Robust adaptive heuristic conflict-free path planning method and system

Abstract

The present application relates to a kind of adaptive heuristic conflict-free path planning method and system with robustness, belong to the technical field of air traffic management, specifically for flight conflict detection and release.The present application is used to quickly generate robust conflict-free path that meets safety interval constraint, can be flexibly switched local search and global search strategy according to search stage, realize effective balance between optimization performance and efficiency, especially suitable for air traffic control automation system that needs to dynamically update conflict-free path proposal with second-level frequency.The adaptive heuristic conflict-free path planning system of the present application includes conflict detection module, conflict resolution module, visualization module and interactive module, wherein, conflict resolution module uses the conflict resolution module, and generates conflict-free path for flight.

Description

Technical Field

[0001] This invention relates to the field of air traffic management technology, specifically to a robust adaptive heuristic conflict-free trajectory planning method and system for flight conflict detection and resolution. Background Technology

[0002] Conflict detection and resolution (CDR) is a critical task in ensuring safe aviation operations. When the system detects a potential conflict, timely intervention is required. At this time, controllers typically use radar to guide aircraft maneuvers, ensuring that the distance between any two aircraft is never less than the minimum safe separation. Taking a heading deflection strategy as an example, after receiving instructions from controllers, the aircraft will make a changeover at the first recommended waypoint. From the current heading deflection angle Then, at the second recommended waypoint, change course. This guides the aircraft to redirect towards the target waypoint or sector exit, generating a conflict-free path that meets safety separation requirements through heading guidance, thereby ensuring the aircraft's operational safety within the airspace. However, the entire process—from system recommendation generation to controller review and decision-making, and finally communication with the crew to implement the maneuver—is often subject to uncertainty in human-machine interaction and air-to-ground communication time, preventing the aircraft from perfectly adhering to the system's recommended path for conflict avoidance.

[0003] In the process of realizing this invention, the inventors discovered at least the following problems in the prior art:

[0004] The uncertainty in response time of human-computer interaction and air-to-ground communication increases the complexity of nonlinear conflict-free trajectory optimization problems, making it difficult for traditional methods to quickly find feasible and robust conflict resolution solutions under real-time conditions. Existing methods for optimization problems considering uncertainties mainly include Monte Carlo methods, chance-constraint methods, or robust optimization.

[0005] Monte Carlo methods evaluate the safety and feasibility of candidate trajectories under different uncertainty scenarios by performing extensive random sampling of communication delays or decision response times. However, these methods are computationally inefficient, cannot meet the real-time requirement of dynamically updating trajectory suggestions every few seconds, and exhibit poor stability of evaluation results when the sample size is insufficient.

[0006] Opportunity-constrained optimization (OCE) sets a probability confidence level to constrain the escape trajectory to meet safe separation conditions under a certain confidence level. Although it can model the effects of uncertainty in a relatively clear form, the complex nonlinear coupling between the maneuver time distribution and the safety constraints makes high-dimensional integrals difficult to calculate. In flight conflict resolution scenarios, the solution process often fails to converge or only yields approximate solutions.

[0007] Robust optimization methods construct a set of uncertainties (such as the maximum-minimum boundary interval of communication time) to find a feasible path solution in the worst case. However, the path generated by this method deviates significantly from the original path, increases flight costs, and may not find a feasible solution when the airspace complexity is high. Summary of the Invention

[0008] In view of this, the present invention proposes a robust adaptive heuristic conflict-free trajectory planning method and system, which is used to quickly generate robust conflict-free trajectories that meet safety interval constraints. It can flexibly switch between local search and global search strategies according to the search phase, and achieve an effective balance between performance optimization and efficiency. It is especially suitable for air traffic control automation systems that need to dynamically update conflict-free trajectory suggestions at a frequency of seconds.

[0009] This invention provides a robust adaptive heuristic conflict-free trajectory planning method, the specific steps of which are as follows:

[0010] Step 101: Obtain geographic information, planned flight paths, and flight status of all flights within the flight sector;

[0011] Step 102: Update the flight status and feasible solutions for conflict resolution of all flights in the flight sector, and sort all flights in the flight sector according to the time they entered the flight sector to obtain the flight set;

[0012] Step 103: Perform conflict detection on the flights in the flight set in chronological order. If a conflict is detected on the current flight, proceed to step 104; if no conflict is detected on the current flight, perform conflict detection on the next flight. If the entire flight set has been traversed, return to step 102.

