝{2×Length-(D1+D3)}×cosθ
and S5.2, establishing a machine learning KNN model recognition test data model according to the design data and the test data of the landing gear of the common aircraft model of civil aviation.
7. The method for analyzing the runway airplane running behavior of claim 6, wherein the step S5.2 of establishing the machine learning KNN model and testing the classified airplane specifically comprises the following steps:
s5.2.1, standardizing data, and processing the data into the same format;
s5.2.2, selecting K value according to the number of the airplane types to be classified;
s5.2.3, calculating Euclidean distances from the wheel track test data points to all the training wheel track data points;
s5.2.4, sorting the distances from big to small, and selecting the top k nearest categories;
s5.2.5, counting the K categories according to the categories to which the K categories belong, wherein the category with the largest number is the category of the model to which the current wheel track sample belongs;
s5.2.6, repeating the above S5.2.3-S5.2.5 steps for other data that need to be classified.
8. The method for analyzing the runway airplane running behavior of claim 1, wherein in step S6, the average speed V of the runway is12The calculation formula of (A) is as follows:
V1＝V2＝V12＝D12/(t1-t2)
wherein, V1For the instantaneous speed, V, of the aircraft at the monitoring device COMS12For the instantaneous speed, V, of the aircraft at the monitoring device COMS212For the average speed of the aircraft between the monitoring devices COMS1 to COMS2Degree, i.e. average speed of running, D12To monitor the distance, t, between the devices COMS1 and COMS21Time, t, for aircraft landing gear nose wheel passing through COMS12The time for the nose wheel of the landing gear of the aircraft to pass through the COMS 2.
9. The method as claimed in claim 1, wherein the time interval for dividing the airplane event is set to 2S in step S3.
10. The method for analyzing the runway airplane run behavior state of claim 1, wherein a distance measuring system for implementing the method comprises three monitoring devices COMS1, COMS2 and COMS3, wherein two monitoring devices COMS1 and COMS3 are opposite monitoring devices symmetrically arranged along the two sides of the centerline of the runway, the other monitoring device COMS2 is arranged at a distance from the COMS1 in the same direction and forms a same-direction monitoring device with COMS1, and the monitoring devices adopt a laser distance measuring or infrared distance measuring mode.
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