Flight ordering planning method based on flight path operation

Abstract

The invention discloses a flight ordering planning method based on flight path operation. The method comprises the following steps: obtaining an operation time deviation value of an in / out flight; quantifying the uncertainty of the running time deviation value of the in / out flights; and establishing an in / out flight optimization scheduling random chance constraint model, introducing the uncertainty corresponding to the operation time deviation into the model through random chance constraint, and solving to obtain an optimal scheduling moment, sorting, runway and time slot distribution scheme of each flight. According to the method, the operation uncertainty is converted into the specific numerical value constraint through the random chance constraint, it is guaranteed that the flight take-off and landing time meets the safety requirement under the preset confidence level, and the delay occurrence probability is greatly reduced; the problem of flight delay in the actual operation process is solved, and the effectiveness of the flight scheduling means is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of flight sequencing and scheduling, and in particular relates to a flight sequencing planning method based on track operation. Background Art

[0002] Currently, flight delays are primarily caused by six main factors: weather, traffic control, mechanical failure, passenger factors, flight adjustments, and queue-jumping. Some of these factors are predictable, while others are not. Terminals are crucial airspace resources, and a prominent cause of flight congestion and delays is the mismatch between demand and capacity. Flight scheduling optimization helps fully utilize available terminal and runway capacity, minimizing delays. However, various factors in flight operations can lead to uncertainty in arrival and departure times, complicating flight operations management decisions. Therefore, flight scheduling optimization helps fully utilize available terminal and runway capacity, minimizing delays.

[0003] Trajectory-Based Operation (TBO) uses the aircraft's four-dimensional trajectory as its foundation. This information is shared between air traffic control, airlines, and aircraft, enabling trajectory negotiation and time constraint negotiation between pilots and ground personnel. This allows for precise control of aircraft flight paths and interval management. This improves the accuracy of aircraft arrival times from minutes to tens of seconds. By predicting aircraft flight paths in advance, TBO can also reduce conflicts during flight and reduce airspace congestion in en-route and terminal areas. Therefore, TBO not only increases air traffic flow but also enables smoother flight, improving flight operations.

[0004] Most relevant research is based on deterministic scenarios. The uncertain factors that affect the operation of airport take-off and landing flights have begun to enter the field of vision of researchers, and more and more flight operation optimization research considering uncertainty has gradually emerged. Methods for dealing with uncertain optimization problems include stochastic programming methods, fuzzy programming methods, robust optimization methods, etc. The choice of method depends on the information available, the characteristics of the problem, and the decision-making perspective of the decision maker. Domestic research on the uncertainty of arrival / departure times has mainly focused on the analysis and prediction of flight delays. In recent years, the focus has been on the prediction of flight delays under the influence of weather. In terms of the quantification of uncertainty in traffic demand forecasting, domestic and foreign research has mainly focused on the prediction of sectoral probabilistic traffic demand, and a small number of studies have focused on the prediction of probabilistic traffic demand at airports.

[0005] Currently, a prominent cause of flight congestion and delays in terminal areas is the mismatch between traffic and capacity. An effective long-term solution to alleviate delays is to expand airport infrastructure and increase overall airspace capacity. This approach requires significant investment in both money and time, but may not be feasible in some terminal areas where airspace capacity is nearing saturation. Furthermore, the four-dimensional trajectory information of aircraft on different flight routes will inevitably vary. For a scheduled flight, the daily four-dimensional trajectory information will also vary with weather conditions, payload, and cruising altitude, resulting in a dynamic and uncertain 4D trajectory (4DT). Furthermore, in daily operations, various uncertainties such as weather, airline factors, and air traffic control intervention can also contribute to flight operational uncertainty. All of these factors complicate air traffic management. Summary of the Invention

[0006] In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a flight sequencing planning method based on trajectory operation, which uses the probability distribution method to fit the expected arrival / departure time deviations of flights in historical operation data; based on the expected arrival / departure time deviation value parameters of flights fitted by the probability distribution method, a flight scheduling random chance constraint model is established that takes into account the uncertainty of arrival / departure time prediction, generates a flight take-off and landing time series that is more in line with the actual occurrence, can reduce the deviation between the expected take-off and landing time and the actual take-off and landing time, so as to solve the problem of flight delays in the actual operation process and enhance the effectiveness of flight scheduling means.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] The present invention provides a flight sequence planning method based on trajectory operation, comprising the following steps:

[0009] 1) Obtain the running time deviation value of the arriving / departing flight;

[0010] 2) Quantify the uncertainty of arrival / departure flight operation time deviation values;

[0011] 3) Establish a random chance constraint model for the optimal scheduling of arrival / departure flights, introduce the uncertainty corresponding to the running time deviation into the model through random chance constraints, and solve the optimal scheduling time, sequence, runway and time slot allocation plan for each flight.

