Fusion flight safety analysis method and system for fixed-wing unmanned aerial vehicle

Abstract

The invention belongs to but not limited to the technical field of flight safety, and particularly relates to a fixed-wing unmanned aerial vehicle fusion flight safety analysis method and system, and the method comprises the steps: S1, building an improved Event model; s2, longitudinal collision risk calculation; s3, calculating a lateral collision risk; and S4, calculating the collision risk in the vertical direction. The invention provides a collision risk assessment method based on an MGFPE improved model by improving an Event model. The provided collision assessment method is subjected to precision calculation, and compared with a traditional cuboid and sphere collision box, the MGFPE improved model is optimal in precision. The optimized safety interval is subjected to a simulation experiment, a certain airport in the southwest is introduced to serve as a simulation experiment scene, a capacity evaluation method based on the dynamic interval is used, the calculated leg capacity is 20.9 sortie, compared with the leg capacity 9.6 sortie before optimization, the leg capacity is improved by 2.18 times, and the leg capacity is greatly improved.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the technical field of flight safety, and particularly relates to a fixed-wing unmanned aerial vehicle integrated flight safety analysis method and system. Background Art

[0002] According to the "Implementation Plan for Accelerating the Cultivation and Development of the Low-Altitude Economy (2024 - 2027) and Several Measures", the implementation plan and measures for accelerating the cultivation and development of the low-altitude economy are proposed. The low-altitude economy will usher in a period of rapid development as an emerging comprehensive economic form. According to the "Interim Regulations on the Flight Management of Unmanned Aerial Vehicles (Decree No. 761 of the State Council)", the normal operation of unmanned aerial vehicles is mainly concentrated in segregated airspace. With the further deepening of the airspace opening degree, integrated flight in mixed airspace will become the main operation mode of unmanned aerial vehicles and manned aircraft. Therefore, the research on flight standards such as safety risks and safety intervals between the two or among multiple aircraft types is of great significance.

[0003] In the current research on safety risks and safety intervals at home and abroad, in the establishment and quantification of basic safety risk models: Professor Reich first proposed the Reich model in the civil aviation field to achieve the quantitative expression of the risk level of parallel routes. Based on the Reich model, Brooker proposed the Event model, which strengthened the model's ability to handle complex risk analysis. Bai et al. established a collision avoidance model for unmanned aerial vehicles (UAVs) based on Markov and solved it using the Monte Carlo method. Zhang et al. established a collision risk model for cylindrical UAVs in non-segregated airspace; in the research on safety risks and safety intervals in specific aircraft and specific airspace environments: Deng Li et al. established a collision probability model between manned aircraft and UAVs to solve the operational safety problem near the terminal area. Wang Lili et al. established a collision risk assessment model in different directions and a collision risk assessment model based on random speed distribution. Zhang Honghai et al. considered the characteristics of multi-rotor UAVs and established an air collision probability model for UAVs to calculate the safety interval of multi-rotor UAVs in free airspace. Wang Xinglong et al. established different types of safety intervals in the study of the safety intervals of multi-type eVTOLs in low-altitude urban areas; domestic and foreign scholars have integrated multiple factors in the comprehensive airspace, making the research on safety risks and safety intervals enter a more complex and systematic stage: Gao Yang et al. established a collision risk prediction model for general aviation and route passenger aircraft considering five dimensions of airspace, people, aircraft, environment, and management based on the flight characteristics of aircraft in the terminal area airspace. Gao Junjie et al. established a safety flight risk assessment model by combining factors such as the flight characteristics of UAVs, airspace conditions, flight modes, and air traffic controllers to make a reasonable risk assessment of the safe flight of UAVs. Zhou Duyi et al. proposed a method for identifying hot spots of flight area conflicts based on dynamic safety intervals for precise monitoring of flight area surface operation conflicts. In terms of the improvement and comprehensive evaluation of safety types: Different methods are used to improve the Event model and evaluate safety risks and safety intervals. Zhang Zhaoning et al. established a flight following model based on reaction time and proposed a calculation method for safety intervals considering reaction time. Yang Wenda et al. proposed a three-dimensional deterministic conflict detection algorithm based on the velocity obstacle method on the basis of an ellipsoidal protection area. Li Nan et al. established a safety risk model that comprehensively considers collision probability and conflict rate to comprehensively calculate the safety risks of UAVs in the airspace.

[0004] In view of the above analysis, the technical problems that urgently need to be solved in the existing technology are as follows: The existing research mostly focuses on small UAVs or between manned aircraft, lacking the calculation of collision risks between large fixed-wing UAVs and manned aircraft. Moreover, with the introduction of new policies and the update of actual operational requirements, the low-altitude flight scenario has gradually changed from non-segregated airspace and specific airspace to the integrated flight in mixed airspace. The low-altitude flight mission objectives in cities have also changed from single flight tests, experiments, etc. to multi-type and normalized operations. A complete mathematical description that integrates a more refined and quantifiable flight conflict model with the safety target level of the International Civil Aviation Organization urgently needs to be created. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method and system for analyzing the safety of fixed-wing UAV integrated flight. In order to improve the safety level of the integrated flight of large fixed-wing UAVs and manned aircraft, through the full discussion of the geometric characteristics in the spatial dimension, and the flight performance such as the aerodynamic characteristics and navigation accuracy of the aircraft, an improved Event collision model (Improved Event Model for Multidimensional Geometry and Flight Performance, MGFPE) is created to solve the problem of evaluating flight conflicts of composite factors, effectively improving the refinement and quantifiability of the results. Combining the flight conflict safety target levels of the International Civil Aviation Organization, a safety interval and application method for integrated flight procedure design oriented to safety risks are constructed. The simulation laboratory is carried out according to the operation conditions of small and medium-sized airports in the southwestern region, and the purpose of increasing airspace flow is achieved through the formulation of an integrated flight track planning scheme. The established improved model is compared with other models through contrast tests to verify the feasibility of the model.

