Mining area geological disaster automatic identification method based on unmanned aerial vehicle image

Abstract

The invention relates to the technical field of geological disaster automatic identification, and discloses a mining area geological disaster automatic identification method based on an unmanned aerial vehicle image, and the method comprises the steps: S100, collecting the first altitude of the highest point in an area and the shape of the area based on the obtained land type information, and calculating the data of a first flight path of the flight of an unmanned aerial vehicle; the system can calculate the flight path of the unmanned aerial vehicle based on the altitude height and the area shape of a mining area geological disaster monitoring area, ensures that the flight height is safe enough, avoids collision with the terrain or obstacles, and can efficiently collect the geological data of the mining area through two-time flight and scanning, including sparse and dense point cloud data, thereby improving the mining area geological disaster monitoring accuracy. The system provides a basis for early recognition of geological disasters, can accurately position point cloud data according to positioning, altitude and attitude data acquired in real time, and provides accurate support for subsequent three-dimensional modeling and disaster analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic identification of geological disasters, and in particular relates to a method for automatic identification of geological disasters in mining areas based on drone images. Background Art

[0002] The collection of land type information is an important means for humans to understand the laws of nature. A global map of land type information for a country or region is a state secret and is related to the country's strategic direction in the economic field. It also has very important reference value for preventing natural disasters.

[0003] As an important part of land type information collection, topographic surveying and mapping refers to the general term for all surveying and mapping work involved in the entire process from topographic data collection to the compilation of results documents.

[0004] With the rapid development of 3D modeling technology, 3D modeling technology has gradually been applied to terrain surveying and mapping. A 3D model of the terrain is constructed based on the collected terrain data, making the terrain surveying and mapping data visual. However, the current collection of terrain data usually requires terrain surveyors to carry relevant equipment into the field to collect data, and the collection process takes a lot of time. Summary of the Invention

[0005] In response to the problems existing in the prior art, the present invention provides a method for automatically identifying geological hazards in mining areas based on drone images, which can overcome the above problems or at least partially solve the above problems.

[0006] The present invention is implemented as follows: a method for automatically identifying geological hazards in mining areas based on drone images, the method comprising the following steps: S100, collecting a first altitude of the highest point in the area and the shape of the area based on the acquired land type information, and calculating data of a first flight path of the UAV; S110, sending data of the first flight path to the drone, so that the drone flies around the land type information collection area along the first flight path; S120, scanning the land type information collection area while the UAV is flying along the first flight path, obtaining sparse point cloud data of the land type information collection area, and obtaining second altitude data of all scanned points in the land type information collection area; S130, calculating data of a second flight path of the UAV based on all the data of the second altitude and the data of the first flight path; S140, sending data of the second flight path to the UAV, so that the UAV flies around the land type information collection area along the second flight path; S150 , performing a second scan on the land type information collection area while the UAV is flying along the second flight path to obtain dense point cloud data of the land type information collection area.

[0007] Based on the first altitude of the highest point in the mining area and the area of the area, the data of the first flight path of the UAV is calculated, specifically including: Obtaining a first altitude, and calculating a first flight altitude of the UAV, where the first flight altitude is equal to the sum of the first altitude and a first preset distance; Planning a horizontal flight path of the UAV using a scanline method, and calculating a first flight path based on a first flight altitude and the horizontal flight path; Calculating data of a second flight path of the UAV based on all the data of the second altitude and the data of the first flight path, the method specifically includes: Calculating a second flight altitude based on all second altitude data so that the second flight altitude of the drone at any scanning point is equal to the sum of the second altitude of the scanning point and the second preset distance; calculating a second flight path based on the horizontal flight path and the second flight altitude; When the UAV flies along the first flight path, it collects images of the mining area to obtain sparse point cloud data of the mining area, and obtains the second altitude data of all scanned points in the mining area. When the UAV flies along the second flight path, it collects images of the mining area for the second time to obtain dense point cloud data of the mining area, which specifically includes: Obtain the drone's positioning data, altitude data, and attitude data in real time; Calculate the point cloud position data of sparse point cloud and dense point cloud based on positioning data, altitude data and posture data; While the UAV is flying along the first flight path, secondary image acquisition is performed on the mining area to obtain dense point cloud data. The method further includes: If there is an unknown area in the mining area that has not been scanned, calculating data of a third flight path for the UAV based on the shape and position data of the unknown area, so that the UAV flies around the unknown area along the third flight path; Collect images of unknown areas and obtain point cloud data of unknown areas; During the UAV's flight along the second flight path, secondary image acquisition is performed on the mining area to obtain dense point cloud data. The method further includes: causing the drone to fly around the mining area in a second flight path; Three image acquisitions are performed on the mining area to collect color information of the mining area, including color data and position data of the color point cloud; After establishing a three-dimensional model of the mining area based on the sparse point cloud and the dense point cloud, the method further includes: Processing the sparse point cloud position data, dense point cloud position data and color point cloud position data, and transforming the coordinates of the sparse point cloud, dense point cloud and color point cloud into the same coordinate system; Based on the transformed sparse point cloud, dense point cloud and color point cloud, a color 3D model of the mining area is established.

