CN114822088A - Capacity flow cooperative optimization method based on flight normality target - Google Patents

Capacity flow cooperative optimization method based on flight normality target Download PDF

Info

Publication number
CN114822088A
CN114822088A CN202210748867.XA CN202210748867A CN114822088A CN 114822088 A CN114822088 A CN 114822088A CN 202210748867 A CN202210748867 A CN 202210748867A CN 114822088 A CN114822088 A CN 114822088A
Authority
CN
China
Prior art keywords
flight
capacity
airport
flights
sector
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210748867.XA
Other languages
Chinese (zh)
Inventor
石潇竹
陈飞飞
丁辉
张婧婷
张明伟
童明
徐善娥
鲍帆
黄吉波
田靖
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CETC 28 Research Institute
Original Assignee
CETC 28 Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CETC 28 Research Institute filed Critical CETC 28 Research Institute
Priority to CN202210748867.XA priority Critical patent/CN114822088A/en
Publication of CN114822088A publication Critical patent/CN114822088A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/003—Flight plan management

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a capacity flow cooperative optimization method based on a flight normality target, which can comprehensively consider the space-time distribution of national air traffic demands, the service capability of an airspace network and the capacity increase limit of each airspace unit according to the flight normality optimization target on the basis of carrying out preliminary analysis on the flight operation efficiency under the current flight schedule and the airspace service capability, locate a flight and an airspace with key problems, and generate flight time optimization and airspace capacity increase suggestions; the method aims to realize the operation efficiency target of nationwide flights through capacity flow collaborative optimization and provide technical support for users to carry out capacity flow collaborative management work on the strategic flow management level.

