A working system for drone airports to inspect the coverage status of natural resources

Abstract

The present invention discloses a working system for inspecting the coverage status of natural resources in an airport by using a drone, comprising the following contents: a data collection module, used for collecting aerial images of an airport area by using a drone, and constructing a reference image of the airport area by using orthogonal points and oblique points; a data analysis module, used for performing anomaly analysis of the airport area by using an image anomaly feature analysis method; and a data display module, used for displaying the anomaly analysis results in real time through a remote terminal.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a working system for a drone airport to inspect the coverage status of natural resources. Background Art

[0002] Currently, the natural resource inspection method based on the past artificial ground has the following problems:

[0003] 1) At present, there are many tasks in natural resource survey and monitoring, the grassroots work is burdened with heavy workload, tight time, many map blocks and heavy tasks. Many map blocks are difficult to reach because they are located in mountains, swamps, abandoned land, cliffs or opposite rivers.

[0004] 2) Traditional manual investigation and monitoring methods require high human resources and economic costs, pose safety risks and the risk of casualties, and are not conducive to the implementation of regular inspections.

[0005] 3) In emergencies, roads are severely damaged and it is often impossible to drive people to the scene, resulting in the inability to continue work and delayed response.

[0006] The existing technology makes extensive use of drones to carry out corresponding natural resource inspections, but the massive amount of data obtained contains a lot of redundant and invalid data, which urgently requires technical personnel in this field to solve the corresponding technical problems. Summary of the invention

[0007] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a working system for airport drone inspection of natural resource coverage status.

[0008] In order to achieve the above-mentioned object of the present invention, the present invention provides a working system for a drone airport to inspect the coverage status of natural resources, comprising:

[0009] The data collection module is used to collect aerial images of the airport area through drones and construct a reference image of the airport area through orthogonal points and oblique points;

[0010] A data analysis module is used to perform airport area anomaly analysis by using an image anomaly feature analysis method;

[0011] The data display module is used to display the abnormal analysis results in real time through the remote terminal.

[0012] Preferably, the data collection module comprises:

[0013] The orthogonal point is located at the center of the image patch. The camera lens is perpendicular to the ground (the shooting angle is within the range of -85 degrees to -90 degrees). The height is adjusted until the shooting range overlooks the entire image patch. The direction is the direction from the previous position of the aircraft to the center of this image patch.

[0014] The oblique shooting point has the same height as the orthogonal shooting point. The camera lens is tilted to shoot from the ground (shooting angle is within the range of -5 degrees to -85 degrees), and the direction is pointing to the center of the map spot. The camera shooting range can cover the entire map spot on the route to the center of the map spot.

[0015] Use the minimum distance sorting method to connect the photo points in series: from my location, find the nearest spot center point to form the first route, and then from the spot center point, find the spot center point closest to the spot to generate the second route, connect all the spot center points in sequence to form the route of this flight, and generate the orthogonal points and oblique points of each spot on this route.

[0016] Preferably, the data analysis module comprises:

[0017] Use drones to take panoramic views of the drone airport surrounding areas at orthogonal and oblique points; used to obtain the natural resource status of the drone airport;

[0018] Extract feature points from each panoramic view of the drone airport surrounding area, and match the same feature points in the panoramic view of the drone airport surrounding area; then traverse the orthogonal point and oblique point images to identify the overlapping areas that need to be fused.

[0019] Preferably, the data analysis module comprises:

[0020] Select two panoramic images of the drone airport surroundings and assign weights to each pixel of the image to determine the contribution of the pixel in the final fused image; use the weight function from pixel i to the image boundary:

[0021] W(i,j)=exp(-d(i,j) 2 / (2*σ 2 )), where d(i,j) 2 is the distance from pixel i to the image boundary reference point j, and σ is the parameter controlling the width of the weight distribution;

[0022] Where p is any channel of the Lab color space, namely L, a, and b, Svalid represents the number of images with normal distribution of natural resources in the fused image, and (x, y) is the coordinate value of pixel i.

[0023] Preferably, the data analysis module comprises:

[0024] Smooth the boundaries of the fused image and calculate the final value of the fused image by weight, which is

[0025]

[0026] Among them, I1(x, y) and I2(x, y) are the pixel values ​​corresponding to the two images, W1(i, j) and W2(i, j) are the weight functions corresponding to the image pixels to the boundaries, and the fusion operation of all images is completed by traversing.