[0006] The present invention is implemented as follows. A method for analyzing the safety of fixed-wing UAV integrated flight includes:

[0007] S1. Establish an improved Event model;

[0008] S2. Calculate the longitudinal collision risk;

[0009] S3. Calculate the lateral collision risk;

[0010] S4. Calculate the vertical collision risk.

[0011] Furthermore, in S1, the frequency of the aircraft crossing the interval layer and the probability of the aircraft appearing in the extended collision box are calculated through the generated extended collision box, so as to calculate the collision risk;

[0012] Longitudinal collision model:

[0013] Among them, the Generalized Event Risk Hypothesis (GERH), f GERHx represents the frequency of losing intervals per hour in longitudinal flight, P z (0) represents the lateral overlap probability of two aircraft at the same altitude layer, ux, uy, and uz respectively represent the relative velocities in the three-dimensional directions of longitudinal, lateral, and vertical, a, b, and c are the length, width, and height of the aircraft, Sy represents the lateral interval, and Q represents the probability that aircraft B is located within the longitudinal extended collision box.

[0014] Furthermore, an Improved Event Model for Multidimensional Geometry and Flight Performance (MGFPE) is established. Taking the center of gravity of the airliner as the centroid and using the ellipsoid parameters a, b, and c as the collision box. In a pair of conflicting aircraft, aircraft A is regarded as collision box A, and aircraft B is regarded as a particle on the separation layer. When A crosses the separation layer along the x, y, and z directions, if B is on the crossing path of A, it is regarded as a collision in the lateral, longitudinal, and vertical directions. Let the longitudinal, lateral, and vertical component velocities of the two aircraft be u x 、u y 、u z respectively. When the collision box enters the separation layer area and crosses the separation layer, an extended collision box will be generated. Since the traditional Event model has problems such as large calculation difficulty and low accuracy in calculating Q, resulting in large calculation errors, an area calculation formula based on the Monte Carlo method is proposed. The collision risks in the longitudinal, lateral, and vertical directions are calculated respectively through the improved calculation method. The collision risk is the product of the frequency of aircraft A crossing the separation layer and the probability of B appearing in the extended collision box.

[0015] Furthermore, S2 specifically includes:

[0016] When the collision box enters the separation layer area and crosses the separation layer, the generated extended collision box is ABCD. ABCD is used as the improved longitudinal extended collision box, and A1B1C1D1 is the area where the collision box crosses the separation layer. The area of the extended collision box is S ABCD ,the area of the area crossing the separation layer is S A1B1C1D1 ,the time for the collision box to cross the separation layer is t, JC = 2b, IC = 2c, and the area of the extended collision box is:

[0017] To improve the calculation accuracy, the Monte Carlo method is used for calculation.

[0018] Center coordinates:

[0019]

[0020] Ellipse equation:

[0021] The equation of the first ellipse T1:

[0022]

[0023] The equation of the second ellipse T2:

[0024]

[0025] Slope of the straight line:

[0026]

[0027] Equations of two tangent lines:

[0028] Equation of tangent line A1B1:

[0029]

[0030] Equation of tangent line C1D1:

[0031]

[0032] Substitute the tangent line equation into the first ellipse equation and convert it to the standard form:

[0033] A1x 2 + B1x + C1 = 0

[0034]

[0035] Substitute it into the second ellipse equation and convert it to the standard form:

[0036] A2x 2 + B2x + c2 = 0

[0037]

[0038] Use the quadratic formula to solve for the two solutions:

[0039]

[0040] Obtain the tangent point coordinates through calculation:

[0041]

[0042] Calculate the ratio of the area enclosed by the two ellipses and the two tangent lines to the total area through Monte Carlo, and calculate the probability Q that aircraft B is located within the longitudinal expansion collision box.

[0043] Calculation process of longitudinal collision risk:

[0044] N x = 2f GERHx * p y * p z

[0045]

[0046] Final expression of longitudinal collision risk:

[0047]

[0048] Furthermore, S3 specifically includes:

[0049] When the collision box enters the spacer layer region and traverses the spacer layer, the generated extended collision box is ABCD; center coordinates:

[0050]

[0051] Ellipse equation:

[0052] First ellipse T1 equation:

[0053]

[0054] Second ellipse T2 equation:

[0055]

[0056] Slope:

[0057]

[0058] Tangent equation:

[0059] Tangent A1B1 equation:

[0060]

[0061] Tangent C1D1 equation:

[0062]

[0063] The final expression of the improved lateral collision risk is:

[0064]

[0065] Furthermore, S4 specifically includes:

[0066] When the collision box enters the spacer layer region and traverses the spacer layer, the generated extended collision box is ABCD; vertical direction:

[0067]

[0068] Ellipse equation:

[0069] First ellipse T1 equation:

[0070]

[0071] Second ellipse T2 equation:

[0072]

[0073] Slope:

[0074]

[0075] Tangent equation:

[0076] Equation of tangent line A1B1:

[0077]

[0078] Equation of tangent line C1D1:

[0079]

[0080] The final expression of the vertical collision risk is:

[0081]

[0082] Another object of the present invention is to provide a fixed-wing UAV integrated flight safety analysis system for implementing the above-mentioned fixed-wing UAV integrated flight safety analysis method, including:

[0083] A model establishment module for establishing an improved Event model;

[0084] A longitudinal risk calculation module for calculating the longitudinal collision risk;

[0085] A lateral risk calculation module for calculating the lateral collision risk;

[0086] A vertical risk calculation module for calculating the vertical collision risk.

[0087] Another object of the present invention is to provide a computer device, which includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the above-mentioned fixed-wing UAV integrated flight safety analysis method.

[0088] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to execute the steps of the above-mentioned fixed-wing UAV integrated flight safety analysis method.

[0089] Another object of the present invention is to provide an information data processing terminal, which includes the above-mentioned fixed-wing UAV integrated flight safety analysis system.

[0090] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solutions to be protected by the present invention are:

[0091] The present invention proposes an airspace capacity optimization technology based on an improved Event collision model for multidimensional geometry and flight performance (MGFPE). This method constructs an improved model for spatial dimension geometric features and flight performance, and through the analysis of flight safety risks in the hybrid airspace fusion, refined and quantifiable three-dimensional safety intervals in space are obtained. Compared with the simple cuboid or sphere collision boxes in traditional methods, through the full discussion of the geometric features of spatial dimensions and flight performance such as the aerodynamic characteristics and navigation accuracy of aircraft, the creation of the improved Event collision model for multidimensional geometry and flight performance (MGFPE) can more accurately consider the actual geometric features of aircraft, including wingspan, fuselage length, etc., avoiding the problem of reduced airspace capacity caused by overestimating the collision volume in traditional methods. Through a three-dimensional collaborative calculation method, the present invention significantly improves the accuracy of safety interval calculation.

[0092] In the process of collision risk assessment, the present invention uses the MGFPE improved model to conduct a precision comparative analysis with the traditional cuboid and sphere collision box models. The experimental results show that in the calculation of longitudinal collision risk, the Q value of the MGFPE improved model is 0.469667, significantly better than 0.837312 of the cuboid model and 0.563421 of the sphere model; in lateral collisions, the Q value of the MGFPE improved model is 0.145764, also better than 0.433783 of the cuboid model and 0.429063 of the sphere model; in the calculation of vertical collision risk, the Q value of the MGFPE improved model is 0.69831, showing better overall performance compared with 0.927036 of the cuboid model and 0.502096 of the sphere model. The above data indicate that the MGFPE improved model can more accurately calculate the three-dimensional safety interval, thereby optimizing the airspace capacity.

[0093] To verify the actual effect of the improved method proposed by the present invention, a certain airport in the southwest is introduced as a simulation experiment scenario in this paper, and a capacity assessment method based on dynamic intervals is used for verification. The experimental results show that the optimized flight segment capacity is 20.9 flights per hour, which is 2.18 times higher than 9.6 flights per hour before optimization. This result indicates that optimizing the airspace capacity through the MGFPE improved model can significantly improve the utilization efficiency of limited airspace, providing technical support for the design of future high-density air routes.

[0094] In the actual application process, the innovation of the present invention lies in constructing a collision model that conforms to the actual shape of the aircraft, and comprehensively evaluating the safety intervals in the longitudinal, lateral, and vertical directions by establishing a three-dimensional probability model. Different from the traditional collision risk decomposition method, the three-dimensional collaborative calculation method of the present invention can more accurately capture the coupling effect of the multi-dimensional movement of the aircraft, thereby significantly improving the calculation accuracy and rationality of the safety interval.

[0095] In terms of economic benefits, the simulation verification of an airport in the southwestern region by the present invention shows that by optimizing the safety interval, the flight segment capacity has been increased from 9.6 flights per hour to 20.9 flights per hour, greatly improving the airspace capacity and the utilization efficiency of the limited airspace. In the commercial application scenario, the reasonable safety interval calculation method proposed by the present invention can provide support for the design of future high-density air routes, and provide a theoretical basis and technical guarantee for the multi-aircraft collaborative operation.

[0096] The improved method proposed by the present invention overcomes the bias in the traditional technology. In the aviation field, it is generally believed that the sphere model can be equivalent to a complex shape in a statistical sense. Especially in the field of unmanned aerial vehicles, the sphere model is widely used for the establishment of collision boxes. However, due to its physical characteristics, the sphere model cannot accurately reflect the aerodynamic shape of the aircraft, resulting in significant errors in the calculation of collision risk. By introducing the ellipsoid model, the present invention not only matches the actual shape of the aircraft better in geometric modeling, but also significantly optimizes the safety interval by constructing a three-dimensional probability model in the longitudinal, lateral, and vertical directions.