Description

Capacity flow cooperative optimization method based on flight normality target
Technical Field
The invention relates to a flight capacity flow collaborative optimization method, in particular to a capacity flow collaborative optimization method based on a flight normality target.
Background
With the rapid development of the civil aviation industry, the contradiction between the limited airspace resources and the continuously-increasing traffic demands is increasingly prominent, so that the problem of flight delay is more and more serious, and the economic benefit of the operation of an airline company and the satisfaction degree of passengers are reduced. In actual operation, when an airspace is in an over-capacity problem, a control department often issues a flow management measure in the problem airspace to limit the number of flights entering the airspace, so that flight delay is caused. In order to improve the operation efficiency of national flights and reduce the control intervention in actual operation, the problem of content demand unbalance in a national airspace network needs to be planned in advance at a strategic flow management layer; on one hand, the scheduling of the flight schedule can be optimized, so that the traffic demand can be better matched with limited airspace resources; on the other hand, the spatial service capability can be expanded, and the ever-increasing traffic demand can be better adapted. However, the disadvantage of the single approach is that the optimization scheme may include a large number of flights to be optimized or airspace to be optimized and a large amplitude to be optimized, which makes the optimization scheme difficult to implement. The method provides a capacity flow collaborative optimization method based on flight normal targets on the basis of carrying out preliminary analysis on flight operation efficiency of a current schedule, can generate national flight time and optimization suggestions of airspace networks according to different flight normal targets, relieves the defects of the single means, and provides technical support for carrying out capacity flow collaborative management work on a strategic flow management level by a user.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of providing a capacity flow cooperative optimization method based on a flight normality target aiming at the defects of the prior art.
In order to solve the technical problem, the invention discloses a capacity flow cooperative optimization method based on a flight normality target, which comprises the following steps of:
step 1, preparing basic data; acquiring basic data required by the method, and performing primary processing on the basic data;
step 2, analyzing flight operation efficiency according to airspace service capacity; screening flights which cannot be normally executed according to an original plan according to capacity limits of nationwide airports and sectors, and analyzing flight operation efficiency;
step 3, calculating flight ranges needing to be guaranteed based on flight normality targets; calculating the flight range which needs to be guaranteed by adjusting the time or expanding the airspace service capacity according to the set flight normality optimization target;
step 4, generating an airspace network optimization scheme according to flights needing to be guaranteed; positioning a key problem airspace according to the flight range needing to be guaranteed, and providing a capacity optimization suggestion for the key problem airspace;
step 5, generating a flight time optimization scheme according to flights needing to be guaranteed; and positioning the flight with the key problem according to the flight range needing to be ensured, and generating a flight time optimization scheme.
Has the advantages that: the method aims to improve the overall operation efficiency of the flight and alleviate the defect of a single optimization means through the capacity flow collaborative optimization. The method can comprehensively consider the space-time distribution of national air traffic demands, the service capacity of an airspace network and the capacity increase limit of each airspace unit according to the flight normality optimization target, locate the flight and the airspace with key problems, generate flight time optimization and airspace capacity expansion suggestions, and provide technical support for the user to carry out national capacity flow cooperative management work at the strategic flow management level.
Drawings
The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a schematic diagram of the overall process flow of the present invention.
FIG. 2 is a schematic diagram illustrating the principle of flight normality enhancement through capacity flow cooperative optimization according to the present invention.
FIG. 3 is a schematic diagram of the processing flow of the generation of the spatial domain network optimization scheme of the present invention.
Fig. 4 is a schematic flow chart illustrating the process of predicting airspace flows based on flight sequencing results according to the present invention.
FIG. 5 is a flow chart illustrating a process for screening recommendations for flight shedding based on airspace expansion limits, in accordance with the present invention.
Fig. 6 is a schematic diagram of the calculation flow of spatial domain optimization information according to the present invention.
FIG. 7 is a schematic flow chart illustrating the process of screening schedule-optimized flights of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
The method comprises the following steps:
step 1, preparing basic data; acquiring calculation data required by the method, and performing primary processing on the calculation data;
step 2, analyzing flight operation efficiency according to airspace service capacity; screening flights which cannot be normally executed according to an original plan according to capacity limits of nationwide airports and sectors, and analyzing flight operation efficiency;
step 3, calculating flight ranges needing to be guaranteed based on flight normality targets; calculating the flight range which needs to be guaranteed by adjusting the time or expanding the airspace service capacity according to the set flight normality optimization target;
step 4, generating an airspace network optimization scheme according to flights needing to be guaranteed; positioning a key problem airspace according to the flight range needing to be guaranteed, and providing a capacity optimization suggestion for the key problem airspace;
step 5, generating a flight time optimization scheme according to flights needing to be guaranteed; and positioning the flight with the key problem according to the flight range needing to be ensured, and generating a flight time optimization scheme.
The overall process flow, as shown in fig. 1:
step 1, preparing basic data,
the function of the step is as follows: and acquiring calculation data required by the method, and performing primary processing on the calculation data according to calculation requirements.
The method comprises the following steps:
step 1-1, variable definition;
step 1-2, acquiring basic data;
step 1-3, processing basic data;
step 1-1, variable definition:
Figure 205471DEST_PATH_IMAGE001
: date of analysis of the method; the strategic flow management stage is defined as 7 days in the future to the end of the current voyage season, and a user can select a certain day in the interval range according to the requirement of the user;
Figure 388191DEST_PATH_IMAGE002
: national flight schedule queue, including and analyzing dates
Figure 495824DEST_PATH_IMAGE001
All related nationwide flight plans;
Figure 156613DEST_PATH_IMAGE003
：
Figure 298881DEST_PATH_IMAGE002
the total number of the flight plans in the queue;
Figure 386923DEST_PATH_IMAGE004
: national flight scheduling queue
Figure 981852DEST_PATH_IMAGE002
The ith flight plan;
Figure 711911DEST_PATH_IMAGE005
: flight
Figure 646369DEST_PATH_IMAGE004
The flight number of;
Figure 967629DEST_PATH_IMAGE006
: flight
Figure 987537DEST_PATH_IMAGE004
The value is a non-negative integer, the initial value is 0, and the user can set the priority according to the self requirement;
Figure 786866DEST_PATH_IMAGE007
: flight
Figure 841410DEST_PATH_IMAGE004
The takeoff airport of (1);
Figure 599150DEST_PATH_IMAGE008
: flight
Figure 106355DEST_PATH_IMAGE004
Landing airports;
Figure 715234DEST_PATH_IMAGE009
: flight
Figure 889863DEST_PATH_IMAGE004
The planned takeoff time of (c);
Figure 490609DEST_PATH_IMAGE010
: flight
Figure 750689DEST_PATH_IMAGE004
The planned landing time of (c);
Figure 157399DEST_PATH_IMAGE011
: flight
Figure 717694DEST_PATH_IMAGE004
With a sequenced takeoff time of the initial value
Figure 754920DEST_PATH_IMAGE012
；
Figure 236717DEST_PATH_IMAGE013
: flight
Figure 447118DEST_PATH_IMAGE004
With an initial value of
Figure 65181DEST_PATH_IMAGE014
；
Figure 538888DEST_PATH_IMAGE015
: flight
Figure 304719DEST_PATH_IMAGE004
The sequencing takeoff delay of (1) is in seconds;
Figure 990915DEST_PATH_IMAGE016
: flight
Figure 791381DEST_PATH_IMAGE004
When the value of 0 is 0, the sequencing adjustment state is not adjusted, when the value of 1 is 1, the time is advanced, when the value of 2 is 2, the delay is shown, when the value of 3 is 3, the reduction is shown, and the initial value is 0;
Figure 170410DEST_PATH_IMAGE017
: flight
Figure 626799DEST_PATH_IMAGE004
The fan-passing queue comprises flights
Figure 913424DEST_PATH_IMAGE004
All via sector information of (1);
Figure 302817DEST_PATH_IMAGE018
: flight
Figure 118326DEST_PATH_IMAGE004
The jth sector in the past sector queue;
Figure 796432DEST_PATH_IMAGE019
: flight
Figure 152327DEST_PATH_IMAGE004
The jth sector in the sector-passing queue
Figure 865068DEST_PATH_IMAGE018
The code of (1);
Figure 320320DEST_PATH_IMAGE020
: flight
Figure 542180DEST_PATH_IMAGE004
The jth sector in the past sector queue
Figure 373869DEST_PATH_IMAGE018
The planned fan in time of (c);
Figure 3434DEST_PATH_IMAGE021
: flight
Figure 629587DEST_PATH_IMAGE004
The jth sector in the past sector queue
Figure 344602DEST_PATH_IMAGE018
The sort fan-in time;
Figure 245562DEST_PATH_IMAGE022
: a national airport queue containing all airport information throughout the country;
Figure 401737DEST_PATH_IMAGE023
: national airport fleet
Figure 526688DEST_PATH_IMAGE022
The number of airports contained in the database;
Figure 666682DEST_PATH_IMAGE024
: national airport fleet
Figure 105754DEST_PATH_IMAGE022
The u-th airport of (1);
Figure 647594DEST_PATH_IMAGE025
: airport
Figure 677867DEST_PATH_IMAGE024
The four-word code of (1);
Figure 101895DEST_PATH_IMAGE026
: a national sector queue; the sector information of all nationwide is contained;
Figure 79078DEST_PATH_IMAGE027
: national sector queue
Figure 475424DEST_PATH_IMAGE026
The number of sectors included in (1);
Figure 942178DEST_PATH_IMAGE028
: national sector queue
Figure 791185DEST_PATH_IMAGE026
The v-th sector in (a);
Figure 572059DEST_PATH_IMAGE029
: sector area
Figure 885229DEST_PATH_IMAGE028
The code of (1);
Figure 460567DEST_PATH_IMAGE030
: the calculation time range of the method is that, wherein,
Figure 62449DEST_PATH_IMAGE031
for analyzing the date
Figure 443752DEST_PATH_IMAGE001
00:00:00, and
Figure 549111DEST_PATH_IMAGE032
for analyzing the date
Figure 97947DEST_PATH_IMAGE001
23:59: 59;
Figure 921547DEST_PATH_IMAGE033
: in the method, the default value of the time slice is 3600 seconds, namely 1 hour, and a user can adjust the time slice according to the requirement.
Figure 106541DEST_PATH_IMAGE034
: the method calculates the number of time slices in a time range, and the initial value is 0;
Figure 66406DEST_PATH_IMAGE035
: calculating a time horizon
Figure 45864DEST_PATH_IMAGE036
The k time slice in which
Figure 622339DEST_PATH_IMAGE037
Is the start time of the time slice,
Figure 283127DEST_PATH_IMAGE038
is the cut-off time of the time slice;
Figure 363079DEST_PATH_IMAGE039
: airport
Figure 513437DEST_PATH_IMAGE024
Capacity value at kth time slice;
Figure 108367DEST_PATH_IMAGE040
: sector area
Figure 572846DEST_PATH_IMAGE028
Capacity value at kth time slice;
Figure 304042DEST_PATH_IMAGE041
: airport
Figure 94143DEST_PATH_IMAGE042
The approach capacity (approach rate) at the kth time slice;
Figure 114052DEST_PATH_IMAGE043
: airport
Figure 178960DEST_PATH_IMAGE042
Off-field capacity (off-field rate) at the kth time slice;
Figure 967924DEST_PATH_IMAGE044
: at airports
Figure 725664DEST_PATH_IMAGE042
Flight number of takeoff within the kth time slice;
Figure 967290DEST_PATH_IMAGE045
: at airports
Figure 835889DEST_PATH_IMAGE042
Flight number landed in the kth time slice;
step 1-2, acquiring basic data:
step 1-2-1, acquiring national airspace basic data:
according to the set analysis date
Figure 744939DEST_PATH_IMAGE001
Acquiring national airport and sector basic information;
acquiring all airport information of the whole country and forming a national airport queue
Figure 408002DEST_PATH_IMAGE022
The total number of airports is
Figure 402502DEST_PATH_IMAGE046
；
Figure 30053DEST_PATH_IMAGE047
In each airport
Figure 855927DEST_PATH_IMAGE024
The specific information of (1) includes: code
Figure 893153DEST_PATH_IMAGE048
；
Acquiring all sector information of the whole country and forming a whole country sector queue
Figure 374950DEST_PATH_IMAGE049
Total number of sectors is
Figure 585351DEST_PATH_IMAGE050
；
Figure 203415DEST_PATH_IMAGE049
Each sector in
Figure 677121DEST_PATH_IMAGE051
The specific information of (1) includes: code
Figure 442952DEST_PATH_IMAGE052
；
Step 1-2-2, extracting nationwide flight plans:
according to the set analysis date
Figure 925886DEST_PATH_IMAGE001
Screening the flight plans which take off from or land at the national airport or appear in the national airspace within the date from the schedule to form a national flight plan queue
Figure 929614DEST_PATH_IMAGE002
The total number of plans is
Figure 308643DEST_PATH_IMAGE053
;
Generation using 4D trajectory prediction techniques
Figure 499453DEST_PATH_IMAGE002
Each plan in
Figure 51657DEST_PATH_IMAGE004
The trajectory prediction information of (1), wherein,
Figure 644312DEST_PATH_IMAGE054
；
the flight trajectory prediction information includes: flight number
Figure 256559DEST_PATH_IMAGE055
(ii) a Take-off airport
Figure 200244DEST_PATH_IMAGE056
(ii) a Landing airport
Figure 228243DEST_PATH_IMAGE057
(ii) a Flight priority
Figure 737722DEST_PATH_IMAGE058
(ii) a Planned takeoff time
Figure 458553DEST_PATH_IMAGE059
(ii) a Planned landing time
Figure 889535DEST_PATH_IMAGE060
(ii) a Fan passing queue
Figure 783541DEST_PATH_IMAGE061
