Airport scene operation efficiency optimization system and method based on intelligent technology

Abstract

The invention discloses an airport scene operation efficiency optimization system and method based on an intelligent technology. According to the invention, by arranging the taxiway data acquisition and prediction module, the aircraft state and scheduling data acquisition module, the taxiway resource real-time monitoring module, the path planning and optimization result generation module and the global decision and feedback module, intelligent management of airport scene operation is realized. Predicting the use condition of the taxiway in advance through a taxiway congestion prediction algorithm based on time sequence analysis; dynamically generating an optimal sliding path and scheduling plan based on a path planning and scheduling optimization algorithm of reinforcement learning; and carrying out global operation optimization decision making by using a Bayesian decision network to generate a global optimal scheme. The airport operation efficiency is improved, taxiway congestion is reduced, and the taxiing time of the aircraft is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of airport surface operation management and optimization, and in particular to an airport surface operation efficiency optimization system and method based on intelligent technology. Background Art

[0002] The existing airport surface operation system mainly relies on manual monitoring and dispatching, and is often unable to achieve efficient and intelligent processing when faced with complex taxiway resource allocation, aircraft dispatching, and taxiway path optimization. Especially during peak air traffic periods, taxiways are prone to congestion, aircraft taxiing time is too long, and dispatch conflicts seriously affect the overall operation efficiency of the airport.

[0003] Although some airports have introduced automated dispatch systems, existing systems generally lack the ability to predict taxiway congestion and are unable to effectively allocate resources based on real-time and historical data. At the same time, there is a lack of a system that can conduct comprehensive analysis and optimize decision-making based on the overall operation status of the airport, making it difficult to improve the overall efficiency of airport operations.

[0004] Therefore, there is an urgent need for a solution based on intelligent technology that can collect data in real time, predict congestion, optimize scheduling routes, and make global intelligent decisions. Summary of the invention

[0005] The purpose of the invention is to provide an airport surface operation efficiency optimization system and method based on intelligent technology, which has the advantages of real-time monitoring, congestion prediction, path optimization and global decision-making, and solves the problems of taxiway congestion that cannot be predicted in advance and low efficiency in scheduling and path planning.

[0006] To achieve the above object, the present invention provides the following technical solution: an airport surface operation efficiency optimization system based on intelligent technology, the system comprising the following modules:

[0007] Taxiway data collection and prediction module: used to collect real-time taxiway usage and historical data, and use the airport taxiway dynamic congestion prediction algorithm to predict the future taxiway congestion status based on the aircraft taxiing speed and occupancy, and output the taxiway congestion prediction result Y t Aircraft status and scheduling data acquisition module: used to obtain the physical status, queue status, take-off and landing plan information of the aircraft, and provide data on the aircraft's current location, taxiing status and scheduling priority as input to the aircraft path planning and scheduling optimization algorithm;

[0008] Taxiway resource real-time monitoring module: used to monitor the taxiway occupancy and resource allocation status in real time, and compare the current taxiway usage with the taxiway congestion prediction result Y tMatching, providing real-time data support for path planning algorithms;

[0009] Path planning and optimization result generation module: through aircraft path planning and scheduling optimization algorithm, combined with taxiway congestion prediction result Y t and aircraft scheduling data to generate the aircraft's optimal taxiing path and scheduling plan Z t , ensuring aircraft taxi efficiently and avoiding congestion;

[0010] Global evidence data collection and processing module: used to collect other evidence data in global operations, including weather conditions, fuel status, taxiway resource status, and provide support data for the airport's global intelligent decision-making and optimization algorithms;

[0011] Global decision-making and feedback module: Based on the airport's global intelligent decision-making and optimization algorithm, using the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t and other global evidence E, formulate a global optimization decision plan for airport surface operations, including the reallocation of taxiway resources and adjustment of aircraft scheduling priorities.

[0012] The method for optimizing airport surface operation efficiency based on intelligent technology includes the following steps:

[0013] S1. Taxiway data collection and congestion prediction:

[0014] S11: Obtain the real-time usage, historical data and taxiing speed information of the taxiway through the taxiway data collection and prediction module; S12: Preprocess the collected data, including data cleaning, denoising and formatting; S13: Use the airport taxiway dynamic congestion prediction algorithm to predict the future taxiway congestion based on real-time and historical data, and generate the taxiway congestion prediction result Y t ;

[0015] S2. Aircraft status and dispatch data collection:

[0016] S21: Acquire the physical status data of the aircraft from the aircraft status and scheduling data acquisition module; S22: Acquire the takeoff and landing plan information of the aircraft, including the estimated takeoff time, landing time and aircraft priority; S23: Integrate the aircraft status and scheduling data to generate input data for path planning and scheduling optimization;

[0017] S3: Taxiway resource monitoring and data processing

[0018] S31: Use the taxiway resource real-time monitoring module to monitor the current occupancy of the taxiway in real time and obtain the taxiway resource usage status; S32: Use the taxiway congestion prediction result Y tCompare and analyze with real-time monitoring data to ensure that the prediction results are consistent with the actual situation or correct them in time; S33: Process the monitoring data and generate real-time taxiway resource data for path planning to ensure that the scheduling algorithm obtains the latest taxiway usage;

