Method and device for multi-landing-point aircraft approach sequencing and dispatching, and medium

Abstract

This application provides a method, apparatus, and medium for aircraft arrival sequencing and scheduling at multiple takeoff and landing points, relating to the field of air traffic management. The method includes: acquiring the airspace location, flight data, and takeoff and landing locations of multiple aircraft awaiting arrival; determining the initial service queue for each takeoff and landing point based on the airspace location of each aircraft and the takeoff and landing location of each takeoff and landing point; optimizing each initial service queue to determine the corresponding optimized service queue; updating the takeoff and landing points of at least one target aircraft determined based on configured cross-regional indicators based on the idle time slots of each aircraft in the determined optimized service queues to obtain target takeoff and landing points; and determining the target service queue for each takeoff and landing point based on the target takeoff and landing points of each target aircraft. This application improves airspace efficiency and scheduling flexibility by using zoned flow control and optimized scheduling, and dynamically updating takeoff and landing points through idle time slots and cross-regional indicators.

Description

Technical Field

[0001] This application relates to the field of air traffic management, and more specifically, to a method, apparatus, and medium for scheduling aircraft arrivals at multiple takeoff and landing points. Background Technology

[0002] In air traffic operations, the sorting and scheduling of aircraft arrivals in the terminal area of ​​takeoff and landing sites is a crucial link in ensuring efficient and safe operations. With the continuous growth of air traffic, the traditional static queue management model is difficult to adapt to the dynamic and complex operational needs: on the one hand, aircraft arrivals need to be initially diverted according to airspace location, takeoff and landing point affiliation, etc., and traditional manual or simple rule-based sorting is prone to resource misallocation, resulting in an increase in total delay; on the other hand, the sorting of aircraft in the queue needs to take into account both time efficiency and operational rules, and the lack of intelligent dynamic optimization methods makes it difficult to balance overall efficiency. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus and medium for scheduling aircraft arrivals at multiple take-off and landing points, which solves the problem that traditional methods are difficult to coordinate and balance delay, priority and airspace resource utilization in the scheduling of aircraft arrivals at multiple take-off and landing points.

[0004] Firstly, a method for aircraft arrival sequencing and scheduling for multiple takeoff and landing points is provided, which may include: Acquire the airspace location, flight data, and take-off and landing locations of multiple aircraft awaiting arrival; Based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point, an initial service queue is determined for each take-off and landing point; the initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point. The rolling temporal particle swarm optimization algorithm is used to optimize each initial service queue and determine the optimized service queue corresponding to each initial service queue. Based on the available time slots of each aircraft in each optimized service queue, the take-off and landing points of at least one target aircraft determined based on the configured cross-regional indicators are updated to obtain the target take-off and landing points; the cross-regional indicators are determined through the flight data corresponding to each aircraft. Based on the target take-off and landing points of each target aircraft, the target service queue for each take-off and landing point is determined.

[0005] In one possible implementation, a rolling temporal particle swarm optimization algorithm is used to optimize each initial service queue and determine the optimized service queue corresponding to each initial service queue, including: For any initial service queue, based on the flight data of each aircraft in the initial service queue, a queue function is constructed with the objective of minimizing the weighted sum of total delay time and arrival priority; The rolling time-domain particle swarm optimization algorithm is used to treat multiple aircraft as particles and use the queue function as the fitness function to optimize the landing order of multiple aircraft, thereby obtaining an optimized service queue.

[0006] In one possible implementation, the expression for the queue function is:

[0007] in, For entry and exit signage, For entry identification, The initial landing and takeoff points for aircraft. Let be the set of takeoff and landing points, and the number of takeoff and landing points is... , Indicates aircraft, This refers to the collection of aircraft that need to land within the flight plan, the number of which is... , For the required arrival time, For the estimated arrival time, For aircraft landing priority This represents the total delay time.

[0008] In one possible implementation, the constraints on the parameters of the queue function are as follows:

[0009]

[0010]

[0011]

[0012]

[0013] in, This refers to the earliest arrival time of the aircraft at the corresponding takeoff and landing point. This refers to the latest arrival time of the aircraft at the corresponding takeoff and landing point. For aircraft Arrive at the take-off and landing point Boundary time of the terminal area boundary, and These are the aircraft's arrival reference time and remaining flight time, respectively. The safe time interval for receiving each aircraft at the takeoff and landing points. For the take-off and landing point aircraft Decision variables.

