CN117746693A - Method for discriminating air risk of specific unmanned aerial vehicle in airport terminal area - Google Patents
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Abstract
The application belongs to the technical field of traffic management, discloses a method for discriminating the risk of the air of a specific unmanned aerial vehicle in an airport terminal area, and belongs to the technical field of traffic control systems. The method for judging the empty risk of the unmanned aerial vehicle of the specific class suitable for the airport terminal area comprises the following steps: step 1: first flight data of an unmanned aerial vehicle and second flight data of the unmanned aerial vehicle of the test airport are collected. Step 2: the minimum required collision probability of unmanned aerial vehicle flying in the adjacent airspace of the airport is obtained. In the technical scheme provided by the application, through collecting the flight data of the manned aircraft at the test airport, and the flight data of the unmanned aircraft, and then obtain the space overlapping probability of unmanned aircraft and manned aircraft, then calculate final collision risk with the space overlapping probability.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to an empty risk judging method applicable to an airport terminal area specific unmanned aerial vehicle.
Background
With the development of unmanned aerial vehicle technology, unmanned aerial vehicle's fly height, flight stability, and flight distance are all increasing constantly. In the background of the increased flight height and flight distance of the unmanned aerial vehicle, the collision probability of the unmanned aerial vehicle to the ground target, which is originally considered during operation, has gradually evolved into an empty collision probability.
In particular, in an airspace near an airport, there is an overlapping space between the flying height of the unmanned aerial vehicle and the flying height of the unmanned aerial vehicle, and the flying height of the unmanned aerial vehicle increases and becomes larger, so that the collision probability between the unmanned aerial vehicle and the unmanned aerial vehicle also increases gradually.
In the field of airport management at present, in order to avoid flight accidents caused by collision between an unmanned aerial vehicle and an unmanned aerial vehicle, an isolated airspace is marked on a place far away from a runway for the unmanned aerial vehicle, or the unmanned aerial vehicle can use the airspace close to an airport at the cost of stopping the unmanned aerial vehicle or waiting on the ground. The reason for the management mode is that the current calculation mode of the unmanned aerial vehicle safety accident is based on the calculation and prediction of the ground collision probability, so that the collision probability of the unmanned aerial vehicle and the unmanned aerial vehicle in the airport adjacent airspace cannot be accurately calculated, and further the safety of the unmanned aerial vehicle in the airport cannot be evaluated. Therefore, the airport cannot take off and experiment of the unmanned aerial vehicle, freight transportation of the unmanned aerial vehicle and the like at present, and the utilization rate of a good low-altitude airspace nearby the airport is affected.
Disclosure of Invention
The content of the present application is intended to introduce concepts in a simplified form that are further described below in the detailed description. The section of this application is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
As a first aspect of the present application, in order to solve the technical problem of low utilization of a low-altitude airspace near an airport, some embodiments of the present application provide a method for determining a risk of sky for an unmanned aerial vehicle of a specific class in an airport terminal area, including the following steps:
step 1: collecting first flight data of an unmanned aerial vehicle of a test airport and second flight data of the unmanned aerial vehicle, wherein the first flight data comprise flight tracks of the unmanned aerial vehicle and time and speed of the unmanned aerial vehicle at a corresponding position; the second flight data comprise the flight track of the manned aircraft, the time and the speed of the manned aircraft at the corresponding position;
setting a part of the maximum flight range of the unmanned aerial vehicle, which is overlapped with the maximum flight range of the manned aerial vehicle, as a collision airspace;
step 2: acquiring the minimum required collision probability of the unmanned aerial vehicle flying in an adjacent airspace of an airport;
step 3: calculating probability distribution of the space position of the unmanned aerial vehicle in the conflict airspace according to the first flight data to obtain a first data set;
calculating probability distribution of the space position of the manned aircraft in the conflict airspace according to the second flight data to obtain a second data set;
step 4: according to the first data set and the second data set, calculating the space overlapping probability when the unmanned aerial vehicle and the manned aerial vehicle operate, and according to the space overlapping probability when the unmanned aerial vehicle and the manned aerial vehicle operate, calculating the collision probability of the unmanned aerial vehicle and the manned aerial vehicle;
step 5: and determining the flight risk of the unmanned aerial vehicle according to the difference value between the collision probability of the unmanned aerial vehicle and the minimum required collision probability.
