A flight mission string generation method, device and storage medium

Abstract

The present invention discloses a flight task string generation method, device and storage medium, the method comprising: obtaining flight segment data of a flight crew, generating a duty task that meets preset business rules; establishing nodes and edges according to the duty task, and constructing a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual starting point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual starting edge and a virtual ending edge; setting weight values ​​of the duty task edge and the overnight edge in the first network graph, and merging nodes that meet preset merging rules to obtain a second network graph; based on a shortest path problem labeling algorithm with resource constraints, obtaining the optimal path of the second network graph, and obtaining a flight task string. The embodiment of the present invention can optimize the network graph structure, which helps to efficiently obtain the optimal solution for flight crew shifts.

Description

Technical Field

[0001] The present invention relates to the technical field of civil aviation logistics optimization and scheduling, and in particular to a flight mission string generation method, device and storage medium. Background Art

[0002] Since the crew scheduling problem of large airlines is huge and difficult to model and solve, it is usually decomposed into two sub-problems: crew pairing and crew rostering. Crew pairing is responsible for pairing individual flights into a flight task string that departs from and returns to the base, while crew rostering is responsible for assigning the flight task string to specific crews.

[0003] The flight crew scheduling problem is an NP-hard (Non-deterministic Polynomial-time hard) problem. For large airlines, a single aircraft model may involve tens of thousands of flights in a month. The number of legal flight task strings that may be generated is extremely large and cannot be generated by brute force enumeration. In practical applications, it is also difficult to find the optimal flight task string, which results in a time-consuming and poorly effective flight crew scheduling plan. Summary of the invention

[0004] The present invention provides a flight mission string generation method, device and storage medium to solve the efficiency problem of obtaining the optimal flight mission string in the prior art, which can optimize the network graph structure and help to efficiently obtain the optimal solution for flight crew scheduling.

[0005] To achieve the above object, an embodiment of the present invention provides a flight task string generation method, comprising:

[0006] Obtain flight segment data of the flight crew and generate duty tasks that meet preset business rules;

[0007] Establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual starting point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual start edge and a virtual end edge;

[0008] Setting weight values ​​of the on-duty task edges and overnight edges in the first network graph, and merging nodes that meet the preset merging rules to obtain a second network graph;

[0009] Based on the shortest path problem labeling algorithm with resource constraints, the optimal path of the second network graph is obtained to obtain the flight task string.

[0010] As an improvement of the above solution, the duty task is obtained by the following steps:

[0011] Selecting the flight segment data whose interval exceeds a preset time unit, and cutting them by the time unit to obtain a plurality of cut data;

[0012] Combine the flights of each of the cut data according to the first connection rule, and enumerate all the duty tasks;

[0013] The first connection rule includes connecting through take-off and landing stations and connecting through stopover time.

[0014] As an improvement of the above solution, the departure node has five attributes of the departure airport, departure time, aircraft type group, inbound node list and outbound node list of the duty task;

[0015] The arrival node has five attributes of the arrival airport, arrival time, aircraft type group, inbound node list and outbound node list of the duty mission;

[0016] The virtual starting point is used to connect all departure nodes based on the same departure airport;

[0017] The virtual destination is used to connect all arrival nodes based on the same arrival airport.

[0018] As an improvement of the above scheme, the edges are established by the following rules:

[0019] Establishing a duty task edge between a departure node and an arrival node;

[0020] When the second connection rule is satisfied between the two on-duty tasks, an overnight edge is established between the arrival node of the first on-duty task edge and the departure node of the second on-duty task edge;

[0021] When the virtual starting point and the departure node of the duty task edge are based on the same departure airport, a virtual starting edge is established;

[0022] When the virtual end point and the arrival node of the duty task edge are based on the same arrival airport, a virtual termination edge is established.

[0023] As an improvement of the above solution, the second connection rule includes:

[0024] The arrival airport of the arrival node of the first duty task edge is consistent with the departure airport of the departure node of the second duty task edge;

[0025] The time interval between the arrival time of the first duty task side and the departure time of the second duty task side meets the preset overnight duration;

[0026] The aircraft type group of the arrival node of the first duty task edge is consistent with the aircraft type group of the departure node of the second duty task edge.

