Airport aviation noise mitigation method based on flight plan route optimization

Abstract

The invention discloses an airport aviation noise mitigation method based on flight plan route optimization, and the method comprises the steps: obtaining flight related information, and generating a flight plan table; generating a flight trajectory data table based on the navigation point information of the flight route; based on the airport surrounding noise sensitive point data, estimating airport surrounding sensitive point noise by using a noise evaluation model in combination with flight trajectory data; defining decision variables, and optimizing flight programs of all flights within one day; a multi-objective optimization model considering noise influence, operation time and flight plans is constructed, flight route optimization constraints are determined, and coordination and balance between environmental benefits and efficient and safe operation of an airport are achieved; and solving the multi-objective optimization model by adopting a non-dominated sorting genetic algorithm II with an elitism strategy. According to the method, noise sensitive points around an airport are classified, and a differentiated noise limitation standard is set for each category; and the balanced regulation and control of the noise level of each region are realized by reasonably deploying the flight route.

Description

Technical Field

[0001] The present invention belongs to the technical field of flight route optimization, and particularly relates to an airport aviation noise mitigation method based on flight plan route optimization. Background Art

[0002] Aviation noise has become one of the important environmental problems faced by modern society, and its harm cannot be underestimated. The roar generated by aircraft engines not only interferes with the daily life and sleep quality of residents, but may also cause a series of health problems. In addition, the social and economic losses caused by aircraft noise are also very significant, affecting the real estate value, public health expenditure and quality of life in the area. With the continuous rapid growth of air traffic flow and the increasing population density around airports, the problem of aviation noise will become increasingly prominent and gradually become a bottleneck problem restricting the sustainable development of the civil aviation industry.

[0003] In this context, it is particularly urgent to explore and implement efficient noise control measures. Among them, the noise reduction method based on flight plan route optimization can effectively reduce the noise at sensitive points around the airport by reasonably allocating the flight procedures of flights throughout the day. Due to the small adjustment range, this method has limited interference to airport operations and requires low requirements for operators, which is convenient for rapid implementation in the existing aviation system. However, the current research on flight plan route optimization focuses on noise reduction in local areas, such as reducing the noise at the runway end, and fails to fully consider reducing the noise levels of sensitive points with different spatial distributions around the airport. Even if the noise-sensitive points around the airport are considered, the heterogeneous needs of various types of noise-sensitive points around the airport are not fully taken into account. In fact, there are significant differences in the noise tolerance standards for different types of sensitive points. For areas that require a high-quiet environment, such as schools, residential areas and hospitals, since these places need to provide a quiet environment for learning, resting or working, more strict noise control standards must be adopted. For areas with a higher noise tolerance, such as business parks and industrial parks, since they may be accompanied by higher environmental noise, the relevant standards can be relaxed accordingly. Therefore, there is an urgent need for an airport aviation noise mitigation method that can improve the noise pollution in various noise-sensitive areas around the airport through scientific and reasonable flight route allocation. Summary of the Invention

[0004] Object of the Invention: The present invention provides an airport aviation noise mitigation method based on flight plan route optimization, aiming to improve the noise pollution in various noise-sensitive areas around the airport through scientific and reasonable flight route allocation.

[0005] Technical Solution: The airport aviation noise mitigation method based on flight plan route optimization described in the present invention includes the following steps:

[0006] (1) Obtain flight-related information and generate a flight schedule;

[0007] (2) Generate a flight track data table using the trajectory generation model and the flight schedule based on the navigation point information of the flight route.

[0008] (3) Estimate the noise at the noise-sensitive points around the airport by applying the noise assessment model and combining the flight track data with the data of the noise-sensitive points around the airport.

[0009] (4) Define decision variables and optimize the flight procedures for all flights within a day.

[0010] (5) Construct a multi-objective optimization model considering noise impact, operation time, and flight plan, determine the optimization constraints for the flight route, and achieve a coordinated balance between environmental benefits and the efficient and safe operation of the airport.

[0011] (6) Solve the multi-objective optimization model using the Non-dominated Sorting Genetic Algorithm II with elitist preservation strategy.

