Three-dimensional scheduling method and system suitable for low-altitude multi-aircraft hybrid take-off and landing field

Abstract

The present invention relates to aircraft scheduling, and in particular to a three-dimensional scheduling method and system suitable for a low-altitude multi-aircraft mixed take-off and landing field. The method comprises the following steps: performing three-dimensional segmentation of an airspace based on a Beidou grid code, and performing real-time mapping of the airspace status of the three-dimensional grid units; dividing the three-dimensional grid units according to the grid status; setting priority weights according to the mission types of the aircraft, and generating access rights in real time in combination with the grid status; introducing a time dimension into the Beidou grid code, segmenting the airspace into a plurality of four-dimensional space-time grid units, and performing conflict risk prediction on all the four-dimensional space-time grid units; and generating avoidance paths for aircraft with conflict risks using an improved quantum approximate optimization algorithm based on the conflict risk prediction results. The technical solution provided by the present invention can effectively overcome the defects of the prior art in that it is difficult to effectively plan avoidance paths for multiple aircraft and it is impossible to efficiently resolve conflicts between multiple aircraft.

Description

Technical Field

[0001] The present invention relates to aircraft scheduling, and in particular to a three-dimensional scheduling method and system suitable for a low-altitude multi-aircraft mixed take-off and landing field. Background Art

[0002] With the gradual opening of low-altitude airspace and the large-scale application of drones and general aviation aircraft in emergency rescue, logistics distribution, urban inspection and other fields, the competition and conflict risks of airspace resources in low-altitude mixed takeoff and landing scenarios of multiple aircraft are becoming increasingly prominent. Traditional airspace scheduling methods mainly rely on two-dimensional plane division and static rule management, which have the following technical limitations:

[0003] 1) Insufficient utilization of three-dimensional airspace: Existing technologies mostly use two-dimensional electronic fences or fixed routes, which do not fully utilize vertical spatial resources. This results in low utilization of the airspace around take-off and landing sites and makes it difficult to adapt to low-altitude complex terrain and dynamic obstacle environments.

[0004] 2) Weak dynamic scheduling capabilities: The system lacks real-time status awareness and dynamic division mechanisms for three-dimensional grid cells. This makes it impossible to adjust grid access permissions in real time based on factors such as airspace occupancy and weather changes, which can easily lead to local congestion or waste of resources.

[0005] 3) Low efficiency in conflict prediction and collaborative avoidance: Low-altitude aircraft trajectories are affected by wind fields, building disturbances, and other factors, resulting in high uncertainty. Traditional geometric conflict detection methods have difficulty accurately predicting four-dimensional spacetime (three-dimensional space + time) conflict risks, and multi-aircraft collaborative avoidance path planning is prone to falling into local optimality.

[0006] Existing technologies fail to effectively combine the three-dimensional encoding advantages of Beidou grid codes with the global search capabilities of quantum optimization, making it impossible to achieve refined modeling, dynamic access permission allocation, and efficient conflict resolution in low-altitude airspace. Therefore, a three-dimensional scheduling method and system suitable for low-altitude multi-aircraft mixed takeoff and landing sites is urgently needed. This method, through three-dimensional airspace segmentation, four-dimensional space-time conflict prediction, and improved quantum approximate optimization algorithms, can improve the safety and operational efficiency of low-altitude multi-aircraft mixed takeoff and landing scenarios. Summary of the Invention

[0007] In response to the above-mentioned shortcomings of the existing technology, the present invention provides a three-dimensional scheduling method and system suitable for low-altitude multi-aircraft mixed take-off and landing fields, which can effectively overcome the defects of the existing technology that it is difficult to effectively plan the avoidance paths of multiple aircraft and cannot efficiently resolve multi-aircraft conflicts.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field includes the following steps:

[0010] S1. Divide the airspace into three dimensions based on the Beidou grid code and map the airspace status of the three-dimensional grid cells in real time.

[0011] S2. Divide the three-dimensional grid cells according to the grid state;

[0012] S3. Set priority weights based on the aircraft's mission type and generate access permissions in real time based on the grid status;

[0013] S4. Introduce the time dimension into the BeiDou grid code, divide the airspace into multiple four-dimensional space-time grid cells, and perform conflict risk prediction for all four-dimensional space-time grid cells;

[0014] S5. Based on the conflict risk prediction results, an improved quantum approximate optimization algorithm is used to generate an avoidance path for aircraft with conflict risks.

