CN110648560A - FAB flow management method based on distributed decision model - Google Patents

Abstract

The invention relates to the technical field of air traffic management area control, and discloses a FAB flow management method based on a distributed decision model, comprising the following steps: A. A proposed model: Step 1, defining the interference between any two flight trajectories Or the conflict is flight interaction; Step 2, establish a distributed decision model, which is based on a meta-heuristic method, using a hybrid algorithm combining simulated annealing and hill-climbing local search to separate a given set of interacting aircraft trajectories , the distributed decision-making for resolving flight interactions stems from an innovative data structure called the FAB‑Flight Interaction Matrix, which captures flight interaction information between and within FABs. The FAB flow management method based on the distributed decision model can solve the problem that the current management methods are too single, and the lack of coordination between airlines and air traffic control units makes the environment in the air field more and more deteriorated, which will adversely affect air traffic safety. .

Description

A FAB Traffic Management Method Based on Distributed Decision Model

technical field

The invention relates to the technical field of air traffic management area control, in particular to a FAB flow management method based on a distributed decision model.

Background technique

In the past five years, my country's civil aviation traffic has grown at an average annual rate of 10%. The rapid growth of air traffic has led to a sharp increase in the number of aircraft in the airspace, which requires optimization of airspace sector flow management. The dynamic sector division of airspace information needs to be combined with the real airspace environment, traffic conditions and air control conditions to analyze the main influencing factors of sector division, and these three aspects need to be fully considered. At present, the basic methods of air traffic flow management are advance flow management, pre-flight flow management, and real-time flow management. The number of flights in my country has increased at a rate of 15%, and the overlapping and overlapping of various flight activities has become more and more concentrated, which has increased the difficulty of air traffic flow management. The lack of coordination among units has made the environment in the air field worse and worse, which will have a negative impact on air traffic safety.

SUMMARY OF THE INVENTION

(1) Technical problems solved

Aiming at the shortcomings of the prior art, the present invention provides a FAB flow management method based on a distributed decision model, which has the advantages of improving air traffic safety, and solves the problem that the current management methods are too single, and the airlines and air traffic control units lack coordination. The environment in the air field is getting worse and worse, which will have a negative impact on air traffic safety.

(2) Technical solutions

In order to achieve the purpose of improving air traffic safety, the present invention provides the following technical solutions: a FAB flow management method based on a distributed decision model, comprising the following steps:

A. The proposed model:

Step 1, define the interference or conflict between any two flight trajectories as flight interaction;

Step 2, establish a distributed decision-making model. The distributed decision-making model is based on a meta-heuristic method and adopts a hybrid algorithm combining simulated annealing and hill-climbing local search to separate a given set of interacting aircraft trajectories for solving flight interactions. The distributed decision-making of FAB stems from an innovative data structure called FAB-Flight Interaction Matrix, which captures flight interaction information between and within FABs, this model enables effective information sharing among various FABs to achieve no interaction 4-D trajectory planning by computing and resetting each flight plan in a given FAB to separate a given set of aircraft trajectories in both the spatial and temporal domains;

B. Functional Airspace Block-Flight Interaction Matrix:

Step 3, establish a FAB model and FAB-flight interaction matrix, the FAB-flight interaction matrix captures the number of interactions caused by the aircraft during the process from the control FAB to the intermediate FAB, and then solves the flight interaction by implementing the time-space separation in the control FAB, And update the flight plan accordingly, when solved, recalculate the flight interactions, and update the FAB flight interaction matrix, this process will continue until all flight interactions are resolved;

C. Traffic flow management strategy:

First, the intermediate FAB with the highest number of flight interactions is identified as the candidate FAB, then the ATFM strategy (spatiotemporal separation) is applied to control the randomly selected (adaptive proportional selection) flight of the FAB Cj, the fitness proportional method means the probability of selecting a flight Only proportional to the number of interactions it causes, the flight interactions are recalculated according to the revised flight plan, and the FAB flight interaction matrix is updated, the process is repeated until the FAB-flight interaction matrix minimum overall interaction, where the decisions made by each FAB are determined by the optimization process Evaluate, then, the interaction information based on the FABs-flight interaction matrix is fed back to each FAB, which then makes new decisions and repeats the process until a solution is reached that results in the smallest overall interaction;

