CN113096449B - Flight big data-based shutdown position resource arrangement method and system - Google Patents
Flight big data-based shutdown position resource arrangement method and system Download PDFInfo
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- CN113096449B CN113096449B CN202110321898.2A CN202110321898A CN113096449B CN 113096449 B CN113096449 B CN 113096449B CN 202110321898 A CN202110321898 A CN 202110321898A CN 113096449 B CN113096449 B CN 113096449B
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Abstract
The invention discloses a flight big data-based method and a flight big data-based system for arranging flight stop resources, wherein the flight big data-based method comprises the following steps: establishing a probability distribution prediction model of the predicted wheel-loading time of each inbound flight and the predicted wheel-unloading time of each outbound flight in the airport; combining inbound flights and outbound flights, and calculating the operation conflict probability of each inbound-outbound flight combination based on the probability distribution prediction model of the inbound and outbound flights; screening inbound-outbound flight combinations meeting the tolerance threshold requirement, and arranging feasible linking combination schemes of all flights in an airport; and screening arrangement combinations meeting the requirements from the connection combination scheme based on the screening conditions of the maximum bridge-leaning rate, the minimum passenger experience threshold value exceeding and the shortest sliding time. The invention has better goodness of fit with the historical operation condition of the flight, accurate flight scheduling time sequence, can achieve the double effects of the increase of the turnover rate of the corridor bridge and the decrease of the change rate of the flight, can effectively improve the satisfaction degree of passengers, and shortens the ground taxi time of the airplane.
Description
Technical Field
The invention relates to the technical field of flight operation control, in particular to a flight stop position resource arrangement method and system based on flight big data.
Background
Flight operation control, which is all decisions of civil aviation units on the initiation, delay, change and termination of flight operation; the arrangement of the parking space resources is that the operation command department of the civil aviation airport determines the parking space arrangement plan of the flight according to the use condition of the parking space resources.
At present, the ground guarantee of the flights of the civil aviation airport generally takes the time of scheduled take-off/scheduled landing of the flights as reference, the operation conflict in time cannot occur in the scheduling, and a certain time interval is reserved as redundancy. On one hand, the starting and stopping points occupied by flight guarantee operations and airport apron resources are flights arriving/leaving the parking spaces; on the other hand, the actual operation of the flight is not always performed according to the scheduled time. The two are influenced by factors such as flight plans, ground taxi paths, flight delays, planning and arrangement and the like, so that the two have larger difference in time.
For example:
the two flights on the left side of fig. 1 seem to be scheduled for a sufficient time interval, but in actual operation, the departure of the first flight frequently delays, and the later flight always arrives in advance, which causes conflict and passive adjustment of the two flights in actual operation.
The two groups of flights on the right side of the graph 1 are seemingly overlapped in time and cannot be combined and connected; however, in actual operation, the front group of flights always take off in advance, and the rear group of flights always arrive in a delayed way, so that the two groups of flights miss the opportunity of normal connection in actual operation.
The reason for the above problems is that the current arrangement of the parking lot resources generally adopts the planning time as a reference, and the reference value has a large error for actual operation; no matter how large an interval or protection redundancy is set, the method cannot adapt to actual operation by adopting more advanced combination algorithms.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a flight big data-based flight stop position resource arrangement method and system.
The invention discloses a flight big data-based stop position resource arrangement method, which comprises the following steps:
establishing a probability distribution prediction model of predicted gear-loading time of each inbound flight in an airport;
establishing a probability distribution prediction model of the estimated wheel-removing time of each outbound flight in the airport;
combining inbound flights with outbound flights, and calculating the operation conflict probability of each inbound-outbound flight combination based on the probability distribution prediction model of the inbound and outbound flights;
screening inbound-outbound flight combinations meeting the tolerance threshold requirement based on the operation conflict probability and a preset tolerance threshold;
arranging a feasible linking combination scheme of all flights in the airport based on the inbound-outbound flight combination meeting the tolerance threshold requirement;
screening one or more groups of first splicing combination schemes with the maximum bridging rate from the splicing combination schemes;
screening one or more groups of second engagement combination schemes exceeding the lowest passenger experience critical value from the first engagement combination schemes;
screening one or more groups of third splicing combination schemes with the shortest slide time from the second splicing combination schemes;
and taking the unique first splicing combination scheme, the unique second splicing combination scheme, the unique third splicing combination scheme or the first calculated third splicing combination scheme as a final arrangement combination.
