Method for modeling airport scene dynamics based on multiple types of target behaviors

Abstract

The application relates to the technical field of dynamic modeling, in particular to an airport scene dynamic modeling method based on behaviors of multiple types of targets, which comprises the following steps: data acquisition and preprocessing, multiple types of target identification and classification, trajectory prediction and preliminary modeling, dynamic behavior modeling based on behavior mode learning, multi-modal information fusion and model optimization, conflict detection and prediction, self-adaptive behavior adjustment strategy, model verification and simulation, real-time updating and iterative optimization. Through machine learning and deep learning technologies, the application can automatically learn and adapt to dynamic changes and newly emerging behavior modes of an airport scene, accurately predict target behaviors by combining radar, video, GPS multi-modal information and Kalman filtering, particle filtering and DQN prediction algorithms, pay attention to not only behaviors of single targets but also interaction behaviors among targets, including complex scenes such as avoidance between an airplane and a vehicle and a pedestrian crossing a runway, so that more comprehensive scene dynamic analysis is provided.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of dynamic modeling, in particular to an airport scene dynamic modeling method based on multi-type target behavior. BACKGROUND

[0002] Multi-type target behavior analysis refers to the process of identifying, classifying, predicting and understanding the behavior of multiple types of targets existing in a specific environment. In the airport environment, this analysis is particularly important because the airport is a highly dynamic and complex transportation system involving multiple different types of targets operating simultaneously in a limited space. The multi-type targets mainly include aircraft vehicles and pedestrians. The behavior patterns of these targets are different, and there may be complex interaction behaviors between them, such as aircraft and vehicle avoidance, pedestrian and aircraft safety distance maintenance, etc.

[0003] Therefore, in general, traditional airport scene dynamic analysis modeling methods rely on rule-based systems or simple mathematical models. These methods have the problems of poor flexibility, accuracy and scalability. Rule-based systems often have difficulty adapting to the dynamic changes of the airport scene and new behavior patterns, while simple mathematical models may not accurately capture the complex interaction behaviors and nonlinear dynamics between targets. Therefore, traditional methods often struggle to handle multi-type targets and large-scale data, and are difficult to extend to more complex airport environments.

[0004] In summary, an airport scene dynamic modeling method based on multi-type target behavior is needed to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide an airport scene dynamic modeling method based on multi-type target behavior to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical solutions:

[0007] The airport scene dynamic modeling method based on multi-type target behavior comprises the following steps:

[0008] S1. Collect real-time data of the airport scene using radar, lidar, camera and other sensors. Through data cleaning and format unification, the collected raw data is converted into a standard format that can be used for modeling.

[0009] S2. Apply machine learning algorithms to identify targets from preprocessed data, distinguish different types of targets such as aircraft, vehicles and pedestrians, and use classification algorithms to finely classify the identified targets, including distinguishing different types of aircraft or vehicles.

[0010] S3. Use a Kalman filter or particle filter algorithm to predict the trajectory of the identified target. Based on the predicted trajectory, a preliminary dynamic model of the airport scene is established, including the target's position, velocity, and direction information.

[0011] S4. Apply deep reinforcement learning algorithms to learn and simulate the behavior patterns of different types of targets at airports, including aircraft taxi path selection and vehicle avoidance behavior;

[0012] S5. Combine radar, video, and GPS information and use a multimodal information fusion algorithm to optimize the preliminary model.

[0013] S6. Develop conflict detection algorithms to predict future conflicts based on dynamic models, use spatiotemporal prediction models to predict conflicts, and calculate conflict probabilities;

[0014] S7. Adaptive behavior adjustment strategies based on conflict prediction results, including dynamic adjustments to aircraft taxi speeds and vehicle routes, and the application of heuristic search algorithms or reinforcement learning algorithms to adjust the strategy;

[0015] S8. Validate the established dynamic model using historical or simulated data to assess its accuracy and reliability. Use simulation software to perform dynamic scene simulations and observe the model's performance in different scenarios.

[0016] S9. Deploy the model to the actual airport system to collect new data in real time and update the model, and iterate and optimize the model regularly.

[0017] Preferably, the step S1 is implemented as follows:

[0018] S1.1. Use radar sensors to obtain basic information about the target's position and velocity on the airport surface. Use lidar to obtain the target's three-dimensional shape and precise position. Use cameras to capture video images of the airport surface for subsequent target identification.

