Obstacle evaluation method and system based on airport clearance management

Abstract

The invention relates to the technical field of aviation safety management, and discloses an obstacle assessment method and system based on airport clearance management. An obstacle assessment method based on airport clearance management comprises the following steps: S1, real-time data acquisition: performing high-frequency scanning on an airport clearance zone by using a laser radar to generate high-density three-dimensional point cloud data, and constructing a clearance zone digital twin base map through a point cloud data processing technology; an ADS-B signal is accessed, and aircraft position data are analyzed in real time; the aircraft position data and the digital twin base map are fused, and a spatial relation map of the temporary obstacle and the aircraft is constructed; meteorological data of an airport is collected based on a sensor. According to the invention, through dynamic evaluation models such as space intrusion factors and height conflict factors, obstacle risks are quantified and aircraft operation states are associated, so that the system can automatically identify sudden threats of temporary obstacles, and the defect that a traditional static model cannot adapt to dynamic scenes is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation safety management, and in particular to an obstacle assessment method and system based on airport clearance management. Background Art

[0002] In recent years, with the rapid growth of civil aviation traffic and the accelerated advancement of urban construction, the management of temporary obstacles in airport clear areas has faced severe challenges. Traditional clear area management methods rely primarily on manual inspections and static obstacle databases. However, with the growth of civil aviation traffic and the acceleration of urban development, the clear area safety issues caused by temporary obstacles have become increasingly prominent.

[0003] Traditional airspace management suffers from several drawbacks: static assessment models cannot adapt to the dynamic changes of temporary obstacles (such as construction cranes and celebration balloons). Manual inspections are inefficient and difficult to quantify risk. Furthermore, existing systems lack real-time linkage with air traffic control dispatchers. Summary of the Invention

[0004] The purpose of the present invention is to provide an obstacle assessment method and system based on airport clearance management to solve the above technical problems:

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An obstacle assessment method based on airport clearance management includes the following steps:

[0007] S1. Real-time data acquisition, including: using LiDAR to scan the airport's clear area frequently to generate high-density three-dimensional point cloud data, and then using point cloud data processing technology to construct a digital twin base map of the clear area; accessing ADS-B signals to analyze aircraft position data in real time; fusing aircraft position data with the digital twin base map to construct a spatial relationship map between temporary obstacles and aircraft; and collecting airport meteorological data based on sensors.

[0008] S2. Build a dynamic assessment model to calculate the obstacle threat index and generate a corresponding warning strategy based on the threshold control of the threat index. The warning strategy includes: SMS notification to the tower, automatic NOTAM notification, triggering air traffic control radar plotting, and driving away drones;

[0009] S3. Execute graded responses according to the early warning strategy and record the response process, including: recording data collection time, threat index calculation process, early warning strategy triggering time and response measure execution results.

[0010] As a further technical solution, when the laser radar is used to perform high-frequency scanning of the airport clear area in step S1, edge computing nodes are used to pre-process the point cloud data, including: deploying edge computing equipment near the laser radar equipment, denoising, filtering and compressing the original point cloud data obtained by the scan on the edge side, removing redundant data, and transmitting only key feature data to the central server.

[0011] As a further technical solution, the process of receiving the ADS-B signal and parsing the aircraft position data in step S1 includes:

[0012] After receiving the ADS-B signal, the edge computing gateway decodes the signal locally and extracts the core information data of the aircraft, which includes the aircraft's position, speed and altitude. It then performs preliminary screening of abnormal data based on preset rules and sends only valid data to the central server.

[0013] As a further technical solution, a dynamic assessment model is established to calculate the obstacle threat index, including the following process:

[0014] Extracting key feature parameters from the fact data, the key features including the height of the obstacle relative to the runway, the horizontal distance between the aircraft and the obstacle, the current speed of the aircraft, and the deviation of the aircraft heading from the obstacle azimuth;

[0015] Obtain the influencing factors of the obstacle threat index based on the above key characteristic parameters, including the spatial intrusion factor , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors ;

[0016] By formula Calculate the obstacle threat index ;In the formula, is the preset weight coefficient;

[0017] According to the preset range that the obstacle threat index falls into, a corresponding early warning strategy is generated.

