Intelligent air route conflict detection method based on three-dimensional grid

Abstract

The invention discloses an air route intelligent conflict detection method based on a three-dimensional grid, and the method comprises the steps: firstly, carrying out the deep analysis of the spatial domain structure characteristics of a designated region, and constructing a three-dimensional grid model based on the spatial domain structure characteristics; then, on the basis of the three-dimensional grid model, fine arrangement of an airspace and an air route is carried out; and finally, carrying out grid coding on the marked air route, and comprehensively judging the air route conflict in combination with the marked airspace. The method can prevent the unmanned aerial vehicles in various industry fields from conflicting, dangerous approaching and collision events, and is of great significance to guarantee the safe operation of the low-altitude unmanned aerial vehicles.

Description

Technical Field

[0001] The invention belongs to the field of low-altitude flight route intelligent management and relates to an intelligent conflict detection method based on a three-dimensional grid route. Background Art

[0002] With the booming development of the low-altitude economy, low-altitude flights will be diversified in the future, including multi-model, large-scale, high-density flights, participation of multiple operators, and a wide range of service scenarios. The growing demand for low-altitude flight activities in various industries and fields and the increase in flight traffic have led to an increase in flight conflicts, threatening the safety of low-altitude flights. Flight route conflict detection is a research focus of air traffic management and is the key to ensuring the safety of low-altitude flights.

[0003] The current low-altitude UAV route conflict detection method mainly relies on the detection of flight planning and interval management of the two-dimensional airspace model. However, there are the following technical problems and shortcomings:

[0004] 1. It cannot accurately reflect the actual flying environment of the UAV, including obstacles such as terrain, buildings, and trees, nor can it intuitively display the relative position relationship between the UAV route and obstacles. The flight environment is unclear, which seriously affects the operator's understanding and judgment.

[0005] 2. The traditional two-dimensional plane model cannot better reflect the actual flight situation of the UAV, and the flight trajectory tracking of the UAV's tiny movement is relatively fuzzy. It fails to detect in time whether there is a potential conflict risk between the routes of the UAVs, thereby reducing the accuracy of conflict detection.

[0006] 3. Flight planning, route planning and conflict detection based on the two-dimensional airspace model increase unnecessary flight adjustments and waiting time, reducing airspace utilization and overall flight efficiency.

[0007] 4. The simplification of the route conflict detection process based on flight planning and interval management makes the flight reliability of drones in complex environments lower, reducing the public's trust in drone technology.

[0008] Based on this, in order to prevent conflicts, dangerous approaches, and collisions among drones in various industries and fields, based on the current complex airspace structure, and on the premise of ensuring safety, an intelligent and efficient conflict detection method is used based on the three-dimensional grid airspace to detect route conflicts in a timely manner, which is of great significance to ensuring the safe operation of low-altitude drones. Summary of the invention

[0009] The purpose of the invention is to provide a method for intelligent conflict detection based on three-dimensional grid routes. First, a three-dimensional grid airspace model is constructed by analyzing the regional airspace structure, including regional three-dimensional grid data collection, three-dimensional grid management, and three-dimensional grid modeling. Secondly, route demarcation and route management are performed based on the three-dimensional grid airspace, and finally, efficient and accurate route conflict detection is achieved through a multi-dimensional detection algorithm. The present invention can prevent conflicts, dangerous approaches, and collisions between drones in various industries and fields, and is of great significance to ensuring the safe operation of low-altitude drones.

[0010] The object of the present invention is achieved through the following technical solutions:

[0011] A three-dimensional grid-based intelligent conflict detection method comprises the following steps:

[0012] Step 1: Analyze the spatial structure characteristics of the designated area and build a three-dimensional grid model based on this;

[0013] Step 2: Based on the three-dimensional grid model, fine-scale delineation of airspace and routes is carried out;

[0014] Step 3: Grid-code the designated routes and comprehensively judge route conflicts based on the designated airspace.