[0015] Among them, in the improved quantum approximate optimization algorithm, when constructing the transverse field Hamiltonian, on the one hand, the weights are adjusted according to the characteristics of the multi-aircraft conflict avoidance problem, and the transverse field strength is increased for the four-dimensional space-time grid units where there is a risk of conflict; on the other hand, the double-qubit mixing term is introduced to enhance the entanglement between quantum states, improve the global search capability, and avoid the algorithm from falling into local optimality.

[0016] Preferably, in S1, the airspace is divided into three dimensions based on the Beidou grid code, and the airspace status is mapped in real time to the three-dimensional grid cells, including:

[0017] S11. Use the GeoSOT global earth 3D model to divide the airspace within a preset altitude range into a 3D grid with centimeter-level accuracy, and assign a corresponding Beidou grid code to each 3D grid cell.

[0018] S12. Combined with the low-altitude three-dimensional grid map, each three-dimensional grid unit is marked as "occupied" or "idle", and updated in real time through the Beidou satellite navigation system to achieve real-time mapping of the airspace status of the three-dimensional grid unit.

[0019] Preferably, the Beidou grid code is a binary integer code containing three dimensions: longitude, latitude and altitude. Through the recursive nesting characteristics of binary coding, multi-scale dynamic aggregation and decoupling of the airspace from centimeters to kilometers is realized to perform multi-scale computable identification of the airspace.

[0020] Preferably, S2 divides the three-dimensional grid cells according to the grid state, including:

[0021] By overlaying the Beidou grid code with the Geographic Information System (GIS), the three-dimensional grid cells covering high-risk areas are marked as no-fly zones and marked in red, prohibiting all aircraft from entering.

[0022] The three-dimensional grid cells covered by the free airspace are marked as flyable areas and marked in green, allowing aircraft to pass through on a priority basis;

[0023] The three-dimensional grid cells covered by the potential conflict area are marked as buffer zones and colored yellow. Access permissions need to be dynamically adjusted based on the priority weights of the aircraft.

[0024] Preferably, S3 sets priority weights according to the mission type of the aircraft and generates access permissions in real time based on the grid status, including:

[0025] S31. Set the priority weight according to the mission type of the aircraft:

[0026] For aircraft performing rescue missions, the first priority weight is set for them, allowing them to pass through the flyable area and buffer zone and enjoy priority passage within the buffer zone;

[0027] For aircraft carrying out manned missions, a second priority weight is set for them, allowing them to pass through the flyable area and the buffer zone, and enjoy the right of way within the buffer zone, second only to aircraft carrying out rescue missions;

[0028] For aircraft performing logistics tasks, a third priority weight is set for them, allowing them to only cross the flyable area and to avoid aircraft with higher priority weights.

[0029] For aircraft performing inspection tasks, a fourth priority weight is set for them, allowing them to only cross the flyable area and must avoid aircraft with higher priority weights.

[0030] S32. With the goal of minimizing the total flight time, multiple aircraft paths are allocated, and access permissions for each three-dimensional grid cell are generated in real time based on the grid status.

[0031] Preferably, S4 introduces the time dimension into the Beidou grid code, divides the airspace into multiple four-dimensional space-time grid units, and performs conflict risk prediction on all four-dimensional space-time grid units, including:

[0032] S41. Introducing the time dimension into the Beidou grid code, constructing a four-dimensional space-time grid code, and dividing the airspace into multiple four-dimensional space-time grid units based on the four-dimensional space-time grid code;

[0033] S42. Use the Gaussian mixture model (GMM) to model the probability distribution of aircraft trajectories, and combine it with the centimeter-level accuracy of the four-dimensional space-time grid code to predict the collision risk of all four-dimensional space-time grid cells;

[0034] Among them, the four-dimensional space-time grid code is a binary integer code containing four dimensions: longitude, latitude, altitude and time.

[0035] Preferably, in S5, based on the conflict risk prediction result, an improved quantum approximate optimization algorithm is used to generate an avoidance path for the aircraft with conflict risk, including:

[0036] S51. Convert the multi-aircraft conflict avoidance problem into a combinatorial optimization problem. The path selection of each aircraft is represented by a binary variable. The conflict conditions are converted into constraints between the variables to construct the classic objective function C by minimizing the total flight distance.