[0097] In future applications, the technical solution of the present invention can be further extended to aspects such as high-density traffic management, airspace capacity optimization, and multi-aircraft collaborative operation in complex airspace environments. By introducing a more refined three-dimensional risk assessment model, the present invention provides important technical support for the efficient utilization and safety management of airspace resources, laying a foundation for the optimization of future airspace operation modes. Brief Description of the Drawings

[0098] Figure 1 is a schematic diagram of the Reich basic model provided by an embodiment of the present invention;

[0099] Figure 2 is a schematic diagram of the Event basic model provided by an embodiment of the present invention;

[0100] Figure 3 is a schematic diagram of the extended collision box provided by an embodiment of the present invention;

[0101] Figure 4 is a schematic diagram of the improved Event model provided by an embodiment of the present invention;

[0102] Figure 5Schematic diagram of the improved longitudinal collision risk model provided by the embodiments of the present invention;

[0103] Figure 6 Schematic diagram of the improved longitudinal extended collision box provided by the embodiments of the present invention;

[0104] Figure 7 Schematic diagram of the improved lateral collision risk model provided by the embodiments of the present invention;

[0105] Figure 8 Schematic diagram of the improved lateral extended collision box provided by the embodiments of the present invention;

[0106] Figure 9 Schematic diagram of the improved vertical collision risk model provided by the embodiments of the present invention;

[0107] Figure 10 Schematic diagram of the improved vertical extended collision box provided by the embodiments of the present invention;

[0108] Figure 11 Schematic diagram of the Q values of the improved model in the longitudinal, lateral, and vertical directions provided by the embodiments of the present invention;

[0109] Figure 12 Schematic diagram of the comparison of the Q values of the three models in the three directions provided by the embodiments of the present invention;

[0110] Figure 13 Schematic diagram of the longitudinal safety interval of the three models provided by the embodiments of the present invention;

[0111] Figure 14 Schematic diagram of the lateral safety interval of the three models provided by the embodiments of the present invention;

[0112] Figure 15 Schematic diagram of the vertical safety interval of the three models provided by the embodiments of the present invention;

[0113] Figure 16 Schematic diagram of the original airspace and protected area scope provided by the embodiments of the present invention;

[0114] Figure 17 Schematic diagram of the optimized airspace and protected area scope provided by the embodiments of the present invention;

[0115] Figure 18 Schematic diagram of the optimized arrival and departure separation mode provided by the embodiments of the present invention;

[0116] Figure 19 Schematic diagram of the comparison before and after the optimization of the program design provided by the embodiments of the present invention;

[0117] Figure 20 Flowchart of the fixed-wing UAV integrated flight safety analysis method provided by the embodiments of the present invention;

[0118] Figure 21 It is the structural diagram of the fixed-wing UAV integrated flight safety analysis system provided by the embodiments of the present invention. Detailed implementation manners

[0119] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0120] The fixed-wing UAV integrated flight safety analysis method proposed by the present invention conducts refined analysis on the flight safety risks of UAVs through an improved Event model. First, in step one, an improved Event model is established. This model takes the flight path, speed and attitude data of the fixed-wing UAV as input parameters, and comprehensively considers the influence of uncertainty factors such as environmental factors (such as wind speed, wind direction, air pressure), communication delay, and positioning error on the flight safety of the UAV. In particular, an adaptive data fusion algorithm based on Kalman filtering and particle filtering is introduced into the improved model, so as to be able to more accurately identify the actual flight state of the UAV and its potential risk factors. The construction of this model provides high-precision basic data support for subsequent risk calculation and assessment.

[0121] In step two, based on the established improved Event model, collision risk models in the longitudinal, lateral and vertical directions are respectively constructed. The longitudinal collision risk model takes the Extended Collision Box (ECB) as the core, and calculates the frequency of the UAV passing through the interval layer during the trajectory planning process by introducing the uncertainty distribution characteristics of the flight path. Specifically, the longitudinal extended collision box calculates the collision risk quantification index by fusing the dynamic characteristics of the UAV flight speed and attitude change, and using the intersection of the path probability density function and the trajectory intersection area. This index can not only reflect the degree of collision risk of the UAV in the longitudinal direction, but also provide an optimization basis for the dynamic safety interval in the multi-UAV cooperative flight scenario.

[0122] The establishment of the lateral collision risk model (Step 3) mainly uses the Monte Carlo method to analyze the collision risk between the UAV and surrounding obstacles or other aircraft in the lateral direction. By constructing a random distribution model of the UAV's lateral motion trajectory and combining the surrounding environment information (such as the spatial position distribution of obstacles, relative speed, etc.), the Monte Carlo method can simulate the UAV's lateral motion process with high precision. In the simulation calculation, repeated sampling and probability statistics are used to quantify the lateral collision probability distribution under different risk levels. By comparing with the traditional static collision risk model, the lateral collision risk model proposed by the present invention can more accurately reflect the interaction between the UAV and dynamic obstacles during the actual flight process, thereby improving the accuracy and real-time performance of collision risk assessment.

