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

Abstract

The present invention relates to the field of digital twin technology, and specifically provides an airport flight zone risk management method, system, terminal and storage medium, including: constructing a three-dimensional model of the airport, and setting a control area in the three-dimensional model; collecting monitoring data of the airport, and identifying mobile targets and the motion trajectory of the mobile targets from the monitoring data, and generating intrusion warning information if the motion trajectory intersects with the control area; obtaining aircraft demand information, the demand information includes take-off demand information and landing demand information, and generating tasks to be performed based on the demand information; collecting meteorological data through meteorological sensors; and allocating take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information. The present invention realizes timely warning of illegal intrusions in the flight zone by constructing a digital twin model and monitoring the environmental data of the airport flight zone, and reasonably allocates runways based on illegal intrusion warnings, thereby realizing effective control of flight safety.

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 continuous expansion of the global aviation industry, the safety management of airport flight areas is facing unprecedented complexity and challenges. In this context, ensuring the safety, efficiency and smoothness of flight area operations has become a top priority for aviation management departments and airport operators. Traditional safety management methods mainly rely on manual monitoring and judgment based on the long-term experience accumulated by staff. Although this method can maintain basic operating order to a certain extent, it seems powerless in the face of increasing and diverse potential risks.

[0003] Specifically, manual monitoring is limited by factors such as human vision, concentration, and reaction speed, and it is often difficult to achieve comprehensive coverage and real-time monitoring of every corner of the flight area, every minute and every second. At the same time, although empirical judgment can reflect historical laws and common problems to a certain extent, its accuracy and timeliness are often not fully guaranteed when facing new threats, emergencies, or complex and changing operating environments. Therefore, the ability of traditional management methods to identify potential risks, respond in a timely manner, and effectively prevent accidents can no longer meet the needs of the current development of the aviation industry. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an airport flight zone 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:

[0006] Build a 3D model of the airport and set up control areas in the 3D model;

[0007] 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;

[0008] 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;

[0009] Collect meteorological data through meteorological sensors;

[0010] Assign take-off and landing runways for pending missions based on meteorological data and intrusion warning information.

[0011] In an optional implementation, constructing a three-dimensional model of the airport and setting a control area in the three-dimensional model includes:

[0012] 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.

[0013] In an optional implementation, monitoring data of the airport is collected, and a mobile target and a motion trajectory of the mobile target are identified from the monitoring data, and if the motion trajectory intersects with the control area, intrusion warning information is generated, including:

[0014] Set up detection points around the control area at the airport and deploy lidar at the detection points;

[0015] Using a surveillance camera to collect surveillance video, and using a target recognition algorithm to identify a moving target from the surveillance video;

[0016] 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;

[0017] 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;

[0018] Predicting the expected position coordinates of the moving target based on the coordinate sequence using a long short-term memory network model;

[0019] 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.

[0020] In an optional implementation, before generating the intrusion warning information, the method further includes:

[0021] Obtain the type of mobile target identified by the target recognition algorithm;

[0022] Determine whether the mobile target has the authority to enter the control area based on the preset type authorization information:

[0023] If yes, a prompt message is generated indicating that the control area is in operation state;

[0024] 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.

[0025] In an optional embodiment, the types include workers, work vehicles and other intrusion objects.

[0026] In an optional implementation, obtaining aircraft demand information, the demand information including takeoff demand information and landing demand information, and generating a task to be performed based on the demand information, includes:

[0027] Obtaining aircraft demand information received by the tower, the demand information including flight number, take-off or landing, current location information, and expected time;

[0028] Generate tasks to be executed based on demand information and cache the tasks to be executed in the task queue;

[0029] 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.

[0030] In an optional implementation, allocating take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information includes:

[0031] 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;

[0032] 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.

[0033] Maintain an execution task queue for each runway without an exception mark;

[0034] 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;

[0035] 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;

[0036] 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.

[0037] In a second aspect, the present invention provides an airport flight zone risk management system, comprising:

[0038] Model building module, used to build a 3D model of the airport and set control areas in the 3D model;

[0039] 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;

[0040] 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;

[0041] A meteorological monitoring module is used to collect meteorological data through meteorological sensors;

[0042] 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.

[0043] In a third aspect, a terminal is provided, including:

[0044] A memory for storing the airport flight area risk management program;

[0045] 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.

[0046] In a fourth aspect, a computer-readable storage medium is provided, on which an airport flight zone risk management program is stored. When the airport flight zone risk management program is executed by a processor, the steps of the airport flight zone risk management method provided in the first aspect are implemented.

