A flight arrival and departure scheduling optimization method and system based on big data

Abstract

The present invention is applicable to the field of air traffic management technology, and provides a flight arrival and departure scheduling optimization method and system based on big data. When the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, the expected waiting time is determined, and it is judged whether the expected waiting time is greater than the preset waiting time threshold; if it is determined that the expected waiting time is greater than the preset waiting time threshold, the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends is determined. By combining the special situation processing time and the real-time taxiing requirements, the fuel efficiency and taxiing efficiency can be improved simultaneously. When a special situation causes the target aircraft to wait on the runway, the present invention determines the taxiing strategy for the target aircraft to leave the runway with the optimal taxiing time based on the expected waiting time and ground traffic conditions, and accurately calculates the taxiing power requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of air traffic management, and in particular relates to a flight arrival and departure scheduling optimization method and system based on big data. Background Art

[0002] With the rapid development of air transportation and the continuous improvement of airport operation capacity, flight arrival and departure scheduling has become a key link in airport operations. During the arrival and departure of flights, aircraft taxiing and waiting on the runway often occur, especially in special circumstances caused by runway occupation, air traffic congestion or emergencies, the target aircraft often needs to wait for instructions on the runway. During this period, the target aircraft usually keeps the engine at idle speed so that it can start, taxi or take off quickly after receiving the instruction. However, during the long waiting process, keeping the engine idling will lead to unnecessary fuel consumption, increase the operating costs of the airport, and also have an adverse impact on the environment. Therefore, how to optimize the waiting and taxiing strategies of aircraft under special circumstances to achieve improved fuel efficiency has become an important topic in the current aviation management field.

[0003] In the prior art, aircraft usually keep the engine idle on the runway or taxiway according to the instructions of ground traffic control to ensure that they can respond to ground taxiing or take-off instructions at any time. However, the engine idle power used in the prior art is usually fixed. No matter how the taxiing demand of the aircraft changes, the idle power of the engine cannot be dynamically adjusted, which leads to the following problems: when the idle power is set too low, the aircraft may not be able to respond quickly to the taxiing demand, especially when additional power is required during the taxiing process, the electric taxiing system cannot fully provide thrust support; when the idle power is set too high, it will cause fuel waste, especially in the case of long waiting, the engine power overflow will lead to additional fuel consumption and environmental pollution. Summary of the invention

[0004] The purpose of the present invention is to provide a flight arrival and departure scheduling optimization method and system based on big data, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a flight arrival and departure scheduling optimization method based on big data, the method comprising:

[0006] When the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, an expected waiting time is determined, and it is judged whether the expected waiting time is greater than a preset waiting time threshold;

[0007] If it is determined that the expected waiting time is greater than the preset waiting time threshold, determine the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends;

[0008] Analyze the taxiing force required for the target aircraft to leave the scheduled runway under the optimal taxiing time, and determine whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system on the target aircraft;

[0009] If it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system, the idle power distribution strategy of the target aircraft's engine is formulated based on the difference between the two.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, when the target aircraft enters a predetermined runway and receives a waiting instruction for a special situation, the steps of determining an expected waiting time and judging whether the expected waiting time is greater than a preset waiting time threshold include:

[0011] When a target aircraft enters the scheduled runway and receives a holding instruction for a special situation, interpret the holding instruction and determine the expected holding time of the target aircraft;

[0012] Get a preset waiting time threshold, and determine whether the expected waiting time is greater than the preset waiting time threshold.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, if it is determined that the expected waiting time is greater than the preset waiting time threshold, the step of determining the optimal taxiing time for the target aircraft to leave the predetermined runway after the special situation ends includes:

[0014] If it is determined that the expected waiting time is greater than the preset waiting time threshold, the ground traffic flow of the scheduled runway after the special situation ends is estimated;

[0015] Assess the congestion level of the intended runway based on the estimated ground traffic flow;

[0016] Calculate the basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed, and adjust the basic taxiing time according to the congestion level to obtain the optimal taxiing time.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the steps of calculating the basic taxiing time required for the target aircraft to leave the predetermined runway at a standard taxiing speed and adjusting the basic taxiing time according to the congestion level to obtain the optimal taxiing time include:

[0018] Obtain the real-time GPS data of the target aircraft and analyze the remaining taxiing distance of the target aircraft on the scheduled runway based on the real-time GPS data;

[0019] The basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed is calculated based on the remaining taxiing distance and the standard taxiing speed;

[0020] The basic taxiing time is adjusted according to the congestion level to obtain the optimal taxiing duration.

[0021] As a further limitation of the technical solution of the embodiment of the present invention, the steps of analyzing the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time and determining whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system carried by the target aircraft include:

[0022] Retrieve the preset comparison table, find the corresponding standard sliding power according to the optimal sliding time and the remaining sliding distance, and set it as the required sliding power;

[0023] Obtain the maximum achievable traction power of the target aircraft's electric taxiing system;

[0024] Determine whether the required taxiing force is greater than the maximum traction power of the electric taxiing system. If not, shut down the engine of the target aircraft.

[0025] As a further limitation of the technical solution of the embodiment of the present invention, if it is determined that the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system, the step of formulating the idle power distribution strategy of the engine of the target aircraft according to the difference between the two comprises:

[0026] If it is determined that the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system, determining a first operating power of the target aircraft engine corresponding to the required taxiing force, and converting the maximum achievable traction power of the electric taxiing system into a second operating power of the target aircraft engine;

[0027] Subtracting the second operating power from the first operating power to obtain the dynamic idle power of the engine;

[0028] After the dynamic idle power is obtained, the engine of the target aircraft is started and the dynamic idle power is maintained to maintain idle operation.

