Free airway airspace strategic track conflict management method

Abstract

The invention discloses a free airway airspace strategic track conflict management method. The method comprises the following steps: S1, based on weather forecast provided by an ensemble forecast system, carrying out ensemble track prediction considering weather element time-varying characteristics; s2, measuring a conflict risk between the track points by using a conflict detection method based on a four-dimensional grid; s3, taking the revised waypoint selected by each flight and the height and Mach number at each waypoint as decision variables, and minimizing the total conflict risk of all flight paths, the total deviation of all flights and user preference flight paths, and the total unpredictability of all flights as objective functions; constructing a strategic conflict management model by taking a flight height layer constraint, a Mach number constraint and a restricted area constraint as constraint conditions; and S4, optimizing the constructed strategic conflict management model, and carrying out track adjustment according to the obtained optimized track. According to the method, a solution which has high robustness and meets the requirements of all parties can be provided for track conflict management in the strategic stage.

Description

Technical Field

[0001] The invention relates to a free route airspace strategic track conflict management method, belonging to the technical field of air traffic management. Background Art

[0002] The current air traffic management system is constrained by a fixed airspace structure, and the room for improvement of system capacity is limited, making it difficult to accommodate the growing demand for air traffic. The introduction of the concept of free route airspace gives each flight a higher degree of freedom to plan its flight trajectory, effectively improving the efficiency of system operation. However, unstructured traffic leads to a more dispersed temporal and spatial distribution of aircraft conflicts, which increases the difficulty and workload of air traffic controllers. The introduction of the strategic trajectory conflict management method provides controllers with an effective decision support tool. Strategic trajectory conflict management can effectively reduce the workload of controllers and improve the overall efficiency of the system by adjusting aircraft trajectories in the strategic stage to avoid potential flight conflicts. However, current research on strategic trajectory conflict management fails to consider the impact of uncertainty in meteorological elements on system operational performance such as safety and efficiency; in addition, related research only takes safety as a single optimization goal, ignoring the differences in needs among various air traffic management entities. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a strategic trajectory conflict management method for free-route airspace. The method takes into account the time-varying characteristics of the meteorological forecast data of the ensemble forecast system, predicts the collective trajectory of flights and evaluates the conflict risk of the collective trajectory. On this basis, a multi-objective strategic conflict management model is optimized to provide a solution with high robustness and coordination of the needs of all parties for trajectory conflict management in the strategic stage.

[0004] The technical solution proposed by the present invention is as follows:

[0005] A method for managing strategic track conflicts in free-route airspace comprises the following steps:

[0006] S1. Based on the weather forecast provided by the ensemble forecast system, an ensemble trajectory prediction is performed taking into account the time-varying nature of meteorological elements to obtain the four-dimensional trajectory of each flight; among which, the four-dimensional trajectory traj of flight f is f The four-dimensional coordinates of the kth sampling point in The time dimension Indicates that flight f arrives at a given waypoint time window;

[0007] S2, using the conflict detection method based on four-dimensional grid to measure the conflict risk between track points; where flight f is at sampling point P f,k Risk of conflict Sampling point P of flight ff,k and the sampling point P of another flight f' detected in an adjacent grid cell f',k' Risk of conflict between The evaluation condition Q is that the horizontal and vertical spacing between two sampling points are both less than the minimum safety interval and there is an interval intersection between the passing time windows;

[0008] S3. Taking the rerouting waypoints selected by each flight and the altitude and Mach number at each waypoint as decision variables, minimizing the total conflict risk of all trajectories, minimizing the total deviation of all flights from the user's preferred trajectories, and minimizing the total unpredictability of all flights as objective functions, and taking the allowed flight altitude layer constraints, allowed Mach number constraints, and restricted area constraints as constraints, a strategic conflict management model is constructed;

[0009] S4. Optimize the constructed strategic conflict management model and adjust the trajectory based on the obtained optimized trajectory.

[0010] Preferably, the total conflict risk of all tracks u is the decision variable, K f is the total number of sampling points in the track of flight f, and F is the set of all flights.

