102 results about "Mineral identification" patented technology

The invention discloses a method for automatically identifying rock minerals based on spectral information, and particularly relates to the field of rock mineral identification, which comprises the following steps: acquiring spectral information of mixed minerals, screening mineral types matched with a static spectral feature library, and establishing feature-mineral type association. Similar mineral types are retrieved, a set number of samples are obtained, and the samples are put according to the collection coordinates; carrying out time sequence reflection spectrum data acquisition on the similar mineral samples, extracting spectral features and carrying out timestamp alignment; performing three-dimensional structuring on the spectral features, extracting dynamic spectral feature elements by adopting a sliding window, and constructing a three-dimensional matrix; distortion identification is carried out on the time window spectrum characteristic elements of the sunshine cycle, and distortion fragments are eliminated; establishing a feature library according to the effective dynamic spectrum feature element proportion, and generating a ready signal to trigger model training; and training a rock mineral identification model based on the convolutional neural network, and carrying out whole-region accurate scanning and automatic identification on the trained model.

The invention provides an altered mineral recognition model construction method and system and a storage medium, and relates to the technical field of geological exploration, and the method comprises the steps: obtaining remote sensing data of a geological resource environment hyperspectral microsatellite for a target region; performing feature enhancement on the remote sensing data through a band ratio method to obtain band ratio features of the remote sensing data; performing principal component analysis on the remote sensing data to obtain principal component characteristics of the remote sensing data; splicing the waveband ratio feature, the principal component feature and the remote sensing data to obtain a high-dimensional feature matrix, and allocating an actual target mineral proportion to each pixel in the high-dimensional feature matrix according to preset mineral sample data; and training the initial recognition model according to the high-dimensional feature matrix and the actual target mineral proportion, and taking the trained initial recognition model as an altered mineral recognition model. According to the method, through multi-dimensional feature extraction and an advanced machine learning algorithm, the recognition effect on altered minerals is remarkably improved.

The invention discloses a hyperspectral satellite target rock and mineral identification method and system based on spectral characteristic parameters. The method comprises the following steps: S1, collecting and preprocessing a standard rock and mineral reflection spectrum curve; s2, collecting a regional hyperspectral satellite image and carrying out image preprocessing; s3, identifying diagnostic spectral reflection and absorption characteristics of the target rock mine based on the standard curve; s4, screening and extracting a diagnostic spectral range of the hyperspectral satellite image; s5, calculating diagnostic spectral characteristic parameters of the hyperspectral image target rock mine; and S6, on the basis of the S5, processing the spectral characteristic parameters obtained by calculation, and realizing target rock and mine identification based on multi-dimensional diagnosis spectral characteristics. According to the method, the application potential of the hyperspectral satellite image in the field of remote sensing geology is effectively improved and the scientificity and the accuracy of the hyperspectral satellite image in large-range rock and ore recognition drawing application are improved through the target rock and ore standard spectral curve characteristic analysis, hyperspectral satellite image processing and spectral characteristic parameter estimation methods.

The invention relates to the cross technical field of artificial intelligence and mineralogy, and discloses an AI mineral automatic identification method based on an optical microphotograph, comprising the following steps: step S1: data acquisition and preprocessing; s2, data classification and labeling: constructing a rock microscopic image data set by adopting a two-stage labeling process; s3, building and training a deep learning model; s4, model optimization and quantitative feature learning; s5, developing a software platform; and S6, hardware system combination and full-process automation are carried out. In the aspect of operation convenience, a full-automatic slice scanning hardware system is constructed and combined with an intelligent software platform, so that full-process automatic operation from automatic rock slice scanning, automatic mineral analysis to automatic PDF report generation is realized, manual intervention is greatly reduced, the operation difficulty and complexity are reduced, and the working efficiency is improved. And even an operator without rich experience can easily complete complex mineral identification work.

The invention discloses a mineral identification method and system based on multi-modal knowledge distillation and a storage medium, and relates to the technical field of mineral identification, and the mineral identification method comprises the steps: obtaining various mineral images and physical attribute data, and coding the images and physical attribute data; inputting various mineral images and coded vectors into a mixed mineral recognition model to obtain the probability of each mineral category; wherein the mineral image and the encoded vector are used for training a multi-modal teacher model, the multi-modal teacher model is used for training a single-modal student model, and a mineral category similarity relation and physical attribute characteristics learned by the multi-modal teacher model are migrated to the single-modal student model through a knowledge distillation strategy; and if the input data only contains the mineral image, outputting the probability of each mineral category by using the trained single-mode student model, otherwise, outputting the probability of each mineral category by using the multi-mode teacher model. According to the method, multi-modal features can be fused to improve the mineral recognition accuracy, and high precision is still kept in the absence of physical attributes.

