Method and device for imaging the surface of raw ore based on laser-induced breakdown spectroscopy

Abstract

The application relates to the technical field of ore detection, and discloses a raw ore surface imaging method and device based on laser-induced breakdown spectroscopy. The method comprises the following steps: acquiring a raw ore surface image, establishing a mapping relationship between an image coordinate system and a laser scanning coordinate system, determining a detection area, generating a dot matrix scanning grid according to image features and scanning resolution, controlling a focus module to focus point by point and collect LIBS spectra according to scanning detection point coordinates, identifying abnormal detection points according to focusing quality parameters and spectral features, re-adjusting the focus points of abnormal points and adjacent points and supplementally scanning, adaptively determining an encryption scanning area and adding detection points according to the element attribute variation amplitude and the focus position variation amplitude of adjacent detection points, and generating an element distribution map matched with the surface topography of the raw ore based on the LIBS spectra and the coordinate mapping relationship. Through adaptive scanning, point-by-point automatic focusing and abnormal supplementary scanning cooperative control, the reliability of element imaging is improved.

Description

Technical Field

[0001] This application relates to the field of ore detection technology, specifically to a method and apparatus for imaging the surface of raw ore based on laser-induced breakdown spectroscopy. Background Technology

[0002] Laser-induced breakdown spectroscopy (LIBS) is a detection technique that uses a high-energy pulsed laser to ablate the sample surface, creating plasma, and then collects the plasma emission spectrum to perform elemental analysis. It has advantages such as fewer sample pretreatment steps, fast detection speed, and the ability to perform simultaneous multi-element analysis, and therefore has application potential in fields such as ore element detection, mineral identification, and mineral resource evaluation.

[0003] Raw ore is a natural stone that has been mined from a mine and has not been processed by crushing, sorting, or impurity removal. It is usually composed of various mineral particles, gangue components, and enriched phases of useful elements, and has obvious heterogeneity. The distribution of different elements varies in mineral particles, boundaries, and enriched areas.

[0004] Related technologies typically involve applying a LIBS laser to the surface of raw ore and performing surface elemental detection and analysis based on a fixed focal length. However, raw ore surfaces are often characterized by irregular morphology, high roughness, and uneven mineral particle distribution, resulting in variations in surface height and local tilt at different detection points. When the laser focus deviates from the ore surface at the corresponding detection point, it causes changes in laser energy density and sample ablation, leading to instability in the plasma excitation state. This, in turn, results in fluctuations in spectral intensity, a decrease in signal-to-noise ratio, poorer detection repeatability, and increased quantitative error. Therefore, using only a fixed focal length cannot guarantee the consistency of LIBS spectral acquisition conditions at different detection points, thus affecting the accuracy of the raw ore surface elemental distribution analysis results.

[0005] Furthermore, raw ore typically contains multiple mineral components, and there may be spectral overlap between the characteristic spectral lines of different elements. Different mineral matrices also produce varying degrees of matrix effects on the elemental spectral line responses. If element identification or semi-quantitative analysis is based solely on the intensity of a single characteristic spectral line, it is easily affected by noise, baseline fluctuations, spectral line overlap, and differences in the mineral matrix, leading to misidentification of elements or deviations in semi-quantitative results. For abnormal detection points caused by defocusing, abnormal spectral signals, or abrupt changes in local mineral composition, related technologies often lack a processing mechanism that combines focusing status and spectral quality for anomaly identification and targeted supplementary scanning. Therefore, outliers, missing data, or local spatial distribution distortions may appear in the generated elemental distribution map. Summary of the Invention

[0006] In view of this, this application provides a method and apparatus for imaging the surface of raw ore based on laser-induced breakdown spectroscopy. The main purpose is to solve the problems of irregular surface morphology, local height variation, and heterogeneous mineral composition of raw ore, which lead to laser defocusing, spectral quality fluctuations, difficulty in timely identification and effective supplementary measurement of abnormal detection points, and difficulty in fully collecting the local distribution characteristics of abrupt changes in elemental properties. This improves the reliability of LIBS spectral acquisition and elemental analysis results at different detection points.

[0007] According to a first aspect of this application, a method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy is provided, the method comprising: Acquire images of the surface of the raw ore and establish a coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system; In the image of the raw ore surface, a selected detection area is determined. Based on the image features, area boundaries, preset scanning resolution, and preset or custom scanning method of the selected detection area, an initial set of scanning points and a scanning path matching the selected detection area are generated. The detection points to be scanned are determined sequentially according to the scanning path. Based on the scanning coordinates of the detection points in the laser scanning coordinate system, the autofocus module is controlled to adjust the relative position between the laser focus and the surface of the detection point, so that the laser beam is focused on the surface of the detection point. The focus position parameters and focus quality parameters are recorded, and the initial scanning LIBS spectrum corresponding to the detection point is acquired. Based on the focusing quality parameters and LIBS spectral quality characteristics, detection points that do not meet the preset focusing quality conditions or preset spectral quality conditions are identified as abnormal detection points. Based on the surface height information of the abnormal detection points and the focal position parameters of the non-abnormal detection points around the abnormal detection points, the predicted focal position and refocusing search interval of the abnormal detection points are determined. With the predicted focal position as the initial position, the abnormal detection points are automatically refocused and supplemented within the refocusing search interval to obtain the supplemented LIBS spectrum. Based on the variation range of elemental characteristic parameters and focal position between adjacent detection points, and the comparison results of the two variation ranges with the corresponding preset thresholds, the encrypted scanning area is determined according to the preset combination rules; supplementary detection points are inserted in the encrypted scanning area, the set of scanning points and the scanning path are updated, and automatic focusing and LIBS spectrum acquisition are performed on the supplementary detection points to obtain the encrypted scanning LIBS spectrum; The initial scan LIBS spectrum, the supplementary scan LIBS spectrum, and the encrypted scan LIBS spectrum that meet the preset focusing quality conditions and preset spectral quality conditions are determined as valid LIBS spectra. Based on the coordinate mapping relationship, the elemental analysis results corresponding to the effective LIBS spectrum are mapped to the original ore surface image to generate an elemental distribution map spatially registered with the original ore surface image.

[0008] According to a second aspect of this application, a surface imaging device for raw ore based on laser-induced breakdown spectroscopy is provided, the device comprising: Image acquisition device, 3D motion platform, laser light source, autofocus module, spectral acquisition module and control module; The image acquisition device is used to acquire images of the surface of the raw ore; The three-dimensional motion platform is used to carry the raw ore and drive the raw ore to move relative to the laser along the first and second horizontal directions that are perpendicular to each other, so as to move different detection points on the surface of the raw ore to the laser action position in sequence. The autofocus module includes an axial drive mechanism set along the laser optical axis. The autofocus module can move along the laser optical axis under the drive of the axial drive mechanism to evaluate the focus quality of different candidate focus positions and adjust the relative position between the laser focus and the detection point on the surface of the raw ore according to the focus quality evaluation results, so as to determine the target focus position parameters and focus quality parameters corresponding to the detection point. The laser is used to excite the detection point at the target focal point; The spectral acquisition module is used to acquire the LIBS spectrum corresponding to the detection point; The control module is connected to the image acquisition device, the 3D motion platform, the laser, the autofocus module, and the spectral acquisition module, and is configured as follows: Establish the coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system, determine the detection area in the raw ore surface image, and generate the initial set of scanning points and scanning path corresponding to the detection area; The scanning path controls the three-dimensional motion platform to move along the first and second horizontal directions to move each detection point to the laser action position in sequence. The autofocus module is controlled to move along the laser optical axis to autofocus each detection point. The laser and the spectral acquisition module are controlled to perform LIBS spectral acquisition on the detection points that have completed autofocus and record the focal position parameters and focusing quality parameters of each detection point. Based on the focusing quality parameters and LIBS spectral quality characteristics, the abnormal detection point is determined. The three-dimensional motion platform is controlled to move the abnormal detection point to the laser action position, and the autofocus module is controlled to move along the laser optical axis to refocus the abnormal detection point. The laser and spectral acquisition module are controlled to perform supplementary scanning on the refocused abnormal detection point. The encrypted scanning area is determined based on the variation range of element characteristic parameters and focal position between adjacent detection points. Supplementary detection points are inserted within the encrypted scanning area, the set of scanning points and the scanning path are updated, and automatic focusing and LIBS spectral acquisition are performed on the supplementary detection points. The LIBS spectra that meet the preset focusing quality conditions and preset spectral quality conditions are determined as effective LIBS spectra. Based on the coordinate mapping relationship, the elemental analysis results corresponding to the effective LIBS spectra are mapped to the original ore surface image to generate an elemental distribution map spatially registered with the original ore surface image.

[0009] According to a third aspect of this application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the first aspect described above.

[0010] According to a fourth aspect of this application, a computer-readable storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the method of the first aspect described above.

[0011] The laser-induced breakdown spectroscopy-based imaging method and apparatus for raw ore surfaces provided in this application have at least the following advantages compared with related technologies: By establishing the coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system, and generating a matching set of scanning points and scanning path according to the selected detection area, the LIBS element analysis results of the detection points can be accurately mapped to the corresponding positions in the raw ore surface image. By automatically focusing before acquiring LIBS spectra at each detection point and recording the focal position parameters and focusing quality parameters, the fluctuations in laser energy density and plasma excitation state caused by changes in the surface height of the raw ore can be reduced, thereby improving the consistency of spectral acquisition conditions at different detection points. By combining focusing quality parameters and spectral quality characteristics to identify anomalous detection points, and integrating the surface height information of the anomalous detection points with the focal position parameters of surrounding effective detection points, the predicted focal position and refocusing search range of the anomalous detection points are determined. Automatic refocusing and supplementary scanning of the anomalous detection points can reduce the impact of local protrusions, pits, or abrupt changes in surface height on the focal prediction results, reduce data loss and outliers caused by defocusing or spectral anomalies, and improve the integrity of effective spectral data. Furthermore, by determining the density scanning area based on the variation amplitude of elemental characteristic parameters and focal position between adjacent detection points, the scanning point density can be increased in areas with significant changes in elemental composition or surface morphology, preserving local elemental distribution details while avoiding high-density scanning of the entire detection area. By decomposing spectral line overlap, focusing and compensating for characteristic spectral line intensity parameters, and selecting the corresponding semi-quantitative analysis model in conjunction with the mineral matrix type, the influence of spectral line interference, defocusing fluctuations and mineral matrix differences on elemental analysis results can be reduced, thereby improving the reliability of qualitative and semi-quantitative analysis results of elements on the surface of raw ore. By mapping the qualitative and semi-quantitative results of elements corresponding to each detection point to the image of the original ore surface, an element distribution map corresponding to the spatial location on the original ore surface can be formed, which can intuitively characterize the spatial occurrence characteristics of the elements to be tested in mineral grains, grain boundaries and local enrichment areas. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0014] Figure 1 This is a schematic flowchart of a method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy, provided in an embodiment of this application. Figure 2 A flowchart illustrating an example of a laser-induced breakdown spectrum-based ore surface imaging system provided in this application is shown. Figure 3 A flowchart illustrating an example provided in an embodiment of this application is shown; Figure 4 This application provides an embodiment of the distribution map of elements to be measured within a selected detection area on the surface of a raw ore. Figure 5 This is a schematic diagram of the structure of a raw ore surface imaging device based on laser-induced breakdown spectroscopy, provided in an embodiment of this application. Detailed Implementation

[0015] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0016] The following describes, with reference to the accompanying drawings, a method and apparatus for imaging the surface of raw ore based on laser-induced breakdown spectroscopy, according to embodiments of this application.

[0017] In related technologies, due to the frequent overlap and interference of spectral lines of different elements in raw ore samples, and the significant matrix effect of different mineral matrices on LIBS spectra, elemental identification based solely on the intensity of a single spectral line is easily affected by noise, baseline drift, wavelength shift, spectral line overlap, and matrix differences, leading to misjudgments or quantitative deviations. Furthermore, the mechanisms for identifying and rescanning abnormal scanning points are not perfect, making it difficult to guarantee the stability and continuity of elemental distribution detection results.

[0018] Whole-sample analysis of raw ore typically only yields the average composition of the sample. The lack of discrete point detection methods with spatial coordinate correlation makes it difficult to continuously reflect the spatial occurrence of the elements within the ore's micro-regions. Therefore, two-dimensional elemental imaging of the raw ore surface can correlate elemental detection results with spatial locations, visually displaying element-enriched areas, depleted areas, mineral grain boundaries, and elemental symbiotic relationships. This provides a spatial basis for raw ore grade evaluation, mineral identification, and sorting process optimization.

[0019] This application provides a method and apparatus for imaging the surface of raw ore based on laser-induced breakdown spectroscopy. The main purpose is to at least solve the problems of related technologies that, when performing LIBS detection on the surface of raw ore based on a fixed focal length, are difficult to adapt to the height fluctuations of the raw ore surface, are prone to fluctuations in laser energy density and plasma excitation state at different detection points, and are difficult to effectively repair abnormal detection points and easily miss local element distribution characteristics, thereby improving the consistency, completeness and reliability of the elemental analysis results of the raw ore surface.

[0020] like Figure 1 As shown in the figure, this application provides a method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy, including the following steps.

[0021] Step 101: Obtain the surface image of the raw ore and establish the coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system.

[0022] In some embodiments, the raw ore to be inspected can be placed on a three-dimensional motion platform, and an image acquisition device positioned above the raw ore can acquire images of its surface. The raw ore surface images can be used to display surface features such as the contour, texture, color distribution, mineral grain boundaries, and cracks of the raw ore surface.

[0023] It should be noted that taking a top-down view of the raw ore surface using the image acquisition device is only one possible implementation. In other implementations, the image acquisition device can also acquire images of the raw ore surface at a preset tilt angle, and establish the correspondence between image coordinates and actual physical coordinates through image correction or coordinate calibration.

[0024] In this embodiment, the surface image of the raw ore has a corresponding image coordinate system, and the three-dimensional motion platform has a corresponding actual physical coordinate system. The actual physical coordinate system can be used as the laser scanning coordinate system, or it can have a predetermined coordinate transformation relationship with the laser scanning coordinate system.

[0025] Specifically, the image coordinates corresponding to multiple calibration positions in the image of the raw ore surface, as well as the actual physical coordinates of each calibration position on the three-dimensional motion platform, can be obtained. Based on the correspondence between multiple sets of image coordinates and actual physical coordinates, a coordinate mapping relationship between the image coordinate system and the laser scanning coordinate system can be established.

[0026] Coordinate mapping relationships can be represented using scaling transformations, affine transformations, perspective transformations, calibration matrices, or other coordinate transformation models. Through coordinate mapping relationships, any image position in the original ore surface image can be converted into an actual scanning position that a 3D motion platform can execute.

[0027] Step 102: Determine the selected detection area in the ore surface image. Based on the image features, area boundaries, preset scanning resolution, and preset or custom scanning method of the selected detection area, generate an initial set of scanning points and a scanning path that match the selected detection area.

[0028] In some embodiments, users can select, circle, outline, or freely draw on the surface image of the raw ore through the client interface to determine the selected detection area for element detection.

[0029] The selected detection area can be the entire surface of the raw ore, or a portion of the area containing specific mineral particles, mineral boundaries, fissures, color difference areas, or suspected element enrichment areas.

[0030] After the selected detection area is determined, the image coordinates of each detection point in the selected detection area in the image coordinate system can be determined based on the image features, area boundaries, preset scanning resolution, and preset or custom scanning methods of the selected detection area.

[0031] The preset scanning resolution can be determined based on the laser spot size, the preset distance between adjacent detection points, the detection accuracy requirements, and the scanning efficiency requirements. The distance between adjacent detection points can be equal to, less than, or greater than the laser spot diameter.

[0032] Based on the coordinate mapping relationship established in step 101, the image coordinates corresponding to each detection point are converted into scanning coordinates in the laser scanning coordinate system to generate an initial set of scanning points, and the scanning path is determined according to a preset or custom scanning method.

[0033] Optionally, step 102 may specifically include: obtaining the image coordinates corresponding to the pixel positions in the image of the raw ore surface, and the actual physical coordinates of the raw ore surface positions represented by the pixel positions on the three-dimensional motion platform; establishing a coordinate mapping relationship based on the correspondence between the image coordinates and the actual physical coordinates; determining the image coordinates of each detection point in the selected detection area in the image coordinate system based on the region boundary, laser spot size, preset scanning resolution, and scanning method; converting the image coordinates of each detection point into scanning coordinates in the laser scanning coordinate system based on the coordinate mapping relationship, generating an initial set of scanning points, and determining the scanning path according to the scanning method; wherein, the scanning method includes at least one of point scanning, line scanning, raster scanning, contour following scanning, and custom path scanning, and the initial set of scanning points includes an irregular set of points formed by cropping according to the region boundary of the selected detection area or a set of regular points of a preset size.

[0034] For example, when the selected detection area is a regular rectangular area, an m×n set of regular scanning points can be generated; when the selected detection area is an irregular area, a set of regular points covering the selected detection area can be generated first, and then trimmed according to the area boundary to form a set of irregular scanning points that match the selected detection area.

[0035] In some embodiments, a contour-following scanning path can be generated based on the mineral particle boundary or contour features in the selected detection area to scan along the mineral particle boundary or a specific image feature area.

[0036] The above method can realize the correspondence between the image coordinates, scan coordinates and actual physical positions of the detection points in the selected detection area, and control the three-dimensional motion platform to complete the positioning of each detection point in sequence according to the scan path.

[0037] Step 103: Determine the detection points to be scanned sequentially according to the scanning path. Based on the coordinates of the detection points to be scanned in the laser scanning coordinate system, control the autofocus module to adjust the relative position between the laser focus and the surface of the detection point to be scanned, so that the laser beam is focused on the surface of the detection point to be scanned. Record the focus position parameters and focus quality parameters, and collect the initial scanning LIBS spectrum corresponding to the detection point to be scanned.

[0038] Optionally, step 103 may specifically include: determining the current detection point according to the scanning path, and controlling the three-dimensional motion platform to move along a first horizontal direction and a second horizontal direction that are perpendicular to each other, based on the scanning coordinates of the current detection point, so as to move the current detection point to the laser action position; acquiring the surface height information of the current detection point, and determining the initial focusing position or focusing search range along the laser optical axis based on the surface height information; controlling the autofocus module to move along the laser optical axis from the initial focusing position or within the focusing search range, so that the laser focus moves axially relative to the surface of the current detection point, and evaluating the focusing quality of different candidate focus positions to determine the target focus position that meets the preset focusing conditions; recording the axial position of the autofocus module corresponding to the target focus position as the focus position parameter of the current detection point, and recording the focusing evaluation result corresponding to the target focus position as the focusing quality parameter; exciting the current detection point at least once at the target focus position, and acquiring the LIBS spectrum corresponding to the current detection point through the spectral acquisition module.

[0039] Specifically, the three-dimensional motion platform can be controlled to move along a first horizontal direction and a second horizontal direction that are perpendicular to each other, so as to move the point to be scanned to the laser action position; the autofocus module is activated, the autofocus module is controlled to move along the laser optical axis, and the focus quality of different candidate focus positions is evaluated. Based on the focus quality evaluation results, the relative position between the laser focus and the surface of the point to be scanned is adjusted.

[0040] In some embodiments, the current detection point is determined sequentially according to the scanning path determined in step 102, and the three-dimensional motion platform is controlled to move along the X and Y directions according to the scanning coordinates of the current detection point in the laser scanning coordinate system, so as to move the current detection point to the laser action position.

[0041] The three-dimensional motion platform includes an X-axis motion mechanism and a Y-axis motion mechanism for planar positioning; the autofocus module includes an axial drive mechanism arranged along the laser optical axis, which drives the autofocus module or its focusing optical components to move along the laser optical axis to adjust the relative position between the laser focus and the surface of the raw ore.

[0042] After the current detection point moves to the laser's position, the surface height information of the current detection point can be obtained through the autofocus module. Based on the surface height information, the initial focusing position or focusing search range along the laser optical axis can be determined.

[0043] The autofocus module can use distance measurement, reflected light detection, image sharpness evaluation, confocal displacement detection, spot size evaluation, or other detection methods that can characterize the focus state to obtain the surface height information or focus evaluation information of the current detection point.

[0044] Based on the initial focusing position or focusing search range, the autofocus module or its focusing optical components are controlled to move along the laser optical axis, causing the laser focus to undergo axial displacement relative to the surface of the original ore within the focusing search range. The autofocus module then evaluates the focusing quality of different candidate focus positions.

[0045] Based on the focusing evaluation results corresponding to each candidate focal position, the target focal position that meets the preset focusing conditions is determined, so that the focal point of the laser beam is located at or close to the surface of the current detection point.

[0046] In some embodiments, the candidate focus position with the best focus evaluation result can be determined as the target focus position; alternatively, the candidate focus position with the focus evaluation result reaching a preset focus quality threshold can be determined as the target focus position.

[0047] The axial position of the autofocus module corresponding to the target focus position is recorded as the focus position parameter of the current detection point, and the focus evaluation result corresponding to the target focus position is recorded as the focus quality parameter of the current detection point.

[0048] The focal position parameter may include at least one of the following: the axial position of the autofocus module, the axial displacement of the autofocus module or focusing optical component along the laser optical axis, the height of the raw ore surface, or the relative position between the laser focal point and the reference plane.

[0049] Focus quality parameters may include at least one of the following: focus evaluation value, focus offset, defocus amount, positioning error, spot size evaluation value, reflected signal intensity, or image sharpness evaluation value.

[0050] After determining the target focal position, the laser is controlled to excite the current detection point at least once at the target focal position, and the initial scan LIBS spectrum corresponding to the current detection point is acquired through the spectral acquisition module.

[0051] When the same detection point is subjected to multiple laser excitations, multiple sets of LIBS spectra can be obtained. The target spectrum can be selected from multiple sets of spectra according to the preset spectral screening conditions. Alternatively, multiple sets of spectra that meet the conditions can be averaged, accumulated, or feature-fused.

[0052] The above-described positioning, autofocusing, parameter recording, and spectral acquisition processes are sequentially performed on each detection point in the initial scan point set according to the scanning path, thereby obtaining the focal position parameters, focusing quality parameters, and initial scan LIBS spectrum corresponding to each detection point.

[0053] By performing automatic focusing before acquiring LIBS spectra at each detection point, the deviation of the laser focus caused by changes in the surface height of the raw ore can be reduced, thereby reducing the fluctuations in laser energy density, sample ablation, and plasma excitation state between different detection points and improving the consistency of the initial scanned LIBS spectra.

[0054] Step 104: Based on the focusing quality parameters and LIBS spectral quality characteristics, identify detection points that do not meet the preset focusing quality conditions or preset spectral quality conditions as abnormal detection points; based on the surface height information of the abnormal detection points and the focal position parameters of the non-abnormal detection points around the abnormal detection points, determine the predicted focal position and refocusing search interval of the abnormal detection points; using the predicted focal position as the initial position, refocus and supplement the scanning of the abnormal detection points within the refocusing search interval to obtain the supplemented LIBS spectrum.

[0055] Optionally, abnormal detection points are determined based on focus quality parameters and LIBS spectral quality characteristics, and focus readjustment and supplementary scanning are performed on these abnormal detection points. Specifically, this may include: determining the spectral quality parameters of each scanned detection point based on the LIBS spectrum; the spectral quality parameters include at least one of total spectral intensity, signal-to-noise ratio, baseline fluctuation amplitude, and number of effective characteristic spectral lines; comparing the focus quality parameters with a preset focus quality threshold, and comparing the spectral quality parameters with a preset spectral quality threshold, identifying scanned detection points that do not meet either the preset focus quality threshold or the preset spectral quality threshold as abnormal detection points; determining adjacent detection points within a preset spatial neighborhood of the abnormal detection points, and identifying adjacent effective detection points from among the adjacent detection points that meet both the preset focus quality threshold and the preset spectral quality threshold; acquiring the surface height information of the abnormal detection points; and, based on the surface height information of the abnormal detection points and the focus position parameters of the adjacent effective detection points... The system determines the predicted focal position of the abnormal detection point; based on the surface height difference between the abnormal detection point and adjacent valid detection points, the dispersion of the focal position parameters of adjacent valid detection points, and the preset minimum search range, it determines the refocusing search interval; when the surface height difference or dispersion exceeds the corresponding preset threshold, the refocusing search interval is expanded; the autofocus module is controlled to use the predicted focal position as the initial position and redetermine the target focal position of the abnormal detection point within the refocusing search interval; the abnormal detection point is supplemented with excitation at the newly determined target focal position, and the corresponding supplemented LIBS spectrum is acquired; when the focus quality parameters of the abnormal detection point after supplementation meet the preset focus quality threshold, and the spectral quality parameters of the supplemented LIBS spectrum meet the preset spectral quality threshold, the initial scanned LIBS spectrum of the abnormal detection point is replaced with the supplemented LIBS spectrum; otherwise, the abnormal detection point is marked as an invalid detection point.

[0056] In this embodiment, step 104 includes anomaly detection point identification and rescanning.

[0057] First, the spectral quality parameters of each detection point are determined based on the initial scanned LIBS spectrum corresponding to each scanned detection point.

[0058] Spectral quality parameters may include at least one of the following: total spectral intensity, effective band intensity, signal-to-noise ratio, baseline fluctuation amplitude, number of effective characteristic spectral lines, spectral saturation, and dispersion between repeatedly acquired spectra.

[0059] The focusing quality parameters corresponding to each detection point are compared with the preset focusing quality threshold, and the spectral quality parameters corresponding to each detection point are compared with the preset spectral quality threshold.

[0060] When the focusing quality parameter of the detection point does not meet the preset focusing quality threshold, or the spectral quality parameter of the detection point does not meet the preset spectral quality threshold, the detection point is identified as an abnormal detection point.

[0061] Within a preset spatial neighborhood of the abnormal detection point, multiple adjacent detection points are determined, and from these adjacent detection points, the adjacent valid detection points that simultaneously satisfy the preset focusing quality threshold and the preset spectral quality threshold are selected.

[0062] Based on the surface height information of the abnormal detection point and the average, median, distance-weighted average, interpolation results, or trends of the focal position parameters of multiple adjacent valid detection points, the predicted focal position of the abnormal detection point is determined. The refocusing search interval is determined based on the surface height difference between the abnormal detection point and adjacent valid detection points, the dispersion of the focal position parameters of adjacent valid detection points, and a preset minimum search range. When the surface height difference or dispersion exceeds a corresponding preset threshold, the refocusing search interval is expanded. For example, the surface height corresponding to the abnormal detection point can be predicted based on the average, median, distance-weighted average, interpolation results, or trends of the focal position parameters of multiple adjacent valid detection points, and the refocusing search interval can be set around the predicted surface height.

[0063] Within the refocusing search range, the autofocus module is controlled to re-search for the target focus position, and at the newly determined target focus position, abnormal detection points are excited to collect supplementary LIBS spectra.

[0064] The focusing quality parameters of the abnormal detection points after the supplementary scan and the spectral quality parameters of the supplementary scan LIBS spectrum are re-evaluated. When the supplementary scan LIBS spectrum meets the preset focusing quality threshold and the preset spectral quality threshold, the initial scanned LIBS spectrum of the corresponding detection point can be replaced with the supplementary scan LIBS spectrum.

[0065] When both the initial scan LIBS spectrum and the supplementary scan LIBS spectrum meet the quality requirements, the spectrum with higher quality can be selected as the target spectrum for the corresponding detection point based on the spectral quality score.

[0066] When the LIBS spectrum after supplementary scanning still does not meet the preset focusing quality threshold or the preset spectral quality threshold, the corresponding detection point can be marked as an invalid detection point, and represented by null value, invalid mark or neighborhood estimate in subsequent element imaging process.

[0067] Step 105: Based on the variation range of element characteristic parameters and focal position between adjacent detection points, and the comparison results of the two variation ranges with the corresponding preset thresholds, determine the encrypted scanning area according to the preset combination rules; insert supplementary detection points within the encrypted scanning area, update the set of scanning points and the scanning path, and perform automatic focusing and LIBS spectrum acquisition on the supplementary detection points to obtain the encrypted scanning LIBS spectrum.

[0068] Optionally, based on the variation amplitude of elemental characteristic parameters and the variation amplitude of focal position between adjacent detection points, and according to the comparison results of the two variation amplitudes with corresponding preset thresholds, the encrypted scanning area is determined according to a preset combination rule. This may include: extracting the elemental characteristic parameters of the element to be measured from the effective LIBS spectra corresponding to adjacent detection points, and determining the variation amplitude of elemental characteristic parameters between adjacent detection points based on the difference in elemental characteristic parameters; determining the variation amplitude of focal position between adjacent detection points based on the difference in focal position parameters; comparing the variation amplitude of elemental characteristic parameters with a first preset threshold, and comparing the variation amplitude of focal position with a second preset threshold; when the variation amplitude of elemental characteristic parameters exceeds the first preset threshold, the encrypted scanning area is determined. When at least one of the preset threshold and the focal position change exceeds a second preset threshold, the region between the corresponding adjacent detection points and its preset neighborhood are defined as the encrypted scanning region. Based on the degree to which the change in elemental characteristic parameters or the change in focal position exceeds the corresponding preset threshold, the number of supplementary detection points or the scanning spacing within the encrypted scanning region is determined, wherein the scanning spacing of the supplementary detection points is less than the scanning spacing of the initial scanning point set. The supplementary detection points are added to the scanning point set and the scanning path is updated. The supplementary detection points are then automatically focused and LIBS spectra are acquired according to the updated scanning path. The encrypted scanning LIBS spectra that meet the preset focus quality threshold and the preset spectral quality threshold are added to the effective LIBS spectrum.

[0069] After identifying and rescanning abnormal detection points, local encrypted scanning can be performed based on the variation range of element characteristic parameters and focal position between adjacent detection points.

[0070] Specifically, elemental characteristic parameters for scanning decisions are extracted from the initial scan LIBS spectrum or supplementary scan LIBS spectrum corresponding to adjacent detection points that meet preset focusing quality conditions and preset spectral quality conditions.

[0071] Elemental characteristic parameters may include the peak intensity, integrated peak area, spectral line intensity ratio, multi-line fusion value, or other parameters that can reflect the response changes of the element to be measured.

[0072] The variation range of element feature parameters between adjacent detection points is determined based on the difference in element feature parameters corresponding to adjacent detection points, or the ratio of the difference in element feature parameters to the distance between adjacent detection points.

[0073] Meanwhile, the magnitude of the change in focal position between adjacent detection points is determined based on the difference in focal position parameters corresponding to adjacent detection points, or the ratio of the difference in focal position parameters to the distance between adjacent detection points.

[0074] The variation range of element feature parameters is compared with the first preset threshold, and the variation range of focus position is compared with the second preset threshold.

[0075] When the change in element feature parameters exceeds at least one of the first preset threshold and the change in focus position exceeds the second preset threshold, the region between the corresponding adjacent detection points and its preset neighborhood are determined as the encrypted scanning region.

[0076] Significant variations in elemental characteristic parameters may indicate the presence of elemental enrichment boundaries, mineral grain boundaries, or abrupt changes in elemental content in the corresponding region; significant variations in focal position may indicate the presence of obvious surface height changes, grain protrusions, depressions, or fissures in the corresponding region.

[0077] The number of supplementary detection points or the scanning interval within the encrypted scanning area are determined based on the degree to which the variation range of element feature parameters or the variation range of focal position exceeds the corresponding preset threshold.

[0078] For example, the greater the degree to which the change exceeds a preset threshold, the smaller the scanning interval can be set or the more supplementary detection points can be inserted. The scanning interval of the supplementary detection points is smaller than the scanning interval of the initial set of scanning points.

[0079] Add the supplementary detection points to the scan point set, and update the scan path according to the positional relationship between the supplementary detection points and the original scan path.

[0080] Following the updated scanning path, each supplementary detection point was sequentially located, automatically focused, and its focal position parameters and focus quality parameters were recorded. LIBS spectra were then acquired to obtain encrypted scanning LIBS spectra.

[0081] By performing abnormal scanning, data loss and outliers caused by local defocusing, abnormal surface reflection, or abnormal spectral signals can be reduced. By performing local densification scanning, the density of scanning points can be increased in areas with significant elemental variations or large surface morphology changes, thereby preserving the spatial details of local elemental boundaries and enriched areas on the original ore surface.

[0082] Step 106: Determine the initial scan LIBS spectrum, the supplementary scan LIBS spectrum, and the encrypted scan LIBS spectrum that meet the preset focusing quality conditions and preset spectral quality conditions as valid LIBS spectra.

[0083] In some embodiments, the initial scan LIBS spectrum, the supplementary scan LIBS spectrum, and the encrypted scan LIBS spectrum that meet the preset focusing quality conditions and the preset spectral quality conditions are determined as valid LIBS spectra.

[0084] Specifically, for detection points where the initial scan LIBS spectrum has been replaced with a supplementary scan LIBS spectrum, the supplementary scan LIBS spectrum can be used as the effective LIBS spectrum for that detection point; for detection points that do not exhibit abnormalities and meet the quality requirements, the initial scan LIBS spectrum can be used as the effective LIBS spectrum; and for supplementary detection points in the encrypted scan area, the encrypted scan LIBS spectrum that meets the quality requirements can be used as the effective LIBS spectrum.

[0085] Step 107: Based on the coordinate mapping relationship, map the elemental analysis results corresponding to the effective LIBS spectrum to the original ore surface image to generate an elemental distribution map spatially registered with the original ore surface image.

[0086] Optionally, the elemental analysis results corresponding to the effective LIBS spectra are mapped to the surface image of the raw ore. This includes: calling a semi-quantitative analysis model corresponding to the mineral matrix category, inputting the focused-corrected characteristic spectral parameters and focusing quality factor into the semi-quantitative analysis model to obtain the semi-quantitative results of the elements to be measured at the corresponding detection points; wherein, the semi-quantitative analysis model is established according to different mineral matrix categories, and the semi-quantitative results include at least one of content grade, relative content characterization value, and concentration range; determining the corresponding position of each detection point in the raw ore surface image based on the scanning coordinates and coordinate mapping relationship of each detection point in the laser scanning coordinate system; determining the image region corresponding to each detection point in the raw ore surface image based on the laser spot size, and assigning the qualitative and semi-quantitative results of the elements corresponding to each detection point to the corresponding image region to generate an elemental distribution map for characterizing the spatial occurrence characteristics of the elements to be measured on the raw ore surface.

[0087] After obtaining the effective LIBS spectrum, preprocessing such as denoising, baseline correction, wavelength calibration, intensity normalization, or abnormal pulse removal can be performed on the effective LIBS spectrum.

[0088] Based on a preset elemental spectral library and preset wavelength matching conditions, one or more candidate characteristic spectral lines corresponding to the element to be measured are extracted from the effective LIBS spectrum.

[0089] The algorithm detects whether candidate feature spectral lines overlap with other spectral lines within the same spectral band. When overlap is detected, multi-peak fitting, spectral line deconvolution, or other spectral interference decomposition algorithms can be used to process the corresponding overlapping spectral bands to obtain the peak components corresponding to each candidate feature spectral line and the feature spectral line intensity parameters after overlap interference correction.

[0090] When no spectral line overlap is detected, the directly extracted feature spectral line intensity parameters can be used as the feature spectral line intensity parameters after overlap interference correction for the corresponding candidate feature spectral lines.

[0091] Based on the focusing quality parameters corresponding to each effective LIBS spectrum, and the pre-established mapping relationship between the focusing quality parameters and the spectral response, the focusing compensation factor corresponding to each detection point is determined.

[0092] The intensity parameters of the characteristic spectral lines after the overlap interference correction are compensated and corrected by the focusing compensation factor to obtain the intensity parameters of the characteristic spectral lines after focusing correction, thereby reducing the influence of the residual defocus state at different detection points on the intensity of the spectral line response.

[0093] For multiple candidate feature spectral lines corresponding to the same element to be tested, the corresponding weights can be determined based on the signal-to-noise ratio, relative intensity, degree of spectral interference, fitting residual, or stability of each candidate feature spectral line.

[0094] The intensity parameters of the candidate spectral lines after focusing correction and their corresponding weights are weighted and fused to obtain the confidence level of the presence of the element at the corresponding detection point.

[0095] The element's existence confidence level is compared with a preset judgment threshold to determine the qualitative result of the element to be tested at the corresponding detection point.

[0096] Optionally, before mapping the elemental analysis results corresponding to the effective LIBS spectra to the original ore surface image, the elemental and mineral matrix analysis results corresponding to the effective LIBS spectra can be determined. Specifically, this may include: extracting one or more candidate feature spectral lines corresponding to the elements to be tested from the effective LIBS spectra based on a preset elemental spectral library and preset wavelength matching conditions; detecting whether there is spectral overlap between the feature spectral lines of different elements to be tested; when spectral overlap is detected, using a spectral interference decomposition algorithm to perform multi-peak decomposition on the corresponding overlapping spectral segments to obtain the peak components corresponding to each candidate feature spectral line and the feature spectral line intensity parameters after overlap interference correction; determining the focus compensation factor corresponding to each detection point based on the focus quality parameters corresponding to each effective LIBS spectrum and the pre-established mapping relationship between the focus quality parameters and the relative response attenuation degree of the feature spectral lines; wherein, the mapping relationship is established by acquiring the LIBS spectra of the reference sample under different focus states and based on the ratio between the feature spectral line response under each focus state and the feature spectral line response under a preset standard focus state. The intensity parameters of the feature spectral lines after overlap interference correction are compensated and corrected according to the focusing compensation factor to obtain the intensity parameters of the feature spectral lines after focusing correction. Based on the intensity parameters of the feature spectral lines corresponding to the same analyte and their corresponding weights, the element presence confidence of the analyte is determined. Based on the comparison between the element presence confidence and the preset judgment threshold, the element qualitative result of the analyte at the corresponding detection point is determined. Spectral features for characterizing the mineral matrix are extracted from the effective LIBS spectrum, and the spectral features are classified based on the preset mineral matrix classification model to determine the mineral matrix category at the corresponding detection point.

[0097] In some implementations, spectral analysis of the effective LIBS spectrum includes: extracting one or more candidate characteristic spectral lines corresponding to the analyte from the effective LIBS spectrum; decomposing spectral interference in spectral segments with overlapping spectral lines; correcting the characteristic spectral line intensity parameters according to the mapping relationship between the focusing quality parameter and the spectral response; and performing weighted fusion of multiple characteristic spectral lines corresponding to the same analyte to determine the elemental qualitative result of the corresponding detection point.

[0098] In some implementations, spectral features for characterizing the mineral matrix are extracted from the effective LIBS spectrum, the mineral matrix category of the corresponding detection point is determined based on the mineral matrix classification model, and a semi-quantitative analysis model corresponding to the mineral matrix category is invoked to obtain at least one of the content level, relative content characterization value, and concentration range of the element to be measured at the corresponding detection point.

[0099] Furthermore, spectral features for characterizing the mineral matrix can be extracted from the effective LIBS spectrum. These spectral features may include at least one of the following: full-spectrum features, major element spectral features, spectral line intensity ratios, continuous background features, or spectral features after dimensionality reduction.

[0100] Input the spectral features into the preset mineral matrix classification model to determine the mineral matrix category of the corresponding detection point.

[0101] Mineral matrix classification models can employ discriminant analysis, support vector machine, decision tree, random forest, neural network, or other models capable of classifying minerals based on spectral characteristics.

[0102] Based on the mineral matrix category of the corresponding detection point, a target semi-quantitative analysis model corresponding to the mineral matrix category is selected from multiple pre-established semi-quantitative analysis models.

[0103] The intensity parameters of the characteristic spectral lines of the corresponding element to be measured after focusing correction are input into the target semi-quantitative analysis model to obtain the semi-quantitative results of the element to be measured at the corresponding detection point.

[0104] Multiple semi-quantitative analysis models can be established based on calibrated samples of different mineral matrix types. Semi-quantitative results can include at least one of the following: content grade, relative content characterization value, and concentration range.

[0105] It should be noted that, since the characteristic spectral line intensity parameters have been compensated and corrected according to the focusing compensation factor, in a preferred embodiment, the same focusing quality factor is no longer repeatedly input into the semi-quantitative analysis model to avoid repeated compensation of the focusing state.

[0106] Based on the scanning coordinates of each detection point in the laser scanning coordinate system and the coordinate mapping relationship established in step 101, the corresponding image position of each detection point in the image of the original ore surface is determined.

[0107] Based on the laser spot size and image ratio, the corresponding image region of each detection point in the image of the raw ore surface is determined, and the qualitative and semi-quantitative results of the elements corresponding to each detection point are assigned to the corresponding image region to generate an element distribution map that characterizes the spatial occurrence characteristics of the elements to be tested on the surface of the raw ore.

[0108] Elemental distribution maps may include at least one of the following: single-element distribution maps, multi-element overlay distribution maps, element presence confidence maps, element content grade maps, relative content distribution maps, concentration range maps, and mineral matrix category maps.

[0109] In some embodiments, for image regions where spectra are not directly acquired between adjacent detection points, nearest neighbor assignment, linear interpolation, or other spatial interpolation processing can be performed based on the elemental analysis results of adjacent detection points.

[0110] When performing spatial interpolation, the interpolation range can be limited according to the mineral matrix type or mineral grain boundary, so that the elemental analysis results are interpolated only in the same mineral matrix region or the same mineral grain region, in order to avoid unreasonable cross-boundary smoothing between different mineral regions.

[0111] Through the above steps, the LIBS elemental analysis results of each detection point can be accurately correlated with the spatial location in the surface image of the original ore, and the spatial occurrence characteristics of the elements to be tested in mineral grains, grain boundaries, fractures and local enrichment areas can be intuitively displayed.

[0112] Furthermore, such as Figure 2 As shown, a laser-induced breakdown spectrum-based imaging system for raw ore surfaces can be constructed to implement the above method.

[0113] The system may include a laser, a spectrometer, an image acquisition device, an autofocus module, a three-dimensional motion platform, control and data processing equipment, and corresponding communication interfaces.

[0114] The three-dimensional motion platform is used to place the raw ore to be tested and to move the raw ore along the X and Y directions according to the control command to achieve planar positioning of the detection point; the automatic focusing module is used to move along the laser optical axis to adjust the relative position between the laser focus and the surface of the raw ore, thereby achieving laser focus height adjustment.

[0115] The image acquisition device is used to acquire images of the surface of the raw ore and send the images to the control and data processing equipment.

[0116] The laser is used to emit pulsed laser light at the target focal point onto the surface of the raw ore, causing localized ablation of the raw ore surface and forming plasma.

[0117] The spectrometer is used to receive plasma emitted light and send the collected LIBS spectrum to the control and data processing equipment.

[0118] The autofocus module is used to acquire surface height information or focus evaluation information of the detection point, move along the laser optical axis to adjust the focus position, and feed back the focus position parameters and focus quality parameters to the control and data processing equipment.

[0119] The control and data processing equipment is used to establish the coordinate mapping relationship between the image coordinate system and the laser scanning coordinate system, generate a set of scanning points and scanning paths, control the coordinated operation of the three-dimensional motion platform, laser, spectrometer, image acquisition device and autofocus module, and perform anomaly detection, supplementary scanning, encrypted scanning, spectral analysis and element distribution map generation operations.

[0120] An autofocus module may include a focusing optical path, a focus detection unit, and a focus drive control unit.

[0121] In one alternative embodiment, the focusing optical path may include a semi-transparent mirror, a dichroic mirror, and an objective lens. The semi-transparent mirror can be used to change the propagation direction of the detection light or imaging light, the dichroic mirror can be used to separate the laser band and the plasma emission light band, and the objective lens can be used to focus the laser beam onto the surface of the raw ore.

[0122] It should be noted that the specific structure of the autofocus module is not limited to the above-mentioned combination of optical components, and can also be implemented using a laser displacement sensor, a confocal displacement sensor, a visual focusing device, or other surface height detection devices.

[0123] Through the above system, it is possible to follow Figure 1 The process shown involves image acquisition, scan planning, point-by-point autofocus, LIBS spectral acquisition, anomaly scan supplementation, local densification scan, and elemental analysis of the surface of the raw ore, ultimately generating an elemental distribution map spatially registered with the image of the raw ore surface.

[0124] For example, the elemental distribution map of the raw ore surface can be generated by following these steps: 1) Preprocess the effective LIBS spectra corresponding to each detection point. The preprocessing includes at least one of noise removal, smoothing, baseline correction, and wavelength calibration. Noise removal is used to reduce the influence of dark current noise and random noise, smoothing is used to reduce random fluctuations in the spectral signal, baseline correction is used to eliminate continuous background and baseline drift, and wavelength calibration is used to correct the wavelength shift generated during the spectral acquisition process. 2) Based on the preset element spectral library and preset wavelength matching conditions, extract one or more candidate feature spectral lines corresponding to the element to be measured from the preprocessed effective LIBS spectrum, and extract at least one of the following parameters for each candidate feature spectral line: peak intensity, integrated peak area, half peak width, signal-to-noise ratio, peak position deviation, background intensity, and peak shape evaluation parameters. 3) Detect whether there is spectral line overlap between each candidate feature spectral line and other spectral lines in the same spectral band. When spectral line overlap is detected, a spectral line interference decomposition algorithm is used to perform multi-peak decomposition on the corresponding overlapping spectral band to obtain the peak components corresponding to each candidate feature spectral line and the feature spectral line intensity parameters after overlap interference correction; when no spectral line overlap is detected, the directly extracted feature spectral line intensity parameters are used as the feature spectral line intensity parameters after overlap interference correction for the corresponding candidate feature spectral line. 4) Based on the focusing quality parameters of the corresponding detection points and the pre-established mapping relationship between the focusing quality parameters and the spectral response, determine the focusing compensation factor, and use the focusing compensation factor to compensate and correct the characteristic spectral line intensity parameters after the overlap interference correction, so as to obtain the focusing-corrected characteristic spectral line intensity parameters. 5) Determine the corresponding weight based on at least one of the following factors for each candidate feature spectral line corresponding to the same element to be tested: signal-to-noise ratio, peak position matching degree, peak shape quality, spectral line interference degree, and spectral line stability. Then, perform weighted fusion based on the focused-corrected feature spectral line intensity parameters of each candidate feature spectral line and the corresponding weight to obtain the element presence confidence of the element to be tested at the corresponding detection point. When the element presence confidence reaches the preset judgment threshold, it is determined that the element to be tested exists at the corresponding detection point; otherwise, it is determined that the element to be tested is not detected or is detected with low confidence. 6) Extract spectral features for characterizing the mineral matrix from the effective LIBS spectrum. The spectral features include at least one of the following: major element spectral features, full spectrum features, element spectral line intensity ratio, element combination features, and dimension-reduced spectral features. Determine the mineral matrix category of the corresponding detection point based on the preset mineral matrix classification model. 7) Based on the mineral matrix category of the corresponding detection point, determine the target semi-quantitative analysis model from multiple pre-established semi-quantitative analysis models, and input the focused-corrected characteristic spectral intensity parameters or multi-spectral fusion parameters corresponding to the element to be measured into the target semi-quantitative analysis model to obtain the semi-quantitative results of the element to be measured at the corresponding detection point; the semi-quantitative results include at least one of the following: content grade, relative content characterization value, and concentration range; 8) Associate the qualitative and semi-quantitative results of elements corresponding to each detection point with the scanning coordinates of each detection point, and based on the coordinate mapping relationship between the image coordinate system and the laser scanning coordinate system, map the qualitative and semi-quantitative results of elements corresponding to each detection point to the corresponding image area in the original ore surface image to generate an element distribution map of the elements to be tested; it is also possible to generate a mineral matrix category distribution map according to the mineral matrix category corresponding to each detection point.

[0125] Optionally, before extracting the spectral features corresponding to the detection point based on the characteristic spectral lines of the element to be tested, and determining the mineral matrix category corresponding to the detection point based on the spectral features, the method further includes: detecting whether there are spectral anomalous regions among the characteristic spectral lines of multiple elements to be tested; if spectral anomalous regions are detected, then using a spectral interference decomposition algorithm to decompose the overlapping peaks in the spectral anomalous regions into sub-peaks corresponding to multiple elements to be tested, generating interference-corrected characteristic spectral lines corresponding to multiple elements to be tested; and determining whether the detection point meets the preset focusing quality conditions based on the focusing quality factor corresponding to the focusing quality parameter of the detection point; if it is determined that the detection point does not meet the preset focusing quality conditions, then correcting the spectral intensity of the characteristic spectral lines of the element to be tested corresponding to the detection point, generating intensity-corrected characteristic spectral lines.

[0126] For example, in spectral anomaly regions where spectral lines overlap or adjacent elements interfere, a spectral interference decomposition algorithm is used to correct overlapping peaks. This algorithm can be implemented based on Gaussian functions, Lorentz functions, Voigt functions, pseudo-Voigt functions, multi-peak fitting, constrained peak position fitting, standard spectral library matching, or machine learning models. By decomposing overlapping peaks into sub-peaks corresponding to multiple elements, the corrected peak intensity or corrected peak area of ​​the spectral lines of the element to be measured is obtained, thereby reducing the impact of adjacent spectral line interference on qualitative and semi-quantitative results.

[0127] Optionally, based on the focusing quality parameters and LIBS spectral quality characteristics of each detection point, anomalies are identified at the detection points, and abnormal scanning points are determined from among the detection points. Based on the surface height information of the abnormal detection points and the focal position parameters of the surrounding effective detection points that meet the preset reference conditions, the predicted focal position and refocusing search interval are determined. When the focal position parameters of the surrounding effective detection points do not meet the preset reference conditions, the predicted focal position is determined based on the surface height information, and the refocusing search interval is expanded. Using the predicted focal position as the initial position, the abnormal detection points are refocused and re-scanned to obtain the supplemented LIBS spectrum.

[0128] For example, the surface height information of the abnormal detection point and the surface height information of the surrounding valid detection points are obtained; based on the surface height difference between the abnormal detection point and the surrounding valid detection points, the spatial distribution of the surrounding valid detection points, and the dispersion of the focal position parameters, the focal position parameters of the surrounding valid detection points are evaluated for reliability.

[0129] When the surface height change between the abnormal detection point and the surrounding valid detection points is continuous, and the dispersion of the focal position parameter does not exceed the preset dispersion threshold, the surrounding valid detection points are determined as adjacent reference detection points, and the predicted focal position of the abnormal detection point is determined based on the surface height information of the abnormal detection point and the focal position parameter of the adjacent reference detection point.

[0130] When an abnormal detection point is a local highest point, a local lowest point, or is located in a region of abrupt change in surface height, near the edge of an ore, a crack, or a hole, making the focal position parameters of the surrounding valid detection points unreliable, the weight of the focal position parameters of the surrounding valid detection points in the focal position prediction is reduced or canceled, and the predicted focal position of the abnormal detection point is determined according to the pre-established mapping relationship between surface height and focal position.

[0131] The refocusing search interval is determined based on the prediction uncertainty of the predicted focal point location, the surface height difference between the abnormal detection point and the surrounding valid detection points, the dispersion of the focal point location parameters of the surrounding valid detection points, and the preset minimum search range. When the abnormal detection point is a local highest or lowest point, or when the surface height difference, dispersion, or prediction uncertainty exceeds the corresponding preset threshold, the refocusing search interval is expanded.

[0132] The autofocus module uses the predicted focus position as its initial position and re-searches for the target focus position corresponding to the abnormal detection point within the refocus search interval. It then controls the laser to supplement the excitation of the abnormal detection point at the target focus position, obtaining the supplemented LIBS spectrum through the spectral acquisition module. If the pre-set focus quality or pre-set spectral quality conditions are still not met after the supplemented scan, the abnormal detection point is marked as abnormal and uniformly processed during subsequent spatial consistency correction.

[0133] Optionally, based on the variation range of element attributes and focal position of adjacent detection points in the selected detection area, an encrypted scanning area is determined, and at least one supplementary detection point is inserted between adjacent detection points in the encrypted scanning area to generate an encrypted dot matrix scanning grid.

[0134] For example, after scanning all detection points of the dot matrix scanning grid, the system adaptively densifies the grid based on the elemental analysis results and focusing parameter changes of the collected detection points. When the changes in the signal intensity, elemental confidence, semi-quantitative results, or mineral matrix category between adjacent detection points exceed a preset threshold, the area is identified as a region of drastic elemental variation, and a new detection point is added within that area. When the changes in focusing parameters, Z-axis compensation, surface height estimation, or focusing quality factor between adjacent detection points exceed a preset threshold, the area is identified as a region of significant surface undulation, and a new detection point is added within that area. Densification scanning can insert one or more supplementary detection points between existing adjacent detection points, or it can reduce the point spacing in local areas to form a locally high-density dot matrix.

[0135] For example, such as Figure 3As shown, this illustrates the entire process from ore surface image acquisition, scanning coordinate system establishment, point-by-point automatic focusing and spectral acquisition, anomaly point identification and rescanning, adaptive grid densification, to spectral preprocessing, focus quality correction, qualitative and quantitative analysis, and finally, the generation of an elemental distribution map. First, the raw ore to be tested is placed on a three-dimensional motion platform, and its surface morphology is captured using a top-down camera, i.e., the ore surface image. Then, a corresponding scanning coordinate system is established, and automatic focusing is performed according to the coordinates of the detection points in the dot matrix scanning grid, with focus parameters recorded. LIBS spectra are acquired, and anomaly detection is performed. If an abnormal scanning point is detected, automatic focusing is performed again for rescanning; if no abnormal scanning point is found, valid data is stored, and adaptive densification scanning and spectral preprocessing can be performed. After these operations, elemental analysis of the raw ore can be performed, such as quantitative analysis, focus correction, qualitative and interference processing, etc. Finally, an elemental distribution map of the raw ore surface is generated, such as a two-dimensional elemental distribution diagram.

[0136] Correspondingly, Figure 4 The diagram shows a two-dimensional distribution of the concentration of a certain element in a selected area on the surface of the raw ore. Different shades of color in the diagram represent the concentration distribution of the element in the selected detection area on the surface of the raw ore. The scale of the two-dimensional distribution diagram can be 0 μm, 0.25 μm, 0.5 μm, etc.

[0137] This approach improves or resolves issues in LIBS detection such as poor adaptability to surface undulations, insufficient spatial representation of the scanning area, difficulty in automatically identifying anomalies, significant spectral interference, insufficient matrix effect correction, and low reliability of elemental distribution results. This embodiment establishes a scanning coordinate system by performing top-down imaging of the raw ore surface and generating a dot matrix scanning grid within the user-selected detection area according to the set spot size and spacing between adjacent spots. At each detection point, an autofocus module acquires focusing parameters and controls the Z-axis motion mechanism of the three-dimensional motion platform to adjust the laser focus position, while simultaneously recording focusing quality parameters. The acquired LIBS spectrum undergoes preprocessing, feature spectral line extraction, multi-spectral line weighted confidence qualitative analysis, overlapping peak interference decomposition, focusing quality factor correction, spatial consistency correction, and quantitative analysis. Abnormal scanning points are identified and supplemented, and adaptively encrypted scanning is performed on areas with drastic elemental changes or large surface undulations. Finally, an elemental distribution map of the raw ore surface is generated, thereby improving the accuracy and stability of ore surface element detection.

[0138] Based on the above Figure 1 The method shown in this embodiment also provides a surface imaging device for raw ore based on laser-induced breakdown spectroscopy, such as... Figure 5 As shown, the device includes an image acquisition device 1, a three-dimensional motion platform 2, a laser 3, an autofocus module 4, a spectrum acquisition module 5, and a control module 6.

[0139] Image acquisition device 1 is used to acquire images of the surface of the raw ore; The three-dimensional motion platform 2 is used to carry the raw ore and drive the raw ore to move relative to the laser along the first horizontal direction and the second horizontal direction that are perpendicular to each other, so as to move different detection points on the surface of the raw ore to the laser action position in sequence. The autofocus module 4 includes an axial drive mechanism arranged along the laser optical axis. The autofocus module can move along the laser optical axis under the drive of the axial drive mechanism to evaluate the focus quality of different candidate focus positions and adjust the relative position between the laser focus and the detection point on the surface of the raw ore according to the focus quality evaluation results, so as to determine the target focus position parameters and focus quality parameters corresponding to the detection point. Laser 3 is used to excite the detection point at the target focal position; Spectrum acquisition module 5 is used to acquire the LIBS spectrum corresponding to the detection point; Control module 6 is connected to the image acquisition device, the 3D motion platform, the laser, the autofocus module, and the spectral acquisition module, and is configured as follows: Establish the coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system, determine the detection area in the raw ore surface image, and generate the initial set of scanning points and scanning path corresponding to the detection area; The scanning path controls the three-dimensional motion platform to move along the first and second horizontal directions to move each detection point to the laser action position in sequence. The autofocus module is controlled to move along the laser optical axis to autofocus each detection point. The laser and the spectral acquisition module are controlled to perform LIBS spectral acquisition on the detection points that have completed autofocus and record the focal position parameters and focusing quality parameters of each detection point. Based on the focusing quality parameters and LIBS spectral quality characteristics, the abnormal detection point is determined. The three-dimensional motion platform is controlled to move the abnormal detection point to the laser action position, and the autofocus module is controlled to move along the laser optical axis to refocus the abnormal detection point. The laser and spectral acquisition module are controlled to perform supplementary scanning on the refocused abnormal detection point. The encrypted scanning area is determined based on the variation range of element characteristic parameters and focal position between adjacent detection points. Supplementary detection points are inserted within the encrypted scanning area, the set of scanning points and the scanning path are updated, and automatic focusing and LIBS spectral acquisition are performed on the supplementary detection points. The LIBS spectra that meet the preset focusing quality conditions and preset spectral quality conditions are determined as effective LIBS spectra. Based on the coordinate mapping relationship, the elemental analysis results corresponding to the effective LIBS spectra are mapped to the original ore surface image to generate an elemental distribution map spatially registered with the original ore surface image.

[0140] Based on the above, Figure 1 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 The method shown.

[0141] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0142] Based on the above, Figure 1 The method shown, and Figure 5 To achieve the above objectives, the present application also provides an electronic device, comprising a storage medium and a processor; the storage medium for storing a computer program; and the processor for executing the computer program to implement the above-described virtual device embodiments. Figure 1 The method shown.

[0143] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0144] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0145] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. This application establishes a scanning coordinate system by performing top-view imaging of the surface of the raw ore, and generates a dot matrix scanning grid within the user-selected detection area according to the set spot size and the spacing between adjacent spots; the raw ore is moved along the X and Y directions by a three-dimensional motion platform to achieve planar positioning of each detection point; the focus is adjusted and the focus quality parameters are recorded by moving the autofocus module along the laser optical axis. The acquired LIBS spectrum is preprocessed, feature spectral lines are extracted, multi-spectral line weighted confidence qualitative analysis is performed, overlapping peak interference is decomposed, focus quality factor is corrected, spatial consistency is corrected, and quantitative analysis is performed; abnormal scanning points are identified and re-scanned, and adaptive densification scanning is performed on areas with drastic elemental changes or large surface undulations, finally generating an elemental distribution map of the raw ore surface, thereby improving the accuracy and stability of ore surface element detection.

[0147] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0148] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy, characterized in that, The method includes: Acquire an image of the surface of the raw ore and establish a coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system; In the ore surface image, a selected detection area is determined. Based on the image features, area boundaries, preset scanning resolution, and preset or custom scanning method of the selected detection area, an initial set of scanning points and a scanning path matching the selected detection area are generated. The detection points to be scanned are determined sequentially according to the scanning path. Based on the coordinates of the detection points to be scanned in the laser scanning coordinate system, the autofocus module is controlled to adjust the relative position between the laser focus and the surface of the detection point to be scanned, so that the laser beam is focused on the surface of the detection point to be scanned. The focus position parameters and focus quality parameters are recorded, and the initial scanning LIBS spectrum corresponding to the detection point to be scanned is acquired. Based on the focusing quality parameters and LIBS spectral quality characteristics, detection points that do not meet the preset focusing quality conditions or preset spectral quality conditions are identified as abnormal detection points. Based on the surface height information of the abnormal detection points and the focal position parameters of the non-abnormal detection points around the abnormal detection points, the predicted focal position and refocusing search interval of the abnormal detection points are determined. With the predicted focal position as the initial position, the abnormal detection points are automatically refocused and supplemented within the refocusing search interval to obtain the supplemented LIBS spectrum. Based on the variation range of elemental characteristic parameters and focal position between adjacent detection points, and the comparison results of the two variation ranges with the corresponding preset thresholds, the encrypted scanning area is determined according to the preset combination rules; supplementary detection points are inserted within the encrypted scanning area, the set of scanning points and the scanning path are updated, and the supplementary detection points are automatically focused and LIBS spectrum is acquired to obtain the encrypted scanning LIBS spectrum. The initial scan LIBS spectrum, the supplementary scan LIBS spectrum, and the encrypted scan LIBS spectrum that meet the preset focusing quality conditions and preset spectral quality conditions are determined as valid LIBS spectra. Based on the coordinate mapping relationship, the elemental analysis results corresponding to the effective LIBS spectrum are mapped to the original ore surface image to generate an elemental distribution map spatially registered with the original ore surface image.

2. The method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy as described in claim 1, characterized in that, The step involves determining a selected detection area in the ore surface image, and generating an initial set of scan points and a scan path matching the selected detection area based on the image features, area boundaries, preset scan resolution, and preset or custom scan method of the selected detection area. This includes: Obtain the image coordinates corresponding to the pixel positions in the surface image of the raw ore, and the actual physical coordinates of the raw ore surface positions represented by the pixel positions on the three-dimensional motion platform. Establish the coordinate mapping relationship based on the correspondence between the image coordinates and the actual physical coordinates. Based on the region boundary, laser spot size, preset scanning resolution, and scanning mode of the selected detection region, determine the image coordinates of each detection point within the selected detection region in the image coordinate system; Based on the coordinate mapping relationship, the image coordinates of each detection point are converted into scanning coordinates in the laser scanning coordinate system to generate the initial set of scanning points, and the scanning path is determined according to the scanning method. The scanning method includes at least one of point scanning, line scanning, grid scanning, contour following scanning, and custom path scanning. The initial set of scanning points includes an irregular set of points formed by cropping according to the region boundary of the selected detection area or a set of regular points of a preset size.

3. The method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy according to claim 2, characterized in that, The process involves sequentially determining the detection points to be scanned along the scanning path, and based on the coordinates of the detection points in the laser scanning coordinate system, controlling the autofocus module to adjust the relative position between the laser focus and the surface of the detection point, so that the laser beam is focused on the surface of the detection point. Focus position parameters and focus quality parameters are recorded, and the initial scanning LIBS spectrum corresponding to the detection point is acquired, including: The current detection point is determined according to the scanning path, and the three-dimensional motion platform is controlled to move along the first horizontal direction and the second horizontal direction that are perpendicular to each other, based on the scanning coordinates of the current detection point, so as to move the current detection point to the laser action position. Obtain the surface height information of the current detection point, and determine the initial focusing position or focusing search range along the laser optical axis based on the surface height information; The autofocus module is controlled to start from the initial focusing position or move along the laser optical axis within the focusing search range, so that the laser focus moves axially relative to the surface of the current detection point, and the focusing quality of different candidate focus positions is evaluated to determine the target focus position that meets the preset focusing conditions. The axial position of the autofocus module corresponding to the target focus position is recorded as the focus position parameter of the current detection point, and the focus evaluation result corresponding to the target focus position is recorded as the focus quality parameter; The current detection point is excited at least once at the target focal position, and the initial scan LIBS spectrum corresponding to the current detection point is acquired by the spectral acquisition module.

4. The method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy according to claim 3, characterized in that, Anomaly detection points are determined based on focusing quality parameters and LIBS spectral quality characteristics, and focus refocusing and supplementary scanning are performed on these anomaly detection points, including: The spectral quality parameters of each scanned detection point are determined based on the LIBS spectrum. The spectral quality parameters include at least one of the following: total spectral intensity, signal-to-noise ratio, baseline fluctuation amplitude, and number of effective characteristic spectral lines. The focusing quality parameter is compared with a preset focusing quality threshold, and the spectral quality parameter is compared with a preset spectral quality threshold. Scanned detection points that do not meet the preset focusing quality threshold or the preset spectral quality threshold are identified as abnormal detection points. Within a preset spatial neighborhood of the abnormal detection point, adjacent detection points are determined, and from these adjacent detection points, adjacent valid detection points that satisfy the preset focusing quality threshold and the preset spectral quality threshold are determined. The system acquires the surface height information of the abnormal detection point; determines the predicted focal position of the abnormal detection point based on the surface height information and the focal position parameters of the adjacent valid detection points; determines the refocusing search interval based on the surface height difference between the abnormal detection point and the adjacent valid detection points, the dispersion of the focal position parameters of the adjacent valid detection points, and a preset minimum search range; expands the refocusing search interval when the surface height difference or the dispersion exceeds a corresponding preset threshold; controls the autofocus module to re-determine the target focal position of the abnormal detection point within the refocusing search interval, using the predicted focal position as the initial position; and performs supplementary excitation on the abnormal detection point at the re-determined target focal position, acquiring the supplementary scan LIBS spectrum corresponding to the abnormal detection point. When the focusing quality parameters of the abnormal detection point after rescanning meet the preset focusing quality threshold, and the spectral quality parameters of the rescanned LIBS spectrum meet the preset spectral quality threshold, the rescanned LIBS spectrum replaces the initial scanned LIBS spectrum of the abnormal detection point; otherwise, the abnormal detection point is marked as an invalid detection point.

5. The method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy according to claim 4, characterized in that, The step of determining the encrypted scanning area based on the variation amplitude of element feature parameters and the variation amplitude of focal position between adjacent detection points, and according to the comparison results of the two variation amplitudes with corresponding preset thresholds, and in accordance with preset combination rules, includes: The elemental characteristic parameters of the element to be measured are extracted from the effective LIBS spectra corresponding to adjacent detection points, and the variation range of the elemental characteristic parameters between adjacent detection points is determined based on the difference of the elemental characteristic parameters between adjacent detection points. The magnitude of the change in focal position between adjacent detection points is determined based on the difference in focal position parameters between adjacent detection points. The variation range of the element feature parameters is compared with a first preset threshold, and the variation range of the focus position is compared with a second preset threshold; When the change in the element feature parameter exceeds the first preset threshold and the change in the focal position exceeds the second preset threshold, the region between the corresponding adjacent detection points and its preset neighborhood are determined as the encrypted scanning region. Based on the degree to which the change in the element feature parameters or the change in the focal position exceeds the corresponding preset threshold, the number of supplementary detection points or the scanning interval within the encrypted scanning area is determined, wherein the scanning interval of the supplementary detection points is less than the scanning interval of the initial set of scanning points. The supplementary detection points are added to the set of scanning points and the scanning path is updated. The supplementary detection points are then automatically focused and LIBS spectra are acquired according to the updated scanning path. The encrypted scanning LIBS spectra that meet the preset focus quality threshold and the preset spectral quality threshold are added to the effective LIBS spectra.

6. The method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy according to claim 1, characterized in that, Before mapping the elemental analysis results corresponding to the effective LIBS spectrum to the surface image of the raw ore, the method further includes: Based on a preset element spectral line library and preset wavelength matching conditions, one or more candidate feature spectral lines corresponding to the element to be measured are extracted from the effective LIBS spectrum. The algorithm detects whether there is spectral overlap between the characteristic spectral lines of different test elements. When spectral overlap is detected, the algorithm uses a spectral interference decomposition algorithm to perform multi-peak decomposition on the corresponding overlapping spectral segments to obtain the peak components corresponding to each candidate characteristic spectral line and the characteristic spectral line intensity parameters after overlap interference correction. Based on the focusing quality parameters corresponding to the effective LIBS spectrum and the pre-established mapping relationship between the focusing quality parameters and the spectral response, a focusing compensation factor is determined, and the characteristic spectral line intensity parameters after the overlap interference correction are compensated and corrected according to the focusing compensation factor to obtain the focusing-corrected characteristic spectral line intensity parameters. Based on the focused correction of the intensity parameters of the characteristic spectral lines corresponding to the same element to be tested and their corresponding weights, the element presence confidence of the element to be tested is determined, and based on the comparison between the element presence confidence and the preset judgment threshold, the element qualitative result of the element to be tested at the corresponding detection point is determined. Spectral features for characterizing the mineral matrix are extracted from the effective LIBS spectrum, and the spectral features are classified based on a preset mineral matrix classification model to determine the mineral matrix category of the corresponding detection point.

7. The method for imaging the surface of raw ore based on laser-induced breakdown spectroscopy according to claim 6, characterized in that, The process of mapping the elemental analysis results corresponding to the effective LIBS spectrum to the surface image of the raw ore includes: The semi-quantitative analysis model corresponding to the mineral matrix category is invoked, and the focused corrected characteristic spectral parameters and focusing quality factor are input into the semi-quantitative analysis model to obtain the semi-quantitative results of the element to be measured at the corresponding detection point. The semi-quantitative analysis model is established according to different mineral matrix categories, and the semi-quantitative results include at least one of content grade, relative content characterization value and concentration range; Based on the scanning coordinates of each detection point in the laser scanning coordinate system and the coordinate mapping relationship, determine the corresponding position of each detection point in the image of the raw ore surface; Based on the size of the laser spot, the corresponding image region of each detection point in the image of the raw ore surface is determined, and the qualitative and semi-quantitative results of the elements corresponding to each detection point are assigned to the corresponding image region to generate an element distribution map for characterizing the spatial occurrence characteristics of the elements to be tested on the surface of the raw ore.

8. A surface imaging device for raw ore based on laser-induced breakdown spectroscopy, characterized in that, The device includes: an image acquisition device, a three-dimensional motion platform, a laser, an autofocus module, a spectrum acquisition module, and a control module; The image acquisition device is used to acquire images of the surface of the raw ore; The three-dimensional motion platform is used to carry the raw ore and drive the raw ore to move relative to the laser along a first horizontal direction and a second horizontal direction that are perpendicular to each other, so as to move different detection points on the surface of the raw ore to the laser action position in sequence. The autofocus module includes an axial drive mechanism arranged along the laser optical axis. The autofocus module can move along the laser optical axis under the drive of the axial drive mechanism to evaluate the focus quality of different candidate focus positions and adjust the relative position between the laser focus and the detection point on the surface of the raw ore according to the focus quality evaluation results, so as to determine the target focus position parameters and focus quality parameters corresponding to the detection point. The laser is used to excite the detection point at the target focal position; The spectral acquisition module is used to acquire the LIBS spectrum corresponding to the detection point; The control module is connected to the image acquisition device, the three-dimensional motion platform, the laser, the autofocus module, and the spectral acquisition module, respectively, and is configured as follows: Establish a coordinate mapping relationship between the image coordinate system of the raw ore surface image and the laser scanning coordinate system, determine the detection area in the raw ore surface image, and generate the initial scanning point set and scanning path corresponding to the detection area; The scanning path is used to control the three-dimensional motion platform to move along the first horizontal direction and the second horizontal direction to move each detection point to the laser action position in sequence. The autofocus module is also controlled to move along the laser optical axis to autofocus each detection point. The laser and the spectral acquisition module are controlled to perform LIBS spectral acquisition on the detection points that have completed autofocus and record the focal position parameters and focal quality parameters of each detection point. Based on the focusing quality parameters and LIBS spectral quality characteristics, anomaly detection points are determined. The three-dimensional motion platform is controlled to move the anomaly detection points to the laser action position, and the autofocus module is controlled to move along the laser optical axis to refocus the anomaly detection points. The laser and spectral acquisition module are then controlled to perform supplementary scanning on the refocused anomaly detection points. The encrypted scanning area is determined based on the variation range of element characteristic parameters and the variation range of focal position between adjacent detection points. Supplementary detection points are inserted within the encrypted scanning area, the set of scanning points and the scanning path are updated, and the supplementary detection points are automatically focused and LIBS spectral acquisition is performed. The LIBS spectra that meet the preset focusing quality conditions and preset spectral quality conditions are determined as effective LIBS spectra. Based on the coordinate mapping relationship, the elemental analysis results corresponding to the effective LIBS spectra are mapped to the surface image of the raw ore to generate an elemental distribution map spatially registered with the surface image of the raw ore.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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