Airport flight area risk management method and system, terminal and storage medium

Abstract

The invention relates to the technical field of digital twinning, and particularly provides an airport flight area risk management method and system, a terminal and a storage medium, and the method comprises the steps: constructing a three-dimensional model of an airport, and setting a management and control area in the three-dimensional model; collecting monitoring data of an airport, identifying a moving target and a movement track of the moving target from the monitoring data, and generating intrusion early warning information if the movement track intersects with the management and control area; aircraft demand information is obtained, the demand information comprises take-off demand information and landing demand information, and a to-be-executed task is generated based on the demand information; collecting meteorological data through a meteorological sensor; and distributing a take-off and landing runway for the task to be executed based on the meteorological data and the intrusion early warning information. According to the invention, the digital twinborn model is constructed and the environment data of the airport flight area is monitored, so that the illegal invasion of the flight area is early warned in time, and the runway is reasonably distributed based on the illegal invasion early warning, so that the flight safety is effectively managed and controlled.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin technology, and specifically relates to an airport flight area risk management method, system, terminal and storage medium. Background Art

[0002] With the rapid development and continued expansion of the global aviation industry, airport aerodrome safety management faces unprecedented complexity and challenges. Against this backdrop, ensuring safe, efficient, and smooth aerodrome operations has become a top priority for aviation authorities and airport operators. Traditional safety management methods rely primarily on manual monitoring and judgment based on accumulated experience. While this approach can maintain basic operational order to a certain extent, it is insufficient in the face of the increasing number and diversity of potential risks.

[0003] Specifically, manual monitoring, limited by factors such as human field of vision, attention span, and reaction speed, often struggles to achieve comprehensive and real-time coverage of every corner of the airspace, every minute and every second. Furthermore, while empirical judgment can reflect historical patterns and common problems to a certain extent, its accuracy and timeliness often cannot be fully guaranteed when faced with new threats, emergencies, or complex and changing operating environments. Therefore, traditional management methods are no longer able to meet the current needs of the aviation industry in terms of identifying potential risks, responding promptly, and effectively preventing accidents. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an airport flight area risk management method, system, terminal and storage medium to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides an airport flight zone risk management method, comprising: Build a 3D model of the airport and set up control areas in the 3D model; Collecting airport surveillance data, and identifying mobile targets and their motion trajectories from the surveillance data, and generating intrusion warning information if the motion trajectories intersect with the control area; Acquiring aircraft demand information, the demand information including takeoff demand information and landing demand information, and generating a task to be executed based on the demand information; Collect meteorological data through meteorological sensors; Assign take-off and landing runways for pending missions based on meteorological data and intrusion warning information.

[0006] In an optional embodiment, constructing a three-dimensional model of the airport and setting a control area in the three-dimensional model includes: Obtain surveying and mapping data of the airport, and use modeling software to construct a three-dimensional model based on the surveying and mapping data.

[0007] In an optional embodiment, monitoring data of an airport is collected, and a mobile target and its trajectory are identified from the monitoring data. If the trajectory intersects with the control area, intrusion warning information is generated, including: Set up inspection points around the control area at the airport and deploy lidar at the inspection points; Using a surveillance camera to collect surveillance video, and using a target recognition algorithm to identify a moving target from the surveillance video; Based on the area to which the actual positioning coordinates of the surveillance camera belong, determine the detection points in the same area and obtain the laser point cloud data of the detection points in the same area; Identify the position coordinates of the moving target based on the laser point cloud data, and save the position coordinates at different times as a coordinate sequence; Predicting the expected position coordinates of the moving target based on the coordinate sequence using a long short-term memory network model; It is determined whether the expected position coordinates are within the control area, and if the expected position coordinates are within the control area, an intrusion warning message is generated.

[0008] In an optional embodiment, before generating the intrusion warning information, the method further includes: Get the type of mobile target identified by the target recognition algorithm; Determine whether the mobile target has the authority to enter the control area based on the preset type authorization information: If so, a prompt message is generated indicating that the control area is in operation status; If not, an intrusion warning message is generated, and the sound and light alarms in the corresponding area are controlled to play an expulsion voice.

[0009] In an optional embodiment, the types include workers, work vehicles and other intrusion objects.

[0010] In an optional embodiment, obtaining aircraft requirement information, the requirement information including takeoff requirement information and landing requirement information, and generating a task to be performed based on the requirement information include: Obtaining aircraft demand information received by the tower, the demand information including flight number, take-off or landing, current location information, and expected time; Generate tasks to be executed based on demand information and cache them in the task queue; Sort the tasks in the task queue by expected time from earliest to latest, or adjust the order of tasks in the task queue based on administrator reordering instructions.