[0013] Step 104: For flights with conflicts to be resolved, determine whether there is a feasible solution for resolving the conflict. If there is no feasible solution for resolving the conflict, proceed to step 105. If there is a feasible solution for resolving the conflict, proceed to step 110.

[0014] Step 105: Generate a candidate set of starting deflection points for flights awaiting conflict resolution. ;

[0015] Step 106: If the candidate set of initial deflection points is... If the value is empty, then the flight awaiting conflict resolution has no conflict-free track, so the next flight is taken as the current flight, and the process returns to step 103; if the initial deflection point candidate set is empty... If not empty, proceed to step 107;

[0016] Step 107: Based on the candidate set of initial deflection points Obtain the initial deflection point for generating a conflict-free track. Regression deflection point candidate set , , To generate the total number of starting deflection points for conflict-free tracks, proceed to step 108; if the candidate set of starting deflection points... If all starting deflection points for generating conflict-free tracks have been traversed and no conflict-free track can be generated, then the next flight will be used as the current flight, and the process will return to step 103.

[0017] Step 108: Determine the starting deflection point for generating a conflict-free track. Regression deflection point candidate set Mid-regression deflection point Conduct a feasibility assessment. , This indicates the total number of turning deflection points, and determines the regression deflection point. The corresponding starting deflection point for generating conflict-free tracks The current deflection point pair The conflict-free track is determined to satisfy both controller load constraints and minimum safe separation constraints. If both constraints are satisfied, the current deflection point pair is feasible. The feasibility of the next deflection point pair is then assessed, and all feasible deflection pairs are obtained. The process proceeds to step 109. If none of the deflection point pairs satisfy controller load constraints and minimum safe separation constraints, the process returns to step 107.

[0018] Step 109: Evaluate all feasible deflection pairs obtained in Step 108. Based on the trade-off between flight cost and robustness, select the feasible deflection pair corresponding to the minimum objective as the recommended conflict-free path. Update the feasible solution for conflict resolution of the current flight to the recommended conflict-free path, then take the next flight as the current flight and return to Step 103.

[0019] Step 110: Generate a neighborhood space for the conflict resolution regression deflection point in the feasible solutions for conflict resolution;

[0020] Step 111: Traverse all conflict resolution and return deflection points in the neighborhood space, and determine whether the conflict-free track formed by the current conflict resolution feasible point pair satisfies the controller load constraint and the minimum safe separation constraint. If there are no feasible points in the neighborhood space, return to step 105; if there are feasible points in the neighborhood space, proceed to step 112.

[0021] Step 112: Evaluate all feasible conflict resolution pairs. Based on flight cost and robustness, select the feasible solution corresponding to the minimum objective as the conflict-free path for the current flight. Update the feasible conflict resolution solution for the current flight to the recommended conflict-free path. Then, take the next flight as the current flight and return to step 103.

[0022] Optionally, the geographic information within the flight sector includes the boundaries, entry points, exit points, waypoints, and route geographic information of the flight sector.

[0023] Optionally, the planned flight path for all flights within the flight sector includes the time, location, and waypoints at which the flight is scheduled to enter the flight sector, as well as the time of waypoints and the time and location at which the flight is scheduled to leave the flight sector.

[0024] Optionally, the flight status of all flights within a flight sector includes the current position, current speed, and heading of the corresponding aircraft.

[0025] Optionally, generate a candidate set of regression deflection points. The specific steps are as follows: using the candidate set of initial deflection points The initial deflection point for generating a conflict-free track Construct a local coordinate system with the origin as the origin. The axis points from the initial deflection point where the conflict-free track is generated. The direction to the target waypoint The axis is perpendicular to it; a finite search region is constructed within the local coordinate system, and this finite search region is divided into multiple grids. The center point of each grid is taken as the starting deflection point for generating a conflict-free track. Generated candidate set of regression deflection points Candidate positions.

[0026] Optionally, the finite search area is located at the intersection of two symmetrical circles, the intersection of which passes through the starting deflection point for generating a conflict-free track. And the target waypoint or flight sector exit point, the centers of the two symmetrical circles are at the starting deflection point for generating a conflict-free path. On the perpendicular bisector of the line connecting the target waypoint or the exit point of the flight sector.

