An airport flight scheduling method and device based on big data, and a medium

Abstract

The application discloses an airport flight scheduling method and device based on big data, and a medium, to solve the technical problem that the existing airport flight scheduling is greatly different from the actual scheduling. It includes: determining the transit duration of each transfer of the to-be-scheduled flight of the flight schedule according to the historical scheduling information of the flight schedule, and determining the average duration corresponding to the to-be-scheduled flight according to the transit duration of each transfer, taking the average duration as the reference duration of the to-be-scheduled flight; determining the influence coefficient corresponding to each influence factor that has an influence on the to-be-scheduled flight according to the reference duration and the historical scheduling information, and determining the expected turnaround time corresponding to the to-be-scheduled flight according to each influence coefficient and the reference duration; arranging the flight time corresponding to all to-be-scheduled flights of the day based on the expected turnaround time corresponding to all to-be-scheduled flights of the day, and scheduling the flight schedule of the to-be-scheduled flight in the airport according to the arranged flight time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of flight scheduling, and in particular to an airport flight scheduling method based on big data, an apparatus and a medium. BACKGROUND

[0002] Big data, also known as huge data, refers to the amount of data involved being so large that it cannot be captured, managed, processed, and arranged into information to help enterprise decision-making in a reasonable time through mainstream software tools. It is a data set that greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis, with four characteristics of massive data size, fast data flow, diverse data types, and low value density.

[0003] Currently, in the daily scheduling of the airport, the flight scheduling arrangement is often unreasonable, and the airport flight scheduling is greatly different from the actual scheduling, resulting in time waste or flight delay. SUMMARY

[0004] The embodiments of the present application provide an airport flight scheduling method based on big data, an apparatus and a medium, to solve the technical problem that the existing airport flight scheduling arrangement is unreasonable, the flight scheduling is greatly different from the actual scheduling, resulting in time waste or flight delay.

[0005] In one aspect, the embodiments of the present application provide an airport flight scheduling method based on big data, comprising:

[0006] determining a flight schedule of a flight to be scheduled in an airport, and obtaining historical scheduling information corresponding to the flight schedule;

[0007] determining a transit duration of each transit of the flight to be scheduled according to the historical scheduling information, and determining an average duration corresponding to the flight to be scheduled according to the transit duration of each transit, taking the average duration as a reference duration of the flight to be scheduled;

[0008] determining an influence coefficient corresponding to each influence factor having an influence on the flight to be scheduled according to the reference duration and the historical scheduling information, and determining a predicted turnaround duration corresponding to the flight to be scheduled according to each influence coefficient and the reference duration;

[0009] scheduling flight times corresponding to all flights to be scheduled in the airport on the day based on the predicted turnaround duration corresponding to all flights to be scheduled in the airport on the day, and scheduling flight schedules of flights to be scheduled in the airport according to the scheduled flight times.

[0010] In an implementation form of the application, the determining the transit duration of each transit of the to-be-scheduled flight according to the historical scheduling information specifically comprises:

[0011] In the historical scheduling information corresponding to the flight schedule, a plurality of flight transit data groups of the to-be-scheduled flight are determined; each flight transit data group comprises a last landing time and a next take-off time of the to-be-scheduled flight;

[0012] The transit duration corresponding to the to-be-scheduled flight is determined according to the last landing time and the next take-off time.

[0013] In an implementation form of the application, the determining the transit duration corresponding to the to-be-scheduled flight according to the last landing time and the next take-off time specifically comprises:

[0014] In the case that the stopover mode and the aircraft model of the to-be-scheduled flight are the same, the transit durations of the to-be-scheduled flights of different airlines are counted;

[0015] In the case that the airline and the aircraft model of the to-be-scheduled flight are the same, the transit durations of the to-be-scheduled flights of different stopover modes are counted;

[0016] And in the case that the airline and the stopover mode of the to-be-scheduled flight are the same, the transit durations of the to-be-scheduled flights of different aircraft models are counted.