[0014] In one possible implementation, based on the idle time slots of each aircraft in each determined optimized service queue, the take-off and landing points of at least one target aircraft, determined based on configured cross-regional indicators, are updated to obtain the target take-off and landing points, including: For any optimized service queue, extract the idle time slots of each aircraft in the optimized service queue, and construct an idle time map of the optimized service queue based on the idle time slots; The target aircraft corresponding to the configured cross-regional indicators are sorted in descending order of cross-regional indicators to obtain a cross-regional sequence that includes multiple target aircraft. For any target aircraft, determine the cross-region time for the target aircraft to reach the target take-off and landing point corresponding to the optimized service queue; If the cross-regional time successfully matches the idle time slot in the idle time map, the take-off and landing point of the target aircraft is updated to obtain the target take-off and landing point.

[0015] In one possible implementation, the configuration process for the cross-regional metric includes: The configured cross-region index algorithm is used to process the flight data corresponding to each aircraft to determine the initial cross-region index of each aircraft. Initial cross-region indicators that exceed a preset threshold are identified as cross-region indicators.

[0016] In one possible implementation, the cross-regional index algorithm is as follows:

[0017] in, The initial cross-regional index for aircraft i, To delay time, For the safe time interval of each aircraft, For aircraft Landing priority.

[0018] Secondly, a multi-takeoff-landing point-oriented aircraft arrival sequencing and scheduling device is provided, the device may include: The acquisition unit is used to acquire the airspace position, flight data, and take-off and landing positions of multiple aircraft to be approached. The determining unit is used to determine the initial service queue for each take-off and landing point based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point; the initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point. The optimization unit is used to optimize each initial service queue using the rolling time-domain particle swarm optimization algorithm and determine the optimized service queue corresponding to each initial service queue. The update unit is used to update the take-off and landing points of at least one target aircraft determined based on the idle time slots of each aircraft in each optimized service queue, based on the configured cross-regional indicators, to obtain the target take-off and landing points; the cross-regional indicators are determined through the flight data corresponding to each aircraft. The determining unit is also used to determine the target service queue for each take-off and landing point based on the target take-off and landing points of each target aircraft.

[0019] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0020] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0021] This application provides a method, apparatus, and medium for aircraft arrival sequencing and scheduling for multiple take-off and landing points. The method includes: acquiring the airspace location, flight data, and take-off and landing locations of multiple aircraft to be arrived; determining the initial service queue for each take-off and landing point based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point; optimizing each initial service queue using a rolling time-domain particle swarm optimization algorithm to determine the optimized service queue corresponding to each initial service queue; updating the take-off and landing points of at least one target aircraft determined based on configured cross-regional indicators based on the idle time slots of each aircraft in the determined optimized service queues to obtain the target take-off and landing points; and determining the target service queue for each take-off and landing point based on the target take-off and landing points of each target aircraft. This method achieves efficient sorting of the initial service queue under the premise of meeting safety constraints by dividing the flow into zones and using rolling time-domain particle swarm optimization, effectively reducing the scheduling pressure of a single take-off and landing point and the complexity of global optimization. By combining idle time slots and cross-zone indicators to dynamically update the target take-off and landing points and generate the target service queue, it not only ensures the differentiated needs of high-priority aircraft, but also improves the overall utilization efficiency and scheduling flexibility of airspace resources through the collaboration of multiple take-off and landing points. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A system architecture diagram of an aircraft approach sequencing and scheduling method for multiple take-off and landing points provided in this application embodiment; Figure 2 A flowchart illustrating an aircraft arrival sequencing and scheduling method for multiple takeoff and landing points provided in this application embodiment; Figure 3 A flowchart illustrating the rolling temporal particle swarm optimization algorithm provided in an embodiment of this application; Figure 4 A schematic diagram of the cross-regional scheduling process provided in an embodiment of this application; Figure 5 A schematic diagram of the structure of an aircraft arrival sequencing and scheduling device for multiple take-off and landing points provided in this application embodiment; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] The aircraft arrival sequencing and scheduling method for multiple takeoff and landing points provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1 As shown, the system may include: an aircraft cluster, a terminal, and a processor; An aircraft cluster comprises multiple aircraft, each of which transmits its airspace position and flight data to a processor; in this application, an aircraft cluster is represented by an aircraft set.