D. Mathematical modeling:

Step 4, abstract the above into a mathematical model to solve the interaction between trajectories;

a. Interaction between trajectories:

The interaction between trajectories indicates when two or more trajectories occupy the same space in the same time period;

b. Allocation of route/departure time:

To separate trajectories in three-dimensional (3-D) space and time domain, formulate a route/departure time assignment technique based on finding alternative 4-D trajectories for each flight to minimize the total interaction between trajectories;

E. Interaction:

First, discretize the airspace, use a four-dimensional grid (three-dimensional space-time) for conflict detection, and perform discretization sampling as a time series of a three-dimensional grid Δt=t _n -t _n-1 , the size of each cell in the three-dimensional grid Determined by the minimum separation requirements (N _h and N _v ), in a 4D grid, the size of each cell is defined by the minimum separation, demand and discretization time step Δt, then, for each given 4D coordinate p _i,k (x _i,k ,y _i,k ,z _i,k ,t _i,k ) determine the corresponding grid, C _i,j,k,t four-dimensional grid contains p _i,k ( _{xi, k} ,y _i,k ,z _i,k ,t _i,k ), then determine the corresponding grid C _i,j,k,t for each such grid, and check the grids around it in turn (there are 3 ³ = 27 such adjacent grids, including C _i,j,k,t itself), if a small grid is occupied by an aircraft other than aircraft i itself, measure the horizontal and vertical distances d _h and vertical distances between the corresponding aircraft coordinates d _v , when dh < N _h and d _v < N _v , the _number of violations of protection found;

F. Algorithm:

The four-dimensional trajectory planning method of the distributed ATFM model relies on the interaction minimization problem introduced in the third part, and its objective function value is simulated by the interaction detection scheme developed in the fourth part.

Preferably, for the FAB-flight interaction matrix, airspace users (airlines, airports, ANSPs, etc.) submit relevant information (flight plans, flight schedules, capacity) to the control center, which then applies centralized traffic flow Manage strategies to meet demand and capacity and other airspace constraints and generate revised flight plans, which are then used as input to the FAB-Flight Interaction Matrix, a two-dimensional (2-D) matrix that captures the and the flight interaction information within the matrix, one dimension of the matrix is called the control FAB and the other dimension is called the intermediate FAB.

Preferably, for the FAB-flight interaction matrix,

The control FAB is defined as: 1. The FAB where the flight departs, 2. The first FAB that passes through when entering a given airspace;

The definition of the intermediate FAB is: 1. The FAB that a given flight traverses; 2. The FAB where the given flight lands when it lands in the given airspace; 3. The last FAB that passes when leaving the given airspace.

Preferably, the mathematical model is based on a route/departure time allocation technique that finds alternative 4-D trajectories for each flight in the interaction when it is reached that results in a minimum overall interaction.

Preferably, the flight plan is a route and a departure time.

Preferably, the time-space interval is the position from the takeoff/activation flight, and the flight interaction is using a revised flight plan.

(3) Beneficial effects

Compared with the prior art, the present invention provides a FAB traffic management method based on a distributed decision model, which has the following beneficial effects:

1. The FAB flow management method based on the distributed decision model replaces the existing airspace sector division technology by designing a functional sector airspace optimization method, which improves the flight information and flow information between sectors. Sharing, at the same time, improves the quality of service in the airspace, reduces the probability of aircraft congestion in the air, reduces fuel consumption and exhaust emissions on the route, and improves flight safety. This method is more objective and efficient than the traditional division method based on controller load, it will not be affected by subjective factors such as people, and its function is more powerful. It will also reduce the workload of air traffic controllers and pilots. It is the future development trend of the airspace sector optimization method, which can continuously coordinate and schedule civil aviation flights in the growing airspace in a safe, accurate, fast and green way.

