CN114492902A - Flight recovery method and device, computer equipment and storage medium - Google Patents

Flight recovery method and device, computer equipment and storage medium Download PDF

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CN114492902A
CN114492902A CN202011259740.9A CN202011259740A CN114492902A CN 114492902 A CN114492902 A CN 114492902A CN 202011259740 A CN202011259740 A CN 202011259740A CN 114492902 A CN114492902 A CN 114492902A
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王文杰
刘国岭
黄美雯
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SF Technology Co Ltd
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Abstract

The application relates to a flight recovery method, a flight recovery device, a computer device and a storage medium. The method comprises the following steps: acquiring a flight recovery application; searching for a flight task; performing time slicing on the task time, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight to be flown through a preset flight recovery model; acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model; and according to the second flight task time slice and the corresponding relation, flight recovery is carried out. The application provides a scheme of a two-stage flight solving recovery method, which comprises the steps of firstly solving in a first flight task time slice with a longer time scale through a preset flight recovery model. And then, optimizing and adjusting the flight recovery scheme of the first-stage solution scheme in the time scale of the second flight task time slice, thereby reducing the complexity of solving the flight recovery problem and meeting the requirement of real-time solution.

Description

Flight recovery method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a flight recovery method, an apparatus, a computer device, and a storage medium.
Background
With the development of the logistics industry, the aviation logistics gradually become an undisassembly part in the logistics. Under the current logistics aviation network, aviation goods can be directly or indirectly air-transported from a departure city to a destination city according to a flight plan and then quickly distributed to customers through a city land transportation network. However, the original flight plan cannot be executed on time due to emergency situations such as airport flow control, typhoon or airplane failure, and in order to reduce the influence on the cargo timeliness, the flight plan needs to be readjusted, such as advancing or delaying the takeoff of the airplane, changing the airplane or airplane type, canceling or adding a new flight, and the like.
To quickly cope with sudden situations, flight adjustments need to be completed in a short time (e.g., several minutes), which is a huge challenge for operation control personnel, especially large-scale adjustments. At present, the mainstream flight recovery mode is performed by adopting a flight recovery model, however, the model solving speed of the current freight flight recovery model is too slow, and if the flight recovery model is directly and simply relied on for flight recovery, the flight recovery efficiency is seriously influenced.
Disclosure of Invention
In view of the above, it is necessary to provide a flight recovery method, an apparatus, a computer device, and a storage medium capable of flight recovery efficiency.
A flight recovery method, the method comprising:
acquiring a flight recovery application, and searching a flight task corresponding to the flight recovery application;
performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight to be flown through a preset flight recovery model;
acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models of optimization objectives and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, flight recovery is carried out.
In one embodiment, before the obtaining, by the preset flight recovery model, the correspondence between the first flight mission time slice and the to-be-flown flight class, the method further includes:
obtaining model input parameters, decision variables, a first optimization objective and a first model constraint, wherein the first optimization objective comprises the maximum flight task number, the minimum flight adjustment times, the dispatching times and the minimum flight delay sum;
and under the condition of meeting the first model constraint, constructing a preset flight recovery model by taking the first optimization target as a target based on the combined relation of the flight task time slice and the flight to be flown.
In one embodiment, the first model constraint comprises: flight task airline shelf number constraints, flight task time coincidence constraints, flight task order constraints, airport flow control constraints, and flight task time slicing and flight constraints.
In one embodiment, after the obtaining, by the preset flight recovery model, the corresponding relationship between the first flight mission time slice and the to-be-flown flight class, the method further includes:
acquiring static limit of flight tasks;
screening the matching of the flight task time slice and the flight to be flown according to the flight task static limitation, and acquiring the combination relation of the first flight task time slice and the flight to be flown which accords with the flight task static limitation;
performing flight recovery according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice which accords with the static limit of the flight task and the flight to be flown, performing flight recovery.