In the technical scheme provided by the application, through collecting the flight data of the manned aircraft at the test airport, and the flight data of the unmanned aircraft, and then obtain the space overlapping probability of unmanned aircraft and manned aircraft, then calculate final collision risk with the space overlapping probability. The collision risk obtained by adopting the scheme is calculated by using the spatial overlap probability. The calculated collision probability is therefore actually the largest collision probability, essentially the calculated probability without regard to a series of operations or measures that the unmanned aerial vehicle would deliberately avoid the unmanned aerial vehicle. Therefore, the probability can intuitively reflect the collision condition of the unmanned aerial vehicle and the manned aerial vehicle when the unmanned aerial vehicle flies in the airports, and further can push the unmanned aerial vehicle to be used in each airport in a large scale based on the collision condition, so that the utilization rate of a good low-altitude airspace near the airport is increased.
There is a large amount of flight data in flight, whether it be a manned or unmanned aircraft, which records the longitude and latitude of the aircraft in flight, the rate of flight, and the time of flight. Thereby, the flight path of the aircraft can be drawn. However, the flight tracks recorded in the existing flight data are all points of the aircraft in space; furthermore, the collision probability of the unmanned aerial vehicle and the unmanned aerial vehicle is predicted by using the flight data, and the collision of the unmanned aerial vehicle and the unmanned aerial vehicle can be considered only when the paths of the unmanned aerial vehicle and the unmanned aerial vehicle are intersected, so that the influence of the unmanned aerial vehicle and the unmanned aerial vehicle on the volume is ignored, the prediction of the collision probability is inaccurate, and the collision probability has no reference value.
Further, step 1 includes the following steps:
step 11: obtaining an average size of the unmanned aerial vehicle and an average size of the unmanned aerial vehicle, wherein the average size of the unmanned aerial vehicle is X 1 、Y 1 、Z 1 Wherein X is 1 、Y 1 、Z 1 Average length, average width, and average altitude of unmanned aerial vehicle, respectively, and X 1 ≥Y 1 ；
Average size of manned aircraft is X 2 、Y 2 、Z 2 Wherein X is 2 、Y 2 、Z 2 Average length, average width, and average altitude of unmanned aerial vehicle, respectively, and X 2 ≥Y 2 ；
Step 12: calculating the size of a first crash module based on the average size of the unmanned aerial vehicle, wherein the size of the first crash module is 2X in diameter 1 With a height of 2Z 1 Is a cylindrical structure;
calculating the size of a second crash module based on the average size of the manned aircraft, wherein the size of the second crash module is 2X in diameter 2 With a height of 2Z 2 Is a cylindrical structure;
step 13: collecting first flight data of an unmanned aerial vehicle of a test airport and second flight data of the unmanned aerial vehicle, wherein the first flight data comprise flight tracks of the unmanned aerial vehicle and time and speed of the unmanned aerial vehicle at a corresponding position; the second flight data comprise the flight track of the manned aircraft, the time and the speed of the manned aircraft at the corresponding position;
setting a part of the maximum flight range of the unmanned aerial vehicle, which is overlapped with the maximum flight range of the manned aerial vehicle, as a collision airspace;
step 14: adding a first collision template to the first flight data, replacing the unmanned aerial vehicle described in the first flight data with a single point;
adding a second collision template to the second flight data, replacing the manned aircraft described in the second flight data with a single point;
step 15: obtaining first distribution data of the unmanned aerial vehicle in a conflict airspace according to flight data of the unmanned aerial vehicle, wherein the first distribution data comprises the number of times of occurrence, the occurrence time and the occurrence flight rate of the unmanned aerial vehicle in each unit space;
and obtaining second distribution data of the manned aircraft in the conflict airspace according to the flight data of the manned aircraft, wherein the second distribution data comprises the number of times and the time of occurrence of the manned aircraft in each unit space and the flight rate of the manned aircraft when the manned aircraft occurs.
In the technical scheme provided by the application, aiming at the problem of overlarge data volume, the air space is simplified, and the airspace above the airport or nearby the airport is simplified into the airspace where the manned aircraft and the unmanned aircraft can cross. In this way, the spatial domain that needs to be considered is greatly reduced. Then, in order to increase the accuracy of collision probability prediction of the unmanned aerial vehicle and the unmanned aerial vehicle, corresponding template volumes are set, and in the existing coordinate description mode, the unmanned aerial vehicle and the unmanned aerial vehicle are replaced by corresponding first collision templates and second collision templates in a mode that the unmanned aerial vehicle and the unmanned aerial vehicle are described as a simple mass point, so that the accuracy of collision probability prediction of the unmanned aerial vehicle and the unmanned aerial vehicle is guaranteed.
