Artificial intelligence-based low-altitude air route risk map construction method and system

Abstract

The present application relates to the technical field of unmanned aerial vehicle route planning, and discloses a low-altitude route risk map construction method and system based on artificial intelligence, comprising a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint judgment module, a low-altitude route map construction module, a low-altitude route risk analysis module, and an optimal path calibration output module. The first constraint condition of low-altitude route environment perception is acquired to determine the low-altitude route flyable area, key feature parameters are extracted from the low-altitude route data, the second constraint condition analysis is performed on the low-altitude route, the low-altitude route is intelligently planned according to the analysis result, the low-altitude route map is constructed, the constant line risk analysis is performed on the low-altitude route, the optimal path is calibrated, the optimal path is output to the user terminal, and the path three-dimensional display is performed on the low-altitude route map, so that the flight task execution efficiency and safety are improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) route planning technology, and more specifically to a method and system for constructing low-altitude route risk maps based on artificial intelligence. Background Technology

[0002] With the rise of emerging industries such as general aviation, drone logistics, and urban air mobility, low-altitude flight activities are becoming increasingly frequent. As an important channel for these flight activities, the safety and efficiency of low-altitude routes are directly related to the healthy development of the entire low-altitude economy. However, low-altitude routes face many risk factors, such as changing weather conditions, terrain obstacles, and air traffic conflicts. Traditional route planning and risk assessment methods are difficult to meet the growing flight demands.

[0003] On the other hand, with the increasingly widespread development and utilization of low-altitude airspace, the application scenarios for drones and low-altitude aircraft are constantly expanding. How to efficiently and accurately construct low-altitude flight path risk maps has become a crucial issue for ensuring flight safety. Traditional radar surveillance systems and other technologies are insufficient to effectively capture low-altitude flying targets such as drones, resulting in blind spots in low-altitude airspace management. Artificial intelligence technology, by integrating multi-sensor data and utilizing algorithms such as computer vision and machine learning, can achieve real-time and accurate positioning, identification, and tracking of low-altitude flying targets, improving the efficiency and safety of low-altitude traffic management.

[0004] However, existing low-altitude airway risk assessment methods mainly rely on static grid division, biological activity analysis, and traditional risk assessment models. These methods still have shortcomings in terms of dynamism, intelligence, and globality, making it difficult to meet the needs of complex and ever-changing urban low-altitude environments. They may not be able to update risk information in real time, leading to discrepancies between risk maps and the actual environment. In some complex low-altitude airway scenarios, such as densely populated urban high-rise areas and airport perimeters, there are numerous obstacles and complex air traffic rules. Existing methods may struggle to accurately assess risks in these scenarios and cannot provide reasonable route planning suggestions for aircraft. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, this invention provides an artificial intelligence-based low-altitude airway risk map construction system to address the problems existing in the background art.

[0006] The present invention provides the following technical solution: an artificial intelligence-based low-altitude airway risk map construction system, comprising: a low-altitude environment perception constraint module, a low-altitude airway feature extraction module, a dynamic risk constraint determination module, a low-altitude airway map construction module, a low-altitude airway risk analysis module, and an optimal path calibration output module;

[0007] The low-altitude environment perception constraint module obtains the first constraint condition of the low-altitude airway environment perception and determines the flyable area of ​​the low-altitude airway.

[0008] The low-altitude airway feature extraction module is used to extract key feature parameters from low-altitude airway data, including terrain relief, meteorological change rate and air traffic density, and convert the parameters into a standardized input format.

[0009] The dynamic risk constraint determination module performs a second constraint analysis on low-altitude routes based on the key feature parameters extracted by the low-altitude route feature extraction module, and intelligently plans low-altitude route routes based on the analysis results.

[0010] The low-altitude airway map construction module constructs a low-altitude airway map based on the low-altitude airway routes intelligently planned by the dynamic risk constraint judgment module.

[0011] The low-altitude airway risk analysis module performs constant-direction risk analysis on the intelligently planned low-altitude airway routes and determines the optimal path based on the constant-direction risk analysis results.

[0012] The optimal path calibration output module outputs the optimal path to the user terminal based on the optimal path calibrated by the low-altitude airway risk analysis module, and displays the path in three dimensions on the low-altitude airway map.

[0013] Preferably, the low-altitude environment perception constraint module is used to obtain the first constraint condition for low-altitude route environment perception, and the first constraint condition for low-altitude route environment perception is as follows:

[0014] By fusing data from multiple sensors, the low-altitude environment is monitored in real time to obtain environmental perception data of the low-altitude environment. The multiple sensors include: lidar, weather radar and infrared imaging equipment.