In one embodiment, before obtaining, by the preset flight adjustment model, the second flight mission time slice in the first flight mission time slice that minimizes delayed or advanced takeoff, the method further includes:
obtaining a second model constraint and a second optimization target, wherein the second optimization target comprises a minimum flight adjustment time and a minimum flight delay;
and searching a second flight task time slice meeting the second optimization target in a preset time interval before and after the first flight task time slice in the historical data according to the second model constraint, and constructing a preset flight adjustment model.
In one embodiment, the obtaining, by the preset flight tuning model, the second flight mission time slice in the first flight mission time slice includes:
acquiring a corresponding assignment relation between the belonging flight task and the flight to be flown according to the belonging flight task corresponding to the first flight task time slice;
and acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and a preset flight adjustment model.
A flight recovery device, the device comprising:
the application acquisition module is used for acquiring a flight recovery application and searching a flight task corresponding to the flight recovery application;
the flight recovery processing module is used for performing time slicing on the task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring the corresponding relation between the first flight task time slice and the to-be-flown flight through a preset flight recovery model;
the flight adjustment processing module is used for acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, the preset flight recovery model and the preset flight adjustment model are different models with optimization objectives and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance;
and the flight recovery module is used for recovering the flight according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown.
In one embodiment, the method further comprises a first model building module for: obtaining model input parameters, decision variables, a first optimization objective and a first model constraint, wherein the first optimization objective comprises the maximum flight task number, the minimum flight adjustment times, the dispatching times and the minimum flight delay sum; and under the condition of meeting the first model constraint, constructing a preset flight recovery model based on the combined relation of the flight mission time slice and the flight class to be flown by taking the first optimization target as a target.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a flight recovery application, and searching a flight task corresponding to the flight recovery application;
performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight to be flown through a preset flight recovery model;
acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models of optimization targets and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, flight recovery is carried out.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a flight recovery application, and searching a flight task corresponding to the flight recovery application;
performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight to be flown through a preset flight recovery model;
acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models of optimization targets and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, flight recovery is carried out.
The flight recovery method, the flight recovery device, the computer equipment and the storage medium acquire flight recovery requests; searching a flight task corresponding to the flight recovery application; performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight to be flown through a preset flight recovery model; acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model; and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery. The flight recovery method comprises the steps that firstly, a flight recovery model is preset in the first stage, flight recovery problems are solved in a first flight task time slice with a long time scale, and then optimization adjustment is carried out on the flight recovery schemes in the second stage, so that complexity of solving the flight recovery problems is reduced, solving efficiency is improved, and requirements of real-time solving are met.
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FIG. 1 is a diagram of an application environment of a flight recovery method in one embodiment;
FIG. 2 is a flow diagram of a flight recovery method in one embodiment;
FIG. 3 is a schematic flow chart diagram illustrating steps for constructing a default flight recovery model in one embodiment;
FIG. 4 is a schematic flow chart diagram illustrating steps for constructing a default flight tuning model in one embodiment;
FIG. 5 is a schematic sub-flow chart of step 205 of FIG. 2 in one embodiment;
FIG. 6 is a block diagram of the structure of a flight recovery device in one embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The flight recovery method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. When flight recovery operation is required, the terminal 102 may submit a flight recovery application to the server 104, and the server 104 obtains the flight recovery application; searching a flight task corresponding to the flight recovery application; performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the to-be-flown class through a preset flight recovery model; acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models with optimization targets and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance; and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a flight recovery method is provided, which is illustrated by taking the method as an example applied to the server 104 in fig. 1, and includes the following steps:
step 201, obtaining a flight recovery application, and searching a flight task corresponding to the flight recovery application.
The flight recovery task means that the original flight plan cannot be executed on time when a flight encounters an emergency, such as airport flow control, typhoon or airplane failure, and the like, and the flight plan needs to be readjusted to reduce the influence on air transportation, such as advancing or delaying airplane takeoff, changing airplane or changing airplane type, canceling or newly adding flights, and the like. To quickly cope with sudden situations, flight adjustments need to be completed in a short time (e.g., several minutes), which is a huge challenge for operation control personnel, especially large-scale adjustments. When the corresponding flight recovery adjustment is required, the terminal may send a corresponding flight recovery application to the server to request flight recovery. The flight task corresponding to the flight recovery application refers to a flight shift which cannot take off on time in the flight plan due to an accident and needs flight recovery adjustment.