[0047] The beneficial effect of the present invention lies in that the airport flight zone risk management method, system, terminal and storage medium provided by the present invention, by constructing a digital twin model and monitoring the environmental data of the airport flight zone, can realize timely early warning of illegal intrusion into the flight zone, and reasonably allocate runways based on the illegal intrusion warning, thereby realizing effective control of flight safety.

[0048] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0051] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0052] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0054] 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 the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0055] The key terms appearing in the present invention are explained below.

[0056] ‌‌Digital twin refers to the digital model of a physical product in virtual space, which contains product information from product conception to product delisting throughout its life cycle. It maps data such as physical models, sensor updates, and operation history in virtual space to reflect the entire life cycle of the corresponding physical equipment.‌

[0057] The airport flight zone risk management method provided in the embodiment of the present invention is executed by a computer device, and accordingly, the airport flight zone risk management system runs in the computer device.

[0058] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be an airport flight zone risk management system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0059] like Figure 1 As shown, the method includes:

[0060] Step 110, construct a three-dimensional model of the airport and set a control area in the three-dimensional model.

[0061] First, we use advanced geographic information systems (GIS) and 3D modeling technology to accurately construct a 3D model of the airport based on the actual layout, topography, and building structure of the airport. The model not only includes the physical structure of the airport, but also covers key areas such as runways, aprons, taxiways, control towers, and terminals. Subsequently, in the 3D model, various control areas are clearly divided and set up according to the needs of safety management and operational efficiency, such as no-fly zones, restricted zones, and safety buffer zones, to ensure that these areas can be effectively identified and controlled in subsequent management and monitoring.

[0062] Step 120, 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.

[0063] The airport's surveillance data is collected in real time through surveillance cameras, radar systems and other equipment deployed at key locations of the airport. Advanced image recognition, target tracking and big data analysis technologies are used to accurately identify mobile targets (such as vehicles, personnel, surveillance cameras, etc.) from the surveillance data and continuously track their movement trajectories. When it is found that the movement trajectory of a mobile target intersects with the preset control area, the intrusion warning mechanism is immediately triggered to generate detailed intrusion warning information, including the type of intrusion target, location, speed and expected intrusion time, so that timely countermeasures can be taken.

[0064] Step 130 , obtaining aircraft demand information, the demand information including takeoff demand information and landing demand information, and generating a task to be performed based on the demand information.

[0065] Connect with the airport's air traffic management system to obtain real-time takeoff and landing information of aircraft. This information includes but is not limited to aircraft model, estimated takeoff / landing time, destination / departure point, load, etc. Based on this information, combined with the airport's operating rules and current conditions, automatically generate takeoff and landing tasks to be executed, including task sequence, required runway, estimated time, etc.

[0066] Step 140, collecting meteorological data through meteorological sensors.

[0067] The weather sensor network installed at the airport collects weather data of the airport and its surrounding areas in real time, including but not limited to wind speed, wind direction, visibility, temperature, humidity, air pressure, etc. These data are crucial for assessing flight conditions, making flight plans, and ensuring flight safety.

[0068] Step 150, allocating take-off and landing runways for the tasks to be performed based on the meteorological data and the intrusion warning information.

[0069] Based on 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 operating efficiency (such as reducing aircraft waiting time, optimizing runway use, etc.), runways are reasonably allocated for takeoff and landing tasks to be executed. This step requires the use of advanced algorithms and models to ensure that while ensuring flight safety, the airport's operating efficiency and capacity are maximized. At the same time, the task allocation plan needs to be dynamically adjusted according to real-time conditions to deal with possible emergencies.

[0070] To facilitate understanding of the present invention, the airport flight zone risk management method provided by the present invention is further described below based on the principle of the airport flight zone risk management method of the present invention and in combination with the process of performing risk management on the airport flight zone in the embodiment.

[0071] Specifically, the airport flight area risk management methods include:

[0072] S1. Build a three-dimensional model of the airport and set up control areas in the three-dimensional model.

[0073] 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.

[0074] In an example, the specific method of constructing a 3D model is as follows:

[0075] First, the surveying and mapping data is cleaned and organized to remove redundant information and ensure the accuracy and completeness of the data. For data from different sources and in different formats, format conversion and data fusion may be required for subsequent processing.

[0076] Using the elevation information in the surveying and mapping data, combined with the Geographic Information System (GIS) technology, the terrain model of the airport is constructed. This usually includes the creation of a digital elevation model (DEM) and a digital surface model (DSM) to accurately reflect the ups and downs and changes of the airport ground.