[0015] Based on environmental perception data of the low-altitude environment, boundary conditions for low-altitude flight routes are delineated, and the boundary conditions are the first constraint conditions.

[0016] The area within which low-altitude routes are flyable is determined based on the first constraint.

[0017] Preferably, the low-altitude airway feature extraction module is used to extract key feature parameters from the low-altitude airway data. The key feature parameters are regional feature parameters of the flyable area of ​​the low-altitude airway, including: terrain relief parameters, meteorological change parameters, and air traffic density parameters.

[0018] The acquisition of the terrain undulation parameters includes: using high-resolution digital elevation model data, the slope of the flyable area of ​​the low-altitude airway is obtained through GIS software; the terrain undulation is calculated using digital elevation model data to describe the terrain along the low-altitude airway; the roughness of the flyable area of ​​the low-altitude airway is obtained using high-density point cloud data obtained by lidar and point cloud processing software; and the elevation standard deviation of the flyable area of ​​the low-altitude airway is obtained using the elevation model.

[0019] The meteorological change parameters are obtained as follows: the actual wind speed of the wind field is detected using the Doppler principle, and the wind speed of the flyable area of ​​the low-altitude route is obtained by combining temperature compensation; the precipitation in the flyable area of ​​the low-altitude route is obtained by establishing the relationship between cloud top temperature and pixel precipitation points and using the difference between satellite information and ground rain gauges; and the visibility in the flyable area of ​​the low-altitude route is obtained by observing the brightness comparison between the target and the background.

[0020] The acquisition of the air traffic density parameters involves: obtaining the number of aircraft per unit area and the distance to the nearest aircraft within a preset time window in the flyable area of ​​the low-altitude airway by using radar monitoring and air traffic management system statistics on flight plan data along the low-altitude airway.

[0021] Preferably, the dynamic risk constraint determination module performs a second constraint analysis on the low-altitude airway based on the key feature parameters extracted by the low-altitude airway feature extraction module, and intelligently plans the low-altitude airway route based on the analysis results. The specific content of this intelligent planning is as follows:

[0022] Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the terrain undulation index of the flyable area of ​​the low-altitude airway is analyzed. The parameters include: the slope, roughness and elevation standard deviation of the flyable area of ​​the low-altitude airway.

[0023] Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the meteorological fluctuation index of the flyable area of ​​the low-altitude airway is analyzed. The key feature parameters include: wind speed, precipitation and visibility of the flyable area of ​​the low-altitude airway.

[0024] Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the air traffic conflict probability index of the flyable area of ​​the low-altitude airway is analyzed. The key feature parameters include: the area per unit area and the number of aircraft within a preset time window and the distance to the nearest aircraft.

[0025] Preferably, the specific content of the second constraint analysis for the low-altitude flight path is as follows:

[0026] A dynamic risk constraint judgment model was established, and a nonlinear coupling analysis was performed on the terrain undulation index, meteorological fluctuation index and air traffic conflict probability index of the flyable area of ​​low-altitude air routes to calculate the comprehensive risk assessment index of the flyable area of ​​low-altitude air routes.

[0027] Based on the first constraint condition of low-altitude airway environmental perception, a segmentation threshold for the second constraint condition is preset. The segmentation threshold includes a maximum segmentation threshold and a minimum segmentation threshold, and the maximum segmentation threshold is greater than the minimum segmentation threshold. The comprehensive risk assessment index of the low-altitude airway flyable area is compared with the maximum segmentation threshold and the minimum segmentation threshold: when the comprehensive risk assessment index of the low-altitude airway flyable area is less than or equal to the minimum segmentation threshold, the area is marked as a green flyable area; when the comprehensive risk assessment index of the low-altitude airway flyable area is greater than the minimum segmentation threshold and less than the maximum segmentation threshold, the area is marked as a yellow deceleration flight area; when the comprehensive risk assessment index of the low-altitude airway flyable area is greater than or equal to the maximum segmentation threshold, the area is marked as a red warning area.

[0028] Based on the constraint results of the second constraint, the green flyable area and the yellow reduced-speed flight area are retained, and low-altitude flight routes are intelligently planned.

[0029] Preferably, the low-altitude route map construction module constructs a low-altitude route map based on the low-altitude route intelligently planned by the dynamic risk constraint judgment module. The low-altitude route map includes a dynamic three-dimensional display of different routes and provides flight status reminders for areas of different colors: when a moving target enters a yellow deceleration flight area from a green flyable area, the low-altitude route map issues a deceleration command; when a moving target enters a green flyable area from a yellow deceleration flight area, the low-altitude route map issues an acceleration command.