Specifically, when an airport staff at the terminal 102 encounters an accident and affects the normal takeoff of a flight, a corresponding flight recovery application may be sent to the server 104 for performing corresponding flight recovery processing by the server 104 in order to perform corresponding recovery on the affected flight. And the server 104 may perform corresponding flight recovery solution through a pre-established recovery model to obtain a flight recovery scheme. In a specific embodiment, the flight recovery method of the present application is specifically used for recovering a freight flight in a logistics aviation network, and the adjustment manner of the freight flight includes, for example, advancing or delaying aircraft takeoff, changing an aircraft or a model, canceling or adding a flight, and the like. Meanwhile, for the adjusted flight plan, the flight delay is required to be minimized, the number of the adjusted flights is reduced as much as possible, and the restrictions of matching flight tasks with airplane capacity, airplane take-off and landing airports, airplane passing time, transfer plate goods, link flights and the like are required to be met.
And 203, performing time slicing on the task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight to be flown through a preset flight recovery model.
The task time corresponding to the flight task refers to a time range that can be adjusted for each flight task, for example, in one embodiment, the task time corresponding to one flight task specifically refers to a time period from half an hour ahead to 24 hours later than the departure time of the flight task. The execution time slice refers to the division of the corresponding task execution time into time periods, for example, the departure time of the flight task is adjusted within half an hour in advance to 24 hours delayed according to the 30 minute scale, and the time slices with different time scales of each flight task can be obtained. And then, solving by presetting a flight recovery model, and obtaining the corresponding relation between the first flight task time slice and the flight to be flown under the specified constraint and optimization target. The corresponding relationship between the first flight task time slice and the flight waiting class specifically refers to the one-to-one corresponding assignment relationship between the flight task time slice i and the airplane p to be flown class.
Specifically, the possible execution time of the flight task may be sliced and analyzed according to a preset slicing rule to obtain a first flight task time slice, and then the flight recovery process is optimized through a preset flight recovery model which is constructed in advance based on a preset constraint condition, an optimization target and the like to obtain a corresponding relationship between the optimized first flight task time slice and the flight to be flown. At this time, the flight waiting time corresponds to a long time period, so that the flight waiting time also needs to be subdivided and adjusted again.
Step 205, acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models with optimization targets and model constraints, the preset flight recovery model performs flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model performs flight recovery solution based on the dimension to be flown with adjustment precision as guidance.
The preset flight adjusting model is used for further subdividing and optimizing the flights to be subdivided in the first flight task time slice, so that the final flight recovery scheme can achieve the optimal optimization effect. Specifically, in order to improve the processing efficiency of the operation process, a longer time segment may be selected for the task time execution time slice corresponding to the flight task before the preset flight recovery model is analyzed. Therefore, the complexity of solving in the flight recovery process is reduced by reducing the search space, and after the initial optimization is performed through the preset flight recovery model to obtain the first flight task time slice corresponding to each flight task in the flight recovery application, in order to further improve the fineness of the flight adjustment process, the optimization can be performed in the first flight task time slice through the preset flight adjustment model to obtain the second flight task time slice.
And step 207, according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery.
Specifically, since the second flight task time slice is also a time slice selected from the first flight task time slice, the corresponding relationship between the first flight task time slice and the flight to be flown can be inherited, and the flight to be flown corresponds to the second flight task time slice.
The flight recovery method comprises the steps of obtaining a flight recovery application; searching a flight task corresponding to the flight recovery application; performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the flight waiting through a preset flight recovery model; acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model; and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery. The method comprises the steps that in the first stage, flight recovery problems are solved in a first flight task time slice with a long time scale through a preset flight recovery model. And then, in the second stage, the flight recovery scheme is optimized and adjusted in the time scale of the second flight task time slice of the first-stage solution scheme, so that the complexity of solving the flight recovery problem is reduced, the solution efficiency is improved, and the requirement of real-time solution is met.