The present risk targets are set based on collision of unmanned aerial vehicles or collision of unmanned aerial vehicles. The risk objectives to be observed in the event of a collision between a manned aircraft and an unmanned aircraft are not yet defined. Therefore, how to find a general risk target to obtain a greater advantage in risk assessment, the present application provides the following technical solutions:
further, step 2: acquiring the minimum required collision probability M of the unmanned aerial vehicle flying in an adjacent airspace of an airport; m=g×h, where M is the minimum required collision probability, G is the generic aircraft accident rate, and H is the aircraft collision rate duty cycle.
According to the technical scheme, the average accident rate of the unmanned aerial vehicle in the general aviation and the commercial aviation and the ratio of the collision rate of the aircraft in the accident rate are counted, the average accident rate and the ratio of the collision rate of the aircraft are multiplied, the general aviation is converted by taking the universal time rate as a unit, the commercial aviation is converted by taking the number of millions of hours of collision, and the equivalent target safety level of the unmanned aerial vehicle in the general aviation and the commercial aviation is obtained, so that the finally obtained risk basis is more suitable for the situation of the general aviation in China, and the unmanned aerial vehicle has greater advantages when used for risk assessment.
Further, step 3 includes the following steps:
step 31: according to the first distribution data, the flight height data of the unmanned aerial vehicle in the vertical direction of the conflict airspace is counted, and then a probability density function F (h) of the unmanned aerial vehicle in the vertical direction is fitted:
；
wherein F (h) contains k groups of formulas altogether, k is the kth segment of Gaussian distribution of the unmanned aerial vehicle, and h is the height of the unmanned aerial vehicle; mu (mu) 1 ' is the average value of the 1 st segment Gaussian distribution, mu 2 ' is the average value of the 2 nd Gaussian distribution, mu k ' is the average value of the k-th segment Gaussian distribution; delta 1 ' is the standard deviation of the 1 st Gaussian distribution, delta 2 ' is the standard deviation of the 2 nd Gaussian distribution, delta k ' is the standard deviation of the k-th Gaussian distribution, and e is a natural constant;
step 32: fitting a probability density function f (x, y) of the unmanned aerial vehicle in the horizontal direction according to the first distribution data:
，
wherein x and y are respectively the abscissa and the ordinate of the unmanned aerial vehicle, and the abscissa and the ordinate are coordinates from which the height data are removed; mu (mu) x ,μ y Respectively the average values in the transverse direction and the longitudinal direction; delta x 、δ y The method is characterized in that the method is a standard deviation of transverse and longitudinal track distribution of the unmanned aerial vehicle, rho is a correlation coefficient of X and Y, rho generally oscillates between-1 and +1, if rho is 0, X is uncorrelated with Y or independent, if the two parameters are completely independent, an equivalent principle exists in mathematics, and f (X, Y) =f (X) f (Y) can simplify calculation.
Further, the step 3 further includes the following steps:
step 33: according to the second distribution data, carrying out statistics on flight height data of the manned aircraft in the vertical direction of the conflict airspace, and fitting a probability density function f (q) of the manned aircraft in the vertical direction;
；
q is the flying height of the manned aircraft; mu (mu) 1 Is the average value of the flying height of the manned aircraft; delta 1 The standard deviation of the flying height distribution of the manned aircraft;
step 34: fitting a probability density function g(s) of the manned aircraft in the horizontal direction according to the second distribution data:
，
wherein s is the ordinate of the horizontal track of the manned aircraft; mu (mu) g Is the average value; mu (mu) g Is the standard deviation delta of the longitudinal distribution of the manned aircraft along the nominal track g Is the standard deviation of the ordinate of the horizontal trajectory of the man-machine.
Further, step 4 includes the steps of:
step 41: calculating coincidence probability P of unmanned aerial vehicle and manned aerial vehicle in vertical direction v ；，h∈[h 0 ,h 1 ]，q∈[h 0 ,h 1 ]The method comprises the steps of carrying out a first treatment on the surface of the q and h are both P v Q is the altitude of the unmanned aerial vehicle, h 0 Is the lower limit of the flying height of the manned aircraft and the unmanned aircraft, h 1 The upper limit of the flying height of the manned aircraft and the unmanned aircraft;
step 42: calculating the coincidence probability P of unmanned aerial vehicle and manned aerial vehicle in horizontal direction H ；；
x 0 X is the abscissa of unmanned aerial vehicle flying into conflicting airspace E For the abscissa of the collision airspace of the flying of the manned aircraft, y max The maximum value of the unmanned aerial vehicle in the ordinate direction; y is min Is the minimum value of the unmanned aerial vehicle distributed in the ordinate direction; g(s) is a probability density function of the horizontal direction of the manned aircraft in the collision space domain, s epsilon y max ，y min ]The method comprises the steps of carrying out a first treatment on the surface of the f (x, y) is a two-dimensional joint probability density function of the unmanned aerial vehicle in the horizontal direction in the collision space domain;
step 43: according to P v And P H Calculating collision probability P of unmanned aircraft and unmanned aircraft, p=nλp v P H The method comprises the steps of carrying out a first treatment on the surface of the Where n and λ are both hyper-parameters.