[0012] Furthermore, the step 1) specifically includes:

[0013] Obtain flight operation data for a certain period of time from the airport's historical data and extract the estimated take-off and landing times of incoming / outgoing flights Actual take-off and landing time And calculate to get the running time deviation value Δt of each flight m m , the expression is as follows:

[0014]

[0015] Furthermore, the step 2) specifically includes: using a probability distribution fitting method to perform statistical modeling on the runtime deviation value obtained in step 1) to quantify the uncertainty of the runtime deviation. The specific process is as follows:

[0016] 21) Construct a set of runtime deviation samples {Δt m};

[0017] 22) Perform distribution fitting on all deviation data to obtain that all deviation data obey normal distribution;

[0018] 23) The maximum likelihood estimation method was used to estimate the parameters of the data and obtain the normal distribution parameters: mean μ and standard deviation σ;

[0019] 24) The Kolmogorov-Smirnov test was used to verify the normal distribution fitting results;

[0020] 25) According to the above fitting results, determine the inverse cumulative distribution function of the entry Inverse cumulative distribution function Complete the quantification of the uncertainty of the arrival / departure flight operation time deviation value, and obtain the key parameters describing the deviation distribution characteristics, including: mean μ, standard deviation σ, arrival inverse cumulative distribution function Inverse cumulative distribution function

[0021] Furthermore, the step 3) specifically includes:

[0022] A random chance constraint model for flight optimal scheduling is constructed with the goal of minimizing flight delay costs and with flight sequencing, runway allocation, safety intervals, and time slot scheduling as constraints. The uncertainty of the running time deviation value is introduced into the model through random chance constraints, as follows:

[0023] 31) Construct the objective function for minimizing flight delay costs;

[0024] The objective function is to minimize the total delay cost, which includes the arrival flight delay cost A c and departure flight delay cost D c ; The objective function expression is as follows:

[0025]

[0026] in, is the optimized arrival time of incoming flight m, is the planned arrival time of incoming flight m, is the operating time deviation of the arrival flight, The optimized take-off time for departing flight n, Planned takeoff time for departing flight n, is the running time deviation of the departure flight, A is the set of arrival flights, D is the set of departure flights, m∈A, n∈D, A c is the unit delay cost of the arriving flight, D c is the unit delay cost of the departing flight, minZ is the minimum delay cost of the arriving and departing flights;

[0027] 32) Set flight sorting constraints;

[0028] A flight sorting constraint is implemented to ensure that any two flights on the same runway have a unique order. The expression is as follows:

[0029]

[0030] Among them, p mn To indicate that flight m is scheduled before flight n, p nm represents the situation where flight n is ranked before flight m; p mn =1 means flight m is ranked before flight n; p mn =0 means flight m is ranked after flight n;

[0031] 33) Set runway allocation constraints;

[0032] A runway allocation constraint is implemented to ensure that each flight is assigned only one runway. The expression is as follows:

[0033]

[0034] Where R represents the set of all available runways, x mr is an indicator variable indicating whether flight m is assigned to runway r, x mr =1 means flight m is assigned to runway r;

[0035] 34) Set safety interval constraints;

[0036] Implement safety interval constraints between arriving flights as follows:

[0037]

[0038] in, is the optimized arrival time of incoming flight m, is the optimized arrival time of incoming flight n, is the minimum safe time slot between arrival flights m and n, where M is a positive number set to perform appropriate constraints;

[0039] Implement safety distance restrictions between departing flights as follows:

[0040]

[0041] in, is the optimized take-off time of departure flight m, is the optimized take-off time of departure flight n, is the minimum safe time gap between departing flights m and n;

[0042] Safety spacing restrictions are implemented between arriving and departing flights as follows:

[0043]

[0044] in, is the safety interval between incoming flight m and departing flight n;

