An autonomous collaborative traffic complexity management method based on spatio-temporal reachable space

Abstract

The present invention discloses an autonomous collaborative traffic complexity management method based on spatio-temporal reachable space, including: performing track prediction based on relevant data; generating a rasterized spatio-temporal reachable space according to the predicted track and aircraft performance; establishing a multi-objective autonomous four-dimensional track planning model based on the spatio-temporal reachable space; and calculating the four-dimensional track of the aircraft through the multi-objective autonomous four-dimensional track planning model by using a multi-aircraft cluster collaborative optimization algorithm based on a hybrid centralized and distributed strategy. On the basis of considering the intentions and performance of the aircraft, the present invention generates a rasterized spatio-temporal reachable space for the horizontal route change of the aircraft according to the predicted track, constructs a multi-objective autonomous four-dimensional track planning model that simultaneously considers track complexity and flight efficiency, and uses a multi-aircraft cluster collaborative optimization algorithm based on a hybrid centralized and distributed strategy to solve the multi-aircraft track planning problem, and can provide a higher-quality solution for the tactical track planning process for complexity management within a reasonable time.

Description

Technical Field

[0001] The present invention belongs to the technical field of air traffic management, relates to traffic complexity management, and particularly relates to an autonomous collaborative traffic complexity management method based on spatio-temporal reachable space. Background Art

[0002] With the rapid development of the civil aviation transportation industry, the demand for air traffic has increased significantly. The current ground-based air traffic management system capacity is gradually saturated, resulting in frequent airspace congestion, serious threats to the safety of airspace operations, and a decline in system efficiency. In order to improve the predictability, flexibility, and efficiency of future air traffic and release additional system capacity, the International Civil Aviation Organization has proposed an operational concept of traffic complexity management in the Aviation System Block Upgrade. Current research on complexity management only focuses on reducing the operational complexity of flights and does not consider the flight efficiency goals of flights. At the same time, relevant research has not considered the constraint of the required arrival time of flights passing through waypoints on their flight tracks. Current research adopts completely centralized or distributed control strategies when planning flight tracks of flights. However, the centralized control strategy has high computational costs and is difficult to apply to the tactical planning stage, while the distributed control strategy may lead to a reduction in the optimality of the system due to the lack of global information. Summary of the Invention

[0003] Object of the Invention: In order to overcome the deficiencies in the prior art, the present invention provides an autonomous collaborative traffic complexity management method based on spatio-temporal reachable space. On the basis of considering the intentions and performance of aircraft, a grid-based spatio-temporal reachable space for horizontal rerouting of aircraft is generated according to the predicted track, and a multi-objective autonomous four-dimensional track planning model that simultaneously considers track complexity and flight efficiency is constructed based on this constraint. A multi-aircraft cluster collaborative optimization algorithm based on a hybrid centralized and distributed strategy is used to solve the multi-aircraft track planning problem, which can provide a higher-quality solution for the tactical track planning process for complexity management within a reasonable time.

[0004] Technical Solution: To achieve the above object, the present invention provides an autonomous collaborative traffic complexity management method based on spatio-temporal reachable space, including the following steps:

[0005] S1: Perform track prediction based on relevant data;

[0006] S2: Generate a grid-based spatio-temporal reachable space according to the predicted track and aircraft performance;

[0007] S3: Establish a multi-objective autonomous four-dimensional track planning model based on the spatio-temporal reachable space;

[0008] S4: Calculate the four-dimensional track of the aircraft through the multi-objective autonomous four-dimensional track planning model by using a multi-aircraft cluster collaborative optimization algorithm based on a hybrid centralized and distributed strategy.

[0009] Furthermore, the relevant data in step S1 includes flight plans of flights, flight performance of aircraft, airspace information. The method for predicting the flight track is as follows: Obtain the flight plan of the flight, the flight performance of the aircraft, and the airspace information data, and predict the four-dimensional flight track of the flight based on the obtained data, including the three-dimensional coordinates of the waypoints passed by the flight and the required arrival time.