Further, step 5 includes the steps of:
step 51: presetting risk levels, and setting probability difference values for different risk levels;
step 52: and calculating a difference value W between the collision probability P of the unmanned aerial vehicle and the minimum required collision probability M, and calculating the risk level of collision of the unmanned aerial vehicle and the unmanned aerial vehicle according to the W.
The beneficial effects of this application lie in:
in the technical scheme that this application provided, compare among the prior art from unmanned aerial vehicle's flight trajectory, flight rate, and the collision probability that the collision probability of aircraft was considered from the mode of pluralism to the obstacle avoidance equipment that each equipment was carried when flying, but outstanding adoption probability density function's mode describes the collision probability, so compare in considering flight trajectory, flight rate, and in collision equipment's the scheme, can consider less data volume in this scheme, and then can be with the calculated amount that surplus comes out for the collision template of accounting for unmanned vehicles and unmanned vehicles, and then guaranteed the accuracy of collision probability prediction.
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The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to provide a further understanding of the application with regard to the other features, objects and advantages of the application. The drawings of the illustrative embodiments of the present application and their descriptions are for the purpose of illustrating the present application and are not to be construed as unduly limiting the present application.
In addition, the same or similar reference numerals denote the same or similar elements throughout the drawings. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
In the drawings:
fig. 1 is a flowchart of a method for identifying the risk of the air gap of a specific unmanned aerial vehicle in an airport terminal area.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it is to be understood that the present application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and examples of the present application are for illustrative purposes only and are not intended to limit the scope of the present application.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments and features of embodiments in this application may be combined with each other without conflict.
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a method for discriminating the empty risk of a specific unmanned aerial vehicle in an airport terminal area comprises the following steps:
step 1: collecting first flight data of an unmanned aerial vehicle of a test airport and second flight data of the unmanned aerial vehicle, wherein the first flight data comprise flight tracks of the unmanned aerial vehicle and time and speed of the unmanned aerial vehicle at a corresponding position; the second flight data includes a flight trajectory of the manned aircraft, a time and a velocity of the manned aircraft at the corresponding location.
And setting a part, which is overlapped with the maximum flight range of the unmanned aerial vehicle, as a collision airspace.
Specifically, step 1 includes the following steps:
step 11: obtaining an average size of the unmanned aerial vehicle and an average size of the unmanned aerial vehicle, wherein the average size of the unmanned aerial vehicle is X 1 、Y 1 、Z 1 Wherein X is 1 、Y 1 、Z 1 Average length, average width, and average altitude of unmanned aerial vehicle, respectively, and X 1 ≥Y 1 。
Average size of manned aircraft is X 2 、Y 2 、Z 2 Wherein X is 2 、Y 2 、Z 2 Average length, average width, and average altitude of unmanned aerial vehicle, respectively, and X 2 ≥Y 2 。
Step 12: calculating the size of a first crash module based on the average size of the unmanned aerial vehicle, wherein the size of the first crash module is 2X in diameter 1 With a height of 2Z 1 Is a cylindrical structure of (a).
Calculating the size of a second crash module based on the average size of the manned aircraft, wherein the size of the second crash module is 2X in diameter 2 With a height of 2Z 2 Is cylindrical in shapeStructure is as follows.
Step 13: collecting first flight data of an unmanned aerial vehicle of a test airport and second flight data of the unmanned aerial vehicle, wherein the first flight data comprise flight tracks of the unmanned aerial vehicle and time and speed of the unmanned aerial vehicle at a corresponding position; the second flight data includes a flight trajectory of the manned aircraft, a time and a velocity of the manned aircraft at the corresponding location. And setting a part, which is overlapped with the maximum flight range of the unmanned aerial vehicle, as a collision airspace.
