Risk identification and emergency response methods when drones fly in airspace near highways

Abstract

The present invention belongs to the field of unmanned aerial vehicle (UAV) emergency control systems and relates to a risk identification and emergency response method for UAVs flying in airspace near highways. The method constructs a six-degree-of-freedom dynamic model and incorporates environmental factors and control intentions to obtain the equilibrium state of the UAV. The equilibrium state is dynamically compared with real-time data to obtain a state deviation, and the state deviation is used to deduce the failure probability of the UAV, thereby achieving sensitive early warning of possible UAV failures. In addition, the method constructs a two-layer linkage framework of electromagnetic safety boundaries and physical safety boundaries, and combines failure probability to achieve safety risk identification, ensuring that the safety area between the UAV and the fleet on the highway can be characterized by risk values ​​according to speed, fleet density and interference intensity. The method also takes into account the failure probability of the UAV when flying in the air. When the failure probability is greater than a preset threshold, the UAV is judged to be out of control and a corresponding control strategy is adopted to ensure flight safety when the UAV is partially or completely out of control.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) emergency control systems, and specifically designs a risk identification and emergency response method for a UAV flying in the airspace adjacent to a highway. Background Art

[0002] In recent years, with the rapid development of quadcopter drone technology, it has demonstrated significant application value in areas such as capturing images near highways and emergency monitoring. While this location offers the advantage of a lower obstacle density compared to urban airspace, drone flight safety faces two risks: First, drones must avoid physical collisions with dynamic traffic flows and infrastructure; second, the dense communication signals on highways can easily disrupt drone communication links or distort navigation signals, indirectly leading to collisions. Furthermore, if a drone's own abnormal conditions, such as power failure or control malfunctions, are not promptly warned, its crash trajectory could directly intrude upon the highway's traffic zone, potentially causing a catastrophic secondary accident. However, in the above scenarios, current research has the following shortcomings: existing research mostly focuses on collision avoidance between drones or between drones and static obstacles. Few technologies construct composite risk models for dynamic highway scenarios, especially lacking modeling of electromagnetic interference and physical collisions. For example, Chinese patent CN 119577439 A discloses a drone fault diagnosis method and system that integrates random convolution data enhancement and deep learning. This method not only has the above problems but also relies on training with massive data samples. In addition, current research on drone risk identification pays little attention to the possible fault states of drones. For example, Chinese patent CN 118968823 A discloses a method for identifying the risks and levels of low-altitude drone operations. This method collects drone operation data in real time, sets static alarm areas, and sets dynamic alarm areas based on the operation data. However, this method is difficult to deal with possible fault risks and cannot guarantee flight safety when the drone is partially or completely out of control. Summary of the Invention

[0003] In view of the shortcomings and deficiencies of existing technologies, the present invention provides a risk identification and emergency response method for unmanned aerial vehicles (UAVs) flying in airspace adjacent to highways. This method constructs a six-degree-of-freedom dynamic model, incorporates environmental factors and control intent, and combines driving information to determine the UAV's equilibrium state. The equilibrium state is then dynamically compared with real-time data to obtain position and attitude state deviations. The UAV's failure probability is derived from this state deviation, and sensitive early warning of possible UAV failures is achieved based on the numerical changes in the failure probability. This approach eliminates the reliance on massive data sets. Furthermore, the method considers electromagnetic communication and physical collisions separately, abstracting a continuous high-speed convoy on a highway into a spatiotemporal extended safety boundary that considers the reachable domain of a time window. A two-layer linkage framework of electromagnetic and physical safety boundaries is constructed. Safety risk identification is achieved by combining the fault margin and fault probability, which characterize the UAV's state deviation, to ensure that the safety zone between the UAV and the convoy on the highway can be characterized by a risk value based on speed, convoy density, and interference intensity. The method also considers the UAV's failure probability while in flight. When the UAV failure probability is determined to be greater than a preset threshold, the UAV is determined to be out of control, and a corresponding control response strategy is implemented to ensure flight safety in the event of partial or complete loss of control.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A risk identification and emergency response method for a UAV flying in airspace adjacent to a highway, the method comprising the following steps:

[0006] Step S1: Data collection, obtaining the surrounding environment data of the drone and basic information of nearby vehicles;

[0007] Step S2: Establish a six-degree-of-freedom dynamic model that includes wind force; convert the joystick signal from the operator into a kinematic state change, calculate the equilibrium state of the UAV based on the established six-degree-of-freedom dynamic model, compare the current state of the UAV obtained in real time with the equilibrium state, obtain the state deviation between the equilibrium state and the current state, calculate the instantaneous fault margin of the position loop and the instantaneous fault margin of the attitude loop based on the state deviation, and thus obtain the UAV's power failure probability, attitude failure probability, and current comprehensive failure probability;

[0008] Step S3: Construct a physical security boundary for the fleet and infrastructure. Simultaneously, perform real-time detection of the frequency bands emitted by vehicles and infrastructure on the road and compare them with the current communication frequency band of the drone. If the frequency bands coincide with the current drone communication frequency, an electromagnetic security boundary is generated. A global security boundary is generated based on the physical and electromagnetic security boundaries.

[0009] Step S4: A collision box is created for the drone, and a determination is made as to whether the overall failure probability exceeds a preset threshold. If not, the operator controls the drone. The drone's current safety risk is identified based on the collision box and the global safety boundary from step S3, and the operator is given a warning based on the safety risk level. If the overall failure probability exceeds the preset threshold, automatic control is initiated, and step S5 is executed.

[0010] Step S5: Identify the fault type of the drone, calculate the comprehensive fault fall path domain of the drone based on the current state of the drone, the power failure probability of the drone, and the attitude failure probability, and obtain the fault state risk value of the drone in the fault situation; then judge the degree of loss of control of the drone based on the comprehensive fault probability. If it is partially out of control, control the flight direction of the drone to move in the direction with a low fault state risk value. When the calculated fault state risk value is zero, the drone is forced to perform a safe landing; if it is completely out of control, map the comprehensive fault fall path domain to the global coordinate system to form the final fall impact boundary. Based on the global safety boundary and the formed final fall impact boundary, issue avoidance instructions to vehicles around the fall site, and protect any infrastructure if there is any.

[0011] As a preferred embodiment of the present invention, the six-degree-of-freedom dynamic model established in step S2 is:

[0012] ;

[0013] in, 、 、 The current center of gravity of the drone The second-order differential of 、 、 for 、 、 The second-order time differential of 、 for 、 The first-order time differential of is the coordinate system of the drone body The rotation angle of the axis, is the coordinate system of the drone body The rotation angle of the axis, is the coordinate system of the drone body The rotation angle of the axis, The UAV orbits in the body coordinate system The moment of inertia of the shaft, The UAV orbits in the body coordinate system The moment of inertia of the shaft, The UAV orbits in the body coordinate system The moment of inertia of the shaft, For the quality of the drone, is the total lift of the four rotors; is the rotation torque of the front and rear channels, is the pitch angle control quantity; is the rotation torque of the left and right channels, is the roll angle control quantity; is the rotational torque of the fuselage plane, is the yaw angle control quantity, Represents the disturbance caused by the wind near the highway to the UAV in the global coordinate system, and the expression is:

[0014] ;

[0015] in, For wind power The disturbance force of the rotor, For the The anti-torque disturbance of each rotor, is the length of the drone's arm.

[0016] As a preferred embodiment of the present invention, after receiving the operator's joystick signal in step S2, the received signal is normalized and then converted into the matrix , convert the joystick signal into the rotor speed change required by the six-degree-of-freedom dynamic model, and then calculate the second-order differential change value of each state of the drone based on the established six-degree-of-freedom dynamic model and the rotor speed change. By integrating the second-order differential change value of each state of the drone, the speed, position, attitude angular velocity and attitude angle of the drone in the equilibrium state are obtained.

[0017] As a preferred embodiment of the present invention, the instantaneous fault margin of the position loop in step S2 is:

[0018] ;

[0019] in, represents the 2-norm in mathematics, Indicates the current position of the drone, i.e. , is the position of the drone in equilibrium; is the achievable speed range of the UAV. For drones The actual speed set at the moment, For drones The set of velocities at equilibrium at all times; and , is the time decay factor; For control The weight coefficient of the item's importance;

[0020] The instantaneous fault margin of the attitude loop is:

[0021] ;

[0022] in, 、 Indicates the current attitude angle and attitude angular velocity of the drone, satisfying 、 , 、 are the attitude angle and attitude angular velocity of the UAV in equilibrium state, is the maximum attitude angle that the drone can achieve, is the range of attitudes that the UAV can achieve in all directions; and , is the time decay factor, For control The weight coefficient of the item's importance;

[0023] Probability of UAV power failure and attitude failure probability Expressed as:

[0024] ;

[0025] ;

[0026] The comprehensive failure probability of the UAV is expressed as:

[0027] ;

[0028] in, and are the Rayleigh distribution scale parameters of position and attitude angle, and is the weight coefficient, ranging from 0.2 to 0.7. The value range is 0.2-0.7, and the sum of the two should be equal to 1.

[0029] As a preferred embodiment of the present invention, the method for constructing the physical safety boundary of the fleet in step S3 is as follows: obtain vehicle information of the fleet on the highway, predict the trajectory in the future period based on the current state of the vehicle and future control input, and construct a three-dimensional semi-ellipsoid reachable area for each vehicle. , then for a column there are The joint collision volume of the convoy of vehicles is represented as ;

[0030] The construction method of the physical security boundary of the infrastructure is: the geometric center coordinates of the infrastructure are , the length, width and height of the infrastructure itself , based on the security boundary space domain constructed Strictly defined by the three-dimensional coordinate constraints:

[0031] ;

[0032] in, Parameter variables representing the location coordinates of infrastructure;

[0033] The infrastructure includes high-voltage power lines, photovoltaic panels and highway toll booths.

[0034] As a preferred embodiment of the present invention, the method for generating the electromagnetic safety boundary in step S3 is: obtaining the communication frequency coincidence device Maximum power emitted during communication , set the maximum power allowed at the drone to be , inversely solve the communication propagation free space path formula to obtain the safe distance , the expression is:

[0035] ;

[0036] Where, is the signal wavelength, is the reflection coefficient of the metal railing, and its value range is , safe distance The distance between the drones is centered on the infrastructure that emits the communication frequency band, and the pointing direction of the safe distance is projected through the rotation matrix to obtain the global coordinate system. X g 、Y g 、Z g Axis distance 、 、 ;

[0037] Based on the safety distance obtained , constructed with For length, For width, For a geometric body with a height of , the electromagnetic safety boundary is:

[0038] ;

[0039] in, The position variable representing the object that overlaps with the drone communication frequency band, , , Represents the geometry in The projection length of the safety distance calculated at any moment on the three coordinate axes of the global coordinate system;

[0040] All the established electromagnetic safety boundaries are synthesized and calculated. Assuming that there are N devices with the same communication frequency band as the drone, the electromagnetic safety boundary within the detectable range of the drone at the current location is .

[0041] As a preferred method of the present invention, the method of establishing the UAV collision box in step S4 is to set the volume of the UAV to be a cylinder, and set the maximum horizontal size of the UAV to be , then the corresponding cylindrical radius of the body is , the height is , is half the height of the drone, , That is the minimum circumscribed sphere radius of the drone body cylinder. Acts as a collision box for the drone.

[0042] As a preferred embodiment of the present invention, the calculation method of the security risk in step S4 is:

[0043] ;

[0044] in, is the safety risk characterization value of the drone at its current location, Represents the drone collision box and the global safety boundary The intersection volume of is the volume of the drone collision box, is the weight factor;

[0045] In step S4, the safety risk level threshold of the drone is set. and , when the safety risk characterization value is When the safety risk characterization value is When the safety risk characterization value is When the drone is at a high risk, the risk level will be superimposed on the control interface layer and displayed in blue; when the drone is at a medium risk, the risk level will be superimposed on the control interface layer and displayed in yellow, and intermittent voice broadcasts will be made to convey the risk level of the current area to the operator; when the drone is at a high risk, the risk level will be superimposed on the control interface layer and displayed in red, and intermittent voice broadcasts will be made to convey the risk level of the current area to the operator. The operator will actively adjust the drone's heading or altitude based on voice prompts and visual feedback to ensure that it maintains a safe distance from the highway infrastructure and traffic flow.

[0046] As a preferred embodiment of the present invention, in step S5, the fault fall path domains of the drone caused by power system failure and control system failure are solved respectively. After obtaining the fall path domains of the drone power system failure and control system failure, the convex hulls of the two different fault types are weighted and calculated to obtain the comprehensive fault fall path domain of the drone at the current position;

[0047] When solving the failure fall path domain of a UAV caused by power system failure and control system failure, the parameter space range is set respectively, and Latin hypercube sampling is used to generate N groups of parameter samples in the parameter space. For each group of parameters, based on the established six-degree-of-freedom dynamic model, the fourth-order Runge-Kutta method is used to obtain the UAV trajectory points, and then N trajectories are generated through the Monte Carlo sampling algorithm. Finally, the convex hull of the point set is calculated using the fast convex hull algorithm.

