Control method and equipment of material moisture online monitoring system and storage medium

Abstract

The invention discloses a control method and device of a material moisture online monitoring system and a storage medium, and relates to the technical field of material moisture monitoring, and the method comprises the steps: starting a neutron generator assembly, and starting a neutron detector assembly and a gamma detector when the starting duration of the neutron generator assembly is greater than or equal to the preheating preset duration; based on a detection result of the neutron detector assembly, determining an accumulated scattering neutron share of the detected material, and obtaining a corresponding target hydrogen density matched with the accumulated scattering neutron share from a preset association library; based on a gamma detector, counting the monitored gamma rays in a classified manner according to the pulse amplitude, generating a gamma-ray energy spectrum, and extracting a water hydrogen characteristic peak area from the gamma-ray energy spectrum; according to the target hydrogen density, the water-hydrogen characteristic peak area and the energy spectrum calibration coefficient of the gamma-ray energy spectrum, the curing hydrogen ratio is determined; and determining the free water content of the detected material according to the cured hydrogen ratio. According to the invention, through dual-mode cooperative detection, the problem of low accuracy of moisture monitoring is solved.

Description

Technical Field

[0001] The present application relates to the technical field of material moisture monitoring, and in particular to a control method, device, and storage medium for an online material moisture monitoring system. Background Art

[0002] In the production of industrial materials, fluctuations in moisture content directly impact product properties, reaction rates, energy consumption, and equipment stability. Related technologies use the hydrogen atom moderation effect to infer moisture content during online moisture monitoring. However, because all hydrogen-containing species in the material contribute to the signal, it is impossible to distinguish between solidified water and free water, resulting in inaccurate moisture monitoring results.

[0003] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a control method, equipment and storage medium for an online material moisture monitoring system, aiming to solve the technical problem that all hydrogen-containing substances in the material contribute signals, making it impossible to distinguish between solidified water and free water, thereby leading to inaccurate moisture monitoring results.

[0005] To achieve the above objectives, the present application proposes a control method for an online material moisture monitoring system, the method comprising: Starting the neutron generator assembly, and when the startup time of the neutron generator assembly is greater than or equal to the preset preheating time, turning on the neutron detector assembly and the gamma detector; Determine the cumulative scattered neutron fraction of the material under test based on the detection result of the neutron detector assembly, and obtain the cumulative scattered neutron fraction from a preset correlation library to match the corresponding target hydrogen density; generating a gamma energy spectrum based on the gamma rays monitored by the gamma detector according to pulse amplitude classification counting, and extracting the water hydrogen characteristic peak area in the gamma energy spectrum; determining a solidified hydrogen ratio according to the target hydrogen density, the water hydrogen characteristic peak area, and the energy spectrum calibration coefficient of the gamma energy spectrum; The free water content of the tested material is determined according to the solidified hydrogen ratio.

[0006] In one embodiment, the pulse feature of the detection result is extracted to generate an original pulse signal; filtering the noise signal in the original pulse signal to output a purified scattered neutron pulse; The data processing system calculates the cumulative scattered neutron fraction within a fixed time window based on the number of scattered neutron pulses and the number of neutron emissions from the neutron generator assembly; According to the accumulated scattered neutron fraction, the corresponding hydrogen density is matched in the correlation library to determine the target hydrogen density.

[0007] In one embodiment, if the data processing system fails to match the cumulative scattered neutron fraction in the correlation library based on the cumulative scattered neutron fraction, a backup neutron fraction having the smallest difference with the cumulative scattered neutron fraction is selected from the correlation library; Determining a spare hydrogen density corresponding to the spare neutron fraction in the associated library; The backup hydrogen density is used as the target hydrogen density, and a prompt message is outputted indicating that the hydrogen density corresponding to the neutron fraction does not exist.

[0008] In one embodiment, based on the gamma rays monitored by the gamma detector, the electrical pulse signals corresponding to the gamma rays are determined according to pulse amplitude classification; sampling the pulse peak voltage of the electrical pulse signal, and mapping the pulse peak voltage to a channel address to generate a mapping relationship between the channel address and the pulse peak voltage; Based on the mapping relationship, accumulating the pulse peak voltage within a fixed time window to generate the gamma energy spectrum; According to the channel address corresponding to the water hydrogen characteristic peak, the water hydrogen characteristic peak is extracted from the gamma energy spectrum and integrated to obtain the water hydrogen characteristic peak area.

[0009] In one embodiment, the neutron generator assembly is started, and when the startup time of the neutron generator assembly is greater than or equal to a preset preheating time, a preheating completion prompt is output, so that the neutron generator assembly outputs a stable yield of neutrons; When the preheating of the neutron generator assembly is completed, the neutron detector assembly and the gamma detector are started, and the timestamp calibration of the neutron generator assembly, the neutron detector assembly and the gamma detector is synchronously triggered.

[0010] In one embodiment, a plurality of preset neutron fractions are determined based on the neutron scattering monitoring results of the neutron detector assembly on each preset material; The data processing system calculates the hydrogen density of the preset material based on the preset moisture content, the bulk density of the preset material, and the hydrogen mass fraction by substituting them into the hydrogen density formula; The preset neutron fraction is correlated with the hydrogen density corresponding to the preset material to generate the correlation library.

[0011] In one embodiment, a plurality of preset gamma energy spectra are determined based on the gamma ray monitoring results of the gamma detector on each preset material; Based on the preset gamma energy spectrum, extracting and integrating the water hydrogen characteristic peak to obtain the preset water hydrogen characteristic peak area; Based on the data processing system, the preset hydrogen density, preset moisture content, and preset water hydrogen characteristic peak area corresponding to the preset material are substituted into the fitting function to calculate the material type coefficient; The material type coefficient is associated and bound with the preset material to generate a coefficient association library.

[0012] In one embodiment, the data processing system selects a target material type coefficient corresponding to the material type from a coefficient association library based on the material type corresponding to the material; Replacing the original coefficient in the initial formula according to the target material type coefficient to generate a revised formula; Based on the cumulative scattered neutron fraction, the solidified hydrogen fraction, and the correction formula, the free water content of the measured material is generated by calculating the correction formula.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes an online monitoring device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the control method of the material moisture online monitoring system as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the control method of the material moisture online monitoring system as described above are implemented.

[0015] The present application provides a control method for an online material moisture monitoring system, including a neutron detector that captures the backscattered neutron counts of the material and directly locks the target hydrogen density from a preset correlation library based on the strong correlation between the neutron fraction and the hydrogen density. At the same time, the gamma detector performs pulse amplitude classification statistics on the detected gamma rays to generate a gamma energy spectrum, accurately extracts the characteristic peak area representing the hydrogen element in the water molecule, and then substitutes the target hydrogen density, the characteristic peak area of ​​water hydrogen and the energy spectrum calibration coefficient into the solidified hydrogen calculation formula to output the solidified hydrogen fraction. Finally, the neutron fraction and the solidified hydrogen fraction are jointly input into a dynamic correction formula to achieve a high-precision correction output of the free water content of the material. The present application uses neutron detection to capture macroscopic hydrogen density and gamma energy spectrum to identify the characteristic peaks of microscopic water molecules. The physical mechanisms of the two complement each other, avoiding the risk of a single detector being interfered with by material component fluctuations or environmental noise at the particle action level.

[0016] In summary, this application, through dual-modal collaborative detection of scattered neutrons and gamma ray spectra, coupled with a dynamic correction mechanism, addresses the related art problem of inferring moisture content from the hydrogen atom moderation effect during online moisture monitoring of materials. However, because all hydrogen-containing substances in the material contribute signals, it is impossible to distinguish between solidified water and free water, which leads to inaccurate moisture monitoring results. This improves the monitoring accuracy of free water content and significantly enhances its adaptability to materials with complex components. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of the first embodiment of the control method of the material moisture online monitoring system of the present application; Figure 2 This is a flow chart of a second embodiment of the control method of the material moisture online monitoring system of the present application; Figure 3 This is a flow chart of a third embodiment of the control method of the material moisture online monitoring system of the present application; Figure 4 This is a flow chart of a fourth embodiment of the control method of the material moisture online monitoring system of the present application; Figure 5 This is a flow chart of a seventh embodiment of the control method of the material moisture online monitoring system of the present application; Figure 6 This is a flow chart of an eighth embodiment of the control method of the material moisture online monitoring system of the present application; Figure 7 This is a schematic diagram of the system structure of this application; Figure 8 This is a structural diagram of the online monitoring equipment of this application.

[0020] 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 DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0022] Related technologies use the hydrogen atom slowing effect to infer moisture content during online material moisture monitoring. However, because all hydrogen-containing substances in the material contribute to the signal, it is impossible to distinguish between solidified water and free water, resulting in inaccurate moisture monitoring results.

[0023] The present application provides a solution: first, the neutron generator assembly is started, and when the startup time of the neutron generator assembly is greater than or equal to the preset preheating time, the neutron detector assembly and the gamma detector are turned on, and then based on the detection result of the neutron detector assembly, the cumulative scattered neutron fraction of the material under test is determined, and the cumulative scattered neutron fraction is obtained from a preset correlation library to match the corresponding target hydrogen density, and then, based on the gamma rays monitored by the gamma detector according to pulse amplitude classification counting, a gamma energy spectrum is generated, and the water hydrogen characteristic peak area is extracted from the gamma energy spectrum, and then, according to the target hydrogen density, the water hydrogen characteristic peak area and the energy spectrum calibration coefficient of the gamma energy spectrum, the solidified hydrogen ratio is determined, and finally, the free water content of the material under test is determined based on the solidified hydrogen ratio.

[0024] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device or online monitoring device capable of implementing the above functions. The following uses online monitoring equipment as an example to illustrate this embodiment and the following embodiments.

[0025] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The present invention provides a method for controlling a material moisture online monitoring system. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the control method of the material moisture online monitoring system of the present application.

[0027] In this embodiment, the control method of the material moisture online monitoring system includes steps S10 to S50: Step S10: starting the neutron generator assembly, and when the starting time of the neutron generator assembly is greater than or equal to the preheating preset time, turning on the neutron detector assembly and the gamma detector.

[0028] In this embodiment, the neutron generator assembly refers to a nuclear physics device that produces a stable neutron beam. It generates neutrons by bombarding a target with accelerated deuterium ions, initiating a fusion reaction. It is configured as a DD neutron tube and is a sealed neutron generator for deuterium-deuterium nuclear fusion reactions. The preheating preset duration refers to the heating / equilibration time required for the device to reach a stable operating state after startup. A stable neutron yield refers to a neutron output state that meets industrial-grade measurement statistical accuracy requirements. The neutron detector assembly refers to a He-3 proportional counter tube array that captures moderated neutrons through reactions with helium nuclei and generates electrical pulses. A gamma detector refers to a scintillator or semiconductor probe that converts gamma photon energy into an electrical signal via a photoelectric conversion device based on the interaction of gamma photons with matter through the photoelectric effect, Compton effect, or electron pair effect.

[0029] As an optional implementation, the neutron generator assembly is started, and when the preheating time is greater than or equal to the preset preheating time, the neutron yield of the neutron generator assembly is stabilized, the high-voltage power supply and gamma detector of the neutron detector assembly are manually triggered, and the timestamps of the neutron generator assembly, the neutron detector assembly, and the gamma detector are aligned.

[0030] As another optional embodiment, when the output neutron flux of the neutron generator assembly reaches a preset stability threshold, the neutron generator assembly triggers a detection instruction and sends it to the neutron detector assembly and the gamma detector, activating the data acquisition circuit of the neutron detector assembly and the gamma detector.

[0031] As another optional embodiment, the neutron generator assembly is started to preheat until the yield is stable, and the operator starts the neutron detector assembly and the gamma detector through a controller via wireless remote control.

[0032] Step S20: determining the cumulative scattered neutron fraction of the material under test based on the detection result of the neutron detector assembly, and obtaining a target hydrogen density corresponding to the cumulative scattered neutron fraction from a preset correlation library.

[0033] In this embodiment, scattered neutrons refer to neutrons whose energy is attenuated and whose direction is deflected after colliding with a material nucleus. The cumulative scattered neutron fraction is the ratio of detector counts to the total neutron source emission, reflecting the material's efficiency in moderating neutrons. The correlation library is a pre-stored dataset mapping neutron fractions to hydrogen density, obtained through calibration with standard samples. The target hydrogen density is the mass of hydrogen atoms per unit volume of the material.

[0034] As an optional implementation, based on scattered neutrons captured by neutron detector assemblies arranged horizontally above the material layer, a beam monitor simultaneously records the total number of neutrons emitted by the neutron generator assembly. The scattered neutron counts are accumulated within each fixed time window, and the cumulative scattered neutron fraction is calculated. This cumulative scattered neutron fraction is transmitted to a database via a bus. This cumulative scattered neutron fraction is used as an index to query a pre-stored hydrogen density mapping table in a correlation library, and the target hydrogen density corresponding to this cumulative scattered neutron fraction is determined and output.

[0035] As another optional implementation, based on the cumulative scattered neutron fraction obtained by the neutron detector assembly, the density compensation submodule in the correlation library is called to automatically correct the mapping relationship and output a density-normalized target hydrogen density value.

[0036] Step S30 , generating a gamma energy spectrum based on the gamma rays monitored by the gamma detector according to pulse amplitude classification counting, and extracting the water hydrogen characteristic peak area in the gamma energy spectrum.

[0037] In this embodiment, pulse amplitude classification counting refers to quantifying the peak voltage of the electrical pulse generated by gamma rays in the detector into discrete channel values ​​using an analog-to-digital converter, and then accumulating the counts by amplitude interval to generate energy spectrum data. Gamma rays are high-energy photons released by the interaction of neutrons with the atomic nuclei of the material. A gamma spectrum is a histogram with channel values ​​on the horizontal axis and counts on the vertical axis, representing the intensity distribution of gamma rays of different energies. The water hydrogen characteristic peak is the characteristic peak at 2.223 MeV in the gamma spectrum, which represents the concentration of total hydrogen atoms in the material. Its peak area is positively correlated with the total hydrogen density. The peak area refers to the net cumulative count value of the channel interval covered by the characteristic peak after subtracting the background, reflecting the concentration of the target nuclei.

[0038] As an optional implementation, a gamma detector captures gamma rays emitted by the material within a fixed time window, generating a corresponding pulse signal from the gamma rays. This pulse signal is amplified by a preamplifier and then converted into quantized channels via digital-to-analog conversion. A multi-channel analyzer then classifies and accumulates the channels to generate a gamma spectrum. The spectrum preprocessing module automatically deducts the ambient background and uses a first-order derivative peak-finding algorithm to locate the center of the characteristic water hydrogen peak within a preset energy window. The background baseline is linearly interpolated using the preset half-width at half-maximum of the peak center as the boundary. The net count within the integration boundary is output as the peak area value, which is the characteristic water hydrogen peak area.

[0039] As another optional implementation, a gamma detector collects gamma rays corresponding to the material, and a digital-to-analog converter records the pulse amplitude distribution in high-resolution mode with a preset number of channels. Spectrum processing software applies a Gaussian smoothing filter to the pulse amplitude distribution and then loads the energy calibration file to generate the gamma spectrum. By precisely locating the preset channel address of the characteristic water hydrogen peak, a preset optimization algorithm is used to fit a Gaussian function superimposed on a linear background model, combined with an integral fitting function, to obtain the characteristic water hydrogen peak area.

[0040] Step S40 , determining the solidified hydrogen ratio according to the target hydrogen density, the water hydrogen characteristic peak area, and the energy spectrum calibration coefficient of the gamma energy spectrum.

[0041] In this embodiment, energy spectrum calibration refers to the combined calibration parameters of detector efficiency, geometry, and energy scale, used to convert gamma peak area into the physical quantity of hydrogen density. The solidified hydrogen formula is a mathematical relationship used to calculate the percentage of bound hydrogen. The solidified hydrogen percentage refers to the mass percentage of chemically bound hydrogen in the material, reflecting the hydrogen contribution from non-free water.

[0042] As an optional implementation method, the target hydrogen density corresponding to the material and the characteristic peak area of ​​water hydrogen in the gamma energy spectrum are substituted into the solidified hydrogen formula through the data processing system, and the energy spectrum calibration coefficient corresponding to the energy spectrum in the database is loaded, and the solidified hydrogen ratio is calculated through the solidified hydrogen formula.

[0043] Step S50: determining the free water content of the tested material according to the solidified hydrogen ratio.

[0044] In this embodiment, the free water content refers to the mass percentage of free water after compensation for solidified hydrogen interference, representing the free water that can participate in physical and chemical reactions.

[0045] As an optional embodiment, the data processing system inputs a correction formula for calculation based on the neutron fraction and solidified hydrogen ratio corresponding to the material, loads the target material type coefficient corresponding to the material type into the coefficient association library, and dynamically calculates and generates the free water content of the measured material.

[0046] For example, in a coking plant's coal conveyor belt online moisture monitoring system, when the neutron generator assembly outputs a stable neutron yield, the neutron detector assembly (He-3 proportional counter tube array) and the gamma detector (semiconductor detection module) are automatically activated. The neutron detector assembly counts scattered neutrons reflected by the coal seam in real time, calculates the neutron fraction using a beam monitor, matches the corresponding hydrogen density value in a precalibrated correlation library, and outputs the target hydrogen density. The gamma detector simultaneously collects inelastically scattered gamma rays, which are then classified and counted by pulse amplitude using a 14-bit ADC to generate a 2048-channel gamma spectrum. After smoothing using a Savitzky-Golay filter, the characteristic water hydrogen peak area is extracted within the 2.22–2.23 MeV energy window using a first-order derivative peak search algorithm. The target hydrogen density, characteristic water hydrogen peak area, and energy spectrum calibration coefficient are substituted into the solidified hydrogen formula: solidified hydrogen fraction = (total hydrogen density - free water hydrogen density) / total hydrogen density × 100%, to dynamically calculate the solidified hydrogen fraction of the current coal seam. Finally, the neutron fraction and solidified hydrogen fraction are input into the process correction formula: free water content = k × neutron fraction × (1-β × solidified hydrogen fraction), where k = 0.18 and β = 0.85 are coking coal calibration parameters, and the free water content of the measured material is output in real time.

[0047] Furthermore, the material moisture monitoring process: Turn on the neutron tube, preheat for 20 minutes, and begin measurement after the neutron yield stabilizes. Acquiring measurement data: Set the measurement time; start the gamma detector and neutron detector; accumulate measurement data in the gamma spectrum processing system and neutron data processing system; stop acquiring data from the gamma detector and neutron detector after the measurement time has elapsed. Processing measurement data: Based on a Monte Carlo model of sample composition, standard geometry, and detector position, Monte Carlo simulation is used to generate a database covering different combinations of material composition (matrix type), water hydrogen content, and intrinsic hydrogen content (non-aqueous hydrogen or hydrogen in inorganic compounds contained in the material). Quickly search the database for matching moisture content in the material based on the measured neutron data; and analyze the gamma spectrum using a gamma spectrum analysis algorithm. Correcting the gamma spectrum analysis: Correct the gamma spectrum analysis results based on the database and the measured moisture content. Output the spectrum analysis results and moisture content data.

[0048] Thanks to the dual-modal collaborative detection of scattered neutrons and gamma ray spectra, as well as a dynamic correction mechanism, this technology solves the problem of inferring the moisture content of materials through the hydrogen atom moderation effect during online moisture monitoring. However, because all hydrogen-containing substances in the material contribute signals, it is impossible to distinguish between solidified water and free water, which in turn leads to inaccurate moisture monitoring results. This technology improves the monitoring accuracy of free water content and significantly enhances its adaptability to materials with complex components. At the same time, non-contact detection based on neutron detector assemblies and gamma detectors, combined with a DD neutron tube that can be started and stopped on and off, avoids both wear on the sensors during contact detection and the harmful radiation caused by the inability to switch the isotope neutron source on and off, achieving non-contact precision measurement.

[0049] Based on any of the above embodiments, in the second embodiment of the present application, refer to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the control method of the material moisture online monitoring system of the present application. Step S20 includes steps A11 to A14: Step A11: extract the pulse feature of the detection result and generate an original pulse signal.

[0050] In this embodiment, the original pulse signal refers to an unshaped voltage pulse sequence output by the neutron detector assembly to capture and extract scattered neutrons.

[0051] As an optional embodiment, when the neutron generator assembly emits fast neutrons to penetrate the material, the high-voltage power supply drives the counter tube ionization reaction through the detection result of the neutron detector assembly to extract the pulse characteristics of the detection result and generate an original pulse signal.

[0052] Step A12: filtering the noise signal in the original pulse signal and outputting a purified scattered neutron pulse.

[0053] In this embodiment, the noise signal refers to invalid pulses caused by non-target particles or electronic noise. The purified scattered neutron pulse refers to only the neutron event pulses retained after the denoising process, and the pulse amplitude is positively correlated with the neutron energy.

[0054] As an optional implementation, the raw pulse signal output by the neutron detector assembly is transmitted to the signal processing chassis via a double-shielded coaxial cable. A first-stage high-pass filter removes power supply frequency interference from the raw pulse signal. A second-stage differential circuit suppresses baseline drift. A pulse amplitude discriminator then removes cosmic ray and low-energy gamma noise from the raw pulse signal. Finally, a programmable gate array real-time pileup rejection module filters pileup events from the raw pulse signal, outputting a purified scattered neutron pulse.

[0055] As another optional implementation, the neutron detector assembly generates an original pulse signal, eliminates amplitude and time fluctuations of the original pulse signal through a constant ratio timing circuit, and then uses a pulse shape discrimination module to separate the noise of the original pulse signal by utilizing the decay time difference between neutron and gamma pulses to output a purified scattered neutron pulse.

[0056] In step A13, the data processing system calculates the cumulative scattered neutron fraction within a fixed time window according to the number of scattered neutron pulses and the number of neutron emissions of the neutron generator assembly.

[0057] In this embodiment, the cumulative fixed time window refers to a preset time statistical interval used to limit the data acquisition range. The target scattered neutron count refers to the cumulative value of effective scattered neutron pulses within the time window. The total neutron emission count refers to the total number of neutrons emitted by the neutron generator within the same time window.

[0058] As an optional implementation, a cumulative fixed time window is set to synchronously start the neutron detector assembly and the neutron generator assembly. The neutron detector assembly collects scattered neutron pulses. The scattered neutron pulses are filtered by a pulse amplitude discriminator to filter the electromagnetic noise and then accumulated to obtain the target scattered neutron number. At the same time, based on the total number of neutron emissions recorded by the beam monitor, the dual data are transmitted to the data processing system through the bus to generate a real-time neutron share value.

[0059] Step A14: Match the corresponding hydrogen density in the correlation library according to the accumulated scattered neutron fraction to determine the target hydrogen density.

[0060] As an optional implementation, the cumulative scattered neutron share calculated by the data processing system is transmitted to the overall control system via Ethernet, the cumulative scattered neutron share value is used as an index to scan the associated library, and a bilinear interpolation algorithm is used to match the corresponding hydrogen density to determine and output the target hydrogen density.

[0061] As another optional implementation, the material is placed in a neutron irradiation chamber, neutrons are emitted by the neutron generator assembly, and the cumulative scattered neutron fraction is measured by the neutron detector assembly. The local correlation library is called to perform nearest neighbor matching to determine and output the target hydrogen density.

[0062] For example, in a coking plant coal conveyor belt online moisture monitoring system, when the neutron generator assembly's DD neutron tube outputs a stable neutron beam, the neutron detector assembly's He-3 proportional counter array vertically captures scattered neutrons reflected by the coal seam, generating a raw pulse signal. This signal is filtered through a two-stage RC filter and a pulse amplitude discriminator to filter electromagnetic noise, outputting purified scattered neutron pulses to the counting module. The number of scattered neutron pulses within a fixed 5-second time window is accumulated along with the number of neutron emissions recorded by the neutron generator beam monitor. Using the fraction formula: neutron fraction = target number of scattered neutrons / total number of neutron emissions, the cumulative scattered neutron fraction is calculated in real time to be 0.18. Using the fraction value as an index, a pre-calibrated correlation library is scanned, and a bilinear interpolation algorithm is used to match the corresponding hydrogen density to 0.12 grams per cubic centimeter.

[0063] Due to the real-time filtering of pulse noise and dynamic calibration of the total number of emissions, the problem of distortion of slowing down efficiency caused by fluctuations in material composition is solved, and the anti-interference ability and stability of online moisture monitoring are improved.

[0064] Based on any of the above embodiments, in the third embodiment of the present application, refer to Figure 3 , Figure 3 This is a flow chart of the third embodiment of the control method of the material moisture online monitoring system of this application. Step A14 includes steps B11 to B13: Step B11: If the data processing system fails to match the cumulative scattered neutron fraction in the association library based on the cumulative scattered neutron fraction, a spare neutron fraction having the smallest difference with the cumulative scattered neutron fraction is selected from the association library.

[0065] In this embodiment, the spare neutron fraction refers to a reference value in the associated library that is closest to the current fraction.

[0066] As an optional implementation, when the data processing system does not have an exact match in the associated library based on the neutron fraction, the data processing system automatically starts a binary search algorithm to screen the closest spare neutron fraction in the library.

[0067] As another optional implementation, if the neutron fraction corresponding to the unknown material exceeds the range of the associated library, a nearest neighbor algorithm is called to screen the spare neutron fractions.

[0068] Step B12: determining the spare hydrogen density corresponding to the spare neutron fraction in the associated library.

[0069] In this embodiment, the reserve hydrogen density refers to the hydrogen atomic mass density value corresponding to the reserve neutron fraction in the associated library. The corresponding hydrogen density refers to the hydrogen atomic mass density value in the associated library that exactly matches the current neutron fraction.

[0070] As an optional implementation, the corresponding spare hydrogen density is read from the associated library through the spare neutron fraction.

[0071] Step B13: Using the reserve hydrogen density as the target hydrogen density, and outputting a prompt message indicating that the hydrogen density corresponding to the neutron fraction does not exist.

[0072] In this example, the target hydrogen density refers to the final output material hydrogen atomic mass density. Here, the reserve hydrogen density is used as the result. The prompt message refers to the abnormal warning text / code generated by the system, indicating that the current neutron fraction has no exact match in the associated library.

[0073] As an optional implementation, the overall control system automatically selects the spare hydrogen density corresponding to the closest spare neutron fraction as the target hydrogen density output, and simultaneously triggers a red flashing alarm "No matching library item for neutron fraction" on the control interface.

[0074] As another optional implementation, the overall control system locks the spare neutron share selected by the nearest neighbor algorithm and corresponds to the spare hydrogen density as the target hydrogen density, and stamps a red "out-of-library interpolation" stamp on the first page of the test report to prompt the operator to update the data of the associated library.

[0075] As another optional implementation, the backup hydrogen density is output as the target hydrogen density, and a prompt message is displayed indicating that the hydrogen density corresponding to the neutron fraction does not exist, so that the operator can add the mapping relationship of the hydrogen density corresponding to the neutron fraction into the association library.

[0076] For example, in the moisture monitoring system of the coal conveyor belt of a coking plant, when the cumulative scattered neutron fraction is 0.157 and there is no exact match in the coal volatile matter association library, the system automatically starts the binary selection method to screen the closest backup neutron fraction of 0.16, and extracts its corresponding backup hydrogen density of 0.108 grams per cubic centimeter from the library as the target hydrogen density output. At the same time, it triggers a red pop-up alarm on the central control screen "The cumulative scattered neutron fraction is 0.157 and there is no match. The backup value of 0.108 grams per cubic centimeter is used". The log records the error code E404 and pushes a text message or automatic email to request the calibration department to expand the library to the engineer.

[0077] By using the forced replacement of backup values ​​and a multi-level alarm mechanism, the system interruption problem caused by incomplete coverage of associated libraries is solved, the continuous operation capability under extreme working conditions is improved, and a precise closed-loop driving basis is provided for dynamic optimization of the database.

[0078] Based on any of the above embodiments, in the fourth embodiment of the present application, refer to Figure 4 , Figure 4 This is a flow chart of the fourth embodiment of the control method of the material moisture online monitoring system of the present application. Step S30 includes steps C11 to C14: Step C11 : Based on the gamma rays monitored by the gamma detector, determine the electrical pulse signals corresponding to the gamma rays according to pulse amplitude classification.

[0079] In this embodiment, the interaction between neutrons and intrinsic hydrogen refers to the radiation capture reaction of hydrogen nuclei in the material bombarded by fast neutrons, which releases 2.223 MeV characteristic gamma rays. The electrical pulse signal refers to the voltage pulse sequence output by the detector, with the pulse amplitude proportional to the gamma photon energy.

[0080] As an optional embodiment, when the neutrons emitted by the neutron generator assembly penetrate the material, based on the gamma rays captured by the neutron detector assembly within a fixed time window, the photon energy of the gamma rays is converted into a charge pulse through the crystal photoelectric effect, and an electrical pulse signal is output through a charge-sensitive preamplifier.

[0081] Step C12: sampling the pulse peak voltage of the electrical pulse signal, and mapping the pulse peak voltage to a channel address, thereby generating a mapping relationship between the channel address and the pulse peak voltage.

[0082] In this embodiment, pulse peak voltage refers to the highest voltage value of a single pulse. Channel address refers to the energy channel number of a multi-channel analyzer, which is used to discretize energy intervals. The mapping relationship refers to the correspondence between pulse peak voltage and channel address.

[0083] As an optional implementation, the electrical pulse signal output by the gamma detector is captured and converted into a pulse peak voltage by a high-speed analog-to-digital converter. The high-speed analog-to-digital converter quantizes the pulse peak voltage into a digital value within a preset range. A linear mapping equation is used to generate a channel address from this digital value. Energy calibration parameters are also applied to correct nonlinear deviations in real time, and the mapping relationship between the channel address and pulse peak voltage is output.

[0084] As another optional implementation, the electrical pulse signal output by the gamma detector is transmitted to the analog-to-digital conversion module through a high-temperature resistant charge amplifier. The adaptive baseline recovery circuit eliminates the temperature drift interference and captures the pulse peak voltage. The analog-to-digital conversion module generates a mapping relationship between the channel address and the pulse peak voltage according to a pre-programmed linear table of pulse peak voltage and channel address segments.

[0085] Step C13: Based on the mapping relationship, accumulate the pulse peak voltages within a fixed time window to generate the gamma energy spectrum.

[0086] As an optional implementation, based on the mapping relationship between channel address and pulse peak voltage, the channel address values ​​corresponding to all pulse peak voltages within a fixed time window are accumulated, and the multi-channel analyzer accumulates the counts by channel address classification to generate a gamma energy spectrum with a preset number of channels.

[0087] As another optional implementation, based on the mapping relationship between channel address and pulse peak voltage, the pulse peak voltage within a fixed time window is accumulated, converted into a channel address of a preset number according to a preprogrammed piecewise linear mapping table, and generated into a compressed gamma energy spectrum package.

[0088] Step C14: extracting the water hydrogen characteristic peak from the gamma spectrum according to the channel address corresponding to the water hydrogen characteristic peak and integrating it to obtain the water hydrogen characteristic peak area.

[0089] In this example, the channel address corresponding to the water hydrogen characteristic peak is the central channel address of the 2.223 MeV characteristic peak in the gamma spectrum. Extracting the water hydrogen characteristic peak involves locating the channel address interval in the energy spectrum and identifying and isolating the 2.223 MeV characteristic peak. Integration involves accumulating the net counts of the channels covered by the characteristic peak.

[0090] As an optional implementation, based on the location of the characteristic peak of water hydrogen in the gamma energy spectrum, a Gaussian function is used to fit the peak shape, with a preset number of channels as the peak area, and the area under the fitting function curve is integrated to generate the characteristic peak area of ​​water hydrogen.

[0091] As another optional implementation, the channel address of the characteristic peak of water hydrogen is automatically located according to the gamma energy spectrum, and the preset half-width at the center of the peak is used as the boundary. The boundary points are connected by linear interpolation to generate a background baseline. After deducting the background counts channel by channel, the net count value of the preset interval is integrated to output the area of ​​the characteristic peak of water hydrogen.

[0092] For example, in a coking plant's coal conveyor belt moisture online monitoring system, a gamma detector monitors the electrical pulse signals generated by the interaction of neutrons with hydrogen nuclei in coal, generating 2.223 MeV gamma rays. A high-speed analog-to-digital conversion module captures the peak voltage of the pulses and converts them to 491 channels using the linear mapping equation: channel address = voltage × 4096 / 10 V. All pulse channels within a fixed 5-second time window are accumulated to generate a 1024-channel gamma spectrum with an energy range of 0–3 MeV. Based on the pre-calibrated 750 channels for the characteristic water hydrogen peak, the energy spectrum peak region (channels 735–765) is automatically located. The net counts are integrated using a trapezoidal background subtraction method to output the water hydrogen characteristic peak area.

[0093] The precise mapping of gamma-ray energy and channel address solves the signal attenuation and drift problems in complex industrial environments, improves the stability of characteristic peak positioning, and provides reliable moisture monitoring capabilities for continuous production.

[0094] Based on any of the above embodiments, in the fifth embodiment of the present application, step S10 includes steps D11 to D12: Step D11, starting the neutron generator assembly, and when the starting time of the neutron generator assembly is greater than or equal to the preset preheating time, outputting a preheating completion prompt so that the neutron generator assembly can output a stable yield of neutrons.

[0095] In this embodiment, the preheating completion prompt refers to a preheating completion prompt message issued when the neutron generator component is preheated to a preset time and outputs a stable yield of neutrons.

[0096] As an optional implementation, a preheating timer is automatically activated upon startup of the neutron generator assembly. During this time, the high-voltage power supply voltage is stepped up, the cooling water circulation system maintains the tube temperature, and the neutron yield fluctuation rate is monitored in real time. The neutron generator assembly is preheated to a preset time and outputs a stable neutron yield.

[0097] As another optional implementation, after the neutron generator assembly is turned on, a preheating procedure of a preset time is executed, the ion source RF power is increased to a preset power, the liquid cooling system controls the temperature and the synchronous vacuum pump maintains the cavity pressure, and the neutron generator assembly is preheated to the preset time and outputs a stable yield of neutrons.

[0098] Step D12: When the neutron generator assembly is preheated, start the neutron detector assembly and the gamma detector, and synchronously trigger the timestamp calibration of the neutron generator assembly, the neutron detector assembly, and the gamma detector.

[0099] In this embodiment, calibrating the timestamp refers to aligning the device clock using the GPS / PTP protocol.

[0100] As an optional embodiment, when the neutron yield fluctuation rate of the neutron generator assembly is less than a preset fluctuation rate, the neutron detector assembly and the gamma detector are automatically started, and the timestamps of the three devices are synchronized through a GPS receiver.

[0101] As another optional embodiment, after the yield of the neutron generator assembly is stabilized, the neutron detector assembly and the gamma detector are manually started, and the timestamps of the three devices are synchronized through the fiber optic switch using a precision clock protocol.

[0102] For example, in a coking plant coal conveyor belt moisture online monitoring system, after starting the neutron generator assembly's DD neutron tube, a 20-minute preheating procedure is performed. The high-voltage power supply is stepped up to 100 kV, and cooling water is circulated to maintain the tube temperature ≤50°C. Neutron yield fluctuations are monitored in real time until they stabilize. Once the yield reaches the target, the He-3 array of the neutron detector assembly is simultaneously started and boosted to 650 V. The gamma detector CZT module is also started and biased to 400 V. The timestamps of the three devices are aligned using a GPS receiver and the PTP protocol. Transmission delays are eliminated using the FPGA's embedded clock compensation algorithm, generating a uniformly timed neutron scattering pulse and gamma spectrum data stream.

[0103] By using step-by-step pressure increase and closed-loop monitoring of yield, the measurement distortion problem caused by cold start fluctuations of the neutron source is solved, and the reliability of the data base is improved.

[0104] Based on any of the above embodiments, in the sixth embodiment of the present application, before step S20, the control method of the material moisture online monitoring system further includes steps E11 to E13: Step E11 : determining a plurality of preset neutron fractions according to the neutron scattering monitoring results of the neutron detector assembly on each preset material.

[0105] In this embodiment, the preset material refers to a standard sample prepared at a target moisture content to ensure uniform moisture distribution and consistent physical properties. The preset neutron fraction refers to the ratio of scattered neutron counts to total neutron emissions, reflecting the moderation efficiency of materials with different preset moisture contents.

[0106] As an optional implementation method, take the same batch of materials, divide them into several equal parts, spray them with deionized water to the preset moisture content, and after balancing in a sealed constant temperature box for a preset time, use an infrared moisture meter to check the uniformity, and mark the balanced materials as the preset material group.

[0107] As an optional embodiment, the sample is vertically irradiated by a neutron emitter assembly, and the neutron detector assembly is arranged in a circular manner to capture scattered neutrons. The scattered neutron counts within a preset time window and the total number of neutron emissions recorded by the beam monitor are accumulated to calculate the preset neutron fraction.

[0108] As another optional implementation, a preset material is fixed in the shielding cabin, the neutron generator assembly bombards the preset material fixed in the shielding cabin according to a pulse mode, the neutron detector assembly records the spatial distribution of scattered neutrons, accumulates and generates a scattering count matrix with the preset time, and calculates and outputs the preset neutron fraction in combination with the total number of neutron emissions calibrated by the target current integrator.

[0109] In step E12, the data processing system substitutes the preset moisture content, the bulk density of the preset material, and the hydrogen mass fraction into a hydrogen density formula to calculate the hydrogen density of the preset material.

[0110] In this embodiment, the hydrogen mass fraction refers to the proportion of hydrogen atoms in a unit mass.

[0111] As an optional implementation, based on the preset moisture content, bulk density and hydrogen mass fraction of the coal material, a hydrogen density formula is input to calculate the preset hydrogen density.

[0112] As another optional implementation, the data processing system reads the preset moisture content, the bulk density of the preset material and the hydrogen mass fraction, and substitutes them into the hydrogen density formula to calculate the hydrogen density of the preset material.

[0113] Step E13: Correlate the preset neutron fraction with the hydrogen density corresponding to the preset material to generate the correlation library.

[0114] In this embodiment, the correlation refers to establishing a mathematical mapping relationship between the neutron fraction and the preset material hydrogen density.

[0115] As an optional implementation, the preset neutron fraction and the hydrogen density of the corresponding preset material are imported into a least square fitting module to generate a linear regression equation, and the original data points are stored in an associated library.

[0116] As another optional implementation, the hydrogen density of the preset material is configured based on the preset neutron fraction and the preset moisture content verified by the Karl Fischer method, and a cubic spline interpolation algorithm is called to construct a nonlinear association library.

[0117] As another optional implementation, a Monte Carlo model is used to simulate the neutron moderation effect of a material to generate a correction curve for the hydrogen density and neutron fraction of a preset material. The neutron fraction of the preset material is correlated with the Karl Fischer moisture value and, combined with the porosity correction factor, a three-dimensional correlation library is generated.

[0118] For example, in a coking plant's coal moisture calibration laboratory, preset materials were prepared according to preset moisture contents of 8% / 12% / 16% / 20% / 24%. The same batch of pulverized coal was spray-humidified, then sealed and equilibrated at a constant temperature for 24 hours. Infrared spot checks revealed a uniformity deviation of <±0.3%. Neutron scattering monitoring of the preset materials was performed using a DD neutron tube and a He-3 detector array. Accumulated scattered neutron counts within a 10-second time window were 12,450 / 18,600 / 24,800 / 31,000 / 37,200. The total number of neutrons recorded by the beam monitor was 100,000, resulting in the calculated preset neutron fractions of 0.1245 / 0.1860 / 0.2480 / 0.3100 / 0.3720. Based on the preset moisture content, the measured bulk density (1.2 g / cm³ obtained by vibration compaction), and a hydrogen mass fraction of 0.111, the hydrogen density formula (hydrogen density = moisture content × bulk density × 0.111) was used to calculate the preset hydrogen densities to be 0.106 / 0.159 / 0.212 / 0.266 / 0.319 g / cm³. The preset neutron fractions and corresponding hydrogen densities were then imported into the least squares fitting module to generate a linear correlation library.

[0119] By using mathematical fitting and dynamic database packaging to solve the problem of large interpolation errors in the traditional table lookup method, the moisture inversion accuracy is improved, and a traceable calibration benchmark is provided for the online monitoring system.

[0120] Based on any of the above embodiments, in the seventh embodiment of the present application, refer to Figure 5 , Figure 5 This is a flow chart of the seventh embodiment of the control method of the material moisture online monitoring system of the present application. Before step S50, the control method of the material moisture online monitoring system further includes steps F11 to F14: Step F11 : determining a plurality of preset gamma energy spectra according to the gamma ray monitoring results of the gamma detector on each preset material.

[0121] In this embodiment, the preset gamma energy spectrum refers to the gamma energy spectrum corresponding to different preset materials.

[0122] As an optional embodiment, a preset material group is vertically irradiated by a neutron generator assembly, and a gamma detector captures hydrogen and captures gamma rays at a preset inclination angle. The pulse signal is classified by amplitude by an analog-to-digital converter to generate a preset gamma energy spectrum with a preset number of channels.

[0123] Step F12: extracting and integrating the water hydrogen characteristic peak based on the preset gamma spectrum to obtain the preset water hydrogen characteristic peak area.

[0124] In this embodiment, the preset water hydrogen characteristic peak area refers to the sum of the net counts of characteristic peaks corresponding to different preset materials.

[0125] As an optional implementation method, the center channel address of the water hydrogen characteristic peak is located in the preset gamma energy spectrum generated by monitoring with a gamma detector, and the preset half-width is used as the boundary. The boundary points are connected by linear interpolation to generate a background baseline. After deducting the background channel by channel, the net count value of the preset channel range is accumulated, and the preset water hydrogen characteristic peak area is output by integration.

[0126] As another optional implementation, the preset gamma energy spectrum uses a Gaussian fitting algorithm to accurately locate the water hydrogen characteristic peak, with the preset channel address as the peak area, and the area under the integral fitting function curve is used as the preset water hydrogen characteristic peak area.

[0127] Step F13: Based on the data processing system, the preset hydrogen density, preset moisture content, and preset water hydrogen characteristic peak area corresponding to the preset material are substituted into the fitting function to calculate the material type coefficient.

[0128] In this embodiment, the preset hydrogen density refers to the mass of hydrogen atoms per unit volume of the standard material, measured and calibrated using the neutron scattering method. The preset moisture content refers to the actual moisture content of the standard material. The fitting function for the unknown coefficients refers to the mathematical model for the parameters to be solved. The material type coefficient refers to the parameter in the fitting function that characterizes the material properties.

[0129] As an optional implementation, the preset hydrogen density, the preset water hydrogen characteristic peak area and the preset moisture content of the preset material group are substituted into the multivariate linear fitting function, and the material type coefficient is solved by the least squares method.

[0130] Step F14: Associating and binding the material type coefficient with the preset material to generate a coefficient association library.

[0131] In this embodiment, the coefficient association library refers to a data set that stores the mapping relationship between material types and material type coefficients.

[0132] As an optional implementation, the solved material type coefficient is associated and bound with the preset material type to generate a coefficient association library.

[0133] For example, in a coal moisture calibration laboratory, gamma ray monitoring was performed using a DD neutron tube and a CZT gamma detector on a predefined material group with moisture contents of 8%, 12%, 16%, 20%, and 24%. The pulse amplitudes were binned and counted using a 14-bit ADC to generate a 1024-channel predefined gamma spectrum. The 2.223 MeV water hydrogen characteristic peak (channel address 750 ± 3) was automatically located and linearly integrated after background subtraction to obtain the predefined water hydrogen characteristic peak area. The predefined hydrogen densities (0.054 / 0.082 / 0.110 / 0.138 / 0.166 g / cm3), the predefined moisture contents (8.0% / 12.0% / 16.0% / 20.0% / 24.0%), and the peak areas were substituted into a multivariate linear fit function: moisture = α × hydrogen density + β × peak area + γ. The material type coefficients were calculated using the least squares method: α = 0.72, β = 0.0003, and γ = 0.15. Bind this coefficient with the material properties: volatile matter 28%, ash content 12%, and particle size 0.5mm to generate a MySQL coefficient association library.

[0134] The problem of accuracy degradation of the single neutron scattering method caused by interference from material composition is solved by multi-parameter fitting of the characteristic peak area of ​​the gamma energy spectrum and the hydrogen density, thereby improving the robustness of moisture inversion of complex materials.

[0135] Based on any of the above embodiments, in the eighth embodiment of the present application, refer to Figure 6 , Figure 6 This is a flow chart of the eighth embodiment of the control method of the material moisture online monitoring system of the present application. Step S50 includes steps G11 to G13: In step G11, the data processing system selects the target material type coefficient corresponding to the material type from the coefficient association library based on the material type corresponding to the material.

[0136] In this embodiment, the target material type coefficient refers to an inversion parameter that matches the current material type.

[0137] As an optional implementation method, scan the preset material batch QR code to obtain the material type, call the coefficient association library of the cloud database, and filter the target material type coefficient according to the batch number field.

[0138] As another optional implementation, the material type is identified in real time by a near-infrared spectrometer, the coefficient association library is queried through a communication protocol, and the target material type coefficient is screened using the material type as an index.

[0139] Step G12: replacing the original coefficient in the initial formula according to the target material type coefficient to generate a revised formula.

[0140] In this embodiment, the initial formula refers to the original mathematical model for moisture inversion. The original coefficients refer to the default parameters in the initial formula. The correction formula refers to an empirical mathematical relationship used to correct the free water content in combination with the empirical parameters.

[0141] As an optional implementation, the original coefficients in the initial formula are replaced with target material type coefficients based on the data processing system to generate a revised formula.

[0142] Step G13: Based on the cumulative scattered neutron fraction, the solidified hydrogen fraction, and the correction formula, the free water content of the measured material is generated by calculating the correction formula.

[0143] As an optional implementation, the data processing system inputs the acquired cumulative scattered neutron fraction and the solidified hydrogen fraction analyzed by gamma ray spectrum into a correction formula to calculate and generate the free water content of the material being tested.

[0144] For example, in a coking plant's coal conveyor belt moisture online monitoring system, near-infrared spectroscopy was used to identify the coal material type as Type_A. The target material type coefficients α = 0.72, β = 0.0003, and γ = 0.15 were selected from the MySQL coefficient association library. These coefficients were substituted into the original formula: Moisture = 0.5 × Hydrogen Density + 0.0005 × Peak Area + 0.2, generating a revised formula: Moisture = 0.72 × Hydrogen Density + 0.0003 × Peak Area + 0.15. The real-time calculated cumulative scattered neutron fraction of 0.18 and the solidified hydrogen fraction of 25% determined by gamma spectroscopy were input into the revised formula to calculate the free water content of the measured material.

[0145] Further, refer to Figure 7 , Figure 7This is a schematic diagram of the system architecture of this application. The system architecture diagram centers around an inverted trapezoidal "test material" container. A neutron generator assembly extends vertically upward through the center of the container's bottom, emitting a stable, controllable fast neutron beam into the material layer. Neutron detector assemblies, symmetrically arranged on either side, capture thermal neutrons slowed by scattered hydrogen atoms in the material. Simultaneously, two gamma detectors at the top, arranged at a 30° angle, monitor the 2.223 MeV characteristic gamma rays emitted by neutron-hydrogen interactions. The raw pulse signals output by the neutron detectors are transmitted to the "Neutron Data Processing" module (marked by the dashed box on the right), where pulse noise filtering and fixed time window counting statistics are used to generate neutron fractions. The electrical pulse signals collected by the gamma detectors are input into the "Gamma Spectrum Processing System," where pulse amplitude classification generates a gamma spectrum, from which the characteristic peak area of ​​water hydrogen is extracted and the solidified hydrogen fraction is calculated. The dual-channel data is fused using an inversion algorithm to determine the free water content of the test material. Logic instructions are transmitted via the yellow feedback line to the "Neutron Tube Control System" to dynamically adjust the neutron emission intensity and frequency. The overall control system monitors the neutron tube temperature, beam stability and cooling status in real time, optimizes the neutron generator operating parameters through control lines to form a closed loop, and realizes real-time, adaptive and high-precision monitoring of material moisture. Each subsystem drives industrial-grade moisture closed-loop control under the protection of a timing coordination mechanism.

[0146] The core principle is to exploit the large interaction cross section and strong moderating capacity of hydrogen (1H) in water molecules with high-energy neutrons. High-energy neutrons generated by a DD neutron tube interact with the material, causing elastic collisions between the hydrogen in the water and the neutrons, which alter the neutron energy and direction of motion, causing some neutrons to be scattered back by the material. The fraction of scattered neutrons is proportional to the hydrogen content in the material. Correlation curves between the water content and the fraction of scattered neutrons are calibrated through experimental measurement or Monte Carlo calculations of mineral compositions. The measured neutron scattering fraction can be used to determine the water content in the material. Furthermore, the activation analysis gamma detector measures the characteristic gamma rays produced by the interaction of hydrogen with thermal neutrons. The intensity of the characteristic hydrogen peak in the gamma-ray energy spectrum is correlated with both the water hydrogen content and the inherent hydrogen content. Monte Carlo simulations accurately reconstruct the gamma-ray energy spectra generated by the neutron activation reaction for materials of different compositions. Spectral matching is used to determine the water hydrogen content, while eliminating interference from non-hydrogen elements and inherent hydrogen.

[0147] By adopting the dual-parameter fusion correction formula of neutron fraction and gamma energy spectrum corresponding to the solidified hydrogen ratio, the interference problem of solidified hydrogen on free water inversion is solved, and the precise stripping of free water is achieved, providing reliable moisture content data for high-precision moisture monitoring closed loop.

[0148] The present application provides an online monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the control method of the material moisture online monitoring system in the above-mentioned embodiment 1.

[0149] Reference below Figure 8 , which shows a schematic diagram of the structure of an online monitoring device suitable for implementing the embodiments of the present application. The online monitoring device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital monitoring devices, personal digital assistants (PDAs), tablet computers (PADs), portable multimedia players (PMPs), mobile monitoring terminals, and fixed terminals such as online monitoring computers and desktop computers. Figure 8 The online monitoring device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0150] like Figure 8As shown, the online monitoring device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the online monitoring device. Processing device 1001, ROM 1002, and 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 may be connected to I / O interface 1006: input devices 1007, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication devices 1009. The communication device 1009 can allow the online monitoring device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an online monitoring device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or provided instead.

[0151] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0152] The online monitoring device provided in this application utilizes the control method for the online material moisture monitoring system described in the aforementioned embodiment, resolving the technical issue in related technologies where all hydrogen-containing substances in the material contribute signals, making it impossible to distinguish between solidified water and free water, leading to inaccurate moisture monitoring results. Compared to the prior art, the beneficial effects of the online monitoring device provided in this application are the same as those of the control method for the online material moisture monitoring system described in the aforementioned embodiment. Other technical features of this online monitoring device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0153] 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 one or more embodiments or examples in a suitable manner.

[0154] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0155] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the control method of the material moisture online monitoring system in the above-mentioned embodiment.

[0156] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores 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.

[0157] The computer-readable storage medium may be included in the online monitoring device, or may exist independently without being assembled into the online monitoring device.

[0158] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the online monitoring device, the online monitoring device: starts the neutron generator assembly, and when the startup time of the neutron generator assembly is greater than or equal to the preset preheating time, turns on the neutron detector assembly and the gamma detector; determines the cumulative scattered neutron fraction of the material under test based on the detection result of the neutron detector assembly, and obtains the cumulative scattered neutron fraction from a preset association library to match the corresponding target hydrogen density; generates a gamma energy spectrum based on the gamma rays monitored by the gamma detector according to the pulse amplitude classification counting, and extracts the water hydrogen characteristic peak area from the gamma energy spectrum; determines the solidified hydrogen ratio based on the target hydrogen density, the water hydrogen characteristic peak area and the energy spectrum calibration coefficient of the gamma energy spectrum; and determines the free water content of the material under test based on the solidified hydrogen ratio.

[0159] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0160] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0161] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0162] The computer-readable storage medium provided herein stores computer-readable program instructions (i.e., a computer program) for executing the control method for the online material moisture monitoring system described above. This computer-readable storage medium can address the technical issue in related art where all hydrogen-containing substances in the material contribute signals, making it impossible to distinguish between solidified water and free water, leading to inaccurate moisture monitoring results. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided herein are similar to those of the control method for the online material moisture monitoring system provided in the aforementioned embodiments and are not further elaborated here.

[0163] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A control method for a material moisture online monitoring system, wherein the material moisture online monitoring system comprises a data processing system, a neutron generator assembly, a neutron detector assembly and a gamma detector, characterized in that: The control of the material moisture online monitoring system includes: Starting the neutron generator assembly, and when the startup time of the neutron generator assembly is greater than or equal to the preset preheating time, turning on the neutron detector assembly and the gamma detector; Determine the cumulative scattered neutron fraction of the material under test based on the detection result of the neutron detector assembly, and obtain the cumulative scattered neutron fraction from a preset correlation library to match the corresponding target hydrogen density; generating a gamma energy spectrum based on the gamma rays monitored by the gamma detector according to pulse amplitude classification counting, and extracting the water hydrogen characteristic peak area in the gamma energy spectrum; determining a solidified hydrogen ratio according to the target hydrogen density, the water hydrogen characteristic peak area, and the energy spectrum calibration coefficient of the gamma energy spectrum; The free water content of the tested material is determined according to the solidified hydrogen ratio.

2. The control method of the material moisture online monitoring system according to claim 1, characterized in that: The step of determining the cumulative scattered neutron fraction of the material under test based on the detection result of the neutron detector assembly, and obtaining the cumulative scattered neutron fraction from a preset correlation library to match the corresponding target hydrogen density includes: Extracting the pulse characteristics of the detection result to generate an original pulse signal; filtering the noise signal in the original pulse signal to output a purified scattered neutron pulse; The data processing system calculates the cumulative scattered neutron fraction within a fixed time window based on the number of scattered neutron pulses and the number of neutron emissions from the neutron generator assembly; According to the accumulated scattered neutron fraction, the corresponding hydrogen density is matched in the correlation library to determine the target hydrogen density.

3. The control method of the material moisture online monitoring system according to claim 2, characterized in that: The step of matching the corresponding hydrogen density in the correlation library according to the accumulated scattered neutron fraction to determine the target hydrogen density includes: If the data processing system fails to match the cumulative scattered neutron fraction in the association library based on the cumulative scattered neutron fraction, then selecting a spare neutron fraction having the smallest difference with the cumulative scattered neutron fraction from the association library; Determining a spare hydrogen density corresponding to the spare neutron fraction in the associated library; The backup hydrogen density is used as the target hydrogen density, and a prompt message is outputted indicating that the hydrogen density corresponding to the neutron fraction does not exist.

4. The control method of the material moisture online monitoring system according to claim 1, characterized in that: The steps of generating a gamma energy spectrum based on the gamma rays monitored by the gamma detector according to pulse amplitude classification counting and extracting the water hydrogen characteristic peak area in the gamma energy spectrum include: Based on the gamma rays monitored by the gamma detector, determining the electrical pulse signals corresponding to the gamma rays according to pulse amplitude classification; sampling the pulse peak voltage of the electrical pulse signal, and mapping the pulse peak voltage to a channel address to generate a mapping relationship between the channel address and the pulse peak voltage; Based on the mapping relationship, accumulating the pulse peak voltage within a fixed time window to generate the gamma energy spectrum; According to the channel address corresponding to the water hydrogen characteristic peak, the water hydrogen characteristic peak is extracted from the gamma energy spectrum and integrated to obtain the water hydrogen characteristic peak area.

5. The control method of the material moisture online monitoring system according to claim 1, characterized in that: The step of starting the neutron generator assembly and turning on the neutron detector assembly and the gamma detector when the startup time of the neutron generator assembly is greater than or equal to the preheating preset time includes: Starting the neutron generator assembly, and when the startup time of the neutron generator assembly is greater than or equal to the preset preheating time, outputting a preheating completion prompt so that the neutron generator assembly can output a stable yield of neutrons; When the preheating of the neutron generator assembly is completed, the neutron detector assembly and the gamma detector are started, and the timestamp calibration of the neutron generator assembly, the neutron detector assembly and the gamma detector is synchronously triggered.

6. The control method of the material moisture online monitoring system according to claim 1, characterized in that: Before the step of determining the cumulative scattered neutron fraction of the tested material based on the detection result of the neutron detector assembly and obtaining a match between the cumulative scattered neutron fraction and the corresponding target hydrogen density in a preset correlation library, the control method of the material moisture online monitoring system further includes: Determining a plurality of preset neutron fractions based on neutron scattering monitoring results of the neutron detector assembly on each preset material; The data processing system calculates the hydrogen density of the preset material based on the preset moisture content, the bulk density of the preset material, and the hydrogen mass fraction by substituting them into the hydrogen density formula; The preset neutron fraction is correlated with the hydrogen density corresponding to the preset material to generate the correlation library.

7. The control method of the material moisture online monitoring system according to claim 1, characterized in that: Before the step of determining the free water content of the tested material according to the solidified hydrogen ratio, the control method of the material moisture online monitoring system further includes: Determining a plurality of preset gamma energy spectra according to the gamma ray monitoring results of the gamma detector on each preset material; Based on the preset gamma energy spectrum, extracting and integrating the water hydrogen characteristic peak to obtain the preset water hydrogen characteristic peak area; Based on the data processing system, the preset hydrogen density, preset moisture content, and preset water hydrogen characteristic peak area corresponding to the preset material are substituted into the fitting function to calculate the material type coefficient; The material type coefficient is associated and bound with the preset material to generate a coefficient association library.

8. The control method of the material moisture online monitoring system according to claim 1, characterized in that: The step of determining the free water content of the tested material according to the solidified hydrogen ratio includes: The data processing system selects a target material type coefficient corresponding to the material type from a coefficient association library based on the material type corresponding to the material; Replacing the original coefficient in the initial formula according to the target material type coefficient to generate a revised formula; Based on the cumulative scattered neutron fraction, the solidified hydrogen fraction, and the correction formula, the free water content of the measured material is generated by calculating the correction formula.

9. An online monitoring device, characterized in that: The online monitoring device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the control method of the material moisture online monitoring system according to any one of claims 1 to 8.

10. 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, the steps of the control method of the material moisture online monitoring system according to any one of claims 1 to 8 are implemented.

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