The invention relates to the technical field of mineral exploration, and discloses a mineral resource intelligent identification method based on multi-source remote sensing data, and the method constructs an intelligent identification model based on a deviation analysis and correction mechanism through integrating the multi-source remote sensing data, geological data and mineral reaction characteristic signals, can dynamically adapt to different environmental conditions, and improves the identification efficiency. According to the method, the mineral resource exploration precision and reliability are remarkably improved, through dynamic correction and multi-coefficient integration, the misjudgment rate is effectively reduced, the recognition accuracy and precision ratio of the mineral target area are improved, potential mining areas can be rapidly screened out in a large-range area, manual intervention is reduced, the exploration efficiency is remarkably improved, and the method is suitable for popularization and application. By generating a plurality of mineral marker guidance coefficients KWX, the mineral resource potential is comprehensively evaluated from multiple angles, and the accuracy and stability of mineral recognition are remarkably improved.

The invention belongs to the technical field of vanadium enrichment mechanisms of phosphorus vanadium ores, and discloses a research method of a vanadium enrichment mechanism in a diagenetic stage of phosphorus vanadium ores, which comprises the following steps: S1, sample collection and treatment; s2, rock ore identification and mineral composition analysis; s3, analyzing the occurrence state and microcell morphology of vanadium; s4, performing geochemical analysis on the whole rock; s5, in-situ microcell geochemical analysis is carried out; s6, phosphorus nodule analysis; s7, comprehensive analysis and mineralization model establishment; according to the method, mineralogy, total rock geochemistry, micro-area surface scanning and other multi-scale and multi-means research methods are comprehensively utilized, and the vanadium occurrence state, the ore-forming fluid evolution and the phosphorus-vanadium coupling relation are finely described; in-situ microcell geochemical analysis is introduced into phosphorus nodule analysis, the internal structure, the growth sequence and element distribution of the phosphorus nodule are directly revealed, obvious frontier and systematicness are achieved, and reliable theoretical support is provided for accurate exploration of vanadium ore resources and green and efficient dressing and smelting process research and development.

The invention provides a molybdenum mineral content measuring method, and belongs to the field of process mineralogy, and the method comprises the following steps: carrying out ore grinding, ore washing and desliming treatment on a to-be-measured sample to obtain slurry and ore washing concentrate; the ore washing concentrate is subjected to gravity separation, and gravity separation concentrate and gravity separation tailings are obtained through separation; mixing the slime and the gravity concentrate to obtain a mixed sample, and performing molybdenum phase analysis to obtain the content of molybdenum oxide; an automatic mineralogical analysis sample of the mixed sample and an automatic mineralogical analysis sample of the gravity separation tailings are prepared respectively, automatic mineralogical analysis is carried out, and a molybdenite content correction coefficient and a molybdenum oxide content correction coefficient are calculated; and calculating the content of various molybdenum minerals in the to-be-detected sample in combination with the correction coefficient and the mineral content data. By optimizing the sample preparation process, molybdenite oxidation is reduced, and the mineral recognition accuracy is improved. Molybdenum oxide and molybdenum sulfide are treated separately, different correction processes are adopted, and the accuracy of ore molybdenum mineral measurement is improved in a targeted mode.

The invention relates to the technical field of hyperspectral alteration, in particular to a satellite hyperspectral alteration mineral remote sensing quantitative recognition method based on deep learning. The method comprises the following steps: acquiring high-resolution 5B satellite hyperspectral data, DEM data and atmospheric parameter data; performing multi-source noise suppression processing on the high-resolution 5B satellite hyperspectral data to obtain a noise correction data set; performing terrain shadow correction based on the noise correction data set and the DEM data, and performing atmospheric correction in combination with the atmospheric parameter data to obtain a pre-corrected hyperspectral data set; through multi-source data fusion, noise and environment interference correction, key wave band screening and multi-dimensional depth feature extraction and fusion, the quality and characterization capability of hyperspectral data are effectively improved, and a solid data basis is provided for subsequent refined mineral recognition and analysis.