[0011] Preferably, the total deviation of all flights from the user's preferred track

[0012] is the deviation of flight f from the user's preferred track, is the operating cost of the planned trajectory of flight f under member e of ensemble forecast E, is the operating cost of the preferred trajectory of flight f under member e of ensemble forecast E, CI f is the cost coefficient of flight f, and are the arrival times of the last and first waypoints of flight f, respectively. and are the initial weight and final weight of flight f respectively, and F is the set of all flights.

[0013] Preferably, the total unpredictability of all flights Unpredictability of flight f Unpredictability of flight f's arrival at waypoint n N f is the total number of waypoints for flight f.

[0014] Preferably, the constructed strategic conflict management model is optimized using a decomposition-based memetic algorithm.

[0015] Further preferably, a conflict risk threshold Φ is introduced into the local search operator of the cultural gene algorithm. TH Used to select the track that needs to be adjusted.

[0016] Further preferably, three parameters p are introduced into the local search operator of the cultural gene algorithm: F 、p S and p R They represent the probability of executing altitude layer allocation, speed adjustment and rerouting, respectively, and are used to control the way the local search operator adjusts the track.

[0017] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0018] The technical solution of the present invention takes into account the time-varying characteristics of the meteorological forecast data of the ensemble forecast system, predicts the collective trajectory of flights in combination with the flight intentions of the flights, evaluates the conflict risk of the predicted trajectory based on the four-dimensional airspace grid and the trajectory transit time window, and on this basis constructs a strategic conflict management model that coordinates the needs of multiple subjects and hybrid multiple maneuvers. This can provide a solution with high robustness and coordinated needs of all parties for trajectory conflict management in the strategic stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the principle of the conflict detection method based on four-dimensional grid;

[0020] Figure 2 Set up schematic diagrams for diversion waypoints;

[0021] Figure 3 It is a flowchart of the decomposition-based genetic algorithm;

[0022] Figure 4 Schematic diagram of the local search operator flow. DETAILED DESCRIPTION

[0023] In view of the shortcomings of the existing technology, the solution of the present invention is to consider the time-varying characteristics of the meteorological forecast data of the ensemble forecast system, predict the collective trajectory of flights and evaluate the conflict risk of the collective trajectory. On this basis, a multi-objective strategic conflict management model is optimized to provide a solution with high robustness and coordination of the needs of all parties for trajectory conflict management in the strategic stage.

[0024] The technical solution proposed by the present invention is as follows:

[0025] A method for managing strategic track conflicts in free-route airspace comprises the following steps:

[0026] S1. Based on the weather forecast provided by the ensemble forecast system, an ensemble trajectory prediction is performed taking into account the time-varying nature of meteorological elements to obtain the four-dimensional trajectory of each flight; among which, the four-dimensional trajectory traj of flight f is f The four-dimensional coordinates of the kth sampling point in The time dimension Indicates that flight f arrives at a given waypoint time window;

[0027] S2, using the conflict detection method based on four-dimensional grid to measure the conflict risk between track points; where flight f is at sampling point P f,k Risk of conflict Sampling point P of flight f f,k and the sampling point P of another flight f' detected in an adjacent grid cell f',k' Risk of conflict between The evaluation condition Q is that the horizontal and vertical spacing between two sampling points are both less than the minimum safety interval and there is an interval intersection between the passing time windows;

[0028] S3. Taking the rerouting waypoints selected by each flight and the altitude and Mach number at each waypoint as decision variables, minimizing the total conflict risk of all trajectories, minimizing the total deviation of all flights from the user's preferred trajectories, and minimizing the total unpredictability of all flights as objective functions, and taking the allowed flight altitude layer constraints, allowed Mach number constraints, and restricted area constraints as constraints, a strategic conflict management model is constructed;

[0029] S4. Optimize the constructed strategic conflict management model and adjust the trajectory based on the obtained optimized trajectory.

[0030] In order to facilitate public understanding, the technical solution of the present invention is described in detail below with reference to the accompanying drawings:

[0031] The free route airspace strategic track conflict management method of the present invention specifically comprises the following steps:

[0032] Step 1: Based on the weather forecast provided by the ensemble forecast system, an ensemble track prediction is performed taking into account the time-varying nature of meteorological elements to obtain the four-dimensional track of each flight:

[0033] Ensemble track prediction generates the ensemble track of flights by predicting the tracks of each ensemble member in the weather forecast provided by the ensemble prediction system, and analyzes the distribution characteristics of track parameters through statistical methods to quantify the impact of weather forecast uncertainty.