[0027] Optionally, the radii of the two symmetrical circles for:

[0028]

[0029] in, Indicates the starting deflection point for generating a conflict-free track. Location; Indicates the location of the current flight's destination waypoint or flight sector exit point; This represents the length from the starting deflection point for generating a conflict-free path to the target waypoint or flight sector exit point of the current flight. This indicates the maximum yaw angle constraint.

[0030] Optionally, the yaw angles at both the initial deflection point and the return deflection point for generating a conflict-free track are not greater than the maximum yaw angle constraint. .

[0031] Optionally, the feasible solution for conflict resolution is either the conflict-free path recommended in step 109 or the conflict-free path of the current flight in step 112; the initial value of the feasible solution for conflict resolution is empty.

[0032] Another aspect of the present invention discloses a grid-based adaptive dynamic heuristic conflict-free trajectory planning system, comprising:

[0033] The conflict detection module is used for conflict detection of all flights within the flight sector;

[0034] The conflict resolution module is used to execute the aforementioned adaptive heuristic conflict-free trajectory planning method to generate conflict-free trajectories for flights;

[0035] The Flight Status module is used to maintain the status updates of all flights within the flight sector;

[0036] The visualization module visualizes the real-time flight status and conflict-free flight paths of all flights within the flight sector.

[0037] Compared with the prior art, the present invention has at least the following beneficial effects:

[0038] (1) The adaptive heuristic conflict-free trajectory planning method and system of the present invention can quickly generate robust conflict-free trajectories that meet safety interval constraints in high-density airspace to deal with the uncertainty problem in real-time dynamic airspace.

[0039] (2) The adaptive heuristic conflict-free trajectory planning method and system of the present invention adaptively switches between global search and local search based on whether there is a feasible escape trajectory in the previous round, effectively avoiding repeated global redundant search, effectively improving trajectory acquisition efficiency and improving the update response speed of conflict-free trajectory.

[0040] (3) The adaptive heuristic conflict-free trajectory planning method and system of the present invention considers the robustness of the decoupling trajectory, ensures that the generated trajectory has good execution stability when facing uncertain disturbances such as communication time, supports dynamic generation of conflict decoupling trajectories at a frequency of seconds, and meets the dual requirements of real-time performance and security of air traffic control automation systems in future high-density airspace. Attached Figure Description

[0041] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0042] Figure 1 This is a flowchart of the robust adaptive heuristic conflict-free trajectory planning method of the present invention.

[0043] Figure 2 This is a schematic diagram illustrating the starting deflection point and the returning deflection point for generating conflict-free tracks in this invention.

[0044] Figure 3 This invention provides a method for generating a global search regression deflection point candidate set.

[0045] Figure 4 This invention provides a global search method for evaluating conflict-free flight paths.

[0046] Figure 5 This invention relates to a method for generating local neighborhood spaces.

[0047] Figure 6 This invention relates to a local neighborhood evaluation method.

[0048] Figure 7 This invention provides a schematic diagram of a robust adaptive heuristic conflict-free path planning method. Detailed Implementation

[0049] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0050] A specific embodiment of the present invention, such as Figures 1-7 This paper discloses a robust adaptive heuristic conflict-free trajectory planning method. Addressing the nonlinear conflict-free trajectory optimization problem considering uncertainties in human-computer interaction and air-to-ground communication, the method significantly reduces search complexity by discretizing the continuous deflection point search space into a finite two-dimensional grid structure. Furthermore, combined with a dynamic task update mechanism, a heuristic search strategy of local-global adaptive switching is designed to achieve fast and stable solutions for feasible conflict resolution paths within a finite time. In each update, the algorithm processes flights sequentially according to their entry time into the sector, following a first-come, first-served principle. For each flight, a conflict detection operation is first performed; if a potential conflict is detected, a conflict resolution scheme is activated to generate a conflict-free recommended trajectory that meets the safety interval requirements for that flight. The specific steps are as follows:

[0051] Step 101: Obtain the geographic information, planned flight paths, and flight status of all flights within the flight sector.

[0052] Specifically, the geographic information within a flight sector includes the boundaries, entry points, exit points, waypoints, and route geographic information of the flight sector.