[0017] In an implementation form of the application, the determining the average duration corresponding to the to-be-scheduled flight according to the transit duration of each transit, and taking the average duration as the reference duration of the to-be-scheduled flight specifically comprises:

[0018] The number of transits of the to-be-scheduled flight corresponding to the flight schedule in a preset time interval is determined, and the total transit duration in the preset time interval is counted;

[0019] The average duration corresponding to the to-be-scheduled flight of the flight schedule is determined according to the number of transits and the total transit duration, and the average duration is taken as the reference duration corresponding to the to-be-scheduled flight.

[0020] In an implementation form of the application, the determining the influence coefficient corresponding to each influence factor having an influence on the to-be-scheduled flight according to the reference duration and the historical scheduling information specifically comprises:

[0021] Based on the historical scheduling information, the influence factors having an influence on the to-be-scheduled flight are determined;

[0022] The historical scheduling information is used to obtain a transit duration corresponding to each influencing factor, and an influence coefficient corresponding to each influencing factor is calculated according to the transit duration corresponding to each influencing factor.

[0023] In an implementation manner of the present application, the method further includes:

[0024] The influence coefficient corresponding to each influencing factor is used to calculate a transit duration under the joint action of multiple influencing factors according to the reference duration corresponding to the to-be-scheduled flight.

[0025] The influence degree of the transit duration with respect to the airline, the stop mode and the aircraft type is determined according to the transit duration corresponding to the airline, the stop mode and the aircraft type, and the transit duration is calculated based on the influence degree, so as to obtain the predicted transit duration corresponding to the to-be-scheduled flight.

[0026] In an implementation manner of the present application, after the flight time of the to-be-scheduled flight is arranged, the method further includes:

[0027] The predicted flight time obtained by arrangement is compared with an actual duration, and the influence coefficient corresponding to each influencing factor and the reference duration are adjusted according to a comparison result.

[0028] In an implementation manner of the present application, the historical scheduling information corresponding to the to-be-scheduled flight of the flight sequence is obtained by:

[0029] All related information of the flight sequence in a preset time interval is obtained based on big data crawling, and the historical scheduling information of the flight sequence is determined from the all related information.

[0030] The historical scheduling information at least includes: flight passenger volume, airline, aircraft type, local weather condition, whether it is a holiday, and stop mode of the current flight sequence.

[0031] On the other hand, the present application also provides an airport flight scheduling device based on big data, which includes:

[0032] At least one processor;

[0033] and a memory in communication connection with the at least one processor;

[0034] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform:

[0035] determine a flight leg of a to-be-scheduled flight in the airport, and obtain historical scheduling information corresponding to the flight leg;

[0036] determine a transfer duration of each transfer of the to-be-scheduled flight according to the historical scheduling information, and determine an average duration corresponding to the to-be-scheduled flight according to the transfer duration of each transfer, so as to take the average duration as a reference duration of the to-be-scheduled flight;

[0037] determine an influence coefficient corresponding to each influence factor having an influence on the to-be-scheduled flight according to the reference duration and the historical scheduling information, and determine a predicted turnaround duration corresponding to the to-be-scheduled flight according to each influence coefficient and the reference duration;

[0038] schedule flight times corresponding to all to-be-scheduled flights on the day in the airport based on the predicted turnaround durations corresponding to all to-be-scheduled flights on the day in the airport, and schedule flight legs of to-be-scheduled flights in the airport according to the scheduled flight times.

[0039] In another aspect, the embodiment of the present application further provides a nonvolatile computer storage medium storing computer executable instructions, which are configured to:

[0040] determine a flight leg of a to-be-scheduled flight in the airport, and obtain historical scheduling information corresponding to the flight leg;

[0041] determine a transfer duration of each transfer of the to-be-scheduled flight according to the historical scheduling information, and determine an average duration corresponding to the to-be-scheduled flight according to the transfer duration of each transfer, so as to take the average duration as a reference duration of the to-be-scheduled flight;

[0042] determine an influence coefficient corresponding to each influence factor having an influence on the to-be-scheduled flight according to the reference duration and the historical scheduling information, and determine a predicted turnaround duration corresponding to the to-be-scheduled flight according to each influence coefficient and the reference duration;

[0043] schedule flight times corresponding to all to-be-scheduled flights on the day in the airport based on the predicted turnaround durations corresponding to all to-be-scheduled flights on the day in the airport, and schedule flight legs of to-be-scheduled flights in the airport according to the scheduled flight times.