[0027] As an improvement of the above solution, the preset merging rules include:

[0028] For multiple arrival nodes, when their arrival airports, arrival times, aircraft types and outgoing node lists are consistent, the multiple arrival nodes are merged into a single arrival node;

[0029] For multiple departure nodes, when their departure airports, departure times, aircraft types and inbound node lists are consistent, the multiple departure nodes are merged into a single departure node.

[0030] As an improvement of the above solution, the labeling algorithm based on the shortest path problem with resource constraints to obtain the optimal path of the second network graph includes:

[0031] Select a node as the source point, and when the label is at the source point, initialize various resources and costs on the label;

[0032] Each time a node is reached, the path will be extended with resources, and the label will be accumulated with resources and costs according to the label on the path;

[0033] The labels on the path are accumulated by resource accumulation and cost accumulation according to the influencing factors of the weights of the passed edges and the number of on-duty tasks.

[0034] As an improvement to the above solution, the resource expansion of the path is performed according to the following steps:

[0035] When the label passes through an overnight edge and a duty task edge, if a resource exceeds the upper limit, the label is deleted and the resource expansion of the path is ended; if all resources do not exceed the upper limit, it means that the label is legal;

[0036] Different labels of the current node are compared. If various resources and costs of a first label are better than those of a second label, the first label is superior to the second label, and the second label is deleted.

[0037] To achieve the above purpose, an embodiment of the present invention further provides a flight mission sequence generating device, comprising:

[0038] The duty task generation module is used to obtain the flight segment data of the flight crew and generate the duty tasks that meet the preset business rules;

[0039] A first network graph construction module, used to establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual start point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual start edge and a virtual end edge;

[0040] A second network graph acquisition module, used to set weight values ​​of on-duty task edges and overnight edges in the first network graph, and merge nodes that meet a preset merging rule to obtain a second network graph;

[0041] The optimal path acquisition module is used to obtain the optimal path of the second network graph based on the shortest path problem labeling algorithm with resource constraints to obtain the flight task string.

[0042] To achieve the above objectives, an embodiment of the present invention further provides a flight mission string generation device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the above flight mission string generation method when executing the computer program.

[0043] To achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the above-mentioned flight mission string generation method.

[0044] To achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer program product, which includes a computer program; wherein, when the computer program is running, it controls the device where the program product is located to execute the above-mentioned flight task string generation method.

[0045] Implementing the embodiments of the present invention has the following beneficial effects:

[0046] The flight task string generation method, device, equipment and storage medium provided in the embodiments of the present invention generate a duty task that meets preset business rules by acquiring the flight segment data of the flight crew; establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include departure nodes, arrival nodes, virtual starting points and virtual end points, and the edges include duty task edges, overnight edges, virtual starting edges and virtual ending edges; set weight values ​​of the duty task edges and overnight edges in the first network graph, and merge the nodes that meet the preset merging rules to obtain a second network graph; based on the shortest path problem labeling algorithm with resource constraints, obtain the optimal path of the second network graph to obtain the flight task string, which can optimize the network graph structure and help to efficiently obtain the optimal solution for flight crew scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of a method for generating a flight task string provided by an embodiment of the present invention;

[0048] Figure 2 is a structural diagram of a flight task string provided by an embodiment of the present invention;

[0049] Figure 3 The first network diagram including the duty task edge as the edge provided by the embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of nodes before and after merging provided by an embodiment of the present invention;

[0051] Figure 5 is a structural block diagram of a flight task string generating device provided by an embodiment of the present invention;

[0052] Figure 6 It is a structural block diagram of a flight mission string generating device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In the description of the present invention, it should be noted that the step numbers in the text are only for the convenience of explanation of specific embodiments and do not serve to limit the order in which the steps are executed. The terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0055] See also Figure 1 , Figure 1 : is a flow chart of a flight task string generation method provided by an embodiment of the present invention, the flight task string generation method comprises steps S1 to S4, which are specifically as follows:

[0056] S1. Obtain the flight segment data of the flight crew and generate a duty task that meets the preset business rules;

[0057] S2. Establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual starting point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual starting edge and a virtual ending edge;

[0058] S3, setting weight values ​​of the on-duty task edges and overnight edges in the first network graph, and merging nodes that meet the preset merging rules to obtain a second network graph;

[0059] S4. Based on the shortest path problem labeling algorithm with resource constraints, the optimal path of the second network graph is obtained to obtain the flight task string.

