An airport aviation noise mitigation method based on flight plan route optimization
By constructing a multi-objective optimization model and a non-dominated sorting genetic algorithm II, the airport flight schedule was optimized, which solved the heterogeneity problem of noise-sensitive points around the airport, achieved differentiated control of noise pollution and a balance between airport operations, and improved resident satisfaction and operational efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NAN JING DI HANG SHU ZHI KE JI YOU XIAN GONG SI
- Filing Date
- 2025-04-24
- Publication Date
- 2026-06-16
Smart Images
Figure CN120299303B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of flight route optimization technology, specifically relating to an airport aviation noise mitigation method based on flight plan route optimization. Background Technology
[0002] Aviation noise has become a significant environmental problem facing modern society, and its harm should not be underestimated. The roar of aircraft engines not only disrupts residents' daily lives and sleep quality but can also trigger a series of health problems. Furthermore, the socio-economic losses caused by aircraft noise are substantial, impacting local real estate values, public health expenditures, and quality of life. With the continued rapid growth of air traffic and the increasing population density around airports, aviation noise will become increasingly prominent, gradually becoming a bottleneck restricting the sustainable development of the civil aviation industry.
[0003] Against this backdrop, exploring and implementing efficient noise control measures is particularly urgent. Among these, noise reduction methods based on flight plan route optimization effectively reduce noise at sensitive points around airports by rationally allocating flight procedures throughout the day. Due to the relatively small adjustment range, this method has limited interference with airport operations and requires less skill from operators, making it easy to implement quickly within existing aviation systems. However, current research on flight plan route optimization focuses on noise reduction in localized areas, such as reducing runway end noise, without fully considering the reduction of noise levels at sensitive points with different spatial distributions around airports. Even when considering noise-sensitive points around airports, it fails to adequately address the heterogeneous needs of various noise-sensitive points. In fact, different types of sensitive points have significantly different noise tolerance standards. For areas requiring a highly quiet environment, such as schools, residential areas, and hospitals, stricter noise control standards must be adopted because these places need to provide a quiet environment conducive to learning, rest, or work. Conversely, for areas with higher noise tolerance, such as commercial parks and industrial parks, where there may be inherently higher environmental noise levels, the relevant standards can be relaxed accordingly. Therefore, there is an urgent need for a method to mitigate airport aviation noise by scientifically and rationally allocating flight routes to improve noise pollution in various noise-sensitive areas around airports. Summary of the Invention
[0004] Purpose of the invention: This invention proposes an airport aviation noise mitigation method based on flight plan route optimization, aiming to improve noise pollution in various noise-sensitive areas around the airport through scientific and reasonable flight route allocation.
[0005] Technical solution: The present invention provides an airport noise mitigation method based on flight plan route optimization, comprising the following steps:
[0006] (1) Obtain flight-related information and generate a flight schedule;
[0007] (2) Based on the navigation point information of the flight route, a flight trajectory data table is generated using a trajectory generation model and a flight schedule table;
[0008] (3) Based on the data of noise-sensitive points around the airport, the noise assessment model is combined with flight trajectory data to estimate the noise 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 that considers noise impact, operating time and flight plan, determine flight route optimization constraints, and achieve a coordinated balance between environmental benefits and efficient and safe airport operation;
[0011] (6) The multi-objective optimization model is solved by using the non-dominated sorting genetic algorithm II with an elite retention strategy.
[0012] Furthermore, the implementation process of step (1) is as follows:
[0013] 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;
[0014] Acquire flight arrival and departure flight track data to determine the available flight routes, runways, and corridor entrances for the same flight; merge airport flight plan data with arrival and departure flight track data to generate a flight plan table.
[0015] Furthermore, the navigation point information of the flight route described in step (2) includes the navigation point name, navigation point longitude, and navigation point latitude.
[0016] Furthermore, the flight trajectory data table described in step (2) includes flight time, trajectory longitude, trajectory latitude, trajectory altitude, trajectory airspeed, and flight time.
[0017] Furthermore, the implementation process of step (3) is as follows:
[0018] Read noise-sensitive point data around the airport, including the longitude, latitude, altitude, and type of the sensitive point;
[0019] Using a noise assessment model and combining flight trajectory data, the noise exposure level of each flight at each sensitive point is calculated, and a flight exposure sound level table is generated, including the name of the sensitive point, the longitude of the sensitive point, the latitude of the sensitive point, the type of the sensitive point, and the flight exposure sound level.
[0020] By integrating flight schedules, flight trajectory data tables, and flight exposure sound level tables, a noise data table for available flight routes is constructed, which includes flight number, aircraft type, departure airport, arrival airport, planned flight route, available flight route, arrival and departure, departure time, arrival time, runway, corridor entrance, flight time, sensitive point name, sensitive point type, and flight exposure sound level.
[0021] Furthermore, the implementation process of step (4) is as follows:
[0022] Decision variable X fp The flight allocation scheme is represented by taking the values 0 or 1. Let F and P be the sets of flights f and flight routes p, respectively, and define the decision variable X. fp for:
[0023]
[0024] Where p∈P, f∈F.
[0025] Furthermore, the process of constructing the multi-objective optimization model considering noise impact, operating time, and flight plan in step (5) is as follows:
[0026] The noise impact is measured using the daytime and nighttime equivalent sound levels at each noise-sensitive point, denoted as S, N. s The daytime and nighttime equivalent sound levels for sensitive point S from all day flights:
[0027]
[0028] Where p∈P, f d ∈F d f n ∈F n , s∈S; For daytime flights f d The exposure sound level at sensitive point s, For nighttime flights f n The sound level at the sensitive point s; F d For daytime flight assembly; F n For nighttime flights;
[0029] The second optimization objective is operational efficiency, let t fp If flight time is allocated to flight route p for flight f, then the total operating time N in a day is... t This represents the sum of the flight route operating times allocated to all flights:
[0030]
[0031] The third optimization objective is the change in flight plan, let δ fFor the original flight route of flight f, N C Changes to flight plan:
[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] Constraints are imposed on noise-sensitive point types: Noise-sensitive points around the airport are divided into three types. Type I land use refers to areas requiring quiet environments, and the set of Type I sensitive points is denoted as […]. Class II land use refers to areas where some noise is permitted. Let the set of Class II sensitive points be denoted as... Class III land use refers to areas that are not very sensitive to noise. Let the set of Class III sensitive points be denoted as... Day and night equivalent sound levels for three land types The calculation formula is as follows:
[0037]
[0038] in, For daytime flights f d For sensitive point s A s B s C The resulting sound exposure level, For nighttime flights f n For sensitive point s A s B s C The resulting sound exposure level; for flight set F, its daytime and nighttime equivalent sound level for Class I sensitive points is: Not greater than The daytime and nighttime equivalent sound levels for Class II sensitive points are: Not greater than The average daily equivalent sound level for Class III sensitive points is: Not greater than The formula for the noise-sensitive point type constraint is as follows:
[0039]
[0040] Arrival handover constraint: Within a given time window, the number of arriving flights at the same corridor entrance must not exceed the maximum capacity of that corridor entrance. Let f be the number of arriving flights within a time step Δt. a The set is Flight path p passing through corridor entrance CC The set is P C Entry and handover constraints The formula is as follows:
[0041]
[0042] in, The maximum number of arriving flights specified at corridor entrance C within a time step Δt;
[0043] Departure Release Constraint: Within a given time window, the number of departing flights at the same corridor entrance must not exceed the maximum capacity of that corridor entrance. Let f be the number of departing flights within a time step Δt. e The set is Flight path p passing through corridor entrance C C The set is P C Departure handover constraints The formula is as follows:
[0044]
[0045] in, The maximum number of departing flights at corridor entrance C within a time step Δt;
[0046] Runway capacity constraint: Under the condition of complying with air traffic control rules, the maximum number of aircraft that each runway can serve within a specified dynamic time window. The formula for runway capacity constraint is as follows:
[0047]
[0048] Among them, F Δt For flight f within time step Δt t The set of P R For the flight path p using runway R R The set, This represents the maximum number of departing flights specified for runway R within a time step Δt.
[0049] Furthermore, the Class I land use includes residential areas, schools, and hospitals; the Class II land use includes office buildings, shopping malls, and restaurants; and the Class III land use includes industrial areas, warehousing areas, and parks and squares.
[0050] Furthermore, the implementation process of step (6) is as follows:
[0051] Population initialization is performed by randomly generating individuals that meet the constraints to construct the initial population. Each individual represents 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 value of each flight plan route scheme in the population is calculated, and the degree of its violation of the constraints is evaluated. Appropriate penalties are imposed on solutions that violate the constraints.
[0052] Using the calculated noise impact, operational efficiency, and flight plan changes, individuals are ranked non-dominated according to the Pareto optimality principle; crowding degree is calculated based on the non-dominated ranking, and individuals with higher crowding degree are given priority for retention; a tournament selection method is used to select the next generation of the population, prioritizing individuals with lower non-dominated level and higher crowding degree to enter the mating pool.
[0053] After constructing the mating pool, the algorithm enters the crossover and mutation phase to generate a new offspring population. Subsequently, the parent and offspring populations are merged, and non-dominated sorting and crowding calculation are performed again. Individuals with lower non-dominated levels and higher crowding are selected to form the next generation population. An elite retention strategy is adopted, and all Pareto optimal solutions are stored in an independent archive. If the Pareto optimal solutions in the independent archive remain unchanged for multiple consecutive generations, the algorithm is considered to have converged, and the iteration stops, 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] This invention classifies noise-sensitive areas around airports, dividing different areas into three categories according to land type, and sets differentiated noise limit standards for each category; by rationally allocating flight routes, it achieves balanced control of noise levels in each area, thereby reducing noise interference with the living environment of residents and improving the satisfaction of surrounding residents.
[0056] This invention constructs a multi-objective optimization model that integrates airport noise control, operational efficiency, and flight plan changes into the same model. Based on fully considering the mutual constraints and synergistic effects among the objectives, it uses a non-dominated sorting genetic algorithm II to solve the problem, achieving a coordinated balance between environmental benefits and efficient and safe airport operation, thus reflecting the optimization of the overall system performance.
[0057] This invention targets daily flight schedules and optimizes flight scheduling in advance to achieve effective prediction and control of noise before it is generated. This method can intervene in sensitive areas to reduce noise before the noise actually occurs, thereby reducing noise interference globally, ensuring the quality of the environment around the airport, and building a more harmonious operating system. Attached Figure Description
[0058] Figure 1 This is a flowchart of the present invention. Detailed Implementation
[0059] The present invention will now be described in further detail with reference to the accompanying drawings.
[0060] like Figure 1 As shown, this invention proposes an airport aviation noise mitigation method based on flight plan route optimization. It categorizes noise-sensitive areas around the airport and sets corresponding noise constraints for each category. By selectively adjusting flight routes throughout the day, noise exceeding the noise constraint area is transferred to the area below the constraint within a specified range, effectively controlling noise levels in various areas, thereby reducing resident complaints and improving public satisfaction. Simultaneously, it comprehensively considers the balance between flight operation efficiency and flight plan changes, ensuring that noise optimization is achieved while simultaneously reducing the workload of air traffic controllers, mitigating the risk of operational delays caused by noise reduction measures, and maintaining the airport's efficient and safe operation. Specifically, it includes the following steps:
[0061] Step 1: Obtain flight information:
[0062] Read airport flight plan data, including flight number, aircraft type, planned flight route, arrival and departure, departure airport, landing airport, departure time, and landing time.
[0063] Read arrival and departure flight track data to determine the available flight routes, runways, and corridor entrances for the same flight. Then, merge the airport flight plan data with the arrival and departure flight track data to generate a flight schedule table.
[0064] Step 2: Trajectory generation based on flight plan.
[0065] Read all navigation point information that constitutes the flight route, including navigation point name, navigation point longitude, and navigation point latitude.
[0066] The flight trajectory data table is generated using a trajectory generation model and a flight schedule, including flight time, trajectory longitude, trajectory latitude, trajectory altitude, trajectory airspeed, and flight time.
[0067] Step 3: Estimate noise levels at sensitive points around the airport.
[0068] Read noise-sensitive point data around the airport, including the longitude, latitude, altitude, and type of the sensitive point.
[0069] Using the ECAC model and combined with flight trajectory data, the noise exposure level of each flight at each sensitive point is calculated, and a flight exposure sound level table is generated, including the name of the sensitive point, the longitude of the sensitive point, the latitude of the sensitive point, the type of the sensitive point, and the flight exposure sound level.
[0070] By integrating flight schedules, flight trajectory data tables, and flight exposure sound level tables, the attributes required for optimizing the model are selected, and a flight available flight route noise data table is constructed, including flight number, aircraft type, departure airport, arrival airport, planned flight route, available flight route, arrival and departure, departure time, arrival time, runway, corridor entrance, flight time, sensitive point name, sensitive point type, and flight exposure sound level.
[0071] Step 4: Define decision variables and optimize for all flights within a day.
[0072] Decision variable X fp The flight allocation scheme is represented by taking values of 0 or 1. Let F and P be the sets of flights f and flight routes p, respectively, and define the decision variable X. fp for:
[0073]
[0074] Where p∈P, f∈F.
[0075] Step 5: Construct a multi-objective optimization model, with optimization objectives covering three aspects: noise impact, operating time, and changes in flight plan.
[0076] The noise impact is measured using the daytime and nighttime equivalent sound levels at each noise-sensitive point. Let S and N be the set of sensitive points s. s The daytime and nighttime equivalent sound levels for sensitive point S from all day flights:
[0077]
[0078] Where p∈P, f d ∈F d f n ∈F n , s∈S; For daytime flights f d The exposure sound level at sensitive point s, For nighttime flights f n The sound level exposed at the sensitive point s. F d For daytime flight assembly; F n This is for assemblies for nighttime flights. "Daytime" refers to the period between 6:00 and 22:00 each day; "Nighttime" refers to the period between 22:00 and 6:00 the following day.
[0079] The second optimization objective is operational efficiency, let t fp If flight time is allocated to flight route p for flight f, then the total operating time N in a day is... t This represents the sum of the flight route operating times allocated to all flights:
[0080]
[0081] The third optimization objective is the change in flight plan, let δ f For the original flight route of flight f, N C Changes to flight plan:
[0082]
[0083] Step 6: Construct flight path optimization constraints.
[0084] The multi-objective optimization model employs five constraints: flight route uniqueness constraint, approach handover constraint, departure release constraint, runway capacity constraint, and 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, constraints are imposed on the types of noise-sensitive points. Referring to the current environmental quality standards for aircraft noise around airports, noise-sensitive points around airports are divided into three types. Type I land use refers to areas where quietness is required as much as possible, including residential areas, schools, hospitals, and other similar uses. The set of Type I sensitive points is denoted as […]. Category II land use allows for some noise levels, including office buildings, shopping malls, restaurants, and other similar uses. The set of Category II sensitive points is as follows: Class III land use refers to areas that are not very sensitive to noise, including industrial areas, warehousing areas, parks, squares, and other similar land uses. The set of Class III sensitive points is as follows: Day and night equivalent sound levels for three land types The calculation formula is as follows:
[0088]
[0089] in, For daytime flights f d For sensitive point s A s B s C The resulting sound exposure level, For nighttime flights f n For sensitive point s A s B s C The resulting sound exposure level. For flight set F, its daytime and nighttime equivalent sound level for Class I sensitive points is: The effective sound level for Class II sensitive locations must not exceed 57 dB(A); the equivalent sound level for day and night is [not specified]. The daily average equivalent sound level for Class III sensitive locations must not exceed 62 dB(A); It must not exceed 67 dB(A). The formula for the noise sensitivity point type constraint is as follows:
[0090]
[0091] Regarding arrival handover constraints, according to current air traffic control rules, the number of flights arriving at the same corridor entrance cannot exceed 5 within a 15-minute timeframe. a The set is Flight path p passing through corridor entrance C C The set is P C The formula for the entry handover constraint is as follows:
[0092]
[0093] Regarding departure restrictions, according to current air traffic control rules, the number of departing flights at the same corridor entrance cannot exceed 5 within a 15-minute period, and the number of departing flights within a 15-minute time step is recorded as f. e The set is Flight path p passing through corridor entrance C C The set is P C The departure handover constraint formula is as follows:
[0094]
[0095] Finally, runway capacity is constrained, meaning that, under air traffic control rules, each runway can serve a maximum of three aircraft within 15 minutes. The formula for runway capacity constraint is as follows:
[0096]
[0097] Among them, F 15 For flights within 15 minutes t The set of P R For the flight path p using runway R R A set of.
[0098] Step 7: Solve the multi-objective optimization model based on the non-dominated sorting genetic algorithm.
[0099] A non-dominated sorting genetic algorithm II with an elite retention strategy is employed to solve the optimization model. First, the population is initialized by randomly generating individuals that satisfy the constraints. Each individual represents a complete flight plan route for a day, and each gene within the individual corresponds to the selected flight route for a single flight, represented as a binary vector. Subsequently, the objective function value for each flight plan route in the population is calculated, and the degree to which it violates the constraints is evaluated. Appropriate penalties are imposed on solutions that violate the constraints.
[0100] Using the calculated noise impact, operational efficiency, and flight plan changes, individuals are ranked non-dominated according to the Pareto optimality principle. Crowding degree is then calculated based on the non-dominated ranking, with individuals having higher crowding degrees being prioritized for retention. A tournament selection method is used to select the next generation of individuals, prioritizing those with lower non-dominated levels and higher crowding degrees for entry into the mating pool.
[0101] After constructing the mating pool, the algorithm enters the crossover and mutation phase to generate a new offspring population. Subsequently, the parent and offspring populations are merged, and non-dominated sorting and crowding calculations are performed again. Individuals with lower non-dominated levels and higher crowding are selected to form the next generation. During this process, an elite preservation strategy is employed, using an independent archive to store all Pareto optimal solutions. If the Pareto optimal solutions in the independent archive remain unchanged for 10 consecutive generations, the algorithm is considered to have converged, and iteration can be stopped, thus gradually converging to a set of high-quality multi-objective optimization solutions.
[0102] For Nanjing Lukou International Airport, optimization was performed on 525 flights per day. After optimization, 28 Pareto front optimization schemes were obtained. Specifically, these 28 schemes effectively reduced the total noise level while also demonstrating advantages in operational efficiency. Scheme 23 reduced noise by 15.8 dB compared to the original plan, scheme 26 saved 5176 seconds of total flight time, and scheme 20 required the fewest changes to the original flight plan, totaling only 106 changes. The airport noise mitigation method based on flight plan route optimization proposed in this invention achieves a dynamic balance between aviation noise control and airport operation management, providing a practical and feasible technical path for noise optimization around airports.
[0103] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A method for mitigating airport aviation noise based on flight plan route optimization, characterized in that, Includes the following steps: (1) Obtain flight-related information and generate a flight schedule; (2) Based on the navigation point information of the flight route, a flight trajectory data table is generated using a trajectory generation model and a flight schedule table; (3) Based on the noise-sensitive point data around the airport, use the noise assessment model combined with flight trajectory data to estimate the noise of 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 that considers noise impact, operating time and flight plan, determine flight route optimization constraints, and achieve a coordinated balance between environmental benefits and efficient and safe airport operation; (6) The multi-objective optimization model is solved by using the non-dominated sorting genetic algorithm II with an elite retention strategy; The process of constructing the multi-objective optimization model that considers noise impact, operating time, and flight plan in step (5) is as follows: The noise impact is measured using the daytime and nighttime equivalent sound levels at each noise-sensitive point as the standard, and the sensitive points are recorded. The set is , For all-day flights to sensitive points Day and night equivalent sound levels: in, , , , ; daytime flights Sensitive points The exposure sound level, For night flights Sensitive points The exposure sound level; For daytime flight assembly; For nighttime flights, As a decision variable, the flight allocation scheme is represented by taking the value 0 or 1; The second optimization objective is operational efficiency, making For flights Assigned to flight routes The running time, then the total running time within a day This represents the sum of the flight route operating times allocated to all flights: The third optimization objective is the change in flight plan, making For flights The original flight route, Changes to flight plan: 。 2. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, 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; Acquire flight arrival and departure flight track data to determine the available flight routes, runways, and corridor entrances for the same flight; merge airport flight plan data with arrival and departure flight track data to generate a flight plan table.
3. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, The navigation point information for the flight route described in step (2) includes the 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 mentioned in step (2) includes flight time, trajectory longitude, trajectory latitude, trajectory altitude, trajectory airspeed, and flight time.
5. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, The implementation process of step (3) is as follows: Read noise-sensitive point data around the airport, including the longitude, latitude, altitude, and type of the sensitive point; Using a noise assessment model and combining flight trajectory data, the noise exposure level of each flight at each sensitive point is calculated, and a flight exposure sound level table is generated, including the name of the sensitive point, the longitude of the sensitive point, the latitude of the sensitive point, the type of the sensitive point, and the flight exposure sound level. By integrating flight schedules, flight trajectory data tables, and flight exposure sound level tables, a noise data table for available flight routes is constructed, which includes flight number, aircraft type, departure airport, arrival airport, planned flight route, available flight route, arrival and departure, departure time, arrival time, runway, corridor entrance, flight time, sensitive point name, sensitive point type, and flight exposure sound level.
6. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, The implementation process of step (4) is as follows: Decision variables The flight allocation scheme is represented by either 0 or 1, and the flight is recorded as follows: and flight routes The set is and Define decision variables for: in, , .
7. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, The flight route optimization constraints in step (5) include: Flight route uniqueness constraint: Each flight Only one flight route can be assigned in a flight plan. : Constraints are imposed on noise-sensitive point types: Noise-sensitive points around the airport are divided into three types. Type I land use refers to areas requiring quiet environments, and the set of Type I sensitive points is denoted as […]. Class II land use refers to areas where some noise is permitted; let the set of Class II sensitive points be denoted as . Class III land use refers to areas that are not very sensitive to noise; the set of Class III sensitive points is denoted as... Day and night equivalent sound levels for three land types , , The calculation formula is as follows: in, , , daytime flights Sensitive points , , The resulting sound exposure level, , , For night flights Sensitive points , , The resulting sound exposure level; for flight assemblies For Class I sensitive points, the daytime and nighttime equivalent sound levels are: It must not be greater than dB(A); the daytime and nighttime equivalent sound level for Class II sensitive points is It must not be greater than dB(A); the daily average equivalent sound level for Class III sensitive points is It must not be greater than dB(A); The formula for the noise-sensitive point type constraint is as follows: Arrival handover constraint: Within a given time window, the number of arriving flights at the same corridor entrance must not exceed the maximum capacity of that corridor entrance, with time steps specified. Inbound flights The set is Passing through the corridor Flight routes The set is Entry and handover constraints The formula is as follows: in, In order to time step Inner corridor entrance The maximum number of flights allowed to enter the airport as stipulated; Departure Release Constraints: Within a given time window, the number of departing flights at the same corridor entrance must not exceed the maximum capacity of that corridor entrance, calculated over a time step. Domestic departure flights The set is Passing through the corridor Flight routes The set is Departure handover constraints The formula is as follows: in, In order to time step The corridor entrances specified inside Maximum number of departing flights; Runway capacity constraint: Under the condition of complying with air traffic control rules, the maximum number of aircraft that each runway can serve within a specified dynamic time window. The formula for runway capacity constraint is as follows: in, In order to time step Domestic flights The set, For use of the runway Flight routes The set, In order to time step Inner track The maximum number of departing flights stipulated.
8. The airport aviation noise mitigation method based on flight plan route optimization according to claim 7, characterized in that, The Class I land use includes residential areas, schools, and hospitals; the Class II land use includes office buildings, shopping malls, and restaurants; and the Class III land use includes industrial areas, warehousing areas, and parks and squares.
9. The airport aviation noise mitigation method based on flight plan route optimization according to claim 1, characterized in that, The implementation process of step (6) is as follows: Population initialization is performed by randomly generating individuals that meet the constraints to construct the initial population. Each individual represents 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 value of each flight plan route scheme in the population is calculated, and the degree to which it violates the constraints is evaluated. Appropriate penalties are imposed on solutions that violate the constraints. Using the calculated noise impact, operational efficiency, and flight plan changes, individuals are non-dominatedly ranked according to the Pareto optimality principle. Crowding degree is calculated based on non-dominant ranking, and individuals with higher crowding degree are given priority for retention. The next generation of the population is selected using a tournament selection method, prioritizing individuals with lower non-dominant ranking and higher crowding degree to enter the mating pool. After constructing the mating pool, the algorithm enters the crossover and mutation phase to generate a new offspring population. Subsequently, the parent and offspring populations are merged, and non-dominated sorting and crowding calculation are performed again. Individuals with lower non-dominated levels and higher crowding are selected to form the next generation population. An elite retention strategy is adopted, and all Pareto optimal solutions are stored in an independent archive. If the Pareto optimal solutions in the independent archive remain unchanged for multiple consecutive generations, the algorithm is considered to have converged, and the iteration stops, thus gradually converging to a set of high-quality multi-objective optimization solutions.