[0008] Preferably, the method of the present invention further comprises a processing module, a sending module, a collection module and a positioning module; The processing module is configured to calculate data of a first flight path of the UAV based on the acquired first altitude of the highest point in the mining area and the area shape; The sending module is used to send data of the first flight path to the UAV, so that the UAV flies around the mining area along the first flight path; The acquisition module is used to acquire images of the mining area, obtain sparse point cloud data of the mining area, and obtain data of the second altitude of all scanned points in the mining area when the UAV flies along the first flight path; The processing module is further configured to calculate data of a second flight path of the UAV based on all the data of the second altitude and the first flight path; The sending module is further configured to send data of the second flight path to the drone, so that the drone flies around the mining area along the second flight path; The acquisition module is further configured to perform secondary image acquisition of the mining area while the UAV is flying along the second flight path, thereby acquiring dense point cloud data of the mining area; The positioning module is used to obtain the positioning data, altitude data and attitude data of the drone in real time; and calculate the point cloud position data of the sparse point cloud and the dense point cloud based on the positioning data, altitude data and attitude data.

[0009] The present invention is implemented as follows: an automatic identification device for geological disasters in mining areas based on drone images includes an electronic device, which includes a processor, a memory, a user interface and a network interface. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0010] Compared with the prior art, the present invention has the following beneficial effects: The system in the present invention can calculate the flight path of the UAV based on the altitude and regional shape of the mining area geological disaster monitoring area, ensure that the flight altitude is safe enough, and avoid collision with terrain or obstacles. Through two flights and scans, the system can efficiently collect geological data of the mining area, including sparse and dense point cloud data, providing a basis for early identification of geological disasters. The system can accurately locate the point cloud data based on the real-time positioning, altitude and attitude data, providing accurate support for subsequent three-dimensional modeling and disaster analysis, and can conduct further flights and scans in uncovered areas to ensure comprehensive monitoring of the entire mining area, providing comprehensive technical support for early warning and management of geological disasters in mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of the method provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of the connection of modules provided by an embodiment of the present invention; Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present invention.

[0012] In the figure: 1. Processing module; 2. Sending module; 3. Acquisition module; 4. Positioning module; 5. Processor; 6. Electronic device; 7. User interface; 8. Network interface; 9. Memory. DETAILED DESCRIPTION

[0013] In order to further understand the content, features and effects of the present invention, the following embodiments are given as examples and described in detail with reference to the accompanying drawings.

[0014] The structure of the present invention is described in detail below with reference to the accompanying drawings.

[0015] like Figures 1 to 3 As shown, an embodiment of the present invention provides an automatic identification method for geological hazards in mining areas based on drone images. In reverse engineering, the collection of point data on the surface of an object obtained by measuring or scanning equipment is called a point cloud.

[0016] A sparse point cloud is a collection of small, widely spaced point data acquired using a measurement device. These sparse points are known as feature points, or points within the scanned object that have distinct characteristics and are easy to detect and match. These points can represent the object's simple geometric shape and outline, such as corners and edges.