[0010] Furthermore, the method for generating the spatio-temporally reachable space in step S2 is as follows:

[0011] Based on the predicted four-dimensional flight track information and subject to the constraint of the required arrival time, the spatio-temporally reachable space of the aircraft from the starting waypoint a to the destination waypoint b is set as an ellipse, and the boundary of the ellipse is expressed as:

[0012]

[0013]

[0014] In the formula, (x R , y R ) are the coordinates of the points on the boundary of the spatio-temporally reachable space of the aircraft in the x-y plane, (x a , y a ), (x b , y b ) are the coordinates of waypoint a and waypoint b respectively, v max is the maximum flight speed of the aircraft, Δt = T b - T a is the required flight duration of the aircraft from the starting waypoint to the ending waypoint, where T a , T b are the required arrival times of the aircraft to the starting waypoint a and the ending waypoint b respectively;

[0015] Discretize the airspace into a set of square grid cells of equal size, and the candidate positions of the rerouting waypoints of each aircraft are the center points of the grid cells in the spatio-temporally reachable space.

[0016] Furthermore, the establishment of the multi-objective autonomous four-dimensional flight track planning model in step S3 includes:

[0017] A1: Set decision variables;

[0018] A2: Construct the objective function;

[0019] A3: Set constraint conditions.

[0020] Furthermore, setting the decision variables in step A1 includes the horizontal rerouting maneuver of the aircraft and the allocation of the flight altitude layer of the aircraft;

[0021] Horizontal rerouting maneuver of an aircraft: By assigning the position of an alternative waypoint w=(x R , y R ) to reconstruct the horizontal flight trajectory of the aircraft, where x R and y R are the coordinates of the rerouting waypoint on the x and y axes respectively;

[0022] Allocation of aircraft flight levels: By allocating alternative flight levels l to aircraft to separate traffic flows in the vertical profile.

[0023] Furthermore, the construction of the objective function in step A2 includes:

[0024] Minimizing the trajectory adjustment cost, that is,

[0025] min f(w, l)=D + αΨ

[0026] where f(w, l) is the trajectory adjustment cost of the aircraft; D and Ψ are the deviation from the user-preferred trajectory and the trajectory complexity respectively; α is a coefficient reflecting the relative importance of the two objectives;

[0027] where the deviation D of the user-preferred trajectory is defined as the additional flight distance of the planned trajectory compared to the user-preferred trajectory, that is

[0028] D = D H (w, l)+D V (w, l)

[0029] where D H is the horizontal additional flight distance and D V is the vertical additional flight distance;

[0030] The trajectory complexity is measured using an intrinsic complexity metric based on a linear dynamic system: With the sampling point P i,k of the reference aircraft i as the center, search for the observation vectors of adjacent aircraft within a cylindrical space with a radius of 25 NM and a height of 2000 ft around it. Use the method of minimizing the least mean square to solve the dynamic system model with the smallest error from the observed values, calculate the complex eigenvalues of the coefficient matrix of the dynamic system model. The complexity metric of the sampling point P i,k is related to the negative real part of the complex eigenvalue, and the complexity metrics of all sampling points on the trajectory are summed to obtain the trajectory complexity of the aircraft i.

[0031] Furthermore, the method for setting the constraint conditions in step A3 is to classify the busyness levels of each waypoint based on the probability equal division method according to the probability distribution fitting curve of the waypoint busyness. The constraint conditions specifically include:

[0032] Spatio-temporal reachable space constraint, that is

[0033]

[0034] The maximum turning angle constraint, i.e.,

[0035]

[0036] wherein, are respectively the turn-out angle and turn-in angle of the aircraft, and θ max is the maximum turning angle of the aircraft;

[0037] The maximum flight altitude layer offset constraint, i.e.,

[0038] -Δl Descend ≤l - l origin ≤Δl Climb

[0039] wherein, l origin is the planned flight altitude layer of the aircraft, Δl Climb is the maximum climb flight altitude layer offset, and Δl Descend is the maximum descent flight altitude layer offset;

[0040] The minimum safety interval constraint, i.e.,

[0041]

[0042]

[0043] wherein, are respectively the horizontal and vertical minimum distances between the reference aircraft and the adjacent aircraft; S H , S V are respectively the horizontal minimum safety interval and the vertical minimum safety interval.