[0030] Preferably, the low-altitude airway risk analysis module performs constant-direction risk analysis on the intelligently planned low-altitude airway routes, and determines the optimal path based on the constant-direction risk analysis results, as follows:

[0031] The deviation points of the existing m paths are counted to obtain the number of deviation points of the m paths. The deviation points of the m paths are connected in sequence to form a deviation flight segment. The deviation point represents the waypoint where the change range of the flight angle of the moving target from the starting point coordinate to the ending point coordinate exceeds a preset threshold.

[0032] Based on the starting coordinates and ending coordinates of the moving target, calculate the length of the constant direction line for each eccentric segment in the m paths, and average the length of the constant direction line to obtain the average length of the constant direction line for the m paths.

[0033] Based on the average constant direction line length of m paths, a constant direction line risk analysis is performed on low-altitude air routes. The smaller the average constant direction line length, the lower the flight risk of the corresponding low-altitude air route. The path corresponding to the minimum average constant direction line length is marked as the optimal path.

[0034] Preferably, the optimal path calibration output module outputs the coordinate information, flight altitude, and estimated flight time of the optimal path based on the optimal path calibrated by the low-altitude airway risk analysis module to the user terminal. The user terminal is an unmanned aerial vehicle (UAV) control platform that realizes a three-dimensional visualization of the optimal path in the low-altitude airway map and provides flight operation prompts based on real-time flight conditions.

[0035] The method for constructing low-altitude airway risk maps based on artificial intelligence includes the following steps:

[0036] Step S01: Obtain the first constraint conditions for low-altitude airway environmental perception and determine the flyable area of ​​the low-altitude airway;

[0037] Step S02: Extract key feature parameters from low-altitude airway data, including terrain relief, weather change rate, and air traffic density;

[0038] Step S03: Based on the key feature parameters, perform a second constraint analysis on the low-altitude airway, and intelligently plan the low-altitude airway route based on the analysis results;

[0039] Step S04: Construct a low-altitude airway map based on intelligently planned low-altitude airway routes;

[0040] Step S05: Perform a constant-direction risk analysis on the intelligently planned low-altitude air routes and determine the optimal path based on the results of the constant-direction risk analysis;

[0041] Step S06: Based on the calibrated optimal path, output the optimal path to the user terminal and display the path in three dimensions on the low-altitude airway map.

[0042] The technical effects and advantages of this invention are as follows:

[0043] This invention includes a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint determination module, a low-altitude route map construction module, a low-altitude route risk analysis module, and an optimal path calibration output module. The low-altitude environment perception constraint module forms the first constraint condition, avoiding the limitations of traditional single data sources, and dynamically adjusts the constraint condition by updating environmental data in real time to cope with the rapid changes in the low-altitude environment.

[0044] This study employs nonlinear coupling analysis of terrain undulation index, meteorological fluctuation index, and air traffic conflict probability index in the flyable area of ​​low-altitude routes to calculate a comprehensive risk assessment index. Combining static and dynamic constraints, it intelligently plans low-altitude routes and constructs low-altitude route maps. This allows for real-time capture of dynamic changes in the low-altitude environment, effectively addressing the complex and ever-changing demands of urban low-altitude environments, accurately delineating areas with different risk levels, providing reasonable route planning suggestions for aircraft, reducing flight risks, and improving the efficiency and safety of low-altitude traffic management. Furthermore, it introduces constant-direction risk analysis, calculating the average constant-direction length of each route to assess flight risks and determine the optimal path. By incorporating actual air traffic conditions, the planned routes become safer and more efficient. Attached Figure Description

[0045] Figure 1 A schematic diagram of the structure of an AI-based low-altitude airway risk map construction system.

[0046] Figure 2 This is a flowchart illustrating the method for constructing low-altitude airway risk maps based on artificial intelligence. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The method and system for constructing low-altitude airway risk maps based on artificial intelligence involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, the present invention provides an artificial intelligence-based low-altitude airway risk map construction system, including: a low-altitude environment perception constraint module, a low-altitude airway feature extraction module, a dynamic risk constraint determination module, a low-altitude airway map construction module, a low-altitude airway risk analysis module, and an optimal path calibration output module;

[0049] The low-altitude environment perception constraint module obtains the first constraint condition of the low-altitude airway environment perception and determines the flyable area of ​​the low-altitude airway.

[0050] The low-altitude airway feature extraction module is used to extract key feature parameters from low-altitude airway data, including terrain relief, meteorological change rate and air traffic density, and convert the parameters into a standardized input format.