[0045] Implement safety spacing restrictions between departing and arriving flights as follows:

[0046]

[0047] in, is the safety interval between incoming flight n and departing flight m;

[0048] 35) Setting random chance constraints;

[0049] The random chance constraints for arrival flight m are as follows:

[0050]

[0051] Among them, α is the entry confidence level; is the estimated arrival time of incoming flight m, is the operating time deviation of arrival flight m;

[0052] The random chance constraints for departure flight n are as follows:

[0053]

[0054] Among them, β is the departure confidence level; is the estimated departure time of departing flight n, is the operating time deviation of departure flight n;

[0055] Extracting arrival flight operation time deviations from historical data and departure flight operating time deviations And the probability distribution fitting is used for the deviation data to obtain the normal distribution parameters: mean μ and standard deviation σ;

[0056] The random chance constraint expression of the arrival flight is as follows:

[0057]

[0058] Among them, α is the entry confidence level, Indicates the estimated arrival time of incoming flight m; represents the optimized arrival time of incoming flight m;

[0059] The random chance constraint expression for departure flights is as follows:

[0060]

[0061] Where β is the departure confidence level, Indicates the estimated departure time of departing flight n; Indicates the optimized take-off time of departure flight n;

[0062] Introducing the inverse cumulative distribution function Convert the arrival confidence level α into a deterministic constant, and obtain the arrival flight deterministic constraints as follows:

[0063]

[0064] Introducing the inverse cumulative distribution function Convert the departure confidence level β into a deterministic constant, and obtain the following deterministic constraints for departure flights:

[0065]

[0066] 36) Set time slot and capacity constraints;

[0067] Perform time slot allocation constraints, the expression is as follows:

[0068]

[0069] Where F = A∪D, L mt Indicates whether flight m is assigned to time slot t. If flight m is assigned to time slot t, then L mt =1; F represents the set of all flights;

[0070] The time constraints within the time slot are implemented to ensure that the optimized take-off and landing times of the flights are within the allocated time slots, as follows:

[0071]

[0072] Where T is a time slot set, which means the entire scheduling period is discretely divided into a set of multiple time slots; t∈T is the index number in the specific time slot set T; T b is the time slot start time, S size For each time slot length, is the optimized time slot of flight m;

[0073] Slot capacity restrictions are implemented to prevent multiple flights from being scheduled in the same slot, as follows:

[0074]

[0075] 37) Integrate and solve the random chance constraint model for optimal scheduling of arrival / departure flights;

[0076] Integrate the objective functions and constraints constructed in steps 31) to 36) into a complete random chance constraint model for optimal scheduling of arrival / departure flights, and solve the model to obtain the optimal scheduling time, sequence, runway and time slot allocation for each flight;

[0077] The objective function of the random chance constraint model for optimal scheduling of arrival / departure flights is as follows:

[0078]

[0079] in, represents the delay cost of the incoming flight, represents the delay cost of the departing flight, Z represents the target value of the total delay cost of the entire model, MinZ represents the minimum value of the total delay, and A c is the unit delay cost of the arriving flight, D c is the unit delay cost of the departing flight, represents the optimized arrival time of incoming flight m, is the planned arrival time of incoming flight m, is the optimized take-off time of departure flight n, Planned takeoff time for departing flight n, It represents the deterministic constant obtained by transforming the approach confidence level α through the inverse cumulative distribution function, which is used to offset the safety interval requirement. It represents the deterministic constant obtained by transforming the departure confidence level β through the inverse cumulative distribution function;

[0080] The random chance constraints for optimal scheduling of arrival / departure flights include:

[0081] 371) Flight sorting constraints:

[0082]

[0083] Among them, Pmn is an indicator variable. If flight m is before flight n, then P mn =1, otherwise 0; P nm is an indicator variable. If flight n is before flight m, then P nm =1, otherwise 0; F represents the set of all flights; in the flight sorting constraint, for any two flights on the same runway, it is guaranteed that one of them must be sorted before the other;

[0084] 372) Runway allocation constraints:

[0085]

[0086] Among them, X mr is an indicator variable. If flight m is assigned to runway r, then X mr =1, otherwise 0; R represents the set of all available runways; the runway allocation constraint must ensure that each flight is assigned to only one runway;

[0087] 373) Safety interval constraints:

[0088]

[0089] in, represents the optimized arrival time of incoming flight m, is the optimized arrival time of incoming flight n, is the minimum safe time slot between arrival flights m and n, M is a set positive number used to make appropriate constraints; when P mn =1(1-P mn ) takes 0, the constraint is strictly enforced; when P mn =0 (1-P mn ) is set to 1, the constraint is relaxed, and A represents the set of all incoming flights; the safety interval constraint must ensure that the optimized arrival time interval is not less than the specified minimum safety time slot;

[0090] 374) Running time constraints within a time slot:

[0091]

[0092] Among them, T b is the time slot start time, (t-1) represents the time slot number starting from the first time slot, S size For each time slot length, represents the optimized operating time of flight m (applicable to arrival or departure); L mt is an indicator variable (whether flight m is assigned to a time slot, if assigned, it is 1, (1-L mt ) is 0), M is a sufficiently large positive number, when (1-L mt)=0, the constraint has no effect; T represents the set of all time slots; the running time constraint within the time slot indicates that the start time assigned to time slot t is not higher than the optimized time of flight m and the optimized running time of flight m does not exceed the end time assigned to time slot t.

[0093] The method of the present invention solves the constructed random chance constraint model for optimal scheduling of arrival / departure flights to obtain a flight scheduling solution containing the optimal scheduling time, sequence, runway and time slot allocation for each flight. The solution specifically includes:

[0094] Optimal flight arrival scheduling time: the optimal arrival time of each incoming flight after the model is solved and

[0095] Optimal departure scheduling time for flights: The optimal take-off time for each departing flight after solving the model and

[0096] Flight sequence: The order of flights operating on the same runway. mn With P nm ;

[0097] Runway Allocation: The number of take-off and landing runways x allocated for each flight mr ;

[0098] Time slot allocation: The number of operating slots L allocated to each flight mt .

[0099] The scheme takes into account the uncertainty of operation time with a preset confidence level while satisfying all safety and operational constraints, aiming to minimize overall flight delays.

[0100] Beneficial effects of the present invention:

[0101] 1. The present invention uses historical data to obtain uncertainty parameters that truly reflect operating deviations through normal distribution fitting, thereby making scheduling optimization more in line with actual operating rules.

[0102] 2. The random chance constraint in the present invention converts operational uncertainty into specific numerical constraints, ensuring that flight take-off and landing times meet safety requirements at a preset confidence level, significantly reducing the probability of delays.

[0103] 3. The present invention comprehensively considers flight sequencing, runway allocation, safety intervals and time slot scheduling, thereby achieving scientific allocation of airport resources and ensuring flight safety.

[0104] 4. The model parameters in the present invention can be flexibly adjusted according to different airports and operating environments, and have high universality and promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0106] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and drawings. The contents mentioned in the embodiments are not intended to limit the present invention.

[0107] Reference Figure 1 As shown, a flight sequence planning method based on trajectory operation of the present invention comprises the following steps:

[0108] 1) Obtain the operating time deviation value of the arriving / departing flight; specifically including:

[0109] Obtain flight operation data for a certain period of time (such as one year or one month) from the airport's historical data and extract the estimated take-off and landing times of incoming / outgoing flights Actual take-off and landing time And calculate to get the running time deviation value Δt of each flight m m , the expression is as follows:

[0110]

[0111] 2) Quantifying the uncertainty of the arrival / departure flight operation time deviation value; specifically, using a probability distribution fitting method to perform statistical modeling on the operation time deviation value obtained in step 1) to quantify the uncertainty of the operation time deviation. The specific process is as follows:

[0112] 21) Construct a set of runtime deviation samples {Δt m};

[0113] 22) Perform distribution fitting on all deviation data to obtain that all deviation data obey normal distribution;

[0114] 23) The maximum likelihood estimation method was used to estimate the parameters of the data and obtain the normal distribution parameters: mean μ and standard deviation σ;

[0115] 24) The Kolmogorov-Smirnov test was used to verify the normal distribution fitting results;

[0116] 25) According to the above fitting results, determine the inverse cumulative distribution function of the entry Inverse cumulative distribution function Complete the quantification of the uncertainty of the arrival / departure flight operation time deviation value, and obtain the key parameters describing the deviation distribution characteristics, including: mean μ, standard deviation σ, arrival inverse cumulative distribution function Inverse cumulative distribution function