[0077] Based on the location, shape, size and other information about buildings and facilities in the surveying data, modeling is performed using 3D modeling software (such as AutoCAD, SketchUp, 3ds Max, Revit, etc.). This step requires meticulous restoration of the geometric forms and spatial relationships of various buildings (such as terminals, control towers, hangars, etc.) and facilities (such as runways, taxiways, aprons, lighting systems, etc.) in the airport.

[0078] In order to improve the realism and visual effects of the 3D model, it is necessary to add appropriate texture maps to the model based on the texture images provided in the surveying data or taken on site. This includes detailed information such as the material, color, and pattern of the building surface, as well as the ground texture of areas such as runways and aprons.

[0079] After completing the modeling of each part, the model needs to be optimized to reduce the amount of model data and improve rendering efficiency. At the same time, the models of each part are integrated to form a complete and unified airport 3D model.

[0080] 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 effect, etc., as well as simulating actual operating scenarios for functional testing.

[0081] S2. 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.

[0082] S201. Set up inspection points around the control area at the airport and deploy lidar at the inspection points.

[0083] S202: Collect surveillance video using a surveillance camera, and identify moving targets from the surveillance video using a target recognition algorithm.

[0084] In one example, a method for identifying a moving target is as follows:

[0085] Choose a surveillance camera equipped with a high-definition camera, ensuring that the camera has high resolution, good image quality, and stable zoom capability.

[0086] Collect surveillance videos and ensure that the videos can be transmitted to ground stations or cloud servers in real time for storage and processing.

[0087] The collected surveillance video is preprocessed, 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.

[0088] Detect moving targets in images using target detection algorithms (such as frame difference method, background modeling method, optical flow method, etc.). The algorithm analyzes features such as pixel changes and optical flow differences in the image to identify potential moving targets.

[0089] Extract features of detected moving targets, including shape, color, texture and other feature information. Use deep learning algorithms or computer vision technology to match the extracted features to determine the identity or type of the target.

[0090] After identifying the moving target, the target tracking algorithm (such as Kalman filter, particle filter, etc.) is used to continuously track the target. The algorithm predicts the position of the next frame based on the target's motion trajectory, speed and other information to achieve real-time tracking of the target.

[0091] The identified moving target information (such as location, speed, type, etc.) is output to the ground station or cloud server in real time. The identification results can be displayed through a visual interface.

[0092] 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.

[0093] In one example, the fusion method of surveillance video and laser point cloud is as follows:

[0094] It can be understood that the method for determining the actual location coordinates of the surveillance camera includes:

[0095] GPS positioning: If the surveillance camera is equipped with a GPS module, its latitude and longitude coordinates can be directly obtained.

[0096] Map matching: If the camera does not have GPS, but its location can be roughly determined by other means (such as network IP address, installation location records, etc.), it can be matched with a map to obtain more precise location information.

[0097] 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).

[0098] The method for determining the detection points in the same area is as follows:

[0099] Area division: According to 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.

[0100] Select detection points: In 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 temporary adjustments based on monitoring requirements).

[0101] Laser point cloud data collection based on determined detection points:

[0102] Ensure that the laser scanning system (such as LiDAR) has been correctly configured, including parameters such as scanning frequency, scanning angle, resolution, etc. Start the laser scanning system and scan the selected detection points. The system will automatically collect laser point cloud data according to the preset parameters and scanning path. Process the collected laser point cloud data, including denoising, filtering, and registration steps to improve the accuracy and usability of the data. Fusion the processed laser point cloud data with the video data of the surveillance camera. This can be achieved by projecting the laser point cloud data onto the image plane of the camera, or by spatially aligning the camera's image data with the laser point cloud data.

[0103] S204: 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.

[0104] In one example, the location coordinate processing method includes:

[0105] Data preprocessing: Further preprocessing of laser point cloud data, including removing noise points, filtering ground points, etc., to improve the accuracy of target recognition. 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 are consistent.

[0106] Target positioning: Based on the results of target recognition, determine the precise position coordinates of the moving target in the laser point cloud data. This can be achieved by calculating the center point of the target or the center of gravity of the bounding box, or more complex algorithms can be used to estimate the actual position of the target.

[0107] 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.

[0108] 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 of the target position coordinates changing over time.

[0109] 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 needs of subsequent processing and analysis.

[0110] S205: Predict the expected position coordinates of the mobile target based on the coordinate sequence using a long short-term memory network model.

[0111] In one example, the prediction method is as follows:

[0112] Coordinate sequence organization: Make sure 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, usually a two-dimensional array or tensor, where each row represents the coordinate data of a time step.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Model parameter setting: Set the number of units in the LSTM layer, which determines the memory capacity of the model. Set the loss function (such as mean square error MSE) and optimizer (such as Adam) to train the model.