[0044] The embodiment of the present application provides an airport flight scheduling method, device and medium based on big data, which at least has the following beneficial effects:

[0045] The historical scheduling information corresponding to the flight schedule of the to-be-scheduled flight can be acquired through big data, and then the transfer time length of the to-be-scheduled flight can be determined in the historical scheduling information, and the influencing factor and the corresponding influence coefficient of the to-be-scheduled flight can also be determined, so as to determine the expected turnaround time length of the to-be-scheduled flight. In this way, the flight times of all to-be-scheduled flights on the day can be arranged according to the expected turnaround time length of all to-be-scheduled flights on the day, so that the arranged flight times are more in line with the actual situation. Starting from the historical scheduling information in airport operation, the airport flight scheduling system based on big data technology can help the airport to predict the turnaround time length of a specified flight on a certain day, and through the predicted turnaround time length, the airport can arrange flights in a targeted manner. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and serve to explain the principles of the application. In the drawings:

[0047] Figure 1 A flowchart of an airport flight scheduling method based on big data provided by an embodiment of the application;

[0048] Figure 2 An internal structure diagram of an airport flight scheduling device based on big data provided by an embodiment of the application. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions and advantages of the application clearer, the technical solutions of the application will be described below in connection with the embodiments of the application and the corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the application, but not all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the application.

[0050] The embodiment of the application provides an airport flight scheduling method, device and medium based on big data, which can obtain historical scheduling information corresponding to a flight schedule of a to-be-scheduled flight through big data, and then can determine the transit duration of the to-be-scheduled flight in the historical scheduling information, and can also determine the influencing factor and the corresponding influence coefficient of the to-be-scheduled flight, so as to determine the expected turnaround duration of the to-be-scheduled flight. In this way, the flight time of all to-be-scheduled flights on the day can be arranged according to the expected turnaround duration of all to-be-scheduled flights on the day, so that the arranged flight time is more in line with the actual situation. Starting from the historical scheduling information in the airport operation, the airport flight scheduling system based on the big data technology can help the airport to predict the turnaround duration of a specified flight on a certain day. Through the predicted turnaround duration, the airport can arrange the flights in a targeted manner. The technical problem that the existing airport flight scheduling arrangement is unreasonable, the flight scheduling is greatly different from the actual scheduling, and time is wasted or flights are delayed is solved.

[0051] The technical solutions provided by the embodiments of the application are described in detail below with reference to the drawings.

[0052] Figure 1 A flowchart of an airport flight scheduling method based on big data provided by the embodiment of the application is shown in FIG. 1. Figure 1 As shown in FIG. 1, the airport flight scheduling method based on big data provided by the embodiment of the application comprises the following steps.

[0053] 101. Determine the flight schedule of a to-be-scheduled flight in an airport, and obtain the historical scheduling information corresponding to the flight schedule.

[0054] The server determines all to-be-scheduled flights on the day and the flight schedule of each to-be-scheduled flight according to the actual business needs of the airport, and then obtains the historical scheduling information corresponding to the current flight schedule according to the flight schedule of the to-be-scheduled flight and through the big data technology.

[0055] Specifically, the server crawls all related information of the current flight schedule within a preset time interval based on big data, and determines the historical scheduling information of the flight schedule in all related information. It should be noted that the historical scheduling information in the embodiment of the application at least includes: flight passenger volume, airline, aircraft type, local weather condition, whether it is a holiday, and the stopover mode of the current flight.

[0056] 102. Determine the transit duration of each transit of the to-be-scheduled flight of the flight schedule according to the historical scheduling information, and determine the average duration corresponding to the to-be-scheduled flight according to the transit duration of each transit, so as to take the average duration as the reference duration of the to-be-scheduled flight.