[0037] S52. Map the classical objective function C to the problem Hamiltonian H C :

[0038] ;

[0039] in, 、 Respectively The Pauli-Z matrix of the j-th quantum bit, , n is the number of quantum bits, which represents the number of binary variables for the aircraft path selection. For a aircraft, a total of quantum bits are required , b is the number of path options, 、 are weight coefficients, representing the path cost of a single aircraft and the path cost of conflicts between multiple aircraft respectively;

[0040] S53. Constructing the transverse field Hamiltonian H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability:

[0041] ;

[0042] in, 、 Respectively The Pauli-X matrix of the j-th quantum bit, is the two-qubit mixing term;

[0043] S54、Parameterized quantum circuit: According to the problem Hamiltonian H C and the transverse field Hamiltonian H B , generating parameterized quantum states :

[0044] ;

[0045] in, and are all parameters to be optimized, p is the number of layers of the parameterized quantum circuit, that is, the Hamiltonian H of the alternating application problem in the parameterized quantum circuit. C , transverse field Hamiltonian H B The number of times, is the initial superposition state;

[0046] S55. Measuring parameterized quantum states In the problem Hamiltonian H C Expected value under , as an approximation of the classic objective function C:

[0047] ;

[0048] S56, using the linear approximation constrained optimization algorithm COBYLA to treat the optimization parameters 、 Perform iterative optimization to minimize the expected value , until the iteration end condition is met and the optimized parameters are obtained 、 , and enter S57;

[0049] S57, according to the optimized parameters 、 , for the final quantum state Perform multiple measurements, count the frequencies of binary strings, and select the solution with the highest frequency as the approximate optimal avoidance path combination.

[0050] A three-dimensional scheduling system applicable to a low-altitude multi-aircraft mixed take-off and landing field is used to implement the above-mentioned three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field, and includes the following components:

[0051] The airspace 3D gridding and status mapping module divides the airspace into 3D parts based on the Beidou grid code and performs real-time mapping of the airspace status of the 3D grid cells.

[0052] Grid state analysis and area division module, which divides the three-dimensional grid cells according to the grid state;

[0053] The grid access permission dynamic allocation module sets priority weights based on the aircraft's mission type and generates access permissions in real time based on the grid status;

[0054] The four-dimensional space-time conflict prediction module introduces the time dimension into the Beidou grid code, divides the airspace into multiple four-dimensional space-time grid cells, and performs conflict risk prediction for all four-dimensional space-time grid cells;

[0055] The quantum optimization avoidance path planning module uses an improved quantum approximate optimization algorithm to generate avoidance paths for aircraft with conflict risks based on conflict risk prediction results;

[0056] Among them, in the improved quantum approximate optimization algorithm, when constructing the transverse field Hamiltonian, on the one hand, the weights are adjusted according to the characteristics of the multi-aircraft conflict avoidance problem, and the transverse field strength is increased for the four-dimensional space-time grid units where there is a risk of conflict; on the other hand, the double-qubit mixing term is introduced to enhance the entanglement between quantum states, improve the global search capability, and avoid the algorithm from falling into local optimality.

[0057] Preferably, the quantum optimization avoidance path planning module generates an avoidance path for an aircraft with conflict risk using an improved quantum approximate optimization algorithm based on the conflict risk prediction result, including:

[0058] S51. Convert the multi-aircraft conflict avoidance problem into a combinatorial optimization problem. The path selection of each aircraft is represented by a binary variable. The conflict conditions are converted into constraints between the variables to construct the classic objective function C by minimizing the total flight distance.

[0059] S52. Map the classical objective function C to the problem Hamiltonian H C ;

[0060] S53. Constructing the transverse field Hamiltonian H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability:

[0061] S54、Parameterized quantum circuit: According to the problem Hamiltonian H C and the transverse field Hamiltonian H B , generating parameterized quantum states :

[0062] ;

[0063] in, and are all parameters to be optimized, p is the number of layers of the parameterized quantum circuit, that is, the Hamiltonian H of the alternating application problem in the parameterized quantum circuit. C , transverse field Hamiltonian H B The number of times, is the initial superposition state;

[0064] S55. Measuring parameterized quantum states In the problem Hamiltonian H C Expected value under , as an approximation of the classic objective function C:

[0065] ;

[0066] S56, using the linear approximation constrained optimization algorithm COBYLA to treat the optimization parameters 、 Perform iterative optimization to minimize the expected value , until the iteration end condition is met and the optimized parameters are obtained 、 , and enter S57;

[0067] S57, according to the optimized parameters 、 , for the final quantum state Perform multiple measurements, count the frequencies of binary strings, and select the solution with the highest frequency as the approximate optimal avoidance path combination.