[0018] As a further technical solution, the process of obtaining the influencing factors of the obstacle threat index includes:

[0019] (1);

[0020] (2);

[0021] (3);

[0022] (4);

[0023] The spatial invasion factor is calculated by formula (1)-(4): , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors ;

[0024] in, is the deviation between the aircraft heading and the obstacle azimuth; is the horizontal distance between the aircraft's current position and the obstacle; is the distance standard deviation; It is the minimum safe height distance; is the current altitude of the aircraft; is the aircraft’s current climb rate; is the current flight speed of the aircraft; is the obstacle height; is the minimum turning radius of the aircraft; 、 is the preset weight; 、 They are current visibility and preset standard visibility respectively; 、 They are the current wind speed and the preset standard wind speed respectively.

[0025] As a further technical solution, the method further includes an emergency processing flow process:

[0026] When an obstacle floating in the air is detected, the trajectory prediction subroutine is started;

[0027] Combined with the WRF weather model to calculate the future drift path, dangerous sectors are displayed on electronic charts, and dynamic monitoring of movable road obstacles is implemented:

[0028] When a movable road obstacle intrudes into the critical line of the danger sector, an audible and visual alarm is triggered.

[0029] An obstacle assessment system based on airport clearance management, the system comprising:

[0030] The data acquisition module is used to obtain real-time airport clear zone data, including: a lidar scanning unit for high-frequency three-dimensional scanning of the clear zone to generate high-density point cloud data; an ADS-B signal receiving unit for analyzing the real-time position, speed, and altitude data of aircraft; and a meteorological sensor unit for collecting visibility and wind speed data.

[0031] The data processing module includes edge computing nodes and a central server; the edge computing nodes are used to denoise, filter, and compress the lidar point cloud data, and to construct a digital twin base map of the clear area; the central server is used to fuse the aircraft position data with the digital twin base map to generate a spatial relationship map between obstacles and aircraft;

[0032] Dynamic assessment module, used to calculate obstacle threat index;

[0033] The early warning response module is used to trigger graded response measures based on the threat index threshold, including SMS notification to the tower, sending NOTAM notices, air traffic control radar plotting and drone expulsion.

[0034] As a further technical solution, the dynamic evaluation module includes:

[0035] A spatial intrusion factor calculation unit that assesses the intrusion risk based on the proximity of the aircraft's predicted trajectory to the obstacle profile;

[0036] The altitude conflict factor calculation unit calculates the vertical conflict probability by combining the aircraft's climb rate and the obstacle height;

[0037] The environmental enhancement factor calculation unit dynamically adjusts the threat weight according to meteorological data; the comprehensive threat index generation unit is used to calculate the obstacle threat index.

[0038] Beneficial effects of the present invention:

[0039] (1) The present invention effectively solves the core defects of traditional airspace management through multi-source real-time data fusion and dynamic threat modeling. First, by utilizing the collaborative collection of lidar, ADS-B and meteorological sensors, a high-precision digital twin base map is constructed to achieve real-time three-dimensional perception of temporary obstacles, overcoming the problems of low efficiency of manual inspections and lag in static databases. Second, through dynamic assessment models such as spatial intrusion factors and height conflict factors, obstacle risks are quantified and associated with aircraft operating status, enabling the system to automatically identify sudden threats from temporary obstacles, making up for the deficiency of traditional static models in adapting to dynamic scenarios.

[0040] (2) This invention significantly improves response timeliness through edge computing, a central server collaborative architecture, and a hierarchical warning mechanism. Edge nodes process point clouds and ADS-B data on-site, reducing transmission delays and ensuring real-time threat assessment. Automatic hierarchical response based on threat indices enables linkage with air traffic control dispatch, avoiding delays caused by manual intervention. Compared with traditional methods, this solution shortens the closed-loop time from obstacle detection to disposal, and at the same time, incorporates meteorological influences through environmental enhancement factors, improving the reliability of assessments in complex weather conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below with reference to the accompanying drawings.