[0012] Further, the implementation process of step (1) is as follows:

[0013] Obtain the airport flight plan data, including flight number, aircraft type, planned flight route, arrival and departure, departure airport, arrival airport, departure time, and arrival time.

[0014] Obtain the flight track data of flight arrivals and departures, determine the available flight routes, runways, and corridors used by the same flight; merge the airport flight plan data with the flight track data of arrivals and departures to generate a flight schedule.

[0015] Further, the navigation point information of the flight route in step (2) includes the navigation point name, navigation point longitude, and navigation point latitude.

[0016] Further, the flight track data table in step (2) includes flight time, track longitude, track latitude, track altitude, track airspeed, and flight time of the flight.

[0017] Further, the implementation process of step (3) is as follows:

[0018] Read the data of the noise-sensitive points around the airport, including sensitive point longitude, sensitive point latitude, sensitive point altitude, and sensitive point type.

[0019] Apply the noise assessment model and combine it with the flight track data to calculate the noise exposure level of each flight at each sensitive point, and generate a flight exposure sound level table, including sensitive point name, sensitive point longitude, sensitive point latitude, sensitive point type, and flight exposure sound level.

[0020] Integrate the flight schedule, flight trajectory data table, and flight exposure sound level table to construct a flight available flight route noise data table, including flight number, aircraft type, departure airport, arrival airport, planned flight route, available flight route, takeoff and landing, takeoff time, landing time, runway, corridor entrance, flight time of the flight, name of the sensitive point, type of the sensitive point, and flight exposure sound level.

[0021] Furthermore, the implementation process of step (4) is as follows:

[0022] Decision variable X fp Represents the flight allocation plan by taking 0 or 1. Denote the sets of flights f and flight routes p as F and P, and define the decision variable X fp As:

[0023]

[0024] Where p ∈ P, f ∈ F.

[0025] Furthermore, the implementation process of constructing the multi-objective optimization model considering noise impact, operation time, and flight plan in step (5) is as follows:

[0026] The noise impact is measured by the day-night equivalent sound level of each noise sensitive point. Denote the set of sensitive points s as S, and N s Is the day-night equivalent sound level of all flights in a day for the sensitive point s:

[0027]

[0028] Where p ∈ P, f d ∈ F d ,f n ∈ F n ,s ∈ S; Is the exposure sound level of the daytime flight f d To the sensitive point s, Is the exposure sound level of the nighttime flight f n To the sensitive point s; F d Is the set of daytime flights; F n Is the set of nighttime flights;

[0029] The second optimization objective is operation efficiency. Let t fp Be the operation time when the flight f is assigned to the flight route p. Then the total operation time N in a day t Is expressed as the accumulation of the operation times of the flight routes assigned to all flights:

[0030]

[0031] The third optimization objective is the flight plan change amount. Let δ fIt is the original flight route of flight f, N C is the flight plan change amount:

[0032]

[0033] Furthermore, the flight route optimization constraints in step (5) include:

[0034] Flight route uniqueness constraint: Each flight f can only be assigned one flight route p in a flight plan scheme:

[0035]

[0036] Conduct constraints on noise-sensitive point types: The noise-sensitive points around the airport are divided into three types. Type Ⅰ land is a place that requires quietness, and the set of type Ⅰ sensitive points is denoted as Type Ⅱ land is a place where some noise is allowed, and the set of type Ⅱ sensitive points is denoted as Type Ⅲ land is a place that is less sensitive to noise, and the set of type Ⅲ sensitive points is denoted as The day-night equivalent sound level of the three land types The calculation formula is as follows:

[0037]

[0038] Among them, is the sound exposure level generated by flight f during the day d to sensitive point s A , s B , s C ; for the flight set F, its day-night equivalent sound level for type Ⅰ sensitive points is shall not be greater than n For the day-night equivalent sound level of type Ⅱ sensitive points is A , s B , s C ; for the day-night equivalent sound level of type Ⅲ sensitive points is shall not be greater than For the day-night equivalent sound level of type Ⅱ sensitive points is shall not be greater than For the average day-night equivalent sound level of type Ⅲ sensitive points is shall not be greater than The formula for the noise-sensitive point type constraint is as follows:

[0039]