[0027] Preferably, the data analysis module comprises:

[0028] Among them, D x,y is the color change degree of the current fused image pixel to the image boundary reference point y, and k is the total number of fused images;

[0029] Map the registration map of the drone airport surrounding panorama at the same coordinates to the newly created image, ensure the natural transition of the overlapping areas of the fused area in the newly created image, and use color space conversion to adjust the colors to make them consistent;

[0030] Substitute the calculation result of the current weight function W(i,j) into the mean color change calculation formula CL mid Compared with the average color change C of the previous fused image, when the fusion change is greater than a threshold M, it is marked as a fusion boundary anomaly and enters the natural resource change judgment stage; otherwise, the next fused image judgment is performed.

[0031] Preferably, the data display module comprises:

[0032] The images obtained by the drone need to load GPS information in real time, mark the positioning results, and automatically adjust the tracking window size. After the fused image collected at the drone airport is judged, it will be transmitted to the remote platform for recording and processing, so as to realize real-time monitoring and early warning of the natural resource vegetation coverage status of the drone airport.

[0033] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0034] 1) Use drone airports to conduct real-time natural resource inspections, and be able to arrange and intelligently schedule work tasks for a large number of map spots within a certain range, customize corresponding flight routes, flight heights, photo points and other key content, and replace manual monitoring to quickly and accurately complete various information collection and investigation and monitoring tasks.

[0035] 2) Compared with traditional manual survey and monitoring methods, airport drones can not only cover a wider area (theoretically more than 10 kilometers near the airport), but also fly all-weather, unmanned, and intelligently dispatched, which reduces the cost of data acquisition and processing, and can significantly reduce the investment in manpower, material and financial resources. Airport drones can plan and operate routes highly flexibly as needed, and can be adjusted for different terrains, climates and environmental conditions to obtain more accurate data, reduce the links and errors of manual operations, and reduce labor costs. With the continuous development of drone technology and the continuous reduction of costs, the economic efficiency of drone surveys and monitoring will gradually improve, and it can also form national and local data assets. In the long run, it can significantly reduce government costs. At the same time, airport drones can avoid the situation where humans enter dangerous areas, reduce safety risks and the probability of casualties, and are an effective way to reduce the cost of surveys and monitoring.

[0036] 3) Airport drones are a powerful means of real-time monitoring and early warning. They can be started and stopped at any time, and can respond to emergencies more quickly and timely (generally, it only takes 5 minutes to reach the scene for a mission, and 10 minutes to complete the target locking and photo taking work). They have better real-time monitoring capabilities than traditional satellite remote sensing technology. Airport drones can be equipped with real-time transmission equipment to transmit monitoring data to ground stations or command centers in real time to achieve real-time monitoring and early warning. Airport drones can also detect potential safety hazards and abnormal situations in a timely manner, improving the safety and operating efficiency of airport drones.

[0037] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0039] Figure 1 It is a schematic diagram of the working of the present invention;

[0040] Figure 2 It is a specific implementation effect diagram of the present invention. DETAILED DESCRIPTION

[0041] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0042] like Figure 1 and 2 As shown, the present invention discloses a working system for an airport drone to inspect the coverage status of natural resources, including the following contents:

[0043] The data collection module is used to collect aerial images of the airport area through drones and construct a reference image of the airport area through orthogonal points and oblique points;

[0044] A data analysis module is used to perform airport area anomaly analysis by using an image anomaly feature analysis method;

[0045] The data display module is used to display the abnormal analysis results in real time through the remote terminal.

[0046] Preferably, the data collection module comprises:

[0047] The orthogonal point is located at the center of the image patch. The camera lens is perpendicular to the ground (the shooting angle is within the range of -85 degrees to -90 degrees). The height is adjusted until the shooting range overlooks the entire image patch. The direction is the direction from the previous position of the aircraft to the center of this image patch.

[0048] The oblique shooting point has the same height as the orthogonal shooting point. The camera lens is tilted to shoot from the ground (shooting angle is within the range of -5 degrees to -85 degrees), and the direction is pointing to the center of the map spot. The camera shooting range can cover the entire map spot on the route to the center of the map spot.

[0049] Use the minimum distance sorting method to connect the photo points in series: from my location, find the nearest spot center point to form the first route, and then from the spot center point, find the spot center point closest to the spot to generate the second route, connect all the spot center points in sequence to form the route of this flight, and generate the orthogonal points and oblique points of each spot on this route.