[0019] S4: Aircraft path planning and scheduling optimization

[0020] S41: Based on taxiway congestion prediction result Y t and aircraft scheduling data, using aircraft path planning and scheduling optimization algorithms to calculate the optimal aircraft taxiing path; S42: Generate the optimal taxiing path and scheduling plan Z through reinforcement learning algorithms t , ensuring that aircraft can take off or land on time and avoid congestion on the taxiway; S43: Generate the optimal taxiing path and scheduling plan Z based on the calculation results t , and output the optimal path and scheduling plan;

[0021] S5: Global evidence collection and analysis

[0022] S51: Obtain global evidence data of current airport surface operation through global evidence data collection and processing module; S52: Analyze global evidence data to evaluate the potential impact of external factors on surface operation; S53: Generate auxiliary data for global decision support to ensure that all influencing factors are taken into account in the global decision-making process;

[0023] S6: Global Operation Optimization Decision Generation

[0024] S61: Based on the airport global intelligent decision-making and optimization algorithm, combined with the taxiway congestion prediction results Y t , optimal taxiing path and scheduling plan Z t and global evidence E, calculate the global optimization decision of airport surface operation; S62: reallocate taxiway resources and adjust aircraft scheduling priorities according to the global operation optimization decision; S63: generate the final optimization operation plan, output the global adjustment suggestions and feed them back to the airport operation control system for execution.

[0025] Preferably, the airport taxiway dynamic congestion prediction algorithm is used to predict the taxiway congestion in the future, and its specific formula is as follows:

[0026] where Y t represents the prediction result of taxiway congestion at time t, that is, the taxiway congestion in a certain period of time in the future; c: is a constant term, representing the intercept in the model; φ i : is the autoregressive coefficient, which is used to indicate the impact of taxiway congestion in the past on the current moment; θ i: is the moving average coefficient, which is used to represent the impact of the prediction error at the past moment on the current moment; ε t : is the prediction error at time t.

[0027] Preferably, the aircraft path planning and scheduling optimization algorithm includes:

[0028] Q value update formula: Where Q(s t ,a t ,Y t ): indicates that in state s t Next, select action a t The expected return value when α: represents the learning rate, which controls the update speed of the Q value; r t : is the reward value, reflecting the quality of path selection; γ: is the discount factor, indicating the degree of influence of future rewards; :Indicates that in the next state S t+1 The maximum Q value obtained in

[0029] Preferably, the aircraft path planning and scheduling optimization algorithm also includes an optimal path generation formula: Where: Z t : represents the optimal taxiing path and scheduling plan generated at time t; Indicates that in state s t and congestion prediction Y t Next, select the action a that maximizes the Q value t .

[0030] Preferably, the steps of the aircraft path planning and scheduling optimization algorithm include the following steps:

[0031] S1. Status update: Get the status s of the aircraft at the current time t t , including the physical location of the aircraft and the congestion prediction of the taxiway Y t and queue status; S2, action selection: through reinforcement learning algorithm, according to the state s t and taxiway congestion prediction Y t , select the current optimal action a t , that is, the taxiing path that the aircraft should take; S3, reward calculation: when taking action a t Then, according to the actual congestion of the taxiway and the taxiing time, the corresponding reward r is calculated. t , used to feedback the effect of aircraft taxiing path selection; S4, Q value update: based on reward r t and the state S at the next time t+1 t+1 , update the Q value to continuously optimize the path selection; S5, optimal path generation: through reinforcement learning, the optimal taxiing path and scheduling plan Z are finally obtainedt .

[0032] Preferably, the airport global intelligent decision-making and optimization algorithm is based on a Bayesian decision network, combined with the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t And global evidence E, generate the optimal decision for global operation, the specific formula is as follows:

[0033] Where: P(X|Y t ,Z t ,E): Given the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t , the probability of the airport's global operating state X occurring under the condition of global evidence E, that is, the probability of the global optimization decision; P(Y t ,Z t ,E|X): Taxiway congestion prediction result Y under a specific global decision X t , optimal taxiing path and scheduling plan Z t and the joint probability of other global evidence E; P(X): the prior probability of the global running state X; P(Y t ,Z t ,E): The joint probability of taxiway congestion prediction, path optimization results and global evidence, as a normalization constant. .

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The present invention realizes dynamic prediction of future taxiway usage by setting a taxiway congestion prediction algorithm based on time series analysis, effectively avoiding the problems of unreasonable taxiway resource allocation and long aircraft waiting time.

[0036] 2. By adopting the path optimization and scheduling algorithm of reinforcement learning, the present invention can dynamically adjust the taxiing path and scheduling order of the aircraft according to real-time data, minimize the aircraft's stay time on the taxiway, and improve the overall operating efficiency.