[0040] Approach handover constraint: Within a given time window, the number of approach flights at the same corridor entrance shall not exceed the maximum capacity of that corridor entrance. Denote the set of approach flights f a within the time step Δt as The flight route p passing through corridor entrance CC The set is P C , approach handover constraint The formula is as follows:

[0041]

[0042] Among them, is the maximum number of approach flights specified for corridor C within the time step Δt;

[0043] Departure release constraint: Within a given time window, the number of departure flights at the same corridor shall not exceed the maximum capacity of that corridor. Denote the set of departure flights f e as the flight route p passing through corridor C C The set is P C , departure handover constraint The formula is as follows:

[0044]

[0045] Among them, is the maximum number of departure flights specified for corridor C within the time step Δt;

[0046] Runway capacity constraint: Under the condition of complying with air traffic control rules, within a specified dynamic time window, the maximum number of aircraft that each runway can serve. The formula for the runway capacity constraint is as follows:

[0047]

[0048] Among them, F Δt is the set of flights f within the time step Δt t , P R is the set of flight routes p using runway R R , is the maximum number of departure flights specified for runway R within the time step Δt.

[0049] Furthermore, the Type I land use includes residential areas, schools, and hospitals; the Type II land use includes office buildings, shopping malls, and restaurants; the Type III land use includes industrial areas, storage areas, and park squares.

[0050] Furthermore, the implementation process of step (6) is as follows:

[0051] The population is initialized by randomly generating individuals that satisfy the constraint conditions to construct the initial population. Each individual is a complete flight plan route scheme for a day, and each gene in the individual corresponds to the flight route selected for a single flight, represented by a binary vector. Subsequently, the objective function values of each flight plan route scheme in the population are calculated, and the degree of violation of the constraint conditions is evaluated. Appropriate penalties are imposed on the solutions that violate the constraints.

[0052] Using the calculated noise impact, operation efficiency, and flight plan change amount, non-dominated sorting of individuals is performed according to the Pareto optimality principle; and the crowding degree is calculated based on non-dominated sorting. Individuals with a larger crowding degree are preferentially retained. The tournament selection method is used to select the next generation population, and individuals with a lower non-dominated rank and a higher crowding degree are preferentially selected to enter the mating pool.

[0053] After the mating pool is constructed, the algorithm enters the crossover and mutation stages to generate a new offspring population; subsequently, the parent and offspring populations are merged, and non-dominated sorting and crowding degree calculation are performed again. Individuals with a lower non-dominated rank and a larger crowding degree are selected to form the next generation population; the elitist retention strategy is adopted, and all Pareto optimal solutions are saved using an independent archive; if the Pareto optimal solutions in the independent archive remain unchanged for multiple consecutive generations, it is considered that the algorithm has converged, and the iteration is stopped, thus gradually converging to a set of high-quality multi-objective optimization solutions.

[0054] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] The present invention classifies the noise-sensitive points around the airport, divides different regions into three categories according to land types, and sets differentiated noise limit standards for each category; by reasonably allocating flight routes, the balanced regulation of the noise levels in each region is realized, thereby reducing the interference of noise on the living environment of residents and improving the satisfaction of the surrounding residents.

[0056] The present invention constructs a multi-objective optimization model, integrating airport noise control, operation efficiency, and flight plan change amount into the same model; on the basis of fully considering the mutual constraints and synergistic effects among the objectives, it is solved by the non-dominated sorting genetic algorithm II to achieve the coordinated balance between environmental benefits and the efficient and safe operation of the airport, reflecting the optimization of the overall system performance.

[0057] The present invention aims at the daily flight plan, and through optimizing and adjusting the flight scheduling in advance, realizes the effective prediction and control before the generation of noise; this method can carry out noise reduction intervention on sensitive areas before the actual generation of noise, thereby reducing the noise interference globally, ensuring the environmental quality around the airport, and constructing a more harmonious operation system. Brief Description of the Drawings

[0058] Figure 1 This is the flowchart of the present invention. Detailed implementation manners

[0059] The present invention will be further described in detail below with reference to the accompanying drawings.

