Airspace risk modeling method for autonomous operation scene

Abstract

The invention discloses an airspace risk modeling method for an autonomous operation scene, and belongs to the field of air transportation safety. In order to meet the requirements of airspace risk assessment refinement and instantaneity in a future autonomous operation mode, firstly, an aircraft ellipsoid risk influence area is established to determine a risk influence range, and an airspace is rasterized to improve the model calculation efficiency; secondly, constructing an airspace risk assessment model by comprehensively considering the real-time risk influence of factors such as distance, speed and course on each grid of the airspace based on an artificial potential field theory; setting a simulation scene, calculating the correlation between the conflict index and the airspace risk value, and explaining the reasonability of the model; and finally, providing an airspace hot spot grid marking method, and visualizing airspace hot spot distribution in combination with various grid scales. Based on the artificial potential field theory, the invention provides a quick calculation method for the risk of any position point of the airspace under an aircraft autonomous operation model, and realizes multi-particle perception of the airspace security situation from the perspective of spatial geography by combining a hot spot grid marking method and multiple scales of grids. And a reference is provided for air traffic management and track adjustment of an airborne auxiliary system in a future autonomous operation mode.

Description

Technical Field

[0001] The present invention relates to the field of civil aviation transportation safety, and in particular to an airspace risk modeling method for autonomous operation scenarios. Background Art

[0002] In order to meet the requirements for accuracy and timeliness of airspace risk assessment under future autonomous operation modes and adapt to the complex aviation environment with high flight density and multiple aircraft types in the future, there is an urgent need for an airspace risk assessment method under the aircraft autonomous operation mode to enhance the ability to recognize the airspace safety situation under the future autonomous operation mode.

[0003] At present, many experts and scholars focus on the research on airspace safety situation awareness, taking conflicts between aircraft as the starting point to measure the airspace safety situation or to endow air traffic with physical structure based on complex networks to achieve a global assessment of the airspace safety situation. However, existing research on airspace operation risk assessment focuses on the traditional air traffic management model, and few studies focus on the future autonomous operation model. In the autonomous operation mode, aircraft rely on their own perception, decision-making and control systems to complete flight missions. Real-time risk measurement is the key to ensuring the safety and reliability of autonomous operation. Existing research is difficult to meet the requirements for real-time risk measurement at any point in the airspace.

[0004] Therefore, it is necessary to provide an airspace risk modeling method for autonomous operation scenarios to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an airspace risk modeling method for autonomous operation scenarios, which comprehensively considers factors such as distance, speed, and heading to achieve real-time risk assessment of each location point in the airspace, and combines the airspace hotspot grid marking method to achieve multi-granular perception of the airspace safety situation from a spatial geographic perspective.

[0006] To achieve the above object, the present invention provides an airspace risk modeling method for autonomous operation scenarios, comprising the following steps:

[0007] Step 1: Establish the aircraft ellipsoid risk impact area to determine the risk impact range and rasterize the airspace;

[0008] Step 2: Based on the artificial potential field theory, an airspace risk assessment model is constructed by comprehensively considering the real-time risk impact of factors such as distance, speed, and heading on each grid in the airspace;

[0009] Step 3: Set up a simulation scenario, calculate the correlation between the conflict index and the airspace risk value, and verify the rationality of the model;

[0010] Step 4: Propose a spatial hotspot grid labeling method and visualize the spatial hotspot distribution by combining multiple grid scales.

[0011] Step 1 specifically includes:

[0012] Step 1.1: Establish the aircraft ellipsoid risk impact area;

[0013] Step 1.2: Rasterization of the airspace.

[0014] Step 2 specifically includes:

[0015] Step 2.1: Based on the artificial potential field theory, construct a general expression for the impact of distance, speed, heading and other factors on the real-time risk of each location in the airspace;

[0016] Step 2.2: Modify the distance risk function;

[0017] Step 2.3: Based on the risk influencing factors such as distance, speed, and heading angle, a risk calculation formula for specific airspace locations is proposed.