[0037] 3. The introduction of the global operation optimization decision algorithm combines taxiway congestion prediction, path optimization results and other global evidence for comprehensive analysis to generate the global optimal decision, ensure the efficiency and safety of airport surface operations, and reduce the decision-making delay caused by manual intervention.

[0038] 4. The present invention can monitor the airport operation status in real time through the setting of real-time monitoring and feedback system, timely discover potential problems and feedback optimization suggestions, thus improving the intelligence and automation level of the overall airport dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram showing the modules of the airport surface operation optimization system based on intelligent technology of the present invention;

[0040] Figure 2 It is a flow chart of the airport surface operation efficiency optimization method based on intelligent technology of the present invention;

[0041] Figure 3 This is a structural diagram of the airport taxiway congestion prediction formula of the present invention;

[0042] Figure 4 A schematic diagram of Q value update for aircraft path planning and scheduling optimization of the present invention;

[0043] Figure 5 Generate a flow chart for the optimal path and scheduling of the present invention;

[0044] Figure 6 The figure is a flowchart of the steps of the aircraft path planning and scheduling optimization algorithm of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The present invention provides an airport surface operation efficiency optimization system based on intelligent technology. The system consists of multiple functional modules and can collect data in real time, predict taxiway congestion, optimize aircraft taxiing paths and scheduling, and make global intelligent decisions, thereby improving the overall operation efficiency of the airport.

[0047] 1. Taxiway data collection and prediction module: This module is responsible for real-time collection of taxiway usage data and historical data, including taxiway congestion status, aircraft taxiing speed, taxiway occupancy time and other information. By analyzing the historical taxiway usage and current aircraft taxiing speed, the system uses a time series prediction algorithm to predict future taxiway congestion. The generated congestion prediction result Y tIt can provide decision-making basis for subsequent aircraft path planning and scheduling, ensuring that the system can identify possible congestion points in advance and optimize resource allocation. The module first obtains the occupancy of the taxiway in real time through sensors and monitoring equipment, and predicts the future state of the taxiway through historical data and current taxiing speed. Through big data analysis and time series models, the system can predict possible congestion and take measures in advance to avoid unnecessary delays during the taxiing process.

[0048] 2. Aircraft status and scheduling data acquisition module: This module is mainly used to collect the physical status, queue status and scheduling plan information of the aircraft. Through this module, the system can obtain the current position, taxiing status and scheduled take-off and landing plans of the aircraft in real time. The data of this module is used as input to the path planning and scheduling optimization algorithm to optimize the taxiing path and scheduling sequence of the aircraft. This module relies on the positioning system installed in various parts of the airport and the aircraft's own navigation data to obtain the physical status and scheduling information of the aircraft in real time. These data will be integrated and passed to the system's path optimization module to ensure that the aircraft can taxi along the optimal path and minimize waiting time.

[0049] 3. Taxiway resource real-time monitoring module: The taxiway resource real-time monitoring module is used to monitor the current occupancy of the taxiway in real time to ensure that the system has the latest usage status of the taxiway. This module also compares the real-time data of the taxiway with the predicted data Y t Compare to confirm the accuracy of the prediction and dynamically adjust the path planning based on the latest monitoring data. By monitoring the actual use of the taxiway, the system can promptly detect differences with the predicted data. If the actual occupancy of the taxiway changes, the system will dynamically adjust the previous path planning plan and provide the aircraft with an updated taxi path to ensure smooth taxiing of the aircraft.

[0050] 4. Path planning and optimization result generation module: This module generates the optimal aircraft taxiing path and scheduling sequence through the "aircraft path planning and scheduling optimization algorithm". The system will use the taxiway congestion prediction result Y t Combined with the aircraft scheduling data, the aircraft taxiing path is continuously optimized through the reinforcement learning algorithm to generate the optimal scheduling plan Z t , ensuring that aircraft can efficiently avoid congested areas and arrive at their destinations on time. This module uses a reinforcement learning algorithm to dynamically adjust the taxi path based on the current taxiway congestion prediction results and aircraft status. Each time a path is selected, the system calculates the congestion and time consumption of different paths and selects the path that minimizes the taxi time. At the same time, the system updates the scheduling plan in real time to ensure that the taxi paths of all aircraft do not conflict with each other.

[0051] 5. Global evidence data collection and processing module: This module is used to collect other data related to the global operation of the airport, such as weather conditions, aircraft fuel status, and the overall use of taxiway and runway resources. These data will serve as input for global intelligent decision-making and optimization algorithms to help the system make more accurate decision analysis. Through sensors and external data sources, this module collects external factors related to airport operations (such as weather, fuel conditions, etc.). These data will affect the aircraft's taxi path and scheduling priority. The system will comprehensively consider these global factors and make more accurate scheduling decisions.