[0060] It is understandable that in order to better reflect the actual operation status and constraints of the flight, the weight values ​​of the duty mission edge and the overnight edge need to be calculated. Taking the duty mission edge as an example, it is necessary to evaluate the influencing factors of weights such as cost, duty time, flight time, and number of plateau segments, and calculate the corresponding weight values. These weight values ​​are helpful in calculating a better flight mission string in the subsequent solution.

[0061] In an embodiment of the present invention, flight segment data is effectively integrated to construct a first network graph with on-duty task edges as edges. By reasonably setting weight values, node merging and a shortest path problem labeling algorithm with resource constraints, the constructed second network graph is optimized for path optimization, thereby efficiently obtaining the optimal flight task string.

[0062] In an optional embodiment, the duty task is obtained by the following steps:

[0063] Selecting the flight segment data whose interval exceeds a preset time unit, and cutting them by the time unit to obtain a plurality of cut data;

[0064] Combine the flights of each of the cut data according to the first connection rule, and enumerate all the duty tasks;

[0065] The first connection rule includes connecting through take-off and landing stations and connecting through stopover time.

[0066] For example, the first step of the process is to combine multiple flight segments (Segments, meaning non-stop flights) that are connected end to end into a duty task (Duty) that complies with certain business rules using "one day" as the time unit. For example, the flight segments from the 1st to the 10th of a month are combined, and they are cut into 10 pieces of data using "one day" as the time unit. The flights in each piece of data are combined according to the rules of take-off and landing station connection and stopover time connection, and all legal Duties are enumerated.

[0067] It is understandable that for the generation of flight task strings, all Duties need to be connected through overnight tasks (LO) or set-position tasks (TVL) into flight task strings (Pairing) that comply with certain business rules and end with rest tasks (RST). It can be seen that the input of pairing is a set of flights for several consecutive days, and the output is a pairing set obtained by combining these flights according to the rules. Therefore, the pairing problem can be solved by constructing a network graph model, that is, converting the generation of pairing problems into the generation of graph paths.

[0068] like Figure 2 The figure shows the structure of a pairing. The pairing departs from CAN (Guangzhou) and finally lands at CAN (Guangzhou), then performs a rest mission (RST) to complete a pairing mission. The pairing has three duties. The first duty has only one flight, departing from CAN (Guangzhou) and landing at PEK (Beijing). The crew has fixed sign-in / sign-out operations when performing duty tasks; after performing the first overnight mission (LO), it enters the second duty, which also has only one flight from PEK (Beijing) to AMS (Amsterdam); continue to perform the second overnight mission (LO), enter the third duty, which includes 2 flights. The first flight departs from AMS (Amsterdam) and lands at PEK (Beijing). After a transit time, the second flight is executed, departing from PEK (Beijing) and returning to the CAN base.

[0069] In an optional embodiment, the departure node has five attributes of the departure airport, departure time, aircraft type group, inbound node list and outbound node list of the duty task;

[0070] The arrival node has five attributes of the arrival airport, arrival time, aircraft type group, inbound node list and outbound node list of the duty mission;

[0071] The virtual starting point is used to connect all departure nodes based on the same departure airport;

[0072] The virtual destination is used to connect all arrival nodes based on the same arrival airport.

[0073] In an optional embodiment, the edge is established by the following rules:

[0074] Establishing a duty task edge between a departure node and an arrival node;

[0075] When the second connection rule is satisfied between the two on-duty tasks, an overnight edge is established between the arrival node of the first on-duty task edge and the departure node of the second on-duty task edge;

[0076] When the virtual starting point and the departure node of the duty task edge are based on the same departure airport, a virtual starting edge is established;

[0077] When the virtual end point and the arrival node of the duty task edge are based on the same arrival airport, a virtual termination edge is established.

[0078] In an optional embodiment, the second connection rule includes:

[0079] The arrival airport of the arrival node of the first duty task edge is consistent with the departure airport of the departure node of the second duty task edge;

[0080] The time interval between the arrival time of the first duty task side and the departure time of the second duty task side meets the preset overnight duration;

[0081] The aircraft type group of the arrival node of the first duty task edge is consistent with the aircraft type group of the departure node of the second duty task edge.

[0082] In the embodiment of the present invention, nodes and edges are constructed based on the duty task data, and a first network graph including the duty task edges as edges is constructed, which provides a structural basis for the subsequent optimization algorithm.