Description of drawings

FIG. 1 is a conceptual schematic diagram of information sharing between FABs without interaction trajectory planning of a FAB traffic management method based on a distributed decision model proposed by the present invention;

2 is a schematic diagram of FAB-A, FAB-B and FAB-C and four flight scenarios (A, B, C and D) of a FAB traffic management method based on a distributed decision model proposed by the present invention;

3 is a schematic diagram of a flight interaction matrix update process of a FAB traffic management method based on a distributed decision model proposed by the present invention;

4 is a schematic diagram of interaction of sampling points of trajectory i of a FAB traffic management method based on a distributed decision model proposed by the present invention;

5 is a schematic diagram of the initial trajectory and the alternate trajectory of the virtual waypoint M of a FAB flow management method based on a distributed decision model proposed by the present invention;

6 is a schematic diagram of a four-dimensional grid for conflict detection of a FAB traffic management method based on a distributed decision model proposed by the present invention;

FIG. 7 is a schematic structural diagram of a hybrid algorithm of simulated annealing and hill-climbing local search of a FAB traffic management method based on a distributed decision model proposed by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Please refer to Figure 1-7, a FAB traffic management method based on distributed decision model, including the following steps:

A. The proposed model:

Step 1, define the interference or conflict between any two flight trajectories as flight interaction;

Step 2, establish a distributed decision-making model. The distributed decision-making model is based on a meta-heuristic method and adopts a hybrid algorithm combining simulated annealing and hill-climbing local search to separate a given set of interacting aircraft trajectories for solving flight interactions. The distributed decision-making of FAB stems from an innovative data structure called FAB-Flight Interaction Matrix, which captures flight interaction information between and within FABs, this model enables effective information sharing among various FABs to achieve no interaction 4-D trajectory planning, by computing and resetting each flight plan (route and takeoff time) in a given FAB to separate a given set of aircraft trajectories in the spatial and temporal domains;

As shown in Figure 1, for the FAB-Flight Interaction Matrix, airspace users (airlines, airports, ANSPs, etc.) submit relevant information (flight plans, schedules, capacity) to the control center, which then applies Centralize traffic flow management strategies to meet demand and capacity and other airspace constraints and generate revised flight plans, which are then used as input to the FAB-Flight Interaction Matrix, a two-dimensional (2-D) matrix that captures Flight interaction information between and within FABs, one dimension of the matrix is called the control FAB and the other dimension is called the intermediate FAB.

For the FAB-flight interaction matrix, the control FAB is defined as: 1. the FAB where the flight departs, 2. the first FAB that passes through when entering a given airspace;

The definition of the intermediate FAB is: 1. The FAB that a given flight traverses; 2. The FAB where the given flight lands when it lands in the given airspace; 3. The last FAB that passes when leaving the given airspace.

As shown in Figure 2, flight A enters FAB-B from the outside, so it is the controlling FAB, flight A goes through FAB-A and ends at FAB-C, so they are called intermediate FABs, likewise, flight B takes off and terminates to FAB-B, so FAB-B is both the control and intermediate FAB for flight B, for flight C, the origin is FAB-A (controlling FAB), which crosses FAB-C (intermediate FAB) before leaving the airspace, so the flight There may be multiple intermediate FABs, but only one controlling FAB.

B. Functional Airspace Block-Flight Interaction Matrix:

Step 3, build a FAB model and FAB-flight interaction matrix, the FAB-flight interaction matrix captures the number of interactions caused by the aircraft during the process from the control FAB to the intermediate FAB, and then realizes the time-space interval (from take-off/activation) in the control FAB. position of the flight) to resolve the flight interactions and update the flight plan accordingly, when resolved, recalculate the flight interactions (using the revised flight plan), and update the FAB flight interaction matrix, this process continues until all flight interactions are resolved ;

