A strategic trajectory conflict management method for free-route airspace

Abstract

The present invention discloses a method for strategic trajectory conflict management in free-route airspace. The method comprises the following steps: S1, based on the weather forecast provided by the ensemble forecast system, performing ensemble trajectory prediction taking into account the time-varying nature of meteorological elements; S2, using a conflict detection method based on a four-dimensional grid to measure the conflict risk between trajectory points; S3, using 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, the total deviation of all flights from the user's preferred trajectory, and the total unpredictability of all flights as the objective function, and using flight altitude constraints, Mach number constraints, and restricted area constraints as constraints to construct a strategic conflict management model; S4, optimizing the constructed strategic conflict management model, and adjusting the trajectory using the obtained optimized trajectory. The present invention can provide a solution with high robustness and coordination with the needs of all parties for trajectory 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, with limited room for improvement in system capacity, making it difficult to accommodate the growing demand for air traffic. The introduction of the free-route airspace concept gives each flight greater freedom to plan its flight trajectory, effectively improving the system's operational efficiency. However, unstructured traffic leads to a more dispersed temporal and spatial distribution of aircraft conflicts, increasing the difficulty and workload of air traffic controllers. The proposed strategic trajectory conflict management method provides controllers with an effective decision-making support tool. Strategic trajectory conflict management can effectively reduce controller workload and improve the overall system efficiency by adjusting aircraft trajectories to avoid potential flight conflicts during the strategic phase. However, current research on strategic trajectory conflict management fails to consider the impact of meteorological uncertainty on system operational performance, such as safety and efficiency. In addition, related research only uses 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. Taking into account the time-varying characteristics of the weather forecast data of the ensemble forecast system, the collective trajectory of flights is predicted and the conflict risk of the collective trajectory is evaluated. 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 solutions proposed in the present invention are as follows:

[0005] A method for strategic trajectory conflict management in free-route airspace comprises the following steps:

[0006] S1. Based on the weather forecast provided by the ensemble prediction system, the ensemble trajectory prediction is performed taking into account the time-varying nature of meteorological factors 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 Time dimension Indicates that flight f arrives at a given waypoint time window;

[0007] S2, using the conflict detection method based on the 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. Using the diversion waypoints selected by each flight, as well as 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 trajectory, and minimizing the total unpredictability of all flights as objective functions, and using the allowed flight altitude constraint, allowed Mach number constraint, and restricted area constraint 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 between flight f and the user’s preferred trajectory, is the operating cost of the planned trajectory of flight f under member e of ensemble forecast E, is the operating cost of flight f’s preferred trajectory under member e of ensemble forecast E, CI f is the cost coefficient of flight f, and are the arrival times of flight f at the last and first waypoints, 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 meme 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 assignment, 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 trajectories 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 maneuvering modes. It 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 Schematic diagram of the principle of the conflict detection method based on four-dimensional grid;

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

[0021] Figure 3 This is a flowchart of the decomposition-based meme algorithm;

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

[0023] In response to 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, the 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 solutions proposed in the present invention are as follows:

[0025] A method for strategic trajectory conflict management in free-route airspace comprises the following steps:

[0026] S1. Based on the weather forecast provided by the ensemble prediction system, the ensemble trajectory prediction is performed taking into account the time-varying nature of meteorological factors 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 Time dimension Indicates that flight f arrives at a given waypoint time window;

[0027] S2, using the conflict detection method based on the 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. Using the diversion waypoints selected by each flight, as well as 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 trajectory, and minimizing the total unpredictability of all flights as objective functions, and using the allowed flight altitude constraint, allowed Mach number constraint, and restricted area constraint 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 includes the following steps:

[0032] Step 1: Based on the weather forecast provided by the ensemble prediction system, perform ensemble trajectory prediction considering the time-varying nature of meteorological factors to obtain the four-dimensional trajectory of each flight:

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

[0034] For each ensemble member, an aircraft dynamics model for 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 = 9.80665 m / s 2 is the acceleration due to gravity, c T is the fuel consumption rate.