[0117] 3) Establish a random chance constraint model for optimal scheduling of arrival / departure flights. The uncertainty corresponding to the running time deviation is introduced into the model through random chance constraints. The optimal scheduling time, sequence, runway and time slot allocation scheme for each flight is obtained. Specifically, the following are included:

[0118] A random chance constraint model for flight optimal scheduling is constructed with the goal of minimizing flight delay costs and with flight sequencing, runway allocation, safety intervals, and time slot scheduling as constraints. The uncertainty of the running time deviation value is introduced into the model through random chance constraints, as follows:

[0119] 31) Construct the objective function for minimizing flight delay costs;

[0120] The objective function is to minimize the total delay cost, which includes the arrival flight delay cost A c and departure flight delay cost D c ; The objective function expression is as follows:

[0121]

[0122] in, is the optimized arrival time of incoming flight m, is the planned (estimated) arrival time of incoming flight m, is the operating time deviation of the arrival flight, The optimized take-off time for departing flight n, is the planned (estimated) departure time of departing flight n, is the running time deviation of the departure flight, A is the set of arrival flights, D is the set of departure flights, m∈A, n∈D, A c is the unit delay cost of the arriving flight, D c is the unit delay cost of the departing flight, minZ is the minimum delay cost of the arriving and departing flights;

[0123] 32) Set flight sorting constraints;

[0124] A flight sorting constraint is implemented to ensure that any two flights on the same runway have a unique order. The expression is as follows:

[0125]

[0126] Among them, p mn To indicate that flight m is scheduled before flight n, p nm represents the situation where flight n is ranked before flight m; p mn =1 means flight m is ranked before flight n; p mn=0 means flight m is ranked after flight n;

[0127] 33) Set runway allocation constraints;

[0128] A runway allocation constraint is implemented to ensure that each flight is assigned only one runway. The expression is as follows:

[0129]

[0130] Where R represents the set of all available runways, x mr is an indicator variable indicating whether flight m is assigned to runway r, x mr =1 means flight m is assigned to runway r;

[0131] 34) Set safety interval constraints;

[0132] Implement safety interval constraints between arriving flights as follows:

[0133]

[0134] in, is the optimized arrival time of incoming flight m, is the optimized arrival time of incoming flight n, is the minimum safe time slot between arrival flights m and n, where M is a positive number set to perform appropriate constraints;

[0135] Implement safety distance restrictions between departing flights as follows:

[0136]

[0137] in, is the optimized take-off time of departure flight m, is the optimized take-off time of departure flight n, is the minimum safe time gap between departing flights m and n;

[0138] Safety spacing restrictions are implemented between arriving and departing flights as follows:

[0139]

[0140] in, is the safety interval between incoming flight m and departing flight n;

[0141] Implement safety spacing restrictions between departing and arriving flights as follows:

[0142]

[0143] in, is the safety interval between incoming flight n and departing flight m;

[0144] 35) Setting random chance constraints;

[0145] The random chance constraints for arrival flight m are as follows:

[0146]

[0147] Among them, α is the entry confidence level; is the estimated arrival time of incoming flight m, is the operating time deviation of arrival flight m;

[0148] The random chance constraints for departure flight n are as follows:

[0149]

[0150] Among them, β is the departure confidence level; is the estimated departure time of departing flight n, is the operating time deviation of departure flight n;

[0151] Extracting arrival flight operation time deviations from historical data and departure flight operating time deviations And the probability distribution fitting is used for the deviation data to obtain the normal distribution parameters: mean μ and standard deviation σ;

[0152] The random chance constraint expression of the arrival flight is as follows:

[0153]

[0154] Among them, α is the entry confidence level, Indicates the estimated arrival time of incoming flight m; represents the optimized arrival time of incoming flight m;

[0155] The random chance constraint expression for departure flights is as follows:

[0156]

[0157] Where β is the departure confidence level, Indicates the estimated departure time of departing flight n; Indicates the optimized take-off time of departure flight n;

[0158] Introducing the inverse cumulative distribution function Convert the arrival confidence level α into a deterministic constant, and obtain the arrival flight deterministic constraints as follows:

[0159]