；
Wherein the queue of passing fan
Figure 85210DEST_PATH_IMAGE017
Therein comprises
Figure 242521DEST_PATH_IMAGE004
Each sector of the way
Figure 697817DEST_PATH_IMAGE062
Code of
Figure 129935DEST_PATH_IMAGE063
And scheduled sector entry time
Figure 817268DEST_PATH_IMAGE020
(ii) a Flight priority
Figure 676640DEST_PATH_IMAGE064
The initial value is 0, and the user can set the initial value according to the self requirement.
Note: the 4D track prediction technology is a general technology in a civil aviation air traffic control system, and can predict the information of key points and sectors of each route passed by a flight according to a flight plan, and the 4D track prediction technology is not important here and is not detailed here.
Step 1-2-3, acquiring national airspace capacity data:
1) calculating a time range setting: according to the set analysis date
Figure 347793DEST_PATH_IMAGE065
Generating a calculation time range for the method
Figure 786864DEST_PATH_IMAGE066
Wherein
Figure 125442DEST_PATH_IMAGE031
For analyzing the date
Figure 358977DEST_PATH_IMAGE065
00:00:00, and
Figure 783005DEST_PATH_IMAGE067
for analyzing the date
Figure 494609DEST_PATH_IMAGE065
23:59: 59;
2) time slice division:
default time slice in the method
Figure 687693DEST_PATH_IMAGE033
3600 seconds, namely 1 hour, and the user can adjust the time according to the requirement;
number of time slices:
Figure 154446DEST_PATH_IMAGE068
（1）
each time slice is made as
Figure 269033DEST_PATH_IMAGE069
Wherein
Figure 784328DEST_PATH_IMAGE070
Is the start time of the kth time slice,
Figure 831918DEST_PATH_IMAGE038
is the cut-off time of the kth time slice, and
Figure 672835DEST_PATH_IMAGE071
；
3) acquiring the capacity of each time slice of the national airport:
screening
Figure 71456DEST_PATH_IMAGE072
Each airport in the queue
Figure 390442DEST_PATH_IMAGE073
In the calculation time range
Figure 292539DEST_PATH_IMAGE074
Capacity information of each time slice in the system
Figure 304357DEST_PATH_IMAGE075
I.e. by
Figure 924694DEST_PATH_IMAGE073
Capacity value at kth time slice;
4) acquiring the capacity of each time slice of the national sector:
screening
Figure 838249DEST_PATH_IMAGE026
Each sector in the queue
Figure 798115DEST_PATH_IMAGE028
In the calculation time range
Figure 777572DEST_PATH_IMAGE076
Capacity information of each time slice in the system
Figure 354047DEST_PATH_IMAGE077
I.e. by
Figure 811573DEST_PATH_IMAGE028
Capacity value at kth time slice;
note: the capacity information can be derived from static capacity data of national airports and sectors published by the civil aviation air administration in China, and a user can modify or set the capacity information according to the self requirement.
Step 1-3, processing basic data:
1-3-1, decomposing the entering and leaving capacity of an airport:
the user can set the entering and leaving capacity of the airport according to the self requirement, and if the entering and leaving capacity is not set, the following method can be adopted for calculation.
For the
Figure 625946DEST_PATH_IMAGE078
Each airport in the queue
Figure 245146DEST_PATH_IMAGE024
The following operations were carried out:
1) counting the take-off and landing requirements of each time slice of the airport: queue according to nationwide flight plan
Figure 840075DEST_PATH_IMAGE002
Every flight in the middle
Figure 38975DEST_PATH_IMAGE004
Take-off airport, landing airport, planned take-off time
Figure 239012DEST_PATH_IMAGE079
And planned landing time
Figure 825852DEST_PATH_IMAGE010
Statistical airport
Figure 580181DEST_PATH_IMAGE024
In the calculation time range
Figure 113930DEST_PATH_IMAGE076
Take-off rack per time slice k in
Figure 965212DEST_PATH_IMAGE080
And landing rack
Figure 395056DEST_PATH_IMAGE081
；
2) Dividing capacity according to the take-off and landing requirements:
to increase utilization of airport capacity resources, airport capacity is broken down according to the take-off and landing requirements for each time slice.
Then:
Figure 167840DEST_PATH_IMAGE082
（2）
Figure 36439DEST_PATH_IMAGE083
（3）
step 1-3-2, acquiring flight sequencing information:
considering national airspace service capability, aiming at ensuring national airports and sectors not to be over-capacity, adopting a combined method pair of time adjustment and flight reduction
Figure 679910DEST_PATH_IMAGE002
Adjusting the medium flights to generate each flight
Figure 811814DEST_PATH_IMAGE004
The flight ordering information includes:
1) sequencing of takeoff time
Figure 603052DEST_PATH_IMAGE084
(ii) a 2) Sequencing landing time
Figure 213025DEST_PATH_IMAGE085
(ii) a 3) Sequencing delay
Figure 242161DEST_PATH_IMAGE015
(ii) a 4) Flight adjustment status
Figure 810546DEST_PATH_IMAGE016
(ii) a 5) Flight queue of passing fan
Figure 360519DEST_PATH_IMAGE086
Each sector in
Figure 774183DEST_PATH_IMAGE087
Rank into sector time of
Figure 392246DEST_PATH_IMAGE021
。
Note: the related flight sequencing method is described in the patent "a flight operation performance pre-evaluation method based on schedule", and is not described herein again.
Step 2, analyzing flight operation efficiency according to airspace service capacity
The function of the step is as follows: and screening flights which cannot be normally executed according to the original plan according to the capacity limit of national airports and sectors, generating a flight adjustment queue, and further analyzing the operation efficiency of the flights.
The method comprises the following steps:
step 2-1, variable definition;
step 2-2, screening flights needing to be adjusted;
step 2-3, optimizing the sequence of the flight adjustment queue;
step 2-4, analyzing flight operation efficiency;
step 2-1, variable definition:
Figure 397111DEST_PATH_IMAGE088
: flight adjustment queue, comprising
Figure 366204DEST_PATH_IMAGE089
All flights that need to be adjusted or subtracted in time.
Figure 317980DEST_PATH_IMAGE090
：
Figure 56128DEST_PATH_IMAGE091
The total number of the flight plans in the queue is 0 as an initial value;
Figure 497474DEST_PATH_IMAGE092
: the method defaults to the maximum flight delay, sets the maximum flight delay as 9999-60 seconds by default, and a user can adjust the flight delay according to requirements;
Figure 688284DEST_PATH_IMAGE093
: the number of flights in the whole country does not need to be adjusted, and the initial value is 0;
Figure 443750DEST_PATH_IMAGE094
: the number of flights needing delay in nationwide flights is 0 in an initial value;
Figure 98723DEST_PATH_IMAGE095
: the number of flights in the whole country needs to be reduced, and the initial value is 0;
Figure 648653DEST_PATH_IMAGE096
: the national flights need to be erected at an advanced time, and the initial value is 0;
Figure 326759DEST_PATH_IMAGE097
: the number of flights in the whole country needing time adjustment is 0;
Figure 682654DEST_PATH_IMAGE098
: estimating normality of nationwide flights, wherein the initial value is 0;
step 2-2, screening flights needing to be adjusted:
according to the flight sequencing result in the step 1-3-2, for
Figure 395395DEST_PATH_IMAGE089
Each flight in the queue
Figure 116226DEST_PATH_IMAGE004
If the flight is satisfied
Figure 343945DEST_PATH_IMAGE099
It means that the flight needs to be adjusted or subtracted and added to the flight
Figure 175635DEST_PATH_IMAGE091
In queue, and order
Figure 539620DEST_PATH_IMAGE100
；
Step 2-3, optimizing the sequence of the flight adjustment queue:
in order to distinguish the severity of the flight operation problem, the flight sequencing information in the step 1-3-2 is comprehensively considered
Figure 696932DEST_PATH_IMAGE091
Each flight in the queue
Figure 84051DEST_PATH_IMAGE004
Delay condition of
Figure 781749DEST_PATH_IMAGE015
Priority of the system
Figure 469082DEST_PATH_IMAGE064
And adjusting the state
Figure 531716DEST_PATH_IMAGE101
Optimized in the order of severity from high to low
Figure 486026DEST_PATH_IMAGE091
The order of flights in the queue.
Step 2-3-1, updating the delay information of the suggested reduction flight:
for
Figure 659518DEST_PATH_IMAGE091
Each flight in the queue
Figure 201358DEST_PATH_IMAGE004
If the flight is in the adjusted state
Figure 497210DEST_PATH_IMAGE101
A value of 3 indicates that the flight is recommended to be reduced, and the flight is allowed to be depleted
Figure 124501DEST_PATH_IMAGE102
；
Step 2-3-2, sequencing according to flight delay conditions:
according to
Figure 101684DEST_PATH_IMAGE091
Every flight in the flight
Figure 560347DEST_PATH_IMAGE004
Delay condition of
Figure 230363DEST_PATH_IMAGE015
Sorting and updating according to the sequence of delay from big to small
Figure 813791DEST_PATH_IMAGE091
Flight order in the queue;
step 2-3-3, sorting according to flight priority:
to highlight the operation problem of high priority flights, on the basis of step 2-3-2
Figure 656982DEST_PATH_IMAGE091
Every flight in the flight
Figure 173414DEST_PATH_IMAGE004
Priority of
Figure 748752DEST_PATH_IMAGE064
The priority is sorted from high to low, and the updating is carried out
Figure 881793DEST_PATH_IMAGE091
Flight order in the queue;
step 2-4, analyzing flight operation efficiency:
analyzing the flight sequence information in the step 1-3-2 under the current airspace service capability,
Figure 200779DEST_PATH_IMAGE065
the method for the national flight operation condition of the date comprises the following steps:
step 2-4-1, flight delay number index calculation: for the
Figure 368455DEST_PATH_IMAGE091
Every flight in the flight
Figure 114694DEST_PATH_IMAGE103
If it is satisfied
Figure 203873DEST_PATH_IMAGE101
Equal to 2, the flight is a delayed flight, and is added to the delay frame number statistic, i.e.
Figure 123287DEST_PATH_IMAGE104
；
Step 2-4-2, flight reduction frame index calculation:
for the
Figure 348732DEST_PATH_IMAGE091
Every flight in the flight
Figure 531452DEST_PATH_IMAGE103
If it is satisfied
Figure 639085DEST_PATH_IMAGE101
If 3, the flight is a proposed abatement flight and added to the abatement rack statistics, i.e., the flight is a proposed abatement flight
Figure 299874DEST_PATH_IMAGE105
；
Step 2-4-3, calculating the flight time advanced setting index:
for the
Figure 442142DEST_PATH_IMAGE091
Every flight in the flight
Figure 536043DEST_PATH_IMAGE103
If it is satisfied
Figure 130972DEST_PATH_IMAGE101
If the number of the flights equals to 1, the flight is an advanced-time flight and is added into the advanced-time frame number statistic, namely
Figure 595452DEST_PATH_IMAGE106
；
And 2-4, calculating flight number indexes without adjustment:
the flight with the advanced time is taken as the flight needing to be subjected to time adjustment, and a user can change a statistical mode according to the self requirement.
Figure 795489DEST_PATH_IMAGE107
（4）
Figure 116749DEST_PATH_IMAGE108
（5）
Step 2-4-5, flight normality index calculation: defining the flight occupation ratio without adjustment as the flight normality
Figure 136658DEST_PATH_IMAGE109
The index reflects the maximum potential that the flight can normally run based on the current schedule.
The calculation formula is as follows:
Figure 670407DEST_PATH_IMAGE110
（6）
note: although various flight normality statistical methods are published by the air traffic control bureau of civil aviation at present, the methods are changing all the time. The method is characterized in that the maximum potential of national flights for normal operation under the current airspace service capacity is mined and an optimization scheme is provided at the strategic flow management level, so that a flight normality statistical method is defined as a formula (6), and a user can change a statistical mode according to the requirement of the user.
Step 3, calculating flight ranges needing to be guaranteed based on flight normality targets; the function of the step is as follows: and calculating the flight range which needs to be guaranteed by adjusting the time or expanding the airspace service capacity according to the set flight normality optimization target.
The principle of the steps is as follows:
the existing airspace network is marked as an airspace network A, and nationwide flight scheduling queues are arranged
Figure 521688DEST_PATH_IMAGE111
Recording the flight queue A, and obtaining the estimated flight normality of the flight queue A when the flight queue A runs in the airspace network A based on the steps 2-4
Figure 951533DEST_PATH_IMAGE112
。
If the normality of flights needs to be improved, the traffic demand can be selected and optimized to better match the existing airspace service capacity on one hand, and the airspace service capacity can be selected and expanded to better adapt to the traffic demand on the other hand; examples are as follows:
1) from the perspective of optimizing traffic demand, if the flights in the flight queue A are completely corrected according to the sequencing result of the step 1-3-2, a flight queue B is generated. According to the flight sequencing result of the step 1-3-2, the flight queue B can meet the service capacity of the airspace network A, no flight needs to be adjusted or reduced, and the flight normality of the flight queue B when the flight queue B runs in the airspace network A is 100%.
2) From the perspective of expanding airspace service capacity, according to the plan information of each flight in the flight queue A, the calculation time range of each airport and sector in the country is respectively counted
Figure 521054DEST_PATH_IMAGE113
And expanding the capacity of the airport or the sector according to the flow peak value of each time slice in the space domain network B, and recording the space domain network with expanded service capacity as the space domain network B. The airspace network B has sufficient service capacity to ensure that the flights in the flight queue a can be executed according to the original plan, and the flight normality of the flight queue a when operating in the airspace network B is 100%.
The normality of flights can be improved from the perspective of optimizing traffic demand or from the perspective of expanding airspace service capacity, but the disadvantage of a single means is that the optimization scheme possibly comprises a large number of flights to be optimized or airspaces to be optimized, and the optimization scheme is difficult to implement.
The method selects two means of comprehensive use of time optimization and airspace expansion so as to achieve the flight normality optimization goal and simultaneously alleviate the defects of the single means. According to step 2-2, the flight adjustment queue
Figure 327336DEST_PATH_IMAGE114
