Method and device for characterizing uranium minerals in low-grade uranium ores

Abstract

The application discloses a low-grade uranium ore mineral characterization method and device, and belongs to the technical field of mineral analysis and testing, and solves the deficiency of a traditional low-grade uranium ore analysis and testing method. The application obtains the element concentration of each pixel on a low-grade uranium ore sample by using mu-XRF, and constructs a single-element concentration matrix. According to the global element concentration mean value and standard deviation of the low-grade uranium ore sample, the threshold value of the global single element is calculated, and the single-element connected domain is extracted according to the single-element concentration matrix and the threshold value. The high-value anomaly area is identified based on the single-element concentration matrix and the single-element connected domain. The high-value anomaly area of the low-grade uranium ore sample is analyzed by using TIMA, and the analysis result of the low-grade uranium ore sample is obtained. Compared with the traditional manual characterization method, the application has the advantages of fast analysis speed, high efficiency, strong automation and high precision, and can also provide more abundant analysis information.

Description

Technical Field

[0001] This invention belongs to the field of mineral analysis and testing technology, and specifically relates to a method and apparatus for characterizing uranium minerals in low-grade uranium ore. Background Technology

[0002] my country's uranium resources mainly consist of hard-rock deposits in the south and sandstone deposits in the north. Geological exploration units have conducted extensive investigations into these two types of resources and achieved positive results. However, for a long time, there has been limited exploration of uranium resources associated with large- to super-large polymetallic deposits. It is estimated that the uranium reserves in these deposits are considerable and will be an important part of future uranium resources. The low-grade uranium ore in these deposits is characterized by "low grade, large scale, and complex composition." The uranium mineral particles are at the micro-nano scale and are dispersed among metallic minerals and gangue minerals.

[0003] Traditional uranium mineral characterization methods primarily relied on researchers' subjective experience, using polarizing microscopes and electron probe microanalysis. The main procedure involved preparing the sample into an electron probe slide or optical slide, observing the sample under a polarizing microscope to identify uranium minerals, taking microscopic photographs of the minerals, and finally using electron probe microanalysis to analyze the composition of the minerals and determine their type, while simultaneously capturing backscattered images. However, this method suffers from several problems when characterizing uranium minerals in low-grade uranium ores. Firstly, low-grade uranium ore samples contain low levels of uranium, resulting in highly dispersed uranium minerals. Secondly, the ore composition is often complex, and the uranium minerals can be very small, making it difficult for researchers to quickly locate them using polarizing microscopes, often requiring a lengthy search. Furthermore, researchers' observations of the optical properties and chemical composition of uranium minerals depend heavily on personal experience, introducing a high degree of subjectivity and leading to inaccuracies in the precise identification of uranium minerals. Summary of the Invention

[0004] In view of the above analysis, the present invention aims to provide a method and apparatus for characterizing uranium minerals in low-grade uranium ore, so as to solve one or more of the above-mentioned problems existing in the prior art.

[0005] The objective of this invention is achieved as follows:

[0006] A mineral analysis method for low-grade uranium ore, comprising the following steps:

[0007] The elemental concentration of each pixel on a low-grade uranium ore sample was obtained using μ-XRF, and a single-element concentration matrix was constructed.

[0008] Based on the mean and standard deviation of the elemental concentration across the entire test domain of the low-grade uranium ore sample, the threshold of a single element across the entire domain is calculated, and the connected components of a single element are extracted based on the single element concentration matrix and the threshold.

[0009] High-value anomaly regions are identified based on single-element concentration matrices and single-element connected components; among them, regions with concentrations greater than those in the single-element connected components within the regions determined by the single-element concentration matrix are high-value anomaly regions.

[0010] TIMA analysis was used to analyze high-value anomaly areas in low-grade uranium ore samples, and the analytical results of the low-grade uranium ore samples were obtained.

[0011] The uranium mineral characterization method for low-grade uranium ore in this application utilizes μ-XRF to obtain the elemental concentration of each pixel on the low-grade uranium ore sample, constructing a single-element concentration matrix. Based on the mean and standard deviation of the elemental concentration across the entire test area of ​​the low-grade uranium ore sample, a threshold for each single element is calculated, and single-element connected components are extracted based on the single-element concentration matrix and the thresholds. High-value anomaly regions are identified based on the single-element concentration matrix and the single-element connected components. TIMA analysis is used to analyze the high-value anomaly regions of the low-grade uranium ore sample, obtaining the analytical results of the low-grade uranium ore sample. Compared with traditional manual characterization methods, this application offers faster analysis speed, higher efficiency, stronger automation, and higher accuracy, while also providing richer analytical information.