As a further improvement of the present invention, the establishing a probability distribution prediction model of predicted gear-in time of each inbound flight in the airport includes:
collecting the actual round-trip time of each inbound flight in the past preset time as a sample set;
calculating the flight number ratio of each actual occurrence point in sequence based on the sample set;
and establishing a probability distribution model of estimated gear-loading time of inbound flights by taking the time as an abscissa and taking the flight number ratio of each occurrence point as an ordinate.
As a further improvement of the present invention, the establishing a probability distribution prediction model of the predicted wheel withdrawal time of each outbound flight in the airport includes:
collecting the actual wheel gear removing time of each outbound flight within the past preset time as a sample set;
calculating the flight number ratio of each actual occurrence point in sequence based on the sample set;
and the probability distribution prediction model predicts the estimated wheel-gear-withdrawal time of the departure flight by taking the time as an abscissa and the flight number ratio of each occurrence point as an ordinate.
As a further improvement of the present invention, calculating the operation conflict probability of the inbound-outbound flight combination comprises:
calculating the operation conflict probability of a certain time point as follows: the departure flight number ratio is multiplied by the sum of the number ratios of all incoming flights before the time point;
the sum of the operation conflict probabilities at all the time points is the operation conflict probability of the inbound-outbound flight combination.
As a further improvement of the invention, the preset tolerance threshold is X%, and the inbound-outbound flight combination with the operation conflict probability within X% is screened as a sample of a subsequent arrangement and connection combination scheme.
As a further improvement of the invention, the connection combination scheme comprises all inbound flights and outbound flights of the airport, and each group of corresponding inbound flight and outbound flight combination meets the tolerance threshold requirement.
As a further improvement of the present invention, the screening one or more sets of second splicing combination schemes from the first splicing combination schemes that exceed the threshold value of passenger experience lowest comprises:
in each of the first splicing combination schemes:
judging whether the taxi time of each inbound flight is greater than a first critical threshold value, and if so, marking as '1'; otherwise, recording as "0";
judging whether the taxi time of each departure flight is greater than a first critical threshold value, and if so, recording as '1'; otherwise, recording as "0";
judging whether the ferrying time of each inbound flight is greater than a second critical threshold value, and if so, marking as '1'; otherwise, recording as "0";
judging whether the ferrying time of each outbound flight is greater than a second critical threshold value, and if so, marking as '1'; otherwise, recording as "0";
taking the sum of the "scores" as the "score" of the first adaptor combination scheme;
screening one or more sets of combination schemes with the lowest "score" from all the first adaptor combination schemes as second adaptor combination schemes.
As a further improvement of the present invention, the screening one or more sets of third tandem combination schemes with the shortest slide time from the second tandem combination schemes includes:
in each second connection combination scheme, calculating the sum of the taxi time of all inbound flights and outbound flights to be used as the taxi time of the second connection combination scheme;
and comparing the sliding time of the second connection combination scheme, and screening one or more groups of combination schemes with the shortest sliding time to serve as a third connection combination scheme.
The invention discloses a flight big data-based shutdown position resource arrangement system, which comprises:
the first establishing module is used for establishing a probability distribution prediction model of predicted gear-loading time of each inbound flight in an airport;
the second establishing module is used for establishing a probability distribution prediction model of the predicted wheel-removing time of each outbound flight in the airport;
the calculation module is used for combining inbound flights and outbound flights and calculating the operation conflict probability of each inbound-outbound flight combination based on the probability distribution prediction model of the inbound and outbound flights;
the first screening module is used for screening the inbound-outbound flight combination meeting the tolerance threshold requirement based on the operation conflict probability and a preset tolerance threshold;
the arrangement module is used for arranging a feasible linking combination scheme of all flights in the airport based on the inbound-outbound flight combination meeting the tolerance threshold requirement;
a second screening module for screening one or more sets of first tandem combination schemes with the largest bridging rate from the tandem combination schemes;
a third screening module, configured to screen one or more second engagement combination schemes from the first engagement combination schemes that exceed a lowest passenger experience threshold;
a fourth screening module, configured to screen one or more groups of third splicing combination schemes with the shortest sliding time from the second splicing combination schemes;
and the output module is used for taking the unique first engagement combination scheme, the unique second engagement combination scheme, the unique third engagement combination scheme or the first calculated third engagement combination scheme as a final arrangement combination.