[0029] A flight arrival and departure scheduling optimization system based on big data, the system includes: an expected waiting time judgment module, an optimal taxiing time determination module, a required taxiing power judgment module and an idle power allocation strategy formulation module, wherein:

[0030] An expected waiting time determination module is used to determine the expected waiting time when the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, and to determine whether the expected waiting time is greater than a preset waiting time threshold;

[0031] An optimal taxiing time determination module is used to determine the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends if it is determined that the expected waiting time is greater than a preset waiting time threshold;

[0032] A required taxiing force determination module is used to analyze the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time, and to determine whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system carried by the target aircraft;

[0033] The idle power distribution strategy formulation module is used to formulate the idle power distribution strategy of the target aircraft's engine according to the difference between the required taxiing power and the maximum achievable traction power of the electric taxiing system if it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the expected waiting time determination module specifically includes:

[0035] an expected waiting time determination unit, for interpreting the waiting instruction and determining the expected waiting time of the target aircraft when the target aircraft enters the predetermined runway and receives a waiting instruction for a special situation;

[0036] The expected waiting time determination unit is used to obtain a preset waiting time threshold and determine whether the expected waiting time is greater than the preset waiting time threshold.

[0037] As a further limitation of the technical solution of the embodiment of the present invention, the optimal taxiing duration determination module specifically includes:

[0038] A ground traffic flow estimation unit, for estimating the ground traffic flow of the scheduled runway after the special situation ends if it is determined that the expected waiting time is greater than a preset waiting time threshold;

[0039] A congestion level assessment unit, used for assessing the congestion level of a predetermined runway according to an estimated ground traffic flow;

[0040] The basic taxi time adjustment unit is used to calculate the basic taxi time required for the target aircraft to leave the scheduled runway at a standard taxi speed, and adjust the basic taxi time according to the congestion level to obtain the optimal taxi time.

[0041] As a further limitation of the technical solution of the embodiment of the present invention, the optimal taxiing duration determination module further includes:

[0042] A remaining taxiing distance determination unit is used to obtain real-time GPS data of the target aircraft and analyze the remaining taxiing distance of the target aircraft on the predetermined runway based on the real-time GPS data;

[0043] A basic taxiing time calculation unit, used to calculate the basic taxiing time required for the target aircraft to leave the predetermined runway at the standard taxiing speed according to the remaining taxiing distance and the standard taxiing speed;

[0044] The optimal taxiing duration determining unit is used to adjust the basic taxiing time according to the congestion level to obtain the optimal taxiing duration.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] By combining the special situation processing time and the real-time taxiing demand, the fuel efficiency and taxiing efficiency are improved simultaneously. When the special situation causes the target aircraft to wait on the runway, the present invention determines the taxiing strategy for the target aircraft to leave the runway with the optimal taxiing time according to the expected waiting time and the ground traffic conditions, and accurately calculates the taxiing power requirement. By comparing the taxiing power with the maximum traction capacity of the electric taxiing system, when the power of the electric taxiing system is insufficient to support the entire taxiing, the present invention reasonably formulates the engine's idle power distribution strategy to ensure that the engine provides sufficient thrust to supplement the deficiencies of the electric taxiing system while maintaining minimum energy consumption, thereby achieving efficient use of fuel and avoiding unnecessary power overflow. In addition, the adjustment of the dynamic idle speed of the engine ensures that during the taxiing process, it can both quickly respond to emergencies and maintain an energy-saving operating mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0048] Figure 2 A flowchart for determining whether the expected waiting time is greater than a preset waiting time threshold in the method provided in an embodiment of the present invention;

[0049] Figure 3 A flow chart of evaluating the congestion level of a predetermined runway in the method provided in an embodiment of the present invention;

[0050] Figure 4 A flow chart of determining the optimal taxiing duration in the method provided in an embodiment of the present invention;

[0051] Figure 5 A flow chart of determining whether the required sliding force is greater than the maximum traction force of the electric sliding system in the method provided in an embodiment of the present invention;

[0052] Figure 6 A flow chart of formulating an idle power distribution strategy for an engine of a target aircraft in a method provided in an embodiment of the present invention;

[0053] Figure 7 An application architecture diagram of a system provided by an embodiment of the present invention;

[0054] Figure 8 A structural block diagram of an expected waiting time determination module in a system provided by an embodiment of the present invention;

[0055] Fig. 9 This is a structural block diagram of an optimal coasting duration determination module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0058] Specifically, a flight arrival and departure scheduling optimization method based on big data, the method specifically includes the following steps:

[0059] Step S100, when the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, an expected waiting time is determined, and it is judged whether the expected waiting time is greater than a preset waiting time threshold.

[0060] Specifically, Figure 2 A flow chart for determining whether the expected waiting time is greater than a preset waiting time threshold is shown.

[0061] Among them, when the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, determining the expected waiting time and judging whether the expected waiting time is greater than the preset waiting time threshold specifically includes the following steps:

[0062] Step S101, when the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, interpreting the waiting instruction and determining the expected waiting time of the target aircraft;

[0063] Step S102, obtaining a preset waiting time threshold, and determining whether the expected waiting time is greater than the preset waiting time threshold.

[0064] In the embodiment of the present invention, the target aircraft is mainly an airplane, and the predetermined runway refers to a specific runway designated for takeoff, landing or ground taxiing of the target aircraft. This is usually allocated by the airport ground control center according to the current flight plan and air traffic conditions to ensure the order and safety of airport operations. The predetermined runway can be understood as a specific location used when the target aircraft performs a specific mission.

[0065] Special circumstances usually include temporary closure of airport runways, obstacles on runways or taxiways, temporary air traffic congestion, severe weather such as thunderstorms or fog, and ground safety incidents such as unauthorized personnel or vehicles entering the runway area. The waiting instructions are sent to the target aircraft by the ground control center through the communication system, and the content usually includes the specific waiting location, estimated waiting time, waiting reason, and subsequent taxiing path or related instructions after the waiting is over. With this information, pilots can clearly know what to do during the waiting period to ensure flight safety and efficient management of the airport ground.

[0066] The expected waiting time is based on a variety of factors, including the specific nature of the special situation, the occupancy status of the runway, the current air and ground traffic flow, and real-time weather conditions. Ground controllers will combine this information and determine how long the target aircraft will need to wait through the airport's ground traffic management system or air traffic management system. The expected waiting time can be derived from the analysis of historical data, real-time traffic monitoring, and the estimated resolution time based on the current special situation.

[0067] When the expected waiting time is not greater than the preset waiting time threshold, the target aircraft will remain at the original position and keep the engine in a low-power idle state to ensure that it can quickly respond to taxiing or take-off instructions that may be issued at any time. In this case, the target aircraft does not need to perform additional power switching or shut down the engine, but enters a standby state to quickly resume operation.

