Obstacle assessment method and system based on airport clearance management

Abstract

The application relates to the technical field of aviation safety management, and discloses an obstacle evaluation method and system based on airport clearance management. The obstacle evaluation method based on airport clearance management comprises the following steps: S1, real-time data acquisition, including: high-frequency scanning of an airport clearance area by means of a laser radar to generate high-density three-dimensional point cloud data, and then constructing a clearance area digital twin base map through point cloud data processing technology; accessing ADS-B signals to analyze aircraft position data in real time; fusing the aircraft position data with the digital twin base map to construct a spatial relationship atlas of temporary obstacles and aircrafts; and collecting meteorological data of the airport based on sensors. Through dynamic evaluation models such as a spatial intrusion factor and a height conflict factor, the application quantifies obstacle risks and correlates aircraft operation states, so that the system can automatically identify the sudden threats of temporary obstacles, and the deficiency that a traditional static model cannot adapt to dynamic scenes is made up.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aviation safety management, and particularly relates to an obstacle assessment method and system based on airport clearance management. BACKGROUND

[0002] In recent years, with the rapid growth of civil aviation transport volume and the accelerated urban construction, the temporary obstacle management of the airport clearance area is facing severe challenges. The traditional clearance management mainly relies on manual patrol and static obstacle database. However, with the growth of civil aviation transport volume and the acceleration of urban development, the clearance safety problem caused by temporary obstacles is increasingly prominent.

[0003] The traditional clearance management has the following defects: the static assessment model cannot adapt to the dynamic changes of temporary obstacles (such as construction cranes and celebration balloons), and manual patrol is low in efficiency and difficult to quantify risks. In addition, the existing system lacks real-time linkage measures with air traffic control scheduling. SUMMARY

[0004] The present application relates to the technical field of aviation safety management, and particularly relates to an obstacle assessment method and system based on airport clearance management.

[0005] The object of the present application can be achieved by the following technical solutions:

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

[0007] S1, real-time data acquisition, including: using a laser radar to perform high-frequency scanning on the airport clearance area to generate high-density three-dimensional point cloud data, and then constructing a clearance area digital twin base map through point cloud data processing technology; accessing ADS-B signals to analyze aircraft position data in real time; fusing the aircraft position data with the digital twin base map to construct a spatial relationship graph of temporary obstacles and aircraft; collecting meteorological data of the airport based on sensors;

[0008] S2, establishing a dynamic assessment model, calculating an obstacle threat index, and generating a warning strategy according to the threshold control of the threat index, the warning strategy including: short message notification to the tower, automatic sending of NOTAM notice, triggering of air traffic radar plotting, and unmanned aerial vehicle driving away;

[0009] S3, executing a hierarchical response according to the warning strategy, and recording the response process, including: recording the data acquisition time, the threat index calculation process, the warning strategy triggering time, and the response measure execution result.

[0010] As a further technical solution, in step S1, when using lidar to perform high-frequency scanning of the airport airspace, edge computing nodes are used to preprocess the point cloud data, including: deploying edge computing devices near the lidar equipment, performing noise reduction, filtering and compression processing on the edge side of the scanned raw point cloud data, removing redundant data, and transmitting only key feature data to the central server.

[0011] As a further technical solution, the process of accessing 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, including the aircraft's position, speed, and altitude. Based on preset rules, abnormal data is initially filtered, and only valid data is sent to the central server.

[0013] As a further technical solution, the process of establishing a dynamic assessment model and calculating the obstacle threat index includes:

[0014] Key feature parameters are extracted from factual data. These key features include 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 between the aircraft's heading and the obstacle's azimuth angle.

[0015] Based on the above key characteristic parameters, the influencing factors of the obstacle threat index are obtained, including the space intrusion factor. Highly conflicting factors Emergency obstacle avoidance factors and environmental enhancement factors ;

[0016] Through formula Calculate and obtain the obstacle threat index ;In the formula, Preset weighting coefficients;

[0017] Based on the preset range into which the obstacle threat index falls, 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 factor ;

[0024] Wherein, is the deviation of the aircraft heading and the azimuth of the obstacle; is the horizontal distance between the current position of the aircraft and the obstacle; is the distance standard deviation; is the minimum safety height interval; is the current height of the aircraft; is the current climb rate of the aircraft; is the current flight speed of the aircraft; is the height of the obstacle; is the minimum turning radius of the aircraft; , is the preset weight; , is the current visibility and the preset standard visibility, respectively; , is the current wind speed and the preset standard wind speed, respectively.

