A Method for Selecting Air Traffic Flow Control Strategies Considering Multiple Objectives

Abstract

The present invention discloses a method for selecting an air traffic flow control strategy considering multiple objectives, including: flight data collection; constructing an evaluation index system for air traffic flow control strategies; constructing a comprehensive evaluation model for air traffic flow control strategies; taking the minimization of the index values in the evaluation index system for air traffic flow control strategies as the optimization objective, determining decision variables under different air traffic flow control strategies, and solving the index values of each flight based on the decision variables under different air traffic flow control strategies according to the collected flight data; calculating the optimal comprehensive evaluation cost index of each flight under the corresponding air traffic flow control strategy according to the comprehensive evaluation model; and selecting and using the air traffic strategy with the lowest comprehensive evaluation cost index. This method provides a decision-making basis for airlines to select flight routes and for air traffic controllers to manage traffic flow, and has practical application value in air traffic flow management.

Description

Technical Field

[0001] The present invention relates to the technical field of air traffic flow management, and particularly to a method for selecting an air traffic flow control strategy considering multiple objectives. Background Art

[0002] With the rapid development of civil aviation, the number of flights is increasing day by day, while airspace resources are limited, so it often leads to airspace congestion and then large-scale flight delays. One of the major purposes of air traffic flow management is to maintain the safe and efficient utilization of airspace and airport resources through traffic management, balance the contradiction between demand and airspace capacity, and reduce delays. At the same time, in recent years, under the background of the dual-carbon goal, as an important part of global economic activities and a "major carbon emitter", the aviation transportation industry has become increasingly urgent in reducing carbon emissions. And carbon emissions are closely related to fuel consumption, which is of great significance for improving operation efficiency, optimizing route structure, and better conducting air traffic flow control. In addition, in order to ensure the safe operation of aircraft and the effective utilization of airspace resources, air traffic flow control must be carried out within the safety workload level of air traffic controllers.

[0003] In the prior art, the Chinese patent with the application number 201310301325.9 mainly considers air congestion and flight delays, and the Chinese patent with the application number 201910880163.6 mainly pre-adjusts the future air traffic flow from the perspective of chance constraints, which is one-sided. Therefore, it is necessary to propose a comprehensive evaluation and optimization method that simultaneously considers minimizing flight delay costs, carbon emissions, and air traffic controller workloads, providing a decision-making basis for airlines to select flight routes and air traffic controllers to manage traffic flow, and having more practical application value in air traffic flow management. Summary of the Invention

[0004] Object of the Invention: The technical problem to be solved by the present invention is to provide a method for selecting an air traffic flow control strategy considering multiple objectives in view of the deficiencies of the prior art.

[0005] To solve the above technical problem, the present invention discloses a method for selecting an air traffic flow control strategy considering multiple objectives, including the following steps:

[0006] Step 1, collect flight data and air traffic control data;

[0007] Step 2, construct an evaluation index system for air traffic flow control strategies;

[0008] Step 3, construct a comprehensive evaluation model for air traffic flow control strategies;

[0009] Step 4: Taking the minimization of the index values in the air traffic flow control strategy evaluation index system as the optimization goal, determining the decision variables under different air traffic flow control strategies, and solving the index values of each flight based on the decision variables under different air traffic flow control strategies according to the collected flight data;

[0010] Step 5: Calculating the optimal comprehensive evaluation cost index of each flight under the corresponding air traffic flow control strategy according to the comprehensive evaluation model;

[0011] Step 6: Selecting and using the air traffic strategy with the lowest comprehensive evaluation cost index.

[0012] Furthermore, in Step 2, an air traffic flow control strategy evaluation index system is constructed, including taking flight delay cost, carbon emissions, and controller workload as the first-level evaluation indexes of air traffic flow control strategies. Among them, the flight delay cost evaluation index includes secondary indexes of ground delay cost and in-air delay cost; the carbon emissions evaluation index includes secondary indexes of the total carbon emissions in the LTO (Land take-off, take-off and landing) stage and the total carbon emissions in the CCD (Climb-Cruise-Descend, climb-cruise-descent) stage; the controller workload evaluation index includes secondary indexes of speed adjustment instructions and direction adjustment instructions, thus forming an air traffic flow control strategy evaluation index system; calculating the flight delay cost DC, carbon emissions CE, and controller workload CL of flights under different air traffic flow control strategies.

[0013] Furthermore, Step 3 constructs an air traffic flow control strategy comprehensive evaluation model as follows: Since the dimensions of the three evaluation indexes are different, the three evaluation indexes are normalized, and the normalized values of the three evaluation indexes are weighted and summed to obtain the air traffic flow control strategy comprehensive evaluation cost index EV:

[0014] EV = w1 * DC G + w2 * CE G + w3 * CL G

[0015] where DC G , CE G , CL G respectively represent the normalized flight delay cost, carbon emissions, and controller workload; w1, w2, and w3 respectively represent the weights corresponding to the normalized flight delay cost, carbon emissions, and controller workload in the comprehensive evaluation cost index EV.