[0015] Further, step 1 comprises the following steps:

[0016] Step 1-1: Based on low-altitude oblique photography technology, use a low-altitude multi-rotor aircraft, a digital camera and a positioning system to conduct full-coverage oblique aerial photography of the designated area to obtain oblique image data of the ground;

[0017] The focal length, principal point coordinates, distortion coefficients of the camera in the camera file, the three-dimensional coordinates of the camera when shooting, and the pitch angle, yaw angle and roll angle information of the camera when shooting are used for calculation to generate a three-dimensional point cloud. The point cloud forms a grid. The constructed grid is used to generate a preliminary three-dimensional model. The texture information in the oblique image data is mapped to the preliminary three-dimensional model. After the texture mapping is completed, the three-dimensional model data with real texture and geometric shape is obtained, and then combined with the digital line map elevation information and ground features to form digital orthophoto impact data;

[0018] Step 1-2: using grid coding technology to assign unique spatial coding identifiers to the three-dimensional model data and digital orthophoto data to obtain a three-dimensional grid code;

[0019] Steps 1-3: Use three-dimensional grid coding to construct a grid world for the specified area, divide the space occupied by all elements of the unmanned system into fine and three-dimensional parts, and use "1" to represent inaccessible grids and "0" to represent accessible grids, thus constructing a three-dimensional grid map model that reflects the regional airspace structure.

[0020] Further, step 2 comprises the following steps:

[0021] Step 2-1: Complete the definition and drawing of the airspace in the three-dimensional grid model 120 meters above the designated area; the defined airspace includes no-fly zones, airworthy zones, suitable flying zones, take-off and landing zones, restricted flying zones, alternate landing zones, and other specific areas customized according to needs; during the drawing process, select a variety of graphic shapes to match and outline the shape and scope of the defined airspace;

[0022] Step 2-2: Plan and design routes in a three-dimensional grid model from above to 120 meters below the designated area, and build in a conflict analysis mechanism for route planning. Each route includes route name, type, and grid level.

[0023] Step 2-3: Display all the route information that has been drawn, and support viewing and deleting the route data.

[0024] Further, step 3 comprises the following steps:

[0025] Step 3-1, grid coding the drawn route according to the grid level and route height input by the three-dimensional grid model;

[0026] Step 3-2: According to the route grid range, query the underlying surface grid information for data grid matching. If the underlying surface grid contains obstacle information, the output is conflicting.

[0027] Step 3-3, according to the route grid range, query the no-fly zone and route demarcation information in the airspace demarcation, if no no-fly zone and route demarcation grid is matched, output no conflict;

[0028] Step 3-4: perform an overlap check on the usage time of the matched no-fly zone and the flight time of the matched route. If it is found that there is an overlap with the usage time of the no-fly zone or the flight time of the route, output that there is a conflict, otherwise output that there is no conflict.

[0029] The beneficial effects of the present invention are:

[0030] The present invention cleverly maps the planned airspace and its routes into the three-dimensional grid system by performing three-dimensional grid modeling on the designated area, and adopts an advanced hierarchical management mode to achieve refined management of route planning. By using a multi-dimensional detection algorithm, efficient and accurate route conflict detection can be performed, which is convenient for ground operation and control personnel to make accurate judgments and ensure the safe operation of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the 3D grid map data collection process.

[0032] Figure 2 Draw a schematic diagram for the route.

[0033] Figure 3 Schematic diagram of route management

[0034] Figure 4 This is a schematic diagram of the route conflict detection process.

[0035] Figure 5 Schematic diagram of the oblique photography model.

[0036] Figure 6 This is a schematic diagram of the orthophoto of the specified area.

[0037] Figure 7 Schematic diagram of the three-dimensional grid graph model.

[0038] Figure 8 The figure is a flow chart of the intelligent conflict detection method based on three-dimensional grid routes. DETAILED DESCRIPTION

[0039] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0040] This embodiment shows a method for intelligent conflict detection based on three-dimensional grid routes. Figure 8 As shown, the following steps are included:

[0041] Step 1: For the designated area, conduct an in-depth analysis of its airspace structure characteristics, including but not limited to topography, airspace use restrictions, existing flight activity patterns, etc., and build a three-dimensional grid model based on this.