[0011] In an optional embodiment, allocating take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information includes: Based on the correspondence between the preset meteorological data and the mission execution time and the current meteorological data, the current takeoff mission execution time and landing mission execution time are obtained; Monitor the duration of the intrusion warning information. If the duration reaches the set time threshold, an abnormality mark is generated for the runway to which the intrusion warning information belongs. If the intrusion warning information is eliminated, the corresponding abnormality mark is deleted. Maintain an execution task queue for each runway without an exception mark; Count the task types of tasks to be executed in each task execution queue, and generate the estimated waiting time for each task execution queue based on the task type, takeoff task execution time, and landing task execution time; Assign the most highly ordered task to be executed in the task queue to the execution task queue with the shortest estimated waiting time, and update the estimated waiting time of the corresponding execution task queue; Monitor the maximum difference in expected waiting time of multiple execution task queues. If the maximum difference exceeds a set time difference threshold, transfer some of the to-be-executed tasks in the execution task queue with the largest expected waiting time to the execution task queue with the smallest expected waiting time.

[0012] In a second aspect, the present invention provides an airport flight area risk management system, comprising: Model building module, used to build a 3D model of the airport and set control areas in the 3D model; An intrusion warning module is used to collect airport monitoring data, identify mobile targets and their motion trajectories from the monitoring data, and generate intrusion warning information if the motion trajectory intersects the control area; A demand analysis module is used to obtain aircraft demand information, including takeoff demand information and landing demand information, and generate tasks to be executed based on the demand information; Meteorological monitoring module, used to collect meteorological data through meteorological sensors; The runway allocation module is used to allocate take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information.

[0013] According to a third aspect, a terminal is provided, including: a memory for storing an airport flight area risk management program; A processor is used to implement the steps of the airport airfield risk management method provided in the first aspect when executing the airport airfield risk management program.

[0014] In a fourth aspect, a computer-readable storage medium is provided, on which an airport aerodrome risk management program is stored. When the airport aerodrome risk management program is executed by a processor, the steps of the airport aerodrome risk management method provided in the first aspect are implemented.

[0015] The beneficial effect of the present invention is that the airport apron risk management method, system, terminal and storage medium provided by the present invention can realize timely early warning of illegal intrusion into the apron by constructing a digital twin model and monitoring the environmental data of the airport apron, and reasonably allocate runways based on the illegal intrusion warning, thereby realizing effective control of flight safety.

[0016] In addition, the present invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0019] Figure 2 FIG. 4 is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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 making creative efforts should fall within the scope of protection of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] The key terms appearing in the present invention are explained below.

[0024] A digital twin is a digital model of a physical product in virtual space, containing product information throughout its entire lifecycle, from conception to decommissioning. It maps physical models, sensor updates, operational history, and other data in virtual space, reflecting the entire lifecycle of the corresponding physical equipment.

[0025] The airport flight zone risk management method provided by the embodiment of the present invention is executed by a computer device. Accordingly, the airport flight zone risk management system runs in the computer device.

[0026] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution entity may be an airport flight zone risk management system. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0027] like Figure 1 As shown, the method includes: Step 110: construct a three-dimensional model of the airport and set a control area in the three-dimensional model.

[0028] First, using advanced geographic information systems (GIS) and 3D modeling technology, a precise 3D model of the airport was constructed based on its actual layout, topography, and building structure. This model encompassed not only the airport's physical structure but also key areas such as runways, aprons, taxiways, control towers, and terminals. Subsequently, within the 3D model, various control areas, such as no-fly zones, restricted zones, and safety buffer zones, were clearly demarcated and established based on the needs of safety management and operational efficiency. This ensured that these areas could be effectively identified and controlled during subsequent management and monitoring.

[0029] Step 120 , collecting monitoring data of the airport, and identifying the mobile target and the movement trajectory of the mobile target from the monitoring data, and generating intrusion warning information if the movement trajectory intersects with the control area.

[0030] Surveillance cameras, radar systems, and other equipment deployed at key locations throughout the airport collect real-time surveillance data. Leveraging advanced image recognition, target tracking, and big data analysis technologies, the system accurately identifies moving targets (such as vehicles, personnel, and surveillance cameras) from the surveillance data and continuously tracks their movements. If a moving target's trajectory intersects with a pre-defined control area, the intrusion warning mechanism is immediately triggered, generating detailed intrusion warning information, including target type, location, speed, and estimated intrusion time, enabling timely response measures.

[0031] Step 130: Acquire aircraft requirement information, including takeoff requirement information and landing requirement information, and generate a task to be executed based on the requirement information.

[0032] Connecting with the airport's air traffic management system, it obtains real-time information on aircraft takeoff and landing requirements. This information includes, but is not limited to, aircraft model, estimated takeoff / landing time, destination / departure point, and payload. Based on this information, combined with the airport's operating rules and current conditions, it automatically generates pending takeoff and landing missions, including mission sequence, required runways, and estimated times.