[0068] The technical means described above belong to the existing technology. Those skilled in the art can use the existing traffic management system, communication system and engine management system to obtain relevant information and execute the above instructions and operations. These technologies have been widely used in airport management, flight safety and aircraft dispatch.

[0069] In the prior art, when the engine is in idle preparation state, its power is usually fixed and cannot be dynamically adjusted according to actual needs. This design leads to efficiency and responsiveness problems in some cases: when the power setting is too low, even if the aircraft is equipped with an electric taxiing system, the target aircraft may not be able to respond quickly to instructions and leave the runway as soon as possible because the idle power is not enough to supplement the required thrust. When the power setting is too high, power overflow is likely to occur, resulting in unnecessary fuel consumption, reduced fuel utilization, and increased operating costs and emissions. Therefore, there is a significant contradiction between fuel efficiency and taxiing responsiveness in the existing idle preparation technology, and it is difficult to provide flexible and economical thrust adjustment in a changing ground traffic environment.

[0070] Furthermore, the flight arrival and departure scheduling optimization method based on big data also includes the following steps:

[0071] Step S200: If it is determined that the expected waiting time is greater than the preset waiting time threshold, determine the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends.

[0072] Specifically, Figure 3 A flow chart for evaluating the congestion level of a predetermined runway is shown.

[0073] If it is determined that the expected waiting time is greater than the preset waiting time threshold, determining the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends specifically includes the following steps:

[0074] Step S201, if it is determined that the expected waiting time is greater than the preset waiting time threshold, the ground traffic flow of the scheduled runway after the special situation ends is estimated;

[0075] Step S202, evaluating the congestion level of the scheduled runway according to the estimated ground traffic flow;

[0076] Step S203, calculating the basic taxiing time required for the target aircraft to leave the scheduled runway at a standard taxiing speed, and adjusting the basic taxiing time according to the congestion level to obtain the optimal taxiing time.

[0077] In an embodiment of the present invention, estimating the ground traffic flow of the scheduled runway after the special situation ends is usually achieved through the airport ground traffic management system combined with various data of the air traffic management system. These systems will collect the aircraft dynamics on each runway and taxiway inside the airport in real time, including the number of taxiing aircraft, parking positions, taxiing speeds, taxiing paths, and the approach and departure plans of other aircraft. At the same time, by integrating information such as weather forecasts, air traffic, ground equipment and obstacle status, the system can comprehensively analyze and predict the traffic flow of the scheduled runway in the future period after the special situation ends. This traffic flow prediction often relies on big data technology, analysis of historical traffic patterns, and real-time sensor data to evaluate the traffic load of the runway after the special situation ends.

[0078] In the existing technology, ground traffic management systems (such as the airport collaborative decision-making system A-CDM, or the air traffic management system ATC) have the function of real-time monitoring and prediction of ground traffic. By combining historical data modeling with real-time traffic data, the system can generate traffic forecasts for the airport in the future and identify possible peak or trough periods. These forecasts usually apply machine learning algorithms, big data analysis models, or algorithms based on empirical rules to make reasonable estimates of current and future traffic conditions. For example, the use of runways and taxiways at different time points can be determined by combining the operating time period, flight schedules, seasonal changes, and weather changes.

[0079] After estimating the ground traffic flow of the scheduled runway, the congestion level can be evaluated by calculating the utilization rate and waiting time of the runway. The existing technology usually adopts the runway capacity model and flow model, which take into account factors such as the maximum handling capacity of the runway, the taxiing speed of the aircraft, the number of aircraft on the runway, and the efficiency of traffic flow. The system can evaluate the congestion level of the runway based on the current traffic flow and the predicted future traffic flow. For example, if the system predicts that the number of aircraft on the runway will approach or exceed the design capacity of the runway after the special situation ends, the system will evaluate it as having a high level of congestion; on the contrary, if the traffic flow is low and the aircraft maintain a large distance, it will be evaluated as having a low level of congestion.

[0080] This assessment method is usually automated through the real-time data collection module and congestion prediction module of the ground traffic management system, using historical traffic data, traffic flow patterns and real-time monitoring information to form a congestion forecast for the runway during a specific period of time. The assessment results can be divided into different levels of congestion, such as low, medium and high congestion, to provide support for the taxiing plan of the target aircraft.

[0081] In the existing technology, many large airports have adopted this method of ground traffic flow prediction and congestion assessment, such as London Heathrow Airport and Frankfurt Airport. Through the A-CDM system, these airports can effectively predict ground traffic conditions, coordinate ground taxiing, boarding and departure activities, and thus improve ground operation efficiency. These systems are based on existing traffic management theories and multi-source data fusion technology, relying on ground sensing equipment (such as ground radar, GPS sensors, etc.) and data analysis platforms, and can provide airport managers and air traffic controllers with real-time ground traffic conditions and congestion assessment results. Therefore, these technologies are all existing technologies and have been successfully applied in many modern airports. Those skilled in the art can obtain relevant information through the existing system to achieve ground traffic flow prediction and congestion assessment.

[0082] Specifically, Figure 4 A flow chart for determining the optimal taxiing duration is shown.

[0083] The following steps are used to calculate the basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed and adjust the basic taxiing time according to the congestion level to obtain the optimal taxiing time:

[0084] Step S2031, acquiring real-time GPS data of the target aircraft, and analyzing the remaining taxiing distance of the target aircraft on the predetermined runway according to the real-time GPS data;

[0085] Step S2032, calculating the basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed according to the remaining taxiing distance and the standard taxiing speed;

[0086] Step S2033: adjusting the basic gliding time according to the congestion level to obtain the optimal gliding time.

[0087] In an embodiment of the present invention, the standard taxiing speed is usually set by the airport management department based on factors such as the airport ground conditions, runway and taxiway design features, and safety regulations to ensure that the aircraft can move at a safe and controllable speed during taxiing while taking into account the overall operating efficiency of the airport.