[0053] Specifically, the planned flight track of all flights within the flight sector includes the planned time and position of the flight entering the flight sector, the waypoints and the time of the waypoints, and the planned time and position of leaving the flight sector; the flight status of all flights within the flight sector includes the current position information (longitude, latitude, altitude) of the corresponding aircraft, as well as the current speed and heading.

[0054] Step 102: At preset update intervals, update the flight status and feasible solutions for conflict resolution of all flights in the flight sector, and sort all flights in the flight sector according to the time order of entry into the flight sector to obtain the flight set.

[0055] It is understandable that a feasible solution for conflict resolution refers to a feasible conflict-free flight path obtained by the flight to be resolved in the previous update cycle when a conflict was detected (i.e., the conflict-free flight path recommended by the current flight in step 109 or the conflict-free flight path of the current flight in step 112). In particular, the initial value of a feasible solution for conflict resolution is empty.

[0056] Preferably, the preset update time interval is 5 seconds.

[0057] Step 103: Perform conflict detection on the current flight in the flight set in chronological order. If a conflict is detected on the current flight, proceed to step 104. If no conflict is detected on the current flight, perform conflict detection on the next flight. If the entire flight set has been traversed, return to step 102.

[0058] Furthermore, if at any given moment a flight is less than the minimum safe separation distance from other flights, it is considered a conflict. Once a conflict is detected, the flight's planned trajectory, current flight status (position, speed, heading), and the location where it lost its safe separation are recorded.

[0059] Furthermore, the point at which the safe separation is lost refers to the critical point where the flight and other flights within the flight sector are less than the minimum safe separation.

[0060] Step 104: For flights with conflicts that need to be resolved, determine whether there is a feasible solution for resolving the conflict. If there is no feasible solution for resolving the conflict, proceed to step 105. If there is a feasible solution for resolving the conflict, proceed to step 110.

[0061] Step 105: Generate a candidate set of starting deflection points for flights awaiting conflict resolution in Step 103. .

[0062] Understandably, the initial deflection point refers to the point where the aircraft corresponding to the flight, after flying along the planned track to that point, first implements a course deflection and begins to perform conflict resolution.

[0063] Specifically, the method for generating the candidate set of starting deflection points is as follows: starting from the aircraft corresponding to the flight awaiting conflict resolution at the current moment until the moment the safe separation point is lost, search for the earliest and latest moments when controller workload can be occupied within this period, obtain the positions corresponding to the earliest and latest moments, and construct a set of points with a length of [missing information - likely a number]. The lines are spaced apart. Select all starting deflection points that satisfy the controller workload constraints; these starting deflection points constitute the candidate set of starting deflection points for flights awaiting conflict resolution. .

[0064] Step 106: If the candidate set of initial deflection points is... If the value is empty, then the flight awaiting conflict resolution has no conflict-free track and no feasible solution. Therefore, conflict detection is performed on the next flight, and the process returns to step 103. If the initial deflection point candidate set... If not empty, proceed to step 107 to begin a global heuristic search.

[0065] Step 107: Based on the candidate set of initial deflection points Obtain the initial deflection point for generating a conflict-free track. Regression deflection point candidate set Proceed to step 108; if the candidate set of starting deflection points... After traversing all the starting deflection points for generating conflict-free tracks, and since no conflict-free tracks can be generated, update the flight and return to step 103.

[0066] Specifically, see Figure 3 Generate a candidate set of regression deflection points The specific steps are as follows: using the candidate set of initial deflection points The initial deflection point for generating a conflict-free track Construct a local coordinate system with the origin as the origin. The axis points from the initial deflection point where the conflict-free track is generated. The direction to the target waypoint The axis is perpendicular to it; a finite search region is constructed within the local coordinate system, and this finite search region is divided into multiple grids. The center point of each grid is taken as the starting deflection point for generating a conflict-free track. Generated candidate set of regression deflection points Candidate positions; , This represents the total number of initial deflection points for generating conflict-free tracks.

[0067] Furthermore, the finite search region is located at the intersection of two symmetrical circles, the intersection of which passes through the starting deflection point for generating a conflict-free track. The center of two symmetrical circles, along with the target waypoint or flight sector exit point. and At the initial deflection point for generating a conflict-free track On the perpendicular bisector of the line connecting the target waypoint or the exit point of the flight sector, and the radii of the two symmetrical circles. for:

[0068]

[0069] in, Indicates the starting deflection point for generating a conflict-free track. Location; Indicates the location of the current flight's destination waypoint or flight sector exit point; This represents the length from the starting deflection point for generating a conflict-free path to the target waypoint or flight sector exit point of the current flight. This indicates the maximum yaw angle constraint.