[0012] As one preferred embodiment, the process of extracting single-element connected components based on a single-element concentration matrix and a threshold includes the following steps:

[0013] Wavelet transform is applied to the single-element concentration matrix to obtain the enhancement matrix;

[0014] Calculate the adaptive global threshold under global and local statistical features;

[0015] By combining the enhancement matrix and the adaptive global threshold, single-element connected components are extracted.

[0016] As a preferred embodiment, the process of identifying high-value anomaly regions based on single-element concentration matrices and single-element connected components includes the following steps:

[0017] Concentration contour maps of each element are created based on the single-element concentration matrix, and regions with concentrations greater than those of the single-element connected domains are marked as high-value anomaly areas.

[0018] As a preferred embodiment, the process of identifying high-value anomaly regions based on single-element concentration matrices and single-element connected components further includes the following steps:

[0019] Update single-element connected components based on multi-scale verification;

[0020] Anomalies are identified in the single-element concentration matrix based on the updated single-element connected components, and spatial continuity is constrained. Smooth high-value anomaly regions are generated based on smooth traps.

[0021] As one preferred embodiment, the process of using TIMA to analyze high-value anomaly regions in low-grade uranium ore samples to obtain analytical results for the low-grade uranium ore samples includes the following steps:

[0022] Collect BSE images and energy spectrum information of high-value anomaly regions;

[0023] Based on the energy spectrum information, the energy spectrum data of each pixel is determined in order to determine the energy spectrum and element content of each mineral particle in the low-grade uranium ore sample;

[0024] The boundaries of mineral grains were determined by combining BSE images, and the energy spectrum and elemental content were compared with the standard mineral energy spectrum and composition in the database to determine the types and names of uranium minerals in the low-grade uranium ore samples.

[0025] Using TIMA, the number, size, mass percentage, and volume percentage of uranium minerals in high-value anomaly areas were obtained. Combined with BSE images, the spatial location of uranium minerals in low-grade uranium ore samples, their contact relationships with other minerals, their replacement relationships, and their mineral symbiotic relationships were accurately located.

[0026] As one preferred embodiment, the process of collecting energy spectrum information includes the following steps:

[0027] Optimize the EDS dwell time to enhance the EDS reception intensity for energy spectrum data of low-abundance elements, as shown in the following formula:

[0028] ;

[0029] in, Indicates the base duration of stay. Represents the grayscale response coefficient. This represents the image gray level at pixel (i, j). This represents the maximum gray level of the high-value anomaly region.

[0030] As one preferred embodiment, the low-grade uranium ore sample is coated with an electron probe sheet of a low-grade uranium ore block.

[0031] A device for characterizing uranium minerals in low-grade uranium ore, comprising:

[0032] The matrix construction module is used to obtain the elemental concentration of each pixel on a low-grade uranium ore sample using μ-XRF and construct a single-element concentration matrix.

[0033] The element processing module is used to calculate the threshold of a single element in the entire test area based on the mean and standard deviation of the element concentration in the low-grade uranium ore sample, and to extract the connected components of a single element based on the single element concentration matrix and the threshold.

[0034] The region segmentation module is used to identify high-value anomaly regions based on the single-element concentration matrix and the single-element connected component; among the regions determined by the single-element concentration matrix, the regions with concentrations greater than those of the single-element connected components are high-value anomaly regions.

[0035] The results analysis module is used to analyze high-value anomaly areas in low-grade uranium ore samples using TIMA to obtain the analysis results of low-grade uranium ore samples.

[0036] At least one embodiment of this application also provides a data control device, including:

[0037] One or more memories that store computer-executable instructions non-transitory;

[0038] One or more processors are configured to run computer-executable instructions, wherein the computer-executable instructions are executed by the one or more processors to implement the method for characterizing uranium minerals in low-grade uranium ore according to any embodiment of the present application.

[0039] At least one embodiment of this application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement a method for characterizing uranium minerals in low-grade uranium ore according to any embodiment of this application.

[0040] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings.