[0068] Preferably, in S52, the classical objective function C is mapped to the problem Hamiltonian H C :

[0069] ;

[0070] in, 、 Respectively The Pauli-Z matrix of the j-th quantum bit, , n is the number of quantum bits, which represents the number of binary variables for the aircraft path selection. For a aircraft, a total of quantum bits are required , b is the number of path options, 、 are weight coefficients, representing the path cost of a single aircraft and the path cost of conflicts between multiple aircraft respectively;

[0071] Constructing the transverse field Hamiltonian in S53 H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability:

[0072] ;

[0073] in, 、 Respectively The Pauli-X matrix of the j-th quantum bit, is the two-qubit mixing term.

[0074] Compared with the prior art, the three-dimensional scheduling method and system for low-altitude multi-aircraft mixed take-off and landing fields provided by the present invention have the following beneficial effects:

[0075] 1) Refined utilization of three-dimensional airspace to break through traditional management bottlenecks

[0076] Three-dimensional segmentation: Constructing three-dimensional grid units based on Beidou grid codes, breaking the limitations of two-dimensional planes, making full use of vertical spatial resources, and increasing the airspace capacity around take-off and landing sites;

[0077] Real-time status mapping: Dynamically maps airspace status to three-dimensional grid cells, providing accurate data support for scheduling and avoiding idle resources or excessive competition;

[0078] 2) Dynamic permission allocation to ensure task priority

[0079] Differentiated priorities: Priority weights are set based on the aircraft's mission type to ensure that high-priority aircraft have priority passage;

[0080] Grid status linkage: Dynamically adjust access permissions based on real-time airspace occupancy, balance efficiency and safety, and reduce congestion risks;

[0081] 3) Four-dimensional space-time conflict prediction to achieve forward-looking risk management

[0082] Time dimension fusion: Expanding the Beidou grid code into a four-dimensional space-time grid code to accurately predict conflict risks;

[0083] 4) Improve the quantum approximate optimization algorithm to enhance the efficiency of avoidance path planning

[0084] Constructing the transverse field Hamiltonian: Adjusting weights based on the characteristics of the multi-vehicle conflict avoidance problem, increasing the transverse field intensity for four-dimensional space-time grid cells with conflict risks, guiding the algorithm to focus on the low-conflict solution space and accelerating convergence;

[0085] Global search enhancement: Introducing a two-qubit mixing term enhances the entanglement between quantum states, avoids local optimality, and efficiently generates avoidance paths in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0087] Figure 1 It is a schematic diagram of the process of the present invention;

[0088] Figure 2 This is a flow chart of the process of using the improved quantum approximate optimization algorithm in the present invention to generate an avoidance path for aircraft with conflict risks. DETAILED DESCRIPTION

[0089] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0090] A three-dimensional scheduling method suitable for low-altitude multi-aircraft mixed take-off and landing fields, such as Figure 1 As shown, S1, based on the Beidou grid code, the airspace is divided into three dimensions and the airspace status of the three-dimensional grid cells is mapped in real time, specifically including:

[0091] S11. Use the GeoSOT global earth 3D model to divide the airspace within a preset altitude range into a 3D grid with centimeter-level accuracy, and assign a corresponding Beidou grid code to each 3D grid cell.

[0092] S12. Combined with the low-altitude three-dimensional grid map, each three-dimensional grid unit is marked as "occupied" or "idle", and updated in real time through the Beidou satellite navigation system to achieve real-time mapping of the airspace status of the three-dimensional grid unit.

[0093] Specifically, the Beidou grid code is a binary integer code that contains three dimensions: longitude, latitude, and altitude. Through the recursive nesting characteristics of binary coding, it realizes multi-scale dynamic aggregation and decoupling of the airspace from centimeters to kilometers, so as to perform multi-scale computable identification of the airspace.

[0094] S2. Divide the three-dimensional grid cells according to the grid state, including:

[0095] By overlaying the Beidou grid code with the Geographic Information System (GIS), the three-dimensional grid cells covering high-risk areas are marked as no-fly zones and marked in red, prohibiting all aircraft from entering.

[0096] The three-dimensional grid cells covered by the free airspace are marked as flyable areas and marked in green, allowing aircraft to pass through on a priority basis;

[0097] The three-dimensional grid cells covered by the potential conflict area are marked as buffer zones and colored yellow. Access permissions need to be dynamically adjusted based on the priority weights of the aircraft.

[0098] S3. Priority weights are set based on the aircraft's mission type, and access permissions are generated in real time based on the grid status. Specifically, the following are included:

[0099] S31. Set the priority weight according to the mission type of the aircraft:

[0100] For aircraft performing rescue missions, the first priority weight is set for them, allowing them to pass through the flyable area and buffer zone and enjoy priority passage within the buffer zone;

[0101] For aircraft carrying out manned missions, a second priority weight is set for them, allowing them to pass through the flyable area and the buffer zone, and enjoy the right of way within the buffer zone, second only to aircraft carrying out rescue missions;

[0102] For aircraft performing logistics tasks, a third priority weight is set for them, allowing them to only cross the flyable area and to avoid aircraft with higher priority weights.