[0033] Step 140: Collect meteorological data through meteorological sensors.

[0034] A network of weather sensors installed at airports collects real-time weather data for the airport and its surrounding areas, including but not limited to wind speed, wind direction, visibility, temperature, humidity, and air pressure. This data is crucial for assessing flight conditions, developing flight plans, and ensuring flight safety.

[0035] Step 150: Allocate a take-off and landing runway for the mission to be performed based on the meteorological data and the intrusion warning information.

[0036] Based on a comprehensive consideration of meteorological data (such as the impact of wind direction and speed on aircraft takeoff and landing), intrusion warning information (such as whether there are potential security threats that need to be avoided), and airport operational efficiency (such as reducing aircraft waiting time and optimizing runway utilization), runways are rationally allocated for upcoming takeoff and landing missions. This step requires the use of advanced algorithms and models to ensure flight safety while maximizing airport operational efficiency and capacity. Furthermore, the task allocation plan must be dynamically adjusted based on real-time conditions to address potential emergencies.

[0037] To facilitate understanding of the present invention, the airport aerodrome risk management method provided by the present invention is further described below based on the principles of the airport aerodrome risk management method of the present invention and in combination with the process of performing risk management on the airport aerodrome in the embodiment.

[0038] Specifically, airport flight area risk management methods include: S1. Build a 3D model of the airport and set up control areas in the 3D model.

[0039] Obtain surveying and mapping data of the airport, and use modeling software to construct a three-dimensional model based on the surveying and mapping data.

[0040] In one example, the specific method of constructing a 3D model is as follows: First, the surveying and mapping data is cleaned and organized to remove redundant information and ensure data accuracy and completeness. For data from different sources and formats, format conversion and data fusion may be required for subsequent processing.

[0041] Using the elevation information from surveying and mapping data, combined with Geographic Information System (GIS) technology, we construct a terrain model of the airport. This typically involves creating a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) to accurately reflect the undulations and changes in the airport surface.

[0042] Based on the location, shape, and size information of buildings and facilities from the surveying data, 3D modeling software (such as AutoCAD, SketchUp, 3ds Max, and Revit) is used to create the model. This step requires meticulously recreating the geometric form and spatial relationships of the airport's various buildings (such as terminals, control towers, and hangars) and facilities (such as runways, taxiways, aprons, and lighting systems).

[0043] To enhance the realism and visual quality of 3D models, appropriate texture maps need to be added to the models based on texture images provided by surveying and mapping data or taken on-site. This includes detailed information such as the material, color, and pattern of building surfaces, as well as ground textures for areas like runways and aprons.

[0044] After completing the modeling of each component, the model needs to be optimized to reduce the amount of data required and improve rendering efficiency. At the same time, the models of each component are integrated to form a complete and unified 3D model of the airport.

[0045] Finally, the constructed 3D model is verified and tested to ensure its accuracy, completeness, and usability. This includes checking the model's geometric accuracy, texture quality, rendering effects, and functional testing by simulating actual operating scenarios.

[0046] S2. Collect monitoring data of the airport, and identify mobile targets and their motion trajectories from the monitoring data. If the motion trajectories intersect with the control area, generate intrusion warning information.

[0047] S201. Set up inspection points around the control area at the airport and deploy lidar at the inspection points.

[0048] S202: Using a surveillance camera to collect surveillance video, and using a target recognition algorithm to identify a moving target from the surveillance video.

[0049] In one example, a method for identifying a moving target is as follows: Choose a surveillance camera equipped with a high-definition camera, ensuring that the camera has high resolution, good image quality, and stable zoom capability.

[0050] Collect surveillance videos and ensure that the videos can be transmitted to ground stations or cloud servers in real time for storage and processing.

[0051] The collected surveillance video is pre-processed, including denoising, contrast enhancement, image smoothing, etc., to improve image quality. The video frames are converted into an image format suitable for target recognition algorithm processing.

[0052] Detect moving objects in images using target detection algorithms (such as inter-frame difference, background modeling, and optical flow). The algorithms analyze features such as pixel changes and optical flow differences in the image to identify potential moving objects.

[0053] Extract features of detected moving targets, including shape, color, texture, and other characteristic information. Use deep learning algorithms or computer vision technology to match the extracted features to determine the identity or type of the target.

[0054] After identifying a moving target, a target tracking algorithm (such as a Kalman filter or particle filter) is used to continuously track the target. The algorithm predicts the target's position in the next frame based on its trajectory, speed, and other information, enabling real-time tracking of the target.