[0051] The dynamic risk constraint determination module performs a second constraint analysis on low-altitude routes based on the key feature parameters extracted by the low-altitude route feature extraction module, and intelligently plans low-altitude route routes based on the analysis results.

[0052] The low-altitude airway map construction module constructs a low-altitude airway map based on the low-altitude airway routes intelligently planned by the dynamic risk constraint judgment module.

[0053] The low-altitude airway risk analysis module performs constant-direction risk analysis on the intelligently planned low-altitude airway routes and determines the optimal path based on the constant-direction risk analysis results.

[0054] The optimal path calibration output module outputs the optimal path to the user terminal based on the optimal path calibrated by the low-altitude airway risk analysis module, and displays the path in three dimensions on the low-altitude airway map.

[0055] In this embodiment, it should be specifically noted that the low-altitude environment perception constraint module is used to obtain the first constraint condition for low-altitude route environment perception, and the first constraint condition for low-altitude route environment perception is as follows:

[0056] By fusing data from multiple sensors, the low-altitude environment is monitored in real time to obtain environmental perception data of the low-altitude environment. The multiple sensors include: lidar, weather radar and infrared imaging equipment.

[0057] Based on environmental perception data of the low-altitude environment, boundary conditions for low-altitude flight routes are delineated, and the boundary conditions are the first constraint conditions.

[0058] The area within which low-altitude air routes are flyable is determined based on the first constraint condition.

[0059] LiDAR is used to accurately capture terrain contours and obstacle distribution, weather radar provides meteorological parameters such as wind speed, rainfall, and air pressure for the current area, and infrared imaging equipment can identify heat source distribution to detect potential air traffic conflicts or human interference. These multi-source data, after filtering and spatiotemporal alignment, form the first layer of environmental constraints and determine the boundaries of the flyable area. For example, in a certain urban area, if there is a cluster of high-rise buildings, the area is marked as a high-risk flight zone, and airspace exceeding a certain altitude range is excluded from the flyable area.

[0060] In this embodiment, it should be specifically noted that the low-altitude airway feature extraction module is used to extract key feature parameters from the low-altitude airway data. The key feature parameters are regional feature parameters of the flyable area of ​​the low-altitude airway, including: terrain relief parameters, meteorological change parameters, and air traffic density parameters.

[0061] The acquisition of the terrain undulation parameters includes: using high-resolution digital elevation model data, the slope of the flyable area of ​​the low-altitude airway is obtained through GIS software; the terrain undulation is calculated using digital elevation model data to describe the terrain along the low-altitude airway; the roughness of the flyable area of ​​the low-altitude airway is obtained using high-density point cloud data obtained by lidar and point cloud processing software; and the elevation standard deviation of the flyable area of ​​the low-altitude airway is obtained using the elevation model.

[0062] The meteorological change parameters are obtained as follows: the actual wind speed of the wind field is detected using the Doppler principle, and the wind speed of the flyable area of ​​the low-altitude route is obtained by combining temperature compensation; the precipitation in the flyable area of ​​the low-altitude route is obtained by establishing the relationship between cloud top temperature and pixel precipitation points and using the difference between satellite information and ground rain gauges; and the visibility in the flyable area of ​​the low-altitude route is obtained by observing the brightness comparison between the target and the background.

[0063] The acquisition of the air traffic density parameters involves: obtaining the number of aircraft per unit area and the distance to the nearest aircraft within a preset time window in the flyable area of ​​the low-altitude airway by using radar monitoring and air traffic management system statistics on flight plan data along the low-altitude airway.

[0064] In this embodiment, it should be specifically explained that the dynamic risk constraint determination module performs a second constraint analysis on the low-altitude airway based on the key feature parameters extracted by the low-altitude airway feature extraction module, and intelligently plans the low-altitude airway route based on the analysis results. The specific content of this analysis is as follows:

[0065] Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the terrain undulation index of the flyable area of ​​the low-altitude airway is analyzed. These parameters include: slope, roughness, and elevation standard deviation of the flyable area. The formula for calculating the terrain undulation index of the flyable area of ​​the low-altitude airway is as follows: ,in A topographic relief index indicating the flyable area for low-altitude routes. This represents the average slope of each flyable area. This represents the maximum measured slope in each flyable area. This represents the roughness of each flyable region. This represents the maximum roughness measured in each flyable region. This represents the standard deviation of elevation for each flyable area. This represents the maximum standard deviation of the measured elevation in each flyable area. , and Let i and n represent the weighting coefficients, and let i represent the area numbers of the low-altitude airway flyable areas, i = 1, 2, 3, ..., n, and n represent the total number of low-altitude airway flyable areas under the first constraint.