[0103] For aircraft performing inspection tasks, a fourth priority weight is set for them, allowing them to only cross the flyable area and must avoid aircraft with higher priority weights.

[0104] S32. With the goal of minimizing the total flight time, multiple aircraft paths are allocated, and access permissions for each three-dimensional grid cell are generated in real time based on the grid status.

[0105] S4. Introduce the time dimension into the BeiDou grid code, divide the airspace into multiple four-dimensional space-time grid cells, and perform conflict risk prediction for all four-dimensional space-time grid cells, including:

[0106] S41. Introducing the time dimension into the Beidou grid code, constructing a four-dimensional space-time grid code, and dividing the airspace into multiple four-dimensional space-time grid units based on the four-dimensional space-time grid code;

[0107] S42. Use the Gaussian mixture model (GMM) to model the probability distribution of aircraft trajectories, and combine it with the centimeter-level accuracy of the four-dimensional space-time grid code to predict the collision risk of all four-dimensional space-time grid cells;

[0108] Among them, the four-dimensional space-time grid code is a binary integer code containing four dimensions: longitude, latitude, altitude and time.

[0109] S5. Based on the conflict risk prediction results, an improved quantum approximate optimization algorithm is used to generate an avoidance path for aircraft with conflict risks.

[0110] In the technical solution of the present application, in the improved quantum approximate optimization algorithm, when constructing the transverse field Hamiltonian, on the one hand, the weights are adjusted according to the characteristics of the multi-aircraft conflict avoidance problem, and the transverse field strength is increased for the four-dimensional space-time grid units where there is a risk of conflict; on the other hand, a double-qubit mixing term is introduced to enhance the entanglement between quantum states, improve the global search capability, and prevent the algorithm from falling into local optimality.

[0111] In S5, based on the conflict risk prediction results, an improved quantum approximate optimization algorithm is used to generate an avoidance path for aircraft with conflict risks, such as Figure 2 Shown, including:

[0112] S51. Convert the multi-aircraft conflict avoidance problem into a combinatorial optimization problem. The path selection of each aircraft is represented by a binary variable. The conflict conditions are converted into constraints between the variables to construct the classic objective function C by minimizing the total flight distance.

[0113] S52. Map the classical objective function C to the problem Hamiltonian H C :

[0114] ;

[0115] in, 、 Respectively The Pauli-Z matrix of the j-th quantum bit, , n is the number of quantum bits, which represents the number of binary variables for the aircraft path selection. For a aircraft, a total of quantum bits are required , b is the number of path options, 、 are weight coefficients, representing the path cost of a single aircraft and the path cost of conflicts between multiple aircraft respectively;

[0116] S53. Constructing the transverse field Hamiltonian H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability:

[0117] ;

[0118] in, 、 Respectively The Pauli-X matrix of the j-th quantum bit, is the two-qubit mixing term;

[0119] S54、Parameterized quantum circuit: According to the problem Hamiltonian H C and the transverse field Hamiltonian H B , generating parameterized quantum states :

[0120] ;

[0121] in, and are all parameters to be optimized, p is the number of layers of the parameterized quantum circuit, that is, the Hamiltonian H of the alternating application problem in the parameterized quantum circuit. C , transverse field Hamiltonian H B The number of times, is the initial superposition state;

[0122] S55. Measuring parameterized quantum states In the problem Hamiltonian H C Expected value under , as an approximation of the classic objective function C:

[0123] ;

[0124] S56, using the linear approximation constrained optimization algorithm COBYLA to treat the optimization parameters 、 Perform iterative optimization to minimize the expected value , until the iteration end condition is met and the optimized parameters are obtained 、 , and enter S57;

[0125] S57, according to the optimized parameters 、 , for the final quantum state Perform multiple measurements, count the frequencies of binary strings, and select the solution with the highest frequency as the approximate optimal avoidance path combination.

[0126] Based on the above-disclosed three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field, the technical solution of this application further discloses a three-dimensional scheduling system applicable to a low-altitude multi-aircraft mixed take-off and landing field, which is used to execute the above-disclosed three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field, and includes the following components:

[0127] The airspace 3D gridding and status mapping module divides the airspace into 3D parts based on the Beidou grid code and performs real-time mapping of the airspace status of the 3D grid cells.

[0128] Grid state analysis and area division module, which divides the three-dimensional grid cells according to the grid state;

[0129] The grid access permission dynamic allocation module sets priority weights based on the aircraft's mission type and generates access permissions in real time based on the grid status;

[0130] The four-dimensional space-time conflict prediction module introduces the time dimension into the Beidou grid code, divides the airspace into multiple four-dimensional space-time grid cells, and performs conflict risk prediction for all four-dimensional space-time grid cells;

[0131] The quantum optimization avoidance path planning module uses an improved quantum approximate optimization algorithm to generate avoidance paths for aircraft with conflict risks based on conflict risk prediction results;

[0132] Among them, in the improved quantum approximate optimization algorithm, when constructing the transverse field Hamiltonian, on the one hand, the weights are adjusted according to the characteristics of the multi-aircraft conflict avoidance problem, and the transverse field strength is increased for the four-dimensional space-time grid units where there is a risk of conflict; on the other hand, the double-qubit mixing term is introduced to enhance the entanglement between quantum states, improve the global search capability, and avoid the algorithm from falling into local optimality.

[0133] Specifically, the quantum optimization avoidance path planning module uses an improved quantum approximate optimization algorithm to generate an avoidance path for aircraft with conflict risks based on the conflict risk prediction results, including:

[0134] S51. Convert the multi-aircraft conflict avoidance problem into a combinatorial optimization problem. The path selection of each aircraft is represented by a binary variable. The conflict conditions are converted into constraints between the variables to construct the classic objective function C by minimizing the total flight distance.

[0135] S52. Map the classical objective function C to the problem Hamiltonian H C ;

[0136] S53. Constructing the transverse field Hamiltonian H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability:

[0137] S54、Parameterized quantum circuit: According to the problem Hamiltonian H C and the transverse field Hamiltonian H B , generating parameterized quantum states :

[0138] ;

[0139] in, and are all parameters to be optimized, p is the number of layers of the parameterized quantum circuit, that is, the Hamiltonian H of the alternating application problem in the parameterized quantum circuit. C , transverse field Hamiltonian H B The number of times, is the initial superposition state;

[0140] S55. Measuring parameterized quantum states In the problem Hamiltonian H C Expected value under , as an approximation of the classic objective function C:

[0141] ;

[0142] S56, using the linear approximation constrained optimization algorithm COBYLA to treat the optimization parameters 、 Perform iterative optimization to minimize the expected value , until the iteration end condition is met and the optimized parameters are obtained 、 , and enter S57;

[0143] S57, according to the optimized parameters 、 , for the final quantum state Perform multiple measurements, count the frequencies of binary strings, and select the solution with the highest frequency as the approximate optimal avoidance path combination.

[0144] Specifically, in S52, the classical objective function C is mapped to the problem Hamiltonian H C :

[0145] ;

[0146] in, 、 Respectively The Pauli-Z matrix of the j-th quantum bit, , n is the number of quantum bits, which represents the number of binary variables for the aircraft path selection. For a aircraft, a total of quantum bits are required , b is the number of path options, 、 are weight coefficients, representing the path cost of a single aircraft and the path cost of conflicts between multiple aircraft respectively;

[0147] Constructing the transverse field Hamiltonian in S53 H B, used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability:

[0148] ;

[0149] in, 、 Respectively The Pauli-X matrix of the j-th quantum bit, is the two-qubit mixing term.

[0150] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A three-dimensional scheduling method applicable to low-altitude multi-aircraft mixed take-off and landing fields, characterized by: The following steps are involved: S1. Divide the airspace into three dimensions based on the Beidou grid code and map the airspace status of the three-dimensional grid cells in real time. S2. Divide the three-dimensional grid cells according to the grid state; S3. Set priority weights based on the aircraft's mission type and generate access permissions in real time based on the grid status; S4. Introduce the time dimension into the BeiDou grid code, divide the airspace into multiple four-dimensional space-time grid cells, and perform conflict risk prediction for all four-dimensional space-time grid cells; S5. Based on the conflict risk prediction results, an improved quantum approximate optimization algorithm is used to generate an avoidance path for aircraft with conflict risks. Among them, in the improved quantum approximate optimization algorithm, when constructing the transverse field Hamiltonian, the weights are adjusted according to the characteristics of the multi-aircraft conflict avoidance problem, and the transverse field intensity is increased for the four-dimensional space-time grid units with conflict risks. At the same time, the double-qubit mixing term is introduced to enhance the entanglement between quantum states and improve the global search capability.

2. The three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 1 is characterized in that: S1 divides the airspace into three dimensions based on the Beidou grid code and performs real-time mapping of the airspace status of the three-dimensional grid cells, including: S11. Use the GeoSOT global earth 3D model to divide the airspace within a preset altitude range into a 3D grid with centimeter-level accuracy, and assign a corresponding Beidou grid code to each 3D grid cell. S12. Combined with the low-altitude 3D grid map, each 3D grid cell is marked as "occupied" or "idle", and updated in real time through the Beidou satellite navigation system to achieve real-time mapping of the airspace status of the 3D grid cell.

3. The three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 2 is characterized in that: The Beidou grid code is a binary integer code containing three dimensions: longitude, latitude, and altitude. Through the recursive nesting characteristics of binary coding, multi-scale dynamic aggregation and decoupling of airspace from centimeters to kilometers is achieved, so as to perform multi-scale computable identification of airspace.

4. The three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 1 is characterized in that: In S2, the three-dimensional grid cells are divided according to the grid status, including: By overlaying the Beidou grid code with the Geographic Information System (GIS), the three-dimensional grid cells covering high-risk areas are marked as no-fly zones and marked in red, prohibiting all aircraft from entering. The three-dimensional grid cells covered by the free airspace are marked as flyable areas and marked in green, allowing aircraft to pass through on a priority basis; The three-dimensional grid cells covered by the potential conflict area are marked as buffer zones and colored yellow. Access permissions need to be dynamically adjusted based on the priority weights of the aircraft.

5. The three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 4 is characterized in that: In S3, priority weights are set based on the aircraft's mission type, and access permissions are generated in real time based on the grid status, including: S31. Set priority weights based on the aircraft's mission type: For aircraft performing rescue missions, the first priority weight is set for them, allowing them to pass through the flyable area and buffer zone and enjoy priority passage within the buffer zone; For aircraft carrying out manned missions, a second priority weight is set for them, allowing them to pass through the flyable area and the buffer zone, and enjoy the right of way within the buffer zone, second only to aircraft carrying out rescue missions; For aircraft performing logistics tasks, a third priority weight is set for them, allowing them to only cross the flyable area and to avoid aircraft with higher priority weights. For aircraft performing inspection tasks, a fourth priority weight is set for them, allowing them to only cross the flyable area and must avoid aircraft with higher priority weights. S32. With the goal of minimizing the total flight time, multiple aircraft paths are allocated, and access permissions for each three-dimensional grid cell are generated in real time based on the grid status.

6. The three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 1 is characterized in that: S4 introduces the time dimension into the BeiDou grid code, divides the airspace into multiple four-dimensional space-time grid cells, and performs conflict risk prediction for all four-dimensional space-time grid cells, including: S41. Introducing the time dimension into the Beidou grid code, constructing a four-dimensional space-time grid code, and dividing the airspace into multiple four-dimensional space-time grid units based on the four-dimensional space-time grid code; S42. Use the Gaussian mixture model (GMM) to model the probability distribution of aircraft trajectories, and combine it with the centimeter-level accuracy of the four-dimensional space-time grid code to predict the collision risk of all four-dimensional space-time grid cells; Among them, the four-dimensional space-time grid code is a binary integer code containing four dimensions: longitude, latitude, altitude and time.

7. The three-dimensional scheduling method applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 1 is characterized in that: In S5, based on the conflict risk prediction results, an improved quantum approximate optimization algorithm is used to generate an avoidance path for aircraft with conflict risks, including: S51. Convert the multi-aircraft conflict avoidance problem into a combinatorial optimization problem. The path selection of each aircraft is represented by a binary variable. The conflict conditions are converted into constraints between the variables to construct the classic objective function C by minimizing the total flight distance. S52. Map the classical objective function C to the problem Hamiltonian H C : ； in, 、 Respectively The Pauli-Z matrix of the j-th quantum bit, , n is the number of quantum bits, which represents the number of binary variables for the aircraft path selection. For a aircraft, a total of quantum bits are required , b is the number of path options, 、 are weight coefficients, representing the path cost of a single aircraft and the path cost of conflicts between multiple aircraft respectively; S53. Constructing the transverse field Hamiltonian H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability: ； in, 、 Respectively The Pauli-X matrix of the j-th quantum bit, is the two-qubit mixing term; S54、Parameterized quantum circuit: According to the problem Hamiltonian H C and the transverse field Hamiltonian H B , generating parameterized quantum states : ； in, and are all parameters to be optimized, p is the number of layers of the parameterized quantum circuit, that is, the Hamiltonian H of the alternating application problem in the parameterized quantum circuit. C , transverse field Hamiltonian H B The number of times, is the initial superposition state; S55. Measuring parameterized quantum states In the problem Hamiltonian H C Expected value under , as an approximation of the classic objective function C: ； S56, using the linear approximation constrained optimization algorithm COBYLA to treat the optimization parameters 、 Perform iterative optimization to minimize the expected value , until the iteration end condition is met and the optimized parameters are obtained 、 , and enter S57; S57, according to the optimized parameters 、 , for the final quantum state Perform multiple measurements, count the frequencies of binary strings, and select the solution with the highest frequency as the approximate optimal avoidance path combination.