[0055] The identified moving target information (such as location, speed, type, etc.) is output to the ground station or cloud server in real time. The recognition results can be displayed through a visual interface.

[0056] S203 : Based on the area to which the actual positioning coordinates of the surveillance camera belong, determine the detection points in the same area, and obtain laser point cloud data of the detection points in the same area.

[0057] In one example, the fusion method of surveillance video and laser point cloud is as follows: It is understood that the method for determining the actual location coordinates of the surveillance camera includes: GPS positioning: If the surveillance camera is equipped with a GPS module, its latitude and longitude coordinates can be directly obtained.

[0058] Map matching: If the camera does not have GPS, but its location can be roughly determined through other means (such as network IP address, installation location records, etc.), it can be matched with a map to obtain more precise location information.

[0059] On-site measurement: For cameras whose location information cannot be directly obtained, their location coordinates can be determined through on-site measurement (such as using tools such as rangefinders and total stations).

[0060] The method for determining the detection points in the same area is as follows: Area division: Based on the location coordinates of the surveillance camera, combined with map information or preset area division rules, the area where the camera is located is divided into one or more sub-areas.

[0061] Select detection points: Within each sub-area, select appropriate detection points based on monitoring requirements and the coverage of the laser scanning system. These detection points can be fixed locations (such as building corners, road intersections, etc.) or dynamically set (such as temporarily adjusted according to monitoring needs).

[0062] Laser point cloud data collection based on determined detection points: Ensure that the laser scanning system (such as LiDAR) is correctly configured, including parameters such as scanning frequency, scanning angle, and resolution. Start the laser scanning system and scan the selected detection points. The system will automatically collect laser point cloud data based on the preset parameters and scanning path. Process the collected laser point cloud data, including denoising, filtering, and alignment, to improve data accuracy and usability. Fuse the processed laser point cloud data with the video data from the surveillance camera. This can be achieved by projecting the laser point cloud data onto the camera's image plane or spatially aligning the camera's image data with the laser point cloud data.

[0063] S204 , identifying the position coordinates of the moving target based on the laser point cloud data, and saving the position coordinates at different times as a coordinate sequence.

[0064] In one example, the location coordinate processing method includes: Data preprocessing: Further preprocessing of the laser point cloud data, including noise removal and ground point filtering, is performed to improve target recognition accuracy. If the laser point cloud data contains multiple scan frames, time synchronization and inter-frame registration are required to ensure that the data between different frames is consistent.

[0065] Target localization: Based on the target recognition results, the precise coordinates of the moving target in the laser point cloud data are determined. This can be achieved by calculating the target center point or the center of gravity of the bounding box, or more complex algorithms can be used to estimate the actual position of the target.

[0066] Timestamp recording: When collecting laser point cloud data, the timestamp of each data frame is recorded. This can be achieved through the built-in clock of the laser scanning system or an external time synchronization device.

[0067] Coordinate sequence construction: For each identified moving target, a coordinate sequence is constructed based on its position coordinates and timestamps in different time frames. The coordinate sequence can be represented as an array or data structure showing the target position coordinates changing over time.

[0068] Data storage: Save the constructed coordinate sequence to an appropriate storage medium, such as a database, file system, etc. The storage format can be a text file, binary file, database table, etc., depending on the requirements of subsequent processing and analysis.

[0069] S205: Utilize a long short-term memory network model to predict the expected position coordinates of the moving target based on the coordinate sequence.

[0070] In one example, the prediction method is as follows: Coordinate sequence organization: Ensure that the coordinate sequence of the moving target has been constructed according to the previous steps, including timestamps and location coordinates. Organize the coordinate sequence data into a format suitable for LSTM model input, typically a two-dimensional array or tensor, where each row represents the coordinate data of a time step.

[0071] Feature Engineering: Extract and transform the coordinate sequence data as needed. For example, the position change (speed, acceleration, etc.) between adjacent time steps can be calculated as an additional feature input.

[0072] Data normalization: Normalize the coordinate sequence data to improve the training efficiency and prediction performance of the model. Normalization can be achieved by scaling the data to a specific range (such as 0 to 1).

[0073] LSTM model design: Design a neural network model that includes an LSTM layer. The LSTM layer can be followed by one or more fully connected layers (Dense layers) to output the predicted location coordinates.

[0074] Model parameter setting: Set the number of units in the LSTM layer, which determines the model's memory capacity. Set the loss function (such as mean squared error (MSE)) and optimizer (such as Adam) to train the model.

[0075] Model compilation: Compile the model using a deep learning framework such as TensorFlow or PyTorch. Specify the loss function, optimizer, and evaluation metric (such as accuracy or mean squared error).

[0076] Data partitioning: The coordinate sequence data is divided into a training set and a test set. The training set is used to train the model, and the test set is used to evaluate the performance of the model.