In one embodiment, as shown in fig. 3, before step 203, the method further includes:
step 302, obtaining model input parameters, decision variables, a first optimization objective and a first model constraint, wherein the first optimization objective comprises a maximum flight task number, a minimum flight adjustment number, a dispatching number and a minimum flight delay sum;
and 304, under the condition of meeting the first model constraint, constructing a preset flight recovery model by taking the first optimization target as a target based on the combined relation of the flight task time slice and the flight to be flown.
The model input parameters refer to parameter data of an input model, and include slice setting, airport original data and the like. In one embodiment, the model input parameters may specifically include the following parameters: siFlight task time cutterSlice i start time. e.g. of the typeiFlight task time slice i end time. c. CktThe maximum number of plane stops at the airport k at the time t. gktThe maximum number of airplanes is guaranteed at the moment t of an airport k. f. ofipIf the aircraft p is allowed to execute the flight mission time slice i. O ispMinimum time to pass for airplane p. a isilWhether the flight task time slice i belongs to the flight task l. biktIf flight mission time slice i lands at airport k within time t. u. ofiktWhether or not the flight mission time slice i takes off at airport k within time t. diWhether flight mission time slice i has a delay (0 for early or punctual takeoff and 1 for delay). dtiFlight mission time slice i delay time (also positive if the flight path takes ahead). distijpFlight time slice i reaches the flight time distance between the point of flight task time slice j and the departure point. w is aijWhether or not there is an empty flight from flight task time slice i to flight task time slice j (1, otherwise 0). Decision variables are specifically things that the airport staff side can master. Also called controlled variables, i.e. variables that can be controlled. In one embodiment, the model decision variables specifically include: x is the number ofijpIf the airplane p executes the flight task time slice i and then executes the flight task time slice j. y isip: whether or not the flight mission time slice i is carried out by the airplane p. z is a radical oflpWhether or not the flight task l is carried by the airplane p. v. ofpWhether the airplane p has a flight mission. These several variables. The first optimization objective includes a maximum number of flight tasks, a minimum number of flight adjustments and dispatches, and a minimum total flight delay, and based on the input parameters and the decision variables, the corresponding optimization objective may specifically be:
maximize the number of flight tasks performed:
Figure BDA0002774251370000091
minimizing flight adjustment and dispatching times:
Figure BDA0002774251370000092
minimizing flight delay total:
Figure BDA0002774251370000093
the model constraint conditions in the flight recovery model may specifically include flight task airline number constraints, flight task time coincidence constraints, flight task order constraints, airport flow control constraints, flight task time slicing and flight constraints, and the like. In a specific embodiment, based on the input parameters and the decision variables, the corresponding constraint conditions may specifically be:
each flight mission is assigned to at most one aircraft:
Figure BDA0002774251370000094
the assigned flight tasks of each aircraft do not coincide in time and minimum station-crossing time needs to be guaranteed between the tasks:
If i＝0 or j＝n+1:
sj≥ei+M*(xijp-1)+Op
Figure BDA0002774251370000101
sj≥ei+M*(xijp-1)+Op*(wij+1)+distijp
Else:
xijp＝0
for i∈{0,1,…,n},j∈{1,…,n+1},i≠j,p∈{1,…,P}
the sequence of the assigned flight tasks of each airplane is limited:
Figure BDA0002774251370000102
Figure BDA0002774251370000103
at airport k, aircraft limits are guaranteed for time t:
Figure BDA0002774251370000104
at airport k, aircraft limits are guaranteed for time t:
Figure BDA0002774251370000105
limiting each aircraft on-board flight mission time slice i:
yip≤fip for i∈{1,…,n},p∈{1,…,P}
flight task time slicing and flight limitation:
Figure BDA0002774251370000106
airport k-flow control limits (half an hour time scale between t1 and t 2):
Figure BDA0002774251370000107
Figure BDA0002774251370000108
turn-to-plate, online flight limit (transfer-to-plate must precede follow-up flight):
Figure BDA0002774251370000111
the online flight has a limit at the same time:
zl1p＝zl2p for(l1,l2)∈{(l11,l21),…,(l1m,l2m)}
whether the aircraft p executes a route:
Figure BDA0002774251370000112
based on the model input parameters, the decision variables, the first optimization objective and the first model constraint, a corresponding preset flight recovery model can be constructed based on the combined relation between the flight task time slice and the flight to be flown. And then during actual processing, based on actual input parameters, solving to obtain the corresponding relation between the optimized corresponding first flight task time slice and the flight to be flown, thereby performing subsequent flight recovery optimization. In this embodiment, the preset flight recovery model is constructed based on the model input parameters, the decision variables, the first optimization objective and the first model constraint, so that when a flight is recovered, the flight can be quickly optimized by the preset flight recovery model, and the optimization efficiency of flight recovery is improved.
