Method, system and terminal for benchmarking real-time video and geographic parameters of unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle real-time video and geographic parameter benchmarking method, system and terminal, and the method comprises the steps: obtaining the position information of a target unmanned aerial vehicle and the parameter proportion of a target camera in real time, and carrying out the projection to obtain video image information; obtaining a distance from the target unmanned aerial vehicle to the video picture according to the video picture information to obtain a latitude and longitude set of the video picture; and importing the longitude and latitude set into map software, and adding a grid type map layer into the map software to obtain a target map. Through the real-time matching and overlapping process of the unmanned aerial vehicle video return picture and the base map of the map, the basic parameters of the unmanned aerial vehicle camera are matched with the positioning information of the unmanned aerial vehicle, and the matched video picture is optimized according to the camera parameters, so that the real-time performance of the video content is effectively improved, and the accuracy of the video content is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information matching, and particularly to a method, system, terminal and computer-readable storage medium for matching real-time video of an unmanned aerial vehicle with geographic parameters. Background Art

[0002] Unmanned aerial vehicles have the characteristics of small size, light weight, low cost, flexible operation and high safety, and are widely used in various fields such as aerial photography, monitoring, search and rescue, resource exploration and agriculture.

[0003] At present, although the unmanned aerial vehicle platform systems on the market have basically solved the problems of video acquisition and short-distance transmission, mainly using radio or network to achieve real-time transmission from the unmanned aerial vehicle to the ground end. However, the aerial photography video of the unmanned aerial vehicle is independent of spatial parameters, and it is impossible to know the corresponding real position during aerial photography, that is, it is impossible to match with the coordinate points on the map in real time.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0005] The main purpose of the present invention is to provide a method, system, terminal and computer-readable storage medium for matching real-time video of an unmanned aerial vehicle with geographic parameters, aiming to solve the problem in the existing technology that the position information of the video cannot be obtained in real time during aerial photography of the unmanned aerial vehicle, resulting in inaccurate positioning according to the video and low referenceability of the video.

[0006] To achieve the above object, the present invention provides a method for matching real-time video of an unmanned aerial vehicle with geographic parameters, and the method for matching real-time video of an unmanned aerial vehicle with geographic parameters includes the following steps:

[0007] Real-time obtain the position information of the target unmanned aerial vehicle and the parameter ratio of the target camera, and perform projection according to the position information and the parameter ratio to obtain the video frame information of the video frame;

[0008] According to the video frame information, obtain the distance from the target unmanned aerial vehicle to the video frame, and according to the distance and the position information, obtain the longitude and latitude set of the video frame;

[0009] Import the longitude and latitude set into a map software, add a raster layer to the map software, and mark the longitude and latitude set on the raster layer to obtain a target map.

[0010] Optionally, in the method for matching real-time video of an unmanned aerial vehicle with geographic parameters, the real-time obtain the position information of the target unmanned aerial vehicle and the parameter ratio of the target camera, and perform projection according to the position information and the parameter ratio to obtain the video frame information of the video frame, specifically includes:

[0011] Obtain the longitude, latitude, altitude and azimuth of the target UAV in real time, and obtain the parameter ratio of the target camera;

[0012] According to the altitude, the azimuth and the parameter ratio, project the imaging video of the target camera onto the ground to obtain a video picture with the same ratio as the parameter ratio, and determine video picture information based on the video picture;

[0013] Wherein, the azimuth represents the included angle between the target UAV and the due north direction, and the altitude of the target UAV is the same as the altitude of the target camera.

[0014] Optionally, for the method of aligning the real-time video of the UAV with geographical parameters, wherein, according to the altitude, the azimuth and the parameter ratio, project the imaging video of the target camera onto the ground to obtain a video picture with the same ratio as the parameter ratio, and determine video picture information based on the video picture, specifically including:

[0015] Project the video picture onto the ground according to the altitude;

[0016] Determine the shape of the video picture according to the parameter ratio, wherein the shape of the video picture includes a rectangle;

[0017] Determine the oblique length of the video picture according to the altitude and the azimuth;

[0018] Determine the length and width of the video picture according to the oblique length and the parameter ratio to obtain the video picture;

[0019] Determine the positions of all vertices of the video picture information according to the parameter ratio, the length and the width;

[0020] Integrate all the vertex positions, the oblique length, the parameter ratio, the length and the width to obtain the video picture information.

