CN115394124B - Large-scale air traffic flow optimization method based on network flow decoupling - Google Patents
Large-scale air traffic flow optimization method based on network flow decoupling Download PDFInfo
- Publication number
- CN115394124B CN115394124B CN202211330503.6A CN202211330503A CN115394124B CN 115394124 B CN115394124 B CN 115394124B CN 202211330503 A CN202211330503 A CN 202211330503A CN 115394124 B CN115394124 B CN 115394124B
- Authority
- CN
- China
- Prior art keywords
- sub
- network
- flight
- airport
- navigation
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005457 optimization Methods 0.000 title claims abstract description 31
- 230000010006 flight Effects 0.000 claims abstract description 42
- 230000002068 genetic effect Effects 0.000 claims abstract description 17
- 230000008569 process Effects 0.000 claims description 14
- 230000001419 dependent effect Effects 0.000 claims description 3
- 108090000623 proteins and genes Proteins 0.000 description 33
- 210000000349 chromosome Anatomy 0.000 description 19
- 238000005215 recombination Methods 0.000 description 4
- 230000006798 recombination Effects 0.000 description 4
- 238000000354 decomposition reaction Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012248 genetic selection Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 206010064571 Gene mutation Diseases 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Biophysics (AREA)
- Strategic Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Marketing (AREA)
- Bioinformatics & Computational Biology (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Aviation & Aerospace Engineering (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the technical field of aviation network planning, relates to a large-scale air traffic flow optimization method based on network flow decoupling, and solves the problems of low efficiency and low safety of large-scale air traffic scheduling and planning methods in the prior art. The method of the present invention comprises integrating the navigation networkGDecomposed into multiple sub-navigation networksG ＇ (ii) a For each sub-navigation networkG ＇ Corresponding three-dimensional graph 3 is constructed according to flight informationD‑G ＇ (ii) a Respectively optimizing and solving each sub-navigation network through genetic algorithmG ＇ Until each sub-navigation network is obtainedG ＇ And (4) solving the optimal solution of the total conflict times of flights of all the sub-navigation networks and the total time of the takeoff delay. The optimization method can relieve the conflict of all flights in the whole designated airspace, and improves the efficiency and the safety of scheduling and planning.
Description
Technical Field
The invention belongs to the technical field of aviation network planning, and relates to a large-scale air traffic flow optimization method based on network flow decoupling.
Background
With the development of the air transportation industry and the advantages of rapid, convenient and on-time air traffic and the like, more and more people tend to select air traffic as a travel mode, which also brings great pressure to air traffic flow management, and how to rapidly and safely perform optimization management on large-scale air traffic flow becomes the focus of attention of people. Such as: chinese patent applications CN112529278A and CN105023068A were studied for the scheduling and planning of air routes, respectively.
However, the adjustment method in the prior art is too dependent on the monitoring and control of the ground center on the flight with the conflict and does not fully consider the situation of the whole navigation network, so that potential safety hazards exist, and even domino effect is brought because only unfavorable adjustment on the flight with the conflict is considered; in addition, a large-scale navigation network cannot be planned, and planning efficiency is low.
Disclosure of Invention
Aiming at the problems, the invention provides a large-scale air traffic flow optimization method based on network flow decoupling, which is used for solving the problems of low efficiency and low safety of the conventional large-scale air traffic scheduling and planning method.
The invention provides a large-scale air traffic flow optimization method based on network flow decoupling, which comprises the following steps of:
step S1: will whole navigation networkGDecomposed into sub-navigation networksG ＇ (ii) a Each sub-navigation networkG ＇ Comprising at least one sub-airline flightF j ，jIs a positive integer;
step S2: for each sub-navigation networkG ＇ According to sub-navigation networkG ＇ All sub-airline network flights in (1)F j The flight information constructs a corresponding sub-navigation network three-dimensional graph 3D-G ＇ (ii) a Three-dimensional graph 3 based on sub-navigation networkD-G ＇ Obtaining a sub-navigation network using a graph traversal algorithmG ＇ The total number of conflicts of all the sub-navigation network flights;
and step S3: based on each sub-navigation network obtained in step S2G ＇ Optimizing the collision times of flights of all the sub-navigation networks by a genetic algorithmG ＇ (ii) a Until each sub-navigation network is obtainedG ＇ The solutions of the total number of conflicts of flights and the total time of takeoff delay of all the sub-navigation networks are larger than the minimum threshold value or the terminal condition is reached.