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

[0026] When an airborne floating obstacle is detected, start the trajectory prediction subroutine;

[0027] Combine the WRF weather model to calculate the future drift path and display the dangerous sector on the electronic chart, and dynamically monitor the movable road obstacle:

[0028] When the movable road obstacle invades the critical line of the dangerous sector, trigger the sound and light alarm.

[0029] An obstacle evaluation system based on airport clearance management, the system comprises:

[0030] A data acquisition module for acquiring airport clearance area data in real time, comprising: a laser radar scanning unit for high-frequency three-dimensional scanning of the clearance area to generate high-density point cloud data; an ADS-B signal receiving unit for analyzing real-time position, speed and height data of the aircraft; a weather sensor unit for collecting visibility and wind speed data;

[0031] The data processing module comprises an edge computing node and a central server; the edge computing node is used for denoising, filtering and compressing the laser radar point cloud data, and constructing a digital twin base map of the clear zone; the central server is used for fusing the aircraft position data and the digital twin base map, and generating a spatial relationship graph of the obstacles and the aircraft;

[0032] The dynamic evaluation module is used for calculating the obstacle threat index.

[0033] The early warning response module is used for triggering a hierarchical response measure according to the threat index threshold, including SMS notification of the tower, sending of a NOTAM notice, air traffic control radar plotting and UAV driving away.

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

[0035] The space invasion factor calculation unit is used for evaluating the invasion risk based on the proximity of the aircraft predicted trajectory and the obstacle profile;

[0036] The height conflict factor calculation unit is used for calculating the vertical conflict probability in combination with the aircraft climb rate and the obstacle height;

[0037] The environment enhancement factor calculation unit is used for dynamically adjusting the threat weight according to the meteorological data; and the comprehensive threat index generation unit is used for calculating the obstacle threat index.

[0038] The present application has the following advantages:

[0039] (1) The present application effectively solves the core defects of traditional clear zone management through multi-source real-time data fusion and dynamic threat modeling. First, the collaborative collection of laser radar, ADS-B and weather sensors is used to construct a high-precision digital twin base map, realize real-time three-dimensional perception of temporary obstacles, and overcome the problems of low efficiency of manual patrol and lag of static database. Secondly, through dynamic evaluation models such as space invasion factor and height conflict factor, the risk of obstacles is quantified and related to the aircraft operating state, so that the system can automatically identify the sudden threat of temporary obstacles, making up for the deficiency of traditional static models that cannot adapt to dynamic scenarios.

[0040] (2) The present application significantly improves the response timeliness through the edge computing, central server cooperative architecture and hierarchical early warning mechanism. The edge node processes the point cloud and ADS-B data on site, reduces the transmission delay, and ensures the real-time of threat evaluation; and the automatic hierarchical response based on the threat index realizes the linkage with air traffic control scheduling, avoiding the delay of manual intervention. Compared with the traditional method, the present application shortens the closed-loop time from obstacle discovery to disposal, and improves the evaluation reliability in complex weather by integrating the meteorological influence through the environment enhancement factor. BRIEF DESCRIPTION OF DRAWINGS

[0041] The application will be further described below with reference to the drawings.

[0042] Figure 1 The flow chart of the obstacle assessment method based on airport clearance management in the application;

[0043] Figure 2 The framework diagram of the obstacle assessment system based on airport clearance management in the application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0045] Please refer to Figure 1 The obstacle assessment method based on airport clearance management shown in the figure comprises the following steps:

[0046] S1, real-time data acquisition, comprising: using a laser radar to perform high-frequency scanning on an airport clearance area to generate high-density three-dimensional point cloud data, and then constructing a clearance area digital twin base map through point cloud data processing technology; accessing an ADS-B signal to analyze aircraft position data in real time; fusing the aircraft position data with the digital twin base map to construct a spatial relationship atlas of temporary obstacles and aircraft; and collecting meteorological data of the airport based on a sensor. The system comprehensively utilizes the laser radar scanning to generate three-dimensional point cloud data to construct the digital twin base map, synchronously integrates the real-time position data of the aircraft of the ADS-B and the meteorological sensor data to form a complete airspace situation awareness system. The data processing adopts an edge computing architecture to complete point cloud denoising and ADS-B signal analysis on the device side, thereby ensuring data timeliness.