[0044] Furthermore, the specific step S4 is as follows:

[0045] Detect pairwise conflicts between aircraft pairs according to the initial flight plan (i.e., the track preferred by the user). If there are conflicts between aircraft pairs, it means that there is a mutual dependence relationship between the aircraft pairs, and the aircraft with potential conflicts are grouped together;

[0046] For aircraft in different groups, a distributed control strategy of "the later-entering aircraft is adjusted first" is adopted, that is, if it is predicted that two aircraft are involved in critical events (i.e., conflicts and highly complex situations), the later-entering aircraft into the airspace needs to adjust its track to avoid critical events;

[0047] For aircraft within the same group, a local centralized control strategy is adopted to achieve collaborative track planning by coordinating the aircraft within the group;

[0048] For the aircraft cluster grouping with only one aircraft, a traversal search algorithm is used to search for the optimal trajectory with the minimum trajectory adjustment cost;

[0049] For the aircraft cluster grouping with multiple aircraft, the optimal flight trajectories of all aircraft are generated from a global perspective with the goal of minimizing the total trajectory adjustment cost of all aircraft, that is

[0050]

[0051] where f g is the total trajectory adjustment cost of all aircraft in group g, and f i is the trajectory adjustment cost of aircraft i; a variable-resolution search algorithm based on a hierarchical strategy is used to approximate the optimal solution within a reasonable calculation time.

[0052] Furthermore, the solution of the variable-resolution search algorithm based on the hierarchical strategy includes:

[0053] Generate solution spaces with low, medium, and high levels of resolution, and the corresponding grid sizes are 20 nautical miles, 10 nautical miles, and 5 nautical miles respectively;

[0054] Traverse and search all solutions in the low-resolution solution space (W g , L g ), and the feasible solution with the minimum total trajectory adjustment cost is the optimal solution at the current resolution level; 20NM Search all solutions around the optimal solution obtained in the medium-resolution solution space (W

[0055] , L g , L g ) 10NM to optimize the optimal solution;

[0056] Search all solutions around the current optimal solution in the high-resolution solution space (W g , L g ) 5NM to obtain an approximate optimal solution.

[0057] Beneficial effects: Compared with the prior art, the present invention, on the basis of considering the intentions and performances of aircraft, generates a grid-based spatio-temporal reachable space for horizontal route changes of aircraft according to the predicted trajectories, and constructs a multi-objective autonomous four-dimensional trajectory planning model that simultaneously considers trajectory complexity and flight efficiency based on this constraint. A multi-aircraft cluster collaborative optimization algorithm based on a hybrid centralized and distributed strategy is used to solve the multi-aircraft trajectory planning problem, which can provide a solution that takes into account minimizing the trajectory adjustment cost and the optimal solution for the tactical trajectory planning process for complexity management within a reasonable time. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1It is a schematic flow chart of the method of the present invention;

[0059] Figure 2 It is a schematic spatio-temporal reachable space diagram of optional diversion waypoints;

[0060] Figure 3 It is a schematic flow chart of the multi-aircraft cluster collaborative optimization algorithm;

[0061] Figure 4 It is a schematic flow chart of the variable resolution search algorithm. Specific embodiments

[0062] The present invention will be further clarified below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent forms of modification of the present invention fall within the scope defined by the appended claims of this application.

