A collision risk quantitative evaluation method applied to low-altitude airspace

Abstract

The application relates to a collision risk quantitative evaluation method applied to low-altitude airspace, and belongs to the technical field of low-altitude risk evaluation. The method solves the problem that an effective method for evaluating collision risks of multiple types of aircraft is lacked in the prior art. The method comprises the following steps: S0, obtaining data information of low-altitude airspace, judging whether an intruding aircraft is a cooperative unmanned aerial vehicle, executing step S1 if the intruding aircraft is the cooperative unmanned aerial vehicle, executing step S2 if the intruding aircraft is a non-cooperative unmanned aerial vehicle, and executing step S3 if the intruding aircraft is a manned aircraft; S1, determining conflict resolution measures involved, generating and outputting a collision probability of the local machine and the cooperative unmanned aerial vehicle, and executing step S4; S2, determining conflict resolution measures involved, generating and outputting a collision probability of the local machine and the non-cooperative unmanned aerial vehicle, and executing step S4; S3, determining conflict resolution measures involved, generating and outputting a collision probability of the local machine and the manned aircraft, and executing step S4; and S4, evaluating a safety situation of the local machine and the intruding aircraft according to the received collision probability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-altitude risk assessment, and particularly relates to a collision risk quantitative assessment method applied to low-altitude airspace. BACKGROUND

[0002] Low-altitude airspace is a valuable resource, and is an important activity space for new types of carrier tools such as unmanned aerial vehicles and eVOTL, and contains great economic value and social value. In recent years, the explosive growth of the number and operation scale of unmanned aerial vehicles has brought unprecedented challenges to low-altitude safety. Obviously, how to objectively and effectively assess the collision risk between low-altitude aircraft is the primary problem faced by the development of low-altitude traffic.

[0003] At present, the applicable objects of the collision risk assessment method are mostly concentrated between unmanned aerial vehicles or between unmanned aerial vehicles and civil passenger aircraft, and there is a lack of a unified framework and method for collision risk assessment of multiple types of aircraft. In addition, the existing assessment method defaults the risk as a continuously deteriorating process, and does not consider the various risk containment means in modern air traffic management, so the assessment results obtained by this method are usually large, and do not have objectivity and practical significance.

[0004] In summary, there is an urgent need in the technical field to establish a unified framework suitable for collision risk assessment of multiple types of aircraft in low-altitude airspace, and to analyze the risk prevention and control means implemented by the management unit, to quantitatively assess the failure probability of each stage of the prevention and control means according to the time sequence of the accident, and to obtain a scientific and quantitative assessment method suitable for the above framework. SUMMARY

[0005] In order to solve the above problems, the present application provides a collision risk quantitative assessment method applied to low-altitude airspace.

[0006] According to the embodiment of the present application, a collision risk quantitative assessment method applied to low-altitude airspace is provided, which comprises:

[0007] Step S0, obtaining data information of low-altitude airspace, judging the type of invading aircraft relative to the host aircraft, if the invading aircraft belongs to a cooperative unmanned aerial vehicle, executing step S1, if it belongs to a non-cooperative unmanned aerial vehicle, executing step S2, and if it belongs to a manned aircraft, executing step S3, wherein the host aircraft is an unmanned aerial vehicle;

[0008] Step S1, determining the timeline for collision risk assessment of cooperative unmanned aerial vehicles and the conflict resolution measures involved, constructing a conflict probability assessment model based on track uncertainty in the encounter process and an arrival time estimation model based on heading adjustment, and statistically analyzing the DAA endogenous failure ratio, generating and outputting the collision probability of the host aircraft and the cooperative unmanned aerial vehicle, and executing step S4;

[0009] Step S2, determining the timeline for collision risk assessment of non-cooperative UAV and the involved conflict resolution measures, constructing a dynamic GAS model based on random position and heading to characterize the probability of procedural conflict resolution failure, generating and outputting the collision probability of the host aircraft with non-cooperative UAV, and performing step S4;

[0010] Step S3, determining the timeline for collision risk assessment of manned aircraft and the involved conflict resolution measures, constructing a GAS model based on the terminal area arrival and departure route structure and operation rules to quantify the probability of procedural conflict resolution failure; combining the above-mentioned arrival time estimation model based on heading adjustment to quantify the probability of tactical conflict resolution failure, generating and outputting the collision probability of the host aircraft with manned aircraft, and performing step S4;

[0011] Step S4, according to the received collision probability, evaluating the safety situation of the host aircraft with the invading aircraft, and providing basis for formulating safety prevention and control strategy;

[0012] The step S1 specifically comprises the following steps:

[0013] Step S1.1, determining the timeline for collision risk assessment of cooperative UAV and the involved conflict resolution measures, which include strategic conflict resolution, tactical conflict resolution and airborne collision avoidance of the host aircraft relative to the cooperative UAV;

[0014] Step S1.2, constructing a conflict probability evaluation model based on track uncertainty in the encounter process to obtain the failure probability of strategic conflict resolution;

[0015] Step S1.3, constructing a cooperative UAV-based arrival time estimation model based on heading adjustment to obtain the failure probability of tactical conflict resolution;

[0016] Step S1.4, based on the historical data statistics DAA endogenous failure rate to obtain the failure probability of airborne collision avoidance;

[0017] Step S1.5, based on the failure probabilities of strategic conflict resolution, tactical conflict resolution and airborne collision avoidance for cooperative UAV, obtaining the collision probability of the host aircraft with cooperative UAV, outputting and performing step S4.

[0018] Optionally, the step S1.2 specifically comprises the following steps:

[0019] Step S1.2.1, modeling the collision area of the host aircraft with the cooperative UAV, selecting a cylinder as the collision area, expanding the collision area based on the original size, and constructing the collision area of the host aircraft with the cooperative UAV as:

[0020]

[0021] where L en is the radius of the collision region, H en is the half-height of the collision region, R a is the actual radius of the host vehicle, H a is the actual half-height of the host vehicle, σ r is the radius expansion factor of the collision region, σ h is the height expansion factor of the collision region.