[0017] A dense point cloud is a collection of dense point data captured using a scanning device, with a large number of points and small spacing between them. Dense point clouds can accurately depict the shape and appearance of objects, enabling the reconstruction of a 3D scene or the entire object.

[0018] Color point cloud is a collection of point data obtained based on the principle of photogrammetry. The point data includes the three-dimensional coordinates and color information (RGB).

[0019] This embodiment discloses a method for collecting land type information based on a drone. Figure 1 , comprising the following steps: S100, collecting the first altitude of the highest point in the area and the area shape based on the acquired land type information, and calculating data of a first flight path of the UAV.

[0020] S110 , sending data of a first flight path to a drone, so that the drone flies around a land type information collection area along the first flight path.

[0021] S120 , scanning the land type information collection area while the UAV is flying along the first flight path, obtaining sparse point cloud data of the land type information collection area, and obtaining second altitude data of all scanned points in the land type information collection area.

[0022] S130 , calculating data of a second flight path of the UAV based on all the data of the second altitude and the data of the first flight path.

[0023] S140 , sending data of the second flight path to the UAV, so that the UAV flies around the land type information collection area along the second flight path.

[0024] S150 , performing a second scan on the land type information collection area while the UAV is flying along the second flight path to obtain dense point cloud data of the land type information collection area.

[0025] Specifically, the system pre-acquires the altitudes of all mountains within the land type information collection area and sets the altitude of the highest mountain as the first altitude. Based on the first altitude and the area's shape, the system then calculates data for a first flight path for the drone and transmits the first flight path data to the drone, causing the drone to fly high around the land type information collection area along the first flight path. Since the drone's flight altitude remains above all objects within the area, collisions with objects within the area are prevented. While the drone is flying along the first flight path, a system pre-installed on the drone scans the area, collecting sparse point cloud data—data on the main features of the area. The system acquires data on the second altitudes of all scanned points within the area, facilitating subsequent calculation of the drone's secondary flight path based on the second altitude data. Based on the data on all second altitudes and the first flight path data, the system calculates data on a second flight path for the drone and transmits it to the drone, causing the drone to fly low around the area along the second flight path, preventing collisions with objects within the area. The system performs a second scan of the land type information collection area. Since the UAV is closer to the land type information collection area, the data scanned by the system is more refined, that is, dense point cloud data is collected. During the two flights of the UAV, the system can complete the data collection of the land type information collection area through two scans, thereby improving the efficiency of terrain data collection. In this embodiment, collecting sparse point cloud data and dense point cloud data is a conventional technical means in the relevant technical field and will not be further described here. Among them, the method of scanning the land type information collection area can be to use a laser radar for scanning, or to use a camera for static photography scanning, or to use a structured light sensor for scanning. In this embodiment, it is preferred to use a laser radar for scanning.

[0026] LiDAR is a radar system that uses laser beams to detect target characteristics such as position and velocity. Its operating principle is to transmit a detection signal (laser beam) toward the target. The received echo signal reflected from the target is then compared with the detection signal. After appropriate processing, relevant target information such as range, direction, altitude, speed, attitude, and shape can be obtained. In this embodiment, the LiDAR includes a laser ranging system, an optical-mechanical scanning unit, a control unit, a global positioning system, an inertial measurement system, and a storage unit.

[0027] In a possible implementation, step S100 specifically further includes the following steps: Obtaining a first altitude, calculating a first flight altitude of the UAV, where the first flight altitude is equal to the sum of the first altitude and a first preset distance. Planning a horizontal flight path of the UAV using a scanline method, calculating the first flight path based on the first flight altitude and the horizontal flight path.

[0028] Specifically, the system obtains a first flight altitude by adding a first altitude to a first preset distance, so that the UAV always flies at a high altitude at the first flight altitude. The first preset distance is determined according to the effective scanning distance of the scanning device, and this embodiment does not make any specific limitations. The horizontal flight path of the UAV is planned using a scanning line method, which includes the following steps: first, the plane projection shape of the land type information collection area is approximated as a rectangular area, the UAV flies along a straight line through the rectangular area, turns at the edge of the area, and then flies in the opposite direction along a parallel straight line, and repeats this process to traverse the entire rectangular area line by line. The spacing between the straight lines is determined according to the effective scanning radius of the scanning device, so as to complete the scanning of the entire rectangular area. The horizontal flight path of the UAV in the horizontal direction is calculated to coincide with the first flight altitude in the vertical direction to obtain the first flight path.