[0117] 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.

[0118] Data division: 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.

[0119] Model training: Use the training set data to train the LSTM model. Update the model weights through multiple iterations (epochs) and batches (batchsize).

[0120] Model evaluation: Evaluate the performance of the model on the test set. Use evaluation metrics such as mean squared error to measure the predictive accuracy of the model.

[0121] Input new data: Get new coordinate sequence data and process it according to the same preprocessing steps.

[0122] Model prediction: Use the trained LSTM model to predict new data and output the predicted location coordinates.

[0123] S206: Determine whether the expected position coordinates are within the control area.

[0124] 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 ranges, which can be defined by a geographic information system (GIS) or a simple polygonal bounding box. The judgment method can be to calculate the distance between the predicted coordinates and the boundary of the control area, or to determine whether the predicted coordinates fall within the polygon of the control area.

[0125] S207. If the expected position coordinates are within the control area, the type of 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.

[0126] A database or rule set of type authorization information is preset, which records which types of mobile targets are allowed to enter which control areas. According to the identified mobile target type and control area, the type authorization information is queried to determine whether the mobile target has the authority to enter the control area.

[0127] Generate corresponding prompt information or warning information based on the judgment result: If the mobile target has the authority to enter the control area, a prompt information is generated that the control 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 the authority to enter the control area, an intrusion warning information is generated, and the sound and light alarm in the corresponding area is controlled to play the expulsion voice to warn and expel the unauthorized mobile target.

[0128] 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.

[0129] Obtain aircraft demand information received by the tower, the demand information including flight number, take-off or landing, current location information, and expected time; generate pending tasks based on the demand information, and cache the pending tasks in a 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.

[0130] In an example, the task generation method is as follows:

[0131] Obtaining aircraft demand information received by the tower: The tower system receives demand information from aircraft in real time through communication links. Such demand information includes but is not limited to: flight number (a number that uniquely identifies a flight), takeoff or landing instructions (clarifying 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 expected time (the time when the aircraft is expected to take off or land).

[0132] Verify and parse demand information: Perform format verification and integrity check on the received demand information to ensure the accuracy and completeness of the information. Parse the demand information to extract key information such as flight number, take-off / landing instructions, current location, expected time, etc., in preparation for subsequent processing.

[0133] Generate tasks to be executed based on demand information: Generate corresponding tasks to be executed based on the parsed demand information. Each task should contain 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).

[0134] 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 table or queue service in a database).

[0135] Sort the tasks in the task queue from earliest to latest by expected time: Sort the tasks in the task queue by their expected time. This ensures that the tasks with the earliest expected time are processed first, improving the efficiency and accuracy of tower scheduling.

[0136] Adjust the order of pending tasks in the task queue based on administrator reordering instructions: In some cases, the administrator 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 to allow administrators to enter reordering instructions. According to the administrator's reordering instructions, the order of tasks in the task queue is adjusted accordingly.

[0137] S4. Collect meteorological data through meteorological sensors.

[0138] According to the needs of airport flight area meteorological monitoring, select appropriate meteorological sensors. 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.

[0139] S5. Allocate take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information.

[0140] S501. 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.

[0141] S501.1: First, the system needs to pre-set a set of rules for the correspondence between meteorological data and mission execution time. These rules may be based on historical data, safety standards, and analysis of the impact of meteorological conditions on flight operations.

[0142] 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.

[0143] S501.3: Calculate the current take-off mission execution time and landing mission execution time based on the current meteorological data and the preset corresponding relationship rules. These times may be adjusted due to different meteorological conditions to ensure flight safety.

[0144] S502: monitor the duration of the intrusion warning information. If the duration reaches a set time threshold, generate an abnormal mark for the runway to which the intrusion warning information belongs. If the intrusion warning information is eliminated, delete the corresponding abnormal mark.

[0145] S502.1: The system continuously monitors intrusion warning information, which may be provided by the Runway Intrusion Detection System (RIDS) or other safety systems.

[0146] 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 abnormal mark for the runway, indicating that the runway is currently in an unsafe state.

[0147] S502.3: If the intrusion warning information is eliminated (such as through manual confirmation of safety or automatic detection and recovery), the system will immediately delete the corresponding abnormal identification and restore the normal use of the runway.

[0148] S503: Maintain an execution task queue for each runway without an abnormal mark.

[0149] S503.1: The system traverses all runways and selects only those runways without abnormal markings for subsequent operations.