[0022] Step S1.2.2, the uncertainty of the trajectory of the cooperative unmanned aerial vehicle is modeled, and the error envelope of the cooperative unmanned aerial vehicle is elliptical in the horizontal direction, and the two axes of the ellipse are along the track direction and perpendicular to the track direction respectively;

[0023] Step S1.2.3, a coordinate system for calculating the collision probability of the host vehicle and the cooperative unmanned aerial vehicle is established, wherein first, a probability distribution model of the position of the host vehicle or the cooperative unmanned aerial vehicle in the global coordinate system is constructed respectively:

[0024]

[0025]

[0026] where P is the spatial distribution of the position of the host vehicle or the cooperative unmanned aerial vehicle, θ' is the heading angle of the host vehicle or the cooperative unmanned aerial vehicle at the predicted time, is the climb angle of the host vehicle or the cooperative unmanned aerial vehicle at the predicted time, is the predicted position of the host vehicle or the cooperative unmanned aerial vehicle in the global coordinate system, is the predicted error of the host vehicle or the cooperative unmanned aerial vehicle in the body coordinate system, matrix A = diag(σ1, σ2, σ3), diag represents a diagonal matrix, σ1, σ2 and σ3 are the position errors of the host vehicle or the cooperative unmanned aerial vehicle in three directions of the coordinate axis, N3 represents a three-dimensional normal distribution, and R is a coordinate transformation matrix;

[0027] Then the predicted error of the host vehicle or the cooperative unmanned aerial vehicle in the global coordinate system is:

[0028] Q = RAR T ;

[0029] Taking the host vehicle as a reference unmanned aerial vehicle and the intruding cooperative unmanned aerial vehicle as a random unmanned aerial vehicle, the relative position AP of the reference unmanned aerial vehicle and the random unmanned aerial vehicle and the combined error matrix M are:

[0030]

[0031] where P r is the position of the reference unmanned aerial vehicle, P s is the position of the random unmanned aerial vehicle, Q rQ s is the predicted error of the random UAV in the global coordinate system;

[0032] Step S1.2.4, coordinate transformation is performed to calculate the analytical solution of the collision probability in the three-dimensional space,

[0033] The conflict probability of the local UAV and the cooperative UAV in the horizontal direction is constructed as:

[0034]

[0035] where Δx c represents the integral interval length in the x-axis direction, Δy c represents the integral interval length in the y-axis direction, Δy is the distance of the random UAV relative to the reference UAV in the y-axis direction, p(x) and p(y) are the probability density functions of the standard normal distribution decoupled to the x-axis and the y-axis, respectively, x represents the function variable of p(x), and y represents the function variable of p(y);

[0036] The conflict probability of the local UAV and the cooperative UAV in the vertical direction can be defined as the integral of the one-dimensional normal distribution in the vertical direction of the combined conflict region:

[0037]

[0038] where h is the half height of the combined conflict region of the local UAV and the cooperative UAV, Δz is the distance of the random UAV relative to the reference UAV in the z-axis direction, p(z) is the one-dimensional normal distribution function, and z represents the function variable of p(z);

[0039] The conflict probability of the local UAV and the cooperative UAV in the encounter process, i.e., the strategic conflict resolution failure probability of the local UAV and the cooperative UAV, is:

[0040] Optionally, the step S1.3 specifically includes the following steps:

[0041] Step S1.3.1, the arrival time of the local UAV and the cooperative UAV is calculated, i.e., the time from the strategic conflict resolution failure to the adoption of the maneuvering action:

[0042]

[0043] where D x represents the allowed flight distance, and Δv is the relative speed between the local UAV and the cooperative UAV;

[0044] Step S1.3.2, a tactical conflict resolution failure probability model of the local UAV relative to the cooperative UAV is established:

[0045]

[0046] wherein, represents the probability of the local machine relative to the cooperative unmanned aerial vehicle tactical conflict mitigation failure, represents a normal distribution function.

[0047] Optionally, the step S1.4 comprises: based on historical data statistics DAA endogenous failure rate, to obtain the failure probability of airborne collision avoidance

[0048] The step S1.5 specifically comprises: based on the failure probability of the cooperative unmanned aerial vehicle for strategic conflict mitigation, tactical conflict mitigation and airborne collision avoidance, obtaining the collision probability of the local machine and the cooperative unmanned aerial vehicle:

[0049]

[0050] Output the obtained collision probability of the local machine and the cooperative unmanned aerial vehicle, and perform step S4.

[0051] Optionally, the step S2 specifically comprises the following steps:

[0052] Step S2.1, determining the timeline for non-cooperative unmanned aerial vehicle collision risk assessment, and the conflict mitigation measures involved, which includes the program mitigation of the local machine relative to the non-cooperative unmanned aerial vehicle;

[0053] Step S2.2, setting the area swept by the local machine at a certain time as a rectangle as a combined collision area, and the non-cooperative unmanned aerial vehicle overlapping with the combined collision area as an intruder;

[0054] Step S2.3, calculating the volume of the area swept by the local machine as:

[0055]

[0056] wherein, is the radius of the combined collision area of the local machine and the non-cooperative unmanned aerial vehicle, is the half height of the combined collision area of the local machine and the non-cooperative unmanned aerial vehicle, Δv is the relative speed between the local machine and the non-cooperative unmanned aerial vehicle, t mission is the time of task execution;

[0057] Step S2.4, determining whether the non-cooperative unmanned aerial vehicle has a collision risk:

[0058]

[0059] wherein, (x′ j , y′ j , z′ jrepresents the spatial position of the jth non-cooperative UAV, represents the spatial region swept by the host relative to the jth non-cooperative UAV;

[0060] Step S2.5, based on historical data, assesses the number of non-cooperative UAVs N that occur during the execution of the task uav The program mitigation failure probability of the non-cooperative UAV is:

[0061]

[0062] Step S2.6, the program mitigation failure probability of the host relative to the non-cooperative UAV is the collision probability of the host and the non-cooperative UAV:

[0063]

[0064] wherein, is the probability of non-cooperative UAV program conflict mitigation failure;

[0065] The output collision probability of the host and the non-cooperative UAV is obtained, and step S4 is performed.