Step 14: adding a first collision template to the first flight data, replacing the unmanned aerial vehicle described in the first flight data with a single point; a second collision template is added to the second flight data to replace the manned aircraft described in the second flight data with a single point.
Specifically, in the existing unmanned aerial vehicle and the existing recording of flight data of the unmanned aerial vehicle, the unmanned aerial vehicle is used as an independent point and recorded into a corresponding coordinate system, and then the flight track of the corresponding point is obtained. That is, the flight data collected by us is actually the flight data of a point in the corresponding space, and the flight data includes the speed, time, and other factors of the point. This is because, in the flight trajectory recording, the aircraft is too small compared to airspace, and the aircraft is directly shortsighted as a point in order to reduce the calculation amount.
However, in this embodiment, since the collision rate needs to be calculated by the probability of overlap of the unmanned aerial vehicle and the manned aerial vehicle in space, it is impossible to calculate that the unmanned aerial vehicle and the manned aerial vehicle collide when the particles coincide, and the collision is considered when there is a coincidence. In this way, corresponding first and second crash templates are provided. The first collision template is brought into the first flight data, which essentially is the flight data originally described by the point in the first flight data, replaced by the block, or object, of the first collision template. The same applies to the second flight data.
Step 15: and obtaining first distribution data of the unmanned aerial vehicle in the conflict airspace according to the flight data of the unmanned aerial vehicle, wherein the first distribution data comprises the number of times of occurrence, the time of occurrence and the flight rate of the unmanned aerial vehicle in each unit space.
And obtaining second distribution data of the manned aircraft in the conflict airspace according to the flight data of the manned aircraft, wherein the second distribution data comprises the number of times and the time of occurrence of the manned aircraft in each unit space and the flight rate of the manned aircraft when the manned aircraft occurs.
Step 2: the minimum required collision probability M of the unmanned aerial vehicle flying in the adjacent airspace of the airport is obtained. M=g×h, where M is the minimum required collision probability, G is the generic aircraft accident rate, and H is the aircraft collision rate duty cycle.
Because the general aviation accident occurs for various reasons, such as collision barriers of aircrafts, bird strikes, tire burst, runway rush out, component falling off and the like, the accident rate counted by G is all the reasons, and the target safety level is the collision rate, so that the ratio of the collision rate of aircrafts is the ratio of the collision in the aviation accident, and the ratio of the collision rate is different every year, thus being averaged.
Step 3: calculating probability distribution of the space position of the unmanned aerial vehicle in the conflict airspace according to the first flight data to obtain a first data set; and calculating the probability distribution of the spatial position of the manned aircraft in the conflict airspace according to the second flight data to obtain a second data set.
Further, step 3 includes the following steps:
step 31: according to the first distribution data, the flight height data of the unmanned aerial vehicle in the vertical direction of the conflict airspace is counted, and then a probability density function F (h) of the unmanned aerial vehicle in the vertical direction is fitted:
；
wherein F (h) contains k groups of formulas altogether, k is the kth segment of Gaussian distribution of the unmanned aerial vehicle, and h is the height of the unmanned aerial vehicle; mu (mu) 1 ' is the average value of the 1 st segment Gaussian distribution, mu 2 ' is the average value of the 2 nd Gaussian distribution, mu k ' is the average value of the k-th segment Gaussian distribution; delta 1 ' is the standard deviation of the 1 st Gaussian distribution, delta 2 ' is the standard deviation of the 2 nd Gaussian distribution, delta k ' is the standard deviation of the k-th gaussian distribution and e is a natural constant.
Step 32: fitting a probability density function f (x, y) of the unmanned aerial vehicle in the horizontal direction according to the first distribution data:
；
wherein x and y are respectively the abscissa and the ordinate of the unmanned aerial vehicle, and the abscissa and the ordinate are coordinates from which the height data are removed; mu (mu) x ,μ y Respectively the average values in the transverse direction and the longitudinal direction; delta x 、δ y The standard deviation of the transverse and longitudinal track distribution of the unmanned aerial vehicle, wherein ρ is the correlation coefficient of x and y.
Step 33: according to the second distribution data, the flight height data of the manned aircraft in the vertical direction of the conflict airspace is counted, and a probability density function f (q) of the manned aircraft in the vertical direction is fitted:
；
q is the flying height of the manned aircraft; mu (mu) 1 Is the average value of the flying height of the manned aircraft; delta 1 Is the standard deviation of the flying height distribution of the manned aircraft.