[0039] The weather forecast data provided by the ensemble prediction system evolves over time and space. Based on the raw weather forecast data, the Shepard interpolation method is used to calculate weather data for any spatial and temporal location in the airspace. The ground speed of each 4D trackpoint on the flight trajectory is determined by the temperature and wind speed data at the corresponding spatial and temporal location:

[0040]

[0041] in, is the ground speed of the flight at position (x, y, z, t), is the true airspeed, 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 the point. 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 specifies 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 the 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 other sampling points P of adjacent flight 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 less 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 Conflict risk Φ 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 as u f =(W f ,FL f ,M f ), the set of decision variables for all flights is represented as u={u f |f∈F}.

[0063] The waypoints in the free route airspace are divided into entry and exit points and rerouting points. The entry and exit points are the waypoints where the flight enters or leaves the free route airspace sector, such as Figure 2 As shown, 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) Constructing 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 trajectory, 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 between flight f and the user’s preferred trajectory, is the operating cost of the planned trajectory of flight f under member e of ensemble forecast E, is the operating cost of flight f’s preferred trajectory under member e of ensemble forecast E, CI f is the cost coefficient of flight f, and are the arrival times of flight f at the last and first waypoints, 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 that flight f passes through, 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, i.e.

[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 accelerate the convergence speed of the algorithm, the present invention adopts a decomposition-based genetic algorithm to solve it. The decomposition-based algorithm framework decomposes the original multi-objective optimization problem into a set of single-objective sub-problems, and uses genetic operators and local search operators to optimize the sub-problems. Figure 3 As shown in Figure 2, the decomposition-based meme algorithm specifically includes the following steps:

[0091] Initialization: Generate the 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 individual 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 EA i ∈Y dominated solution; if there is no solution dominating y in EA i , then y i Insert into EA; sort the solutions in set Z = X ∪ Y based on 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 evolution process until the pre-set 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 the track set that needs 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 layer assignment, 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 individual 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 strategic trajectory conflict management in free-route airspace, characterized in that: The following steps are involved: S1. Based on the weather forecast provided by the ensemble prediction system, the ensemble trajectory prediction is performed taking into account the time-varying nature of meteorological factors 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 Time dimension Indicates that flight f arrives at a given waypoint The ground speed of each 4D track point on the flight trajectory is determined by the temperature and wind speed data of the corresponding time and space position: in, is the ground speed of the flight at position (x, y, z, t), is the true airspeed, w a is the radial wind along the track, w c For horizontal wind; S2, using the conflict detection method based on the 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. Using the reroute 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 using the allowed flight altitude constraints, allowed Mach number constraints, and restricted area constraints as constraints, a strategic conflict management model is constructed; the total deviation of all flights from the user's preferred trajectories is is the deviation between flight f and the user’s preferred trajectory, is the operating cost of the planned trajectory of flight f under member e of ensemble forecast E, is the operating cost of flight f’s preferred trajectory 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; S4. Optimize the constructed strategic conflict management model and adjust the trajectory based on the obtained optimized trajectory.

2. The method for strategic trajectory conflict management in free route airspace according to 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 method for strategic trajectory conflict management in free route airspace according to 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.

4. The method for strategic trajectory conflict management in free route airspace according to claim 1, wherein: The constructed strategic conflict management model is optimized using a decomposition-based cultural gene algorithm.

5. The method for strategic trajectory conflict management in free route airspace according to claim 4, characterized in that: Introducing a conflict risk threshold Φ into the local search operator of the cultural gene algorithm TH Used to select the track that needs to be adjusted.

6. The method for strategic trajectory conflict management in free route airspace according to claim 4, characterized in that: 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 assignment, speed adjustment and rerouting, respectively, and are used to control the way the local search operator adjusts the track.

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