Unmanned aerial vehicle pegmatite spectrum acquisition device and lithium-containing pegmatite identification method and system

Abstract

The invention discloses an unmanned aerial vehicle pegmatite spectrum acquisition device and a lithium-containing pegmatite identification method and system, and relates to the field of data acquisition and processing. Through reasonable design, the hyperspectral sensor is carried on the unmanned aerial vehicle, the hyperspectral data of the pegmatite can be collected in real time through the unmanned aerial vehicle, and a data basis is provided for identification of the lithium-containing pegmatite. The method comprises the following steps: processing acquired unmanned aerial vehicle hyperspectral data, reflectivity spectral data and the like to obtain unmanned aerial vehicle hyperspectral space vector cloud, lithium-containing pegmatite spectral vector cloud and lithium-free pegmatite spectral vector cloud, and taking the spectral vector cloud as input of a constructed network model to obtain an identification result. And the identification real-time performance of the pegmatite can be improved. The image containing the lithium-containing pegmatite information is reserved on the basis of the recognition result by adopting the spectral angle comparison method, the accuracy of lithium-containing pegmatite recognition can be further improved, and then the problem that lithium-containing pegmatite recognition cannot be carried out for the hyperspectrum of the unmanned aerial vehicle in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of data acquisition and processing, and in particular to an unmanned aerial vehicle (UAV) pegmatite spectrum acquisition device, and a lithium-containing pegmatite identification method and system. Background Art

[0002] The infrared spectrum of UAV hyperspectral research is based on the electronic transition of metal cations and the molecular vibration caused by water, hydroxyl ions, carbonate and sulfate (Hunt and Ashley, 1979; Yan Shouxun et al., 2003; Lian Yunchang et al., 2005). In the identification of lithium minerals, infrared spectroscopy can be used to identify ore-bearing pegmatites and non-ore-bearing pegmatites (Wang Shanshan et al., 2023; Ren Guangli et al., 2022). Because the spectral diagnostic characteristics of clay minerals dominate the spectral diagnostic characteristics of lithium minerals, the spectra of clay minerals and lithium minerals are difficult to distinguish (Cardoso-Fernandes et al., 2021). A large number of lithium-bearing mineral spectral measurement results show that lepidolite and muscovite are difficult to distinguish in the spectrum.

[0003] In recent years, satellite remote sensing has gradually been used to study the identification of pegmatite lithium ores or brine lithium ores (Cardoso-Fernandes et al., 2020). Satellite hyperspectral and multispectral data are used to extract lithium mineral information, such as using ZY1E spectrum and GF2 to delineate lithium mineralization anomalies (Gao Yihang et al., 2023), using neural networks to extract pegmatite information (Li Wanyue et al., 2023), and using hyperspectral data to accurately extract lithium minerals (Booysen et al., 2022). ASTER is used to identify remote sensing anomalies of pegmatite lithium ores and delineate target areas (Jin Moushun et al., 2019; Xu Xingwang et al., 2019; Cardoso-Fernandes et al., 2019). However, satellite multispectral and hyperspectral data with low spatial resolution can rarely directly detect small-sized geological bodies (Eskandari et al., 2023). Satellite high-resolution remote sensing provides high spatial resolution. For example, WorldView-3 has a maximum resolution of 0.3 meters and provides 8 visible and near-infrared (VNIR) bands and 8 short-wave infrared (SWIR) bands. It is a powerful tool for identifying pegmatites (Worldview-3 Specifications, 2013; Kruse and Perry, 2012; Jin et al., 2019). It only provides several short-wave infrared bands and cannot accurately identify lithium-bearing pegmatites. In the early stage, the WorldView-3 SWIR short-wave infrared band was used to carry out research in the Tashdaban lithium mine field. It was found that the WorldView-3 SWIR image sampling spectra of pegmatite veins and surrounding rocks were basically the same, and it was impossible to distinguish between pegmatites and surrounding rocks.

[0004] UAV systems bridge the observation gap between airborne surveys and ground surveys. They can carry different sensors to obtain high-resolution spatial, spectral, and temporal data, which helps to achieve rapid and efficient breakthroughs in geological prospecting (Beyer et al., 2019; Heincke et al., 2019; Van der Meer et al., 2012). Spectral imaging of UAV systems has been successfully applied to rare earth exploration (Booysen et al., 2019). Jackisch et al. (2020) conducted a study in Finland based on UAV photography, using traditional methods and machine learning methods for processing, combined with ground surveys. The distinction between ore-bearing rocks and waste rocks in the northern region of the apatite mine was made by Eskandari et al. (2023) using remote sensing and drones for geological mapping and chromite exploration. Martelet et al. (2021) used drone multispectral for geological mapping and mineralization identification. However, there are few studies on pegmatite lithium mines conducted by drones. Among them, Bai et al. (2024) used an airborne Hyspex imaging spectrometer to directly map the Jingerquan pegmatite region in Xinjiang. However, drone remote sensing was greatly affected by weather conditions and could not identify lithium-bearing pegmatites based on drone hyperspectral. Summary of the invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present application provides a UAV pegmatite spectrum acquisition device, a lithium-containing pegmatite identification method and system.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a UAV pegmatite spectrum acquisition device, the device comprising: a UAV and a hyperspectral sensor;

[0008] A housing is provided on the body of the drone; the housing is used to install the hyperspectral sensor; a shock absorbing structure or a correction structure is provided at the connection between the hyperspectral sensor and the drone.

[0009] Optionally, the device further comprises a landing gear; the landing gear is mounted on the UAV; when the UAV lands, the landing gear is used to form a gap between the hyperspectral sensor and the ground.

[0010] Optionally, a battery shielding film is provided in the accommodating cavity.

[0011] Optionally, the UAV is a vertical take-off fixed-wing UAV; and the hyperspectral sensor is a full-spectrum imager in the 400nm-2500nm band.