[0016] Further, when a certain index among flight delay cost, carbon emissions, and controller workload reaches a certain threshold, the negative impact brought by its relatively large cost will show exponential growth. For example, large-scale flight delays will spread in the network, causing cascading failures of airport nodes; the higher the carbon emission cost, the higher the tax rate imposed on airlines; when the controller workload exceeds a certain threshold, their work efficiency may drop significantly, making it difficult to meet the requirements of busy airspace and resulting in airspace paralysis. Therefore, in this application, according to the values of each index after normalization, the weights are dynamically adjusted to make the comprehensive evaluation result of the flow control strategy more in line with the actual situation. The solution process of the weights w1, w2, and w3 corresponding to the flight delay cost, carbon emissions, and controller workload after normalization is as follows: The normalized flight delay cost DC G , carbon emissions CE G , and controller workload index CL G are respectively substituted into the weight calculation function to obtain the corresponding weights w1, w2, and w3 in the multi-objective evaluation. The weight calculation function is:

[0017]

[0018] In the formula, x is the cost index after normalization, and f(x) is the weight corresponding to this index. Using this function to calculate the corresponding weights of the indicators, that is:

[0019]

[0020] Further, the different air traffic flow control strategies in step 4 include ground holding strategy, in-air speed adjustment and in-air holding strategy, and rerouting strategy. Step 4 includes:

[0021] Step 4.1, under the ground holding strategy, taking the flight delay cost DC and carbon emissions CE as optimization objectives, taking the flight departure time as the decision variable, considering the actual airport landing and takeoff capacity limit, and deploying the takeoff time of flights in the pre-tactical stage to reduce the in-air holding caused by congestion after the aircraft takes off, and optimizing to obtain a set of Pareto front solutions, that is, the optimized flight departure time set. Under the feasible solution of each aircraft's departure time, calculate the corresponding flight delay cost, carbon emissions, and controller workload;

[0022] Step 4.2, under the in-air speed adjustment and in-air holding strategy, taking the flight delay cost DC and carbon emissions CE as optimization objectives, taking the aircraft cruise speed as the decision variable, considering the aircraft flight performance limit and safety limit, accelerating the flight speed of the leading aircraft, reducing the delay time of the entire queue, and optimizing to obtain a set of Pareto front solutions, that is, the optimized aircraft cruise speed set. Under the feasible solution of each aircraft's cruise speed, calculate the corresponding flight delay cost, carbon emissions, and controller workload;

[0023] Step 4.3, under the rerouting strategy, the flight delay cost DC and carbon emissions CE are taken as optimization objectives, and the aircraft flight path is taken as the decision variable. Considering constraints such as the capacity restrictions of arrival and departure points and the capacity restrictions of key waypoints, a set of Pareto frontier solutions is optimized, that is, the set of optimized aircraft flight paths. Under each feasible solution of the aircraft flight path, the corresponding flight delay cost, carbon emissions and controller workload are calculated.

[0024] Modeling the air traffic flow control problem as a multi-objective comprehensive evaluation problem has the following advantages: it comprehensively considers three evaluation indicators: flight delay cost, carbon emissions and controller workload. Compared with the traditional flow control program that only considers minimizing the cost of flight delays in the air and allocating time slots according to flight priority, its evaluation indicators cover a wider range. While achieving results that are not much different from those of traditional flow control programs, it can minimize carbon emissions and controller workload costs, reduce the overall cost of flow control strategies, and better meet the needs of airlines and passengers, environmental protection and air traffic management agencies.

[0025] Furthermore, step 4.1 includes: establishing a multi-objective optimization model with minimizing flight delay cost DC and carbon emission CE as the objective function, and the constraints are as follows:

[0026]

[0027] T f,dep ≤t f,dep <T f,dep +η f (4)

[0028] Formula 1 to Formula 3 are the airport takeoff capacity, landing capacity and total capacity constraints with a step length t of 15 minutes respectively; AP is the set of all current airports, and C p denote the take-off capacity, landing capacity and total capacity of airport p respectively, They represent the set of flights whose take-off and landing times are [t, t+15) at airport p, and || represents the number of elements in the set; Formula 4 is the waiting time constraint, which limits the flight waiting time to no more than the threshold η f , the threshold is determined by the flight priority, t f,dep represents the departure time of the flight f in operation; T f,dep represents the departure time of flight f in the flight plan;

[0029] The optimization obtains a set of Pareto frontier solutions, that is, the optimized flight departure time set. Under the feasible solution of each aircraft departure time set, the corresponding flight delay cost, carbon emissions and controller workload are calculated.

[0030] Furthermore, step 4.2 includes: establishing a multi-objective optimization model with minimizing flight delay cost DC and carbon emissions CE as the objective functions, and the constraints are as follows:

[0031]

[0032] This constraint indicates that the speed of flight f during the cruise phase is between the minimum level flight speed and the maximum safe speed of the corresponding aircraft type of flight f, and the data is obtained by querying the aircraft type;

[0033] A set of Pareto front solutions is obtained through optimization, that is, the optimized aircraft cruise speed set. Under the feasible solutions of each aircraft cruise speed set, the corresponding flight delay cost, carbon emissions, and air traffic controller workload are calculated.