[0021] Optionally, for the method of aligning the real-time video of the UAV with geographical parameters, wherein, according to the video picture information, obtain the distance from the target UAV to the video picture, and according to the distance and the position information, obtain the longitude and latitude set of the video picture, specifically including:

[0022] Obtain the distances from the target UAV to all vertices on the video picture according to all the vertex positions;

[0023] Obtain the vertex azimuth of each vertex according to the azimuth, all the vertex positions and the distance of each corresponding vertex;

[0024] Input the vertex azimuth angle and the corresponding vertex position of each vertex into the constructed longitude and latitude model, output the longitude and latitude of each vertex, and integrate all the vertex longitudes and latitudes to obtain the longitude and latitude set of the video frame.

[0025] Optionally, in the method for aligning the real-time video of the unmanned aerial vehicle with geographical parameters, the step of obtaining the vertex azimuth angle of each vertex according to the azimuth angle, all the vertex positions, and the distance of each corresponding vertex specifically includes:

[0026] Determine a plurality of projection angles of the video frame according to the diagonal length, the length, and the width, where the projection angle represents an interior angle of a right triangle formed by the diagonal length, the length, and the width;

[0027] Obtain the projection point of the target camera on the video frame, and connect the projection point to each vertex according to all the vertex positions to obtain corresponding projection lines;

[0028] Obtain the vertex azimuth angle of each vertex relative to the projection point according to each projection line.

[0029] Optionally, in the method for aligning the real-time video of the unmanned aerial vehicle with geographical parameters, the step of importing the longitude and latitude set into a map software, adding a grid layer in the map software, and marking the longitude and latitude set on the grid layer to obtain a target map specifically includes:

[0030] Import the longitude and latitude set into the deployed map software, and add a grid layer in the map software;

[0031] Mark all the vertex longitudes and latitudes in the longitude and latitude set at different positions on the layer according to the video frame information to obtain a target map;

[0032] Crop the target map according to the parameter ratio and all the vertex longitudes and latitudes to obtain a target video frame with the same ratio as the parameter ratio;

[0033] Enlarge the target video frame according to the size of the grid layer, and cover it on the grid layer.

[0034] Optionally, in the method for aligning the real-time video of the unmanned aerial vehicle with geographical parameters, after the step of enlarging the target video frame according to the size of the grid layer and covering it on the grid layer, it further includes:

[0035] When the height of the target unmanned aerial vehicle changes, update the video frame information in real time according to the updated height;

[0036] According to the updated video frame information, the longitude and latitude set is updated in real time, and according to the updated longitude and latitude information, the target video frame is updated to obtain the real-time target video frame.

[0037] In addition, to achieve the above object, the present invention further provides a system for matching real-time video of an unmanned aerial vehicle with geographical parameters, wherein the system for matching real-time video of an unmanned aerial vehicle with geographical parameters includes:

[0038] A frame deployment module, configured to obtain the position information of a target unmanned aerial vehicle and the parameter ratio of a target camera in real time, and perform projection according to the position information and the parameter ratio to obtain the video frame information of the video frame;

[0039] An information positioning module, configured to obtain the distance from the target unmanned aerial vehicle to the video frame according to the video frame information, and obtain the longitude and latitude set of the video frame according to the distance and the position information;

[0040] A longitude and latitude information matching module, configured to import the longitude and latitude set into a map software, add a grid-type layer in the map software, and mark the longitude and latitude set on the grid-type layer to obtain a target map.

[0041] In addition, to achieve the above object, the present invention further provides a terminal, wherein the terminal includes: a memory, a processor, and a program for matching real-time video of an unmanned aerial vehicle with geographical parameters stored on the memory and executable on the processor, and when the program for matching real-time video of an unmanned aerial vehicle with geographical parameters is executed by the processor, the steps of the method for matching real-time video of an unmanned aerial vehicle with geographical parameters as described above are implemented.

[0042] In addition, to achieve the above object, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program for matching real-time video of an unmanned aerial vehicle with geographical parameters, and when the program for matching real-time video of an unmanned aerial vehicle with geographical parameters is executed by a processor, the steps of the method for matching real-time video of an unmanned aerial vehicle with geographical parameters as described above are implemented.