[0123] In Step 4, by comprehensively analyzing the collision probabilities in the longitudinal, lateral, and vertical directions, a comprehensive evaluation framework for the flight safety risk of the UAV is constructed. This framework fuses the collision probabilities in different directions through the weighted factor method or the fuzzy logic algorithm to obtain the overall flight safety risk value. Based on the magnitude of the risk value, the flight safety risk is further classified, and risk warning information is generated in real time. In particular, the real-time risk assessment mechanism proposed by the present invention combines edge computing and cloud computing technologies to achieve a low-latency and high-precision risk warning function in the UAV flight control system. Through comparison and verification with actual flight data, this method can significantly improve flight safety in complex airspace environments and provide a scientific basis for the safe operation of UAVs.

[0124] 1 Establishment of the collision model

[0125] 1.1 Reich model

[0126] The basic model was proposed by Professor P.G. Reich in 1966. By establishing a collision template and an adjacent layer around the center point A of the aircraft, and regarding the center point B of another aircraft as a particle, the sum of the probabilities of the particle B entering the collision template in multiple dimensions such as lateral, longitudinal, and vertical is calculated, and the number of collisions between the two aircraft is derived. The basic model is as Figure 1 shown. Thus, a mathematical description of the number of collisions between two aircraft per unit time is established, realizing the quantitative calculation of the multi-dimensional collision probability between two aircraft.

[0127] 1.2 Event basic model

[0128] Since the Reich collision basic model has the ability to calculate the multi-dimensional collision probability, there is room for optimization in the refinement method and degree of the multi-dimensional flight performance characteristics. Therefore, the Event basic model is proposed based on the Reich collision basic model by taking the refinement of the fuzziness of each parameter as the improvement direction. Event fully considers parameter preprocessing and the motion characteristics of the aircraft, improving the calculation ability of the model, asFigure 2 As shown Figure 3 It is the extended collision box generated when the cuboid collision box enters the spacer layer area and traverses the spacer layer. Through the generated extended collision box, the frequency of the aircraft traversing the spacer layer and the probability of the aircraft appearing in the extended collision box can be calculated, so as to calculate the collision risk.

[0129] Longitudinal collision model:

[0130] Among them, the Generalized Event Risk Hypothesis (GERH), f GERHx represents the frequency of losing the interval per hour during longitudinal flight, and P z (0) represents the lateral overlap probability of two aircraft at the same altitude layer, and u x , u y , u z represent the relative velocities in the three-dimensional directions of longitudinal, lateral, and vertical respectively. a, b, and c are the length, width, and height of the aircraft, and S y represents the lateral interval, and Q represents the probability that aircraft B is located within the longitudinal extended collision box.

[0131] 1.3 Establish an improved Event model

[0132] Testing the collision box for parameter ambiguity and performance characteristics reveals that the ellipsoid collision model has advantages in the mathematical expression of aircraft size and collision development status, etc., and can more precisely describe the vertical interval of the aircraft to achieve the purpose of reducing errors and improving airspace utilization, as Figure 4 shown

[0133] 2 Calculation of collision risk for the improved Event model

[0134] By establishing the MGFPE improved model and the collision box, with the center of gravity of the airliner as the centroid and the ellipsoid parameters a, b, and c as the collision box, in a pair of conflicting aircraft, aircraft A is regarded as collision box A, and aircraft B is regarded as a particle on the spacer layer. When A traverses the spacer layer along the x, y, and z directions, if B is on the traversing path of A, it is regarded as a lateral, longitudinal, or vertical collision. Let the longitudinal, lateral, and vertical component velocities of the two aircraft be u x , uy, u z . When the collision box enters the spacer layer area and traverses the spacer layer, an extended collision box will be generated. Since the traditional Event model has problems such as large calculation difficulty and low accuracy when calculating Q, resulting in large calculation errors, an area calculation formula based on the Monte Carlo method is proposed, and the collision risks in the longitudinal, lateral, and vertical directions are calculated respectively through the improved calculation method. The collision risk is the product of the frequency of aircraft A traversing the spacer layer and the probability of B appearing in the extended collision box.

[0135] 2.1 Longitudinal, lateral, vertical and other multi-dimensional direction collision risk calculation

[0136] 2.1.1 Improved longitudinal collision risk model

[0137] The improved Event longitudinal collision risk model is as Figure 5 shown below:

[0138] When the collision box enters the interval layer area and crosses the interval layer, the generated extended collision box is ABCD, as Figure 6 shown below:

[0139] Figure 6 In the figure, ABCD is the improved longitudinal extended collision box, and A1B1C1D1 is the area where the collision box crosses the interval layer. The area of the extended collision box is S ABCD , the area of the area where it crosses the interval layer is S A1B1C1D1 , the time for the collision box to cross the interval layer is t, JC = 2b, IC = 2c, and the area of the extended collision box is:

[0140] To improve the calculation accuracy, the Monte Carlo method is used for calculation.

[0141] Center coordinates:

[0142]

[0143] Ellipse equation:

[0144] The equation of the first ellipse T1:

[0145]

[0146] The equation of the second ellipse T2:

[0147]

[0148] Slope of the line:

[0149]

[0150] Equations of two tangents:

[0151] Equation of tangent A1B1:

[0152]

[0153] Equation of tangent C1D1:

[0154]

[0155] Substitute the tangent equation into the first ellipse equation and convert it to the standard form:

[0156] A1x 2 +B1x + C1 = 0

[0157]

[0158] Substitute it into the second ellipse equation and convert it to the standard form:

[0159] A2x 2 +B2x + c2 = 0

[0160]

[0161] Use the quadratic formula to solve for the two solutions:

[0162]

[0163] Obtain the tangent point coordinates through calculation:

[0164]

[0165] Calculate the ratio of the area enclosed by the two ellipses and the two tangents to the total area through Monte Carlo, and calculate the probability Q that aircraft B is located within the longitudinal extended collision box.