Compared with the prior art, the invention has the beneficial effects that:
the invention calculates the probability of occurrence of flight connection combination conflict, sets the range of conflict probability which can be received by a user, controls the implementation result within the set range and makes optimal arrangement on the flight plan from the whole situation; meanwhile, the highest corridor and bridge resource utilization efficiency, the shortest ground sliding time and the lowest machine position change rate are pursued, and the method is a more accurate machine position resource control method; the invention has better goodness of fit with the historical operation condition of the flight, accurate flight scheduling time sequence, can achieve the double effects of the increase of the turnover rate of the corridor bridge and the decrease of the change rate of the flight, can effectively improve the satisfaction degree of passengers, and shortens the ground taxi time of the airplane.
Drawings
FIG. 1 is a diagram illustrating the correlation between scheduling operation conflicts for an existing stand;
FIG. 2 is a flowchart illustrating a method for scheduling flight stop resources based on flight big data according to an embodiment of the present invention;
FIG. 3 is an analysis chart of the discomfort experienced by a passenger versus the taxi and ferry time of an aircraft according to an embodiment of the present invention;
fig. 4 is a block diagram of a flight big data-based flight number resource arrangement system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
in order to solve the technical problem that the scheduling time is adopted as reference in the background technology and cannot be adapted to actual operation, the invention establishes a probability distribution model of the historical operation condition of each flight number through flight big data, calculates the probability of operation conflict of flight combination, controls the operation conflict probability of the overall scheduling of the system within a tolerance range, and combines bridge rate, passenger experience, taxi time and the like to formulate a parking lot resource scheduling method capable of predicting and simulating the operation condition of the flight.
Specifically, the method comprises the following steps:
as shown in fig. 2, the present invention provides a flight position resource arrangement method based on flight big data, including:
the specific establishment method comprises the following steps:
collecting actual schedule time of each inbound flight within past preset time as a sample set; calculating the flight number ratio of each actual occurrence point in sequence based on the sample set; and establishing a probability distribution model of estimated gear-loading time of inbound flights by taking the time as an abscissa and the flight number ratio of each occurrence point as an ordinate.
Example (b):
the airport includes 100 inbound flights, marked as A1, A2,. cndot.. cndot.100, and obtains the actual round shift time of the past 100 routes of inbound flight A1, T1, T2,. cndot.. cndot.100, such as: t1 is 8:30, T2 is 8:32, T3 is 8:40, T4 is 8:35, · and T100 is 8:35 as a sample set; calculating the flight number ratio of each actual occurrence point in sequence, for example, if the flight number of 8:35 is 5, the flight number ratio is 5%; and establishing a probability distribution model of the estimated time of the gear taking the time as an abscissa and the flight number ratio of each occurrence point as an ordinate.
Step 2, establishing a probability distribution prediction model of the estimated wheel-removing time of each departure flight in the airport;
the specific establishment method comprises the following steps:
collecting the actual wheel gear removing time of each outbound flight within the past preset time as a sample set; based on the sample set, sequentially calculating the flight number ratio of each actual occurrence point; and the probability distribution prediction model takes time as an abscissa and the flight number ratio of each occurrence point as an ordinate and predicts the wheel-withdrawing time of the departure flight.
The embodiment is as follows:
the airport includes 100 outbound flights, recorded as B1, B2,. cndot. cndot., B100, and obtains the actual round trip gear time of the past 100 routes of the outbound flight B1, T1, T2,. cndot. T100, for example: t1 is 8:40, T2 is 8:42, T3 is 8:50, T4 is 8:45, · and T100 is 8:45 as a sample set; calculating the flight number ratio of each actual occurrence point in sequence, for example, if the flight number of 8:45 is 5, the flight number ratio is 5%; and establishing a probability distribution model of the estimated gear-removing time by taking the time as an abscissa and taking the ratio of the number of flights of each occurrence point as an ordinate.