[0019] S1.2. Remove noise and outliers from radar and LiDAR data, including erroneous data due to weather or equipment failure, and perform denoising and enhancement on video images to improve image quality.

[0020] S1.3. Convert radar, LiDAR, and camera data into a unified spatiotemporal coordinate system to ensure data consistency and format the data into a format that can be used as input for machine learning models.

[0021] Preferably, the step S2 is implemented as follows:

[0022] S2.1. Apply a convolutional neural network (CNN) to detect objects in preprocessed video images, identifying aircraft, vehicles, and pedestrians. Use the YOLO object detection algorithm to improve recognition speed and accuracy.

[0023] S2.2. Classify the identified objects using a support vector machine (SVM) to distinguish between different types of passenger aircraft, cargo aircraft, or vehicles. Extract the size, shape, and texture of the objects as input to the SVM classifier.

[0024] S2.3. Record the position, speed, and direction attribute information of each target to provide basic data for subsequent trajectory prediction and dynamic modeling.

[0025] Preferably, the step S3 is implemented as follows:

[0026] S3.1. Use the Kalman filter algorithm to predict the trajectory of the identified target and estimate the target's future position and velocity.

[0027] For nonlinear or complex scenarios, particle filtering algorithms are used to improve prediction accuracy;

[0028] S3.2. Based on the predicted trajectory, establish a preliminary dynamic model of the airport scene, including the target's position, velocity, and direction. Use the nearest neighbor algorithm to correlate the target's trajectories at different time points to form a complete motion trajectory.

[0029] S3.3. Store the data of the preliminary dynamic model in a database to facilitate subsequent analysis and optimization.

[0030] Preferably, the step S4 is implemented as follows:

[0031] S4.1. Apply the DQN training model to learn and simulate the behavior patterns of different types of targets in the airport scene, design reward functions and penalty mechanisms, and guide the model to learn taxi path selection and vehicle avoidance behavior strategies.

[0032] S4.2. Use the trained DRL model to simulate target behavior at the airport, generate simulated trajectory and behavior data, and verify the accuracy and reliability of the model using the simulated data.

[0033] S4.3. Analyze simulation data to capture and simulate complex interactions between targets, such as avoidance between aircraft and vehicles and pedestrians crossing the runway. Use behavioral analysis methods to further analyze and understand these interactions.

[0034] Preferably, the step S5 is implemented as follows:

[0035] S5.1. Combine information from multiple modalities, including radar, video, and GPS, and use factor graph models to optimize the preliminary model. Design a fusion strategy to integrate data from different modalities and improve model accuracy and robustness.

[0036] S5.2. Use the fused data to optimize the preliminary dynamic model, correct for errors introduced by a single sensor, and adjust the model parameters using a gradient descent algorithm to improve the model's prediction accuracy.

[0037] S5.3. Verify the effectiveness of multimodal information fusion by comparing the model prediction results before and after fusion, and evaluate the optimized model using the mean square error evaluation indicator.

[0038] Preferably, the implementation steps of step S6 are:

[0039] S6.1. Develop a conflict detection algorithm based on the dynamic model to predict possible future aircraft taxi path intersections and vehicle collisions. Design conflict detection rules, including distance thresholds and speed difference thresholds, to determine whether a conflict has occurred.

[0040] S6.2. Use the ST-GCN spatiotemporal prediction model to predict conflicts, calculate the probability and likely time of conflict, analyze the relative positions and speed changes between targets, and predict the development trend of the conflict.

[0041] S6.3. Generate conflict warning information based on the conflict prediction results and provide it to airport managers for decision-making support. They design warning strategies, including lead time and warning levels, to ensure that managers can take timely measures to avoid conflicts.

[0042] Preferably, the implementation steps of step S7 are:

[0043] S7.1. Based on the conflict prediction results, design adaptive behavior adjustment strategies, including dynamically adjusting aircraft taxi speed and vehicle routes, and consider multiple solutions, including changing taxi paths, pausing taxiing, and accelerating or decelerating.

[0044] S7.2. Use the A* algorithm or reinforcement learning algorithm to search for the optimal adjustment solution in the solution space. Design an evaluation function, evaluate each adjustment solution, and select the optimal solution as the execution strategy.

[0045] S7.3. Send the optimal adjustment plan to the relevant targets for execution, including sending it to the aircraft or vehicle pilots through the communication system, and monitor the implementation of the adjustment plan in real time to ensure that the conflict is effectively resolved.

