Metal mine goaf identification method and system based on UAV aeromagnetic measurement

Abstract

The present invention provides a method and system for identifying goaf areas in metal mines based on aeromagnetic measurement by unmanned aerial vehicles, which include acquiring aeromagnetic data of an area to be identified; preprocessing the acquired aeromagnetic data; gridding the preprocessed aeromagnetic data to obtain a magnetic anomaly contour map; using the identification results of high magnetic anomalies and / or gradient zone anomalies as identification targets for goaf areas according to the magnetic anomaly contour map; and obtaining the buried depth of goaf areas in metal mines after magnetic profile inversion based on the aeromagnetic data of the identification target areas. The present invention realizes the rapid identification of goaf areas in metal mines in complex human interference areas, can quickly complete magnetic field scanning measurement of target areas, and quickly completes the identification of goaf targets, with low danger, high operating efficiency, and low working cost.

Description

Technical Field

[0001] The present invention relates to the field of aerial geophysical prospecting technology, and in particular to a method and system for identifying goaf areas in metal mines based on unmanned aerial vehicle (UAV) aeromagnetic measurement. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] At present, many old mining areas are gradually surrounded by towns or industrial and mining enterprises, and even become construction land for urban construction.

[0004] The inventors found that due to historical reasons, the mining data of many old mining areas are incomplete or even lost, which poses a huge safety hazard. In addition, due to serious human interference in urban areas, the current main method of exploration and detection is pit measurement and drilling verification, which is extremely difficult and dangerous. Summary of the invention

[0005] In order to address the shortcomings of the prior art, the present invention provides a method and system for identifying metal mine goafs based on unmanned aerial vehicle aeromagnetic measurement, which realizes the rapid identification of metal mine goafs in complex human interference areas, can quickly complete magnetic field scanning measurement of the target area, and quickly complete goaf target identification, with low risk, high operating efficiency and low working cost.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a method for identifying goaf areas in metal mines based on unmanned aerial vehicle aeromagnetic measurement.

[0008] A method for identifying goaf areas in metal mines based on UAV aeromagnetic measurement includes the following processes:

[0009] Obtain aeromagnetic data of the area to be identified;

[0010] Preprocess the acquired aeromagnetic data;

[0011] The pre-processed aeromagnetic data is gridded to obtain a magnetic anomaly contour map;

[0012] According to the magnetic anomaly contour plane map, the identification results of high magnetic anomalies and / or gradient zone anomalies are used as the identification target areas of the goaf areas;

[0013] Based on the aeromagnetic data of the identified target area, the buried depth of the metal mine goaf is obtained after magnetic profile inversion.

[0014] Furthermore, according to the identified target area, the profile of the UAV aeromagnetic measurement is set according to the direction of the vertical magnetic anomaly, and the profile passes through the center of the high-value magnetic anomaly or the center of the dense gradient zone.

[0015] Furthermore, the acquired aeromagnetic data are preprocessed, including: magnetic diurnal variation correction and normal field correction.

[0016] Furthermore, the verification hole positions were obtained based on the magnetic profile inversion results.

[0017] Furthermore, the minimum curvature method is used to grid the aeromagnetic data, obtain the grid data of the magnetic field ΔT data in the survey area, perform aeromagnetic ΔT-based magnetic pole processing, and obtain the aeromagnetic ΔT-based magnetic pole contour line plane result map.

[0018] Further, the acquisition of aeromagnetic data includes:

[0019] Obtain oblique photography data and perform three-dimensional reconstruction to obtain a three-dimensional real scene model;

[0020] Generate a digital elevation model based on the three-dimensional real scene model and display it, and receive location annotations related to flight safety;

[0021] Obtaining the flight start point, end point and flight altitude of the UAV, and generating an initial route according to the digital elevation model;

[0022] According to the initial route and the elevation change rate, the waypoints are determined in combination with the marked positions, and the aeromagnetic measurement flight path is determined and sent to the UAV.

[0023] Furthermore, the acquisition of aeromagnetic data also includes:

[0024] Obtain the aeromagnetic measurement data sent by the drone and generate an aeromagnetic measurement results map;

[0025] The aeromagnetic survey result map is superimposed on the three-dimensional real scene model for users to view and compare.

[0026] A second aspect of the present invention provides a metal mine goaf identification system based on unmanned aerial vehicle aeromagnetic measurement.