[0034] Furthermore, step 4.3 includes: establishing a multi-objective optimization model with minimizing flight delay cost DC and carbon emissions CE as the objective functions, and the constraints are as follows:

[0035] r f ∈{r f1 ,r f2 ,...,r fN}(5)

[0036]

[0037] In formula 5, r f represents the flight path of flight f, and {r f1 ,r f2 ,...,r fN} is an ordered set of the shortest N paths of flight f in the airway network, and each set starts from the inbound waypoint and ends at the initial approach fix;

[0038] In formula 6, C i represents the capacity of the i-th airway point d i , d s is the set of arrival / departure points and pre-identified key airway points, represents the set of flights passing through the i-th airway point d i at the time interval [t - 1, t + 1);

[0039] First, obtain the set of airway points d s in the available airspace according to the actual operating conditions, establish an airway network based on the connection status between airway points, use the Dijkstra algorithm to generate the shortest N paths of flight f, and save its airway points as r fn , 1 ≤ n ≤ N, 1 ≤ N ≤ 10;

[0040] An optimized set of Pareto front solutions, that is, the set of optimized aircraft flight paths, is obtained. Under each feasible solution of the aircraft flight path set, the corresponding flight delay cost, carbon emissions, and controller workload are calculated.

[0041] Furthermore, step 5 includes:

[0042] Step 5.1: For the flight delay cost, carbon emissions, and controller workload corresponding to each feasible solution of the flight departure time in the optimized set of flight departure times obtained in step 4.1, calculate the comprehensive evaluation cost index of the ground waiting strategy under each feasible solution of the flight departure time according to step 3; among the comprehensive evaluation cost indexes of all feasible solutions of the flight departure time, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the ground waiting strategy. GW ;

[0043] Step 5.2: For the flight delay cost, carbon emissions, and controller workload corresponding to each feasible solution of the aircraft cruise speed in the optimized set of aircraft cruise speeds obtained in step 4.2, calculate the comprehensive evaluation cost index of the in-air speed adjustment and in-air waiting strategy under each feasible solution of the aircraft cruise speed according to step 3; among the comprehensive evaluation cost indexes of all feasible solutions of the aircraft cruise speed, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the in-air waiting and in-air speed adjustment strategy. AS ;

[0044] Step 5.3: For the flight delay cost, carbon emissions, and controller workload corresponding to each feasible solution of the aircraft flight path in the optimized set of aircraft flight paths obtained in step 4.3, calculate the comprehensive evaluation cost index of the flight diversion strategy under each feasible solution of the aircraft flight path according to step 3; among the comprehensive evaluation cost indexes of all feasible solutions of the aircraft flight path, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the flight diversion strategy. WC 。

[0045] This application provides a method for selecting an air traffic flow control strategy considering multiple objectives. Corresponding constraint conditions are constructed for the ground waiting strategy, flight diversion strategy, in-air speed adjustment, and in-air waiting strategy respectively. With minimizing the flight delay cost, carbon emissions, and controller workload as the objective function, a set of Pareto feasible solutions under each control strategy, as well as the flight delay cost, carbon emissions, and controller workload index values corresponding to the feasible solutions, are obtained. A variable weight comprehensive evaluation index system for traffic flow control strategies is constructed. According to the flight delay cost, carbon emissions, and controller workload index values under each feasible solution, the corresponding weights are found, and the comprehensive evaluation cost index EV corresponding to the feasible solutions under the ground waiting strategy, flight diversion strategy, in-air speed adjustment, and in-air waiting strategy control strategies is calculated by weighted summation. GW, EV AS , EV WC . After implementing the three traffic control strategies, the comprehensive evaluation cost index EV GW , EV AS , EV WC are compared to contrast the performance differences of each traffic control strategy, and the air traffic strategy with the lowest comprehensive evaluation cost index is selected and used.

[0046] Beneficial effects: The present application proposes a method for selecting an air traffic flow control strategy that simultaneously considers minimizing flight delay costs, carbon emissions, and controller workload. It overcomes the traditional air traffic flow control strategy that mainly focuses on reducing flight delays and conflicts and lacks a comprehensive trade-off for carbon emission indicators. By comprehensively evaluating the three indicators of flight delay costs, carbon emissions, and controller workload with different dimensions, the present invention can provide a decision-making basis for airlines to select flight routes and for controllers to manage traffic flow in the pre-tactical stage, and has practical application value in air traffic flow management. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The following further specifically describes the present invention in conjunction with the drawings and specific embodiments, and the above and / or other advantages of the present invention will become clearer.

[0048] Figure 1 is a flowchart of a method for selecting an air traffic flow control strategy considering multiple objectives provided by an embodiment of the present application.

[0049] Figure 2 is a schematic diagram of a weight calculation function provided by an embodiment of the present application. SPECIFIC EMBODIMENTS

[0050] For the convenience of those skilled in the art, the present invention is further described below in conjunction with embodiments and the drawings. The content mentioned in the embodiments does not limit the present invention.

[0051] Referring to Figure 1 as shown, this embodiment provides a method for selecting an air traffic flow control strategy considering multiple objectives, and the steps are as follows:

[0052] Step 1, collect flight data and air traffic control data: The flight data includes the planned departure time, planned landing time, aircraft type, departure airport, and landing airport of the flight; the air traffic control data includes the capacity of waypoints, the takeoff capacity, landing capacity, and total capacity of each airport. The data format description of the flight plan is shown in Table 1.