[0042] Figure 1 is a flow chart of the obstacle assessment method based on airport clearance management in the present invention;

[0043] Figure 2 This is a structural diagram of the obstacle assessment system based on airport clearance management in the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] See also Figure 1 As shown, an obstacle assessment method based on airport clearance management includes the following steps:

[0046] S1. Real-time data acquisition includes: frequent lidar scanning of the airport's airspace to generate high-density three-dimensional point cloud data. Point cloud data processing technology is then used to construct a digital twin basemap of the airspace; ADS-B signals are connected to analyze aircraft position data in real time; aircraft position data is integrated with the digital twin basemap to construct a spatial relationship map between temporary obstacles and aircraft; and sensor-based airport meteorological data is collected. The system comprehensively utilizes lidar scanning to generate three-dimensional point cloud data to construct a digital twin basemap, and simultaneously integrates ADS-B aircraft real-time position data and meteorological sensor data to form a complete airspace situational awareness system. Data processing utilizes an edge computing architecture, completing point cloud denoising and ADS-B signal parsing on the device side to ensure data timeliness.

[0047] S2. A dynamic assessment model is established to calculate the obstacle threat index. Based on the threshold control of the threat index, corresponding warning strategies are generated. These warning strategies include: SMS notification to the control tower, automatic NOTAM notification, triggering air traffic control radar plotting, and drone removal. Based on the spatial relationship map, key features such as obstacle height, aircraft distance, speed and heading difference are extracted. A comprehensive threat index is generated through multi-dimensional calculations such as spatial intrusion factor and altitude conflict factor, and graded warning thresholds are set. When floating objects such as balloons are detected, the trajectory prediction module is activated to predict the risk area.

[0048] S3. Execute a graded response based on the early warning strategy and record the response process, including data collection time, threat index calculation process, early warning strategy triggering time, and response execution results. Differentiated measures are automatically triggered based on threat level, forming a closed-loop management system from information notification to active dispersal. The response process is fully documented for analysis and optimization.

[0049] Through the above technical solution, this embodiment provides an obstacle assessment method based on airport clearance management. Specifically, through multi-source real-time data fusion and dynamic threat modeling, the core defects of traditional clearance management are effectively solved. By utilizing the collaborative collection of lidar, ADS-B and meteorological sensors, a high-precision digital twin base map is constructed to achieve real-time three-dimensional perception of temporary obstacles, overcoming the problems of low efficiency of manual inspections and lag in static databases. Through dynamic assessment models such as spatial intrusion factors and height conflict factors, obstacle risks are quantified and associated with aircraft operating status, so that the system can automatically identify sudden threats of temporary obstacles, making up for the deficiency of traditional static models that cannot adapt to dynamic scenarios. In addition,

[0050] When the airport clear area is scanned frequently using a laser radar in step S1, edge computing nodes are used to pre-process the point cloud data, including: deploying edge computing equipment near the laser radar equipment, denoising, filtering, and compressing the scanned raw point cloud data on the edge side, removing redundant data, and transmitting only key feature data to the central server.

[0051] The process of receiving the ADS-B signal and parsing the aircraft position data in step S1 includes:

[0052] After receiving the ADS-B signal, the edge computing gateway decodes the signal locally and extracts the core information data of the aircraft, which includes the aircraft's position, speed and altitude. It then performs preliminary screening of abnormal data based on preset rules and sends only valid data to the central server.

[0053] This technical solution effectively addresses the high bandwidth and processing latency issues inherent in traditional data transmission solutions, enabling the system to support full-clearance zone scanning at a shorter frequency. Feature extraction technology also ensures that transmitted data is directly relevant to aviation safety. It should be noted that the lightweight algorithms employed by edge nodes enable real-time processing on processors such as Intel Core i5 processors, significantly reducing deployment costs. Edge nodes compensate for signal coverage blind spots, improving reception success rates. Furthermore, filtering out anomalous data reduces server load. Finally, local coordinate transformation reduces computational overhead at the central end.