[0060] As Figure 1 shown, the present invention proposes an airport aviation noise mitigation method based on flight plan route optimization, classifies the noise-sensitive areas around the airport, and formulates corresponding noise constraints for different types of noise-sensitive areas. By specifically adjusting the flight routes of flights throughout the day, the noise in the areas exceeding the noise constraints is grafted to the areas not exceeding the noise constraints within the specified range, thereby effectively controlling the noise levels in various areas, reducing resident complaints, and improving public satisfaction. At the same time, comprehensively considering the balance between flight operation efficiency and the change amount of the flight plan, it is ensured that during the process of achieving noise optimization, the workload of air traffic controllers is reduced, the operation delay risk caused by noise reduction measures is alleviated, and the airport maintains an efficient and safe operation state. Specifically, it includes the following steps:

[0061] Step 1: Obtain flight-related information:

[0062] Read the airport flight plan data, including flight number, aircraft type, planned flight route, arrival and departure, departure airport, arrival airport, departure time, and arrival time.

[0063] Read the arrival and departure flight track data to determine the available flight routes, runways, and corridors used by the same flight. Subsequently, merge the airport flight plan data with the arrival and departure flight track data to generate a flight schedule.

[0064] Step 2: Generate a trajectory based on the flight plan.

[0065] Read all the navigation point information that makes up the flight route, including navigation point name, navigation point longitude, and navigation point latitude.

[0066] Use the trajectory generation model and the flight schedule to generate a flight trajectory data table, including flight time, track longitude, track latitude, track altitude, track airspeed, and flight time of the flight.

[0067] Step 3: Estimate the noise at sensitive points around the airport.

[0068] Read the data of noise-sensitive points around the airport, including sensitive point longitude, sensitive point latitude, sensitive point altitude, and sensitive point type.

[0069] Apply the ECAC model and combine it with the flight trajectory data to calculate the noise exposure levels of each flight at each sensitive point, and generate a flight exposure sound level table, including sensitive point name, sensitive point longitude, sensitive point latitude, sensitive point type, and flight exposure sound level.

[0070] Integrate the flight schedule, flight trajectory data table, and flight exposure sound level table, screen out the attributes required for the optimization model, and construct a flight available flight route noise data table, including flight number, aircraft type, departure airport, arrival airport, planned flight route, available flight route, approach and departure, departure time, arrival time, runway, corridor, flight time of the flight, name of the sensitive point, type of the sensitive point, and flight exposure sound level.

[0071] Step 4: Define the decision variables and optimize for all flights within a day.

[0072] Decision variable X fp Represents the flight allocation plan by taking 0 or 1. Denote the sets of flights f and flight routes p as F and P, and define the decision variable X fp as:

[0073]

[0074] where p ∈ P, f ∈ F.

[0075] Step 5: Construct a multi-objective optimization model, and the optimization objectives cover three aspects: noise impact, operation time, and flight plan change amount.

[0076] The noise impact uses the day-night equivalent sound level of each noise-sensitive point as the measurement standard. Denote the set of sensitive points s as S, and N s is the day-night equivalent sound level of all flights in a day on the sensitive point s:

[0077]

[0078] where, p ∈ P, f d ∈ F d , f n ∈ F n , s ∈ S; is the exposure sound level of the daytime flight f d on the sensitive point s, is the exposure sound level of the nighttime flight f n on the sensitive point s. F d is the set of daytime flights; F n is the set of nighttime flights. "Daytime" refers to the period between 6:00 and 22:00 every day; "Nighttime" refers to the period between 22:00 and 6:00 the next day.

[0079] The second optimization objective is the operation efficiency. Let t fp be the operation time of the flight f assigned to the flight route p, then the total operation time N t within a day is expressed as the accumulation of the operation times of the flight routes assigned to all flights:

[0080]

[0081] The third optimization objective is the flight plan change amount. Let δ f be the original flight route of flight f, and N C be the flight plan change amount:

[0082]

[0083] Step 6: Construct the flight route optimization constraints.

[0084] The multi-objective optimization model adopts five constraint conditions, namely the flight route uniqueness constraint, the approach handover constraint, the departure release constraint, the runway capacity constraint, and the noise-sensitive point type constraint.

[0085] For the flight route uniqueness constraint, each flight f can only be assigned one flight route p in a flight plan scheme:

[0086]

[0087] Secondly, the noise-sensitive point type is constrained. Referring to the current aircraft noise environmental quality standard in the surrounding area of the airport, the noise-sensitive points around the airport are divided into three types. Type I land is where quietness is required as much as possible, including residential areas, schools, hospitals and other similar land uses. Denote the set of Type I sensitive points as Type II land is where some noise is allowed, including office buildings, shopping malls, restaurants and other similar land uses. The set of Type II sensitive points is Type III land is where noise is less sensitive, including industrial areas, storage areas, park squares and other similar land uses. The set of Type III sensitive points is The day-night equivalent sound levels of the three land types are calculated as follows:

[0088]

[0089] where is the sound exposure level generated by flight f during the day d at sensitive point s A , s B , s C . The sound exposure level generated by flight f at night at sensitive point s n , s A , s B , s C . For the flight set F, its day-night equivalent sound level for Type I sensitive points is It shall not be greater than 57 dB(A); for Class II sensitive points, the day-night equivalent sound level is It shall not be greater than 62 dB(A); for Class III sensitive points, the average day-night equivalent sound level is It shall not be greater than 67 dB(A). The formula for the constraint of the noise sensitive point type is as follows:

[0090]

[0091] For the approach handover constraint, according to the current air traffic control rules, within 15 minutes, the number of approach flights at the same corridor entrance shall not exceed 5. Denote the set of approach flights f a within the time step of 15 minutes as The set of flight routes p C passing through corridor entrance C is C P. The approach handover constraint formula is as follows:

[0092]

[0093] For the departure release constraint, according to the current air traffic control rules, within 15 minutes, the number of departure flights at the same corridor entrance shall not exceed 5. Denote the set of departure flights f e within the time step of 15 minutes as The set of flight routes p C passing through corridor entrance C is C P. The departure handover constraint formula is as follows:

[0094]

[0095] Finally, the runway capacity is constrained, which means that under the condition of complying with the air traffic control rules, within 15 minutes, the maximum number of aircraft that each runway can serve does not exceed 3. The formula for the runway capacity constraint is as follows:

[0096]

[0097] where, F 15 is the set of flights f t within 15 minutes, and P R is the set of flight routes p R using runway R.

[0098] Step 7: Solve the multi-objective optimization model based on the non-dominated sorting genetic algorithm.

[0099] The non-dominated sorting genetic algorithm II with elitist retention strategy is used to solve the optimization model. First, the population is initialized by randomly generating individuals that satisfy the constraint conditions to construct the initial population. Each individual is a complete flight plan route scheme for a day, and each gene in the individual corresponds to the flight route selected by a single flight, represented by a binary vector. Subsequently, the objective function values of each flight plan route scheme in the population are calculated, and the degree of violation of the constraint conditions is evaluated, and appropriate penalties are imposed on the solutions that violate the constraints.

[0100] Using the calculated noise impact, operating efficiency, and flight plan change amount, the individuals are non-dominated sorted according to the Pareto optimality principle. And the crowding degree is calculated based on the non-dominated sorting. The individuals with larger crowding degree are preferentially retained. The tournament selection method is used to select the next generation population, and the individuals with lower non-dominated rank and higher crowding degree are preferentially selected into the mating pool.

[0101] After the mating pool is constructed, the algorithm enters the crossover and mutation stages to generate a new offspring population. Subsequently, the parent and offspring populations are merged, and non-dominated sorting and crowding degree calculation are performed again. The individuals with lower non-dominated rank and larger crowding degree are selected to form the next generation population. In this process, the elitist retention strategy is adopted, and an independent archive is used to save all Pareto optimal solutions. If the Pareto optimal solutions in the independent archive remain unchanged for 10 consecutive generations, it is considered that the algorithm has converged and the iteration can be stopped, thus gradually converging to a set of high-quality multi-objective optimization solutions.

[0102] For Nanjing Lukou International Airport, 525 flights in a day are optimized. After optimization, a total of 28 Pareto front optimization schemes are obtained. Specifically, the 28 groups of schemes effectively reduce the total noise value and also show certain advantages in operating efficiency; the 23rd group of optimization schemes reduces 15.8 dB compared with the original plan, the 26th group of schemes saves 5176 seconds of total flight time compared with the original plan, and the 20th group of schemes has the least number of changes to the original flight plan, a total of 106 times. The airport aviation noise mitigation method based on flight plan route optimization proposed by the present invention realizes a dynamic balance in aviation noise control and airport operation management, providing a practical technical path for the noise optimization around the airport.

[0103] The above-described embodiments merely represent several implementation manners of the present application, and the description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. An airport aviation noise mitigation method based on flight plan route optimization, characterized in that, It includes the following steps: (1) Obtain flight-related information and generate a flight schedule plan; (2) Based on the navigation point information of the flight route, use a trajectory generation model and the flight schedule plan to generate a flight trajectory data table; (3) Based on the noise-sensitive point data around the airport, use a noise assessment model combined with the flight trajectory data to estimate the noise at the sensitive points around the airport; (4) Define decision variables and optimize the flight procedures for all flights within a day; (5) Construct a multi-objective optimization model considering noise impact, operation time, and flight plan, determine the flight route optimization constraints, and achieve a coordinated balance between environmental benefits and the efficient and safe operation of the airport; (6) Use the non-dominated sorting genetic algorithm II with an elitist retention strategy to solve the multi-objective optimization model.

2. The method for mitigating airport aviation noise based on flight plan route optimization according to claim 1, wherein The implementation process of step (1) is as follows: Obtain airport flight plan data, including flight number, aircraft type, planned flight route, arrival and departure, departure airport, arrival airport, departure time, and arrival time; Obtain flight track data for flight arrivals and departures, determine the available flight routes, runways, and corridors used by the same flight; merge the airport flight plan data with the arrival and departure flight track data to generate a flight schedule plan.

3. A method for alleviating airport aviation noise based on flight plan route optimization according to claim 1, characterized in that The navigation point information of the flight route in step (2) includes navigation point name, navigation point longitude, and navigation point latitude.

4. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, The flight trajectory data table in step (2) includes flight time, track longitude, track latitude, track altitude, track airspeed, and flight time of the flight.

5. A method for mitigating airport aviation noise based on flight plan route optimization according to claim 1, characterized in that, The implementation process of step (3) is as follows: Read the noise-sensitive point data around the airport, including sensitive point longitude, sensitive point latitude, sensitive point altitude, and sensitive point type; Use a noise assessment model, combined with the flight trajectory data, to calculate the noise exposure level of each flight at each sensitive point, and generate a flight exposure sound level table, including sensitive point name, sensitive point longitude, sensitive point latitude, sensitive point type, and flight exposure sound level; Integrate the flight schedule, flight trajectory data table, and flight exposure sound level table to construct a flight available flight route noise data table, including flight number, aircraft type, departure airport, arrival airport, planned flight route, available flight route, arrival and departure, departure time, arrival time, runway, corridor, flight time of the flight, sensitive point name, sensitive point type, and flight exposure sound level.

6. The method for mitigating airport aviation noise based on flight plan route optimization according to claim 1, wherein The implementation process of step (4) is as follows: Decision variable X fp It represents the flight allocation plan by taking 0 or 1. Denote the sets of flights f and flight routes p as F and P, and define the decision variable X fp as follows: where p ∈ P, f ∈ F.

7. A method for mitigating airport aviation noise based on the optimization of flight plan routes according to claim 1, characterized in that, The implementation process of constructing the multi-objective optimization model considering noise impact, operation time, and flight plan in step (5) is as follows: The impact of noise is measured by the day-night equivalent sound level at each noise-sensitive point. Denote the set of sensitive points s as S, and N s is the day-night equivalent sound level of all-day flights at sensitive point s: where p ∈ P, f d ∈ F d , f n ∈ F n , s ∈ S; is the exposure sound level of the daytime flight f d to the sensitive point s, is the exposure sound level of the nighttime flight f n to the sensitive point s; F d is the set of daytime flights; F n is the set of nighttime flights; The second optimization objective is the operating efficiency. Let t fp be the operating time for flight f assigned to flight route p. Then the total operating time N within a day t is expressed as the accumulation of the operating times of the flight routes assigned to all flights: The third optimization objective is the flight plan change amount, denoted as δ f is the original flight route of flight f, and N C is the flight plan change amount:

8. A method for mitigating airport aviation noise based on optimizing flight plan routes according to claim 1, characterized in that, The flight route optimization constraints in step (5) include: Flight route uniqueness constraint: Each flight f can only be assigned one flight route p in a flight plan scheme: Constrain the types of noise-sensitive points: The noise-sensitive points around the airport are divided into three types. Type I land is a place that requires quietness, and the set of Type I sensitive points is denoted as Type II land is a place where some noise is allowed, and the set of Type II sensitive points is denoted as Type III land is a place that is less sensitive to noise, and the set of Type III sensitive points is denoted as The day-night equivalent sound level of the three land types The calculation formula is as follows: Among them, is the daytime flight f d to the sensitive point s A , s B , s C the sound exposure level generated; is the night flight f n to the sensitive point s A , s B , s C the sound exposure level generated; for the flight set F, its day-night equivalent sound level for type Ⅰ sensitive points is shall not be greater than For the day-night equivalent sound level of type Ⅱ sensitive points is shall not be greater than For the average day-night equivalent sound level of type Ⅲ sensitive points is shall not be greater than The formula for the noise sensitive point type constraint is as follows: Approach handover constraint: within a given time window, the number of approach flights at the same corridor entrance shall not exceed the maximum capacity of that corridor entrance. Denote the set of approach flights f within the time step Δt a as the set of flight routes p passing through corridor entrance C C is P C , the approach handover constraint is given by the following formula: Among them, is the maximum number of incoming flights specified for corridor entrance C within the time step Δt; Departure release constraint: within a given time window, the number of departing flights at the same corridor entrance shall not exceed the maximum capacity of that corridor entrance. Denote the set of departing flights f within the time step Δt e as the set of flight routes p passing through corridor entrance C C is P C , and the departure handover constraint is given by the following formula: Among them, is the maximum number of departing flights at corridor mouth C specified within the time step Δt; Runway capacity constraint: Under the condition of observing air traffic control rules, within a specified dynamic time window, the maximum number of aircraft that each runway can serve. The formula for the runway capacity constraint is as follows: Among them, F Δt is the set of flights f t within the time step Δt, P R is the set of flight routes p R using runway R, and is the maximum number of departing flights specified by runway R within the time step Δt.

9. A method for mitigating airport aviation noise based on the optimization of flight plan routes according to claim 8, characterized in that, The type I land use includes residential areas, schools, and hospitals; the type II land use includes office buildings, shopping malls, and restaurants; the type III land use includes industrial areas, storage areas, and park squares.

10. A method for mitigating airport aviation noise based on flight plan route optimization according to claim 1, characterized in that The implementation process of step (6) is as follows: Perform population initialization. The initial population is constructed by randomly generating individuals that satisfy the constraint conditions. Each individual represents a complete flight plan route scheme for one day. Each gene in the individual corresponds to the flight route selected for a single flight and is represented by a binary vector. Subsequently, calculate the objective function value of each flight plan route scheme in the population and evaluate the degree to which it violates the constraint conditions. Apply appropriate penalties to the solutions that violate the constraints. Use the calculated noise impact, operating efficiency, and flight plan change amount to perform non-dominated sorting on the individuals according to the Pareto optimality principle. Based on the non-dominated sorting, calculate the crowding degree. Individuals with a larger crowding degree are preferentially retained. The tournament selection method is used to select the next generation population, and individuals with a lower non-dominated rank and a higher crowding degree are preferentially selected to enter the mating pool. After constructing the mating pool, the algorithm enters the crossover and mutation stages to generate a new offspring population. Subsequently, merge the parent and offspring populations, perform non-dominated sorting and crowding degree calculation again, and select individuals with a lower non-dominated rank and a larger crowding degree to form the next generation population. Adopt the elitist retention strategy and use an independent archive to save all Pareto optimal solutions. If the Pareto optimal solutions in the independent archive remain unchanged for multiple consecutive generations, it is considered that the algorithm has converged, stop the iteration, and thus gradually converge to a set of high-quality multi-objective optimization solutions.

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