A target glide path acquisition system

Abstract

The present invention relates to a target taxiing path acquisition system, and relates to the field of data processing technology. The system implements the following steps: setting a leader bee and a follower bee, updating pheromones and acquiring the fitness of a feasible solution of a path generated by each follower bee, sequentially acquiring the target priority generated after each iteration starting from the first iteration, sequentially acquiring the target priority generated after each iteration starting from the first iteration, performing multiple iterations based on the target priority, converting the acquired leader bee or follower bee into a scout bee to perform a random search and acquire the number of iterations corresponding to all the leader bees, acquiring the target taxiing path when the number of iterations corresponding to all the leader bees exceeds a second target number of iterations, and repeating the above steps to acquire the target taxiing path when there is a leader bee whose number of iterations does not exceed the second target number of iterations. The system can improve search efficiency, shorten the taxiing time of a flight, and make the acquired taxiing path more optimal.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a system for acquiring a target glide path. Background Art

[0002] Traffic congestion on taxiways is a critical issue affecting the normal operation of airports. Optimizing taxi paths for all arriving and departing aircraft has become a pressing issue. Existing methods for obtaining target taxi paths rely on bee swarm algorithms. This approach typically results in the preceding aircraft's taxi path being collision-free and the shortest path, while subsequent aircraft are affected by the preceding aircraft, resulting in suboptimal taxi paths. Consequently, achieving a global optimal path is impossible. Summary of the Invention

[0003] In response to the above technical problems, the present invention adopts a technical solution: a target glide path acquisition system, comprising: a processor and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented:

[0004] S100: Set the leading bee and the following bee, update the pheromone and obtain the fitness of the feasible solution of the path generated by each follower bee.

[0005] S200 , based on a first target iteration number β, sequentially obtain target priorities generated after each iteration starting from the first iteration, wherein the target priorities are fitness values of feasible solutions of paths generated after each iteration by the leading bee and the following bee.

[0006] S300, when the target priority obtained in the nth iteration is less than the candidate priority, determine β=β+n-1, so that the leading bee and the following bee continue to iterate according to the first target iteration number β, where n≤β, and the candidate priority is the minimum feasible solution fitness searched in the same bee team before iterating the first target iteration number.

[0007] S400, repeatedly execute steps S200 to S300 until there is no target priority less than the candidate priority after iterating the first target iteration number β, transform the obtained leading bee or follower bee into a scout bee to perform random search and obtain the iteration numbers corresponding to all leading bees.

[0008] S500, when the number of iterations corresponding to all leading bees exceeds the second target number of iterations, the target glide path corresponding to each target flight is obtained, wherein the obtained target glide path is the glide path corresponding to each target flight when the fitness value of the iteratively updated feasible solution is the minimum.

[0009] S600: When the number of iterations corresponding to the existence of a leading bee does not exceed the second target number of iterations, steps S100 to S500 are repeatedly executed to obtain a target taxiing path corresponding to each target flight.

[0010] Compared with the prior art, the present invention has significant advantages. By means of the above technical solution, the target glide path acquisition system provided by the present invention can achieve considerable technological advancement and practicality, and has wide industrial application value. It has at least the following advantages:

[0011] The present invention is a system for acquiring a target glide path, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the following steps are implemented: setting a leading bee and a following bee, updating pheromones and acquiring a feasible solution fitness of a path generated by each following bee, acquiring the target priority generated after each iteration starting from the first iteration based on a first target iteration number, when there is a target priority obtained in the nth iteration that is less than the candidate priority, re-determining the first target iteration number so that the leading bee and the following bee continue to iterate according to the first target iteration number, repeating the steps of updating pheromones and acquiring a feasible solution fitness of a path generated by each following bee and the above steps until there is no target priority less than the candidate priority after iterating the first target iteration number, and then the obtained leading bee or following bee is returned to the target path. The bees are transformed into scout bees to perform random searches and obtain the iteration numbers corresponding to all leading bees. When the iteration numbers corresponding to all leading bees exceed the second target iteration number, the target taxi path corresponding to each target flight is obtained. When there is a leading bee whose iteration number does not exceed the second target iteration number, the above steps are repeated to obtain the target taxi path corresponding to each target flight. The present invention can introduce the pheromone mechanism into the bee colony algorithm to obtain the target taxi path when the airport is in an abnormal state, so that the bees can draw on historical experience when searching for the taxiway path and avoid repeated searches, thereby significantly improving the search efficiency. At the same time, by optimizing the relevant parameters in the bee colony algorithm, the algorithm has stronger adaptability and robustness in solving the taxiway path, shortens the flight taxiing time, and makes the obtained taxiing path better.