[0160] Introducing the inverse cumulative distribution function Convert the departure confidence level β into a deterministic constant, and obtain the following deterministic constraints for departure flights:

[0161]

[0162] 36) Set time slot and capacity constraints;

[0163] Perform time slot allocation constraints, the expression is as follows:

[0164]

[0165] Where F = A∪D, L mt Indicates whether flight m is assigned to time slot t. If flight m is assigned to time slot t, then L mt =1; F represents the set of all flights;

[0166] The time constraints within the time slot are implemented to ensure that the optimized take-off and landing times of the flights are within the allocated time slots, as follows:

[0167]

[0168] Where T is a time slot set, which means the entire scheduling period is discretely divided into a set of multiple time slots; t∈T is the index number in the specific time slot set T; T b is the time slot start time, S size For each time slot length, is the optimized time slot of flight m;

[0169] Slot capacity restrictions are implemented to prevent multiple flights from being scheduled in the same slot, as follows:

[0170]

[0171] 37) Integrate and solve the random chance constraint model for optimal scheduling of arrival / departure flights;

[0172] Integrate the objective functions and constraints constructed in steps 31) to 36) into a complete random chance constraint model for optimal scheduling of arrival / departure flights, and solve the model to obtain the optimal scheduling time, sequence, runway and time slot allocation for each flight;

[0173] The objective function of the random chance constraint model for optimal scheduling of arrival / departure flights is as follows:

[0174]

[0175] in, represents the delay cost of the incoming flight, represents the delay cost of the departing flight, Z represents the target value of the total delay cost of the entire model, MinZ represents the minimum value of the total delay, and A c is the unit delay cost of the arriving flight, D c is the unit delay cost of the departing flight, represents the optimized arrival time of incoming flight m, is the planned (estimated) arrival time of incoming flight m, is the optimized take-off time of departure flight n, is the planned (estimated) departure time of departing flight n, It represents the deterministic constant obtained by transforming the approach confidence level α through the inverse cumulative distribution function, which is used to offset the safety interval requirement. It represents the deterministic constant obtained by transforming the departure confidence level β through the inverse cumulative distribution function;

[0176] The random chance constraints for optimal scheduling of arrival / departure flights include:

[0177] 371) Flight sorting constraints:

[0178]

[0179] Among them, P mn is an indicator variable. If flight m is before flight n, then P mn =1, otherwise 0; P nm is an indicator variable. If flight n is before flight m, then P nm =1, otherwise 0; F represents the set of all flights; in the flight sorting constraint, for any two flights on the same runway, it is guaranteed that one of them must be sorted before the other;

[0180] 372) Runway allocation constraints:

[0181]

[0182] Among them, X mr is an indicator variable. If flight m is assigned to runway r, then X mr =1, otherwise 0; R represents the set of all available runways; the runway allocation constraint must ensure that each flight is assigned to only one runway;

[0183] 373) Safety interval constraints:

[0184]

[0185] in, represents the optimized arrival time of incoming flight m, is the optimized arrival time of incoming flight n, is the minimum safe time slot between arrival flights m and n, M is a set positive number used to make appropriate constraints; when P mn =1(1-P mn ) takes 0, the constraint is strictly enforced; when P mn =0 (1-P mn ) is set to 1, the constraint is relaxed, and A represents the set of all incoming flights; the safety interval constraint must ensure that the optimized arrival time interval is not less than the specified minimum safety time slot;

[0186] 374) Running time constraints within a time slot:

[0187]

[0188] Among them, T b is the time slot start time, (t-1) represents the time slot number starting from the first time slot, S size For each time slot length, represents the optimized operating time of flight m (applicable to arrival or departure); L mt is an indicator variable (whether flight m is assigned to a time slot, if assigned, it is 1, (1-L mt ) is 0), M is a sufficiently large positive number, when (1-L mt )=0, the constraint has no effect; T represents the set of all time slots; the running time constraint within the time slot indicates that the start time assigned to time slot t is not higher than the optimized time of flight m and the optimized running time of flight m does not exceed the end time assigned to time slot t.

[0189] The present invention has many specific application paths. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principles of the present invention. These improvements should also be considered as the scope of protection of the present invention.

Claims

1. A flight sequencing planning method based on trajectory operation, characterized in that: Here are the steps: 1) Obtain the running time deviation value of the arriving / departing flight; 2) Quantify the uncertainty of arrival / departure flight operation time deviation values; 3) Establish a random chance constraint model for the optimal scheduling of arrival / departure flights, introduce the uncertainty corresponding to the running time deviation into the model through random chance constraints, and solve the optimal scheduling time, sequence, runway and time slot allocation plan for each flight.

2. The flight sequence planning method based on trajectory operation according to claim 1, characterized in that: The step 1) specifically includes: Obtain flight operation data for a certain period of time from the airport's historical data and extract the estimated take-off and landing times of incoming / outgoing flights Actual take-off and landing time And calculate to get the running time deviation value Δt of each flight m m , the expression is as follows:

3. The flight sequence planning method based on trajectory operation according to claim 2, characterized in that: The step 2) specifically includes: using a probability distribution fitting method to perform statistical modeling on the runtime deviation value obtained in step 1) to quantify the uncertainty of the runtime deviation. The specific process is as follows: 21) Construct a set of runtime deviation samples {Δt m }; 22) Perform distribution fitting on all deviation data to obtain that all deviation data obey normal distribution; 23) The maximum likelihood estimation method was used to estimate the parameters of the data and obtain the normal distribution parameters: mean μ and standard deviation σ; 24) The Kolmogorov-Smirnov test was used to verify the normal distribution fitting results; 25) According to the above fitting results, determine the inverse cumulative distribution function of the entry Inverse cumulative distribution function Complete the quantification of the uncertainty of the arrival / departure flight operation time deviation value, and obtain the key parameters describing the deviation distribution characteristics, including: mean μ, standard deviation σ, arrival inverse cumulative distribution function Inverse cumulative distribution function 4. The flight sequence planning method based on trajectory operation according to claim 3, characterized in that: The step 3) specifically includes: A random chance constraint model for flight optimal scheduling is constructed with the goal of minimizing flight delay costs and with flight sequencing, runway allocation, safety intervals, and time slot scheduling as constraints. The uncertainty of the running time deviation value is introduced into the model through random chance constraints, as follows: 31) Construct the objective function for minimizing flight delay costs; The objective function is to minimize the total delay cost, which includes the arrival flight delay cost A c and departure flight delay cost D c ; The objective function expression is as follows: in, is the optimized arrival time of incoming flight m, is the planned arrival time of incoming flight m, is the operating time deviation of the arrival flight, The optimized take-off time for departing flight n, Planned takeoff time for departing flight n, is the running time deviation of the departure flight, A is the set of arrival flights, D is the set of departure flights, m∈A, n∈D, A c is the unit delay cost of the arriving flight, D c is the unit delay cost of the departing flight, minZ is the minimum delay cost of the arriving and departing flights; 32) Set flight sorting constraints; A flight sorting constraint is implemented to ensure that any two flights on the same runway have a unique order. The expression is as follows: Among them, p mn To indicate that flight m is scheduled before flight n, p nm represents the situation where flight n is ranked before flight m; p mn =1 means flight m is ranked before flight n; p mn =0 means flight m is ranked after flight n; 33) Set runway allocation constraints; A runway allocation constraint is implemented to ensure that each flight is assigned only one runway. The expression is as follows: Where R represents the set of all available runways, x mr is an indicator variable indicating whether flight m is assigned to runway r, x mr =1 means flight m is assigned to runway r; 34) Set safety interval constraints; Implement safety interval constraints between arriving flights as follows: in, is the optimized arrival time of incoming flight m, is the optimized arrival time of incoming flight n, is the minimum safe time slot between arrival flights m and n, where M is a positive number set to perform appropriate constraints; Implement safety distance restrictions between departing flights as follows: in, is the optimized take-off time of departure flight m, is the optimized take-off time of departure flight n, is the minimum safe time gap between departing flights m and n; Safety spacing restrictions are implemented between arriving and departing flights as follows: in, is the safety interval between incoming flight m and departing flight n; Implement safety spacing restrictions between departing and arriving flights as follows: in, is the safety interval between incoming flight n and departing flight m; 35) Setting random chance constraints; The random chance constraints for arrival flight m are as follows: Among them, α is the entry confidence level; is the estimated arrival time of incoming flight m, is the operating time deviation of arrival flight m; The random chance constraints for departure flight n are