[0042] As an example, this embodiment uses a drone equipped with a tilted five-lens camera to take high-precision, all-round photos of a designated area, extract and match image feature points, construct an irregular triangulated network (TIN) model, and generate a tilted three-dimensional model with an accuracy of 0.3 meters. Combined with the elevation information and terrain features of the digital line map (DLG), further processing is performed to generate an orthophoto (DOM) with an accuracy of 1 meter. Based on national standards and Beidou grid technology, the tilted three-dimensional model and orthophoto data are integrated to create a full-space stereo layered three-dimensional grid model, showing the spatial information of all elements in the area, and providing accurate map support for the operation and management of unmanned equipment. Specifically, the following steps are included:

[0043] Step 1-1: 3D grid data collection

[0044] See also Figure 1 As shown, based on the low-altitude oblique photography technology, a low-altitude multi-rotor aircraft, a digital camera and a positioning system are used to conduct full-coverage oblique aerial photography of the designated area to obtain oblique image data of the ground.

[0045] The camera's focal length, principal point coordinates, distortion coefficient, the camera's three-dimensional coordinates at the time of shooting, and the camera's pitch, yaw, and roll angles at the time of shooting in the camera file are used for calculation to generate an accurate three-dimensional point cloud. The point cloud forms a grid, and a preliminary three-dimensional model (a "white model" without texture) is generated using the constructed grid. The texture information in the oblique image data is accurately mapped to the preliminary three-dimensional model by judging the correspondence between the image and the model surface. After completing the texture mapping, the three-dimensional model data with real texture and geometric shape is obtained, which is then combined with the digital line map (DLG) elevation information and ground features to form digital orthophoto influence (DOM) data.

[0046] This step uses high-precision surveying and mapping technology to obtain detailed three-dimensional data on the terrain, buildings, flight restriction areas, etc. in the area. The three-dimensional model of a certain area is as follows: Figure 5 As shown:

[0047] Range: 12.7km in a certain area 2 ;

[0048] Format: OSGB, 3D Tiles;

[0049] Accuracy: 0.3m;

[0050] Coordinate system: CGCS2000 coordinate system, 1985 National Height Datum;

[0051] Coding basis: Beidou grid position code, Beidou segmentation time code.

[0052] The orthophoto map (DOM) of a certain area is as follows: Figure 6 As shown:

[0053] Range: 12.7km in a certain area 2 ;

[0054] Format: Tiff;

[0055] Accuracy: 1m;

[0056] Coordinate system: CGCS2000 coordinate system, 1985 National Height Datum;

[0057] Coding basis: Beidou grid position code, Beidou segmentation time code.

[0058] Step 1-2: 3D grid map management

[0059] The grid coding technology is used to give a unique spatial coding identifier to the three-dimensional model data and DOM data to obtain a three-dimensional grid code.

[0060] In this step, the national standard "Earth Space Grid Coding Rules" (GB / T40087-2021) is followed, and grid coding technology is used to give every inch of space in the designated area a unique spatial coding identification that is computable and easy to retrieve. This identification system has the characteristics of multi-scale, uniqueness, three-dimensionality, and full time and space. It provides a framework support for the realization of the integrated expression capability of "land, sea, air, space, and electricity", underground and underwater, and realizes the refined management of construction elements in designated areas. Multi-source data segmentation coding can provide the ability to aggregate multiple types of data maps with GeoSOT coding, including but not limited to the following data types: vector maps, DOM data, meteorological data, three-dimensional model data, infrastructure data, etc.

[0061] Steps 1-3: 3D mesh modeling

[0062] Use three-dimensional grid coding to construct a grid world for a specified area, covering buildings, ground, trees, etc., and divide the space occupied by all elements of the unmanned system (underlying surface, no-fly zone, meteorological environment, communication services) into a refined and three-dimensional subdivision, and convert it into a one-dimensional grid code for efficient calculation. Use "0" and "1" to express the situation in the entire space, with "1" being inaccessible and "0" being accessible, providing a basis for drone activities, monitoring and security, and using three-dimensional modeling technology to construct a three-dimensional grid map model that accurately reflects the regional airspace structure. A three-dimensional grid map of a certain area is shown below: Figure 7 As shown:

[0063] Range: 12.7km in a certain area 2 , airspace below 120m;

[0064] Format: 3D Tiles;

[0065] Accuracy: ≤4m;

[0066] Coordinate system: CGCS2000 coordinate system, 1985 National Height Datum;

[0067] Coding basis: Beidou grid position code, Beidou segmentation time code.