[0053] Table 1 Example of flight data format

[0054] Call sign Aircraft type Departure airport Arrival airport Scheduled departure time Scheduled arrival time PIC1261 A320 ZUGY VVCR 2019 / 8 / 1 00:01 2019 / 8 / 1 02:56 CSN6710 A320 ZJSY ZJHK 2019 / 8 / 1 00:02 2019 / 8 / 1 00:56 CBJ5588 NULL ZGNN ZJSY 2019 / 8 / 1 00:05 2019 / 8 / 1 01:25 AXM2139 A320 ZHHH WBKK 2019 / 8 / 1 00:05 2019 / 8 / 1 03:56 HVN713 A321 ZSQD VVCR 2019 / 8 / 1 00:05 2019 / 8 / 1 04:13 MMA705 A319 ZSHC VYMD 2019 / 8 / 1 00:05 2019 / 8 / 1 03:34 SLK967 B738 ZGSZ WSSS 2019 / 8 / 1 00:05 2019 / 8 / 1 03:28 CHH6027 B738 ZGGG ZUCK 2019 / 8 / 1 00:05 2019 / 8 / 1 01:47 SLK969 B738 ZGSZ WSSS 2019 / 8 / 1 00:05 2019 / 8 / 1 05:20

[0055] Step 2, construct an evaluation index system for air traffic flow control strategies;

[0056] Take flight delay cost, carbon emissions, and controller workload as the first-level evaluation indicators for traffic flow control strategies. Among them, the flight delay cost evaluation indicator includes secondary indicators such as ground delay cost and in-air delay cost; the carbon emissions evaluation indicator includes secondary indicators such as the total carbon emissions during the LTO phase and the total carbon emissions during the CCD phase; the controller workload evaluation indicator includes secondary indicators such as speed adjustment instructions and direction adjustment instructions. Thus, an evaluation index system for air traffic flow control strategies is formed. The calculation formulas for flight delay cost, carbon emissions, and controller workload cost are expressed as follows:

[0057] (21) Flight delay cost DC, the calculation formula is:

[0058]

[0059] Among them, DC represents the flight delay cost, t f,dep , t f,arr respectively represent the takeoff time and landing time of flight f during operation, T f,dep , T f,arr respectively represent the takeoff time and landing time of flight f in the flight plan; η is a coefficient representing the ratio of in-air delay cost to ground delay cost, and is a positive number greater than 1; F represents the set of all flights.

[0060] If the ground waiting strategy acts on flight f, then the takeoff time t f,dep of flight f is the takeoff time after flight f executes the ground waiting strategy. t f,arr can be calculated from the segment distance and the average cruising speed, that is

[0061]

[0062] Among them, lon i and lat i respectively represent the longitude and latitude of waypoint d i ; r f represents the flight path of flight f, that is, the set of waypoints passed through, n represents the total number of segments of the flight path r f , i represents the waypoint index, 1 ≤ i ≤ n - 1; v f represents the average cruising speed of flight f.

[0063] (22) Carbon emissions, abbreviated as CE, specifically:

[0064]

[0065] Among them, E f,LTORepresents the total carbon emissions (kg) of flight f during the LTO phase; E f,CCD Represents the total carbon emissions (kg) of flight f during the CCD phase. The specific calculation formula is as follows:

[0066]

[0067] E f,CCD = 60T f,CCD F f,CCD I

[0068]

[0069] Among them, m f,j Represents the number of cycles of flight f in the j-th phase during the LTO process. j = 1 is the takeoff phase, j = 2 is the climb phase, j = 3 is the approach phase, and j = 4 is the taxiing phase; n f Represents the number of engines of flight f; T f,j Represents the flight time (min) of flight f in the j-th phase during the LTO process. ICAO defines the time consumed by the aircraft during takeoff, climb, approach, and taxiing as 0.7 min, 2.2 min, 4.0 min, and 26.0 min respectively; F f,j Represents the single-engine fuel flow rate (kg / s) of flight f in the j-th phase; I represents the carbon dioxide emission factor, taking the constant 3.115; T f,CCD Represents the flight time (min) of flight f during the CCD process; F f,CCD Represents the single-engine fuel flow rate (kg / s) of flight f during the CCD process;; L f,CCD Represents the flight mileage (km), V f，CCD Is the flight speed (km / h) of aircraft f during the CCD process; M f,CCD Is the flight Mach number of flight f during the CCD process;

[0070] (23) Controller workload, abbreviated as CL, mainly considers the controller's workload of air-ground communication, specifically:

[0071]

[0072] Among them, f s Represents the number of times flight f adjusts its speed, f t Represents the number of times flight f adjusts its heading.

[0073] If the speed of the aircraft corresponding to flight f is adjusted in the in-air speed adjustment strategy, it is regarded as issuing two speed adjustment instructions. After the aircraft selects its flight path, the included angles between adjacent flight segments are statistically analyzed. If the included angle is greater than 30°, the aircraft makes a turn at this point, which is regarded as the controller issuing two heading adjustment instructions.

[0074] Step 3: Construct a comprehensive evaluation model for air traffic flow control strategies;

[0075] Since the dimensions of the three evaluation indicators are different, they need to be normalized and then weighted and summed up.

[0076]

[0077] EV = w1 * DC G + w2 * CE G + w3 * CL G (34)

[0078] where EV represents the comprehensive evaluation cost index of air traffic flow control strategies, DC G , CE G , CL G represent the flight delay cost, carbon emissions, and controller workload after normalization, respectively; DC min , CE min , CL min represent the minimum values of the flight delay cost, carbon emissions, and controller workload, respectively; DC max , CE max , CL max represent the maximum values of the flight delay cost, carbon emissions, and controller workload, respectively; w1, w2, and w3 represent the weights corresponding to the flight delay cost, carbon emissions, and controller workload after normalization in the comprehensive evaluation cost index.