[0066] Optionally, the step S3 specifically comprises the following steps:

[0067] Step S3.1, determine the timeline for manned aircraft collision risk assessment and the conflict mitigation measures involved, including the host relative to manned aircraft program mitigation and tactical conflict mitigation;

[0068] Step S3.2, determine the region swept by the host relative to the manned aircraft:

[0069]

[0070] wherein, is the radius of the combined collision region of the host and the manned aircraft, is the half-height of the combined collision region of the host and the manned aircraft, and Δv is the relative speed between the host and the manned aircraft, t mission is the time of task execution;

[0071] Step S3.3, calculate the probability that the take-off and landing flight of the manned aircraft falls into the region swept by the host relative to the manned aircraft:

[0072] L sw / L tol

[0073] wherein, L sw is the length of the manned aircraft flight segment in the region swept by the host, and L tol is the length of the overall take-off and landing flight path of the manned aircraft;

[0074] Step S3.4, for the entire airport terminal area, the probability of failure of the program to resolve the conflict of the aircraft relative to the manned aircraft is:

[0075]

[0076] wherein, is the length of the sweep of the aircraft relative to the mth landing and takeoff runway, is the total length of the mth landing and takeoff runway, and M' represents the total number of landing and takeoff runways in the entire airport terminal area during the mission of the aircraft;

[0077] Step S3.5, the probability of failure of the tactical conflict resolution of the aircraft relative to the manned aircraft is:

[0078]

[0079] wherein, represents a normal distribution function, t ap ' represents the time between the failure of the program to resolve the conflict and the taking of a maneuvering action;

[0080] Step S3.6, the collision probability of the aircraft and the manned aircraft is:

[0081]

[0082] The output collision probability of the aircraft and the manned aircraft is obtained, and step S4 is performed.

[0083] The application provides a collision risk quantitative evaluation method applied to low-altitude airspace, including an air collision risk evaluation framework for multiple types of aircraft, a collision risk quantitative evaluation method of an aircraft and a cooperative unmanned aerial vehicle, a collision risk quantitative evaluation method of an aircraft and a non-cooperative unmanned aerial vehicle, and a collision risk quantitative evaluation method of an aircraft and a manned aircraft. The collision risk quantitative evaluation method of the aircraft and the cooperative unmanned aerial vehicle involves three links of strategic conflict resolution, tactical conflict resolution and airborne collision avoidance, respectively proposes a conflict probability evaluation model in an encounter process based on track uncertainty, an arrival time estimation model based on heading adjustment, and a DAA system failure evaluation method to calculate the failure probability of the three links, so as to obtain the collision probability of the aircraft relative to the cooperative unmanned aerial vehicle. The collision risk quantitative evaluation method of the aircraft and the non-cooperative unmanned aerial vehicle involves a program conflict resolution link, and a dynamic GAS model based on random position and heading is proposed to calculate the failure probability of the link, so as to obtain the collision probability of the aircraft relative to the non-cooperative unmanned aerial vehicle. The collision risk quantitative evaluation method of the aircraft and the manned aircraft involves two links of program conflict resolution and tactical conflict resolution, and respectively uses a GAS model based on the structure of the approach and departure runway and the operation rule and an arrival time estimation model based on the heading adjustment to calculate the failure probability of the two links, so as to obtain the collision probability of the aircraft relative to the manned aircraft.

[0084] Compared with the prior art, the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application has at least the following beneficial effects: a collision risk quantitative evaluation method for multiple types of aircraft is introduced, collision accidents are divided into several stages based on the risk prevention and control means currently implemented by the management unit, the failure risk of mitigation measures is modeled at each stage, and various environmental factors such as wind error, navigation error, flight technology error, communication delay, reaction delay, etc. are considered, so that the evaluation result is more objective and close to the actual situation. In addition, referring to the risk evaluation method of high-altitude civil aviation, the quantitative index of risk is defined as "number of collision accidents / hour" to connect the air collision risk and the ground loss cost, and lay a foundation for subsequent integrated risk evaluation of air-ground. BRIEF DESCRIPTION OF DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. The features and advantages of the present application can be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as any limitation on the present application. For those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0086] Figure 1 A framework diagram of the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application.

[0087] Figures 2a to 2c A relative geometric relationship diagram during the encounter of two aircraft in an example of the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application.

[0088] Figure 3a A two-dimensional plane diagram after geometric coordinate conversion in an example of the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application.

[0089] Figure 3b A Z-axis diagram after geometric coordinate conversion in an example of the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application. Figure 4 A minimum distance diagram required for adjusting the heading in an example of the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application.

[0090] Figure 5 An approach time estimation diagram in an example of the collision risk quantitative evaluation method applied to low-altitude airspace provided by the embodiment of the present application.

[0091] Figure 6A schematic diagram of a dynamic GAS model based on random position and heading in an example of a collision risk quantitative assessment method applied to low-altitude airspace provided in accordance with an embodiment of the present invention.

[0092] Figure 7 A schematic diagram of a GAS model based on an arrival and departure route structure in an example of a collision risk quantitative assessment method applied to low-altitude airspace provided in accordance with an embodiment of the present invention.

[0093] Figure 8 The present invention provides a flowchart of a method for quantitatively assessing collision risk in low-altitude airspace according to an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein can be combined with each other without conflict.

[0095] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0096] A method for quantitatively assessing collision risk in low-altitude airspace provided in accordance with an embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0097] like Figures 1 to 8 As shown, a collision risk quantitative assessment method for low-altitude airspace provided in accordance with an embodiment of the present invention is a collision risk assessment framework for cooperative UAVs, non-cooperative UAVs, and manned aircraft, including the following steps:

[0098] Step S0: Obtain low-altitude airspace data and determine the type of intruding aircraft relative to the aircraft. If the intruding aircraft is a cooperative UAV, proceed to step S1; if it is a non-cooperative UAV, proceed to step S2; if it is a manned aircraft, proceed to step S3. Wherein, the aircraft is a UAV.

[0099] Step S1: Determine the timeline for cooperative UAV collision risk assessment and the conflict mitigation measures involved. Build a conflict probability assessment model based on trajectory uncertainty during encounters to quantify the probability of strategic conflict mitigation failure. Build an arrival time estimation model based on heading adjustment to quantify the probability of tactical conflict mitigation failure. Utilize DAA historical failure rates to quantify the probability of airborne collision avoidance failure. Generate and output the collision probability between the aircraft and the cooperative UAV, and execute step S4.

[0100] In step S2, determine the timeline for the collision risk assessment of non-cooperative UAVs and the conflict mitigation measures involved. Construct a dynamic GAS model based on random positions and headings to characterize the probability of procedural conflict mitigation failure. Generate and output the collision probability between the aircraft and the non-cooperative UAV, and proceed to step S4.