[0166] Longitudinal collision risk calculation process:

[0167] N x = 2f GERHx *p y *p z

[0168]

[0169] Final expression of longitudinal collision risk:

[0170]

[0171] 2.1.2 Improved lateral collision risk model

[0172] The improved Event lateral collision risk model is as Figure 7 shown. When the collision box enters the interval layer area and crosses the interval layer, the generated extended collision box is ABCD, as Figure 8 shown:

[0173] Center coordinates:

[0174]

[0175] Ellipse equation:

[0176] Equation of the first ellipse T1:

[0177]

[0178] Equation of the second ellipse T2:

[0179]

[0180] Slope:

[0181]

[0182] Tangent equation:

[0183] Equation of the tangent A1B1:

[0184]

[0185] Equation of the tangent C1D1:

[0186]

[0187] From 2.1.1, the final expression of the improved lateral collision risk is:

[0188]

[0189] 2.1.3 Improved vertical collision risk model

[0190] The improved Event vertical collision risk model is as Figure 9 shown. When the collision box enters the spacer layer area and crosses the spacer layer, the generated extended collision box is ABCD, as Figure 10 shown:

[0191] Vertical direction:

[0192]

[0193] Ellipse equation:

[0194] Equation of the first ellipse T1:

[0195]

[0196] Equation of the second ellipse T2:

[0197]

[0198] Slope:

[0199]

[0200] Tangent equation:

[0201] Equation of the tangent A1B1:

[0202]

[0203] Equation of the tangent line C1D1:

[0204]

[0205] As can be seen from 2.1.1, the final expression of the vertical collision risk is:

[0206]

[0207] 3 Experimental Simulation and Analysis

[0208] 3.1 Parameter Settings

[0209] The following are the main parameters for calculating the collision risk: the size of the collision box, the overlapping probabilities and speeds in the longitudinal, lateral, and vertical directions. The manned aircraft selected is the A320, and the unmanned aircraft selected is the Twin-Tail Scorpion unmanned aircraft. The specific parameters are as follows:

[0210] Table 1 Calculation Parameters

[0211]

[0212] 3.2 Model Verification

[0213] 3.2.1 Comparative Experiment

[0214] Compare the improved Event ellipsoid collision model with the traditional cuboid collision model and sphere collision model, calculate the ratio of the Q values before and after improvement, analyze the collision risk model, compare the collision risk values in the longitudinal, lateral, and vertical directions, and combine with the safety target level specified by ICAO: 5.0*10 -9 times / flying hour to calculate the safety intervals in the longitudinal, lateral, and vertical directions.

[0215] Perform Monte Carlo calculations on the MGFPE improved model, perform iterative calculations in units of ten thousand, and finally obtain the Q value based on the Monte Carlo calculation method, as Figure 11 shown. After averaging, the Q values of the MGFPE improved model in the longitudinal, lateral, and vertical directions are finally: 0.469667, 0.145764, and 0.69831 respectively.

[0216] The comparison chart of the Q values of the three models in the three directions is as Figure 12As shown in the figure, in terms of longitudinal collision, the values of Q for the cuboid, sphere, and MGFPE improved model are 0.837312, 0.563421, and 0.469667 respectively; in terms of lateral collision, the Q values of the cuboid, sphere, and MGFPE improved model are 0.433783, 0.429063, and 0.145764 respectively; in terms of collision in the vertical direction, the Q values of the cuboid, sphere, and MGFPE improved model are 0.927036, 0.502096, and 0.69831 respectively.

[0217] According to the analysis of the obtained data, it can be seen that the calculation results of the Q values of the improved MGFPE improved model in the longitudinal and lateral directions are significantly better than those of the cuboid and sphere. Generally speaking, the calculation advantage of the ellipsoid is significant, and it can optimize the safety interval to a great extent.

[0218] 3.2.2 Safety Interval Calculation

[0219] Through model calculation, the minimum safety intervals in the longitudinal, lateral, and vertical directions that meet the safety target level are obtained as Figure 13 、 Figure 14 、 Figure 15 shown.

[0220] Figure 13 、 14 、The horizontal lines in 15 are the safety target levels stipulated by ICAO, and the curves are the corresponding collision risks. It can be seen from Figure 13 、 14 、15 that as the interval increases, the collision risk will decrease accordingly. By analyzing the three figures, it can be seen that under the condition of meeting the safety target level, the safety intervals in the longitudinal, lateral, and vertical directions of the MGFPE improved model are all optimal. The calculation results are shown in Table 2:

[0221] Table 2 Calculation Results of Safety Interval Values

[0222]

[0223] It can be seen from the comparative analysis in Table 2 that the optimization of the safety interval by the MGFPE improved model is better than that of the cuboid and sphere. In terms of the selection of the optimal safety interval, according to the analysis, the longitudinal safety interval of the MGFPE improved model is selected as the optimal safety interval, and the safety interval value is about 4329, approximately equal to 4500 meters.