[0027] A metal mine goaf identification system based on UAV aeromagnetic measurement, comprising:

[0028] The data acquisition module is configured to: acquire aeromagnetic data of the area to be identified;

[0029] The preprocessing module is configured to: preprocess the acquired aeromagnetic data;

[0030] The grid processing module is configured to: perform grid processing on the pre-processed aeromagnetic data to obtain a magnetic anomaly contour line plane map;

[0031] The target area identification module is configured to: according to the magnetic anomaly contour plane map, use the identification results of high magnetic anomaly and / or gradient zone anomaly as the identification target area of ​​the goaf area;

[0032] The buried depth identification module is configured to obtain the buried depth of the metal mine goaf after performing magnetic profile inversion based on the aeromagnetic data of the identified target area.

[0033] The third aspect of the present invention is a computer-readable storage medium having a program stored thereon, characterized in that when the program is executed by a processor, the steps of the method for identifying metal mine goafs based on unmanned aerial vehicle aeromagnetic measurement as described in the first aspect of the present invention are implemented.

[0034] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for identifying metal mine goafs based on unmanned aerial vehicle aeromagnetic measurement as described in the first aspect of the present invention are implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The method and system for identifying metal mine goafs based on UAV aeromagnetic measurement described in the present invention realize the rapid identification of metal mine goafs in complex human interference areas, can quickly complete the magnetic field scanning measurement of the target area, and quickly complete the goaf target identification, with low risk, high operating efficiency and low working cost.

[0037] 2. The method and system for identifying metal mine goafs based on UAV aeromagnetic measurement described in the present invention realize centimeter-level high-precision three-dimensional real-scene modeling based on oblique photography, thereby guiding the planning of UAV aeromagnetic measurement routes and improving the efficiency and safety of UAV aeromagnetic measurement operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] Figure 1 A schematic flow chart of a method for identifying goaf areas in metal mines based on UAV aeromagnetic measurement provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0043] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0044] Embodiment 1:

[0045] like Figure 1 As shown, Embodiment 1 of the present invention provides a method for identifying goaf areas in metal mines based on drone aeromagnetic measurement, comprising the following process:

[0046] S1: According to the scope of the target survey area, design a UAV aeromagnetic survey route covering the entire survey area and complete the aeromagnetic data collection of the survey area;

[0047] S2: Use the designed computer software to automatically process the acquired raw data with magnetic diurnal variation correction and normal field correction to generate low-altitude high-precision magnetic field data in the survey area.

[0048] S2.1: Magnetic diurnal variation correction: Before the start of the UAV aeromagnetic measurement work, a magnetic diurnal variation station is set up in the survey area or its surrounding area (not more than 30km) where the magnetic field is calm, the magnetic gradient change is small, there is no human interference, and the terrain is flat and open. A magnetometer with the same accuracy as the aviation magnetometer is set up, and magnetic diurnal variation observations are carried out at intervals of 3s (or less). 30 minutes after the end of the UAV aeromagnetic measurement, the magnetic diurnal variation observation is ended, and the original observation data of the magnetic diurnal variation magnetometer is exported. After subtracting the magnetic field value of the base point of the magnetic diurnal variation station, the magnetic diurnal variation data of the survey area on that day (including measurement time and magnetic diurnal variation value) is obtained. The magnetometer diurnal variation correction software is used, and a correction method combining direct correction and interpolation correction is adopted to subtract the magnetic diurnal variation value from the aeromagnetic measurement data to complete the magnetic diurnal variation correction.

[0049] S2.2: Normal field correction: According to the longitude and latitude coordinates of the aeromagnetic measurement point, the corresponding International Geomagnetic Reference Field value (IGRF) is calculated using the International Geomagnetic Reference Field model. The International Geomagnetic Reference Field value (IGRF) is subtracted from the UAV aeromagnetic measurement data after the magnetic diurnal variation correction to obtain the Earth's normal field correction.

[0050] S3: The low-altitude high-precision magnetic field data of the survey area are gridded using the minimum curvature method to obtain the gridded file of the magnetic field ΔT data of the survey area, and then aeromagnetic ΔT magnetic pole processing is performed to eliminate the influence of oblique magnetization, obtain the aeromagnetic ΔT magnetic pole contour plane result map, and highlight the boundary characteristics of the magnetic geological body.

[0051] Minimum curvature gridding method: The minimum curvature method is used to interpolate the drone aeromagnetic data to form a gridded data volume file. The minimum curvature gridding method is a grid interpolation method that attempts to generate the smoothest possible surface while respecting the data as strictly as possible.

[0052] S4: The computer first identifies the high magnetic anomaly traps and gradient zone anomalies in the aeromagnetic ΔT magnetic pole contour plane result map according to the set recognition parameters, and then highlights the identified magnetic anomalies as the target area for identifying the magnetic ore body goaf area.