[0088] In the process of analyzing the remaining taxi distance of the target aircraft on the scheduled runway based on real-time GPS data, the real-time position (including geographic coordinates) is first obtained by the GPS receiving device on the aircraft, and the ground traffic management system matches these positions with the digital runway map of the airport. In this way, the ground traffic management system can determine the remaining taxi distance between the target aircraft's current position and the runway exit. This distance is calculated by comparing the real-time coordinates of the target aircraft with the geographic information of the runway path.

[0089] After obtaining the remaining taxiing distance, combined with the standard taxiing speed, the basic taxiing time required for the target aircraft to leave the scheduled runway can be calculated. This time represents the time required for the target aircraft to complete taxiing at the standard speed under ideal conditions without additional obstacles.

[0090] Next, the basic taxi time is adjusted based on the runway congestion level to obtain the optimal taxi time. Specifically, the congestion level can be set as a percentage adjustment factor. When the congestion level is low, the percentage factor will be negative, thereby shortening the basic taxi time; and when the congestion level is high, the factor will be positive, thereby extending the basic taxi time. For example:

[0091] In the case of low congestion, the system may set a percentage factor of -20%, indicating that the basic taxi time can be reduced by 20%. This will allow the target aircraft to taxi in a more efficient manner and leave the runway quickly.

[0092] In cases of high congestion, the system may set a percentage factor of +30%, indicating that the basic taxi time needs to be increased by 30% to allow sufficient time for possible delays during taxiing and interactions with other aircraft.

[0093] In this way, the congestion level is directly converted into a percentage adjustment factor and applied to the basic taxiing time, which enables the taxiing time to be flexibly adjusted to match the actual ground traffic conditions, thereby calculating the optimal taxiing duration.

[0094] For example: Assume that the target aircraft is currently located in the middle of the scheduled runway, the remaining taxiing distance is 1,500 meters, the standard taxiing speed is 10 meters per second, and the calculated basic taxiing time is 150 seconds. If the runway congestion level is assessed as "low", the percentage adjustment factor set by the system is -20%, then the basic taxiing time will be shortened by 20%, that is, the adjusted taxiing time is 120 seconds. If the runway congestion level is assessed as "high", the percentage adjustment factor set is +30%, then the taxiing time will increase by 30%, that is, the adjusted taxiing time is 195 seconds.

[0095] Furthermore, the flight arrival and departure scheduling optimization method based on big data also includes the following steps:

[0096] Step S300, analyzing the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time, and determining whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system carried by the target aircraft.

[0097] Specifically, Figure 5 A flow chart for determining whether the required taxiing power is greater than the maximum traction power of the electric taxiing system is shown.

[0098] The step of analyzing the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time and determining whether the required taxiing force is greater than the maximum achievable traction force of the electric taxiing system carried by the target aircraft specifically includes the following steps:

[0099] Step S301, calling a preset comparison table, finding a corresponding standard sliding force according to the optimal sliding time and the remaining sliding distance, and setting it as the required sliding force;

[0100] Step S302, obtaining the maximum achievable traction power of the electric taxiing system of the target aircraft;

[0101] Step S303, determining whether the required taxiing force is greater than the maximum traction power of the electric taxiing system, if not, shutting down the engine of the target aircraft.

[0102] In an embodiment of the present invention, the preset comparison table is set according to the taxiing requirements and various operating conditions of the target aircraft, and is mainly used to find the standard taxiing power required by the target aircraft under different taxiing distances and taxiing times. The data in the preset comparison table is usually set based on a large amount of historical operating data, actual taxiing test results, and the power system parameters of the target aircraft. The data in the table will cover the traction requirements of different types of aircraft under various taxiing conditions. For example, the table will list the required traction power corresponding to different remaining taxiing distances, different taxiing speeds and taxiing times. The setting of the comparison table takes into account factors such as the weight of the target aircraft, ground friction resistance, runway slope, wind direction, etc., to ensure that reasonable power guidance can be provided to the aircraft under different conditions to ensure taxiing safety and fuel efficiency.

[0103] In actual operation, "finding the corresponding standard taxiing force based on the optimal taxiing time and the remaining taxiing distance, and setting it as the required taxiing force" is accomplished by querying the preset comparison table. First, the system will look for the taxiing force value that matches these two parameters in the preset comparison table based on the optimal taxiing time calculated previously and the remaining taxiing distance obtained through GPS data analysis. For example, the system will search for the closest combination of taxiing time and taxiing distance in the table to find the corresponding standard taxiing force. This taxiing force represents the most reasonable traction power required for the aircraft under the current ground conditions and taxiing targets to ensure that the target aircraft remains stable and controllable during taxiing. The standard taxiing force will be set as the required taxiing force to guide the power output required for the next taxiing phase.

[0104] The purpose of judging "whether the required taxiing power is greater than the maximum traction power of the electric taxiing system, and if not, shutting down the engine of the target aircraft" is to save fuel to the greatest extent possible while ensuring that the electric taxiing system is sufficient to provide the power required for taxiing. When the required taxiing power does not exceed the maximum traction capacity of the electric taxiing system, it means that the power of the electric taxiing system is sufficient to support the taxiing of the target aircraft on the predetermined runway. Therefore, in this case, the engine can be shut down and the aircraft can be driven to taxi only by the electric taxiing system to reduce fuel consumption, reduce operating costs, and reduce carbon emissions. This method ensures that the aircraft does not require additional engine thrust support when taxiing, and at the same time optimizes fuel use by rationally utilizing the traction power of the electric taxiing system.

[0105] Furthermore, the flight arrival and departure scheduling optimization method based on big data also includes the following steps:

[0106] Step S400: If it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system, an idle power distribution strategy for the target aircraft's engine is formulated according to the difference between the two.

[0107] Specifically, Figure 6 A flow chart for formulating an idle power distribution strategy for the engines of a target aircraft is shown.

[0108] If it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system, then formulating the idle power distribution strategy of the target aircraft's engine according to the difference between the two specifically includes the following steps:

[0109] Step S401, if it is determined that the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system, determining a first operating power of the target aircraft engine corresponding to the required taxiing force, and converting the maximum achievable traction power of the electric taxiing system into a second operating power of the target aircraft engine;

[0110] Step S402, subtracting the second operating power from the first operating power to obtain the dynamic idle power of the engine;

[0111] Step S403, after obtaining the dynamic idle power, start the engine of the target aircraft and keep the dynamic idle power to maintain idle operation.