[0068] Step 2: Based on the three-dimensional grid model, carry out detailed delineation of airspace and routes.

[0069] This process needs to take into account the flight requirements of drones, and also needs to fully integrate airspace usage rules, safety interval requirements, and potential conflict risk points to improve the efficiency and accuracy of demarcation. The steps are as follows:

[0070] Step 2-1: Airspace demarcation

[0071] Flexible and accurate airspace definition and drawing are completed in a three-dimensional grid model 120 meters above the designated area. A variety of airspace demarcation types fully cover no-fly zones, airworthy zones, suitable flying zones, take-off and landing zones, restricted flying zones, alternate landing zones, and other specific areas customized according to needs to meet the complex and ever-changing airspace management needs. During the drawing process, a variety of graphic shapes such as circles, polygons, cylinders, and polyhedrons are selected to accurately match and outline the shape and scope of the defined airspace, realizing highly customized airspace planning. In addition, a wealth of airspace demarcation data management functions are set up, which not only improves the flexibility and efficiency of airspace management, but also ensures the rational allocation and utilization of airspace resources, providing a solid guarantee for the safety and smoothness of aviation activities.

[0072] Step 2-2: Route planning

[0073] Carefully plan and design routes within a three-dimensional grid model from above the designated area to 120 meters below. The route planning has a built-in conflict analysis mechanism that can effectively identify and avoid potential route conflicts during the route planning stage to ensure the safety and rationality of the route. In order to facilitate unified management, the designated routes will be displayed in a clear and intuitive list format, and you can easily view the detailed information of each route, including key information such as route name, type, and grid level. This greatly improves the flexibility and efficiency of route management.

[0074] By entering the route name, selecting the route type, entering the grid level, route altitude, selecting the flight time, route color, and route drawing. Ground operation monitoring personnel draw routes in the command and dispatch system, such as Figure 2 shown.

[0075] Step 2-3: Route Management

[0076] In the process of route management, all the route information that has been drawn is clearly displayed in the form of a list, including key elements such as route name, route type, grid level, and current route status. On this basis, the selected route can be accurately focused and highlighted in the center of the map, which makes it easier for users to intuitively understand the specific direction and coverage of the route, greatly ensuring the timeliness and security of route data.

[0077] The command and dispatch system can display the route information, including route name, route type, grid level, status and other information, and supports viewing and deleting route data. Figure 3 shown.

[0078] Step 3: Grid-code the designated routes and comprehensively judge route conflicts in combination with the designated airspace. This algorithm has the ability to efficiently process three-dimensional spatial data and can accurately identify possible route conflicts.

[0079] In the three-dimensional grid airspace model, intelligent algorithms are used to conduct refined conflict analysis of routes, and an efficient conflict detection function is built in. Potential route conflicts are accurately identified and evaluated during the route planning stage, thereby effectively improving the safety and rationality of route planning. Finally, the detailed results of the conflict detection are quickly fed back to each unmanned equipment manufacturer to help them optimize the route design based on the feedback, ensure the safe and efficient operation of unmanned equipment in complex airspace, and jointly promote the intelligent upgrade and widespread application of unmanned system technology. The route conflict detection algorithm process is as follows Figure 4 As shown:

[0080] Step 3-1, grid coding: grid coding the drawn route according to the grid level and route altitude input;

[0081] Step 3-2, underlying surface matching: call the data I / O interface, query the underlying surface grid information according to the route grid range, and match the data grid. If the underlying surface grid contains obstacle information, the output is conflicting;

[0082] Step 3-3, airspace demarcation matching: query the no-fly zone and route demarcation information in the airspace demarcation according to the route grid range. If no no-fly zone or route demarcation grid is matched, output no conflict;

[0083] Step 3-4, time overlap check: perform overlap check on the usage time of the matched no-fly zone and the flight time of the matched route. If the check shows that there is overlap with the usage time of the no-fly zone or the flight time of the route, then output that there is a conflict, otherwise output that there is no conflict.