[0042] Figure 1 A flowchart of a method for characterizing uranium minerals in low-grade uranium ore according to an embodiment of the application;

[0043] Figure 2 A flowchart of a preferred embodiment of a method for characterizing uranium minerals in low-grade uranium ore;

[0044] Figure 3 Flowchart of a method for characterizing uranium minerals in low-grade uranium ore according to another preferred embodiment;

[0045] Figure 4 This is a structural diagram of a uranium mineral characterization device module in low-grade uranium ore according to an embodiment of the application;

[0046] Figure 5 A schematic block diagram of a data control device provided by the present invention;

[0047] Figure 6 This is a schematic diagram of a non-transitory computer-readable storage medium provided by the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] To facilitate understanding of the embodiments of this application, further explanation and description will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of this application. In the drawings, the dimensions and relative dimensions of components may be exaggerated for clarity and / or descriptive purposes. When exemplary embodiments can be implemented differently, a specific process sequence may be performed in a different order than that described. For example, two consecutively described processes may be performed substantially simultaneously or in the reverse order of their description. Furthermore, the same reference numerals denote the same components.

[0050] The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “(the)” are also intended to include the plural forms. Furthermore, when the terms “comprising” and / or “including” and variations thereof are used in this specification, it indicates the presence of the stated features, integrals, steps, operations, parts, components, and / or groups thereof, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, parts, components, and / or groups thereof. It should also be noted that, as used herein, the terms “substantially,” “about,” and other similar terms are used as approximate terms rather than as terms of degree, thus explaining the inherent biases in measurements, calculated values, and / or provided values ​​that would be recognized by one of ordinary skill in the art.

[0051] This application provides a method for characterizing uranium minerals in low-grade uranium ore. This method is based on the synergistic analysis of μ-XRF and TIMA, enabling rapid, efficient, and accurate identification of the types, spatial distribution, mineral assemblage, content, and microstructure of uranium minerals in samples. μ-XRF stands for Micro-area X-ray Fluorescence Spectrometer, and TIMA stands for Integrated Automated Quantitative Analysis System for Minerals.

[0052] Figure 1 Here is a flowchart of a method for characterizing uranium minerals in low-grade uranium ore according to an embodiment of the application, as follows: Figure 1 As shown, a method for characterizing uranium minerals in low-grade uranium ore according to an embodiment of the application includes steps S100 to S103:

[0053] S100, using μ-XRF (micro-area X-ray fluorescence spectrometry) to obtain the elemental concentration of each pixel on a low-grade uranium ore sample, and constructing a single-element concentration matrix;

[0054] S101. Based on the mean and standard deviation of the elemental concentration across the entire test domain of the low-grade uranium ore sample, calculate the threshold of a single element across the entire domain, and extract the connected components of a single element based on the single element concentration matrix and the threshold.

[0055] S102, high-value anomaly regions are identified based on single-element concentration matrices and single-element connected components; among them, regions with concentrations greater than those in single-element connected components within the regions determined by the single-element concentration matrix are high-value anomaly regions.

[0056] S103. The high-value anomaly zone of the low-grade uranium ore sample was analyzed using TIMA (Integrated Automated Quantitative Mineral Analysis System) to obtain the analysis results of the low-grade uranium ore sample.

[0057] In this embodiment of the application, for low-grade uranium deposits, based on the geotectonic location and regional tectonic geological background of the study area, regional geological maps, ore deposit geological maps and ore body profiles of different scales are systematically collected. Previous geological reports and results are absorbed and digested to clarify the basic geological characteristics of low-grade uranium mineralization, including ore grade, mineralization scale, mineralization type, mineralization period and stage, and to determine the type of low-grade uranium deposit.

[0058] During field geological surveys, detailed observations were conducted on representative low-grade uranium ores in the study area. The structure, texture, and mineral composition of the ore were recorded, and representative low-grade uranium ore specimens were collected. Preferably, the size of the low-grade uranium ore specimens was 3cm × 6cm × 9cm. The mineral structure and ore composition of the low-grade uranium ore specimens were observed, and gamma values ​​were measured using a gamma meter to obtain radiation dose data. The grade and class of the ore were then assessed to determine whether it met the requirements for low-grade mineralization.

[0059] Samples that have reached low-grade mineralization, i.e., samples with a radiation dose greater than 200 ppm, are cut into 2.5 cm × 5 cm cubes. These cubes are then coarsely and finely ground using diamond abrasive and boron carbide on a polishing machine to achieve a smooth and flat surface. Preferably, 400-mesh or 800-mesh diamond abrasive and 1200-mesh boron carbide are used.

[0060] The cube is adhered to the electron probe sheet, and finally the electron probe sheet is polished with a diamond suspension until the surface is smooth and flat, forming the low-grade uranium ore sample of this embodiment. Preferably, the particle size of the diamond suspension in the polishing machine is 0.05 μm. Preferably, an electron probe sheet with a thickness of 30 μm is selected.