[0034] For each ensemble member, an aircraft dynamics model in the cruise phase is established based on the aircraft database:

[0035]

[0036] T=D

[0037] L=mg

[0038] Where s is the flight distance of the aircraft, t is the time, v g is the ground speed of the aircraft; T, D, L are thrust, air resistance and lift respectively, m is the weight of the aircraft, g = 9.80665m / s 2 is the acceleration due to gravity, c T The fuel consumption rate.

[0039] The weather forecast data provided by the ensemble forecast system has the characteristics of evolving with the change of spatial position and time. Based on the original weather forecast data, the Shepard interpolation method is used to calculate the weather data at any spatial and temporal position in the airspace. The ground speed of each four-dimensional track point on the flight trajectory is determined by the temperature and wind speed data at the corresponding spatial and temporal position:

[0040]

[0041] in, is the ground speed of the flight at position (x, y, z, t), is the true air speed, w a is the radial wind along the track, w c In the cruise phase, the true airspeed of an aircraft is usually determined by the Mach number M and the local speed of sound v sound The product of is:

[0042] v TAS =M·v sound

[0043]

[0044] Among them, γ A =1.4, R A =287.05J / kg K are the specific heat ratio and adiabatic constant of air respectively; Temp is the absolute temperature.

[0045] Based on the trajectories generated by all ensemble members, the ensemble trajectory is constructed, and the trajectories are discretized with a given fixed interval. The influence of meteorological factors on trajectory prediction is quantified by statistically analyzing the distribution of trajectories at the time of passing through points. Assume that flight f∈F has K f sampling points, the ensemble forecast E has N mem According to the aircraft dynamics differential formula, each set member e∈E={1,...,N mem The time when the next flight arrives at the kth sampling point:

[0046]

[0047] For the kth sampling point of the track, construct the arrival time set The arrival time window is determined accordingly in, and are the upper and lower bounds of the time window, respectively.

[0048] Considering the uncertainty of weather forecast, the four-dimensional trajectory traj of each flight f is f A set of four-dimensional coordinates The sequence representation indicates that the aircraft needs to be Reach the given waypoint within

[0049]

[0050] Step 2: Use the conflict detection method based on the four-dimensional grid to measure the conflict risk between track points:

[0051] In order to avoid the huge computational time requirements of the traditional pairwise conflict detection method, the present invention adopts a conflict detection method based on a four-dimensional grid to measure the conflict risk between track points. The basic principle of the conflict detection method based on a four-dimensional grid is as follows: Figure 1 As shown, the spatial domain is first divided into a set of four-dimensional space-time grid cells using the spatial domain discretization method. The size of each grid cell is determined by the minimum safety interval N h The four-dimensional coordinates of each flight track sampling point are mapped and stored in the corresponding grid cells according to the predefined discrete time step. Since the arrival time of each sampling point is represented by a time window, it is necessary to identify all grid cells within this time interval. For the kth sampling point P of flight f f,k , check all adjacent grid cells in its spatiotemporal dimension, if there are sampling points P of other adjacent flights f' f',k' , then calculate the horizontal distance d between the two H and vertical spacing d V .

[0052] When the reference track point and the adjacent track point overlap in the time and space domain, the conflict risk between the two is evaluated. The evaluation condition Q is the horizontal distance d between the two. H and vertical spacing d V All are smaller than the minimum safety interval and there is an interval intersection between the passing time windows:

[0053]

[0054] The conflict risk between track sampling points is defined as the ratio of the duration of the intersection of the intervals of the two time windows to the total duration of the combined interval:

[0055]

[0056] Flight f at sampling point P f,k The risk of conflict f,k Defined as sampling point P f,k The sum of the conflict risks with the sampling points of all other tracks detected in adjacent grid cells:

[0057]

[0058] Step 3: Build a strategic conflict management model:

[0059] (1) Set decision variables:

[0060] The strategic conflict management model constructed by the present invention considers three maneuvers: mixed rerouting, altitude layer allocation and speed adjustment. Therefore, the decision variables set include the rerouting waypoints selected by each flight, the altitude and Mach number at each waypoint. f The trajectory of the waypoints, the corresponding decision variables are:

[0061]

[0062] The set of decision variables corresponding to each flight is represented by u f =(W f ,FL f ,M f ), the set of decision variables for all flights is represented by u = {u f |f∈F}.