[0070] Furthermore, the first Candidate set of regression deflection points Satisfying the maximum yaw angle constraint To ensure that the heading deflection at both the initial deflection point and the return deflection point meets the aircraft's maximum deflection performance limit, see [reference needed]. Figure 2 The yaw angle at the starting deflection point for generating a conflict-free track. and the yaw angle at the corresponding regression deflection point All are not greater than the maximum yaw angle constraint .

[0071] As can be understood, the regression deflection point refers to the position where, after flying along the heading from the initial deflection point for a period of time, the aircraft readjusts its heading to the target waypoint or the exit of the flight sector. The candidate set of regression deflection points is a grid set generated based on the current initial deflection point.

[0072] Step 108: Determine the starting deflection point for generating a conflict-free track. Regression deflection point candidate set Mid-regression deflection point Conduct a feasibility assessment. This indicates the total number of turning deflection points, and determines the regression deflection point. The corresponding starting deflection point for generating conflict-free tracks The current deflection point pair The resulting conflict-free track is checked against controller load constraints and minimum safe separation constraints. If neither is satisfied, the current deflection point pair is infeasible (i.e., no feasible return deflection point exists). The feasibility of the next deflection point pair is then assessed. If all deflection point pairs are infeasible, the process returns to step 107 and iterates through the next starting deflection point. If both controller load constraints and minimum safe separation constraints are satisfied, the current deflection point pair is feasible (i.e., a feasible return deflection point exists). The feasibility of the next deflection point pair is then assessed, and the starting deflection point for generating the conflict-free track is obtained. Regression deflection point candidate set For all corresponding feasible deflection pairs, proceed to step 109;

[0073] Step 109: Evaluate all feasible deflection pairs obtained in Step 108. Based on the dual objective function of flight cost and robustness, weigh flight cost and robustness, select the feasible deflection pair that minimizes the objective as the recommended conflict-free path for the current flight. Update the feasible solution for conflict resolution of the current flight to the recommended conflict-free path, then take the next flight as the current flight and return to Step 103.

[0074] Specifically, such as Figure 4 Flight costs refer to the delays caused by conflict avoidance. Specifically, robustness refers to the resilience of the system to feasible deflection pairs. One feasible regression deflection point coordinates Find the largest range of positive integers. Such that no grid point within the range of positive integers belongs to the infeasible set. The expression is:

[0075]

[0076] in, This is the set of infeasible grid points, i.e., the region that does not meet the safety interval constraint; The set of natural numbers; and Representing the range of positive integers respectively Values ​​within the range of the X and Y axes. Indicates the first The larger the redundant feasible space in the surrounding area of ​​a feasible regression deflection point, the stronger the robustness of the feasible deflection pair. and They represent the first The coordinates of the feasible regression deflection points on the X and Y axes.

[0077] Understandable, Figure 4In the diagram, the numbers in the grid points represent the robustness index.

[0078] Step 110: Perform a local neighborhood search, generating a neighborhood space for the conflict resolution regression deflection points in the feasible solutions for conflict resolution. .

[0079] Specifically, such as Figure 5 Neighborhood space The neighborhood range is Then the grid range of this neighborhood space is ,in, These represent the coordinate sets of the conflict resolution regression deflection point set on the X and Y axes, respectively. These represent the range values ​​of the domain space along the X and Y axes, respectively.

[0080] Furthermore, neighborhood space The conflict resolution and return deflection point in the middle satisfies the maximum yaw angle constraint. And it lies within the finite search region of two symmetrical circles.

[0081] Preferably, .

[0082] Step 111: Traverse the neighborhood space All conflict resolution and regression deflection points, for the first A conflict resolution and return to the deflection point Conduct a feasibility assessment. Determine the feasible points for resolving the current conflict. The resulting conflict-free flight path This indicates whether the conflict resolution starting deflection point in the current feasible conflict resolution point pair satisfies the controller load constraint and the minimum safe separation constraint. If it does not satisfy the controller load constraint and the minimum safe separation constraint, the neighborhood space... If no feasible point is found, the local search fails, and the process returns to step 105; if the controller load constraint and the minimum safe interval constraint are satisfied, the neighborhood space... If a feasible point exists, proceed to step 112.