[0047] S2, establishing a dynamic assessment model, calculating an obstacle threat index, and generating a warning strategy according to the threshold control of the threat index, the warning strategy comprising: sending a short message to a tower station, automatically sending a NOTAM notice, triggering air traffic control radar plotting, and triggering a drone to drive away. Based on the spatial relationship atlas, key features such as obstacle height, aircraft distance, and speed and heading difference are extracted, a comprehensive threat index is generated through multi-dimensional calculation of a spatial intrusion factor and a height conflict factor, and a graded warning threshold is set. When a balloon or other floating object is detected, a trajectory prediction module is also started to predict a risk area.

[0048] S3, performing a hierarchical response according to the early warning strategy, and recording the response process, including: recording the data collection time, the threat index calculation process, the early warning strategy triggering time and the response measure execution result. According to the threat level, automatic triggering of differentiated measures is formed from information notification to active driving to form a closed-loop management, while the complete disposal process is recorded for analysis and optimization.

[0049] Through the above technical solution, the 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 using the collaborative collection of laser radar, ADS-B and weather sensors, a high-precision digital twin base map is constructed, real-time three-dimensional perception of temporary obstacles is realized, and the problems of low efficiency of manual patrol and lag of static database are overcome. Through dynamic evaluation models such as space intrusion factor and height conflict factor, the risk of obstacles is quantified and correlated with the aircraft operation state, so that the system can automatically identify the sudden threat of temporary obstacles, making up for the shortcomings of traditional static models that cannot adapt to dynamic scenarios. In addition,

[0050] In the step S1, when the airport clearance area is scanned at a high frequency by the laser radar, an edge computing node is used for preprocessing of point cloud data, including: deploying an edge computing device near the laser radar equipment, performing denoising, filtering and compression processing on the original point cloud data obtained by scanning on the edge side, removing redundant data, and only transmitting key feature data to the central server.

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

[0052] After the edge computing gateway receives the ADS-B signal, the signal is decoded locally, and the core information data of the aircraft is extracted, including the position, speed and height of the aircraft. Based on the preset rules, the abnormal data is preliminarily screened, and only the valid data is sent to the central server.

[0053] The above technical solution effectively solves the problems of large data backhaul bandwidth pressure and high processing delay in the traditional scheme, so that the system can support a full clearance area scanning frequency in a shorter time, and at the same time, through the feature extraction technology, it is ensured that all the transmitted data are directly related to aviation safety. It should be noted that the lightweight algorithm used by the edge node can realize real-time processing on an Intel Core i5 level processor, greatly reducing the deployment cost. Through the edge node, the signal coverage blind area compensation is realized, and the reception success rate is improved. Secondly, the abnormal data filtering reduces the server load; finally, the local coordinate conversion reduces the computational overhead of the central end.

[0054] The process of establishing a dynamic evaluation model to calculate the obstacle threat index includes:

[0055] Key feature parameters are extracted from factual data. These key features include 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 between the aircraft's heading and the obstacle's azimuth angle.

[0056] Based on the above key characteristic parameters, the influencing factors of the obstacle threat index are obtained, including the space intrusion factor. Highly conflicting factors Emergency obstacle avoidance factors and environmental enhancement factors , Used to quantify the multiplier effect of visibility and wind speed on operational difficulty;

[0057] Through formula Calculate and obtain the obstacle threat index ;In the formula, Preset weighting coefficients;

[0058] Based on the preset range into which the obstacle threat index falls, a corresponding early warning strategy is generated.

[0059] Through the above technical solution, this embodiment provides a process for establishing a dynamic assessment model and calculating the obstacle threat index. The model transforms traditional qualitative judgment into quantitative calculation by establishing a four-dimensional assessment system encompassing space, altitude, maneuverability, and environment. Specifically, the space intrusion factor uses a Gaussian probability distribution to predict track deviation, the altitude conflict factor incorporates a time dimension to calculate dynamic altitude differences, the emergency obstacle avoidance factor is combined with the aircraft performance envelope, and the environmental enhancement factor quantifies and compensates for meteorological impacts. Compared to traditional methods, its advantages lie in eliminating misjudgments based on single indicators through multi-factor fusion, the ability to adjust weighting coefficients according to the operational characteristics of different airports, and the implementation of a tiered response mechanism for precise resource allocation.