In one embodiment, after step 203, the method further comprises: acquiring static limit of flight tasks; and screening the matching of the flight task time slice and the flight to be flown according to the static limit of the flight task, and acquiring the combination relation of the first flight task time slice and the flight to be flown which accords with the static limit of the flight task.
Step 205 comprises: and according to the second flight task time slice and the corresponding relation between the first flight task time slice which accords with the static limit of the flight task and the flight to be flown, performing flight recovery.
The static limitation of the flight mission refers to static limitation in the aviation network, such as airplane take-off and landing airport limitation, including airport take-off and landing airplane or airplane type limitation, airplane and flight mission matching limitation, and the like. The matching of each flight mission time slice with each airplane can be calculated directly based on the static limit of the flight mission, so as to screen out the combination relationship of the flight mission time slices and the airplanes which meet the static limit. And then screening the corresponding relation between the first flight task time slice and the flight to be flown, which is obtained from the preset flight recovery model, so as to obtain the combined relation between the first flight task time slice and the flight to be flown, which accords with the static limit of the flight task. This may avoid adding these static limits to the pre-set flight recovery model, increasing the model complexity. In the embodiment, the static limits of the flight tasks are separated, and the matching between each flight task time slice and each airplane is directly calculated, so that the combined relationship between the flight task time slices and the airplanes which meet the static limits is screened out. The static limitation can be effectively prevented from being added into the model constraint, the complexity of the flight recovery model is reduced, and the operation efficiency is improved.
As shown in fig. 4, in one embodiment, before step 205, the method further includes:
step 401, obtaining a second model constraint and a second optimization objective, where the second optimization objective includes minimizing flight adjustment times and minimizing flight delay.
And step 403, according to the second model constraint, searching a second flight task time slice meeting a second optimization objective in a preset time interval before and after the first flight task time slice in the historical data, and constructing a preset flight adjustment model.
In the second stage of model calculation, the preset flight adjustment model can inherit the model input parameters and decision variables in the preset flight recovery model, so that the second stage of flight adjustment can be performed only by redefining new constraint conditions and optimization targets according to the output result of the preset flight recovery model to obtain a second flight task time slice in the first flight task time slice. Specifically, in one embodiment, the preset optimization objectives in the flight adjustment model may specifically include minimizing the number of flight adjustments and minimizing the total flight delay, where the corresponding optimization objectives are specifically:
minimizing the number of flight adjustments:
Figure BDA0002774251370000121
minimizing flight delay total:
Figure BDA0002774251370000122
and the second model constraint includes:
task attribution:
zlp＝1 for(l,p),l∈{1,…,L},p∈{1,…,P}
airline and flight restrictions:
Figure BDA0002774251370000123
turn-to-plate, online flight limit (transfer-to-plate must precede follow-up flight):
Figure BDA0002774251370000124
the order of the assigned airline tasks of each aircraft is limited:
Figure BDA0002774251370000131
Figure BDA0002774251370000132
the assigned flight path tasks of each aircraft do not coincide in time and minimum station-crossing time needs to be ensured between tasks:
If i＝0or j＝n+1:
sj≥ei+M*(xijp-1)+Op
Figure BDA0002774251370000133
sj≥ei+M*(xijp-1)+Op*(wij+1)+distijp
Else:
xijp＝0
for i∈{0,1,…,n},j∈{1,…,n+1},i≠j,p∈{1,…,P}
whether the aircraft p executes a route:
Figure BDA0002774251370000134
based on the second model constraint and the second optimization objective, a corresponding preset flight adjustment model can be established, the preset flight adjustment model is used for searching a second flight task time slice meeting the second optimization objective in a preset time interval before and after the first flight task time slice in the historical data according to the second model constraint to pre-establish the preset flight adjustment model for flight adjustment, and the preset flight adjustment model carries out flight recovery solution based on the dimension of the flight to be flown with the adjustment precision as the guide. When flight recovery is needed, after the corresponding relation between the first flight task time slice and the flight to be flown is obtained through the preset flight recovery model, more refined adjustment is carried out through the preset flight adjustment model, so that the model complexity is reduced, the search space is reduced, and the model solving speed is increased.
As shown in FIG. 5, in one embodiment, step 205 comprises:
step 502, according to the belonging flight task corresponding to the first flight task time slice, obtaining a corresponding assignment relationship between the belonging flight task and the flight to be flown.
And step 504, acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and the preset flight adjustment model.
Specifically, in the calculation of the first stage, according to the belonging flight task corresponding to the first flight task time slice, the corresponding assignment relationship between the belonging flight task and the flight to be flown is obtained. And then, acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and a preset flight adjustment model. In one embodiment, the assignment relationship (i, p) of the flight task time slice i and the flight to-be-flown flight p in one-to-one correspondence can be obtained through the flight recovery model, the assignment relationship (l, p) of the flight task l and the flight to-be-flown flight p can be obtained according to the belonging flight task of the flight task time slice i, at this stage, the assignment relationship of the flight task l and the flight to-be-flown flight p is fixed, and the flight task time slice with the minimum delay or the minimum takeoff advance is searched according to the 5-minute scale in the first and second half time of the flight task time slice i. In this embodiment, the flight task time slice is extracted by using the belonging flight task corresponding to the first flight task time slice, so that the function of flight adjustment can be more efficiently implemented, and the processing efficiency can be improved.
It should be understood that although the various steps in the flow diagrams of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in a strict order unless explicitly stated herein, and may be performed in other orders. Moreover, at least some of the steps in fig. 2-5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or in alternation with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 6, there is provided a flight recovery apparatus including:
the application obtaining module 601 is configured to obtain a flight recovery application, and search a flight task corresponding to the flight recovery application.
The flight recovery processing module 603 is configured to perform time slicing on the task time corresponding to the flight task, obtain a first flight task time slice, and obtain a corresponding relationship between the first flight task time slice and the to-be-flown flight through a preset flight recovery model.
The flight adjustment processing module 605 is configured to obtain a second flight task time slice in the first flight task time slice through a preset flight adjustment model, where the preset flight recovery model and the preset flight adjustment model are different models with optimization objectives and model constraints, the preset flight recovery model performs flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model performs flight recovery solution based on the dimension to be flown with adjustment accuracy as guidance.
And the flight recovery module 607 is configured to perform flight recovery according to the second flight task time slice and the corresponding relationship between the first flight task time slice and the flight to be flown.
In one embodiment, the method further comprises a first model building module for: obtaining model input parameters, decision variables, a first optimization target and a first model constraint, wherein the first optimization target comprises the maximum flight number, the minimum flight number adjustment times, the number of dispatching times and the minimum flight delay sum; and under the condition of meeting the first model constraint, constructing a preset flight recovery model based on the combined relation of the flight task time slice and the flight to be flown by taking a first optimization target as a target.
In one embodiment, the first model constraint comprises: flight task airline shelf number constraints, flight task time coincidence constraints, flight task order constraints, airport flow control constraints, and flight task time slicing and flight constraints.
In one embodiment, the apparatus further comprises a static restriction processing module configured to: acquiring flight task static limit; screening the matching of the flight task time slice and the flight to be flown according to the static limit of the flight task, and acquiring a combined relation of the first flight task time slice and the flight to be flown which accords with the static limit of the flight task; the flight recovery module 607 is specifically configured to: and according to the second flight task time slice and the corresponding relation between the first flight task time slice which accords with the static limit of the flight task and the flight to be flown, performing flight recovery.
In one embodiment, the system further comprises a second model building module, configured to: acquiring a second model constraint and a second optimization target, wherein the second optimization target comprises a minimum flight adjustment time and a minimum flight delay; and searching a second flight task time slice meeting a second optimization target in a preset time interval before and after the first flight task time slice in the historical data according to the second model constraint, and constructing a preset flight adjustment model.
In one embodiment, the flight adjustment processing module 607 is specifically configured to: acquiring a corresponding assignment relation between the belonging flight task and the flight class to be flown according to the belonging flight task corresponding to the first flight task time slice; and acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and a preset flight adjustment model.
For the specific definition of the flight recovery device, reference may be made to the above definition of the flight recovery method, and details are not described here. The various modules in the flight recovery apparatus can be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor calls and executes operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing flight recovery data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a flight recovery method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of:
acquiring a flight recovery application, and searching a flight task corresponding to the flight recovery application;
performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the to-be-flown class through a preset flight recovery model;
acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models with optimization targets and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension of the flight to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension of the flight to be flown with adjustment precision as guidance;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining model input parameters, decision variables, a first optimization target and a first model constraint, wherein the first optimization target comprises the maximum flight task number, the minimum flight adjustment times, the dispatching times and the minimum flight delay sum; and under the condition of meeting the first model constraint, constructing a preset flight recovery model based on the combined relation of the flight task time slice and the flight class to be flown by taking the first optimization target as a target.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring static flight task limits; and screening the matching of the flight task time slice and the flight to be flown according to the static limit of the flight task, and acquiring the combination relation of the first flight task time slice and the flight to be flown which accords with the static limit of the flight task.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a second model constraint and a second optimization target, wherein the second optimization target comprises a minimum flight adjustment time and a minimum flight delay; and searching a second flight task time slice meeting a second optimization target in a preset time interval before and after the first flight task time slice in the historical data according to the second model constraint, and constructing a preset flight adjustment model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a corresponding assignment relation between the belonging flight task and the to-be-flown flight according to the belonging flight task corresponding to the first flight task time slice; and acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and a preset flight adjustment model.
In one embodiment, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a flight recovery application, and searching a flight task corresponding to the flight recovery application;
performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the to-be-flown class through a preset flight recovery model;
acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models with optimization targets and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension of the flight to be flown and with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension of the flight to be flown and with adjustment precision as guidance;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining model input parameters, decision variables, a first optimization objective and a first model constraint, wherein the first optimization objective comprises the maximum flight task number, the minimum flight adjustment times, the dispatching times and the minimum flight delay sum; and under the condition of meeting the first model constraint, constructing a preset flight recovery model based on the combined relation of the flight task time slice and the flight class to be flown by taking the first optimization target as a target.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring static limit of flight tasks; and screening the matching of the flight task time slice and the flight to be flown according to the static limit of the flight task, and acquiring the combination relation of the first flight task time slice and the flight to be flown which accords with the static limit of the flight task.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a second model constraint and a second optimization target, wherein the second optimization target comprises a minimum flight adjustment time and a minimum flight delay; and searching a second flight task time slice meeting a second optimization target in a preset time interval before and after the first flight task time slice in the historical data according to the second model constraint, and constructing a preset flight adjustment model.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a corresponding assignment relation between the belonging flight task and the flight to be flown according to the belonging flight task corresponding to the first flight task time slice; and acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and a preset flight adjustment model.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A flight recovery method, the method comprising:
acquiring a flight recovery application, and searching a flight task corresponding to the flight recovery application;
performing time slicing on task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring a corresponding relation between the first flight task time slice and the to-be-flown flight through a preset flight recovery model;
acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, wherein the preset flight recovery model and the preset flight adjustment model are different models of optimization targets and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance;
and according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown, performing flight recovery.