[0083] For example, Figure 3 Shown is a first network graph including on-duty mission edges as edges.

[0084] In an optional embodiment, the preset merging rule includes:

[0085] For multiple arrival nodes, when their arrival airports, arrival times, aircraft types and outgoing node lists are consistent, the multiple arrival nodes are merged into a single arrival node;

[0086] For multiple departure nodes, when their departure airports, departure times, aircraft types and inbound node lists are consistent, the multiple departure nodes are merged into a single departure node.

[0087] It is understandable that, since the attribute information of the departure node and the arrival node is simple, it is easy to generate departure nodes or arrival nodes with exactly the same attributes. For these nodes with the same attributes, only one needs to be retained, so the nodes can be processed.

[0088] In an embodiment of the present invention, the network graph structure is optimized by utilizing the definitions of nodes and edges, combined with weight value settings and node merging rules, so that the shortest path problem labeling algorithm with resource constraints performs better in dealing with complex crew scheduling problems.

[0089] For example, Figure 4 As shown, Figure 4 (a) in the figure is the graph before node merging. Figure 4 (b) in the figure is the graph after the nodes are merged.

[0090] In an optional embodiment, the obtaining of the optimal path of the second network graph based on the shortest path problem labeling algorithm with resource constraints includes:

[0091] Select a node as the source point, and when the label is at the source point, initialize various resources and costs on the label;

[0092] Each time a node is reached, the path will be extended with resources, and the label will be accumulated with resources and costs according to the label on the path;

[0093] The labels on the path are accumulated by resource accumulation and cost accumulation according to the influencing factors of the weights of the passed edges and the number of on-duty tasks.

[0094] It is understandable that a label generally contains multiple resources. A label on a node represents the various resources consumed and the accumulated cost of a legal sub-path from the source point to the node. When the label is at the source point, the various resources are initialized to 0, indicating that no resources are consumed; because the core business information is stored in the edge structure, when the path is extended, each time it passes through an arrival node, the label represented by the path will accumulate resources and costs according to the influencing factors of the weight of the edge passed and the number of on-duty tasks, representing the total cost of the current path and the situation of each resource item consumed after passing through the edge. This process is also called resource extension.

[0095] In an optional embodiment, resource expansion of the path is performed according to the following steps:

[0096] When the label passes through an overnight edge and a duty task edge, if a resource exceeds the upper limit, the label is deleted and the resource expansion of the path is ended; if all resources do not exceed the upper limit, it means that the label is legal;

[0097] Different labels of the current node are compared. If various resources and costs of a first label are better than those of a second label, the first label is superior to the second label, and the second label is deleted.

[0098] It can be understood that when the node is reached, two operations are required for each resource expansion of the path. The first is to check whether the label is in violation of the rules (a certain resource exceeds the resource limit) after passing through an overnight edge and a duty task edge. If it is in violation, it is deleted and the expansion is terminated. The second is to compare different labels of the current node (a label on the node saves the cost of a path to reach the node and the information of various resources consumed). When the cost and resources of the first label are better than the second label, the second label will be deleted even if it is legal, because all path extensions from the second label are consistent, and the extended path will be worse than the first label. The cost of the second label must be greater than the first label, and the resource items consumed are more than the first label. It is only possible that it is easier to exceed the upper limit than the first label. Therefore, there is no need to expand, and the second label can be deleted in advance. This operation is called the first label "superior" the second label.

[0099] In the crew scenario, a basic label structure includes: total cost, total duty time, total flight time, total number of plateau segments and number of duty tasks. It is easy to see that the influencing factors of the partial structure of the label are the same as the weight of the duty task edge, but in addition to the cost, the cumulative value of each resource saved in the label has a specific upper limit requirement. Specifically, for the first label to "excel" the second label, the following conditions must be met: the total cost of the first label is less than or equal to the total cost of the second label, the total duty time of the first label is less than or equal to the total duty time of the second label, the total flight time of the first label is less than or equal to the total flight time of the second label, the total number of plateau segments of the first label is less than or equal to the total number of plateau segments of the second label, and the number of duty tasks of the first label is less than or equal to the number of duty tasks of the second label.

[0100] Among them, the number of on-duty tasks increases by 1 every time the label passes a node, so it does not need to be saved in the node attributes.