[0063] Waypoints in free route airspace are divided into entry and exit points and rerouting points. Entry and exit points are waypoints where flights enter or leave the free route airspace sector, such as Figure 2 As shown in the figure, the rerouting waypoints are a set of virtual points located near the initial flight segment. For flight f, the set of entry and exit points W f,E and two consecutive entry and exit points and The set of rerouting points between Respectively expressed as:

[0064]

[0065] Where R represents the number of rerouting points between two consecutive entry and exit points.

[0066] In order to determine the location of each optional diversion waypoint, the relative coordinate axes of each flight segment are defined: the vertical axis of the relative coordinate axis is parallel to the initially planned route, and the horizontal axis is perpendicular to the vertical axis. The rth diversion point between the entry waypoint and the departure waypoint is represented by a relative coordinate Indicates that and are the longitudinal and transverse components respectively.

[0067] (2) Construct the objective function:

[0068] The objective function constructed by the present invention is a multi-objective optimization function, specifically including:

[0069] Minimize the total conflict risk of all tracks, that is,

[0070]

[0071] Among them, Φ tot is the total conflict risk, Φ f Indicates the conflict risk of a single track;

[0072] Minimize the deviation from the user's preferred track, that is,

[0073]

[0074] Among them, D tot is the total deviation of all flights from the user's preferred trajectory, D f is the deviation of flight f from the user's preferred track, is the operating cost of the planned trajectory of flight f under member e of ensemble forecast E, is the operating cost of the preferred trajectory of flight f under member e of ensemble forecast E, CI f is the cost coefficient of flight f, and are the arrival times of the last and first waypoints of flight f, respectively. and are the initial weight and final weight of flight f, respectively;

[0075] Minimize the unpredictability of the trajectory, that is

[0076]

[0077] Among them, σ tot is the total unpredictability of all flights, σ f is the unpredictability of flight f, N f is the number of waypoints passed by flight f, is the unpredictability of flight f arriving at the nth waypoint, that is, the duration of the arrival time window.

[0078] (3) Set constraints:

[0079] The constraints include:

[0080] The allowed flight level constraints are

[0081]

[0082] in, and are the minimum and maximum flight altitudes allowed for flight f, is the maximum altitude layer change;

[0083] The allowed Mach number constraint is

[0084]

[0085] in, and are the minimum and maximum Mach numbers allowed for flight f, is the maximum Mach number change;

[0086] Restricted area constraints, i.e.

[0087]

[0088] in, is the restricted grid cell, i.e., the grid cell occupied by the restricted area; The grid cells occupied by the flight track.

[0089] Step 4: Optimize the constructed strategic conflict management model and adjust the trajectory based on the obtained optimized trajectory:

[0090] The above multi-objective optimization problem can be solved by various existing optimization algorithms, such as non-dominated sorting genetic algorithm, multi-objective differential evolution algorithm, etc. In order to speed up the convergence of the algorithm, the present invention adopts a decomposition-based genetic algorithm to solve it. The original multi-objective optimization problem is decomposed into a set of single-objective sub-problems through a decomposition-based algorithm framework, and the sub-problems are optimized by using genetic operators and local search operators. Figure 3 As shown in FIG. 1 , the decomposition-based genetic algorithm specifically includes the following steps:

[0091] Initialization: Generate an initial population X = {x1,...,x N} and external archives

[0092] Decomposition: According to the weight vector λ 1 ,...,λ N Decompose the original problem into N subproblems {Pr1,Pr2,...,Pr N}, calculate each sub-problem Pr i The neighborhood B(i)={i1,...,i T};

[0093] Assignment: Assign representative individuals to each subproblem to generate a representative population The representative individual of each sub-problem is the optimal individual for that sub-problem;

[0094] Optimization: From the subproblem Pr i Randomly select two indexes k and l from the neighborhood B(i) of individual and Implement genetic operators to obtain offspring individuals Then, based on the mixed strategy, Implement the local search operator to obtain the offspring individuals y i ; Repeat this step for all subproblems to generate the offspring population Y = {y1,...,y N};

[0095] Update and select: Remove all y in the EA i ∈Y dominated by the solution; if there is no solution dominating y in EA i , then y i Insert into EA; Sort the solutions in the set Z = X ∪ Y based on the fast non-dominated sorting and crowding distance method; Select the best N solutions in the sorted set Z to generate the next generation population X; Repeat the evolutionary process until the preset maximum generation number g is reached max .