[0077] Model training: Use the training set data to train the LSTM model. Update the model weights through multiple iterations (epochs) and batches (batch size).

[0078] Model evaluation: Evaluate the performance of the model on the test set. Use evaluation metrics such as mean squared error to measure the model's predictive accuracy.

[0079] Input new data: Get new coordinate sequence data and process it according to the same preprocessing steps.

[0080] Model prediction: Use the trained LSTM model to predict new data and output the predicted location coordinates.

[0081] S206: Determine whether the expected position coordinates are within the control area.

[0082] After obtaining the predicted location coordinates, it is necessary to determine whether the coordinates are within the preset control area. The control area can be one or more specific geographic areas, which can be defined using a geographic information system (GIS) or a simple polygonal bounding box. This determination can be made by calculating the distance between the predicted coordinates and the control area boundary, or by determining whether the predicted coordinates fall within the control area polygon.

[0083] S207. If the expected position coordinates are within the control area, the type of the mobile target identified by the target recognition algorithm is obtained; based on the preset type authorization information, it is determined whether the mobile target has the authority to enter the control area: if so, a prompt message indicating that the control area is in operation is generated; if not, an intrusion warning message is generated, and the sound and light alarm in the corresponding area is controlled to play an expulsion voice.

[0084] A database or set of rules for type authorization information is pre-set, which records which types of mobile targets are allowed to enter which control areas. Based on the identified mobile target type and control area, the type authorization information is queried to determine whether the mobile target has permission to enter the control area.

[0085] Based on the judgment results, corresponding prompt information or warning information is generated: If the mobile target has permission to enter the controlled area, a prompt message is generated indicating that the controlled area is in operation, and relevant personnel can be notified through the monitoring system interface, SMS, email, etc. If the mobile target does not have permission to enter the controlled area, an intrusion warning message is generated, and the sound and light alarms in the corresponding area are controlled to play a voice prompt to warn and drive away the unauthorized mobile target.

[0086] S3. Obtain aircraft demand information, where the demand information includes takeoff demand information and landing demand information, and generate a task to be executed based on the demand information.

[0087] Obtain aircraft demand information received by the tower, the demand information including flight number, takeoff or landing, current location information, and expected time; generate pending tasks based on the demand information, and cache the pending tasks in the task queue; sort the pending tasks in the task queue from early to late according to the expected time, or adjust the order of pending tasks in the task queue based on the administrator's reordering instructions.

[0088] In an example, the task generation method is as follows: Obtaining aircraft demand information received by the control tower: The control tower system receives real-time demand information from aircraft via communication links. This demand information includes, but is not limited to, the flight number (a unique identifier of a flight), takeoff or landing instructions (specifying whether the aircraft is scheduled to take off or land), current location information (the aircraft's current latitude, longitude, altitude, and other geographic location data), and desired time (the aircraft's desired takeoff or landing time).

[0089] Verify and parse request information: Perform format verification and integrity checks on received request information to ensure its accuracy and completeness. Parse the request information to extract key information such as flight number, takeoff / landing instructions, current location, and expected time to prepare for subsequent processing.

[0090] Generate pending tasks based on demand information: Generate corresponding pending tasks based on the parsed demand information. Each task should include key information such as flight number, takeoff / landing type, expected time, current location, and target location (for takeoff tasks, the target location may be the takeoff runway; for landing tasks, the target location may be the landing runway or apron).

[0091] Cache pending tasks to the task queue: Add the generated pending tasks to the task queue, which is used to store all pending flight tasks. The task queue can be a memory-based queue structure (such as Python's queue module) or a persistent storage queue (such as a database table or queue service).

[0092] Sort pending tasks in the task queue by expected time from earliest to latest: Sort pending tasks in the task queue by their expected time. This ensures that the task with the earliest expected time is prioritized, improving the efficiency and accuracy of tower scheduling.

[0093] Adjust the order of pending tasks in the task queue based on administrator reordering instructions: In some cases, administrators may need to adjust the order of tasks in the task queue based on special circumstances or priorities. The system should provide an administrator interface or API interface that allows administrators to enter reordering instructions. Based on the administrator's reordering instructions, the order of tasks in the task queue will be adjusted accordingly.

[0094] S4. Collect meteorological data through meteorological sensors.

[0095] Select appropriate meteorological sensors based on the airport's flight zone weather monitoring needs. Common sensors include temperature sensors, humidity sensors, air pressure sensors, wind speed sensors, wind direction sensors, and precipitation sensors. These sensors can measure key meteorological elements such as air temperature, relative humidity, atmospheric pressure, wind speed, wind direction, and precipitation.

[0096] S5. Allocate take-off and landing runways for the mission to be executed based on meteorological data and intrusion warning information.