[0066] Based on the key feature parameters extracted by the low-altitude flight path feature extraction module, the meteorological fluctuation index of the flyable area of ​​the low-altitude flight path is analyzed. These key feature parameters include wind speed, precipitation, and visibility in the flyable area. The formula for calculating the meteorological fluctuation index of the flyable area of ​​the low-altitude flight path is as follows: ,in A meteorological fluctuation index indicating the flyable area of ​​low-altitude air routes. This represents the difference between the maximum and minimum wind speeds in each flyable area during the data collection period. This indicates the duration of the data collection period for each flyable area. This indicates the precipitation in each flyable area during the data collection period. This represents the maximum visibility in each flyable area during the data collection period. , and These represent the weighting coefficients;

[0067] Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the air traffic conflict probability index of the flyable area of ​​the low-altitude airway is analyzed. These key feature parameters include: the number of aircraft per unit area of ​​the area and within a preset time window, and the distance to the nearest aircraft. The formula for calculating the air traffic conflict probability index of the flyable area of ​​the low-altitude airway is as follows: ,in An index representing the probability of air traffic conflict in areas where low-altitude air routes are flyable. This indicates the number of aircraft per unit area and within a preset time window in each flyable area. This represents the area per unit of the flyable zone. Indicates the preset time window, This represents the distance from the center measurement point within each flyable area to the nearest aircraft. This indicates the preset safe distance.

[0068] In this embodiment, it should be specifically explained that the details of the second constraint condition analysis for the low-altitude flight path are as follows:

[0069] A dynamic risk constraint judgment model is established, and a nonlinear coupling analysis is performed on the terrain undulation index, meteorological fluctuation index, and air traffic conflict probability index of the flyable area of ​​low-altitude routes. The comprehensive risk assessment index of the flyable area of ​​low-altitude routes is calculated using the following formula: ,in A comprehensive risk assessment index indicating the flyable area of ​​low-altitude air routes;

[0070] Based on the first constraint condition of low-altitude airway environmental perception, a segmentation threshold for the second constraint condition is preset. The segmentation threshold includes a maximum segmentation threshold and a minimum segmentation threshold, and the maximum segmentation threshold is greater than the minimum segmentation threshold. The comprehensive risk assessment index of the low-altitude airway flyable area is compared with the maximum segmentation threshold and the minimum segmentation threshold: when the comprehensive risk assessment index of the low-altitude airway flyable area is less than or equal to the minimum segmentation threshold, the area is marked as a green flyable area; when the comprehensive risk assessment index of the low-altitude airway flyable area is greater than the minimum segmentation threshold and less than the maximum segmentation threshold, the area is marked as a yellow deceleration flight area; when the comprehensive risk assessment index of the low-altitude airway flyable area is greater than or equal to the maximum segmentation threshold, the area is marked as a red warning area.

[0071] Based on the constraint results of the second constraint, the green flyable area and the yellow reduced-speed flight area are retained, and low-altitude flight routes are intelligently planned.

[0072] In this embodiment, it should be specifically explained that the low-altitude route map construction module constructs a low-altitude route map based on the low-altitude route intelligently planned by the dynamic risk constraint judgment module. The low-altitude route map includes a dynamic three-dimensional display of different routes and provides flight status reminders for areas of different colors: when a moving target enters a yellow deceleration flight area from a green flyable area, the low-altitude route map issues a deceleration command; when a moving target enters a green flyable area from a yellow deceleration flight area, the low-altitude route map issues an acceleration command.

[0073] In this embodiment, it should be specifically explained that the low-altitude route risk analysis module performs constant-direction risk analysis on the intelligently planned low-altitude route and determines the optimal path based on the constant-direction risk analysis results, as follows:

[0074] The deviation points of the existing m paths are counted to obtain the number of deviation points of the m paths. The deviation points of the m paths are connected in sequence to form a deviation flight segment. The deviation point represents the waypoint where the change range of the flight angle of the moving target from the starting point coordinate to the ending point coordinate exceeds a preset threshold.

[0075] Based on the starting coordinates and ending coordinates of the moving target, calculate the length of the constant direction line for each eccentric segment in the m paths, and average the length of the constant direction line to obtain the average length of the constant direction line for the m paths.