[0050] Preferably, the data analysis module comprises:

[0051] The drone is used to take panoramic pictures of the area around the drone airport. The automatically generated photo points are connected to the orthogonal points and oblique points of each map spot and the association relationship is recorded according to the generation rules; the relationship between photos and map spots is many-to-many: one photo can be associated with multiple map spots; one map spot can have multiple photos.

[0052] These include:

[0053] Use drones to take panoramic views of the drone airport surrounding areas at orthogonal and oblique points; used to obtain the natural resource status of the drone airport;

[0054] Extract feature points from each panoramic image of the drone airport surrounding area, and match the same feature points in the panoramic image of the drone airport surrounding area; then traverse the orthogonal point and oblique point images to identify the overlapping areas that need to be fused;

[0055] Select two panoramic images of the drone airport surroundings and assign weights to each pixel of the image to determine the contribution of the pixel in the final fused image; use the weight function from pixel i to the image boundary:

[0056] W(i,j)=exp(-d(i,j) 2 / (2*σ 2 )), where d(i,j) 2 is the distance from pixel i to the image boundary reference point j, and σ is the parameter controlling the width of the weight distribution;

[0057] Where p is any channel of the Lab color space, i.e., L, a, b, Svalid represents the number of images with normal distribution of natural resources in the fused image, and (x, y) is the coordinate value of pixel i;

[0058] Smooth the boundaries of the fused image and calculate the final value of the fused image by weight, which is

[0059]

[0060] Among them, I1(x, y) and I2(x, y) are the pixel values ​​corresponding to the two images, W1(i, j) and W2(i, j) are the weight functions corresponding to the image pixels to the boundaries, and the fusion operation of all images is completed by traversing.

[0061] Among them, D x,y is the color change degree of the current fused image pixel to the image boundary reference point y, and k is the total number of fused images;

[0062] Map the registration map of the drone airport surrounding panorama at the same coordinates to the newly created image, ensure the natural transition of the overlapping areas of the fused area in the newly created image, and use color space conversion to adjust the colors to make them consistent;

[0063] Substitute the calculation result of the current weight function W(i,j) into the mean color change calculation formula CL mid Compared with the average color change C of the previous fused image, when the fusion change is greater than a threshold M, it is marked as a fusion boundary anomaly and enters the natural resource change judgment stage; otherwise, the next fused image judgment is performed. According to actual observations and statistics, the effect is better when M=2.3.

[0064] For each fused image after determination, a drone inspection of the airport's natural resource coverage is carried out. The flight path and altitude of the drone are planned to verify the coverage of the detected natural resources and calibrate the image key frames.

[0065] At the same time, the images acquired by the drone need to load GPS information in real time, mark the positioning results, automatically adjust the tracking window size, and transmit the fused images collected at the drone airport to the remote platform for recording and processing after judgment, so as to achieve real-time monitoring and early warning of the natural resource vegetation coverage status of the drone airport. According to the actual application effect and feedback, the technical route is optimized and improved to improve the accuracy and efficiency of the application.

[0066] The beneficial effects of the above technical solution are:

[0067] 1. An airport drone monitoring network can be constructed to achieve unified resource discovery and dispatching of drone resources, automatic route planning and operation control. A command and dispatch work network with four levels of vertical linkage and horizontal coordination can be established at the city-district-township-village level to form a hierarchical monitoring and hierarchical responsibility work pattern. Integrate local airport drone resources to create an airport drone monitoring network for natural resource survey, monitoring and supervision, give full play to resource utilization efficiency, reduce costs and increase efficiency, and improve the capacity and level of natural resource governance. Taking the actual survey and monitoring work in a certain district and county as an example, the updated survey of the state issued and autonomously extracted monitoring spots is about 7,000, and the district and county area is 568 square kilometers. If airport drones are used, the cost can be significantly reduced.

[0068] 2. Improve the unified video perception and real-time push capabilities of airport drones in real-time scenarios, and promote the upgrade of investigation and monitoring work to real-time. Accelerate the transformation from task-driven to innovation-driven, deepen the application of new generation information technologies such as artificial intelligence, big data, cloud computing, blockchain, 5G and AI space-time, and drones serve the entire life cycle of natural resources and housing, water conservancy, agriculture, emergency and other departments. For example, airports can use drones to provide aerial photography services and provide government departments, enterprises or individuals with investigation, monitoring and verification, basic mapping, monitoring supervision, emergency rescue and other services. This service model can bring additional sources of income and increase economic benefits.