Step 34: fitting a probability density function g(s) of the manned aircraft in the horizontal direction according to the second distribution data:
，
wherein s is the ordinate of the horizontal track of the manned aircraft; mu (mu) g Is the average value; mu (mu) g Is the standard deviation of the longitudinal distribution of the manned aircraft along the nominal track.
By converting the coordinate system and setting the x-transverse direction, i.e. the direction of the unmanned aerial vehicle track, to the x-direction, only the distribution of the ordinate is considered, i.e. mu, to simplify the calculation g For the average value of the longitudinal coordinates of the horizontal track of the man-machine, the nominal track is a standard flight track for strictly executing various information such as course, gradient, distance and the like marked by a navigation chart manual, but the actual situation is influenced by wind power, mechanical, equipment precision and the like, the nominal track is difficult to accurately achieve, that is, the actual track of the airplane can deviate from the nominal track left and right or up and down.
In step 3, the probability of the distribution of the unmanned aerial vehicle and the manned aerial vehicle in the collision space is actually described, but the probability of the distribution of the unmanned aerial vehicle is described by adopting two dimensions, namely the horizontal direction and the vertical direction, so that a probability distribution model can be simplified, and the calculation amount can be reduced.
Step 4: and calculating the space overlapping probability of the unmanned aerial vehicle and the manned aerial vehicle when the unmanned aerial vehicle operates according to the first data set and the second data set, and calculating the collision probability of the unmanned aerial vehicle and the manned aerial vehicle according to the space overlapping probability of the unmanned aerial vehicle and the manned aerial vehicle when the unmanned aerial vehicle operates.
In step 3, the probability of the distribution of the unmanned aerial vehicle and the unmanned aerial vehicle in the collision space is calculated, and in practice, the probability that the unmanned aerial vehicle and the unmanned aerial vehicle overlap each other or have a overlapping portion is calculated, namely the maximum collision probability of the unmanned aerial vehicle and the unmanned aerial vehicle. Based on this, the following manner is provided.
Step 41: calculating coincidence probability P of unmanned aerial vehicle and manned aerial vehicle in vertical direction v ；，h∈[h 0 ,h 1 ]，q∈[h 0 ,h 1 ]The method comprises the steps of carrying out a first treatment on the surface of the q and h are both P v Q is the altitude and the manned aircraft, h is the altitude of the unmanned aircraft, h 0 Is the lower limit of the flying height of the manned aircraft and the unmanned aircraft, h 1 For flying height of manned aircraft and unmanned aircraftLimiting;
step 42: calculating the coincidence probability P of unmanned aerial vehicle and manned aerial vehicle in horizontal direction H ；；x 0 X is the abscissa of unmanned aerial vehicle flying into conflicting airspace E For the abscissa of the collision airspace of the flying of the manned aircraft, y max The maximum value of the unmanned aerial vehicle in the ordinate direction; y is min Is the minimum value of the unmanned aerial vehicle distributed in the ordinate direction; g(s) is a probability density function of the horizontal direction of the manned aircraft in the collision space domain, s epsilon y max ，y min ]The method comprises the steps of carrying out a first treatment on the surface of the f (x, y) is a two-dimensional joint probability density function of the unmanned aerial vehicle in the horizontal direction in the collision space domain;
step 43: according to P v And P H Calculating collision probability P of unmanned aircraft and unmanned aircraft, p=nλp v P H . Wherein n and lambda are super parameters respectively, and are required to be set according to the needs. In this embodiment, n is a conversion coefficient for converting into a required measurement unit or measurement system. Lambda is an influencing factor parameter, and in the scheme, the probability (namely collision probability) that the manned aircraft and the unmanned aircraft are mutually overlapped is calculated under the condition of no influence, but in actual airport management, various obstacle avoidance measures such as obstacle avoidance radar, route planning and the like exist, and the obstacle avoidance measures are set when the newly added factors are considered.
Step 5: and determining the flight risk of the unmanned aerial vehicle according to the difference value between the collision probability of the unmanned aerial vehicle and the minimum required collision probability.
Further, step 5 includes the steps of:
step 51: presetting risk levels, and setting probability difference values for different risk levels;
step 52: and calculating a difference value W between the collision probability P of the unmanned aerial vehicle and the minimum required collision probability M, and calculating the risk level of collision of the unmanned aerial vehicle and the unmanned aerial vehicle according to the W.
The preset risk level can be set according to the requirement, and the setting mode is not exemplified here.
The foregoing description is only illustrative of the principles of the technology being employed and of some of the preferred embodiments of the present application. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present application is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually replaced with (but not limited to) features having similar functions as disclosed in the embodiments of the present application.