[0063] The present invention provides an autonomous collaborative traffic complexity management method based on spatio-temporal reachable space, as Figure 1 shown, which includes the following steps:

[0064] S1: Obtain data such as flight plans of flights, flight performance of aircraft, airspace information, etc., and predict the four-dimensional flight track of the flight according to the obtained data, including the three-dimensional coordinates of waypoints passed by the flight and the required arrival time.

[0065] S2: Generate a rasterized spatio-temporal reachable space based on the predicted flight track and aircraft performance:

[0066] As Figure 2 shown, according to the predicted four-dimensional flight track information, constrained by the required arrival time, the spatio-temporal reachable space of the aircraft from the starting waypoint a to the destination waypoint b is set as an ellipse, and the boundary of the ellipse is expressed as:

[0067]

[0068]

[0069] In the formula, (x R , y R ) are the coordinates of the points on the boundary of the spatio-temporal reachable space of the aircraft in the x-y plane, (x a , y a ), (x b , y b ) are the coordinates of waypoint a and waypoint b respectively, v max is the maximum flight speed of the aircraft, Δt = T b - T a is the required flight duration of the aircraft from the starting waypoint to the ending waypoint, where Ta , T b are the required arrival times of the aircraft to the starting waypoint a and the ending waypoint b respectively;

[0070] The airspace is discretized into a set of square grid cells of equal size, and the candidate positions of the rerouting waypoints of each aircraft are the center points of the grid cells in the spatio-temporally reachable space.

[0071] S3: Establish a multi-objective autonomous four-dimensional trajectory planning model based on the spatio-temporally reachable space, including:

[0072] A1: Set decision variables

[0073] The decision variables include the horizontal rerouting maneuver of the aircraft and the allocation of the aircraft flight altitude layer;

[0074] Horizontal rerouting maneuver of the aircraft: Reconstruct the horizontal flight trajectory of the aircraft by assigning the optional waypoint position w = (x R , y R ), where x R and y R are the coordinates of the rerouting waypoint on the x and y axes respectively;

[0075] Allocation of the aircraft flight altitude layer: Allocate the optional flight altitude layer l to the aircraft to separate the traffic flow in the vertical profile.

[0076] A2: Construct the objective function

[0077] The construction of the objective function includes:

[0078] Minimize the trajectory adjustment cost, that is,

[0079] min f(w, l) = D + αΨ

[0080] where f(w, l) is the trajectory adjustment cost of the aircraft; D and Ψ are the deviation amount from the user-preferred trajectory and the trajectory complexity respectively; α is a coefficient reflecting the relative importance of the two objectives;

[0081] where the deviation amount D of the user-preferred trajectory is defined as the additional flight distance of the planned trajectory compared with the user-preferred trajectory, that is

[0082] D = D H (w, l) + D V (w, l)

[0083] where D H is the horizontal additional flight distance, and D V is the vertical additional flight distance;

[0084] The trajectory complexity is measured by using the intrinsic complexity index based on the linear dynamic system: With the sampling points P of the reference aircrafti,k Search for the observation vectors of adjacent aircraft within a cylindrical space centered at [specific center] with a radius of 25 NM and a height of 2000 ft, that is

[0085]

[0086]

[0087] Among them, is the position observation vector of aircraft j, x i , y i , z i are the position coordinates of aircraft j in the x, y, and z-axis directions respectively; is the speed observation vector of aircraft j, vx i , vy i , vz i are the components of the speed vector in the x, y, and z-axis directions;

[0088] Use the method of minimizing the least mean square to solve the dynamic system model with the smallest error from the observed values, that is

[0089]

[0090] Among them, M is the number of aircraft identified in the search space, A is the transformation matrix of the dynamic system model, is the stationary state of the dynamic system model;

[0091] Calculate the complex eigenvalues of matrix A. The complexity index of sampling point P i,k is related to the negative real part of the complex eigenvalues, that is

[0092]

[0093] Among them, is the complexity index of sampling point P i,k and is the real part of the complex eigenvalues of matrix A;

[0094] The track complexity of aircraft i is obtained by summing the complexity indices of all sampling points on the track, that is

[0095]

[0096] Among them, N i is the number of track sampling points of aircraft i.

