Calibration methods, equipment and storage media for material composition analysis

Abstract

This application discloses a calibration method, device, and storage medium for material composition analysis, relating to the field of automated analysis technology. The method includes: matching a first gamma spectrum obtained from the detection data of a first gamma detector with material parameters from a parameter library; calculating an equivalent mass thickness based on the characteristic peak area of ​​a second gamma spectrum obtained from the acquisition data of a second gamma detector, the material parameters, and the initial gamma-ray characteristic peak area of ​​a radioactive source; generating a correction factor through scaling transformation based on the equivalent mass thickness and the material parameters; and correcting the initial material composition matched in the parameter library using the correction factor to obtain material composition data. This method solves the problem of excessive deviation in material composition monitoring data and reduces the error in cement composition analysis under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of automated analysis technology, and in particular to a calibration method, apparatus and storage medium for material composition analysis. Background Technology

[0002] Monitoring the composition of cement is crucial during the production process. Real-time and effective online component monitoring can ensure the quality of cement production. Related technologies utilize neutron activation analyzers for online analysis of cement composition. However, frequent cement stacking on the conveyor belt leads to uneven cement thickness distribution. The upper layer of cement can shield the gamma rays released by the lower layer, resulting in significant deviations in the material composition monitoring data.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a calibration method, device and storage medium for material composition analysis, which aims to solve the technical problem in the related technology that the cement composition is analyzed online by a neutron activation analyzer, but the cement stacking phenomenon on the conveyor belt is frequent, resulting in uneven cement thickness distribution. The upper layer of cement will shield the gamma rays released by the lower layer of cement, which will lead to excessive deviation of the material composition monitoring data.

[0005] To achieve the above objectives, this application proposes a calibration method for material composition analysis, the method comprising:

[0006] The first gamma energy spectrum, which is parsed from the detection data of the first gamma detector, is matched with the material parameters in the parameter library.

[0007] Based on the area of ​​the second gamma energy spectrum characteristic peak obtained from the data collected by the second gamma detector, the material parameters, and the area of ​​the initial gamma ray characteristic peak of the radioactive source, the equivalent mass thickness is calculated by inversion.

[0008] Based on the equivalent mass thickness and the material parameters, a scaling transformation generates a correction factor;

[0009] The material composition data is obtained by correcting the initial material composition of the first gamma spectrum in the parameter library using the correction factor.

[0010] In one embodiment, preset materials are configured according to preset components and preset component ratios, and the standard density corresponding to the preset materials is calculated according to the density formula;

[0011] The area of ​​the characteristic peak of the preset energy region corresponding to the preset material is detected and obtained by the first gamma detector.

[0012] The area of ​​the second gamma energy spectrum characteristic peak corresponding to the preset material is obtained by analyzing the data collected by the second gamma detector, and the mass decay coefficient is calculated based on the area of ​​the second gamma energy spectrum characteristic peak and the area of ​​the initial gamma ray characteristic peak.

[0013] The parameter library is generated by associating the component ratio corresponding to the preset material, the characteristic peak area of ​​the preset energy region, the standard density, and the mass decay coefficient.

[0014] In one embodiment, the radioactive source produces neutrons that react with the atomic nuclei of the material to produce first gamma rays;

[0015] The first gamma detector detects the first gamma ray and fits a linear relationship between the corresponding channel address and energy based on the captured first gamma ray.

[0016] Based on the linear relationship between the channel address and energy, the first gamma spectrum is generated according to the cumulative distribution of energy corresponding to the channel address.

[0017] In one embodiment, characteristic peaks of each energy region are extracted from the first gamma spectrum, and the energy region characteristic peak area of ​​the region corresponding to each energy region characteristic peak is calculated according to the peak area formula.

[0018] Based on the characteristic peak area of ​​the energy region, the initial material composition corresponding to the characteristic peak area of ​​the energy region is matched and screened in the parameter library;

[0019] In the parameter library, match the initial material composition and the material type with the smallest difference in component ratio;

[0020] Based on the material type, determine the material parameters corresponding to the characteristic peak of the energy region from the parameter library.

[0021] In one embodiment, the second gamma detector detects the second gamma rays passing through the material, resolves the area of ​​the second gamma energy spectrum characteristic peak corresponding to the second gamma ray, and generates the transmittance by comparing the area of ​​the second gamma energy spectrum characteristic peak with the area of ​​the initial gamma ray characteristic peak.

[0022] Based on the standard density and mass decay coefficient in the material parameters, a linear decay coefficient is generated by multiplying them together.

[0023] Substitute the mass attenuation coefficient and the penetration rate into the equivalent mass inversion formula to output the initial equivalent mass, and introduce the mass thickness conversion factor in the material parameters to correct the initial equivalent mass, thereby generating the equivalent mass thickness.

[0024] In one embodiment, an exponential function calculation of penetration path attenuation is performed based on the equivalent mass thickness and the mass thickness conversion factor and mass attenuation coefficient in the material parameters to generate a self-absorption correction factor;

[0025] A density compensation factor is generated by comparing the standard density in the material parameters with the target density obtained by inverting the equivalent mass thickness.

[0026] The initial material composition is corrected based on the self-absorption correction factor and the density compensation factor to generate the material composition data.

[0027] In one embodiment, the convergence value of the initial material composition and the material composition data is calculated to obtain the convergence data;

[0028] The convergence data is compared with a preset convergence threshold, and the convergence result is determined according to the preset convergence judgment conditions.

[0029] If the convergence result is successful, the material composition data will be output as the final result.

[0030] In one embodiment, a target difference between the equivalent mass thickness and the preset mass thickness is generated by comparing the equivalent mass thickness with the preset mass thickness.

[0031] Based on the target difference, a preset correction rule is matched to generate a correction strategy corresponding to the target difference;

[0032] The material composition analysis system adjusts the conveyor speed of the conveyor belt and the feeding rate of the feeder according to the correction strategy to correct the material thickness.

[0033] In addition, to achieve the above objectives, this application also proposes a material composition analysis device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the calibration method for material composition analysis as described above.

[0034] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the calibration method for material composition analysis as described above.

[0035] This application provides a calibration method for material composition analysis, including accurately capturing the first gamma rays released by atomic nuclei during neutron activation using a first gamma detector, generating a first gamma energy spectrum containing elemental fingerprint information, and intelligently extracting characteristic peak area data for each energy region. Subsequently, this characteristic peak area is dynamically matched with a pre-built parameter library to accurately pinpoint the current material matrix type and corresponding nuclide parameters. Simultaneously, a second gamma detector is used to measure the intensity attenuation of 0.662 MeV gamma rays after penetrating the material. Combining the initial intensity of the radioactive source with the matched material parameters, the equivalent mass thickness of the material is derived. These two thickness values ​​are then input into a density inversion formula to calculate the real-time density of the material. Finally, self-absorption and density compensation factors are generated based on the material parameters and real-time density. An iterative correction algorithm is used to dynamically calibrate the original component characteristic peak area, outputting stable and reliable material composition data. This method solves the core pain points in industrial online component analysis caused by material thickness fluctuations, such as variations in gamma ray attenuation paths, characteristic peak intensity drift, and amplified analysis errors. Through dual-detector collaborative measurement and intelligent matching with the parameter library, closed-loop correction of thickness, density, and composition is achieved.