[0012] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart implemented when a processor of a target glide path acquisition system according to an embodiment of the present invention executes a computer program. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] Example

[0017] This embodiment provides a target glide path acquisition system, the system comprising: a processor and a memory storing a computer program, when the computer program is executed by the processor, the following steps are implemented, such as: Figure 1 As shown:

[0018] Specifically, the system further includes: a target airport map and a target feature data list, wherein the target feature data list includes a plurality of target feature data, and the target feature data is taxiing feature data corresponding to the target flight.

[0019] Specifically, the target airport map is a map of the target airport, wherein the target airport is an airport in an abnormal state, and the abnormal state is a state that affects the normal operation of flights at the airport, for example, abnormal states such as blizzard weather.

[0020] Furthermore, the target airport map is a target airport taxiway graph structure G, wherein G=(V, E), V is a node set formed by nodes on the target airport surface, E is an edge set, and the edges in E represent taxiways connecting the nodes.

[0021] Specifically, the target flight is an aircraft that takes off or stops at a target airport.

[0022] Specifically, the taxiing characteristic data includes flight type, flight number, taxiing start time, taxiing stop time, flight start node number, and flight end node number.

[0023] Furthermore, the flight type includes inbound flights and outbound flights, wherein, when the flight type is an inbound flight, the start taxiing time is the time from the flight leaving the runway and entering the taxiway system after landing, and the stop taxiing time is the time for the flight to taxi to the parking position and engage the wheel block.

[0024] Furthermore, when the flight type is a departing flight, the start taxiing time is the time when the flight starts to unblock at the parking stand, and the stop taxiing time is the time when the flight ends taxiing to the runway holding area.

[0025] Furthermore, the node number at which the flight starts and the node number at which the flight ends are the numbers corresponding to the nodes in V.

[0026] S100: Set the leading bee and the following bee, update the pheromone and obtain the fitness of the feasible solution of the path generated by each follower bee.

[0027] Specifically, S100 also includes the following steps:

[0028] S101, randomly generating a number of initial sliding paths and randomly setting the pheromone concentrations between the nodes of the sliding paths to form a pheromone matrix.

[0029] Specifically, those skilled in the art know that the initial sliding path can be generated and the pheromone concentration can be randomly set according to actual needs, both of which fall within the scope of protection of the present invention and will not be described in detail here.

[0030] Preferably, the pheromone concentrations between the nodes of the sliding path are randomly set to form a pheromone matrix with values of 1.

[0031] S102, obtaining the fitness value of the feasible solution corresponding to each leading bee, and updating the pheromone matrix corresponding to each leading bee.

[0032] Specifically, the feasible solution fitness T meets the following conditions:

[0033] , where, when the e-th target flight taxis from node i to node j, ɛ e ij =1, when the e-th target flight does not taxi from node i to node j, ɛ e ij =0, t e ijis the time it takes for the e-th target flight to taxi from node i to node j. The value of e ranges from 1 to N, N is the number of target flights, i ranges from 1 to M, j ranges from 1 to M, i≠j, and M is the number of nodes in V.

[0034] Specifically, the transfer probability of the pheromone matrix is obtained, where the probability P of the leading bee k in the pheromone matrix transferring from node i to node j at time t is k ij (t) meet the following conditions:

[0035] P k ij (t) = (τ ij (t) α × (1 / d ij ) β / (∑ s∈Uk (τ is (t) α × (1 / d is ) β ), τ ij (t) is the pheromone concentration on the path connecting node i and node j at time t, d ij is the distance between node i and node j, Uk is the set of nodes to be visited by the kth leader bee, τ is (t) is the pheromone concentration on the path connecting node i and node s at time t, d is is the distance between node i and node s, α is a constant between 0 and 1, β is a constant between 0 and 1, and node s is any node among the nodes to be visited by the kth leader bee.

[0036] S103 , setting a first target number of iterations, wherein the first target number of iterations is the number of new paths generated by each follower bee for iterative update.

[0037] Specifically, the value range of the first target number of iterations is 10 to 20. Those skilled in the art know that the first target number of iterations can be selected according to actual needs, which falls within the protection scope of the present invention and will not be repeated here.