[0150] S503.2: Maintain an independent execution task queue for each selected runway to store the take-off and landing tasks to be executed on the runway.

[0151] 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.

[0152] S504.1: The system counts the types of tasks to be executed in each execution task queue, including take-off tasks and landing tasks.

[0153] S504.2: Calculate the estimated waiting time for each task queue based on the task type, takeoff task execution time and landing task execution time, and the number of tasks in the current queue. This time may take into account factors such as safety intervals between tasks and runway utilization efficiency.

[0154] 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.

[0155] S505.1: The system first sorts all execution task queues according to a certain order (such as task priority, estimated waiting time, etc.).

[0156] S505.2: Then, the task to be executed with the highest ranking is assigned to the execution task queue with the shortest estimated waiting time. This step aims to balance the utilization efficiency of each runway and reduce the waiting time.

[0157] 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.

[0158] S506, monitoring the maximum difference in estimated waiting time of multiple execution task queues, and if the maximum difference exceeds a set time difference threshold, transferring some of the tasks to be executed in the execution task queue with the largest estimated waiting time to the execution task queue with the smallest estimated waiting time.

[0159] S506.1: The system continuously monitors the estimated waiting times of multiple execution task queues and calculates the maximum difference between them.

[0160] S506.2: If the maximum difference exceeds the set time difference threshold (e.g., 30 minutes), it indicates that some runways are seriously backlogged while other runways are relatively idle.

[0161] S506.3: In this case, the system transfers some of the tasks to be executed in the execution task queue with the longest estimated waiting time to the execution task queue with the shortest estimated waiting time. This step is intended to further deal with the runway that has been released from the abnormal state, optimize the efficiency of runway use, and reduce the overall waiting time.

[0162] In some embodiments, the airport flight zone risk management system may include a plurality of functional modules composed of computer program segments. The computer programs of the various program segments 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 Description) Functions of airport airfield risk management.

[0163] 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. The functional modules of the 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. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0164] Model building module, used to build a 3D model of the airport and set control areas in the 3D model;

[0165] 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;

[0166] 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;

[0167] A meteorological monitoring module is used to collect meteorological data through meteorological sensors;

[0168] 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.

[0169] 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:

[0170] 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.

[0171] Optionally, as an embodiment of the present invention, monitoring data of the airport is collected, and a mobile target and a motion trajectory of the mobile target are identified from the monitoring data, and if the motion trajectory intersects with the control area, intrusion warning information is generated, including:

[0172] Set up detection points around the control area at the airport and deploy lidar at the detection points;

[0173] Using a surveillance camera to collect surveillance video, and using a target recognition algorithm to identify a moving target from the surveillance video;

[0174] 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;

[0175] 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;

[0176] Predicting the expected position coordinates of the moving target based on the coordinate sequence using a long short-term memory network model;

[0177] 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.

[0178] Optionally, as an embodiment of the present invention, before generating the intrusion warning information, the method further includes:

[0179] Obtain the type of mobile target identified by the target recognition algorithm;

[0180] Determine whether the mobile target has the authority to enter the control area based on the preset type authorization information:

[0181] If yes, a prompt message is generated indicating that the control area is in operation state;

[0182] 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.

[0183] Optionally, as an embodiment of the present invention, the types include operating personnel, operating vehicles and other intrusion objects.

[0184] Optionally, as an embodiment of the present invention, obtaining 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:

[0185] Obtaining aircraft demand information received by the tower, the demand information including flight number, take-off or landing, current location information, and expected time;

[0186] Generate tasks to be executed based on demand information and cache the tasks to be executed in the task queue;

[0187] 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.

[0188] 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:

[0189] 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;

[0190] 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.

[0191] Maintain an execution task queue for each runway without an exception mark;

[0192] 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;

[0193] 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;

[0194] 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.

[0195] Figure 3 The present invention provides a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the airport flight zone risk management method provided in an embodiment of the present invention.

[0196] 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, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0197] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal 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 the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0198] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. 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.

[0199] The communication unit 330 is used 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.

[0200] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0201] 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 airfield, 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.

[0202] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling 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 each embodiment of the present invention.

[0203] 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.

[0204] 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that 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, which can be electrical, mechanical or other forms.

[0205] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0206] 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.

[0207] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these 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; Allocate take-off and landing runways for pending missions based on meteorological data and intrusion warning information; 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.

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 and operating vehicles.

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. 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; Runway allocation module, used to allocate take-off and landing runways for tasks to be performed based on meteorological data and intrusion warning information; 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. 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 1-6 when executing the airport airfield risk management program.

9. 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 6 are implemented.

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