There are two broad classes of flights that require time of day adjustments and suggest a reduction. The proposed reduced flights indicate that available time slots cannot be allocated to the flights under the current airspace service capacity, and the method guarantees the flights in an airspace capacity expansion mode for reducing flight reduction behaviors in actual operation; and for the type of flights needing to be adjusted in time, the flight time is required to be adjusted, which means that the flight can not be executed according to the original plan under the current airspace service capability, and in order to reduce the flight delay condition in actual operation, the method provides the time optimization suggestion for the type of flights according to the flight sequencing information in the step 1-3-2.
Based on the thought, the flight normality optimization target set by the user is realized
Figure 33124DEST_PATH_IMAGE115
The method adjusts the queue from the flight
Figure 899449DEST_PATH_IMAGE114
In a proper amountAnd generating a flight time optimization scheme and an airspace capacity expansion scheme according to the flight sequencing information of the flights.
Suppose a slave queue
Figure 893950DEST_PATH_IMAGE114
The screened flights include
Figure 566240DEST_PATH_IMAGE116
Flights for which the rack needs to be adjusted in time, an
Figure 64217DEST_PATH_IMAGE117
Shelf-advised subtractive flights; wherein
Figure 163760DEST_PATH_IMAGE116
The flight needing to be adjusted in time is used for generating a time optimization scheme, and the flight queue A is corrected according to flight sequencing information of the flight optimization scheme to generate a flight queue C;
Figure 645557DEST_PATH_IMAGE118
the flight recommended to be subtracted is used for generating an airspace network optimization scheme, and an airspace network C is generated by expanding the service capacity of the airspace network A, so that the part of flights can be executed in the network C according to an original plan; from
Figure 793642DEST_PATH_IMAGE114
Total flight number of medium screening
Figure 474022DEST_PATH_IMAGE119
Equation (7) and equation (8) need to be satisfied.
Figure 682149DEST_PATH_IMAGE120
And is and
Figure 447980DEST_PATH_IMAGE121
（7）
Figure 665334DEST_PATH_IMAGE122
（8）
to prove that the flight queue C can achieve the flight normality optimization target when being actually executed in the airspace network C
Figure 132045DEST_PATH_IMAGE123
The following explanation is also needed.
If the flight queue C is executed in the airspace network A, the queue C is selected in advance
Figure 573390DEST_PATH_IMAGE124
The flights needing to be adjusted in time are corrected according to the flight sequencing information, so that the service capacity of the airspace network A is not exceeded according to the flight sequencing information in the step 1-3-2, and the rest remains in the queue C
Figure 764200DEST_PATH_IMAGE125
The overhead flight needs to be adjusted in time, an
Figure 519667DEST_PATH_IMAGE126
The overhead flight needs to be eliminated; order to
Figure 174639DEST_PATH_IMAGE127
Temporary variables for flight normality during the calculation process of the method are shown in the formula (9).
Figure 255727DEST_PATH_IMAGE128
（9）
The airspace network C has more service capacity than the airspace network A, and is only used for supporting the queue of the flight adjustment
Figure 933833DEST_PATH_IMAGE114
Of medium size
Figure 289728DEST_PATH_IMAGE117
The shelf flight is executed as it was originally scheduled,thus, when flight queue C is executing in network C, queue C remains
Figure 736890DEST_PATH_IMAGE129
The overhead flight needs to be adjusted in time, an
Figure 520038DEST_PATH_IMAGE130
The overhead flight needs to be eliminated; the combination of the formula (10) proves that at least one operation mode exists, so that the flight normality optimization goal can be realized when the flight queue C is executed in the airspace network C. The principle is shown in fig. 2.
Figure 419861DEST_PATH_IMAGE131
（10）
The step 3 comprises the following steps:
step 3-1, variable definition;
step 3-2, setting a flight normality optimization target;
3-3, calculating the flight range needing to be guaranteed;
wherein, in step 3-1, variables are defined:
Figure 313868DEST_PATH_IMAGE115
: setting flight normality optimization targets;
Figure 881116DEST_PATH_IMAGE132
: the method calculates temporary variables of flight normality in the process;
Figure 835165DEST_PATH_IMAGE133
: aiming at the flight normality optimization target, the total number of flights is guaranteed through time adjustment and airspace expansion, and the initial value is 0;
Figure 487863DEST_PATH_IMAGE134
: normal for flightFlight number needing to be adjusted is screened out by the sexual optimization target and is initially 0;
Figure 123244DEST_PATH_IMAGE117
: reducing the flight number, and aiming at the flight number recommended to be reduced and screened by the flight normality optimization target, ensuring the flight number by expanding the space domain, wherein the initial value is 0;
step 3-2, setting flight normality optimization targets: the invention aims to improve the flight normality in actual operation by using two means of time optimization and airspace service capacity expansion; therefore, the flight normality optimization target set by the user needs to be set
Figure 607315DEST_PATH_IMAGE115
Limit and satisfy
Figure 669949DEST_PATH_IMAGE135
；
Step 3-3, calculating the flight range needing to be guaranteed: this step optimizes the objective according to flight normality
Figure 606681DEST_PATH_IMAGE115
The calculation is required from
Figure 780173DEST_PATH_IMAGE114
Total number of flights screened in queue
Figure 118751DEST_PATH_IMAGE133
(ii) a Because the economic loss of the airline company caused by flight elimination in actual operation is higher than that caused by delayed flight, the method preferentially brings the possibly eliminated flights into the flight screening so as to reduce the flight elimination behavior in actual operation; the user can adjust the preference of screening flights according to the needs of the user.
Step 3-3-1, reducing flight quantity calculation:
first only from
Figure 617865DEST_PATH_IMAGE114
And screening the flights suggested to be eliminated from the queue, and judging whether the normality optimization target is achieved or not.
Order to
Figure 985436DEST_PATH_IMAGE136
Then, then
Figure 24936DEST_PATH_IMAGE137
（11）
If it is satisfied with
Figure 421282DEST_PATH_IMAGE138
If the flight is normal, the flight is judged to be unable to achieve the normal target
Figure 825719DEST_PATH_IMAGE139
Continuing to execute the step 3-3-2; otherwise, it orders
Figure 737043DEST_PATH_IMAGE140
Jumping to step 3-3-3;
step 3-3-2, calculating the flight amount of the time adjustment:
order to
Figure 517917DEST_PATH_IMAGE141
Then, then
Figure 565508DEST_PATH_IMAGE142
（12）；
Step 3-3-3, calculating total adjusting flight quantity:
Figure 406425DEST_PATH_IMAGE143
（13）
step 4, generating an airspace network optimization scheme according to flights needing to be guaranteed; the function of the step is as follows: the method can position the key problem airspace according to the flight range needing to be guaranteed, and provide capacity optimization suggestions of the airspace. The processing flow is shown in fig. 3:
the method comprises the following steps:
step 4-1, variable definition;
step 4-2, setting parameters;
4-3, predicting airspace flow based on the flight sequencing result;
4-4, generating an airspace network optimization scheme;
step 4-1, variable definition:
Figure 539466DEST_PATH_IMAGE144
: airport
Figure 124031DEST_PATH_IMAGE145
The upper limit of the capacity increase amplitude of (1), unit%, the initial value is 100%;
Figure 229390DEST_PATH_IMAGE146
: airport
Figure 37946DEST_PATH_IMAGE145
The upper limit of the advance capacity lifting amplitude is 100 percent in unit percent;
Figure 658283DEST_PATH_IMAGE147
: airport
Figure 780960DEST_PATH_IMAGE145
The upper limit of the lifting amplitude of the off-field capacity is 100 percent in unit percent;
Figure 740826DEST_PATH_IMAGE148
: sector area
Figure 985862DEST_PATH_IMAGE149
The upper limit of the capacity increase amplitude of (1), unit%, the initial value is 100%;
Figure 296758DEST_PATH_IMAGE150
: flight
Figure 957546DEST_PATH_IMAGE151
The processing state of (1), comprising: 0 represents that the processing is not participated in, and 1 represents that the processing is performed at this time;
Figure 99815DEST_PATH_IMAGE152
: entering the sector at the k time slice according to the flight sequencing result
Figure 453436DEST_PATH_IMAGE149
The flight number of (2) is 0;
Figure 986048DEST_PATH_IMAGE153
: according to the flight sequencing result, at the airport
Figure 512845DEST_PATH_IMAGE145
The flight number of the takeoff in the kth time slice, namely the takeoff flow, is 0;
Figure 712882DEST_PATH_IMAGE154
: according to the flight sequencing result, at the airport
Figure 989403DEST_PATH_IMAGE145
The flight number of landing in the kth time slice, namely landing flow, is 0;
Figure 71628DEST_PATH_IMAGE155
: entering a sector at the kth time slice
Figure 74219DEST_PATH_IMAGE149
The initial value of the temporary variable of the flight number of (1) is 0;
Figure 925501DEST_PATH_IMAGE156
: at airports
Figure 886504DEST_PATH_IMAGE145
The temporary variable of the flight number of the takeoff in the kth time slice is set to be 0;
Figure 393708DEST_PATH_IMAGE157
: at airports
Figure 996728DEST_PATH_IMAGE145
The initial value of the temporary variable of the descending flight number in the kth time slice is 0;
Figure 171357DEST_PATH_IMAGE158
: temporary variables for reducing flight quantity, wherein the initial value is 0;
Figure 772103DEST_PATH_IMAGE159
: the airspace network optimization scheme comprises the airspace names, types and capacity growth values which need to be optimized;
Figure 828921DEST_PATH_IMAGE160
：
Figure 438894DEST_PATH_IMAGE159
the number of airspaces contained in the space domain;
Figure 202450DEST_PATH_IMAGE161
：
Figure 301993DEST_PATH_IMAGE159
the m-th airspace needing to be optimized, namely the airspace to be optimized;
Figure 518211DEST_PATH_IMAGE162
：
Figure 728612DEST_PATH_IMAGE163
the spatial domain code of (1);
Figure 612255DEST_PATH_IMAGE164
：
Figure 820382DEST_PATH_IMAGE163
the value 0 represents a sector, and the value 1 represents an airport;
Figure 586213DEST_PATH_IMAGE165
：
Figure 272409DEST_PATH_IMAGE163
the initial value of the capacity increase value of (2) is 0;
Figure 276137DEST_PATH_IMAGE166
：
Figure 717483DEST_PATH_IMAGE163
the entrance capacity growth value of (2) is only effective for airports, and the initial value is 0;
Figure 705031DEST_PATH_IMAGE167
：
Figure 460497DEST_PATH_IMAGE163
the departure capacity increase value of (2) is only effective for airports, and the initial value is 0;
Figure 793432DEST_PATH_IMAGE168
: the maximum value of the deviation of the total flow and the capacity of each time slice of the xth airspace object is 0, x is a subscript of the airspace object, and the airspace object type is an airport or a sector;
Figure 405679DEST_PATH_IMAGE169
: the maximum value of deviation between the takeoff flow and the departure capacity of each time slice of the xth airspace object is 0, x is a subscript of the airspace object, and the type of the airspace object is an airport or a sector;
Figure 880523DEST_PATH_IMAGE170
: the maximum value of deviation between the landing flow and the entrance capacity of each time slice of the x-th airspace object is 0, x is a subscript of the airspace object, and the airspace object type is an airport or a sector.
Step 4-2, setting parameters:
in order to improve the feasibility of the space domain optimization scheme, the maximum capacity increase amplitude of each space domain is limited.
Step 4-2-1, limiting the airport capacity increase amplitude:
for national airport queues
Figure 439680DEST_PATH_IMAGE171
Each airport in
Figure 886842DEST_PATH_IMAGE145
The following settings were all developed:
the lifting amplitude limit of the airport capacity: order to
Figure 404411DEST_PATH_IMAGE172
The user can modify the operation according to the self requirement;
the lift range limit of the airport departure capacity: order to
Figure 569813DEST_PATH_IMAGE173
The modification can be carried out according to the self requirement;
the lifting amplitude limit of the airport approach capacity: order to
Figure 667082DEST_PATH_IMAGE174
The device can be modified according to the self requirement;
step 4-2-2, limiting the increase range of the sector capacity:
for national sector queues
Figure 296647DEST_PATH_IMAGE175
Each sector in the queue
Figure 922800DEST_PATH_IMAGE149
The following settings were all developed:
order to
Figure 637815DEST_PATH_IMAGE176
And the user can modify the operation according to the self requirement.
4-3, predicting airspace flow based on the flight sequencing result:
predicting the flow of airports and sectors in the whole country according to the flight sequencing result in the step 1-3-2; because step 1-3-2 takes into account the national airport and sector capacity limits in the ranking, the traffic values for each airspace object calculated here will not exceed their capacity limits. The processing flow is shown in fig. 4:
step 4-3-1, emptying the flight processing state:
scheduling queues for nationwide flights
Figure 273196DEST_PATH_IMAGE177
Every flight in the flight
Figure 694950DEST_PATH_IMAGE151
Let it
Figure 819901DEST_PATH_IMAGE178
；
4-3-2, screening flights to be processed:
from
Figure 959895DEST_PATH_IMAGE177
The first flight in the queue begins, and the current flight is taken
Figure 398967DEST_PATH_IMAGE179
First flight of 0
Figure 737544DEST_PATH_IMAGE151
Let it
Figure 236659DEST_PATH_IMAGE180
Carrying out subsequent operation; if all flights have been processed, the calculation of step 4-3 is completed, skipping step 4-3-3 to step 4-3-6.
Step 4-3-3, judging the ordering adjustment state of the flight:
if the flight's order adjusts the status
Figure 395108DEST_PATH_IMAGE181
If the number is 3, the flight is recommended to be reduced, and the flow statistics is not needed to be participated in, and the step 4-3-2 is returned to be executed; otherwise, the step 4-3-4 is continuously executed.
Step 4-3-4, updating the flow of the takeoff airport of the flight:
according to flight
Figure 372291DEST_PATH_IMAGE151
Take-off airport
Figure 565375DEST_PATH_IMAGE182
And sequencing departure times
Figure 235391DEST_PATH_IMAGE183
Suppose flight is
Figure 84398DEST_PATH_IMAGE151