2. The method of claim 1, wherein before obtaining the correspondence between the first flight mission time slice and the flight to be flown through the preset flight recovery model, the method further comprises:
obtaining model input parameters, decision variables, a first optimization objective and a first model constraint, wherein the first optimization objective comprises the maximum flight task number, the minimum flight adjustment times, the dispatching times and the minimum flight delay sum;
and under the condition of meeting the first model constraint, constructing a preset flight recovery model by taking the first optimization target as a target based on the combined relation of the flight task time slice and the flight to be flown.
3. The method of claim 2, wherein the first model constraints include flight mission airline number constraints, flight mission time coincidence constraints, flight mission sequence constraints, airport flow control constraints, and flight mission time slicing and flight constraints.
4. The method of claim 1, wherein after obtaining the correspondence between the first flight mission time slice and the flight to be flown through the preset flight recovery model, the method further comprises:
acquiring static limit of flight tasks;
screening the matching of the flight task time slice and the flight to be flown according to the flight task static limitation, and acquiring the combination relation of the first flight task time slice and the flight to be flown which accords with the flight task static limitation;
the flight recovery according to the second flight task time slice and the corresponding relationship between the first flight task time slice and the flight to be flown includes:
and according to the second flight task time slice and the corresponding relation between the first flight task time slice which accords with the static limit of the flight task and the flight to be flown, performing flight recovery.
5. The method of claim 1, wherein the obtaining, through the preset flight tuning model, a first flight mission time slice that minimizes delayed or advanced takeoff before a second flight mission time slice within the first flight mission time slice further comprises:
obtaining a second model constraint and a second optimization target, wherein the second optimization target comprises a minimum flight adjustment time and a minimum flight delay;
and searching a second flight task time slice meeting the second optimization target in a preset time interval before and after the first flight task time slice in the historical data according to the second model constraint, and constructing a preset flight adjustment model.
6. The method of claim 1, wherein obtaining a second flight mission time slice in the first flight mission time slice through a preset flight tuning model comprises:
acquiring a corresponding assignment relation between the belonging flight task and the to-be-flown flight according to the belonging flight task corresponding to the first flight task time slice;
and acquiring a second flight task time slice in the first flight task time slice according to the corresponding assignment relation and a preset flight adjustment model.
7. A flight recovery apparatus, the apparatus comprising:
the application acquisition module is used for acquiring a flight recovery application and searching a flight task corresponding to the flight recovery application;
the flight recovery processing module is used for performing time slicing on the task time corresponding to the flight task, acquiring a first flight task time slice, and acquiring the corresponding relation between the first flight task time slice and the to-be-flown flight through a preset flight recovery model;
the flight adjustment processing module is used for acquiring a second flight task time slice in the first flight task time slice through a preset flight adjustment model, the preset flight recovery model and the preset flight adjustment model are different models with optimization objectives and model constraints, the preset flight recovery model carries out flight recovery solution based on the dimension to be flown with adjustment efficiency as guidance, and the preset flight adjustment model carries out flight recovery solution based on the dimension to be flown with adjustment precision as guidance;
and the flight recovery module is used for recovering the flight according to the second flight task time slice and the corresponding relation between the first flight task time slice and the flight to be flown.
8. The apparatus of claim 7, further comprising a first model building module to: obtaining model input parameters, decision variables, a first optimization objective and a first model constraint, wherein the first optimization objective comprises the maximum flight task number, the minimum flight adjustment times, the dispatching times and the minimum flight delay sum; and under the condition of meeting the first model constraint, constructing a preset flight recovery model based on the combined relation of the flight task time slice and the flight to be flown by taking the first optimization target as a target.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
CN202011259740.9A 2020-11-12 2020-11-12 Flight recovery method and device, computer equipment and storage medium Pending CN114492902A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024127625A1 (en) * 2022-12-16 2024-06-20 日本電気株式会社 Flight plan management device, flight plan management method, and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024127625A1 (en) * 2022-12-16 2024-06-20 日本電気株式会社 Flight plan management device, flight plan management method, and storage medium

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