[0052] 6. Global decision-making and feedback module: This module is based on global intelligent decision-making and optimization algorithms, combined with taxiway congestion prediction results Y t , Path optimization result Z t and global evidence E, to globally optimize airport surface operations. The global decision plan generated by the system can adjust the allocation of taxiway resources, the scheduling order and priority of aircraft, thereby ensuring the overall efficiency and safety of airport surface operations. The global decision and feedback module comprehensively analyzes the predicted congestion of taxiways, aircraft scheduling paths and other external factors through the Bayesian decision network. The system generates the optimal decision based on the current global operating status and feeds it back to each module for execution to ensure that all parts of the system operate in coordination and maximize the overall operating efficiency of the airport.

[0053] The present invention provides an airport surface operation efficiency optimization method based on intelligent technology, which mainly realizes intelligent management and optimization of airport surface operation through the following steps:

[0054] S1: Taxiway data collection and congestion prediction

[0055] S11: The taxiway data collection and prediction module obtains taxiway usage data in real time, including taxiway occupancy status, aircraft taxiing speed and historical usage data. These data provide a basis for subsequent congestion prediction.

[0056] S12: Preprocess the collected data, including data cleaning, denoising and formatting, to ensure data accuracy. Data preprocessing can filter out invalid data, ensure the quality of input data, and thus improve the accuracy of prediction results.

[0057] S13: Use the airport taxiway congestion prediction algorithm, combined with real-time data and historical data, to dynamically predict the future use of the taxiway and generate the taxiway congestion prediction result Y t Through time series analysis or machine learning models, possible congestion can be identified in advance, thus providing early warning and optimization solutions for aircraft scheduling.

[0058] S2: Aircraft status and dispatch data collection

[0059] S21: Through the aircraft status and scheduling data acquisition module, the physical status information and scheduling plan of the aircraft are obtained in real time, including the current position of the aircraft, queue status, and take-off and landing plans. This module can ensure that the real-time status of the aircraft can be fed back to the system at any time, providing accurate data for path planning.

[0060] S22: Collect aircraft scheduling priority information and adjust aircraft priorities based on aircraft urgency (such as fuel status or flight delays). This information will serve as input to path planning and scheduling optimization algorithms to ensure that aircraft take off and land on time and avoid unnecessary delays.

[0061] S23: Integrate the physical status of the aircraft and the scheduling priority information to provide input data for path planning to ensure the rationality of scheduling and optimization of the path.

[0062] S3: Taxiway resource monitoring and data processing

[0063] S31: The taxiway resource monitoring module monitors the current use of the taxiway in real time to ensure that the system can keep track of the taxiway resource allocation status at any time. Through this module, the system can adjust the path and scheduling plan in advance when taxiway resources are tight to avoid conflicts between aircraft.

[0064] S32: The taxiway congestion prediction result Y t Compare and correct with real-time monitoring data to ensure that the forecast data is consistent with the actual situation. Through comparative analysis, the system can adjust the forecast results in time and perform corresponding optimization operations according to the actual situation.

[0065] S33: Process the monitoring data and generate the latest taxiway resource data for path planning. Ensure that the system can obtain the latest taxiway usage status each time the path is planned, and avoid the path planning scheme from becoming invalid due to changes in resource status.

[0066] S4: Aircraft path planning and scheduling optimization

[0067] S41: Based on taxiway congestion prediction result Y t The system uses aircraft path planning and scheduling optimization algorithms to calculate the optimal aircraft taxi path based on the aircraft scheduling data. By comprehensively considering the taxiway congestion and aircraft scheduling priorities, the system can generate the optimal taxi path for each aircraft, ensuring that the aircraft can taxi efficiently and reduce waiting time on the taxiway.

[0068] S42: The system generates the optimal taxiing path and scheduling plan Z based on the feedback of real-time data through reinforcement learning algorithm t Through continuous learning and optimization, the system can continuously update the taxi path according to the dynamically changing airport surface conditions to ensure that the path is always in the optimal state.

[0069] S43: Generate the optimal taxiing path and scheduling plan Z based on the calculation results t , and outputs optimized paths and scheduling plans. The system ensures that aircraft can take off and land on time and avoid congestion on the taxiway.

[0070] S5: Global evidence collection and analysis

[0071] S51: Through the global evidence data collection and processing module, the system can collect other evidence data related to the global operation of the airport, including weather conditions, fuel status, taxiway resource status, etc. The setting of this module ensures that the system can consider the potential impact of the external environment on aircraft scheduling when performing path planning and scheduling optimization.

[0072] S52: Analyze global evidence data, evaluate the impact of external factors on scene operations, and generate global decision support data. The system integrates this data into the path planning and scheduling optimization algorithm to ensure the accuracy and operability of the optimization results.

[0073] S6: Global Operation Optimization Decision Generation

[0074] S61: Based on the airport global intelligent decision-making and optimization algorithm, combined with the taxiway congestion prediction results Y t , optimal taxiing path and scheduling plan Z t The system conducts global decision analysis based on other global evidence E and generates an optimal decision plan for global operation. When optimizing taxiway resource allocation and scheduling priorities, the system considers a variety of external factors to ensure the comprehensiveness of the decision.