[0112] In the embodiment of the present invention, the "first operating power" refers to the power output that the target aircraft must obtain from the engine to meet the taxiing power required for the current taxiing. It is calculated based on the required taxiing power through the power system characteristics of the target aircraft. Specifically, the system will find the corresponding engine power output through the engine performance curve based on the required taxiing power combined with the current mass of the aircraft, the ground friction coefficient, the taxiing speed and other parameters to ensure that the taxiing power demand is met. This process usually relies on the aircraft's flight management system (FMS) or power system management module to complete. By inputting the traction demand, the system automatically obtains the corresponding output power from the engine power curve.

[0113] The "second operating power" converts the maximum achievable traction power of the electric taxiing system into the power requirement of the target aircraft engine through the power conversion relationship. The electric taxiing system provides the traction output by the motor, which is converted into the corresponding engine power to characterize the required engine operating power if the engine is required to provide the same thrust as the electric taxiing system. Usually, this conversion is based on the power conversion factor of the engine or the comparison of the performance curves of the electric taxiing system and the engine.

[0114] The principle of "subtracting the second operating power from the first operating power to obtain the dynamic idle power of the engine" is to determine the additional thrust that the engine actually needs to provide by calculating the difference. In this scenario, since the electric taxiing system itself can provide a part of the thrust (maximum traction power), the remaining part needs to be supplemented by the engine. Therefore, the first operating power represents the total power to meet the taxiing needs, and the second operating power represents the part of the power that the electric taxiing system can already provide. Subtracting the second operating power from the first operating power can determine the minimum additional power that the engine needs to output, that is, the dynamic idle power. This power value is calculated in real time based on multiple factors such as the current taxiing conditions, ground friction, and the power that the electric taxiing system can provide, so as to ensure that the engine only provides additional power support when necessary, so as to save fuel and reduce power overflow.

[0115] Maintaining the engine running through dynamic idle power can save fuel consumption to the greatest extent while meeting taxiing requirements, while reducing engine wear. Dynamic idle power is based on precise calculations to ensure that the engine only provides the minimum power output required during taxiing, avoiding the energy waste caused by fixed idle power. In addition, when the engine is kept in a dynamic idle state, it can respond to changes in taxiing conditions at any time. Especially when emergency thrust is required, the engine can quickly switch from idle to high power output to ensure the safe taxiing of the aircraft. This method not only improves fuel efficiency, but also enhances the response sensitivity and safety during taxiing.

[0116] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0117] Among them, in another preferred embodiment provided by the present invention, a flight arrival and departure scheduling optimization system based on big data includes:

[0118] The expected waiting time determination module 100 is used to determine the expected waiting time when the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, and to determine whether the expected waiting time is greater than a preset waiting time threshold.

[0119] Specifically, Figure 8 It shows a structural block diagram of the expected waiting time determination module 100 in the system provided by an embodiment of the present invention.

[0120] Among them, in the preferred implementation mode provided by the present invention, the expected waiting time determination module 100 specifically includes:

[0121] The expected waiting time determining unit 101 is used to interpret the waiting instruction and determine the expected waiting time of the target aircraft when the target aircraft enters the predetermined runway and receives a waiting instruction for a special situation;

[0122] The expected waiting time determination unit 102 is used to obtain a preset waiting time threshold and determine whether the expected waiting time is greater than the preset waiting time threshold.

[0123] In the embodiment of the present invention, the target aircraft is mainly an airplane, and the predetermined runway refers to a specific runway designated for takeoff, landing or ground taxiing of the target aircraft. This is usually allocated by the airport ground control center according to the current flight plan and air traffic conditions to ensure the order and safety of airport operations. The predetermined runway can be understood as a specific location used when the target aircraft performs a specific mission.

[0124] Special circumstances usually include temporary closure of airport runways, obstacles on runways or taxiways, temporary air traffic congestion, severe weather such as thunderstorms or fog, and ground safety incidents such as unauthorized personnel or vehicles entering the runway area. The waiting instructions are sent to the target aircraft by the ground control center through the communication system, and the content usually includes the specific waiting location, estimated waiting time, waiting reason, and subsequent taxiing path or related instructions after the waiting is over. With this information, pilots can clearly know what to do during the waiting period to ensure flight safety and efficient management of the airport ground.

[0125] The expected waiting time is based on a variety of factors, including the specific nature of the special situation, the occupancy status of the runway, the current air and ground traffic flow, and real-time weather conditions. Ground controllers will combine this information and determine how long the target aircraft will need to wait through the airport's ground traffic management system or air traffic management system. The expected waiting time can be derived from the analysis of historical data, real-time traffic monitoring, and the estimated resolution time based on the current special situation.

[0126] When the expected waiting time is not greater than the preset waiting time threshold, the target aircraft will remain at the original position and keep the engine in a low-power idle state to ensure that it can quickly respond to taxiing or take-off instructions that may be issued at any time. In this case, the target aircraft does not need to perform additional power switching or shut down the engine, but enters a standby state to quickly resume operation.

[0127] The technical means described above belong to the existing technology. Those skilled in the art can use the existing traffic management system, communication system and engine management system to obtain relevant information and execute the above instructions and operations. These technologies have been widely used in airport management, flight safety and aircraft dispatch.

[0128] In the prior art, when the engine is in idle preparation state, its power is usually fixed and cannot be dynamically adjusted according to actual needs. This design leads to efficiency and responsiveness problems in some cases: when the power setting is too low, even if the aircraft is equipped with an electric taxiing system, the target aircraft may not be able to respond quickly to instructions and leave the runway as soon as possible because the idle power is not enough to supplement the required thrust. When the power setting is too high, power overflow is likely to occur, resulting in unnecessary fuel consumption, reduced fuel utilization, and increased operating costs and emissions. Therefore, there is a significant contradiction between fuel efficiency and taxiing responsiveness in the existing idle preparation technology, and it is difficult to provide flexible and economical thrust adjustment in a changing ground traffic environment.

[0129] Furthermore, the flight arrival and departure scheduling optimization system based on big data also includes:

[0130] The optimal taxiing time determination module 200 is used to determine the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends if it is determined that the expected waiting time is greater than the preset waiting time threshold.