[0097] S501 : Based on the preset correspondence between meteorological data and mission execution time and current meteorological data, obtain the current takeoff mission execution time and landing mission execution time.

[0098] S501.1: First, the system must pre-define a set of rules for the correspondence between weather data and mission execution time. These rules may be based on historical data, safety standards, and analysis of the impact of weather conditions on flight operations.

[0099] S501.2: Obtain current meteorological data in real time, including but not limited to key meteorological elements such as temperature, humidity, wind speed, wind direction, and visibility.

[0100] S501.3: Calculate the current takeoff and landing times based on current weather data and pre-set mapping rules. These times may be adjusted based on weather conditions to ensure flight safety.

[0101] S502: Monitor the duration of the intrusion warning information. If the duration reaches a set time threshold, generate an abnormality mark for the runway to which the intrusion warning information belongs. If the intrusion warning information is eliminated, delete the corresponding abnormality mark.

[0102] S502.1: The system continuously monitors intrusion warning information, which may be provided by the Runway Intrusion Detection System (RIDS) or other safety systems.

[0103] S502.2: If the duration of the intrusion warning information for a runway reaches the set time threshold (such as 5 minutes), the system automatically generates an abnormality mark for the runway, indicating that the runway is currently in an unsafe state.

[0104] S502.3: If the intrusion warning information is eliminated (e.g., through manual confirmation of safety or automatic detection recovery), the system will immediately delete the corresponding abnormality mark and restore the normal use of the runway.

[0105] S503: Maintain an execution task queue for each runway without an abnormality mark.

[0106] S503.1: The system traverses all runways and selects only those runways without abnormal identification for subsequent operations.

[0107] S503.2: Maintain an independent execution task queue for each selected runway to store the takeoff and landing tasks to be executed on the runway.

[0108] S504: Count the task types of the tasks to be executed in each task execution queue, and generate an estimated waiting time for each task execution queue based on the task type, takeoff task execution time, and landing task execution time.

[0109] S504.1: The system counts the types of tasks to be executed in each execution task queue, including takeoff tasks and landing tasks.

[0110] S504.2: Calculate the estimated waiting time for each task queue based on the task type, takeoff task execution time, landing task execution time, and the number of tasks currently in the queue. This time may take into account factors such as safe spacing between tasks and runway utilization efficiency.

[0111] S505: Allocate the to-be-executed task that is ranked highest in the task queue to the execution task queue with the shortest estimated waiting time, and update the estimated waiting time of the corresponding execution task queue.

[0112] S505.1: The system first sorts all execution task queues according to a certain order (such as task priority, estimated waiting time, etc.).

[0113] S505.2: Then, assign the task with the highest order to the task queue with the shortest estimated waiting time. This step aims to balance the efficiency of each runway and reduce waiting time.

[0114] S505.3: After assigning a task, the system immediately updates the estimated waiting time of the corresponding execution task queue to reflect the latest task status and runway usage.

[0115] S506. Monitor the maximum difference in expected waiting time of multiple execution task queues. If the maximum difference exceeds a set time difference threshold, transfer some of the to-be-executed tasks in the execution task queue with the longest expected waiting time to the execution task queue with the shortest expected waiting time.

[0116] S506.1: The system continuously monitors the estimated waiting times of multiple execution task queues and calculates the maximum difference between them.

[0117] S506.2: If the maximum difference exceeds the set time difference threshold (e.g., 30 minutes), it indicates that some runways are heavily backlogged while other runways are relatively idle.

[0118] S506.3: In this case, the system transfers some pending tasks from the task queue with the longest estimated waiting time to the task queue with the shortest estimated waiting time. This step is intended to further address the runway's abnormal status, optimize runway utilization, and reduce overall waiting time.

[0119] In some embodiments, the airport flight zone risk management system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the airport flight zone risk management system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 (Describe) the function of airport airfield risk management.

[0120] In this embodiment, the airport flight zone risk management system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown in FIG. The functional modules of system 200 may include: a model building module 210, an intrusion warning module 220, a demand analysis module 230, a weather monitoring module 240, and a runway allocation module 250. As used herein, a module refers to a series of computer program segments that can be executed by at least one processor and perform fixed functions, and is stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0121] Model building module, used to build a 3D model of the airport and set control areas in the 3D model; An intrusion warning module is used to collect airport monitoring data, identify mobile targets and their motion trajectories from the monitoring data, and generate intrusion warning information if the motion trajectory intersects the control area; A demand analysis module is used to obtain aircraft demand information, including takeoff demand information and landing demand information, and generate tasks to be executed based on the demand information; Meteorological monitoring module, used to collect meteorological data through meteorological sensors; The runway allocation module is used to allocate take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information.