[0026] The terminal is used to send the take-off and landing positions of multiple take-off and landing points in the terminal area of ​​the take-off and landing field to the processor. The processor is used to receive the airspace position and flight data of each aircraft sent to the processor, as well as the take-off and landing positions of multiple take-off and landing points in the terminal area of ​​the take-off and landing field, in order to execute the aircraft approach sequencing and scheduling method for multiple take-off and landing points provided in this application.

[0027] In air traffic operations, aircraft arrival sequencing and scheduling in the terminal area of ​​takeoff and landing sites are crucial for ensuring efficient and safe operations. With the continuous growth of air traffic, traditional static queue management models are ill-suited to the dynamic and complex operational demands. On the one hand, aircraft arrivals require initial diversion based on airspace location and takeoff / landing point affiliation; traditional manual or simple rule-based sequencing easily leads to resource misallocation, resulting in increased overall delay and insufficient priority response. On the other hand, aircraft sequencing within the queue must balance time efficiency and operational rules; the lack of intelligent dynamic optimization methods makes it difficult to balance overall efficiency and individual priority. Therefore, this application provides an aircraft arrival sequencing and scheduling method for multiple takeoff and landing points, solving the problem that traditional methods in multi-takeoff and landing point aircraft arrival scheduling struggle to coordinate and balance delay, priority, and airspace resource utilization.

[0028] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0029] Figure 2 This is a flowchart illustrating an aircraft arrival sequencing and scheduling method for multiple takeoff and landing points, provided as an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Obtain the airspace location, flight data, and take-off and landing locations of multiple aircraft to be approached.

[0030] Specifically, an aircraft's airspace position can be obtained through its onboard GPS or other positioning devices. It can then actively broadcast its three-dimensional coordinates (latitude, longitude, altitude, etc.) to ground receiving stations, which are then integrated and processed by the ground system to achieve real-time monitoring of the aircraft's airspace position. In some embodiments, ground-deployed air traffic control equipment can determine the aircraft's airspace position by emitting electromagnetic waves and receiving reflected signals from the aircraft, calculating information such as distance and bearing; for example, using three-dimensional spatial coordinates represented by latitude, longitude, and altitude.

[0031] Flight data, including but not limited to Time to Arrival (RTA), Estimated Time to Arrival (ETA), aircraft type, approach priority (such as emergency flight identification, flight schedule priority, etc.), and historical operating parameters (such as typical approach time), are used to subsequently assess aircraft approach requirements and scheduling suitability.

[0032] The takeoff and landing positions of multiple takeoff and landing points are represented by spatial positioning information in the takeoff and landing field coordinate system, including runway number, runway threshold coordinates, available takeoff and landing directions, etc., which serve as the basic resource constraints for the allocation and sorting of aircraft approach queues.

[0033] Step S220: Based on the airspace location of each aircraft and the take-off and landing locations of each take-off and landing point, determine the initial service queue for each take-off and landing point.

[0034] The initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point, and the landing aircraft are subsequently indicated as the aircraft to be approached.

[0035] Specifically, the airspace jurisdiction of each take-off and landing point can be delineated first. Taking the take-off and landing position of each take-off and landing point j as the core, and in conjunction with the terminal area airspace planning, define its exclusive receiving airspace for aircraft that need to enter the area (such as a fan-shaped or circular airspace area centered on the take-off and landing point), ensuring that the boundaries of each area are clear and there is no overlap or conflict.

[0036] Next, the aircraft is matched with the take-off and landing points. For each aircraft i, its airspace jurisdiction is determined based on its airspace location, and it is assigned to the corresponding take-off and landing point j. If the airspace location of an aircraft is in the boundary area of ​​multiple take-off and landing point jurisdictions, a unique initial assigned take-off and landing point can be determined by a preset priority rule (such as priority of closest take-off and landing point, priority of lowest current load of take-off and landing point).

[0037] Next, for each takeoff and landing point j, all aircraft i assigned to that takeoff and landing point are integrated into an independent queue, forming the initial service queue for that takeoff and landing point. The initial service queue contains at least one aircraft (in actual processing, the aircraft in the initial service queue are represented by their respective aircraft identifiers), and are initially sorted according to the time order in which the aircraft entered the airspace under the jurisdiction of that takeoff and landing point, serving as the initial sequence for subsequent optimization.