[0046] Preferably, the implementation steps of step S8 are:

[0047] S8.1. Verify the established dynamic model using historical data to evaluate the accuracy and reliability of the model, compare the model prediction results with the actual data, and calculate the error index;

[0048] S8.2. Use OMNeT++ to perform dynamic simulation of the scene, simulate airport operation under different scenarios, design simulation experiments, and verify the performance of the model under different conditions, including different weather and different traffic flow;

[0049] S8.3. Analyze the simulation results to evaluate the simulation performance and applicability of the model, and further adjust and optimize the model according to the simulation results.

[0050] Preferably, the step S9 implements the following steps:

[0051] S9.1. Deploy the optimized model to the actual airport system to realize real-time data collection and model updating, design data interface and communication protocol to ensure seamless integration of the model with the airport system;

[0052] S9.2. Collect new data of the airport scene in real time, update and correct the model, and use data synchronization algorithm to ensure the consistency of the model with the new data;

[0053] S9.3. Iteratively optimize the model periodically to adapt to the dynamic changes of the airport scene and newly emerging target behavior patterns, analyze the model performance indicators including prediction accuracy and calculation efficiency, and continuously improve and optimize the model;

[0054] S9.4. Establish a model maintenance and upgrade mechanism to ensure long-term stable operation of the model, regularly test the performance of the model and troubleshoot faults, and timely handle problems of the model.

[0055] Compared with the prior art, the beneficial effects of the present application are: the present application can automatically learn and adapt to the dynamic changes of the airport scene and newly emerging behavior patterns through machine learning and deep learning technology, and can realize accurate prediction of target behavior by combining radar, video, GPS multi-modal information and Kalman filter, particle filter, DQN prediction algorithm, not only focusing on the behavior of a single target, but also considering the interaction between targets, including complex scenarios such as avoidance of aircraft and vehicles, and pedestrian crossing runway, thereby providing more comprehensive dynamic analysis of the scene. At the same time, the method of the present application can also process multiple types of targets and large-scale data, and is suitable for different sizes and complexities of airport environment. Through real-time data collection and model updating mechanism, the method can keep synchronization with the airport scene, and provide timely decision support for airport management personnel. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The flowchart of the airport scene dynamic modeling method based on multiple target behaviors of the present application is shown. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0058] Example

[0059] See also Figure 1 The present invention proposes a method for dynamic modeling of airport scenes based on multi-type target behaviors, specifically comprising the following steps:

[0060] S1. Data collection and preprocessing:

[0061] S1.1. Use radar sensors to obtain basic information about the target's position and velocity on the airport surface. Use lidar to obtain the target's three-dimensional shape and precise position. Use cameras to capture video images of the airport surface for subsequent target identification.

[0062] S1.2. Remove noise and outliers from radar and LiDAR data, including erroneous data due to weather or equipment failure, and perform denoising and enhancement on video images to improve image quality.

[0063] S1.3. Convert radar, LiDAR, and camera data into a unified spatiotemporal coordinate system to ensure data consistency and format the data for input into machine learning models.

[0064] S2. Multi-type target recognition and classification:

[0065] S2.1. Apply a convolutional neural network (CNN) to detect objects in preprocessed video images, identifying aircraft, vehicles, and pedestrians. Use the YOLO object detection algorithm to improve recognition speed and accuracy.

[0066] S2.2. Classify the identified objects using a support vector machine (SVM) to distinguish between different types of passenger aircraft, cargo aircraft, or vehicles. Extract the size, shape, and texture of the objects as input to the SVM classifier.

[0067] S2.3. Record the position, velocity, and direction attributes of each target to provide basic data for subsequent trajectory prediction and dynamic modeling;

[0068] S3. Trajectory prediction and preliminary modeling:

[0069] S3.1. Use the Kalman filter algorithm to predict the trajectory of the identified target and estimate the target's future position and velocity. For nonlinear or complex scenarios, use the particle filter algorithm to improve prediction accuracy.

[0070] S3.2. Based on the predicted trajectory, establish a preliminary dynamic model of the airport scene, including the target's position, velocity, and direction. Use the nearest neighbor algorithm to correlate the target's trajectories at different time points to form a complete motion trajectory.