[0101] Step S3 determines the timeline for manned aircraft collision risk assessment. A GAS model is established based on the terminal area arrival and departure route structure and operating rules to quantify the probability of procedural conflict mitigation failure. The aforementioned arrival time estimation model based on heading adjustment is combined to quantify the probability of tactical conflict mitigation failure. The collision probability between the aircraft and the manned aircraft is generated and output, and step S4 is executed. The manned aircraft can be, for example, a passenger airliner, transport aircraft, or other manned aircraft.

[0102] Step S4: Based on the received collision probability, the safety situation of the aircraft and the intruding aircraft is evaluated to provide a basis for formulating a safety prevention and control strategy.

[0103] like Figure 1 、 Figures 2a to 2c 、 Figures 3a to 3b 、 Figure 4 and Figure 5 As shown, step S1 specifically includes the following steps, which are used to calculate the collision probability between the local aircraft and the cooperative UAV.

[0104] Step S1.1: Determine the timeline for the cooperative UAV collision risk assessment and the conflict mitigation measures involved. The conflict mitigation measures include strategic conflict mitigation, tactical conflict mitigation, and airborne collision avoidance of the aircraft relative to the cooperative UAV.

[0105] Step S1.2, construct a conflict probability assessment model during the encounter process based on trajectory uncertainty to quantify the probability of failure of strategic conflict mitigation, which specifically includes the following sub-steps.

[0106] Step S1.2.1 models the collision area between the local aircraft and the cooperative UAV. Referring to the definition of the risk envelope (DAA WellClear) in the Detect-and-Avoid document released by NASA, this implementation method selects a cylinder as the collision area. Taking into account the non-horizontal maneuvers such as climbing and descending during the movement of the UAV and the interference of environmental factors such as wind and air pressure that may be encountered, the collision area is expanded accordingly based on the original size. Combined with the concept of the minimum envelope circle, the collision area between the local aircraft and the cooperative UAV is constructed as follows:

[0107]

[0108] Among them, (L en , H en) are the radius and half height of the collision area, (R a , H a ) are the actual radius and half-height of the aircraft, (σ r ,σ h ) are the radius and height expansion factor of the collision area respectively.

[0109] In step S1.2.2, the uncertainty of the cooperative UAV's trajectory is modeled. The error envelope of the cooperative UAV is approximately elliptical in the horizontal direction, with the two axes of the ellipse along and perpendicular to the track. According to the Performance-Based Navigation (PBN) Manual, the overall positioning error of the cooperative UAV is determined by the total system error (TSE), which is the sum of the navigation system error (NSE), the flight technical error (FTE), and the path definition error (PDE). The path definition error is the difference between the defined path and the desired path and is usually negligible. The flight technical error typically increases along the track with flight time, while the navigation system error is usually constant. Therefore, the error envelope of the cooperative UAV is approximately elliptical in the horizontal direction, with the two axes of the ellipse along and perpendicular to the track.

[0110] Step S1.2.3, establish a coordinate system for calculating the collision probability between the local aircraft and the cooperative UAV. Assume that the position error of the aircraft (i.e., the local aircraft or the cooperative UAV) follows a Gaussian distribution, and assume that the error is independent in the three directions of the aircraft's body coordinate axis, namely σ1, σ2, and σ3. Construct a position distribution model for the local aircraft or the cooperative UAV in the global coordinate system. After defining the prediction time span, the predicted position of the local aircraft or the cooperative UAV in the global coordinate system is The prediction error of the local or cooperative UAV in the body coordinate system is The coordinate transformation matrix is ​​R, and the probability distribution of the position of the local or cooperative UAV in the global coordinate system is obtained:

[0111]

[0112]

[0113] Where P is the spatial distribution of the position of the local or cooperative UAV, θ′ is the heading angle of the UAV (i.e., the local or cooperative UAV) at the prediction time, is the climb angle of the UAV (i.e., own aircraft or cooperative UAV) at the prediction time, Matrix A=diag(σ1, σ2, σ3), diag represents a diagonal matrix, and N3 represents a three-dimensional normal distribution.

[0114] Therefore, the prediction error of the local or cooperative UAV in the global coordinate system can be obtained as: Q = RAR T .

[0115] The construction method of the position probability distribution of the local and cooperative unmanned aerial vehicle in the global coordinate system is the same as the above method. In the calculation of the collision probability of the local and cooperative unmanned aerial vehicle, in this embodiment, the local unmanned aerial vehicle is taken as the reference unmanned aerial vehicle, the invading cooperative unmanned aerial vehicle is taken as the random unmanned aerial vehicle, the prediction error of the reference unmanned aerial vehicle is superimposed on the prediction error of the random unmanned aerial vehicle, the collision region of the random unmanned aerial vehicle is superimposed on the collision region of the reference unmanned aerial vehicle, and the collision coordinate system is constructed with the reference unmanned aerial vehicle as the origin and the random unmanned aerial vehicle as the mass point, as shown in Figure 2a and Figure 2b The relative position of the reference unmanned aerial vehicle and the random unmanned aerial vehicle and the combined error matrix are as follows:

[0116]

[0117] Wherein, P r is the position of the reference unmanned aerial vehicle, P s is the position of the random unmanned aerial vehicle, Q r and Q s are the prediction errors of the reference unmanned aerial vehicle and the random unmanned aerial vehicle in the global coordinate system respectively, ΔP is the relative position of the reference unmanned aerial vehicle and the random unmanned aerial vehicle, and M is the combined error matrix of the reference unmanned aerial vehicle and the random unmanned aerial vehicle.

[0118] Based on the above idea, the instantaneous collision probability of the reference unmanned aerial vehicle and the random unmanned aerial vehicle can be calculated. The main purpose of this model is to calculate the total collision probability of the reference unmanned aerial vehicle and the random unmanned aerial vehicle during the entire encounter process (in a period of time). Therefore, it is assumed that the speed and error of the reference unmanned aerial vehicle and the random unmanned aerial vehicle are constant during the encounter. In this embodiment, in order to make the evaluation result more conservative, the maximum error is modeled. The combined collision region is extended in the direction parallel to the relative speed to form an extended collision region, and the length thereof depends on the encounter time, as shown in Figure 2c The collision probability (the probability of strategic conflict mitigation failure) between the reference unmanned aerial vehicle and the random unmanned aerial vehicle is equal to the integral of the error probability density function in the extended region. Next, the 3-D space will be decoupled by the coordinate transformation idea and the analytical solution of the integral will be determined.