As shown in Table 1, for N FABs in a given space A, a 2-D matrix of N rows and N columns is listed. The row vectors of the matrix represent the number of flight interactions caused by the control FAB C _j in the intermediate FAB I _i for i=1, 2, . . . , N. The column vector of the matrix represents the number of flight interactions caused by the control FAB C _j for j = 1, 2, ..., N in an intermediate FAB I _i :

Table 1 Flight interaction matrix (1)I _i =[INT(C _j ,I _A )INT(C _j ,I _B )INT(C _j ,I _i )

(2)

As shown in Table 1, the flight interactions caused by the flight controlled by FAB C _A in the middle FAB I _A are given by row FAB C _A and column FAB I _A , and represented by INT(C _A , I _A ). Similarly, the number of flight interactions caused by flights controlled by FAB _{C C} in the same intermediate FAB (i.e. FAB IC ) is given by row FAB _{C C} and column FAB IC ,

Thus, the total number U of flight interactions in a given FAB i can be given by summing the column vectors:

(3)

The total flight interaction V in a given airspace A (including N FABs) can be given by:

(4)

For a given airspace A, the average contribution of each FAB to the overall flight interaction is given by

(5) U _jre1 =U _j /V, j=1...N

C. Traffic flow management strategy:

First, the intermediate FAB with the highest number of flight interactions is identified as a candidate FAB [Equation 6]:

(6) FAB I _i =MAX(U _A , U _B , . . . , U _N )

Then, for FAB I _i , the control FAB C _j that generates the largest number of flight interactions is determined [equation (7)]:

(7) FAB C _j =MAX(INT(C _A ,I _i ),INT(C _B ,I _i ),...,INT(C _N ,I _i )), then apply the ATFM strategy (spatiotemporal separation) To control the randomly selected (adaptive proportional selection) flight of FAB Cj, the fitness proportional method means that the probability of selecting a flight is only proportional to the number of interactions it causes, recalculate flight interactions according to the revised flight plan, and update the FAB flight interaction matrix , the process is repeated until the FABs-flight interaction matrix minimum overall interaction, Figure 3 illustrates the update process, where the decisions made by each FAB are evaluated by the optimization process, and then the interaction information based on the FABs-flight interaction matrix is fed back to each FAB, then FAB makes new decisions and repeats the process until a solution is reached that results in the smallest overall interaction;

D. Mathematical modeling:

Step 4, abstract the above into a mathematical model (i.e., when reaching the minimum overall interaction, based on the route/departure time allocation technique, find an alternative 4-D trajectory for each flight in the interaction), solve the interaction between the trajectories effect;

a. Interaction between trajectories:

The interaction between trajectories indicates when two or more trajectories occupy the same space for the same time period, it differs from a conflict situation, which simply corresponds to a violation of the minimum separation (i.e. 5 miles horizontally and 1000 feet vertically) , the concept of interaction takes into account the duration of the conflict, the temporal separation, the topology of the intersection of trajectories, the distance between trajectories.

Consider a given set of N discrete 4-D trajectories _{, where} each trajectory _{i is the} A time series, specifying that an aircraft must arrive at a given point ( _xi,k , _yi,k , _{zi ,k} ) at time ti, where k=1, . . . , Ki, Ki is the number of sampling points for trajectory _i .

Considering point k of trajectory i, the interaction at point P _i,k (denoted as Φ _i,k ) can be defined as the total number of violations of the guard volume at point Pi, and Fig. 4 shows that at point P _i,k Measured interactions in the horizontal plane between N=3 trajectories;

The interaction associated with trajectory i (denoted Φ _i ) is thus defined as

(8)

Finally, for the entire traffic situation, the total interaction Φ _tot between trajectories is simply defined as

(9)

It can be observed that the measurement of the interaction between trajectories implicitly takes into account the duration of the conflict between trajectories;

b. Allocation of route/departure time:

To separate trajectories in three-dimensional (3-D) space and time domain, formulate a route/departure time assignment technique based on finding alternative 4-D trajectories for each flight to minimize the total interaction between trajectories;

Given data: problem instances are given by:

1) A set of initial N discretized 4-D trajectories and associated control FABs;

2) Discretization time step _Δt ;

3) The allowed number of virtual waypoints M;

4) The maximum allowable advance departure time for each flight i,

5) Shift step length δ _s when starting;

6) The maximum allowable delayed departure time offset for each flight i,

7) The maximum routing length expansion factor allowed for each flight i, 0≤d _i ≤1;

8) The length i, _Li,0 of the initial flight segment of each flight.