[0054] The process of establishing a dynamic assessment model and calculating the obstacle threat index includes:

[0055] Extracting key feature parameters from the fact data, the key features including the height of the obstacle relative to the runway, the horizontal distance between the aircraft and the obstacle, the current speed of the aircraft, and the deviation of the aircraft heading from the obstacle azimuth;

[0056] Obtain the influencing factors of the obstacle threat index based on the above key characteristic parameters, including the spatial intrusion factor , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors , Used to quantify the multiplying effect of visibility and wind speed on operational difficulty;

[0057] By formula Calculate the obstacle threat index ;In the formula, is the preset weight coefficient;

[0058] According to the preset range that the obstacle threat index falls into, a corresponding early warning strategy is generated.

[0059] Through the above-mentioned technical solution, this embodiment provides a process for establishing a dynamic assessment model and calculating the obstacle threat index. The model transforms traditional qualitative judgments into quantitative calculations by establishing a four-dimensional assessment system based on space, altitude, maneuverability, and environment. The spatial intrusion factor uses a Gaussian probability distribution to predict track deviations, the altitude conflict factor introduces a time dimension to calculate dynamic altitude differences, the emergency obstacle avoidance factor incorporates the aircraft performance envelope, and the environmental enhancement factor achieves quantitative compensation for meteorological impacts. Compared to traditional methods, its advantages lie in the elimination of single-factor misjudgments through multi-factor fusion, the ability to adjust weight coefficients based on the operational characteristics of different airports, and the ability to implement precise resource allocation through a hierarchical response mechanism.

[0060] The process of obtaining the influencing factors of the obstacle threat index includes:

[0061] (1);

[0062] (2);

[0063] (3);

[0064] (4);

[0065] The spatial invasion factor is calculated by formula (1)-(4): , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors ;

[0066] in, is the deviation between the aircraft heading and the obstacle azimuth; is the horizontal distance between the aircraft's current position and the obstacle; is the distance standard deviation; It is the minimum safe height distance; is the current altitude of the aircraft; is the aircraft’s current climb rate; is the current flight speed of the aircraft; is the obstacle height; is the minimum turning radius of the aircraft; 、 is the preset weight; 、 They are current visibility and preset standard visibility respectively; 、 They are the current wind speed and the preset standard wind speed respectively.

[0067] Through the above technical solution, this embodiment provides a specific process for obtaining the influencing factor of the obstacle threat index. The spatial intrusion factor is calculated by formulas (1)-(4): , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors Among them, the spatial invasion factor Used to assess whether the aircraft's future trajectory is likely to intrude into the obstacle safety buffer zone; height conflict factor Used to assess whether the aircraft's current altitude and climb rate may cause a collision with an obstacle; emergency obstacle avoidance factor Used to assess whether the aircraft's maneuverability is sufficient to avoid obstacles; environmental enhancement factor This takes into account the amplifying effect of meteorological conditions on threats.

[0068] The method also includes an emergency handling process:

[0069] When an obstacle floating in the air is detected, the trajectory prediction subroutine is started;

[0070] Combined with the WRF weather model to calculate the future drift path, dangerous sectors are displayed on electronic charts, and dynamic monitoring of movable road obstacles is implemented:

[0071] When a movable road obstacle intrudes into the critical line of the danger sector, an audible and visual alarm is triggered. This emergency response process targets two types of dynamic threats: floating objects in the air (such as balloons and drones) and movable road obstacles (such as vehicles and equipment). Through the dual protection mechanism of trajectory prediction and real-time monitoring, it significantly improves the active defense capability of airport clearance management. Through the above technical solution, this embodiment provides an emergency response process for dynamic obstacles.

