Control methods, equipment and storage media for online moisture monitoring systems of materials

Abstract

This application discloses a control method, device, and storage medium for an online material moisture monitoring system, relating to the field of material moisture monitoring technology. The method includes: starting a neutron generator assembly; when the start-up time of the neutron generator assembly is greater than or equal to a preset preheating time, activating a neutron detector assembly and a gamma detector; determining the cumulative scattered neutron share of the measured material based on the detection results of the neutron detector assembly, and obtaining the cumulative scattered neutron share to match the corresponding target hydrogen density from a preset correlation library; generating a gamma spectrum based on the gamma rays monitored by the gamma detector according to pulse amplitude classification and counting, and extracting the water hydrogen characteristic peak area from the gamma spectrum; determining 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 spectrum; and determining the free water content of the measured material based on the solidified hydrogen ratio. This application solves the problem of low accuracy in moisture monitoring through dual-modal collaborative detection.

Description

Technical Field

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

[0002] In industrial material production, fluctuations in material moisture content directly affect product properties, reaction rates, energy consumption, and equipment stability. Related technologies for online monitoring of material moisture content infer the moisture content through the hydrogen atom moderation effect. However, because all hydrogen-containing substances in the material contribute signals, it is impossible to distinguish between solidified water and free water, leading to inaccurate moisture monitoring results.

[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 objective of this application is to provide a control method, device, and storage medium for an online material moisture monitoring system, aiming to solve the technical problem that the moisture monitoring results are inaccurate because all hydrogen-containing substances in the material contribute signals, making it impossible to distinguish between solidified water and free water.

[0005] To achieve the above objectives, this application proposes a control method for an online material moisture monitoring system, the method comprising:

[0006] The neutron generator assembly is started, and when the start-up time of the neutron generator assembly is greater than or equal to the preheating preset time, the neutron detector assembly and the gamma detector are turned on.

[0007] Based on the detection results of the neutron detector assembly, the cumulative scattered neutron share of the tested material is determined, and the cumulative scattered neutron share is matched with the corresponding target hydrogen density in a preset association library.

[0008] Based on the gamma rays monitored by the gamma detector according to pulse amplitude classification and counting, a gamma energy spectrum is generated, and the characteristic peak area of ​​water hydrogen is extracted from the gamma energy spectrum.

[0009] The solidified hydrogen ratio is determined based on the target hydrogen density, the area of ​​the water-hydrogen characteristic peak, and the energy spectrum calibration coefficient of the gamma spectrum.

[0010] The free water content of the tested material is determined based on the percentage of solidified hydrogen.

[0011] In one embodiment, the pulse features of the detection result are extracted to generate the original pulse signal;

[0012] The noise signal in the original pulse signal is filtered out, and the purified scattered neutron pulse is output.

[0013] 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 component.

[0014] Based on the cumulative scattered neutron share, the corresponding hydrogen density is matched in the associated library to determine the target hydrogen density.

[0015] In one embodiment, if the data processing system fails to match the cumulative scattered neutron share in the association library, it then selects the backup neutron share with the smallest difference from the cumulative scattered neutron share from the association library.

[0016] Determine the spare hydrogen density corresponding to the spare neutron share in the associated library;

[0017] The spare hydrogen density is used as the target hydrogen density, and a prompt message is output indicating that the hydrogen density corresponding to the neutron fraction does not exist.

[0018] In one embodiment, based on the gamma rays monitored by the gamma detector, the electrical pulse signal corresponding to the gamma ray is determined according to the pulse amplitude classification.

[0019] The peak voltage of the electrical pulse signal is sampled, and the peak voltage is mapped to a channel address to generate a mapping relationship between the channel address and the peak voltage.

[0020] Based on the mapping relationship, the peak voltage of the pulse within a fixed time window is accumulated to generate the gamma spectrum;

[0021] Based on the channel address corresponding to the water-hydrogen characteristic peak, the water-hydrogen characteristic peak is extracted from the gamma spectrum and integrated to obtain the area of ​​the water-hydrogen characteristic peak.

[0022] In one embodiment, the neutron generator assembly is started, and when the start-up time of the neutron generator assembly is greater than or equal to the preheating preset time, a preheating completion prompt is output so that the neutron generator assembly outputs a stable yield of neutrons;

[0023] When the neutron generator assembly has finished preheating, 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 triggered synchronously.

[0024] In one embodiment, multiple preset neutron shares are determined based on the neutron scattering monitoring results of each preset material by the neutron detector assembly;

[0025] The data processing system calculates the hydrogen density of the preset material by substituting the preset moisture content, the preset bulk density of the material, and the hydrogen mass fraction into the hydrogen density formula.

[0026] The preset neutron fraction is correlated with the hydrogen density of the corresponding preset material to generate the correlation library.

[0027] In one embodiment, multiple preset gamma energy spectra are determined based on the gamma ray monitoring results of the gamma detector on each preset material.

[0028] Based on the preset gamma spectrum, the water hydrogen characteristic peak is extracted and integrated to obtain the preset water hydrogen characteristic peak area.

[0029] 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.

[0030] The material type coefficient is associated and bound with the preset material to generate a coefficient association library.

[0031] In one embodiment, the data processing system filters out the target material type coefficient corresponding to the material type from the coefficient association library based on the material type corresponding to the material;

[0032] A corrected formula is generated by replacing the original coefficients in the initial formula with the target material type coefficients.

[0033] Based on the cumulative scattered neutron fraction, the solidified hydrogen ratio, and the correction formula, the free water content of the tested material is generated through calculation using the correction formula.

[0034] In addition, to achieve the above objectives, this application also proposes an online monitoring 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 control method for the online material moisture monitoring system as described above.

[0035] 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 control method of the online material moisture monitoring system described above.

[0036] This application provides a control method for an online material moisture monitoring system. The method includes a neutron detector that captures the backscattered neutrons from the material and, based on the strong correlation between neutron fraction and hydrogen density, directly locks the target hydrogen density from a pre-set correlation library. Simultaneously, a gamma detector performs pulse amplitude classification and statistical analysis on the detected gamma rays to generate a gamma spectrum, accurately extracting the characteristic peak area representing hydrogen in water molecules. The target hydrogen density, the water-hydrogen characteristic peak area, and the energy spectrum calibration coefficient are then substituted into a solidified hydrogen calculation formula to output the solidified hydrogen percentage. Finally, the neutron fraction and the solidified hydrogen percentage are combined and input into a dynamic correction formula to achieve high-precision correction of the free water content of the material. This application utilizes neutron detection to capture macroscopic hydrogen density and gamma spectroscopy to identify microscopic water molecule characteristic peaks. The two methods complement each other's physical mechanisms, mitigating the risk of single detectors being affected by material composition fluctuations or environmental noise interference at the particle interaction level.