[0029] In one possible embodiment, the second flight altitude is calculated based on all second altitude data, so that the second flight altitude of the drone at any scanning point is equal to the sum of the second altitude of the scanning point and the second preset distance. The second flight path is calculated based on the horizontal flight path and the second flight altitude.

[0030] Specifically, horizontally, the drone continues to fly along a horizontal flight path. Vertically, the drone first acquires the second altitude data for all scanned points. The second altitude is then added to the second preset distance to obtain a second flight altitude. When the drone is directly above any scanned point, the distance from the scanned point is the value of the second flight altitude. The second preset distance is determined based on the effective scanning range of the scanning device and is not specifically limited in this embodiment.

[0031] In one possible implementation, positioning data, altitude data, and attitude data of the drone are acquired in real time, and point cloud position data of the sparse point cloud and the dense point cloud are calculated based on the positioning data, altitude data, and attitude data.

[0032] Specifically, the drone is pre-installed with GPS, barometer, and attitude sensor. When the system scans, the GPS obtains the positioning data of the scanning position and sends it to the system. The barometer measures the air pressure data of the drone's position and sends it to the system. The system calculates the altitude data of the drone's position based on the air pressure data. The attitude sensor obtains the attitude data of the drone and sends it to the system. Based on the positioning data, altitude data, attitude data, and the device-to-scan distance data detected by the scanning device, the point cloud position data of the sparse point cloud and the dense point cloud are calculated, so that the system can subsequently unify the coordinate system of the sparse point cloud and the dense point cloud. Among them, obtaining and calculating point cloud position data is a conventional technical means in the relevant field and will not be further elaborated here.

[0033] In a possible implementation, step S150 further includes the following steps: If there are unknown areas within the land type information collection area that have not been scanned, the system calculates the drone's third flight path based on the shape and location data of the unknown area, allowing the drone to fly along the third flight path around the unknown area. The system scans the unknown area and obtains point cloud data for the unknown area, thereby further completing data collection for the land type information collection area.

[0034] In a possible implementation, step S150 further includes the following steps: The second flight path data is transmitted to the drone, causing the drone to fly around the land type information collection area along the second flight path. The land type information collection area is scanned three times to collect color information of the land type information collection area. The color information includes color data and position data of the color point cloud.

[0035] Specifically, the drone continues to fly along the second flight path around the land type information collection area, and the system collects color data and position data of the color point cloud in the land type information collection area. In this embodiment, the color point cloud is preferably collected by using a panoramic camera to collect color data of the color point cloud and obtaining positioning data of the scanning location using GPS to calculate the position data of the color point cloud.

[0036] In one possible implementation, the sparse point cloud location data, the dense point cloud location data, and the color point cloud location data are processed, and the coordinates of the sparse point cloud, the dense point cloud, and the color point cloud are transformed into the same coordinate system. Based on the transformed sparse point cloud, the dense point cloud, and the color point cloud, a color 3D model of the land type information collection area is constructed.

[0037] Specifically, in order to ensure that the same type of point cloud data obtained by scanning the same area at multiple scanning positions can be stitched together, first, the system obtains the overlapping parts of the point cloud data obtained at two adjacent scanning positions, overlaps the overlapping parts to complete the stitching, and then completes the stitching of all point cloud data in sequence using the same method. Then, the coordinates of the sparse point cloud, dense point cloud, and color point cloud in the default coordinate system are converted to the coordinates of the geodetic coordinate system, where the default coordinate system is the coordinate system with the drone as the center point during the data acquisition process. The geodetic coordinate system is a real-world coordinate system established with the reference ellipsoid as the reference surface in geodetic measurement. Converting the coordinates of different types of scanned point cloud data in the default coordinate system to the coordinates of a unified geodetic coordinate system can complete the stitching of different types of scanned point cloud data, and can make the system coordinates consistent with the real spatial state of the scanned target, so that the point cloud data truly reflects the spatial conditions of the site, and prepare for the next step of topographic mapping.