[0076] The expression for the length of the constant-direction line of each skew segment in the m paths is:

[0077] ,in This represents the length of the constant-direction line for each eccentric segment in the m paths. This represents the meridian arc length of the starting point of each eccentric segment in the m paths. This represents the meridian arc length of the endpoint of each bias segment in the m paths. Let represent the heading angle of each eccentric segment in the m paths, 'a' represent the semi-major axis of the Earth ellipsoid, and 'e' represent the eccentricity of the ellipsoid. This represents the current latitude of each bias segment in the m paths. This represents the longitude of the starting point of each biased flight segment in the m paths. This represents the longitude of the endpoint of each biased segment in the m paths;

[0078] When the latitude change is not zero, the length of the heading line is directly proportional to the latitude change, and the heading angle becomes more pronounced as it approaches north-south. The larger the value, the shorter the path.

[0079] Based on the average constant direction line length of m paths, a constant direction line risk analysis is performed on low-altitude air routes. The smaller the average constant direction line length, the lower the flight risk of the corresponding low-altitude air route. The path corresponding to the minimum average constant direction line length is marked as the optimal path.

[0080] In this embodiment, it should be specifically noted that the optimal path calibration output module outputs the coordinate information, flight altitude, and estimated flight time of the optimal path based on the optimal path calibrated by the low-altitude airway risk analysis module to the user terminal. The user terminal is an unmanned aerial vehicle (UAV) control platform that realizes a three-dimensional visualization of the optimal path in the low-altitude airway map and provides flight operation prompts based on real-time flight conditions.

[0081] like Figure 2 As shown in this embodiment, it should be specifically explained that the method for constructing a low-altitude airway risk map based on artificial intelligence includes the following steps:

[0082] Step S01: Obtain the first constraint conditions for low-altitude airway environmental perception and determine the flyable area of ​​the low-altitude airway;

[0083] Step S02: Extract key feature parameters from low-altitude airway data, including terrain relief, weather change rate, and air traffic density;

[0084] Step S03: Based on the key feature parameters, perform a second constraint analysis on the low-altitude airway, and intelligently plan the low-altitude airway route based on the analysis results;

[0085] Step S04: Construct a low-altitude airway map based on intelligently planned low-altitude airway routes;

[0086] Step S05: Perform a constant-direction risk analysis on the intelligently planned low-altitude air routes and determine the optimal path based on the results of the constant-direction risk analysis;

[0087] Step S06: Based on the calibrated optimal path, output the optimal path to the user terminal and display the path in three dimensions on the low-altitude airway map.

[0088] In this embodiment, it should be specifically noted that the main difference between this embodiment and the prior art is that this embodiment is equipped with a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint judgment module, a low-altitude route map construction module, a low-altitude route risk analysis module, and an optimal path calibration output module. The low-altitude environment perception constraint module forms the first constraint condition, avoiding the limitations of the traditional single data source, and dynamically adjusts the constraint condition by updating the environmental data in real time to cope with the rapid changes in the low-altitude environment.

[0089] This study employs nonlinear coupling analysis of terrain undulation index, meteorological fluctuation index, and air traffic conflict probability index in the flyable area of ​​low-altitude routes to calculate a comprehensive risk assessment index. Combining static and dynamic constraints, it intelligently plans low-altitude routes and constructs low-altitude route maps. This allows for real-time capture of dynamic changes in the low-altitude environment, effectively addressing the complex and ever-changing demands of urban low-altitude environments, accurately delineating areas with different risk levels, providing reasonable route planning suggestions for aircraft, reducing flight risks, and improving the efficiency and safety of low-altitude traffic management. Furthermore, it introduces constant-direction risk analysis, calculating the average constant-direction length of each route to assess flight risks and determine the optimal path. By incorporating actual air traffic conditions, the planned routes become safer and more efficient.