[0037] In summary, this application addresses the technical problem of inaccurate moisture monitoring results caused by the inverse relationship between hydrogen atom moderation effects and the dual-mode detection of scattered neutrons and gamma spectroscopy, along with a dynamic correction mechanism. This is achieved through dual-mode synergistic detection of scattered neutrons and gamma spectroscopy, and a dynamic correction mechanism. The latter addresses the inaccuracy caused by the inability to distinguish between solidified water and free water due to the contribution of all hydrogen-containing substances in the material. This application improves the accuracy of free water content monitoring and significantly enhances adaptability to materials with complex compositions. Attached Figure Description

[0038] 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.

[0039] 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.

[0040] Figure 1 This is a flowchart illustrating the first embodiment of the control method for the online material moisture monitoring system of this application;

[0041] Figure 2 This is a flowchart illustrating the second embodiment of the control method for the online material moisture monitoring system of this application;

[0042] Figure 3 This is a flowchart illustrating the third embodiment of the control method for the online material moisture monitoring system of this application;

[0043] Figure 4 This is a flowchart illustrating the fourth embodiment of the control method for the online material moisture monitoring system of this application;

[0044] Figure 5 This is a flowchart illustrating the seventh embodiment of the control method for the online material moisture monitoring system of this application;

[0045] Figure 6 This is a flowchart illustrating the eighth embodiment of the control method for the online material moisture monitoring system of this application;

[0046] Figure 7 This is a schematic diagram of the system structure of this application;

[0047] Figure 8 This is a schematic diagram of the online monitoring equipment of this application.

[0048] 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

[0049] 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.

[0050] In the online monitoring of material moisture, related technologies infer the moisture content by using the hydrogen atom moderation effect. However, because all hydrogen-containing substances in the material contribute signals, it is impossible to distinguish between solidified water and free water, leading to inaccurate moisture monitoring results.

[0051] This application provides a solution as follows: First, the neutron generator assembly is started. When the start-up time of the neutron generator assembly is greater than or equal to the preheating preset time, the neutron detector assembly and the gamma detector are turned on. Then, based on the detection results of the neutron detector assembly, the cumulative scattered neutron share of the tested material is determined, and the cumulative scattered neutron share is matched with the target hydrogen density by obtaining the target hydrogen density from a preset association library. Next, based on the gamma rays monitored by the gamma detector according to pulse amplitude classification and counting, a gamma spectrum is generated, and the water hydrogen characteristic peak area is extracted from the gamma spectrum. Then, based on the target hydrogen density, the water hydrogen characteristic peak area, and the energy spectrum calibration coefficient of the gamma spectrum, the solidified hydrogen ratio is determined. Finally, based on the solidified hydrogen ratio, the free water content of the tested material is determined.

[0052] 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 online monitoring device capable of performing the above functions. The following description uses an online monitoring device as an example to illustrate this embodiment and the subsequent embodiments.

[0053] 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.

[0054] This application provides a control method for an online material moisture monitoring system, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the control method for the online material moisture monitoring system of this application.

[0055] In this embodiment, the control method of the online moisture monitoring system for materials includes steps S10 to S50:

[0056] Step S10: Start the neutron generator assembly. When the startup time of the neutron generator assembly is greater than or equal to the preheating preset time, turn on the neutron detector assembly and the gamma detector.

[0057] In this embodiment, the neutron generator assembly refers to a nuclear physics device that generates a stable neutron beam. It outputs neutrons by accelerating deuterium ions to bombard a target material, triggering a fusion reaction. It is configured as a DD neutron tube, a sealed neutron generator for deuterium-deuterium nuclear fusion reactions. The preheating preset time refers to the heating / equilibration time required for the device to reach a stable operating state after startup. Stable neutron output refers to the neutron output state that meets the requirements of industrial-grade measurement and statistical accuracy. The neutron detector assembly refers to a He-3 proportional counter array, which captures slowed neutrons and generates electrical pulses through the reaction of neutrons with helium nuclei. The gamma detector refers to a scintillator or semiconductor probe that converts photon energy into electrical signals via a photoelectric conversion device based on the photoelectric effect, Compton effect, or electron-electron pair effect interaction between gamma photons and matter.

[0058] As an optional implementation, when the neutron generator assembly is started and the preheating time is greater than or equal to the preset preheating time, so as to stabilize the neutron output of the neutron generator assembly, 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.

[0059] As another optional implementation, when the output neutron flux of the neutron generator component reaches a preset stable threshold, the neutron generator component triggers a detection command and sends it to the neutron detector component and the gamma detector, activating the data acquisition circuit of the neutron detector component and the gamma detector.

[0060] As an alternative implementation, the neutron generator assembly is preheated until the output stabilizes, and the operator wirelessly remotely starts the neutron detector assembly and gamma detector via a controller.

[0061] Step S20: Based on the detection results of the neutron detector assembly, determine the cumulative scattered neutron share of the tested material, and obtain the cumulative scattered neutron share from a preset association library to match the corresponding target hydrogen density.

[0062] In this embodiment, scattered neutrons refer to neutrons whose energy decays and direction is deflected after a fast neutron collides with the atomic nuclei of matter. The cumulative scattered neutron share is the ratio of detector counts to the total amount emitted by the neutron source, reflecting the material's neutron moderation efficiency. The association library refers to a pre-stored mapping dataset of neutron share and hydrogen density, obtained through standard sample calibration. The target hydrogen density refers to the mass of hydrogen atoms per unit volume of material.

[0063] As an optional implementation, based on the scattered neutrons captured by the neutron detector assembly horizontally arranged above the material layer, the beam monitor synchronously records the total number of neutrons emitted by the neutron generator assembly. Within each fixed time window, the scattered neutron count is accumulated, and the cumulative scattered neutron share is calculated. The cumulative scattered neutron share is transmitted to a database via a bus. Using the cumulative scattered neutron share as an index, a pre-stored hydrogen density mapping table in the associated database is queried to determine and output the target hydrogen density corresponding to that cumulative scattered neutron share.

[0064] As another optional implementation, based on the cumulative scattered neutron fraction obtained by the neutron detector component, the density compensation submodule in the associated library is called to automatically correct the mapping relationship and output the target hydrogen density value after density normalization.

[0065] Step S30: Based on the gamma rays monitored by the gamma detector according to the pulse amplitude classification and counting, a gamma energy spectrum is generated, and the area of ​​the water hydrogen characteristic peak is extracted from the gamma energy spectrum.

[0066] In this embodiment, pulse amplitude classification counting refers to quantizing the peak voltage of the electrical pulses generated by gamma rays in the detector into discrete channel address values ​​using an analog-to-digital converter, and then accumulating the counts according to amplitude intervals to generate energy spectrum data. Gamma rays are high-energy photons released by the interaction of neutrons with the atomic nuclei of materials. The gamma spectrum is a histogram with the channel address on the horizontal axis and the count on the vertical axis, characterizing the intensity distribution of gamma rays of different energies. The water hydrogen characteristic peak refers to the characteristic peak at 2.223 MeV in the gamma spectrum, characterizing the concentration of total hydrogen atoms in the material, and its peak area is positively correlated with the total hydrogen density. The peak area refers to the net accumulated count value of the channel address interval covered by the characteristic peak after deducting the background, reflecting the target nucleus concentration.

[0067] As an optional implementation, a gamma detector captures gamma rays emitted by materials within a fixed time window, generates corresponding pulse signals from the gamma rays, amplifies the pulse signals through a preamplifier, and then converts them into quantized channel addresses via digital-to-analog conversion. A multichannel analyzer categorizes and accumulates these channel addresses to generate a gamma spectrum. The spectrum preprocessing module automatically subtracts the environmental background and uses a first-order derivative peak-finding algorithm to locate the center of the water-hydrogen characteristic peak within a preset energy window. A linear interpolation baseline is then established using a preset half-width at half-maximum (FWHM) at the peak center as the boundary. The net count within the integral boundary is output as the peak area value, which is used as the area of ​​the water-hydrogen characteristic peak.

[0068] As an alternative implementation, a gamma detector collects gamma rays corresponding to the material, and a digital-to-analog converter records the pulse amplitude distribution in a high-resolution mode with a preset number of channels. Energy spectrum processing software performs Gaussian smoothing filtering on the pulse amplitude distribution and then loads an energy scale file to generate a gamma spectrum. By precisely locating the preset water-hydrogen characteristic peak addresses, a preset optimization algorithm is used to fit a Gaussian function superimposed with a linear background model, combined with an integral fitting function, to obtain the water-hydrogen characteristic peak area.

[0069] Step S40: Determine the solidified hydrogen ratio based on the target hydrogen density, the area of ​​the water-hydrogen characteristic peak, and the energy spectrum calibration coefficient of the gamma spectrum.

[0070] In this embodiment, the energy spectrum calibration system refers to the comprehensive calibration parameters of detector efficiency, geometric factor, and energy scale, used to convert the gamma peak area into the physical quantity of hydrogen density. The solidified hydrogen formula refers to the mathematical relationship for calculating solidified hydrogen, used to calculate the proportion of bound hydrogen. The solidified hydrogen proportion refers to the mass percentage of chemically bound hydrogen in the material, reflecting the hydrogen contribution from non-free water.

[0071] As an optional implementation method, the target hydrogen density corresponding to the material and the area of ​​the water-hydrogen characteristic peak in the gamma spectrum are substituted into the solidification hydrogen formula through the data processing system, and the energy spectrum calibration coefficients corresponding to the energy spectrum in the database are loaded. The solidification hydrogen ratio is then calculated through the solidification hydrogen formula.

[0072] Step S50: Determine the free water content of the tested material based on the solidified hydrogen ratio.

[0073] In this embodiment, the free water content refers to the percentage of free water mass after compensation for hydrogen interference from solidification, representing the free water that can participate in physicochemical reactions.

[0074] As an optional implementation, the data processing system calculates the free water content of the tested material based on the neutron content and solidified hydrogen ratio corresponding to the material, and loads the target material type coefficient corresponding to the material type into the coefficient association library.

[0075] For example, in the online moisture monitoring system of the coal conveyor belt in a coking plant, when the neutron generator component outputs a stable yield of neutrons, the neutron detector component (He-3 proportional counter array) and the gamma detector (semiconductor detection module) are automatically activated. The neutron detector component counts the scattered neutrons returned by the coal seam in real time and calculates the neutron share through the beam monitor. It then matches the corresponding hydrogen density value in a pre-calibrated correlation library to output the target hydrogen density. The gamma detector simultaneously acquires inelastic scattered gamma rays, which are classified and counted by a 14-bit ADC according to pulse amplitude to generate 2048 gamma energy spectra. After being smoothed by Savitzky-Golay filtering, the water hydrogen characteristic peak area is extracted within the 2.22–2.23 MeV energy window using a first-order derivative peak-finding algorithm. The target hydrogen density, water hydrogen characteristic peak area, and energy spectrum calibration coefficient are substituted into the solidified hydrogen formula: solidified hydrogen proportion = (total hydrogen density - free water hydrogen density) / total hydrogen density × 100%, dynamically calculating the solidified hydrogen proportion of the current coal seam. Finally, the neutron fraction and solidified hydrogen ratio are input into the process correction formula: Free water content = k × neutron fraction × (1 - β × solidified hydrogen ratio), where k = 0.18 and β = 0.85 are the calibration parameters for coking coal, and the free water content of the tested material is output in real time.

[0076] Further, the material moisture monitoring process is as follows: The neutron tube is turned on and preheated for 20 minutes. Measurement begins after the neutron yield stabilizes. Data acquisition: The measurement time is set; the gamma detector and neutron detector begin operation; the gamma spectral processing system and neutron data processing system accumulate measurement data; data acquisition by the gamma and neutron detectors stops after the measurement time is met. Data processing: Based on the sample composition, standard geometry, and detector position Monte Carlo model, a database covering different combinations of material composition (matrix type), moisture hydrogen content, and inherent hydrogen content (non-aqueous hydrogen or hydrogen in inorganic compounds contained in the material) is generated through Monte Carlo simulation; the moisture content of the matched material is quickly searched from the database based on the measured neutron data; the gamma spectrum is analyzed using a gamma spectral analysis algorithm. Gamma spectral analysis correction: The gamma spectral analysis results are corrected based on the database and measured moisture content. Output of the gamma spectral analysis results and moisture content data.

[0077] By employing dual-mode synergistic detection of scattered neutrons and gamma spectra, along with a dynamic correction mechanism, this technology overcomes the problem of inferring material moisture content through the hydrogen atom moderation effect during online monitoring. Previously, all hydrogen-containing substances in the material contributed signals, making it impossible to distinguish between solidified water and free water, leading to inaccurate moisture monitoring results. This approach improves the accuracy of free water content monitoring and significantly enhances adaptability to materials with complex compositions. Furthermore, the non-contact detection based on the neutron detector assembly and gamma detector, combined with a switchable DD neutron tube, avoids sensor wear associated with contact detection and harmful radiation caused by the inability to control the isotope neutron source, achieving precise non-contact measurement.

[0078] Based on any of the above embodiments, in Embodiment 2 of this application, referring to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the control method for the online moisture monitoring system of materials in this application. Step S20 includes steps A11-A14:

[0079] Step A11: Extract the pulse features of the detection results to generate the original pulse signal.

[0080] In this embodiment, the original pulse signal refers to the unshaped voltage pulse sequence output by the scattered neutrons captured and extracted by the neutron detector assembly.

[0081] As an optional implementation, when the neutron generator assembly emits fast neutrons that penetrate the material, the high-voltage power supply drives the counting tube to ionize and extract the pulse characteristics of the detection results based on the detection results of the neutron detector assembly, thereby generating the original pulse signal.

[0082] Step A12: Filter the noise signal in the original pulse signal and output the purified scattered neutron pulse.

[0083] In this embodiment, noise signal refers to invalid pulses caused by non-target particles or electronic noise. Purified scattered neutron pulses refer to neutron event pulses retained only after denoising processing, and the pulse amplitude is positively correlated with the neutron energy.

[0084] 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 frequency interference from the raw pulse signal, followed by a second-stage differentiating circuit to suppress baseline drift. A pulse amplitude discriminator then removes cosmic rays and low-energy gamma noise from the raw pulse signal. Finally, a programmable gate array (PGA) real-time accumulation rejection module filters accumulation events from the raw pulse signal, outputting a purified scattered neutron pulse.

[0085] As another alternative implementation, the neutron detector assembly generates the original pulse signal, eliminates the amplitude and time fluctuations of the original pulse signal through a constant ratio timing circuit, and then uses the pulse shape discrimination module to separate the noise of the original pulse signal by utilizing the difference in decay time between neutron and gamma pulses, and outputs a purified scattered neutron pulse.

[0086] Step A13: 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 component.

[0087] 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 number refers to the cumulative value of effective scattered neutron pulses within the time window. The total number of neutron emissions refers to the total number of neutrons emitted by the neutron generator within the same time window.

[0088] As an optional implementation, a fixed cumulative time window is set to simultaneously start the neutron detector component and the neutron generator component. The neutron detector component collects the scattered neutron pulses. After the scattered neutron pulses are filtered for electromagnetic noise by the pulse amplitude discriminator, they are 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 two data are transmitted to the data processing system through the bus to generate a real-time neutron share value.

[0089] Step A14: Based on the cumulative scattered neutron share, match the corresponding hydrogen density in the association library to determine the target hydrogen density.

[0090] As an optional implementation, the cumulative scattered neutron share calculated by the data processing system is transmitted to the central 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.

[0091] As another alternative implementation, the material is placed in a neutron irradiation cavity, neutrons are emitted by a neutron generator assembly, and the cumulative scattered neutron fraction is measured by a neutron detector assembly. Then, the local association library is called to perform nearest neighbor matching to determine and output the target hydrogen density.

[0092] For example, in the online moisture monitoring system of a coking plant's coal conveyor belt, when the DD neutron tube of the neutron generator assembly outputs a stable neutron beam, the He-3 proportional counter array of the neutron detector assembly vertically captures the scattered neutrons reflected back by the coal seam, generating a raw pulse signal. This signal is filtered for electromagnetic noise by a two-stage RC filter and a pulse amplitude discriminator, 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 share formula: neutron share = number of target scattered neutrons / total number of neutron emissions, the cumulative scattered neutron share is calculated in real time to be 0.18. Using the share value as an index, a pre-calibrated association library is scanned, and a bilinear interpolation algorithm is used to match the corresponding hydrogen density of 0.12 grams per cubic centimeter.

[0093] By using real-time pulse noise filtering and dynamic calibration of the total number of emissions, the problem of slowing efficiency distortion caused by material composition fluctuations is solved, thus improving the anti-interference capability and stability of online moisture monitoring.

[0094] 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 control method for the online moisture monitoring system of the present application. Step A14 includes steps B11-B13:

[0095] Step B11: If the data processing system fails to match the cumulative scattered neutron share in the association library, it then filters out the backup neutron share with the smallest difference from the cumulative scattered neutron share from the association library.

[0096] In this embodiment, the spare neutron share refers to the reference value in the associated database that is closest to the current share.

[0097] As an optional implementation, when the data processing system finds no exact match in the association database based on the neutron share, the data processing system automatically initiates a binary search algorithm to filter the closest available neutron share in the database.

[0098] As another optional implementation, if the neutron share corresponding to the unknown material exceeds the range of the associated library, the nearest neighbor algorithm is called to filter the alternative neutron share.

[0099] Step B12: Determine the spare hydrogen density corresponding to the spare neutron share in the associated library.

[0100] In this embodiment, the spare hydrogen density refers to the hydrogen atom mass density value corresponding to the spare neutron share in the associated library. The corresponding hydrogen density refers to the hydrogen atom mass density value in the associated library that precisely matches the current neutron share.

[0101] As an optional implementation, the corresponding spare hydrogen density is read from the associated library using the spare neutron share.

[0102] Step B13: Use the spare hydrogen density as the target hydrogen density, and output a prompt message indicating that the hydrogen density corresponding to the neutron fraction does not exist.

[0103] In this embodiment, the target hydrogen density refers to the final output hydrogen atomic mass density of the material; here, the backup hydrogen density is directly used as the result. The warning message refers to the abnormal warning text / code generated by the system, indicating that the current neutron share has no exact match in the associated library.

[0104] As an optional implementation, the overall control system automatically selects the backup hydrogen density corresponding to the closest backup neutron share as the target hydrogen density output, and at the same time triggers a red flashing alarm on the control interface that reads "No matching item in the neutron share library".

[0105] As an alternative implementation, the overall control system locks the spare hydrogen density corresponding to the spare neutron share selected by the nearest neighbor algorithm as the target hydrogen density, and affixes a red "external interpolation" stamp on the first page of the detection report to prompt the operator to update the data of the associated library.

[0106] As another optional implementation, the spare hydrogen density is output as the target hydrogen density, and a prompt message is displayed indicating that the hydrogen density corresponding to the neutron share does not exist, so that the operator can add the hydrogen density mapping relationship corresponding to the neutron share into the association library.

[0107] For example, in the moisture monitoring system of a coal conveyor belt in a coking plant, when the cumulative scattered neutron fraction is 0.157 and there is no exact match in the coal volatile matter correlation library, the system automatically starts a binary search to screen for the closest backup neutron fraction of 0.16, extracts its corresponding backup hydrogen density of 0.108 g / cm³ from the library as the target hydrogen density output, and simultaneously triggers a red pop-up alarm on the central control screen: "Cumulative scattered neutron fraction 0.157 has no match, use backup value 0.108 g / cm³", logs error code E404 and pushes an SMS or automatic email requesting the calibration department to expand the library to the engineer.

[0108] By using backup value substitution and multi-level alarm mechanisms, the system interruption problem caused by incomplete coverage of related databases was resolved, the continuous operation capability under extreme conditions was improved, and a precise closed-loop driving basis was provided for dynamic database optimization.

[0109] Based on any of the above embodiments, in Embodiment 4 of this application, referring to Figure 4 , Figure 4 This is a flowchart illustrating the fourth embodiment of the control method for the online moisture monitoring system of materials in this application. Step S30 includes steps C11 to C14:

[0110] Step C11: Based on the gamma rays monitored by the gamma detector, determine the electrical pulse signal corresponding to the gamma rays according to the pulse amplitude classification.

[0111] In this embodiment, the interaction between neutrons and intrinsic hydrogen refers to the radiative capture reaction that occurs when fast neutrons bombard hydrogen nuclei in the material, releasing characteristic 2.223 MeV gamma rays. The electrical pulse signal refers to the sequence of voltage pulses output by the detector, with the pulse amplitude proportional to the gamma photon energy.

[0112] As an optional implementation, when the neutrons emitted by the neutron generator assembly penetrate the material, the gamma rays captured by the neutron detector assembly within a fixed time window are converted into charge pulses through the crystal photoelectric effect, and the electrical pulse signal is output through a charge-sensitive preamplifier.

[0113] Step C12: Sample the peak voltage of the electrical pulse signal and map the peak voltage to a channel address to generate a mapping relationship between the channel address and the peak voltage.

[0114] In this embodiment, the pulse peak voltage refers to the highest voltage value of a single pulse. The channel address refers to the energy channel number of the multichannel analyzer, used to discretize the energy range. The mapping relationship refers to the correspondence between the pulse peak voltage and the channel address.

[0115] 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 (ADC). The high-speed ADC then quantizes the pulse peak voltage into a digital value within a preset range. A channel address value is generated from the digital value using a linear mapping equation, while simultaneously applying energy calibration parameters to correct nonlinear deviations in real time, outputting the mapping relationship between the channel address and the pulse peak voltage.

[0116] As another optional implementation, the electrical pulse signal output by the gamma detector is transmitted to the analog-to-digital converter module through a high-temperature charge amplifier. The adaptive baseline recovery circuit captures the pulse peak voltage after eliminating temperature drift interference. The analog-to-digital converter 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.

[0117] Step C13: Based on the mapping relationship, accumulate the peak voltage of the pulse within a fixed time window to generate the gamma spectrum.

[0118] 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 multichannel analyzer is accumulated and counted according to channel address to generate a gamma spectrum with a preset number of channels.

[0119] 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 channel addresses of a preset number of channels according to a pre-programmed piecewise linear mapping table, and compressed into a gamma spectrum compressed package.

[0120] Step C14: Based on the channel address corresponding to the water hydrogen characteristic peak, extract the water hydrogen characteristic peak from the gamma spectrum and integrate it to obtain the area of ​​the water hydrogen characteristic peak.

[0121] In this embodiment, the channel address corresponding to the water-hydrogen characteristic peak refers to the center channel address of the 2.223 MeV characteristic peak in the gamma spectrum. Extracting the water-hydrogen characteristic peak means locating the channel address interval in the energy spectrum, identifying and isolating the 2.223 MeV characteristic peak. Integration refers to the summation of the net counts of the channels covered by the characteristic peak.

[0122] As an optional implementation method, based on locating the characteristic peak address of water hydrogen in the gamma spectrum, the peak shape is fitted with a Gaussian function, the peak region is set with a preset number of channels, and the area under the fitted function curve is integrated to generate the characteristic peak area of ​​water hydrogen.

[0123] As another optional implementation method, the characteristic peak address of water and hydrogen is automatically located according to the gamma spectrum. The baseline is generated by linearly interpolating the boundary points with a preset half height and width of the peak center as the boundary. After deducting the background count for each channel, the net count value of the preset interval is integrated to output the area of ​​the characteristic peak of water and hydrogen.

[0124] For example, in the online moisture monitoring system of the coal conveyor belt in a coking plant, a gamma detector monitors in real time the 2.223 MeV gamma-ray output electrical pulse signal generated by the interaction of neutrons with hydrogen nuclei in coal. A high-speed analog-to-digital converter captures the peak voltage of the pulse and converts it to channel address 491 using the linear mapping equation: channel address = voltage × 4096 / 10 V. Within a fixed time window of 5 seconds, all pulse channels generate 1024 gamma energy spectra with an energy range of 0–3 MeV. Based on the pre-calibrated water-hydrogen characteristic peak channels (channel 750), the energy spectrum peak regions (channels 735–765) are automatically located, and the water-hydrogen characteristic peak area is output using the trapezoidal background subtraction method for net counting.

[0125] By accurately mapping gamma ray energy to channel address, the problem of signal attenuation and drift in complex industrial environments is solved, improving the stability of characteristic peak positioning and providing reliable moisture monitoring capabilities for continuous production.

[0126] Based on any of the above embodiments, in Embodiment 5 of this application, step S10 includes steps D11~D12:

[0127] Step D11: Start the neutron generator assembly. When the start-up time of the neutron generator assembly is greater than or equal to the preheating preset time, output a prompt indicating that preheating is complete, so that the neutron generator assembly outputs a stable output of neutrons.

[0128] In this embodiment, the preheating completion notification refers to the notification message issued when the neutron generator component has preheated to a preset duration and outputs a stable yield of neutrons.

[0129] As an optional implementation, a preheating timer is automatically activated after the neutron generator assembly is started. During this period, the high-voltage power supply 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.

[0130] As another optional implementation, after the neutron generator assembly is powered on, a preheating program of a preset time is executed, the ion source radio frequency power is increased to a preset power, the liquid cooling system temperature control synchronous vacuum pump maintains the cavity pressure, and the neutron generator assembly is preheated to a preset time and outputs a stable yield of neutrons.

[0131] Step D12: When the neutron generator assembly has finished preheating, start the neutron detector assembly and the gamma detector, and simultaneously trigger the timestamp calibration of the neutron generator assembly, the neutron detector assembly and the gamma detector.

[0132] In this embodiment, calibrating the timestamp refers to aligning the device clock using the GPS / PTP protocol.

[0133] As an optional implementation, when the neutron yield fluctuation rate of the neutron generator component is less than the preset fluctuation rate, the neutron detector component and the gamma detector are automatically activated, and the timestamps of the three devices are synchronized through the GPS receiver.

[0134] As an alternative implementation, once the output of the neutron generator assembly is stable, the neutron detector assembly and gamma detector are manually started, and the timestamps of the three devices are synchronized through a fiber optic switch using a precision clock protocol.

[0135] For example, in the online moisture monitoring system of the coal conveyor belt in a coking plant, after starting the DD neutron tube of the neutron generator component, a 20-minute preheating program is executed. The high-voltage power supply is stepped up to 100 kV, and cooling water circulation maintains the tube temperature ≤50℃. The neutron output fluctuation rate is monitored in real time until it stabilizes. After the output reaches the target, the He-3 neutron detector component is started simultaneously and the voltage is boosted to 650 V. The CZT gamma detector module is started and a bias voltage of 400 V is applied. The timestamps of the three devices are aligned with the PTP protocol through the GPS receiver. The transmission delay is eliminated by the clock compensation algorithm embedded in the FPGA, generating a unified time-stamped neutron scattering pulse and gamma energy spectrum data stream.

[0136] By using stepped voltage boosting and closed-loop monitoring of output, the measurement distortion problem caused by cold start fluctuations of the neutron source was solved, thus improving the reliability of the data substrate.

[0137] Based on any of the above embodiments, in Embodiment Six of this application, before step S20, the control method of the online material moisture monitoring system further includes steps E11 to E13:

[0138] Step E11: Based on the neutron scattering monitoring results of the neutron detector assembly on each preset material, determine multiple preset neutron shares.

[0139] In this embodiment, the preset material refers to a standard sample prepared according to the target moisture content to ensure uniform moisture distribution and consistent physical properties. The preset neutron share refers to the ratio of the scattered neutron count to the total number of neutrons emitted, reflecting the moderating efficiency of materials with different preset moisture contents.

[0140] As an optional implementation method, take the same batch of materials, divide them into several portions, spray them with deionized water to a preset moisture content, and after balancing in a sealed constant temperature chamber for a preset time, use an infrared moisture meter to check the uniformity. Mark the balanced materials as a preset material group.

[0141] As an optional implementation, the sample is vertically irradiated by a neutron emitter assembly, and scattered neutrons are captured by a neutron detector assembly arranged in a ring. The number of scattered neutrons within a preset time window is accumulated with the total number of neutron emissions recorded by the beam monitor to calculate the preset neutron share.

[0142] As another optional implementation, a preset material is fixed inside the shielded chamber. The neutron generator assembly bombards the preset material fixed inside the shielded chamber in a pulse mode. The neutron detector assembly records the spatial distribution of scattered neutrons, accumulates the data with a preset time to generate a scattering count matrix, and calculates and outputs the preset neutron share by combining the total number of neutrons emitted calibrated by the target current integrator.

[0143] In step E12, the data processing system calculates the hydrogen density of the preset material by substituting the preset moisture content, the bulk density of the preset material, and the hydrogen mass fraction into the hydrogen density formula.

[0144] In this embodiment, the hydrogen mass fraction refers to the proportion of hydrogen atoms per unit mass.

[0145] As an optional implementation, the preset hydrogen density is calculated by inputting the hydrogen density formula based on the preset moisture content, bulk density, and hydrogen mass fraction of the coal material.

[0146] As another optional implementation, the data processing system reads the preset moisture content, the preset bulk density of the material, and the hydrogen mass fraction, and substitutes them into the hydrogen density formula to calculate the hydrogen density of the preset material.

[0147] Step E13: Correlate the preset neutron fraction with the hydrogen density of the corresponding preset material to generate the association library.

[0148] In this embodiment, the interrelationship refers to establishing a mathematical mapping relationship between the neutron fraction and the preset hydrogen density of the material.

[0149] As an optional implementation, the preset neutron fraction and the hydrogen density of the corresponding preset material are imported into the least squares fitting module to generate a linear regression equation, while the original data points are stored in the associated library.

[0150] As another optional implementation, the hydrogen density of the preset material is configured based on the preset neutron fraction and the Karl Fischer method to verify the preset moisture content, and a nonlinear association library is constructed by calling the cubic spline interpolation algorithm.

[0151] As an alternative implementation, a Monte Carlo model is used to simulate the neutron moderation effect of the material, generating a corrected curve for the hydrogen density and neutron fraction of the preset material. The neutron fraction of the preset material is correlated with the Karl Fischer moisture value, and a three-dimensional correlation library is generated by combining it with a porosity correction coefficient.

[0152] For example, in the coal moisture calibration laboratory of a coking plant, preset materials were prepared according to preset moisture contents: 8% / 12% / 16% / 20% / 24%. The same batch of coal powder was spray-humidified and then kept at a constant temperature and sealed for 24 hours. The uniformity deviation was measured by infrared sampling and found to be <±0.3%. Neutron scattering of the preset materials was monitored by a DD neutron tube and a He-3 detector array. The cumulative neutron counts over a 10-second time window were 12450 / 18600 / 24800 / 31000 / 37200 times. The total number of emissions recorded by the beam monitor was 100000 times. The preset neutron fractions were calculated to be 0.1245 / 0.1860 / 0.2480 / 0.3100 / 0.3720. Based on the preset moisture content and measured bulk density (1.2 g / cm³ obtained by vibration compaction) and a hydrogen mass fraction of 0.111, the hydrogen density formula is input: Hydrogen density = Moisture content × Bulk density × 0.111. The preset hydrogen density is calculated to be 0.106 / 0.159 / 0.212 / 0.266 / 0.319 g / cm³. The preset neutron fraction and corresponding hydrogen density are then imported into the least squares fitting module to generate a linear correlation library.

[0153] By solving the problem of large interpolation errors in the traditional table lookup method through mathematical fitting and dynamic database encapsulation, the accuracy of moisture inversion is improved, providing a traceable calibration benchmark for online monitoring systems.

[0154] Based on any of the above embodiments, in Embodiment Seven of this application, referring to Figure 5 , Figure 5 This is a flowchart illustrating the seventh embodiment of the control method for the online material moisture monitoring system of this application. Before step S50, the control method for the online material moisture monitoring system further includes steps F11 to F14:

[0155] Step F11: Based on the gamma ray monitoring results of the gamma detector on each preset material, determine multiple preset gamma energy spectra.

[0156] In this embodiment, the preset gamma spectrum refers to the gamma spectrum corresponding to different preset materials.

[0157] As an optional implementation, a preset material group is vertically irradiated by a neutron generator assembly, and a gamma detector captures hydrogen and gamma rays at a preset tilt angle. The pulse signal is then classified by an analog-to-digital converter to generate a preset gamma spectrum with a preset number of channels.

[0158] Step F12: Based on the preset gamma spectrum, extract the water hydrogen characteristic peak and integrate it to obtain the preset water hydrogen characteristic peak area.

[0159] In this embodiment, the preset water-hydrogen characteristic peak area refers to the net sum of the characteristic peak counts corresponding to different preset materials.

[0160] As an optional implementation, the center address of the water hydrogen characteristic peak in the generated preset gamma spectrum is located by monitoring the gamma detector. The boundary points are connected by linear interpolation with a preset half-width as the boundary to generate the background baseline. The background is subtracted from each channel, and the net count value of the preset number of channels is accumulated. The area of ​​the preset water hydrogen characteristic peak is then integrated and output.

[0161] As another optional implementation, the preset gamma spectrum uses a Gaussian fitting algorithm to accurately locate the water hydrogen characteristic peak, with the preset channel address as the peak region, and the area under the integral fitting function curve as the area of ​​the preset water hydrogen characteristic peak.

[0162] Step F13: Based on the data processing system, substitute the preset hydrogen density, preset moisture content, and preset water-hydrogen characteristic peak area corresponding to the preset material into the fitting function to calculate the material type coefficient.

[0163] 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 of the parameters to be solved. The material type coefficient refers to the parameter in the fitting function that characterizes the material properties.

[0164] As an optional implementation, the preset hydrogen density, preset water-hydrogen characteristic peak area, and preset moisture content of the preset material group are substituted into a multivariate linear fitting function, and the material type coefficient is solved by the least squares method.

[0165] Step F14: Associate and bind the material type coefficient with the preset material to generate a coefficient association library.

[0166] In this embodiment, the coefficient association library refers to a dataset that stores the mapping relationship between material types and material type coefficients.

[0167] As an optional implementation, the solved material type coefficients are associated with preset material types to generate a coefficient association library.

[0168] For example, in a coal moisture calibration laboratory, gamma-ray monitoring was performed on a preset material group with moisture contents of 8% / 12% / 16% / 20% / 24% using a DD neutron tube and a CZT gamma detector. The pulse amplitude was classified and counted by a 14-bit ADC to generate 1024 preset gamma energy spectra. The 2.223 MeV water-hydrogen characteristic peak (channel address 750±3) was automatically located, and the preset water-hydrogen characteristic peak area was obtained by linear background subtraction and integration. The preset hydrogen density (0.054 / 0.082 / 0.110 / 0.138 / 0.166 g / cm³), preset moisture content (8.0% / 12.0% / 16.0% / 20.0% / 24.0%), and peak area were substituted into the multivariate linear fitting function: moisture = α × hydrogen density + β × peak area + γ. The material type coefficients were solved using the least squares method: α = 0.72, β = 0.0003, γ = 0.15. The coefficient is bound to the material properties: volatile matter 28%, ash content 12%, particle size 0.5mm to generate a MySQL coefficient association library.

[0169] By using multi-parameter fitting of the characteristic peak area of ​​the gamma spectrum with hydrogen density, the accuracy degradation problem of the single neutron scattering method due to interference from material composition is solved, thus improving the robustness of moisture inversion for complex materials.

[0170] Based on any of the above embodiments, in Embodiment Eight of this application, referring to Figure 6 , Figure 6 This is a flowchart illustrating the eighth embodiment of the control method for the online material moisture monitoring system of this application. Step S50 includes steps G11 to G13:

[0171] Step G11: The data processing system, based on the material type corresponding to the material, filters out the target material type coefficient corresponding to the material type from the coefficient association library.

[0172] In this embodiment, the target material type coefficient refers to the inversion parameter that matches the current material type.

[0173] As an optional implementation, the material type is obtained by scanning the preset material batch QR code, and the coefficient association library in the cloud database is called to filter the target material type coefficient by the batch number field.

[0174] As another optional implementation, the material type is identified in real time by a near-infrared spectrometer, and the coefficient association library is queried through a communication protocol to filter the target material type coefficients by using the material type as an index.

[0175] Step G12: Replace the original coefficients in the initial formula with the target material type coefficients to generate a modified formula.

[0176] In this embodiment, the initial formula refers to the original mathematical model for water inversion. The original coefficients refer to the default parameters in the initial formula. The correction formula refers to the empirical mathematical relationship used to correct the free water content by incorporating empirical parameters.

[0177] As an optional implementation, a modified formula is generated by replacing the original coefficients in the initial formula with the target material type coefficients based on the data processing system.

[0178] Step G13: Based on the cumulative scattered neutron fraction, the solidified hydrogen ratio, and the correction formula, the free water content of the tested material is generated through calculation using the correction formula.

[0179] As an optional implementation method, the cumulative scattered neutron fraction and the solidified hydrogen fraction obtained by the data processing system are input into a correction formula to calculate the free water content of the material being tested.

[0180] For example, in the online moisture monitoring system for coal conveyor belts in a coking plant, the coal material type is identified as Type_A based on near-infrared spectroscopy. The target material type coefficients are selected from the MySQL coefficient association database as α=0.72, β=0.0003, and γ=0.15. These coefficients replace the original coefficients in the initial formula: Moisture = 0.5 × Hydrogen Density + 0.0005 × Peak Area + 0.2, generating a corrected formula: Moisture = 0.72 × Hydrogen Density + 0.0003 × Peak Area + 0.15. The cumulative scattered neutron fraction of 0.18 calculated in real time and the solidified hydrogen percentage of 25% determined by gamma spectroscopy are input into the corrected formula to calculate the free water content of the measured material.

[0181] Furthermore, refer to Figure 7 , Figure 7This is a schematic diagram of the system structure of this application. The system structure uses an inverted trapezoidal "material to be tested" container as its core. A neutron generator assembly extends vertically upwards through the center of the container's bottom, emitting a stable and controllable fast neutron beam into the material layer. Symmetrically distributed neutron detector assemblies on both sides capture thermal neutrons slowed down by hydrogen atoms scattered by the material. Simultaneously, two gamma detectors at the top, arranged at a 30° angle, monitor the 2.223 MeV characteristic gamma rays released by neutron-hydrogen interactions. The raw pulse signals output by the neutron detectors are transmitted to the "neutron data processing" module (shown in the dashed box on the right), where pulse noise filtering and fixed-time-window counting statistics generate the neutron fraction. The electrical pulse signals collected by the gamma detectors are input to the "gamma spectrum processing system," where pulse amplitude classification generates a gamma spectrum, extracting the characteristic peak area of ​​water hydrogen and calculating the percentage of solidified hydrogen. The dual-channel data is fused and inverted using an algorithm to generate the free water content of the tested material. Logic instructions are transmitted via the yellow feedback line to the "neutron tube control system," which dynamically adjusts the neutron emission intensity and frequency. The overall control system monitors the neutron tube temperature, beam stability, and cooling status in real time. It optimizes the neutron generator operating parameters through control lines to form a closed loop, enabling real-time, adaptive, and high-precision monitoring of material moisture. Under the protection of a time-series coordination mechanism, each subsystem drives industrial-grade moisture closed-loop control.

[0182] The core principle utilizes the large interaction cross-section and strong moderation ability of hydrogen (¹H) in water molecules with high-energy neutrons. High-energy neutrons generated by the DD neutron tube interact with the material, causing elastic collisions between hydrogen in the water and neutrons. This alters the energy and direction of the neutrons, resulting in some neutrons being scattered back by the material. The proportion of scattered neutrons is directly proportional to the hydrogen content in the material. By experimentally measuring or calculating the correlation curve between the water content of mineral components and the proportion of scattered neutrons, the measured neutron scattering proportion can determine the water content in the material. Furthermore, the gamma detector in the activation analysis can measure the characteristic gamma rays generated by the interaction of hydrogen with thermal neutrons. The intensity of the hydrogen characteristic peak in the gamma-ray energy spectrum is related to the hydrogen content in the water and the inherent hydrogen content. During Monte Carlo simulation, the gamma-ray energy spectra generated by different material components during the neutron activation reaction are accurately reconstructed. The hydrogen content in the water is then determined through energy spectrum matching, while simultaneously eliminating interference from non-hydrogen elements and inherent hydrogen elements.

[0183] By using a dual-parameter fusion correction formula that combines neutron fraction and gamma spectrum corresponding to the proportion of solidified hydrogen, the interference of solidified hydrogen on free water inversion is solved, enabling precise stripping of free water and providing reliable moisture content data for high-precision closed-loop moisture monitoring.

[0184] This 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 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 control method of the material moisture online monitoring system in Embodiment 1 above.

[0185] The following is for reference. Figure 8 The diagram illustrates a structural schematic of an online monitoring device suitable for implementing embodiments of this application. The online monitoring device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital monitoring devices, personal digital assistants (PDAs), tablet computers (PADs), portable media players (PMPs), mobile monitoring terminals, etc., as well as fixed terminals such as online monitoring computers, desktop computers, etc. Figure 8 The online monitoring 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.

[0186] like Figure 8As shown, the online monitoring 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 online monitoring device. The processing unit 1001, the ROM 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 the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the online monitoring device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show online monitoring devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0187] 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.

[0188] The online monitoring device provided in this application employs the control method of the material moisture online monitoring system in the above embodiments, which solves the technical problem in related technologies where all hydrogen-containing substances in the material contribute signals, making it impossible to distinguish between solidified water and free water, thus leading to inaccurate moisture monitoring results. Compared with the prior art, the beneficial effects of the online monitoring device provided in this application are the same as those of the control method of the material moisture online monitoring system provided in the above embodiments, and other technical features of this online monitoring device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0189] 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.

[0190] 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.

[0191] 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 execute the control method of the online material moisture monitoring system in the above embodiments.

[0192] 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.

[0193] The aforementioned computer-readable storage medium may be included in the online monitoring device; or it may exist independently and not be assembled into the online monitoring device.

[0194] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the online monitoring device, cause the online monitoring device to: start the neutron generator assembly; when the startup duration of the neutron generator assembly is greater than or equal to a preset preheating duration, turn on the neutron detector assembly and the gamma detector; based on the detection results of the neutron detector assembly, determine the cumulative scattered neutron share of the tested material, and obtain the cumulative scattered neutron share from a preset association library to match the corresponding target hydrogen density; based on the gamma rays monitored by the gamma detector according to pulse amplitude classification and counting, generate a gamma energy spectrum, and extract the water hydrogen characteristic peak area from the gamma energy spectrum; determine 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 determine the free water content of the tested material based on the solidified hydrogen ratio.

[0195] 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).

[0196] 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.

[0197] 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.

[0198] 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 executing the control method of the above-described online material moisture monitoring system. This solves the technical problem in related technologies where all hydrogen-containing substances in the material contribute signals, making it impossible to distinguish between solidified water and free water, thus leading to inaccurate moisture monitoring results. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the control method of the online material moisture monitoring system provided in the above embodiments, and will not be elaborated upon here.

[0199] 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 control method for an online material moisture monitoring system, the online material moisture monitoring system comprising a data processing system, a neutron generator assembly, a neutron detector assembly, and a gamma detector, characterized in that, The control of the online material moisture monitoring system includes: The neutron generator assembly is started, and when the start-up time of the neutron generator assembly is greater than or equal to the preheating preset time, the neutron detector assembly and the gamma detector are turned on. Based on the detection results of the neutron detector assembly, the cumulative scattered neutron share of the tested material is determined, and the cumulative scattered neutron share is matched with the corresponding target hydrogen density in a preset association library. Based on the gamma rays monitored by the gamma detector according to pulse amplitude classification and counting, a gamma energy spectrum is generated, and the characteristic peak area of ​​water hydrogen is extracted from the gamma energy spectrum. The solidified hydrogen ratio is determined based on the target hydrogen density, the area of ​​the water-hydrogen characteristic peak, and the energy spectrum calibration coefficient of the gamma spectrum. The free water content of the tested material is determined based on the percentage of solidified hydrogen.

2. The control method of the online material moisture monitoring system as described in claim 1, characterized in that, The step of determining the cumulative scattered neutron fraction of the tested material based on the detection results of the neutron detector assembly, and obtaining the cumulative scattered neutron fraction from a preset association library to match the corresponding target hydrogen density includes: Extract the pulse features from the detection results to generate the original pulse signal; The noise signal in the original pulse signal is filtered out, and the purified scattered neutron pulse is output. 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 component. Based on the cumulative scattered neutron share, the corresponding hydrogen density is matched in the associated library to determine the target hydrogen density.

3. The control method of the online material moisture monitoring system as described in claim 2, characterized in that, The step of determining the target hydrogen density by matching the corresponding hydrogen density in the association library based on the cumulative scattered neutron fraction includes: If the data processing system fails to match the cumulative scattered neutron share in the association database, it will select the backup neutron share with the smallest difference from the cumulative scattered neutron share from the association database. Determine the spare hydrogen density corresponding to the spare neutron share in the associated library; The spare hydrogen density is used as the target hydrogen density, and a prompt message is output indicating that the hydrogen density corresponding to the neutron fraction does not exist.

4. The control method of the online material moisture monitoring system as described in claim 1, characterized in that, The step of generating a gamma spectrum based on the gamma rays monitored by the gamma detector according to pulse amplitude classification and counting, and extracting the characteristic peak area of ​​water hydrogen from the gamma spectrum includes: Based on the gamma rays monitored by the gamma detector, the electrical pulse signals corresponding to the gamma rays are determined according to the pulse amplitude classification. The peak voltage of the electrical pulse signal is sampled, and the peak voltage is mapped to a channel address to generate a mapping relationship between the channel address and the peak voltage. Based on the mapping relationship, the peak voltage of the pulse within a fixed time window is accumulated to generate the gamma spectrum; Based on the channel address corresponding to the water-hydrogen characteristic peak, the water-hydrogen characteristic peak is extracted from the gamma spectrum and integrated to obtain the area of ​​the water-hydrogen characteristic peak.

5. The control method of the online material moisture monitoring system as described in claim 1, characterized in that, The step of activating the neutron generator assembly, and then activating the neutron detector assembly and the gamma detector when the neutron generator assembly activation time is greater than or equal to the preheating preset time, includes: The neutron generator assembly is started, and when the start-up time of the neutron generator assembly is greater than or equal to the preset preheating time, a preheating completion prompt is output so that the neutron generator assembly outputs a stable yield of neutrons; When the neutron generator assembly has finished preheating, 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 triggered synchronously.

6. The control method of the online material moisture monitoring system as described in claim 1, characterized in that, Before the step of determining the cumulative scattered neutron fraction of the tested material based on the detection results of the neutron detector assembly, and obtaining the cumulative scattered neutron fraction from a preset association library to match the corresponding target hydrogen density, the control method of the online moisture monitoring system for the material further includes: Based on the neutron scattering monitoring results of the neutron detector assembly on each preset material, multiple preset neutron shares are determined; The data processing system calculates the hydrogen density of the preset material by substituting the preset moisture content, the preset bulk density of the material, and the hydrogen mass fraction into the hydrogen density formula. The preset neutron fraction is correlated with the hydrogen density of the corresponding preset material to generate the correlation library.

7. The control method of the online material moisture monitoring system as described in claim 1, characterized in that, Before the step of determining the free water content of the tested material based on the solidified hydrogen ratio, the control method of the online moisture monitoring system for the material further includes: Based on the gamma ray monitoring results of the gamma detector on each preset material, multiple preset gamma energy spectra are determined. Based on the preset gamma spectrum, the water hydrogen characteristic peak is extracted and integrated 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 online material moisture monitoring system as described in claim 1, characterized in that, The step of determining the free water content of the tested material based on the solidified hydrogen ratio includes: The data processing system filters out the target material type coefficient corresponding to the material type from the coefficient association library based on the material type corresponding to the material; A corrected formula is generated by replacing the original coefficients in the initial formula with the target material type coefficients. Based on the cumulative scattered neutron fraction, the solidified hydrogen ratio, and the correction formula, the free water content of the tested material is generated through calculation using 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, the computer program being configured to implement the steps of the control method for the online material moisture monitoring system as described in 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, it implements the steps of the control method of the online material moisture monitoring system as described in any one of claims 1 to 8.

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