[0131] Specifically, Fig. 9 The structure block diagram of the optimal coasting duration determination module 200 in the system provided by the embodiment of the present invention is shown.

[0132] Among them, in the preferred embodiment provided by the present invention, the optimal taxiing duration determination module 200 specifically includes:

[0133] A ground traffic flow estimation unit 201 is used to estimate the ground traffic flow of the scheduled runway after the special situation ends if it is determined that the expected waiting time is greater than a preset waiting time threshold;

[0134] A congestion level assessment unit 202, for assessing the congestion level of a predetermined runway according to the estimated ground traffic flow;

[0135] A basic taxiing time adjustment unit 203 is used to calculate the basic taxiing time required for the target aircraft to leave the scheduled runway at a standard taxiing speed, and adjust the basic taxiing time according to the congestion level to obtain an optimal taxiing time;

[0136] The remaining taxiing distance determination unit 204 is used to obtain the real-time GPS data of the target aircraft and analyze the remaining taxiing distance of the target aircraft on the predetermined runway according to the real-time GPS data;

[0137] A basic taxiing time calculation unit 205 is used to calculate the basic taxiing time required for the target aircraft to leave the predetermined runway at the standard taxiing speed according to the remaining taxiing distance and the standard taxiing speed;

[0138] The optimal coasting duration determining unit 206 is used to adjust the basic coasting time according to the congestion level to obtain the optimal coasting duration.

[0139] In an embodiment of the present invention, estimating the ground traffic flow of the scheduled runway after the special situation ends is usually achieved through the airport ground traffic management system combined with various data of the air traffic management system. These systems will collect the aircraft dynamics on each runway and taxiway inside the airport in real time, including the number of taxiing aircraft, parking positions, taxiing speeds, taxiing paths, and the approach and departure plans of other aircraft. At the same time, by integrating information such as weather forecasts, air traffic, ground equipment and obstacle status, the system can comprehensively analyze and predict the traffic flow of the scheduled runway in the future period after the special situation ends. This traffic flow prediction often relies on big data technology, analysis of historical traffic patterns, and real-time sensor data to evaluate the traffic load of the runway after the special situation ends.

[0140] In the existing technology, ground traffic management systems (such as the airport collaborative decision-making system A-CDM, or the air traffic management system ATC) have the function of real-time monitoring and prediction of ground traffic. By combining historical data modeling with real-time traffic data, the system can generate traffic forecasts for the airport in the future and identify possible peak or trough periods. These forecasts usually apply machine learning algorithms, big data analysis models, or algorithms based on empirical rules to make reasonable estimates of current and future traffic conditions. For example, the use of runways and taxiways at different time points can be determined by combining the operating time period, flight schedules, seasonal changes, and weather changes.

[0141] After estimating the ground traffic flow of the scheduled runway, the congestion level can be evaluated by calculating the utilization rate and waiting time of the runway. The existing technology usually adopts the runway capacity model and flow model, which take into account factors such as the maximum handling capacity of the runway, the taxiing speed of the aircraft, the number of aircraft on the runway, and the efficiency of traffic flow. The system can evaluate the congestion level of the runway based on the current traffic flow and the predicted future traffic flow. For example, if the system predicts that the number of aircraft on the runway will approach or exceed the design capacity of the runway after the special situation ends, the system will evaluate it as having a high level of congestion; on the contrary, if the traffic flow is low and the aircraft maintain a large distance, it will be evaluated as having a low level of congestion.

[0142] This assessment method is usually automated through the real-time data collection module and congestion prediction module of the ground traffic management system, using historical traffic data, traffic flow patterns and real-time monitoring information to form a congestion forecast for the runway during a specific period of time. The assessment results can be divided into different levels of congestion, such as low, medium and high congestion, to provide support for the taxiing plan of the target aircraft.

[0143] In the existing technology, many large airports have adopted this method of ground traffic flow prediction and congestion assessment, such as London Heathrow Airport and Frankfurt Airport. Through the A-CDM system, these airports can effectively predict ground traffic conditions, coordinate ground taxiing, boarding and departure activities, and thus improve ground operation efficiency. These systems are based on existing traffic management theories and multi-source data fusion technology, relying on ground sensing equipment (such as ground radar, GPS sensors, etc.) and data analysis platforms, and can provide airport managers and air traffic controllers with real-time ground traffic conditions and congestion assessment results. Therefore, these technologies are all existing technologies and have been successfully applied in many modern airports. Those skilled in the art can obtain relevant information through the existing system to achieve ground traffic flow prediction and congestion assessment.

[0144] The standard taxiing speed is usually set by the airport management department based on factors such as airport ground conditions, runway and taxiway design characteristics, and safety regulations to ensure that aircraft can move at a safe and controllable speed during taxiing, while taking into account the overall operating efficiency of the airport.

[0145] In the process of analyzing the remaining taxi distance of the target aircraft on the scheduled runway based on real-time GPS data, the real-time position (including geographic coordinates) is first obtained by the GPS receiving device on the aircraft, and the ground traffic management system matches these positions with the digital runway map of the airport. In this way, the ground traffic management system can determine the remaining taxi distance between the target aircraft's current position and the runway exit. This distance is calculated by comparing the real-time coordinates of the target aircraft with the geographic information of the runway path.

[0146] After obtaining the remaining taxiing distance, combined with the standard taxiing speed, the basic taxiing time required for the target aircraft to leave the scheduled runway can be calculated. This time represents the time required for the target aircraft to complete taxiing at the standard speed under ideal conditions without additional obstacles.

[0147] Next, the basic taxi time is adjusted based on the runway congestion level to obtain the optimal taxi time. Specifically, the congestion level can be set as a percentage adjustment factor. When the congestion level is low, the percentage factor will be negative, thereby shortening the basic taxi time; and when the congestion level is high, the factor will be positive, thereby extending the basic taxi time. For example:

[0148] In the case of low congestion, the system may set a percentage factor of -20%, indicating that the basic taxi time can be reduced by 20%. This will allow the target aircraft to taxi in a more efficient manner and leave the runway quickly.