[0119] Step S1.2.4, coordinate system transformation is performed, and the analytical solution of the collision probability in the three-dimensional space is calculated.

[0120] Let p and q represent the positions of the aircraft (i.e., the reference unmanned aerial vehicle or the random unmanned aerial vehicle) in the original coordinate system and the transformed coordinate, respectively. It is assumed that q = Tp, where T is the change matrix to be determined, W represents the inverse matrix of the change matrix T, and W = T -1 , then p = Wq, and the combined error in the change matrix is Based on Cholesky theorem, the combined error matrix M = LL T , where L is a lower triangular matrix, the combined error in the transformation matrix can be expressed as:

[0121]

[0122] where H represents an orthogonal rotation matrix (HH T = I), I represents a unit matrix.

[0123] So far, the combined error has been transformed from the ellipsoid of the original coordinate system to the unit sphere of the new coordinate system (decoupled), and the combined collision area has been transformed from the cylinder of the original coordinate system to the ellipsoid. Since the aircraft maintains horizontal flight during the encounter, it can be assumed that the horizontal and vertical motions are independent, and the above transformation can be decoupled into the collision probabilities in the horizontal and vertical directions by simple scaling, as shown in Figure 3a and Figure 3b Then the embodiment calculates the analytical solutions of the two directions respectively, and due to the independence, the total probability of the two directions is the product of the two.

[0124] As shown in Figure 3a , for the horizontal probability, first, the transformed coordinate system is rotated using the above-mentioned orthogonal rotation matrix H, so that the relative velocity of the two aircraft, the reference UAV and the random UAV, is taken as the positive direction of the x-axis. Assuming that the relative velocity Δv = v r -v s between the two aircraft, the partially transformed velocity in the transformed coordinate system is then

[0125]

[0126] where v r is the speed of the reference UAV, v s is the speed of the random UAV, and Δv x represents the partially transformed velocity matrix in the x-axis component, and Δv y represents the partially transformed velocity matrix in the y-axis component.

[0127] Then the integral expression for calculating the two-dimensional collision probability of the two aircraft and the upper and lower limits of the integral are determined. As shown in Figure 3a , the boundary on the y-axis is the maximum and minimum values of the elliptical boundary, and the integral region on the x-axis is the area swept by the relative velocity within the encounter time. Let Δp c and Δq c represent the boundary distance on the y-axis in the original coordinate system and the transformed coordinate system respectively, then:

[0128]

[0129] where, is the radius of the combined collision region of the host and cooperative UAVs, W = T -1 = LH -1 , if let Δq c = [Δx c , Δy c ], and the discriminant is obtained after squaring the above formula, and let the discriminant be equal to zero, the element Δy c in the vector Δq c can be obtained, and the other element Δx c is related to the encounter time, as shown in the following formula:

[0130]

[0131] where, Δx c represents the integral interval length in the x-axis direction, Δy c represents the integral interval length in the y-axis direction, a, b, c represent the elements of the matrix W T W, which are known quantities. d sr is the distance swept by the relative velocity within the encounter period, Δv is the relative velocity, t encounter is the encounter time, is the radius of the combined collision region of the host and cooperative UAVs. In summary, the conflict probability of the host and cooperative UAVs in the horizontal direction can be represented as:

[0132]

[0133] where, Δy is the distance of the random UAV relative to the reference UAV in the y-axis direction, p(x) and p(y) are the probability density functions of the standard normal distribution decoupled to the x-axis and y-axis, respectively, x represents the function variable of p(x), and y represents the function variable of p(y).

[0134] The conflict probability of the host and cooperative UAVs in the vertical direction can be defined as the integral of the one-dimensional normal distribution in the vertical direction of the combined conflict region:

[0135]

[0136] where, is the half-height of the combined collision region of the host and cooperative UAVs, Δz is the distance of the random UAV relative to the reference UAV in the z-axis direction, p(z) is a one-dimensional normal distribution function, and z represents the function variable of p(z).

[0137] In summary, the conflict probability in the encounter process, i.e., the probability of failure of the strategic conflict mitigation of the host and cooperative UAVs, is:

[0138] Step S1.3, constructing a heading adjustment based arrival time estimation model of the cooperative UAV to quantify the tactical conflict mitigation failure probability, as shown in Figure 4 and Figure 5 , comprising the following sub-steps:

[0139] Step S1.3.1, calculating the arrival time of the ownship and the cooperative UAV, i.e. the time from the strategic conflict mitigation failure to the taking of the maneuver action:

[0140]

[0141] wherein D x represents the allowed flight distance, and Δv is the relative speed between the ownship and the cooperative UAV.

[0142] This step S1.3.1 specifically comprises the following steps.

[0143] Step S1.3.1.1, calculating the minimum maneuver distance required for the ownship to make the heading adjustment.

[0144] During the heading adjustment of the ownship, it is assumed that the maximum turning maximum inclination that the ownship can take is The maximum lateral turning overload n can be expressed as The minimum turning radius of the ownship is:

[0145]

[0146] wherein v re is the relative speed between the ownship and the cooperative UAV, g is the gravitational acceleration, and R turn is the minimum turning radius of the ownship.

[0147] As shown in Figure 4 , when the ownship makes a turn with the minimum radius R turn , if the trajectory of the ownship overlaps with the combined collision region, the distance L m between the ownship and the intruding cooperative UAV at this time is the minimum maneuver distance. Assuming that the heading angle of the relative speed Δv between the ownship and the cooperative UAV is θ, and the azimuth angle of the relative speed Δv between the ownship and the cooperative UAV is β, taking the x-axis as the directional reference, the included angle between the heading angle and the azimuth angle of the relative speed is β1 = |β - θ|. Then, the minimum maneuver distance is calculated in a triangle with γ1 as the vertex angle by using the triangle theorem, and the value of γ1 depends on the heading angle θ, if θ > β, then γ1 = π / 2 + β1, if θ ≤ β, then γ1 = π / 2 - β1. In the above triangle, the minimum maneuver distance L m can be obtained by:

[0148]

[0149] where γ1represents the angle of the triangle (as shown in Figure 4 ), is the radius of the combined collision area of the ownship and the cooperative UAV.