[0072] See also Figure 2 As shown, an obstacle assessment system based on airport clearance management, the system includes:

[0073] The data acquisition module, used to acquire real-time airport clear zone data, includes: a lidar scanning unit, utilizing a mechanical / solid-state lidar with a scanning frequency ≥20Hz and a point cloud density >100 points / ㎡, for high-frequency 3D scanning of the clear zone and generation of high-density point cloud data; an ADS-B signal receiving unit, employing a 1090MHz multi-channel receiver with a decoding delay <10ms and a coverage radius of 200km, for analyzing real-time aircraft position, speed, and altitude data; and a meteorological sensor unit, integrating an ultrasonic anemometer and a forward scatter visibility meter, for collecting visibility and wind speed data.

[0074] The data processing module includes edge computing nodes and a central server; the edge computing nodes are used to denoise, filter, and compress the lidar point cloud data, and to construct a digital twin base map of the clear area; the central server is used to fuse the aircraft position data with the digital twin base map to generate a spatial relationship map between obstacles and aircraft;

[0075] Dynamic assessment module, used to calculate obstacle threat index;

[0076] The early warning response module is used to trigger graded response measures based on the threat index threshold, including SMS notification to the tower, sending NOTAM notices, air traffic control radar plotting and drone expulsion.

[0077] Through the above technical solution, this embodiment provides an obstacle assessment system based on airport clearance management. The system realizes real-time three-dimensional perception, dynamic threat assessment and intelligent hierarchical response of obstacles in the airport clearance area through multi-source data fusion of lidar, ADS-B and meteorological sensors, combined with edge-cloud collaborative computing architecture, and transforms the passive management mode of traditional manual inspections into a fully automatic, high-precision active protection system, greatly shortening the obstacle identification time and significantly improving airport operation safety and airspace management efficiency.

[0078] The dynamic evaluation module includes:

[0079] The space invasion factor calculation unit evaluates the invasion risk based on the proximity of the aircraft predicted trajectory and the obstacle profile;

[0080] The height conflict factor calculation unit calculates the vertical conflict probability by combining the aircraft climb rate and the obstacle height;

[0081] The environment enhancement factor calculation unit dynamically adjusts the threat weight according to the meteorological data; and the comprehensive threat index generation unit is used to calculate the obstacle threat index.

[0082] Through the above technical solution, the embodiment provides specific content of the dynamic evaluation module. The dynamic evaluation module realizes accurate quantification of the obstacle threat index through multi-dimensional fusion calculation of the space invasion, height conflict and environment enhancement factors. Compared with the traditional single-dimensional evaluation method, the accuracy and timeliness of risk evaluation are improved, and the active early warning capability of airport clearance management is also enhanced.

[0083] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still belong to the patent coverage of the present application.

Claims

1. An obstacle assessment method based on airport clearance management, characterized in that: The following steps are involved: S1. Real-time data acquisition, including: using LiDAR to scan the airport's clear area frequently to generate high-density three-dimensional point cloud data, and then using point cloud data processing technology to construct a digital twin base map of the clear area; accessing ADS-B signals to analyze aircraft position data in real time; fusing aircraft position data with the digital twin base map to construct a spatial relationship map between temporary obstacles and aircraft; and collecting airport meteorological data based on sensors. S2. Build a dynamic assessment model to calculate the obstacle threat index and generate a corresponding warning strategy based on the threshold control of the threat index. The warning strategy includes: SMS notification to the tower, automatic NOTAM notification, triggering air traffic control radar plotting, and driving away drones; S3. Execute graded responses according to the early warning strategy and record the response process, including: recording data collection time, threat index calculation process, early warning strategy triggering time and response measure execution results.

2. The obstacle assessment method based on airport clearance management according to claim 1, characterized in that: When the airport clear area is scanned frequently using a laser radar in step S1, edge computing nodes are used to pre-process the point cloud data, including: deploying edge computing equipment near the laser radar equipment, denoising, filtering, and compressing the scanned raw point cloud data on the edge side, removing redundant data, and transmitting only key feature data to the central server.