[0043] In the present invention, the position information of the target unmanned aerial vehicle (UAV) and the parameter ratio of the target camera are obtained in real time, and projection is performed according to the position information and the parameter ratio to obtain the video frame information of the video; according to the video frame information, the distance from the target UAV to the video frame is obtained, and according to the distance and the position information, the longitude and latitude set of the video frame is obtained; the longitude and latitude set is imported into a map software, a grid layer is added in the map software, and the longitude and latitude set is marked on the grid layer to obtain a target map. Calculate the average betweenness centrality of the simulated network; construct a result analysis model, input the development data, the number of nodes of the nodes, the number of node connection relationship groups, and the average betweenness centrality into the result analysis model, and output the simulation result of the urban development difference. Through a process of real-time matching and overlaying of the UAV video backhaul screen and the map base map, the present invention matches the basic parameters of the UAV camera with the positioning information of the UAV, and optimizes the matched video frame according to the camera parameters, effectively improving the real-time performance of the video content and the accuracy of the video content. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flowchart of a preferred embodiment of the method for matching the real-time video of the UAV with geographical parameters of the present invention;

[0045] Figure 2 is a specific flowchart of a preferred embodiment of the method for matching the real-time video of the UAV with geographical parameters of the present invention;

[0046] Figure 3 is an overlay effect diagram of a preferred embodiment of the method for matching the real-time video of the UAV with geographical parameters of the present invention;

[0047] Figure 4 is a cropped schematic diagram after overlay of a preferred embodiment of the method for matching the real-time video of the UAV with geographical parameters of the present invention;

[0048] Figure 5 is a structural diagram of a preferred embodiment of the system for matching the real-time video of the UAV with geographical parameters of the present invention;

[0049] Figure 6 is a schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The method for matching the real-time video of the UAV with geographical parameters according to a preferred embodiment of the present invention, asFigure 1 As shown, the method for aligning the real-time video of the UAV with geographical parameters includes the following steps:

[0052] Step S10: Real-time obtain the position information of the target UAV and the parameter ratio of the target camera, and perform projection according to the position information and the parameter ratio to obtain the video frame information of the video frame.

[0053] Among them, the position information of the target UAV mainly includes longitude, latitude, altitude, and azimuth angle. The azimuth angle represents the angle between the UAV and the due north direction. At the same time, obtain the parameter ratio of the target camera. In addition, the field of view angle and resolution of the target camera can also be obtained, which can improve the accuracy of the video frame subsequently; further, as Figure 2 shown, the position information of the target UAV can be uploaded regularly according to the time set by the user.

[0054] Specifically, real-time obtain the longitude, latitude, altitude, and azimuth angle of the target UAV, and obtain the parameter ratio of the target camera; according to the altitude, the azimuth angle, and the parameter ratio, project the imaging video of the target camera onto the ground to obtain a video frame with the same ratio as the parameter ratio, and determine the video frame information according to the video frame; among them, the azimuth angle represents the angle between the target UAV and the due north direction, and the altitude of the target UAV is the same as the altitude of the target camera.

[0055] Further, project the video frame onto the ground according to the altitude; determine the shape of the video frame according to the parameter ratio, where the shape of the video frame includes a rectangle; determine the diagonal length of the video frame according to the altitude and the azimuth angle; determine the length and width of the video frame according to the diagonal length and the parameter ratio to obtain the video frame; determine the positions of all vertices of the video frame information according to the parameter ratio, the length, and the width; integrate all the vertex positions, the diagonal length, the parameter ratio, the length, and the width to obtain the video frame information.

[0056] Among them, first take the position of the target UAV as the projection point. According to the altitude of the UAV and the field of view angle of the camera parameters, the diagonal distance of the rectangular projection of the camera video on the ground can be inferred, and then the length and width of the projected video can be obtained according to the known parameter ratio of the camera and the Pythagorean theorem; among them, the parameter ratio can be set according to the user's needs to improve the operability of the video frame.

[0057] Step S20: Obtain the distance from the target UAV to the video frame according to the video frame information, and obtain the longitude and latitude set of the video frame according to the distance and the position information.

[0058] Among them, after determining the positions of each vertex in the video frame according to the above steps, the distances from the target UAV (or the target camera) to each vertex can be obtained, and then the longitude and latitude data of each vertex can be obtained, so as to realize the matching of the backhaul video frame and the map, superimpose the video parameters and the spatial parameters on each other, improve the integrity of the video data, and thus reduce the cost of the map video recognition process.