In that
Figure 859413DEST_PATH_IMAGE171
The u-th airport in the queue
Figure 907003DEST_PATH_IMAGE145
Take off at the kth time slice, then order
Figure 75816DEST_PATH_IMAGE184
。
And 4-3-5, updating the flow of the landing airport of the flight:
according to flight
Figure 412120DEST_PATH_IMAGE151
Landing airport
Figure 793422DEST_PATH_IMAGE185
And sequencing the landing time
Figure 164361DEST_PATH_IMAGE186
Suppose flight is
Figure 910600DEST_PATH_IMAGE151
In that
Figure 530937DEST_PATH_IMAGE171
The u-th airport in the queue
Figure 653614DEST_PATH_IMAGE145
When the kth time slice of (1) falls, then order
Figure 879059DEST_PATH_IMAGE187
。
And 4-3-6, updating the flow of the flight path sector:
according to flight
Figure 858516DEST_PATH_IMAGE151
Past fan queue
Figure 169412DEST_PATH_IMAGE188
And each sector therein
Figure 892517DEST_PATH_IMAGE189
Sequencing of Fan-in time
Figure 706890DEST_PATH_IMAGE190
Suppose flight is
Figure 122827DEST_PATH_IMAGE151
Enter at the k-th time slice
Figure 921019DEST_PATH_IMAGE175
The v sector in the queue
Figure 651078DEST_PATH_IMAGE149
Then give an order
Figure 116694DEST_PATH_IMAGE191
(ii) a And returning to execute the step 4-3-2.
4-4, generating an airspace network optimization scheme:
according to the reduction flight amount obtained in the step 3-3 and ensured by airspace capacity expansion
Figure 641216DEST_PATH_IMAGE192
Adjusting queues from flights
Figure 926704DEST_PATH_IMAGE193
And screening corresponding number of recommended reduction flights, positioning a key problem airspace according to the flights, and providing a capacity optimization proposal.
And 4-4-1, screening the flights suggested to be reduced according to capacity expansion limit:
taking into account the limitations of the capacity growth range of airports and sectors throughout the country
Figure 991612DEST_PATH_IMAGE193
Screening out in queue
Figure 317594DEST_PATH_IMAGE194
The rack needs to reduce flights by the suggestions of airspace capacity expansion guarantee. The specific processing flow is shown in fig. 5:
step 4-4-1-1, emptying the flight processing state: adjusting queues for flights
Figure 105029DEST_PATH_IMAGE193
Every flight in the middle
Figure 346654DEST_PATH_IMAGE151
To make it process the state
Figure 215253DEST_PATH_IMAGE178
(ii) a Order to
Figure 124303DEST_PATH_IMAGE195
；
Step 4-4-1-2, judging whether the screening is finished:
if it is satisfied with
Figure 787366DEST_PATH_IMAGE196
Or is or
Figure 781866DEST_PATH_IMAGE193
All flights in the queue have been processed (i.e., the flight queue is processed)
Figure 391839DEST_PATH_IMAGE179
Equal to 1), skipping step 4-4-1-3 to step 4-4-1-11; otherwise, the step 4-4-1-3 is continued.
4-4-1-3, screening flights to be processed;
from
Figure 217713DEST_PATH_IMAGE193
The first flight in the queue begins, and the current flight is taken
Figure 989360DEST_PATH_IMAGE179
First flight of 0
Figure 551052DEST_PATH_IMAGE151
Let it
Figure 699136DEST_PATH_IMAGE180
；
Step 4-4-1-4, judging the ordering adjustment state of the flight:
if the flight's order adjusts the status
Figure 582779DEST_PATH_IMAGE181
If not, the flight is not the flight recommended to be subtracted, the step returns to the step 4-4-1-2, otherwise, the step 4-4-1-5 is continuously executed.
Step 4-4-1-5, updating the flow of the take-off airport of the flight:
according to flight
Figure 853223DEST_PATH_IMAGE151
Take-off airport and planned take-off time
Figure 353474DEST_PATH_IMAGE197
Suppose a flight
Figure 305250DEST_PATH_IMAGE151
In that
Figure 308978DEST_PATH_IMAGE171
The u-th airport in the queue
Figure 422428DEST_PATH_IMAGE198
Take off at the kth time slice, then order
Figure 675554DEST_PATH_IMAGE199
And is and
Figure 165442DEST_PATH_IMAGE200
。
step 4-4-1-6, judging whether the flow of the takeoff airport of the flight exceeds the capacity increase amplitude:
if it is satisfied with
Figure 820414DEST_PATH_IMAGE201
Returning to execute the step 4-4-1-2;
if so:
Figure 167081DEST_PATH_IMAGE202
returning to execute the step 4-4-1-2;
otherwise, continuing to execute the step 4-4-1-7;
and 4-4-1-7, updating the flow of the landing airport of the flight:
according to flight
Figure 110767DEST_PATH_IMAGE151
Landing airport and planned landing time
Figure 138766DEST_PATH_IMAGE203
Suppose flight is
Figure 648244DEST_PATH_IMAGE151
In that
Figure 634655DEST_PATH_IMAGE171
The u-th airport in the queue
Figure 800057DEST_PATH_IMAGE198
When the kth time slice of (1) falls, then order
Figure 694064DEST_PATH_IMAGE204
And is and
Figure 995732DEST_PATH_IMAGE205
。
step 4-4-1-8, judging whether the flow of the landing airport of the flight exceeds the capacity increase amplitude:
if it is satisfied with
Figure 949782DEST_PATH_IMAGE206
Returning to execute the step 4-4-1-2;
if so:
Figure 602480DEST_PATH_IMAGE207
returning to execute the step 4-4-1-2; otherwise, continuing to execute the step 4-4-1-9;
and 4-4-1-9, updating the flow of the flight path sector:
according to flight
Figure 34598DEST_PATH_IMAGE151
Past fan queue
Figure 721931DEST_PATH_IMAGE188
And each sector therein
Figure 790425DEST_PATH_IMAGE208
Scheduled fan in time
Figure 727157DEST_PATH_IMAGE209
Suppose flight is
Figure 431808DEST_PATH_IMAGE151
Enter at the k-th time slice
Figure 708068DEST_PATH_IMAGE175
The v sector in the queue
Figure 3920DEST_PATH_IMAGE149
Then give an order
Figure 365631DEST_PATH_IMAGE210
And is and
Figure 139552DEST_PATH_IMAGE211
；
step 4-4-1-10, judging whether the traffic of the approach sector of the flight exceeds the capacity increase amplitude:
for flights
Figure 535899DEST_PATH_IMAGE151
Any sector of the way
Figure 2652DEST_PATH_IMAGE149
If there is a flight
Figure 117239DEST_PATH_IMAGE151
Enter sector at kth time slice
Figure 632534DEST_PATH_IMAGE149
When it is satisfied with
Figure 680124DEST_PATH_IMAGE212
Returning to the step 4-4-1-2;
otherwise, continuing to execute the step 4-4-1-11;
and 4-4-1-11, updating the selected reduction flight quantity: order to
Figure 521041DEST_PATH_IMAGE213
；
Updating flights
Figure 919661DEST_PATH_IMAGE151
Take-off airport flow of
Figure 238647DEST_PATH_IMAGE214
；
Updating flights
Figure 406323DEST_PATH_IMAGE151
Landing airport traffic of
Figure 418142DEST_PATH_IMAGE215
；
Updating flights
Figure 241741DEST_PATH_IMAGE151
Traffic per sector of the path, order
Figure 895576DEST_PATH_IMAGE216
；
Returning to the step 4-4-1-2;
4-4-2, generating an airspace network optimization scheme:
and generating an airspace network optimization scheme according to the capacity flow matching condition of each airport and sector in China. The processing flow is shown in fig. 6:
step 4-4-2-1, emptying protocol:
optimization scheme for empty airspace network
Figure 121021DEST_PATH_IMAGE217
And order
Figure 100479DEST_PATH_IMAGE218
；
Step 4-4-2-2, counting airports needing optimization:
for national airport queues
Figure 411374DEST_PATH_IMAGE219
Each of the airports in
Figure 863041DEST_PATH_IMAGE220
And circularly performing the following treatment:
1) calculating the deviation between the flow and the capacity of each time slice:
computer airport
Figure 333206DEST_PATH_IMAGE220
Deviation of takeoff flow from field leaving capacity at each time slice k
Figure 217985DEST_PATH_IMAGE221
Deviation of landing flow from approach volume
Figure 812914DEST_PATH_IMAGE222
And total flow and capacity deviation
Figure 11815DEST_PATH_IMAGE223
(ii) a On the basis, the airport is counted
Figure 8589DEST_PATH_IMAGE224
Maximum deviation value of takeoff flow and off-field capacity at each time slice
Figure 798691DEST_PATH_IMAGE225
Maximum deviation of landing flow from approach volume
Figure 553020DEST_PATH_IMAGE226
And maximum deviation of total flow from capacity
Figure 883507DEST_PATH_IMAGE227
；
If it is not
Figure 938051DEST_PATH_IMAGE228
Then give an order
Figure 367895DEST_PATH_IMAGE229
；
If it is used
Figure 671838DEST_PATH_IMAGE230
Then give an order
Figure 540437DEST_PATH_IMAGE231
；
If it is not
Figure 449487DEST_PATH_IMAGE232
Then give an order
Figure 315812DEST_PATH_IMAGE233
；
2) Screening dilatation airports and calculating dilatation degree:
if the airport
Figure 107050DEST_PATH_IMAGE220
Satisfies the following conditions:
Figure 717023DEST_PATH_IMAGE234
define the airport as the airspace to be optimized
Figure 542897DEST_PATH_IMAGE235
Let us order
Figure 314544DEST_PATH_IMAGE236
，
Figure 61920DEST_PATH_IMAGE237
，
Figure 278181DEST_PATH_IMAGE238
，
Figure 896244DEST_PATH_IMAGE239
，
Figure 901109DEST_PATH_IMAGE240
；
Will be provided with
Figure 604623DEST_PATH_IMAGE241
Optimization scheme for adding to airspace network
Figure 618715DEST_PATH_IMAGE242
In, and
Figure 356864DEST_PATH_IMAGE243
。
step 4-4-2-3, counting sectors needing optimization:
for national sector queues
Figure 735893DEST_PATH_IMAGE244
Each sector in
Figure 989019DEST_PATH_IMAGE245
And circularly performing the following treatment:
1) calculating the deviation between the flow and the capacity of each time slice:
computing sector
Figure 478907DEST_PATH_IMAGE245
Deviation of flow from capacity at each time slice k
Figure 337141DEST_PATH_IMAGE246
On the basis of the sector statistics
Figure 949388DEST_PATH_IMAGE245
Maximum deviation of flow rate and capacity in each time slice
Figure 158653DEST_PATH_IMAGE247
；
If it is not
Figure 717810DEST_PATH_IMAGE248
Then give an order
Figure 164972DEST_PATH_IMAGE249
；
2) Screening expansion sectors and calculating the expansion degree:
if sector
Figure 948120DEST_PATH_IMAGE245
Satisfy the requirement of
Figure 113522DEST_PATH_IMAGE250
Then define the sector as the space domain to be optimized
Figure 210791DEST_PATH_IMAGE235
Let us order
Figure 574776DEST_PATH_IMAGE251
，
Figure 466509DEST_PATH_IMAGE252
，
Figure 119207DEST_PATH_IMAGE253
；
Will be provided with
Figure 816905DEST_PATH_IMAGE241
Optimization scheme for adding to airspace network
Figure 238659DEST_PATH_IMAGE242
In, and
Figure 115609DEST_PATH_IMAGE243
。
step 5, generating a flight time optimization scheme according to flights needing to be guaranteed
The function of the step is as follows: the flight with the key problem can be positioned according to the flight range needing to be guaranteed, and a flight time optimization scheme is generated. The processing flow is shown in fig. 7:
the method comprises the following steps:
step 5-1, variable definition;
step 5-2, generating a flight time optimization scheme;
step 5-1, variable definition:
Figure 255603DEST_PATH_IMAGE254
: flight time optimization scheme comprising optimization goal for achieving flight normality
Figure 491412DEST_PATH_IMAGE255
Adjusting queues from flights
Figure 33252DEST_PATH_IMAGE256
Screening out flights needing to be subjected to time adjustment;
Figure 266787DEST_PATH_IMAGE257
: flight time optimization scheme
Figure 690815DEST_PATH_IMAGE258
The initial value of the total number of flights in the flight list is 0;
Figure 933578DEST_PATH_IMAGE259
：
Figure 595503DEST_PATH_IMAGE258
the nth flight needing to be optimized, namely the flight to be optimized;
Figure 796677DEST_PATH_IMAGE260
：
Figure 380106DEST_PATH_IMAGE259
the flight number of;
Figure 223297DEST_PATH_IMAGE261
：
Figure 474149DEST_PATH_IMAGE259
the flight adjustment state type of (1) is that the value 0 represents the time adjustment and the value 1 represents the proposed reduction;
Figure 111804DEST_PATH_IMAGE262
：
Figure 182528DEST_PATH_IMAGE259
a recommended takeoff time;
Figure 767093DEST_PATH_IMAGE263
：
Figure 934770DEST_PATH_IMAGE259
a suggested landing time;
step 5-2, generating a flight time optimization scheme:
adjusting queues from flights
Figure 477746DEST_PATH_IMAGE264
Medium screening
Figure 301346DEST_PATH_IMAGE265
Setting flights needing to be subjected to time adjustment to form a flight time optimization scheme;
step 5-2-1, emptying protocol:
flight emptying time optimization scheme
Figure 424023DEST_PATH_IMAGE258
And order
Figure 711785DEST_PATH_IMAGE266
；
Step 5-2-2, emptying flight processing state:
adjusting queues for flights
Figure 425663DEST_PATH_IMAGE264
Every flight in the flight
Figure 470979DEST_PATH_IMAGE267
To make it process the state
Figure 199944DEST_PATH_IMAGE268
；
Step 5-2-3, judging whether the screening is finished:
if it is satisfied with
Figure 279895DEST_PATH_IMAGE269
Or is or
Figure 430254DEST_PATH_IMAGE264
All flights in the queue have been processed (i.e., the queue is ready to be used for flight service)
Figure 962866DEST_PATH_IMAGE270
Equal to 1), the process of step 5-2 is completed; otherwise, continuing to execute the step 5-2-4.
Step 5-2-4, screening flights to be processed:
from
Figure 489663DEST_PATH_IMAGE264
The first flight in the queue begins, and the current flight is taken
Figure 424121DEST_PATH_IMAGE270
First flight of 0
Figure 745381DEST_PATH_IMAGE267
Let it
Figure 562027DEST_PATH_IMAGE271
Continuing to execute the step 5-2-5;
step 5-2-5, judging the ordering adjustment state of the flight:
if the flight's order adjusts the status
Figure 830197DEST_PATH_IMAGE272
If the number is 3, the flight belongs to the flight recommended to be subtracted, the step 5-2-3 is returned, otherwise, the step 5-2-6 is continuously executed.
And 5-2-6, updating the flight time optimization scheme:
will the flight
Figure 884741DEST_PATH_IMAGE267
Defined as flights to be optimized
Figure 376902DEST_PATH_IMAGE259
And order
Figure 680844DEST_PATH_IMAGE273
，
Figure 487126DEST_PATH_IMAGE274
，
Figure 192914DEST_PATH_IMAGE275
，
Figure 324818DEST_PATH_IMAGE276
；
Will be provided with
Figure 850478DEST_PATH_IMAGE259
Adding to flight time optimization scheme
Figure 460450DEST_PATH_IMAGE258
In, and
Figure 224007DEST_PATH_IMAGE277
；
and returning to execute the step 5-2-3.
In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, where the computer storage medium is capable of storing a computer program, and the computer program, when executed by the data processing unit, may execute the inventive content of the capacity flow collaborative optimization method based on the flight normality objective provided by the present invention and some or all of the steps in each embodiment. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
It is clear to those skilled in the art that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general-purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be substantially or partially embodied in the form of a computer program, that is, a software product, which may be stored in a storage medium and includes several instructions for enabling a device (which may be a personal computer, a server, a single chip microcomputer MUU, or a network device) including a data processing unit to execute the method in the embodiments or some parts of the embodiments of the present invention.