3. The obstacle assessment method based on airport clearance management according to claim 2, characterized in that: The process of receiving the ADS-B signal and parsing the aircraft position data in step S1 includes: After receiving the ADS-B signal, the edge computing gateway decodes the signal locally and extracts the core information data of the aircraft, which includes the aircraft's position, speed and altitude. It then performs preliminary screening of abnormal data based on preset rules and sends only valid data to the central server.

4. The obstacle assessment method based on airport clearance management according to claim 3, characterized in that: The process of establishing a dynamic assessment model and calculating the obstacle threat index includes: Extracting key feature parameters from the fact data, the key features including the height of the obstacle relative to the runway, the horizontal distance between the aircraft and the obstacle, the current speed of the aircraft, and the deviation of the aircraft heading from the obstacle azimuth; Obtain the influencing factors of the obstacle threat index based on the above key characteristic parameters, including the spatial intrusion factor , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors ; By formula Calculate the obstacle threat index ;In the formula, is the preset weight coefficient; According to the preset range that the obstacle threat index falls into, a corresponding early warning strategy is generated.

5. The obstacle assessment method based on airport clearance management according to claim 4, characterized in that: The process of obtaining the influencing factors of the obstacle threat index includes: （1）； （2）； （3）； （4）； The spatial invasion factor is calculated by formula (1)-(4): , high conflict factor , emergency obstacle avoidance factor and environmental enhancement factors ; in, is the deviation between the aircraft heading and the obstacle azimuth; is the horizontal distance between the aircraft's current position and the obstacle; is the distance standard deviation; It is the minimum safe height distance; is the current altitude of the aircraft; is the aircraft’s current climb rate; is the current flight speed of the aircraft; is the obstacle height; is the minimum turning radius of the aircraft; 、 is the preset weight; 、 They are current visibility and preset standard visibility respectively; 、 They are the current wind speed and the preset standard wind speed respectively.

6. The obstacle assessment method based on airport clearance management according to claim 3, characterized in that: The method also includes an emergency response process: When an obstacle floating in the air is detected, the trajectory prediction subroutine is started; Combined with the WRF weather model to calculate the future drift path, dangerous sectors are displayed on electronic charts, and dynamic monitoring of movable road obstacles is implemented: When a movable road obstacle intrudes into the critical line of the danger sector, an audible and visual alarm is triggered.

7. An obstacle assessment system based on airport clearance management, configured to execute the obstacle assessment method based on airport clearance management according to any one of claims 1 to 6, characterized in that: The system comprises: The data acquisition module is used to obtain real-time airport clear zone data, including: a lidar scanning unit for high-frequency three-dimensional scanning of the clear zone to generate high-density point cloud data; an ADS-B signal receiving unit for analyzing the real-time position, speed, and altitude data of aircraft; and a meteorological sensor unit for collecting visibility and wind speed data. The data processing module includes edge computing nodes and a central server; the edge computing nodes are used to denoise, filter, and compress the lidar point cloud data, and to construct a digital twin base map of the clear area; the central server is used to fuse the aircraft position data with the digital twin base map to generate a spatial relationship map between obstacles and aircraft; Dynamic assessment module, used to calculate obstacle threat index; The early warning response module is used to trigger graded response measures based on the threat index threshold, including SMS notification to the tower, sending NOTAM notices, air traffic control radar plotting and drone expulsion.

8. The obstacle assessment system based on airport clearance management according to claim 7, characterized in that: The dynamic evaluation module includes: A spatial intrusion factor calculation unit that assesses the intrusion risk based on the proximity of the aircraft's predicted trajectory to the obstacle profile; The altitude conflict factor calculation unit calculates the vertical conflict probability by combining the aircraft's climb rate and the obstacle height; The environmental enhancement factor calculation unit dynamically adjusts the threat weight according to meteorological data; the comprehensive threat index generation unit is used to calculate the obstacle threat index.

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