[0057] The server can determine the transit duration of the to-be-scheduled flight at each transfer according to the obtained historical scheduling information, and further determine the average duration corresponding to the to-be-scheduled flight according to the transit duration of the to-be-scheduled flight at each transfer, and then take the determined average duration as the reference duration of the to-be-scheduled flight.

[0058] Specifically, the server can determine a plurality of flight transfer data groups of the to-be-scheduled flight in the historical scheduling information corresponding to the flight schedule. It should be noted that each flight transfer data group in the embodiment of the present application includes the last landing time and the next take-off time of the to-be-scheduled flight. Then, the server can determine the transit duration corresponding to the to-be-scheduled flight according to the last landing time and the next take-off time.

[0059] The server can count the transit duration of the to-be-scheduled flight of different airlines in the case that the stop mode and the model of the to-be-scheduled flight are the same, count the transit duration of the to-be-scheduled flight of different stop modes in the case that the airline and the model of the to-be-scheduled flight are the same, and count the transit duration of the to-be-scheduled flight of different models in the case that the airline and the stop mode of the to-be-scheduled flight are the same. In this way, the turnaround time of the flight can be saved by arranging the airlines with longer turnaround time to use the jetway or other ways to improve efficiency, thereby improving the operation efficiency of the entire airport.

[0060] The server first determines the number of transfers of the to-be-scheduled flight corresponding to the flight schedule in a preset time interval, and counts the total transit duration in the preset time interval, and then determines the average duration corresponding to the to-be-scheduled flight of the current flight schedule according to the number of transfers and the total transit duration, and takes the average duration as the reference duration corresponding to the to-be-scheduled flight.

[0061] 103、According to the reference duration and the historical scheduling information, determine the influence coefficient corresponding to each influence factor that has an influence on the to-be-scheduled flight, and determine the predicted turnaround time corresponding to the to-be-scheduled flight according to each influence coefficient and the reference duration.

[0062] The server also needs to determine a plurality of influence factors that have an influence on the flight time of the to-be-scheduled flight before calculating the predicted turnaround time, and determine the influence coefficient corresponding to each influence factor according to the historical scheduling information and the transit duration corresponding to each influence factor, so that the server can determine the predicted turnaround time of the to-be-scheduled flight according to the influence coefficient corresponding to each influence factor and the reference duration of the to-be-scheduled flight.

[0063] Specifically, the server determines the influencing factors that affect the scheduled flight based on the historical scheduling information, obtains the transfer time corresponding to each influencing factor in the historical scheduling information, and calculates the influence coefficient corresponding to each influencing factor based on the transfer time corresponding to each influencing factor.

[0064] The server calculates the turnaround time under the combined effect of multiple influencing factors based on the benchmark duration of the flight to be scheduled and the influence coefficient of each influencing factor. It then determines the degree of influence of the airline, docking method, and aircraft model on the transfer time based on the transfer time corresponding to the three factors: airline, docking method, and aircraft model. Based on the degree of influence, the server calculates the turnaround time to obtain the estimated turnaround time corresponding to the flight to be scheduled.

[0065] 104. Based on the estimated turnaround time corresponding to all flights to be scheduled at the airport on that day, the flight times corresponding to all flights to be scheduled on that day are arranged, and the flight numbers of the flights to be scheduled at the airport are scheduled according to the arranged flight times.

[0066] In one embodiment of the present application, after the server schedules the flights to be scheduled at the airport according to the scheduled flight time, it compares the scheduled predicted flight time with the actual duration, and adjusts the influence coefficient and benchmark duration corresponding to each influencing factor based on the comparison result, and continuously optimizes so as to obtain more accurate calculation results, optimize the airport flight scheduling plan, and improve the airport's operating efficiency.

[0067] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides an airport flight scheduling device based on big data, the structure of which is as follows: Figure 2 shown.