[0079] When a certain type of cost reaches a certain threshold, the negative impact brought by its larger cost will show exponential growth. For example, a large area of flight delays will spread in the network, causing the cascading failure of airport nodes; the higher the carbon emission cost, the higher the tax rate imposed on airlines; when the controller workload exceeds a certain threshold, their work efficiency may drop significantly, making it difficult to meet the requirements of busy airspace and resulting in airspace paralysis. Therefore, in this embodiment, according to the values of the normalized cost indices, the weights are dynamically adjusted to make the comprehensive evaluation result of the flow control strategy more in line with the actual situation.

[0080] Substitute the flight delay cost DC G , carbon emissions CE G , and controller workload index CL G after normalization into the weight calculation function f(x) to obtain the corresponding weights w1, w2, and w3 in the multi-objective evaluation. The weight calculation function f(x) is:

[0081]

[0082] Where x is the normalized cost index, and f(x) is the weight corresponding to this index. The weights corresponding to the indicators are calculated using this function, i.e.:

[0083]

[0084] Step 4: Taking the minimization of the indicator values in the air traffic flow control strategy evaluation index system as the optimization objective, determine the decision variables under different air traffic flow control strategies, and solve the indicator values of each flight based on the decision variables under different air traffic flow control strategies according to the collected flight data; different air traffic flow control strategies include ground waiting strategy, in-air speed adjustment and in-air waiting strategy, and rerouting strategy,

[0085] Step 5: Calculate the optimal comprehensive evaluation cost index of each flight under the corresponding air traffic flow control strategy according to the comprehensive evaluation model;

[0086] Step 4.1: Evaluate the impact generated after implementing the ground waiting strategy;

[0087] Taking the flight delay cost DC and carbon emissions CE as the optimization objectives, taking the flight departure time as the decision variable, considering the actual airport landing and takeoff capacity constraints, allocate the takeoff time of flights in the pre-tactical stage to reduce the in-air waiting caused by congestion after the aircraft takes off.

[0088] Establish a multi-objective optimization model with the minimization of flight delay cost DC and carbon emissions CE as the objective function:

[0089]

[0090] The constraint conditions are as follows:

[0091]

[0092] T f,dep ≤t f,dep <T f,dep +η f (4)

[0093] Formulas 1 to 3 are the airport takeoff capacity, landing capacity, and total capacity constraints with a step size of 15 minutes respectively. AP is the set of all current airports, and C p represent the takeoff capacity, landing capacity, and total capacity of airport p respectively, and these data are obtained from historical data statistics as a theoretical value, represent the set of flights with takeoff time or landing time in [t, t + 15) at airport p respectively, i.e.,

[0094]

[0095] where They are the sets of flights with the departure airport and the landing airport located at airport p respectively. || represents the number of elements in the set.

[0096] Formula 4 is the waiting time constraint, which restricts the waiting time of flights not to exceed the threshold η f , and this threshold is determined by the priority of the flight, t f,dep represents the departure time of flight f in operation; T f,dep represents the departure time of flight f in the flight plan.

[0097] A set of Pareto front solutions is obtained through optimization, that is, the optimized set of flight departure times. Under the feasible solutions of each aircraft departure time set, the corresponding flight delay cost, carbon emissions, and air traffic controller workload are calculated.

[0098] Step 5.1, normalize the three indicators according to Formulas (31) to (33), and then calculate the weights corresponding to the three indicators according to Formulas (36) to (38) to obtain the weights w1, w2, w3 used in its comprehensive evaluation cost index. Calculate the comprehensive evaluation cost index EV' of each feasible solution according to Formula (34) GW .

[0099] EV' GW = w1 * DC G + w2 * CE G + w3 * CL G

[0100] Among the comprehensive evaluation cost indexes corresponding to the feasible solutions of all aircraft departure time sets, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the ground holding strategy GW .

[0101] Step 4.2, evaluate the impacts generated after implementing the airspeed adjustment and air waiting strategies;

[0102] Take the flight delay cost DC and carbon emissions CE as the optimization objectives, take the aircraft cruise speed as the decision variable, consider the aircraft flight performance limitations and safety limitations, increase the flight speed of the leading aircraft, and reduce the delay time of the entire queue.

[0103] Establish a multi-objective optimization model with minimizing the flight delay cost DC and carbon emissions CE as the objective function:

[0104]

[0105] The constraint conditions are as follows:

[0106]

[0107] This constraint indicates that the speed of flight f during the cruise phase lies between the minimum level flight speed of the aircraft type corresponding to flight f and the maximum safe speed , and the data is obtained by querying the aircraft type.

[0108] After optimizing to obtain a set of Pareto front solutions, that is, the set of optimized aircraft cruise speeds, the corresponding flight delay costs, carbon emissions, and controller workloads are calculated for each feasible solution in the set of aircraft cruise speeds.