[0083] Step 112: Evaluate all feasible current conflict resolution pairs, weigh flight costs and robustness, and select the feasible solution corresponding to the minimum objective. If the conflict-free path of the current flight is used as the current flight's conflict resolution solution, and the recommended conflict-free path is updated, then the next flight is used as the current flight, and the process returns to step 103 to obtain the conflict-free path of the next flight.

[0084] Specifically, such as Figure 6 The evaluation method is the same as in step 109, and will not be repeated here.

[0085] Understandably, feasible current conflict resolution points include the starting deflection point from the previous round and the regression deflection point selected in the neighborhood for this round.

[0086] Another aspect of this invention specifically discloses a grid-based adaptive dynamic heuristic conflict-free trajectory planning system. The system uses the aforementioned method for adaptive dynamic heuristic conflict-free trajectory planning and includes a conflict detection module for detecting conflicts among all aircraft within a flight sector; a conflict resolution module for executing the aforementioned adaptive dynamic heuristic conflict-free trajectory planning method to generate conflict-free trajectories for flights; a flight status module for maintaining the status updates of all flights within the flight sector, such as trajectory updates, position and heading changes; a visualization module for visualizing the real-time flight status and conflict-free trajectories of all aircraft within the flight sector; and an interaction module for controllers to confirm generated suggestions and interact with the system.

[0087] See Figure 7 The present invention describes the structure and process of a robust adaptive heuristic conflict-free trajectory planning system, which includes the following steps:

[0088] Step 1: The flight dynamics module continuously updates the planned flight path and real-time flight dynamics information of each flight at a fixed frequency and sends them to the conflict detection module to detect whether there are potential flight conflicts within the sector.

[0089] Step 2: The conflict detection module monitors the flight dynamics of all flights within the sector to detect potential conflicts. If a conflict exists, the conflict information is sent to the conflict resolution module.

[0090] Step 3, Conflict Resolution Module: The proposed conflict resolution algorithm is used to plan a conflict-free flight path, which is then provided to the visualization module for review and confirmation by the controller.

[0091] Step 4: The controller decides whether to accept the system's suggestions by displaying the interactive module, and then provides feedback to the system through the interactive module.

[0092] Step 5: If you accept the suggestion, click "Accept Suggestion" to provide conflict-free flight paths to the Flight Dynamics module to update the flight's future flight path; if you do not accept the suggestion, click "Disagree with Suggestion," and the system will regenerate a suggestion for the flight in the next round of detection and resolution.

[0093] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A robust adaptive heuristic conflict-free path planning method, characterized in that, The specific steps are as follows: Step 101: Obtain the geographical information in the flight sector, the planned flight paths of all flights, and the flight status; Step 102: Update the flight status and conflict resolution feasible solution of all flights in the flight sector, sort all flights in the flight sector in time sequence according to the time of entering the flight sector, and obtain a flight set; Step 103: Detect the conflict of the flights in the flight set in time sequence, if the current flight detects that there is a conflict, go to step 104; if the current flight detects that there is no conflict, detect the conflict of the next flight, and if the flight set is completely traversed, return to step 102; Step 104: For the flight to be conflict resolution, determine whether there is a conflict resolution feasible solution, if there is no conflict resolution feasible solution, go to step 105, if there is a conflict resolution feasible solution, go to step 110; Step 105: generating a candidate set of initial deflection points for the flight to be conflict resolution ; Step 106: If the candidate set of initial deflection points is empty, the flight to be conflict resolution has no conflict-free flight path, then the next flight is taken as the current flight, and the process returns to step 103; if the candidate set of initial deflection points is not empty, the process goes to step 107. Step 107: Select the initial deflection point from the candidate set of initial deflection points, and the process goes to step 108. Step 108: If the candidate set of deflection points is empty, the process returns to step 103; if the candidate set of deflection points is not empty, the process goes to step 109. Step 107: obtaining the starting deflection point candidate set based on the starting deflection point candidate set that generates a conflict-free flight path Step 108: obtaining the regression deflection point candidate set based on the starting deflection point candidate set that generates a conflict-free flight path , Step 109: obtaining the total number of starting deflection points that generate a conflict-free flight path from the regression deflection point candidate set Step 110: if all the starting deflection points in the regression deflection point candidate set that generate a conflict-free flight path are exhausted, the next flight is taken as the current flight, and the process returns to step 103. Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair , Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 108: judging the feasibility of the current deflection point pair Step 109: Evaluate all feasible deflection pairs obtained in step 108, select the feasible deflection pair corresponding to the minimum target based on the trade-off between flight cost and robustness, and take the feasible deflection pair as the recommended conflict-free flight path; update the conflict resolution feasible solution of the current flight to the recommended conflict-free flight path, and take the next flight as the current flight, and return to step 103; Step 110: Generate a neighborhood space for the conflict resolution return deflection point in the conflict resolution feasible solution; Step 111: Traverse all conflict resolution return deflection points in the neighborhood space, determine whether the conflict-free flight path formed based on the current conflict resolution feasible point pair meets the controller load constraint and meets the minimum safety interval constraint, if there is no feasible point in the neighborhood space, return to step 105; if there is a feasible point in the neighborhood space, go to step 112; Step 112: Evaluate all feasible conflict resolution feasible point pairs, select the feasible solution corresponding to the minimum target based on flight cost and robustness, and take the feasible solution as the conflict-free flight path of the current flight, update the conflict resolution feasible solution of the current flight to the recommended conflict-free flight path, and take the next flight as the current flight, and return to step 103.

