CN115204466A - International airline route planning method with traffic limitation - Google Patents

International airline route planning method with traffic limitation Download PDF

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CN115204466A
CN115204466A CN202210697154.5A CN202210697154A CN115204466A CN 115204466 A CN115204466 A CN 115204466A CN 202210697154 A CN202210697154 A CN 202210697154A CN 115204466 A CN115204466 A CN 115204466A
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陈创希
伍翔
许南
常先英
吴东岳
张苗苗
曾力舜
谢静娜
罗德贵
蒋禄妹
柳亚昆
瞿也丰
李翰林
高嘉许
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Abstract

The invention discloses a method for planning an international airline route with traffic limitation, which comprises the following steps: 1) Loading the whole network route data to generate a route network diagram; 2) Cutting the navigation line network graph to obtain a directed graph G = { V, E }, wherein V is a set of all nodes in the directed graph G, and E is a set of all navigation sides in the directed graph G; 3) Calculating the shortest distance from the starting point to all the nodes under the condition without limit, calculating the shortest distance from all the nodes to the end point under the condition without limit, and 4) calculating the edge weight of each height layer at each navigation side; 5) Selecting the average value of the edge weights of all height layers at each navigation side as the estimated weight of the navigation side; 6) Determining a horizontal airway satisfying airway restrictions; 7) Determining a vertical airway; 8) And outputting an optimization result. The invention can automatically plan the optimal airway according to the optimization target selected by the user, airway restriction, meteorological wind temperature and other conditions.

Description

International airline route planning method with traffic limitation
Technical Field
The invention relates to the technical field of civil aviation operation command and dispatch release, in particular to an international airline route planning method with traffic limitation.
Background
Before executing a civil flight, an airline distributor must determine an actual flight profile, calculate and determine a portable commercial load, and determine the fuel quantity and flight time required by the flight of the current flight according to meteorological conditions, navigation information, conditions of an airport and a landing site, navigation and navigation rules, loading capacity and equipment conditions of an airplane, airplane performance limits, various complex operation limits and laws and regulations. The air route planning is one of core technologies of a flight planning system, and an airline company makes a computer flight plan and a dispatch release of each flight through the flight planning system so as to standardize operation management, improve the working efficiency, control the flight operation risk, save the flight operation cost and increase the operation benefit.
The current method for planning the air route is mainly divided into two categories, the first category is an Optimal Control Theory method (Optimal Control Theory Approach), an Optimal path is obtained under the condition that the air route restriction is not considered, then the rule check is carried out, if the Optimal path violates the related air route restriction rule and is corrected into the path meeting the restriction on the basis of the path, the navigation distance, the navigation time, the navigation cost and the possible deviation of the corrected path and the obtained Optimal path are larger, and once the Optimal path violates the related air route restriction rule, the actual result quality of the air route planning cannot be guaranteed. The second type is a Network Optimization method (Network Optimization Approach), the optimal path is calculated by adopting a fixed pre-estimated weight value at the route edge of the Network, however, the method has large influence on oil consumption, speed and cost due to aircraft weight, wind direction, temperature and the like, and the error of an Optimization result is large.
Disclosure of Invention
The invention aims to provide an international airline route planning method with traffic limitation, which can automatically plan an optimal route according to the optimization target selected by a user, the route limitation, meteorological wind temperature and other conditions.