[0053] S5: According to the identified target area and the direction of the vertical magnetic anomaly, a high-precision aeromagnetic measurement profile of an unmanned aerial vehicle is deployed, passing through the center of the high-value magnetic anomaly or the center of the dense gradient zone. According to the aeromagnetic data processing steps, magnetic diurnal variation correction and normal field correction are performed, and then the magnetic profile inversion is performed to preliminarily determine the buried depth of the magnetic body goaf area, and the verification hole position is obtained based on the inversion results.

[0054] The aeromagnetic measurement method used in this embodiment includes:

[0055] (1): Obtain oblique photography data and perform 3D reconstruction to obtain a 3D real scene model.

[0056] (1.1): Determine the scope of the survey area, design the UAV oblique photography route, determine the flight parameters, complete the oblique photography work within the survey area, and collect the oblique photography image data and corresponding GPS information within the survey area;

[0057] (1.2): By solving and processing the real-scene photos, a centimeter-level three-dimensional real-scene model of the survey area is generated.

[0058] (2): Generate a digital elevation model of the survey area based on the three-dimensional real scene model and display it, and receive location annotations related to flight safety.

[0059] (2.1): Extract the centimeter-level precision digital elevation model of the survey area and display it; specifically, create a grid on the plane, and assign a corresponding height value to each grid according to the height data of the three-dimensional real scene model to obtain the digital elevation model;

[0060] (2.2): Receive user annotations of locations that may affect the flight safety of the drone, such as marking obstacles that may affect flight safety. Preferably, the magnetic field interference influence radius of the obstacle is also marked.

[0061] (3): Obtain the flight starting point, terminal point and flight altitude of the UAV, and generate an initial route based on the digital elevation model.

[0062] In this embodiment, the initial route is generated according to the preset flight altitude of the drone at an altitude above the ground.

[0063] (4): According to the initial route and elevation change rate, the waypoints are determined in combination with the marked positions, and the aeromagnetic measurement flight path is obtained and sent to the UAV.

[0064] (4.1): The initial route is sampled at equal intervals according to the preset initial density to obtain the initial waypoint set;

[0065] (4.2): Projecting the initial route onto the digital elevation model, and adding or deleting waypoints according to elevation changes;

[0066] Specifically, the elevation difference between adjacent initial waypoints is obtained. If the elevation difference exceeds a first set threshold, waypoints are added between the adjacent initial waypoints according to a preset mapping relationship between the altitude difference and the sampling density. If the elevation differences between multiple consecutive initial waypoints are all less than a second set threshold, one or more of the initial waypoints are deleted. This process is repeated until the elevation differences between adjacent waypoints are all between the first set threshold and the second set threshold.

[0067] (4.3): If the initial route passes through the marked position, the route is automatically planned to bypass. Specifically, the marked position is included in the waypoint set, the waypoint height is increased, and the start climbing waypoint and the start descending waypoint are determined according to the climbing angle requirement of the drone and the influence range of the magnetic field interference of obstacles, and the start climbing waypoint and the start descending waypoint are added to the waypoint set.

[0068] (4.4): Calculate the elevation change rate of each waypoint, and for waypoints whose elevation change rate exceeds a third set threshold, increase the altitude of the waypoint, and determine the flight route at the waypoint based on the set danger zone.

[0069] In order to ensure the safe and stable flight of the drone, so that the drone will not collide in any posture, and to ensure that there is a safe distance between the drone and the ground or obstacles, in this embodiment, an initial maneuvering radius is set for the waypoint, and the spherical area with the waypoint as the center and the initial maneuvering radius as the radius is the danger zone. In step (4.4), in order to ensure the stability and safety of the drone flight, the waypoint height is increased by a maneuvering radius, and at the same time, the maneuvering radius of the waypoint is expanded, that is, the scope of the danger zone is expanded.

[0070] Under normal circumstances, during the measurement process, the UAV flies at a constant speed in a flat area. At measuring points with a large elevation change rate, it needs to significantly slow down or even stop for measurement, which results in large losses. This embodiment sets a dangerous area and raises the flight altitude so that the UAV can smoothly fly over waypoints with a large elevation change rate without significantly slowing down or stopping, thereby ensuring the stability and safety of the UAV flight.

[0071] (4.5): Based on the obtained waypoint set, the corresponding flight altitude and flight route of each waypoint, the flight path is obtained and sent to the drone.

[0072] The UAV completes the UAV aeromagnetic survey operation according to the received flight path and collects aeromagnetic survey data.

[0073] (5): Obtain the aeromagnetic measurement data sent by the drone and generate an aeromagnetic measurement results map.

[0074] (5.1): Obtain aeromagnetic measurement data and flight path, and perform preprocessing to remove measurement data from takeoff, landing, turning and outside the measurement area boundary.

[0075] The preprocessing includes: obtaining the coordinate range of the turning points in the survey area, magnetic daily variation data, scale, coordinate system, gridding parameters, filter combination, color scale model and other parameters, using a human-computer interactive calculation program to complete the survey line segmentation, and remove the measurement data outside the take-off, landing, turning and survey area boundary. Specifically, the drone track coordinates are matched with the waypoint coordinates, and the data before the first waypoint and after the last waypoint are deleted, and only the aeromagnetic measurement data between the waypoints are retained.

[0076] (5.2): coordinate system conversion, diurnal variation correction, normal field correction, ΔT magnetic anomaly gridding, polarization, vertical first-order derivative calculation and horizontal first-order X and Y direction derivative calculation processing are performed in sequence, and a UAV aeromagnetic survey line distribution map, ΔT magnetic anomaly profile plane map, ΔT magnetic anomaly contour plane map, ΔT polarization magnetic anomaly contour plane map, vertical first-order derivative contour plane map and horizontal first-order X and Y direction derivative contour plane map are generated. In this embodiment, the aeromagnetic measurement data is converted to the same coordinate system as the three-dimensional real scene model.

[0077] (5.3): Receive user’s information on magnetic anomaly identification and fault structure delineation. Specifically, conduct preliminary magnetic anomaly delineation and fault structure inference in the survey area based on magnetic data interpretation principles and reference models.

[0078] (6): Overlay the aeromagnetic survey results with the 3D real-life model for users to view and compare.

[0079] The circled magnetic anomalies and inferred structures are matched with the real-life model objects and topographic features, false anomalies caused by man-made objects and human interference are screened out and marked, and the remaining magnetic anomalies are matched with the topography and landforms, combined with geological data for inferred interpretation to improve the efficiency and accuracy of the inferred interpretation.

[0080] Embodiment 2:

[0081] Embodiment 2 of the present invention provides a metal mine goaf identification system based on drone aeromagnetic measurement, comprising:

[0082] The data acquisition module is configured to: acquire aeromagnetic data of the area to be identified;

[0083] The preprocessing module is configured to: preprocess the acquired aeromagnetic data;

[0084] The grid processing module is configured to: perform grid processing on the pre-processed aeromagnetic data to obtain a magnetic anomaly contour line plane map;

[0085] The target area identification module is configured to: according to the magnetic anomaly contour plane map, use the identification results of high magnetic anomaly and / or gradient zone anomaly as the identification target area of ​​the goaf area;

[0086] The buried depth identification module is configured to obtain the buried depth of the metal mine goaf after performing magnetic profile inversion based on the aeromagnetic data of the identified target area.

[0087] The working method of the system is the same as the method for identifying metal mine goafs based on drone aeromagnetic measurement provided in Example 1, and will not be repeated here.

[0088] Embodiment 3:

[0089] Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, characterized in that when the program is executed by a processor, the steps of the method for identifying metal mine goafs based on drone aeromagnetic measurement as described in Embodiment 1 of the present invention are implemented.

[0090] Embodiment 4:

[0091] Embodiment 4 of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for identifying metal mine goafs based on unmanned aerial vehicle aeromagnetic measurement as described in Embodiment 1 of the present invention are implemented.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying goaf areas in metal mines based on drone aeromagnetic measurement, characterized in that: The process includes: Obtain aeromagnetic data of the area to be identified; Preprocess the acquired aeromagnetic data; The pre-processed aeromagnetic data is gridded to obtain a magnetic anomaly contour map; According to the magnetic anomaly contour plane map, the identification results of high magnetic anomalies and / or gradient zone anomalies are used as the identification target areas of the goaf areas; Based on the aeromagnetic data of the identified target area, the buried depth of the metal mine goaf is obtained after magnetic profile inversion; Specifically: S1: According to the scope of the target survey area, design a UAV aeromagnetic survey route covering the entire survey area and complete the aeromagnetic data collection of the survey area; S2: Use the designed computer software to automatically process the acquired raw data with magnetic diurnal variation correction and normal field correction to generate low-altitude high-precision magnetic field data in the survey area; S2.1: Magnetic diurnal variation correction: Before the start of the UAV aeromagnetic measurement, a magnetic diurnal variation station is set up in the survey area or its surroundings where the magnetic field is calm, the magnetic gradient change is small, there is no human interference, and the terrain is flat and open. A magnetometer with the same accuracy as the aviation magnetometer used in the work is set up, and the magnetic diurnal variation observation is carried out at 3s intervals. 30 minutes after the end of the UAV aeromagnetic measurement, the magnetic diurnal variation observation is ended, and the original observation data of the magnetic diurnal variation magnetometer is exported. After subtracting the magnetic field value of the base point of the magnetic diurnal variation station, the magnetic diurnal variation data of the survey area on that day is obtained. The magnetometer diurnal variation correction software is used, and a correction method combining direct correction and interpolation correction is adopted to subtract the magnetic diurnal variation value from the aeromagnetic measurement data to complete the magnetic diurnal variation correction; S2.2: Normal field correction: According to the longitude and latitude coordinates of the aeromagnetic measurement point, the corresponding international geomagnetic reference field value is calculated using the international geomagnetic reference field model. The international geomagnetic reference field value is subtracted from the drone aeromagnetic measurement data after the magnetic diurnal variation correction to complete the earth's normal field correction; S3: The low-altitude high-precision magnetic field data of the survey area is gridded using the minimum curvature method to obtain the gridded file of the magnetic field ΔT data of the survey area, and then the aeromagnetic ΔT magnetic pole processing is performed to eliminate the influence of oblique magnetization, obtain the aeromagnetic ΔT magnetic pole contour plane result map, and highlight the boundary characteristics of the magnetic geological body; Minimum curvature gridding method: The minimum curvature method is used to interpolate the drone aeromagnetic data to form a gridded data volume file. The minimum curvature gridding method is a grid interpolation method that attempts to generate the smoothest possible surface while respecting the data as strictly as possible; S4: The computer first identifies the high magnetic anomaly traps and gradient zone anomalies in the aeromagnetic ΔT magnetic pole contour plane result map according to the set identification parameters, and then focuses on marking the identified magnetic anomalies as the target area for identifying the magnetic ore body goaf area; S5: According to the identified target area and the direction of the vertical magnetic anomaly, a high-precision aeromagnetic measurement profile of an unmanned aerial vehicle is deployed, passing through the center of the high-value magnetic anomaly or the center of the dense gradient zone. According to the aeromagnetic data processing steps, magnetic diurnal variation correction and normal field correction are performed, and then the magnetic profile inversion is performed to preliminarily determine the buried depth of the magnetic body goaf area, and the verification hole position is obtained based on the inversion results.

2. The method for identifying metal mine goaf based on drone aeromagnetic measurement as claimed in claim 1, characterized in that: Acquisition of aeromagnetic data, including: Obtain oblique photography data and perform three-dimensional reconstruction to obtain a three-dimensional real scene model; Generate a digital elevation model based on the three-dimensional real scene model and display it, and receive location annotations related to flight safety; Obtaining the flight start point, end point and flight altitude of the UAV, and generating an initial route according to the digital elevation model; According to the initial route and the elevation change rate, the waypoints are determined in combination with the marked positions, and the aeromagnetic measurement flight path is determined and sent to the UAV.

3. The method for identifying metal mine goaf based on drone aeromagnetic measurement as claimed in claim 2, characterized in that: The acquisition of aeromagnetic data also includes: Obtain the aeromagnetic measurement data sent by the drone and generate an aeromagnetic measurement results map; The aeromagnetic survey result map is superimposed on the three-dimensional real scene model for users to view and compare.

4. A metal mine goaf identification system based on drone aeromagnetic measurement, used to execute the metal mine goaf identification method based on drone aeromagnetic measurement as claimed in any one of claims 1 to 3, characterized in that: include: The data acquisition module is configured to: acquire aeromagnetic data of the area to be identified; The preprocessing module is configured to: preprocess the acquired aeromagnetic data; The grid processing module is configured to: perform grid processing on the pre-processed aeromagnetic data to obtain a magnetic anomaly contour line plane map; The target area identification module is configured to: according to the magnetic anomaly contour plane map, use the identification results of high magnetic anomaly and / or gradient zone anomaly as the identification target area of ​​the goaf area; The buried depth identification module is configured to obtain the buried depth of the metal mine goaf after performing magnetic profile inversion based on the aeromagnetic data of the identified target area.

5. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for identifying metal mine goafs based on unmanned aerial vehicle aeromagnetic measurement as described in any one of claims 1 to 3 are implemented.

6. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for identifying metal mine goafs based on unmanned aerial vehicle aeromagnetic measurement as described in any one of claims 1 to 3 are implemented.

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