Flight release prediction method and system under flow control condition

Abstract

The invention belongs to the technical field of data mining of civil aviation industry, and discloses a flight release prediction method and system under a flow control condition. The method comprises the following steps: acquiring a plurality of airport operation index parameters based on a safe air traffic flow scene, quantifying the plurality of acquired airport operation index parameters through a weighting algorithm, and establishing a flight plan adjustment efficiency index data model suitable for flight safe operation; and according to the established flight plan adjustment efficiency index data model, a flight plan adjustment scheme of flight safe operation under a flow control condition is obtained through a deployment priority rule. According to the method, the operation indexes are summarized, the data model is established, the efficiency of the flight plan adjustment plan is automatically analyzed through an algorithm rule, and the optimal flight plan adjustment scheme is selected.

Description

Technical Field

[0001] The present invention belongs to the technical field of data mining in the civil aviation industry, and in particular relates to a method and system for predicting flight release under flow control conditions. Background Art

[0002] The weather at the departure airport, route and destination airport is bad (such as thunderstorms and typhoons in summer, ice and snow in winter), or there are military exercises or emergencies in the area, or there are too many flights taking off and landing in the airspace within a certain period of time, and the aircraft cannot stay in the air, so once the number of flights exceeds a certain number, necessary safety measures must be taken. In order to ensure flight safety and the safety of passengers' lives and property, air traffic control will issue a traffic control notice.

[0003] Flow control is to maintain safe air traffic flow by limiting the number of aircraft entering air traffic control nodes per unit time. For flight safety, takeoffs or landings need to be carried out in an orderly manner. At this time, the take-off time of some flights needs to be postponed, and different flights have different impacts on airport operation efficiency.

[0004] When an airport is affected by adverse conditions, the air traffic control will issue an MDRS warning (Massive Flight Delay Response Mechanism, which is an emergency response mechanism adopted by the civil aviation department to deal with large-scale flight delays at airports, routes, airspace, etc.) and conduct traffic control. The airport's traffic capacity will decrease, and the Transport Management Committee (including airports, airlines, and air traffic control) will organize a meeting to adjust the flight plan. The adjustment results will have an impact on the airport, air traffic control, airlines, and passengers. Under the flight plan adjustment principles required by the authority, it is necessary to optimize the flight plan adjustment plan to balance the rights and interests of airports, air traffic control, airlines, and passengers.

[0005] Airports are mainly concerned about ensuring that flights do not exceed capacity or quantity (i.e. the upper limit on the number of take-offs and landings per hour) and that flight clearance is efficient; air traffic control is mainly concerned about smooth flight operations and low pressure on controllers; airlines are mainly concerned about small marginal losses after flight adjustments and low pressure on passenger services; passengers are mainly concerned about being able to travel normally and that flights are not delayed.

[0006] Through the above analysis, the problems and defects of the existing technology are as follows: the existing technology cannot effectively analyze and adjust the schedules of different flights, so that airports, airlines and air traffic control can adjust flight schedules more reasonably; it cannot effectively reduce the number of canceled flights and reduce flight delay time, and passenger satisfaction needs to be improved. Summary of the invention

[0007] In order to overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a method and system for predicting flight release under flow control conditions.

[0008] The technical solution is as follows: A method for predicting flight release under flow control conditions, comprising:

[0009] S1, based on a safe air traffic flow scenario, obtaining multiple airport operation index parameters, wherein the multiple airport operation index parameters include an airport operation hour capacity index, a flight release normal rate index, an airline marginal loss index, a controller command load index, and a passenger satisfaction index;

[0010] S2, quantify the obtained multiple airport operation index parameters through a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation;

[0011] S3, based on the established flight plan adjustment efficiency index data model, obtains the flight plan adjustment plan for safe flight operation under flow control conditions by allocating priority rules.