[0075] S62: Based on the global decision results, taxiway resources are reallocated and aircraft scheduling priorities are adjusted to ensure that each aircraft can taxi and take off and land under the best conditions.

[0076] S63: Generate the final optimized operation plan, output the global scheduling suggestions, and feed them back to the airport operation control system for execution to ensure the global coordination and optimization of airport surface operations.

[0077] The airport taxiway dynamic congestion prediction algorithm of the present invention is mainly used to predict the taxiway congestion in the future. The algorithm can identify possible congestion points in advance through the analysis of historical data and current real-time data, thereby providing important decision-making basis for subsequent path planning and scheduling optimization. Its prediction model is based on the idea of ​​time series analysis, combining the characteristics of the autoregressive model (AR) and the moving average model (MA), and can effectively capture the time variation trend of taxiway usage.

[0078] Congestion prediction formula

[0079] The prediction formula is as follows: In this formula: Y t It represents the prediction result of taxiway congestion at time t, that is, the taxiway congestion in a certain time period in the future;

[0080] c: is a constant term, representing the intercept in the model, which usually reflects the baseline level of taxiway resource utilization, for example, the average occupancy rate of the taxiway or the baseline taxiing time of aircraft when other factors are not considered.

[0081] φ i : is the autoregressive coefficient, which is used to indicate the impact of taxiway congestion in the past on the current time; coefficient φ i The larger the value, the greater the impact of past congestion on the current situation. The coefficients are obtained through the autoregressive model (AR model) in time series analysis, which fits the time dependence of taxiway congestion based on historical data.

[0082] θ i : is the moving average coefficient, which is used to represent the impact of past forecast errors on the current moment. The moving average model can smooth past forecast errors to improve the accuracy of the model. The coefficient is calculated by the moving average model (MA model) in the time series, which solves the problem of random errors in the forecast.

[0083] ε t : is the prediction error at time t, that is, the prediction deviation caused by unobservable factors, such as sudden emergencies, weather changes, and other data that are not in the model.

[0084] Data collection: The system obtains the current taxiing speed, occupancy time and other data from the real-time monitoring equipment of the taxiway, and combines it with historical data to provide input for the congestion prediction algorithm. The integrity and accuracy of the data directly affect the prediction results, so during the data collection process, the system will clean and denoise the data.

[0085] Prediction model construction: The algorithm uses autoregression and moving average models, combined with historical congestion and prediction errors, to build a time series model. The autoregression part (AR) captures the time dependence of taxiway congestion, and the moving average part (MA) processes past prediction errors to improve the accuracy of the overall prediction.

[0086] Congestion prediction result generation: After the model is established, the system calculates the taxiway congestion in the future in real time by inputting current and historical data. t It provides the system with early warning of airport taxiway resource allocation and provides key data support for subsequent path planning and scheduling optimization.

[0087] Feedback and correction: The system will give the prediction result Y t Real-time comparison is performed to verify the consistency between the prediction results and the actual taxiway occupancy. If the prediction is found to be inconsistent with the actual situation, the system will automatically adjust and correct the model based on the new data to ensure the reliability of the prediction results.

[0088] The aircraft path planning and scheduling optimization algorithm of the present invention is a Q-Learning algorithm based on reinforcement learning. The algorithm can generate the optimal taxiing path and scheduling plan for the aircraft at each moment according to the current state of the aircraft and the taxiway congestion prediction results.

[0089] Q value update formula: Where Q(s t ,a t ,Y t ): indicates that in state s t Next, select action a t The update of Q value is based on the reinforcement learning algorithm, which optimizes the taxiing path of the aircraft by repeatedly adjusting the path selection.

[0090] α: represents the learning rate, which controls the update speed of the Q value. A larger learning rate will make the system adapt to new situations quickly. Its value is between [0,1]. A higher learning rate means that the system is more sensitive to new information, while a lower learning rate means that the system is more conservative. The learning rate is usually adjusted to a suitable value through experiments.

[0091] r t : is a reward value that reflects the quality of the path selection; it is usually calculated based on factors such as taxiway congestion and aircraft taxiing time. A positive reward value indicates a good path selection, while a negative reward value indicates a poor path selection (e.g. congestion).

[0092] γ: Discount factor, which indicates the impact of future rewards; usually between [0,1]. A larger discount factor means that the system pays more attention to long-term benefits, while a smaller discount factor means that the system pays more attention to current benefits.

[0093] Indicates that in the next state S t+1 Based on the maximum Q value obtained, the system will evaluate the possible options at the next moment and choose the path and action that can bring the greatest reward. By continuously updating the Q value, the system gradually learns and optimizes the aircraft's taxiing path, allowing the aircraft to taxi efficiently and take off and land on time even in congested conditions.

[0094] The system calculates the optimal taxiing path and scheduling plan Z through reinforcement learning t , the final path selection is based on the action selection that maximizes the Q value, that is:

[0095] The optimal path generation formula is: in:

[0096] Z t : represents the optimal taxiing path and scheduling plan generated at time t;

[0097] Indicates that in state s t and congestion prediction Y t Next, select the action a that maximizes the Q value t ,The system determines the optimal taxiing path and scheduling strategy through repeated learning.