[0096] In order to reduce the frequent adjustment of the track, the present invention further improves the design of the local search operator. The process of the improved local search operator is as follows: Figure 4 As shown. Introducing the conflict risk threshold Φ in the local search operator TH Used to select the track that needs to be adjusted, and only select the track with a conflict risk greater than the conflict risk threshold Φ TH The track is adjusted to generate a set of tracks that need to be adjusted; in order to control the track adjustment method adopted by the local search operator, three parameters p are introduced F , p S and p R They represent the probability of executing altitude allocation, speed adjustment and diversion respectively. The three parameters meet the following conditions:

[0097] p F +p S +p R =1

[0098] Generate a random number, and select the corresponding maneuver to generate the local search offspring individuals according to the interval in which the random number falls. When the conflict risk of offspring individuals generated by local search Smaller than the offspring individuals generated by the genetic operator Then update the offspring individual y iOffspring individuals generated for local search

Claims

1. A method for managing strategic track conflicts in free-route airspace, characterized in that: The following steps are involved: S1. Based on the weather forecast provided by the ensemble forecast system, an ensemble trajectory prediction is performed taking into account the time-varying nature of meteorological elements to obtain the four-dimensional trajectory of each flight; among which, the four-dimensional trajectory traj of flight f is f The four-dimensional coordinates of the kth sampling point in The time dimension Indicates that flight f arrives at a given waypoint time window; S2, using the conflict detection method based on four-dimensional grid to measure the conflict risk between track points; where flight f is at sampling point P f,k Risk of conflict Sampling point P of flight f f,k and the sampling point P of another flight f' detected in an adjacent grid cell f',k' Risk of conflict between The evaluation condition Q is that the horizontal and vertical spacing between two sampling points are both less than the minimum safety interval and there is an interval intersection between the passing time windows; S3. Taking the rerouting waypoints selected by each flight and the altitude and Mach number at each waypoint as decision variables, minimizing the total conflict risk of all trajectories, minimizing the total deviation of all flights from the user's preferred trajectories, and minimizing the total unpredictability of all flights as objective functions, and taking the allowed flight altitude layer constraints, allowed Mach number constraints, and restricted area constraints as constraints, a strategic conflict management model is constructed; S4. Optimize the constructed strategic conflict management model and adjust the trajectory based on the obtained optimized trajectory.

2. The free route airspace strategic track conflict management method as claimed in claim 1, characterized in that: Total conflict risk for all tracks u is the decision variable, K f is the total number of sampling points in the track of flight f, and F is the set of all flights.

3. The free route airspace strategic track conflict management method as claimed in claim 1, characterized in that: Total deviations of all flights from the user's preferred track is the deviation of flight f from the user's preferred track, is the operating cost of the planned trajectory of flight f under member e of ensemble forecast E, is the operating cost of the preferred trajectory of flight f under member e of ensemble forecast E, CI f is the cost coefficient of flight f, and are the arrival times of the last and first waypoints of flight f, respectively. and are the initial weight and final weight of flight f respectively, and F is the set of all flights.

4. The free route airspace strategic track conflict management method as claimed in claim 1, characterized in that: Total unpredictability of all flights Unpredictability of flight f Unpredictability of flight f's arrival at waypoint n N f is the total number of waypoints for flight f.

5. The free route airspace strategic track conflict management method as claimed in claim 1, characterized in that: The constructed strategic conflict management model is optimized using a decomposition-based cultural gene algorithm.

6. The method for strategic track conflict management in free route airspace as claimed in claim 5, characterized in that: Introducing a conflict risk threshold Φ into the local search operator of the genetic algorithm TH Used to select the track that needs to be adjusted.

7. The free route airspace strategic track conflict management method as claimed in claim 5, characterized in that: Three parameters p are introduced into the local search operator of the genetic algorithm: F 、p S and p R They represent the probability of executing altitude layer allocation, speed adjustment and rerouting, respectively, and are used to control the way the local search operator adjusts the track.

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