The invention relates to a Mars mineral identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a Mars surface image in a preset wavelength range; acquiring a plurality of mineral pattern spots of the Mars surface image in a pixel-by-pixel manner; extracting deep spatial spectral features of different mineral pattern spots through a pre-trained multi-scale adaptive convolutional neural network; and classifying minerals contained in the Mars surface image according to the deep spatial spectral features of all mineral pattern spots to obtain a classification result. According to the method, the Mars surface image is obtained by combining near-infrared hyperspectral imaging, deep spatial spectral features of different mineral pattern spots are extracted based on the pre-trained multi-scale adaptive convolutional neural network, and rapid, lossless and accurate identification of Mars minerals can be realized.

The invention relates to the technical field of horizontal well monitoring, in particular to a shale oil horizontal well trajectory judgment method based on rock debris mineral parameters, which comprises the following steps: taking to-be-analyzed rock debris of a newly drilled horizontal well for sample preparation to obtain a rock debris sample; obtaining mineral information of the rock debris sample by using an automatic mineral identification system; and screening the mineral information of the rock debris sample by using the mineral characteristic standard of each small layer to obtain the small layer to which the rock debris sample belongs. The rock debris of the horizontal well is used for manufacturing the rock debris sample, the automatic mineral recognition system is used for obtaining the mineral information of the rock debris sample, the small layer position corresponding to the mineral information of the rock debris sample is obtained in combination with the mineral characteristic standard of each small layer, and compared with rock core sampling, the mode for achieving small layer judgment is low in cost, the sample is easy to obtain, and the accuracy is high. The method is suitable for wide popularization.

The invention discloses a mineral identification method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a sample microscopic tomography image and a scanning electron microscope mineral detection image matched with the sample microscopic tomography image; according to the sample microscopic tomography image and the scanning electron microscope mineral detection image, training a U-Net model to obtain a mineral identification model; and inputting a to-be-recognized microscopic tomography image into the mineral recognition model to obtain a mineral recognition result output by the mineral recognition model. According to the technical scheme provided by the invention, accurate identification of core minerals is realized, so that high-precision reconstruction and quantitative characterization of the three-dimensional digital core are realized.

The invention discloses a mineral identification method based on full-spectrum hyperspectral remote sensing data, which belongs to the technical field of spectrum detection and specifically comprises the following steps: acquiring full-spectrum hyperspectral remote sensing data of a to-be-detected area; dividing the target area into a plurality of grids, and generating a hyperspectral curve according to the reflectivity of a ground object; any grid is used as an undetermined grid, the spectrum difference degree of the undetermined grid and the adjacent grids is calculated, and all the adjacent grids are traversed when the difference degree is lower than a threshold value; obtaining an undetermined grid set, and calculating a mean value to determine a central grid; calculating the product of the spectral difference degree between the central grids and the Euclidean distance, screening the closest central grid, marking a corresponding set as a target area, setting a new grid, dividing the target area again, calculating the spectral difference degree with a peripheral grid by taking a new undetermined grid as a center, marking the spectral difference degree with the peripheral grid being lower than a threshold value as a new target area, and repeating until the nth grid, obtaining a final target area; and extracting spectrum and texture characteristic parameters, comparing with a mineral spectrum curve, and outputting a mineral identification result.

The invention relates to a rock CT image mineral identification method based on Latin hypercube random search, and the method comprises the steps: carrying out the CT scanning of a to-be-identified rock, and obtaining a slice gray-scale map of the to-be-identified rock; according to the slice grey-scale map of the to-be-identified rock, obtaining a two-dimensional distribution map of each mineral in a target area of the to-be-identified rock; according to the two-dimensional distribution diagram of each mineral, obtaining the area proportion and the area proportion range of each mineral, and the dispersion index and the dispersion index range of each mineral; determining constraint conditions according to an area proportion and an area proportion range, and constructing a mathematical model based on a loss function of a dispersion index; the mathematical model is solved based on a Latin hypercube random search method, and the gray threshold segmentation interval of each mineral is obtained; and recognizing the mineral components of the rock to be recognized according to the gray threshold segmentation interval of each mineral. According to the invention, the mineral particles in the rock can be accurately identified.