[0098] The execution steps of the aircraft path planning and scheduling optimization algorithm are:

[0099] S1. Status update: The system first obtains the status s of the aircraft at the current time t t , including the physical location of the aircraft and the congestion prediction of the taxiway Y t and queue status. By collecting these data, the system provides basic information for subsequent path planning.

[0100] S2, action selection: through reinforcement learning algorithm, according to the state s t and taxiway congestion prediction Y t , select the current optimal action a t , which is the taxi path the aircraft should take; the system evaluates the Q value of each feasible path and selects the path that can bring the greatest reward.

[0101] S3, reward calculation: after taking action a t Then, according to the actual congestion of the taxiway and the taxiing time, the corresponding reward r is calculated. t, which is used to give feedback on the effect of aircraft taxiing path selection; the higher the reward value, the more optimized the current path selection is.

[0102] S4. Q value update: based on reward r t and the state S at the next time t+1 t+1 , update the Q value to continuously optimize the path selection;

[0103] S5. Optimal path generation: Through reinforcement learning, the optimal taxiing path and scheduling plan Z are finally obtained. t , ensuring that aircraft can taxi efficiently and take off and land on time even in congested conditions.

[0104] The airport global intelligent decision-making and optimization algorithm is based on the Bayesian decision network, combined with the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t And global evidence E, generate the optimal decision for global operation, the specific formula is as follows:

[0105] Where: P(X|Y t ,Z t ,E): Given the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t , the probability of the airport's global operating state X occurring under the condition of global evidence E, that is, the probability of the global optimization decision; P(Y t ,Z t ,E|X): Taxiway congestion prediction result Y under a specific global decision X t , optimal taxiing path and scheduling plan Z t The joint probability of the global evidence E and other global evidence E; this value indicates the degree of match between the predicted taxiway condition and the actual situation when the system makes a decision. P(X): The prior probability of the global operating state X; this value is usually set based on historical data and experience. t ,Z t ,E): The joint probability of taxiway congestion prediction, path optimization results and global evidence is used as a normalization constant to ensure the validity of the probability distribution of the Bayesian formula.

[0106] Data input and processing: The system first collects the taxiway congestion prediction results Y t , optimal taxiing path and scheduling plan Z t and global evidence E, these data will serve as input for global decision analysis, helping the system to comprehensively evaluate the operation of the airport surface.

[0107] Global state evaluation: Through the Bayesian network, the system can calculate the probability of the global airport operation state X based on the current input data. The state X includes a comprehensive evaluation of factors such as taxiway resource allocation, aircraft scheduling priority, taxi time, etc.

[0108] Optimal decision generation: P(X|Y t ,Z t ,E), the system can generate the optimal global operation decision X. This means that the system not only considers the current taxiway conditions and aircraft scheduling, but also comprehensively adjusts the allocation and scheduling priorities of airport resources according to external factors (such as weather) to ensure the efficiency and safety of the overall operation. Feedback and optimization: The system will continuously monitor the taxiway usage and aircraft scheduling results in actual operation, and feed this data back to the Bayesian decision network to update the model parameters. If the predicted results do not match the actual results, the system will automatically correct the decision strategy to ensure that future optimization results are more accurate.

[0109] Example 1: Taxiway resource optimization and aircraft scheduling during airport peak hours

[0110] Airport peak hours are critical moments for airport surface operations, and problems such as taxiway resource shortages and extended aircraft waiting times usually occur. Traditional airport taxiway scheduling methods are unable to dynamically adjust taxiway paths based on real-time data, resulting in unreasonable allocation of taxiway resources, frequent congestion, and low aircraft scheduling efficiency. To this end, the present invention provides an airport surface operation efficiency optimization system based on intelligent technology, which uses taxiway congestion prediction and global intelligent scheduling algorithms to alleviate these problems.

[0111] The technical purpose of this embodiment is to improve the efficiency of taxiway resource utilization, optimize aircraft scheduling strategies, reduce the occurrence of congestion, and shorten aircraft taxiing time under airport peak conditions by applying the system of the present invention, thereby improving the overall operating efficiency of the airport.

[0112] The present invention uses a taxiway congestion prediction algorithm based on time series and a path planning algorithm based on reinforcement learning. By combining real-time data with historical data, the taxiing path and scheduling priority of the aircraft are dynamically adjusted. Compared with the existing static scheduling method, the present invention can automatically adjust the taxiing plan according to the congestion situation, significantly improving the intelligence level of the system.

[0113] This embodiment is particularly suitable for the taxiway resource optimization scenario when aircraft frequently take off and land during airport peak hours. During peak hours, the airport will encounter a surge in the number of aircraft. In this complex environment, the present invention can quickly adjust the scheduling plan to minimize the waiting time of aircraft on the taxiway.