Claims (10)

1. A capacity flow cooperative optimization method based on flight normality targets is characterized by comprising the following steps:
step 1, preparing basic data; acquiring basic data required by the method, and performing primary processing on the basic data;
step 2, analyzing flight operation efficiency according to airspace service capacity; screening flights which cannot be normally executed according to an original plan according to capacity limits of nationwide airports and sectors, and analyzing flight operation efficiency;
step 3, calculating flight ranges needing to be guaranteed based on flight normality targets; calculating the flight range which needs to be guaranteed by adjusting the time or expanding the airspace service capacity according to the set flight normality optimization target;
step 4, generating an airspace network optimization scheme according to flights needing to be guaranteed; positioning a key problem airspace according to the flight range needing to be guaranteed, and providing a capacity optimization suggestion for the key problem airspace;
step 5, generating a flight time optimization scheme according to flights needing to be guaranteed; and positioning the flight with the key problem according to the flight range needing to be ensured, and generating a flight time optimization scheme.
2. The flight-normality-objective-based capacity flow collaborative optimization method according to claim 1, wherein the basic data in step 1 comprises:
Figure 788006DEST_PATH_IMAGE001
: date of analysis of the method;
Figure 642830DEST_PATH_IMAGE002
: national flight schedule queue, including and analyzing dates
Figure 625829DEST_PATH_IMAGE001
All related nationwide flight plans;
Figure 224301DEST_PATH_IMAGE003
：
Figure 970497DEST_PATH_IMAGE002
the total number of the flight plans in the queue;
Figure 261801DEST_PATH_IMAGE004
: national flight scheduling queue
Figure 732096DEST_PATH_IMAGE002
The ith flight in;
Figure 868680DEST_PATH_IMAGE005
: flight
Figure 740821DEST_PATH_IMAGE004
The flight number of;
Figure 937447DEST_PATH_IMAGE006
: flight
Figure 895038DEST_PATH_IMAGE004
The value is a non-negative integer, and the initial value is 0;
Figure 835313DEST_PATH_IMAGE007
: flight
Figure 561960DEST_PATH_IMAGE004
The takeoff airport of (1);
Figure 460646DEST_PATH_IMAGE008
: flight
Figure 905534DEST_PATH_IMAGE004
Landing airports;
Figure 383920DEST_PATH_IMAGE009
: flight
Figure 230653DEST_PATH_IMAGE004
The planned takeoff time of (c);
Figure 769082DEST_PATH_IMAGE010
: flight
Figure 701266DEST_PATH_IMAGE004
The planned landing time of (c);
Figure 717763DEST_PATH_IMAGE011
: flight
Figure 684582DEST_PATH_IMAGE004
With a sequenced takeoff time of the initial value
Figure 659491DEST_PATH_IMAGE009
；
Figure 813392DEST_PATH_IMAGE012
: flight
Figure 899160DEST_PATH_IMAGE004
With an initial value of
Figure 454906DEST_PATH_IMAGE010
；
Figure 600717DEST_PATH_IMAGE013
: flight
Figure 970475DEST_PATH_IMAGE004
The sequencing takeoff delay of (1) is in seconds;
Figure 594354DEST_PATH_IMAGE014
: flight
Figure 270186DEST_PATH_IMAGE004
When the value of 0 is 0, the sequencing adjustment state is not adjusted, when the value of 1 is 1, the time is advanced, when the value of 2 is 2, the delay is shown, when the value of 3 is 3, the reduction is shown, and the initial value is 0;
Figure 852477DEST_PATH_IMAGE015
: flight
Figure 980970DEST_PATH_IMAGE004
The fan-passing queue comprises flights
Figure 408541DEST_PATH_IMAGE004
All via sector information of (1);
Figure 938879DEST_PATH_IMAGE016
: flight
Figure 426492DEST_PATH_IMAGE004
The jth sector in the past sector queue;
Figure 776702DEST_PATH_IMAGE017
: flight
Figure 7963DEST_PATH_IMAGE004
The jth sector in the past sector queue
Figure 127229DEST_PATH_IMAGE016
The code of (1);
Figure 785744DEST_PATH_IMAGE018
: flight
Figure 888829DEST_PATH_IMAGE004
The jth sector in the past sector queue
Figure 392622DEST_PATH_IMAGE016
The planned fan in time of (c);
Figure 897553DEST_PATH_IMAGE019
: flight
Figure 726969DEST_PATH_IMAGE004
The jth sector in the past sector queue
Figure 317350DEST_PATH_IMAGE016
The sort fan-in time;
Figure 624835DEST_PATH_IMAGE020
: a national airport queue containing all airport information throughout the country;
Figure 718693DEST_PATH_IMAGE021
: national airport fleet
Figure 984589DEST_PATH_IMAGE020
The number of airports contained in the database;
Figure 796687DEST_PATH_IMAGE022
: national airport fleet
Figure 925441DEST_PATH_IMAGE020
The u-th airport of (1);
Figure 139384DEST_PATH_IMAGE023
: airport
Figure 576182DEST_PATH_IMAGE022
The four-word code of (1);
Figure 875576DEST_PATH_IMAGE024
: a national sector queue; including all sector information nationwide;
Figure 790443DEST_PATH_IMAGE025
: national sector queue
Figure 858893DEST_PATH_IMAGE024
The number of sectors included in (1);
Figure 201012DEST_PATH_IMAGE026
: national sector queue
Figure 987703DEST_PATH_IMAGE024
The v-th sector in (a);
Figure 440681DEST_PATH_IMAGE027
: sector area
Figure 629217DEST_PATH_IMAGE026
The code of (1);
Figure 407817DEST_PATH_IMAGE028
: the calculation time range of the method is that, wherein,
Figure 416224DEST_PATH_IMAGE029
for analyzing the date
Figure 672893DEST_PATH_IMAGE001
00:00:00, and
Figure 981515DEST_PATH_IMAGE030
for analyzing the date
Figure 399858DEST_PATH_IMAGE001
23:59: 59;
Figure 895561DEST_PATH_IMAGE031
: in the method, the default value of the time slice size is 3600 seconds, namely 1 hour;
Figure 221500DEST_PATH_IMAGE032
: the method calculates the number of time slices in a time range, and the initial value is 0;
Figure 119049DEST_PATH_IMAGE033
: calculating a time horizon
Figure 973873DEST_PATH_IMAGE028
The k time slice in which
Figure 222452DEST_PATH_IMAGE034
Is the start time of the time slice,
Figure 820923DEST_PATH_IMAGE035
is the cut-off time of the time slice;
Figure 572978DEST_PATH_IMAGE036
: airport
Figure 858423DEST_PATH_IMAGE022
Capacity value at kth time slice;
Figure 328719DEST_PATH_IMAGE037
: sector area
Figure 996460DEST_PATH_IMAGE026
Capacity value at kth time slice;
Figure 603022DEST_PATH_IMAGE038
: airport
Figure 65228DEST_PATH_IMAGE039
The entry capacity, i.e., the entry rate, at the kth time slice;
Figure 22819DEST_PATH_IMAGE040
: airport
Figure 963094DEST_PATH_IMAGE039
The field-off capacity, i.e. field-off rate, at the kth time slice;
Figure 424162DEST_PATH_IMAGE041
: at airports
Figure 57269DEST_PATH_IMAGE039
Flight number of takeoff within the kth time slice;
Figure 236577DEST_PATH_IMAGE042
: at airports
Figure 246121DEST_PATH_IMAGE039
Flight number landing within the kth time slice of (1).
3. The capacity flow collaborative optimization method based on the flight normality objective as claimed in claim 2, wherein in the step 2, flight operation performance is analyzed according to the basic data obtained in the step 1 to obtain the following indexes:
Figure 827275DEST_PATH_IMAGE043
: flight adjustment queue, comprising
Figure 365704DEST_PATH_IMAGE002
All flights needing to be adjusted or subtracted at any time;
Figure 297888DEST_PATH_IMAGE044
：
Figure 579965DEST_PATH_IMAGE043
the total number of the flight plans in the queue is 0 as an initial value;
Figure 281205DEST_PATH_IMAGE045
: the default maximum flight delay is set in the method9999 x 60 seconds;
Figure 256114DEST_PATH_IMAGE046
: the number of flights in the whole country does not need to be adjusted, and the initial value is 0;
Figure 941173DEST_PATH_IMAGE047
: the number of flights needing delay in nationwide flights is 0 in an initial value;
Figure 761362DEST_PATH_IMAGE048
: the number of flights in the whole country needs to be reduced, and the initial value is 0;
Figure 317108DEST_PATH_IMAGE049
: the national flights need to be erected at an advanced time, and the initial value is 0;
Figure 462918DEST_PATH_IMAGE050
: the number of flights in the whole country needing time adjustment is 0;
Figure 98256DEST_PATH_IMAGE051
: the normality estimation of nationwide flights is carried out, and the initial value is 0.
4. The flight-normality-objective-based capacity flow collaborative optimization method according to claim 3, wherein the step 3 comprises:
step 3-1, variable definition;
step 3-2, setting a flight normality optimization target;
3-3, calculating the flight range needing to be guaranteed;
in step 3-1, variable definition includes:
Figure 987715DEST_PATH_IMAGE052
: setting a flight normality optimization target;
Figure 397967DEST_PATH_IMAGE053
: temporary variables for flight normality;
Figure 714679DEST_PATH_IMAGE054
: aiming at the flight normality optimization target, the total number of flights is guaranteed through time adjustment and airspace expansion, and the initial value is 0;
Figure 843172DEST_PATH_IMAGE055
: flight number needing to be adjusted is screened out aiming at the flight normality optimization target, and the initial value is 0;
Figure 270742DEST_PATH_IMAGE056
: reducing the flight number, and aiming at the flight number recommended to be reduced and screened by the flight normality optimization target, ensuring the flight number by expanding the space domain, wherein the initial value is 0;
step 3-2, setting flight normality optimization targets:
flight normality in actual operation is improved by using two means of time optimization and airspace service capacity expansion; optimizing objectives for set flight normality
Figure 801081DEST_PATH_IMAGE052
Limit to meet
Figure 554273DEST_PATH_IMAGE057
；
Step 3-3, calculating the flight range needing to be guaranteed:
this step optimizes the objective according to flight normality
Figure 904483DEST_PATH_IMAGE052
The calculation is required from
Figure 870165DEST_PATH_IMAGE043
Total number of flights screened in queue
Figure 255010DEST_PATH_IMAGE054
(ii) a From
Figure 913525DEST_PATH_IMAGE043
Preferentially selecting flights which are suggested to be eliminated from the queue; the specific method comprises the following steps:
step 3-3-1, reducing flight quantity calculation:
first only from
Figure 751031DEST_PATH_IMAGE043
And (3) screening flights which are suggested to be eliminated in the queue, and judging whether the normality optimization goal is achieved:
order to
Figure 520403DEST_PATH_IMAGE058
Then, then
Figure 759755DEST_PATH_IMAGE059
Figure 589171DEST_PATH_IMAGE060
（11）
If it is satisfied with
Figure 179552DEST_PATH_IMAGE061
If the flight is normal, the flight is judged to be unable to achieve the normal target
Figure 487036DEST_PATH_IMAGE062
Continuing to execute the step 3-3-2; otherwise, it orders
Figure 315315DEST_PATH_IMAGE063
Skipping to the step 3-3-3;
step 3-3-2, calculating the flight amount of the time adjustment:
order to
Figure 581211DEST_PATH_IMAGE064
Then, then
Figure 393310DEST_PATH_IMAGE065
Figure 498626DEST_PATH_IMAGE066
（12）；
Step 3-3-3, calculating total adjusting flight quantity:
Figure 978149DEST_PATH_IMAGE067
（13）。
5. the flight-normality-objective-based capacity flow collaborative optimization method according to claim 4, wherein the step 4 comprises:
step 4-1, variable definition;
step 4-2, setting parameters;
4-3, predicting airspace flow based on the flight sequencing result;
and 4-4, generating a space domain network optimization scheme.
6. The flight normality objective-based capacity flow collaborative optimization method according to claim 5, wherein in the step 4-1, the variable definition comprises:
Figure 149367DEST_PATH_IMAGE068
: airport
Figure 448761DEST_PATH_IMAGE022
The upper limit of the capacity increase amplitude of (1), unit%, the initial value is 100%;
Figure 98048DEST_PATH_IMAGE069
: airport
Figure 432078DEST_PATH_IMAGE022
The upper limit of the promotion amplitude of the approach volume is 100 percent in unit percent;
Figure 774197DEST_PATH_IMAGE070
: airport
Figure 560888DEST_PATH_IMAGE022
The upper limit of the lifting amplitude of the off-field capacity is 100 percent in unit percent;
Figure 13866DEST_PATH_IMAGE071
: sector area
Figure 202402DEST_PATH_IMAGE026
The upper limit of the capacity increase amplitude of (1), unit%, the initial value is 100%;
Figure 449844DEST_PATH_IMAGE072
: flight
Figure 989409DEST_PATH_IMAGE004
The processing state of (1), comprising: the value of 0 indicates that the processing is not participated in the processing, and the value of 1 indicates that the processing is performed;
Figure 246078DEST_PATH_IMAGE073
: entering the sector at the k time slice according to the flight sequencing result
Figure 289121DEST_PATH_IMAGE026
The initial value of the flight number of the flight is 0;
Figure 973043DEST_PATH_IMAGE074
: according to the flight sequencing result, at the airport
Figure 734325DEST_PATH_IMAGE022
The flight number of the takeoff in the kth time slice, namely the takeoff flow, is 0;
Figure 529106DEST_PATH_IMAGE075
: according to the flight sequencing result, at the airport
Figure 692234DEST_PATH_IMAGE022
The flight number of landing in the kth time slice, namely landing flow, is 0;
Figure 547058DEST_PATH_IMAGE076
: enter sector at kth time slice
Figure 530057DEST_PATH_IMAGE026
The initial value of the temporary variable of the flight number of (1) is 0;
Figure 128529DEST_PATH_IMAGE077
: at airports
Figure 880584DEST_PATH_IMAGE022
The temporary variable of the flying flight number taking off in the kth time slice has an initial value of 0;
Figure 455046DEST_PATH_IMAGE078
: at airports
Figure 925341DEST_PATH_IMAGE022
The initial value of the temporary variable of the descending flight number in the kth time slice is 0;
Figure 61924DEST_PATH_IMAGE079
: temporary variables for reducing flight quantity, wherein the initial value is 0;
Figure 934066DEST_PATH_IMAGE080
: the airspace network optimization scheme comprises the airspace names, types and capacity growth values which need to be optimized;
Figure 396271DEST_PATH_IMAGE081
：
Figure 88283DEST_PATH_IMAGE080
the number of airspaces contained in the space domain;
Figure 294137DEST_PATH_IMAGE082
：
Figure 286364DEST_PATH_IMAGE080
the m-th airspace needing to be optimized, namely the airspace to be optimized;
Figure 653891DEST_PATH_IMAGE083
：
Figure 833200DEST_PATH_IMAGE082
the spatial domain code of (1);
Figure 577165DEST_PATH_IMAGE084
：
Figure 158319DEST_PATH_IMAGE082
the value 0 represents a sector, and the value 1 represents an airport;
Figure 962327DEST_PATH_IMAGE085
：
Figure 628931DEST_PATH_IMAGE082
the initial value of the capacity increase value of (2) is 0;
Figure 911008DEST_PATH_IMAGE086
：
Figure 612248DEST_PATH_IMAGE082
the entrance capacity growth value of (2) is only effective for airports, and the initial value is 0;
Figure 321578DEST_PATH_IMAGE087
：
Figure 741058DEST_PATH_IMAGE082
the departure capacity increase value of (2) is only effective for airports, and the initial value is 0;
Figure 826826DEST_PATH_IMAGE088
: the maximum value of the deviation of the total flow and the capacity of each time slice of the x-th space domain object is 0, x is the subscript of the space domain object, and the type of the space domain object isAn airport or sector;
Figure 382572DEST_PATH_IMAGE089
: the maximum value of deviation between the takeoff flow and the departure capacity of each time slice of the xth airspace object is 0, x is a subscript of the airspace object, and the type of the airspace object is an airport or a sector;
Figure 262803DEST_PATH_IMAGE090
: the maximum value of deviation between the landing flow and the entrance capacity of each time slice of the x-th airspace object is 0, x is a subscript of the airspace object, and the airspace object type is an airport or a sector.
7. The flight-normality-objective-based capacity flow collaborative optimization method according to claim 6, wherein in the step 4-2, the parameter setting comprises:
limiting the maximum increase amplitude of the capacity of each airspace;
step 4-2-1, limiting the airport capacity increase amplitude:
for national airport queues
Figure 898141DEST_PATH_IMAGE020
Each airport in
Figure 522020DEST_PATH_IMAGE022
The following settings were all developed:
the lifting amplitude limit of the airport capacity: order to