A method for planning an international airline route with a traffic limit comprises the following steps:
1) Loading the whole network route data to generate a route network diagram;
2) Cutting a navigation network graph according to a set takeoff airport and a set landing airport, and compressing the navigation network graph to obtain a directed graph G = { V, E } for optimization, wherein V is a set of all nodes in the directed graph, and E is a set of all navigation sides;
3) Using Dijkstra algorithm to calculate the shortest distance from the starting point to all nodes without limit and establish a shortest path dictionary MINDCit D Calculating the shortest distance from all nodes to the end point without restriction and establishing a shortest path dictionary MINDICT A The method is used for estimating the weight of the navigation side and estimating the estimated distance from the current point to the terminal point by the subsequent A-star algorithm;
4) Calculating the edge weight of each height layer at each navigation side according to a set optimization target and data comprising wind temperature, wind speed and course angle;
5) Selecting the average value of the edge weights of all height layers at the side of each route as the estimated weight of the route edge;
6) Determining a horizontal route meeting route limit by using the A-algorithm;
7) Determining a vertical airway according to the obtained horizontal airway;
8) Outputting an optimization result;
in order to achieve the purpose of controlling the route congestion, the air traffic control mechanism issues some restrictions on the route trend and the flow, part of the restrictions need to be combined with route points/routes/airspaces passed by the preorders to judge whether the conditions are met, the restrictions cannot be filtered in advance, and the restrictions must be checked and conservatively estimated while being planned, and the current global route restrictions are about one hundred thousand, and the restrictions can be generally classified into three types by combing the restrictions:
(1) Not available-Not accessible: when condition X is true, Y must be false;
(2) Comulsory-must pass through: when condition X is false, Y must be true;
(3) Only available-can pass through: y may be true only if condition X is true; where condition X and condition Y may be the passage of a waypoint, a combination of waypoints, a route, a portion of a route, or a airspace in a certain time period, type (3) may be converted to that when condition X is false, Y must be false, so the present invention classifies the route restriction rules as: (1) Not available-Not-accessible, (2) complex-must-pass;
the a-Star algorithm is the most effective direct search method for solving the shortest path in the static road network, and is also an effective algorithm for solving a plurality of search problems. The closer the distance estimation value in the algorithm is to the actual value, the faster the final search speed is. The invention adopts an improved A-algorithm, and the estimation function is as follows:
f (n) = g (n) + h (n); where f (n) is a cost estimate for each possible node, which is composed of two parts, where g (n) is the actual cost from the start point to the current node, h (n) is a cost estimate from the current node to the end point, and where h (n) is the pre-computed shortest distance from each waypoint node to the destination airport without constraints, each node retaining a plurality of extended paths, and adjusting h (n) to be the shortest path through all necessary nodes when there are waypoint-related constraints.
The invention also has the following preferred design:
in the step 6), the determination process of the horizontal route meeting the route limit is as follows:
(1) Establishing an open list and a close list, initializing the open list and the close list to be empty, and initializing the states of all nodes to be open;
(2) Initializing two rule sets R na And R cmp ，R na To optimize the Not available type rules of class (1), R, involved in the scope cmp Optimizing the rule of the type of the Compulsory (2) involved in the scope;
(3) Adding the starting point S into the open list, and representing the node { pre-node, cost, weight, time, C by using a five-dimensional label n Represents the preorder node, cost estimate, weight after the plane passes the point, time and must pass the node set, wherein pre-node, C n Initialization to null, cost initialization to MINDICT A If the open list is empty or the shortest path from the K starting points to the end point is found, the program is terminated, wherein K can be taken according to experience;
(4) If the open list is not empty, finding the node n with the minimum cost, namely the minimum f (n) value from the open list for path expansion;
(5) Deleting the node n from the open list, skipping the node if the state of the node n in the close list is close, selecting the next node, otherwise, carrying out rule check on the corresponding path, adding the path of the node into the close list if the rule is not violated, modifying the state of the node into close if the number of the nodes in the close list reaches K after the nodes are placed in the node n, and finding a path from a starting point to a terminal point if the node n is the terminal point, wherein the value of K is set according to experience so as to ensure that a feasible and better result is obtained;
(6) Traversing the node m of the node n with the outgoing edge, if m is not close, calculating the cost f (m), because the weight of the airplane reaching the starting point of each edge has great influence on the oil consumption, the cost and the time of the edge, when the edge is expanded, calculating the weight of the edge to be expanded from the starting point forward again according to the actual weight of the airplane when the edge is reached, updating MINDICT D And recording the weight of the aircraft after passing the edge, and checking whether R is triggered or not for the current expansion node cmp If yes, checking whether the necessary node set has the necessary node specified by the limitation, if yes, ignoring, otherwise, updating the necessary node set C m If necessary, set C m If the current node is empty, h (m) = the unrestricted shortest distance from the current node to the target point, otherwise, h (m) is the shortest path from the current node to the destination point through the inevitable point, namely the unrestricted shortest distance from the current node to the start point in the inevitable set + the unrestricted shortest distance between the middle point and the point in the inevitable set + the unrestricted shortest distance from the end point of the inevitable set to the target point, and the cost f (m) of the current expansion node is updated and added into the open list;
the weight of each navigation side is determined by the estimation weight of the shortest path without limit from the reverse direction to the end point and the estimation weight of the shortest path with limit from the starting point to the forward direction, so the weight is called as the bidirectional dynamic estimation weight;
(7) And (5) repeating the steps (4) to (6) until the shortest path with the target point is obtained or the open list is empty.
In order to narrow the optimization range, digging by using historical data of an actual airway within two years of an airway to be optimized, cutting an airway graph, and compressing the airway network graph, wherein the compression mode of the airway network graph in the step 2) is as follows: and for the nodes with the access degrees of 1, if the nodes are not the nodes in any path-related constraint condition and any restricted object, deleting the nodes, connecting a predecessor node and a successor node of the nodes to generate virtual edges, wherein the weight of each virtual edge is the sum of the weights of the nodes and the predecessor node and the weights of two navigation sides of the nodes and the successor node.