[0068] Figure 2 This is a schematic diagram of the internal structure of an airport flight scheduling device based on big data provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0069] at least one processor;

[0070] and, a memory communicatively coupled to the at least one processor;

[0071] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0072] Determine the flight schedule of the flight to be scheduled at the airport and obtain the historical scheduling information corresponding to the flight schedule;

[0073] According to the historical scheduling information, a transit time length of each transit of the to-be-scheduled flight of the flight schedule is determined, and according to the transit time length of each transit, an average time length corresponding to the to-be-scheduled flight is determined, so as to take the average time length as a reference time length of the to-be-scheduled flight;

[0074] According to the reference time length and the historical scheduling information, an influence coefficient corresponding to each influence factor having an influence on the to-be-scheduled flight is determined, and according to each influence coefficient and the reference time length, a predicted turnaround time length corresponding to the to-be-scheduled flight is determined.

[0075] Based on the predicted turnaround time lengths corresponding to all the to-be-scheduled flights of the airport on the day, flight times corresponding to all the to-be-scheduled flights on the day are arranged, and according to the arranged flight times, flight schedules of the to-be-scheduled flights in the airport are scheduled.

[0076] The embodiment of the present application also provides a nonvolatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are configured to:

[0077] A flight schedule of a to-be-scheduled flight in an airport is determined, and historical scheduling information corresponding to the flight schedule is obtained;

[0078] According to the historical scheduling information, a transit time length of each transit of the to-be-scheduled flight of the flight schedule is determined, and according to the transit time length of each transit, an average time length corresponding to the to-be-scheduled flight is determined, so as to take the average time length as a reference time length of the to-be-scheduled flight;

[0079] According to the reference time length and the historical scheduling information, an influence coefficient corresponding to each influence factor having an influence on the to-be-scheduled flight is determined, and according to each influence coefficient and the reference time length, a predicted turnaround time length corresponding to the to-be-scheduled flight is determined.

[0080] Based on the predicted turnaround time lengths corresponding to all the to-be-scheduled flights of the airport on the day, flight times corresponding to all the to-be-scheduled flights on the day are arranged, and according to the arranged flight times, flight schedules of the to-be-scheduled flights in the airport are scheduled.

[0081] The embodiments in the present application are described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. Especially, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0082] The device and medium provided by the embodiment of the present application are one-to-one corresponding with the method, so the device and medium also have the similar beneficial technical effects as the method, and since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0083] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, apparatus, or computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, magnetic disks, CD-ROMs, optical storage media such as DVD s, etc.) embodying computer program instructions.

[0084] The application is described in relation to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It is understood that each flow and / or block in the flow diagrams and / or block diagrams, and combinations of flows and / or blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified by the flow or flows and / or block or blocks.

[0085] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagrams and / or block diagrams flow or flows and / or block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified by the flow or flows and / or block or blocks.

[0086] The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagrams and / or block diagrams flow or flows and / or block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified by the flow or flows and / or block or blocks.

[0087] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0088] Memory can include non-persistent memory, such as volatile random access memory (RAM) and / or non-volatile memory, such as read only memory (ROM), electrically erasable read only memory (EEPROM), flash memory, or other memory technologies, which can be either volatile or non-volatile. Memory is an example of computer readable media.

[0089] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the identified element.