[0036] In summary, this application employs a technical solution that precisely captures the first gamma rays generated by neutron activation using a first gamma detector and extracts the characteristic peak area. This is combined with a second gamma detector measuring the attenuation intensity of the 0.662 MeV gamma rays. A parameter library is used to intelligently match material matrix parameters, and high-precision composition data is generated based on dual-thickness inversion and density iteration correction. This solution addresses the technical problem in related technologies where online analysis of cement composition using a neutron activation analyzer often results in cement stacking on the conveyor belt, causing uneven cement thickness distribution. The upper layer of cement can shield the gamma rays released by the lower layer, leading to excessive deviations in material composition monitoring data. This approach reduces the error in cement composition analysis under complex working conditions. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic flowchart of the first embodiment of the calibration method for material composition analysis in this application.

[0040] Figure 2This is a schematic flowchart of the second embodiment of the calibration method for material composition analysis in this application;

[0041] Figure 3 This is a schematic flowchart of the third embodiment of the calibration method for material composition analysis in this application.

[0042] Figure 4 This is a schematic flowchart of the fourth embodiment of the calibration method for material composition analysis in this application;

[0043] Figure 5 This is a flowchart illustrating the fifth embodiment of the calibration method for material composition analysis in this application;

[0044] Figure 6 This is a schematic flowchart of the sixth embodiment of the calibration method for material composition analysis in this application;

[0045] Figure 7 This is a schematic diagram of the system structure of this application;

[0046] Figure 8 This is a schematic diagram of the material composition analysis equipment of this application.

[0047] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0049] In related technologies, cement composition is analyzed online using a neutron activation analyzer. However, cement stacking on the conveyor belt occurs frequently, resulting in uneven cement thickness distribution. The upper layer of cement can shield the gamma rays released by the lower layer of cement, leading to excessive deviations in the material composition monitoring data.

[0050] This application provides a solution: First, based on the detection data of the first gamma detector, the first gamma spectrum is analyzed and matched with the material parameters in the parameter library. Then, based on the characteristic peak area of ​​the second gamma spectrum analyzed from the acquisition data of the second gamma detector, the material parameters, and the initial gamma ray characteristic peak area of ​​the radioactive source, the equivalent mass thickness is calculated by inversion. Then, according to the equivalent mass thickness and the material parameters, a scaling transformation is performed to generate a correction factor. Finally, the initial material composition matched with the first gamma spectrum in the parameter library is corrected by the correction factor to obtain the material composition data.

[0051] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or material composition analysis device capable of performing the above functions. The following description uses a material composition analysis device as an example to illustrate this embodiment and the subsequent embodiments.

[0052] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0053] This application provides a calibration method for material composition analysis, referring to... Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the calibration method for material composition analysis in this application.

[0054] In this embodiment, the calibration method for material composition analysis includes steps S10 to S40:

[0055] Step S10: Based on the detection data of the first gamma detector, the first gamma energy spectrum is analyzed and matched with the parameter library to obtain the material parameters.

[0056] In this embodiment, the first gamma detector refers to a radiation sensor used to detect gamma rays. It converts photon energy into electrical pulses through the photoelectric effect or Compton scattering, thus being a high-energy gamma detector. The first gamma spectrum refers to the histogram of gamma ray count rate and energy distribution recorded by a multichannel analyzer, characterizing the nuclear reaction features of elements. The characteristic peak area of ​​the energy region refers to the net count integral value of a specific energy peak in the energy spectrum, reflecting the content of the target element. The parameter library refers to a pre-stored structured dataset of material-related parameters. Material parameters include key physical quantities for nuclear analysis such as material type, mass decay coefficient, characteristic peak area of ​​the energy region, mass decay coefficient of material composition, standard density, and mass thickness conversion factor.

[0057] As an optional implementation, a first gamma detector captures the instantaneous gamma rays excited by the radioactive source in real time. After being shaped by a preamplifier, several gamma energy spectra are generated by a multichannel analyzer. The gamma energy spectra are smoothed by filtering, and the characteristic peaks of each energy region are identified using the second derivative zero-crossing point localization method. The net area value of the characteristic peaks in each energy region is calculated by combining Gaussian fitting with a nonlinear iterative peak stripping algorithm. The area of ​​each characteristic peak is used as an index for matching in a parameter library. The data item closest to the index is designated as the target item, and the material parameter corresponding to the target item is the material parameter corresponding to the characteristic peak of the energy region.

[0058] Step S20: Based on the area of ​​the second gamma energy spectrum characteristic peak obtained from the data collected by the second gamma detector, the material parameters, and the area of ​​the initial gamma ray characteristic peak of the radioactive source, the equivalent mass thickness is calculated by inversion.

[0059] In this embodiment, the second gamma detector refers to a low-energy gamma detector that only monitors 0.662 MeV gamma rays. The area of ​​the characteristic peak of the second gamma spectrum refers to the count rate of 0.662 MeV gamma rays after penetrating the material, as measured by the second gamma detector. The area of ​​the initial gamma ray characteristic peak refers to the baseline count rate of rays of the same energy when there is no material. Inversion calculation refers to applying the Lambert-Beer law to convert the ray attenuation rate into equivalent mass thickness data.

[0060] As an optional implementation, a linear attenuation coefficient is generated by calling the mass attenuation coefficient and standard density of the material parameters in the parameter library. The area of ​​the characteristic peak of the second gamma spectrum detected by the second gamma detector and the area of ​​the initial gamma-ray characteristic peak are substituted into the transmittance formula to generate the transmittance. The initial equivalent mass is output by inputting the mass attenuation coefficient and transmittance into the equivalent mass formula. Based on the initial equivalent mass, a mass thickness conversion factor from the material parameters is introduced for correction to generate the equivalent mass thickness.

[0061] As another optional implementation, the mass attenuation coefficient in the parameter library is called, and the area of ​​the second gamma energy spectrum characteristic peak of the second gamma detector and the area of ​​the initial gamma ray characteristic peak are substituted into the transmittance formula to calculate the equivalent mass thickness.

[0062] As an alternative implementation, the linear attenuation coefficient corresponding to the material parameter in the parameter library is obtained by multiplying the mass attenuation coefficient corresponding to the material parameter by the standard density. The ratio of the area of ​​the characteristic peak of the second gamma spectrum detected by the second gamma detector to the area of ​​the initial gamma ray characteristic peak is calculated, and the resulting ratio is used as the transmittance. The calculated mass attenuation coefficient and transmittance are substituted into the equivalent mass formula to output the initial equivalent mass.

[0063] (1)

[0064] Based on the initial equivalent mass, a mass thickness conversion factor is introduced from the material parameters, and combined with the equivalent mass correction formula, to generate the equivalent mass thickness.

[0065] (2)

[0066] Among them, formula (1) is the equivalent quality formula. This refers to the initial equivalent mass, and T refers to the transmittance. This refers to the mass attenuation coefficient, and formula (2) is the equivalent mass correction formula. It is the equivalent mass thickness. This refers to the mass thickness conversion factor.

[0067] Step S30: Based on the equivalent mass thickness and the material parameters, a scaling transformation is performed to generate a correction factor.

[0068] In this embodiment, scaling transformation refers to mapping the original data to a new dimension or range through mathematical operations to reveal physical laws or achieve data comparability. The correction factor refers to the self-absorption factor that compensates for the attenuation of characteristic gamma penetration paths and the density compensation factor that corrects for deviations in the number of nuclides per unit volume.

[0069] As an optional implementation, the mass thickness conversion factor and mass attenuation coefficient in the material parameters are called, and a self-absorption correction factor is generated through an exponential function calculation of penetration path attenuation, simultaneously generating a density compensation factor. The initial material composition is divided by the correction factor to generate material composition data, wherein the correction factor includes the self-absorption correction factor and the density compensation factor.

[0070] As another optional implementation method, the self-absorption correction factor is generated by calling the mass thickness conversion factor, mass attenuation coefficient and equivalent mass thickness corresponding to the material in the material parameters and calculating the exponential function formula of penetration path attenuation.

[0071] (3)

[0072] Based on the equivalent mass thickness and the preset mass thickness, a density compensation factor is generated by combining the density correction formula.

[0073] (4)

[0074] Among them, formula (3) is the formula for an exponential function. This refers to the self-absorption correction factor. It is the equivalent mass thickness. This refers to the mass attenuation coefficient. This refers to the mass thickness conversion factor. Formula (4) is the density compensation factor. This refers to the density compensation factor. It is the equivalent mass thickness. This refers to the preset mass thickness, which is the preset equivalent mass thickness used to define the standard equivalent mass thickness.

[0075] Furthermore, after the correction factor is calculated, iterative updates can be generated. A correction strategy is generated based on the difference between the equivalent mass thickness and the preset mass thickness, and the equipment is adjusted according to this strategy. The adjustment process then repeats steps S10 to S30, generating the correction parameters and producing an updated correction factor. If the relative deviation between the previous and updated correction factors is less than a preset correction threshold, the correction factor is deemed to meet the requirements and is output. Otherwise, the updated correction factor is used as the new initial value to trigger the next round of correction iterations. If the results of three consecutive iterations are all greater than the preset correction threshold, the output is frozen and marked.

[0076] Step S40: Correct the initial material composition of the first gamma spectrum in the parameter library using the correction factor to obtain material composition data.

[0077] In this embodiment, the material composition data is the percentage of the elements contained in the material.

[0078] As an optional implementation method, a self-absorption correction factor is calculated based on the mass thickness conversion factor and mass attenuation coefficient in the material parameters. A density compensation factor is generated simultaneously. The original characteristic peak area is divided by the correction factor to obtain the corrected peak area. The corrected peak area is then substituted into the calibration formula to output the corrected material composition data.

[0079] As another optional implementation, the initial material composition matched in the parameter library is input into the calibration formula based on the self-absorption correction factor and density compensation factor to generate corrected material composition data.

[0080] (5)

[0081] Formula (5) is the calibration formula. This refers to the revised material composition data. This refers to the initial material composition. This refers to the self-absorption correction factor. This refers to the density compensation factor.

[0082] For example, a lanthanum bromide first gamma detector was used to acquire the transient gamma spectrum excited by the neutron generator in real time. After generating 4096 energy spectrum data through a multichannel analyzer, the Compton background was stripped by an algorithm, and the net area value of the Ca 4.44 MeV characteristic peak of 12500 cps was extracted. This peak area was successfully matched with the limestone matrix characteristic peak threshold range [11000-14000 cps] in the parameter library, and the corresponding mass attenuation coefficient μ_m=0.032, geometric correction factor δ=0.85, and Ca calibration coefficient k=0.0042% / cps were applied. By using a second gamma detector to measure the intensity I_meas = 17500 cps of the 0.662 MeV rays from the Cs-137 source after penetrating the material, and the initial intensity I_0 = 50000 cps, the equivalent mass thickness ρ·d = -ln(0.35) / 0.032 = 32.2 was calculated according to Lambert's law. Combined with the preset mass thickness d_meas = 25 cm, the density inversion formula ρ = (ρ·d) / (δ×d_meas) = ​​32.2 / (0.85×25) = 1.51 was used. Finally, using this density value, a self-absorption correction factor F_att=exp(-0.032×1.51×0.85)=0.92 and a density compensation factor F_density=1.51 / 2.40=0.63 are generated. The original Ca characteristic peak area is corrected to obtain S_corr=12500 / (0.92×0.63)=21580cps. The output CaO content C=k×S_corr=0.0042×21580=42.0%. After two iterations and convergence, the material composition data is locked.

[0083] Furthermore, a multi-parameter library is pre-built: a Monte Carlo model containing sample composition, standard geometry, and detector position is constructed. Through Monte Carlo simulation, a γ-ray spectrum database covering different combinations of material composition (matrix type), density (ρ), and thickness (d) is generated. Real-time extraction of dual features: Composition analysis extracts the characteristic peak area of ​​the energy region in the gamma spectrum of the high-energy γ detector; thickness analysis, where the low-energy γ detector only measures 0.662 MeV γ-ray energy, obtains thickness information through material composition and the multi-parameter library. Composition-thickness correction: the composition feature quantity and the measured thickness value are matched with the database to iteratively correct the composition analysis process. Under a selected matrix, the equivalent mass thickness (ρ·d) is inverted using the thickness-density feature quantity. Thickness feedback control: the travel speed of the belt scale and the feeding speed of the feeder are controlled based on the equivalent mass thickness value to stabilize the material thickness. Core principle: The thickness is determined by the attenuation of γ-rays generated by an independent single-energy γ-ray source. Thickness and material composition influence each other, requiring iterative determination of the optimal thickness-material relationship. Thickness corrects the energy spectrum and composition analysis. Decoupling Mechanism: Based on the principle of component identification, high-energy gamma rays (1 MeV~10 MeV) mainly originate from transient gamma rays generated by neutron activation. Their characteristic peak ratios (e.g., 28Si peak, 40Ca peak) primarily reflect elemental abundance. Thickness leads to greater loss of lower-energy gamma rays, resulting in lower analytical concentrations of elements with low-energy characteristic peaks. Thickness-Density Inversion Principle: Low-energy gamma rays (<1 MeV) mainly originate from 137Cs sources. The decrease in count in the low-energy region depends only on ρ·d (the product of density and thickness). System Composition: First, a 137Cs radioactive source and a low-energy gamma detector are added to the conventional neutron activation analysis equipment. The low-energy gamma detector measures the 0.662 MeV gamma rays emitted by the 137Cs source. A scintillator detector (such as sodium iodide or bismuth germanate) is selected, and its size should not be too large, preferably 2 to 3 inches. Second, a high-energy gamma detector measures the high-energy gamma rays generated during neutron activation, with an energy response of 1 MeV~10 MeV. A large-size sodium iodide scintillator is generally selected. Third, the gamma-ray spectrum analysis and thickness were combined to provide the optimal corrected component measurement results.

[0084] By accurately capturing the first gamma ray generated by neutron activation and extracting the characteristic peak area through the first gamma detector, and measuring the 0.662 MeV gamma ray attenuation intensity through the second gamma detector, the technical solution of intelligently matching the material matrix parameters with the linkage parameter library and generating high-precision composition data based on dual thickness inversion and density iteration correction solves the technical problem of excessive deviation in material composition monitoring data and reduces the error of cement composition analysis under complex working conditions.

[0085] Based on any of the above embodiments, in Embodiment 2 of this application, referring to Figure 2 , Figure 2This is a schematic flowchart of the second embodiment of the calibration method for material composition analysis in this application. Before step S10, steps A11 to A14 are also included:

[0086] Step A11: Configure the preset materials according to the preset components and preset component ratios, and calculate the standard density corresponding to the preset materials according to the density formula.

[0087] In this embodiment, the preset composition refers to the theoretical proportion of the target element or compound in the material. The preset composition ratio refers to the mass fraction of each component in the mixture. The preset material refers to an artificial standard sample prepared according to the preset composition ratio. The density formula refers to the formula for calculating the theoretical density of the mixture. The standard density refers to the density reference value of the preset material under ideal compaction conditions.

[0088] As an optional implementation method, materials are weighed according to a preset component ratio, mixed evenly in a three-dimensional mixer, and the mixed sample is pressed into a preset shape. Its volume and mass are then measured using a true density meter. The volume and mass are substituted into the density formula to calculate the standard density.

[0089] Step A12: Detect and obtain the characteristic peak area of ​​the preset energy region corresponding to the preset material using the first gamma detector.

[0090] In this embodiment, the area of ​​the characteristic peak in the preset energy region refers to the net count integral value of the standard sample in the characteristic energy range of a specific nuclide, which is used to establish a calibration benchmark for component analysis.

[0091] As an optional implementation, the material is placed at the center of the irradiation cavity of a deuterium-tritium neutron generator, where fast neutrons emitted by the radiation source bombard the material, inducing an inelastic scattering reaction. A first gamma detector collects transient gamma rays with a preset time-gated method, generates a gamma spectrum through a multichannel analyzer, and after a preset number of iterations using a single network pruning algorithm to remove the Compton background, the area of ​​the characteristic peaks in each energy region is output by performing a trapezoidal integral net count near the characteristic peaks in each energy region.

[0092] As an alternative implementation, a first gamma detector is used to measure the material under liquid nitrogen cooling. A neutron irradiation source excites an iron nucleus radiation capture reaction, and characteristic gamma-ray energy spectra are collected. A Watson function is applied to fit the Compton edge background, and a Gaussian fit is used to extract the net peak area in the characteristic peak region. After live-time correction and normalization with the efficiency of the first gamma detector, the characteristic peak area value for the preset energy region is output.

[0093] Step A13: The area of ​​the second gamma energy spectrum characteristic peak corresponding to the preset material is obtained by analyzing the data collected by the second gamma detector, and the mass attenuation coefficient is calculated based on the area of ​​the second gamma energy spectrum characteristic peak and the area of ​​the initial gamma ray characteristic peak.

[0094] As an optional implementation, the area of ​​the pre-set second gamma energy spectrum characteristic peak is collected by a second gamma detector to obtain the penetration intensity of the gamma ray, the penetration rate is calculated, the linear attenuation coefficient is inverted based on Lambert's law, and the mass attenuation coefficient is calculated in combination with the standard density.

[0095] As an alternative implementation, a preset second gamma-ray spectrum characteristic peak area is detected using a second gamma detector, and an initial second gamma-ray spectrum characteristic peak area is determined from a database. The transmittance is then determined by comparing the preset second gamma-ray spectrum characteristic peak area with the initial second gamma-ray characteristic peak area. The mass attenuation coefficient is then calculated from this transmittance.

[0096] Step A14: Associate the material type corresponding to the preset material, the characteristic peak area of ​​the preset energy region, the standard density, and the mass decay coefficient to generate the parameter library.

[0097] In this embodiment, material type refers to the material matrix category classified according to different component contents.

[0098] As an optional implementation method, the material type, characteristic peak area of ​​the preset energy region, standard density, and mass decay coefficient of the preset material are bound together to generate data records and store them in the parameter library.

[0099] For example, according to a preset composition ratio of 70% limestone and 30% clay, calcium carbonate powder and kaolin powder were weighed, mixed by ball milling, and pressed into standard cylindrical samples. The volume V = 33.5 (mass m = 90.45 g) was determined using the Archimedes displacement method, and the standard density ρ = 2.7 was calculated. The sample was placed at the center of the irradiation field of a deuterium-tritium neutron generator, and the inelastic scattering gamma spectrum was collected by a lanthanum bromide high-energy detector. The characteristic peak area of ​​calcium at 3.69 MeV (12500 cps) and silicon at 1.78 MeV (4200 cps) were extracted by SNIP background subtraction and Gaussian fitting. Simultaneously, the intensity of 0.662 MeV radiation after the Cs-137 source penetrated the sample was measured using a NaI low-energy detector: I_meas = 18500 cps (initial intensity I_0 = 50000 cps). The inversion mass attenuation coefficient μ_m = -ln(0.37) / (2.7×5) = 0.073 cm. Finally, the material type was labeled as "limestone-clay mixed matrix," and the characteristic peak area data, standard density, and mass attenuation coefficient were correlated to generate a parameter library record {matrix code: 3, Ca_peak: 12500, Si_peak: 4200, density: 2.7, mu_m: 0.073}.

[0100] By employing a technical chain involving standard sample preparation, dual-modal gamma-ray measurement, and parameter fusion library construction, the problems of large parameter calibration errors and incomparable cross-device data caused by the lack of a traceability chain for standard materials in traditional neutron activation analysis have been solved, thereby reducing the uncertainty of the calibration coefficients for cement composition analysis.

[0101] Based on any of the above embodiments, in Embodiment 3 of this application, referring to Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the calibration method for material composition analysis in this application. Step S10 includes steps B11-B13:

[0102] In step B11, the radioactive source generates neutrons, which react with the atomic nuclei of the material to produce the first gamma rays.

[0103] In this embodiment, neutrons are electrically neutral nuclei that undergo inelastic scattering or radiative capture nuclear reactions with the atomic nuclei of the material.

[0104] As an alternative implementation, a deuterium-tritium neutron tube is used to bombard the material. Fast neutrons undergo inelastic scattering with the atomic nuclei, exciting energy levels and releasing gamma rays upon de-excitation.

[0105] As another optional implementation, a deuterium-deuterium neutron generator is configured in combination with a beryllium target. After being slowed down, the neutrons undergo a radiation capture reaction with hydrogen atoms in the material to generate gamma rays.

[0106] Step B12: The first gamma detector detects the first gamma ray and fits a linear relationship between the corresponding channel address and energy based on the captured first gamma ray.

[0107] In this embodiment, the channel address refers to the digitized channel number corresponding to the pulse amplitude in the multichannel analyzer. The energy linearity relationship refers to a direct proportional mapping function describing the channel address and photon energy.

[0108] As an optional implementation, a first gamma detector coupled with a photomultiplier tube is used to collect the first gamma ray. The output pulse from the preamplifier is sent to a multichannel analyzer via a shaping circuit to measure the peak position of the radiation source. The slope and intercept are calculated using a two-point calibration method. A linear relationship between channel address and energy is established based on the slope and intercept.

[0109] As an alternative implementation, a first gamma detector is used. After trapezoidal pulse shaping, the channel addresses of a preset number of channels are input, and standard source calibration is performed to obtain low-energy peak addresses, medium-energy peaks, and high-energy peaks. A piecewise linear fitting strategy is adopted to generate a linear relationship between channel address and energy based on the energy inversion of each measured channel address.

[0110] Step B13: Based on the linear relationship between the channel address and energy, the first gamma spectrum is generated according to the cumulative distribution of the energy corresponding to the channel address.

[0111] In this embodiment, the cumulative distribution refers to the cumulative statistical histogram of the corresponding gamma photon counts for each address.

[0112] As an optional implementation, the data of each sub-region is synchronously mapped according to the linear relationship between the channel address and energy. When merging the cumulative distribution, a Poisson statistical error weighted average is performed. The pulse stacking effect is corrected by the energy spectrum deformation compensation algorithm, and finally a high-fidelity first gamma energy spectrum with a preset count rate is generated.

[0113] As an alternative implementation, channel addresses are mapped to energies in real time based on the linear relationship between channel address and energy. After suppressing the random background with time conformity correction, the cumulative distribution is used to generate a statistically optimized gamma spectrum using Monte Carlo resampling technology.

[0114] For example, a deuterium-tritium neutron generator bombards cement raw materials on a conveyor belt. Fast neutrons undergo inelastic scattering reactions with the nuclei of carbon, oxygen, and calcium atoms in the material, exciting the nuclei to de-excite and release characteristic gamma rays of 4.44 MeV (carbon), 6.13 MeV (oxygen), and 3.69 MeV (calcium). A lanthanum bromide high-energy detector captures the transient luminescent signals, which are then processed by a 4096-channel multichannel analyzer to generate energy spectrum data. The channel addresses are calibrated using Cs-137 (0.662 MeV) and Co-60 (1.33 MeV) standard sources, and the energy linear relationship is E = 0.0013C - 0.02, where C is the channel address. The first gamma spectrum is generated by counting to the power of 10.

[0115] By using neutron activation to excite elemental fingerprint gamma rays, high-precision energy spectrum analysis, and dual-thickness synergistic density inversion, the technical problems of characteristic peak intensity distortion and large traditional analysis errors caused by cement composition fluctuations and thickness variations have been solved, thus improving the accuracy of online analysis of cement composition content.

[0116] Based on any of the above embodiments, in Embodiment 4 of this application, referring to Figure 4 , Figure 4 This is a schematic flowchart of the fourth embodiment of the calibration method for material composition analysis in this application. Step S10 includes steps C11-C14:

[0117] Step C11: Extract the characteristic peaks of each energy region from the first gamma spectrum, and calculate the energy region characteristic peak area of ​​the region corresponding to each energy region characteristic peak according to the peak area formula.

[0118] In this embodiment, the peak area formula refers to the algorithm flow for calculating the net count of characteristic peaks by subtracting the background and integrating the peak boundary.

[0119] As an optional implementation, in the first gamma energy spectrum generated by the first gamma detector, the characteristic peaks of each energy region are located, and the peak region is defined by a preset half-width of the peak center channel address. The Compton continuous background is stripped by a single network pruning algorithm iterating a preset number of times, and the remaining net peak count is subjected to trapezoidal numerical integration to output the area of ​​the characteristic peaks of the energy region.

[0120] As an alternative implementation, the first gamma energy spectrum data acquired by the first gamma detector is used to identify characteristic peaks through the second derivative zero-crossing detection method. An automatic peak-finding algorithm is then used to determine peak boundaries. Within a preset interval, a Watson function is applied to fit the background curve. The original counts are subtracted from the fitted background values, and the net peak areas are generated by accumulating them channel by channel. For overlapping peak regions, constrained least squares method is used for spectral separation, outputting the independent energy region characteristic peak areas for each element.

[0121] Step C12: Based on the characteristic peak area of ​​the energy region, match and filter the initial material composition corresponding to the characteristic peak area of ​​the energy region from the parameter library.

[0122] In this embodiment, the initial material composition refers to the reference values ​​of element or compound content preset in the database.

[0123] As an optional implementation, the characteristic peak areas of each energy region in the first gamma spectrum are extracted, the peak area ratio is calculated, the corresponding characteristic peak area of ​​the energy region is retrieved in the parameter library, a preset ratio threshold range is matched, and the associated initial material composition data is called to determine the initial material composition corresponding to the characteristic peak area of ​​the energy region.

[0124] As an optional implementation, the characteristic peak area of ​​the energy region is normalized to generate a feature vector. A cosine similarity algorithm is then used to traverse and compare the vectors in a parameter database, matching and selecting the corresponding benchmark records. The initial material composition corresponding to the characteristic peak area of ​​this energy region is determined using these benchmark records.

[0125] Step C13: In the parameter library, match the initial material composition and the material type with the smallest difference in component ratio.

[0126] In this embodiment, the component ratio refers to the percentage relationship of the mass fraction of each component. Minimum difference matching refers to selecting the matrix category with the smallest deviation from the measured components using a similarity algorithm.

[0127] As an optional implementation, the initial material composition is used as an index to match and filter in the parameter library, and the preset material type whose composition ratio group is close to the initial material composition is taken as the material type corresponding to the initial material composition.

[0128] As another optional implementation, the extracted measured component data is traversed through all material component records in the parameter library, the Euclidean distance of the initial components of each material is calculated, and the preset material type with the smallest distance is selected as the material type corresponding to the initial material component.

[0129] As another alternative implementation, the initial material composition is processed by a random forest classification method to obtain the corresponding feature vector. This feature vector is then input into a pre-trained model, which outputs the matrix type probability distribution. The material type is then selected based on the highest probability.

[0130] Step C14: Based on the material type, determine the material parameters corresponding to the characteristic peak of the energy region from the parameter library.

[0131] As an optional implementation, the material type is used as an index to match and determine the material parameters corresponding to the characteristic peak of the high-energy region in the parameter library.

[0132] For example, in an online monitoring system for a cement plant conveyor belt, a lanthanum bromide detector collects the transient gamma spectrum generated by neutron activation, extracting the characteristic peak area of ​​calcium (3.69 MeV, 12800 cps) and silicon (1.78 MeV, 4500 cps). The peak areas are input into a parameter library for similarity matching, retrieving reference peak areas for a limestone matrix: calcium 12500±300 cps / silicon 4200±200 cps, with a similarity of 98%, locking the initial material composition as 2.3% calcium oxide / 13.5% silicon dioxide. Based on this composition ratio, the calcium-silicon ratio is calculated to be 3.13, and the closest material type in the matching database is "limestone-clay mixed matrix (code 3)". The associated parameters for this type are: mass attenuation coefficient μ_m = 0.071 cm (0.662 MeV), geometric correction factor δ = 0.82, and calcium calibration coefficient k = 0.00408 % / cps.

[0133] By employing a four-level dynamic matching mechanism based on characteristic peak area, composition, material type, and physical parameters, the problem of matrix identification errors caused by fluctuations in raw material sources and the high misjudgment rate of traditional methods in cement has been solved, thus improving the accuracy of material type identification.

[0134] Based on any of the above embodiments, in Embodiment 5 of this application, referring to Figure 5 , Figure 5 This is a schematic flowchart of the fifth embodiment of the calibration method for material composition analysis in this application. Step S20 includes steps D11 to D13:

[0135] Step D11: The second gamma ray passing through the material is detected by the second gamma detector, the area of ​​the second gamma energy spectrum characteristic peak corresponding to the second gamma ray is analyzed, and the ratio of the area of ​​the second gamma energy spectrum characteristic peak to the initial gamma ray characteristic peak area is used to generate the transmittance.

[0136] In this embodiment, the second gamma ray penetrating the material refers to the gamma photon stream emitted by the radiation source and attenuated by the material; the second gamma ray is the target gamma ray with a set energy intensity. The initial gamma ray characteristic peak area refers to the initial characteristic peak area of ​​the target gamma ray with the set energy intensity. The penetration rate is the ratio of the penetration intensity to the initial intensity, characterizing the degree of attenuation of the radiation by the material.

[0137] As an optional implementation, the area of ​​the initial second gamma-ray characteristic peak is obtained by directly facing the radiation source with a second gamma detector and collecting data for a preset number of seconds under a no-material, unloaded state. After placing the preset material, the area of ​​the second gamma-ray energy spectrum characteristic peak after penetration is simultaneously collected. The initial penetration rate is calculated by comparing the area of ​​the second gamma-ray energy spectrum characteristic peak with the initial area of ​​the second gamma-ray characteristic peak. After time correction and background subtraction, the final penetration rate is output.

[0138] As an alternative implementation, a preset energy window for the second gamma ray is set using a multichannel analyzer, and the initial area of ​​the second gamma ray characteristic peak is collected when there is no material. After material penetration, the area of ​​the second gamma ray characteristic peak is obtained, and the original transmittance is calculated. After superimposing temperature drift compensation and pulse accumulation correction, the corrected transmittance is output.

[0139] Step D12: Based on the material standard density and mass decay coefficient in the material parameters, multiply them to generate a linear decay coefficient.

[0140] In this embodiment, the linear attenuation coefficient refers to the intensity attenuation rate of gamma rays passing through a unit length of material, which is obtained by multiplying the standard density by the mass attenuation coefficient.

[0141] As an optional implementation, the standard density and mass decay coefficient in the material parameters are called, and the two are multiplied to generate a linear decay coefficient.

[0142] As another alternative implementation, in the processing of mixed materials, a weighted standard density and a mass decay coefficient are calculated to generate a linear decay coefficient.

[0143] Step D13: Substitute the mass attenuation coefficient and penetration rate into the equivalent mass inversion formula to output the initial equivalent mass, and introduce the mass thickness conversion factor in the material parameters to correct the initial equivalent mass and generate the equivalent mass thickness.

[0144] In this embodiment, the mass thickness conversion factor refers to the correction coefficient for compensating for geometric path deviations.

[0145] As an optional implementation, the mass attenuation coefficient and penetration rate from the material parameters are called and substituted into the inversion formula to calculate the initial equivalent mass thickness. The matrix mass thickness conversion factor is retrieved from the parameter library and corrected to obtain the equivalent mass thickness. When the mass thickness conversion factor version number does not match the current material repose angle, a Monte Carlo simulation is triggered to update the value of the mass thickness conversion factor.

[0146] As an alternative implementation, an initial equivalent mass thickness is obtained by using an effective mass attenuation coefficient combined with the penetration rate. The correlation curve between porosity and mass thickness conversion factors is then used to generate the corrected equivalent mass thickness.

[0147] For example, the intensity of 0.662 MeV gamma rays from a Cs-137 source penetrating a cement raw material layer was measured using a low-energy NaI detector, resulting in I_meas = 17500 cps (initial intensity I_0 = 50000 cps), and the transmittance T = 0.35 was calculated. The standard density ρ_ref = 2.71 g / cm³ and the mass attenuation coefficient μ_m = 0.073 cm³ from the limestone matrix parameter library were used to generate a linear attenuation coefficient μ = ρ_ref × μ_m = 0.198. Substituting μ_m and T into the equivalent mass inversion formula ρ·d_init = -ln(0.35) / 0.073 = 34.6 g / cm², and correcting with a mass thickness conversion factor δ = 0.85, the equivalent mass thickness ρ·d = 34.6 / 0.85 = 40.7 g / cm².

[0148] By using penetration rate, dual-thickness synergistic inversion, and dynamic factor correction chain, the problem of equivalent mass thickness distortion caused by abrupt changes in cement bulkiness and excessive error in thickness analysis using traditional methods have been solved, thus improving the accuracy of equivalent mass thickness.

[0149] Based on any of the above embodiments, in Embodiment Six of this application, referring to Figure 6 , Figure 6 This is a schematic flowchart of the sixth embodiment of the calibration method for material composition analysis in this application. Step S40 includes steps E11 to E13:

[0150] Step E11: Based on the equivalent mass thickness and the mass thickness conversion factor and mass attenuation coefficient in the material parameters, perform an exponential function calculation of the penetration path attenuation to generate a self-absorption correction factor.

[0151] In this embodiment, the penetration path attenuation exponential function refers to the self-absorption attenuation model based on Lambert's law. The self-absorption correction factor is a coefficient used to correct the attenuation of characteristic gamma rays propagating within the material.

[0152] As an optional implementation method, the equivalent mass thickness, mass thickness conversion factor, and mass decay coefficient in the material parameters are called and substituted into the exponential decay function model to generate a self-absorption correction factor.

[0153] As an alternative implementation, the equivalent mass thickness, mass thickness conversion factor, and mass decay coefficient from the material parameters are loaded to calculate the basic self-absorption correction factor. Temperature correction is then added, and the calculation is performed piecewise using a layered model to obtain the final self-absorption correction factor.

[0154] Step E12: The standard density in the material parameters is compared with the target density obtained by inversion through the equivalent mass thickness to generate a density compensation factor.

[0155] In this embodiment, the density compensation factor refers to the ratio of the actual density to the equivalent density, which is used to correct the nuclide counting rate error caused by density deviation.

[0156] As an optional implementation, the standard density corresponding to the material is called, and the equivalent density obtained by inverting the equivalent mass thickness is used to calculate the initial density compensation factor. A temperature influence factor is then introduced, and the initial density compensation factor is compensated according to the temperature influence rules to generate the final density compensation factor.

[0157] Step E13: Correct the initial material composition according to the self-absorption correction factor and the density compensation factor to generate material composition data.

[0158] As an optional implementation method, the corrected material composition data is generated by calculating the correction based on the initial material composition and combining the self-absorption correction factor and the density compensation factor.

[0159] For example, in the online monitoring system of cement raw material conveyor belt, a deuterium-tritium neutron source bombards the material to excite transient gamma rays. A lanthanum bromide high-energy detector collects the energy spectrum and extracts the characteristic peak area of ​​calcium at 3.69 MeV, which is 12500 cps. Based on the equivalent mass thickness ρ·d = 24.6 g / cm², the mass thickness conversion factor δ = 0.85, and the calcium peak mass attenuation coefficient μ_m = 0.032, the self-absorption correction factor F_att = exp(-0.032 × 24.6 × 0.85) = 0.72 is calculated. The actual density ρ = 2.46 g / cm³ is obtained by synchronous laser ranging with a measured thickness d_meas = 10 cm. This is compared with the equivalent density of the limestone matrix ρ_ref = 2.4 g / cm³ to generate a density compensation factor F_density = 2.46 / 2.4 = 1.025. The initial calcium composition C_Ca0=42.3% is called from the parameter library. After two-factor correction, the final CaO content C_Ca=42.3% / (0.72×1.025)=57.2% is obtained. The silica content of 14.3% is output simultaneously and the raw material ratio optimization command is triggered.

[0160] By using a dual-factor dynamic correction model based on self-absorption and density, the problems of distortion in the characteristic gamma-ray attenuation path caused by fluctuations in the bulkiness of cement and excessive errors in traditional neutron activation analysis have been solved, thus improving the accuracy of online material analysis.

[0161] Based on any of the above embodiments, in Embodiment 7 of this application, after step E13, steps F11 to F13 are further included:

[0162] Step F11: Calculate the convergence value of the initial material composition and the material composition data to obtain the convergence data.

[0163] In this embodiment, the convergence value refers to the absolute or relative deviation between two adjacent component calculation results in consecutive iterations. The convergence data is the final stable component value that meets the preset threshold condition.

[0164] As an optional implementation, the relative deviation between the current iteration material composition and the initial material composition is calculated, and this relative deviation is used as the convergence data.

[0165] Step F12: Compare the converged data with a preset convergence threshold, and determine the convergence result according to the preset convergence judgment conditions.

[0166] In this embodiment, the preset convergence threshold refers to the upper limit of the absolute or relative deviation for determining convergence. The preset convergence judgment conditions include logical rules such as the deviation threshold and the number of consecutive times the target is met. The convergence result refers to the decision conclusion that the iteration terminates and the effective components are output.

[0167] As an optional implementation, the value of the converged data is compared with a preset convergence threshold, and based on the comparison result, a corresponding convergence result is generated according to the preset convergence judgment condition.

[0168] Step F13: If the convergence result is convergence, the material composition data is output as the final result.

[0169] In this embodiment, the final output refers to publishing the converged and confirmed component data to the downstream system.

[0170] As an optional implementation, if the relative deviation of the convergence result is less than the preset convergence threshold, then convergence is determined and the convergence data is output. Otherwise, the material composition data is used as a new initial value to trigger the next round of correction iteration. If the results of three consecutive iterations are all greater than the preset convergence threshold, then the output is frozen and marked.

[0171] For example, the online conveyor system of a cement plant obtains an initial calcium composition of 42.3% from the parameter library. After correction by a self-absorption correction factor (F_att=0.72) and a density compensation factor (F_density=1.025), the first round of calcium composition data is generated as 57.2%. The calculated absolute deviation from the initial value is 14.9% > the threshold of 0.5%, so a second round of correction is initiated, resulting in 42.1% with a deviation of 0.47%. The third round results in 42.05% with a deviation of 0.12%. After calculating two relative deviations of 0.12% < 0.5% for the sequence [42.3, 42.1, 42.05], the standard deviation is 0.13% < 0.2%, and the convergence result is determined to be "converged". The final calcium composition of 42.05% is then output to the material composition data.

[0172] By employing a multi-round iterative deviation control and dynamic convergence decision-making mechanism, the problem of abrupt changes in cement composition analysis results due to fluctuations in raw materials or thickness, as well as the excessively high single-correction error of traditional methods, has been solved, thereby improving the stability of material composition output.

[0173] Based on any of the above embodiments, in Embodiment 8 of this application, after step S40, steps G11~G13 are further included:

[0174] Step G11: Based on the equivalent mass thickness, compare it with the preset mass thickness to generate a target difference between the equivalent mass thickness and the preset mass thickness.

[0175] In this embodiment, the preset mass thickness refers to the preset equivalent mass thickness, used to define the standard equivalent mass thickness. The target difference refers to the dimensionless deviation coefficient or absolute difference between the equivalent mass thickness and the preset mass thickness after normalization, used to quantify material density anomalies.

[0176] As an optional implementation, the equivalent mass thickness obtained through thickness inversion calculation and the preset mass thickness are called, and the equivalent mass thickness and the preset mass thickness are input into the target difference calculation formula to calculate the target difference.

[0177] As another optional implementation, the equivalent mass thickness is subtracted from the preset mass thickness to generate a target difference between the equivalent mass thickness and the preset mass thickness.

[0178] Step G12: Based on the target difference, match a preset correction rule to generate a correction strategy corresponding to the target difference.

[0179] In this embodiment, the preset correction rule refers to a strategy library that maps deviations to actions based on historical data and expert experience. The correction strategy refers to a set of device control commands triggered for the target difference.

[0180] As an optional implementation, based on the target difference, and using the target difference as an index, the corresponding correction rule items are selected from the preset correction rules. Based on each correction rule item and the device parameters, a correction strategy that meets the device parameter requirements is generated.

[0181] As another optional implementation, corresponding correction rule items are selected from preset correction rules based on the target difference, and an initial strategy is generated based on the correction rule items. The control parameters in the initial strategy are adjusted according to the limitations of the device parameters to generate a correction strategy.

[0182] In step G13, the material composition analysis system adjusts the conveyor speed of the conveyor belt and the feeding rate of the feeder according to the correction strategy to correct the material thickness.

[0183] In this embodiment, the conveyor belt speed refers to the motor speed control of the material conveyor line. The feeder feeding rate refers to the material feeding rate control parameter per unit time. The feedback correction thickness refers to the thickness value remeasured based on the control results for closed-loop verification.

[0184] As an optional implementation, the system detects the target difference, invokes a correction strategy to generate feed rate adjustment instructions and conveyor speed adjustment instructions, and the logic controller completes the adjustment of the corresponding equipment parameters within a preset time. After the equipment parameters are adjusted, the equivalent mass thickness and the preset mass thickness are calculated in real time, and then the new target difference is calculated. The corrected thickness data is then fed back to the composition analysis module to trigger composition recalculation.

[0185] For example, when the cement raw material layer has a preset mass thickness d_meas=30cm due to its looseness, while the equivalent mass thickness ρ·d based on the Cs-137 source penetration rate inversion is 24 grams per square centimeter, the system calculates the target difference Δ_r=|24 / (2.4×30)-1|×100%=-66.7%, and triggers a "three-level compaction strategy" by matching the preset correction rule: First, increase the feeder speed to 150% of the rated value for 20 seconds to accelerate material feeding. Second, reduce the conveyor belt speed to 0.8m / s to extend the compaction time. Third, activate the high-frequency vibrating roller: amplitude 1.5 mm / frequency 40Hz to strengthen interlayer density. After adjustment, the laser rangefinder feedback correction thickness d_meas_new=25cm, remeasured ρ·d=26.4, new target difference Δ_r'=|26.4 / (2.4×25)-1|×100%=10%, the component analysis module recalculated the CaO content to 42.3% based on the new thickness, the system locked the adjustment parameters and generated an optimization log.

[0186] Furthermore, at time t, the material parameter library is matched using the first gamma detector, and the equivalent mass thickness is derived from the source intensity using the second gamma detector. A correction factor is generated based on thickness iteration to calibrate the initial composition of the high-energy spectrum matching, and the corrected material composition data is output. At time t+1, the equivalent mass thickness calculated through thickness inversion and the preset mass thickness are called, and these are input into the target difference calculation formula to obtain the target difference. Based on the target difference, the corresponding correction rule item is selected from the preset correction rules, and the corresponding initial strategy is generated based on the correction rule item. The control parameters in the initial strategy are adjusted according to the limitations of the equipment parameters to generate a correction strategy. The correction strategy is called to generate feed rate adjustment instructions and conveyor speed adjustment instructions. The logic controller completes the adjustment of the corresponding equipment parameters within a preset time. After the equipment parameter adjustment is completed, the equivalent mass thickness and the preset mass thickness are calculated in real time, and then a new target difference is calculated. When the target difference is less than the preset difference, a recalculation of the material composition data is triggered to obtain the target material composition data. This forms a logical closed loop of material composition analysis, thickness control, and composition re-analysis.

[0187] Furthermore, referring to Figure 7 , Figure 7 This is a schematic diagram of the system structure of this application. The workflow of the material composition and thickness joint analysis system is as follows: The material to be tested is placed in a container at the center of the system. A fast neutron beam is emitted by a neutron tube below the container under the drive of the neutron tube control system. After being slowed down by the reflector material, the atomic nuclei of the material are excited. The characteristic high-energy gamma rays generated by the nuclear reaction are captured by high-energy gamma detectors 1 and 2 symmetrically arranged above the container. The dual detectors are designed for material lateral distribution compensation and data cross-verification. Their signals are transmitted to the gamma spectrum processing system through blue wires to perform SNIP background subtraction, characteristic peak identification, and net peak area calculation. Cs-137 source 1 and Cs-137 source 2 on both sides below the container emit 0.662 MeV gamma rays that penetrate the material. The attenuation intensity is measured by a low-energy gamma detector arranged in the center. The signal is input into the gamma thickness measurement system to calculate the penetration rate and equivalent mass thickness. All data are converged to the composition and thickness joint analysis module. Based on the high-energy gamma characteristic peak area matching parameter library, the matrix type is determined. The thickness data is linked to iteratively correct the self-absorption effect and density deviation, and finally, stable composition and thickness values ​​are output. The neutron tube control system synchronizes and coordinates the timing of neutron pulse and gamma acquisition. The dual-source layout ensures the measurement accuracy of wide-range material penetration distribution. The triangular configuration design of the detector and source maximizes the elimination of geometric shadow interference.

[0188] By using real-time difference analysis between equivalent mass thickness and preset mass thickness, rule-driven closed-loop control, and feedback correction mechanism, the problems of excessive cement composition analysis error and frequent manual intervention have been solved, thus improving the accuracy of cement layer thickness control.

[0189] This application provides a material composition analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the calibration method for material composition analysis in the first embodiment described above.

[0190] The following is for reference. Figure 8 This document illustrates a structural schematic diagram of a material composition analysis device suitable for implementing embodiments of this application. The material composition analysis device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, neutron activation analyzers, personal digital assistants (PDAs), tablet computers (PADs), portable multimedia players (PMPs), and material composition analysis mobile terminals, as well as fixed terminals such as composition analyzers and desktop computers. Figure 8 The material composition analysis device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0191] like Figure 8As shown, the material composition analysis device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the material composition analysis device. The processing unit 1001, the read-only memory 1002, and the RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the material composition analysis equipment to communicate wirelessly or wiredly with other devices to exchange data. Although a material composition analysis equipment with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0192] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0193] The material composition analysis equipment provided in this application, employing the calibration method for material composition analysis in the above embodiments, can solve the technical problem in related technologies where online analysis of cement composition using a neutron activation analyzer frequently results in cement stacking on the conveyor belt, causing uneven cement thickness distribution. The upper layer of cement can shield the gamma rays released by the lower layer, leading to excessive deviations in material composition monitoring data. Compared with the prior art, the beneficial effects of the material composition analysis equipment provided in this application are the same as those of the calibration method for material composition analysis provided in the above embodiments, and other technical features of this material composition analysis equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0194] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0195] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0196] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the calibration method for material composition analysis in the above embodiments.

[0197] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0198] The aforementioned computer-readable storage medium may be included in the material composition analysis equipment; or it may exist independently and not be assembled into the material composition analysis equipment.

[0199] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the material composition analysis device, the material composition analysis device: obtains material parameters by matching the first gamma energy spectrum, which is parsed from the detection data of the first gamma detector, with a parameter library; calculates the equivalent mass thickness by inverting the area of ​​the characteristic peak of the second gamma energy spectrum, which is parsed from the acquisition data of the second gamma detector, the material parameters, and the area of ​​the initial gamma ray characteristic peak of the radioactive source; generates a correction factor by scaling transformation based on the equivalent mass thickness and the material parameters; and corrects the initial material composition matched with the first gamma energy spectrum in the parameter library using the correction factor to obtain material composition data.

[0200] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0201] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0202] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0203] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for performing the calibration method for the above-described material composition analysis. This solves the technical problem in related technologies where online analysis of cement composition using a neutron activation analyzer often results in cement stacking on the conveyor belt, causing uneven cement thickness distribution. The upper layer of cement can shield the gamma rays released by the lower layer, leading to excessive deviations in the material composition monitoring data. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the calibration method for material composition analysis provided in the above embodiments, and will not be elaborated upon here.

[0204] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A calibration method for material composition analysis, applied to a material composition analysis system, the material composition analysis system comprising a first gamma detector and a second gamma detector, the second gamma detector being used to detect target gamma rays of a set energy intensity, characterized in that... The method includes: The first gamma energy spectrum, which is parsed from the detection data of the first gamma detector, is matched with the material parameters in the parameter library. Based on the area of ​​the second gamma energy spectrum characteristic peak obtained from the data collected by the second gamma detector, the material parameters, and the area of ​​the initial gamma ray characteristic peak of the radioactive source, the equivalent mass thickness is calculated by inversion. Based on the equivalent mass thickness and the material parameters, a scaling transformation generates a correction factor; Based on the equivalent mass thickness and the mass thickness conversion factor and mass attenuation coefficient in the material parameters, an exponential function calculation of the penetration path attenuation is performed to generate a self-absorption correction factor. A density compensation factor is generated by comparing the standard density in the material parameters with the target density obtained by inverting the equivalent mass thickness. Based on the self-absorption correction factor and the density compensation factor, the initial material composition is corrected to generate material composition data.

2. The calibration method for material composition analysis as described in claim 1, characterized in that, Before the step of matching the first gamma energy spectrum obtained from the detection data of the first gamma detector with the material parameters in the parameter library, the calibration method for material composition analysis further includes: According to the preset components and preset component ratios, the preset materials are configured, and the standard density corresponding to the preset materials is calculated according to the density formula; The area of ​​the characteristic peak of the preset energy region corresponding to the preset material is detected and obtained by the first gamma detector. The area of ​​the second gamma energy spectrum characteristic peak corresponding to the preset material is obtained by analyzing the data collected by the second gamma detector, and the mass decay coefficient is calculated based on the area of ​​the second gamma energy spectrum characteristic peak and the area of ​​the initial gamma ray characteristic peak. The parameter library is generated by associating the component ratio corresponding to the preset material, the characteristic peak area of ​​the preset energy region, the standard density, and the mass decay coefficient.

3. The calibration method for material composition analysis as described in claim 1, characterized in that, The step of matching the first gamma energy spectrum obtained from the detection data of the first gamma detector with the material parameters in the parameter library includes: The radioactive source produces neutrons, which react with the atomic nuclei of the material to produce the first gamma rays; The first gamma detector detects the first gamma ray and fits a linear relationship between the corresponding channel address and energy based on the captured first gamma ray. Based on the linear relationship between the channel address and energy, the first gamma spectrum is generated according to the cumulative distribution of energy corresponding to the channel address.

4. The calibration method for material composition analysis as described in claim 1, characterized in that, The step of matching the first gamma energy spectrum obtained from the detection data of the first gamma detector with the material parameters in the parameter library includes: The characteristic peaks of each energy region are extracted from the first gamma spectrum, and the energy region characteristic peak area of ​​the corresponding region is calculated according to the peak area formula. Based on the characteristic peak area of ​​the energy region, the initial material composition corresponding to the characteristic peak area of ​​the energy region is matched and screened in the parameter library; In the parameter library, match the initial material composition and the material type with the smallest difference in component ratio; Based on the material type, determine the material parameters corresponding to the characteristic peak of the energy region from the parameter library.

5. The calibration method for material composition analysis as described in claim 1, characterized in that, The step of inverting and calculating the equivalent mass thickness based on the area of ​​the second gamma energy spectrum characteristic peak obtained from the data collected by the second gamma detector, the material parameters, and the initial gamma ray characteristic peak area of ​​the radioactive source includes: The second gamma detector detects the second gamma rays passing through the material, analyzes the area of ​​the second gamma energy spectrum characteristic peak corresponding to the second gamma ray, and generates the transmittance by comparing the area of ​​the second gamma energy spectrum characteristic peak with the area of ​​the initial gamma ray characteristic peak. Based on the standard density and mass decay coefficient in the material parameters, a linear decay coefficient is generated by multiplying them together. Substitute the mass attenuation coefficient and the penetration rate into the equivalent mass inversion formula to output the initial equivalent mass, and introduce the mass thickness conversion factor in the material parameters to correct the initial equivalent mass, thereby generating the equivalent mass thickness.

6. The calibration method for material composition analysis as described in claim 1, characterized in that, After the step of correcting the initial material composition based on the self-absorption correction factor and the density compensation factor to generate material composition data, the calibration method for material composition analysis further includes: Calculate the convergence value of the initial material composition and the material composition data to obtain convergence data; The convergence data is compared with a preset convergence threshold, and the convergence result is determined according to the preset convergence judgment conditions. If the convergence result is successful, the material composition data will be output as the final result.

7. The calibration method for material composition analysis as described in claim 1, characterized in that, After the step of correcting the initial material composition of the first gamma spectrum in the parameter library using the correction factor to obtain the material composition data, the calibration method for material composition analysis further includes: Based on the equivalent mass thickness, a target difference between the equivalent mass thickness and the preset mass thickness is generated by comparing them. Based on the target difference, a preset correction rule is matched to generate a correction strategy corresponding to the target difference; The material composition analysis system adjusts the conveyor speed of the conveyor belt and the feeding rate of the feeder according to the correction strategy to correct the material thickness.

8. A material composition analysis device, characterized in that, The material composition analysis device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the calibration method for material composition analysis as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the calibration method for material composition analysis as described in any one of claims 1 to 7.

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