[0224] 3.2.3 Application of Integrated Flight Procedure Design

[0225] Based on the safety interval calculation results of 3.2.2, taking a certain airport in the southwest as an example, the optimized safety interval is verified according to the "dynamic safety interval" and the "risk collision standard". By creating an arrival and departure separation mode for the transfer flights between the two airports, a design and optimization plan for the flight tracks of unmanned aerial vehicles (UAVs) and manned aircraft in a mixed airspace that meets the civil aviation flight safety standards is constructed under limited airspace conditions, thereby improving airspace traffic flow.

[0226] Figure 16 As shown, it is the airspace environment and protected area scope of a certain airport in the southwest. Due to airspace constraints, only a single-route flight procedure can be designed between the two airports. Figure 17 The protected area scope is redrawn according to the optimized safety interval. From Figure 17 it can be seen that after the safety interval is optimized, the existing plannable airspace scope can be increased. Figure 18 The flight procedure between the two airports is redesigned in the optimized airspace. By adding a baseline turn procedure, arrival and departure separation between the two airports is achieved.

[0227] Figure 19 Taking a certain airport in the southwest as an example, the left part in Figure 19 shows that if the original protected area is not optimized and a baseline turn procedure is added, there will be a problem of protected area conflict. After optimization, the available airspace scope expands, and at the same time, the design requirements of the baseline turn procedure are met. As shown in the right part in

[0228] Therefore, arrival and departure procedure separation can be achieved by adding a baseline turn procedure, so that a more reasonable and efficient flight procedure design can be carried out in a complex airspace environment, and airspace utilization rate and operation traffic flow can be improved at the operation level.

[21] Taking a certain airport in the southwest as an example, before optimization, the route length between the two airports was 92,600 meters. After adding the arrival and departure procedures, the route length is 209,000 meters. According to the "Interim Regulations on the Flight Management of Unmanned Aerial Vehicles", it is required that UAVs must maintain at least 10 kilometers (5.4 nautical miles) from manned aircraft or comply with air traffic control requirements.

[0229] As Figure 20 shown, the flight safety analysis method for fixed-wing UAV integration includes:

[0230] S1, establishing an improved Event model;

[0231] S2, calculating the longitudinal collision risk;

[0232] S3, calculating the lateral collision risk;

[0233] S4. Vertical direction collision risk calculation.

[0234] As Figure 21 shown, the fixed-wing UAV integrated flight safety analysis system includes:

[0235] A model establishment module for establishing an improved Event model;

[0236] A longitudinal risk calculation module for calculating the longitudinal collision risk;

[0237] A lateral risk calculation module for calculating the lateral collision risk;

[0238] A vertical risk calculation module for calculating the vertical direction collision risk.

[0239] The fixed-wing UAV integrated flight safety analysis method of the present invention first establishes an improved Event collision risk analysis model through the model establishment module. During the model establishment process, the fixed-wing UAV is taken as the analysis object, and the flight path, speed, and attitude data of the UAV are entered into the system as basic information. At the same time, various uncertainty factors such as environmental factors, communication delay, and positioning accuracy are considered to form the basis of the integrated risk analysis model. The improved Event model can more accurately reflect the actual flight situation of the UAV and possible collision risk scenarios, providing accurate input data for subsequent risk calculations.

[0240] Based on the established improved Event model, the longitudinal risk calculation module uses the longitudinal safety distance and UAV speed distribution data to quantitatively calculate the longitudinal collision risk between UAVs or between UAVs and other air targets. Specifically, by analyzing the longitudinal flight speed difference, the possibility of track overlap, and the uncertainty factors of communication and navigation, the longitudinal collision probability is calculated, and the longitudinal risk quantification index is obtained through statistical methods, clearly revealing the probability and severity of longitudinal collision events.

[0241] Based on the longitudinal risk analysis, the lateral risk calculation module further considers factors such as the lateral deviation of the UAV flight path, lateral position error, and wind speed influence to conduct a refined analysis and calculation of the lateral collision risk. This module uses the UAV trajectory prediction algorithm to calculate the lateral distance distribution characteristics between the UAV and obstacles or other aircraft, and uses probability methods to determine the risk level and probability distribution of lateral collisions, thus helping decision-makers accurately identify potential lateral collision risks.

[0242] The vertical risk calculation module quantifies the collision risk in the vertical direction between a fixed-wing UAV and other aircraft or obstacles based on the uncertainty of the vertical flight path and the characteristics of altitude deviation. The module first determines the safe vertical separation, establishes a risk calculation function through factors such as flight altitude distribution, vertical speed change, navigation accuracy, and communication delay, and finally obtains a quantitative index of vertical collision risk to evaluate the possibility of vertical collision and the risk level, providing a key reference for the flight safety strategy of the UAV.

[0243] The present invention comprehensively analyzes the risk indicators in the longitudinal, lateral, and vertical dimensions to form a fused multi-dimensional collision risk evaluation result. The system automatically identifies high-risk areas or flight phases with a higher risk level according to the requirements of the actual flight mission and the threshold of the risk level, and provides corresponding safety warnings and risk prompts. The system visualization module displays the comprehensive result of the risk analysis in the form of intuitive charts and risk maps to help operators and supervisors quickly understand the safety status of UAV flight.