Optionally, the specific process of step S1 is: integral navigation networkGComprises a plurality of flights; integral navigation networkGComprises at least one waypoint; integral air routeNetGIncluding at least one airport; determining waypoint dependent airports for each waypoint: determining a flight subordinate airport of the flight according to each waypoint subordinate airport;
the specific process of determining the subordinate airport of each waypoint is as follows: initializing the number of times each waypoint is passed by a flight from a respective airport to 0; traversing integral navigation networkGEvery time a flight departs from an airport and passes through an waypoint, adding 1 to the number of times the waypoint is passed by the flight departing from the airport; traversing the times of the flights taking off from each airport passing through each route point, and taking the airport corresponding to the maximum time of each route point as a subordinate airport of the route point;
the specific process of determining the slave airport of the flight according to the slave airport of each waypoint is as follows: traversing integral navigation networkGCounting the number of times of appearance of slave airports serving as each waypoint in all waypoints passed by each flight, and taking an airport with the largest number of times of appearance of the slave airport of each waypoint as a flight slave airport of the corresponding flight;
traversing all flight subordinate airports, wherein the same flight and the corresponding subordinate airports of the flight subordinate airports form the same sub-navigation networkG ＇ And counting in the sub-navigation networkG ＇ Total number of flights inj。
Optionally, the specific process of step S2 is: obtaining each sub-navigation network flightF j The three-dimensional map coordinates of the take-off airport and the three-dimensional map coordinates of the passing waypoints of the sub-airline network flightF j The three-dimensional graph coordinates of the take-off airport and the three-dimensional graph coordinates of the passing route points form a sub-route networkG ＇ All the nodes form a sub-navigation networkG ＇ FIG. 3 is a three-dimensional view ofD-G ＇ 。
Will three-dimensional figure 3D-G ＇ Nodes with the same longitude and latitude coordinates are collected to obtain a set node, and no sub-navigation network with the same coordinates existsG ＇ Is a single nodeThe collection node and the individual nodes form a statistic node; calculating the coordinate time distance between each statistical node and any other statistical node, and counting as a conflict when the coordinate time distances of the two statistical nodes are less than the safety time; traversing the coordinate time distance between each statistical node and any other statistical node to obtain the sub-navigation networkG ＇ Total number of conflicts between all sub-airline network flightsc。
Optionally, step S4: all the sub-navigation networks obtained in the step S3G ＇ The total time length of the take-off delay is added to obtain the whole navigation networkGThe overall flight delay duration optimization value of (2).
Compared with the prior art, the invention can at least realize the following beneficial effects:
(1) The optimization method can relieve the conflict by delaying the takeoff time of the flights in the airport for all the flights in the whole designated airspace, ensures that the total delay time of all the flights is reduced as much as possible under the condition of small airspace conflict amount, and improves the efficiency and the safety of large-scale air traffic scheduling and planning.
(2) The optimization method of the invention divides the whole navigation network by the waypoints and the takeoff airports of the flights, divides two flights which pass through the same waypoint for a plurality of times into the same sub-navigation network, and effectively reduces the possibility of conflict among the flights.
(3) The optimization method of the invention carries out decomposition pretreatment on the large-scale integral navigation network, reduces the coupling relation between the sub-navigation networks, processes each sub-navigation network independently and reduces the complexity of the optimization process.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention.
FIG. 1 is a flow chart illustrating a sub-route network decomposition of an overall route network in the optimization method of the present invention;
FIG. 2 is a schematic diagram of a subordinate airport for determining waypoints in the optimization method of the present invention;
FIG. 3 is a schematic diagram of a large-scale airspace sub-navigation network after decomposition according to the optimization method of the present invention;
FIG. 4 is a flow chart of the construction of a three-dimensional map of waypoints in the optimization method of the present invention;
FIG. 5 is a flowchart of the optimization of the genetic algorithm in the optimization method of the present invention;
FIG. 6 is an overall flow chart of the optimization method of the present invention.