[0059] Specifically, according to all the vertex positions, the distances from the target UAV to all the vertices on the video frame are obtained; according to the azimuth angle, all the vertex positions and the distances of each corresponding vertex, the vertex azimuth angle of each vertex is obtained; the vertex azimuth angle and the corresponding vertex position of each vertex are input into the pre-constructed longitude and latitude model, and the vertex longitude and latitude of each vertex are output, and all the vertex longitudes and latitudes are integrated to obtain the longitude and latitude set of the video frame.

[0060] Further, according to the oblique length, the length and the width, a plurality of projection angles of the video frame are determined, wherein the projection angle represents an interior angle of a right triangle formed by the oblique length, the length and the width; the projection point of the target camera on the video frame is obtained, and according to all the vertex positions, the projection point is connected to each vertex to obtain corresponding projection lines; according to each projection line, the vertex azimuth angle of each vertex relative to the projection point is obtained.

[0061] Among them, the longitude and latitude information of the target UAV can be obtained in real time through technical supports such as the interface document for docking the UAV to obtain its location. Based on the video frame (the projection of the video frame is a rectangle), a right triangle and its different interior angles can be obtained, and then combined with the azimuth angle of the target UAV, the azimuth angles of each vertex can be inferred. Then, with the help of a pre-deployed analysis library (for example, the geospatial analysis library Turf.js), according to the method destination in the analysis library (by accepting the location information of a point, and the distance and direction angle information of the target point, calculating the geographical location of the target point), the longitude and latitude of each vertex are obtained and form a set (i.e., the longitude and latitude set).

[0062] Step S30: Import the longitude and latitude set into a map software, add a raster layer in the map software, and mark the longitude and latitude set on the raster layer to obtain a target map.

[0063] Among them, the raster layer refers to a layer category in the mapbox map engine, which is built-in in the engine. This raster layer is used to display the previously added UAV video frame on the map, use the longitude and latitude of each vertex to frame a range on the map, and display it on top of the original layer of the map, so as to realize the matching and superposition of the spatial parameters and the video data.

[0064] Specifically, import the set of longitude and latitude into the deployed map software, and add a raster layer in the map software; according to the video frame information, mark all the vertex longitudes and latitudes in the set of longitude and latitude at different positions on the layer respectively to obtain a target map; according to the parameter ratio and all the vertex longitudes and latitudes, crop the target map to obtain a target video frame with the same ratio as the parameter ratio; according to the size of the raster layer, expand the target video frame and cover it on the raster layer.

[0065] Among them, in the map software (such as the Mapbox map engine mentioned in this embodiment), first add a data source (i.e., the set of longitude and latitude in the present invention), secondly add a layer (i.e., the raster layer), and then mark the set of longitude and latitude on the raster layer, so as to realize the matching and superposition of spatial parameters and video data (the superposition result is as Figure 3 shown, where "30m" in the lower left corner represents the scale of the map). Further, according to the user's requirements (given a range, as Figure 4 shown), the superimposed video frame can be cropped, and the cropped frame can be expanded and covered on the raster layer to realize the superimposed playback of the video on the map (i.e., aligning the aerial video frame of the drone with the geographical information parameters), improving the content integrity of the map data, facilitating a comprehensive grasp of the on-site situation, and improving the efficiency of on-site analysis.

[0066] Further, when the height of the target drone changes, according to the updated height, the video frame information is updated in real time; according to the updated video frame information, the set of longitude and latitude is updated in real time, and according to the updated longitude and latitude information, the target video frame is updated to obtain a real-time target video frame.

[0067] Among them, when the target drone moves, according to the longitude, latitude and azimuth angle of the drone, the set of longitude and latitude of the video frame is updated in real time. When the height of the target drone changes, the azimuth angle and its longitude and latitude of each vertex of the video frame can be updated according to the real-time changing height information (and azimuth angle), so as to realize the real-time update of the video frame and improve the real-time nature of on-site information.

[0068] The present invention realizes a process of real-time matching and superposition of the drone video transmission frame and the map base map, matches the basic parameters of the drone camera with the positioning information of the drone, and optimizes the matched video frame according to the camera parameters, effectively improving the real-time nature of the video content and the accuracy of the video content.