[0090] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0091] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A low-altitude airway risk map construction system based on artificial intelligence, characterized in that: include: The system includes a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint determination module, a low-altitude route map construction module, a low-altitude route risk analysis module, and an optimal path calibration output module. The low-altitude environment perception constraint module obtains the first constraint condition of the low-altitude airway environment perception and determines the flyable area of ​​the low-altitude airway. By fusing data from multiple sensors, the low-altitude environment is monitored in real time to obtain environmental perception data of the low-altitude environment. The multiple sensors include: lidar, weather radar and infrared imaging equipment. Based on environmental perception data of the low-altitude environment, boundary conditions for low-altitude flight routes are delineated, and the boundary conditions are the first constraint conditions. The area within which low-altitude air routes are flyable is determined based on the first constraint condition. The low-altitude airway feature extraction module is used to extract key feature parameters from low-altitude airway data, including terrain relief parameters, meteorological change parameters, and air traffic density, and convert the parameters into a standardized input format. The acquisition of the terrain undulation parameters includes: using high-resolution digital elevation model data, the slope of the flyable area of ​​the low-altitude airway is obtained through GIS software; the terrain undulation is calculated using digital elevation model data to describe the terrain along the low-altitude airway; the roughness of the flyable area of ​​the low-altitude airway is obtained using high-density point cloud data obtained by lidar and point cloud processing software; and the elevation standard deviation of the flyable area of ​​the low-altitude airway is obtained using the elevation model. The meteorological change parameters are obtained as follows: the actual wind speed of the wind field is detected using the Doppler principle, and the wind speed of the flyable area of ​​the low-altitude route is obtained by combining temperature compensation; the precipitation in the flyable area of ​​the low-altitude route is obtained by establishing the relationship between cloud top temperature and pixel precipitation points and using the difference between satellite information and ground rain gauges; and the visibility in the flyable area of ​​the low-altitude route is obtained by observing the brightness comparison between the target and the background. The acquisition of the air traffic density parameters is as follows: by using radar monitoring and air traffic management system to collect flight plan data along the low-altitude airway, the number of aircraft per unit area and within a preset time window in each area of ​​the flyable low-altitude airway and the distance to the nearest aircraft are obtained. Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the terrain undulation index of the flyable area of ​​the low-altitude airway is analyzed. The parameters include: the slope, roughness and elevation standard deviation of the flyable area of ​​the low-altitude airway. Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the meteorological fluctuation index of the flyable area of ​​the low-altitude airway is analyzed. The key feature parameters include: wind speed, precipitation and visibility of the flyable area of ​​the low-altitude airway. Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the air traffic conflict probability index of the flyable area of ​​the low-altitude airway is analyzed. The key feature parameters include: the area per unit area of ​​the region and the number of aircraft within a preset time window and the distance to the nearest aircraft. The dynamic risk constraint determination module performs a second constraint analysis on low-altitude routes based on the key feature parameters extracted by the low-altitude route feature extraction module, and intelligently plans low-altitude route routes based on the analysis results. The low-altitude airway map construction module constructs a low-altitude airway map based on the low-altitude airway routes intelligently planned by the dynamic risk constraint judgment module. The low-altitude airway risk analysis module performs constant-direction risk analysis on the intelligently planned low-altitude airway routes and determines the optimal path based on the constant-direction risk analysis results. The optimal path calibration output module outputs the optimal path to the user terminal based on the optimal path calibrated by the low-altitude airway risk analysis module, and displays the path in three dimensions on the low-altitude airway map.

2. The low-altitude airway risk map construction system based on artificial intelligence according to claim 1, characterized in that: The specific details of the second constraint analysis for low-altitude routes are as follows: A dynamic risk constraint judgment model is established, and a nonlinear coupling analysis is performed on the terrain undulation index, meteorological fluctuation index and air traffic conflict probability index of the flyable area of ​​low-altitude air routes to calculate the comprehensive risk assessment index of the flyable area of ​​low-altitude air routes. Based on the first constraint condition of low-altitude airway environmental perception, a segmentation threshold for the second constraint condition is preset. The segmentation threshold includes a maximum segmentation threshold and a minimum segmentation threshold, and the maximum segmentation threshold is greater than the minimum segmentation threshold. The comprehensive risk assessment index of the low-altitude airway flyable area is compared with the maximum segmentation threshold and the minimum segmentation threshold: when the comprehensive risk assessment index of the low-altitude airway flyable area is less than or equal to the minimum segmentation threshold, the area is marked as a green flyable area; when the comprehensive risk assessment index of the low-altitude airway flyable area is greater than the minimum segmentation threshold and less than the maximum segmentation threshold, the area is marked as a yellow deceleration flight area; when the comprehensive risk assessment index of the low-altitude airway flyable area is greater than or equal to the maximum segmentation threshold, the area is marked as a red warning area. Based on the constraint results of the second constraint, the green flyable area and the yellow reduced-speed flight area are retained, and low-altitude flight routes are intelligently planned.

3. The low-altitude airway risk map construction system based on artificial intelligence according to claim 1, characterized in that: The low-altitude airway map construction module constructs a low-altitude airway map based on the low-altitude airway routes intelligently planned by the dynamic risk constraint judgment module. The low-altitude airway map includes a dynamic three-dimensional display of different routes and provides flight status reminders for areas of different colors: when a moving target enters a yellow deceleration flight area from a green flyable area, the low-altitude airway map issues a deceleration command; when a moving target enters a green flyable area from a yellow deceleration flight area, the low-altitude airway map issues an acceleration command.