The alternate departure times and alternate routes assigned to each flight are modeled as follows:

可能的提前时隙和可能的飞行i的延迟时隙，因此，定义了飞行i的所有可能的出发时间偏移的集合Δ_i，Alternative departure time: the departure time of each flight can be changed by a positive (delayed) or negative (advance) time shift, let δ _i ∈ Δ _i be the departure time offset due to flight i, where Δ _i is flight i A set of acceptable time shifts for ; therefore, the takeoff time ti for flight _i is ti = t _{i ,0} + δ _i , where t _i,0 is the originally planned takeoff time i for flight i. The departure time offset _δi will be limited to the interval middle. The usual practice at airports makes us rely on the discretization of this time interval using a time-shift step size δ _s . This produces

possible advance slots and the delay slots for possible flight i, thus defining the set Δi of all possible departure time offsets for flight _i ,

(10)

Alternative trajectory design: In this work, an alternative trajectory is constructed by placing a set of virtual waypoints, expressed as:

(11)

(12) In the vicinity of the initial route segment, and then by reconnecting consecutive waypoints with straight segments, as shown in Figure 5, in order to limit the route length extension, an alternate route profile for the route must be satisfied.

where Li ( _{wi )} is the length of the surrogate airway profile determined by _wi . Figure 4 shows initial and alternative trajectories constructed with M=2 waypoints, where the position of each waypoint is constrained to a rectangle of possible positions. Let W be the set of all possible normalized longitudinal positions of the mth virtual waypoint on trajectory i. For each trajectory i, the normalized longitudinal component w is set to lie in that interval,

(13)

where bi is a (user-defined) parameter that defines the range of possible normalized longitudinal components of the mth virtual waypoint on trajectory i. To obtain a regular trajectory, the normalized longitudinal components of two adjacent waypoints must not overlap, i.e.,

(14)

So the user should choose bi

(15)

Let W be the set of all possible normalized lateral positions of the mth virtual waypoint on trajectory i. Similarly, the normalized transverse component w is restricted to the following interval:

(16)

where 0≤a≤1 are (user-defined) model parameters that define the range of possible normalized lateral positions of the mth virtual waypoint on trajectory i, chosen a priori to satisfy equation (12);

Set up compact vector notation:

δ: ₌ δ1, _δ2 ,..., _δN ,w: ₌ w1, _w2 ,..., _wN

Use _ui to represent the components of u. It is a vector whose components are related to the modification of the ith trajectory; therefore, the decision variable is

u:=(δ, w)

(17) min _{u = (δ, w)} Φ _tot (u)

The conditions are:

δ _i ∈Δ _i

和分别由等式13和等式16定义；where i=1,...,N, m=1,...,M,

and are defined by Equation 13 and Equation 16, respectively;

E. Interaction:

First, discretize the space domain, and use a four-dimensional grid (three-dimensional space-time) for conflict detection. As shown in Figure 6, the time series as a three-dimensional grid is discretized and sampled Δt=t _n -t _n-1 , and the three-dimensional grid The size of each cell in is determined by the minimum separation requirement (N _h and N _v ), and in a 4D grid, the size of each cell is defined by the minimum separation, requirement and discretization time step Δt, then, for each The given four-dimensional coordinates p _i,k (x _i,k ,y _i,k ,z _i,k ,t _i,k ) determine the corresponding grid, and the C _i,j,k,t four-dimensional grid contains p _i , _k (x _i,k ,y _i,k ,z _i,k ,t _i,k ), then determine the corresponding meshes C _i,j,k,t for each such mesh, and examine the meshes around it in turn grid (there are 3 ³ = 27 such adjacent grids, including C _i,j,k,t itself), if a small grid is occupied by an aircraft other than aircraft i itself, measure the level between the corresponding aircraft coordinates Distance dh and vertical distance d _v , when _{dh < N h and d v < N v} , the _number of violations of protection is found;