2. The adaptive heuristic conflict-free path planning method of claim 1, wherein, The geographical information in the flight sector includes the boundary, entry point, exit point, waypoint and route geographical information of the flight sector.

3. The adaptive heuristic conflict-free path planning method of claim 1, wherein, The planned flight path of all flights in the flight sector includes the time, position and route waypoint of the flight plan entering the flight sector, the time of the route waypoint, the planned time of leaving the flight sector and the position.

4. The adaptive heuristic conflict-free path planning method of claim 1, wherein, The flight status of all flights in the flight sector includes the current position, current speed and heading of the aircraft corresponding to the flight.

5. The adaptive heuristic conflict-free path planning method of claim 1, wherein, Generating a set of regression deflection point candidates The specific steps are: taking the initial deflection point candidate set Generate a conflict-free track starting from the initial deflection point The origin of the local coordinate system is constructed, The axis points to the direction from the initial deflection point To the target waypoint, The axis is its vertical direction; in the local coordinate system, a limited search area is constructed, the limited search area is divided into multiple grids, and the center points of each grid are taken as the candidate positions of the generated regression deflection point Set of regression deflection point candidates ​ 6. The adaptive heuristic conflict-free path planning method of claim 5, wherein, The limited search area is located in the intersection area of two symmetric circles, the intersection of which passes through the start deflection point of the conflict-free flight path and the target waypoint or flight sector exit point, the centers of the two symmetric circles being on the perpendicular bisector of the line connecting the start deflection point of the conflict-free flight path and the target waypoint or flight sector exit point.

7. The adaptive heuristic conflict-free path planning method of claim 6, wherein, the radius of the two symmetrical circles is: wherein, represents the position of the start deflection point for generating a conflict-free flight path; represents the position of the target waypoint or flight sector exit point of the current flight; represents the length of the start deflection point for generating a conflict-free flight path to the target waypoint or flight sector exit point of the current flight; represents the maximum yaw angle constraint.​ 8. The adaptive heuristic conflict-free path planning method of claim 7, wherein, The yaw angle of the start deflection point and the yaw angle of the return deflection point of the generated conflict-free flight path are both not greater than the maximum yaw angle constraint .

9. The adaptive heuristic conflict-free path planning method according to any one of claims 1-8, characterized in that, The conflict resolution feasible solution is the recommended conflict-free flight path in step 109 or the conflict-free flight path of the current flight in step 112; the initial value of the conflict resolution feasible solution is empty.

10. A grid-based adaptive dynamic heuristic conflict-free path planning system, characterized in that, It comprises: A conflict detection module for detecting the conflict of all flights in the flight sector; A conflict resolution module for performing the adaptive heuristic conflict-free flight path planning method of any one of claims 1-9 to generate a conflict-free flight path for the flight; A flight status module for maintaining the status update of all flights in the flight sector; A visualization module for visualizing the real-time flight status and conflict-free flight path of all flights in the flight sector.

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