[0069] Further, as Figure 5As shown, based on the above method for matching real-time drone videos with geographical parameters, the present invention also correspondingly provides a system for matching real-time drone videos with geographical parameters, wherein the system for matching real-time drone videos with geographical parameters includes:

[0070] A screen deployment module 51, configured to obtain the position information of the target drone and the parameter ratio of the target camera in real time, and perform projection according to the position information and the parameter ratio to obtain the video screen information of the video screen;

[0071] An information positioning module 52, configured to obtain the distance from the target drone to the video screen according to the video screen information, and obtain the longitude and latitude set of the video screen according to the distance and the position information;

[0072] A longitude and latitude information matching module 53, configured to import the longitude and latitude set into a map software, add a grid-type layer in the map software, and mark the longitude and latitude set on the grid-type layer to obtain a target map.

[0073] Further, as Figure 6 shown, based on the above method and system for matching real-time drone videos with geographical parameters, the present invention also correspondingly provides a terminal, and the terminal includes a processor 10, a memory 20, and a display 30. Figure 6 Only some components of the terminal are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0074] The memory 20 may be an internal storage unit of the terminal in some embodiments, such as the hard disk or memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk equipped on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 20 may also include both the internal storage unit and the external storage device of the terminal. The memory 20 is used to store application software installed on the terminal and various types of data, such as the program code installed on the terminal. The memory 20 may also be used to temporarily store data that has been output or will be output. In one embodiment, a program 40 for matching real-time drone videos with geographical parameters is stored on the memory 20, and the program 40 for matching real-time drone videos with geographical parameters can be executed by the processor 10, thereby implementing the method for matching real-time drone videos with geographical parameters in this application.

[0075] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chips, which are used to run the program code stored in the memory 20 or process data, such as executing the method for matching the real-time video of the drone with geographical parameters, etc.

[0076] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display the information in the terminal and to display a visual user interface. The components of the terminal communicate with each other through a system bus.

[0077] In one embodiment, when the processor 10 executes the program 40 for matching the real-time video of the drone with geographical parameters stored in the memory 20, the following steps are implemented:

[0078] Real-time obtain the position information of the target drone and the parameter ratio of the target camera, and project according to the position information and the parameter ratio to obtain the video frame information of the video frame;

[0079] According to the video frame information, obtain the distance from the target drone to the video frame, and according to the distance and the position information, obtain the longitude and latitude set of the video frame;

[0080] Import the longitude and latitude set into a map software, add a raster layer in the map software, and mark the longitude and latitude set on the raster layer to obtain a target map.

[0081] Among them, the real-time obtaining the position information of the target drone and the parameter ratio of the target camera, and projecting according to the position information and the parameter ratio to obtain the video frame information of the video frame specifically includes:

[0082] Real-time obtain the longitude and latitude, altitude, and azimuth angle of the target drone, and obtain the parameter ratio of the target camera;

[0083] According to the altitude, the azimuth angle, and the parameter ratio, project the imaging video of the target camera onto the ground to obtain a video frame with the same ratio as the parameter ratio, and determine the video frame information according to the video frame;

[0084] Among them, the azimuth angle represents the angle between the target drone and the due north direction, and the altitude of the target drone is the same as the altitude of the target camera.

[0085] Among them, according to the height, the azimuth angle, and the parameter ratio, project the imaging video of the target camera onto the ground to obtain a video frame with the same ratio as the parameter ratio, and determine video frame information according to the video frame, specifically including:

[0086] Project the video frame onto the ground according to the height;

[0087] Determine the shape of the video frame according to the parameter ratio, where the shape of the video frame includes a rectangle;

[0088] Determine the oblique length of the video frame according to the height and the azimuth angle;

[0089] Determine the length and width of the video frame according to the oblique length and the parameter ratio to obtain the video frame;

[0090] Determine the positions of all vertices of the video frame information according to the parameter ratio, the length, and the width;

[0091] Integrate all the vertex positions, the oblique length, the parameter ratio, the length, and the width to obtain the video frame information.

[0092] Among them, according to the video frame information, obtain the distance from the target UAV to the video frame, and according to the distance and the position information, obtain the longitude and latitude set of the video frame, specifically including:

[0093] Obtain the distances from the target UAV to all vertices on the video frame according to all the vertex positions;

[0094] Obtain the vertex azimuth angle of each vertex according to the azimuth angle, all the vertex positions, and the distance corresponding to each vertex;

[0095] Input the vertex azimuth angle of each vertex and the corresponding vertex position into the constructed longitude and latitude model, output the vertex longitude and latitude of each vertex, and integrate all the vertex longitude and latitudes to obtain the longitude and latitude set of the video frame.