In order to avoid underestimating the interaction and avoid using smaller values, resulting in a large number of trajectory samples and a long calculation time, an inner loop algorithm is proposed to detect the interaction between two sampling times and, by inserting planes with a sufficiently small step size Position, interpolation is performed only if there are no interactions, detected at time, and then, one checks each pair of these interpolated points, the algorithm stops when an interaction is identified or each pair of interpolated points is checked.

F. Algorithm:

The four-dimensional trajectory planning method of the distributed ATFM model relies on the interaction minimization problem introduced in the third part, and its objective function value is simulated by the interaction detection scheme developed in the fourth part. This paper adopts a hybrid meta-heuristic method to deal with The air traffic assignment problem, which relies on the classical simulated annealing (SA) algorithm, which allows the system to intensify the search for potential candidate solutions, and two different local search (LS) modules, while the simulated annealing algorithm allows the system to search from local Escape from traps, thus ensuring exploration of the solution space.

This is a classic meta-heuristic stochastic optimization method that allows occasional moves that worsen the value of the objective function, escaping local minima, and such worsening moves become less and less with the number of iterations.

The concept of the simulated annealing algorithm is based on a strong analogy with physical annealing of materials. This process involves bringing the solid into a lower energy state after increasing the temperature. It can be summarized as the following two steps:

1) Bring the solid to a very high temperature until the structure melts;

2) The solid is cooled according to a very specific cooling scheme to the minimum energy solid state.

In the liquid phase, where the particles are randomly distributed, it turns out that the minimum energy state can be reached, very long, with a sufficiently high initial temperature and short cooling time. If this were not the case, the solid would be in a metastable state with non-minimum energy, which is called hardening, the sudden cooling of the solid. The truth in thermoregulated physical systems is analogous to the control parameters in optimization problems, where the objective function of the problem is analogous to the energy state of the system. Because, from a numerical solution of I, the simulated annealing algorithm can perform multiple iterations, each iteration yielding a random neighborhood. Actions to improve the cost function are always accepted. Therefore, at a given temperature, the lower the increase in the objective function, the more likely it is to accept a move, and the higher the temperature, the more likely it is to accept the worst action.

The proposed hybrid algorithm combines the simulated annealing algorithm and the local search algorithm, so that the local search algorithm is considered as the inner loop of the simulated annealing algorithm, which will be executed when the predefined conditions are met, as shown in Fig. 7.

The simulated annealing process is as follows:

First, we evaluate the objective function (w,δ) _C under the current configuration. Denoted as φ _C . Then, a neighborhood solution (w,δ) _N is generated by the neighborhood function. Then, a new solution is generated for the selected flight based on the predefined neighborhood structure. Accepted if the neighborhood solution improves the objective function value. Otherwise, it is accepted with probability e - ^Δφ/T , where Δφ = φ _N - φ _C is the energy difference between the current state C and the new state N. When the maximum number of iterations η _T is reached at a given temperature, the temperature is decreased according to a predefined schedule provided by the user, and the process is repeated until a predetermined final temperature T _final is reached.

局部搜索模块对应于以下两种策略：Local Search Module: The local search module we use is a heuristic that accepts new solutions only if the objective function is reduced. The process repeats until no further improvement is found, or until the maximum number of iterations

The local search module corresponds to the following two strategies:

1) Enhancing the search for a particular trajectory, given flight i, this state utilization step focuses on improving the current solution by applying local changes from the neighborhood structure to flight i (only decision variables are affected;

2) Enhancing the search for interaction trajectories, this state development step, given flight i, applies local changes from the neighborhood structure to each flight that is both subject to the same control FAB as flight i and currently has the same flight i to interact.