[0048] As a preferred embodiment of the present invention, the expression of the fault state risk value in step S5 is:

[0049] ;

[0050] in, Indicates The intersection volume of the comprehensive fault fall path domain obtained at the moment and the global safety boundary, Indicates The volume of the integrated fault fall path domain obtained under the state at time t.

[0051] Advantages and beneficial effects of the present invention:

[0052] (1) The present invention abstracts the high-speed convoy and surrounding infrastructure into three-dimensional geometric bodies that evolve dynamically over time, retaining the core features while filtering out redundant detail data, and using simple expressions to achieve dynamic monitoring of the convoy and infrastructure.

[0053] (2) The present invention constructs an electromagnetic-physical dual-level adaptive safety boundary framework for highway traffic flow and its surrounding infrastructure. The physical layer generates a three-dimensional dynamic risk cube of vehicles and infrastructure in real time by constructing an envelope; the electromagnetic layer dynamically calculates the electromagnetic safety distance based on frequency band conflict judgment by comparing the communication frequency bands of the equipment and the drone; and a dual-level risk coupling analysis mechanism is designed to realize a dynamic safety boundary that can be adaptively adjusted according to the traffic speed, density and environmental interference intensity.

[0054] (3) The present invention establishes a theoretical benchmark model based on the six-degree-of-freedom dynamic characteristics of the UAV, monitors flight data in real time, and dynamically compares the real-time monitored flight data with the equilibrium state calculated based on the operator's remote control signal conversion, thereby accurately quantifying possible failures of the UAV.

[0055] (4) The present invention introduces the fault margin index to establish a mapping physical model relationship between state deviation and failure probability, accurately quantify the possible failures of drones, and break through the limitations of traditional risk identification methods in data dependence.

[0056] (5) The present invention breaks through the limitations of the traditional single fault model and constructs a position-attitude dual-parameter fusion fault probability model based on the Rayleigh distribution, realizes the joint quantification of power attenuation and control failure, and calculates the failure probability of the UAV in real time. When the failure probability exceeds the preset threshold, the possible falling trajectory of the UAV is calculated, and the safety risk of the UAV in the state of possible failure is expressed by calculating the fault state risk value.

[0057] (6) The present invention constructs a hierarchical fault response system, which refines the UAV out-of-control state into a dual mode of partial out-of-control and complete out-of-control. Different response strategies are adopted for different out-of-control states, so that the UAV can find a low-risk location to land by flying when it is not completely out of control; when the UAV is completely out of control and loses the ability to fly, the probability of collision with the highway traffic flow is calculated, the emergency response mechanism is activated, and relevant resources are mobilized for timely processing to ensure that flight safety can still be guaranteed to a certain extent when a complete out-of-control occurs.

[0058] (7) This invention overcomes the core problems of insufficient quantification of the interaction between drones and space facilities near highways, low sensitivity to environmental disturbances, and rigid safety boundaries, and provides a high-precision and high-efficiency solution for airspace safety monitoring and risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of the risk identification and emergency response method when the UAV of the present invention flies in the airspace near a highway. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solutions and advantages of the present invention, the present application is described in detail below with reference to the accompanying drawings, but this is not intended to limit the scope of protection of the present invention.

[0061] like Figure 1 As shown, the risk identification and emergency response method provided by this embodiment when a drone flies in the airspace near a highway includes the following steps:

[0062] Step S1. Data acquisition:

[0063] Monitor and obtain drone body data, including the drone's current angular velocity, flight speed, body weight, current drone position, rotor moment of inertia, three-axis moment of inertia in the global coordinate system (roll axis, pitch axis, yaw axis), and real-time control commands issued by the operator. At the same time, collect surrounding environment information, including wind speed vector and electromagnetic field distribution data, and perform three-dimensional dynamic modeling of the highway surrounding environment based on the multi-modal sensors carried by the drone (such as lidar, depth camera, millimeter wave radar), and calculate the relative distance between the drone and the infrastructure and the moving fleet in real time.

[0064] Step S2. Obtain the UAV failure probability:

[0065] Step S2.1. Establish the dynamic model of the quadrotor drone:

[0066] In this embodiment, the body coordinate system of the drone is to establish a spatial rectangular coordinate system with the location of the drone as the base point. The body coordinate system B The geometric center of gravity of the quadrotor drone As the origin, the two rigid frames of the quadrotor are Axis and axis, the body can be confirmed according to the right-hand rule The direction of the axis. Define the global coordinate system E The origin is the point where the axis points to the north. axis, The direction of the axis when rotated 90° counterclockwise is axis, the body can be confirmed according to the right-hand rule The direction of the axis is perpendicular to the body and upward.

[0067] Based on the drone's body coordinate system, use the rotation matrix Convert to the global coordinate system. In this embodiment, a rotation matrix is ​​used to complete this process, so that the global coordinate system and the drone body coordinate system can be converted to each other. The conversion between the two coordinate systems can be achieved by rotating three times around the coordinate axis.

[0068] Specifically, the rotation matrix The expression is:

[0069] ;

[0070] in, is the coordinate system of the drone body The rotation angle of the axis, that is, the pitch angle; is the coordinate system of the drone body The rotation angle of the axis, that is, the roll angle; is the coordinate system of the drone body The rotation angle of the axis, that is, the yaw angle.

[0071] In this embodiment, the dynamic model of a quadcopter derived from the Newton-Euler method is used as the dynamic basis. With yaw, roll, and pitch as the attitude angles, the six-degree-of-freedom motion equation of the drone is established based on the mutual coupling of the attitude angles. The expression is:

[0072] ;

[0073] This formula describes the motion equation of a quadrotor drone, where the center of gravity of the drone is represented by express, for The second-order differential of For the quality of the drone, It is the resultant external force matrix composed of the forces acting on the drone body in the global coordinate system, mainly related to the lift force on the drone in the body coordinate system. ,gravity and wind disturbances About gravity and wind disturbances The expression is:

[0074] ;

[0075] in, is the acceleration due to gravity, and its value is 9.8;

[0076] ;

[0077] ；

[0078] in, For wind power The disturbance force of the rotor, is the air density, is the lift coefficient of the UAV under wind force, set , is the relative speed between the UAV rotor and the wind, Considered as the projected area of ​​the UAV rotor disc to the side wind, , is the radius of a single rotor;

[0079] set up is the wind speed in the global coordinate system, then the expression of the relative speed between the UAV rotor and the wind force is:

[0080] ;

[0081] in, For the UAV in the global coordinate system Xg 、 Yg、Zg A collection of three-axis velocities.

[0082] In this embodiment, is the set of attitude angular velocities of the UAV, that is , for The first-order differential of is the third-order identity matrix, , Used to describe the UAV's moment of inertia in three directions in the body coordinate system, where The UAV orbits in the body coordinate system The moment of inertia of the shaft, The UAV orbits in the body coordinate system The moment of inertia of the shaft, The UAV orbits in the body coordinate system The moment of inertia of the shaft; Represents the disturbance caused by the wind near the highway to the drone in the global coordinate system, which is calculated by the following formula:

[0083] ;

[0084] in, For the The anti-torque disturbance of the rotor is expressed as: ;in, is the torque coefficient of the UAV under wind force, set , is the length of the drone's arm.

[0085] Taking gravity into account, the lift force on the drone in the body coordinate system can be expressed as , F1 to F4 represent the lift provided by the four rotors respectively, convert the total lift into the lift in the global coordinate system, and define the rotor speed as the speed of the rotor located at the left front of the body , right front rotor speed , right rear rotor speed and the speed of the left rear rotor , then we get Xg 、 Yg、Zg Lift forces on the three axes , its mathematical formula is:

[0086] ;

[0087] in, is the lift coefficient, ranging from ;

[0088] Based on the dynamic model established above, the total external torque of the UAV in the body coordinate system is It can be expressed as:

[0089] ;

[0090] in, The gyroscopic torques generated by the gyroscopic effects of the four rotors of the drone on the roll, pitch and yaw axes are represented as 、 、 , whose expression is ; is the moment of inertia of the drone rotor (assuming the four rotors have the same moment of inertia), is the sum of the total rotational speeds of the four rotors, Indicates that the lift provided by the drone rotor is along the body coordinate system 、 、 The torque generated by the axis is a set; based on the UAV dynamics, the torque can be explicitly expressed as ;and , , In the body coordinate system 、 、 The angular velocities of the three axes, 、 、 They are , , The differential of the angular velocity around the axis, the superscript T represents the transpose symbol. Then convert the torque in the body coordinate system to the torque in the global coordinate system ; Define a set , is the total lift of the four rotors, so as the position height control quantity, is the rotational torque of the front and rear channels, is the rotational torque of the left and right channels, so it can be used as the pitch angle control quantity and the roll angle control quantity. It is the rotation torque of the fuselage plane, which can be used as the yaw angle control quantity. The actual information of the UAV can be obtained by measuring the rotor speed.

[0091]

[0092]

[0093]

[0094]

[0095] in, is the thrust coefficient of the UAV , is the counter torque coefficient .

[0096] Therefore, the expression of the resultant external force matrix F composed of the forces acting on the UAV body in the global coordinate system is:

[0097] ;

[0098] in, For wind power The disturbance force of the rotor, is the acceleration due to gravity, and its value is 9.8;

[0099] In summary, the six-degree-of-freedom dynamic model of the quadrotor drone can be expressed as:

[0100] ;

[0101] in, 、 、 The current center of gravity of the drone The second-order differential of 、 、 for 、 、 The second-order time differential of 、 for 、 The first-order time differential of .

[0102] Step S2.2. Get the balance state of the drone:

[0103] Based on the input set obtained, calculate the drone state expected by the operator or signal transmission, and set the time window The equilibrium state after is:

[0104] ;

[0105] In this embodiment, it is considered that the allowable delay of signal transmission is , that is, the drone at time The instruction can be received. For the current moment, It is a very small period of time, ranging from 0.05s to 0.2s. is the defined time window; after the drone receives the operator’s joystick signal, it normalizes the received signal to obtain ;in, The vertical stick (throttle) controls the total lift , Pitch stick (front and back), controls the pitch moment, Roll the joystick (left and right) to control the rolling torque. The yaw stick (rotation) controls the yaw moment, and then converts the stick signal into the control quantity required by the dynamic model through linear mapping and physical constraints:

[0106] Define the transformation matrix The purpose of setting this matrix is ​​to convert the joystick signal from the operator into the matrix required for the rotor speed of the drone after receiving it. It should be a 4×4 matrix; satisfy ;in, For the The square of the rotor speed change, that is, at time The change after receiving the operator's signal; the conversion matrix It should be obtained by calibration of different drones.

[0107] Substitute the square of the above rotor speed change into the six-degree-of-freedom dynamic model in step S2.1 to obtain the second-order differential change value of each state of the drone 、 、 、 、 、 .

[0108] Integrate the obtained parameters to obtain the velocity of the UAV in equilibrium state:

[0109]

[0110]

[0111]

[0112] The equilibrium position of the UAV in the global coordinate system is:

[0113]

[0114] in, For drones at all times The initial position when is the initial velocity in the global coordinate system; for The coordinates of the equilibrium position at the moment; based on the drone’s own GPS positioning system, record the drone’s The position coordinates after time are and the speed is ;

[0115] Again 、 、 Integrate to obtain the angular velocity in equilibrium:

[0116] in, 、 、 For the moment The attitude angular velocity of the UAV at this time is used to derive the attitude angle in the equilibrium state. The expression is:

[0117]

[0118] in, For the moment The drone attitude angle is collected and recorded at the moment The equilibrium state of the UAV in the global coordinate system According to the gyroscope installed on the drone, the drone is The set of attitude angles in the global coordinate system at the moment .

[0119] Step S2.3. Fault Margin:

[0120] In order to further quantify the relationship between the state deviation between the equilibrium state and the current state and the probability of drone failure, this embodiment introduces a new risk judgment indicator, namely, the fault margin, based on the real-time state deviation between the equilibrium state and the current state.

[0121] The instantaneous fault margin of the position loop is defined as:

[0122] ;

[0123] in, represents the 2-norm in mathematics, Indicates the current position of the drone, i.e. , is the position of the drone in equilibrium; is the achievable speed range of the UAV. For drones The actual speed set at the moment, For drones The set of velocities at equilibrium at all times; and , is the time decay factor ( ); For control The weight coefficient of the item's importance is ( );

[0124] The instantaneous fault margin of the attitude loop is defined as:

[0125] ;

[0126] in, 、 Represents the current attitude angle and attitude angular velocity of the drone, and the parameters in the formula satisfy 、 , 、 are the attitude angle and attitude angular velocity of the UAV in equilibrium state, is the maximum attitude angle that the drone can achieve, is the range of attitudes that the UAV can achieve in all directions; and , is the time decay factor ( ), For control The weight coefficient of the item's importance is ( );

[0127] Calculating global fault margin ;in, 、 Both represent weight coefficients, all have values ​​greater than zero and should satisfy .

[0128] Step S2.4. Obtain failure probability:

[0129] The probability of possible failure of the drone is obtained based on different fault margins. In this embodiment, the probability of possible failure of the drone is considered to obey the Rayleigh distribution. The historical operation status information of the drone is obtained through the drone's onboard equipment, the characteristic value is extracted, and the Rayleigh distribution scale parameters of the position and attitude angle are obtained by fitting the historical fault data. and , to characterize the fault characteristic value of the UAV's driving behavior, the UAV's power failure probability and attitude failure probability It can be expressed as:

[0130]

[0131]

[0132] Based on the weighted accumulation of the above two failure probabilities, the comprehensive failure probability of the drone is obtained. , the expression is:

[0133] ;

[0134] in, and is the weight coefficient, ranging from 0.2 to 0.7. The value range is 0.2-0.7, and the sum of the two should be equal to 1.

[0135] Step S3. Build a global security boundary:

[0136] Step S3.1. Generate physical security perimeter:

[0137] Based on the moment The center coordinates of the vehicle position near the highway and the infrastructure around the highway (toll booths, high-voltage power towers, photovoltaic panels, etc.) Propose collision boxes;

[0138] ;

[0139] in, Refers to vehicles and infrastructure on the highway (mainly high-voltage wires, photovoltaic panels, and facilities at toll stations). Based on the acquired data, the attribute coordinate set of the collision box is obtained. ,in The length, width and height of the fleet or infrastructure.

[0140] Modeling and analysis of the motion status of a convoy on a highway; in this embodiment, vehicle information on the convoy side of the highway near the drone is obtained. The vehicle information includes the autonomous vehicle's trajectory prediction for the next time, the vehicle's position information, and vehicle parameters; under the autonomous vehicle control system, the vehicle will predict its trajectory for a period of time in the future based on its current state (such as speed, position, and heading angle) and future control inputs (such as acceleration and steering angle).

[0141] For example: Through The current state of the vehicle and control input , you can get the future time Movement trajectory , and then construct the vehicle at time by numerical integration The reachable domain model at ; where For vehicles current( time) position, For vehicles The heading angle at the moment (i.e. the angle of the vehicle's direction relative to the global coordinate system), For the moment speed; For vehicles exist The acceleration of time, For vehicles exist Front wheel steering angle at the moment.

[0142] In this embodiment, the time distance between the vehicle and the head of the vehicle within the monitoring range of the drone is less than the threshold value. The continuous vehicles are defined as a cooperative convoy, and their future motion reachable domain is assumed to be a semi-ellipsoidal shape. The mathematical representation of the vehicle's three-dimensional semi-ellipsoidal reachable area is:

[0143] ;

[0144] in, For the A car in The position coordinates at the moment, is the square of the maximum reachable distance of the vehicle along the driving direction, ,in is the maximum acceleration of the vehicle in the time window, For vehicles in The speed of time, is the maximum distance that the vehicle can reach when turning, which is obtained from the turning radius of the vehicle, that is, ,in For the The wheelbase of the car, For the A car in The front wheel steering angle at the moment, is the design height of the vehicle, ,Right now For the The design height of the vehicle, Representative vehicle The position coordinate parameter variable.

[0145] The safety boundary of a single vehicle is composed of the possible vehicle trajectory vectors. The joint collision volume of the vehicle convoy can be expressed as .

[0146] For collision detection modeling of stationary infrastructure along highways, its spatial representation adopts a double-layer cuboid structure system based on the global coordinate system. First, the core parameters of infrastructure collision are defined as the geometric center coordinates, i.e. The length, width and height of the facility itself , the safety boundary space domain constructed based on this is strictly defined by the three-dimensional coordinate constraints as follows:

[0147] ;

[0148] in, A parameter variable representing the location coordinates of the infrastructure.

[0149] Step S3.2. Generate electromagnetic safety boundary:

[0150] Infrastructure near highways will also have a certain electromagnetic impact on the flight and communication of drones. Since the impact caused by most infrastructure can be ignored, only the impact of high-voltage power lines, photovoltaic panels and toll station facilities around highways on drone communications is considered here.

[0151] In this embodiment, before generating the electromagnetic boundary, the following conditions should be met: the drone flies along the highway, and at the same time, the frequency bands emitted by vehicles traveling on the highway, high-voltage power lines and photovoltaic panels installed beside the highway, and facilities at the highway toll station are monitored in real time, and compared with the drone's current communication frequency band.

[0152] Specifically, it should be able to identify the various wireless communication or electromagnetic radiation frequency bands that may be used by the above four sources, and compare and analyze them with the drone's own communication frequency band; when it is found that the communication frequency band of a certain device or infrastructure in the vehicle overlaps, that is, there is a potential interference risk, an electromagnetic safety boundary should be established with the infrastructure with the same communication frequency band as the drone as the geometric center.

[0153] Get communication frequency coincidence device Maximum power emitted during communication , set the maximum power allowed at the drone to be , according to the communication propagation free space path formula:

[0154] ;

[0155] in, is the signal wavelength, is the reflection coefficient of the metal railing, and its value range is , The phase difference between the direct wave and the reflected wave, is the direct path distance, and the unknown quantity of the equation is obtained by experimental fitting through simulating the UAV flight environment.

[0156] Reverse the above equation to obtain the safe distance , the expression is:

[0157] ;

[0158] Where, safety distance The distance between the drones is centered on the infrastructure that emits the communication frequency band. The pointing direction of the distance is projected through the rotation matrix to obtain the global coordinate system. X g 、Y g 、Z g Axis distance 、 、 ; Based on the safety distance obtained , constructed with For length, For width, For a geometric body with a height of , the electromagnetic safety boundary is:

[0159] ;

[0160] in, The position variable representing the object that overlaps with the drone communication frequency band, , , Represents the geometry at time The projected length of the safety distance calculated at the time on the three coordinate axes of the global coordinate system;

[0161] All the established electromagnetic safety boundaries are synthesized and calculated. Assuming that there are N devices with the same communication frequency band as the drone, the electromagnetic safety boundary within the detectable range of the drone at the current location is .

[0162] Step S4. Identification of drone safety risks:

[0163] Establish the UAV collision box. For the convenience of calculation, the volume of the UAV is assumed to be a cylinder. The maximum horizontal size of the UAV is set to , then the corresponding cylindrical radius of the body is , the height is , is half of the height of the drone, which can be obtained from solid geometry knowledge , This is the minimum circumscribed sphere radius of the UAV body cylinder. In subsequent calculations, the minimum circumscribed sphere Acts as a collision box for the drone.