[0114] In order to verify the application effect of the present invention during the peak period of the airport, we designed several comparative experiments. The experimental scenarios include peak period and normal period, and are compared with the traditional static scheduling algorithm (existing technology). The comparison items include: taxi time, aircraft waiting time, taxiway congestion, number of scheduling adjustments, scheduling response time, aircraft take-off and landing punctuality rate, taxiway resource utilization rate, and system resource occupancy rate.

[0115]

[0116] The experimental results show that during the peak hours at the airport, the system of the present invention significantly reduces the taxiing time and waiting time of aircraft, improves the dispatch response speed and the utilization rate of taxiway resources. Compared with the traditional dispatch system, the present invention has obvious advantages in multiple indicators such as congestion and punctuality.

[0117] Example 2: Airport global scheduling optimization under severe weather conditions

[0118] In severe weather conditions, the use of airport taxiways and runways will be seriously affected, and aircraft scheduling becomes more complicated. Traditional scheduling systems are usually unable to respond quickly to changes in the external environment, resulting in long aircraft taxiing time and frequent take-off and landing delays. To this end, the present invention provides a global intelligent scheduling optimization system based on a Bayesian decision network to deal with scheduling problems in severe weather.

[0119] The technical purpose of this embodiment is to introduce a global intelligent scheduling system, combined with real-time monitoring data of the taxiway, weather conditions and other external factors, to dynamically adjust the taxiway resource allocation and aircraft scheduling strategy to reduce delays and improve airport operation efficiency.

[0120] Based on the Bayesian decision network, the present invention can comprehensively evaluate taxiway congestion, aircraft status, and global evidence data (such as weather and runway conditions) under severe weather conditions to generate globally optimized scheduling decisions. Compared with the prior art, the present invention can respond to changes in the external environment in real time and automatically adjust the taxiing plan, thereby maintaining efficient operation of the airport under severe conditions.

[0121] This embodiment is suitable for scenarios where airport operations are significantly affected by weather conditions such as heavy rain and strong winds. Through the global intelligent scheduling system of the present invention, the airport can dynamically adjust the scheduling strategy according to changes in the external environment to minimize delays and congestion caused by weather reasons.

[0122] The experiment compared the performance of the system of the present invention with that of the existing dispatch system under severe weather conditions. The evaluation indicators included: taxi time, number of delays, aircraft take-off and landing time deviation, dispatch response speed, number of dispatch adjustments, aircraft take-off and landing cancellation rate, passenger satisfaction, system scalability, etc.

[0123]

[0124]

[0125] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The airport surface operation efficiency optimization system based on intelligent technology is characterized by: The system includes the following modules: Taxiway data collection and prediction module: used to collect real-time taxiway usage and historical data, and use the airport taxiway dynamic congestion prediction algorithm to predict the future taxiway congestion status based on the aircraft taxiing speed and occupancy, and output the taxiway congestion prediction result Y t ; Aircraft status and scheduling data acquisition module: used to obtain the physical status, queue status, take-off and landing plan information of the aircraft, and provide data on the aircraft's current location, taxiing status and scheduling priority as input to the aircraft path planning and scheduling optimization algorithm; Taxiway resource real-time monitoring module: used to monitor the taxiway occupancy and resource allocation status in real time, and compare the current taxiway usage with the taxiway congestion prediction result Y t Matching, providing real-time data support for path planning algorithms; Path planning and optimization result generation module: through aircraft path planning and scheduling optimization algorithm, combined with taxiway congestion prediction result Y t and aircraft scheduling data to generate the aircraft's optimal taxiing path and scheduling plan Z t , ensuring aircraft taxi efficiently and avoiding congestion; Global evidence data collection and processing module: used to collect other evidence data in global operations, including weather conditions, fuel status, taxiway resource status, and provide support data for the airport's global intelligent decision-making and optimization algorithms; Global decision-making and feedback module: Based on the airport's global intelligent decision-making and optimization algorithm, using the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t and other global evidence E, formulate a global optimization decision plan for airport surface operations, including the reallocation of taxiway resources and adjustment of aircraft scheduling priorities.