Reference numerals are as follows:
1. a first flight; 2. a second flight; 3. a third flight; a. a first airport; b. a second airport; c. a third airport; 11. a first waypoint; 22. a second waypoint.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
A specific embodiment of the present invention, as shown in fig. 1-6, discloses a large-scale air traffic flow optimization method based on network flow decoupling, comprising the following steps:
step S1: will whole navigation networkGDecomposed into multiple independent sub-navigation networksG ＇ (ii) a Each sub-navigation networkG ＇ Including a plurality of sub-airline network flightsF j ，j=1,2,3…。
Step S2: for each sub-navigation networkG ＇ (ii) a According to sub-navigation networkG ＇ All sub-route network flights in (1)F j The flight information constructs a corresponding sub-navigation network three-dimensional graph 3D-G ＇ (ii) a Three-dimensional graph 3 based on sub-navigation networkD-G ＇ Obtaining a sub-navigation network using a graph traversal algorithmG ＇ The total number of conflicts of all the sub-navigation network flights; wherein the flight information comprises sub-route network flightsF j The take-off airport longitude and latitude coordinates, the passing waypoint longitude and latitude coordinates, the take-off time, the take-off delay time and the flight time.
And step S3: based on each sub-navigation network obtained in step S2G ＇ Conflict of all sub-navigation network flightsNumber of times, optimization of sub-navigation networks by genetic algorithmsG ＇ (ii) a Until each sub-navigation network is obtainedG ＇ The solution of the total number of conflicts of flights and the total time of departure delay of all the sub-navigation network is larger than the minimum threshold value or reaches the termination condition.
And step S4: all the sub-navigation networks obtained in the step S3G ＇ Adding the values when the solutions of the total time length of the takeoff delay are larger than the minimum threshold value or reach the termination condition to obtain the integral navigation networkGThe overall flight delay time optimization value of the overall navigation network. Integral navigation networkGThe integral flight delay time length optimization value of the integral navigation network is convenient for understanding the optimization effect of large-scale air traffic flow.
Optionally, as shown in fig. 1 to fig. 3, the specific process of step S1 is:
integral navigation networkGComprises a plurality of flights; integral navigation networkGComprises at least one waypoint; integral navigation networkGIncluding at least one airport;
(1) Determining the subordinate airport of each waypoint:
step S1-1: initializing the number of times each waypoint is passed by flights departing from the respective airport to 0;
step S1-2: traversing integral navigation networkGEvery time a flight takes off from an airport and passes an waypoint, adding 1 to the number of times the waypoint is passed by flights taking off from the airport;
step S1-3: and traversing the times that the flights taking off from each airport pass through each route point, and taking the airport corresponding to the maximum time of each route point as a slave airport of the route point.
Referring to fig. 2, a first flight 1 flies from a first airport a to a second airport b, a second flight 2 flies from the first airport a to a third airport c, and a third flight 3 flies from the second airport b to the third airport c, specifically, in fig. 2, the first flight 1 is a thin solid line, the second flight 2 is a thick solid line, and the third flight 3 is a dotted line. The first waypoint 11 is passed by the first flight 1 and the second flight 2 taken off by the first airport a for 1 time respectively, and is passed by the third flight 3 taken off by the second airport b for 1 time, the number of times that the first waypoint 11 is passed by the flight taken off by the first airport a is 2, and the number of times that the flight taken off by the second airport b passes is 1, so that the waypoint 11 belongs to the first airport a; similarly, the second flight 2 and the third flight 3 of the second waypoint 22 from the first airport a and the second airport b pass 1 time respectively, and the number of the flights of the second waypoint 22 from the first airport a and the second airport b is the same, so that the subordinate airport is randomly selected from the first airport a and the second airport b, and the second airport b is assumed to be randomly selected.
(2) Determining the subordinate airport of the flight: traversing integral navigation networkGThe number of times of appearance of the slave airport serving as each waypoint in all waypoints passed by each flight is counted, and the airport serving as each waypoint and having the largest number of times of appearance of the slave airport is taken as the flight slave airport of the corresponding flight.
Referring to fig. 2, the first flight 1 only passes through the first waypoint 11, and the slave airport of the first waypoint 11 is the first airport a, so the slave airport of the first flight 1 is the first airport a; the second flight 2 passes through the first waypoint 11 and the second waypoint 22, and the subordinate airport of the first waypoint 11 is the first airport a, and the subordinate airport of the second waypoint 22 is the second airport b, so that the times of selecting the first airport a and the second airport b as the subordinate airports are the same in all waypoints through which the second flight 2 passes, one of the first airport a and the second airport b is randomly selected as the subordinate airport of the second airport 2, and the second airport a is supposed to be randomly selected as the subordinate airport of the second flight 2 at this time; similarly, the second airport b is randomly picked as the slave airport for the second flight 3.
(3) Traversing all flight subordinate airports, wherein the same flights of the flight subordinate airports and the corresponding subordinate airports form a sub-navigation networkG ＇ And counting in the sub-navigation networkG ＇ Total number of flights inj，j=1,2,3 \8230, whereby the whole navigation network is connectedGDecomposing to obtain at least one independent sub-navigation networkG ＇ Navigation networkG ＇ Including a plurality of sub-airline network flightsF j Counting flights of the sub-navigation networkF j Waypoints of passing sub-airline network flightsw jq ，q=1,2,3 \8230. Referring to fig. 3, the abscissa of the drawing is longitude, the ordinate is latitude, and the symbols "x", "dots", and "five-pointed star" in the drawing represent all the waypoints in the decomposed three sub-navigation networks, respectively. Traverse waypointsw k Airport and airline flights.
Optionally, as shown in fig. 4, the specific process of step S2 is:
obtaining sub-airline network flightsF j The three-dimensional map coordinates of the take-off airport and the three-dimensional map coordinates of the passing waypoints of the sub-airline network flightF j The three-dimensional graph coordinates of the take-off airport and the three-dimensional graph coordinates of the passing route points form a sub-route networkG ＇ All the nodes form a sub-navigation networkG ＇ FIG. 3 is a three-dimensional view ofD-G ＇ (ii) a Sub-route network flightF j Taking-off airport three-dimensional graph with coordinates of sub-navigation network flightF j Longitude, latitude and departure time of the departure airport of (1); the three-dimensional map coordinates of the passing waypoints are the longitude and latitude of the waypoint and the time of passing the waypoint; the time of passing by the waypoints is take-off time, take-off delay time and flight time, wherein the flight time is the flight of the sub-navigation networkF j From the takeoff airport to the 1 st waypoint or between two adjacent waypoints;
will three-dimensional figure 3D-G ＇ Nodes with the same longitude and latitude coordinates are collected to obtain a set node, and no sub-navigation network with the same coordinates existsG ＇ The nodes are independent nodes, and the aggregation nodes and the independent nodes form statistical nodes, because of the sub-navigation networkG ＇ One of the waypoints can be passed by flights of different sub-navigation networks at different times, and the node set with the same coordinates before counting the total conflict times can improve the overall optimization efficiency; calculating the coordinate time distance between each statistical node and any other statistical node when the two statistical nodesWhen the coordinate time distance is less than the safety time, counting as a conflict, wherein the safety time is the safety distance divided by the average flight speed of civil aviation flights, and traversing the coordinate time distance between each statistical node and any other statistical node to obtain the sub-navigation networkG ＇ Total number of conflicts in all sub-navigation network flightscPreferably, the safe distance is 5 nautical miles and the safe time is 40s.
Optionally, the specific process of step S3 is:
step S3-1: clustering a sub-navigation network:
general navigation networkG ＇ The population is a genetic populationPGenetic populationPIn which a plurality of unexploded chromosomes are arrangedR i ，i=1,2,38230; each unexploded chromosomeR i Thereon is provided withj(ii) an unexploded gene; sub-route network flightF j At each undeveloped chromosomeR i Undeveloped gene of (1)g ij Genes not evolvedg ij According to flight of sub-navigation networkF j Time delay of undeveloped takeoffδ j A binary coded setting value.
Step S3-2: calculating the fitness of the non-evolved gene:
based on step S2 neutron navigation netG ＇ Total number of conflicts for all sub-route network flightscTo obtain the fitness of the unexploded genefg ij ：
Wherein the content of the first and second substances,δ jmax flight for sub-navigation networkF j The maximum takeoff delay duration of; the maximum takeoff delay time is given by a takeoff airport, and the optimal time is half an hour in order to avoid the influence of overlong flight delay time on the flight plan of the subsequent flight;
calculating fitness of an evolutionary chromosomefR i ：
Wherein the content of the first and second substances,Nfor flights in a sub-navigation networkF j The total number of (c).
Step S3-3: genetic evolution:
step S3-3-1: obtaining sub-airline network flightsF j The takeoff delay time state after the takeoff delay time variation is as follows:
when the gene fitness is less than or equal toεWhen the gene is expressed inxThe probability of (2) generating a gene mutation, the sub-airline network flight after the gene mutationF j The takeoff delay time length is varied, and the varied takeoff delay time length is as follows:
in the formula (I), the compound is shown in the specification,δ ijbest flight for sub-navigation networkF j The corresponding takeoff delay time length when the historical maximum value of the gene fitness is reached;δ ij1 andδ ij2 for flights from sub-airline networksF j Randomly selecting the takeoff delay time length from the historical takeoff delay time lengths;M c which represents a scaling factor, is the ratio of the scaling factor,U c (0, 1) represents a uniform distribution between 0 and 1, parametersfpThe setting is made to be 0.5,N c (0.5 ) represents a random variable subject to a Gaussian distribution with both mean and variance of 0.5,
a cauchy random variable representing a parameter of 1; preferably, the first and second liquid crystal display panels are,x=0.1。
sub-airline network flight corresponding to non-mutated geneF j The takeoff delay time length does not vary, and at the moment, the variation takeoff delay time lengthδ ijm =g ij 。
Step S3-3-2: calculating the fitness of the variant gene:
based on the variation takeoff delay time length in the step S3-3-1δ ijm According to step S2, the sub-navigation network is obtained againG ＇ Total number of conflicts in all sub-navigation network flightsc；
Flight of sub-navigation networkF j Variable takeoff delay durationδ ijm Obtaining variant gene after binary codingg ijm According to the variant geneg ijm Obtaining the fitness of the variant genefg ijm ：
Step S3-3-3: gene crossing:
transforming the geneg ijm Recombination into recombinant parental chromosomesR im From recombinant parent chromosomesR im In the random selection ofAChromosome of parent stripR Am And a first step ofBChromosome of parent stripR Bm Wherein, in the step (A),A∈i，B∈i，A≠B；
first, theAChromosome of parental stripR Am ToaIndividual parent geneg Aam And a firstBChromosome of parent stripR Bm TobIndividual parent geneg Bbm To be provided withyThe probability of (a) is crossed, wherein,a∈j，b∈j，a≠b；
if the gene fitness of the two selected parent genes is different, the parent gene with small gene fitness in the two selected parent genes uses the parent gene with large gene fitness as a filial generation gene of the parent gene to obtain the filial generation gene;
if the gene fitness of the two selected parent genes is the same, the two parent genes are crossed to obtain offspring genes in the following way to obtain the firstaProgeny geneCg Aam And a first step ofbProgeny geneCg Bbm ：
In the formula (I), the compound is shown in the specification,αin order to linearly combine the parameters of the device,flooris a rounded down function; preferably, the first and second electrodes are formed of a metal,y=0.1。
based on the takeoff time delay states corresponding to crossed and uncrossed genes, the sub-navigation network is obtained again according to the step S2G ＇ Total number of conflicts between all sub-airline network flightsc；
The parent gene with crossover and the parent gene without crossover constitute the offspring geneCg ijm Progeny geneCg ijm Recombination into recombinant progeny chromosomesCR im Recombination of offspring chromosomesCR im The fitness of the progeny gene of (2):
thus, traversing the genetic populationPAny two genes in any two chromosomes are subjected to gene crossing to obtain evolved offspring chromosomes, and the offspring chromosomes form an offspring genetic populationP。
Step S3-3-4: calculating the fitness of the chromosomes of the recombination offspring:
wherein the content of the first and second substances, Nfor flights in a sub-navigation networkF j The total number of (c).
Thus, traversing the genetic populationPAny two genes in any two non-evolved chromosomes are subjected to gene crossing to obtain evolved recombinant offspring chromosomes, and the recombinant offspring chromosomes are used as offspringPutting chromosomes into offspring genetic populationP ＇ 。
S3-4: genetic selection:
selecting the genetic evolved product obtained in the step S3-3kHigh fitness chromosome with highest fitness of the left chromosomes in the undeveloped left chromosomes is taken as offspring chromosome to be placed into offspring genetic populationP ＇ Over time, remaink-1 undeveloped chromosome is put back and the genetic selection process is repeated until the progeny genetic populationP ＇ Number of progeny chromosomes and genetic populationPThe number of unexploded chromosomes in (a) is the same.
S3-5: judging whether to stop evolution:
if the progeny is a genetic populationP ＇ The fitness of the chromosome of the middle-appearing offspring is more than or equal toφOr a high fitness chromosome, or a current number of evolutionary eventsE now ≥E max When the optimization is stopped, the fitness of the offspring chromosome is the subsidiary navigation networkG ＇ Solving the total conflict times of flights of all the sub-navigation networks and the total takeoff delay time; else progeny genetic populationP ＇ Executing the step S3-3;
wherein, the first and the second end of the pipe are connected with each other,φfor sub-navigation netG ＇ The minimum threshold value of the solutions of the total number of conflicts of all the sub-navigation network flights and the total time of the takeoff delay,E now the number of current evolutions is represented by,E max indicating a set maximum number of evolutions.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (3)
1. A large-scale air traffic flow optimization method based on network flow decoupling is characterized by comprising the following steps:
step S1: will whole navigation networkGDecomposed into sub-navigation networksG ＇ (ii) a Each sub-navigation networkG ＇ Comprising at least one sub-airline flightF j ，jIs a positive integer;
step S2: for each sub-navigation networkG ＇ According to sub-navigation networkG ＇ All sub-airline network flights in (1)F j The flight information constructs a corresponding sub-navigation network three-dimensional graph 3D-G ＇ (ii) a Three-dimensional graph 3 based on sub-navigation networkD-G ＇ Obtaining a sub-navigation network using a graph traversal algorithmG ＇ The total number of conflicts of all the sub-navigation network flights;
and step S3: based on each sub-navigation network obtained in step S2G ＇ Optimizing the collision times of flights of all the sub-navigation networks by genetic algorithmG ＇ (ii) a Until each sub-navigation network is obtainedG ＇ The solutions of the total number of flight conflicts and the total takeoff delay time of all the sub-navigation networks are larger than a minimum threshold value or reach a termination condition;
the specific process of the step S1 is as follows: integral navigation networkGComprises a plurality of flights; integral navigation networkGComprises at least one waypoint; integral navigation networkGIncluding at least one airport; determining waypoint dependent airports for each waypoint: determining a flight subordinate airport of the flight according to each waypoint subordinate airport;
the specific process of determining the subordinate airport of each waypoint is as follows: initializing the number of times each waypoint is passed by a flight from a respective airport to 0; traversing integral navigation networkGEvery time a flight takes off from an airport and passes an waypoint, adding 1 to the number of times the waypoint is passed by flights taking off from the airport; traversing flights from various airports through each flightTaking the airport corresponding to the maximum times of each waypoint as a subordinate airport of the waypoint;
the specific process of determining the slave airport of the flight according to the slave airport of each waypoint is as follows: traversing integral navigation networkGCounting the occurrence times of the subordinate airports serving as the route points in all the route points passed by each flight, and taking the airport with the largest occurrence times of the subordinate airports of the route points as the flight subordinate airports of the corresponding flights;
traversing all flight subordinate airports, wherein the same flight and the corresponding subordinate airports of the flight subordinate airports form the same sub-navigation networkG ＇ And counting in the sub-navigation networkG ＇ Total number of flights inj。
2. The large-scale air traffic flow optimization method based on network flow decoupling according to claim 1, wherein the specific process of the step S2 is as follows: obtaining each sub-navigation network flightF j The three-dimensional map coordinates of the take-off airport and the three-dimensional map coordinates of the passing waypoints of the sub-navigation network flightF j The three-dimensional graph coordinates of the take-off airport and the three-dimensional graph coordinates of the passing route points form a sub-route networkG ＇ All the nodes form a sub-navigation networkG ＇ FIG. 3 is a three-dimensional view ofD-G ＇ ；
Will three-dimensional figure 3D-G ＇ Nodes with the same longitude and latitude coordinates are collected to obtain a set node, and no sub-navigation network with the same coordinates existsG ＇ The node of (2) is an individual node, and a set node and the individual node form a statistical node; calculating the coordinate time distance between each statistical node and any other statistical node, and counting as a conflict when the coordinate time distances of the two statistical nodes are smaller than the safety time; traversing the coordinate time distance between each statistical node and any other statistical node to obtain the sub-navigation networkG ＇ Total number of conflicts between all sub-airline network flightsc。
3. The large-scale air traffic flow optimization method based on network flow decoupling according to claim 1, wherein the step S4: all the sub-navigation networks obtained in the step S3G ＇ The solutions of the total time of the takeoff delay are added to obtain the whole navigation networkGThe overall flight delay duration optimization value of (2).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211330503.6A CN115394124B (en) | 2022-10-28 | 2022-10-28 | Large-scale air traffic flow optimization method based on network flow decoupling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211330503.6A CN115394124B (en) | 2022-10-28 | 2022-10-28 | Large-scale air traffic flow optimization method based on network flow decoupling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115394124A CN115394124A (en) | 2022-11-25 |
| CN115394124B true CN115394124B (en) | 2023-03-03 |
Family
ID=84114968
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211330503.6A Active CN115394124B (en) | 2022-10-28 | 2022-10-28 | Large-scale air traffic flow optimization method based on network flow decoupling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115394124B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930339A (en) * | 2012-09-28 | 2013-02-13 | 北京航空航天大学 | Method and device for resolving flight conflict |
| EP2993627A1 (en) * | 2014-09-05 | 2016-03-09 | Amadeus S.A.A. | Flight schedule optimization |
| CN114611839A (en) * | 2022-04-13 | 2022-06-10 | 北京航空航天大学 | Large-scale flight regulation and control method based on multi-objective collaborative optimization |
| CN114897343A (en) * | 2022-05-07 | 2022-08-12 | 邹国良 | Flight time resource optimal configuration method and device |
-
2022
- 2022-10-28 CN CN202211330503.6A patent/CN115394124B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930339A (en) * | 2012-09-28 | 2013-02-13 | 北京航空航天大学 | Method and device for resolving flight conflict |
| EP2993627A1 (en) * | 2014-09-05 | 2016-03-09 | Amadeus S.A.A. | Flight schedule optimization |
| CN114611839A (en) * | 2022-04-13 | 2022-06-10 | 北京航空航天大学 | Large-scale flight regulation and control method based on multi-objective collaborative optimization |
| CN114897343A (en) * | 2022-05-07 | 2022-08-12 | 邹国良 | Flight time resource optimal configuration method and device |
Non-Patent Citations (2)
| Title |
|---|
| A strategic flight conflict avoidance approach based on a memetic algorithm;Guan Xiangmin 等;《Chinese Journal of Aeronautics》;20131208;第27卷(第1期);论文第2-4节 * |
| 基于航路耦合容量的协同多航路资源分配;刘方勤等;《航空学报》;20101213(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115394124A (en) | 2022-11-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107807665B (en) | Unmanned aerial vehicle formation detection task cooperative allocation method and device | |
| Guan et al. | A strategic flight conflict avoidance approach based on a memetic algorithm | |
| Zhang et al. | Distributed flight routing and scheduling for air traffic flow management | |
| CN107248033B (en) | Method for decomposing regional tasks observed from air, space and ground | |
| CN112735188B (en) | Air traffic network vulnerability analysis system based on complex network theory | |
| CN114117322A (en) | Method for evaluating network capacity of urban low-altitude unmanned logistics transportation airway | |
| Meng et al. | Flight conflict resolution for civil aviation based on ant colony optimization | |
| WO2024007256A1 (en) | Unmanned aerial vehicle conflict detection method and apparatus of airspace digital grid and storage medium | |
| CN115938162A (en) | Conflict minimization track collaborative planning method considering high-altitude wind time variability | |
| CN115168654A (en) | Multi-attribute decision-based super-network important node identification method | |
| CN115394124B (en) | Large-scale air traffic flow optimization method based on network flow decoupling | |
| CN110991913A (en) | Busy airport peak time congestion risk analysis method | |
| Ghorpade | Airspace configuration model using swarm intelligence based graph partitioning | |
| US20230334994A1 (en) | Multi-Objective Collaborative Optimization Approach for Large- Scale Air Traffic Management | |
| CN112101474A (en) | Aviation traffic network motif recognition method and subgraph structure toughness evaluation method | |
| Scozzaro et al. | Noise abatement trajectories for a uav delivery fleet | |
| Chen et al. | Optimal transport in time-varying small-world networks | |
| Zhang et al. | Learning-to-dispatch: Reinforcement learning based flight planning under emergency | |
| CN113326990A (en) | Helicopter scheduling route planning method based on dynamic genetic algorithm serial fusion | |
| CN110413656B (en) | Rapid flow axis automatic identification method based on high spatial resolution numerical mode | |
| CN113191567A (en) | Multi-forest-area air route scheduling planning method based on double-layer fusion intelligent algorithm | |
| Sheth et al. | Analysis of airspace tube structures | |
| CN111080110A (en) | Airport congestion risk analysis system | |
| Meng et al. | Aircraft category based genetic algorithm for aircraft arrival sequencing and scheduling | |
| CN113240066A (en) | Improved ant colony algorithm based on Shapley value |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |