Method and device for identifying and evaluating vein quartz type high-purity quartz mine

Abstract

The invention discloses a vein quartz type high-purity quartz mine identification and evaluation method and device, relates to the field of vein quartz type high-purity quartz mine quality evaluation, and can solve the problems of time consumption, high cost, strong destructiveness and low efficiency of traditional vein quartz type high-purity quartz mine identification at the present stage. Measuring physical property parameters of the plurality of samples to be identified; wherein the physical property parameters comprise light transmittance, water content, unit cell parameters and particle size related parameters; inputting the physical property parameters of the plurality of samples to be identified into the purity evaluation model, and determining the purity estimation value of the vein quartz sample; determining an evaluation result of the vein quartz sample according to the purity estimation value of the vein quartz sample; wherein the evaluation result is used for representing whether the vein quartz sample has the potential of vein quartz type high-purity quartz ore. The method is used for identifying the purity of the vein quartz type high-purity quartz ore and evaluating the potential of the vein quartz type high-purity quartz ore.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vein quartz type high-purity quartz ore quality evaluation, in particular to a vein quartz type high-purity quartz ore identification and evaluation method and device. BACKGROUND

[0002] High-purity quartz ore is a rock that can obtain silicon dioxide (Si ) purity of not less than 99.995% (4N5) and impurity, inclusion content and particle size meeting the application requirements of high-tech fields such as semiconductors, photovoltaics and optics under current technical and economic conditions through ore dressing and purification. As an important strategic new mineral, it plays a key supporting role in the development of high-tech industries.

[0003] Vein quartz is one of the important sources of high-purity quartz ore. With the increasing demand for high-purity quartz ore in high-tech fields, the demand for evaluation and identification of vein quartz type high-purity quartz ore is increasingly urgent. However, the evaluation and identification method for this type of mineral has not yet formed an efficient, accurate and mature system.

[0004] Traditional vein quartz type high-purity quartz ore evaluation and identification methods usually rely on complex processing and purification tests and strict chemical analysis tests, which not only have a long overall time span and low efficiency, but also have high cost investment. The testing process is destructive to the sample, and it is difficult to meet the actual demand of large-scale sample rapid evaluation. Therefore, developing a rapid, accurate and scientific identification technology has become a problem to be solved. SUMMARY

[0005] The present application provides a vein quartz type high-purity quartz ore identification and evaluation method and device, which can solve the problems of time-consuming, high cost, strong destructiveness and low efficiency of traditional vein quartz type high-purity quartz ore identification at the present stage.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: In a first aspect, the present application provides a vein quartz type high-purity quartz ore identification and evaluation method, comprising: preparing vein quartz samples into a plurality of identification samples; wherein the plurality of identification samples belong to different preset types, and the preset types include rock powder, inclusion sheet, electron probe sheet and double-sided polished thin section; measuring the physical property parameters of the plurality of identification samples; wherein the physical property parameters include light transmittance, water content, cell parameters and particle size related parameters; inputting the physical property parameters of the plurality of identification samples into a purity evaluation model to determine the purity estimate value of the vein quartz sample; determining the evaluation result of the vein quartz sample according to the purity estimate value of the vein quartz sample; wherein the evaluation result is used to represent whether the vein quartz sample has the potential of vein quartz type high-purity quartz ore.

[0007] In a possible implementation, the vein quartz sample is prepared into multiple samples to be identified, specifically including: sequentially performing coarse crushing to 4 mm screening, medium crushing to 1 mm screening, mixing and dividing, strong magnetic iron removal, fine crushing to 200 mesh, and secondary strong magnetic iron removal on the vein quartz sample to obtain rock powder type samples to be identified; cutting the vein quartz sample into a sheet sample of a preset size, and sequentially performing ultrasonic cleaning, drying, preparing an inclusion sheet, acetone soaking, removing residual glue, and air drying on the sheet sample of the preset size to obtain an inclusion sheet type sample to be identified; cutting the vein quartz sample into a sheet sample of a preset size, and sequentially performing ultrasonic cleaning, drying, and preparing an electron probe sheet on the sheet sample of the preset size to obtain an electron probe sheet type sample to be identified; cutting the vein quartz sample into a sheet sample of a preset size, and sequentially performing ultrasonic cleaning, drying, grinding into a double-sided polished wafer, acetone soaking, anhydrous ethanol cleaning, and secondary drying on the sheet sample of the preset size to obtain a double-sided polished wafer type sample to be identified; and wherein a length deviation of the sheet sample is less than a first threshold value, and a non-parallelism deviation of two end faces of the sheet sample is less than a second threshold value.

[0008] In a possible implementation, physical property parameters of the multiple samples to be identified are measured, specifically including: performing particle size measurement on the rock powder type sample to be identified to obtain particle size related parameters of the vein quartz sample; performing light transmittance measurement on the inclusion sheet type sample to be identified to obtain light transmittance of the vein quartz sample; performing cell parameter measurement on the electron probe sheet type sample to be identified to obtain a cell parameter of the vein quartz sample; and performing water content measurement on the double-sided polished wafer type sample to be identified to obtain water content of the vein quartz sample.

[0009] In a possible implementation, the particle size related parameters include particle size and particle size variation coefficient; the particle size measurement on the rock powder type sample to be identified to obtain the particle size related parameters of the vein quartz sample specifically includes: using an automatic mineral parameter analysis system to perform particle size analysis testing on the rock powder type sample to be identified to obtain a particle size analysis report; wherein the particle size analysis report is used to represent multiple particle size intervals of quartz particles in the rock powder, and an average particle size of the quartz particles in each particle size interval and a number of the quartz particles in the corresponding interval; and the particle size of the vein quartz sample is determined according to the particle size analysis report by using the following formula one: = / n formula one wherein, represents the particle size of the vein quartz sample, i represents the particle size of the quartz particles in the corresponding particle size interval, x i represents the number of the quartz particles in the corresponding particle size interval, mThe number of particle size intervals, n is the sum of all quartz particles; the coefficient of variation of particle size is determined according to the particle size of the vein quartz sample and the standard deviation of the particle size of all quartz particles, which is performed by the following formula two: cv=( σ / )×100% Formula two Wherein, cv represents the coefficient of variation of particle size, σ is the standard deviation of the particle size of all quartz particles.

[0010] In one possible implementation, the transmittance of the sample to be identified of the inclusion slice type is measured to obtain the transmittance of the vein quartz sample, specifically comprising: using an ultraviolet visible near infrared spectrophotometer, the transmittance of the sample to be identified of the inclusion slice type is measured under different wavelengths of light, and the transmittance of the vein quartz sample is obtained.

[0011] In one possible implementation, the cell parameter includes a cell parameter basic value and a quartz axis change rate ratio; the cell parameter of the sample to be identified of the electron probe slice type is measured to obtain the cell parameter of the vein quartz sample, specifically comprising: using an X-ray diffractometer, the sample to be identified of the electron probe slice type is analyzed under a preset condition to obtain diffraction spectrum data; the diffraction spectrum data is processed to determine the cell parameter basic value; according to the cell parameter basic value and the standard quartz cell parameter, the quartz axis change rate ratio is calculated, which is performed by the following formula three: Δ=(Δ / ) / (Δ / ) Formula three Wherein, Δ is the quartz axis change rate ratio, a 0、 c 0 respectively represent the standard quartz cell parameter, Δ a 0、Δ c 0 respectively represent the difference between the cell parameter basic value and the standard quartz cell parameter.

[0012] In one possible implementation, the water content of the sample to be identified of the double-side polished thin section type is measured to obtain the water content of the vein quartz sample, specifically comprising: using a Fourier transform micro infrared spectrometer, the water content of the sample to be identified of the double-side polished thin section type is measured to obtain infrared absorption spectrum data; the infrared absorption spectrum data is normalized according to the sample thickness data to obtain the infrared spectrum absorption coefficient corresponding to the wave number position; according to the infrared spectrum absorption coefficient corresponding to the wave number position, the water content of the vein quartz sample is determined, which is performed by the following formula four: Formula four Wherein, C represents the molar absorption coefficient, K(v) represents the infrared spectrum absorption coefficient corresponding to the wave number position, K(v)dv represents the integral area in a given waveband range, v represents the infrared absorption wave number, λ represents the activation direction factor of OH bond in the crystal, P represents the water content of the quartz sample.

[0013] In a possible implementation, before the physical property parameters of the plurality of to-be-identified samples are input into the purity evaluation model to determine the purity estimation value of the vein quartz sample, the method further includes: performing correlation analysis on the transmittance, water content, cell parameter, and particle size related parameter of the known purity vein quartz sample and the purity data of the known purity vein quartz sample according to a preset statistical algorithm, and constructing the purity evaluation model according to the correlation analysis result.

[0014] In a second aspect, the present application provides a vein quartz type high-purity quartz ore identification and evaluation device, characterized in that the identification and evaluation device comprises: a preparation unit and a processing unit; the preparation unit is used for preparing a vein quartz sample into a plurality of to-be-identified samples; wherein the plurality of to-be-identified samples respectively belong to different preset types, and the preset types include rock powder, inclusion sheet, electron probe sheet, and double-sided polished thin section; the processing unit is used for measuring physical property parameters of the plurality of to-be-identified samples; wherein the physical property parameters include transmittance, water content, cell parameter, and particle size related parameter; the processing unit is further used for inputting the physical property parameters of the plurality of to-be-identified samples into a purity evaluation model to determine a purity estimation value of the vein quartz sample; and the processing unit is further used for determining an evaluation result of the vein quartz sample according to the purity estimation value of the vein quartz sample; wherein the evaluation result is used to represent whether the vein quartz sample has vein quartz type high-purity quartz ore potential.

[0015] In a third aspect, the present application provides a computer readable storage medium storing one or more programs, the one or more programs including instructions which, when executed by an electronic device of the present application, cause the electronic device to perform the identification and evaluation method as described in the first aspect and any possible implementation of the first aspect.

[0016] In a fourth aspect, the present application provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, and the one or more programs include computer execution instructions; when the electronic device is running, the processor executes the computer execution instructions stored in the memory to cause the electronic device to perform the identification and evaluation method as described in the first aspect and any possible implementation of the first aspect.

[0017] In a fifth aspect, the present application provides a computer program product comprising instructions which, when run on a computer, cause an electronic device of the present application to perform the identification and evaluation method as described in the first aspect and any possible implementation of the first aspect.

[0018] In a sixth aspect, the present application provides a chip system applied to the identification and evaluation device; the chip system comprises one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected through a line; the interface circuit is used to receive a signal from a memory of the identification and evaluation device and send the signal to the processor, and the signal comprises computer instructions stored in the memory. When the processor executes the computer instructions, the identification and evaluation device executes the identification and evaluation method as described in the first aspect and any possible design manner thereof.

[0019] Based on the above technical solution, the present application collects the core physical property parameters such as the light transmittance, water content, cell parameter, particle size and particle size variation coefficient of the vein quartz sample to be identified, constructs a purity evaluation model by means of the partial least squares regression method, obtains a purity estimation value after inputting the parameters, and realizes the evaluation of whether the vein quartz sample has the potential of vein quartz type high-purity quartz ore in combination with the judgment standard that the purity estimation value is greater than or equal to 99.995% (4N5 level) and each physical property parameter meets the corresponding threshold value. The present application effectively solves the problems of long time consumption, high cost, sample damage and difficulty in large-scale and rapid evaluation of the traditional identification method, greatly improves the identification efficiency and accuracy while avoiding sample damage, and meets the needs of the early rapid screening and potential evaluation of vein quartz type high-purity quartz ore, thereby providing reliable technical support for subsequent beneficiation and purification and raw material application in high-tech fields. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 An architectural schematic diagram of a vein quartz type high-purity quartz ore identification and evaluation system provided by an embodiment of the present application is shown; Figure 2 A flowchart of a vein quartz type high-purity quartz ore identification and evaluation method provided by an embodiment of the present application is shown; Figure 3 A schematic diagram of the relationship between the light transmittance and the purity of a vein quartz type high-purity quartz ore provided by an embodiment of the present application is shown; Figure 4 A schematic diagram of the relationship between the water content and the purity of a vein quartz type high-purity quartz ore provided by an embodiment of the present application is shown; Figure 5 A schematic diagram of the relationship between the quartz axis change rate and the purity of a vein quartz type high-purity quartz ore provided by an embodiment of the present application is shown; Figure 6 A schematic diagram of the relationship between the particle size and the purity of a vein quartz type high-purity quartz ore provided by an embodiment of the present application is shown; Figure 7 A schematic diagram of the relationship between the particle size variation coefficient and the purity of a vein quartz type high-purity quartz ore provided by an embodiment of the present application is shown; Figure 8Another flowchart of a vein quartz type high-purity quartz ore identification and evaluation method provided by the embodiment of the present application is shown in the figure. Figure 9 A structural diagram of a vein quartz type high-purity quartz ore identification and evaluation device provided by the embodiment of the present application is shown in the figure. Figure 10 A structural diagram of another vein quartz type high-purity quartz ore identification and evaluation device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0022] In this document, the character " / " generally represents that the associated objects before and after the character " / " are in an "or" relationship. For example, A / B can be understood as A or B.

[0023] The terms "first" and "second" in the specification and claims of the present application are used to distinguish different objects, rather than to describe a specific order of the objects. For example, the first edge service node and the second edge service node are used to distinguish different edge service nodes, rather than to describe the order of the characteristics of the edge service nodes.

[0024] In addition, the terms "comprising" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0025] In addition, in the embodiments of the present application, the words "exemplarily" or "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "exemplarily" or "for example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplarily" or "for example" are intended to present the concept in a specific manner.

[0026] The technical terms related to the present application are described below: 1. vein quartz type high-purity quartz ore The vein quartz type high-purity quartz ore is a kind of high-purity quartz ore with vein quartz as the core raw material, wherein the vein quartz refers to a quartz mineral that is in the form of a vein in a rock crack, has a complete quartz crystal structure, and has a low primary content of impurity elements such as Al, Fe, Ca, and the like.

[0027] According to the related technical standards of the high-purity quartz ore, the ore of this type needs to meet the core index that “after beneficiation and purification, the purity of silicon dioxide (Si ) is not less than 99.995% (i.e. 4N5 level)”, and the content of internal inclusions (such as gas-liquid inclusions, solid inclusions) and the particle size distribution of quartz particles need to meet the raw material requirements of high-tech fields such as quartz crucibles for semiconductor chips, quartz ladles for the photovoltaic industry, and quartz glass for optical instruments.

[0028] In the present application, the vein quartz type high-purity quartz ore is the identification object, and the scheme determines whether the vein quartz sample to be detected has the potential to meet the standard of this type of ore through multi-dimensional physical parameter measurement and analysis, thereby providing preliminary screening basis for subsequent beneficiation and purification and industrial application.

[0029] 2. Automated mineral parameter analysis system

[0030] The automated mineral parameter analysis system (MLA) is a mineral analysis equipment integrating optical detection, image recognition and data automatic processing functions.

[0031] The core working principle is to capture the microscopic image of the mineral sample (such as the rock powder in the present application) through a high-resolution optical lens, identify individual mineral particles in combination with an image segmentation algorithm, and then realize qualitative and quantitative analysis of mineral types through comparison with a standard mineral database. The system can output key parameters including the particle size distribution (such as the proportion of particles in each particle size interval), average particle size, particle size variation coefficient, and particle number statistics of the mineral particles, and the entire analysis process does not require human intervention, thereby greatly improving the efficiency and data repeatability of obtaining mineral parameters.

[0032] In the present application, the system is mainly used for particle size measurement of vein quartz rock powder, thereby providing basic data for subsequent correlation of sample purity with particle size parameters and judgment of the potential of high-purity quartz ore.

[0033] 3. Ultraviolet-visible-near infrared spectrophotometer

[0034] Ultraviolet-Visible-Near Infrared Spectrophotometer is an optical detection equipment designed based on the principle that different substances have different absorption / transmission characteristics for different wavelengths of light. Its detection wavelength range covers ultraviolet light region (200-380 nm), visible light region (380-780 nm), and near-infrared light region (780-2500 nm). The core components include stable light sources (such as deuterium lamps, tungsten lamps), monochromators (used to filter specific wavelengths of light), integrating spheres (to improve the stability of light signal acquisition), and photoelectric detectors.

[0035] When the device is in operation, specific wavelength light is irradiated onto the sample. By detecting the ratio of incident light flux to transmitted light flux, the transmittance or absorbance of the sample is calculated, which reflects the correlation between the optical properties and purity of the sample (e.g., impurities can reduce transmittance).

[0036] In this application, the device is used to measure the transmittance of the vein quartz inclusion slice in the ultraviolet light region (200-380 nm). At least 3 measurements are taken at each wavelength, and the average value is calculated. Then, the final transmittance is calculated through a formula, which is one of the key parameters for judging whether the sample has the potential of high-purity quartz ore.

[0037] 4、X-Ray Diffractometer (XRD): It is a device that analyzes crystal structure by using the phenomenon of X-ray diffraction in crystals (following Bragg's law). The core components include an X-ray source (commonly using a Cu target, which can emit X-rays with a characteristic wavelength of 1.5406 Å), a goniometer (controls the angular rotation of the sample and detector), a detector (captures the diffraction X-ray signal), and a data processing system.

[0038] The working process is as follows: X-rays emitted by the X-ray source irradiate the crystal sample (such as the electron probe slice in this application). When the X-ray wavelength and the crystal plane spacing satisfy a certain relationship, diffraction signals will be generated. The detector captures the signals and converts them into XRD diffraction patterns. Then, through data processing software (such as Jade), the patterns are background-subtracted, curve-smoothed, and diffraction angle-corrected. Finally, the crystal cell parameters (such as the a-axis length, c-axis length, and cell volume v of quartz) are calculated.

[0039] In this application, the device is used to measure the cell parameters of the vein quartz electron probe slice. Subsequently, the quartz axis change rate ratio is calculated by combining with the standard quartz cell parameters, which reflects the structural integrity of the sample and provides a structural basis for the potential judgment of high-purity quartz ore.

[0040] 5、Fourier Transform Micro-Infrared Spectrometer

[0041] Fourier Transform Micro-Infrared Spectrophotometer (FTIR) is a device that combines microscopic observation and infrared spectral analysis. The core principle is to convert the continuous wavelength infrared light emitted by the infrared light source into interference light through an interferometer, irradiate the sample, receive the interference signal containing the sample absorption information, and then convert the interference signal into an infrared absorption spectrum through Fourier transform. Then, the molecular structure of the material (such as the structural water in minerals) is analyzed according to the spectral characteristic peak position and intensity.

[0042] The key parameters of the device include the wave number measurement range (usually 400-4000 cm ¹), beam spot size (which can be accurate to the micron level, such as 50 μm x 50 μm in this application), scanning times and resolution (the more the scanning times, the higher the resolution, and the better the data signal-to-noise ratio).

[0043] In this application, the device is used to measure the water content of vein quartz double-polished thin sections. The wave number measurement range is set to 2000-4000 cm ¹ (which is the characteristic absorption region of the OH bond in water molecules), and the background is scanned after each sample analysis to eliminate interference. At the same time, the sample thickness measured by a high-precision height gauge is used for spectral normalization. Finally, the water mass fraction is calculated through a formula, which is an important indicator for judging the high-purity potential of the sample.

[0044] The above introduces the technical terms related to this application.

[0045] High-purity quartz ore is a rock that can be obtained through beneficiation and purification under current technical and economic conditions, with a silicon dioxide (Si ) purity of not less than 99.995% (4N5) and impurity, inclusion content and particle size meeting the application requirements of high-tech fields such as semiconductors, photovoltaics and optics. As an important strategic new mineral, it plays a key supporting role in the development of high-tech industries.

[0046] Vein quartz is one of the important sources of high-purity quartz ore. With the increasing demand for high-purity quartz ore in high-tech fields, the demand for evaluation and identification of vein quartz type high-purity quartz ore is becoming increasingly urgent. However, there is no efficient and accurate mature system for the evaluation and identification of this type of mineral.

[0047] Traditional evaluation and identification methods for vein quartz type high-purity quartz ore usually rely on complex processing and purification tests and strict chemical analysis tests. Not only is the overall time span long and the efficiency low, but also the cost is high. The testing process is destructive to the sample, and it is difficult to meet the actual demand of large-scale sample rapid evaluation. Therefore, developing a rapid, accurate and scientific identification technology has become a problem to be solved.

[0048] In view of this, in order to solve the problems existing in the prior art, the application provides a vein quartz type high-purity quartz ore identification evaluation method and device, which can solve the problems of time-consuming, high cost, strong destructive and low efficiency of traditional vein quartz type high-purity quartz ore identification at the present stage, and provide a fast and accurate identification technology.

[0049] The vein quartz type high-purity quartz ore identification evaluation method provided by the application will be described in detail below in combination with the accompanying drawings of the specification: Exemplarily, as shown in Figure 1 , Figure 1 The application provides a schematic diagram of the architecture of a vein quartz type high-purity quartz ore identification evaluation system. The identification evaluation system 10 includes a sample preparation module 11, a physical property parameter measurement module 12, a total control module 13, a purity evaluation model 14, and an evaluation module 15.

[0050] The sample preparation module 11 is configured to prepare the vein quartz sample into a plurality of identification samples.

[0051] Optionally, the plurality of identification samples respectively belong to different preset types, and the preset types include rock powder, inclusion sheet, electron probe sheet, and double-sided polished wafer.

[0052] Exemplarily, the sample preparation module 11 performs the operations of coarse crushing to 4mm screening, medium crushing to 1mm screening, mixing and dividing, strong magnetic iron removal, fine crushing to 200 mesh, and strong magnetic secondary iron removal on the vein quartz sample in sequence, to obtain the rock powder type identification sample.

[0053] Exemplarily, the sample preparation module 11 cuts the vein quartz sample into a sheet sample of a preset size, and performs the operations of ultrasonic cleaning, drying, preparing an inclusion sheet, acetone soaking, removing residual glue, and air drying on the sheet sample of the preset size in sequence, to obtain the inclusion sheet type identification sample; wherein the length deviation of the sheet sample is less than a first threshold value, and the non-parallelism deviation of the two end faces of the sheet sample is less than a second threshold value.

[0054] Exemplarily, the sample preparation module 11 cuts the vein quartz sample into a sheet sample of a preset size, and performs the operations of ultrasonic cleaning, drying, and preparing an electron probe sheet on the sheet sample of the preset size in sequence, to obtain the electron probe sheet type identification sample.

[0055] Exemplarily, the sample preparation module 11 cuts the vein quartz sample into a sheet sample of a preset size, and performs the operations of ultrasonic cleaning, drying, grinding into a double-sided polished wafer, acetone soaking, anhydrous ethanol cleaning, and secondary drying on the sheet sample of the preset size in sequence, to obtain the double-sided polished wafer type identification sample.

[0056] It can be understood that the sample preparation module 11 can be electrically connected with instruments required in the sample preparation process, so that the sample preparation module 11 can call the instruments to prepare the quartz vein samples.

[0057] The physical property parameter measurement module 12 is configured to measure the physical property parameters of the plurality of samples to be identified. The physical property parameters include transmittance, water content, cell parameters, and particle size related parameters.

[0058] Specifically, the physical property parameter measurement module 12 measures the physical property parameters by calling various instruments connected with the physical property parameter measurement module 12. For example, the physical property parameter measurement module 12 is connected with an automatic mineral parameter analysis system, an ultraviolet-visible-near infrared spectrophotometer, an X-ray diffractometer, and a Fourier transform infrared spectrometer. The four instruments are called to measure the transmittance, water content, cell parameters, and particle size related parameters.

[0059] It should be noted that the specific process of measuring the physical property parameters of the plurality of samples to be identified by the physical property parameter measurement module 12 can be referred to S201 below, which will not be described here.

[0060] The total control module 13 is configured to control the operation of the entire system. For example, the total control module 13 controls the sample preparation module 11 to prepare the samples to be identified. The total control module 13 controls the physical property parameter measurement module 12 to measure the physical property parameters of the plurality of samples to be identified and input the physical property parameters into the purity evaluation model 14 to determine the purity estimate value.

[0061] The purity evaluation model 14 is configured to determine the purity estimate value of the quartz vein sample according to the input physical property parameters of the plurality of samples to be identified.

[0062] Optionally, the transmittance, water content, cell parameters, and particle size related parameters of the quartz vein samples with known purity are analyzed for correlation with the purity data of the quartz vein samples with known purity according to a preset statistical algorithm. The purity evaluation model 14 is constructed according to the correlation analysis results.

[0063] The evaluation module 15 is configured to determine the evaluation result of the quartz vein sample according to the purity estimate value of the quartz vein sample. The evaluation result is used to represent whether the quartz vein sample has the potential of quartz vein type high-purity quartz ore.

[0064] The above describes a quartz vein type high-purity quartz ore identification and evaluation system.

[0065] As shown in Figure 2 , Figure 2 a flowchart of a quartz vein type high-purity quartz ore identification and evaluation method provided by the present application, comprising the following steps: S201, preparing the quartz vein sample into a plurality of samples to be identified.

[0066] The plurality of samples to be identified respectively belong to different preset types, and the preset types include rock powder, inclusion slice, electron probe slice, and double-sided polished section.

[0067] In a possible implementation, the vein quartz sample is prepared into a plurality of samples to be identified in this step, and the specific process is as follows: (1) Preparation of the sample to be identified of the rock powder type; Optionally, the vein quartz sample is sequentially subjected to coarse crushing to 4 mm sieving, medium crushing to 1 mm sieving, uniform mixing and division, strong magnetic iron removal, fine crushing to 200 mesh, and secondary strong magnetic iron removal, to obtain the sample to be identified of the rock powder type.

[0068] Specifically, for the collected representative vein quartz sample, coarse crushing is first performed: the sample is coarsely crushed to a particle size of no greater than 4 mm using a jaw crusher, and then sieved through a standard sieve with a 4 mm aperture, and particles that do not meet the particle size requirement are removed to ensure uniform sample particle size after coarse crushing; then medium crushing is performed: the coarsely crushed and sieved sample is transferred to a roller crusher, and medium crushing is performed to a particle size of no greater than 1 mm, and then sieving is performed through a standard sieve with a 1 mm aperture to further control the sample particle size; the medium crushed and sieved sample is placed in a sample mixer and mixed uniformly for no less than 10 minutes, and four-fold division is performed to reduce the total sample amount to adapt to the subsequent processing requirements; the divided sample is first subjected to first strong magnetic iron removal using a strong magnetic iron removal device to remove magnetic impurities (such as iron filings and magnetite) mixed in the sample, and after iron removal, the sample is divided into two parts, one part is sealed for storage as a coarse sub-sample for verification, and the other part is transferred to a planetary ball mill for fine crushing, to a particle size of 200 mesh (corresponding to a sieve aperture of about 0.074 mm), and after fine crushing, second iron removal is performed using the strong magnetic iron removal device to ensure complete removal of magnetic impurities, and finally, the sample to be identified of the rock powder type for subsequent particle size measurement is obtained, which includes an analysis master sample and an analysis sub-sample, and is used for formal detection and parallel verification, respectively.

[0069] (2) Preparation of the sample to be identified of the inclusion slice type; Optionally, the vein quartz sample is cut into a sample of a preset size, and the sample of the preset size is sequentially subjected to ultrasonic cleaning, drying, preparation of an inclusion slice, acetone soaking, residual glue removal, and air drying, to obtain the sample to be identified of the inclusion slice type. The length deviation of the sample of the preset size is less than a first threshold value, and the non-parallelism deviation of the two end faces of the sample of the preset size is less than a second threshold value.

[0070] Specifically, a block part without cracks and obvious impurities in the vein quartz sample is selected, and a diamond wire cutting machine is used to cut it into a sheet-shaped sample of a preset size (50 mm x 50 mm x 10 mm). The size accuracy is strictly controlled during the cutting process: the length deviation of the sheet-shaped sample is less than a first threshold value (±0.1 mm), and the deviation of the non-parallelism of the two end faces is less than a second threshold value (±0.05 mm), so as to ensure that the sample has a regular shape. The cut sheet-shaped sample is placed in an ultrasonic cleaner, deionized water is added as a cleaning medium, the cleaning power is set to 300 W, and the cleaning time is set to 15 minutes, so as to remove dust and cutting residues on the surface of the sample. The cleaned sample is placed in an electric heating air oven, and is dried at a constant temperature of 105 DEG C for 2 hours, so as to completely remove the adsorbed water on the surface of the sample. After drying, the sample is bonded to a glass slide using epoxy resin, and a wafer grinder is used to prepare a uniform thickness of the inclusion sheet. After the preparation of the inclusion sheet is completed, the inclusion sheet is placed in a sealed container containing acetone and soaked for 24 hours, so that the sample and the glass slide are separated and the residual epoxy resin glue is dissolved. After soaking, the residual glue layer on the surface of the sample is gently brushed off with a dust-free cotton swab, and then the sample is placed in a fume hood for natural air drying (avoiding direct sunlight to prevent the sample from being damp). Finally, the inclusion sheet type sample to be identified for subsequent transmission rate measurement is obtained.

[0071] (3) Preparation of the electron probe sheet type sample to be identified; Optionally, the vein quartz sample is cut into a sheet-shaped sample of a preset size, and the sheet-shaped sample of the preset size is sequentially subjected to ultrasonic cleaning, drying, and electron probe sheet preparation operations, so as to obtain the electron probe sheet type sample to be identified. The length deviation of the sheet-shaped sample is less than a first threshold value, and the deviation of the non-parallelism of the two end faces of the sheet-shaped sample is less than a second threshold value.

[0072] Specifically, a part with complete crystal structure and no obvious defects in the vein quartz sample is selected, and a diamond wire cutting machine is used to cut it into a sheet-shaped sample of 50 mm x 50 mm x 10 mm according to the cutting standard of the inclusion sheet preparation, so as to ensure that the length deviation is ≤±0.1 mm and the deviation of the non-parallelism of the two end faces is ≤±0.05 mm. The cut sheet-shaped sample is placed in an ultrasonic cleaner, deionized water is added for cleaning for 15 minutes (power 300 W), so as to remove the surface impurities. After cleaning, it is transferred to a 105 DEG C electric heating air oven for drying for 2 hours, so as to remove the water on the surface of the sample. The dried sheet-shaped sample does not need additional bonding or soaking treatment, and is directly sent to a sample coating machine for carbon spraying treatment (coating thickness is about 10 nm, so as to ensure the surface conductivity of the sample and meet the electron probe detection requirements). Then, the sample is trimmed to a size suitable for the sample stage of the electron probe instrument by using a slicing machine. Finally, the electron probe sheet type sample to be identified for subsequent cell parameter measurement is obtained.

[0073] (4) Preparation of the double-side polished wafer type sample to be identified; Optionally, the vein quartz sample is cut into a preset size of a sheet sample, and the preset size of the sheet sample is sequentially subjected to ultrasonic cleaning, drying, grinding into a double-sided polished wafer, acetone immersion, anhydrous ethanol cleaning, and secondary drying operation, to obtain a double-sided polished wafer type of the to-be-identified sample. In this case, the length deviation of the sheet sample is less than a first threshold value, and the non-parallelism deviation of the two end faces of the sheet sample is less than a second threshold value.

[0074] Specifically, a block part with uniform texture and no cracks in the vein quartz sample is selected, cut into a sheet sample with a size of 50 mm×50 mm×10 mm, and the length deviation is controlled to be ±0.1 mm and the non-parallelism deviation of the two end faces is controlled to be ±0.05 mm. The sheet sample is subjected to ultrasonic cleaning with deionized water for 15 minutes to remove surface dust, and then dried in a 105°C oven for 2 hours. The sample after drying is adhered to a glass slide using hot melt adhesive, and a double-sided polishing machine is used to grind one side of the sample first, and then the sample is flipped to grind the other side. The grinding accuracy is controlled throughout the process, so that the final thickness of the wafer is 150-250 μm. The double-sided polished wafer (including the glass slide) after grinding is immersed in an acetone solution for more than 24 hours, so that the hot melt adhesive is fully dissolved, and the rock sample and the glass wafer are separated. The separated rock wafer is repeatedly washed with anhydrous ethanol for 3 times to remove the residual acetone and hot melt adhesive on the surface. Finally, the washed wafer is placed in an electrically heated air oven and dried at a constant temperature of about 100°C for more than 12 hours to completely remove the adsorbed water in the sample surface and internal cracks, and finally obtain a double-sided polished wafer type of the to-be-identified sample for subsequent water content measurement.

[0075] The above describes the specific process of preparing the vein quartz sample into a plurality of to-be-identified samples.

[0076] In a possible implementation, the step can be performed by a sample preparation module included in the identification evaluation system described above, so that the identification evaluation system prepares the vein quartz sample into a plurality of to-be-identified samples.

[0077] S202, measure the physical property parameters of the plurality of to-be-identified samples.

[0078] The physical property parameters include transmittance, water content, cell parameters, and particle size related parameters.

[0079] In a possible implementation, the physical property parameters of the plurality of to-be-identified samples are measured in the step, and the specific process is as follows: S2021, perform particle size measurement on the rock powder type of the to-be-identified sample to obtain the particle size related parameters of the vein quartz sample.

[0080] The particle size related parameters include particle size and particle size variation coefficient. Further, the particle size measurement is performed on the rock powder type of the to-be-identified sample to obtain the particle size related parameters of the vein quartz sample, specifically including: (1) Using an automatic mineral parameter analysis system, a particle size analysis test is performed on a rock powder type sample to be identified to obtain a particle size analysis report.

[0081] In the particle size analysis report, a plurality of particle size intervals of the quartz particles in the rock powder are characterized, as well as the average particle size of the quartz particles in each particle size interval and the number of quartz particles in the corresponding interval.

[0082] Specifically, using MLA for testing, first calibrate the equipment according to the instrument operation procedure: select a standard particle size sample (such as a quartz standard powder with known particle size distribution) for instrument calibration to ensure that the test error is controlled within ±2%; then the pretreated sample is sent into the instrument through the automatic sampling device, and the test parameters are set: sampling speed 5 mL / min, scanning resolution 500 dpi, and the number of single sample tests is not less than 3 times (taking the average value to reduce random error). The instrument identifies and counts the quartz particles in the sample one by one through high-resolution optical imaging and image recognition technology, and finally generates a particle size analysis report; in addition to characterizing a plurality of particle size intervals of the quartz particles in the rock powder, the average particle size of the quartz particles in each interval and the number of quartz particles in the corresponding interval, the report also includes particle morphology parameters (such as circularity, used to assist in judging the integrity of the particles) and the removal record of abnormal particles (such as impurity particles with particle size beyond the reasonable range), to ensure the effectiveness of the subsequent calculation data.

[0083] (2) According to the particle size analysis report, the particle size of the vein quartz sample is determined.

[0084] Specifically, according to the effective data (abnormal particles have been removed) in the particle size analysis report, determine the particle size intervals involved in the calculation: select the particle size interval where the proportion of quartz particles is ≥95% (exclude the interval where impurity particles are concentrated to avoid interference with the results), confirm the average particle size of each interval (i, unit μm, provided directly from the report, which is the arithmetic mean of the upper and lower limit particle sizes of the corresponding interval), the number of particles in the corresponding interval, the number of particle size intervals and the total number of all effective quartz particles.

[0085] Optionally, the above step is performed by the following formula one: = / n formula one wherein, represents the particle size of the vein quartz sample, i represents the particle size of the quartz particles in the corresponding particle size interval, x i represents the number of quartz particles in the corresponding particle size interval, m is the number of particle size intervals, n ​The total of all quartz particles. During the calculation process, Excel or professional data processing software (such as Origin) is used for data operation, and 3 significant digits are retained; if the proportion of the number of particles in a certain interval is ≤0.5% (regarded as a small amount of particles caused by accidental error), it can be removed during calculation to further improve the representativeness of the average particle size.

[0086] (3) According to the particle size of the vein quartz sample and the standard deviation of the particle size of all quartz particles, the particle size variation coefficient is determined.

[0087] Optionally, before calculating the particle size variation coefficient, the standard deviation of the particle size of all effective quartz particles is required, which can be calculated in combination with the particle size of the vein quartz sample obtained in the foregoing steps.

[0088] Optionally, the particle size variation coefficient is determined by the following formula two: cv= ( σ / ) × 100% Formula two Wherein, cv represents the particle size variation coefficient, σ is the standard deviation of the particle size of all quartz particles. After the calculation is completed, the result needs to be verified: if the cv value exceeds the allowed range of instrument test precision (usually ±5%), part of the sample needs to be reselected for repeated testing to check whether the result is abnormal due to uneven sample dispersion or instrument error; the finally output particle size variation coefficient needs to retain 2 decimal places, which is one of the key parameters for judging the uniformity of the particle size distribution of the vein quartz sample and the potential of the high-purity quartz mine.

[0089] The particle size measurement process of the rock powder type sample to be identified is described above.

[0090] S2022, the transmittance of the inclusion slice type sample to be identified is measured to obtain the transmittance of the vein quartz sample; Optionally, in this step, the ultraviolet visible near-infrared spectrophotometer is used to measure the transmittance of the inclusion slice type sample to be identified under different wavelengths of light to obtain the transmittance of the vein quartz sample.

[0091] Specifically, first, the ultraviolet visible near-infrared spectrophotometer is used for testing. Before testing, the instrument needs to be calibrated: a standard transmittance sample (such as a high-purity quartz sheet with known transmittance, with a transmittance standard value error ≤0.1%) is selected for instrument calibration in the wavelength range of 200-380 nm. During the calibration process, the baseline drift value of the instrument needs to be recorded to ensure that the drift value is ≤0.001 Abs (absorbance unit). If it exceeds the range, recalibrate until the accuracy requirement is met.

[0092] Secondly, set the test parameters after calibration: limit the wavelength measurement range to the ultraviolet light region 200~380nm, which is the sensitive interval of impurity elements in quartz for light absorption, and can effectively reflect the optical properties related to sample purity; set the scanning step to 1nm to ensure that each wavelength point data is collected, avoiding missing the key wavelength transmittance information; set the measurement number of each wavelength point to 3 times to reduce random error through multiple measurements; set the instrument resolution to 0.5nm and the integration time to 0.1 second to balance measurement efficiency and data signal-to-noise ratio; at the same time, turn on the "automatic background subtraction" function of the instrument to prepare for subsequent elimination of environmental light, instrument noise and other interference.

[0093] Thirdly, fix the pre-processed inclusion sheet on the sample clamping table of the instrument, adjust the clamping table position to ensure that the inclusion sheet completely covers the light path channel, and the sample plane is perpendicular to the incident light direction to avoid refraction deviation of light caused by sample inclination, affecting the accuracy of transmittance data. Start the measurement according to the instrument operation procedure: first perform background scanning, in the state of no sample blocking the light path, scan the wavelength range of 200~380nm, record the incident light flux at different wavelengths in this interval as the reference light flux for subsequent calculation; after background scanning, put in the inclusion sheet to perform sample scanning, the instrument automatically records the total transmittance light flux through the sample in the same wavelength range, and generates the original transmittance data curve in real time. Monitor the instrument state throughout the measurement process, if the data jumps (such as the total transmittance light flux value of a certain wavelength suddenly decreases or increases by more than 5%), pause the measurement, check whether the inclusion sheet is shifted or the light path is blocked, and re-measure the wavelength interval after excluding the abnormality to ensure that all wavelength point transmittance data is true and effective.

[0094] Finally, calculate the transmittance of each wavelength point according to the incident light flux and transmittance light flux data recorded by the instrument: the transmittance calculation formula of a single wavelength point is Tt=(T2 / T1) 100%, where Tt is the transmittance of the wavelength point, T1 is the incident light flux of the corresponding wavelength, and T2 is the total transmittance light flux of the corresponding wavelength through the sample. After calculation, filter and arrange the data: first, eliminate abnormal data points, if the Tt value of a certain wavelength point measured 3 times has a coefficient of variation >2% (i.e. the data dispersion exceeds the allowed range), re-measure the wavelength point; if it is still abnormal after re-measurement, check whether the sample has local defects (such as micro-cracks), and replace the sample for repeated testing. Then take the average value of the filtered effective data: calculate the arithmetic mean of all effective wavelength points Tt in the wavelength range of 200~380nm, and round the average value to 0.1% as the final transmittance result of the type of inclusion sheet to be identified sample, which will be one of the key optical parameters for subsequent correlation of quartz sample purity and judgment of high-purity quartz ore potential.

[0095] S2023, the cell parameter measurement is performed on the electronic probe piece type sample to be identified, and the cell parameter of the vein quartz sample is obtained; Wherein, the cell parameter includes a cell parameter basic value and a quartz axis change rate ratio. Further, the cell parameter measurement is performed on the electronic probe piece type sample to be identified, and the cell parameter of the vein quartz sample is obtained, specifically including: (1) using an X-ray diffractometer, diffraction analysis is performed on the electronic probe piece type sample to be identified under a preset condition to obtain diffraction spectrum data; Specifically, first, the X-ray diffractometer needs to complete instrument calibration before testing: a standard silicon sample (purity ≥ 99.999%, cell parameter known and error ≤ 0.00001 nm) is selected, and diffraction calibration is performed in the range of 2θ = 20°-80°, and the diffraction peak position deviation is recorded to ensure that the deviation is ≤ 0.02°; At the same time, the stability of the instrument current and voltage is calibrated, and the maximum voltage is set to 60kV, the maximum current is set to 55mA, and the current and voltage fluctuation value within 30 minutes is observed ≤ 1%, if it exceeds the range, it needs to be re-adjusted until the accuracy requirement is met.

[0096] Secondly, after calibration, set the preset test condition: according to the structural characteristics of vein quartz crystal, select Cu target (emitting Kα characteristic X-ray, wavelength λ = 1.5406Å); The scanning range is set to 5°-90°, covering the main characteristic diffraction peaks of quartz, such as 2θ = 20.8°, 26.6°, 36.5°, etc.; The scanning step is 0.02°, which ensures accurate capture of the diffraction peak position; Each step stays for 20s to ensure sufficient diffraction signal intensity and reduce noise interference; The detector counting mode is set to "integral counting", and the "automatic sample rotation" function of the instrument is turned on at the same time, and the rotation speed is 5r / min to ensure that the sample is uniformly irradiated by X-rays.

[0097] Finally, place the glass holder with the fixed sample into the XRD sample table, close the instrument protection door (to avoid X-ray leakage), and start the diffraction analysis program according to the instrument operation procedure: the instrument automatically drives the sample table to rotate, the X-rays emitted by the X-ray source penetrate the sample and are diffracted, the detector captures the diffraction signal in real time and converts it into an electrical signal, which is processed by the data acquisition system to generate diffraction spectrum data containing diffraction peak position (2θ value), diffraction peak intensity, half-width, etc. The spectrum file is saved for subsequent processing.

[0098] (2) data processing of the diffraction spectrum data to determine the cell parameter basic value; Optionally, in this step, professional XRD data processing software such as Jade 6.5 is used to process the diffraction spectrum data.

[0099] Example, import the diffraction pattern data into professional XRD data processing software; first perform pattern preprocessing: 1, background subtraction, use the software "Automatic Background Subtraction" function, set the background smoothing coefficient to 5, eliminate the interference of environmental noise and instrument background on the pattern; 2, curve smoothing, select the "5-Point Smoothing" option, smooth the pattern after background subtraction, and make the diffraction peak profile clearer; 3, diffraction peak identification, through the software "Peak Search" function, set the peak intensity threshold to 100 counts, eliminate weak impurity peaks, automatically identify and mark the characteristic diffraction peaks of quartz, and confirm the correspondence of each characteristic peak by comparing with the standard quartz diffraction data, and eliminate impurity peaks.

[0100] After that, perform diffraction angle correction: select the software "Theta Calibration" option, and use the diffraction peaks of the standard silicon sample for calibration as the reference to correct the diffraction angle of the current pattern, ensuring that the error of 2θ value is ≤0.01°; after correction, start the "Calculate Lattice" function, input the crystal structure type of quartz (hexagonal system, space group P 22), the software will calculate the initial cell parameters based on the 2θ values of the characteristic diffraction peaks and Bragg's law (2d sinθ=λ, d is the interplanar spacing); then enable the "Cell Refinement" function to optimize the initial parameters with diffraction peak intensity as the weight, reduce the system error, and finally obtain the crystal cell parameter basic value with the required precision, including a (cell a axis length, unit nm), c (cell c axis length, unit nm), v (cell volume, unit nm³, calculated from a, c and hexagonal system volume formula).

[0101] It should be noted that the calculated crystal cell parameter basic value is verified in this step: check the software output fitting goodness R value (deviation coefficient of diffraction peak calculation position and measured position), if R<5% indicates that the calculation result matches the measured pattern well; if R≥5%, recheck the pattern preprocessing step, such as whether the characteristic peaks are deleted by mistake, whether the background subtraction is excessive, or replace the test area of the sample for repeated diffraction analysis, until the qualified crystal cell parameter basic value is obtained.

[0102] (3) Calculate the quartz axis change rate ratio according to the crystal cell parameter basic value and the standard quartz crystal cell parameter; In this step, the standard published by the International Diffraction Data Center is selected as the source of the crystal cell parameters of the standard quartz, and the crystal cell parameters of the standard quartz are fixed as: =0.49134nm (standard a axis length), = 0.54052 nm (standard c-axis length), which is the reference value of the high-purity quartz crystal structure, and is used to compare the cell deviation of the measured sample.

[0103] Optionally, first, the difference between the measured cell parameter and the standard parameter is calculated, and then, the quartz axis change rate ratio is calculated according to the cell parameter basic value and the standard quartz cell parameter, which is performed by the following formula three: Δ = (Δ / ) / (Δ / ) Formula three Wherein, Δ is the quartz axis change rate ratio, a 0, c 0 respectively represent the standard quartz cell parameter, and Δ a 0, Δ c 0 respectively represent the difference between the cell parameter basic value and the standard quartz cell parameter.

[0104] It can be understood that, in order to ensure the reliability of the data, the steps S2023 are repeatedly performed on three different test areas of the same sample under the premise of avoiding fissures and inclusion concentration areas, so as to obtain three quartz axis change rate ratios (Δ , , ); the arithmetic mean of the three values is calculated, and the coefficient of variation is calculated, requiring CV≤2%; finally, the average Δ is taken as the quartz axis change rate ratio of the electronic probe piece type sample to be identified, which is used for subsequent correlation analysis with the purity of the vein quartz to determine whether the sample has the potential of high-purity quartz ore.

[0105] S2024, measuring the water content of the double-side polished flake type sample to be identified to obtain the water content of the vein quartz sample.

[0106] Optionally, the present step specifically includes the following procedures: (1) Using a Fourier transform micro infrared spectrometer, the water content of the double-side polished flake type sample to be identified is measured to obtain infrared absorption spectrum data; Optionally, a Fourier transform micro infrared spectrometer is used, and OPUS software is used for spectrum collection and processing. First, the instrument calibration needs to be completed before testing: a standard polystyrene film sample (with known characteristic absorption peak wave number, error ≤1 c ¹) is selected, scanned in the range of 2000-4000 c ¹ wave number, verify the accuracy of the instrument wave number, ensure that the characteristic peak position deviation is ≤0.5 c ¹; at the same time, calibrate the signal-to-noise ratio of the instrument, the background spectrum noise value obtained by scanning under the blank state is ≤0.001 Abs, if it exceeds the range, then adjust the light path or replace the detector until the accuracy requirement is met.

[0107] Secondly, set the test parameters after calibration: the wave number measurement range is 2000-4000 cm-1 1 (The interval is the characteristic absorption interval of the structural water OH bond in the quartz, which can accurately capture the infrared absorption signal of water), the test light type is non-polarized light (to avoid the interference of the anisotropy of the crystal on the absorption signal), the beam spot size is selected as 50 μm x 50 μm (to adapt to the micro area analysis of the wafer and reduce the influence of impurity areas), the scanning times of the sample and the background are both set as 256 times (to ensure that the spectral signal intensity is sufficient and to reduce random noise), and the resolution is set as 4 cm-1 1 (to balance the spectral details and measurement efficiency), and the scanning mode is set as "transmission mode" to match the characteristics of the wafer sample.

[0108] Thirdly, fix the pre-processed double-side polished wafer on the sample holder of the instrument, adjust the height and angle of the sample table to ensure that the wafer plane is perpendicular to the infrared light path and the beam spot completely falls on the defect-free area of the wafer. Perform the measurement in the order of "background first and sample second": first, scan the background in the state without sample shielding, obtain the background infrared spectrum data and save it; then move the sample table to make the beam spot cover the first test area on the wafer, start the sample scanning, and the instrument converts the infrared light into interference signals through the interferometer, and generates the original infrared absorption spectrum data containing wave number and absorption intensity after Fourier transform; after completing the sample scanning of each area, scan the background again to avoid the background drift caused by the change of the environment temperature and humidity, and obtain the net infrared absorption spectrum data of the area by deducting the current background.

[0109] Finally, randomly select 10 non-overlapping test areas in the same sample, each with a distance ≥1 mm, and avoid the edges and cracks. Repeat the above "background scanning→ sample scanning→ background deduction" process to obtain the net infrared absorption spectrum data of the 10 areas, and save them as spc format files recognizable by OPUS software for subsequent processing.

[0110] (2) Normalize the infrared absorption spectrum data according to the sample thickness data to obtain the infrared spectrum absorption coefficient corresponding to the wave number position; Optionally, use a high-precision height gauge (accuracy ≤0.1 μm) to measure the thickness of the double-side polished wafer: on the wafer after the infrared scanning, measure the thickness value of each area 3 times near the positions corresponding to the 10 infrared test areas (distance from the test area ≤0.5 mm to ensure that the thickness data match the spectrum data); avoid pressing the wafer with the height gauge probe during measurement to prevent the wafer from deforming and affecting the data accuracy. Take the arithmetic mean of the 3 thickness values of each area to obtain the average thickness value of the 10 areas, denoted as 、 … The thickness average value d of the sample is obtained by averaging the thickness average values of the 10 regions twice, and the thickness data is recorded to three decimal places, with the unit of microns.

[0111] Further, the OPUS software is opened, and the 10 regional net infrared absorption spectrum data saved in step 1 is imported, and normalization processing is performed: 1. Secondary background subtraction, further eliminate the residual environmental water vapor, instrument background interference, ensure the smoothness of the spectrum baseline through the software "Advanced Background Correction" function; 2. Thickness normalization, call the corresponding regional thickness value measured in step 2 (d1, d2, d3, d4, d5, d6, d7, d8, d9, d10) to ), the software automatically corrects the infrared absorption intensity of different thickness regions to the standard state of "thickness = 1mm" according to the logic of "absorption intensity / thickness" (eliminate the influence of sheet thickness difference on absorption intensity), generate normalized infrared absorption spectrum; 3. Absorption coefficient extraction, in the normalized spectrum, select every wave number point (ν) in the range of 2000-4000c ¹ wave number, read the corresponding absorption intensity value, which is the infrared spectrum absorption coefficient K(ν) (unit: c ¹) corresponding to the wave number position ν.

[0112] Finally, the K(ν) data of the 10 regions are verified for consistency: calculate the coefficient of variation of K(ν) of the 10 regions at the same wave number (such as 3500c ¹), and the coefficient of variation is required to be ≤5% (indicating that the absorption coefficient distribution of each region is uniform, and the sample is well represented); if the coefficient of variation is out of tolerance, check whether the sheet has local composition unevenness, or reselect the test area for retesting. Finally, the arithmetic average value of K(ν) of the 10 regions is taken to obtain the average value of the infrared spectrum absorption coefficient corresponding to the wave number position of the sample as a whole, which is used for subsequent calculation.

[0113] (3) According to the infrared spectrum absorption coefficient corresponding to the wave number position, the water content of the quartz sample is determined, which is executed through the following formula four: Formula four Wherein, C represents the molar absorption coefficient, K(ν) represents the infrared spectrum absorption coefficient corresponding to the wave number position, K(ν)dν represents the integral area in a given wave band range, ν represents the infrared absorption wave number, λ represents the activation direction factor of OH bond in the crystal, and P represents the water content of the quartz sample.

[0114] Specifically, according to the average value of K(ν) obtained in the foregoing steps, the integral wave band is first determined: in the range of 2000-4000c ¹, select the core wave band (3000-3800c ¹, the wave band is the main absorption interval of the structural water OH bond stretching vibration in vein quartz), the integral area of K(v) in the wave band is calculated by the "Integration" function of OPUS software, that is K(v)dv (unit: cm-1) ². The C value of each of the 10 regions is calculated according to the above formula four, and the arithmetic mean of the 10 C values is taken to obtain the average molar absorption coefficient Caverage of the sample, and the last four significant digits are retained.

[0115] For the calculation of the water content of the vein quartz sample, the density p of the vein quartz sample is determined: the conventional value is taken, and the density p of quartz is 2.65 g / cm³. After the calculation is completed, the precision of P is controlled: the result is required to be accurate to 0.0001wt%, and the coefficient of variation of the water content calculation values of the 10 regions is checked, which needs to be ≤3% (to ensure the reliability of the data); if the coefficient of variation exceeds the standard, it is necessary to recheck whether there is an error in the integral area calculation, thickness measurement and other links, and if necessary, some region data is supplemented. The final output of the water content of the sample is the arithmetic mean of the calculation values of the 10 regions, which is one of the key indicators for judging the purity potential of the vein quartz sample.

[0116] The above describes the specific process of measuring the physical property parameters of the plurality of samples to be identified by the identification evaluation system.

[0117] In one possible implementation, the step can be performed by the physical property parameter measurement module included in the identification evaluation system described in the foregoing, so that the identification evaluation system measures the physical property parameters of the plurality of samples to be identified.

[0118] S203, input the physical property parameters of the plurality of samples to be identified into the purity evaluation model, and determine the purity estimate value of the vein quartz sample.

[0119] Optionally, before performing the step, the identification evaluation system also creates the purity evaluation model.

[0120] In one possible implementation, the transmittance, water content, cell parameter, particle size related parameter of the vein quartz sample with known purity are subjected to correlation analysis with the purity data of the vein quartz sample with known purity according to a preset statistical algorithm, and the purity evaluation model is constructed according to the correlation analysis result. The specific creation process of the purity evaluation model is described below S801, which is not described here again.

[0121] Exemplarily, in this step, it is ensured that the multiple quartz vein samples to be identified have completed the physical property parameter measurement according to the requirements of the scheme, and the parameter data meets the accuracy standard: the four types of core physical property parameters of each sample to be identified need to be collected, that is, the average light transmittance in the wavelength range of 200-380 nm (T, unit %, the coefficient of variation of repeated measurement is less than or equal to 2%), the mass fraction of water (P, unit wt%, accurate to 0.0001 wt%), the quartz axis change rate ratio (Δ, unitless, calculated from the cell parameter), and the particle size variation coefficient (cv, unit %, obtained based on the particle size analysis of the rock powder), and at the same time, the abnormal values in the parameter data (such as light transmittance > 100%, water content > 0.1 wt%) are removed to ensure the effectiveness of the input parameters.

[0122] After that, according to the input requirements of the purity evaluation model de, the core physical property parameters of the multiple samples to be identified after arrangement are input into the purity evaluation model constructed in the foregoing step: when inputting, it is ensured that the parameters are matched with the independent variables of the model one by one (the light transmittance T corresponds to the coefficient term of the model , the water content P corresponds to the coefficient term, the quartz axis change rate Δ corresponds to the coefficient term, and the particle size variation coefficient cv corresponds to the coefficient term), and the parameters need to be preprocessed according to the standardization rules (such as [0, 1] interval standardization) when the model is constructed to avoid the influence of the dimension difference on the calculation result.

[0123] Finally, after the model receives the parameters, the purity estimation value of each sample to be identified is output automatically based on the preset linear regression formula; if there are multiple sets of parameter data (such as parameters of different test regions) for the same sample, the arithmetic mean of the purity estimation values obtained by multiple calculations is taken as the final purity estimation value of the sample, and the number of each sample and the corresponding purity estimation value are recorded to form a corresponding table of “sample number-purity estimation value”, which provides a core basis for the subsequent judgment of the potential of the quartz vein type high-purity quartz mine.

[0124] In a possible implementation manner, this step can be executed by the total control module included in the identification evaluation system described in the foregoing step in cooperation with the purity evaluation model, so that the identification evaluation system inputs the physical property parameters of the multiple samples to be identified into the purity evaluation model to determine the purity estimation value of the quartz vein sample.

[0125] S204, determining the evaluation result of the quartz vein sample according to the purity estimation value of the quartz vein sample.

[0126] The evaluation result is used to represent whether the quartz vein sample has the potential of the quartz vein type high-purity quartz mine.

[0127] It can be understood that the core basis of the evaluation decision is that the purity estimation value is greater than or equal to 99.995% (4N5 level), and the transmittance of the sample (200-380 nm average) is greater than or equal to 58%, the water content is less than 0.0150wt%, the particle size variation coefficient is 65%-90%, and the quartz axis change rate is less than 1.

[0128] Further, if the purity estimation value of the sample is greater than or equal to 99.995% and the above parameters meet the standards, the evaluation result is “possesses potential of vein quartz type high-purity quartz mine”; if the purity estimation value is less than 99.995%, or the purity meets the standards but any parameter does not meet the standards, the evaluation result is “does not possess potential of vein quartz type high-purity quartz mine”, which is used to represent whether the sample possesses the corresponding potential.

[0129] For example, different types of physical parameters of the vein quartz type high-purity quartz mine have different degrees of influence on the purity. For example, Figures 3 to 7 as shown in Figures 3 to 7 respectively, the relationship between the five types of parameters of the vein quartz type high-purity quartz mine, including transmittance, water content, quartz axis change rate, particle size, and particle size variation coefficient, and the purity.

[0130] Based on the above technical solution, the purity estimation value of the vein quartz sample is obtained by inputting the core physical parameters of the vein quartz sample to be identified, including transmittance, water content, cell parameters, particle size, and particle size variation coefficient, into the purity evaluation model. Then, the evaluation of whether the vein quartz sample possesses potential of vein quartz type high-purity quartz mine is realized by combining the determination standard of “purity estimation value greater than or equal to 99.995% (4N5 level) and each physical parameter meeting the corresponding threshold value”. The problems of long time consumption, high cost, sample damage, and difficulty in large-scale and rapid evaluation of the traditional identification method are effectively solved. The identification efficiency and accuracy are greatly improved while avoiding sample damage, which can meet the needs of early rapid screening and potential evaluation of the vein quartz type high-purity quartz mine, and provide reliable technical support for subsequent beneficiation and purification and high-tech field raw material application.

[0131] For example, in combination with Figure 2 as shown in Figure 8 before the physical parameters of the plurality of samples to be identified are input into the purity evaluation model to determine the purity estimation value of the vein quartz sample, the method for identifying and evaluating the vein quartz type high-purity quartz mine provided by the present application further includes the following steps: S801, creating a purity evaluation model.

[0132] Optionally, the purity evaluation model can be a partial least squares regression (PLSR) model.

[0133] In a possible implementation, the light transmittance, water content, cell parameter, particle size related parameter of the known purity vein quartz sample are subjected to correlation analysis according to a preset statistical algorithm, and a purity evaluation model is constructed according to the correlation analysis result, and the specific process includes: (1) Preparation of basic data set; Filter the known purity vein quartz samples covering the "non-high-purity- quasi-high-purity-high-purity" levels (≥30 for each level, covering different mineral sources), and the purity is accurately determined by ICP-MS method (error ≤0.001%), as "true purity label". For each sample, four types of core parameters are collected according to the scheme steps: light transmittance (200-380 nm average, denoted as T), water content (mass fraction, denoted as P), quartz axis change rate ratio (denoted as Δ), and particle size variation coefficient (denoted as cv). Each parameter is measured three times to take the average value, forming a "sample number-T-P-Δ-cv-true purity S" structured data set. After excluding outliers, the training set and the validation set are divided according to 7:3.

[0134] (2) Parameter and purity correlation analysis

[0135] A preset statistical algorithm is used: first, single variable analysis is performed by Pearson correlation coefficient method, and key parameters with r absolute value ≥0.6 and t test p<0.05 (usually T, P, Δ, and cv) are selected (T and S are positively correlated, P / Δ and S are negatively correlated, and cv and S are positively correlated); then, PLSR is used for multivariate collaborative analysis, the key parameters are set as the independent variable matrix, S is set as the dependent variable, the principal component (cumulative contribution rate ≥90%) is constructed, the parameter weight (T weight is the highest, P is the second, and Δ and cv are similar) is determined, and the collaborative correlation result is output.

[0136] (3) Purity evaluation model construction and verification

[0137] Based on the correlation result, a linear regression model is constructed using PLSR algorithm: S= ×T+ ×P+ ×Δ+ ×cv+b. Wherein, ~ is the weight coefficient, and b is the constant term, which is optimized by the training set to the fitting degree R²≥0.9). Verification is performed using the validation set: the R² of the predicted purity and the true purity is ≥0.85, the RMSEP is ≤0.08%, the cross-validation RMSEcv is ≤0.05%; the application scope of the model (vein quartz sample, purity 99.5%-99.999%) and sample preparation and environmental requirements are determined, and the model is defined.

[0138] The construction of the purity evaluation model is introduced above.

[0139] Based on the above technical scheme, the present application collects the core physical property parameters such as the light transmittance, water content, cell parameter, particle size and particle size variation coefficient of the vein quartz sample to be identified, constructs a purity evaluation model by means of the partial least squares regression method, inputs the parameters to obtain a purity estimation value, and combines the determination standard that the purity estimation value is greater than or equal to 99.995% (4N5 level) and each physical property parameter meets the corresponding threshold value to realize the evaluation of whether the vein quartz sample has the potential of vein quartz type high-purity quartz ore. The present application effectively solves the problems of long time consumption, high cost, sample damage and difficulty in large-scale and rapid evaluation of the traditional identification method, greatly improves the identification efficiency and accuracy while avoiding sample damage, and meets the needs of early rapid screening and potential evaluation of vein quartz type high-purity quartz ore, thereby providing reliable technical support for subsequent beneficiation and purification and high-tech field raw material application.

[0140] The present application embodiment can divide the function modules or function units of the identification evaluation device according to the above method examples. For example, each function module or function unit can be divided according to each function, or two or more functions can be integrated in one processing module. The integrated module can be realized in the form of hardware or software function module or function unit. The division of modules or units in the present application embodiment is illustrative, and is only a logical function division. In actual implementation, another division mode can be used.

[0141] Exemplarily, as shown in Figure 9 Fig. 1 is a possible structural schematic diagram of an identification evaluation device according to an embodiment of the present application. The identification evaluation device 900 includes a preparation unit 901 and a processing unit 902.

[0142] The preparation unit 901 is configured to prepare the vein quartz sample into a plurality of samples to be identified. The plurality of samples to be identified respectively belong to different preset types, and the preset types include rock powder, inclusion sheet, electron probe sheet and double-sided polished thin section. The processing unit 901 is configured to measure the physical property parameters of the plurality of samples to be identified. The physical property parameters include light transmittance, water content, cell parameter and particle size related parameters. The processing unit 901 is further configured to input the physical property parameters of the plurality of samples to be identified into the purity evaluation model to determine the purity estimation value of the vein quartz sample. The processing unit 901 is further configured to determine the evaluation result of the vein quartz sample according to the purity estimation value of the vein quartz sample. The evaluation result is used to represent whether the vein quartz sample has the potential of vein quartz type high-purity quartz ore.

[0143] Optionally, the preparation unit 901 is further configured to sequentially perform coarse crushing to 4 mm sieving, medium crushing to 1 mm sieving, mixing and dividing, strong magnetic iron removal, fine crushing to 200 mesh, and secondary strong magnetic iron removal on the vein quartz sample to obtain a rock powder type sample to be identified; Optionally, the preparation unit 901 is further configured to cut the vein quartz sample into a sheet sample of a preset size, and sequentially perform ultrasonic cleaning, drying, preparation of an inclusion sheet, acetone soaking, residual glue removal, and air drying on the sheet sample of the preset size to obtain an inclusion sheet type sample to be identified; Optionally, the preparation unit 901 is further configured to cut the vein quartz sample into a sheet sample of a preset size, and sequentially perform ultrasonic cleaning, drying, preparation of an electron probe sheet on the sheet sample of the preset size to obtain an electron probe sheet type sample to be identified; Optionally, the preparation unit 901 is further configured to cut the vein quartz sample into a sheet sample of a preset size, and sequentially perform ultrasonic cleaning, drying, grinding into a double-sided polished wafer, acetone soaking, anhydrous ethanol cleaning, and secondary drying on the sheet sample of the preset size to obtain a double-sided polished wafer type sample to be identified; Optionally, the processing unit 901 is further configured to perform particle size measurement on the rock powder type sample to be identified to obtain a particle size related parameter of the vein quartz sample; Optionally, the processing unit 901 is further configured to perform light transmittance measurement on the inclusion sheet type sample to be identified to obtain a light transmittance of the vein quartz sample; Optionally, the processing unit 901 is further configured to perform cell parameter measurement on the electron probe sheet type sample to be identified to obtain a cell parameter of the vein quartz sample; Optionally, the processing unit 901 is further configured to perform water content measurement on the double-sided polished wafer type sample to be identified to obtain a water content of the vein quartz sample.

[0144] Optionally, the processing unit 901 is further configured to use an automatic mineral parameter analysis system to perform particle size analysis testing on the rock powder type sample to be identified to obtain a particle size analysis report; wherein the particle size analysis report is used to represent a plurality of particle size intervals of quartz particles in the rock powder, and an average particle size of the quartz particles in each particle size interval and a number of quartz particles in the corresponding interval; Optionally, the processing unit 901 is further configured to determine the particle size of the vein quartz sample according to the particle size analysis report by the following formula one: = / n formula one wherein, represents the particle size of the vein quartz sample, i represents the particle size of the quartz particles in the corresponding particle size interval,x i the number of quartz particles corresponding to the particle size interval, m the number of particle size intervals, n the sum of all quartz particles; Optionally, the processing unit 901 is further configured to determine a coefficient of variation of particle size according to the particle size of the vein quartz sample and the standard deviation of the particle size of all quartz particles, by the following Formula Two: cv=( σ / )×100% Formula Two wherein cv represents the coefficient of variation of particle size, σ the standard deviation of the particle size of all quartz particles.

[0145] Optionally, the processing unit 901 is further configured to use an ultraviolet-visible-near infrared spectrophotometer to perform transmittance measurement tests on the sample to be identified of the inclusion flake type under light of different wavelengths, to obtain the transmittance of the vein quartz sample.

[0146] Optionally, the processing unit 901 is further configured to use an X-ray diffractometer to perform diffraction analysis on the sample to be identified of the electron probe flake type under a preset condition, to obtain diffraction spectrum data; Optionally, the processing unit 901 is further configured to perform data processing on the diffraction spectrum data to determine a cell parameter basic value; Optionally, the processing unit 901 is further configured to calculate a quartz axis change rate ratio according to the cell parameter basic value and a standard quartz cell parameter, by the following Formula Three: Δ=(Δ / ) / (Δ / ) Formula Three wherein Δ represents the quartz axis change rate ratio, a 0、 c 0 respectively represent the standard quartz cell parameter, Δ a 0、Δ c 0 respectively represent the difference between the cell parameter basic value and the standard quartz cell parameter.

[0147] Optionally, the processing unit 901 is further configured to use a Fourier transform microscope infrared spectrometer to perform water content measurement on the sample to be identified of the double-side polished flake type, to obtain infrared absorption spectrum data; Optionally, the processing unit 901 is further configured to perform normalization processing on the infrared absorption spectrum data according to the sample thickness data, to obtain an infrared spectrum absorption coefficient corresponding to a wave number position; Optionally, the processing unit 901 is further configured to determine the water content of the quartz vein sample according to the infrared spectrum absorption coefficient corresponding to the wave number position, by the following Formula Four: Formula Four wherein C represents the molar absorption coefficient, K(v) represents the infrared spectrum absorption coefficient corresponding to the wave number position, K(v)dv represents the integral area in a given wave band range, v represents the infrared absorption wave number, λ represents the activation direction factor of the OH bond in the crystal, and P represents the water content of the quartz vein sample.

[0148] Optionally, the processing unit 901 is further configured to perform correlation analysis on the transmittance, water content, cell parameter, particle size related parameter of the known purity quartz vein sample and the purity data of the known purity quartz vein sample according to a preset statistical algorithm, and construct a purity evaluation model according to the correlation analysis result.

[0149] Optionally, the identification evaluation device 900 can further include a storage unit (shown in a dashed box) Figure 9 in the middle), which stores programs or instructions, and when the preparation unit 901 and the processing unit 902 execute the programs or instructions, the identification evaluation device can execute the identification evaluation method described in the above method embodiments.

[0150] In addition, Figure 9 The technical effects of the identification evaluation device can refer to the technical effects of the identification evaluation method described in the above embodiments, which will not be repeated here.

[0151] Exemplarily, Figure 10 is another possible structure schematic diagram of the identification evaluation method involved in the above embodiments. As Figure 10 shown, the identification evaluation device 1000 includes a processor 1002.

[0152] The processor 1002 is configured to control and manage the actions of the identification evaluation device 900, for example, to execute the steps performed by the preparation unit 901 and the processing unit 902 in the above identification evaluation device 900, and / or to execute other processes of the technical solutions described herein.

[0153] The processor 1002 described above can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic device, transistor logic, hardware component, or any combination thereof. It can implement or execute various example logical blocks, modules, and circuits described in connection with the disclosure. The processor can also be a combination of computing functionality, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on.

[0154] Optionally, the identification evaluation apparatus 1000 can further include a communication interface 1003, a memory 1001, and a bus 1004. The communication interface 1003 is configured to support communication between the identification evaluation apparatus 1000 and other network entities. The memory 1001 is configured to store program codes and data of the identification evaluation apparatus.

[0155] The memory 1001 can be a memory in the identification evaluation apparatus, which can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a read-only memory, a flash memory, a hard disk, or a solid state disk, and can also include a combination of the above-mentioned memories.

[0156] The bus 1004 can be an extended industry standard architecture (EISA) bus or the like. The bus 1004 can be divided into an address bus, a data bus, a control bus, and the like. For the sake of convenience and brevity, Figure 10 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0157] Through the description of the above embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional modules is taken as an example, and in actual application, the above-mentioned functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and module described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0158] The embodiments of the present application provide a computer program product containing instructions, which, when the computer program product runs on the electronic device of the present application, causes the computer to perform the identification evaluation method described in the method embodiments.

[0159] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores instructions. When a computer executes the instructions, the electronic device of the present application executes each step performed by the identification and evaluation device in the method flow shown in the method embodiment of the present application.

[0160] The computer readable storage medium, for example, can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other medium of the form of a computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Of course, the storage medium can be a component of the processor. Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors. The processor can be coupled to memory, which can be the computer readable storage medium. The memory can be used for storing data or other computer usable instructions, programs, and / or modules that implement one or more embodiments of the present application. The memory can also be used for storing temporary variables or other intermediate information during execution of computations. The present application also contemplates methods of using these computer readable storage media. These methods can include, for example, implementing the methods described herein on the computer readable storage media.

[0161] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for identifying and evaluating vein-type high-purity quartz ore, characterized in that, The method includes: The vein quartz sample was prepared into multiple samples to be identified; wherein the multiple samples to be identified belong to different preset types, including rock powder, inclusion slices, electron probe slices, and double-sided polished thin slices; The physical properties of the plurality of samples to be identified are measured; wherein the physical properties include transmittance, water content, unit cell parameters, and particle size-related parameters. The physical property parameters of the multiple samples to be identified are input into the purity assessment model to determine the purity estimate of the vein quartz sample. Based on the purity estimate of the vein quartz sample, the evaluation result of the vein quartz sample is determined; wherein, the evaluation result is used to characterize whether the vein quartz sample has the potential to become a high-purity quartz mineral of the vein quartz type.

2. The identification and evaluation method according to claim 1, characterized in that, The preparation of vein quartz samples into multiple samples for identification specifically includes: The vein quartz sample was subjected to a series of operations, including coarse crushing to 4 mm and sieve, medium crushing to 1 mm and sieve, mixing and reducing, strong magnetic iron removal, fine crushing to 200 mesh and strong magnetic secondary iron removal, to obtain a sample of the rock powder type to be identified. The vein quartz sample is cut into sheet-like samples of a preset size, and the sheet-like samples of the preset size are subjected to ultrasonic cleaning, drying, preparation of inclusion sheet, acetone soaking, removal of residual glue, and air drying in sequence to obtain the sample to be identified of the inclusion sheet type. The vein quartz sample is cut into sheet-like samples of a preset size, and the sheet-like samples of the preset size are sequentially subjected to ultrasonic cleaning, drying, and electron probe sheet preparation operations to obtain the sample to be identified of the electron probe sheet type. The vein quartz sample is cut into sheet-like samples of a preset size, and the sheet-like samples of the preset size are subjected to ultrasonic cleaning, drying, grinding into double-sided polished thin sheets, acetone soaking, anhydrous ethanol cleaning, and secondary drying operations in sequence to obtain the double-sided polished thin sheet type of sample to be identified. Wherein, the length deviation of the sheet-like sample is less than a first threshold, and the non-parallelism deviation of the two end faces of the sheet-like sample is less than a second threshold.

3. The identification and evaluation method according to claim 2, characterized in that, The measurement of the physical properties of the plurality of samples to be identified specifically includes: The particle size of the rock powder sample to be identified was measured to obtain the particle size-related parameters of the vein quartz sample. The transmittance of the sample to be identified by the inclusion sheet type was measured to obtain the transmittance of the vein quartz sample. The cell parameters of the sample to be identified by the electron probe sheet type are measured to obtain the cell parameters of the vein quartz sample. The water content of the sample to be identified, which is of the double-sided polished thin sheet type, was measured to obtain the water content of the vein quartz sample.

4. The identification and evaluation method according to claim 3, characterized in that, The particle size-related parameters include particle size and particle size variation coefficient; the particle size measurement of the rock powder sample to be identified, to obtain the particle size-related parameters of the vein quartz sample, specifically includes: An automated mineral parameter analysis system was used to perform particle size analysis on the rock powder sample to be identified, and a particle size analysis report was obtained. The particle size analysis report was used to characterize multiple particle size ranges in the rock powder, as well as the average particle size of the quartz particles in each particle size range and the number of quartz particles in the corresponding range. The particle size of the vein quartz sample was determined based on the particle size analysis report using the following formula: = Formula 1 in, This indicates the particle size of the vein quartz sample. i This indicates the particle size of the quartz particles within the corresponding particle size range. x i This indicates the number of quartz particles in the corresponding particle size range. m This represents the number of particle size intervals. n The sum of all quartz particles; The particle size variation coefficient is determined based on the particle size of the vein quartz sample and the standard deviation of all quartz particle sizes, using the following formula: cv=( σ / Formula 2 (100%) Wherein, cv represents the particle size variation coefficient. σ represents the standard deviation of the particle size of all quartz particles.

5. The identification and evaluation method according to claim 3, characterized in that, The process of measuring the transmittance of the sample to be identified, which is of the inclusion sheet type, to obtain the transmittance of the vein quartz sample specifically includes: The transmittance of the inclusion sheet type of sample to be identified was measured using an ultraviolet-visible-near-infrared spectrophotometer under different wavelengths of light to obtain the transmittance of the vein quartz sample.

6. The identification and evaluation method according to claim 3, characterized in that, The cell parameters include the basic values ​​of the cell parameters and the ratio of the rate of change of the quartz axis; the cell parameter measurement of the sample to be identified by the electron probe sheet type to obtain the cell parameters of the vein quartz sample specifically includes: Using an X-ray diffractometer, diffraction analysis was performed on the electron probe sheet type of the sample to be identified under preset conditions to obtain diffraction pattern data. The diffraction pattern data is processed to determine the basic values ​​of the unit cell parameters; The ratio of the rate of change of the quartz axis is calculated based on the basic values ​​of the unit cell parameters and the standard quartz unit cell parameters, using the following formula: Δ=(Δ / ) / (Δ / ) Formula 3 Where Δ is the ratio of the rate of change of the quartz axis. a 0、 c 0 represents the standard quartz unit cell parameter, Δ a 0, Δ c 0 represents the difference between the basic value of the cell parameter and the standard quartz cell parameter.

7. The identification and evaluation method according to claim 3, characterized in that, The process of measuring the water content of the double-sided polished thin-film sample to obtain the water content of the vein quartz sample specifically includes: The water content of the double-sided polished thin-film sample to be identified was measured using a Fourier transform micro-infrared spectrometer to obtain infrared absorption spectrum data. The infrared absorption spectrum data is normalized based on the sample thickness data to obtain the infrared absorption coefficient corresponding to the wavenumber position. The water content of the vein quartz sample is determined based on the infrared spectral absorption coefficient corresponding to the wavenumber position, using the following formula: Formula 4 Where C represents the molar absorption coefficient, and K(ν) represents the infrared spectral absorption coefficient corresponding to the wavenumber position. K(ν)dν represents the integral area within a given wavelength range, ν represents the infrared absorption wavenumber, λ represents the activation direction factor of OH bonds in the crystal, and P represents the water content of the vein quartz sample.

8. The identification and evaluation method according to any one of claims 1-7, characterized in that, Before inputting the physical property parameters of the plurality of samples to be identified into the purity assessment model to determine the purity estimate of the vein quartz sample, the method further includes: Based on a preset statistical algorithm, a correlation analysis was performed on the transmittance, water content, cell parameters, and particle size-related parameters of vein quartz samples with known purity and the purity data of vein quartz samples with known purity. Based on the results of the correlation analysis, the purity evaluation model was constructed.

9. A device for identifying and evaluating vein-type high-purity quartz ore, characterized in that, The identification and evaluation device includes: a preparation unit and a processing unit; The preparation unit is used to prepare vein quartz samples into multiple samples to be identified; wherein the multiple samples to be identified belong to different preset types, and the preset types include rock powder, inclusion slices, electron probe slices, and double-sided polished thin slices. The processing unit is used to measure the physical properties of the plurality of samples to be identified; wherein the physical properties include transmittance, water content, unit cell parameters, and particle size-related parameters. The processing unit is also used to input the physical property parameters of the plurality of samples to be identified into the purity evaluation model to determine the purity estimate of the vein quartz sample. The processing unit is further configured to determine the evaluation result of the vein quartz sample based on the purity estimate of the vein quartz sample; wherein the evaluation result is used to characterize whether the vein quartz sample has the potential to become a high-purity quartz mineral of the vein quartz type.

10. An electronic device, characterized in that, include: A processor and a memory; wherein the memory is used to store computer execution instructions, and when the electronic device is running, the processor executes the computer execution instructions stored in the memory to cause the electronic device to perform the identification and evaluation apparatus method as described in any one of claims 1-8.

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