The invention relates to a target mineral identification method and device and computer equipment. Comprising the following steps: firstly, primarily identifying concentrate of a main ore belt or a main ore spot, so that the concentrate containing target minerals can be effectively identified, the subsequent data processing pressure is reduced, and the primary detection efficiency and accuracy are improved; next, the pixels of the secondary ore zone are updated to weaken false ore position information caused by the edge and thickness of the ore, and mistakes and omissions of subsequent identification are avoided; and finally, the target ore plot after pixel updating is accurately re-identified based on the main ore plot, so that the overall performance of ore detection is improved.

This invention discloses a Monte Carlo-based mineral assemblage analysis method, belonging to the field of mineral analysis. The invention includes the following steps: multi-source data fusion and preprocessing; physical model construction and initial solution generation; multi-scale feature fusion and extraction; Monte Carlo random sampling and solution space exploration; physical-data dual-driven evaluation and screening; iterative update and model optimization; convergence judgment and result output. This invention effectively solves the problem of multiple solutions in mineral assemblage identification by combining multi-source data fusion, a physical-data dual-driven model, and the Monte Carlo method with multi-scale feature fusion and iterative optimization. This method can integrate geophysical, geochemical, remote sensing, borehole core, and mineral spectral data, and through iterative model updates, gradually converges to the most probable mineral assemblage, providing uncertainty assessment, thereby improving the accuracy and reliability of mineral identification.

The invention discloses a heavy mineral placer prediction method, device and equipment based on paleo-coastline reconstruction, and the method comprises the steps: determining the paleo-sea level relative height of a sample region, and generating a target paleo-coastline through a region growing algorithm; generating an ancient landform elevation model, identifying a plurality of ancient landform units based on the topographic position index, and screening out ancient landform exposure units; performing mineral identification on the ancient landform exposure unit to obtain a heavy mineral abundance map; constructing a hydrodynamic-deposition coupling model, identifying a sediment accumulation area of the sample area, and comparing the sediment accumulation area with the heavy mineral abundance map to obtain a heavy mineral enrichment area; dividing a positive sample area and a negative sample area, and training to obtain a heavy mineral placer prediction model; a mineralization probability distribution diagram of the target area is obtained through prediction, and the heavy mineral target area is determined according to the mineralization probability distribution diagram. The method improves the prediction precision of the mineralization probability of the heavy minerals, thereby improving the efficiency and accuracy of positioning and exploration of heavy mineral placers, and can be applied to the technical field of resource detection.

The invention discloses an underground soil layer identification method based on a static cone penetration test device. The method comprises the steps that the static cone penetration test device collects an original spectral data curve; preprocessing the original spectral data curve to obtain a standardized spectral reflectivity curve and forming a data set; manually annotating the data set and dividing the annotated data set into a training set, a verification set and a test set; training and verifying the constructed mineral recognition model by adopting a training set and a verification set to obtain a trained mineral recognition model; inputting the test set into the trained mineral identification model, and outputting prediction probabilities of n minerals corresponding to a spectral reflectivity curve; and according to the prediction probability of the n minerals, performing soil layer category judgment on the collected soil body to obtain a depth-soil layer layering information table. According to the underground soil layer identification method, on the premise that a soil body structure is not damaged, high-precision identification of fine soil layer changes is achieved through the trained mineral identification model, and manual intervention and subjective judgment errors are greatly reduced.

The application discloses a mineral identification method based on Monte Carlo simulation, comprising the following steps: S100, placing a prepared sample on a sample stage of a scanning electron microscope, controlling the sample stage to move, and splicing out a light microscope image or a BSE image; S200, selecting a target area in the spliced light microscope image or BSE image; S300, causing the scanning electron microscope and the energy spectrometer to scan the target area, and obtaining a mineral distribution map of the sample; wherein the EDS parameters corresponding to the energy spectrometer include a scanning step distance, and the scanning step distance is determined in the following manner: adopting a Monte Carlo simulation method, determining the horizontal width of the element characteristic X-ray of the mineral in the sample; and according to the horizontal width of the characteristic X-ray, determining the scanning step distance. Through the constitution, the identification accuracy and efficiency of the complex mineral in the rock sample are effectively improved.

The application discloses a deep-sea mineral identification method and system based on deep learning, which comprises collecting data, shooting a video of underwater mineral distribution and recording geographical position coordinates of the mineral distribution; processing data, reading the collected video data of underwater mineral distribution according to the time length of one frame, screening out pictures related to the mineral distribution from the video data as a data set; dividing the data set into a training set, a test set and a verification set; deep learning training, putting the pictures of the training set into a deep learning network model for training to obtain a trained deep learning network model; deep learning verification, putting the pictures of the verification set into the trained deep learning network model for verification until the verification is qualified; outputting the pictures of the verification set in the form of a heat map; mining vehicle operation, according to the geographical position coordinates of the mineral, the mining vehicle operates, and the heat map is output to guide the mining vehicle to reach the mineral distribution area. The application can realize high-precision real-time identification of seabed mineral distribution.

The invention relates to the technical field of mineral recognition, in particular to a mineral recognition method based on a neural network. The method comprises the following steps: dividing a target mineral video into a plurality of shot segments to obtain A; traversing A to obtain a visual feature vector Fi, v, a voice feature vector Fi, a and a text feature vector Fi, t of Ai; when i > = p + 1, a fusion feature Mi corresponding to Ai is obtained, Mi = [Fi, v, Fi, a, Fi, t, Fi, b, Oi-1, Oi-2,..., Oi-j,..., Oi-p], Fi and b are mineral clustering result features corresponding to Ai, and Oi-j is a content feature corresponding to the (i-j) th shot segment; inputting the Mi into a trained neural network model, and obtaining structured information corresponding to the Ai; the structured information corresponding to the Ai comprises the scene type of the current shot and the name and state of the appearing mineral product. According to the invention, the accuracy of mineral product identification is improved.

The invention discloses a mineral combination analysis method based on Monte Carlo, and relates to the field of mineral analysis, and the method comprises the following steps: multi-source data fusion and preprocessing; constructing a physical model and generating an initial solution; performing multi-scale feature fusion extraction; monte Carlo random sampling and solution space exploration are carried out; performing physical-data dual-drive evaluation and screening; carrying out iterative updating and model optimization; and carrying out convergence judgment and result output. Through multi-source data fusion, a physical-data dual-drive model and a Monte Carlo method, and in combination with multi-scale feature fusion and iterative optimization, the problem of multiplicity of solutions of mineral combination recognition is effectively solved. According to the method, geophysics, geochemistry, remote sensing, drilling core and mineral spectrum data can be integrated, the model is updated through iteration, the most possible mineral combination is gradually converged, uncertainty evaluation is provided, and therefore the accuracy and reliability of mineral recognition are improved.

This invention provides a method for screening characteristic bands of Martian surface minerals, comprising: acquiring a standard hyperspectral dataset of Martian minerals, dividing it into a training hyperspectral dataset and a test hyperspectral dataset according to a preset division ratio, and performing data preprocessing on each dataset; constructing a band screening supervised learning network model, iteratively training it on the preprocessed training hyperspectral dataset to obtain an optimized band screening supervised learning network model, and determining the band screening results; performing band screening on the preprocessed test hyperspectral dataset based on the band screening results, and determining the test classification accuracy; and, if the test accuracy meets a preset accuracy condition, performing band screening on the Martian mineral hyperspectral dataset to be screened according to the optimized band screening supervised learning network model to obtain the target screening dataset. This method can accurately screen characteristic bands of Martian surface minerals, thereby improving the accuracy of Martian surface mineral identification and classification.

The present application belongs to the technical field of oil and gas exploration, and particularly relates to a kind of exploration method of restricted tidal flat facies carbonate rock gas reservoir. Based on rock, logging data, seismic data, experimental test analysis data, through microfabric analysis, macro and micro combination approach, through rock fabric and mineral identification, restricted tidal flat facies rock development characteristics and regional scope are identified and defined; wherein the rock samples include pore development samples and samples with lithology of dark mudstone and argillaceous carbonate rock; frequency spectrum analysis method, weighted average method, organic matter mass balance method and coherent technology method are used to construct high-frequency sequence lithology-electricity interpretation model and multi-scale through-source fault transport pattern; thus, restricted tidal flat facies carbonate rock gas reservoir accumulation mode is obtained to explore restricted tidal flat facies carbonate rock gas reservoir. The present application innovatively explores restricted tidal flat facies carbonate rock gas reservoir, and solves the technical problems of identification and fine description of this type of gas reservoir.

The invention provides a mineral dynamic identification and accurate sampling method and system based on deep learning, and the method comprises the steps: obtaining mineral image data and laser radar ranging data on a mine car in an underground environment, and carrying out the preprocessing of the data, and obtaining mineral feature data; designing a deep convolutional neural network architecture with multi-scale feature fusion and performing multi-stage training to determine a mineral recognition model; performing target detection by using a mineral recognition model, and realizing multi-target tracking through a Kalman filter and a Hungary algorithm; calculating mine car motion parameters by using FAST corner detection, ORB descriptors and an optical flow algorithm; predicting the future position of the target mineral by using multi-sensor fusion and an extended Kalman filter; and based on the predicted position, precise sampling operation is executed through quintic polynomial interpolation and dynamic compensation control. According to the invention, high-precision mineral identification and dynamic accurate sampling can be realized in a complex underground environment, and the representativeness and production efficiency of mineral sampling are obviously improved.

The invention provides a mineral identification reliability-driven task-type hyperspectral quality evaluation method, which relates to the technical field of hyperspectral remote sensing, and comprises the following steps: initializing P evaluation task containers of P types of target evaluation minerals; executing spatial index segmentation on the hyperspectral data cube to obtain P spatial data subsets; performing mineral exclusive quality analysis process treatment in an isolated manner, and outputting P mineral reliability feature sets; calculating P mineral recognition reliability indexes; mapping and extracting P-pixel-level local signal-to-noise ratios and P-pixel-level absorption depth stability; executing reliability partitioning, and outputting P reliability hierarchical mapping results; and outputting a global mineral recognition reliability thermodynamic diagram. The technical problem that the subsequent mineral recognition result is unreliable due to the fact that a hyperspectral data quality evaluation method in the prior art focuses on evaluation by using global image quality indexes and cannot reflect key diagnosis wavebands on which specific mineral recognition depends is solved.

The invention discloses an intelligent recognition green separation method and system for pegmatite type lithium ores, and relates to the technical field of mineral recognition and separation. Training an intelligent identification and sorting decision model; ore pretreatment and intelligent roughing waste throwing; stage ore grinding and intelligent real-time separation; by means of the method, the high recovery rate of lithium minerals and high-grade concentrate output can be achieved.

The application relates to a U-Net shale authigenic mineral identification method based on a residual structure, and relates to the technical field of geophysics. The method comprises the following steps: acquiring sample backscattering, cathodeluminescence and element distribution image data; constructing a data set and making labels; inputting the data set into a U-Net neural network model based on a residual structure to perform training, so as to obtain a network model capable of identifying different types of authigenic minerals; and using the trained U-Net model to perform pixel-level fine segmentation on different types of authigenic minerals in a to-be-detected image, so as to realize high-precision automatic classification of different types of authigenic minerals. The method effectively solves the problems of great difficulty and low efficiency in identifying different types of authigenic minerals in fine-grained shale, and realizes the needs of rapid and accurate segmentation and quantitative analysis of a large number of samples, thereby providing important microscopic image basis for the research contents of shale oil and gas reservoir physical property changes and reservoir reconstruction schemes.

The embodiment of the application discloses a kind of based on image segmentation's paragenesis mineral identification method and system, the method and system include obtaining mineral image and marking and expanding mineral image segmentation data set, construct with Swin Transformer as encoder, improved feature pyramid as decoder mineral image segmentation model, and using multi-scale pyramid pooling and global attention module to fuse the mineral feature information of different scales, obtain more comprehensive and rich mineral feature representation, effectively integrate the context information of mineral image, strengthen the extraction ability of model for different abstract level features, maintain the richness of feature map, improve mineral segmentation performance, when training, using transfer learning to freeze the weight of the encoder Swin Transformer after pre-training in ImageNet and fine-tuning in mineral classification data set, the model obtained using mineral segmentation data set is trained, the obtained model solves the problem that existing method and system can only identify mineral species in paragenesis mineral image, cannot accurately locate various mineral.

This invention discloses an automatic identification method for fluorite minerals based on a civilian digital camera, belonging to the fields of geological remote sensing and automated mineral processing technology. The method includes the following steps: S1, taking a picture under natural visible light to obtain image P1; S2, taking a picture under ultraviolet light to obtain image P2; S3, setting a position calibration flag; S4, combining images P1 and P2 to form a new raster set M; S5, performing principal component analysis on the new raster set M to obtain the analyzed principal component PC, and determining the principal component of the fluorescent mineral; S6, determining the anomaly threshold and cutting out anomalies; S7, completing the fluorescent mineral identification process. By using a common digital camera to take pictures of the fluorescent minerals to be detected under ultraviolet and natural visible light, the hardware cost is low. The determination of the anomaly threshold and the cutting out of anomalies result in a low detection error rate and broad application prospects.