[0038] Through decision variables (Value can be 0 or 1) Guaranteed uniqueness: when When =1, it indicates that aircraft i is assigned to the initial service queue of takeoff and landing point j; when A value of 0 indicates that the resource has not been allocated. The constraints are satisfied. This means that each aircraft belongs to only one initial service queue at a single takeoff and landing point, avoiding duplicate allocation. It should be noted that the same constraint is applied in subsequent steps to ensure that each aircraft belongs to only one takeoff and landing point.

[0039] This approach decouples the aircraft set F into M independent initial service queues (one initial service queue for each take-off and landing point), enabling the segmentation and flow of aircraft that need to enter the field. This reduces the complexity of subsequent global scheduling and lays the foundation for each take-off and landing point to independently perform rolling time-domain particle swarm optimization, ensuring the orderliness and safety of parallel scheduling of multiple take-off and landing points.

[0040] Step S230: Use the rolling time-domain particle swarm optimization algorithm to optimize each initial service queue and determine the optimized service queue corresponding to each initial service queue.

[0041] Specifically, step 1: For any initial service queue, based on the flight data of each aircraft in the initial service queue, construct a queue function with the objective of minimizing the weighted sum of total delay time and arrival priority; The queue function can be represented as:

[0042] in, For entry and exit signage, For entry identification, The initial landing and takeoff points for aircraft. Let be the set of takeoff and landing points, and the number of takeoff and landing points is... , Indicates aircraft, This refers to the collection of aircraft that need to land within the flight plan, the number of which is... , For the required arrival time, For the estimated arrival time, For aircraft landing priority This represents the total delay time.

[0043] The constraints on the parameters in the queue function are as follows:

[0044]

[0045]

[0046]

[0047]

[0048] in, This refers to the earliest arrival time of the aircraft at the corresponding takeoff and landing point. This refers to the latest arrival time of the aircraft at the corresponding takeoff and landing point. For aircraft Arrive at the take-off and landing point Boundary time of the terminal area boundary, and These are the aircraft's arrival reference time and remaining flight time, respectively. The safe time interval for receiving each aircraft at the takeoff and landing points. For the take-off and landing point aircraft Decision variables.

[0049] Step 2: Using the rolling time-domain particle swarm optimization algorithm, multiple aircraft are set as particles, and the landing order of multiple aircraft is optimized using the queue function as the fitness function to obtain the optimized service queue.

[0050] Based on the number of takeoff and landing points M, the initial size of the rolling time slot window is W=M, meaning that each time slot window contains W aircraft; in some embodiments, the size of the time slot window can also be determined according to the actual situation.

[0051] Initialize the particle swarm size (e.g., S particles), maximum number of iterations, learning factor, and other algorithm parameters; where each particle is encoded as an aircraft sequence, the length of the aircraft sequence is the number of aircraft in the current time slot window, and the position of the particle corresponds to a possible aircraft sorting scheme.

[0052] Furthermore, the particle encoding is to assign the identifiers of W aircraft within the current time slot window in the initial service queue (e.g., i1, i2, ..., i...). W The data is encoded as particles, and the dimension of each particle corresponds to the sorting position of the aircraft. For example, particle P=[i3, i1, i2] means that the sorting is aircraft i3, aircraft i1, aircraft i2. Substitute the aircraft sequence corresponding to the particle into the queue function (which is also the fitness function of the rolling time-domain particle swarm algorithm) and calculate its STD value. The smaller the STD value, the higher the particle fitness (i.e., the better the sorting scheme).

[0053] Then, the sorting sequence is optimized through collaborative search among particles.

[0054] Two particles (parent sequence) are randomly selected, and their positions are partially swapped to generate a child sequence. For example, after the parent sequence P1=[i1, i2, i3, i4] and P2=[i3, i1, i4, i2] are crossed, P3=[i1, i3, i4, i2] is obtained. Randomly select an aircraft from the particles and insert it into another position in the sequence to adjust the order. For example, insert i2 before i1 in P=[i1, i2, i3] to get P′=[i2, i1, i3]. Each particle adjusts its search direction based on its own historical best fitness (individual best) and the group's global best fitness (global best), iterating until the fitness converges within the current time slot window.

[0055] After optimizing the current time slot window, the first aircraft in the sequence is fixed, and the RTA of that aircraft is used as the time base for subsequent optimizations. Slide the time slot window back one position to include the next unoptimized aircraft, and repeat the optimization process. When the time slot window covers all the aircraft in the initial service queue (i.e., all aircraft have completed RTA optimization), stop rolling. The aircraft sequence at this time is the optimized service queue corresponding to the initial service queue.

[0056] In some embodiments, in combination with Figure 3 As shown, for any landing and take-off point, initialize the airspace position, flight data of the aircraft, and the landing and take-off positions of the landing and take-off point; use the first-come-first-served scheduling rule to sort in ascending order according to the expected arrival time ETA of the aircraft to generate an initial service queue; start iterating from time slot window 1; the number of aircraft optimized each time (e.g., w = 3) represents the sorting of 3 consecutive aircraft optimized each time, and the constraint to be satisfied is i < n - w + 1; that is, the total number of aircraft is n, the size of the time slot window is w, and the starting position of the last window is n - w + 1; for example: n = 5, w = 3, the window starting positions are 1, 2, 3, corresponding to the constraint i < 5 - 3 + 1 = 3; if i ≥ n - w + 1, it means the window has covered all aircraft, and stop rolling optimization. When optimizing the aircraft corresponding to each time slot window in the initial service queue, first fix the first aircraft in the time slot window, then iterate and roll the time slot window i, and finally output the optimized service queue.

[0057] This method decomposes the large-scale global scheduling problem into time-ordered sub-problems by constructing a queue function that combines delay and priority and combining the dynamic window optimization mechanism of the rolling horizon particle swarm algorithm. On the premise of meeting safety constraints, it realizes the efficient conversion of the initial service queue to the optimized service queue, reduces the solution complexity, and ensures the differential scheduling requirements of high-priority aircraft, providing an accurate sorting scheme for the coordinated operation of multiple landing and take-off points.

[0058] Step S240: Based on the idle time slots of each aircraft in the determined optimized service queues, update the landing and take-off points of at least one target aircraft determined based on the configured cross-region metrics to obtain the target landing and take-off points.

[0059] Among them, the cross-region metrics are determined through the flight data corresponding to each aircraft.

[0060] Specifically, step 1: For any optimized service queue, extract the idle time slots between the aircraft in the optimized service queue, and based on the idle time slots, construct the idle time spectrum of the optimized service queue. For the optimized service queues of each landing and take-off point j ∈ V obtained by optimizing with the rolling horizon particle swarm algorithm (the optimized service queue contains the aircraft sorting sequence and the required arrival time RTA of each aircraft i), extract the idle time slots in the optimized service queue to construct the idle time spectrum.

[0061] The idle time slots specifically cover three types of scenarios: Category 1: Optimizing the time slot before the first aircraft in the service queue: that is, the time slot from the available start time of the takeoff and landing point to the RTA of the first aircraft, satisfying the safe time interval. The time period; Category 2: The RTA difference between any two adjacent aircraft exceeds... Redundant time periods; Category 3: Optimize the service queue before the end of the current scheduling cycle from the last aircraft's RTA to its takeoff / landing point, ensuring compliance with... The time period.

[0062] Furthermore, the idle time map records the start time, duration, and take-off and landing point identifier of each idle time slot, providing a resource basis for cross-regional scheduling.

[0063] Step 2: Sort the target aircraft corresponding to the configured cross-regional indicators in descending order of cross-regional indicators to obtain a cross-regional sequence that includes multiple target aircraft. The configuration process for this cross-regional indicator includes: The configured cross-region index algorithm is used to process the flight data corresponding to each aircraft to determine the initial cross-region index of each aircraft. The cross-regional indicator algorithm can be expressed as:

[0064] in, The initial cross-regional index for aircraft i, To delay time, For the safe time interval of each aircraft, For aircraft Landing priority.

[0065] Initial cross-regional indicators exceeding a preset threshold are identified as cross-regional indicators. A further preset threshold of 0 is set. Then, the target aircraft are sorted in descending order of cross-regional indicators to obtain the cross-regional sequence.

[0066] This method selects aircraft with a cross-regional index Cross(i)>0 as target aircraft, sorts them from largest to smallest Cross(i), prioritizes high-demand target aircraft, and ensures that resources are tilted toward the scheduling objects that need the most optimization.