Step 3, combining inbound flights and outbound flights, and calculating the operation conflict probability of each inbound-outbound flight combination based on the probability distribution prediction model of the inbound and outbound flights;
wherein the content of the first and second substances,
example (b): the combination of inbound flights and outbound flights is: the inbound flight A1 can form 100 combinations with 100 outbound flights B1, B2, ·, B100. the inbound flight A100 can form 100 combinations with 100 outbound flights B1, B2, ·, B100, thus 10000 combination schemes can be formed together.
Calculating the operation conflict probability of the inbound-outbound flight combination, comprising:
calculating the operation conflict probability of a certain time point as follows: multiplying the wheel-withdrawing ratio of the departure flight by the sum of the wheel-adding ratios of all the inbound flights before the time point; and the sum of the operation conflict probabilities at all the time points is the operation conflict probability of the inbound-outbound flight combination. For example, in 10000 combinations, 100 groups having an operating collision probability of less than 10% are calculated according to the above calculation formula.
Step 4, screening the inbound-outbound flight combination meeting the tolerance threshold requirement based on the operation conflict probability and a preset tolerance threshold;
specifically, the method comprises the following steps:
according to actual working experience, the risk level of the airplane parking place operation conflict probability can be roughly divided into:
safety, within 10%;
low risk, 10% -30%;
medium risk, 30% -70%;
high risk, more than 70%.
According to the invention, a threshold (within 10%) corresponding to the security level can be used as a preset tolerance threshold, so that 1000 groups of inbound-outbound flight combinations with the operation conflict probability within 10% are screened and used as samples of a subsequent arrangement and connection combination scheme; while the remaining 9000 sets of samples that did not meet the tolerance threshold were discarded directly.
Step 5, arranging a feasible linking combination scheme of all flights in the airport based on the inbound-outbound flight combination meeting the tolerance threshold requirement;
wherein, the arrangement conditions of the arrangement combination scheme are as follows: the connection combination scheme comprises all inbound flights and outbound flights of the airport, and each group of corresponding inbound and outbound flight combination meets the tolerance threshold requirement.
For example: b1 and B2 corresponding to A1 are combinations meeting requirements, B3, B4 and B5 corresponding to A2 are combinations meeting requirements, B1 and B5 corresponding to A100 are combinations meeting requirements; then when the mark is made, after A1 corresponds to B1, A100 cannot correspond to B1; after the arrangement, on the premise that the one-to-one correspondence is satisfied and all flights are considered, 100 sets of connection combination schemes are obtained in the embodiment.
Linkage combination scheme 1:
A1—B1、A2—B4、A3—B10、A4—B100、A5—B50、···、A100—B5；
···
linkage combination scheme 100:
A1—B2、A2—B3、A3—B6、A4—B100、A5—B40、···、A100—B1。
step 6, screening one or more groups of first connection combination schemes with the maximum bridge approach rate from the connection combination schemes;
the flights in each connection combination scheme have respective stands, so that the bridge closing rate of the connection combination scheme is calculated based on the stands of all the flights; selecting one or more groups of connection combination schemes with the maximum bridge approach rate as a first connection combination scheme;
for example: according to the invention, the combination scheme with the largest bridge approach rate (70%) is screened from 100 groups of connection combination schemes to be 20 groups, so that the 20 groups of combination schemes are screened as the first connection combination scheme for subsequent re-screening.
7, screening one or more groups of second engagement combination schemes exceeding the lowest passenger experience critical value from the first engagement combination schemes;
the method specifically comprises the following steps:
in each first splicing combination scheme: judging whether the taxi time of each inbound flight is greater than a first critical threshold value, and if so, recording as '1'; otherwise, recording as "0"; judging whether the taxi time of each outbound flight is greater than a first critical threshold value, and if so, marking as '1'; otherwise, recording as "0"; judging whether the ferrying time of each inbound flight is greater than a second critical threshold value, and if so, marking as '1'; otherwise, recording as "0"; judging whether the ferry time of each outbound flight is greater than a second critical threshold value, and if so, recording as '1'; otherwise, recording as "0";
taking the sum of the "scores" as the "score" of the first adaptor combination scheme;
one or more sets of combination schemes with the lowest "score" are screened from all the first adaptor combination schemes as second adaptor combination schemes.
Further, as shown in fig. 3, the first critical threshold of the present invention is 15min, and the first critical threshold is 11 min.