Figure 932273DEST_PATH_IMAGE091
；
The lift range limit of the airport departure capacity: order to
Figure 248985DEST_PATH_IMAGE092
；
Airport approach capacityThe lifting amplitude limit of (2): order to
Figure 377478DEST_PATH_IMAGE093
；
Step 4-2-2, limiting the increase range of the sector capacity:
for national sector queues
Figure 805048DEST_PATH_IMAGE024
Each sector in the queue
Figure 335386DEST_PATH_IMAGE026
The following settings were all made:
order to
Figure 823000DEST_PATH_IMAGE094
。
8. The flight normality objective-based capacity flow collaborative optimization method according to claim 7, wherein in step 4-3, the prediction of the airspace flow based on the flight sequencing result comprises: predicting the flow of airports and sectors in the country according to the flight sequencing result obtained in the step 1;
step 4-3-1, emptying the flight processing state:
scheduling queues for nationwide flights
Figure 438789DEST_PATH_IMAGE002
Every flight in the flight
Figure 670050DEST_PATH_IMAGE004
Let us order
Figure 54895DEST_PATH_IMAGE095
；
4-3-2, screening flights to be processed:
from
Figure 713409DEST_PATH_IMAGE002
The first flight in the queue begins, and the current flight is taken
Figure 816495DEST_PATH_IMAGE072
First flight of 0
Figure 585867DEST_PATH_IMAGE004
Let it
Figure 559640DEST_PATH_IMAGE096
Carrying out subsequent operation; if all flights are processed, the calculation of the step 4-3 is completed, and the step 4-3-3 to the step 4-3-6 are skipped;
step 4-3-3, judging the ordering adjustment state of the flight:
if flight is scheduled
Figure 654635DEST_PATH_IMAGE004
Rank adjusted state of
Figure 979437DEST_PATH_IMAGE014
If the number is 3, the flight is recommended to be reduced, and the flow statistics is not needed to be participated in, and the step 4-3-2 is returned to be executed; otherwise, continuing to execute the step 4-3-4;
step 4-3-4, updating the flow of the takeoff airport of the flight:
according to flight
Figure 552500DEST_PATH_IMAGE004
Take-off airport
Figure 911938DEST_PATH_IMAGE007
And sequencing departure times
Figure 912255DEST_PATH_IMAGE011
Suppose flight is
Figure 724353DEST_PATH_IMAGE004
In that
Figure 829669DEST_PATH_IMAGE020
The u-th airport in the queue
Figure 778033DEST_PATH_IMAGE022
Take off at the kth time slice, then order
Figure 214831DEST_PATH_IMAGE097
；
And 4-3-5, updating the flow of the landing airport of the flight:
according to flight
Figure 514225DEST_PATH_IMAGE004
Landing airport
Figure 163512DEST_PATH_IMAGE008
And sequencing the landing time
Figure 497542DEST_PATH_IMAGE012
Suppose flight is
Figure 105241DEST_PATH_IMAGE004
In that
Figure 626352DEST_PATH_IMAGE020
The u-th airport in the queue
Figure 79330DEST_PATH_IMAGE022
When the kth time slice falls, then order
Figure 267866DEST_PATH_IMAGE098
；
And 4-3-6, updating the flow of the flight path sector:
according to flight
Figure 515308DEST_PATH_IMAGE004
Past fan queue
Figure 789294DEST_PATH_IMAGE015
And each sector therein
Figure 45963DEST_PATH_IMAGE016
Sequencing of Fan-in time
Figure 823426DEST_PATH_IMAGE019
Suppose flight is
Figure 772928DEST_PATH_IMAGE004
Enter at the k-th time slice
Figure 534210DEST_PATH_IMAGE024
The v sector in the queue
Figure 328991DEST_PATH_IMAGE026
Then give an order
Figure 226540DEST_PATH_IMAGE099
；
And returning to execute the step 4-3-2.
9. The flight normality objective-based capacity flow collaborative optimization method according to claim 8, wherein in step 4-4, a spatial domain network optimization scheme is generated, and the method comprises the following steps: according to the reduction flight amount obtained in the step 3-3 and ensured by airspace capacity expansion
Figure 815784DEST_PATH_IMAGE056
Adjusting queues from flights
Figure 798784DEST_PATH_IMAGE043
Screening a corresponding number of recommended reduction flights, positioning a key problem airspace according to the flights, and providing a capacity optimization proposal;
and 4-4-1, screening the flights suggested to be reduced according to capacity expansion limit:
general considerations ofCapacity growth limits of airports and sectors throughout the country, from
Figure 662834DEST_PATH_IMAGE043
Screening out in queue
Figure 674610DEST_PATH_IMAGE056
The frame needs to reduce flights through the suggestion of airspace capacity expansion guarantee;
step 4-4-1-1, emptying the flight processing state:
adjusting queues for flights
Figure 700334DEST_PATH_IMAGE043
Every flight in the middle
Figure 170630DEST_PATH_IMAGE004
To make it process the state
Figure 572793DEST_PATH_IMAGE095
；
Order to
Figure 444934DEST_PATH_IMAGE100
；
Step 4-4-1-2, judging whether the screening is finished:
if it is satisfied with
Figure 641560DEST_PATH_IMAGE101
Or is or
Figure 864731DEST_PATH_IMAGE043
All flights in the queue are processed
Figure 805005DEST_PATH_IMAGE072
If equal to 1, skipping step 4-4-1-3 to step 4-4-1-11;
otherwise, continuing to execute the step 4-4-1-3;
4-4-1-3, screening flights to be processed:
from
Figure 531652DEST_PATH_IMAGE043
The first flight in the queue begins, and the current flight is taken
Figure 899180DEST_PATH_IMAGE072
First flight of 0
Figure 78488DEST_PATH_IMAGE004
Let it
Figure 88033DEST_PATH_IMAGE102
；
Step 4-4-1-4, judging the ordering adjustment state of the flight:
if flight is scheduled
Figure 669187DEST_PATH_IMAGE004
Rank adjusted state of
Figure 473195DEST_PATH_IMAGE014
If not, indicating that the flight does not belong to the flight recommended to be subtracted, returning to the step 4-4-1-2, otherwise, continuing to execute the step 4-4-1-5;
step 4-4-1-5, updating the flow of a take-off airport of the flight:
according to flight
Figure 874220DEST_PATH_IMAGE004
Take-off airport and planned take-off time
Figure 687455DEST_PATH_IMAGE009
Suppose a flight
Figure 123116DEST_PATH_IMAGE004
In that
Figure 98025DEST_PATH_IMAGE020
The u-th airport in the queue
Figure 986347DEST_PATH_IMAGE022
Take off at the kth time slice, then order
Figure 337694DEST_PATH_IMAGE103
And is and
Figure 893440DEST_PATH_IMAGE104
；
step 4-4-1-6, judging whether the flow of the takeoff airport of the flight exceeds the capacity increase amplitude:
if it is satisfied with
Figure 56829DEST_PATH_IMAGE105
Returning to execute the step 4-4-1-2;
if so:
Figure 432446DEST_PATH_IMAGE106
returning to execute the step 4-4-1-2;
otherwise, continuing to execute the step 4-4-1-7;
and 4-4-1-7, updating the flow of the landing airport of the flight:
according to flight
Figure 587484DEST_PATH_IMAGE004
Landing airport and planned landing time
Figure 200999DEST_PATH_IMAGE010
Suppose flight is
Figure 517711DEST_PATH_IMAGE004
In that
Figure 646204DEST_PATH_IMAGE020
The u-th airport in the queue
Figure 73774DEST_PATH_IMAGE022
When the kth time slice of (1) falls, then order
Figure 604113DEST_PATH_IMAGE107
And is and
Figure 91726DEST_PATH_IMAGE108
；
step 4-4-1-8, judging whether the flow of the landing airport of the flight exceeds the capacity increase amplitude:
if it is satisfied with
Figure 707515DEST_PATH_IMAGE109
Returning to execute the step 4-4-1-2;
if so:
Figure 938776DEST_PATH_IMAGE110
returning to execute the step 4-4-1-2;
otherwise, continuing to execute the step 4-4-1-9;
and 4-4-1-9, updating the flow of the flight path sector:
according to flight
Figure 323621DEST_PATH_IMAGE004
Past fan queue
Figure 982136DEST_PATH_IMAGE015
And each sector therein
Figure 819642DEST_PATH_IMAGE111
Scheduled fan in time
Figure 323435DEST_PATH_IMAGE018
Suppose flight is
Figure 828366DEST_PATH_IMAGE004
Enter at the k-th time slice
Figure 923361DEST_PATH_IMAGE024
The v sector in the queue
Figure 248163DEST_PATH_IMAGE026
Then give an order
Figure 821227DEST_PATH_IMAGE112
And is and
Figure 446243DEST_PATH_IMAGE113
；
step 4-4-1-10, judging whether the traffic of the approach sector of the flight exceeds the capacity increase amplitude:
for flight
Figure 446560DEST_PATH_IMAGE004
Any sector of the way
Figure 252799DEST_PATH_IMAGE026
If there is a flight
Figure 363975DEST_PATH_IMAGE004
Enter sector at kth time slice
Figure 843497DEST_PATH_IMAGE026
When it is satisfied with
Figure 749137DEST_PATH_IMAGE114
Returning to the step 4-4-1-2;
otherwise, continuing to execute the step 4-4-1-11;
and 4-4-1-11, updating the selected reduction flight quantity:
order to
Figure 48531DEST_PATH_IMAGE115
；
Updating flights
Figure 963397DEST_PATH_IMAGE004
Take-off airport flow of
Figure 766268DEST_PATH_IMAGE116
；
Updating flights
Figure 108388DEST_PATH_IMAGE004
Landing airport traffic of
Figure 895078DEST_PATH_IMAGE117
；
Updating flights
Figure 613636DEST_PATH_IMAGE004
Traffic per sector of the path, order
Figure 536592DEST_PATH_IMAGE118
；
Returning to the step 4-4-1-2;
step 4-4-2, generating an airspace network optimization scheme, namely generating the airspace network optimization scheme according to the capacity-flow matching condition of airports and sectors all over the country, wherein the method comprises the following steps:
step 4-4-2-1, emptying protocol:
optimization scheme for empty airspace network
Figure 49613DEST_PATH_IMAGE080
And order
Figure 323600DEST_PATH_IMAGE119
；
Step 4-4-2-2, counting airports needing optimization:
for national airport fleet
Figure 580269DEST_PATH_IMAGE020
Each of which isAirport
Figure 623311DEST_PATH_IMAGE022
And circularly performing the following treatment:
1) calculating the deviation between the flow and the capacity of each time slice:
computer airport
Figure 572812DEST_PATH_IMAGE022
Deviation of takeoff flow from field leaving capacity at each time slice k
Figure 334095DEST_PATH_IMAGE120
Deviation of landing flow from approach volume
Figure 863297DEST_PATH_IMAGE121
And total flow and capacity deviation
Figure 26425DEST_PATH_IMAGE122
(ii) a On the basis, the airport is counted
Figure 146827DEST_PATH_IMAGE022
Maximum deviation value of takeoff flow and off-field capacity at each time slice
Figure 129827DEST_PATH_IMAGE123
Maximum deviation of landing flow from approach volume
Figure 728298DEST_PATH_IMAGE124
And maximum deviation of total flow from capacity
Figure 740074DEST_PATH_IMAGE125
；
If it is not
Figure 31378DEST_PATH_IMAGE126
Then give an order
Figure 236094DEST_PATH_IMAGE127
；
If it is not
Figure 903836DEST_PATH_IMAGE128
Then give an order
Figure 41556DEST_PATH_IMAGE129
；
If it is not
Figure 972603DEST_PATH_IMAGE130
Then make an order
Figure 930195DEST_PATH_IMAGE131
；
2) Screening dilatation airports and calculating dilatation degree:
if the airport
Figure 870469DEST_PATH_IMAGE022
Satisfies the following conditions:
Figure 331537DEST_PATH_IMAGE132
define the airport as the airspace to be optimized
Figure 230223DEST_PATH_IMAGE082
Let us order
Figure 409532DEST_PATH_IMAGE133
，
Figure 153497DEST_PATH_IMAGE134
，
Figure 230DEST_PATH_IMAGE135
，
Figure 538659DEST_PATH_IMAGE136
，
Figure 470843DEST_PATH_IMAGE137
；
Will be provided with
Figure 752919DEST_PATH_IMAGE082
Optimization scheme for adding to airspace network
Figure 719738DEST_PATH_IMAGE080
In, and
Figure 429068DEST_PATH_IMAGE138
；
step 4-4-2-3, counting sectors needing to be optimized:
for national sector queues
Figure 582969DEST_PATH_IMAGE024
Each sector in
Figure 668737DEST_PATH_IMAGE026
And circularly performing the following treatment:
1) calculating the deviation between the flow and the capacity of each time slice:
computing sector
Figure 224483DEST_PATH_IMAGE026
Deviation of flow from capacity at each time slice k
Figure 364434DEST_PATH_IMAGE139
On the basis of the sector statistics
Figure 740052DEST_PATH_IMAGE026
Maximum deviation of flow rate and capacity in each time slice
Figure 629511DEST_PATH_IMAGE140
；
If it is not
Figure 570922DEST_PATH_IMAGE141
Then give an order
Figure 622054DEST_PATH_IMAGE142
；
2) Screening expansion sectors and calculating the expansion degree:
if sector
Figure 750547DEST_PATH_IMAGE026
Satisfy the requirement of
Figure 178118DEST_PATH_IMAGE143
Then define the sector as the space domain to be optimized
Figure 708456DEST_PATH_IMAGE082
Let us order
Figure 930490DEST_PATH_IMAGE144
，
Figure 546279DEST_PATH_IMAGE145
，
Figure 777540DEST_PATH_IMAGE146
；
Will be provided with
Figure 162385DEST_PATH_IMAGE082
Optimization scheme for adding to airspace network
Figure 555321DEST_PATH_IMAGE080
In, and
Figure 658406DEST_PATH_IMAGE147
。
10. the flight-normality-objective-based capacity flow collaborative optimization method according to claim 9, wherein the step 5 comprises:
step 5-1, variable definition;
step 5-2, generating a flight time optimization scheme;
and 5-1, defining variables, wherein the variables comprise:
Figure 427779DEST_PATH_IMAGE148
: flight time optimization scheme comprising optimization goal for achieving flight normality
Figure 932709DEST_PATH_IMAGE052
Adjusting queues from flights
Figure 496546DEST_PATH_IMAGE043
Screening out flights needing to be subjected to time adjustment;
Figure 86927DEST_PATH_IMAGE149
: flight time optimization scheme
Figure 659991DEST_PATH_IMAGE148
The initial value of the total number of flights in the flight list is 0;
Figure 753849DEST_PATH_IMAGE150
：
Figure 754166DEST_PATH_IMAGE148
the nth flight needing to be optimized, namely the flight to be optimized;
Figure 831843DEST_PATH_IMAGE151
：
Figure 38676DEST_PATH_IMAGE150
the flight number of;
Figure 252620DEST_PATH_IMAGE152
：
Figure 423838DEST_PATH_IMAGE150
the flight adjustment status type of (1), 0 represents time adjustment, and 1 represents proposed abatement;
Figure 988812DEST_PATH_IMAGE153
：
Figure 638099DEST_PATH_IMAGE150
a recommended takeoff time;
Figure 706549DEST_PATH_IMAGE154
：
Figure 48669DEST_PATH_IMAGE150
a suggested landing time;
step 5-2, generating a flight time optimization scheme:
adjusting queues from flights
Figure 835359DEST_PATH_IMAGE043
Medium screening
Figure 553916DEST_PATH_IMAGE055
Erecting flights needing to be subjected to time adjustment to form a flight time optimization scheme:
step 5-2-1, emptying protocol:
flight emptying time optimization scheme
Figure 476873DEST_PATH_IMAGE148
And order
Figure 989894DEST_PATH_IMAGE155
；
Step 5-2-2, emptying the flight processing state:
adjusting queues for flights
Figure 529460DEST_PATH_IMAGE043
Every flight in the flight
Figure 520550DEST_PATH_IMAGE004
To make it process the state
Figure 563592DEST_PATH_IMAGE095
；
Step 5-2-3, judging whether the screening is finished:
if it is satisfied with
Figure 981935DEST_PATH_IMAGE156
Or is or
Figure 743218DEST_PATH_IMAGE043
All flights in the queue have been processed, i.e.
Figure 803577DEST_PATH_IMAGE072
If the value is equal to 1, finishing the processing of the step 5-2; otherwise, continuing to execute the step 5-2-4;
step 5-2-4, screening flights to be processed:
from
Figure 701126DEST_PATH_IMAGE043
The first flight in the queue begins, and the current flight is taken
Figure 821529DEST_PATH_IMAGE072
First flight of 0
Figure 70108DEST_PATH_IMAGE004
Let it
Figure 668579DEST_PATH_IMAGE157
Continuing to execute the step 5-2-5;
step 5-2-5, judging the ordering adjustment state of the flight:
if flight is scheduled
Figure 686214DEST_PATH_IMAGE004
Rank adjusted state of
Figure 971659DEST_PATH_IMAGE014
If the number is 3, the flight belongs to the flight recommended to be subtracted, and the step 5-2-3 is returned; otherwise, continuing to execute the step 5-2-6;
and 5-2-6, updating the flight time optimization scheme:
will flight
Figure 176375DEST_PATH_IMAGE004
Defined as flights to be optimized
Figure 578537DEST_PATH_IMAGE150
And order
Figure 185099DEST_PATH_IMAGE158
，
Figure 912884DEST_PATH_IMAGE159
，
Figure 870476DEST_PATH_IMAGE160
，
Figure 810750DEST_PATH_IMAGE161
；
Will be provided with
Figure 271818DEST_PATH_IMAGE150
Adding to flight time optimization scheme
Figure 170504DEST_PATH_IMAGE148
In, and
Figure 84233DEST_PATH_IMAGE162
；
and returning to execute the step 5-2-3.
CN202210748867.XA 2022-06-29 2022-06-29 Capacity flow cooperative optimization method based on flight normality target Pending CN114822088A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210748867.XA CN114822088A (en) 2022-06-29 2022-06-29 Capacity flow cooperative optimization method based on flight normality target