2. The airport surface operation efficiency optimization method based on intelligent technology is characterized by: The following steps are involved: S1. Taxiway data collection and congestion prediction: S11: The taxiway data collection and prediction module is used to obtain the real-time usage of the taxiway, historical data and aircraft taxiing speed information; S12: Preprocess the collected data, including data cleaning, denoising and formatting; S13: Use the airport taxiway dynamic congestion prediction algorithm to predict future taxiway congestion based on real-time and historical data and generate taxiway congestion prediction result Y t ; S2. Aircraft status and dispatch data collection: S21: Acquire physical status data of the aircraft from the aircraft status and scheduling data acquisition module; S22: Obtaining aircraft takeoff and landing plan information, including estimated takeoff time, landing time and aircraft priority; S23: Integrate aircraft status and scheduling data to generate input data for path planning and scheduling optimization; S3: Taxiway resource monitoring and data processing S31: Monitor the current occupancy of the taxiway in real time through the taxiway resource real-time monitoring module to obtain the resource usage status of the taxiway; S32: The taxiway congestion prediction result Y t Compare and analyze with real-time monitoring data to ensure that the forecast results are consistent with the actual situation or make timely corrections; S33: Process monitoring data to generate real-time taxiway resource data for path planning, ensuring that the scheduling algorithm obtains the latest taxiway usage; S4: Aircraft path planning and scheduling optimization S41: Based on taxiway congestion prediction result Y t and aircraft scheduling data, using aircraft path planning and scheduling optimization algorithms to calculate the optimal aircraft taxi path; S42: Generate the optimal taxiing path and scheduling plan Z through reinforcement learning algorithm t , ensuring that aircraft can take off or land on time and avoid congestion on the taxiways; S43: Generate the optimal taxiing path and scheduling plan Z based on the calculation results t , and output the optimal path and scheduling plan; S5: Global evidence collection and analysis S51: obtaining global evidence data of current airport surface operation through the global evidence data collection and processing module; S52: Analyze global evidence data and assess the potential impact of external factors on scene operations; S53: Generate auxiliary data for global decision support to ensure that all influencing factors are taken into account in the global decision process; S6: Global Operation Optimization Decision Generation S61: Based on the airport global intelligent decision-making and optimization algorithm, combined with the taxiway congestion prediction results Y t , optimal taxiing path and scheduling plan Z t and global evidence E, the global optimization decision of the computer field operation; S62: Reallocate taxiway resources and adjust aircraft scheduling priorities based on global operational optimization decisions; S63: Generate the final optimized operation plan, output global adjustment suggestions and feed them back to the airport operation control system for execution.

3. The method for optimizing airport surface operation efficiency based on intelligent technology according to claim 2 is characterized by: The airport taxiway dynamic congestion prediction algorithm is used to predict the taxiway congestion in the future. The specific formula is as follows: where Y t It represents the prediction result of taxiway congestion at time t, that is, the taxiway congestion in a certain time period in the future; c: is a constant term, representing the intercept in the model; φ i : is the autoregressive coefficient, which is used to indicate the impact of taxiway congestion in the past on the current moment; θ i : is the moving average coefficient, which is used to represent the impact of the prediction error at the past moment on the current moment; ε t : is the prediction error at time t.

4. The method for optimizing airport surface operation efficiency based on intelligent technology according to claim 2 is characterized by: The aircraft path planning and scheduling optimization algorithm includes: Q value update formula: Where Q(s t ,a t ,Y t ): indicates that in state s t Next, select action a t The expected return value when α: represents the learning rate, which controls the update speed of the Q value; r t : is the reward value, reflecting the quality of path selection; γ: is the discount factor, which indicates the impact of future rewards; Indicates that in the next state S t+1 The maximum Q value obtained in 5. The airport surface operation efficiency method based on intelligent technology according to claim 4 is characterized by: The aircraft path planning and scheduling optimization algorithm also includes an optimal path generation formula: in: Z t : represents the optimal taxiing path and scheduling plan generated at time t; Indicates that in state s t and congestion prediction Y t Next, select the action a that maximizes the Q value t .

6. The airport surface operation efficiency optimization system and method based on intelligent technology according to claim 5 is characterized by: The steps of the aircraft path planning and scheduling optimization algorithm include the following steps: S1. Status update: Get the status s of the aircraft at the current time t t , including the physical location of the aircraft and the congestion prediction of the taxiway Y t and queue status S2, action selection: through reinforcement learning algorithm, according to the state s t and taxiway congestion prediction Y t , select the current optimal action a t , which is the taxi path the aircraft should take; S3, reward calculation: after taking action a t Then, according to the actual congestion of the taxiway and the taxiing time, the corresponding reward r is calculated. t , used to provide feedback on the effect of aircraft taxi path selection; S4. Q value update: based on reward r t and the state S at the next time t+1 t+1 , update the Q value to continuously optimize the path selection; S5. Optimal path generation: Through reinforcement learning, the optimal taxiing path and scheduling plan Z are finally obtained. t .

7. The method for optimizing airport surface operation efficiency based on intelligent technology according to claim 2 is characterized by: The airport global intelligent decision-making and optimization algorithm is based on the Bayesian decision network, combined with the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t And global evidence E, generate the optimal decision for global operation, the specific formula is as follows: Where: P(X|Y t ,Z t ,E): Given the taxiway congestion prediction result Y t , optimal taxiing path and scheduling plan Z t , the probability of the airport's global operating state X occurring under the condition of global evidence E, that is, the probability of the global optimal decision; P(Y t ,Z t ,E|X): Taxiway congestion prediction result Y under a specific global decision X t , optimal taxiing path and scheduling plan Z t and the joint probability of other global evidence E; P(X): prior probability of global operating state X; P(Y t ,Z t ,E): The joint probability of taxiway congestion prediction, path optimization results and global evidence, as a normalization constant.

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