[0018] Step 3 specifically includes:

[0019] Step 3.1: Set up the autonomous running simulation scenario;

[0020] Step 3.2: Calculate the correlation between the conflict index and the airspace risk value.

[0021] Step 4 specifically includes:

[0022] Step 4.1: Spatial hotspot grid marking;

[0023] Step 4.2: Visualize the spatial hotspot distribution by combining multiple grid scales.

[0024] The beneficial effects of the present invention are as follows: based on the theory of artificial potential field, the present invention proposes a method for quickly calculating the risk of any point in the airspace under the aircraft autonomous operation model, and combines the hotspot grid marking method and multiple scale grid sizes to achieve multi-granular perception of the airspace safety situation from a spatial geographic perspective, providing a reference for air traffic management under future autonomous operation modes and track adjustment of airborne auxiliary systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 : Flowchart of an airspace risk modeling approach for autonomous operation scenarios.

[0026] Figure 2 : Schematic diagram of aircraft ellipsoid risk area for an airspace risk modeling approach for autonomous operation scenarios.

[0027] Figure 3 : Schematic diagram of an airspace grid for an airspace risk modeling approach for autonomous operation scenarios.

[0028] Figure 4 : Schematic diagram of the risk impact of aircraft on the surrounding airspace in an airspace risk modeling method for autonomous operation scenarios, where the upper left is the XY cross-sectional risk impact, the upper right is the XZ cross-sectional risk impact, and the lower middle is the three-dimensional risk impact.

[0029] Figure 5 : Schematic diagram of airspace hotspot distribution for an airspace risk modeling method for autonomous operation scenarios, where the upper left is the 10km grid airspace hotspot distribution, the upper right is the 5km grid airspace hotspot distribution, and the lower middle is the 1km grid airspace hotspot distribution. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described below with reference to the accompanying drawings.

[0031] The present invention discloses an airspace risk modeling method for autonomous operation scenarios, and the specific implementation process is as follows: Figure 1 As shown, the following steps are included:

[0032] Step 1: Establish the aircraft ellipsoid risk impact area to determine the risk impact range and rasterize the airspace;

[0033] Step 1.1: Establish the aircraft ellipsoid risk impact area, such as Figure 2 As shown. The major semi-axis of the ellipsoid is a, the minor semi-axis is b, and the vertical semi-axis is c. The mathematical expression is: O is the position of the aircraft, P is any point in the airspace, and θ is The angle between the aircraft's heading and the

[0034] Step 1.2: Rasterize the airspace. The coordinates describing the grid layout are defined as grid coordinates, while the coordinates describing the specific location points are defined as actual coordinates. The grid coordinates are represented by G(X,Y,Z), and the representative grid coordinates are (X,Y,Z), where X, Y, and Z represent the arrangement order of the grid on the three axes, and the actual coordinates are represented by P(X,Y,Z). Use a rectangular grid to divide the airspace, such as Figure 3 As shown, the length, width and height are g respectively. len , g wid and g hei , the actual coordinates of the origin of the coordinate system are (x0, y0), then the actual coordinates of the 9 important positions of G(X,Y,Z) are expressed as follows:

[0035]

[0036] Where: P1 to P8 correspond to vertices 1 to 8 of the rectangular grid, P C Represents the center point of the rectangular grid.

[0037] The airspace is divided into rectangular grids with a length and width of 10 km and a height of 300 m. In order to minimize the error in grid risk calculation, the risk values ​​at nine important locations of the grid are measured and their average is processed to obtain the risk value of the entire grid.

[0038] Step 2: Based on the artificial potential field theory, an airspace risk assessment model is constructed by comprehensively considering the real-time risk impact of factors such as distance, speed, and heading on each grid in the airspace;

[0039] Step 2.1: Based on the artificial potential field theory, construct a general expression for the impact of distance, speed, heading and other factors on the real-time risk of each location in the airspace. The traditional potential field strength model is:

[0040]

[0041] Where: η is the field strength coefficient; d is the distance from the center of the field source to any position; σ is the rate of change of potential field strength; E is the potential field strength.