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

[0061] (1);

[0062] (2);

[0063] (3);

[0064] (4);

[0065] The space intrusion factor is obtained by calculating using formulas (1)-(4). Highly conflicting factors Emergency obstacle avoidance factors and environmental enhancement factors ;

[0066] wherein, is the deviation of the aircraft heading from the obstacle azimuth; is the horizontal distance between the aircraft current position and the obstacle; is the distance standard deviation; is the minimum safety height separation; is the aircraft current altitude; is the aircraft current climb rate; is the aircraft current flight speed; is the obstacle height; is the minimum turning radius of the aircraft; , is the preset weight; , is the current visibility and the preset standard visibility, respectively; , is the current wind speed and the preset standard wind speed, respectively.

[0067] Through the above technical solutions, the embodiment provides a specific process for obtaining the influencing factors of the obstacle threat index. The spatial intrusion factor , the height conflict factor , the emergency obstacle avoidance factor , and the environmental enhancement factor are calculated by formulas (1) to (4). Among them, the spatial intrusion factor is used to evaluate whether the future trajectory of the aircraft is likely to intrude into the safety buffer zone of the obstacle; the height conflict factor is used to evaluate whether the current altitude and the climb rate of the aircraft are likely to cause a collision with the obstacle; the emergency obstacle avoidance factor is used to evaluate whether the aircraft maneuvering capability is sufficient to avoid the obstacle; and the environmental enhancement factor considers the amplification effect of weather conditions on the threat.

[0068] The method further includes an emergency handling flow process:

[0069] When an airborne floating obstacle is detected, a trajectory prediction subroutine is started;

[0070] The future drift path is calculated in combination with the WRF weather model, and a dangerous sector is displayed on the electronic chart, and dynamic monitoring is implemented on the movable road obstacle:

[0071] An audible and light alarm is triggered when the movable road obstacle invades the critical line of the dangerous sector. The emergency handling process is aimed at two types of dynamic threats, i.e., air floats (such as balloons and unmanned aerial vehicles) and movable road obstacles (such as vehicles and equipment). Through the dual guarantee mechanism of trajectory prediction and real-time monitoring, the active defense capability of airport clearance management is significantly improved. Through the above technical solutions, the embodiment provides an emergency handling process for dynamic obstacles

[0072] Referring to Figure 2 An obstacle evaluation system based on airport clearance management, as shown in the drawings, the system comprises:

[0073] A data acquisition module is configured to acquire airport clearance zone data in real time, comprising: a laser radar scanning unit, which adopts a mechanical / solid-state laser radar, has a scanning frequency of ≥20Hz and a point cloud density of >100 points / ㎡, is configured to perform high-frequency three-dimensional scanning on the clearance zone to generate high-density point cloud data; an ADS-B signal receiving unit, which is provided with a 1090MHz multi-channel receiver and has a decoding delay of <10ms and a coverage radius of 200 kilometers, is configured to analyze real-time position, speed and height data of an aircraft; and a meteorological sensor unit, which is integrated with an ultrasonic anemometer and a forward scattering visibility meter, is configured to collect visibility and wind speed data.

[0074] A data processing module, comprising an edge computing node and a central server, is configured to perform denoising, filtering and compression on the laser radar point cloud data, and construct a digital twin base map of the clearance zone; the central server is configured to fuse the aircraft position data and the digital twin base map to generate a spatial relationship graph of obstacles and aircrafts.

[0075] A dynamic evaluation module is configured to calculate an obstacle threat index.

[0076] A warning response module is configured to trigger graded response measures according to a threat index threshold, including SMS notification of the tower, sending of a NOTAM notice, air traffic control radar plotting and unmanned aerial vehicle driving away.

[0077] Through the above technical solutions, the embodiment provides an obstacle evaluation system based on airport clearance management. The system realizes real-time three-dimensional perception, dynamic threat evaluation and intelligent grading response of obstacles in the airport clearance zone through multi-source data fusion of laser radar, ADS-B and meteorological sensors, combined with edge-cloud collaborative computing architecture. The system changes the passive management mode of traditional manual patrol into an active protection system with full automation and high precision, greatly shortens the obstacle identification time, and significantly improves the airport operation safety and air space management efficiency.