[0150] Step S1.3.1.2, the relative position relationship between the ownship and the cooperative UAV is shown in Figure 5 , the relative distance L re between the two aircrafts and the allowed flight distance D x can also be described by the triangle theorem, and the angle γ2of the triangle can be expressed as:

[0151]

[0152] where γ2represents the angle of the triangle (as shown in Figure 5 ), θ is the heading angle of the relative velocity Δv between the ownship and the cooperative UAV, β is the azimuth angle between the two aircrafts, L re is the relative distance between the two aircrafts, L m is the minimum maneuvering distance.

[0153] Then, the allowed flight distance D x , i.e., the distance of the ownship flying along its original trajectory without taking avoidance maneuvers, is calculated using the cosine theorem:

[0154]

[0155] Step S1.3.1.3, the arrival time of the ownship and the cooperative UAV, i.e., the time from the strategic conflict resolution failure to the taking of the maneuvering action, is calculated. The arrival time t ap between the two aircrafts can be obtained by the arrival distance, i.e., the allowed flight distance D x and the relative velocity:

[0156]

[0157] where Δv is the relative velocity between the ownship and the cooperative UAV.

[0158] Step S1.3.2, a model for calculating the probability of the tactical conflict resolution failure of the ownship relative to the cooperative UAV is established.

[0159] The reason for the tactical conflict resolution failure: due to the delay, the time when the tactical conflict resolution measure takes effect in the actual flight is greater than the time when the separation loss occurs between the two aircrafts, i.e., the aircrafts have reached the separation loss state before completing the avoidance action. Based on the above idea, it is assumed that the delay time obeys the normal distribution, and its distribution function is The time of arrival of both aircraft, i.e. the time between the failure of the strategic conflict resolution and the taking of the maneuvering action, is defined as t ap If the tactical conflict resolution fails, i.e. the delay time is greater than t ap Otherwise, the tactical conflict resolution is effective and no collision risk is considered. Therefore, the probability of failure of the tactical conflict resolution can be expressed as:

[0160]

[0161] wherein represents the probability of failure of the tactical conflict resolution of the own aircraft with respect to the cooperative UAV.

[0162] Step S1.4, the failure rate of the DAA is calculated based on the historical data statistics, to obtain the failure probability of the onboard collision avoidance of the own aircraft with respect to the cooperative UAV

[0163] Step S1.5, the product of the failure probabilities of the above-mentioned resolution measures is obtained, to obtain the collision probability of the own aircraft with the cooperative UAV:

[0164]

[0165] wherein are the failure probabilities of the strategic conflict resolution, the tactical conflict resolution and the onboard collision avoidance of the own aircraft with respect to the cooperative UAV, respectively. The obtained failure probability is output and step S4 is executed.

[0166] As shown in Figure 1 , Figure 6 and Figure 8 , step S2 is to evaluate the collision probability of the own aircraft with the non-cooperative UAV. For the non-cooperative UAV, it is possible to appear accidentally during the execution of the task of the own aircraft. Since its flight plan and trajectory are not easy to determine, unlike the above numerical calculation method, a dynamic GAS model based on random position and heading is constructed to calculate the collision probability of the non-cooperative UAV. Specifically, the following steps are included:

[0167] Step S2.1, the timeline for the collision risk assessment of the non-cooperative UAV is determined, and the conflict resolution measures involved are determined. The conflict resolution measures include the procedural resolution of the own aircraft with respect to the non-cooperative UAV.

[0168] Step S2.2, it is assumed that the non-cooperative UAV is similar to the gas molecules randomly distributed in the task airspace, with a random horizontal speed and no tendency to climb or descend. The area swept by the own aircraft within a certain time is regarded as a rectangle as a combined collision area, and the non-cooperative UAV overlapping with the combined collision area is the invader that may have a conflict.

[0169] Step S2.3, calculate the swept volume of the host vehicle. Assume is the radius and half-height of the combined collision region of the host vehicle and the non-cooperative UAV, Δv is the relative velocity between the host vehicle and the non-cooperative UAV, t mission is the time of mission execution. Due to the effect of relative velocity, the swept volume of the host vehicle relative to different intruders will be different, so this model is a dynamic GAS model, and the volume of the swept volume is:

[0170]

[0171] Step S2.4, determine whether there is a collision risk for a non-cooperative UAV. Determine whether there is a conflict between the host vehicle and the non-cooperative UAV, when the host vehicle and the non-cooperative UAV overlap during the execution of the mission, i.e. the non-cooperative UAV is located in the swept volume of the host vehicle, it is considered that there is a conflict risk between the two vehicles, let Otherwise, let The logic of determining whether there is a conflict between the host vehicle and the non-cooperative UAV is shown in the following formula:

[0172]

[0173] where (x′ j , y′ j , z′ j ) represents the spatial position of the jth non-cooperative UAV, represents the swept volume of the host vehicle relative to the jth non-cooperative UAV.

[0174] Step S2.5, calculate the collision risk of the non-cooperative UAV in the airspace, i.e. the program mitigation failure probability. Based on historical data, estimate the number of non-cooperative UAVs that may appear during mission execution, let N uav represent, then the program mitigation failure probability of the non-cooperative UAV can be expressed as:

[0175]

[0176] Step S2.6, the program mitigation failure probability of the host vehicle relative to the non-cooperative UAV is the collision probability of the host vehicle and the non-cooperative UAV:

[0177]

[0178] where, is the probability of program conflict mitigation failure for non-cooperative UAVs.

[0179] Step S3 is to evaluate the collision probability of the host vehicle and manned aircraft, which specifically includes a dynamic GAS model based on random position and heading, as follows: Figure 1 ​、 Figure 7 and Figure 8 as shown, comprising the following steps:

[0180] Step S3.1, determine the timeline of the risk assessment of the manned-unmanned aircraft collision and the involved conflict resolution measures. The conflict resolution measures include the program resolution of the unmanned aircraft relative to the manned aircraft and the tactical conflict resolution.

[0181] Step S3.2, determine the area swept by the unmanned aircraft relative to the manned aircraft. Assuming is the radius and half-height of the area swept by the unmanned aircraft and the manned aircraft combination, t mission is the time of task execution, and Δv is the relative speed between the unmanned aircraft and the manned aircraft. For a certain runway of the airport, the take-off and landing heading is fixed, but the difference in the speed of the manned aircraft leads to different relative speeds, so the swept area is different, and the corresponding calculation formula is:

[0182]

[0183] Step S3.3, calculate the probability of the take-off and landing flight falling into the area swept by the unmanned aircraft relative to the manned aircraft.