In order to reduce the optimization range and improve the optimization efficiency, the nodes and the route edges in the directed graph G are subjected to the following filtering treatment:
a) If the node is not any condition in the path-related constraint and the node in the constrained object and meets NM > C (N + M), wherein N is the degree of entry of the node, M is the degree of exit of the node, and C is a given parameter, deleting the node, and connecting a predecessor node and a successor node of the node to generate a virtual edge;
b) If another path which is shorter than the virtual edge exists between two nodes connected by any virtual edge, deleting the virtual edge with the longer path;
c) The rules which are irrelevant to the passing waypoints, the routes and the airspaces or the time for arriving at a certain waypoint, the route and the airspace and can be directly filtered through the taking-off time and the landing time of flights are called static rules, the static rules are preprocessed according to the known information including set model, set airport and set arriving airport, the corresponding waypoints, the routes and the airspaces are filtered, and the corresponding nodes and route edges are not generated in the directed graph G.
When the international airline route is planned, the global route network is huge, the optimization range is narrowed and nodes which are not on the shortest route due to limitation problems are removed through the navigation chart cutting, the route network diagram compression and the static rule preprocessing, and the optimization speed is effectively improved.
The optimization target of the navigation route comprises 4 types of shortest distance, time saving, oil consumption saving and cost saving, and the invention calculates the edge weight of each height layer at each navigation route aiming at different optimization targets:
if the optimization target is the shortest distance, taking the ground distance of the navigation side as the side weight;
if the optimization target is the most time-saving, the time of the aircraft passing through the navigation side is taken as the side weight;
if the optimization target is the most fuel-saving, taking the oil consumption of the aircraft passing through the side of the navigation road as the side weight;
and if the optimization target is the most cost-saving, taking the cost of the airplane passing through the navigation side as the side weight.
As a possible implementation: the time of the aircraft passing through the navigation side is obtained by the following processes:
estimating the weight of the airplane passing through each node, and searching the shortest path dictionary MINDICt D Obtaining the shortest distance between the starting point and all the nodes
Figure BDA0003703106590000071
Looking up the shortest path dictionary MINDict A Obtaining the shortest distance from all nodes to the terminal
Figure BDA0003703106590000072
The estimated weight of the aircraft passing each node
Figure BDA0003703106590000073
Wherein fc is the unit distance oil consumption,
Figure BDA0003703106590000074
is the shortest distance, W, of the point to the target airport ZFW The airplane has no oil weight;
estimating the time when the aircraft passes each point:
Figure BDA0003703106590000075
where t is the elapsed time in units of distance,
Figure BDA0003703106590000076
is the distance of the point from the starting point, T dep Is the takeoff time;
synthesizing the ground speed according to the vacuum speed, the wind speed and the course angle:
Figure BDA0003703106590000077
wherein v is a As aircraft speed，v w The wind speed is theta, and theta is an included angle between wind and a flight line;
the time when the plane passes by the navigation side
Figure BDA0003703106590000078
Wherein L is e Is the length of the edge of the route.
As a possible implementation, the oil consumption at the roadside is: f e ＝T e *FF e Wherein FF e For the fuel flow rate, T, corresponding to the edge of the route e The time when the aircraft passes by the navigation side.
As a possible implementation, the cost of the navigation side is:
Figure BDA0003703106590000079
wherein
Figure BDA00037031065900000710
The relative cost of fuel oil,
Figure BDA00037031065900000711
For time-related costs,
Figure BDA00037031065900000712
Is the route related cost.
The optimization of the vertical air route adopts a greedy algorithm, the height layer with the minimum cost is selected from the plurality of height layers to be used as the flight height of the airplane for iterative calculation, and iteration is finished when the difference between the calculated landing weight of the airplane and the input standard landing weight is within 10 KG.
The planning method of the invention adds the constraint that the height difference value of the adjacent route edges does not exceed the threshold value in the cruising stage so as to avoid the jumping of the height layer in the cruising stage.
The invention has the following beneficial effects:
1. the invention provides a civil aviation route optimization method for the bidirectional dynamic estimation of the weight, which meets the complex route limit, through an improved A-x algorithm, can expand a plurality of optimal paths under the route related limit, and avoids the condition that the quality of a corrected path result cannot be ensured when the optimal path without considering the limit violates the rule.
2. The invention cuts the navigation chart, compresses the navigation network chart and preprocesses the static rule, reduces the optimization range, eliminates the nodes which are no longer on the shortest path due to the limitation problem and effectively improves the optimization speed.
3. According to the method, aiming at the optimization targets of the shortest distance, the most time-saving, the most oil-saving and the most cost-saving, the weight of the navigation side is respectively estimated, the influence of the weight, the wind direction, the temperature and the speed cost of the airplane is considered, and the error of the optimization result is reduced.
Drawings
FIG. 1 is a flow chart of a method of international airline route planning with traffic restrictions of the present invention;
FIG. 2 is a schematic diagram of a clipping of a navigation network diagram in an embodiment;
FIG. 3 is a schematic diagram showing the result of route planning in the embodiment.
Detailed Description
The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and examples so that those skilled in the art can better understand and implement the technical solutions of the present invention.
A method for planning an international airline route with a traffic restriction, as shown in FIG. 1, comprises the following steps:
1) And loading the whole network route data to generate a route network diagram.
2) And cutting the network map according to the set take-off airport and landing airport, and compressing the network map to obtain the directed graph G = { V, E } for optimization, wherein V is the set of all nodes in the directed graph, and E is the set of all navigation sides.
In this embodiment, the actual route historical data of the airline to be optimized within two years is used for mining and cutting the route map, the cutting mode is as shown in fig. 2, a route network in an ellipse with a take-off airport and a landing airport as focuses and a route network with the take-off airport and the landing airport as centers of circles and within a radius R are obtained as an optimization range, only the route edge in the range is considered, and the calculation scale is effectively reducedWherein R = R 1 *|F 1 F 2 |+375*1852，r 1 Was 0.0045. To ensure that the ellipse is not too small, R has a minimum value of 400 nautical miles and a maximum value of 1000 nautical miles.
The ellipse is a track of a moving point E, wherein the sum of distances from a plane to a starting point F1 and a terminal point F2 is equal to a constant (greater than | F1F2 |), and the F1 and the F2 are two focuses of the ellipse. The mathematical expression is as follows: i EF1| + | EF2| =2a (2 a > | F1F2 |).
In this example, 2a = | F 1 F 2 |+2R。
Compressing the navigation network diagram in the following way: and for the node with the access degree of 1, if the node is not the node in any path-related condition and any path-related restricted object, deleting the node, connecting a predecessor node and a successor node of the node to generate a virtual edge, wherein the weight of the virtual edge is the sum of the weights of the node and the predecessor node as well as the two navigation sides of the node and the successor node.
As a preferred embodiment: in order to reduce the optimization range and improve the optimization efficiency, the following filtering processing is carried out on the nodes and the route edges in the directed graph G:
a) If the node is not a node in any path-related constraint condition and a constraint object and meets NM > C (N + M) (wherein N is the entry degree of the node, M is the exit degree of the node, and C is a given parameter), deleting the node, and connecting a predecessor node and a successor node of the node to generate a virtual edge;
b) If another path which is shorter than the virtual edge exists between two nodes connected by any virtual edge, deleting the virtual edge with the longer path;
c) The static rule is preprocessed according to known information including set model, departure airport and arrival airport, the static rule is filtered to filter out corresponding waypoint, route and airspace, corresponding nodes and route edges are not generated in a directed graph G, the static rule is obtained by filtering and screening notification data, the notification data is one of limiting data in route planning, the static rule is mainly used for explaining the available states of limiting objects such as waypoints, route edges, airspace and the like, the limiting types are generally limited by time and edge height, the limitation is not influenced by the preorder route, and the static rule can be preprocessed to reduce the optimization range.
3) Using Dijkstra algorithm to calculate the shortest distance from the starting point to all nodes without limit and establish a shortest path dictionary MINDCt D Calculating the shortest distance from all nodes to the end point without limitation and establishing a shortest path dictionary MINDICT A And the method is used for estimating the weight of the navigation side and estimating the estimated distance from the current point to the terminal point by a subsequent A-star algorithm.
In this example, MINDIct D The calculation steps are as follows:
1) Declaring an array dis to store the shortest distance from the source point to each vertex and a set T storing vertices where the shortest path has been found;
2) The path weight of the source point s is given as 0 (dis [ s ] = 0), and if there is an edge (s, m) to which s can directly reach for the vertex v, let dis [ m ] = w (s, m), where w (s, m) is the weight of the edge (s, m). Meanwhile, the path length of all other vertexes (namely, vertexes which can not be directly reached by s) is set to be + ∞, and initially, the set T only has the vertex s;
3) Selecting the minimum value from the dis array, and adding the minimum value to the set T, wherein the minimum value is the shortest path from the source point s to the vertex corresponding to the value;
4) Checking whether the newly added vertex can reach other vertices and judging whether the path length of the vertex reaching other points is shorter than the path length of the vertex directly reaching the source point, and if so, replacing the values of the vertices in dis;
5) Find the minimum value from dis again, repeat 3), 4) until the set T contains the target point,
similarly, MINDICT can be obtained A 。
4) And calculating the edge weight of each height layer at each navigation side according to the set optimization target and the data comprising the wind temperature, the wind speed and the course angle.
The optimization target of the navigation route comprises 4 types of shortest distance, time saving, oil consumption saving and cost saving, and the edge weight of each height layer at the side of each navigation route is calculated aiming at different optimization targets.
And if the optimization target is the shortest distance, taking the ground distance of the navigation side as the side weight.
And if the optimization target is the most time-saving, taking the time of the plane passing through the navigation side as the side weight. The time of the aircraft passing through the navigation side is obtained by the following processes:
estimating the weight of the airplane passing through each node, and searching the shortest path dictionary MINDICt D Obtaining the shortest distance between the starting point and all the nodes
Figure BDA0003703106590000121
Looking up the shortest path dictionary MINDCit A Obtaining the shortest distance from all nodes to the terminal
Figure BDA0003703106590000122
The estimated weight of the aircraft passing each node
Figure BDA0003703106590000123
Wherein fc is the fuel consumption per unit distance,
Figure BDA0003703106590000124
the shortest distance, W, from the point to the target airport ZFW The airplane has no oil weight;
estimating the time when the aircraft passes each point:
Figure BDA0003703106590000125
where t is the elapsed time in units of distance,
Figure BDA0003703106590000126
is the distance of the point from the starting point, T dep Is the takeoff time;
synthesizing the ground speed according to the vacuum speed, the wind speed and the course angle:
Figure BDA0003703106590000127
wherein v is a As aircraft speed, v w The wind speed is theta, and theta is an included angle between wind and a flight line;
the time when the aircraft passes the navigation side
Figure BDA0003703106590000128
Wherein L is e Is the length of the edge of the airway.
And if the optimization target is the most fuel-saving, taking the oil consumption of the aircraft passing through the navigation side as the side weight. The oil consumption of the navigation side is as follows: f e ＝T e *FF e Wherein FF e Fuel flow, T, for the fairway side e The time when the aircraft passes by the navigation side.
And if the optimization target is the most cost-saving, taking the cost of the aircraft passing through the side of the navigation as the side weight.
The cost of the navigation side is as follows:
Figure BDA0003703106590000129
wherein
Figure BDA00037031065900001210
A cost associated with fuel oil;
Figure BDA00037031065900001211
time-related costs, mainly time-related aircraft maintenance costs, time-related personnel costs;
Figure BDA00037031065900001212
and is a route-related cost, mainly a route fee.
5) Selecting the average value of the edge weights of all height layers at the side of each route as the estimated weight of the route edge;
6) Using said A * Determining a horizontal route meeting the route limit by an algorithm;
in order to achieve the purpose of controlling the route congestion, the air traffic control mechanism issues some restrictions on the route trend and the flow, part of the restrictions need to be combined with route points/routes/airspaces passed by the preorders to judge whether the conditions are met, the restrictions cannot be filtered in advance, and the restrictions must be checked and conservatively estimated while being planned, and the current global route restrictions are about one hundred thousand, and the restrictions can be generally classified into three types by combing the restrictions:
(1) Not available-Not accessible: when condition X is true, Y must be false;
(2) Comulsory-must pass through: when condition X is false, Y must be true;
(3) Only available-can pass through: y may be true only if condition X is true; where conditions X and Y may be the passage of a waypoint, a combination of waypoints, a route, a portion of a route, or an airspace over a certain period of time, type (3) may translate into that when condition X is false, Y must be false, so the route restriction rules may be classified as: (1) Not available-Not-accessible, (2) complex-must-pass;
the a-Star algorithm is the most effective direct search method for solving the shortest path in the static road network, and is also an effective algorithm for solving a plurality of search problems. The closer the distance estimation value in the algorithm is to the actual value, the faster the final search speed is. The invention adopts the improved A * The algorithm, the valuation function of which is:
f (n) = g (n) + h (n); where f (n) is a cost estimate for each possible node, and is composed of two parts, where g (n) is the actual cost from the start point to the current node, and h (n) is a cost estimate from the current node to the end point, in MINDICT A The pre-calculated shortest distance from each route node to the destination airport without the restriction is used as h (n), each node reserves a plurality of extension paths, and the h (n) is adjusted to be the shortest path passing through all necessary nodes when the relevant restriction of the route exists.
The process of determining the horizontal route which meets the route limit is as follows:
(1) Establishing an open list and a close list, initializing the open list and the close list to be empty, and initializing the states of all nodes to be open;
(2) Initializing two rule sets R na And R cmp ，R na To optimize the Not available type rule of class (1) involved in the scope, R cmp The rule is the rule of the category (2) Comulsory type involved in the optimization scope;
(3) Adding the starting point S into the open list, and representing the node { pre-node, cost, weight, time, C by using a five-dimensional label n Represents the preorder node, cost estimate, weight after the plane passes the point, time and must pass the node set, wherein pre-node, C n Initialization to null, cost initialization to MINDICT A If the open list is empty or the shortest path from the K starting points to the end point is found, the program is terminated, wherein K can be taken according to experience;
(4) If the open list is not empty, finding the node n with the minimum cost, namely the minimum f (n) value from the open list for path expansion;
(5) Deleting the node n from the open list, skipping the node if the state of the node n in the close list is close, selecting the next node, otherwise, carrying out rule check on the corresponding path, adding the path of the node into the close list if the rule is not violated, modifying the state of the node into close if the number of the nodes in the close list reaches K after the nodes are placed in the node n, and finding a path from the starting point to the end point if the node n is the end point;
(6) Traversing the node m of the node n with the outgoing edge, if m is not close, calculating the cost f (m), because the weight of the airplane reaching the starting point of each edge has great influence on the oil consumption, the cost and the time of the edge, when the edge is expanded, calculating the weight of the edge to be expanded from the starting point forward again according to the actual weight of the airplane when the edge is reached, updating MINDICT D And recording the weight of the aircraft after passing the edge, and checking whether R is triggered or not for the current expansion node cmp If yes, checking whether the requisite node specified by the limitation exists in the requisite node set, if yes, ignoring, otherwise updating the requisite set C m If necessary set C m If the current node is empty, h (m) = the shortest distance without limit from the current node to the target point, otherwise h (m) is the shortest path from the current point to the destination point through the must-pass point, that is, the current node is not limited to the starting point in the must-pass setUpdating the cost f (m) of the current expansion node and adding the cost f (m) into the open list;
the weight of each route side is determined by the estimation weight of the shortest path without limit from the reverse direction to the terminal point and the shortest path with limit from the starting point to the terminal point from the forward direction, so the weight is called as the bidirectional dynamic estimation weight;
(7) And (5) repeating the steps (4) to (6) until the shortest path with the target point is obtained or the open list is empty.
7) Determining a vertical airway according to the obtained horizontal airway;
the flight height of the airplane in the air is determined, the optimal height layer can be directly adopted to be used as the flight height layer of the airplane for calculation during vertical optimization of the shortest distance algorithm, the most time-saving and most fuel-saving vertical optimization algorithm adopts the idea of a greedy algorithm, and the height layer with the most time-saving, most fuel-saving and most cost-saving functions is selected from the selectable height layers to be used as the flight height layer of the airplane for calculation.
In the vertical optimization process, when cruise calculation is performed on each edge, a cruise end point, namely a TOD point, needs to be estimated in advance. The following decisions need to be made: 1) Judging whether the edge starts to descend from the starting point of the edge and can reach a BOD point, namely a descending bottom point; 2) Whether the BOD point can be reached or not is determined by descending from the end point; 3) If the former can reach BOD but the latter cannot, the navigation system starts to descend from the edge, calculates TOD point information, and ends the cruise.
As shown in FIG. 3, A is the takeoff airport, G is the target airport, and the horizontal road from A to G is known as ABCDEFG;
the gray line indicates whether or not the aircraft can reach the BOD point by descending from the point position, and the BOD point can be reached by any of TOC, C, D, and E points, but the aircraft cannot be descended to the ground by the remaining horizontal distance at the point F point, so that the descending process is performed from the edge EF, the TOD point is calculated, and the cruise is completed.
And optimizing the vertical air route by adopting a greedy algorithm, selecting the height layer with the minimum cost from the plurality of height layers as the flight height of the airplane to carry out iterative computation, and finishing the iteration when the difference between the computed airplane landing weight and the input standard landing weight is less than 10 KG.
The planning method of the invention adds the constraint that the height difference value of the adjacent route edges does not exceed the threshold value in the cruising stage so as to avoid the jumping of the height layer in the cruising stage.
8) Outputting an optimization result;
the following is the comparison test data of the route planning method of the invention and the route planning software LIDO commonly used by airlines:
the effect of the route planning of the invention is verified by taking Guangzhou to Amsterdam routes as test routes. The data in table 1 are the calculated optimization results for different optimization objectives compared to the conventional route planning software LIDO. During comparison, all the limitations of the routes are considered according to actual conditions. It can be seen that the difference between the prediction result calculated by the present invention and the LIDO prediction result is not more than 1%. The shortest distance results are better than LIDO. The method of the invention achieves the optimization goal, and the result can be used for planning the restricted air route.
Table 1.
Figure BDA0003703106590000171
The above-mentioned embodiments are merely preferred embodiments of the present invention, but should not be construed as limiting the invention, and any variations and modifications based on the concept of the present invention should fall within the scope of the present invention, which is defined by the claims.

Claims (10)

1. A method for planning an international airline route with a traffic limit is characterized by comprising the following steps:
1) Loading the whole network route data to generate a route network diagram;
2) Cutting the air line network graph according to a set takeoff airport and a set landing airport, and compressing the air line network graph to obtain a directed graph G = { V, E } for optimization, wherein V is a set of all nodes in the directed graph G, and E is a set of all navigation sides in the directed graph G;
3) Using Dijkstra algorithm to calculate the shortest distance from the starting point to all nodes without limit and establish a shortest path dictionary MINDCit D Calculating the shortest distance from all nodes to the end point without limitation and establishing a shortest path dictionary MINDICT A The method is used for estimating the weight of the navigation side and estimating the estimated distance from the current point to the terminal point by the subsequent A-star algorithm;
4) Calculating the edge weight of each altitude layer at each navigation side according to a set optimization target and data comprising wind temperature, wind speed and course angle;
5) Selecting the average value of the edge weights of all height layers at each navigation side as the estimated weight of the navigation side;
6) Determining a horizontal route meeting route limit by using the A-x algorithm;
7) Determining a vertical airway according to the obtained horizontal airway;
8) Outputting an optimization result;
the route restriction rules are classified as: (1) Not available-Not-passable; (2) The Compulsory-must pass through,
a is described * The evaluation function of the algorithm is:
f (n) = g (n) + h (n); where f (n) is the cost estimate for each possible node, and is composed of two parts, where g (n) is the actual cost from the start point to the current node, and h (n) is the cost estimate from the current node to the end point, in MINDICT A The pre-calculated shortest distance from each route node to the destination airport without the restriction is used as h (n), each node reserves a plurality of extension paths, and adjusts h (n) to be the shortest path passing through all necessary nodes when the relevant restriction of the route exists.
2. The method of restricted international airline route planning according to claim 1, characterized by: in the step 6), the determination process of the horizontal route meeting the route limit is as follows:
(1) Establishing an open list and a close list, initializing the open list and the close list to be empty, and initializing the states of all nodes to be open;
(2) Initializing two rule sets R na And R cmp ，R na To optimize the Not available type rules of class (1), R, involved in the scope cmp The rule is the rule of the category (2) Comulsory type involved in the optimization scope;
(3) Adding the starting point S into the openlist, and representing the node { pre-node, cost, weight, time, C by using a five-dimensional label n Represents the preorder node, cost estimate, weight after the plane passes the point, time and must pass the node set, wherein pre-node, C n Initialization to null, cost initialization to MINDICT A If the open list is empty or the shortest path from the K starting points to the end point is found, the program is terminated;
(4) If the openlist is not empty, finding the node n with the minimum cost, namely the minimum f (n) value from the openlist to carry out path expansion;
(5) Deleting the node n from the open list, skipping the node if the state of the node n in the close list is close, selecting the next node, otherwise, carrying out rule check on the corresponding path, adding the path of the node into close if the rule is not violated, modifying the state of the node into close if the number of the nodes in close reaches K after the nodes are placed in the node n, and finding a path from the starting point to the end point if the node n is the end point;
(6) Traversing the node m of the node n with the outgoing edge, if m is not close, calculating the cost f (m), because the weight of the airplane reaching the starting point of each edge has great influence on the oil consumption, the cost and the time of the edge, when the edge is expanded, the weight of the edge to be expanded is calculated from the starting point in the forward direction again according to the actual weight of the airplane when the edge is reached, and updating MINDICT D And recording the weight of the aircraft after passing the edge, and checking whether R is triggered or not for the current expansion node cmp If yes, checking whether the requisite node specified by the limitation exists in the requisite node set, if yes, ignoring, otherwise updating the requisite set C m If necessary set C m Null, h (m) = current node toIf not, h (m) is the shortest path from the current point to the end point through the must-pass point, namely the shortest path from the current node to the start point in the must-pass set without limitation + the shortest path between the middle point and the point in the must-pass set without limitation + the shortest path from the end point of the must-pass set to the target point without limitation, and the cost f (m) of the current extended node is updated and added into openlist;
the weight of each navigation side is determined by the estimation weight of the shortest path without limit from the reverse direction to the end point and the estimation weight of the shortest path with limit from the starting point to the forward direction, so the weight is called as the bidirectional dynamic estimation weight;
(7) And (5) repeating the steps (4) to (6) until the shortest path of the target point is obtained or the openlist is empty.
3. The method for planning an international airline route with traffic restriction according to claim 2, wherein the compression manner of the airline network map in the step 2) is: and for the node with the access degree of 1, if the node is not the node in any path-related condition and any path-related restricted object, deleting the node, connecting a predecessor node and a successor node of the node to generate a virtual edge, wherein the weight of the virtual edge is the sum of the weights of the node and the predecessor node as well as the two navigation sides of the node and the successor node.
4. The method of restricted international airline route planning according to claim 3, wherein the following filtering process is performed for the nodes and route edges in the directed graph G:
a) If the node is not any condition in the path-related constraint and the node in the constrained object and meets NM > C (N + M) (wherein N is the degree of entry of the node, M is the degree of exit of the node, and C is a given parameter), deleting the node, and connecting a predecessor node and a successor node of the node to generate a virtual edge;
b) If another path which is shorter than the virtual edge exists between two nodes connected by any virtual edge, deleting the virtual edge with the longer path;
c) The rules which are irrelevant to the passing waypoints, the routes and the airspaces or the time for arriving at a certain waypoint, the route and the airspace and can be directly filtered through the taking-off time and the landing time of flights are called static rules, the static rules are preprocessed according to the known information including set model, set airport and set arriving airport, the corresponding waypoints, the routes and the airspaces are filtered, and the corresponding nodes and route edges are not generated in the directed graph G.
5. The method for planning a restricted international airline route according to claim 1, characterized in that in steps 4) and 5):
if the optimization target is the shortest distance, taking the ground distance of the navigation side as the side weight;
if the optimization target is the most time-saving, the time of the aircraft passing through the navigation side is taken as the side weight;
if the optimization target is the most fuel-saving, taking the oil consumption of the aircraft passing through the side of the navigation road as the side weight;
and if the optimization target is the most cost-saving, taking the cost of the aircraft passing through the side of the navigation as the side weight.
6. The method of restricted international airline route planning according to claim 5, wherein the time for the aircraft to pass by the route side is obtained by:
estimating the weight of the airplane passing through each node, and searching the shortest path dictionary MINDICt D Obtaining the shortest distance between the starting point and all the nodes
Figure FDA0003703106580000051
Looking up the shortest path dictionary MINDict A Obtaining the shortest distance from all nodes to the terminal
Figure FDA0003703106580000052
The estimated weight of the aircraft passing each node
Figure FDA0003703106580000053
Wherein fc is the unit distance oil consumption,
Figure FDA0003703106580000054
the shortest distance, W, from the point to the target airport ZFW The airplane has no oil weight;
estimating the time when the aircraft passes each point:
Figure FDA0003703106580000055
where t is the elapsed time per unit distance,
Figure FDA0003703106580000056
is the distance of the point from the starting point, T dep Is the takeoff time;
synthesizing the ground speed according to the vacuum speed, the wind speed and the course angle:
Figure FDA0003703106580000057
wherein v is a As aircraft speed, v w The wind speed is theta, and theta is an included angle between wind and a flight line;
the time when the plane passes by the navigation side
Figure FDA0003703106580000058
Wherein L is e Is the length of the edge of the airway.
7. The method of claim 5, wherein the fuel consumption at the roadside is: f e ＝T e *FF e Wherein FF e Fuel flow, T, for the fairway side e The time when the aircraft passes by the navigation side.
8. The method of restricted international airline route planning of claim 5, wherein the cost of the route side is:
Figure FDA0003703106580000059
wherein
Figure FDA00037031065800000510
The relative cost of fuel oil,
Figure FDA0003703106580000061
For time-related costs,
Figure FDA0003703106580000062
For the cost associated with the airway.
9. The method for planning a restricted international airline route according to claim 1, characterized in that in step 7): and optimizing the vertical air route by adopting a greedy algorithm, selecting the height layer with the minimum cost from the plurality of height layers as the flight height of the airplane to carry out iterative computation, and finishing the iteration when the difference between the computed airplane landing weight and the input standard landing weight is less than 10 KG.
10. The method of restricted international airline route planning according to claim 9, wherein a constraint is added during the cruise phase that the difference in altitude between adjacent airlines does not exceed a threshold value, to avoid a jump in the altitude layer during the cruise phase.
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