[0067] Step 3: For any target aircraft, determine the cross-region time for the target aircraft to arrive at the target take-off and landing point corresponding to the optimized service queue; Specifically, construct a cross-regional cost matrix. , :in, The additional flight time for an aircraft to travel from its original take-off and landing point j to the target take-off and landing point d, d∈V, (d≠j) is calculated in advance based on the location of the target take-off and landing point and the aircraft's flight performance.

[0068] For target aircraft i (original take-off and landing point j), according to the cross-regional cost matrix middle Iterate through other takeoff and landing points in ascending order (i.e., from shortest to longest extra flight time across regions) and calculate their required arrival time after crossing regions: .

[0069] Step 4: If the cross-regional time and the idle time map are successfully matched, the take-off and landing points of the target aircraft are updated to obtain the target take-off and landing points.

[0070] Specifically, the original takeoff and landing point j of aircraft i is changed to the target takeoff and landing point d. ,Right now Can it match a specific idle time slot in the idle time map of the target take-off and landing point d (i.e., slot start time ≤ ≤ the end time of the time slot, and satisfy constraint).

[0071] If a match is found, update the target takeoff and landing point of aircraft i to d, and update its required arrival time to... Simultaneously, the matching idle time slot is removed from the idle time map of the target take-off and landing point d to avoid redundant resource allocation.

[0072] If the match fails, the target aircraft i will maintain its original takeoff and landing point j and original required arrival time. Then proceed to the matching process for the next target aircraft.

[0073] In some embodiments, combined with Figure 4 As shown, the process first initializes the cross-regional cost matrix, cross-regional index, and idle time map. Target aircraft with cross-regional indexes greater than 0 are sorted. From the sorted target aircraft, the first aircraft (initially i=1) is selected, and its ability to find idle time slots at other take-off and landing points across regions is checked. If a match is successful, the cross-regional operation is performed, updating the target aircraft's required arrival time and take-off / landing point; if it fails, its original take-off / landing point and required arrival time are retained. The process checks if the currently processed aircraft identifier i is less than or equal to the total number of aircraft N requiring cross-regional scheduling. If it is, the next aircraft (i+1) is processed; otherwise, the cross-regional matching process ends. Once all target aircraft requiring cross-regional scheduling have been processed, the cross-regional scheduling result, including the final take-off / landing point allocation and queue sorting for each aircraft, is output as the final optimized output of the overall scheme.

[0074] This approach extracts idle time slots from the optimized service queue, accurately identifies high-demand aircraft by combining cross-regional indicators, and dynamically updates takeoff and landing points based on a cost-first matching logic. Under the premise of ensuring the independent and safe operation of each takeoff and landing point, it transforms the collaborative scheduling of multiple takeoff and landing points into an idle window matching problem, effectively balancing global load and individual latency requirements, and improving the overall utilization efficiency and scheduling flexibility of terminal area airspace resources.

[0075] Step S250: Based on the target take-off and landing points of each target aircraft, determine the target service queue for each take-off and landing point.

[0076] Specifically, after the cross-regional scheduling update, all aircraft have been assigned a unique target take-off and landing point; for example: all aircraft i at the target take-off and landing point (i.e., those satisfying the following conditions) Aircraft with a value of 1 are grouped into the corresponding queue to form the target service queue for that target take-off and landing point. This target service queue contains two types of aircraft: aircraft that have not crossed the zone and target aircraft that have crossed the zone.

[0077] The aggregated target service queues are verified to ensure that there are no duplicate aircraft and that all aircraft have been included in their respective queues without omission.

[0078] This approach integrates cross-regional scheduling results with the original optimized service queue resources, constructing target service queues for each takeoff and landing point based on the target takeoff and landing point. This retains the local efficiency of single-takeoff and landing point optimization while achieving global load balancing through the orderly integration of cross-regional aircraft. Simultaneously, rigorous sorting rules and constraint verification ensure that the target service queues meet safety interval and time range requirements, providing a directly executable scheduling scheme for the safe, orderly, and efficient operation of multi-takeoff and landing terminal areas.

[0079] This application provides a method for aircraft arrival sequencing and scheduling for multiple take-off and landing points. The method includes: acquiring the airspace location, flight data, and take-off and landing locations of multiple aircraft awaiting arrival; determining the initial service queue for each take-off and landing point based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point; optimizing each initial service queue using a rolling temporal particle swarm optimization algorithm to determine the optimized service queue corresponding to each initial service queue; updating the take-off and landing points of at least one target aircraft determined based on configured cross-regional indicators based on the idle time slots of each aircraft in the determined optimized service queues to obtain the target take-off and landing points; and determining the target service queue for each take-off and landing point based on the target take-off and landing points of each target aircraft. This method, through zone-based flow control and rolling temporal particle swarm optimization, achieves efficient sequencing of the initial service queues while satisfying safety constraints, effectively reducing the scheduling pressure of a single take-off and landing point and the complexity of global optimization; by dynamically updating the target take-off and landing points and generating the target service queues by combining idle time slots and cross-regional indicators, it not only ensures the differentiated needs of high-priority aircraft but also improves the overall utilization efficiency and scheduling flexibility of airspace resources through multi-take-off and landing point collaboration.

[0080] Corresponding to the above method, this application also provides an aircraft arrival sequencing and scheduling device for multiple takeoff and landing points, such as... Figure 5 As shown, the device includes: The acquisition unit 510 is used to acquire the airspace position, flight data, and take-off and landing positions of multiple aircraft to be approached; The determining unit 520 is used to determine the initial service queue of each take-off and landing point based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point; the initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point. The optimization unit 530 is used to optimize each initial service queue using the rolling time-domain particle swarm optimization algorithm and determine the optimized service queue corresponding to each initial service queue. The update unit 540 is used to update the take-off and landing points of at least one target aircraft determined based on the idle time slots of each aircraft in each optimized service queue, based on the configured cross-regional indicators, to obtain the target take-off and landing points; the cross-regional indicators are determined through the flight data corresponding to each aircraft. The determining unit 520 is also used to determine the target service queue of each take-off and landing point based on the target take-off and landing points of each target aircraft.

[0081] The functions of each unit in the aircraft arrival sequencing and scheduling device for multiple take-off and landing points provided in the above embodiments of this application can be implemented through the above-described method steps. Therefore, the specific working process and beneficial effects of each unit in the aircraft arrival sequencing and scheduling device for multiple take-off and landing points provided in the embodiments of this application will not be repeated here.

[0082] This application also provides an electronic device, such as... Figure 6 As shown, it includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640.

[0083] Memory 630 is used to store computer programs; When the processor 610 executes the program stored in the memory 630, it performs the following steps: Acquire the airspace location, flight data, and take-off and landing locations of multiple aircraft awaiting arrival; Based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point, an initial service queue is determined for each take-off and landing point; the initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point. The rolling temporal particle swarm optimization algorithm is used to optimize each initial service queue and determine the optimized service queue corresponding to each initial service queue. Based on the available time slots of each aircraft in each optimized service queue, the take-off and landing points of at least one target aircraft determined based on the configured cross-regional indicators are updated to obtain the target take-off and landing points; the cross-regional indicators are determined through the flight data corresponding to each aircraft. Based on the target take-off and landing points of each target aircraft, the target service queue for each take-off and landing point is determined.

[0084] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0085] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0086] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0087] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0088] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0089] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform an aircraft approach sequencing and scheduling method for multiple take-off and landing points as described in any of the above embodiments.

[0090] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the above embodiments of an aircraft approach sequencing and scheduling method for multiple take-off and landing points.

[0091] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected," "coupled," or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0096] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the embodiments in this application are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments in this application.

[0097] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the embodiments of this application and their equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for scheduling aircraft arrivals at multiple takeoff and landing points, characterized in that, The method includes: Acquire the airspace location, flight data, and take-off and landing locations of multiple aircraft awaiting arrival; Based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point, an initial service queue is determined for each take-off and landing point; the initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point. The rolling temporal particle swarm optimization algorithm is used to optimize each initial service queue and determine the optimized service queue corresponding to each initial service queue. Based on the idle time slots between aircraft in each optimized service queue, the take-off and landing points of at least one target aircraft determined based on the configured cross-regional indicators are updated to obtain the target take-off and landing points; the cross-regional indicators are determined through the flight data corresponding to each aircraft. Based on the target take-off and landing points of each target aircraft, determine the target service queue for each take-off and landing point; Specifically, based on the idle time slots of each aircraft in each optimized service queue, the take-off and landing points of at least one target aircraft determined based on configured cross-regional indicators are updated to obtain the target take-off and landing points, including: For any optimized service queue, extract the idle time slots of each aircraft in the optimized service queue, and construct an idle time map of the optimized service queue based on the idle time slots; The target aircraft corresponding to the configured cross-regional indicators are sorted in descending order of cross-regional indicators to obtain a cross-regional sequence that includes multiple target aircraft. For any target aircraft, determine the cross-region time for the target aircraft to reach the target take-off and landing point corresponding to the optimized service queue; If the cross-regional time successfully matches the idle time slot in the idle time map, the take-off and landing point of the target aircraft is updated to obtain the target take-off and landing point.