[0012] In step S1, the airport operating hour capacity index is index 1;

[0013] Indicator 1 = airport operating hour capacity - airport capacity reduction value;

[0014] Among them, the airport capacity reduction value is the maximum number of outbound and inbound flights at the airport within one hour, which is a fixed value.

[0015] In step S1, the flight release normal rate index is index 2;

[0016] Indicator 2 = predicted adjusted airport flight release normal rate / airport flight release normal rate in the current month.

[0017] In step S1, the airline's marginal loss indicator is indicator 3;

[0018] Indicator 3 = (influence value of passenger number + influence value of flight type + influence value of popular routes) / 100;

[0019] Passenger number impact value: The number of passengers is divided into four levels: 0-50, 51-100, 101-150, and above 151. Each flight to be adjusted is assigned a value of 10, 20, 30, 40, and 50 respectively;

[0020] Flight type impact value: According to the flight type of the originating flight, associated flight, and single incoming flight, 30, 15, and 5 points are assigned respectively;

[0021] Popular route impact value: Based on whether the flight is a popular route or not, 20 and 10 points are assigned respectively.

[0022] In step S1, the controller commands the load index to be index 4;

[0023] Indicator 4 = flight density / base flight density + number of backlogged flights / base value of backlogged flights;

[0024] Flight density: the number of flights taking off and landing in the current hour;

[0025] Flight base density: the average number of take-offs and landings per hour in a week;

[0026] Number of operational backlog flights: number of flights with abnormal releases;

[0027] Baseline number of operational backlog flights: 5.

[0028] In step S1, the passenger satisfaction index is index 5;

[0029] Indicator 5 = flight cancellation score / 100 + flight delay score / 100;

[0030] Flight cancellation score: 50 points for a canceled flight and 0 points for an uncancelled flight;

[0031] Flight delay score: If a flight is delayed, the score will be divided into multiple levels according to the delay duration.

[0032] In step S2, the obtained multiple airport operation index parameters are quantified by a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation, including:

[0033] By taking the weighted average of all indicators, the calculation formula for the flight plan adjustment efficiency index is obtained:

[0034]

[0035] In the formula, indicator 1 is set by the airport, and the number of take-off and landing flights per hour cannot exceed the airport's operating hourly capacity; the coefficients corresponding to indicators 2, 3, 4, and 5 are set by themselves, and all coefficients are less than 1 and add up to 1; where, Coefficient i=1.

[0036] In step S3, the allocation priority rules include:

[0037] Allocation priority rule 1: If the airport is in adverse conditions of bad weather or flight control, the value of index 1 is reduced, and the value of coefficient 1 is set not to exceed 0.2;

[0038] Allocation priority rule 2: set the value of coefficient 2 higher than the values ​​of other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority, so as to meet the normal flight release rate condition of the airport.

[0039] Allocation priority rule three: set the value of coefficient 3 higher than the values ​​of other coefficients, and prioritize flights with higher values. The higher the value of the flight, the higher the allocation priority, which meets the condition of giving priority to protecting the revenue of airlines;

[0040] Allocation priority rule 4: set the value of coefficient 4 higher than the values ​​of other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority; meet the conditions for reducing the command load of controllers;

[0041] Allocation priority rule five sets the value of coefficient 5 higher than the values ​​of other coefficients, and prioritizes flights with higher values. The higher the value of the flight, the higher the allocation priority, which meets the conditions for improving passenger satisfaction.

[0042] Another object of the present invention is to provide a prediction system for flight release under flow control conditions, comprising:

[0043] A module for acquiring multiple airport operation index parameters, which is used to acquire multiple airport operation index parameters based on a safe air traffic flow scenario, wherein the multiple airport operation index parameters include an airport operation hour capacity index, a flight release normal rate index, an airline marginal loss index, a controller command load index, and a passenger satisfaction index;

[0044] The flight plan adjustment efficiency index data model establishment module is used to quantify the acquired multiple airport operation index parameters through a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation;

[0045] The flight plan adjustment scheme acquisition module is used to obtain the flight plan adjustment scheme for safe operation of flights under flow control conditions by allocating priority rules based on the established flight plan adjustment efficiency index data model.

[0046] Furthermore, the system is mounted on a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and running on the at least one processor, and the processor implements the above functions when executing the computer program.

[0047] Combined with all the above technical solutions, the beneficial effects of the present invention are as follows: the present invention utilizes the various operating indicators already available at the airport to explore a calculation method for flight release under flow control conditions. The main strategy adopted is to cut fewer flights and adjust more flight schedules. This method selects five main indicators, including the airport operating hour capacity indicator, the flight release normal rate indicator, the airline marginal loss indicator, the controller command load indicator, and the passenger satisfaction indicator. The above operating indicators are summarized and a data model is established. The efficiency of the flight plan adjustment plan is automatically analyzed through algorithm rules, and the best flight plan adjustment plan is selected. The present invention improves the airport operation efficiency, reduces the waiting time for passengers, improves the airport release efficiency, and reduces the passenger complaint rate. The present invention quantifies the main factors affecting the operation of the airport under adverse conditions, so that the commander can adjust the flight plan more scientifically and reasonably. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0049] Figure 1 is a flow chart of a method for predicting flight release under flow control conditions provided by an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of a prediction system for flight release under flow control conditions provided by an embodiment of the present invention;

[0051] In the figure: 1. Multiple airport operation index parameter acquisition module; 2. Flight plan adjustment efficiency index data model establishment module; 3. Flight plan adjustment plan acquisition module. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0053] The innovation of the present invention is that the present invention takes into account various factors that affect the operation of the airport under adverse conditions, and quantifies the impact of each factor on the flight, so that under adverse conditions, the commander can adjust the flight plan more scientifically and reasonably. The present invention quantifies the airport operation hour capacity, flight release normal rate, airline marginal loss, controller command load, passenger satisfaction and other airport operation indicators, designs a weighted algorithm for analyzing and adjusting the time of different flights, so that the airport, airline and air traffic control can adjust the flight time more reasonably.

[0054] Embodiment 1, the airport operation indicators used in the present invention are as follows:

[0055] (1) Airport operating hour capacity indicators.

[0056] Airport operating hour capacity refers to the number of flight takeoffs and landings that the airport can handle and accommodate within one hour. This capacity depends on many factors, including the number of runways, runway length, air traffic control capabilities, ground facilities, weather conditions, etc. The operating hour capacity of different airports is different. Generally speaking, the operating hour capacity of large airports will be larger than that of small airports. The operating hour capacity of each airport is a fixed value.

[0057] The airport's operating hourly capacity must not be exceeded or exceeded. This indicator is a red line indicator and must be met.

[0058] (2) Flight release normal rate indicator.

[0059] In view of the impact of restricted flight releases, it is necessary to reasonably adjust the planned take-off times of flights, reduce the flight density during the affected periods, and improve the normal flight release rate of airports.

[0060] The normal flight release rate is an indicator that reflects the airport's support capabilities, that is, the ratio of the number of normal flights released by the airport to the total number of flights released by the airport. The calculation formula is: Normal flight release rate = Normal release number of flights / Total release number of flights.

[0061] Normal flights: The sum of flights that meet the following conditions:

[0062] Actual take-off time <= planned take-off time + 30 minutes;

[0063] Arrival is late (i.e. planned arrival time <= actual arrival time + 10 minutes) and actual take-off time <= actual arrival time + 10 minutes + (planned departure time - planned landing time) + 30 minutes;

[0064] Total number of flights released: the total number of flights released for departure.

[0065] Normal flight release rate of the airport in the current month: the number of normal flights released from 30 days before the current day to the current day / the total number of flights released from 30 days before the current day to the current day

[0066] (3) Airline marginal loss indicator.

[0067] Choose a flight schedule adjustment plan that will cause the airline to suffer the least loss. The more passengers on the delayed flight, the greater the loss of the airline. The more delayed the originating flight, the greater the loss of the airline. Delays on popular routes will also bring greater losses to the airline.

[0068] (4) Controller command load indicators.

[0069] The main factors to be considered include the difficulty of controller command, flight density and operation backlog.

[0070] Flight density: refers to the number of flights released per unit time. The higher the flight density, the higher the controller's command load.

[0071] Operational backlog: the number of outbound flights that have not yet taken off when their take-off time has arrived.

[0072] (5) Passenger satisfaction index.

[0073] Reducing the number of flight cancellations and flight delays are issues closely related to passenger satisfaction.

[0074] As shown in 1, the method for predicting flight release under flow control conditions provided by an embodiment of the present invention includes:

[0075] S1, based on a safe air traffic flow scenario, obtaining multiple airport operation index parameters, wherein the multiple airport operation index parameters include an airport operation hour capacity index, a flight release normal rate index, an airline marginal loss index, a controller command load index, and a passenger satisfaction index;

[0076] S2, quantify the obtained multiple airport operation index parameters through a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation;

[0077] S3, based on the established flight plan adjustment efficiency index data model, obtains the flight plan adjustment plan for safe flight operation under flow control conditions by allocating priority rules.

[0078] In step S1, the airport operating hour capacity index: the operating hour capacity of each airport is a fixed value, that is, the upper limit of the number of flights taking off and landing at the airport within one hour;

[0079] Flight release normal rate indicator: The higher the flight release normal rate, the higher the airport release efficiency. Therefore, in the application of this algorithm model, it is necessary to balance the factors of the release normal rate. The actual take-off time is no later than 30 minutes after the planned take-off time, which is generally not considered as abnormal release.

[0080] Airlines' marginal loss indicators: Generally, the more passengers there are, the higher the priority of flight release; the priority of originating flights is higher than that of inbound and outbound related flights; the priority of popular routes is also higher;

[0081] Controller command load index: The lower the flight density, the lower the controller load pressure; the fewer the number of backlogged flights, the lower the controller load pressure;

[0082] Passenger satisfaction index: Reducing the number of flight cancellations and reducing flight delays can improve passenger satisfaction.

[0083] For example, indicator 1: airport operating hour capacity indicator;

[0084] Airport operating hour capacity: The maximum flight throughput per hour specified by the airport, generally a fixed value. It can be appropriately reduced under special circumstances. Airport capacity reduction value: Due to various reasons such as weather and military activities, the airport clearance is restricted and the value of the airport operating hour capacity needs to be lowered. The lowered value is the airport capacity reduction value.

[0085] Indicator 1 = airport operating hourly capacity - airport capacity reduction value.

[0086] Indicator 2: Impact value of flight release normal rate;

[0087] Indicator 2 = predicted adjusted airport flight release rate / the airport flight release rate in the current month;

[0088] Indicator 3: Airlines’ marginal loss indicator.

[0089] Impact value of passenger number: The number of passengers is the most important factor affecting this indicator. The number of passengers is divided into four levels: 0-50, 51-100, 101-150, and above 151. Each flight to be adjusted is assigned a value according to the level, as shown in Table 1.

[0090] Table 1 Flight value assignment table by grade

[0091] Number of passengers on flight Points 0-50 10 51-100 20 101-150 30 150 and above 50

[0092] Flight type impact value: According to the flight type, two scores are assigned, as shown in Table 2;

[0093] Table 2 Flight type score assignment table

[0094] Flight Type Points Departure flight 30 Related flights 15 One-way flights 5

[0095] Popular route impact value: According to whether the flight is a popular route (popular routes are designated by the airport), two scores are assigned as shown in Table 3;

[0096] Table 3 Popular route flight score assignment table

[0097] Popular Routes Points yes 20 no 10

[0098] Indicator 3 = (influence value of passenger number + influence value of flight type + influence value of popular routes) / 100;

[0099] Indicator 4: controller command load indicator;

[0100] Flight density: the number of flights taking off and landing in the current hour;

[0101] Flight baseline density: the average number of take-offs and landings per hour from 8:00 to 20:00 in a week;

[0102] Number of operational backlog flights: number of flights with abnormal releases;

[0103] Baseline value for number of backlog flights: 5;

[0104] Indicator 4 = flight density / base flight density + number of backlogged flights / base value of backlogged flights;

[0105] Indicator 5: Passenger satisfaction index;

[0106] Flight cancellation score: 50 points for a canceled flight and 0 points for an uncancelled flight;

[0107] Flight delay score: If a flight is delayed, the score will be divided into 5 different levels according to the delay duration, as shown in Table 4.

[0108] Table 4 Delay time score table

[0109] Delay duration range Points Less than 30 minutes 10 30-60 minutes 20 60-90 minutes 30 90-120 minutes 40 More than 120 minutes 50

[0110] Indicator 5 = flight cancellation score / 100 + flight delay score / 100.

[0111] Exemplarily, in step S2, the airport operation indicators of the airport operation hour capacity, the flight release normal rate, the airline marginal loss, the controller command load, and the passenger satisfaction are quantified by a weighted algorithm to establish a flight plan adjustment efficiency indicator data model suitable for the safe operation of flights;

[0112] Based on a quantitative data model of flight plan adjustment efficiency indicators suitable for safe flight operations, the operating indicators of each airport are analyzed to adjust the schedules of different flights. By calculating the efficiency of the flight plan adjustment plan, the adjustment efficiency index value is obtained, and the optimal adjustment plan is selected and implemented after approval by all member units of the Transport Management Committee.

[0113] Among them, based on the flight plan adjustment efficiency index data model, plan adjustments can be made in the flight dynamic stage to adjust the release order of unexecuted flights and realize flight priority release; the flight dynamic stage refers to the inbound and outbound flights to be executed on the day, and the unexecuted flights are the planning stage.

[0114] Exemplarily, the flight schedule adjustment efficiency index is obtained by taking a weighted average of all the indicators.

[0115] in, Coefficient i = 1;

[0116]

[0117] In the formula, indicator 1 is set by the airport, and the number of take-offs and landings per hour cannot exceed the airport's operating hourly capacity; the coefficients corresponding to indicators 2, 3, 4, and 5 are set by themselves, and all coefficients are less than 1, and the sum is 1.

[0118] In step S2, the first indicator of the airport operating hour capacity is set by the airport, and the number of take-offs and landings per hour cannot exceed the airport operating hour capacity value. The coefficients corresponding to the last four indicators (flight release normal rate, airline marginal loss, controller command load, and passenger satisfaction) can be set by themselves, and all coefficients are less than 1 and add up to 1.

[0119] Exemplarily, in step S3, obtaining a flight plan adjustment plan for safe flight operation under flow control conditions by allocating priority rules includes:

[0120] Allocation priority rule 1: If the airport is in adverse conditions such as bad weather or flight control, the value of index 1 should be appropriately reduced, and the coefficient should not be set to exceed 0.2;

[0121] Allocation priority rule 2: When improving the normal release rate of airport flights, set the value of coefficient 2 higher than other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority;

[0122] Allocation priority rule three: when giving priority to protecting the airline's revenue, set the value of coefficient 3 higher than other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority;

[0123] Allocation priority rule 4: When reducing the controller's command load, set the value of coefficient 4 higher than other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority.

[0124] Allocation priority rule five: when improving passenger satisfaction, set the value of coefficient 5 higher than other coefficients, and give priority to allocating flights with higher values. The higher the value of the flight, the higher the allocation priority.

[0125] Embodiment 2, as Figure 2 As shown, an embodiment of the present invention provides a prediction system for flight release under flow control conditions, including:

[0126] A plurality of airport operation index parameter acquisition module 1 is used to acquire a plurality of airport operation index parameters based on a safe air traffic flow scenario, wherein the plurality of airport operation index parameters include an airport operation hour capacity index, a flight release normal rate index, an airline marginal loss index, a controller command load index, and a passenger satisfaction index;

[0127] The flight plan adjustment efficiency index data model establishment module 2 is used to quantify the obtained multiple airport operation index parameters through a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation;

[0128] The flight plan adjustment scheme acquisition module 3 is used to obtain the flight plan adjustment scheme for safe operation of the flight under the flow control condition by allocating priority rules according to the established flight plan adjustment efficiency index data model.

[0129] Exemplarily, the system is mounted on a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and running on the at least one processor, and the processor implements the above functions when executing the computer program.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] In 2023, a certain airport launched 5 MDRS warnings. The Transportation Management Committee organized a meeting and adjusted the flight plan. In recent times, the above-mentioned flight plan adjustment efficiency analysis method has been adopted, and the effect is relatively ideal. The operation case of this airport is as follows:

[0132] On September 7, the airport was affected by the typhoon. The weather forecast showed that the impact was greater between 15:00 and 20:00, and the greatest impact was around 17:00. After consultation, the Transport Management Committee adjusted the flights between 15:00 and 20:00 according to a 30% reduction in capacity. After the adjustment: Indicator 1 was met: the number of inbound and outbound flights per hour from 15:00 to 20:00 was within 22 flights, and the number of flights per hour from 16:00 to 18:00 was 16 flights; Indicator 2: According to the weather impact, route restrictions in the outer area and the distribution of flight schedules, it is predicted that the airport release rate after the flight schedule adjustment is normal at 85.66%. Indicator 2 is equal to 0.959, and the normal flight release rate on that day is 92.31% , 2.99% higher than the average normal release rate of the month (89.32%); Index 3: China Southern Airlines provides an index of 0.7, HNA Airlines 0.8, and China Eastern Airlines 0.7. Index 3 = the number of flights adjusted by the airline / the total number of flights * index, resulting in Index 3 = 0.725; Index 4: The index provided by air traffic control for the period from 15:00 to 20:00 (5 hours) is 0.9, and the index for 20:00 to 24:00 (4 hours) is 1.1, Index 4 = 0.98 (5 / 9*0.9+4 / 9*1.1); Index 5: The average departure delay of airport flights in September was 9.2 minutes, and the average delay on September 7 was 6.98 minutes, Index 5 = 132% (9.2 / 6.98)

[0133] Flights between 15:00 and 20:00 on September 7 will be reduced and adjusted, affecting a period of more than 4 hours, with a 30% drop in capacity: 26 scheduled take-off times will be adjusted, and the airport will have an hourly planned take-off and landing schedule after the adjustment;

[0134] The normal flight release rate on that day was 92.31%, 2.99% higher than the average normal flight release rate of that month (89.32#), and the average flight delay time was 8.05 minutes.

[0135] On October 9, the on-site blind landing calibration flight was carried out and the scheduled take-off times of 6 departure flights were adjusted. The normal flight release rate on that day was 90.6% and the average flight delay time was 6.98 minutes.

[0136] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting flight release under flow control conditions, characterized in that: The method includes: S1, based on a safe air traffic flow scenario, obtaining multiple airport operation index parameters, wherein the multiple airport operation index parameters include an airport operation hour capacity index, a flight release normal rate index, an airline marginal loss index, a controller command load index, and a passenger satisfaction index; S2, quantify the obtained multiple airport operation index parameters through a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation; S3, based on the established flight plan adjustment efficiency index data model, obtains the flight plan adjustment plan for safe flight operation under flow control conditions by allocating priority rules.

2. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S1, the airport operating hour capacity index is index 1; Indicator 1 = airport operating hour capacity - airport capacity reduction value; Among them, the airport capacity reduction value is the maximum number of outbound and inbound flights at the airport within one hour, which is a fixed value.

3. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S1, the flight release normal rate index is index 2; Indicator 2 = predicted adjusted airport flight release normal rate / airport flight release normal rate for the current month.

4. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S1, the airline's marginal loss index is index 3; Indicator 3 = (influence value of passenger number + influence value of flight type + influence value of popular routes) / 100; Passenger number impact value: The number of passengers is divided into four levels: 0-50, 51-100, 101-150, and above 151. Each flight to be adjusted is assigned a value of 10, 20, 30, 40, and 50 respectively; Flight type impact value: According to the flight type of the originating flight, associated flight, and single incoming flight, 30, 15, and 5 points are assigned respectively; Popular route impact value: Based on whether the flight is a popular route or not, 20 or 10 points are assigned respectively.

5. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S1, the controller commands the load index to be index 4; Indicator 4 = flight density / base flight density + number of backlogged flights / base value of backlogged flights; Flight density: the number of flights taking off and landing in the current hour; Flight base density: the average number of take-offs and landings per hour in a week; Number of operational backlog flights: number of flights with abnormal releases; Baseline number of operational backlog flights:

5.

6. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S1, the passenger satisfaction index is index 5; Indicator 5 = flight cancellation score / 100 + flight delay score / 100; Flight cancellation score: 50 points for a canceled flight and 0 points for an uncancelled flight; Flight delay score: If a flight is delayed, the score will be divided into multiple levels according to the delay duration.

7. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S2, the obtained multiple airport operation index parameters are quantified by a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation, including: By taking a weighted average of all indicators, the calculation formula for the flight plan adjustment efficiency index is obtained: Flight plan adjustment efficiency index In the formula, indicator 1 is set by the airport, and the number of take-off and landing flights per hour cannot exceed the airport's operating hourly capacity; the coefficients corresponding to indicators 2, 3, 4, and 5 are set by themselves, and all coefficients are less than 1 and add up to 1; where, Coefficient i=1.

8. The method for predicting flight release under flow control conditions according to claim 1, characterized in that: In step S3, the allocation priority rules include: Allocation priority rule 1: If the airport is in adverse conditions of bad weather or flight control, the value of index 1 is reduced, and the value of coefficient 1 is set not to exceed 0.2; Allocation priority rule 2: set the value of coefficient 2 higher than the values ​​of other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority, so as to meet the normal flight release rate condition of the airport. Allocation priority rule three: set the value of coefficient 3 higher than the values ​​of other coefficients, and prioritize flights with higher values. The higher the value of the flight, the higher the allocation priority, which meets the condition of giving priority to protecting the revenue of airlines; Allocation priority rule 4: set the value of coefficient 4 higher than the values ​​of other coefficients, and prioritize flights with higher values. The higher the value, the higher the allocation priority; meet the conditions for reducing the command load of controllers; Allocation priority rule five sets the value of coefficient 5 higher than the values ​​of other coefficients, and prioritizes flights with higher values. The higher the value of the flight, the higher the allocation priority, which meets the conditions for improving passenger satisfaction.

9. A prediction system for flight release under flow control conditions, characterized in that: The system implements the method for predicting flight release under flow control conditions as described in any one of claims 1 to 8, and the system comprises: A plurality of airport operation index parameter acquisition modules (1) are used to acquire a plurality of airport operation index parameters based on a safe air traffic flow scenario, wherein the plurality of airport operation index parameters include an airport operation hour capacity index, a flight release regularity index, an airline marginal loss index, a controller command load index, and a passenger satisfaction index; A flight plan adjustment efficiency index data model building module (2) is used to quantify the acquired multiple airport operation index parameters through a weighted algorithm to establish a flight plan adjustment efficiency index data model suitable for flight safety operation; The flight plan adjustment scheme acquisition module (3) is used to obtain a flight plan adjustment scheme for safe flight operation under flow control conditions by allocating priority rules based on the established flight plan adjustment efficiency index data model.

10. The flight release prediction system under flow control conditions according to claim 9, characterized in that: The system is mounted on a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and running on the at least one processor, and the processor implements the above functions when executing the computer program.

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