[0069] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A working system for a drone airport to inspect the coverage status of natural resources, characterized in that: include: The data collection module is used to collect aerial images of the airport area through drones and construct a reference image of the airport area through orthogonal points and oblique points; A data analysis module is used to perform airport area anomaly analysis by using an image anomaly feature analysis method; The data display module is used to display the abnormal analysis results in real time through the remote terminal.

2. The working system of the drone airport for inspecting the coverage status of natural resources according to claim 1 is characterized in that: The data collection module comprises: The orthogonal point is located at the center of the image patch. The camera lens is perpendicular to the ground (the shooting angle is within the range of -85 degrees to -90 degrees). The height is adjusted until the shooting range overlooks the entire image patch. The direction is the direction from the previous position of the aircraft to the center of this image patch. The oblique shooting point has the same height as the orthogonal shooting point. The camera lens is tilted to shoot from the ground (shooting angle is within the range of -5 degrees to -85 degrees), and the direction is pointing to the center of the map spot. The camera shooting range can cover the entire map spot on the route to the center of the map spot. Use the minimum distance sorting method to connect the photo points in series: from my location, find the nearest spot center point to form the first route, and then from the spot center point, find the spot center point closest to the spot to generate the second route, connect all the spot center points in sequence to form the route of this flight, and generate the orthogonal points and oblique points of each spot on this route.

3. The working system of the drone airport for inspecting the coverage status of natural resources according to claim 1 is characterized in that: The data analysis module includes: Use drones to take panoramic views of the drone airport surrounding areas at orthogonal and oblique points; used to obtain the natural resource status of the drone airport; Extract feature points from each panoramic view of the drone airport surrounding area, and match the same feature points in the panoramic view of the drone airport surrounding area; then traverse the orthogonal point and oblique point images to identify the overlapping areas that need to be fused.

4. The working system of the drone airport for inspecting the coverage status of natural resources according to claim 1 is characterized in that: The data analysis module includes: Select two panoramic images of the drone airport surroundings and assign weights to each pixel of the image to determine the contribution of the pixel in the final fused image; use the weight function from pixel i to the image boundary: W(i,j)=exp(-d(i,j) 2 / (2*σ 2 )), where d(i,j) 2 is the distance from pixel i to the image boundary reference point j, and σ is the parameter controlling the width of the weight distribution; Where p is any channel of the Lab color space, namely L, a, and b, Svalid represents the number of images with normal distribution of natural resources in the fused image, and (x, y) is the coordinate value of pixel i.

5. The working system of the drone airport for inspecting the coverage status of natural resources according to claim 1 is characterized in that: The data analysis module includes: Smooth the boundaries of the fused image and calculate the final value of the fused image by weight, which is Among them, I1(x, y) and I2(x, y) are the pixel values ​​corresponding to the two images, W1(i, j) and W2(i, j) are the weight functions corresponding to the image pixels to the boundaries, and the fusion operation of all images is completed by traversing.

6. The working system of the drone airport for inspecting the coverage status of natural resources according to claim 1 is characterized in that: The data analysis module includes: Among them, D x,y is the color change degree of the current fused image pixel to the image boundary reference point y, and k is the total number of fused images; Map the registration map of the drone airport surrounding panorama at the same coordinates to the newly created image, ensure the natural transition of the overlapping areas of the fused area in the newly created image, and use color space conversion to adjust the colors to make them consistent; Substitute the calculation result of the current weight function W(i,j) into the mean color change calculation formula CL mid Compared with the average color change C of the previous fused image, when the fusion change is greater than a threshold M, it is marked as a fusion boundary anomaly and enters the natural resource change judgment stage; otherwise, the next fused image judgment is performed.

7. The working system of the drone airport for inspecting the coverage status of natural resources according to claim 1 is characterized in that: The data display module includes: The images obtained by the drone need to load GPS information in real time, mark the positioning results, and automatically adjust the tracking window size. After the fused image collected at the drone airport is judged, it will be transmitted to the remote platform for recording and processing, so as to realize real-time monitoring and early warning of the natural resource vegetation coverage status of the drone airport.

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