[0164] When the current comprehensive failure probability of the drone does not exceed the preset threshold, based on the physical safety boundary and electromagnetic safety boundary obtained in steps S3.2-S3.3, the physical and electromagnetic safety boundaries are compared separately, and the safety boundary obtained after the operation is used as the global safety boundary. ， Expressed as:

[0165] ， ， ;

[0166] Introducing safety risk characterization values Calculate the risk of the drone at its current location using the expression: , Represents the drone collision box and the global safety boundary The intersection volume of is the volume of the drone collision box, is a weight factor, representing the acceptance range of drone risks.

[0167] In this embodiment, the level threshold of the safety risk characterization value of the drone is set and , that is, when the safety risk characterization value is When the safety risk characterization value is When the safety risk characterization value is , it is a high-level risk.

[0168] In this embodiment, if the comprehensive failure probability of the drone is less than or equal to the preset threshold value during flight, the control right is in the hands of the operator. When the drone is at a low risk, the risk level is superimposed on the control interface layer and displayed in blue. When the drone is at a medium risk, the risk level is superimposed on the control interface layer and displayed in yellow. At this time, the system Intermittent voice broadcasts are made every 10 seconds to convey the risk level of the current area to the operator; when the drone is at a high risk, the risk level is superimposed on the control interface layer and displayed in red. The operator will make an intermittent voice broadcast every second to convey the risk level of the current area to the operator. The operator will actively adjust the heading or altitude of the drone based on the voice prompts and visual feedback to ensure that it maintains a safe distance from the highway infrastructure and traffic flow. 、 They should all be formulated based on expert opinions in actual application situations, while ensuring the control logic of the present invention and enabling the operator to safely control the drone.

[0169] If the comprehensive failure probability of the UAV is greater than the preset threshold, automatic control is turned on and step S5 is executed;

[0170] Step S5. Identify the type of drone failure. Calculate the comprehensive failure fall path domain of the drone at its current location based on the current state of the drone, the probability of power failure, and the probability of attitude failure. Obtain the failure state risk value of the drone in the event of a failure. Then, determine the degree of loss of control of the drone. If it is partially out of control, control the flight direction of the drone to move it in a direction with a lower failure state risk value. When the calculated failure state risk value reaches zero, the drone is forced to perform a safe landing. If it is completely out of control, calculate the fall path domain of the current state, issue a risk avoidance alert to vehicles near the fall site, and notify relevant departments if there is infrastructure involved.

[0171] In this embodiment, a fault threshold should be determined in advance. When the comprehensive fault probability of the drone at a certain time t exceeds the fault threshold, it is considered that the drone may have a fault. The drone status is tracked to obtain the fault type ratio and probability.

[0172] Specifically, in this embodiment, the fault types are divided into two categories, namely power system faults and control system faults. UAV crash models caused by the two types of faults are constructed respectively, and the comprehensive fault crash path domain is solved based on the current position of the UAV.

[0173] When describing a power system failure, it is assumed that the pitch, roll, and yaw angles of the drone are It can reach a state of equilibrium, but in the event of a power system failure, the thrust of the drone's quadrotor will be attenuated to a certain extent, causing the drone's motion state to gradually fail.

[0174] The continuous domain of the falling path of the UAV caused by power system failure is expressed as , that is, the drone from arrive The set of all possible power failure falling trajectories within the time period; among them, is the crash time of the drone, A collection of parameters.

[0175] In this embodiment, the continuous domain of the falling path is based on the current moment ( moment) to calculate some parameters, Including the current moment ( Real-time status at the moment , environmental parameters: ,in is the air density, is the time in the global coordinate system At X g 、Y g , Z g Components of the three axes, fault parameters: ,in is the power attenuation rate after the UAV power system fails in the global coordinate system; this power attenuation rate will affect the control input, that is, ,in , This is the control input after considering power attenuation; define the parameter space The range is ;

[0176] Based on the set parameter space range, Latin hypercube sampling (LHS) is used to generate N groups of parameter samples in the parameter space:

[0177] ;

[0178] For each set of parameters, the dynamic differential equation is solved To get the trajectory of the UAV under this set of parameters, is the trajectory point, That is, in the parameter The drone trajectory points under the , including the drone's position and speed, etc. is the six-degree-of-freedom dynamic equation of the drone in step S2 (including position, velocity, and angular velocity equations), Specifically, the fourth-order Runge-Kutta method is used to solve the numerical solution; through this iterative process, the UAV can be obtained arrive The states at different time steps in time.

[0179] In this embodiment, the path of the drone at different time steps is obtained based on the fourth-order Runge-Kutta method in the above steps, and the Monte Carlo simulation is used to obtain trajectories. In the current state ( The state at the moment The position of each track is expressed as: , use the fast convex hull algorithm QuickHull to calculate the convex hull of the point set, which is mathematically represented as follows:

[0180] ;

[0181] in, are the vertices of the convex hull, is the weight coefficient, is the number of vertices of the convex hull, For the simulation The position of each trajectory is the convex hull parameter of the power system fault.

[0182] In this embodiment, the convex hull calculation is used to obtain the current state ( The spatial coverage area of ​​the falling path obtained under the state at the moment is the failure falling path domain of the UAV caused by the power system failure. .

[0183] A drone's control system failure can disrupt its attitude angles (pitch, roll, and yaw), causing it to crash. This control system failure prevents the drone from maintaining its intended flight attitude, causing its trajectory to gradually deviate from its original trajectory. If recovery is unrecoverable, the resulting crash path will differ from that resulting from a powertrain failure. When considering a drone crash due to a control system failure, it is assumed that the drone maintains its current direction and velocity.

[0184] In order to quantify the trajectory range of such uncontrolled motion, the continuous domain of the falling path caused by control system failure is also defined as , that is, the drone from arrive The set of all possible control failure falling trajectories within time; A set of parameters, including Real-time status at all times , environmental parameters: , fault parameters: .in is the power attenuation rate after the UAV control system fails in the global coordinate system; this power attenuation rate will affect the control input, that is, ,in , This is the control input after considering power attenuation; define the parameter space The range is , that is, the range between the current state and the equilibrium state of the drone status and fault parameters.

[0185] Based on the set parameter space range, Latin hypercube sampling (LHS) is used to generate N groups of parameter samples in the parameter space:

[0186] ;

[0187] Similarly, for each set of parameters, solve the differential equation To obtain the trajectory of the UAV under this set of parameters, the fourth-order Runge-Kutta method is also used for numerical solution; Monte Carlo simulation is used to obtain Similarly, here for , in the current state ( The state at the moment The position of the track is represented by: , use the fast convex hull algorithm QuickHull to calculate the convex hull of the point set, which is mathematically represented as follows:

[0188] ;

[0189] in, are the vertices of the convex hull, is the weight coefficient, is the number of vertices of the convex hull, For the current state ( The state at the moment The position of each trajectory is the convex hull parameter of the control system fault.

[0190] In this embodiment, the convex hull calculation is used to obtain the current state ( The spatial coverage area of ​​the falling path obtained under the state at the moment is the failure falling path domain of the UAV caused by the control system failure. .

[0191] After obtaining the fall path domains of the UAV power system fault and control system fault, the convex hulls of the two different fault types are weighted and synthesized to obtain the comprehensive fall path domain of the UAV at the current position. The expression is:

[0192] ;

[0193] in, In the current state ( The comprehensive fault fall path domain obtained under the state at the moment 、 They are and The variables in (vertices of the convex hull) and and is the weight coefficient, and the weight coefficient should satisfy , .

[0194] In this embodiment, when the comprehensive failure probability of the drone exceeds the preset threshold, the failure state risk value is calculated based on the drone collision box constructed in step S4. , Indicates that in the current state ( The intersection volume of the comprehensive fault fall path domain obtained under the state at the moment (the intersection volume of the global safety boundary) is Indicates that in the current state ( The volume of the comprehensive fault fall path domain obtained under the state at the moment.

[0195] In this embodiment, when the UAV is performing a flight mission controlled by the operator near the highway, it will monitor and calculate its own state deviation in real time, dynamically evaluate the UAV's current power failure probability and attitude failure probability based on the state deviation, and calculate the current comprehensive failure probability; when the comprehensive failure probability exceeds the preset threshold, the UAV will be automatically detected and the state deviation will be calculated. , and continues to exceed When the comprehensive failure probability is less than , greater than When the UAV is out of control but has the ability to continue flying, that is, it is in a partially out-of-control state. At this time, the UAV's failure fall path domain is calculated under possible failure conditions, and the UAV's failure state risk value under possible failure conditions is determined. After obtaining the failure state risk value, the UAV's flight direction is controlled to move in the direction with a lower failure state risk value. When the failure state risk value reaches zero, the UAV is forced to land safely.

[0196] When the comprehensive failure probability of the drone is greater than or equal to When the UAV power fails completely, it is considered that the fault fall path domain containing all possible fall trajectories is generated based on the real-time state parameters and the dynamic model. This area (comprehensive fault fall path domain) is mapped to the global coordinate system to form the final fall impact boundary. The volume of the overlap between this dynamically updated boundary and the global safety boundary is then calculated. If it is detected that the fall area has a geometric intersection with the safety boundary of any vehicle or infrastructure, an avoidance command is immediately sent to the surrounding vehicles, and relevant protection mechanisms are activated for critical infrastructure. At the same time, the fall area prediction results are continuously iterated and updated until a collision occurs or emergency measures take effect.

[0197] In this embodiment, the above parameters (Range 0-1s), (range 0-0.3), (Range 0.3-0.6) should be formulated based on expert opinions in actual application situations, while ensuring the control logic of the present invention and achieving safe control of the drone.

[0198] The above description of the present invention using specific examples is intended only to facilitate understanding of the present invention and is not intended to limit the present invention. A person skilled in the art of the present invention may make several simple deductions, modifications, or substitutions based on the principles of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. Risk identification and emergency response methods when drones fly in the airspace near highways, characterized by: The method comprises the following steps: Step S1: Data collection, obtaining the drone's surrounding environment data and basic information of nearby vehicles; Step S2: Establish a six-degree-of-freedom dynamic model that includes wind force; convert the joystick signal from the operator into a kinematic state change, calculate the equilibrium state of the UAV based on the established six-degree-of-freedom dynamic model, compare the current state of the UAV obtained in real time with the equilibrium state, obtain the state deviation between the equilibrium state and the current state, calculate the instantaneous fault margin of the position loop and the instantaneous fault margin of the attitude loop based on the state deviation, and thus obtain the UAV's power failure probability, attitude failure probability, and current comprehensive failure probability; Step S3: Construct a physical security boundary for the fleet and infrastructure. Simultaneously, perform real-time detection of the frequency bands emitted by vehicles and infrastructure on the road and compare them with the current communication frequency band of the drone. If the frequency bands coincide with the current drone communication frequency, an electromagnetic security boundary is generated. A global security boundary is generated based on the physical and electromagnetic security boundaries. Step S4: A collision box is created for the drone, and a determination is made as to whether the overall failure probability exceeds a preset threshold. If not, the operator controls the drone. The drone's current safety risk is identified based on the collision box and the global safety boundary from step S3, and the operator is given a warning based on the safety risk level. If the overall failure probability exceeds the preset threshold, step S5 is executed. Step S5: Identify the type of UAV failure, calculate the comprehensive failure fall path domain of the UAV at the current position based on the current state of the UAV, the probability of power failure of the UAV, and the probability of attitude failure of the UAV, and obtain the failure state risk value of the UAV in the failure situation; then determine the degree of UAV loss of control based on the comprehensive failure probability. If it is partial loss of control, control the flight direction of the UAV to move in the direction with low failure state risk value. When the calculated failure state risk value is zero, the UAV is forced to perform a safe landing; if it is complete loss of control, map the comprehensive failure fall path domain to the global coordinate system to form the final fall impact boundary. Based on the global safety boundary and the final fall impact boundary formed, issue avoidance instructions to vehicles around the fall site, and protect any infrastructure. Among them, the instantaneous fault margin of the position loop in step S2 is: Among them, ||*||2 represents the 2-norm in mathematics, Indicates the current position of the drone, i.e. is the position of the UAV in equilibrium state; Δs range is the achievable speed range of the UAV, s real is the actual speed set of the UAV at time t1, s ideal is the set of velocities of the drone in equilibrium at time t1; and κ is the time decay factor; λ position For control The weight coefficient of the item's importance; The instantaneous fault margin of the attitude loop is: Among them, η real 、 Indicates the current attitude angle and attitude angular velocity of the drone, satisfying η ideal 、 is the attitude angle and attitude angular velocity of the UAV in equilibrium state, η max is the maximum attitude angle that the drone can achieve, is the range of attitudes that the UAV can achieve in all directions; and k is the time decay factor, λ attitude For control The weight coefficient of the item's importance; The probability of UAV power failure P position and attitude failure probability P posture Expressed as: The comprehensive failure probability of the UAV is expressed as: Among them, σ p and σ a are the Rayleigh distribution scale parameters of position and attitude angle, and ζ' are weight coefficients, The value range is 0.2-0.7, and the value range of ζ' is 0.2-0.

7. The sum of the two should be equal to 1.

2. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 1 is characterized in that: The six-degree-of-freedom dynamic model established in step S2 is: in, is the second-order differential of the current center of gravity position x, y, z of the drone, is the second-order time differential of φ, θ, and ψ, is the first-order time differential of φ and θ, θ is the X coordinate system around the UAV body q The rotation angle of the axis is φ, which is around the Y axis in the drone body coordinate system. q The rotation angle of the axis is ψ, which is around the Z axis in the drone body coordinate system. q Axis rotation angle, J x The drone rotates around X in the body coordinate system g Moment of inertia of the shaft, J y The UAV rotates around Y in the body coordinate system g Moment of inertia of the shaft, J z The UAV rotates around Z in the body coordinate system g The moment of inertia of the axis, m is the mass of the drone, U1 is the total lift of the four rotors; U2 is the rotation torque of the front and rear channels, which is used as the pitch angle control quantity; U3 is the rotation torque of the left and right channels, which is used as the roll angle control quantity; U4 is the rotation torque of the fuselage plane, which is used as the yaw angle control quantity, f = [f x f y f z f φ f θ f ψ ] T Represents the disturbance caused by the wind near the highway to the UAV in the global coordinate system, and the expression is: in, is the disturbance force of wind on the i-th rotor, is the anti-torque disturbance on the i-th rotor, and L is the length of the UAV's arm.

3. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 2, characterized in that: After receiving the operator's joystick signal in step S2, the received signal is normalized, and then converted into the rotor speed change required by the six-degree-of-freedom dynamic model through the conversion matrix H. Then, based on the established six-degree-of-freedom dynamic model and the rotor speed change, the second-order differential change value of each state of the drone is calculated. By integrating the second-order differential change value of each state of the drone, the speed, position, attitude angular velocity and attitude angle of the drone in the equilibrium state are obtained.

4. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 3 is characterized in that: The method for constructing the physical security boundary of the fleet in step S3 is: Obtain vehicle information on the highway fleet, predict the trajectory in the future based on the vehicle's current state and future control input, and construct a three-dimensional semi-ellipsoidal reachable area for each vehicle. Then the joint collision body of a convoy of n vehicles is expressed as The construction method of the physical security boundary of the infrastructure is: the geometric center coordinates of the infrastructure are The length, width and height of the infrastructure itself The secure boundary space domain built on this Strictly defined by the three-dimensional coordinate constraints: Among them, x in ,y in ,z in Parameter variables representing the location coordinates of infrastructure; The infrastructure includes high-voltage power lines, photovoltaic panels and highway toll booths.

5. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 4 is characterized in that: The method for generating the electromagnetic safety boundary in step S3 is: obtaining the maximum power field emitted by the communication frequency coincidence device C during communication c , set the maximum power allowed at the drone to field ths , inversely solve the communication propagation free space path formula to obtain the safe distance d safe，c , the expression is: Where λ is the signal wavelength, Γ is the reflection coefficient of the metal railing, which ranges from 0.5 to 0.9, and the safety distance d is safe，c In order to point to the distance between drones with the infrastructure that emits the communication frequency band as the center, the pointing direction of the safe distance is projected through the rotation matrix to obtain the X coordinate system in the global coordinate system. g 、Y g , Z g Axis distance d c 、e c 、f c ; Based on the obtained safety distance d safe.c , construct with d c is the length, e c is the width, f c For a geometric body with a height of , the electromagnetic safety boundary is: Among them, x ei ,y ei ,z ei The position variable of the object that overlaps with the drone communication frequency band, x i (t0),y i (t0),z i (t0) represents the projection length of the safety distance calculated at time t0 on the three coordinate axes of the global coordinate system; All the established electromagnetic safety boundaries are synthesized and calculated. Assuming that there are N devices with the same communication frequency band as the drone, the electromagnetic safety boundary within the detectable range of the drone at the current location is 6. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 5, characterized in that: The method for establishing the UAV collision box in step S4 is to set the volume of the UAV to a cylinder and set the maximum horizontal size of the UAV to 2r UAV , then the corresponding cylindrical radius of the body is r UAV , height is 2h UAV , h UAV is half the height of the drone, R UAV That is the minimum circumscribed sphere radius of the UAV body cylinder, the minimum circumscribed sphere S UAV Acts as a collision box for the drone.

7. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 6, characterized in that: The security risk in step S4 is calculated as follows: Among them, R risk is the safety risk characterization value of the drone at its current location, V COLL Represents the drone collision box and the global safety boundary The intersection volume, V UAV is the volume of the drone collision box, is the weight factor; In step S4, the safety risk thresholds R1 and R2 of the drone are set. When the safety risk characterization value is 0≤R risk <R1, it is a low-level risk. When the safety risk characterization value R1≤R risk When the safety risk characterization value R risk When ≥R2, it is a high-level risk; when the drone is at a low-level risk, the risk level will be superimposed on the control interface layer and displayed in blue; when the drone is at a medium-level risk, the risk level will be superimposed on the control interface layer and displayed in yellow, and intermittent voice broadcasts will be made to convey the risk level of the current area to the operator; when the drone is at a high-level risk, the risk level will be superimposed on the control interface layer and displayed in red, and intermittent voice broadcasts will be made to convey the risk level of the current area to the operator. The operator will actively adjust the drone's heading or altitude based on voice prompts and visual feedback to ensure that it maintains a safe distance from the highway infrastructure and traffic flow.

8. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 7, characterized in that: In step S5, the fault fall path domains of the UAV caused by power system failure and control system failure are solved respectively. After obtaining the fall path domains of the UAV power system failure and control system failure, the convex hulls of the two different fault types are weighted and calculated to obtain the comprehensive fault fall path domain of the UAV at the current position; When solving the failure fall path domain of a UAV caused by power system failure and control system failure, the parameter space range is set respectively, and Latin hypercube sampling is used to generate N groups of parameter samples in the parameter space. For each group of parameters, based on the established six-degree-of-freedom dynamic model, the fourth-order Runge-Kutta method is used to obtain the UAV trajectory points, and then N trajectories are generated through the Monte Carlo sampling algorithm. Finally, the convex hull of the point set is calculated using the fast convex hull algorithm.

9. The risk identification and emergency response method for a UAV flying in the airspace near a highway according to claim 8, characterized in that: The expression of the fault state risk value in step S5 is: Among them, V overlap Indicates that at t n The intersection volume of the comprehensive fault fall path domain obtained at the moment and the global safety boundary, V(Conv(E UAV (t1))) means that at t n The volume of the integrated fault fall path domain obtained under the state at time t.

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