[0091] The embodiments described above are only examples of the present application and are not intended to limit the present application. The present application can be modified and changed by various ways and means, which should be included in the scope of the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for airport flight scheduling based on big data, characterized in that: The method comprises: Determine the flight number of the flight to be scheduled at the airport and obtain the historical scheduling information corresponding to the flight number; Determine, based on the historical scheduling information, the transfer duration of each transfer of the to-be-scheduled flight of the flight schedule, and determine, based on the transfer duration of each transfer, an average transfer duration corresponding to the to-be-scheduled flight, so as to use the average transfer duration as a benchmark transfer duration for the to-be-scheduled flight; Determining, based on the benchmark duration and the historical scheduling information, an influence coefficient corresponding to each influencing factor affecting the flight to be scheduled, and determining, based on each influence coefficient and the benchmark duration, an estimated turnaround time corresponding to the flight to be scheduled; Based on the estimated turnaround time of all flights to be scheduled at the airport on that day, the flight times of all flights to be scheduled on that day are arranged, and the flights of the flights to be scheduled at the airport are scheduled according to the arranged flight times; The step of determining the average duration of the flights to be scheduled based on the transfer duration of each transfer, and using the average duration as the benchmark duration of the flights to be scheduled, specifically includes: Determine the number of transfers of the flight corresponding to the scheduled flight within a preset time interval, and calculate the total transfer time within the preset time interval; Determine the average duration of the flights to be scheduled according to the number of transfers and the total transfer duration, and use the average duration as the benchmark duration of the flights to be scheduled; Determining, based on the benchmark duration and the historical scheduling information, an influence coefficient corresponding to each influencing factor affecting the flight to be scheduled specifically includes: Determining factors affecting the flight to be scheduled based on the historical scheduling information; Obtaining the transfer duration corresponding to each influencing factor in the historical scheduling information, and calculating the influence coefficient corresponding to each influencing factor according to the transfer duration corresponding to each influencing factor; Determining the estimated turnaround time corresponding to the flight to be scheduled based on each impact coefficient and the benchmark time specifically includes: Calculate the turnaround time under the combined effect of multiple influencing factors based on the benchmark duration corresponding to the flight to be scheduled and the influence coefficient corresponding to each influencing factor; Based on the transfer times corresponding to the three factors of airline, docking method and aircraft type, the degree of influence of the airline, docking method and aircraft type on the transfer time is determined respectively, and based on the degree of influence, the turnaround time is calculated to obtain the estimated turnaround time corresponding to the flight to be scheduled.

2. The airport flight scheduling method based on big data according to claim 1, characterized in that: Determining the transfer duration of each transfer of the scheduled flight of the flight schedule according to the historical scheduling information specifically includes: Determine a plurality of flight transfer data groups of the flight to be scheduled from the historical scheduling information corresponding to the flight; each flight transfer data group includes the last landing time and the next take-off time of the flight to be scheduled; The transfer time corresponding to the flight to be scheduled is determined according to the last landing time and the next take-off time.

3. The airport flight scheduling method based on big data according to claim 2, characterized in that: Determining the transfer time corresponding to the flight to be scheduled based on the last landing time and the next take-off time specifically includes: When the docking mode and aircraft types of the scheduled flights are the same, the transfer time of the scheduled flights of different airlines is counted; When the airlines and aircraft types of the scheduled flights are the same, the transfer time of the scheduled flights with different stopover methods is counted; And when the airlines and docking methods of the flights to be scheduled are the same, the transfer time of the flights to be scheduled with different aircraft types is counted.

4. The airport flight scheduling method based on big data according to claim 1, characterized in that: After scheduling the flights to be scheduled at the airport according to the scheduled flight times, the method further includes: The predicted flight time obtained by scheduling is compared with the actual duration, and based on the comparison results, the influence coefficient and benchmark duration corresponding to each influencing factor are adjusted.

5. The airport flight scheduling method based on big data according to claim 1, characterized in that: The obtaining of the historical scheduling information corresponding to the flight to be scheduled of the flight schedule specifically includes: crawling all relevant information of the flight within a preset time interval based on big data, and determining historical scheduling information of the flight from all the relevant information; The historical scheduling information includes at least: flight passenger volume, airline, aircraft type, local weather conditions, whether it is a holiday, and the docking method of this voyage.

6. An airport flight dispatching device based on big data, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute: Determine the flight number of the flight to be scheduled at the airport and obtain the historical scheduling information corresponding to the flight number; Determine, based on the historical scheduling information, the transfer duration of each transfer of the to-be-scheduled flight of the flight schedule, and determine, based on the transfer duration of each transfer, an average transfer duration corresponding to the to-be-scheduled flight, so as to use the average transfer duration as a benchmark transfer duration for the to-be-scheduled flight; Determining, based on the benchmark duration and the historical scheduling information, an influence coefficient corresponding to each influencing factor affecting the flight to be scheduled, and determining, based on each influence coefficient and the benchmark duration, an estimated turnaround time corresponding to the flight to be scheduled; Based on the estimated turnaround time of all flights to be scheduled at the airport on that day, the flight times of all flights to be scheduled on that day are arranged, and the flights of the flights to be scheduled at the airport are scheduled according to the arranged flight times; The step of determining the average duration of the flights to be scheduled based on the transfer duration of each transfer, and using the average duration as the benchmark duration of the flights to be scheduled, specifically includes: Determine the number of transfers of the flight corresponding to the scheduled flight within a preset time interval, and calculate the total transfer time within the preset time interval; Determine the average duration of the flights to be scheduled according to the number of transfers and the total transfer duration, and use the average duration as the benchmark duration of the flights to be scheduled; Determining, based on the benchmark duration and the historical scheduling information, an influence coefficient corresponding to each influencing factor affecting the flight to be scheduled specifically includes: Determining factors affecting the flight to be scheduled based on the historical scheduling information; Obtaining the transfer duration corresponding to each influencing factor in the historical scheduling information, and calculating the influence coefficient corresponding to each influencing factor according to the transfer duration corresponding to each influencing factor; Determining the estimated turnaround time corresponding to the flight to be scheduled based on each impact coefficient and the benchmark time specifically includes: Calculate the turnaround time under the combined effect of multiple influencing factors based on the benchmark duration corresponding to the flight to be scheduled and the influence coefficient corresponding to each influencing factor; Based on the transfer times corresponding to the three factors of airline, docking method and aircraft type, the degree of influence of the airline, docking method and aircraft type on the transfer time is determined respectively, and based on the degree of influence, the turnaround time is calculated to obtain the estimated turnaround time corresponding to the flight to be scheduled.

7. A non-volatile computer storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Determine the flight number of the flight to be scheduled at the airport and obtain the historical scheduling information corresponding to the flight number; Determine, based on the historical scheduling information, the transfer duration of each transfer of the to-be-scheduled flight of the flight schedule, and determine, based on the transfer duration of each transfer, an average transfer duration corresponding to the to-be-scheduled flight, so as to use the average transfer duration as a benchmark transfer duration for the to-be-scheduled flight; Determining, based on the benchmark duration and the historical scheduling information, an influence coefficient corresponding to each influencing factor affecting the flight to be scheduled, and determining, based on each influence coefficient and the benchmark duration, an estimated turnaround time corresponding to the flight to be scheduled; Based on the estimated turnaround time of all flights to be scheduled at the airport on that day, the flight times of all flights to be scheduled on that day are arranged, and the flights of the flights to be scheduled at the airport are scheduled according to the arranged flight times; The step of determining the average duration of the flights to be scheduled based on the transfer duration of each transfer, and using the average duration as the benchmark duration of the flights to be scheduled, specifically includes: Determine the number of transfers of the flight corresponding to the scheduled flight within a preset time interval, and calculate the total transfer time within the preset time interval; Determine the average duration of the flights to be scheduled according to the number of transfers and the total transfer duration, and use the average duration as the benchmark duration of the flights to be scheduled; Determining, based on the benchmark duration and the historical scheduling information, an influence coefficient corresponding to each influencing factor affecting the flight to be scheduled specifically includes: Determining factors affecting the flight to be scheduled based on the historical scheduling information; Obtaining the transfer duration corresponding to each influencing factor in the historical scheduling information, and calculating the influence coefficient corresponding to each influencing factor according to the transfer duration corresponding to each influencing factor; Determining the estimated turnaround time corresponding to the flight to be scheduled based on each impact coefficient and the benchmark time specifically includes: Calculate the turnaround time under the combined effect of multiple influencing factors based on the benchmark duration corresponding to the flight to be scheduled and the influence coefficient corresponding to each influencing factor; Based on the transfer times corresponding to the three factors of airline, docking method and aircraft type, the degree of influence of the airline, docking method and aircraft type on the transfer time is determined respectively, and based on the degree of influence, the turnaround time is calculated to obtain the estimated turnaround time corresponding to the flight to be scheduled.

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