[0038] S104, the follower bee searches according to the pheromone matrix corresponding to its corresponding leader bee, updates the pheromone and obtains the fitness of the feasible solution of each follower bee-generated path, wherein each follower bee corresponds to one leader bee.

[0039] S200 , based on a first target iteration number β, sequentially obtain target priorities generated after each iteration starting from the first iteration, wherein the target priorities are fitness values of feasible solutions of paths generated after each iteration by the leading bee and the following bee.

[0040] S300, when the target priority obtained in the nth iteration is less than the candidate priority, determine β=β+n-1, so that the leading bee and the following bee continue to iterate according to the first target iteration number β, where n≤β, and the candidate priority is the minimum feasible solution fitness searched in the same bee team before iterating the first target iteration number.

[0041] S400, repeatedly execute steps S200 to S300 until there is no target priority less than the candidate priority after iterating the first target iteration number β, transform the obtained leading bee or follower bee into a scout bee to perform random search and obtain the iteration numbers corresponding to all leading bees.

[0042] S500, when the number of iterations corresponding to all leading bees exceeds the second target number of iterations, the target glide path corresponding to each target flight is obtained, wherein the obtained target glide path is the glide path corresponding to each target flight when the fitness value of the iteratively updated feasible solution is the minimum.

[0043] Specifically, the second target number of iterations is the maximum number of iterations of the leading bee, wherein the value range of the second target number of iterations is 100 to 150. Those skilled in the art know that the second target number of iterations can be selected according to actual needs, which falls within the protection scope of the present invention and will not be repeated here.

[0044] S600: When the number of iterations corresponding to the existence of a leading bee does not exceed the second target number of iterations, steps S100 to S500 are repeatedly executed to obtain a target taxiing path corresponding to each target flight.

[0045] From the above, we can see that when the airport is in an abnormal state, when the pheromone mechanism is introduced into the bee swarm algorithm to obtain the target taxiway path, the bees can draw on historical experience when searching for the taxiway path and avoid repeated searches, thereby significantly improving the search efficiency. At the same time, by optimizing the relevant parameters in the bee swarm algorithm, the algorithm has stronger adaptability and robustness in solving the taxiway path, shortening the flight taxiing time and making the obtained taxiing path better.

[0046] The present embodiment provides a target glide path acquisition system, which implements the following steps: setting a leading bee and a following bee, updating pheromones and acquiring the fitness of a feasible solution for the path generated by each following bee, based on a first target iteration number, sequentially acquiring the target priority generated after each iteration starting from the first iteration, and when there is a target priority obtained in the nth iteration that is less than the candidate priority, re-determining the first target iteration number so that the leading bee and the following bee continue to iterate according to the first target iteration number, repeating the steps of updating pheromones and acquiring the fitness of a feasible solution for the path generated by each following bee and the above steps until there is no target priority less than the candidate priority after iterating the first target iteration number, and converting the acquired leading bee or following bee into a scout bee to perform a random search and acquire Take the number of iterations corresponding to all leading bees. When the number of iterations corresponding to all leading bees exceeds the second target number of iterations, obtain the target taxi path corresponding to each target flight. When there is a leading bee whose number of iterations does not exceed the second target number of iterations, repeat the above steps to obtain the target taxi path corresponding to each target flight. The present invention can introduce the pheromone mechanism into the bee colony algorithm to obtain the target taxi path when the airport is in an abnormal state, so that the bees can draw on historical experience when searching for the taxiway path and avoid repeated searches, thereby significantly improving the search efficiency. At the same time, by optimizing the relevant parameters in the bee colony algorithm, the algorithm has stronger adaptability and robustness in solving the taxiway path, shortens the flight taxiing time, and makes the obtained taxi path better.