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210748867.XA CN114822088A (en) 2022-06-29 2022-06-29 Capacity flow cooperative optimization method based on flight normality target

Publications (1)

Publication Number Publication Date
CN114822088A true CN114822088A (en) 2022-07-29

Family

ID=82522606

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210748867.XA Pending CN114822088A (en) 2022-06-29 2022-06-29 Capacity flow cooperative optimization method based on flight normality target

Country Status (1)

Country Link
CN (1) CN114822088A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111009155A (en) * 2019-12-06 2020-04-14 南京莱斯信息技术股份有限公司 Air traffic flow complexity quantitative analysis method based on airspace structure and flight flow
CN113034980A (en) * 2021-02-26 2021-06-25 中国电子科技集团公司第二十八研究所 Scheduled flight operation efficiency pre-evaluation method
CN113408907A (en) * 2021-06-22 2021-09-17 中国人民解放军空军工程大学 Method, system and equipment for analyzing threat influence of air traffic control system
CN113643571A (en) * 2021-10-18 2021-11-12 中国电子科技集团公司第二十八研究所 Airspace network optimization method based on flight normality target
CN113706933A (en) * 2021-11-01 2021-11-26 中国电子科技集团公司第二十八研究所 Time optimization method based on flight normality target

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111009155A (en) * 2019-12-06 2020-04-14 南京莱斯信息技术股份有限公司 Air traffic flow complexity quantitative analysis method based on airspace structure and flight flow
CN113034980A (en) * 2021-02-26 2021-06-25 中国电子科技集团公司第二十八研究所 Scheduled flight operation efficiency pre-evaluation method
CN113408907A (en) * 2021-06-22 2021-09-17 中国人民解放军空军工程大学 Method, system and equipment for analyzing threat influence of air traffic control system
CN113643571A (en) * 2021-10-18 2021-11-12 中国电子科技集团公司第二十八研究所 Airspace network optimization method based on flight normality target
CN113706933A (en) * 2021-11-01 2021-11-26 中国电子科技集团公司第二十八研究所 Time optimization method based on flight normality target

Similar Documents

Publication Publication Date Title
CN113643571B (en) Airspace network optimization method based on flight normality target
CN113034980B (en) Scheduled flight operation efficiency pre-evaluation method
EP3438821B1 (en) Task-resource scheduling method and device
CN109584638B (en) Regional network-oriented advanced flight time collaborative optimization method
CN107239335B (en) Job scheduling system and method for distributed system
US5392430A (en) Hierarchical scheduling method for processing tasks having precedence constraints on a parallel processing system
CN107944625A (en) Single air station flight season time slot optimization method based on history data driving
CN107544251B (en) Single machine scheduling method for minimizing total stall period based on distributed robust model
Guidara et al. Heuristic based time-aware service selection approach
CN111582584B (en) Air section operation time optimization method based on fairness of airlines
WO2023071257A1 (en) Time optimization method based on flight normality target
JP2001092802A (en) Device and method for optimization and recording medium
CN110751309A (en) Abnormal flight recovery method, electronic equipment and storage medium
CN114911613A (en) Cross-cluster resource high-availability scheduling method and system in inter-cloud computing environment
US8667008B2 (en) Search request control apparatus and search request control method
CN114822088A (en) Capacity flow cooperative optimization method based on flight normality target
CN112035234B (en) Distributed batch job distribution method and device
CN110088730B (en) Task processing method, device, medium and equipment
CN113191533A (en) Warehouse employment prediction method, device, equipment and storage medium
Sherali et al. An air force crew allocation and scheduling problem
CN111523685B (en) Method for reducing performance modeling overhead based on active learning
CN115061811A (en) Resource scheduling method, device, equipment and storage medium
Shin et al. Hippo: Taming hyper-parameter optimization of deep learning with stage trees
Peng et al. A data-driven air cargo redistribution model based on multiple programming
CN107085536B (en) Task management method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination