Dynamic aviation flow regulation and control method

Abstract

The invention relates to a dynamic aviation flow regulation and control method, belongs to the technical field of aviation flow management, solves the problems that global airspace information cannot be utilized, airspace resources are difficult to fully utilize and obtained decisions are not comprehensive in the prior art, and comprises the following steps: S1, obtaining initial data, and building a simulation environment according to the obtained initial data; s2, determining a state set and a function set according to the initial data to generate a multi-tree individual; s3, setting a selection operator, a crossover operator and a mutation operator of multiple tree individuals in genetic programming; s4, a multi-tree individual initialization method is set, and multi-tree individuals with the set population number are obtained; step S5, calculating fitness values of the multi-tree individuals based on a simulation environment; s6, performing evolution learning based on the obtained multi-tree individuals, and outputting the trained optimal multi-tree individuals and optimal rules; and S7, carrying out analog simulation on the obtained optimal multi-tree individual and the optimal rule, and obtaining and outputting a scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation traffic management, and in particular to a dynamic aviation traffic control method. Background Art

[0002] With the booming growth of China's economy and the rapid development of the aviation transportation industry, my country's flight traffic has grown explosively. The continued rapid growth of flight traffic has brought tremendous pressure and challenges to aviation traffic safety. Limited by airspace resources such as sector capacity, air traffic congestion has become a focal issue affecting the development of my country's aviation transportation industry, especially near busy routes and airports, where flight delays are frequent. In addition, bad weather is a major challenge to air traffic control. Weather factors can cause changes in sector capacity, thereby interfering with pre-set route plans and making control plans easily ineffective. Existing measures often require dispatchers to control flights based on historical information. This method cannot utilize global airspace information, and the decisions made are not comprehensive enough. It is difficult to fully utilize airspace resources and alleviate the pressure on the aviation network.

[0003] Therefore, there is a need in the art for an aviation traffic control method that can fully utilize airspace resources and alleviate the pressure on the aviation network. Summary of the invention

[0004] In view of the above problems, the present invention provides a dynamic aviation traffic control method to solve the problem of excessive pressure on the aviation network in the prior art.

[0005] The present invention provides a dynamic aviation traffic control method, comprising the following steps:

[0006] Step S1, obtaining initial data and building a simulation environment based on the obtained initial data, the initial data including obtaining historical weather information, historical flight information, route information and actual aviation sector planning from a database, as well as real-time weather, special control conditions, routes and flight information of the current aircraft;

[0007] Step S2, determining a state set and a function set according to the initial data, which are used to generate a multi-tree individual, wherein the state set includes a sector state set and an aircraft state set, the sector state set is used to construct a flight route rule function, and the aircraft state set is used to construct a flight priority function;

[0008] Step S3, setting the selection operator, crossover operator and mutation operator of the multi-tree individuals in genetic programming, for performing selection, crossover and mutation operations on the generated multi-tree individuals;

[0009] Step S4, setting a multi-tree individual initialization method, initializing each knowledge tree in the majority of individuals, and obtaining a multi-tree individual with a set population size;

[0010] Step S5, determining a method for evaluating the fitness of multiple tree individuals, and calculating the fitness values ​​of multiple tree individuals based on a simulation environment;

[0011] Step S6, performing evolutionary learning based on the obtained multi-tree individuals, and outputting the best multi-tree individuals and optimal rules after training;

[0012] Step S7, simulating the best multi-tree individuals and the best rules, obtaining and outputting a scheduling solution.

[0013] Optionally, the step S2 specifically includes the following steps:

[0014] Step S2.1, select a sector state set for constructing a flight route rule function. The sector state set includes the following features: the flight distance L from the current sector node i to the next neighbor sector node j i,j ; Flight distance S from the next neighbor sector node j to the end sector node d j,d , the destination sector node d is the sector where the destination airport is located; according to the initial data obtained, the maximum flight capacity C on the route formed by the current sector node i to the next neighbor sector node j is calculated i,j , get the number of flights on the route at the current time in real time as f i,j , the neighboring sector node set v of the current sector node i i , the estimated flight time ET of the flight from the current sector node i to the neighboring sector node j i,j ; The number of aircraft assigned to the current sector node i PN i , the normalized density of flight traffic on the route formed by the current sector node i and the neighboring sector node j, and the average traffic density between the current sector node i and all neighboring sector nodes;

[0015] Step S2.2, select an aircraft state set for constructing a flight priority function, the aircraft state set includes the following features: the distance LD from the sector node i where the aircraft is currently located to the sector node d where the target airport is located i,d ND is the distance from the next sector node j where the aircraft is expected to arrive to the sector node d where the target airport is located. j,d , the number of sector nodes that the current flight has flown through FS, the flight time T of the current flight, and the delay FD of the start time of the current flight relative to the departure time of the first flight of the day;

[0016] Step S2.3, select a set of functions related to the problem, including selecting features from operators: {+, -, *, ÷, max, min};

[0017] Step S2.4, constructing a flight route rule function knowledge tree according to the selected sector state set and function set, and constructing a flight priority function knowledge tree according to the selected aircraft state set and function set, and together forming multiple tree individuals.

[0018] Optionally, the sector status set further includes:

[0019] Based on the characteristics of the obtained sector state set, the normalized density of flight flow on the route formed by the current sector node i and the neighboring sector node j is obtained:

[0020] ρ i,j =f i,j / C i,j ;

[0021] And, based on the characteristics of the obtained sector state set, the average traffic density between the current sector node i and all neighboring sector nodes is obtained:

[0022]

[0023] Optionally, the step S3 specifically includes the following steps:

[0024] Step S3.1, setting the selection operator: calculating the fitness of each multi-tree individual and converting the fitness into a ratio; assigning a corresponding sector on the wheel to each multi-tree individual according to the ratio; then rotating the wheel and selecting the multi-tree individual according to the sector it falls on;

[0025] Step S3.2, setting a crossover operator: in the crossover process of two parent multi-tree individuals selected by the selection operator, firstly, randomly select one knowledge tree from the two knowledge trees in each of the two parent multi-tree individuals to directly exchange, and then randomly select a subtree structure from the other knowledge tree in each of the two parent multi-tree individuals, and migrate the subtree structures to the tree structure of the other party's knowledge tree, respectively, to obtain two child multi-tree individuals;

[0026] Step S3.3, set the mutation operator, randomly select a non-leaf node of a knowledge tree in the multi-tree individual, use the knowledge tree of the new expression to replace the subtree with the non-leaf node as the root, and obtain the offspring multi-tree individual. The new expression is the formula after the knowledge tree is decoded.

[0027] Optionally, the step S4 specifically includes the following steps:

[0028] Step S4.1, select features from the sector status set as leaf nodes, select features from the function set as non-leaf nodes, and construct a flight route rule function knowledge tree; select features from the aircraft status set as leaf nodes, and select features from the function set as non-leaf nodes, and construct a flight priority knowledge tree;

[0029] Step S4.2, specifying the minimum height of the generated knowledge tree and the maximum height of the generated knowledge tree;

[0030] Step S4.3, selecting an initialization function from the genFull function and the genGrow function;

[0031] Step S4.4, initialize the population, randomly generate multi-tree individuals of the set population size, each multi-tree individual is composed of two knowledge trees, namely the flight route rule function knowledge tree and the flight priority function knowledge tree.

[0032] Optionally, the step S6 specifically includes the following steps:

[0033] Step S6.1, determine the basic parameters required for evolutionary calculation according to the problem and computing power, including population size, evolutionary generations, crossover probability and mutation probability;

[0034] Step S6.2, initializing the population, including randomly generating a multi-tree individual of the determined population number, each multi-tree individual is composed of two knowledge trees representing the flight route rule function and the flight priority function, and initializing the current generation to 0;

[0035] Step S6.3, traverse each multi-tree individual in the current population and perform operations to generate a descendant population;

[0036] Step S6.4, evaluating the fitness values ​​of multiple tree individuals in the current population and the descendant population;

[0037] Step S6.5, merging the multi-tree individuals in the current population and the offspring population, sorting them from high to low according to the fitness value, and selecting the multi-tree individuals of the determined population number to form a new population;

[0038] Step S6.6, determine whether the current new population meets the termination condition, if so, proceed to the next step, otherwise return to step S6.3, the termination condition is the set termination generation;

[0039] Step S6.7: Select the individual with the highest fitness value from the current new population as the best multi-tree individual, decode the two knowledge trees of the multi-tree individual into specific executable expressions as the extracted optimal rule, and save the best multi-tree individual and the optimal rule.

[0040] Optionally, the step S6.3 specifically includes the following steps:

[0041] Step S6.3.1, randomly select a multi-tree individual from the current population and generate a random operation selector, generating a random number in the range [0,1] to determine the operation type;

[0042] Step S6.3.2, compare the random number with the determined crossover probability. If the random number is less than the crossover probability, randomly select another multi-tree individual from the population, and use the current multi-tree individual as the parent multi-tree individual. Perform a crossover operation through the crossover operator to obtain two child multi-tree individuals. Add the obtained two child multi-tree individuals to the child population.

[0043] Step S6.3.3, compare the random number with the determined mutation probability. If the random number is less than the mutation probability, perform a mutation operation on the current multi-tree individual through the mutation operator, and add the obtained offspring multi-tree individual to the offspring population, and execute step S6.3.5. If the random number is not less than the mutation probability, execute step S6.3.4;

[0044] Step S6.3.4, compare the random number with the determined crossover probability, if the random number is not less than the crossover probability, directly add the current multi-tree individual to the offspring population, otherwise no processing is performed;

[0045] Step S6.3.5, repeat the above steps S6.3.1 to S6.3.4 until every multi-tree individual in the current population is traversed to obtain the offspring population.

[0046] Optionally, step S7 specifically includes the following steps:

[0047] Step S7.1, setting the simulation deadline T of this simulation, the random seed of the random perturbation of the sector capacity, and obtaining the flight route rule function and the flight priority function;

[0048] Step S7.2, initialize the current simulation time t to 0, and the number of flights that have completed scheduling to 0;

[0049] Step S7.3, add the newly departing flights at the current simulation time t to the simulation environment, and update the number of flights accommodated by the corresponding starting sector node; update the number of flights accommodated by all sector nodes at the current simulation time t according to the number of arrived aircraft; update the flight flow on all routes at the current simulation time t;

[0050] Step S7.4, for each flight to be scheduled in the current sector node i at the current simulation time t, calculate the specific value of the flight priority function state set determined in stage 1: LD i,d , N.D. j,d ,FS,T,FD,substitute the obtained state into the flight priority function to calculate the flight priority, and sort the flights in the current sector node i from high to low according to the priority; the same sorting operation is performed for the flights of other sector nodes;

[0051] Step S7.5, for each flight to be scheduled in the current sector node i at the current simulation time t, calculate the specific value of the flight route rule function state set determined in stage 1: L i,j , S j,d , ρ i,j , ET i,j , PN i , substitute the obtained state into the flight route rule function to calculate the priority of neighbor sector nodes, and select the sector node with the highest priority as the next target node;

[0052] Step S7.6, check whether each flight has arrived at the target sector node at the current simulation time t, and update the number of flights that have completed scheduling accordingly;

[0053] Step S7.7, if the current simulation time t is not less than the simulation deadline T, stop the simulation and output the number of flights that have completed scheduling ret, as well as the current scheduling plan; otherwise, update the current simulation time t to t+1 and return to step S7.3.

[0054] Compared with the prior art, a dynamic air traffic control method provided according to an embodiment of the present invention has at least the following beneficial effects:

[0055] 1. The present invention can obtain better route planning in a short time, respond to emergencies in real time, and does not rely on knowledge and experience.

[0056] 2. The present invention has strong generalization ability to cope with complex scenarios.

[0057] 3. The present invention is closer to the real aviation dispatch scenario, and simultaneously learns two rules: aircraft heading and waypoint flight.

[0058] 4. The representation framework of the rules of the present invention is flexible enough to accommodate a wide range of variables and parameters, while maintaining sufficient structure to ensure the effectiveness and interpretability of the rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 The present invention provides a flowchart of a dynamic aviation traffic control method according to an embodiment of the present invention.

[0061] Figure 2 The figure is a schematic diagram schematically showing the current sector and adjacent sectors in aviation sector planning.

[0062] Figure 3 A schematic diagram of multiple tree individuals consisting of a flight route rule function knowledge tree and a flight priority function knowledge tree in an example of applying the dynamic air traffic control method provided according to an embodiment of the present invention.

[0063] Figure 4 A schematic diagram of a process of crossover operation of two multi-tree individuals in an example of a dynamic air traffic control method provided according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0064] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0066] A dynamic air traffic control method provided according to an embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0067] Explanation of symbols:

[0068] L i,j , the flight distance from the sector node i where the aircraft is currently located to the next neighbor sector node j;

[0069] S j,d , the flight distance from the next neighbor sector node j to the destination sector node d;

[0070] ρ i,j , the normalized density of flight flow on the route formed by the current sector node i and the neighboring sector node j;

[0071] C i,j , the maximum flight capacity on the route between the current sector node i and the neighboring sector node j;

[0072] f i,j , the number of flights on the route at the current time is;

[0073] The average traffic density between the current sector node i and all neighboring sector nodes;

[0074] V i , the set of neighboring sector nodes of the current sector node i;

[0075] ET i,j , the estimated flight time of the flight from the current sector node i to the neighboring sector node j;

[0076] PN i , the number of aircraft assigned to the current sector node i;

[0077] LD i,d , the distance from the sector node i where the aircraft is currently located to the sector node d where the target airport is located;

[0078] ND j,d , the distance from the next sector node j where the aircraft is expected to arrive to the sector node d where the target airport is located;

[0079] FS, the number of sector nodes that the current flight has flown through;

[0080] T, the time the current flight has flown;

[0081] FD, the delay of the current flight start time relative to the departure time of the first flight of the day;

[0082] min, minimum height of the knowledge tree;

[0083] max, the maximum height of the knowledge tree;

[0084] population_size, population size;

[0085] current, current algebra;

[0086] seed, random seed;

[0087] T, simulation deadline;

[0088] t, current simulation time;

[0089] ret, the number of flights that have completed scheduling.

[0090] like Figure 1 As shown, a dynamic aviation traffic control method provided according to an embodiment of the present invention includes the following steps.

[0091] Step S1, obtain historical weather information, historical flight information, route information and actual aviation sector planning from the database, as well as real-time weather, special control conditions, current aircraft routes and flights, etc. as initial data, and build a simulation environment based on the obtained initial data.

[0092] The aviation sectors are divided according to the actual aviation sector planning in my country. The historical flight information may include take-off and landing time, flight path, flight speed, total number of flights, sectors passed, delay time, etc. In this implementation, the neighbor sector is a sector that has an adjacent edge with the current sector, and the distance from the current sector node to the neighbor sector node can be regarded as the distance between the geometric centers of the two sectors. For example, Figure 2 As shown, Xi'an 10 sector is adjacent to Xi'an 6 sector, and their node distance is the distance between two center points represented by dots in the two sectors.

[0093] Step S2, determining a state set and a function set according to the initial data, for generating multiple tree individuals.

[0094] Genetic programming is an algorithm based on natural evolution mechanism, which is used to automatically generate computer programs to solve problems. The individual in genetic programming can be an allocation rule, which is usually represented by a tree structure. The tree structure includes leaf nodes and non-leaf nodes, where non-leaf nodes are members of the function set, including common operators (such as addition, subtraction, logical operations, etc.), and leaf nodes are members of the state set, which are composed of characteristic variables that are strongly related to the problem. The step S2 specifically includes the following steps.

[0095] Step S2.1, select a sector state set, the sector state set includes the following features: the flight distance L from the current sector node i to the next neighbor sector node j i,j ; Flight distance S from the next neighbor sector node j to the end sector node d j,d , the destination sector node d is the sector where the destination airport is located; according to the initial data obtained, the maximum flight capacity C on the route formed by the current sector node i to the next neighbor sector node j is calculated i,j , get the number of flights on the route at the current time in real time as f i,j , the neighboring sector node set V of the current sector node i i , the estimated flight time ET of the flight from the current sector node i to the neighboring sector node j i,j ; The number of aircraft assigned to the current sector node i PN i , the normalized density of flight traffic on the route formed by the current sector node i and the neighboring sector node j, and the average traffic density between the current sector node i and all neighboring sector nodes. Among them, the current sector node is the geometric center of the sector where the flight is currently located.

[0096] Based on the above characteristics of the sector state set, the normalized density of flight flow on the route formed by the current sector node i and the neighboring sector node j is obtained:

[0097] ρ i,j =f i,j / C i,j

[0098] Add the flight flow normalized density to the sector state set.

[0099] Based on the above characteristics of the sector state set, the average traffic density between the current sector node i and all neighboring sector nodes is obtained:

[0100]

[0101] Add the average traffic density to the sector state set.

[0102] The selected sector state set is used to construct a flight routing rule function (routing rule).

[0103] Step S2.2, select an aircraft state set, which includes the following features: the distance LD from the sector node i where the aircraft is currently located to the sector node d where the target airport is located i,d ; The distance ND from the next sector node j where the aircraft is expected to arrive to the sector node d where the target airport is located j,d ; The number of sector nodes that the current flight has flown through, FS; The flight time T of the current flight; The delay FD of the start time of the current flight relative to the departure time of the first flight of the day, where the departure time delay of the first flight of the day is 0.

[0104] The selected aircraft status is used to construct the flight priority function (sequencing rule).

[0105] Step S2.3, select a function set related to the problem, including features: {+, -, *, ÷, max, min}. The features of the function set can be common operators, such as addition, subtraction, multiplication, division, and logical operations.

[0106] Step S2.4, construct a knowledge tree of flight route rule function based on the selected sector state set and function set, and construct a knowledge tree of flight priority function based on the selected aircraft state set and function set, and together form a multi-tree individual. Figure 3 As shown, a schematic diagram of a multi-tree individual consisting of a knowledge tree of a flight route rule function and a knowledge tree of a flight priority function is shown. In the knowledge tree of the flight route rule function, features in the sector state set are selected to form leaf nodes, and features in the function set are selected to form non-leaf nodes; in the knowledge tree of the flight priority function, features in the aircraft state set are selected to form leaf nodes, and features in the function set are selected to form non-leaf nodes.

[0107] Step S3, setting the selection operator, crossover operator and mutation operator of the multi-tree individuals in genetic programming, which are used to perform selection, crossover and mutation operations on the generated multi-tree individuals. In the traditional multi-tree representation, the genetic operator usually only acts on one tree in the individual, and the other trees are directly copied from the parent generation to the offspring, and the genetic operator is limited to one type of tree. However, this will result in the offspring not being much different from the parent generation, the population diversity is reduced, the exploration ability is reduced, and the crossover operation cannot improve the diversity of the combination of different types of trees. In order to overcome these shortcomings, the dynamic air traffic control method provided in this embodiment provides a crossover operator that is more suitable for the problem of dynamic air traffic control. This step S3 specifically includes the following steps.

[0108] Step S3.1, set the selection operator: adopt the roulette selection method, the probability of each multi-tree individual selection is proportional to its fitness value, and the individual with higher fitness is more likely to be selected. Specifically, it includes: calculating the fitness of each multi-tree individual and converting the fitness into a ratio; assigning a corresponding "sector" on the roulette wheel to each multi-tree individual according to the ratio; then rotating the roulette wheel and selecting the multi-tree individual according to the sector it falls on.

[0109] During the evolution process, a multi-tree individual is first randomly selected as the parent through the selection operator. If a crossover operation is required, the parent is excluded and then a roulette selection is performed. This operation can retain the structure of the better individuals.

[0110] Step S3.2, set the crossover operator: in the crossover process of two parent multi-tree individuals selected by the selection operator, first randomly select one of the two knowledge trees in each of the two parent multi-tree individuals for direct exchange, and then randomly select a subtree structure from the other knowledge tree in each of the two parent multi-tree individuals, and migrate the subtree structure to the tree structure of the other knowledge tree to form two child multi-tree individuals. For example, if the flight route rule function knowledge tree is selected, first directly exchange the flight route rule function knowledge trees of the two parents, and then randomly select a subtree structure from the two flight priority function knowledge trees, and migrate the subtree structure to the tree structure of the other knowledge tree to form two child multi-tree individuals.

[0111] In the process of crossover through the crossover operator, in order to avoid overfitting and overly complex tree structures, the maximum depth and maximum number of nodes of the knowledge tree can be limited. For example, the maximum depth is limited to 10 and the maximum number of nodes is limited to 35. When the migrated subtree structure causes the limit to be exceeded, the subtree structure will be reselected for crossover. The depth of the knowledge tree is the number of layers of the leaf node farthest from the root node of the knowledge tree, and the number of nodes of the knowledge tree is the number of leaf nodes of the knowledge tree.

[0112] See also Figure 4, showing the crossover process of two parent multi-tree individuals. The dotted circle indicates that the subtree is selected and will be exchanged. The conventional standard crossover operator will stop here, but in this embodiment, another type of tree without a dotted circle in each parent multi-tree individual in the figure will also be exchanged with each other as a whole. Therefore, two offspring are generated. The advantages of this crossover method of two parent multi-tree individuals mainly include that useful subtree blocks are not easily destroyed, and the combination of flight route rule function and flight priority function is more diversified, effectively improving the diversity of the population.

[0113] Step S3.3, set the mutation operator, randomly select a non-leaf node of a knowledge tree in the multi-tree individual, and use the knowledge tree of the new expression to replace the subtree with the non-leaf node as the root. In this implementation, the new knowledge tree is randomly composed of a state set and a function set, and the height of the knowledge tree is set to 2. The height of the knowledge tree is the total number of edges from the root node to the leaf node. The new expression is the formula after the knowledge tree is decoded.

[0114] Step S4, setting a multi-tree individual initialization method, initializing each knowledge tree in the majority of individuals, and obtaining a multi-tree individual with a set population size. The initialization of each knowledge tree in the majority of individuals in step S4 specifically includes the following steps.

[0115] Step S4.1, select leaf nodes from the state set, and select features from the function set as non-leaf nodes. Specifically, select leaf nodes from the sector state set, select features from the function set as non-leaf nodes, and form a flight route rule function knowledge tree, select features from the aircraft state set as leaf nodes, and select features from the function set as non-leaf nodes, and form a flight priority knowledge tree.

[0116] Step S4.2, specifying the minimum height (min) and the maximum height (max) of the generated knowledge tree, which indicates how many layers the generated knowledge tree has at least and at most. In one example, the specified minimum height of the generated knowledge tree is 1 and the maximum height is 6.

[0117] Step S4.3, select the initialization function. During the initialization process, there is a 50% chance of selecting the genFull function and a 50% chance of selecting the genGrow function. The genFull function indicates that all leaf nodes have the same height, that is, a full binary tree, while the leaf nodes in the genGrow function can have different heights and a more random structure.

[0118] Step S4.4, initialize the population, i.e. randomly generate a multi-tree individual with a set population size (population_size), each of which consists of two knowledge trees representing the flight route rule function knowledge tree and the flight priority function knowledge tree. The leaf nodes in the knowledge tree are randomly selected from the state set, and the non-leaf nodes are randomly selected from the function set. Initialize the current generation (current) to 0.

[0119] Step S5, determine the fitness evaluation method of the multi-tree individuals, and calculate the fitness value of the multi-tree individuals based on the simulation environment. In this embodiment, each multi-tree individual participating in the evolutionary iteration is composed of two knowledge trees, which respectively represent the flight route rule function and the flight priority function. The fitness value of the multi-tree individual is calculated by the flight route rule function and the flight priority function.

[0120] All flight plans for a certain day are selected as the basis, and these flights are added to the simulation environment according to the actual departure time. During the flight, due to the impact of emergencies in the simulation environment, such as changes in sector capacity caused by weather reasons, changes in the order of aircraft arrival, etc., when the original flight needs to be adjusted, the flight route rule function and flight priority function obtained by decoding the two knowledge trees in the multi-tree individual are selected and regulated. The total number of flights scheduled before the simulation deadline is calculated as the fitness value. The larger the fitness value, the better the multi-tree individual.

[0121] At the same time, in order to ensure the generalization ability of the flight route rule function and the flight priority function, in this step, the impact of the emergency is a random disturbance, which may be in different sectors and have different degrees of impact. Therefore, for a multi-tree individual, it will be simulated and calculated multiple times in the simulation environment, and the accumulation of multiple fitness value results will be used as the final fitness value.

[0122] Step S6, performing evolutionary learning based on the obtained multi-tree individuals, and outputting the best multi-tree individuals and optimal rules after training.

[0123] Step S6.1, determine the basic parameters required for evolutionary calculation according to the problem and computing power, including population size (population_size), evolutionary generations (iteration), crossover probability (cxpb), and mutation probability (mutpb). The population size is the number of multi-tree individuals involved in the calculation in each iteration, the evolutionary generations are the number of iterations experienced until the calculation stops, the crossover probability is the probability that a multi-tree individual crosses with other multi-tree individuals through a crossover operator during the evolution process, and the mutation probability is the probability that a multi-tree individual mutates through a mutation operator during the evolution process. In one example, optionally, the evolutionary generations can be set to 50 generations, which can achieve better training effects while ensuring a smaller amount of calculation.

[0124] Step S6.2, initialize the population, i.e. randomly generate a multi-tree individual of the determined population size, each of which is composed of two knowledge trees representing the flight route rule function and the flight priority function. The leaf nodes in the tree are randomly selected from the state set, and the non-leaf nodes are randomly selected from the function set. Initialize the current generation to 0.

[0125] Step S6.3, traverse each multi-tree individual in the current population and perform the following operations to generate an offspring population (offspring).

[0126] Step S6.3.1, generate a random operation selector (op_choice): generate a random number in the range [0,1] to determine the operation type;

[0127] Step S6.3.2, compare the random number with the determined crossover probability. If the random number (op_choice) is less than the crossover probability, randomly select another multi-tree individual from the population, and use it and the current multi-tree individual as two parent multi-tree individuals. Perform a crossover operation using the crossover operator, and add the obtained two child multi-tree individuals to the child population.

[0128] Step S6.3.3, compare the random number with the determined mutation probability. If the random number is less than the mutation probability, perform a mutation operation on the current multi-tree individual through the mutation operator, and add the obtained offspring multi-tree individual to the offspring population, and execute step S6.3.5. If the random number is not less than the mutation probability, execute step S6.3.4;

[0129] Step S6.3.4, compare the random number with the determined crossover probability. If the random number is not less than the crossover probability, directly add the current multi-tree individual to the offspring population, otherwise no processing is performed.

[0130] Step S6.3.5, repeat the above steps S6.3.1 to S6.3.4 until every multi-tree individual in the current population is traversed to obtain the offspring population.

[0131] Step S6.4, evaluate the fitness values ​​of multiple tree individuals in the current population and the offspring population. That is, decode the two tree structures, generate the flight route rule function and the flight priority function, and substitute these two rules into the simulation environment established in step S7.2. After simulation, the number of flights completed within the specified time is output as the fitness value of the individual. During the simulation, the capacity of the sector will fluctuate randomly within the range of historical data to simulate the limitations brought by weather, special control conditions and other situations. This operation can ensure that individuals with high fitness values ​​can output better scheduling solutions for different situations, ensuring the generalization ability of the algorithm. This step is the main time-consuming operation in the evolution process, and different individuals can be calculated in parallel according to the actual computing power.

[0132] Step S6.5, merge the multi-tree individuals in the current population and the offspring population, sort them from high to low according to the fitness value, and select the multi-tree individuals of the determined population size (population_size) to form a new population (population).

[0133] Step S6.6, determine whether the current new population meets the termination condition, if so, proceed to the next step, otherwise return to step S6.3.

[0134] The termination condition can be a set termination generation number. For example, if the termination generation number is set to 50 generations, the selection crossover mutation operation will be performed for 50 rounds before the end of the iteration and the next step will be executed. In each round of iteration before the end of the iteration, each time after returning to a new round of selection crossover mutation, the individuals in the population will change and better individuals will be updated.

[0135] Step S6.7: Select the individual with the highest fitness value from the current new population as the best multi-tree individual, decode the two knowledge trees of the multi-tree individual into specific executable expressions as the extracted optimal rule, and save the best multi-tree individual and the optimal rule.

[0136] Step S7, simulating the best multi-tree individuals and the best rules, obtaining and outputting a scheduling plan, which is used to regulate the air traffic.

[0137] Step S7 is a process of applying the optimal rule obtained in step S6 to perform actual flight scheduling. Step S7 specifically includes the following steps.

[0138] Step S7.1, setting the simulation deadline T of this simulation, the random seed of the random perturbation of the sector capacity, and obtaining the flight route rule function and the flight priority function.

[0139] Step S7.2, initialize the current simulation time t to 0, and the number of scheduled flights (ret) to 0. Randomly initialize the maximum flight capacity of each route sector and the maximum flow limit on the route based on the historical data range and the random seed (seed).

[0140] Step S7.3, add the newly departing flights at the current simulation time t to the simulation environment, and update the number of flights accommodated by the corresponding starting sector node; update the number of flights accommodated by all sector nodes at the current simulation time t according to the number of arrived aircraft; update the flight flow on all routes at the current simulation time t.

[0141] Step S7.4, for each flight to be scheduled in the current sector node i at the current simulation time t, calculate the specific value of the flight priority function state set determined in stage 1: LD i,d , N.D. j,d , FS, T, FD. Substitute the obtained state into the flight priority function to calculate the flight priority, and sort the flights in the current sector node i from high to low according to priority.

[0142] The same sorting operation is performed for flights of other sector nodes.