[0149] In cases of high congestion, the system may set a percentage factor of +30%, indicating that the basic taxi time needs to be increased by 30% to allow sufficient time for possible delays during taxiing and interactions with other aircraft.

[0150] In this way, the congestion level is directly converted into a percentage adjustment factor and applied to the basic taxiing time, which enables the taxiing time to be flexibly adjusted to match the actual ground traffic conditions, thereby calculating the optimal taxiing duration.

[0151] For example: Assume that the target aircraft is currently located in the middle of the scheduled runway, the remaining taxiing distance is 1,500 meters, the standard taxiing speed is 10 meters per second, and the calculated basic taxiing time is 150 seconds. If the runway congestion level is assessed as "low", the percentage adjustment factor set by the system is -20%, then the basic taxiing time will be shortened by 20%, that is, the adjusted taxiing time is 120 seconds. If the runway congestion level is assessed as "high", the percentage adjustment factor set is +30%, then the taxiing time will increase by 30%, that is, the adjusted taxiing time is 195 seconds.

[0152] Furthermore, the flight arrival and departure scheduling optimization system based on big data also includes:

[0153] The required taxiing force determination module 300 is used to analyze the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time, and to determine whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system carried by the target aircraft.

[0154] In an embodiment of the present invention, the preset comparison table is set according to the taxiing requirements and various operating conditions of the target aircraft, and is mainly used to find the standard taxiing power required by the target aircraft under different taxiing distances and taxiing times. The data in the preset comparison table is usually set based on a large amount of historical operating data, actual taxiing test results, and the power system parameters of the target aircraft. The data in the table will cover the traction requirements of different types of aircraft under various taxiing conditions. For example, the table will list the required traction power corresponding to different remaining taxiing distances, different taxiing speeds and taxiing times. The setting of the comparison table takes into account factors such as the weight of the target aircraft, ground friction resistance, runway slope, wind direction, etc., to ensure that reasonable power guidance can be provided to the aircraft under different conditions to ensure taxiing safety and fuel efficiency.

[0155] In actual operation, "finding the corresponding standard taxiing force based on the optimal taxiing time and the remaining taxiing distance, and setting it as the required taxiing force" is accomplished by querying the preset comparison table. First, the system will look for the taxiing force value that matches these two parameters in the preset comparison table based on the optimal taxiing time calculated previously and the remaining taxiing distance obtained through GPS data analysis. For example, the system will search for the closest combination of taxiing time and taxiing distance in the table to find the corresponding standard taxiing force. This taxiing force represents the most reasonable traction power required for the aircraft under the current ground conditions and taxiing targets to ensure that the target aircraft remains stable and controllable during taxiing. The standard taxiing force will be set as the required taxiing force to guide the power output required for the next taxiing phase.

[0156] The purpose of judging "whether the required taxiing power is greater than the maximum traction power of the electric taxiing system, and if not, shutting down the engine of the target aircraft" is to save fuel to the greatest extent possible while ensuring that the electric taxiing system is sufficient to provide the power required for taxiing. When the required taxiing power does not exceed the maximum traction capacity of the electric taxiing system, it means that the power of the electric taxiing system is sufficient to support the taxiing of the target aircraft on the predetermined runway. Therefore, in this case, the engine can be shut down and the aircraft can be driven to taxi only by the electric taxiing system to reduce fuel consumption, reduce operating costs, and reduce carbon emissions. This method ensures that the aircraft does not require additional engine thrust support when taxiing, and at the same time optimizes fuel use by rationally utilizing the traction power of the electric taxiing system.

[0157] Furthermore, the flight arrival and departure scheduling optimization system based on big data also includes:

[0158] The idle power distribution strategy formulation module 400 is used to formulate an idle power distribution strategy for the target aircraft's engine according to the difference between the required taxiing power and the maximum achievable traction power of the electric taxiing system if it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system.

[0159] In the embodiment of the present invention, the "first operating power" refers to the power output that the target aircraft must obtain from the engine to meet the taxiing power required for the current taxiing. It is calculated based on the required taxiing power through the power system characteristics of the target aircraft. Specifically, the system will find the corresponding engine power output through the engine performance curve based on the required taxiing power combined with the current mass of the aircraft, the ground friction coefficient, the taxiing speed and other parameters to ensure that the taxiing power demand is met. This process usually relies on the aircraft's flight management system (FMS) or power system management module to complete. By inputting the traction demand, the system automatically obtains the corresponding output power from the engine power curve.

[0160] The "second operating power" converts the maximum achievable traction power of the electric taxiing system into the power requirement of the target aircraft engine through the power conversion relationship. The electric taxiing system provides the traction output by the motor, which is converted into the corresponding engine power to characterize the required engine operating power if the engine is required to provide the same thrust as the electric taxiing system. Usually, this conversion is based on the power conversion factor of the engine or the comparison of the performance curves of the electric taxiing system and the engine.

[0161] The principle of "subtracting the second operating power from the first operating power to obtain the dynamic idle power of the engine" is to determine the additional thrust that the engine actually needs to provide by calculating the difference. In this scenario, since the electric taxiing system itself can provide a part of the thrust (maximum traction power), the remaining part needs to be supplemented by the engine. Therefore, the first operating power represents the total power to meet the taxiing needs, and the second operating power represents the part of the power that the electric taxiing system can already provide. Subtracting the second operating power from the first operating power can determine the minimum additional power that the engine needs to output, that is, the dynamic idle power. This power value is calculated in real time based on multiple factors such as the current taxiing conditions, ground friction, and the power that the electric taxiing system can provide, so as to ensure that the engine only provides additional power support when necessary, so as to save fuel and reduce power overflow.

[0162] Maintaining the engine running through dynamic idle power can save fuel consumption to the greatest extent while meeting taxiing requirements, while reducing engine wear. Dynamic idle power is based on precise calculations to ensure that the engine only provides the minimum power output required during taxiing, avoiding the energy waste caused by fixed idle power. In addition, when the engine is kept in a dynamic idle state, it can respond to changes in taxiing conditions at any time. Especially when emergency thrust is required, the engine can quickly switch from idle to high power output to ensure the safe taxiing of the aircraft. This method not only improves fuel efficiency, but also enhances the response sensitivity and safety during taxiing.