[0042] Aircraft are the main source of airspace safety potential field, and the strength of the potential field generated by them will be affected by their inherent properties and kinematic state parameters. Therefore, based on the traditional potential field strength model that only involves distance, the key influencing factors of the angle between the aircraft speed, heading and the direction of the line connecting the specific position points are comprehensively considered, and a real-time risk assessment model for each position point in the airspace is proposed. The calculation formula is as follows:

[0043]

[0044] Where: r is the risk value; is the distance risk function; is the speed risk function; is the heading risk function; η d is the distance risk coefficient; σ d is the distance risk change rate.

[0045] Step 2.2: Modify the distance risk function. For the distance risk function, d is the distance from the center of the field source to any position. Assume that the position coordinates of a point in space are (x, y, z), and the aircraft is regarded as a particle with the position coordinates (x0, y0, z0). The spatial distance calculation formula is as follows:

[0046]

[0047] Since the measurement standards of horizontal distance and vertical distance are different during aircraft operation, the concept of pseudo-distance is proposed here, that is, to judge the proximity between the position point to be evaluated and the aircraft position in the center of the ellipsoid risk area, and to correct the distance risk function.

[0048] First, determine the general expression of the ellipsoid based on the heading angle, pitch angle, and roll angle. The attitude of the aircraft in space is determined by the heading angle, pitch angle, and roll angle. In order to ensure that the long semi-axis direction of the aircraft ellipsoid risk impact zone is always consistent with the aircraft heading, the ellipsoid is rotated according to the aircraft's heading angle, pitch angle, and roll angle. In three-dimensional space, the rotation of the ellipsoid can be defined by the following three basic matrices, namely:

[0049] Rotation matrix R around the Z axis (heading angle) z (θ yaw ):

[0050]

[0051] Rotation matrix R around the Y axis (pitch angle) y (θ pitch ):

[0052]

[0053] Rotation matrix R around the X axis (roll angle) x (θ roll ):

[0054]

[0055] Among them, θ yaw is the heading angle; θ pitch is the pitch angle; θ roll is the roll angle;

[0056] In the aviation field, a common combination sequence for the rotation positioning of aircraft is to rotate around the Z axis (heading angle) first, then around the Y axis (pitch angle), and finally around the X axis (roll angle) to obtain the rotation matrix R, that is:

[0057] R=R z (θ yaw )×R y (θ pitch )×R x (θ roll )

[0058] Therefore, the coordinates of any point (x, y, z) on the ellipse after rotation are (x′, y′, z′), where (x′, y′, z′) = R×(x, y, z).

[0059] Finally, calculate the pseudo distance. After rotation, the ellipsoid equation is: The pseudo-distance calculation formula is as follows:

[0060]

[0061] When l>1, it indicates that the location to be evaluated is within the ellipsoid risk zone. When l>1, it indicates that the location to be evaluated is not affected by the aircraft ellipsoid risk zone. The distance risk function is updated as follows:

[0062]

[0063] Where: η l is the pseudo-distance risk coefficient; σ l is the pseudorange risk change rate.

[0064] Step 2.3: Combining risk influencing factors such as distance, speed, and heading angle, a formula for calculating the risk of a specific position in the airspace is proposed. The risk value r is a dimensionless absolute risk indicator. Appropriate distance risk function, speed risk function, and heading risk function are designed so that the value range of the risk value r is in the interval [0,1]. For the speed risk function, as the speed of the aircraft increases, its dynamic changes in the airspace are more rapid, which increases its potential risk impact on the surrounding space accordingly; for the heading risk function, the relationship between the aircraft and any position point in space includes two trends: convergence and dispersion. The risk of the convergence trend between aircraft under the same motion state is much greater than the dispersion trend.

[0065] In summary, based on the concept of aircraft safety potential field, combined with risk influencing factors such as distance, speed, and direction angle, a risk calculation formula for any position in the airspace is proposed. The formula is as follows:

[0066]

[0067] Where: r i (t) is the risk value imposed by aircraft i on a specific location at time t; l i (t) is the pseudo distance from aircraft i to a specific location point; v i (t) is the speed of aircraft i at time t; v ref is the reference speed; θ i (t) is the angle between the direction of the line connecting aircraft i and any position point at time t.