Neighborhood structure: The proposed hybrid algorithm relies on the neighborhood structure to decide the next move. First, identify the FAB that produces the highest proportion of interactions. Then, a flight of the identified FAB is chosen such that there is a relevant interaction with trajectory i, φ _i ≥ τ. φ _avg , where τ is a user-defined parameter and φ _avg = φ _tot /N is the average value of the interaction.

To generate a neighborhood solution for a given flight _i from the current configuration ( _wi ,δi) _C , in the next action, a decision must be made whether to modify the position of the point, or to modify the departure time. In general, it is better to find a solution in the time domain, as it does not lead to additional fuel consumption. However, empirical tests have shown that restricting the search to only degrees of freedom results in excessively long computation times. Therefore, a user-defined parameter _pw is introduced to control the probability of modifying the position of the waypoint such that the probability of modification is more precisely the departure time of 1- _pw . On a given flight i, the neighborhood operator generates a new set of virtual waypoints or a new alternative departure time according to this probability _pw .

Hybrid algorithms (SA and LS): operate according to predefined probabilities proportional to the control temperature T.

式中P_SA,max和P_SA,min是执行模拟退火步骤的最大和最小概率(由用户预定义)。(18) The probability of executing the SA step, P _SA ,

where _PSA,max and _PSA,min are the maximum and minimum probabilities (predefined by the user) to perform the simulated annealing step.

(19) The probability of running the LS module, P _LOC , where P _LOC,max and P _LOC,min are the maximum and minimum probabilities (predefined by the user) to perform the simulated annealing step.

(20) The probability of performing both SA and LS at the end is expressed as:

P _SL (T)=1-(P _SA (T)+P _LOC (T))

To sum up, the FAB flow management method based on the distributed decision model replaces the existing airspace sector division technology by designing a functional sector airspace optimization method, which improves the flight information, The sharing of flow information also improves the quality of service in the airspace, reduces the probability of aircraft congestion in the air, reduces fuel consumption and exhaust emissions on the route, and improves flight safety. This method is more objective and efficient than the traditional division method based on controller load, it will not be affected by subjective factors such as people, and its function is more powerful. It will also reduce the workload of air traffic controllers and pilots. It is the future development trend of the airspace sector optimization method, which can continuously coordinate and schedule civil aviation flights in the growing airspace in a safe, accurate, fast and green way.

It should be noted that the term "comprising" or any other variation thereof is intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a list of elements includes not only those elements, but also not expressly listed Other elements, or elements that are inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by the appended claims and their equivalents.

Claims (6)

1. A FAB flow management method based on a distributed decision model is characterized by comprising the following steps:

A. drawing a model:

step 1, defining interference or conflict between any two flight trajectories as flight interaction;

step 2, establishing a distributed decision model, wherein the distributed decision model is based on a meta-heuristic method, a given set of interactive aircraft tracks are separated by adopting a hybrid algorithm combining simulated annealing and mountain climbing local search, the distributed decision for solving flight interaction is derived from an innovative data structure, namely an FAB-flight interaction matrix, the FAB-flight interaction matrix captures flight interaction information between FABs and inside the FABs, the model can realize effective information sharing between the FABs, realize non-interactive 4-D track planning, and achieve the separation of a set of given aircraft tracks in space and time domain by calculating and resetting each flight plan in the given FABs;

B. functional space block-flight interaction matrix:

step 3, establishing a FAB model and a FAB-flight interaction matrix, capturing interaction times caused by an aircraft in the process from controlling the FAB to the intermediate FAB by the FAB-flight interaction matrix, then solving the flight interaction by realizing a time-space interval in the controlling FAB, correspondingly updating a flight plan, recalculating the flight interaction after the solving, and updating the FAB-flight interaction matrix, wherein the process is continued until all flight interactions are solved;