[0047] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A target glide path acquisition system, characterized in that: The system includes: a processor and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: S100, setting the leading bee and the following bee, updating the pheromone and obtaining the fitness of the feasible solution of the path generated by each following bee; wherein S100 also includes the following steps: S101, randomly generating a number of initial sliding paths and randomly setting pheromone concentrations between nodes of the sliding paths to form a pheromone matrix, wherein each initial sliding path corresponds to a leader bee, and the leader bee randomly searches to generate the initial sliding paths; S102, obtaining the fitness value of the feasible solution corresponding to each leading bee, and updating the pheromone matrix corresponding to each leading bee; S103, setting a first target number of iterations, wherein the first target number of iterations is the number of new paths generated by each follower bee for iterative update; S104, the follower bee searches according to the pheromone matrix corresponding to its corresponding leader bee, updates the pheromone and obtains the fitness of the feasible solution of each follower bee-generated path, wherein each follower bee corresponds to one leader bee; S200, based on a first target iteration number β, sequentially obtaining target priorities generated after each iteration starting from the first iteration, wherein the target priorities are fitness values of feasible solutions of paths generated after each iteration by the leading bee and the following bee; S300, when the target priority obtained in the nth iteration is less than the candidate priority, determining β=β+n-1, so that the leading bee and the following bee continue to iterate according to the first target number of iterations β, where n≤β, and the candidate priority is the minimum fitness of the feasible solution searched in the same bee team before iterating the first target number of iterations; S400, repeatedly executing steps S200 to S300 until no target priority is less than the candidate priority after iterating the first target iteration number β, transforming the obtained leader bee or follower bee into a scout bee to perform a random search and obtain the iteration numbers corresponding to all leader bees; S500, when the number of iterations corresponding to all leading bees exceeds a second target number of iterations, obtaining a target glide path corresponding to each target flight, wherein the obtained target glide path is the glide path corresponding to each target flight when the fitness value of the iteratively updated feasible solution is minimized; the second target number of iterations is the maximum number of iterations of the leading bee; S600: When the number of iterations corresponding to the existence of a leading bee does not exceed the second target number of iterations, steps S100 to S500 are repeatedly executed to obtain a target taxiing path corresponding to each target flight.

2. The target glide path acquisition system according to claim 1, characterized in that: The system further includes a target airport map and a target feature data list, wherein the target feature data list includes a plurality of target feature data, and the target feature data is taxiing feature data corresponding to the target flight.

3. The target glide path acquisition system according to claim 2, characterized in that: The target airport map is a map of the target airport, wherein the target airport is an airport in an abnormal state, and the abnormal state is a state that affects the normal operation of flights at the airport.

4. The target glide path acquisition system according to claim 2, characterized in that: The target airport map is a target airport taxiway graph structure G, where G=(V, E), V is a node set formed by nodes on the target airport surface, and E is an edge set, where the edges in E represent taxiways connecting the nodes.

5. The target glide path acquisition system according to claim 2, characterized in that: The taxiing characteristic data includes flight type, flight number, taxiing start time, taxiing stop time, flight start node number, and flight end node number.

6. The target glide path acquisition system according to claim 5, characterized in that: The flight types include arriving flights and departing flights. When the flight type is an arriving flight, the start taxiing time is the time from when the flight leaves the runway and enters the taxiway system after landing, and the stop taxiing time is the time when the flight taxis to the parking stand and puts the wheels on the block. When the flight type is a departing flight, the start taxiing time is the time from when the flight starts to unblock the wheels at the parking stand, and the stop taxiing time is the time when the flight taxis to the runway holding area.

7. The target glide path acquisition system according to claim 1, characterized in that: The feasible solution fitness T meets the following conditions: , where, when the e-th target flight taxis from node i to node j, ɛ e ij =1, when the e-th target flight does not taxi from node i to node j, ɛ e ij =0, t e ij is the time it takes for the e-th target flight to taxi from node i to node j. The value of e ranges from 1 to N, N is the number of target flights, i ranges from 1 to M, j ranges from 1 to M, i≠j, and M is the number of nodes in the node set formed by the nodes on the target airport surface in the target airport map.

8. The target glide path acquisition system according to claim 1, characterized in that: Get the transfer probability of the pheromone matrix, where the probability P of the leading bee k in the pheromone matrix transferring from node i to node j at time t is k ij (t) meet the following conditions: P k ij (t) = (τ ij (t) α × (1 / d ij ) β / (∑ s∈Uk (τ is (t) α × (1 / d is ) β ), τ ij (t) is the pheromone concentration on the path connecting node i and node j at time t, d ij is the distance between node i and node j, Uk is the set of nodes to be visited by the kth leader bee, τ is (t) is the pheromone concentration on the path connecting node i and node s at time t, d is is the distance between node i and node s, α is a constant between 0 and 1, β is a constant between 0 and 1, and node s is any node among the nodes to be visited by the kth leader bee.

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