[0163] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0165] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0167] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A flight arrival and departure scheduling optimization method based on big data, characterized in that: The method comprises: When the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, an expected waiting time is determined, and it is judged whether the expected waiting time is greater than a preset waiting time threshold; If it is determined that the expected waiting time is greater than the preset waiting time threshold, determine the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends; If it is determined that the expected waiting time is greater than the preset waiting time threshold, the steps of determining the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends include: If it is determined that the expected waiting time is greater than the preset waiting time threshold, the ground traffic flow of the scheduled runway after the special situation ends is estimated; Assess the congestion level of the intended runway based on the estimated ground traffic flow; Calculate the basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed, and adjust the basic taxiing time according to the congestion level to obtain the optimal taxiing time; The steps of calculating the basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed and adjusting the basic taxiing time according to the congestion level to obtain the optimal taxiing time include: Obtain the real-time GPS data of the target aircraft and analyze the remaining taxiing distance of the target aircraft on the scheduled runway based on the real-time GPS data; The basic taxiing time required for the target aircraft to leave the scheduled runway at the standard taxiing speed is calculated based on the remaining taxiing distance and the standard taxiing speed; Adjust the basic taxiing time according to the congestion level to obtain the optimal taxiing time; Analyze the taxiing force required for the target aircraft to leave the scheduled runway under the optimal taxiing time, and determine whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system on the target aircraft; The steps of analyzing the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time and determining whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system carried by the target aircraft include: Retrieve the preset comparison table, find the corresponding standard sliding power according to the optimal sliding time and the remaining sliding distance, and set it as the required sliding power; Obtain the maximum achievable traction power of the target aircraft's electric taxiing system; Determine whether the required taxiing force is greater than the maximum traction power of the electric taxiing system. If not, shut down the target aircraft's engine; If it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system, the idle power distribution strategy of the target aircraft's engine is formulated based on the difference between the two; If it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system, the steps of formulating the idle power distribution strategy of the target aircraft engine according to the difference between the two include: If it is determined that the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system, determining a first operating power of the target aircraft engine corresponding to the required taxiing force, and converting the maximum achievable traction power of the electric taxiing system into a second operating power of the target aircraft engine; Subtracting the second operating power from the first operating power to obtain the dynamic idle power of the engine; After the dynamic idle power is obtained, the engine of the target aircraft is started and the dynamic idle power is maintained to maintain idle operation.

2. The flight arrival and departure scheduling optimization method based on big data according to claim 1 is characterized in that: When the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, the steps of determining the expected waiting time and judging whether the expected waiting time is greater than a preset waiting time threshold include: When a target aircraft enters the scheduled runway and receives a holding instruction for a special situation, interpret the holding instruction and determine the expected holding time of the target aircraft; Get a preset waiting time threshold, and determine whether the expected waiting time is greater than the preset waiting time threshold.

3. A flight arrival and departure scheduling optimization system based on big data, characterized in that: The system comprises: an expected waiting time determination module, an optimal taxiing time determination module, a required taxiing power determination module and an idle power allocation strategy formulation module, wherein: An expected waiting time determination module is used to determine the expected waiting time when the target aircraft enters the scheduled runway and receives a waiting instruction for a special situation, and to determine whether the expected waiting time is greater than a preset waiting time threshold; An optimal taxiing time determination module is used to determine the optimal taxiing time for the target aircraft to leave the scheduled runway after the special situation ends if it is determined that the expected waiting time is greater than a preset waiting time threshold; The optimal taxiing duration determination module specifically includes: A ground traffic flow estimation unit, for estimating the ground traffic flow of the scheduled runway after the special situation ends if it is determined that the expected waiting time is greater than a preset waiting time threshold; A congestion level assessment unit, used for assessing the congestion level of a predetermined runway according to an estimated ground traffic flow; The basic taxi time adjustment unit is used to calculate the basic taxi time required for the target aircraft to leave the scheduled runway at a standard taxi speed, and adjust the basic taxi time according to the congestion level to obtain the optimal taxi time; A remaining taxiing distance determination unit is used to obtain real-time GPS data of the target aircraft and analyze the remaining taxiing distance of the target aircraft on the predetermined runway based on the real-time GPS data; A basic taxiing time calculation unit, used to calculate the basic taxiing time required for the target aircraft to leave the predetermined runway at the standard taxiing speed according to the remaining taxiing distance and the standard taxiing speed; An optimal taxiing duration determination unit, used to adjust the basic taxiing time according to the congestion level to obtain an optimal taxiing duration; A required taxiing force determination module is used to analyze the taxiing force required for the target aircraft to leave the predetermined runway under the optimal taxiing time, and to determine whether the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system carried by the target aircraft; Retrieve the preset comparison table, find the corresponding standard sliding power according to the optimal sliding time and the remaining sliding distance, and set it as the required sliding power; Obtain the maximum achievable traction power of the target aircraft's electric taxiing system; Determine whether the required taxiing force is greater than the maximum traction power of the electric taxiing system. If not, shut down the target aircraft's engine; An idle power distribution strategy formulation module is used to formulate an idle power distribution strategy for the target aircraft's engine according to the difference between the required taxiing power and the maximum achievable traction power of the electric taxiing system if it is determined that the required taxiing power is greater than the maximum achievable traction power of the electric taxiing system; If it is determined that the required taxiing force is greater than the maximum achievable traction power of the electric taxiing system, determining a first operating power of the target aircraft engine corresponding to the required taxiing force, and converting the maximum achievable traction power of the electric taxiing system into a second operating power of the target aircraft engine; Subtracting the second operating power from the first operating power to obtain the dynamic idle power of the engine; After the dynamic idle power is obtained, the engine of the target aircraft is started and the dynamic idle power is maintained to maintain idle operation.

4. The flight arrival and departure scheduling optimization system based on big data according to claim 3 is characterized in that: The expected waiting time determination module specifically includes: an expected waiting time determination unit, for interpreting the waiting instruction and determining the expected waiting time of the target aircraft when the target aircraft enters the predetermined runway and receives a waiting instruction for a special situation; The expected waiting time determination unit is used to obtain a preset waiting time threshold and determine whether the expected waiting time is greater than the preset waiting time threshold.

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