[0097] A3: Set the constraint conditions

[0098] The constraint conditions specifically include:

[0099] Space-time reachable space constraint, that is

[0100]

[0101] The maximum turning angle constraint, i.e.,

[0102]

[0103] wherein, are the pull-out turning angle and the pull-in turning angle of the aircraft, respectively, and θ max is the maximum turning angle of the aircraft;

[0104] The maximum flight altitude layer offset constraint, i.e.,

[0105] -Δl Descend ≤l - l origin ≤Δl Climb

[0106] wherein, l origin is the planned flight altitude layer of the aircraft, Δl Climb is the maximum climb flight altitude layer offset, and Δl Descend is the maximum descent flight altitude layer offset;

[0107] The minimum safety interval constraint, i.e.,

[0108]

[0109]

[0110] wherein, are the horizontal and vertical minimum distances between the reference aircraft and the adjacent aircraft, respectively; S H , S V are the horizontal minimum safety interval and the vertical minimum safety interval, respectively.

[0111] S4: Through the multi-objective autonomous four-dimensional trajectory planning model, use the multi-aircraft cluster cooperative optimization algorithm based on the hybrid centralized and distributed strategies to calculate the four-dimensional trajectory of the aircraft:

[0112] As Figure 3 shown, detect the pairwise conflicts between aircraft pairs according to the initial flight plan (i.e., the trajectory preferred by the user). If there are conflicts between aircraft pairs, it means that there is a mutual dependence relationship between the aircraft pairs. Divide the aircraft with potential conflicts into a group;

[0113] For different groups of aircraft, adopt a distributed control strategy that follows "the later-entering aircraft is adjusted first", that is, if it is predicted that two aircraft are involved in critical events (i.e., conflicts and highly complex situations), the aircraft that enters the airspace later needs to adjust its trajectory to avoid critical events;

[0114] For the aircraft within the same group, a local centralized control strategy is adopted to achieve collaborative trajectory planning by coordinating the aircraft within the group;

[0115] For the aircraft cluster grouping with only one aircraft, a traversal search algorithm is used to search for the optimal trajectory with the minimum trajectory adjustment cost;

[0116] For the aircraft cluster grouping with multiple aircraft, the optimal flight trajectories of all aircraft are generated from a global perspective with the goal of minimizing the total trajectory adjustment cost of all aircraft, that is

[0117]

[0118] where f g is the total trajectory adjustment cost of all aircraft within group g, and f i is the trajectory adjustment cost of aircraft i; a variable-resolution search algorithm based on a hierarchical strategy is adopted to approximate the optimal solution within a reasonable calculation time.

[0119] As Figure 4 shown, the solution of the variable-resolution search algorithm based on a hierarchical strategy includes:

[0120] Generate solution spaces with low, medium, and high levels of resolution, and the corresponding grid sizes are 20 nautical miles, 10 nautical miles, and 5 nautical miles respectively;

[0121] Traverse and search all solutions in the low-resolution solution space (W g , L g ), and the feasible solution with the minimum total trajectory adjustment cost is the optimal solution at the current resolution level; 20NM Search all solutions around the optimal solution obtained in the medium-resolution solution space (W

[0122] g , L g 10NM g )to optimize the optimal solution;

[0123] Search all solutions around the current optimal solution in the high-resolution solution space (W g , L g ) 5NM to obtain an approximate optimal solution.

[0124] This embodiment also provides an autonomous collaborative traffic complexity management system based on spatio-temporal reachable space. The system includes a network interface, a memory, and a processor; wherein, the network interface is used to receive and send signals during the process of receiving and sending information with other external network elements; the memory is used to store computer program instructions that can run on the processor; the processor is used to execute the steps of the above consensus method when running the computer program instructions.