C. traffic flow management strategy:

first, the intermediate FAB with the highest number of flight interactions is identified as a candidate FAB, then an ATFM strategy (spatio-temporal separation) is applied to control the randomly selected (adaptive proportional selection) flight of FAB Cj, the fitness scaling method means that the probability of selecting a flight is only proportional to the number of interactions it causes, the flight interactions are recalculated according to the revised flight plan and the FAB flight interaction matrix is updated, the process is repeated until the FAB-flight interaction matrix minimum overall interaction, where the decision made by each FAB is evaluated by the optimization process, then interaction information based on the FAB-flight interaction matrix is fed back to each FAB, which then makes a new decision and repeats the process until a solution is reached that results in the minimum overall interaction;

D. mathematical modeling:

step 4, abstracting the above into a mathematical model to solve the interaction between the tracks;

a. interaction between trajectories:

the interaction between the trajectories indicates when two or more trajectories occupy the same space for the same period of time;

b. airline/departure time assignment:

to separate the trajectories in three-dimensional (3-D) space and time domain, a path-based/departure time allocation technique is developed to find an alternate 4-D trajectory for each flight to minimize the overall interaction between the trajectories;

E. interaction:

first, a space domain is discretized, collision detection is performed using a four-dimensional grid (three-dimensional space-time), and discretized samples Δ t ═ t are performed as a time series of the three-dimensional grid_n-t_n-1The size of each cell in the three-dimensional grid is governed by a minimum separation requirement (N)_hAnd N_v) It is decided that in a four-dimensional grid, the size of each cell is defined by the minimum separation, the demand and discretization time steps Δ t, and then, for each given four-dimensional coordinate p_i,k(x_i,k,y_i,k,z_i,k,t_i,k) Determining a corresponding grid, C_i,j,k,tThe four-dimensional lattice contains p_i,k(x_i,k,y_i,k,z_i,k,t_i,k) Then, a determination is made for each such grid C_i,j,k,tAnd sequentially checks the surrounding grids (with 3)³27 such adjacent grids, including C_i,j,k,tItself), if a small grid is occupied by an aircraft other than the aircraft i itself, the horizontal distance d between the coordinates of the corresponding aircraft is measured_hAnd a vertical distance d_vWhen d is_h＜N_hAnd d_v＜N_vWhen the number of violations of protection is found;

F. the algorithm is as follows:

the four-dimensional trajectory planning method of the distributed ATFM model depends on the interactive minimization problem introduced in the third part, and the objective function value of the method is obtained by simulating the interactive detection scheme developed in the fourth part.

2. The FAB traffic management method based on the distributed decision model as claimed in claim 1, wherein: for the FAB-flight interaction matrix, airspace users (airlines, airports, air service providers, etc.) submit relevant information (flight plans, flight times, volumes) to the control center, which then applies centralized traffic flow management strategies to meet demand and capacity and other airspace constraints and generate revised flight plans, which are then used as inputs to the FAB-flight interaction matrix, which is a two-dimensional (2-D) matrix that captures flight interaction information between and within FABs, one dimension of which is referred to as the control FAB and the other dimension as the intermediate FAB.

3. The FAB traffic management method based on the distributed decision model as claimed in claim 1, wherein: for the FAB-flight interaction matrix as described,

the control FAB is defined as: 1. FAB where the flight departs, 2, the first FAB which passes when entering a given airspace;

the definition of intermediate FAB is: 1. FAB that a given flight crosses; 2 FAB where the given flight lands in the given airspace; 3. the FAB that passed last when leaving a given spatial domain.

4. The FAB traffic management method based on the distributed decision model as claimed in claim 1, wherein: the mathematical model finds an alternative 4-D trajectory for each flight in the interaction based on a path/departure time allocation technique when a path is reached that results in a minimum overall interaction.

5. The FAB traffic management method based on the distributed decision model as claimed in claim 1, wherein: the flight plan is a route and a takeoff time.

6. The FAB traffic management method based on the distributed decision model as claimed in claim 1, wherein: the time-space interval is the location of the slave takeoff/active flight, and the flight interaction is the use of a revised flight plan.

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