2. The method as described in claim 1, characterized in that, The rolling time-domain particle swarm optimization algorithm is used to optimize each initial service queue, and the optimized service queue corresponding to each initial service queue is determined, including: For any initial service queue, based on the flight data of each aircraft in the initial service queue, a queue function is constructed with the objective of minimizing the weighted sum of total delay time and arrival priority; The rolling time-domain particle swarm optimization algorithm is used to treat multiple aircraft as particles and the queue function as the fitness function to optimize the landing order of multiple aircraft, thereby obtaining an optimized service queue.

3. The method as described in claim 2, characterized in that, The expression for the queue function is: in, For entry and exit signage, For entry identification, The initial landing and takeoff points for aircraft. Let be the set of takeoff and landing points, and the number of takeoff and landing points is... , Indicates aircraft, This refers to the collection of aircraft that need to land within the flight plan, the number of which is... , For the required arrival time, For the estimated arrival time, For aircraft landing priority This represents the total delay time.

4. The method as described in claim 3, characterized in that, The constraints on each parameter in the queue function are as follows: in, This refers to the earliest arrival time of the aircraft at the corresponding takeoff and landing point. This refers to the latest arrival time of the aircraft at the corresponding takeoff and landing point. For aircraft Arrive at the take-off and landing point Boundary time of the terminal area boundary, and These are the aircraft's arrival reference time and remaining flight time, respectively. The safe time interval for receiving each aircraft at the takeoff and landing points. For the take-off and landing point aircraft Decision variables.

5. The method as described in claim 1, characterized in that, The configuration process for the cross-regional indicators includes: The configured cross-region index algorithm is used to process the flight data corresponding to each aircraft to determine the initial cross-region index of each aircraft; Initial cross-region indicators that exceed a preset threshold are identified as cross-region indicators.

6. The method as described in claim 5, characterized in that, The algorithm for the cross-regional indicator is as follows: in, For aircraft i, the initial cross-regional index, To delay time, For the safe time interval of each aircraft, For aircraft Landing priority.

7. An aircraft arrival sequencing and scheduling device for multiple takeoff and landing points, characterized in that, The device includes: The acquisition unit is used to acquire the airspace position, flight data, and take-off and landing positions of multiple aircraft to be approached. The determining unit is used to determine the initial service queue for each take-off and landing point based on the airspace location of each aircraft and the take-off and landing location of each take-off and landing point; the initial service queue is a queue of the landing sequence of at least one aircraft landing at the corresponding take-off and landing point. The optimization unit is used to optimize each initial service queue using the rolling time-domain particle swarm optimization algorithm and determine the optimized service queue corresponding to each initial service queue. An update unit is used to update the take-off and landing points of at least one target aircraft determined based on configured cross-regional indicators, based on the idle time slots between aircraft in each optimized service queue, to obtain the target take-off and landing points; the cross-regional indicators are determined through the flight data corresponding to each aircraft. The determining unit is also used to determine the target service queue of each take-off and landing point based on the target take-off and landing points of each target aircraft. Specifically, based on the idle time slots of each aircraft in each optimized service queue, the take-off and landing points of at least one target aircraft determined based on configured cross-regional indicators are updated to obtain the target take-off and landing points, including: For any optimized service queue, extract the idle time slots of each aircraft in the optimized service queue, and construct an idle time map of the optimized service queue based on the idle time slots; The target aircraft corresponding to the configured cross-regional indicators are sorted in descending order of cross-regional indicators to obtain a cross-regional sequence that includes multiple target aircraft. For any target aircraft, determine the cross-region time for the target aircraft to reach the target take-off and landing point corresponding to the optimized service queue; If the cross-regional time successfully matches the idle time slot in the idle time map, the take-off and landing point of the target aircraft is updated to obtain the target take-off and landing point.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

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