[0184] Assuming that the position of the take-off and landing route of the manned aircraft is uniformly distributed, the length of the leg in the swept area is L sw , and the length of the overall take-off and landing route is L tol , then the probability of the manned aircraft being exactly in the swept area is L sw / L tol .

[0185] Step S3.4, for the entire airport terminal area, calculate the collision probability of the unmanned aircraft with the manned aircraft during the task execution, i.e., the probability of failure of the program resolution. Assuming that there are M' take-off and landing routes in the entire airport terminal area during the task process of the unmanned aircraft, then the probability of failure of the program resolution of the unmanned aircraft relative to the manned aircraft is:

[0186]

[0187] wherein, is the length swept by the unmanned aircraft relative to the mth take-off and landing route, is the total length of the mth take-off and landing route.

[0188] Step S3.5, the probability of failure of the tactical conflict resolution of the unmanned aircraft relative to the manned aircraft can be obtained based on the arrival time estimation model of the heading adjustment in step S1.3 above:

[0189]

[0190] wherein, denotes a normal distribution function, tap P represents the time between program mitigation failure and taking a maneuver action. This is not expanded here.

[0191] Step S3.6, the collision probability of the manned aircraft is the product of the program mitigation failure probability of the unmanned aircraft relative to the manned aircraft and the tactical conflict mitigation failure probability, and the collision probability of the unmanned aircraft and the manned aircraft is:

[0192]

[0193] Wherein, The obtained collision probability of the unmanned aircraft and the manned aircraft is output, and step S4 is performed.

[0194] Based on the same technical scheme, the application further discloses a software system of the method, and a collision risk assessment system applied to low-altitude airspace, which comprises: a collision risk assessment module of a cooperative unmanned aircraft, program code is written based on the above model, and the strategic conflict mitigation failure probability, the tactical conflict mitigation failure probability, and the airborne conflict avoidance failure probability are calculated respectively, and the product of the three is the overall collision risk. A collision risk assessment module of a non-cooperative unmanned aircraft, program code is written based on the above model, and the program mitigation failure probability is calculated, which is regarded as the overall collision risk. A collision risk assessment module of a manned aircraft, program code is written based on the above model, and the program mitigation failure probability and the tactical conflict mitigation failure probability are calculated, which are regarded as the overall collision risk.

[0195] In the above system, the data processing procedures and methods of each module are consistent with the corresponding steps, which are not described here.

[0196] All the optional technical solutions can be combined to form optional embodiments of the application, which are not described one by one here.

[0197] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0198] The above is only the preferred specific implementation of the application, but the protection scope of the application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. A method for quantitatively assessing collision risk in low-altitude airspace, characterized in that: include: Step S0: Obtain low-altitude airspace data and determine the type of intruding aircraft relative to the aircraft. If the intruding aircraft is a cooperative UAV, proceed to step S1; if it is a non-cooperative UAV, proceed to step S2; if it is a manned aircraft, proceed to step S3, where the aircraft is a UAV. Step S1: Determine the timeline for cooperative UAV collision risk assessment and the conflict mitigation measures involved, construct a collision probability assessment model based on trajectory uncertainty and an arrival time estimation model based on heading adjustment during the encounter process, calculate the DAA endogenous failure rate, generate and output the collision probability between the aircraft and the cooperative UAV, and execute step S4; Step S2: Determine the timeline for the non-cooperative UAV collision risk assessment and the conflict mitigation measures involved, construct a dynamic GAS model based on random positions and headings to characterize the probability of program conflict mitigation failure, generate and output the collision probability between the aircraft and the non-cooperative UAV, and execute step S4; Step S3: Determine the timeline for manned-aircraft collision risk assessment and the conflict mitigation measures involved, and establish a GAS model based on the terminal area arrival and departure route structure and operating rules to quantify the probability of procedural conflict mitigation failure; Combine the above-mentioned arrival time estimation model based on heading adjustment to quantify the probability of tactical conflict mitigation failure, generate and output the collision probability between the own aircraft and the manned aircraft, and execute step S4; Step S4: Based on the received collision probability, the safety situation of the aircraft and the intruding aircraft is evaluated to provide a basis for formulating a safety prevention and control strategy; Wherein, the step S1 specifically includes the following steps: Step S1.1: Determine the timeline for the cooperative UAV collision risk assessment and the conflict mitigation measures involved, including strategic conflict mitigation, tactical conflict mitigation, and airborne collision avoidance of the aircraft relative to the cooperative UAV. Step S1.2: construct a conflict probability assessment model during the encounter process based on trajectory uncertainty to obtain the failure probability of strategic conflict mitigation; Step S1.3: construct an arrival time estimation model based on heading adjustment for cooperative UAVs to obtain the failure probability of tactical conflict mitigation; Step S1.4, calculating the DAA intrinsic failure rate based on historical data to obtain the failure probability of airborne collision avoidance; Step S1.5, based on the failure probability of strategic conflict mitigation, tactical conflict mitigation and airborne collision avoidance for cooperative UAVs, obtain the collision probability between the aircraft and the cooperative UAV, output it and execute step S4.