[0125] This embodiment also provides a computer storage medium, which stores a computer program. When the processor executes the computer program, the above-described method can be implemented. The computer-readable medium can be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media include non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits, or mask read-only memory circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog or digital magnetic tapes or hard disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs), etc. The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also include or rely on stored data. The computer program may include a basic input / output system (BIOS) that interacts with the hardware of a dedicated computer, device drivers that interact with specific devices of a dedicated computer, one or more operating systems, user applications, background services, background applications, etc.

[0126] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a dedicated computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0128] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or multiple processes and / or boxes Figure 1 or steps of the functions specified in multiple boxes.

Claims

1. An autonomous collaborative traffic complexity management method based on spatio-temporal reachable space, characterized in that It includes the following steps: S1: Conduct trajectory prediction based on relevant data; S2: Generate a rasterized spatio-temporal reachable space according to the predicted trajectory and aircraft performance; S3: Establish a multi-objective autonomous four-dimensional trajectory planning model based on the spatio-temporal reachable space; S4: Through the multi-objective autonomous four-dimensional trajectory planning model, use a multi-aircraft cluster cooperative optimization algorithm based on a hybrid centralized and distributed strategy to calculate the four-dimensional trajectory of the aircraft; The method for generating the spatio-temporal reachable space in step S2 is as follows: According to the predicted four-dimensional trajectory information and subject to the required arrival time constraint, the spatio-temporal reachable space of the aircraft from the starting waypoint a to the destination waypoint b is set as an ellipse, and the boundary of the ellipse is expressed as: where (x R , y R ) are the coordinates of a point on the spatio-temporal reachable space boundary of the aircraft in the x-y plane, (x a , y a ), (x b , y b ) are the coordinates of waypoint a and waypoint b respectively, v max is the maximum flight speed of the aircraft, Δt = T b - T a is the required flight duration of the aircraft from the starting waypoint to the ending waypoint, where T a , T b are the required arrival times of the aircraft at the starting waypoint a and the ending waypoint b respectively; Discretize the airspace into a set of square grid cells of equal size, and the candidate positions of the rerouting waypoints of each aircraft are the center points of the grid cells in the spatio-temporal reachable space; The establishment of the multi-objective autonomous four-dimensional trajectory planning model in step S3 includes: A1: Set decision variables; A2: Construct the objective function; A3: Set constraint conditions; The decision variables set in step A1 include the horizontal rerouting maneuver of the aircraft and the aircraft flight altitude layer allocation; Aircraft horizontal diversion maneuver: Reconstruct the horizontal flight trajectory of the aircraft by assigning the position of the alternative waypoint w = (x R , y R ) for the aircraft diversion, where x R and y R are the coordinates of the diversion waypoint on the x and y axes respectively; Aircraft flight altitude layer allocation: Allocate the optional flight altitude layer l to the aircraft to separate the traffic flow in the vertical profile; The construction of the objective function in step A2 includes: Minimize the trajectory adjustment cost, that is, minf(w,l)=D+αΨ where f(w,l) is the trajectory adjustment cost of the aircraft; D and Ψ are the deviation amount from the user-preferred trajectory and the trajectory complexity respectively; α is a coefficient reflecting the relative importance of the two objectives; where the deviation amount D of the user-preferred trajectory is defined as the additional flight distance of the planned trajectory compared with the user-preferred trajectory, that is D = D H (w, l) + D V (w, l) Among them, D H is the horizontal additional flight distance, and D V is the vertical additional flight distance; The intrinsic complexity index based on the linear dynamic system is used to measure the track complexity: taking the sampling point P of the reference aircraft i，k as the center, search for the observation vectors of adjacent aircraft in the cylindrical space, that is Among them, is the position observation vector of aircraft j, where x i , y i , z i are the position