[0109] Step 5.2, normalize the three indicators according to formulas (31) to (33), and then look up the initial weights corresponding to the three indicators in Figure 2 . Then, scale the three initial weight values of flight delay cost, carbon emissions, and controller workload to their sum equal to 1 according to formulas (35) to (37) to obtain the weights w1, w2, and w3 used in the comprehensive evaluation cost index. Calculate the comprehensive evaluation cost index EV’ for each feasible solution according to formula (34) . AS .

[0110] EV' AS = w1 * DC G + w2 * CE G + w3 * CL G

[0111] Among the comprehensive evaluation cost indices corresponding to all feasible solutions in the set of aircraft cruise speeds, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the airspeed adjustment and air waiting strategies AS .

[0112] Step 4.3, evaluate the impact generated after implementing the rerouting strategy;

[0113] Establish a multi-objective optimization model with minimizing the flight delay cost DC and carbon emissions CE as the objective functions:

[0114]

[0115] Take the aircraft flight path as the decision variable, and consider constraints such as the departure point capacity limit and the critical airway point capacity limit

[0116] r f ∈{r f1 ,r f2 ,...,r fN}(5)

[0117]

[0118] In formula 5, r f represents the flight path of flight f, {rf1 , r f2 ,..., r fN} is the ordered set of the N shortest paths of flight f in the airway network. Each set starts from the inbound waypoint and ends at the initial approach fix, that is

[0119] r fn = ordered_dict(d1, d2,..., d k ), 1 ≤ n ≤ N

[0120] where k represents the number of waypoints on the current flight path, and ordered_dict is an ordered dictionary, a data type in python.

[0121] In formula 6, C i represents the capacity of the i-th waypoint d i , and d s is the set of departure / arrival points and pre-identified key waypoints. represents the set of flights passing through the i-th waypoint d i at the time interval [t - 1, t + 1).

[0122] First, obtain the set of waypoints d s in the available airspace according to the actual operating conditions. Establish an airway network based on the connection status between waypoints, use the Dijkstra algorithm to generate the N shortest paths of flight f, and save its waypoints to r fn , 1 ≤ n ≤ N, 1 ≤ N ≤ 10, and N can be specified by the decision maker.

[0123] After obtaining a set of Pareto front solutions, that is, the optimized set of aircraft flight paths, calculate the corresponding flight delay cost, carbon emissions, and controller workload under each feasible solution of the aircraft flight path set.

[0124] Step 5.3, normalize the three indicators according to formulas (31) to (33), and then calculate the weights corresponding to the three indicators according to formulas (36) to (38) to obtain the weights w1, w2, w3 used in its comprehensive evaluation cost index. Calculate the comprehensive evaluation cost index EV' of each feasible solution according to formula (34) WC .

[0125] EV' WC = w1 * DC G + w2 * CE G + w3 * CL G

[0126] Among the comprehensive evaluation cost indexes corresponding to all feasible solutions of the aircraft flight path set, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the rerouting strategy.WC 。

[0127] Step 6: Select and use the air traffic strategy with the lowest comprehensive evaluation cost index.

[0128] Compare the optimal comprehensive evaluation cost indices EV GW 、EV AS 、EV WC after implementing the three air traffic flow control strategies of ground waiting, in-air waiting and speed adjustment, and rerouting, compare the performance differences of each flow control strategy, and select and use the air traffic strategy with the lowest comprehensive evaluation cost index. For example, for a certain part of the flight plan, calculate the cost indices and the comprehensive evaluation cost index before flow control and under the three flow control strategies. The results are shown in the following table:

[0129] Table 2 Calculation Example

[0130]

[0131]

[0132] Among them, although the flight delay cost of the ground waiting strategy is the smallest, the comprehensive evaluation cost index EV AS of the first group of Pareto solutions of the in-air speed adjustment and in-air waiting strategies is the lowest. Therefore, the in-air speed adjustment and in-air waiting strategies are the current optimal control strategies.

[0133] In specific implementation, the present application provides a computer storage medium and a corresponding data processing unit. Among them, the computer storage medium can store a computer program, and when the computer program is executed by the data processing unit, it can run the inventive content of a method for selecting an air traffic flow control strategy considering multiple objectives provided by the present invention and some or all of the steps in each embodiment. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0134] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a computer program, that is, a software product. The computer program software product can be stored in the storage medium and includes several instructions to enable a device (which can be a personal computer, a server, a single-chip microcomputer, a MUU, or a network device, etc.) including a data processing unit to execute the methods described in each embodiment or some parts of the embodiments of the present invention.

[0135] The present invention provides a method for selecting an air traffic flow control strategy considering multiple objectives. There are many methods and ways to specifically implement this technical solution. The above description is only a specific implementation manner of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented by using the prior art.