[0071] S3.3. Store the data of the preliminary dynamic model in a database to facilitate subsequent analysis and optimization;

[0072] S4. Dynamic Behavior Modeling Based on Behavior Pattern Learning

[0073] S4.1. Apply the DQN training model to learn and simulate the behavior patterns of different types of targets in the airport scene, design reward functions and penalty mechanisms, and guide the model to learn taxi path selection and vehicle avoidance behavior strategies.

[0074] S4.2. Use the trained DRL model to simulate target behavior at the airport, generate simulated trajectory and behavior data, and verify the accuracy and reliability of the model using the simulated data.

[0075] S4.3. Analyze simulation data to capture and simulate complex interactions between targets, such as aircraft and vehicles avoiding each other, and pedestrians crossing the runway. Use behavioral analysis methods to further analyze and understand these interactions.

[0076] S5. Multimodal Information Fusion and Model Optimization

[0077] S5.1. Combine information from multiple modalities, including radar, video, and GPS, and use factor graph models to optimize the preliminary model. Design a fusion strategy to integrate data from different modalities and improve model accuracy and robustness.

[0078] S5.2. Use the fused data to optimize the preliminary dynamic model, correcting errors introduced by a single sensor. Use a gradient descent algorithm to adjust the model parameters to improve the model's prediction accuracy. Specifically, in multimodal information fusion, use a gradient descent algorithm to optimize the model parameters to minimize the error between the fused data and the model's prediction.

[0079] Let the model parameters be θ, the fused data be y, and the model prediction be Then the loss function is as follows:

[0080]

[0081] The gradient descent algorithm minimizes the loss function L(θ) by iteratively updating the parameters θ.

[0082]

[0083] Where:

[0084] θ is the model parameter, which in this step serves as the weights and biases in the neural network;

[0085] y is the fused data, which is the information fusion result of multiple modalities such as radar, video, and GPS in this step;

[0086] is the prediction result of the model based on the parameter θ;

[0087] L(θ) is the loss function, which measures the error between the model prediction and the real data;

[0088] α is the learning rate, which controls the step size of parameter updates;

[0089] It is the gradient of the loss function with respect to the parameter θ, indicating the direction of parameter update;

[0090] In each iteration, the gradient of the loss function is calculated and the model parameters are updated according to the gradient until the loss function converges to the minimum value or reaches the preset number of iterations. The model parameters are optimized through the gradient descent algorithm to improve the accuracy and robustness of the model.

[0091] S5.3. Verify the effectiveness of multimodal information fusion by comparing the model prediction results before and after fusion. Evaluate the optimized model using the mean square error (MSE) metric.

[0092] S6. Conflict Detection and Prediction

[0093] S6.1. Develop a conflict detection algorithm based on the dynamic model to predict possible future aircraft taxi path intersections and vehicle collisions. Design conflict detection rules, including distance thresholds and speed difference thresholds, to determine whether a conflict has occurred.

[0094] S6.2. Use the ST-GCN spatiotemporal prediction model to predict conflicts, calculate the probability and likely time of conflict, analyze the relative positions and speed changes between targets, and predict the development trend of the conflict.

[0095] S6.3. Generate conflict warning information based on conflict prediction results and provide it to airport managers for decision-making support. This information includes lead time and warning levels, ensuring managers can take timely action to avoid conflicts.

[0096] S7. Adaptive behavior adjustment strategy

[0097] S7.1. Based on the conflict prediction results, design adaptive behavior adjustment strategies, including dynamically adjusting aircraft taxi speed and vehicle routes, and consider multiple solutions, including changing taxi paths, pausing taxiing, and accelerating or decelerating.

[0098] S7.2. Use the A* algorithm or reinforcement learning algorithm to search for the optimal adjustment solution in the solution space. Design an evaluation function, evaluate each adjustment solution, and select the optimal solution as the execution strategy.

[0099] S7.3. Send the optimal adjustment plan to the relevant targets for execution, including sending it to the aircraft or vehicle pilots via the communication system, and monitor the implementation of the adjustment plan in real time to ensure that the conflict is effectively resolved;

[0100] S8. Model verification and simulation:

[0101] S8.1. Validate the established dynamic model using historical data, assess its accuracy and reliability, compare the model's predictions with actual data, and calculate error metrics.

[0102] S8.2. Use OMNeT++ to perform dynamic simulations of airport operations under different scenarios. Design simulation experiments to verify the model's performance under different conditions, including varying weather conditions and traffic flows.