For example:
the present invention screens 10 sets of combination solutions for the lowest "score" and "10" scores in 20 sets of combination solutions, and therefore screens the 10 sets of combination solutions as a second, cohesive combination solution for subsequent re-screening.
Step 8, screening one or more groups of third engagement combination schemes with the shortest sliding time from the second engagement combination schemes;
the method specifically comprises the following steps:
in each second connection combination scheme, calculating the sum of the taxi time of all inbound flights and outbound flights to be used as the taxi time of the second connection combination scheme; and comparing the sliding time of the second connection combination scheme, and screening one or more groups of combination schemes with the shortest sliding time to serve as a third connection combination scheme.
For example: according to the method, one scheme with the shortest sliding time is selected from 10 combined schemes, and then the scheme is used as a third connection combined scheme.
And 9, taking the unique first splicing combination scheme, the unique second splicing combination scheme, the unique third splicing combination scheme or the first calculated third splicing combination scheme as a final arrangement combination.
For example, the present invention takes the only third splicing combination scheme in step 8 as the final layout combination.
As shown in fig. 4, the present invention provides a flight big data-based flight-stop-position resource arrangement system, including:
a first establishing module, configured to implement step 1;
a second establishing module, configured to implement step 2;
a calculating module for implementing the step 3;
the first screening module is used for realizing the step 4;
the arrangement module is used for realizing the step 5;
a second screening module for implementing the step 6;
a third screening module for implementing the step 7;
a fourth screening module for implementing the step 8;
and an output module for implementing the step 9.
The invention has the advantages that:
the invention calculates the occurrence probability of flight connection combination conflict, sets the conflict probability range which can be received by the user, controls the implementation result within the set range and makes optimal arrangement on the flight plan from the whole situation; meanwhile, the highest corridor and bridge resource utilization efficiency, the shortest ground sliding time and the lowest machine position change rate are pursued, and the method is a more accurate machine position resource control method; the invention has better goodness of fit with the historical operation condition of the flight, has accurate flight position scheduling time sequence, can achieve the double effects of the increase of the turnover rate of the corridor bridge and the decrease of the change rate of the flight position, can effectively improve the satisfaction degree of passengers, and shortens the ground taxi time of the airplane.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A flight big data-based flight stop position resource arrangement method is characterized by comprising the following steps:
establishing a probability distribution prediction model of predicted gear-loading time of each inbound flight in an airport;
establishing a probability distribution prediction model of the estimated wheel-removing time of each departure flight in the airport;
combining inbound flights and outbound flights, and calculating the operation conflict probability of each inbound-outbound flight combination based on the probability distribution prediction model of the inbound and outbound flights;
screening inbound-outbound flight combinations meeting the tolerance threshold requirement based on the operation conflict probability and a preset tolerance threshold;
arranging a feasible linking combination scheme of all flights in the airport based on the inbound-outbound flight combination meeting the tolerance threshold requirement;
screening one or more groups of first splicing combination schemes with the largest bridging rate from the splicing combination schemes;
screening one or more groups of second splicing combination schemes exceeding a passenger experience threshold value to the lowest from the first splicing combination schemes; the method specifically comprises the following steps: in each of the first splicing combination schemes: judging whether the taxi time of each inbound flight is greater than a first critical threshold value P1, and if so, marking as '1'; otherwise, recording as "0"; judging whether the taxi time of each outbound flight is greater than a first critical threshold value P1, and if so, marking as '1'; otherwise, recording as "0"; judging whether the ferrying time of each inbound flight is greater than a second critical threshold value P2, and if so, marking as '1'; otherwise, recording as "0"; judging whether the ferrying time of each outbound flight is greater than a second critical threshold value P2, and if so, marking as '1'; otherwise, recording as "0"; taking the sum of the "scores" as the "score" of the first adaptor combination scheme; screening one or more sets of combination schemes with the lowest "score" from all of the first adaptor combination schemes as second adaptor combination schemes;
screening one or more groups of third splicing combination schemes with the shortest slide time from the second splicing combination schemes;
and using the unique first engagement combination scheme, the unique second engagement combination scheme, the unique third engagement combination scheme or the first calculated third engagement combination scheme as a final arrangement combination.