Claims

1. A method for selecting an air traffic flow control strategy considering multiple objectives, characterized in that It includes the following steps: Step 1, collect flight data and air traffic control data; Step 2, construct an evaluation index system for air traffic flow control strategies; Step 3, construct a comprehensive evaluation model for air traffic flow control strategies; Step 4, taking the minimization of the index values in the air traffic flow control strategy evaluation index system as the optimization goal, determine the decision variables under different air traffic flow control strategies, and solve the index values of each flight based on the decision variables under different air traffic flow control strategies according to the collected flight data; Step 5, calculate the optimal comprehensive evaluation cost index of each flight under the corresponding air traffic flow control strategy according to the comprehensive evaluation model; Step 6, select and use the air traffic strategy with the lowest comprehensive evaluation cost index; Step 2 constructs an evaluation index system for air traffic flow control strategies, including taking flight delay cost, carbon emissions, and controller workload as the primary evaluation indicators for air traffic flow control strategies. Among them, the flight delay cost evaluation indicator includes secondary indicators of ground delay cost and in-air delay cost, the carbon emissions evaluation indicator includes secondary indicators of the total carbon emissions in the LTO phase and the total carbon emissions in the CCD phase, and the controller workload evaluation indicator includes secondary indicators of speed adjustment instructions and heading adjustment instructions; calculate the flight delay cost DC, carbon emissions CE, and controller workload CL of flights under different air traffic flow control strategies: (21) The flight delay cost DC, and the calculation formula is: Among them, DC represents the flight delay cost, t f,dep , t f,arr respectively represent the departure time and arrival time of flight f during operation, T f,dep , T f,arr respectively represent the departure time and arrival time of flight f in the flight schedule; η is a coefficient representing the ratio of the in-air delay cost to the ground delay cost, and is a positive number greater than 1; F represents the set of all flights; If the ground holding strategy is applied to flight f, the departure time t of flight f f,dep is the departure time after the ground holding strategy is executed for flight f; t f,arr can be calculated from the segment distance and the average cruising speed, that is where lon i and lat i are the longitude and latitude of waypoint d i respectively; r f represents the flight path of flight f, that is, the set of waypoints passed through, n represents the total number of flight segments of flight path r f , i represents the waypoint index, 1 ≤ i ≤ n - 1; v f represents the average cruising speed of flight f (22) The carbon emissions CE, and the calculation formula is: Among them, E f,LTO represents the total carbon emissions of flight f during the LTO phase; E f,CCD represents the total carbon emissions of flight f during the CCD phase, and the solution formula is as follows: E f,CCD = 60T f,CCD F f,CCD I where m f,j represents the number of cycles of flight f in the j-th phase during LTO, where j = 1 is the takeoff phase, j = 2 is the climb phase, j = 3 is the approach phase, and j = 4 is the taxiing phase; n f represents the number of engines of flight f; T f,j represents the flight time of flight f in the j-th phase during LTO. ICAO defines the time consumed by an aircraft during takeoff, climb, approach, and taxiing as 0.7 min, 2.2 min, 4.0 min, and 26.0 min respectively; F f,j represents the single-engine fuel flow rate of flight f in the j-th phase; I represents the carbon dioxide emission factor, taking the constant 3.115; T f,CCD represents the flight time of flight f during CCD; F f,CCD represents the single-engine fuel flow rate of flight f during CCD; L f,CCD represents the flight mileage, V f，CCD is the flight speed of aircraft f during CCD; M f,CCD is the flight Mach number of flight f during CCD; (23) The controller workload CL, considering the controller's workload of air-ground communication, and the calculation formula is: Among them, f s represents the number of times the speed of flight f is adjusted, and f t represents the number of times the heading of flight f is adjusted; If the speed of the aircraft corresponding to flight f is adjusted in the in-air speed adjustment strategy, it is regarded as issuing two speed adjustment instructions; after the aircraft selects a flight path, count the included angle between adjacent flight segments. If the included angle is greater than 30°, it means that the aircraft has made a turn at this point, and it is regarded as the controller issuing two heading adjustment instructions.

2. The method for selecting an air traffic flow control strategy considering multiple objectives according to claim 1, wherein Step 3 constructs a comprehensive evaluation model for air traffic flow control strategies, including: normalizing the three evaluation indicators, and performing weighted summation on the normalized values of the three evaluation indicators to obtain the comprehensive evaluation cost index EV of the air traffic flow control strategy; EV = w1 * DC G + w2 * CE G + w3 * CL G Among them, DC G , CE G , and CL G respectively represent the normalized flight delay cost, carbon emissions, and controller workload; w1, w2, and w3 respectively represent the weights corresponding to the normalized flight delay cost, carbon emissions, and controller workload in the comprehensive evaluation cost index EV.

3. The method for selecting an air traffic flow control strategy considering multiple objectives according to claim 2, characterized in that In Step 3, the weight calculation function f(x) is used to calculate the weights w1, w2, and w3 corresponding to the normalized flight delay cost, carbon emissions, and controller workload. The weight calculation function f(x) is expressed as: Then 4. A method for selecting an air traffic flow control strategy considering multiple objectives according to claim 3, characterized in that, In Step 4, different air traffic flow control strategies include ground holding strategy, in-air speed adjustment and in-air holding strategy, and rerouting strategy. Step 4 includes: Step 4.1, under the ground holding strategy, taking the flight delay cost DC and carbon emissions CE as the optimization goals, taking the flight departure time as the decision variable, considering the actual airport landing and takeoff capacity limit, and allocating the takeoff time of flights in the pre-tactical stage to reduce the in-air holding caused by congestion after the aircraft takes off, and optimize to obtain a set of Pareto front solutions, that is, the optimized set of flight departure times. Under each feasible solution of the aircraft departure time, calculate the corresponding flight delay cost, carbon emissions, and controller workload; Step 4.2, under the airspeed adjustment and in-air waiting strategies, taking the flight delay cost DC and carbon emissions CE as the optimization objectives, taking the aircraft cruise speed as the decision variable, considering the aircraft flight performance limitations and safety limitations, accelerating the flight speed of the leading aircraft, reducing the delay time of the entire queue, and optimizing to obtain a set of Pareto front solutions, that is, the optimized aircraft cruise speed set. Under each feasible solution of the aircraft cruise speed, calculate the corresponding flight delay cost, carbon emissions, and controller workload; Step 4.3, under the rerouting strategy, taking the flight delay cost DC and carbon emissions CE as the optimization objectives, taking the aircraft flight path as the decision variable, considering the capacity limitations of the departure and arrival points and the capacity limitations of the key waypoints, and optimizing to obtain a set of Pareto front solutions, that is, the optimized aircraft flight path set. Under each feasible solution of the aircraft flight path, calculate the corresponding flight delay cost, carbon emissions, and controller workload.