[0038] Regarding the coordinate system conversion method, this embodiment preferably performs coordinate conversion based on the positioning data, altitude data, and posture data of the scanning device. First, the system calculates the coordinates of the point cloud data in the default coordinate system based on the positioning data and posture data of the scanning device, as well as the distance between the scanning device and the scanning point. The coordinates of the scanning device in the geodetic coordinate system are then calculated based on the positioning data and altitude data. Finally, the center point of the default coordinate system is converted to the coordinates of the scanning device in the geodetic coordinate system, completing the coordinate system conversion. In this embodiment, the conventional technical means for coordinate system conversion in the relevant technical field will not be further described here.

[0039] This embodiment also discloses a land type information collection system based on drones, referring to Figure 2 The system includes a processing module 11, a sending module 22 and a collection module 33, wherein: The processing module 11 collects the first altitude of the highest point in the area and the area shape based on the acquired land type information, and calculates data of the first flight path of the UAV; The sending module 22 sends data of the first flight path to the UAV, so that the UAV flies around the land type information collection area along the first flight path; When the UAV flies along the first flight path, the acquisition module 33 scans the land type information acquisition area, obtains sparse point cloud data of the land type information acquisition area, and obtains data of the second altitude of all scanned points in the land type information acquisition area; The processing module 11 calculates data of a second flight path of the UAV based on all the data of the second altitude and the first flight path; The sending module 22 sends the data of the second flight path to the UAV, so that the UAV flies around the land type information collection area along the second flight path; When the UAV flies along the second flight path, the acquisition module 33 performs a second scan on the land type information acquisition area to obtain dense point cloud data of the land type information acquisition area.

[0040] In one possible implementation, refer to Figure 2 , the system also includes a positioning module 4; The positioning module 4 obtains the positioning data, altitude data and attitude data of the UAV in real time; and calculates the point cloud position data of the sparse point cloud and the dense point cloud based on the positioning data, altitude data and attitude data.

[0041] In one possible implementation, the system obtains a first altitude and calculates a first flight altitude of the UAV, where the first flight altitude is equal to the sum of the first altitude and a first preset distance; A scan line method is used to plan the horizontal flight path of the UAV, and a first flight path is calculated based on the first flight altitude and the horizontal flight path.

[0042] In one possible implementation, the system calculates the second flight altitude based on all the second altitude data, so that the second flight altitude of the drone at any scanning point is equal to the sum of the second altitude of the scanning point and the second preset distance; A second flight path is calculated based on the horizontal flight path and the second flight altitude.

[0043] In one possible implementation, the system acquires the drone's positioning data, altitude data, and attitude data in real time; Based on the positioning data, altitude data and posture data, the point cloud position data of sparse point cloud and dense point cloud are calculated.

[0044] In one possible implementation, if the system detects that there is an unknown area in the land type information collection area that has not been scanned, the system calculates data of a third flight path for the UAV based on shape and position data of the unknown area, so that the UAV flies around the unknown area along the third flight path. Scan the unknown area and obtain the point cloud data of the unknown area.

[0045] In one possible implementation, the system causes the drone to fly around the land type information collection area in a second flight path; The land type information collection area is scanned three times to collect color information of the land type information collection area, where the color information includes color data and position data of the color point cloud.

[0046] In one possible implementation, the system processes the sparse point cloud position data, the dense point cloud position data, and the color point cloud position data, and transforms the coordinates of the sparse point cloud, the dense point cloud, and the color point cloud into the same coordinate system; Based on the transformed sparse point cloud, dense point cloud and color point cloud, a color 3D model of the land type information collection area is established.

[0047] This application also provides an electronic device 6, referring to Figure 3 The electronic device 6 may include: at least one processor 5 , at least one communication bus, a user interface 7 , a network interface 8 , and at least one memory 9 .

[0048] The communication bus is used to realize the connection and communication between these components.

[0049] The user interface 7 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 7 may also include a standard wired interface and a wireless interface.

[0050] The network interface 8 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0051] The processor 5 may include one or more processing cores. The processor 5 uses various interfaces and lines to connect various parts of the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 9, and calling data stored in the memory 9. Optionally, the processor 5 may use digital signal processing (DSP), field programmable gate array (FPGA), or other similar processors. The processor 5 may be implemented in at least one of the following hardware forms: a Field-Programmable Gate Array (FPGA) or a Programmable Logic Array (PLA). The processor 5 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may be implemented as a separate chip, rather than integrated into the processor 5.

[0052] Memory 9 may include random access memory (RAM) or read-only memory (ROM). Optionally, memory 9 may include non-transitory computer-readable storage medium. Memory 9 may be used to store instructions, programs, code, code sets, or instruction sets. Memory 9 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, sound playback function, image playback function, etc.), instructions for implementing the aforementioned method embodiments, etc.; the data storage area may store data related to the aforementioned method embodiments, etc. Memory 9 may also optionally be at least one storage device located remotely from the aforementioned processor 5. As shown in the figure, memory 9, as a computer storage medium, may include an operating system, a network communication module, a user interface module 7, and an application program for a method for collecting land type information using a drone.

[0053] exist Figure 3 In the electronic device 6 shown, the user interface 7 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 5 can be used to call an application program stored in the memory 9 for a method for collecting land type information based on a drone. When executed by one or more processors 5, the electronic device 6 executes one or more methods in the above-mentioned embodiments.

[0054] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0055] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0056] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with this patent will not depart from the scope of the technical solution of the present invention.

Claims

1. A method for automatically identifying geological hazards in mining areas based on drone images, characterized by: The method comprises the following steps, S100, collecting a first altitude of the highest point in the area and the shape of the area based on the acquired land type information, and calculating data of a first flight path of the UAV; S110, sending data of the first flight path to the drone, so that the drone flies around the land type information collection area along the first flight path; S120, scanning the land type information collection area while the UAV is flying along the first flight path, obtaining sparse point cloud data of the land type information collection area, and obtaining second altitude data of all scanned points in the land type information collection area; S130, calculating data of a second flight path of the UAV based on all the data of the second altitude and the data of the first flight path; S140, sending data of the second flight path to the UAV, so that the UAV flies around the land type information collection area along the second flight path; S150 , performing a second scan on the land type information collection area while the UAV is flying along the second flight path to obtain dense point cloud data of the land type information collection area.

2. The method for automatically identifying geological hazards in mining areas based on drone images according to claim 1, characterized in that: The method further includes a processing module (1), a sending module (2), a collection module (3) and a positioning module (4); The processing module (1) is used to calculate data of a first flight path of the UAV based on the acquired first altitude of the highest point in the mining area and the area shape; The sending module (2) is used to send data of the first flight path to the drone, so that the drone flies around the mining area along the first flight path; The acquisition module (3) is used to acquire images of the mining area, obtain sparse point cloud data of the mining area, and obtain data on the second altitude of all scanned points in the mining area when the UAV is flying along the first flight path; The processing module (1) is further configured to calculate data of a second flight path of the UAV based on all the data of the second altitude and the first flight path; The sending module (2) is further used to send data of the second flight path to the drone, so that the drone flies around the mining area along the second flight path; The acquisition module (3) is further used to perform secondary image acquisition of the mining area when the UAV is flying along the second flight path, thereby obtaining dense point cloud data of the mining area; The positioning module (4) is used to obtain the positioning data, altitude data and attitude data of the UAV in real time; and based on the positioning data, altitude data and attitude data, calculate the point cloud position data of the sparse point cloud and the dense point cloud.

3. An automatic identification device for mining area geological hazards based on drone images, comprising the automatic identification method for mining area geological hazards based on drone images according to any one of claims 1 and 2, characterized in that: The electronic device (6) comprises a processor (5), a memory (9), a user interface (7) and a network interface (8), wherein the memory (9) is used to store instructions, the user interface (7) and the network interface (8) are both used to communicate with other devices, and the processor (5) is used to execute the instructions stored in the memory (9).

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