2. The method of gate stand resource orchestration according to claim 1, wherein the establishing a probability distribution prediction model of predicted time to go into gear for each inbound flight within an airport comprises:
collecting actual schedule time of each inbound flight within past preset time as a sample set;
based on the sample set, sequentially calculating the flight number ratio of each actual occurrence point;
and establishing a probability distribution model of estimated gear-loading time of inbound flights by taking the time as an abscissa and the flight number ratio of each occurrence point as an ordinate.
3. The method of gate stand resource orchestration according to claim 1, wherein building a probability distribution prediction model of predicted wheel-withdrawal time for each outbound flight within an airport comprises:
collecting the actual wheel gear removing time of each outbound flight within the past preset time as a sample set;
calculating the flight number ratio of each actual occurrence point in sequence based on the sample set;
and the probability distribution prediction model takes time as an abscissa and the flight number ratio of each occurrence point as an ordinate and predicts the wheel-withdrawing time of the departure flight.
4. The method of gate stand resource orchestration according to claim 1, wherein calculating an operational conflict probability for the inbound-outbound flight combination comprises:
calculating the operation conflict probability of a certain time point as follows: the departure flight number ratio is multiplied by the sum of the number ratios of all incoming flights before the time point;
the sum of the operation conflict probabilities at all the time points is the operation conflict probability of the inbound-outbound flight combination.
5. The method of gate stand resource orchestration according to claim 1, wherein the preset tolerance threshold is X%, and inbound-outbound flight combinations with an operating conflict probability within X% are screened as samples of subsequent orchestration-engagement combination schemes.
6. The method of claim 1, wherein the connection and combination scheme comprises all inbound flights and outbound flights of the airport, and each corresponding set of inbound and outbound flight combinations meets a tolerance threshold requirement.
7. The method for scheduling stand resources of claim 1, wherein the selecting one or more sets of third engagement combination schemes with the shortest sliding time from the second engagement combination schemes comprises:
in each second connection combination scheme, calculating the sum of the taxi time of all inbound flights and outbound flights to serve as the taxi time of the second connection combination scheme;
and comparing the sliding time of the second linkage combination scheme, and screening one or more groups of combination schemes with the shortest sliding time to serve as a third linkage combination scheme.
8. A system for implementing the stand resource orchestration method according to any one of claims 1-7, comprising:
the first establishing module is used for establishing a probability distribution prediction model of predicted gear-loading time of each inbound flight in an airport;
the second establishing module is used for establishing a probability distribution prediction model of the predicted wheel-removing time of each departure flight in the airport;
the calculation module is used for combining the inbound flights with the outbound flights and calculating the operation conflict probability of each inbound-outbound flight combination based on the probability distribution prediction model of the inbound and outbound flights;
the first screening module is used for screening the inbound-outbound flight combination meeting the tolerance threshold requirement based on the operation conflict probability and a preset tolerance threshold;
the arrangement module is used for arranging a feasible linking combination scheme of all flights in the airport based on the inbound-outbound flight combination meeting the tolerance threshold requirement;
a second screening module, configured to screen one or more groups of first splicing combination schemes with a largest bridging rate from the splicing combination schemes;
a third screening module for screening one or more sets of second engagement combination plans that exceed a lowest threshold passenger experience value from the first engagement combination plans; the method specifically comprises the following steps: in each of the first splicing combination schemes: judging whether the taxi time of each inbound flight is greater than a first critical threshold value P1, and if so, marking as '1'; otherwise, recording as "0"; judging whether the taxi time of each outbound flight is greater than a first critical threshold value P1, and if so, marking as '1'; otherwise, recording as "0"; judging whether the ferrying time of each inbound flight is greater than a second critical threshold value P2, and if so, marking as '1'; otherwise, recording as "0"; judging whether the ferrying time of each outbound flight is greater than a second critical threshold value P2, and if so, marking as '1'; otherwise, recording as "0"; taking the sum of the "scores" as the "score" of the first adaptor combination scheme; screening one or more sets of combination schemes with the lowest "score" from all of the first adaptor combination schemes as second adaptor combination schemes;
a fourth screening module, configured to screen one or more groups of third tandem combination schemes with a shortest sliding time from the second tandem combination schemes;
and the output module is used for taking the unique first splicing combination scheme, the unique second splicing combination scheme, the unique third splicing combination scheme or the first calculated third splicing combination scheme as a final arrangement combination.
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