[0143] Step S7.5, for each flight to be scheduled in the current sector node i at the current simulation time t, calculate the specific value of the flight route rule function state set determined in stage 1: L i,j , S j,d , ρ i,j , ET i,j , PN i Substitute the obtained state into the flight route rule function to calculate the priority of the neighboring sector nodes, and select the sector node with the highest priority as the next target node. If there are multiple flights occupying the same route and the maximum flow that the route can accommodate is exceeded, the high-priority flights will pass through according to the flight order of step 4, and the low-priority flights will be temporarily blocked until the route resources are released.

[0144] Step S7.6, check whether each flight has arrived at the target sector node at the current simulation time t, and update the number of flights that have completed scheduling (ret) accordingly.

[0145] Step S7.7, if the current simulation time t is not less than the simulation deadline T, stop the simulation and output the number of flights that have completed scheduling ret, and output the current scheduling plan; otherwise, update the current simulation time t to t+1 and return to S7.3.

[0146] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0147] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0148] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A dynamic air traffic control method, characterized in that: The following steps are involved: Step S1, obtaining initial data and building a simulation environment based on the obtained initial data, the initial data including obtaining historical weather information, historical flight information, route information and actual aviation sector planning from a database, as well as real-time weather, special control conditions, routes and flight information of the current aircraft; Step S2, determining a state set and a function set according to the initial data, which are used to generate a multi-tree individual, wherein the state set includes a sector state set and an aircraft state set, the sector state set is used to construct a flight route rule function, and the aircraft state set is used to construct a flight priority function; Step S3, setting the selection operator, crossover operator and mutation operator of the multi-tree individuals in genetic programming, for performing selection, crossover and mutation operations on the generated multi-tree individuals; Step S4, setting a multi-tree individual initialization method, initializing each knowledge tree in the majority of individuals, and obtaining a multi-tree individual with a set population size; Step S5, determining a method for evaluating the fitness of multiple tree individuals, and calculating the fitness values ​​of multiple tree individuals based on a simulation environment; Step S6, performing evolutionary learning based on the obtained multi-tree individuals, and outputting the best multi-tree individuals and optimal rules after training; Step S7, simulating the best multi-tree individuals and the best rules, obtaining and outputting a scheduling solution.

2. The method for dynamic air traffic control according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S2.1, select a sector state set for constructing a flight route rule function. The sector state set includes the following features: the flight distance L from the current sector node i to the next neighbor sector node j i,j ; Flight distance S from the next neighbor sector node j to the end sector node d j,d , the destination sector node d is the sector where the destination airport is located; according to the initial data obtained, the maximum flight capacity C on the route formed by the current sector node i to the next neighbor sector node j is calculated i,j , get the number of flights on the route at the current time in real time as f i,j , the neighboring sector node set V of the current sector node i i , the estimated flight time ET of the flight from the current sector node i to the neighboring sector node j i,j ; The number of aircraft assigned to the current sector node i PN i , the normalized density of flight traffic on the route formed by the current sector node i and the neighboring sector node j, and the average traffic density between the current sector node i and all neighboring sector nodes; Step S2.2, select an aircraft state set for constructing a flight priority function, the aircraft state set includes the following features: the distance LD from the sector node i where the aircraft is currently located to the sector node d where the target airport is located i,d ND is the distance from the next sector node j where the aircraft is expected to arrive to the sector node d where the target airport is located. j,d , the number of sector nodes that the current flight has flown through FS, the flight time T of the current flight, and the delay FD of the start time of the current flight relative to the departure time of the first flight of the day; Step S2.3, select a set of functions related to the problem, including selecting features from operators: {+, -, *, ÷, max, min}; Step S2.4, constructing a flight route rule function knowledge tree according to the selected sector state set and function set, and constructing a flight priority function knowledge tree according to the selected aircraft state set and function set, and together forming multiple tree individuals.

3. The dynamic air traffic control method according to claim 2, characterized in that: The sector status set also includes: Based on the characteristics of the obtained sector state set, the normalized density of flight flow on the route formed by the current sector node i and the neighboring sector node j is obtained: r i,j =f i,j / C i,j ; And, based on the characteristics of the obtained sector state set, the average traffic density between the current sector node i and all neighboring sector nodes is obtained:

4. The dynamic air traffic control method according to claim 3, characterized in that: The step S3 specifically comprises the following steps: Step S3.1, setting the selection operator: calculating the fitness of each multi-tree individual and converting the fitness into a ratio; assigning a corresponding sector on the wheel to each multi-tree individual according to the ratio; then rotating the wheel and selecting the multi-tree individual according to the sector it falls on; Step S3.2, setting a crossover operator: in the crossover process of two parent multi-tree individuals selected by the selection operator, firstly, randomly select one knowledge tree from the two knowledge trees in each of the two parent multi-tree individuals to directly exchange, and then randomly select a subtree structure from the other knowledge tree in each of the two parent multi-tree individuals, and migrate the subtree structures to the tree structure of the other party's knowledge tree, respectively, to obtain two child multi-tree individuals; Step S3.3, set the mutation operator, randomly select a non-leaf node of a knowledge tree in the multi-tree individual, use the knowledge tree of the new expression to replace the subtree with the non-leaf node as the root, and obtain the offspring multi-tree individual. The new expression is the formula after the knowledge tree is decoded.

5. The method for dynamic air traffic control according to claim 4, characterized in that: The step S4 specifically comprises the following steps: Step S4.1, selecting features from the sector status set as leaf nodes and features from the function set as non-leaf nodes to form a flight route rule function knowledge tree, and selecting features from the aircraft status set as leaf nodes and features from the function set as non-leaf nodes to form a flight priority knowledge tree; Step S4.2, specifying the minimum height of the generated knowledge tree and the maximum height of the generated knowledge tree; Step S4.3, selecting an initialization function from the genFull function and the genGrow function; Step S4.4, initialize the population, randomly generate multi-tree individuals of the set population size, each multi-tree individual is composed of two knowledge trees, namely the flight route rule function knowledge tree and the flight priority function knowledge tree.

6. The method for dynamic air traffic control according to claim 5, characterized in that: The step S6 specifically comprises the following steps: Step S6.1, determine the basic parameters required for evolutionary calculation according to the problem and computing power, including population size, evolutionary generations, crossover probability and mutation probability; Step S6.2, initializing the population, including randomly generating a multi-tree individual of the determined population number, each multi-tree individual is composed of two knowledge trees representing the flight route rule function and the flight priority function, and initializing the current generation to 0; Step S6.3, traverse each multi-tree individual in the current population and perform operations to generate a descendant population; Step S6.4, evaluating the fitness values ​​of multiple tree individuals in the current population and the descendant population; Step S6.5, merging the multi-tree individuals in the current population and the offspring population, sorting them from high to low according to the fitness value, and selecting the multi-tree individuals of the determined population number to form a new population; Step S6.6, determine whether the current new population meets the termination condition, if so, proceed to the next step, otherwise return to step S6.3, the termination condition is the set termination generation; Step S6.7, select the individual with the highest fitness value from the current new population as the best multi-tree individual, decode the two knowledge trees of the multi-tree individual into specific executable expressions as the extracted optimal rule, and save the best multi-tree individual and the optimal rule.

7. The method for dynamic air traffic control according to claim 6, characterized in that: The step S6.3 specifically includes the following steps: Step S6.3.1, randomly select a multi-tree individual from the current population and generate a random operation selector, generating a random number in the range [0,1] to determine the operation type; Step S6.3.2, compare the random number with the determined crossover probability. If the random number is less than the crossover probability, randomly select another multi-tree individual from the population, and use the current multi-tree individual as the parent multi-tree individual. Perform a crossover operation through the crossover operator to obtain two child multi-tree individuals. Add the obtained two child multi-tree individuals to the child population. Step S6.3.3, compare the random number with the determined mutation probability. If the random number is less than the mutation probability, perform a mutation operation on the current multi-tree individual through the mutation operator, and add the obtained offspring multi-tree individual to the offspring population, and execute step S6.3.

5. If the random number is not less than the mutation probability, execute step S6.3.4; Step S6.3.4, compare the random number with the determined crossover probability, if the random number is not less than the crossover probability, directly add the current multi-tree individual to the offspring population, otherwise no processing is performed; Step S6.3.5, repeat the above steps S6.3.1 to S6.3.4 until every multi-tree individual in the current population is traversed to obtain the offspring population.

8. The method for dynamic air traffic control according to claim 7, characterized in that: The step S7 specifically includes the following steps: Step S7.1, setting the simulation deadline T of this simulation, the random seed of the random perturbation of the sector capacity, and obtaining the flight route rule function and the flight priority function; Step S7.2, initialize the current simulation time t to 0, and the number of flights that have completed scheduling to 0; Step S7.3, add the newly departing flights at the current simulation time t to the simulation environment, and update the number of flights accommodated by the corresponding starting sector node; update the number of flights accommodated by all sector nodes at the current simulation time t according to the number of arrived aircraft; update the flight flow on all routes at the current simulation time t; Step S7.4, for each flight to be scheduled in the current sector node i at the current simulation time t, calculate the specific value of the flight priority function state set determined in stage 1: LD i,d , N.D. j,d ,FS,T,FD,substitute the obtained state into the flight priority function to calculate the flight priority, and sort the flights in the current sector node i from high to low according to the priority; the same sorting operation is performed for the flights of other sector nodes; Step S7.5, for each flight to be scheduled in the current sector node i at the current simulation time t, calculate the specific value of the flight route rule function state set determined in stage 1: L i,j , S j,d , ρ i,j , ET i,j , PN i , substitute the obtained state into the flight route rule function to calculate the priority of neighbor sector nodes, and select the sector node with the highest priority as the next target node; Step S7.6, check whether each flight has arrived at the target sector node at the current simulation time t, and update the number of flights that have completed scheduling accordingly; Step S7.7, if the current simulation time t is not less than the simulation deadline T, stop the simulation and output the number of flights that have completed scheduling ret, as well as the current scheduling plan; otherwise, update the current simulation time t to t+1 and return to step S7.3.

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