[0101] In the embodiment of the present invention, based on the shortest path problem labeling algorithm with resource constraints, the efficiency and accuracy of calculating the flight task string are significantly improved through resource expansion, label legitimacy check, label comparison and optimization and super operations.

[0102] For example, Figure 4 As shown in (b), since the departure node and the arrival node are merged, the number of overnight edges is significantly reduced. From the first arrival node X to the next set of arrival nodes Y1 / Y2 / Y3, they all pass through the same overnight edge. The number of resource expansion operations is n+n, and the time complexity of the resource expansion operation is reduced to O(n). In actual shift applications, dozens or even hundreds of departure nodes or arrival nodes can be merged into one node, which significantly improves the performance of the SPPRC algorithm.

[0103] The flight task string generation method provided in the embodiment of the present invention generates a duty task that meets preset business rules by acquiring the flight segment data of the flight crew; establishes nodes and edges according to the duty task, and constructs a first network graph with the nodes and the edges; wherein the nodes include departure nodes, arrival nodes, virtual starting points and virtual end points, and the edges include duty task edges, overnight edges, virtual starting edges and virtual ending edges; sets weight values ​​for the duty task edges and overnight edges in the first network graph, and merges the nodes that meet the preset merging rules to obtain a second network graph; based on the shortest path problem labeling algorithm with resource constraints, obtains the optimal path of the second network graph to obtain the flight task string, which can optimize the network graph structure and help to efficiently obtain the optimal solution for flight crew scheduling.

[0104] See also Figure 5 , Figure 5 : is a structural block diagram of a flight task sequence generating device 10 provided in an embodiment of the present invention, wherein the flight task sequence generating device 10 comprises:

[0105] The duty task generation module 11 is used to obtain the flight segment data of the flight crew and generate a duty task that meets the preset business rules;

[0106] A first network graph construction module 12 is used to establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual start point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual start edge and a virtual end edge;

[0107] The second network graph acquisition module 13 is used to set the weight values ​​of the on-duty task edges and overnight edges in the first network graph, and merge the nodes that meet the preset merging rules to obtain the second network graph;

[0108] The optimal path acquisition module 14 is used to acquire the optimal path of the second network graph based on the shortest path problem labeling algorithm with resource constraints to obtain the flight task string.

[0109] Optionally, the duty task generating module 11 is further used for:

[0110] Selecting the flight segment data whose interval exceeds a preset time unit, and cutting them by the time unit to obtain a plurality of cut data;

[0111] Combine the flights of each of the cut data according to the first connection rule, and enumerate all the duty tasks;

[0112] The first connection rule includes connecting through take-off and landing stations and connecting through stopover time.

[0113] Optionally, the first network diagram construction module 12 is further used to:

[0114] The departure node has five attributes: the departure airport, departure time, aircraft type group, inbound node list, and outbound node list of the duty mission;

[0115] The arrival node has five attributes of the arrival airport, arrival time, aircraft type group, inbound node list and outbound node list of the duty mission;

[0116] The virtual starting point is used to connect all departure nodes based on the same departure airport;

[0117] The virtual destination is used to connect all arrival nodes based on the same arrival airport.

[0118] Optionally, the first network diagram construction module 12 is further used to:

[0119] Establishing a duty task edge between a departure node and an arrival node;

[0120] When the second connection rule is satisfied between the two on-duty tasks, an overnight edge is established between the arrival node of the first on-duty task edge and the departure node of the second on-duty task edge;

[0121] When the virtual starting point and the departure node of the duty task edge are based on the same departure airport, a virtual starting edge is established;

[0122] When the virtual end point and the arrival node of the duty task edge are based on the same arrival airport, a virtual termination edge is established.

[0123] Optionally, the second connection rule includes:

[0124] The arrival airport of the arrival node of the first duty task edge is consistent with the departure airport of the departure node of the second duty task edge;

[0125] The time interval between the arrival time of the first duty task side and the departure time of the second duty task side meets the preset overnight duration;

[0126] The aircraft type group of the arrival node of the first duty task edge is consistent with the aircraft type group of the departure node of the second duty task edge.

[0127] Optionally, the second network diagram acquisition module 13 is further used to:

[0128] For multiple arrival nodes, when their arrival airports, arrival times, aircraft types and outgoing node lists are consistent, the multiple arrival nodes are merged into a single arrival node;

[0129] For multiple departure nodes, when their departure airports, departure times, aircraft types and inbound node lists are consistent, the multiple departure nodes are merged into a single departure node.

[0130] Optionally, the optimal path acquisition module 14 is further used to:

[0131] Select a node as the source point, and when the label is at the source point, initialize various resources and costs on the label;

[0132] Each time a node is reached, the path will be extended with resources, and the label will be accumulated with resources and costs according to the label on the path;

[0133] The labels on the path are accumulated by resource accumulation and cost accumulation according to the influencing factors of the weights of the passed edges and the number of on-duty tasks.

[0134] Optionally, the optimal path acquisition module 14 is further used to:

[0135] When the label passes through an overnight edge and a duty task edge, if a resource exceeds the upper limit, the label is deleted and the resource expansion of the path is ended; if all resources do not exceed the upper limit, it means that the label is legal;

[0136] Different labels of the current node are compared. If various resources and costs of a first label are better than those of a second label, the first label is superior to the second label, and the second label is deleted.

[0137] It is worth noting that the working process of each module in the flight mission string generation device 10 described in the embodiment of the present invention can refer to the working process of the flight mission string generation method described in the above embodiment, and will not be repeated here.

[0138] The flight task string generation device provided by the embodiment of the present invention generates a duty task that meets preset business rules by acquiring flight segment data of the flight crew; establishes nodes and edges according to the duty task, and constructs a first network graph with the nodes and the edges; wherein the nodes include departure nodes, arrival nodes, virtual starting points and virtual end points, and the edges include duty task edges, overnight edges, virtual starting edges and virtual ending edges; sets weight values ​​of duty task edges and overnight edges in the first network graph, and merges nodes that meet preset merging rules to obtain a second network graph; based on the shortest path problem labeling algorithm with resource constraints, obtains the optimal path of the second network graph to obtain the flight task string, which can optimize the network graph structure and help to efficiently obtain the optimal solution for flight crew scheduling.

[0139] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the above-mentioned flight task string generation method.

[0140] An embodiment of the present invention further provides a computer program product, which includes a computer program; wherein, when the computer program is running, the device where the computer program product is located is controlled to execute the above-mentioned flight task string generation method.

[0141] See also Figure 6 , Figure 6 : is a structural block diagram of a flight task string generation device 20 provided in an embodiment of the present invention, and the flight task string generation device 20 includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above-mentioned flight task string generation method embodiment are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0142] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the flight mission sequence generation device 20.

[0143] The flight mission string generation device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the flight mission string generation device 20 and does not constitute a limitation on the flight mission string generation device 20. The flight mission string generation device 20 may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the flight mission string generation device 20 may also include input and output devices, network access devices, buses, and the like.

[0144] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the flight mission sequence generation device 20, and uses various interfaces and lines to connect various parts of the entire flight mission sequence generation device 20.

[0145] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the flight task string generation device 20 by running or executing the computer program and / or module stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0146] Wherein, if the module / unit integrated in the flight mission sequence generating device 20 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, the steps of the above-mentioned method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0147] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating a flight task string, characterized in that: include: Obtain flight segment data of the flight crew and generate duty tasks that meet preset business rules; Establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual starting point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual start edge and a virtual end edge; Setting weight values ​​of the on-duty task edges and overnight edges in the first network graph, and merging nodes that meet the preset merging rules to obtain a second network graph; Based on the shortest path problem labeling algorithm with resource constraints, the optimal path of the second network graph is obtained to obtain the flight task string; The duty task is obtained through the following steps: Selecting the flight segment data whose interval exceeds a preset time unit, and cutting them by the time unit to obtain a plurality of cut data; Combine the flights of each of the cut data according to the first connection rule, and enumerate all the duty tasks; wherein the first connection rule includes connection through take-off and landing stations and connection through stopover time; The edges are established by the following rules: Establishing a duty task edge between a departure node and an arrival node; When the second connection rule is satisfied between the two on-duty tasks, an overnight edge is established between the arrival node of the first on-duty task edge and the departure node of the second on-duty task edge; When the virtual starting point and the departure node of the duty task edge are based on the same departure airport, a virtual starting edge is established; When the virtual end point and the arrival node of the duty task edge are based on the same arrival airport, a virtual termination edge is established; The preset merging rules include: For multiple arrival nodes, when their arrival airports, arrival times, aircraft types and outgoing node lists are consistent, the multiple arrival nodes are merged into a single arrival node; For multiple departure nodes, when their departure airports, departure times, aircraft types and inbound node lists are consistent, the multiple departure nodes are merged into a single departure node; Wherein, the method of obtaining the optimal path of the second network graph based on the shortest path problem labeling algorithm with resource constraints includes: Select a node as the source point, and when the label is at the source point, initialize various resources and costs on the label; Each time a node is reached, the path will be extended with resources, and the label will be accumulated with resources and costs according to the label on the path; The labels on the path are accumulated by resource accumulation and cost accumulation according to the influencing factors of the weights of the passed edges and the number of on-duty tasks.

2. The flight task sequence generation method according to claim 1, characterized in that: The departure node has five attributes: the departure airport, departure time, aircraft type group, inbound node list, and outbound node list of the duty mission; The arrival node has five attributes of the arrival airport, arrival time, aircraft type group, inbound node list and outbound node list of the duty mission; The virtual starting point is used to connect all departure nodes based on the same departure airport; The virtual destination is used to connect all arrival nodes based on the same arrival airport.

3. The flight task sequence generation method according to claim 1, characterized in that: The second connection rule includes: The arrival airport of the arrival node of the first duty task edge is consistent with the departure airport of the departure node of the second duty task edge; The time interval between the arrival time of the first duty task side and the departure time of the second duty task side meets the preset overnight duration; The aircraft type group of the arrival node of the first duty task edge is consistent with the aircraft type group of the departure node of the second duty task edge.

4. The flight task sequence generation method according to claim 1, characterized in that: Follow the steps below to expand the resources of the path: When the label passes through an overnight edge and a duty task edge, if a resource exceeds the upper limit, the label is deleted and the resource expansion of the path is ended; if all resources do not exceed the upper limit, it means that the label is legal; Different labels of the current node are compared. If various resources and costs of a first label are better than those of a second label, the first label is superior to the second label, and the second label is deleted.

5. A flight mission sequence generating device, characterized in that: include: The duty task generation module is used to obtain the flight segment data of the flight crew and generate the duty tasks that meet the preset business rules; A first network graph construction module, used to establish nodes and edges according to the duty task, and construct a first network graph with the nodes and the edges; wherein the nodes include a departure node, an arrival node, a virtual start point and a virtual end point, and the edges include a duty task edge, an overnight edge, a virtual start edge and a virtual end edge; A second network graph acquisition module, used to set the weight values ​​of the on-duty task edges and overnight edges in the first network graph, and merge the nodes that meet the preset merging rules to obtain a second network graph; An optimal path acquisition module, used to acquire the optimal path of the second network graph based on the shortest path problem labeling algorithm with resource constraints, and obtain the flight task string; Wherein, the duty task generation module is also used for: Selecting the flight segment data whose interval exceeds a preset time unit, and cutting them by the time unit to obtain a plurality of cut data; Combine the flights of each of the cut data according to the first connection rule, and enumerate all the duty tasks; wherein the first connection rule includes connection through take-off and landing stations and connection through stopover time; Wherein, the first network diagram construction module is further used for: Establishing a duty task edge between a departure node and an arrival node; When the second connection rule is satisfied between the two on-duty tasks, an overnight edge is established between the arrival node of the first on-duty task edge and the departure node of the second on-duty task edge; When the virtual starting point and the departure node of the duty task edge are based on the same departure airport, a virtual starting edge is established; When the virtual end point and the arrival node of the duty task edge are based on the same arrival airport, a virtual termination edge is established; The preset merging rules include: For multiple arrival nodes, when their arrival airports, arrival times, aircraft types and outgoing node lists are consistent, the multiple arrival nodes are merged into a single arrival node; For multiple departure nodes, when their departure airports, departure times, aircraft types and inbound node lists are consistent, the multiple departure nodes are merged into a single departure node; Wherein, the optimal path acquisition module is also used for: Select a node as the source point, and when the label is at the source point, initialize various resources and costs on the label; Each time a node is reached, the path will be extended with resources, and the label will be accumulated with resources and costs according to the label on the path; The labels on the path are accumulated by resource accumulation and cost accumulation according to the influencing factors of the weights of the passed edges and the number of on-duty tasks.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is run, it controls the device where the computer-readable storage medium is located to execute the flight task string generation method according to any one of claims 1 to 4.

Images