[0103] S8.3. Analyze the simulation results, evaluate the simulation performance and applicability of the model, and further adjust and optimize the model based on the simulation results.

[0104] S9. Real-time update and iterative optimization:

[0105] S9.1. Deploy the optimized model to the actual airport system, implement real-time data collection and model updates, and design data interfaces and communication protocols to ensure seamless integration of the model and the airport system.

[0106] S9.2. Collect new data on the airport surface in real time, update and revise the model, and use data synchronization algorithms to ensure consistency between the model and the new data.

[0107] S9.3. Regularly iterate and optimize the model to adapt to dynamic changes in the airport surface and emerging target behavior patterns. Analyze model performance indicators, including prediction accuracy and computational efficiency, and continuously improve and optimize the model.

[0108] S9.4. Establish a model maintenance and upgrade mechanism to ensure the long-term stable operation of the model, regularly perform performance testing and troubleshooting on the model, and promptly address any problems that arise with the model.

[0109] Through the above steps, the airport scene dynamic modeling method based on multi-type target behavior of the present invention provides a more flexible, accurate and comprehensive scene dynamic analysis method than traditional methods by combining advanced machine learning and prediction technology, which is of great significance for improving the operating efficiency and safety of airports.

[0110] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The airport scene dynamic modeling method based on multi-type target behavior is characterized by: The following steps are involved: S1. Use radar, lidar, and cameras to collect real-time data from airports. Through data cleaning and formatting, convert the collected raw data into a standardized format suitable for modeling. S2. Apply machine learning algorithms to the preprocessed data to identify targets, distinguishing between aircraft, vehicles, and pedestrians. Use classification algorithms to finely classify the identified targets, including different types of aircraft or vehicles. S3. Use a Kalman filter or particle filter algorithm to predict the trajectory of the identified target. Based on the predicted trajectory, a preliminary dynamic model of the airport scene is established, including the target's position, velocity, and direction information. S4. Apply deep reinforcement learning algorithms to learn and simulate the behavior patterns of different types of targets at airports, including aircraft taxi path selection and vehicle avoidance behavior; S5. Combine radar, video, and GPS information and use a multimodal information fusion algorithm to optimize the preliminary model. S6. Develop conflict detection algorithms to predict future conflicts based on dynamic models, use spatiotemporal prediction models to predict conflicts, and calculate conflict probabilities; S7. Adaptive behavior adjustment strategies based on conflict prediction results, including dynamic adjustments to aircraft taxi speeds and vehicle routes, and application of heuristic search algorithms or reinforcement learning algorithms. S8. Validate the established dynamic model using historical or simulated data to assess its accuracy and reliability. Use simulation software to perform dynamic scene simulations and observe the model's performance in different scenarios. S9. Deploy the model to the actual airport system to collect new data in real time and update the model, and iterate and optimize the model regularly.

2. The airport scene dynamic modeling method based on multi-type target behavior according to claim 1 is characterized in that: The implementation steps of step S1 are: S1.

1. Use radar sensors to obtain basic information about the target's position and velocity on the airport surface. Use lidar to obtain the target's three-dimensional shape and precise position. Use cameras to capture video images of the airport surface for subsequent target identification. S1.

2. Remove noise and outliers from radar and LiDAR data, including erroneous data due to weather or equipment failure, and perform denoising and enhancement on video images. S1.

3. Convert radar, LiDAR, and camera data into a unified spatiotemporal coordinate system to ensure data consistency.

3. The airport scene dynamic modeling method based on multi-type target behavior according to claim 2 is characterized in that: The implementation steps of step S2 are: S2.

1. Apply a convolutional neural network to perform object detection on the preprocessed video image to identify aircraft, vehicles, and pedestrians. S2.

2. Classify the identified objects using an SVM to distinguish between different types of passenger aircraft, cargo aircraft, or vehicles. Extract the size, shape, and texture of the objects as input to the SVM classifier. S2.

3. Record the location, speed, and direction attribute information of each target.

4. The airport scene dynamic modeling method based on multi-type target behavior according to claim 3 is characterized in that: The implementation steps of step S3 are: S3.

1. Use the Kalman filter algorithm to predict the trajectory of the identified target and estimate the target's future position and velocity. For nonlinear or complex scenarios, use the particle filter algorithm to improve prediction accuracy. S3.