[0061] The target elements of each pixel on the low-grade uranium ore sample are collected, and the single-element concentration matrix is ​​obtained based on the count information of each element. Specifically, the surface of the electron probe sheet is gently wiped with alcohol and cotton swabs, allowed to air dry, and then placed in the μ-XRF sample. The μ-XRF software is used for positioning, calibration, and focusing, and the element scanning range and matrix scanning mode are set.

[0062] Preferably, the operating conditions of the μ-XRF software are set as follows: pixel time of 5 ms / pixel, sample stage speed of 4.0 mm / s, maximum pulse count rate of 275,000 counts / s, voltage of 50 kV, anode current of 599 mA, and spot size of 20 μm.

[0063] Among them, U, Pb, Th, Na, K, Ca, P, Mo, and S were selected as the target elements for scanning. Low-grade uranium ore samples were scanned using an electron probe microarray (μ-XRF) to collect the count information of each element.

[0064] The element concentration of each pixel on the electron probe chip is the element count information. Based on this, a single-element concentration matrix is ​​obtained, as shown in the following formula:

[0065] ;

[0066] Where X represents a certain element. This represents the single-element concentration matrix of element X. H represents the element concentration at pixel (i, j), and H and W represent the rows and columns of the matrix.

[0067] Based on the mean and standard deviation of elemental concentrations across the entire testing area of ​​low-grade uranium ore samples, the threshold for a single element across the entire testing area is calculated as follows:

[0068] T global =μX +3σX;

[0069] Among them, T global denoted by threshold, μX represents the mean of the elemental concentrations across the entire region, and σX represents the standard deviation of the elemental concentrations across the entire region.

[0070] Based on the threshold of the global single element, the single-element connected components can be extracted from the concentration of each element in the single-element concentration matrix using morphological closing operations, as shown in the following equation:

[0071] ROI X =Closing({(i,j)| >T global}, kernel);

[0072] Among them, ROI X This represents a single-element connected component of element X.

[0073] To better adapt to the mineral image characteristics of low-grade uranium ore, the preferred embodiments of this application optimize the morphological closing operation, such as... Figure 2 As shown, the process of extracting single-element connected components based on the single-element concentration matrix and threshold in step S101 includes steps S200 to S202:

[0074] S200, perform wavelet transform on the single-element concentration matrix to obtain the enhancement matrix;

[0075] S201, calculate the adaptive global threshold under global and local statistical features;

[0076] S202, combining the enhancement matrix and adaptive global threshold, extracts single-element connected components.

[0077] In step S200, the single-element concentration matrix is ​​decomposed into approximate coefficients (global) and detail coefficients (local). The noise level of the single-element concentration matrix is ​​calculated based on the detail coefficients, and the global threshold is calculated according to the transformation length of the single-element concentration matrix to obtain the wavelet reconstruction function T(x), as follows:

[0078] T(x)=sign(x) × max(∣x∣-threshold,0);

[0079] Here, threshold represents the global threshold, and sign(x) represents the noise level of the detail coefficient x. A wavelet reconstruction function is applied to each detail coefficient, setting the approximation coefficient to 0 when the detail coefficient x < the global threshold, and reducing the approximation coefficient when the detail coefficient x > the global threshold.

[0080] The global threshold is as follows:

[0081] threshold = β × 2log(N);

[0082] Where N represents the total number of pixels corresponding to the single-element concentration matrix.

[0083] Correspondingly, β= ;in, This is the high-frequency coefficient (detail coefficient) matrix after wavelet decomposition. Indicates the middle value. This represents the normal distribution adjustment coefficient, which is preferably set to 0.6745.

[0084] Based on this, the single-element concentration matrix is ​​reconstructed using the processed approximation coefficients and detail coefficients to obtain the enhanced matrix. (E).

[0085] Based on wavelet transform and local statistical features, optimize the threshold T of a single element in the global domain. global The adaptive global threshold is obtained as follows:

[0086] T optimized =d×T global +(1-d) ×T local ;

[0087] Among them, T optimized T represents the adaptive global threshold. local This represents a local threshold. An adaptive global threshold T is used. optimized Replace the original threshold T global。

[0088] Wherein, the local threshold T local The calculation is as follows:

[0089] T local =m×[1+k×(β / R-1)];

[0090] Where m represents the local mean, β= k represents the sensitivity parameter (preferably 0.2-0.5), and R represents the standardization constant (preferably 128).

[0091] In step S202, the ROI can be updated by combining the enhancement matrix and the adaptive global threshold. X =Closing({(i,j)| (E) > T optimized}, kernel). Based on this, the limitations of a single threshold are avoided, and the spatial heterogeneity of elements in low-grade uranium ore is better adapted.

[0092] Based on this, according to the enhancement matrix, concentration contour maps of each element are created, focusing on the spatial distribution pattern of single elements in the whole domain. Using the connected domain of a single element as the lower limit, the regions of concentration matrix above the limit are enclosed by smooth curves. Regions with concentrations greater than the limit are high-value anomaly regions, and regions with concentrations lower than the limit are low-value anomaly regions.

[0093] In this embodiment, due to the rarity and instability of mineral elements in low-grade uranium ore, the delineation of high-value anomaly zones is not sufficiently defined, characterized by interruptions in the regional curves, thus affecting the determination of high-value anomaly zones. Based on this, this embodiment proposes another preferred implementation method, combined with steps S200 to S202, as follows: Figure 2 As shown, the process of identifying high-value anomaly regions based on single-element concentration matrices and single-element connected components in step S102 also includes steps S300 and S301:

[0094] S300, updating single-element connected components based on multi-scale verification;

[0095] S301, based on the updated single-element connected domain, identify outliers in the single-element concentration matrix and constrain spatial continuity, and generate smooth high-value outlier regions based on smooth traps.

[0096] In step S300, spatial statistical analysis is performed by combining the local Moran index of the enhancement matrix of this application with hotspot analysis to constrain single-element connected components. The local Moran index identifies clusters with similar concentration values, facilitating the distinction between high-value and low-value anomalies. Hotspot analysis further identifies significant hotspots (high-value anomalies) and cold spots (low-value anomalies).

[0097] The constraint process for the local Moran index is as follows:

[0098] ;

[0099] Where i and j represent the corresponding pixel positions of the elements. The element concentration at pixel position (i, j) is represented. This represents the variance of the element concentration at each location. This is a spatial weight matrix based on the transformation of the single-element concentration matrix. This represents the average concentration of the element at each location.

[0100] During the constraint process, if A value greater than 0 indicates clustering, representing a cluster of high-value or low-value outliers; if... A value less than 0 indicates a discrete value.

[0101] After clustering based on the improved local Moran index, further constraints are imposed through hotspot analysis, as follows:

[0102] ;

[0103] Where n represents the number of pixels clustered after local Moran's index constraint.

[0104] During the constraint process, The larger the value, the more concentrated the spatial continuity, and the more valuable the high-value anomaly area is in subsequent analysis, and it can be used to screen for high-value anomalies.

[0105] Preferably, the above-mentioned criteria for establishing high-value anomaly regions are combined, as shown in the following formula:

[0106] ;

[0107] in, This represents the baseline value of ROI. This represents the standard deviation of ROI.

[0108] Based on this, the high-value anomaly region meets the following constraints:

[0109] ;

[0110] in, These are empirical values ​​and can be set as percentages. High-value outliers that meet the above constraints are used as the basis for analysis in this application.

[0111] Preferably, such as Figure 3 As shown, step S103, which involves using TIMA to analyze high-value anomaly areas in a low-grade uranium ore sample and obtaining the analytical results, includes steps S400 to S403:

[0112] S400, collects BSE images and energy spectrum information of high-value anomaly regions;

[0113] S401, determine the energy spectrum data of each pixel based on the energy spectrum information, so as to determine the energy spectrum and element content of each mineral particle in the low-grade uranium ore sample;

[0114] S402, combined with BSE (backscattered electron image) images, the boundaries of mineral grains were determined, and the energy spectrum and elemental content were compared with the standard mineral energy spectrum and composition in the database to determine the types and names of uranium minerals in the low-grade uranium ore samples.

[0115] S403 utilizes a comprehensive mineral analysis system to obtain the number of uranium mineral particles, particle size, mass percentage, and volume percentage in high-value anomaly areas. Combined with BSE images, it accurately locates the spatial position of uranium minerals in low-grade uranium ore samples, as well as their contact relationships, replacement relationships, and mineral symbiotic assemblage relationships with other minerals.

[0116] Preferably, the electron probe sheet is subjected to carbon spraying treatment;

[0117] The TIMA system was selected for the comprehensive mineral analysis system. The electron probe was placed in the TIMA sample chamber, and the TIMA software was used to focus, calibrate and capture images of the electron probe. The high-value anomaly area was set as the analysis area, and the working conditions and dot matrix scanning mode were set to acquire BSE images and collect energy dispersive spectroscopy information in the analysis area.

[0118] Preferably, the operating conditions of the TIMA system are set as follows: accelerating voltage of 25 kV, current of 8.59 mA, working distance of 15 mm, current and BSE signal intensity calibrated using a platinum Faraday cup automatic program, and EDS (energy dispersive spectroscopy) signal calibrated using a Mn standard. Dissociation mode is used in the test to simultaneously acquire BSE images and EDS spectral data, with an X-ray count of 1000 at each point. The pixel size is 3 μm, and the spectral step size is 9 μm. The BSE image gain is increased to 130% of the standard value. During the EDS test, the optimal EDS residence time T is optimized using the following formula to enhance the EDS reception intensity of spectral data for low-abundance elements, as follows:

[0119] ;

[0120] in, Indicates the base duration of stay. Represents the grayscale response coefficient. This represents the image gray level at pixel (i, j). This represents the maximum gray level of the high-value anomaly region.

[0121] The energy dispersive spectral data of each pixel in the dot matrix scanning mode of the analysis area are summarized to obtain the energy dispersive spectral spectrum and elemental content of each mineral grain in the sample. The boundaries of the mineral grains are determined by combining the BSE image with the obtained energy dispersive spectral spectrum and elemental content, and the obtained energy dispersive spectral spectrum and elemental content are compared with the standard mineral energy dispersive spectral spectrum and composition in the database to determine the type and name of uranium mineral in the sample.

[0122] Finally, the number, size, mass percentage, and volume percentage of uranium minerals in the analysis area were obtained using TIMA mineral statistics software. Combined with BSE images, the spatial location of uranium minerals at the electron probe slide scale was accurately located, as well as the contact, replacement, and mineral symbiotic relationships between uranium minerals and other minerals.

[0123] The uranium mineral characterization method for low-grade uranium ore in this application involves obtaining the elemental concentration of each pixel on the low-grade uranium ore sample and constructing a single-element concentration matrix. Based on the mean and standard deviation of the elemental concentration across the entire test area of ​​the low-grade uranium ore sample, a threshold for each single element is calculated, and single-element connected components are extracted based on the single-element concentration matrix and the thresholds. High-value anomaly regions are identified based on the single-element concentration matrix and the single-element connected components. A comprehensive mineral analysis system is used to analyze the high-value anomaly regions of the low-grade uranium ore sample to obtain the analysis results. Compared to traditional manual characterization methods, this application offers faster analysis speed, higher efficiency, stronger automation, and higher accuracy, while also providing richer analytical information.

[0124] This application also provides a device for characterizing uranium minerals in low-grade uranium ore.

[0125] Figure 4 This is a structural diagram of a uranium mineral characterization device module in low-grade uranium ore according to an embodiment of the application, as shown below. Figure 4 As shown, one embodiment of the uranium mineral characterization apparatus in low-grade uranium ore includes:

[0126] Matrix construction module 100 is used to obtain the element concentration of each pixel on the low-grade uranium ore sample and construct a single-element concentration matrix;

[0127] The element processing module 101 is used to calculate the threshold of a single element in the entire test area based on the mean and standard deviation of the element concentration in the entire test area of ​​the low-grade uranium ore sample, and to extract the connected components of a single element based on the single element concentration matrix and the threshold.

[0128] The region division module 102 is used to identify high-value anomaly regions based on the single-element concentration matrix and the single-element connected component; wherein, among the regions determined based on the single-element concentration matrix, the regions with concentrations greater than those of the single-element connected components are high-value anomaly regions.

[0129] The results analysis module 103 is used to analyze the high-value anomaly area of ​​the low-grade uranium ore sample using the integrated mineral analysis system, and to obtain the analysis results of the low-grade uranium ore sample.

[0130] The uranium mineral characterization device for low-grade uranium ore in this application embodiment acquires the elemental concentration of each pixel on the low-grade uranium ore sample and constructs a single-element concentration matrix; calculates the threshold of the single element across the entire test area based on the mean and standard deviation of the elemental concentration of the low-grade uranium ore sample, and extracts the single-element connected components based on the single-element concentration matrix and the threshold; identifies high-value anomaly regions based on the single-element concentration matrix and the single-element connected components; and analyzes the high-value anomaly regions of the low-grade uranium ore sample using a comprehensive mineral analysis system to obtain the analysis results of the low-grade uranium ore sample. Compared with traditional manual characterization methods, this application offers faster analysis speed, higher efficiency, stronger automation, and higher accuracy, while also providing richer analytical information.

[0131] At least one embodiment of this application also provides a data control device. Figure 5 This is a schematic block diagram of a data control device provided for at least one embodiment of this application. For example, such as... Figure 5 As shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memories 200 are used to store computer-executable instructions non-transitory; the processors 201 are used to run the computer-executable instructions, which, when run by the processors 201, can cause the processors 201 to perform one or more steps in the method for characterizing uranium minerals in low-grade uranium ore according to any embodiment of this application.

[0132] For the specific implementation and explanation of each step of the uranium mineral characterization method in low-grade uranium ore, please refer to the relevant content in the embodiments of the uranium mineral characterization method in low-grade uranium ore mentioned above, which will not be repeated here. It should be noted that Figure 5 The components of the data control device 20 shown are merely exemplary and not limiting. The data control device 20 may have other components depending on the actual application requirements.

[0133] In one embodiment, the processor 201 and the memory 200 can communicate directly or indirectly with each other. For example, the processor 201 and the memory 200 can communicate via a network connection. The network can include wireless networks, wired networks, and / or any combination of wireless and wired networks; this application does not limit the type and function of the network. Alternatively, the processor 201 and the memory 200 can also communicate via a bus connection. The bus can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 can be located at a remote data server (cloud) or a distributed energy system (local), or at a client (e.g., a mobile device such as a mobile phone). For example, the processor 201 can be a central processing unit (CPU), a tensor processor (TPU), or a graphics processing unit (GPU), etc., with data processing and / or instruction execution capabilities, and can control other components in the data control device 20 to perform desired functions. The central processing unit (CPU) can be an x86 or ARM architecture, etc.

[0134] In one embodiment, memory 200 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer-executable instructions may be stored on the computer-readable storage medium, and processor 201 may execute these computer-executable instructions to implement various functions of data control device 20. Various application programs and various data, as well as various data used and / or generated by the application programs, may also be stored in memory 200.

[0135] It should be noted that the data control device 20 can achieve similar technical effects to the aforementioned method for characterizing uranium minerals in low-grade uranium ore, and the repetitions will not be repeated.

[0136] At least one embodiment of this application also provides a non-transitory computer-readable storage medium. Figure 6 This is a schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of this application. For example, such as... Figure 6 As shown, one or more computer-executable instructions 301 may be stored non-transitory on the non-transitory computer-readable storage medium 30. For example, when the computer-executable instructions 301 are executed by a computer, the computer may perform one or more steps in a method for characterizing uranium minerals in low-grade uranium ore according to any embodiment of this application.

[0137] In one embodiment, the non-transitory computer-readable storage medium 30 can be applied to the data control device 20 described above, for example, it can be the memory 200 in the data control device 20.

[0138] In one embodiment, the description of the non-transitory computer-readable storage medium 30 can be found in the description of the memory 200 in the embodiment of the data control device 20, and will not be repeated hereafter.

[0139] It should be noted that the memory 200 stores different non-transient computer-executable instructions, and the data control device 20 corresponds to a firmware upgrade device. When the computer-executable instructions are run by the processor 201, the processor 201 can perform one or more steps in the method for characterizing uranium minerals in low-grade uranium ore according to any embodiment of this application.

[0140] The following points should be noted regarding this application:

[0141] (1) The accompanying drawings of the embodiments of this application only involve the structures involved in the embodiments of this application. Other structures can be referred to the general design.

[0142] (2) For clarity, the thickness and dimensions of layers or structures are enlarged in the accompanying drawings used to describe embodiments of the invention. It will be understood that when an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements present.

[0143] (3) Where there is no conflict, the embodiments and features in the embodiments of this application can be combined with each other to obtain new embodiments. The above are only specific implementations of this application, but the protection scope of this application is not limited thereto, and the protection scope of this application shall be determined by the protection scope of the claims.

[0144] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for characterizing uranium minerals in low-grade uranium ore, characterized in that, Including the following steps: The elemental concentration of each pixel on a low-grade uranium ore sample was obtained using μ-XRF, and a single-element concentration matrix was constructed. Based on the mean and standard deviation of the total elemental concentration of the low-grade uranium ore sample, the threshold of the total single element is calculated, and the single element connected component is extracted based on the single element concentration matrix and the threshold. High-value anomaly regions are identified based on the single-element concentration matrix and the single-element connected components; wherein, within the regions determined by the single-element concentration matrix, the regions with concentrations greater than those within the single-element connected components are the high-value anomaly regions. The high-value anomaly region of the low-grade uranium ore sample was analyzed using TIMA to obtain the analytical results of the low-grade uranium ore sample, including the following steps: Collect BSE images and energy spectrum information of the high-value anomaly region; Based on the energy spectrum information, determine the energy spectrum data of each pixel to determine the energy spectrum and element content of each mineral particle in the low-grade uranium ore sample; The boundaries of the mineral particles are determined by combining the BSE image, and the energy spectrum and elemental content are compared with the standard mineral energy spectrum and composition in the database to determine the type and name of uranium minerals in the low-grade uranium ore sample. Using a comprehensive mineral analysis system, the number of uranium mineral particles, particle size, mass percentage, and volume percentage in the high-value anomaly zone were obtained. Combined with BSE images, the spatial location of uranium minerals in the low-grade uranium ore sample, their contact relationship, replacement relationship, and mineral symbiotic association relationship were accurately located.

2. The method for characterizing uranium minerals in low-grade uranium ore according to claim 1, characterized in that, The process of extracting single-element connected components based on the single-element concentration matrix and the threshold includes the following steps: Perform wavelet transform on the single-element concentration matrix to obtain the enhancement matrix; Calculate the adaptive global threshold under global and local statistical features; The single-element connected component is extracted by combining the enhancement matrix and the adaptive global threshold.

3. The method for characterizing uranium minerals in low-grade uranium ore according to claim 2, characterized in that, The process of identifying high-value anomaly regions based on the single-element concentration matrix and the single-element connected components includes the following steps: Based on the single-element concentration matrix, a concentration contour map of each element is created, and regions with concentrations greater than those of the single-element connected domains are marked as high-value anomaly regions.

4. The method for characterizing uranium minerals in low-grade uranium ore according to claim 3, characterized in that, The process of identifying high-value anomaly regions based on the single-element concentration matrix and the single-element connected components further includes the following steps: The single-element connected component is updated based on multi-scale verification. Based on the updated single-element connected components, outliers are identified in the single-element concentration matrix and spatial continuity is constrained. Smooth high-value outlier regions are generated based on smooth traps.

5. The method for characterizing uranium minerals in low-grade uranium ore according to claim 1, characterized in that, The process of collecting the energy spectrum information includes the following steps: Optimize the EDS dwell time to enhance the EDS reception intensity for energy spectrum data of low-abundance elements, as shown in the following formula: in, Indicates the base duration of stay. Represents the grayscale response coefficient. This represents the image gray level at pixel (i, j). This represents the maximum gray level of the high-value anomaly region.

6. The method for characterizing uranium minerals in low-grade uranium ore according to any one of claims 1 to 5, characterized in that, The low-grade uranium ore sample was coated with an electron probe sheet from a low-grade uranium ore block.

7. A device for characterizing uranium minerals in low-grade uranium ore, characterized in that, include: The matrix construction module is used to obtain the elemental concentration of each pixel on a low-grade uranium ore sample using μ-XRF and construct a single-element concentration matrix. The element processing module is used to calculate the threshold of a single element in the entire test area based on the mean and standard deviation of the element concentration in the low-grade uranium ore sample, and to extract the connected components of the single element based on the single element concentration matrix and the threshold. The region segmentation module is used to identify high-value anomaly regions based on the single-element concentration matrix and the single-element connected components; wherein, among the regions determined based on the single-element concentration matrix, the regions with concentrations greater than those of the single-element connected components are the high-value anomaly regions. The results analysis module is used to analyze the high-value anomaly region of the low-grade uranium ore sample using TIMA to obtain the analysis results of the low-grade uranium ore sample, including the following steps: Collect BSE images and energy spectrum information of the high-value anomaly region; Based on the energy spectrum information, determine the energy spectrum data of each pixel to determine the energy spectrum and element content of each mineral particle in the low-grade uranium ore sample; The boundaries of the mineral particles are determined by combining the BSE image, and the energy spectrum and elemental content are compared with the standard mineral energy spectrum and composition in the database to determine the type and name of uranium minerals in the low-grade uranium ore sample. Using a comprehensive mineral analysis system, the number of uranium mineral particles, particle size, mass percentage, and volume percentage in the high-value anomaly zone were obtained. Combined with BSE images, the spatial location of uranium minerals in the low-grade uranium ore sample, their contact relationship, replacement relationship, and mineral symbiotic association relationship were accurately located.

8. A non-transitory computer-readable storage medium, characterized in that, A non-transitory computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method for characterizing uranium minerals in low-grade uranium ore as described in any one of claims 1 to 6.

9. A data control device, characterized in that, include: One or more memories that store computer-executable instructions non-transitory; One or more processors configured to run computer-executable instructions, wherein the computer-executable instructions, when run by the one or more processors, implement the method for characterizing uranium minerals in low-grade uranium ore as described in any one of claims 1 to 6.

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