[0068] For a total of m aircraft in the airspace at time t, the risk value of a certain point P in the airspace at the current time is:

[0069]

[0070] Based on the above airspace risk value calculation formula, the risk impact of aircraft on the surrounding airspace is as follows: Figure 4 shown.

[0071] Step 3: Set up a simulation scenario, calculate the correlation between the conflict index and the airspace risk value, and verify the rationality of the model;

[0072] Step 3.1: Set up the autonomous running simulation scenario.

[0073] For the simulation basic conditions, the simulated airspace area is set to 100km×100km, the simulation time is 0.5h, the number of aircraft is 60, the initial speed of the aircraft is randomly generated, the initial speed distribution range is 800~1000km / h, and the time for the aircraft to enter the airspace is randomly allocated. For safety reasons, the aircraft entering the simulated airspace at the same altitude and the same entrance must meet the safety time interval requirement of 5min when entering the airspace.

[0074] For the selection of aircraft entrances and exits, the sector boundary entrances and exits are selected at equal intervals. In order to reflect the high degree of freedom in the future airspace route design, the route design from any entrance to any other exit is allowed.

[0075] In order to reflect the autonomous operation characteristics in the actual operation process, the line connecting the exit and the entrance is regarded as the preset route of the aircraft, the preset route direction is calculated, and different altitude layers are allocated for the eastward route and the westward route. The evolution interval of the aircraft flying along the preset route is set to 1min, and the heading and speed have a 30% probability of changing during each evolution process. The change range of the heading is -30° to 30°, and the change range of the speed is ±0.1V current , where V current is the current aircraft speed. In order to ensure that the aircraft leaves the simulated airspace along the predetermined exit, a specific function is set to regulate the aircraft behavior, that is, when the distance between the aircraft and the exit is less than the set threshold, the aircraft heading is allowed to make a large angle adjustment each time it evolves, so that the heading is quickly adjusted to the exit direction, and then it flies straight to the exit.

[0076] Step 3.2: Calculate the correlation between conflict indicators and airspace risk values. Select the number of conflicts and conflict rate indicators, where conflict rate = number of conflicts / total number of aircraft, adjust the number of flights generated, conduct multiple independent simulation experiments, and calculate the correlation coefficient between the airspace risk value and each indicator under different flight volumes.

[0077] Step 4: Propose a spatial hotspot grid labeling method and visualize the spatial hotspot distribution by combining multiple grid scales.

[0078] Step 4.1: Airspace hotspot grid marking. Based on the existing simulation environment, calculate the risk values ​​of all grids corresponding to different simulated flights, repeat the experiment independently for multiple times, record the 0.8 quantile of all grid risk values ​​for each experiment, take the average value as the risk threshold corresponding to the corresponding simulated flight, fit the functional relationship between the simulated flight and the risk threshold, and mark the grid with a risk value greater than the risk threshold as a hotspot grid.

[0079] Step 4.2: Combine multiple grid scales to visualize the distribution of airspace hotspots. Based on the functional fitting relationship between the simulated flights and the airspace risk value, calculate the risk threshold corresponding to the corresponding airspace simulated flights, and visualize the hotspots by combining three granularity grid sizes of 10km, 5km, and 1km. The schematic diagram is shown below. Figure 5 shown.

Claims

1. An airspace risk modeling method for autonomous operation scenarios, characterized by: The following steps are involved: Step 1: Establish the aircraft ellipsoid risk impact area to determine the risk impact range and rasterize the airspace; Step 2: Based on the artificial potential field theory, an airspace risk assessment model is constructed by comprehensively considering the real-time risk impact of distance, speed, and heading factors on each grid in the airspace; Step 3: Set up simulation scenarios, calculate the correlation between conflict indicators and airspace risk values, and verify the rationality of the model; Step 4: Propose a spatial hotspot grid labeling method and visualize the spatial hotspot distribution by combining multiple grid scales.

2. The airspace risk modeling method for autonomous operation scenarios according to claim 1 is characterized in that: Step 1 specifically includes: Step 1.1: Establish the aircraft ellipsoid risk impact area; Step 1.2: Rasterization of the airspace.