2. Based on the predicted trajectory, a preliminary dynamic model of the airport scene is established, including the target's position, speed, and direction status information. The target trajectory at different time points is associated using the nearest neighbor algorithm. S3.

3. Store the data of the preliminary dynamic model in a database.

5. The airport scene dynamic modeling method based on multi-type target behavior according to claim 4 is characterized in that: The implementation steps of step S4 are as follows: S4.

1. Apply the DQN training model to learn and simulate the behavior patterns of different types of targets in the airport scene, design reward functions and penalty mechanisms, and guide the model to learn taxi path selection and vehicle avoidance behavior strategies. S4.

2. Use the trained DRL model to simulate target behavior at the airport and generate simulated trajectory and behavior data. S4.

3. Analyze simulation data to capture and simulate complex interactions between targets, such as avoidance between aircraft and vehicles and pedestrians crossing the runway. Use behavioral analysis methods to further analyze and understand these interactions.

6. The airport scene dynamic modeling method based on multi-type target behavior according to claim 5 is characterized in that: The implementation steps of step S5 are as follows: S5.

1. Combine information from multiple modalities, including radar, video, and GPS, using factor graph models to optimize the preliminary model and design a fusion strategy to integrate data from different modalities. S5.

2. Use the fused data to optimize the preliminary dynamic model, correct for errors introduced by a single sensor, and adjust the model parameters using a gradient descent algorithm. S5.

3. Verify the effectiveness of multimodal information fusion by comparing the model prediction results before and after fusion, and evaluate the optimized model using the mean square error evaluation indicator.

7. The airport scene dynamic modeling method based on multi-type target behavior according to claim 6 is characterized in that: The implementation steps of step S6 are as follows: S6.

1. Develop a conflict detection algorithm based on the dynamic model to predict possible future aircraft taxi path intersections and vehicle collisions. Design conflict detection rules, including distance thresholds and speed difference thresholds, to determine whether a conflict has occurred. S6.

2. Use the ST-GCN spatiotemporal prediction model to predict conflicts, calculate the probability and likely time of conflict, analyze the relative positions and speed changes between targets, and predict the development trend of the conflict. S6.

3. Generate conflict warning information based on the conflict prediction results and provide it to airport managers for decision support and the design of warning strategies, including lead time and warning level.

8. The airport scene dynamic modeling method based on multi-type target behavior according to claim 7 is characterized in that: The implementation steps of step S7 are: S7.

1. Based on the conflict prediction results, design adaptive behavior adjustment strategies, including dynamically adjusting aircraft taxi speed and vehicle routes, and consider multiple solutions, including changing taxi paths, pausing taxiing, and accelerating or decelerating. S7.

2. Use the A* algorithm or reinforcement learning algorithm to search for the optimal adjustment solution in the solution space, design an evaluation function, and evaluate each adjustment solution. S7.

3. Send the optimal adjustment plan to relevant targets for execution, including sending it to aircraft or vehicle pilots through the communication system, and monitor the execution of the adjustment plan in real time.

9. The airport scene dynamic modeling method based on multi-type target behavior according to claim 8 is characterized in that: The implementation steps of step S8 are: S8.

1. Validate the established dynamic model using historical data, assess its accuracy and reliability, compare the model's predictions with actual data, and calculate error metrics. S8.

2. Use OMNeT++ to perform dynamic simulations of airport operations under different scenarios. Design simulation experiments to verify the model's performance under different conditions, including varying weather conditions and traffic flows. S8.

3. Analyze the simulation results, evaluate the simulation performance and applicability of the model, and further adjust and optimize the model based on the simulation results.

10. The airport scene dynamic modeling method based on multi-type target behavior according to claim 9 is characterized in that: The implementation steps of step S9 are: S9.

1. Deploy the optimized model to the actual airport system to enable real-time data collection and model updates. S9.

2. Collect new data on the airport surface in real time, update and revise the model, and use data synchronization algorithms to ensure consistency between the model and the new data. S9.

3. Regularly iterate and optimize the model to adapt to dynamic changes in the airport surface and emerging target behavior patterns. Analyze model performance indicators, including prediction accuracy and computational efficiency, and continuously improve and optimize the model. S9.

4. Establish a model maintenance and upgrade mechanism to ensure the long-term stable operation of the model, and regularly perform performance testing and troubleshooting on the model.

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