5. The method for selecting an air traffic flow control strategy considering multiple objectives according to claim 4, wherein Step 4.1 includes: establishing a multi-objective optimization model with minimizing the flight delay cost DC and carbon emissions CE as the objective function, and the constraints are as follows: T f,dep ≤t f,dep <T f,dep +η f (4) Equations 1 to 3 are the constraints on the takeoff capacity, landing capacity, and total capacity of the airport with a step size t of 15 minutes; AP is the set of all current airports. and C p represent the takeoff capacity, landing capacity, and total capacity of airport p respectively. represent the sets of flights with takeoff and landing times in [t, t + 15) at airport p respectively, and || represents the number of elements in the set; Equation 4 is the waiting time constraint, which restricts the waiting time of flights not to exceed the threshold η f , which is determined by the priority of the flight, and t f,dep represents the takeoff time of flight f in operation; T f,dep represents the takeoff time of flight f in the flight plan. Optimizing to obtain a set of Pareto front solutions, that is, the optimized flight departure time set. Under each feasible solution of the aircraft departure time set, calculate the corresponding flight delay cost, carbon emissions, and controller workload.

6. The method for selecting an air traffic flow control strategy considering multiple objectives according to claim 5, wherein Step 4.2 includes: establishing a multi-objective optimization model with minimizing the flight delay cost DC and carbon emissions CE as the objective function, and the constraints are as follows: This constraint indicates that the speed of flight f during the cruise phase lies between the minimum level flight speed of the aircraft type corresponding to flight f and the maximum safe speed, and the data is obtained by querying the aircraft type; Optimizing to obtain a set of Pareto front solutions, that is, the optimized aircraft cruise speed set. Under each feasible solution of the aircraft cruise speed set, calculate the corresponding flight delay cost, carbon emissions, and controller workload.

7. A method for selecting an air traffic flow control strategy considering multiple objectives according to claim 6, characterized in that Step 4.3 includes: establishing a multi-objective optimization model with minimizing the flight delay cost DC and carbon emissions CE as the objective function, and the constraints are as follows: r f ∈{r f1 ,r f2 ,...,r fN}(5) r in Equation 5 f represents the flight path of flight f, {r f1 , r f2 ,..., r fN} is an ordered set of the N shortest paths of flight f in the airway network, and each set starts from the inbound waypoint and ends at the initial approach fix; C in Formula 6 i represents the capacity of the i-th waypoint d i , where d s is the set of departure / arrival points and pre-identified key waypoints, represents the set of flights passing through the i-th waypoint d i at the time interval [t - 1, t + 1); First, obtain the set d of waypoints in the available airspace according to the actual operating conditions s , establish an airway network based on the connection status between waypoints, use the Dijkstra algorithm to generate the shortest N paths of flight f, and save its waypoints to r fn , 1 ≤ n ≤ N, 1 ≤ N ≤ 10; Optimizing to obtain a set of Pareto front solutions, that is, the optimized aircraft flight path set. Under each feasible solution of the aircraft flight path set, calculate the corresponding flight delay cost, carbon emissions, and controller workload.

8. A method for selecting an air traffic flow control strategy considering multiple objectives according to claim 7, characterized in that, Step 5 includes: Step 5.1, for the flight delay cost, carbon emissions, and controller workload corresponding to each feasible solution of the flight departure time in the optimized set of flight departure times obtained in Step 4.1, calculate the comprehensive evaluation cost index of the ground holding strategy for each feasible solution of the flight departure time according to Step 3; among the comprehensive evaluation cost indexes of all feasible solutions of the flight departure time, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the ground holding strategy GW ; Step 5.2: For the flight delay cost, carbon emissions, and controller workload corresponding to each aircraft cruise speed feasible solution in the optimized aircraft cruise speed set obtained in Step 4.2, calculate the comprehensive evaluation cost index of the in-air speed adjustment and in-air waiting strategies for each aircraft cruise speed feasible solution according to Step 3; among the comprehensive evaluation cost indices of all aircraft cruise speed feasible solutions, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the in-air waiting and in-air speed adjustment strategies AS ; Step 5.

3. For the flight delay cost, carbon emissions, and controller workload corresponding to each feasible solution of the aircraft flight path obtained in Step 4.3, calculate the comprehensive evaluation cost index of the rerouting strategy for each feasible solution of the aircraft flight path according to Step 3; among the comprehensive evaluation cost indices of all feasible solutions of the aircraft flight path, select the minimum value as the optimal comprehensive evaluation cost index EV after implementing the rerouting strategy WC .

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