[0122] Optionally, as an embodiment of the present invention, constructing a three-dimensional model of an airport and setting a control area in the three-dimensional model includes: Obtain surveying and mapping data of the airport, and use modeling software to construct a three-dimensional model based on the surveying and mapping data.

[0123] Optionally, as an embodiment of the present invention, monitoring data of an airport is collected, and a mobile target and a motion trajectory of the mobile target are identified from the monitoring data. If the motion trajectory intersects with the control area, intrusion warning information is generated, including: Set up inspection points around the control area at the airport and deploy lidar at the inspection points; Using a surveillance camera to collect surveillance video, and using a target recognition algorithm to identify a moving target from the surveillance video; Based on the area to which the actual positioning coordinates of the surveillance camera belong, determine the detection points in the same area and obtain the laser point cloud data of the detection points in the same area; Identify the position coordinates of the moving target based on the laser point cloud data, and save the position coordinates at different times as a coordinate sequence; Predicting the expected position coordinates of the moving target based on the coordinate sequence using a long short-term memory network model; It is determined whether the expected position coordinates are within the control area, and if the expected position coordinates are within the control area, an intrusion warning message is generated.

[0124] Optionally, as an embodiment of the present invention, before generating the intrusion warning information, the method further includes: Get the type of mobile target identified by the target recognition algorithm; Determine whether the mobile target has the authority to enter the control area based on the preset type authorization information: If so, a prompt message is generated indicating that the control area is in operation status; If not, an intrusion warning message is generated, and the sound and light alarms in the corresponding area are controlled to play an expulsion voice.

[0125] Optionally, as an embodiment of the present invention, the types include workers, work vehicles and other intrusion objects.

[0126] Optionally, as an embodiment of the present invention, obtaining aircraft requirement information, the requirement information including takeoff requirement information and landing requirement information, and generating a task to be executed based on the requirement information include: Obtaining aircraft demand information received by the tower, the demand information including flight number, take-off or landing, current location information, and expected time; Generate tasks to be executed based on demand information and cache them in the task queue; Sort the tasks in the task queue by expected time from earliest to latest, or adjust the order of tasks in the task queue based on administrator reordering instructions.

[0127] Optionally, as an embodiment of the present invention, allocating take-off and landing runways for tasks to be executed based on meteorological data and intrusion warning information includes: Based on the correspondence between the preset meteorological data and the mission execution time and the current meteorological data, the current takeoff mission execution time and landing mission execution time are obtained; Monitor the duration of the intrusion warning information. If the duration reaches the set time threshold, an abnormality mark is generated for the runway to which the intrusion warning information belongs. If the intrusion warning information is eliminated, the corresponding abnormality mark is deleted. Maintain an execution task queue for each runway without an exception mark; Count the task types of tasks to be executed in each task execution queue, and generate the estimated waiting time for each task execution queue based on the task type, takeoff task execution time, and landing task execution time; Assign the most highly ordered task to be executed in the task queue to the execution task queue with the shortest estimated waiting time, and update the estimated waiting time of the corresponding execution task queue; Monitor the maximum difference in expected waiting time of multiple execution task queues. If the maximum difference exceeds a set time difference threshold, transfer some of the to-be-executed tasks in the execution task queue with the largest expected waiting time to the execution task queue with the smallest expected waiting time.

[0128] Figure 3 This is a structural diagram of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the airport flight area risk management method provided in an embodiment of the present invention.

[0129] The terminal 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention. The server structure may be a bus structure or a star structure, and may include more or fewer components than shown, or may combine certain components or arrange the components differently.

[0130] Memory 320 can be used to store execution instructions of processor 310. Memory 320 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. When the execution instructions in memory 320 are executed by processor 310, terminal 300 can perform some or all of the steps in the above-described method embodiments.

[0131] The processor 310 is the control center of the storage terminal. It uses various interfaces and lines to connect various parts of the entire electronic terminal. It executes various functions of the electronic terminal and / or processes data by running or executing software programs and / or modules stored in the memory 320, and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0132] The communication unit 330 is configured to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals, or send user data to other terminals.

[0133] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of each embodiment provided herein. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0134] Therefore, the present invention realizes timely warning of illegal intrusion into the airfield by constructing a digital twin model and monitoring the environmental data of the airport flight area, and reasonably allocates runways based on the illegal intrusion warning to achieve effective control of flight safety. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0135] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code, and includes instructions for causing a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0136] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0137] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, and can be electrical, mechanical or other forms.

[0138] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0140] Although the present invention has been described in detail with reference to the accompanying drawings and in conjunction with preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, persons of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any changes or substitutions that can be easily conceived by persons skilled in the art within the technical scope disclosed in the present invention shall be within the scope of protection of the present invention.