[0084] Finally, by establishing a route conflict detection method based on a three-dimensional grid airspace model, we can effectively improve the safety and efficiency of low-altitude UAV flights. This method not only provides more accurate flight guidance for UAVs, but also detects route conflicts in a timely manner and returns the results to each unmanned system operator, so that ground control personnel can make accurate judgments, ensure the harmonious and orderly operation of low-altitude airspace, and ensure the safe operation of UAVs.

[0085] It is understandable that those skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention, and all these changes or substitutions should fall within the protection scope of the claims attached to the present invention.

Claims

1. A method for intelligent conflict detection based on three-dimensional grid routes, characterized in that The following steps are involved: Step 1: Analyze the spatial structure characteristics of the designated area and build a three-dimensional grid model based on this; Step 2: Based on the three-dimensional grid model, fine-scale delineation of airspace and routes is carried out; Step 3: Grid-code the designated routes and comprehensively judge route conflicts based on the designated airspace.

2. The method for intelligent conflict detection based on three-dimensional grid routes according to claim 1 is characterized in that Step 1 contains the following steps: Step 1-1: Based on low-altitude oblique photography technology, use a low-altitude multi-rotor aircraft, a digital camera and a positioning system to conduct full-coverage oblique aerial photography of the designated area to obtain oblique image data of the ground; The focal length, principal point coordinates, distortion coefficients of the camera in the camera file, the three-dimensional coordinates of the camera when shooting, and the pitch angle, yaw angle and roll angle information of the camera when shooting are used for calculation to generate a three-dimensional point cloud. The point cloud forms a grid. The constructed grid is used to generate a preliminary three-dimensional model. The texture information in the oblique image data is mapped to the preliminary three-dimensional model. After the texture mapping is completed, the three-dimensional model data with real texture and geometric shape is obtained, and then combined with the digital line map elevation information and ground features to form digital orthophoto impact data; Step 1-2: using grid coding technology to assign unique spatial coding identifiers to the three-dimensional model data and digital orthophoto data to obtain a three-dimensional grid code; Steps 1-3: Use three-dimensional grid coding to construct a grid world for the specified area, divide the space occupied by all elements of the unmanned system into fine and three-dimensional parts, and use "1" to represent inaccessible grids and "0" to represent accessible grids, thus constructing a three-dimensional grid map model that reflects the regional airspace structure.

3. The intelligent conflict detection method based on three-dimensional grid routes according to claim 1 is characterized in that Step 2 contains the following steps: Step 2-1: Complete the definition and drawing of the airspace in the three-dimensional grid model 120 meters above the designated area; the defined airspace includes no-fly zones, airworthy zones, suitable flying zones, take-off and landing zones, restricted flying zones, alternate landing zones, and other specific areas customized according to needs; During the drawing process, multiple graphic shapes are selected to match and outline the shape and extent of the defined airspace; Step 2-2: Plan and design routes in a three-dimensional grid model from above to 120 meters below the designated area, and build in a conflict analysis mechanism for route planning. Each route includes route name, type, and grid level. Step 2-3: Display all the route information that has been drawn, and support viewing and deleting the route data.

4. The method for intelligent conflict detection based on three-dimensional grid routes according to claim 1 is characterized in that Step 3 contains the following steps: Step 3-1, grid coding the drawn route according to the grid level and route height input by the three-dimensional grid model; Step 3-2: According to the route grid range, query the underlying surface grid information for data grid matching. If the underlying surface grid contains obstacle information, the output is conflicting. Step 3-3, according to the route grid range, query the no-fly zone and route demarcation information in the airspace demarcation, if no no-fly zone and route demarcation grid is matched, output no conflict; Step 3-4: perform an overlap check on the usage time of the matched no-fly zone and the flight time of the matched route. If it is found that there is an overlap with the usage time of the no-fly zone or the flight time of the route, output that there is a conflict, otherwise output that there is no conflict.

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