[0244] The above modules achieve real-time communication and data sharing through data interfaces. The data output of the model establishment module serves as the data input of the longitudinal, lateral, and vertical risk calculation modules to ensure the consistency and coherence of risk analysis. At the same time, the analysis results of each module are fed back to the system database, and the risk model parameters are continuously updated to achieve the dynamic adjustment and real-time optimization of the risk model. This method has strong real-time performance, accuracy, and dynamic adaptability, can significantly improve the flight safety of fixed-wing UAVs, and effectively prevent the occurrence of air collision accidents.

[0245] The application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for analyzing the integrated flight safety of a fixed-wing UAV.

[0246] The application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for analyzing the integrated flight safety of a fixed-wing UAV.

[0247] The application embodiment of the present invention provides an information data processing terminal, which includes a system for analyzing the integrated flight safety of a fixed-wing UAV.

[0248] Figure 16 It is the airspace environment and protected area scope of an airport in the southwest. Due to airspace restrictions, only a one-way flight procedure can be designed between the two airports. In actual operation, there are problems such as limited operation efficiency, easy air traffic congestion, and high safety risks and control difficulties, which restrict the further improvement of the scale and operation efficiency of the airport.

[0249] Figure 17 The protected area range has been redrawn according to the optimized safety interval, reducing the original protected area range of 10,000 m to a protected area range of 4,500 m. Through the optimization of the safety interval, the existing available airspace range is increased, and the flight procedures for the airspace can be redesigned. The effect after the redesign is as Figure 18 shown.

[0250] After the optimization of the safety interval, the available range of the current airspace can be increased. Taking Figure 18 as an example, the original protected area range is optimized. The airspace after optimization meets the requirements of the baseline turn procedure design. A new baseline turn procedure is added between the two airports to achieve the separation of arrivals and departures between the two airports. Thus, more efficient and reasonable flight procedure design can be carried out in a complex airspace environment, and the airspace utilization rate and operation flow can be improved at the operation level. In the actual simulation experiment, the route length between the two airports before optimization is 92,600 meters. After adding the arrival and departure procedures, the route length is 209,000 meters. According to the "Interim Regulations on the Flight Management of Unmanned Aerial Vehicles", the segment capacity is calculated. After establishing the arrival and departure separation procedure, the segment capacity is increased from 9.2 flights to 20.9 flights.

[0251] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those of ordinary skill in the art can understand that the above devices and methods can be implemented using computer-executable instructions and / or included in processor control code, such as provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable logic devices such as field programmable gate arrays, or can be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software, such as firmware.

[0252] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope disclosed by the present invention, as long as they are made within the spirit and principle of the present invention, should be covered by the protection scope of the present invention.

Claims

1. A method for analyzing the integrated flight safety of a fixed-wing unmanned aerial vehicle, characterized in that, It includes the following steps: Step 1: Establish an improved Event model, taking the flight path, speed, and attitude data of a fixed-wing UAV as input parameters, and comprehensively considering environmental factors, communication delays, and positioning errors; Step 2: Based on the improved Event model, establish a longitudinal collision risk model, a lateral collision risk model, and a vertical collision risk model respectively; Step 3: Calculate the collision probabilities of the fixed-wing UAV in the longitudinal, lateral, and vertical directions according to the established collision risk models; Step 4: Based on the calculated collision probabilities, evaluate the flight safety risks of the fixed-wing UAV and divide the risk levels, and generate risk warning information in real time.

2. The method according to claim 1, wherein The longitudinal collision risk model calculates the frequency of the aircraft passing through the separation layer and the probability of the aircraft appearing in the longitudinal extended collision box based on the extended collision box, and obtains the longitudinal collision risk quantification index.

3. The fixed-wing UAV integrated flight safety analysis method according to claim 1, characterized in that The lateral collision risk model uses the Monte Carlo method to calculate the lateral distance distribution characteristics between the aircraft and obstacles or other aircraft to obtain the lateral collision risk level and probability distribution.

4. The fixed-wing UAV integrated flight safety analysis method according to claim 1, wherein The vertical collision risk model obtains the vertical collision risk quantification index by establishing a risk calculation function for flight altitude distribution, vertical speed change, and navigation accuracy.

5. The fixed-wing UAV integrated flight safety analysis method according to claim 1, wherein, The method further includes grading the risks according to the magnitude of the collision probabilities and setting corresponding safety warnings and risk tips for different risk levels.

6. The fixed-wing UAV integrated flight safety analysis method according to claim 1, wherein, The method further includes storing and analyzing the UAV flight historical data through a knowledge graph to realize the dynamic adjustment and real-time optimization of the risk model parameters.

7. A fixed-wing UAV integrated flight safety analysis system for implementing the fixed-wing UAV integrated flight safety analysis method according to any one of claims 1 to 6, characterized in that, It includes: A model establishment module for establishing an improved Event model; A longitudinal risk calculation module for calculating the longitudinal collision risk; A lateral risk calculation module for calculating the lateral collision risk; A vertical risk calculation module for calculating the vertical collision risk.

8. A computer device, which includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the fixed-wing UAV integrated flight safety analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to execute the steps of the fixed-wing UAV integrated flight safety analysis method according to any one of claims 1 to 6.

10. An information data processing terminal, which includes the fixed-wing UAV integrated flight safety analysis system according to claim 7.

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