Claims (7)
1. A method for discriminating the empty risk of a specific unmanned aerial vehicle in an airport terminal area is characterized by comprising the following steps: the method comprises the following steps:
step 1: collecting first flight data of an unmanned aerial vehicle of a test airport and second flight data of the unmanned aerial vehicle, wherein the first flight data comprise flight tracks of the unmanned aerial vehicle and time and speed of the unmanned aerial vehicle at a corresponding position; the second flight data comprise the flight track of the manned aircraft, the time and the speed of the manned aircraft at the corresponding position;
setting a part of the maximum flight range of the unmanned aerial vehicle, which is overlapped with the maximum flight range of the manned aerial vehicle, as a collision airspace;
step 2: acquiring the minimum required collision probability of the unmanned aerial vehicle flying in an adjacent airspace of an airport;
step 3: calculating probability distribution of the space position of the unmanned aerial vehicle in the conflict airspace according to the first flight data to obtain a first data set;
calculating probability distribution of the space position of the manned aircraft in the conflict airspace according to the second flight data to obtain a second data set;
step 4: according to the first data set and the second data set, calculating the space overlapping probability when the unmanned aerial vehicle and the manned aerial vehicle operate, and according to the space overlapping probability when the unmanned aerial vehicle and the manned aerial vehicle operate, calculating the collision probability of the unmanned aerial vehicle and the manned aerial vehicle;
step 5: and determining the flight risk of the unmanned aerial vehicle according to the difference value between the collision probability of the unmanned aerial vehicle and the minimum required collision probability.
2. The method for discriminating the risk of the air of the unmanned aerial vehicle of the specific class of the terminal area of the applicable airport according to claim 1, wherein the method comprises the following steps: step 1 comprises the following steps:
step 11: obtaining an average size of the unmanned aerial vehicle and an average size of the unmanned aerial vehicle, wherein the average size of the unmanned aerial vehicle is X 1 、Y 1 、Z 1 Wherein X is 1 、Y 1 、Z 1 Average length, average width, and average altitude of unmanned aerial vehicle, respectively, and X 1 ≥Y 1 ；
Average size of manned aircraft is X 2 、Y 2 、Z 2 Wherein X is 2 、Y 2 、Z 2 Average length, average width, and average altitude of unmanned aerial vehicle, respectively, and X 2 ≥Y 2 ；
Step 12: calculating the size of a first crash module based on the average size of the unmanned aerial vehicle, wherein the size of the first crash module is 2X in diameter 1 With a height of 2Z 1 Is a cylindrical structure;
calculating the size of a second crash module based on the average size of the manned aircraft, wherein the size of the second crash module is 2X in diameter 2 With a height of 2Z 2 Is a cylindrical structure;
step 13: collecting first flight data of an unmanned aerial vehicle of a test airport and second flight data of the unmanned aerial vehicle, wherein the first flight data comprise flight tracks of the unmanned aerial vehicle and time and speed of the unmanned aerial vehicle at a corresponding position; the second flight data comprise the flight track of the manned aircraft, the time and the speed of the manned aircraft at the corresponding position;
setting a part of the maximum flight range of the unmanned aerial vehicle, which is overlapped with the maximum flight range of the manned aerial vehicle, as a collision airspace;
step 14: adding a first collision template to the first flight data, replacing the unmanned aerial vehicle described in the first flight data with a single point;
adding a second collision template to the second flight data, replacing the manned aircraft described in the second flight data with a single point;
step 15: obtaining first distribution data of the unmanned aerial vehicle in a conflict airspace according to flight data of the unmanned aerial vehicle, wherein the first distribution data comprises the number of times of occurrence, the occurrence time and the occurrence flight rate of the unmanned aerial vehicle in each unit space;
and obtaining second distribution data of the manned aircraft in the conflict airspace according to the flight data of the manned aircraft, wherein the second distribution data comprises the number of times and the time of occurrence of the manned aircraft in each unit space and the flight rate of the manned aircraft when the manned aircraft occurs.
3. The method for discriminating the risk of the air of the unmanned aerial vehicle of the specific class of the terminal area of the applicable airport according to claim 1, wherein the method comprises the following steps: in the step 2, acquiring the minimum required collision probability M of the unmanned aerial vehicle flying in an adjacent airspace of an airport; m=g×h, where M is the minimum required collision probability, G is the generic aircraft accident rate, and H is the aircraft collision rate duty cycle.