2. The method for quantitatively assessing collision risk in low-altitude airspace according to claim 1, characterized in that: The step S1.2 specifically includes the following steps: In step S1.2.1, the collision area between the local aircraft and the cooperative UAV is modeled. A cylinder is selected as the collision area, and the collision area is expanded based on the original size. The collision area between the local aircraft and the cooperative UAV is constructed as follows: Among them, L en is the radius of the collision area, H en is the half height of the collision area, R a is the actual radius of the machine, H a is the actual half-height of the machine, σ r is the radius expansion factor of the collision area, σ h is the height expansion factor of the collision area; Step S1.2.2, modeling the uncertainty of the cooperative UAV's trajectory. The error envelope of the cooperative UAV is elliptical in the horizontal direction, with the two axes of the ellipse along the track direction and perpendicular to the track direction respectively. Step S1.2.3: Establish a coordinate system for calculating the collision probability between the local aircraft and the cooperative UAV. First, construct a probability distribution model for the position of the local aircraft or the cooperative UAV in the global coordinate system: Where P is the spatial distribution of the position of the local or cooperative UAV, θ′ is the heading angle of the local or cooperative UAV at the prediction time, is the climb angle of the local UAV or cooperative UAV at the predicted moment, is the predicted position of the local or cooperative UAV in the global coordinate system, is the prediction error of the local or cooperative UAV in the body coordinate system, Matrix A = diag(σ1,σ2,σ3), where diag represents a diagonal matrix, σ1, σ2, and σ3 represent the position errors of the local or cooperative UAV in the three coordinate axes, N3 represents the three-dimensional normal distribution, and R is the coordinate transformation matrix. Then the prediction error of the local or cooperative UAV in the global coordinate system is: Q=RAR T ; The local drone is regarded as the reference drone, and the invading cooperative drone is regarded as the random drone. The relative position ΔP and the combined error matrix M of the reference drone and the random drone are: Among them, P r is the reference position of the UAV, P s is the position of the random drone, Q r is the prediction error of the reference UAV in the global coordinate system, Q s is the prediction error of the random UAV in the global coordinate system; Step S1.2.4, perform coordinate system transformation and calculate the analytical solution of collision probability in three-dimensional space. The collision probability between the local UAV and the cooperative UAV in the horizontal direction is constructed as: Where Δx c Indicates the length of the integration interval in the x-axis direction, Δy c represents the length of the integration interval in the y-axis direction, Δy is the distance of the random UAV relative to the reference UAV in the y-axis direction, p(x) and p(y) are the probability density functions of the standard normal distribution decoupled to the x-axis and y-axis respectively, x represents the function variable of p(x), and y represents the function variable of p(y); The probability of collision between the aircraft and the cooperative UAV in the vertical direction can be defined as the integral of the one-dimensional normal distribution in the vertical direction of the combined conflict area: in, is the half height of the collision area between the local drone and the cooperative drone combination, Δz is the distance between the random drone and the reference drone in the z-axis direction, p(z) is the one-dimensional normal distribution function, and z represents the function variable of p(z); The probability of conflict between the host aircraft and the cooperative UAV during the encounter, that is, the probability of failure of strategic conflict mitigation between the host aircraft and the cooperative UAV, is:

3. The method for quantitatively assessing collision risk in low-altitude airspace according to claim 2, characterized in that: The step S1.3 specifically includes the following steps: Step S1.3.1, calculate the arrival time of the own aircraft and the cooperative UAV, that is, the time from the failure of strategic conflict mitigation to the taking of maneuvering action: Among them, D x represents the allowed flight distance, Δv is the relative speed between the host and the cooperative UAV; Step S1.3.2: Establish a probability model for the failure of the tactical conflict mitigation of the aircraft relative to the cooperative UAV: in, represents the probability of failure of tactical conflict mitigation of the own aircraft relative to the cooperative UAV, Represents the normal distribution function.

4. The method for quantitatively assessing collision risk in low-altitude airspace according to claim 3 is characterized in that: The step S1.4 includes calculating the DAA intrinsic failure rate based on historical data to obtain the failure probability of airborne collision avoidance The step S1.5 specifically includes obtaining the collision probability between the aircraft and the cooperative UAV based on the failure probabilities of strategic conflict mitigation, tactical conflict mitigation, and airborne collision avoidance for the cooperative UAV: Output the obtained collision probability between the local UAV and the cooperative UAV, and execute step S4.

5. The method for quantitatively assessing collision risk in low-altitude airspace according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S2.1, determining the timeline for the non-cooperative UAV collision risk assessment and the conflict mitigation measures involved, including the own aircraft's procedural mitigation measures relative to the non-cooperative UAV; Step S2.2: Set the area swept by the UAV at a relative speed within a certain period of time as a rectangle, which is used as the combined collision area. The non-cooperative UAVs that overlap with the combined collision area are considered intruders. Step S2.3, calculate the volume of the area swept by the machine as: in, The radius of the combined collision zone between the own aircraft and the non-cooperative drone, is the half height of the collision area between the local UAV and the non-cooperative UAV, Δv is the relative speed between the local UAV and the non-cooperative UAV, t mission The time for task execution; Step S2.4, determine whether there is a collision risk with the non-cooperative UAV: Among them, (x j ′ ,y j ′ ,z j ′ ) represents the spatial position of the jth non-cooperative UAV, represents the spatial area swept by the aircraft relative to the jth non-cooperative UAV; Step S2.5: Evaluate the number of non-cooperative drones N that appear during the mission based on historical data. uav , the probability of program mitigation failure of non-cooperative UAV is: Step S2.6: The failure probability of the program mitigation of the own aircraft relative to the non-cooperative UAV is the collision probability of the own aircraft and the non-cooperative UAV: in, The probability of failure of conflict mitigation procedures for non-cooperative UAVs; Output the obtained collision probability between the local UAV and the non-cooperative UAV, and execute step S4.

6. The method for quantitatively assessing collision risk in low-altitude airspace according to claim 1, characterized in that: The step S3 specifically includes the following steps: Step S3.1, determine the timeline for manned aircraft collision risk assessment and the conflict mitigation measures involved, including procedural mitigation and tactical conflict mitigation of the own aircraft relative to the manned aircraft; Step S3.2, determine the area scanned by the aircraft relative to the manned aircraft: in, is the radius of the collision area between the aircraft and the manned aircraft combination, is the half height of the collision area between the aircraft and the manned aircraft, Δv is the relative speed between the aircraft and the manned aircraft, t mission The time for task execution; Step S3.3, calculate the probability that a manned aircraft's take-off or landing flight falls into the area swept by the aircraft relative to the manned aircraft: L sw / L tol Among them, L sw L is the length of the manned segment in the area swept by the aircraft. tol is the length of the overall take-off and landing pattern of manned aircraft; Step S3.4: For the entire airport terminal area, the probability of failure of the own aircraft's procedure mitigation relative to the manned aircraft is: in, is the length swept by the aircraft relative to the mth take-off and traffic pattern, is the total length of the mth take-off and traffic pattern, M′ represents the total number of take-off and traffic patterns in the entire airport terminal area during the aircraft’s mission; In step S3.5, the probability of tactical conflict mitigation failure of the aircraft relative to the manned aircraft is: in, represents the normal distribution function, t ap ′ represents the time between failure of procedure mitigation and taking maneuvering action; Step S3.6: The collision probability between the aircraft and the manned aircraft is: Output the obtained collision probability between the aircraft and the manned aircraft, and execute step S4.

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