[0012] In a second aspect, the present application provides a method for identifying lithium-bearing pegmatites, the method comprising:

[0013] Acquire reflectivity spectrum data, UAV hyperspectral data, target spectrum, dark current data, attitude data and ground station data; the UAV hyperspectral data is a spectrum image acquired by the UAV pegmatite spectrum acquisition device provided by the present application; the dark current data is the current data acquired before the UAV takes off and after landing; the reflectivity spectrum data is the emissivity data of lithium-bearing pegmatite and the emissivity data of lithium-free pegmatite measured by an infrared spectrometer; the target spectrum is obtained based on targets with different reflectivities; the targets with different reflectivities are set within a set range centered on the take-off position of the UAV; the attitude data includes one or more of the yaw angle, pitch angle and roll angle of the hyperspectral sensor;

[0014] Fitting the UAV hyperspectral data based on the reflectivity spectral data to obtain a fitted infrared spectrum;

[0015] Calibrate key parameters based on the fitted infrared spectrum, and construct a spectrum vector cloud of lithium-bearing pegmatite and a spectrum vector cloud of lithium-free pegmatite based on the key parameters and the reflectivity spectrum data;

[0016] The target spectrum and the dark current data are used to correct the UAV hyperspectral data, and the ground station data and the attitude data are matched to obtain the corrected UAV hyperspectral data;

[0017] Based on the corrected UAV hyperspectral data, constructing a UAV hyperspectral spatial vector cloud according to the key parameters;

[0018] Inputting the drone hyperspectral spatial vector cloud, the lithium-bearing pegmatite spectral vector cloud, and the lithium-free pegmatite spectral vector cloud into the constructed network model, obtaining a recognition result; the recognition result is one or more of an image containing lithium-bearing pegmatite information, an image containing lithium-free pegmatite information, and an image containing both lithium-bearing pegmatite information and lithium-free pegmatite information;

[0019] retaining images containing information of lithium-bearing pegmatite based on the identification results by using a spectral angle comparison method;

[0020] Processing the image containing lithium-bearing pegmatite information by using filtering technology to obtain a binary image, and vectorizing the binary image to form a vector file of lithium-bearing pegmatite distribution;

[0021] A color rendering containing lithium-bearing pegmatite information is generated based on the vector file of the lithium-bearing pegmatite distribution and the drone hyperspectral data.

[0022] Optionally, calibrating key parameters based on the fitted infrared spectrum, and constructing a spectrum vector cloud of lithium-bearing pegmatite and a spectrum vector cloud of lithium-free pegmatite based on the key parameters and the reflectance spectrum data, specifically includes:

[0023] Calibrate the characteristics of lithium-bearing pegmatite and the characteristics of lithium-free pegmatite based on the fitted infrared spectrum, and use the characteristics of lithium-bearing pegmatite and the characteristics of lithium-free pegmatite as key parameters;

[0024] Constructing the spectral vector cloud of the lithium-bearing pegmatite based on the characteristics of the lithium-bearing pegmatite and the reflectivity spectral data;

[0025] The spectral vector cloud of the lithium-free pegmatite is constructed based on the characteristics of the lithium-free pegmatite and the reflectance spectrum data.

[0026] Optionally, the target spectrum and the dark current data are used to correct the UAV hyperspectral data, and the ground station data and the attitude data are matched to obtain the corrected UAV hyperspectral data, which specifically includes:

[0027] According to the acquisition time, the UAV hyperspectral data is divided into the first part of UAV hyperspectral data and the second part of UAV hyperspectral data;

[0028] The first part of the UAV hyperspectral data is radiometrically corrected using the current data collected before the UAV takes off, and the second part of the UAV hyperspectral data is radiometrically corrected using the current data collected after the UAV lands, to obtain the radiometrically corrected UAV hyperspectral data;

[0029] The target spectrum is used as a standard spectrum, and the spectrum data corresponding to the target is obtained from the UAV hyperspectral data after radiation correction, and the obtained spectrum data corresponding to the target is used as white balance. The spectrum data in the UAV hyperspectral data other than the obtained spectrum data corresponding to the target is subjected to reflection correction by using the white balance to obtain the UAV hyperspectral data after reflection correction;

[0030] The DEM model and satellite attitude are used to perform orthorectification on the reflectance-corrected UAV hyperspectral data, and in this correction process, the ground station data and the attitude data are matched to obtain the corrected UAV hyperspectral data.

[0031] Optionally, the image containing the lithium-bearing pegmatite information is retained based on the recognition result by using a spectral angle comparison method, specifically comprising:

[0032] When the recognition result is an image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information, a spectral angle calculation is performed on the spectrum in the drone hyperspectral data and the spectrum in the emissivity data of the lithium-bearing pegmatite to obtain a first spectral angle result;

[0033] The spectrum angle of the spectrum in the UAV hyperspectral data and the spectrum in the emissivity data of the lithium-free pegmatite are calculated to obtain the second spectrum angle result;

[0034] When the first spectral angle result is less than the second spectral angle result, retaining the image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information;

[0035] When the first spectral angle result is greater than or equal to the second spectral angle result, the image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information is not retained.

[0036] Optionally, generating a color rendering containing information about lithium-bearing pegmatites based on the vector file of the lithium-bearing pegmatite distribution and the drone hyperspectral data specifically includes:

[0037] The vector file of the lithium-bearing pegmatite distribution and the drone hyperspectral data are superimposed using coordinate layering to generate a color rendering containing lithium-bearing pegmatite information.

[0038] In a third aspect, the present application provides a lithium-bearing pegmatite identification system, the system comprising:

[0039] The above-mentioned drone pegmatite spectrum acquisition device is used to collect drone hyperspectral data, target spectrum and dark current data; the dark current data is the current data collected before the drone takes off and after landing; the target spectrum is obtained based on targets with different reflectivities; the targets with different reflectivities are set within a set range centered on the drone take-off position;

[0040] A UAV control system is mounted on the UAV and is used to control the flight of the UAV based on flight instructions; the flight instructions include one or more of route, speed, distance and altitude;

[0041] A hyperspectral sensor control system, carried on the UAV, is used to control the working data of the hyperspectral sensor in the UAV pegmatite spectrum acquisition device; the working data of the hyperspectral sensor includes one or more of acquisition time and spectral band;

[0042] A ground-based infrared spectrometer for collecting reflectance spectrum data; the reflectance spectrum data includes the emissivity data of lithium-bearing pegmatite and the emissivity data of lithium-free pegmatite;

[0043] A ground station, used to obtain ground station data and attitude data; the attitude data includes one or more of a yaw angle, a pitch angle, and a roll angle of the hyperspectral sensor;

[0044] The service system interacts with the UAV pegmatite spectrum acquisition device, the UAV control system, the hyperspectral sensor control system, the ground infrared spectrometer and the ground station for implementing the lithium-containing pegmatite identification method provided above.