[0096] Among them, the step of obtaining the vertex azimuth angle of each vertex according to the azimuth angle, all the vertex positions, and the distance corresponding to each vertex specifically includes:

[0097] Determine multiple projection angles of the video frame according to the oblique length, the length, and the width, where the projection angle represents the interior angle of the right triangle formed by the oblique length, the length, and the width;

[0098] Obtain the projection points of the target camera on the video frame. Connect the projection points to each of the vertices according to all the vertex positions to obtain corresponding projection lines.

[0099] According to each projection line, obtain the vertex azimuth angle of each vertex relative to the projection point.

[0100] Among them, the step of importing the longitude and latitude set into a map software, adding a raster layer in the map software, and marking the longitude and latitude set on the raster layer to obtain a target map specifically includes:

[0101] Import the longitude and latitude set into the deployed map software and add a raster layer in the map software.

[0102] According to the video frame information, mark all the vertex longitudes and latitudes in the longitude and latitude set at different positions on the layer to obtain a target map.

[0103] Crop the target map according to the parameter ratio and all the vertex longitudes and latitudes to obtain a target video frame with the same ratio as the parameter ratio.

[0104] Enlarge the target video frame according to the size of the raster layer and cover it on the raster layer.

[0105] Among them, after enlarging the target video frame according to the size of the raster layer and covering it on the raster layer, it further includes:

[0106] When the height of the target UAV changes, update the video frame information in real time according to the updated height.

[0107] According to the updated video frame information, update the longitude and latitude set in real time, and update the target video frame according to the updated longitude and latitude information to obtain a real-time target video frame.

[0108] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a program for aligning UAV real-time video with geographical parameters. When the program for aligning UAV real-time video with geographical parameters is executed by a processor, the steps of the method for aligning UAV real-time video with geographical parameters as described above are implemented.

[0109] In summary, the present invention provides a method for matching real-time video of an unmanned aerial vehicle with geographical parameters and related devices. The method includes: obtaining the position information of a target unmanned aerial vehicle and the parameter ratio of a target camera in real time, and performing projection according to the position information and the parameter ratio to obtain video frame information of a video frame; obtaining the distance from the target unmanned aerial vehicle to the video frame according to the video frame information, and obtaining a set of longitude and latitude of the video frame according to the distance and the position information; importing the set of longitude and latitude into a map software, adding a grid-type layer in the map software, and marking the set of longitude and latitude on the grid-type layer to obtain a target map. Calculate the average betweenness centrality of the simulation network; construct a result analysis model, input the development data, the number of nodes of the nodes, the number of node connection relationship groups, and the average betweenness centrality into the result analysis model, and output a simulation result of urban development differences. The present invention effectively improves the real-time performance of video content and the accuracy of video content through a process of real-time matching and overlaying of the video frame transmitted back by the unmanned aerial vehicle and the map base map, matching the basic parameters of the unmanned aerial vehicle camera with the positioning information of the unmanned aerial vehicle, and optimizing the matched video frame according to the camera parameters.

[0110] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or terminal including that element.

[0111] Certainly, those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium readable by a computer. When the program is executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disk, etc.

[0112] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description. All such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for comparing real-time video of a drone with geographic parameters, characterized in that: The method for comparing the real-time video of the UAV with the geographic parameters includes: Acquire the position information of the target UAV and the parameter ratio of the target camera in real time, and perform projection according to the position information and the parameter ratio to obtain video picture information of the video picture; According to the video screen information, obtain the distance from the target UAV to the video screen, and according to the distance and the position information, obtain the longitude and latitude set of the video screen; The longitude and latitude set is imported into a map software, a raster layer is added to the map software, and the longitude and latitude set is marked on the raster layer to obtain a target map.

2. The method for comparing real-time video of a drone with geographic parameters according to claim 1, characterized in that: The real-time acquisition of the position information of the target UAV and the parameter ratio of the target camera, and the projection according to the position information and the parameter ratio to obtain the video picture information of the video picture, specifically includes: Obtain the latitude, longitude, altitude and azimuth of the target drone in real time, and obtain the parameter ratio of the target camera; According to the height, the azimuth and the parameter ratio, the imaging video of the target camera is projected onto the ground to obtain a video screen with the same ratio as the parameter ratio, and according to the video screen, video screen information is determined; The azimuth angle represents the angle between the target UAV and the true north direction, and the height of the target UAV is the same as the height of the target camera.