3. The airspace risk modeling method for autonomous operation scenarios according to claim 2 is characterized in that: In step 1.1, the mathematical expression of the aircraft ellipsoid risk impact area is: The major semi-axis of the ellipsoid is a, the minor semi-axis is b, and the vertical semi-axis is c; In step 1.2, the airspace grid is divided as follows: the coordinates describing the grid layout position are defined as grid coordinates, and the coordinates describing the specific position points are defined as actual coordinates; the grid coordinates are represented by G(X, Y, Z), the representative grid coordinates are (X, Y, Z), where X, Y, and Z represent the arrangement order of the grid on the three axes, and the actual coordinates are represented by P(X, Y, Z); the airspace is divided into rectangular grids, with length, width, and height respectively g len , g wid and g hei , the actual coordinates of the origin of the coordinate system are (x0, y0), then the actual coordinates of the 9 important positions of G(X,Y,Z) are expressed as follows: Where: P1 to P8 correspond to vertices 1 to 8 of the rectangular grid, P C Represents the center point of the rectangular grid.

4. The airspace risk modeling method for autonomous operation scenarios according to claim 1 is characterized in that: Step 2 specifically includes: Step 2.1: Based on the artificial potential field theory, construct a general expression for the impact of distance, speed, and heading factors on the real-time risk of each location in the airspace; Step 2.2: Modify the distance risk function; Step 2.3: Combining the risk influencing factors of distance, speed, and heading angle, a formula for calculating the risk of specific airspace locations is proposed.

5. The airspace risk modeling method for autonomous operation scenarios according to claim 4 is characterized in that: In step 2.1, based on the artificial potential field theory, the general expression process for calculating the real-time risk value of each location point in the airspace is constructed as follows: The traditional potential field strength model is: Where: η is the field strength coefficient; d is the distance from the center of the field source to any position; σ is the rate of change of the potential field strength; E is the potential field strength; Aircraft is the main source of airspace safety potential field. The strength of the potential field generated by it will be affected by its inherent properties and kinematic state parameters. Therefore, based on the traditional potential field strength model that only involves distance, the key influencing factors of the angle between the aircraft speed, heading and the direction of the line connecting the specific position points are comprehensively considered, and a real-time risk assessment model for each position point in the airspace is proposed. The calculation formula is as follows: Where: r is the risk value; is the distance risk function; is the speed risk function; is the heading risk function; η d is the distance risk coefficient; σ d is the distance risk change rate; In step 2.2, the process of modifying the distance risk function is as follows: For the distance risk function, d is the distance from the center of the field source to any position. Assuming that the position coordinates of a point in space are (x, y, z), and the aircraft is regarded as a particle, its position coordinates are (x0, y0, z0), then the spatial distance calculation formula is as follows: Since the measurement standards of horizontal distance and vertical distance during aircraft operation are different, the concept of pseudo-distance is proposed here, that is, to judge the proximity between the position point to be evaluated and the aircraft position at the center of the ellipsoid risk area, and to correct the distance risk function; First, determine the general expression of the ellipsoid based on the heading angle, pitch angle, and roll angle; the aircraft's posture in space is determined by the heading angle, pitch angle, and roll angle. To ensure that the major semi-axis direction of the aircraft ellipsoid risk impact zone is always consistent with the aircraft heading, the ellipsoid is rotated according to the aircraft's heading angle, pitch angle, and roll angle; in three-dimensional space, the rotation of the ellipsoid is defined by the following three basic matrices, namely: The rotation matrix R of the heading angle z (θ yaw ): The rotation matrix R of the pitch angle y (θ pitch ): The rotation matrix R of the roll angle x (θ roll ): Get the rotation matrix R, that is: R=R z (i yaw )×R y (i pitch )×R x (i roll ) Among them, θ yaw is the heading angle, θ pitch is the pitch angle, θ roll is the roll angle; Therefore, the coordinates of any point (x, y, z) on the ellipse after rotation are (x′, y′, z′), where (x′, y′, z′) = R×(x, y, z); Finally, calculate the pseudo distance; the ellipsoid equation after rotation is: The pseudo-distance calculation formula is as follows: When 0≤l≤1, it indicates that the position to be evaluated is within the ellipsoid risk area; when l>1, it indicates that the position to be evaluated is not affected by the aircraft ellipsoid risk area; The distance risk function is updated as follows: Where: η l is the pseudo-distance risk coefficient; σ l is the pseudorange risk change rate; In step 2.3, combined with the risk influencing factors of distance, speed, and heading angle, the risk calculation formula for a specific airspace location is as follows: Based on the concept of aircraft safety potential field, combined with the risk influencing factors of distance, speed, and direction angle, a risk calculation formula for any position in the airspace is proposed. The formula is as follows: Where: r i (t) is the risk value imposed by aircraft i on a specific location at time t; l i (t) is the pseudo distance from aircraft i to a specific location point; v i (t) is the speed of aircraft i at time t; v ref is the reference speed; θ i (t) is the angle between the direction of the line connecting aircraft i and any position point at time t; For a total of m aircraft in the airspace at time t, the risk value of a certain point P in the airspace at the current time is:

6. The airspace risk modeling method for autonomous operation scenarios according to claim 1 is characterized in that: Step 3 specifically includes: Step 3.1: Set up the autonomous running simulation scenario; Step 3.2: Calculate the correlation between the conflict index and the airspace risk value.

7. The airspace risk modeling method for autonomous operation scenarios according to claim 6 is characterized in that: In step 3.1, setting up the autonomous simulation scenario specifically includes: For the simulation basic conditions, set the simulated airspace area, simulation time, number of aircraft, randomly generate the initial speed of aircraft, and randomly assign the time for aircraft to enter the airspace; for safety considerations, ensure that aircraft enter the simulated airspace at the same altitude and entrance, and the time of entering the airspace must meet the safety time interval requirements; For the selection of aircraft entrances and exits, the sector boundary entrances and exits are selected at intervals; in order to reflect the high degree of freedom in the demarcation of future airspace routes, the route design from any entrance to any other exit is allowed; In order to reflect the autonomous operation characteristics in the actual operation process, the line connecting the exit and the entrance is regarded as the preset route of the aircraft, the preset route direction is calculated, and different altitude layers are allocated to the eastward route and the westward route; the evolution interval, the evolution probability of the heading and speed, and the change range of the aircraft flying along the preset route are set; in order to ensure that the aircraft leaves the simulated airspace along the predetermined exit, a specific function is set to regulate the aircraft behavior; In step 3.2, calculating the correlation between the conflict index and the airspace risk value specifically includes: selecting the number of conflicts and the conflict rate index, where the conflict rate = number of conflicts / total number of aircraft, adjusting the flight generation sorties, conducting multiple independent simulation experiments, and calculating the correlation coefficient between the airspace risk value and each index under different flight volumes.

8. The airspace risk modeling method for autonomous operation scenarios according to claim 1 is characterized in that: Step 4 specifically includes: Step 4.1: Spatial hotspot grid marking; Step 4.2: Visualize the spatial hotspot distribution by combining multiple grid scales.

9. The airspace risk modeling method for autonomous operation scenarios according to claim 8 is characterized in that: In step 4.1, the airspace hotspot grid marking is specifically as follows: based on the existing simulation environment, all grid risk values ​​corresponding to different simulation flights are calculated, the experiment is repeated independently for multiple times, the 0.8 quantile of all grid risk values ​​in each experiment is recorded, the average value is taken as the risk threshold corresponding to the corresponding simulation flight, the functional relationship between the simulation flight and the risk threshold is fitted, and the grid with a risk value greater than the risk threshold is marked as a hotspot grid; In step 4.2, the distribution of airspace hotspots is visualized by combining multiple grid scales. Specifically, based on the functional fitting relationship between the number of simulated flights and the airspace risk value, the risk threshold corresponding to the number of simulated flights in the corresponding airspace is calculated, and the hotspots are visualized by combining three granularity grid sizes of 10km, 5km, and 1km.

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