[0078] The dynamic evaluation module comprises:

[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 a 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 airspace management, characterized in that, Includes the following steps: S1. Real-time data acquisition, including: using lidar to perform high-frequency scanning of the airport airspace 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 airspace; 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. Establish a dynamic assessment model, calculate the obstacle threat index, and generate corresponding early warning strategies based on the threshold control of the threat index. The early warning strategies include: SMS notification to the control tower, automatic sending of NOTAM announcements, triggering air traffic control radar plotting, and drone expulsion. S3. Execute graded responses according to the early warning strategy and record the response process, including: recording the data collection time, threat index calculation process, early warning strategy trigger time, and response measures execution results; In step S1, when using lidar to perform high-frequency scanning of the airport airspace, edge computing nodes are used to preprocess the point cloud data, including: deploying edge computing devices near lidar equipment, performing noise reduction, filtering and compression on the edge side of the scanned raw point cloud data, removing redundant data, and transmitting only key feature data to the central server. The process of accessing 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, including the aircraft's position, speed and altitude. Based on preset rules, abnormal data is initially filtered, and only valid data is sent to the central server. The process of establishing a dynamic assessment model and calculating the obstacle threat index includes: Key feature parameters are extracted from factual data. These key features include 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 between the aircraft's heading and the obstacle's azimuth angle. Based on the above key characteristic parameters, the influencing factors of the obstacle threat index are obtained, including the space intrusion factor. Highly conflicting factors Emergency obstacle avoidance factors and environmental enhancement factors ; Through formula Calculate and obtain the obstacle threat index ;In the formula, Preset weighting coefficients; Based on the preset range into which the obstacle threat index falls, a corresponding early warning strategy is generated. The process of obtaining the influencing factors of the obstacle threat index includes: （1）； （2）； （3）； （4）； The space intrusion factor is obtained by calculating using formulas (1)-(4). Highly conflicting factors Emergency obstacle avoidance factors and environmental enhancement factors ; in, The deviation between the aircraft's heading and the obstacle's azimuth angle; This represents the horizontal distance between the aircraft's current position and the obstacle. This represents the distance from the standard deviation. Minimum safe height spacing; The aircraft's current altitude; This represents the aircraft's current rate of climb. This refers to the aircraft's current flight speed; The height of the obstacle; This refers to the minimum turning radius of the aircraft. , Preset weights; , These are the current visibility and the preset standard visibility, respectively. , These are the current wind speed and the preset standard wind speed, respectively.

2. The obstacle assessment method based on airport airspace management according to claim 1, characterized in that, The method also includes an emergency response procedure: When an airborne obstacle is detected, the trajectory prediction subroutine is activated. By combining the WRF meteorological model to calculate the future drift path and displaying dangerous sector areas on electronic aeronautical charts, dynamic monitoring of movable road obstacles is implemented. An audible and visual alarm is triggered when a movable road obstacle intrudes into the critical line of a danger sector.

3. An obstacle assessment system based on airport airspace management, used to execute the obstacle assessment method based on airport airspace management as described in any one of claims 1 to 2, characterized in that, The system includes: The data acquisition module is used to acquire airport airspace data in real time, including: a lidar scanning unit for high-frequency three-dimensional scanning of the airspace to generate high-density point cloud data; an ADS-B signal receiving unit for analyzing the aircraft's real-time position, speed, and altitude data; 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 lidar point cloud data, and construct a digital twin base map of the airspace; the central server is used to fuse aircraft position data and the digital twin base map to generate a spatial relationship map between obstacles and aircraft. The dynamic assessment module is used to calculate the obstacle threat index; The early warning response module is used to trigger tiered response measures based on threat index thresholds, including SMS notification to the control tower, sending NOTAMs, air traffic control radar plotting, and drone removal.

4. The obstacle assessment system based on airport airspace management according to claim 3, characterized in that, The dynamic evaluation module includes: The space intrusion factor calculation unit assesses the intrusion risk based on the proximity of the aircraft's predicted trajectory to the obstacle's outline. The altitude conflict factor calculation unit combines the aircraft's climb rate with the obstacle's height to calculate the vertical conflict probability. The environmental enhancement factor calculation unit dynamically adjusts threat weights based on meteorological data; the comprehensive threat index generation unit is used to calculate the obstacle threat index.

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