4. The low-altitude airway risk map construction system based on artificial intelligence according to claim 1, characterized in that: The low-altitude airway risk analysis module performs constant-direction risk analysis on the intelligently planned low-altitude airway routes and determines the optimal path based on the constant-direction risk analysis results, as follows: The deviation points of the existing m paths are counted to obtain the number of deviation points of the m paths. The deviation points of the m paths are connected in sequence to form a deviation flight segment. The deviation point represents the waypoint where the change range of the flight angle of the moving target from the starting point coordinate to the ending point coordinate exceeds a preset threshold. Based on the starting coordinates and ending coordinates of the moving target, calculate the length of the constant direction line for each eccentric segment in the m paths, and average the length of the constant direction line to obtain the average length of the constant direction line for the m paths. Based on the average constant direction line length of m paths, a constant direction line risk analysis is performed on low-altitude air routes. The smaller the average constant direction line length, the lower the flight risk of the corresponding low-altitude air route. The path corresponding to the minimum average constant direction line length is marked as the optimal path.

5. The low-altitude airway risk map construction system based on artificial intelligence according to claim 1, characterized in that: The optimal path calibration output module outputs the coordinates, flight altitude, and estimated flight time of the optimal path based on the optimal path calibrated by the low-altitude airway risk analysis module to the user terminal. The user terminal is an unmanned aerial vehicle (UAV) control platform that displays the optimal path in a three-dimensional visualization on the low-altitude airway map and provides flight operation prompts based on real-time flight conditions.

6. A method for constructing low-altitude airway risk maps based on artificial intelligence, used in the artificial intelligence-based low-altitude airway risk map construction system according to any one of claims 1-5, characterized in that: Includes the following steps: Step S01: Obtain the first constraint conditions for low-altitude airway environmental perception and determine the flyable area of ​​the low-altitude airway; By fusing data from multiple sensors, the low-altitude environment is monitored in real time to obtain environmental perception data of the low-altitude environment. The multiple sensors include: lidar, weather radar and infrared imaging equipment. Based on environmental perception data of the low-altitude environment, boundary conditions for low-altitude flight routes are delineated, and the boundary conditions are the first constraint conditions. The area within which low-altitude air routes are flyable is determined based on the first constraint condition. Step S02: Extract key feature parameters from low-altitude airway data, including terrain relief, weather change rate, and air traffic density; The acquisition of the terrain undulation parameters includes: using high-resolution digital elevation model data, the slope of the flyable area of ​​the low-altitude airway is obtained through GIS software; the terrain undulation is calculated using digital elevation model data to describe the terrain along the low-altitude airway; the roughness of the flyable area of ​​the low-altitude airway is obtained using high-density point cloud data obtained by lidar and point cloud processing software; and the elevation standard deviation of the flyable area of ​​the low-altitude airway is obtained using the elevation model. The meteorological change parameters are obtained as follows: the actual wind speed of the wind field is detected using the Doppler principle, and the wind speed of the flyable area of ​​the low-altitude route is obtained by combining temperature compensation; the precipitation in the flyable area of ​​the low-altitude route is obtained by establishing the relationship between cloud top temperature and pixel precipitation points and using the difference between satellite information and ground rain gauges; and the visibility in the flyable area of ​​the low-altitude route is obtained by observing the brightness comparison between the target and the background. The acquisition of the air traffic density parameters is as follows: by using radar monitoring and air traffic management system to collect flight plan data along the low-altitude airway, the number of aircraft per unit area and within a preset time window in each area of ​​the flyable low-altitude airway and the distance to the nearest aircraft are obtained. Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the terrain undulation index of the flyable area of ​​the low-altitude airway is analyzed. The parameters include: the slope, roughness and elevation standard deviation of the flyable area of ​​the low-altitude airway. Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the meteorological fluctuation index of the flyable area of ​​the low-altitude airway is analyzed. The key feature parameters include: wind speed, precipitation and visibility of the flyable area of ​​the low-altitude airway. Based on the key feature parameters extracted by the low-altitude airway feature extraction module, the air traffic conflict probability index of the flyable area of ​​the low-altitude airway is analyzed. The key feature parameters include: the area per unit area of ​​the region and the number of aircraft within a preset time window and the distance to the nearest aircraft. Step S03: Based on the key feature parameters, perform a second constraint analysis on the low-altitude airway, and intelligently plan the low-altitude airway route based on the analysis results; Step S04: Construct a low-altitude airway map based on intelligently planned low-altitude airway routes; Step S05: Perform a constant-direction risk analysis on the intelligently planned low-altitude air routes and determine the optimal path based on the results of the constant-direction risk analysis; Step S06: Based on the calibrated optimal path, output the optimal path to the user terminal and display the path in three dimensions on the low-altitude airway map.

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