8. A three-dimensional dispatching system applicable to a low-altitude multi-aircraft mixed take-off and landing field, for executing the three-dimensional dispatching method applicable to a low-altitude multi-aircraft mixed take-off and landing field as claimed in claim 1, characterized in that: Includes the following components: The airspace 3D gridding and status mapping module divides the airspace into 3D parts based on the Beidou grid code and performs real-time mapping of the airspace status of the 3D grid cells. Grid state analysis and area division module, which divides the three-dimensional grid cells according to the grid state; The grid access permission dynamic allocation module sets priority weights based on the aircraft's mission type and generates access permissions in real time based on the grid status; The four-dimensional space-time conflict prediction module introduces the time dimension into the Beidou grid code, divides the airspace into multiple four-dimensional space-time grid cells, and performs conflict risk prediction for all four-dimensional space-time grid cells; The quantum optimization avoidance path planning module uses an improved quantum approximate optimization algorithm to generate avoidance paths for aircraft with conflict risks based on conflict risk prediction results; Among them, in the improved quantum approximate optimization algorithm, when constructing the transverse field Hamiltonian, the weights are adjusted according to the characteristics of the multi-aircraft conflict avoidance problem, and the transverse field intensity is increased for the four-dimensional space-time grid units with conflict risks. At the same time, the double-qubit mixing term is introduced to enhance the entanglement between quantum states and improve the global search capability.

9. The three-dimensional dispatching system applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 8 is characterized in that: The quantum optimization avoidance path planning module generates an avoidance path for aircraft with conflict risks based on the conflict risk prediction results using an improved quantum approximate optimization algorithm, including: S51. Convert the multi-aircraft conflict avoidance problem into a combinatorial optimization problem. The path selection of each aircraft is represented by a binary variable. The conflict conditions are converted into constraints between the variables to construct the classic objective function C by minimizing the total flight distance. S52. Map the classical objective function C to the problem Hamiltonian H C ; S53. Constructing the transverse field Hamiltonian H B , used to explore solutions in the quantum state space, adjust the weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability; S54、Parameterized quantum circuit: According to the problem Hamiltonian H C and the transverse field Hamiltonian H B , generating parameterized quantum states : ； in, and are all parameters to be optimized, p is the number of layers of the parameterized quantum circuit, that is, the Hamiltonian H of the alternating application problem in the parameterized quantum circuit. C , transverse field Hamiltonian H B The number of times, is the initial superposition state; S55. Measuring parameterized quantum states In the problem Hamiltonian H C Expected value under , as an approximation of the classic objective function C: ； S56, using the linear approximation constrained optimization algorithm COBYLA to treat the optimization parameters 、 Perform iterative optimization to minimize the expected value , until the iteration end condition is met and the optimized parameters are obtained 、 , and enter S57; S57, according to the optimized parameters 、 , for the final quantum state Perform multiple measurements, count the frequencies of binary strings, and select the solution with the highest frequency as the approximate optimal avoidance path combination.

10. The three-dimensional dispatching system applicable to a low-altitude multi-aircraft mixed take-off and landing field according to claim 9 is characterized in that: In S52, the classical objective function C is mapped to the problem Hamiltonian H C : ； in, 、 Respectively The Pauli-Z matrix of the j-th quantum bit, , n is the number of quantum bits, which represents the number of binary variables for the aircraft path selection. For a aircraft, a total of quantum bits are required , b is the number of path options, 、 are weight coefficients, representing the path cost of a single aircraft and the path cost of conflicts between multiple aircraft respectively; Constructing the transverse field Hamiltonian in S53 H B , used to explore solutions in quantum state space, adjust weights according to the characteristics of the multi-aircraft conflict avoidance problem, increase the transverse field strength of the four-dimensional space-time grid cells with conflict risks, and introduce a two-qubit mixing term to enhance the entanglement between quantum states and improve the global search capability: ； in, 、 Respectively The Pauli-X matrix of the j-th quantum bit, is the two-qubit mixing term.

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