Claims

1. A method for risk management of airport flight areas, characterized in that: include: Build a 3D model of the airport and set up control areas in the 3D model; Collecting monitoring data of the airport, and identifying the mobile target and the movement trajectory of the mobile target from the monitoring data, and generating intrusion warning information if the movement trajectory intersects with the control area; Acquiring aircraft demand information, the demand information including take-off demand information and landing demand information, and generating a task to be performed based on the demand information; Collect meteorological data through meteorological sensors; Assign take-off and landing runways for pending missions based on meteorological data and intrusion warning information.

2. The method according to claim 1, characterized in that Build a 3D model of the airport and set control areas in the 3D model, including: Obtain surveying and mapping data of the airport, and use modeling software to build a three-dimensional model based on the surveying and mapping data.

3. The method according to claim 1, characterized in that: Collecting the monitoring data of the airport, and identifying the mobile target and the movement trajectory of the mobile target from the monitoring data, and generating intrusion warning information if the movement trajectory intersects with the control area, including: Set up detection points around the control area at the airport and deploy lidar at the detection points; Using a surveillance camera to collect surveillance video, and using a target recognition algorithm to identify a moving target from the surveillance video; Based on the area to which the actual positioning coordinates of the surveillance camera belong, the detection points in the same area are determined, and the laser point cloud data of the detection points in the same area are obtained; Identify the position coordinates of the moving target based on the laser point cloud data, and save the position coordinates at different times as a coordinate sequence; Predicting the expected position coordinates of the moving target based on the coordinate sequence using a long short-term memory network model; It is determined whether the expected position coordinates are within the control area, and if the expected position coordinates are within the control area, an intrusion warning message is generated.

4. The method according to claim 3, characterized in that Before generating the intrusion warning information, the method further includes: Obtain the type of mobile target identified by the target recognition algorithm; Determine whether the mobile target has the authority to enter the control area based on the preset type authorization information: If yes, a prompt message is generated indicating that the control area is in operation state; If not, an intrusion warning message is generated, and the sound and light alarms in the corresponding area are controlled to play an expulsion voice.

5. The method according to claim 4, characterized in that The types include operating personnel, operating vehicles and other intrusion objects.

6. The method according to claim 1, characterized in that Acquiring aircraft demand information, the demand information including take-off demand information and landing demand information, and generating a task to be performed based on the demand information, including: Obtaining aircraft demand information received by the tower, the demand information including flight number, take-off or landing, current location information, and expected time; Generate tasks to be executed based on demand information and cache the tasks to be executed in the task queue; Sort the tasks to be executed in the task queue from earliest to latest according to the expected time, or adjust the order of the tasks to be executed in the task queue based on the administrator's reordering instructions.

7. The method according to claim 6, characterized in that Allocate take-off and landing runways for pending missions based on meteorological data and intrusion warning information, including: Based on the correspondence between the preset meteorological data and the mission execution time and the current meteorological data, the current take-off mission execution time and the landing mission execution time are obtained; Monitor the duration of the intrusion warning information. If the duration reaches the set time threshold, an abnormal mark is generated for the runway to which the intrusion warning information belongs. If the intrusion warning information is eliminated, the corresponding abnormal mark is deleted. Maintain an execution task queue for each runway without an exception mark; Count the task types of tasks to be executed in each task execution queue, and generate an estimated waiting time for each task execution queue based on the task type, takeoff task execution time, and landing task execution time; Allocate the to-be-executed task with the highest order in the task queue to the execution task queue with the shortest estimated waiting time, and update the estimated waiting time of the corresponding execution task queue; The maximum difference of the estimated waiting time of multiple execution task queues is monitored. If the maximum difference exceeds a set time difference threshold, part of the tasks to be executed in the execution task queue with the largest estimated waiting time is transferred to the execution task queue with the smallest estimated waiting time.

8. An airport flight area risk management system, characterized in that: include: Model building module, used to build a 3D model of the airport and set control areas in the 3D model; An intrusion warning module is used to collect monitoring data of the airport and identify mobile targets and their motion trajectories from the monitoring data, and generate intrusion warning information if the motion trajectory intersects with the control area; A demand analysis module, used to obtain aircraft demand information, the demand information including take-off demand information and landing demand information, and generate tasks to be performed based on the demand information; A meteorological monitoring module is used to collect meteorological data through meteorological sensors; The runway allocation module is used to allocate take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information.

9. A terminal, characterized in that: include: A memory for storing the airport flight area risk management program; A processor, used to implement the steps of the airport airfield risk management method as described in any one of claims 1-7 when executing the airport airfield risk management program.

10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores an airport flight zone risk management program, and when the airport flight zone risk management program is executed by a processor, the steps of the airport flight zone risk management method according to any one of claims 1 to 7 are implemented.

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