[0045] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0046] The present application provides a drone pegmatite spectrum acquisition device, a lithium-containing pegmatite identification method and system. Through reasonable design, a hyperspectral sensor is mounted on a drone, and the hyperspectral data of pegmatites can be collected in real time through the drone, providing a data basis for the identification of lithium-containing pegmatites. By processing the acquired drone hyperspectral data and reflectivity spectrum data, etc., the drone hyperspectral space vector cloud, the lithium-containing pegmatite spectrum vector cloud and the lithium-free pegmatite spectrum vector cloud are obtained. These spectrum vector clouds are used as the input of the constructed network model to obtain the identification results, which can improve the real-time performance of pegmatite identification. By using the spectral angle comparison method based on the identification results, the image containing the lithium-containing pegmatite information is retained, which can further improve the accuracy of lithium-containing pegmatite identification, thereby solving the problem that the existing technology cannot carry out lithium-containing pegmatite identification for drone hyperspectral.

[0047] In addition, the present application provides a shock-absorbing structure or a correction structure at the connection between the hyperspectral sensor and the drone, which can effectively prevent the hyperspectral sensor from being damaged when the drone lands, thereby increasing the service life of the hyperspectral sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A schematic diagram of the structure of a UAV pegmatite spectrum acquisition device provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the connection structure between the hyperspectral sensor and the drone provided in the embodiment of the present application;

[0051] Figure 3 A flow chart of a lithium-containing pegmatite identification method provided in an embodiment of the present application;

[0052] Figure 4 A schematic diagram of the reflectivity of lithium-containing pegmatite provided in an embodiment of the present application;

[0053] Figure 5 A schematic diagram of the reflectivity of lithium-free pegmatite provided in an embodiment of the present application;

[0054] Figure 6 A schematic diagram of constructing an infrared spectrum vector cloud provided in an embodiment of the present application;

[0055] Figure 7 Schematic diagram of drone hyperspectral data provided by the embodiment of the present application;

[0056] Figure 8 A schematic diagram of a hyperspectral spatial vector cloud of a drone provided in an embodiment of the present application;

[0057] Fig. 9 A schematic diagram of the configuration of an input device provided in an embodiment of the present application;

[0058] Fig.10 A schematic diagram of the ground infrared spectrum test results of the wood-cut lithium-bearing pegmatite provided in the embodiment of the present application;

[0059] Fig.11 A schematic diagram of the ground infrared spectrum test results of wooden cut lithium-free pegmatite provided in the embodiment of the present application;

[0060] Fig.12 A schematic diagram of the structure of a typical convolutional neural network provided in an embodiment of the present application;

[0061] Fig.13 A schematic diagram of the identification result screening provided in the embodiment of the present application;

[0062] Fig.14 Field landscape map of lithium-bearing pegmatite provided in the embodiment of the present application;

[0063] Fig.15 Based on the embodiments of the present application Fig.14 The obtained UAV hyperspectral lithium-bearing pegmatite identification result map;

[0064] Fig.16 Based on the embodiments of the present application Fig.14 Color rendering generated with information on lithium-bearing pegmatites;

[0065] Fig.17 A schematic diagram of the altitude value provided in the embodiment of the present application;

[0066] Fig.18 A technical flow chart for identifying lithium-containing pegmatites using a lithium-containing pegmatite identification system provided in an embodiment of the present application.

[0067] Description of reference numerals:

[0068] 1 UAV, 1-1 left wing, 1-2 right wing, 1-3 left rotor, 1-4 right rotor, 1-5 tail wing, 2 hyperspectral sensor, 3 landing gear, 4 shock absorbing structure or correction structure. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] The purpose of this application is to provide a UAV pegmatite spectrum acquisition device, a lithium-containing pegmatite identification method and system, aiming to solve the problem that the existing technology cannot carry out lithium-containing pegmatite identification based on UAV hyperspectral.

[0071] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0072] This embodiment provides a UAV pegmatite spectrum acquisition device, such as Figure 1 As shown, the device includes: a drone 1 and a hyperspectral sensor 2.

[0073] The cabin of the drone 1 is provided with a receiving cavity. For example, the cabin of the drone 1 is cut and modified according to the size of the hyperspectral sensor 2 to obtain the receiving cavity. The receiving cavity is used to install the hyperspectral sensor. Figure 2 As shown, a shock absorbing structure or a correction structure 4 is provided at the connection between the hyperspectral sensor 2 and the drone 1. For example, a spherical shock absorber or a gimbal can be provided for shock absorption or correction.

[0074] In actual application, the UAV can be a vertical fixed-wing UAV, such as Figure 1 As shown, it includes: a left wing 1-1, a right wing 1-2, a left rotor 1-3, a right rotor 1-4, a tail 1-5 and a cabin. The hyperspectral sensor can be a full-spectrum imager with a wavelength band of 400nm-2500nm.

[0075] The UAV pegmatite spectrum acquisition device provided above in this application can solve the problem that current rotorcraft UAVs are unable to carry out operations over a large area due to limitations on flight time and speed, and is suitable for large-area scanning of lithium-containing pegmatites in exposed areas.

[0076] In an exemplary embodiment, in order to avoid damage caused by the grounding of the hyperspectral sensor during landing, the present application may also provide a landing gear 3. The landing gear 3 is installed on the drone 1. For example, the landing gear 3 is welded at the head and tail of the drone 1 to ensure the balance of landing. When the drone 1 lands, the landing gear 3 can ensure that the hyperspectral sensor 2 forms a gap with the ground, thereby increasing the service life of the hyperspectral sensor 2.

[0077] In an exemplary embodiment, electromagnetic shielding is required to avoid the electromagnetic influence of the drone on the hyperspectral sensor 2. Based on this, the embodiment uses an electromagnetic shielding film as an electromagnetic shielding material, and reasonably arranges the accommodating cavity where the hyperspectral sensor 2 is located to shield the electromagnetic influence. The naked hyperspectral sensor is used to test the ground object under the same conditions and the same ground object is tested by installing it in a drone, and it is found that the test results of the shielding effect are completely consistent.

[0078] In an exemplary embodiment, in order to avoid the problem of inconsistent weight of the front and back or left and right of the drone due to the installation of the hyperspectral sensor, it is necessary to consider the weight matching of the front and back and left and right when installing the hyperspectral sensor. After the hyperspectral sensor is installed, the weight consistency needs to be verified. The verification method used in this embodiment is to verify whether the center of gravity remains unchanged before and after the hyperspectral sensor is installed to ensure the safety of the drone flight.