3. The method for comparing real-time video of unmanned aerial vehicle with geographic parameters according to claim 2, characterized in that: The step of projecting the imaging video of the target camera onto the ground according to the height, the azimuth and the parameter ratio to obtain a video screen having the same ratio as the parameter ratio, and determining video screen information according to the video screen specifically includes: Projecting the video image onto the ground according to the height; Determining a shape of the video screen according to the parameter ratio, wherein the shape of the video screen includes a rectangle; Determining the oblique length of the video picture according to the height and the azimuth; Determine the length and width of the video picture according to the oblique length and the parameter ratio to obtain the video picture; Determine all vertex positions of the video picture information according to the parameter ratio, the length and the width; The video picture information is obtained by integrating all the vertex positions, the oblique lengths, the parameter ratios, the lengths and the widths.

4. The method for comparing real-time video of unmanned aerial vehicle with geographic parameters according to claim 3 is characterized in that: The obtaining, according to the video screen information, a distance from the target UAV to the video screen, and obtaining a set of longitude and latitude of the video screen according to the distance and the position information, specifically includes: According to the positions of all the vertices, the distances from the target UAV to all the vertices on the video screen are obtained; Obtaining a vertex azimuth of each vertex according to the azimuth, all the vertex positions and the distance of each corresponding vertex; The vertex azimuth and the corresponding vertex position of each vertex are input into the constructed longitude and latitude model, the longitude and latitude of each vertex are output, and the longitude and latitude of all vertices are integrated to obtain the longitude and latitude set of the video screen.

5. The method for comparing real-time video of unmanned aerial vehicle with geographic parameters according to claim 4 is characterized in that: The step of obtaining the vertex azimuth of each vertex according to the azimuth, all the vertex positions and the distance of each corresponding vertex specifically includes: Determine a plurality of projection angles of the video screen according to the oblique length, the length and the width, wherein the projection angle represents an inner angle of a right triangle formed by the oblique length, the length and the width; Obtaining a projection point of the target camera on the video screen, and connecting the projection point with each vertex according to all the vertex positions to obtain a corresponding projection line; According to each of the projection lines, the vertex azimuth of each of the vertices relative to the projection point is obtained.

6. The method for comparing real-time video of unmanned aerial vehicle with geographic parameters according to claim 4, characterized in that: The step of importing the longitude and latitude set into a map software, adding a grid layer in the map software, and marking the longitude and latitude set on the grid layer to obtain a target map specifically includes: Importing the latitude and longitude set into the deployed map software, and adding a raster layer in the map software; According to the video screen information, all the longitudes and latitudes of the vertices in the longitude and latitude set are marked at different positions of the layer to obtain a target map; According to the parameter ratio and the longitude and latitude of all the vertices, the target map is cropped to obtain a target video screen with the same ratio as the parameter ratio; According to the size of the grid-type layer, the target video screen is enlarged and covered on the grid-type layer.

7. The method for comparing real-time video of a drone with geographic parameters according to claim 6, characterized in that: The target video screen is enlarged according to the size of the grid-type layer and covered on the grid-type layer, and then further includes: When the height of the target drone changes, the video image information is updated in real time according to the updated height; According to the updated video picture information, the longitude and latitude set is updated in real time, and according to the updated longitude and latitude information, the target video picture is updated to obtain the real-time target video picture.

8. A system for comparing real-time video of unmanned aerial vehicles with geographic parameters, characterized in that: The real-time video and geographic parameter matching system of the UAV includes: A picture deployment module is used to obtain the position information of the target UAV and the parameter ratio of the target camera in real time, and project according to the position information and the parameter ratio to obtain the video picture information of the video picture; An information positioning module, used to obtain the distance between the target UAV and the video screen according to the video screen information, and obtain the longitude and latitude set of the video screen according to the distance and the position information; The latitude and longitude information benchmarking module is used to import the latitude and longitude set into the map software, add a grid layer in the map software, mark the latitude and longitude set on the grid layer, and obtain the target map.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a matching program for real-time video of a drone and geographic parameters stored in the memory and executable on the processor. When the matching program for real-time video of a drone and geographic parameters is executed by the processor, the steps of the matching method for real-time video of a drone and geographic parameters as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for matching the real-time video of the drone with geographic parameters. When the program for matching the real-time video of the drone with geographic parameters is executed by the processor, the steps of the method for matching the real-time video of the drone with geographic parameters as described in any one of claims 1 to 7 are implemented.

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