as follows: Among them, β is the departure confidence level; is the estimated departure time of departing flight n, is the operating time deviation of departure flight n; Extracting arrival flight operation time deviations from historical data and departure flight operating time deviations And the probability distribution fitting is used for the deviation data to obtain the normal distribution parameters: mean μ and standard deviation σ; The random chance constraint expression of the arrival flight is as follows: Among them, α is the entry confidence level, Indicates the estimated arrival time of incoming flight m; represents the optimized arrival time of incoming flight m; The random chance constraint expression for departure flights is as follows: Where β is the departure confidence level, Indicates the estimated departure time of departing flight n; Indicates the optimized take-off time of departure flight n; Introducing the inverse cumulative distribution function Convert the arrival confidence level α into a deterministic constant, and obtain the arrival flight deterministic constraints as follows: Introducing the inverse cumulative distribution function Convert the departure confidence level β into a deterministic constant, and obtain the following deterministic constraints for departure flights: 36) Set time slot and capacity constraints; Perform time slot allocation constraints, the expression is as follows: Where F = A∪D, L mt Indicates whether flight m is assigned to time slot t. If flight m is assigned to time slot t, then L mt =1; F represents the set of all flights; The time constraints within the time slot are implemented to ensure that the optimized take-off and landing times of the flights are within the allocated time slots, as follows: Where T is a time slot set, which means the entire scheduling period is discretely divided into a set of multiple time slots; t∈T is the index number in the specific time slot set T; T b is the time slot start time, S size For each time slot length, is the optimized time slot of flight m; Slot capacity restrictions are implemented to prevent multiple flights from being scheduled in the same slot, as follows: 37) Integrate and solve the random chance constraint model for optimal scheduling of arrival / departure flights; Integrate the objective functions and constraints constructed in steps 31) to 36) into a complete random chance constraint model for optimal scheduling of arrival / departure flights, and solve the model to obtain the optimal scheduling time, sequence, runway and time slot allocation for each flight; The objective function of the random chance constraint model for optimal scheduling of arrival / departure flights is as follows: in, represents the delay cost of the incoming flight, represents the delay cost of the departing flight, Z represents the target value of the total delay cost of the entire model, MinZ represents the minimum value of the total delay, and A c is the unit delay cost of the arriving flight, D c is the unit delay cost of the departing flight, represents the optimized arrival time of incoming flight m, is the planned arrival time of incoming flight m, is the optimized take-off time of departure flight n, Planned takeoff time for departing flight n, It represents the deterministic constant obtained by transforming the approach confidence level α through the inverse cumulative distribution function, which is used to offset the safety interval requirement. It represents the deterministic constant obtained by transforming the departure confidence level β through the inverse cumulative distribution function; The random chance constraints for optimal scheduling of arrival / departure flights include: 371) Flight sorting constraints: Among them, P mn is an indicator variable. If flight m is before flight n, then P mn =1, otherwise 0; P nm is an indicator variable. If flight n is before flight m, then P nm =1, otherwise 0; F represents the set of all flights; in the flight sorting constraint, for any two flights on the same runway, it is guaranteed that one of them must be sorted before the other; 372) Runway allocation constraints: Among them, X mr is an indicator variable. If flight m is assigned to runway r, then X mr =1, otherwise 0; R represents the set of all available runways; the runway allocation constraint must ensure that each flight is assigned to only one runway; 373) Safety interval constraints: in, represents the optimized arrival time of incoming flight m, is the optimized arrival time of incoming flight n, is the minimum safe time slot between arrival flights m and n, M is a set positive number used to make appropriate constraints; when P mn =1(1-P mn ) takes 0, the constraint is strictly enforced; when P mn =0 (1-P mn ) is set to 1, the constraint is relaxed, and A represents the set of all incoming flights; the safety interval constraint must ensure that the optimized arrival time interval is not less than the specified minimum safety time slot; 374) Running time constraints within a time slot: Among them, T b is the time slot start time, (t-1) represents the time slot number starting from the first time slot, S size For each time slot length, represents the optimized operating time of flight m; L mt is an indicator variable, M is a sufficiently large positive number, when (1-L mt )=0, the constraint has no effect; T represents the set of all time slots; the running time constraint within the time slot indicates that the start time assigned to time slot t is not higher than the optimized time of flight m and the optimized running time of flight m does not exceed the end time assigned to time slot t.

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