[0079] In an exemplary embodiment, a method for identifying lithium-bearing pegmatites is provided, such as Figure 3 As shown, the method includes:

[0080] Step 100: Acquire reflectance spectrum data, UAV hyperspectral data, target spectrum, dark current data, attitude data, and ground station data.

[0081] In actual application, the UAV hyperspectral data is a spectral image collected by the UAV pegmatite spectrum acquisition device provided by the present application. For example, according to the scope of the survey area, the route can be planned in two ways: north-south and east-west. A whiteboard and targets with different reflectivity (for example, reflectivity of 11%, 33%, and 66%) are arranged near the take-off point as targets for hyperspectral sensor calibration. At the same time, the relationship between exposure time, frame period, altitude, speed, frame period, etc. are configured. The frame period is generally about 80% of the target reflection DN value (Digital Number, i.e., the brightness value of the pixel of the remote sensing image). The speed is matched with the exposure time, frame period, and altitude so that the speed of the hyperspectral sensor is consistent with the speed of the UAV. Set the excitation area and altitude, and gradually obtain the UAV hyperspectral data by flying along the route, and collect dark current data before take-off and after landing.

[0082] The reflectivity spectral data is the emissivity data of lithium-bearing pegmatite and the emissivity data of non-lithium-bearing pegmatite measured by an infrared spectrometer. For example, the infrared spectrometer is configured as a portable spectrometer covering a band range of 400-2500nm, and a white plate with a reflectivity of 1 is used as a calibration standard. The infrared spectrometer is used for measurement. Before measurement, the electrical signal is converted into reflectivity through white plate calibration to obtain a reflectivity spectral curve. The target spectrum is obtained based on targets with different reflectivities. Targets with different reflectivities are set within a set range centered on the take-off position of the drone. The attitude data includes one or more of the yaw angle, pitch angle, and roll angle of the hyperspectral sensor.

[0083] When configuring the ground station and inertial navigation system, the ground station is configured to use RTK with post-differential processing (PPS) as the ground station, and the ground station sampling time interval is configured to be consistent with the timestamp.

[0084] The acquisition of ground station data is configured as fixed point mode. The time interval of the collection points is consistent with the timestamp of the UAV hyperspectral data collection. The points are collected to obtain fixed point data, that is, ground station data.

[0085] Step 101: Fitting the drone hyperspectral data based on the reflectance spectrum data to obtain a fitted infrared spectrum.

[0086] In actual application, an infrared spectrometer can be used to test lithium-bearing pegmatite and lithium-free pegmatite to obtain their reflectance data, and the drone hyperspectral data can be fitted according to the reflectance data to obtain the fitted infrared spectrum.

[0087] Step 102: Calibrate key parameters based on the fitted infrared spectrum, and construct a spectrum vector cloud of lithium-bearing pegmatite and a spectrum vector cloud of lithium-free pegmatite based on the key parameters and reflectance spectrum data.

[0088] In the actual application process, after obtaining the fitted infrared spectrum, the characteristics of lithium-bearing pegmatite and lithium-free pegmatite are calibrated as the key parameters for constructing the infrared spectrum vector cloud. The key parameters obtained are mainly the two characteristics of emissivity and absorption peak, such as high reflectivity at 400nm and an obvious absorption peak at 1000nm. Figure 4 and Figure 5 shown.

[0089] For example, based on the measured lithium-bearing pegmatite and lithium-free pegmatite, the main parameters of the lithium-bearing pegmatite are determined as the POS position: n1, n2, n3, n4,..., and the main parameters of the lithium-free pegmatite are determined as the POS position: m1, m2, m3, m4,...

[0090] According to the parameters of lithium-bearing pegmatite and lithium-free pegmatite, infrared spectrum vector cloud is constructed. The vector in the infrared spectrum vector cloud includes vector length angle and starting position. Figure 6 As shown, the coordinate a j (x j ,y j ) and a m (x m ,y m ) where the x coordinate is the POS position, the y coordinate is the reflectivity (%), j is the vector angle, l j is the vector length.

[0091]

[0092] In the formula, j is the sequence number of the constructed vector, is the vector from j to m, l io is the complex form of the vector, i is the imaginary unit of the complex number, o is the direction angle of the vector, and l is the amplitude.

[0093] Step 103: Use the target spectrum and dark current data to calibrate the UAV hyperspectral data, and match the ground station data and attitude data to obtain the calibrated UAV hyperspectral data.

[0094] In actual application, the implementation process of this step can be:

[0095] (1) According to the acquisition time, the UAV hyperspectral data is divided into the first part of UAV hyperspectral data and the second part of UAV hyperspectral data.

[0096] (2) Since the UAV data collection time is relatively long, generally speaking, the first half of the data collected is corrected by dark current before takeoff, and the second half of the data collected is corrected by dark current after landing. Based on this, the current data collected before the UAV takes off is used to perform radiation correction on the first part of the UAV hyperspectral data, and the current data collected after the UAV lands is used to perform radiation correction on the second part of the UAV hyperspectral data, thus obtaining the radiation-corrected UAV hyperspectral data.

[0097] Among them, DN 0～l / 2 =DN o1 -DN BC1 .

[0098] DN l / 2～l =DN o2 -DNB C2 .

[0099] Where, DN 0-l / 2 is the DN value after radiation correction for the time period from 0 to 1 / 2, DN o1is the original DN value of the time period from 0 to 1 / 2, DN BC1 The dark current value collected during the time period from 0 to 1 / 2, DN l / 2-l is the DN value after radiation correction for the period from l / 2 to l, DN o2 is the original DN value for the time period from 1 / 2 to 1, DN BC2 is the dark current value collected in the time period from l / 2 to l, and l is the data acquisition time.

[0100] (3) The target spectrum is used as the standard spectrum, and the spectral data corresponding to the target is obtained from the UAV hyperspectral data after radiation correction. The obtained spectral data corresponding to the target is used as white balance. The spectral data in the UAV hyperspectral data except the obtained spectral data corresponding to the target is reflectance corrected by using white balance to obtain the UAV hyperspectral data after reflection correction.

[0101] For example, in the drone hyperspectral data after radiation correction, the target modes (11%, 33%, 66%, 99.99%) are circled as white balance, and other data are corrected according to the white balance.

[0102]

[0103] In the formula, ρ target is the reflectivity of the ground, DN target is the DN value after radiation correction, DN panel is the target DN value after radiation correction, ρ panel is the reflectivity of the target.

[0104] (4) The DEM model and satellite attitude are used to orthorectify the UAV hyperspectral data after reflection correction. During this correction process, the ground station data and attitude data are matched to obtain the corrected UAV hyperspectral data.

[0105] For example, the original pose of the hyperspectral sensor is (Y o ,P o ,R o ), this posture represents the initial yaw, pitch and roll angles of the hyperspectral sensor, and the posture of each point (timestamp) is Y q ,P q ,R q , representing the sensor instrument yaw, pitch and roll angles at timestamp q, are first corrected by the instrument raw attitude.

[0106] Y c =Y q -Y o , P c =P q -P O ,R c =Rq -R o are the yaw, pitch and roll angles at timestamp i after correction, respectively.

[0107] The coordinates of each timestamp q after correction are:

[0108]

[0109] In the formula, m represents the mapping coordinate system, u represents the image coordinate system, and x c ,y c ,z c is the coordinate of the projection center of timestamp q in the cartographic coordinate system, x q ,y q ,z q is the coordinate of the ground point in the mapping coordinate system at timestamp q, x, y, -g is the spatial coordinate of the pixel point at timestamp q in the image coordinate system, k is the scale factor k = h / g, h is the flight altitude, T is the transformation matrix, which is expressed by the following formula:

[0110]

[0111] Where W is the WGS84 coordinate system, g is the local horizontal coordinate system, b is the IMU coordinate system, c is the imaging spectrometer coordinate system, and j is the image coordinate system. Y is the transformation matrix from the IMU coordinate system to the local horizontal coordinate system. c ,P c ,R c The yaw, pitch and roll angles at timestamp q after correction, is the transformation matrix from the local horizontal coordinate system to the WGS84 coordinate system. is the transformation matrix from WGS84 coordinate system to cartographic coordinate system, is the transformation matrix from the image coordinate system to the imaging spectrometer coordinate system, is the transformation matrix from the imaging spectrometer coordinate system to the IMU coordinate system.

[0112] Step 104: Based on the corrected UAV hyperspectral data, construct a UAV hyperspectral spatial vector cloud according to key parameters.

[0113] Since the UAV hyperspectral data obtained by the hyperspectral sensor has certain noise, it often needs to be smoothed, but it takes a lot of machine time. Therefore, by building a hyperspectral vector cloud, it can directly participate in the calculation, which greatly saves time. The construction of the UAV hyperspectral vector cloud needs to be consistent with the construction of the infrared spectrum vector cloud to ensure mutual consistency. Input the corrected UAV hyperspectral data, and build the UAV hyperspectral spatial vector cloud based on the key parameters for building the infrared spectrum vector cloud. Among them, the UAV hyperspectral data is as follows: Figure 7As shown in the figure, the constructed UAV hyperspectral spatial vector cloud is as follows Figure 8 shown.

[0114] For example, based on the infrared spectrum calibration parameters and the infrared spectrum vector, the formula is constructed Value band a j ,a m As the parameters for constructing the UAV hyperspectral vector cloud, any point on the image (i.e., vectorized data) can be expressed as:

[0115]

[0116] Among them, j, m are the POS positions of band j and band m, k and l are the number of rows and columns of the drone image matrix, i is the imaginary unit of the complex number, 1...k, 1...t are the rows from 1 to k and the columns from 1 to t of the matrix. In this way, each point of the image can form a new multidimensional data set. Each point in the multidimensional data layer has an amplitude and a phase, which can be compared by the two parameters of amplitude and phase to form an amplitude and phase vector space data cloud.

[0117] To express this in a simple example, for example, the spectral data of lithium-bearing pegmatite is:

[0118] Wavelength nm Reflectivity %

[0119]

[0120] V 400,2100 ={0.2 0.8}=1.8 i13.25 .

[0121] At the same time, the band of point a in the UAV hyperspectral image is expressed as:

[0122] Wavelength nm Reflectivity %

[0123]

[0124] The vector cloud constructed based on the spectral parameters of lithium-bearing pegmatite is:

[0125] a 400,2100 ={0.5 0.7]=1.7 i6.71 .

[0126] Then point a is inconsistent with the spectrum of lithium-bearing pegmatite.

[0127] Step 105: Input the drone hyperspectral spatial vector cloud, the lithium-bearing pegmatite spectral vector cloud, and the lithium-free pegmatite spectral vector cloud into the constructed network model to obtain a recognition result. The recognition result is one or more of an image containing lithium-bearing pegmatite information, an image containing lithium-free pegmatite information, and an image containing both lithium-bearing pegmatite information and lithium-free pegmatite information.

[0128] For example, the data such as the drone hyperspectral space vector cloud, the lithium-bearing pegmatite spectral vector cloud, and the lithium-free pegmatite spectral vector cloud are configured as follows: Fig. 9 Input device shown.

[0129] For example, for data with obvious spectral features, the spectral analysis software uses TSG (spectral geologist) to analyze altered minerals. Tests show that although the spectra of lepidolite and muscovite are not much different, the spectra of spodumene and lepidolite are significantly different from the spectra of quartz. The similarities between the spectra of ore-bearing pegmatite and non-ore-bearing pegmatite are: both have spectral absorption at 2200nm and low reflectivity between 2400 and 2500nm, as well as high reflectivity at 350 to 750nm (the reason why the white color is visible to the naked eye). The difference is that the absorption characteristics of ore-bearing pegmatite at 2200nm are obvious ( Fig.10 ), and also has two small absorption valleys at 2360nm and 2450nm ( Fig.11 ). The spectrometer actually has a spectral resolution of less than 10 nm and is not affected by the atmosphere. The spatial resolution can reach 2 cm, and the electrical signal is stable. The comparison of its spectral characteristics is shown in Table 1. The obvious difference between the spectra of spodumene, lepidolite and quartz in the short-wave infrared range indicates that the hyperspectral method can be used to distinguish between lithium-bearing pegmatites and non-lithium-bearing pegmatites. Therefore, the drone hyperspectral data can be used for lithium pegmatite identification research.

[0130] Table 1 Comparison of spectral characteristics of Tamuqi lithium-bearing pegmatite and non-lithium-bearing pegmatite

[0131]

[0132] Furthermore, in 1986, Rumelhart et al. proposed an artificial neural network back propagation algorithm, which is superior to shallow machine learning models such as support vector machines, BOOSTING, and LOGISTIC regression methods based on statistical learning theory. In 2006, Hinton et al. proposed that multi-hidden layer deep learning artificial neural networks have excellent feature learning capabilities, can overcome training difficulties through layer-by-layer unsupervised training, and use deep structured nonlinear operators to obtain higher-level features and improve recognition rate and accuracy. Based on this, the network model used in this application is a typical convolutional neural network, such as Fig.12 As shown in the figure, it mainly includes input layer, convolution layer, sampling layer, connection layer and output layer.

[0133] For example, convolution filtering is performed on each channel of the input image, and the features of the image training area are taken to obtain feature maps with the same number of convolution kernels. After these features pass through the resampling layer, feature maps with small size but large number are obtained. The feature maps are expanded in a certain way, spliced ​​into the one-dimensional vector connection layer, and output through several connection layers to achieve the recognition task.

[0134] The convolutional layer can be expressed using the following formula: the j-th feature matrix It can be obtained by weighting the convolution of several feature maps in the previous layer:

[0135]

[0136] Where f is the neuron activation function, N j Represents the combination of input feature maps, represents the convolution operation, Convolution kernel matrix, is the bias matrix, Represents the feature matrix of the previous layer.

[0137] Commonly used neuron activation functions include Sigmoid function, Tanh function, ReLU function, etc.

[0138] The sampling layer is also called the "pooling layer". It performs pooling sampling based on the principle of local correlation, reduces the amount of data and retains useful information. It includes:

[0139] Represents the feature matrix of the previous layer.

[0140] In the formula, d represents the sampling function, which includes maximum value sampling, mean value sampling, etc.

[0141] After the convolution layer and the sampling layer, it is connected to a connection layer. Each neuron is connected to each neuron in the next layer. The feature vector of the connection layer in the first layer can be expressed as x l :

[0142] x l =f(w l x l-1 +b l ).

[0143] In the formula, w l is the weight matrix, b l is the bias vector, x l-1 is the feature vector of the l-1th connection layer.

[0144] When the final output layer of the model is a logistic regression layer, each node of the convolutional neural network output represents the probability P(Y=t|x,w,b) of a certain category t of the input data, which is:

[0145]

[0146] In the formula, w is the weight parameter of the last layer, b is the corresponding bias parameter, x is the input data, and w t is the weight parameter of the last layer of category t, s t () is the probability of category t, w j is the weight parameter of the last layer for category j.

[0147] Step 106: Using the spectral angle comparison method based on the recognition result, retain the image containing the lithium-bearing pegmatite information.

[0148] In actual application, the implementation process of step 106 may be:

[0149] (1) When the recognition result is an image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information, the spectral angle of the spectrum in the UAV hyperspectral data and the spectrum in the emissivity data of the lithium-bearing pegmatite is calculated to obtain a first spectral angle result.

[0150] (2) Calculate the spectral angle of the spectrum in the UAV hyperspectral data and the spectrum in the emissivity data of lithium-free pegmatite to obtain the second spectral angle result.

[0151] (3) When the first spectral angle result is smaller than the second spectral angle result, the image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information is retained.

[0152] (4) When the first spectral angle result is greater than or equal to the second spectral angle result, the image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information is not retained.

[0153] For example, the output results of the above-mentioned data processing of lithium-bearing pegmatite and lithium-free pegmatite can be used as the input layer of the judgment device. If it is lithium-bearing pegmatite information, it is retained; if it is lithium-free pegmatite information, it is deleted; if it contains both lithium-bearing pegmatite information and lithium-free pegmatite information, the spectrum of the drone hyperspectral image here is compared with the spectra of lithium-bearing pegmatite and lithium-free pegmatite obtained by ground infrared spectrum, such as Fig.13 As shown, the spectral angle size is judged. If the spectral angle of lithium-bearing pegmatite is smaller than that of lithium-free pegmatite, it is retained, otherwise it is deleted.

[0154] The spectral angle method characterizes each multidimensional space point with its spatial vector and compares the similarity of the spatial vector angles. It is a supervised classification. It requires a known reference spectrum for each category. This reference spectrum can be measured on the ground and stored in the reference spectrum library, or it can be counted from the area of ​​interest of the map unit with known conditions and stored in the reference spectrum library. The formula is as follows:

[0155]

[0156] In the formula, (α, β) is the inner product of n-dimensional vectors α and β. According to the definition of inner product:

[0157] (α,β)=α1β1+α2β2+…+α n β n .

[0158] When α, β are column vectors, (α, β) = α'β = β'α, we have:

[0159]

[0160] Where |α|, |β| are the lengths of vectors α and β,

[0161] By finding the inner product and length of α and β, we can find cosine, and the angle can be found by looking up the table.

[0162] Step 107: Using filtering technology to process the image containing the lithium-bearing pegmatite information to obtain a binary image, and vectorizing the binary image to form a vector file of the distribution of the lithium-bearing pegmatite.

[0163] In practical applications, median filtering can be used to process the two-dimensional image of lithium-bearing pegmatite, such as outputting a two-dimensional matrix X ij , the median filter is:

[0164]

[0165] In the formula, F ij is the median filtering result, A is the filtering window, and the window can be selected as 3×3 or 5×5, etc., until the filtering effect is satisfactory.

[0166] After filtering, a two-dimensional {0, 1} binary image is obtained. Vectorization of the two-dimensional binary image can form a vector file of the distribution of lithium-bearing pegmatite.

[0167] Step 108: Generate a color rendering containing information of lithium-bearing pegmatites based on the vector file of lithium-bearing pegmatite distribution and the drone hyperspectral data.

[0168] In the actual application process, the optimized lithium-bearing pegmatite vector and the R (red): 669.43nm, G (green): 538.96nm, B (blue): 479.25nm in the drone hyperspectral band are input as RGB (red, green and blue) color composite image input items to synthesize the color image. The vector is represented by points, lines and surfaces with the same projection. The grid and vector are superimposed using coordinate layering.

[0169] Among them, the vector f(x,y,z), x,y are the corresponding coordinates, z is the eigenvalue, f(x,y,z) is the vector value, the grid g(x′,y′), x′ and y′ are the corresponding coordinates, g(x′,y′) is the grid gray value

[0170] Let x = x′, y = y′, so as to realize the superposition of the gray value of the grid g(x′, y′) and the vector g(x′, y′).

[0171] The raster and vector are overlaid using the corresponding coordinates and output as GIS vector and raster overlay images, thus forming an image suitable for the human eye (i.e., a color rendering).

[0172] That is, input the drone hyperspectral base map and lithium-bearing pegmatite vectors, and output an overlay image (GIS) suitable for human eye observation, which can be output as a final image in JPG or TIF format through software. Fig.14 Taking the lithium-bearing pegmatite field landscape shown in the figure as an example, the identification results of the lithium-bearing pegmatite obtained by the drone hyperspectral are as follows Fig.15 As shown in the figure, the final image suitable for human eye habits is as follows Fig.16 shown.

[0173] In an exemplary embodiment, before obtaining the drone hyperspectral data, it is necessary to match the timestamp and flight attitude. For example, each frame exposure has a timestamp, and each timestamp must record the attitude of the hyperspectral sensor. Generally speaking, the attitude of the hyperspectral sensor includes one or more data such as point position (GPS), X-direction speed, Y-direction speed, Z-direction speed, altitude, yaw, roll, and pitch.

[0174] In an exemplary embodiment, a lithium-containing pegmatite identification system is provided, which includes: the above-mentioned drone pegmatite spectrum acquisition device, drone control system, hyperspectral sensor control system, ground infrared spectrometer, ground station and service system.

[0175] The above-mentioned drone pegmatite spectrum acquisition device is used to collect drone hyperspectral data, target spectrum and dark current data. The dark current data is the current data collected before the drone takes off and after landing. The target spectrum is obtained based on targets with different reflectivities. Targets with different reflectivities are set within a set range centered on the drone take-off position.

[0176] The UAV control system is mounted on the UAV and is used to control the flight of the UAV based on flight instructions. Flight instructions include one or more of the following: route, speed, distance, and altitude.

[0177] The hyperspectral sensor control system is mounted on the UAV and is used to control the working data of the hyperspectral sensor in the UAV pegmatite spectrum collection device. The working data of the hyperspectral sensor includes one or more of the collection time and spectral band.

[0178] The ground-based infrared spectrometer is used to collect reflectance spectrum data, which includes the emissivity data of lithium-bearing pegmatite and the emissivity data of lithium-free pegmatite.

[0179] The ground station is used to obtain ground station data and attitude data. The attitude data includes one or more of the yaw angle, pitch angle and roll angle of the hyperspectral sensor.

[0180] The service system exchanges data with the UAV pegmatite spectrum acquisition device, the UAV control system, the hyperspectral sensor control system, the ground infrared spectrometer and the ground station, and is mainly used to implement the lithium-bearing pegmatite identification method provided above.

[0181] In an exemplary embodiment, in order to ensure the mobility of the platform, the UAV control system for controlling the UAV and the hyperspectral sensor control system for controlling the hyperspectral sensor are two different systems to match the relationship between the flight altitude, flight distance and flight speed. Fig.17 The altitude shown is the difference between the aircraft altitude in the survey area and the average altitude in the survey area. The speed measured by the hyperspectral sensor system must be consistent with the speed of the drone. The speed is generally the airspeed. The flight distance is related to the viewing angle of the sensor. The relationship between them is shown in the following formula:

[0182] Flight distance ll = htan(a / 2).

[0183] Flight altitude h, viewing angle a, flight distance ll.

[0184] In summary, the overall process of the lithium-containing pegmatite identification method implemented by the lithium-containing pegmatite identification system in this application is as follows: Fig.18 As shown, it calibrates the characteristic spectrum for identifying lithium-bearing pegmatites by distinguishing the infrared spectra of lithium-bearing pegmatites from those without lithium-bearing pegmatites. As an input item, it uses a vector cloud space neural network to identify lithium-bearing pegmatites in the UAV hyperspectral spectrum, forming a system for identifying lithium-bearing pegmatites. This system mainly makes up for the fact that the existing satellite hyperspectral remote sensing with a resolution of 30 meters cannot identify pegmatite veins at the meter and centimeter level. Although the satellite high-resolution remote sensing has a panchromatic band with a resolution of 0.3 meters, it cannot distinguish lithium-bearing pegmatite veins without a high-resolution spectrum. At the same time, it solves the problem that the current rotor UAVs are limited by the flight time and speed and cannot carry out operations on a large scale. The device and system are very suitable for large-scale scanning and identification of lithium-bearing pegmatites in exposed areas.

[0185] In an exemplary embodiment, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the lithium-bearing pegmatite identification method provided above.

[0186] In an exemplary embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned lithium-bearing pegmatite identification method is implemented.

[0187] In an exemplary embodiment, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned lithium-bearing pegmatite identification method.

[0188] In an exemplary embodiment, the present application provides a computer device, which may be a database. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the lithium-bearing pegmatite identification method provided above can be implemented.

[0189] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0190] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0191] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0192] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The same and similar parts between the embodiments can be referenced to each other; at the same time, for those of ordinary skill in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A drone pegmatite spectrum acquisition device, characterized in that: The device includes: a drone and a hyperspectral sensor; A housing is provided on the body of the drone; the housing is used to install the hyperspectral sensor; a shock absorbing structure or a correction structure is provided at the connection between the hyperspectral sensor and the drone.

2. The unmanned aerial vehicle pegmatite spectrum acquisition device according to claim 1, characterized in that: The device also includes a landing gear; the landing gear is installed on the UAV; when the UAV lands, the landing gear is used to form a gap between the hyperspectral sensor and the ground.

3. The unmanned aerial vehicle pegmatite spectrum acquisition device according to claim 1, characterized in that: A battery shielding film is arranged in the accommodating cavity.

4. The unmanned aerial vehicle pegmatite spectrum acquisition device according to claim 1, characterized in that: The UAV is a vertical take-off fixed-wing UAV; the hyperspectral sensor is a full-spectrum imager with a wavelength range of 400nm-2500nm.

5. A method for identifying lithium-containing pegmatite, characterized in that: The lithium-bearing pegmatite identification method comprises: Acquire reflectivity spectrum data, UAV hyperspectral data, target spectrum, dark current data, attitude data and ground station data; the UAV hyperspectral data is a spectrum image acquired by the UAV pegmatite spectrum acquisition device according to any one of claims 1 to 4; the dark current data is current data acquired before the UAV takes off and after landing; the reflectivity spectrum data is the emissivity data of lithium-bearing pegmatite and the emissivity data of lithium-free pegmatite measured by an infrared spectrometer; the target spectrum is obtained based on targets with different reflectivities; the targets with different reflectivities are set within a set range centered on the take-off position of the UAV; the attitude data includes one or more of the yaw angle, pitch angle and roll angle of the hyperspectral sensor; Fitting the UAV hyperspectral data based on the reflectivity spectral data to obtain a fitted infrared spectrum; Calibrate key parameters based on the fitted infrared spectrum, and construct a spectrum vector cloud of lithium-bearing pegmatite and a spectrum vector cloud of lithium-free pegmatite based on the key parameters and the reflectivity spectrum data; The target spectrum and the dark current data are used to correct the UAV hyperspectral data, and the ground station data and the attitude data are matched to obtain the corrected UAV hyperspectral data; Based on the corrected UAV hyperspectral data, constructing a UAV hyperspectral spatial vector cloud according to the key parameters; Inputting the drone hyperspectral spatial vector cloud, the lithium-bearing pegmatite spectral vector cloud, and the lithium-free pegmatite spectral vector cloud into the constructed network model, obtaining a recognition result; the recognition result is one or more of an image containing lithium-bearing pegmatite information, an image containing lithium-free pegmatite information, and an image containing both lithium-bearing pegmatite information and lithium-free pegmatite information; retaining images containing information of lithium-bearing pegmatite based on the identification results by using a spectral angle comparison method; Processing the image containing lithium-bearing pegmatite information by using filtering technology to obtain a binary image, and vectorizing the binary image to form a vector file of lithium-bearing pegmatite distribution; A color rendering containing lithium-bearing pegmatite information is generated based on the vector file of the lithium-bearing pegmatite distribution and the drone hyperspectral data.

6. The method for identifying lithium-bearing pegmatite according to claim 5, characterized in that: Calibrate key parameters based on the fitted infrared spectrum, and construct a spectrum vector cloud of lithium-bearing pegmatite and a spectrum vector cloud of lithium-free pegmatite based on the key parameters and the reflectivity spectrum data, specifically including: Calibrate the characteristics of lithium-bearing pegmatite and the characteristics of lithium-free pegmatite based on the fitted infrared spectrum, and use the characteristics of lithium-bearing pegmatite and the characteristics of lithium-free pegmatite as key parameters; Constructing the spectral vector cloud of the lithium-bearing pegmatite based on the characteristics of the lithium-bearing pegmatite and the reflectivity spectral data; The spectral vector cloud of the lithium-free pegmatite is constructed based on the characteristics of the lithium-free pegmatite and the reflectance spectrum data.

7. The method for identifying lithium-bearing pegmatite according to claim 5, characterized in that: The target spectrum and the dark current data are used to correct the UAV hyperspectral data, and the ground station data and the attitude data are matched to obtain the corrected UAV hyperspectral data, specifically including: According to the acquisition time, the UAV hyperspectral data is divided into the first part of UAV hyperspectral data and the second part of UAV hyperspectral data; The first part of the UAV hyperspectral data is radiometrically corrected using the current data collected before the UAV takes off, and the second part of the UAV hyperspectral data is radiometrically corrected using the current data collected after the UAV lands, to obtain the radiometrically corrected UAV hyperspectral data; The target spectrum is used as a standard spectrum, and the spectrum data corresponding to the target is obtained from the UAV hyperspectral data after radiation correction, and the obtained spectrum data corresponding to the target is used as white balance. The spectrum data in the UAV hyperspectral data other than the obtained spectrum data corresponding to the target is subjected to reflection correction by using the white balance to obtain the UAV hyperspectral data after reflection correction; The DEM model and satellite attitude are used to perform orthorectification on the reflectance-corrected UAV hyperspectral data, and in this correction process, the ground station data and the attitude data are matched to obtain the corrected UAV hyperspectral data.

8. The method for identifying lithium-bearing pegmatite according to claim 5, characterized in that: The spectral angle comparison method is used to retain images containing lithium-bearing pegmatite information based on the identification results, specifically including: When the recognition result is an image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information, a spectral angle calculation is performed on the spectrum in the drone hyperspectral data and the spectrum in the emissivity data of the lithium-bearing pegmatite to obtain a first spectral angle result; The spectrum angle of the spectrum in the UAV hyperspectral data and the spectrum in the emissivity data of the lithium-free pegmatite are calculated to obtain the second spectrum angle result; When the first spectral angle result is less than the second spectral angle result, retaining the image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information; When the first spectral angle result is greater than or equal to the second spectral angle result, the image containing both lithium-bearing pegmatite information and non-lithium-bearing pegmatite information is not retained.

9. The method for identifying lithium-bearing pegmatite according to claim 5, characterized in that: A color rendering containing lithium-bearing pegmatite information is generated based on the vector file of the lithium-bearing pegmatite distribution and the drone hyperspectral data, specifically including: The vector file of the lithium-bearing pegmatite distribution and the drone hyperspectral data are superimposed using coordinate layering to generate a color rendering containing lithium-bearing pegmatite information.

10. A lithium-containing pegmatite identification system, characterized in that: The system comprises: The UAV pegmatite spectrum acquisition device according to any one of claims 1 to 4 is used to collect UAV hyperspectral data, target spectrum and dark current data; the dark current data is current data collected before the UAV takes off and after landing; the target spectrum is obtained based on targets with different reflectivities; the targets with different reflectivities are set within a set range centered on the take-off position of the UAV; A UAV control system is mounted on the UAV and is used to control the flight of the UAV based on flight instructions; the flight instructions include one or more of route, speed, distance and altitude; A hyperspectral sensor control system, carried on the UAV, is used to control the working data of the hyperspectral sensor in the UAV pegmatite spectrum acquisition device; the working data of the hyperspectral sensor includes one or more of acquisition time and spectral band; A ground-based infrared spectrometer for collecting reflectance spectrum data; the reflectance spectrum data includes the emissivity data of lithium-bearing pegmatite and the emissivity data of lithium-free pegmatite; A ground station, used to obtain ground station data and attitude data; the attitude data includes one or more of a yaw angle, a pitch angle, and a roll angle of the hyperspectral sensor; The service system interacts with the UAV pegmatite spectrum acquisition device, the UAV control system, the hyperspectral sensor control system, the ground infrared spectrometer and the ground station for implementing the lithium-containing pegmatite identification method as described in any one of claims 5 to 9.

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