coordinates of aircraft j in the x, y, and z-axis directions respectively; is the velocity observation vector of aircraft j, where vx i , vy i , vz i are the components of the velocity vector in the x, y, and z-axis directions; Adopt the method of minimizing the least mean square to solve the dynamic system model with the minimum error between the observation values, that is Where M is the number of aircraft identified in the search space, and A is the transformation matrix of the power system model, is the stationary state of the power system model; Calculate the complex eigenvalues of matrix A, sampling point P i,k The complexity index of Among them, is the complexity index of the sampling point P i,k and is the real part of the complex eigenvalue of the matrix A; The trajectory complexity of aircraft i is obtained by summing the complexity indexes of all sampling points on the trajectory, that is where N i is the number of track sampling points of aircraft i; The method for setting the constraint conditions in step A3 is to classify the busyness of each waypoint based on the probability equal division method according to the probability distribution fitting curve of the waypoint busyness degree. The constraint conditions specifically include: Spatio-temporal reachable space constraint, that is Maximum turning angle constraint, that is Among them, are respectively the breakaway turning angle and the entry turning angle of the aircraft, and θ max is the maximum turning angle of the aircraft; Maximum flight altitude layer offset constraint, that is -Δl Descend ≤l - l origin ≤Δl Climb where, l origin is the planned flight altitude level of the aircraft, and Δl Climb is the maximum climb flight altitude level offset, and Δl Descend is the maximum descent flight altitude level offset; Minimum safety interval constraint, that is wherein, are respectively the horizontal and vertical minimum distances between the reference aircraft and the adjacent aircraft; S H , S V are respectively the horizontal minimum safety interval and the vertical minimum safety interval; Step S4 is specifically: Detect pairwise conflicts between aircraft pairs according to the initial flight plan. If there are conflicts between aircraft pairs, it means that there is a mutual dependence relationship between the aircraft pairs, and the aircraft with potential conflicts are divided into a group; For different groups of aircraft, adopt a distributed control strategy that follows the "last-in, first-adjust" principle; For the aircraft within the same group, adopt a local centralized control strategy to achieve cooperative trajectory planning by coordinating the aircraft within the group; For the aircraft cluster grouping with only one aircraft, use a traversal search algorithm to search for the optimal trajectory with the minimum trajectory adjustment cost; For the aircraft cluster grouping with multiple aircraft, generate the optimal flight trajectories of all aircraft from a global perspective with the goal of minimizing the total trajectory adjustment cost of all aircraft, that is Among them, f g is the total trajectory adjustment cost of all aircraft within group g, and f i is the trajectory adjustment cost of aircraft i; A variable-resolution search algorithm based on a hierarchical strategy is adopted to approximate the optimal solution within a reasonable calculation time.

2. The autonomous collaborative traffic complexity management method based on spatio-temporal reachable space according to claim 1, wherein The relevant data in step S1 includes flight plans of flights, flight performance of aircraft, airspace information. The method for predicting the flight track is as follows: Obtain the flight plan of the flight, flight performance of the aircraft, and airspace information data, and predict the four-dimensional flight track of the flight based on the obtained data, including the three-dimensional coordinates of waypoints passed by the flight and the required arrival time.

3. A method for autonomous collaborative traffic complexity management based on spatio-temporal reachable space according to claim 1, characterized in that, The solution using the variable-resolution search algorithm based on a hierarchical strategy includes: Generate solution spaces with low, medium, and high levels of resolution, and the corresponding grid sizes are 20 nautical miles, 10 nautical miles, and 5 nautical miles respectively; Traverse and search the low-resolution solution space (W g , L g ). 20NM Among all solutions, the feasible solution with the minimum total track adjustment cost is the optimal solution at the current resolution level; All solutions around the optimal solution obtained by searching in the medium-resolution solution space (W g , L g ) 10NM are used to optimize the optimal solution; Search for all solutions around the current optimal solution in the high-resolution solution space (W g , L g ) 5NM to obtain an approximate optimal solution.

Images