4. A method for discriminating an empty risk of an unmanned aerial vehicle of a specific class for an airport terminal according to claim 3, wherein: step 3 comprises the following steps:
step 31: according to the first distribution data, obtaining flight height data of the unmanned aerial vehicle in the vertical direction of the conflict airspace, and fitting a probability density function F (h) of the unmanned aerial vehicle in the vertical direction:
；
wherein F (h) includes k groups of formulas together, k is unmannedThe kth section of Gaussian distribution of the unmanned aerial vehicle is h; mu (mu) 1 ' is the average value of the 1 st segment Gaussian distribution, mu 2 ' is the average value of the 2 nd Gaussian distribution, mu k ' is the average value of the k-th segment Gaussian distribution; delta 1 ' is the standard deviation of the 1 st Gaussian distribution, delta 2 ' is the standard deviation of the 2 nd Gaussian distribution, delta k ' is the standard deviation of the k-th Gaussian distribution, and e is a natural constant;
step 32: fitting a probability density function f (x, y) of the unmanned aerial vehicle in the horizontal direction according to the first distribution data:
;
wherein x and y are respectively the abscissa and the ordinate of the unmanned aerial vehicle, and the abscissa and the ordinate are coordinates from which the height data are removed; mu (mu) x ,μ y Respectively the average values in the transverse direction and the longitudinal direction; delta x 、δ y The standard deviation of the transverse and longitudinal track distribution of the unmanned aerial vehicle, wherein ρ is the correlation coefficient of x and y.
5. The method for discriminating the risk of air for the unmanned aerial vehicle of the specific class of the terminal area of the applicable airport according to claim 4, wherein the method comprises the following steps: step 3 further comprises the steps of:
step 33: according to the second distribution data, carrying out statistics on flight height data of the manned aircraft in the vertical direction of the conflict airspace, and fitting a probability density function f (q) of the manned aircraft in the vertical direction;
；
q is the flying height of the manned aircraft; mu (mu) 1 Is the average value of the flying height of the manned aircraft; delta 1 The standard deviation of the flying height distribution of the manned aircraft;
step 34: fitting a probability density function g(s) of the manned aircraft in the horizontal direction according to the second distribution data:
，
wherein s is the ordinate of the horizontal track of the manned aircraft; mu (mu) g The average value is longitudinally distributed along a nominal track for the manned aircraft; delta g Is the standard deviation of the longitudinal distribution of the manned aircraft along the nominal track.
6. The method for discriminating the risk of air for the unmanned aerial vehicle of the specific class of the terminal area of the applicable airport according to claim 5, wherein the method comprises the following steps: step 4 comprises the following steps:
step 41: calculating the coincidence probability Pv of the unmanned aerial vehicle and the manned aerial vehicle in the vertical direction;
，h∈[h 0 ,h 1 ]，q∈[h 0 ,h 1 ]the method comprises the steps of carrying out a first treatment on the surface of the q and h are both P v Q is the altitude of the unmanned aerial vehicle, h 0 Is the lower limit of the flying height of the manned aircraft and the unmanned aircraft, h 1 The upper limit of the flying height of the manned aircraft and the unmanned aircraft;
step 42: calculating the coincidence probability P of unmanned aerial vehicle and manned aerial vehicle in horizontal direction H ；
；
x 0 X is the abscissa of unmanned aerial vehicle flying into conflicting airspace E For the abscissa of the collision airspace of the flying of the manned aircraft, y max The maximum value of the unmanned aerial vehicle in the ordinate direction; y is min Is the minimum value of the unmanned aerial vehicle distributed in the ordinate direction; g(s) is a probability density function of the horizontal direction of the manned aircraft in the collision space domain, s epsilon y max ，y min ]The method comprises the steps of carrying out a first treatment on the surface of the f (x, y) is the unmanned aerial vehicle in the collision airspaceA two-dimensional joint probability density function in the horizontal direction;
step 43: according to Pv and P H Calculating collision probability P of unmanned aircraft and unmanned aircraft, p=nλp v P H Where n and λ are both hyper-parameters.
7. The method for discriminating the risk of air for the specific unmanned aerial vehicle in the terminal area of the applicable airport according to claim 6, wherein the method comprises the following steps: step 5 comprises the following steps:
step 51: presetting risk levels, and setting probability difference values for different risk levels;
step 52: and calculating a difference value W between the collision probability P of the unmanned aerial vehicle and the minimum required collision probability M, and calculating the risk level of collision of the unmanned aerial vehicle and the unmanned aerial vehicle according to the W.
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