A method and system for controlling the processing of a building gypsum board

Abstract

The application discloses a building gypsum board processing process control method and system, and relates to the technical field of building material processing control. The method comprises the following steps: acquiring internal structure state of finished gypsum board, material proportioning stage data, slurry preparation stage data, forming and pressing stage data and drying stage data; analyzing the internal structure state of the finished gypsum board, the material proportioning stage data, the slurry preparation stage data, the forming and pressing stage data and the drying stage data, and identifying a process state deviation set; according to the process state deviation set and a risk association rule set, evaluating potential influence on product quality; and according to the potential influence on product quality, adjusting processing process parameters. The application can combine the process state deviation set and the potential influence on product quality to adjust the processing process parameters, so as to realize processing process control, and improve control accuracy and product quality.

Description

Technical Field

[0001] This invention relates to the field of building material processing control technology, and in particular to a method and system for controlling the processing of building gypsum board. Background Technology

[0002] In the production of gypsum board, especially when using recycled construction waste as the main raw material, the complexity and volatility of the raw material composition pose a severe challenge to product quality control. Existing production lines typically use preset, fixed process parameters for control, and their control logic is based on the assumption that the raw material properties are stable and predictable. However, recycled waste is diverse, and different batches may come from buildings of different eras, containing impurities that are difficult to completely remove, such as wallpaper fibers, paint coating particles, or trace amounts of cement powder. These compositional differences mean that even if the material proportioning module ensures a completely consistent macroscopic weight ratio, the microscopic coagulation characteristics and dehydration behavior of the slurry may vary significantly. Existing control systems can only sense external conditions such as material weight, external pressure, and ambient temperature. This disconnect between the control logic and the actual state of the material makes it easy for micro-cracks to form inside the core of the board during the rolling process when the slurry coagulates too quickly; when coagulation is too slow, the fixed drying curve will cause the surface of the board to harden prematurely, forming a dense shell that traps internal moisture and causes delamination and blistering. The existing control system lacks the ability to dynamically adjust molding pressure and drying temperature based on the real-time characteristics of the raw materials, resulting in a high scrap rate, low product quality, severely eroding the cost advantage brought by using waste materials, and low control accuracy.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a method and system for controlling the processing of gypsum board, which can adjust the processing parameters by combining the set of process state deviations and the potential impact on product quality, so as to achieve process control and improve control accuracy and product quality.

[0005] On one hand, embodiments of the present invention provide a method for controlling the processing of building gypsum board, including the following steps: The system acquires data on the internal structure of the finished gypsum board, material proportioning stage data, slurry preparation stage data, molding and pressing stage data, and drying stage data. The material proportioning stage data includes material weight data, the slurry preparation stage data includes slurry viscosity data and slurry conductivity data, the molding and pressing stage data includes slab structural density data and molding and pressing pressure data, and the drying stage data includes steam pressure data. The internal structural state of the finished gypsum board, the material proportioning stage data, the slurry preparation stage data, the molding and pressing stage data, and the drying stage data are analyzed to identify the process state deviation set. The process state deviation set is used to reflect the deviation between the sensor data of different production stages and the corresponding benchmark parameter values. The production stages include the material proportioning stage, the slurry preparation stage, the molding and pressing stage, the drying stage, and the finished product inspection stage. Based on the process state deviation set and the risk association rule set, the potential impact on product quality is assessed. The risk association rule set is used to record the impact of different production stages on subsequent stages. Based on the potential impact on product quality, the processing parameters are adjusted, including the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage.

[0006] In some embodiments, obtaining the internal structural state of the finished gypsum board includes: During the finished product inspection stage, vibration excitation is applied to the finished gypsum board using a micro-vibration excitation device. After applying vibration excitation, the acoustic response signal of the finished gypsum board is collected; The acoustic response signal is subjected to spectral analysis to extract the characteristic parameters of the plate material, including the resonant frequency and damping ratio. Based on the characteristic parameters of the board material, the structural state of the finished gypsum board is evaluated to obtain the internal structural state of the finished gypsum board.

[0007] In some embodiments, adjusting the processing parameters based on the potential impact on product quality includes: Identify sources of abnormalities in the processing procedure based on the potential impact on product quality. If the abnormality in the processing is caused by the slurry coagulation rate exceeding the upper limit, then the current slurry pumping rate in the slurry preparation stage is adjusted, and the current pressing pressure in the molding and pressing stage is adjusted. If the abnormality in the processing is caused by the slurry coagulation rate being lower than the lower limit, then the current heating power in the drying stage is adjusted.

[0008] In some embodiments, adjusting the current slurry pumping rate during the slurry preparation stage includes: Obtain the material filling degree at the feed inlet of the molding machine; Adjust the drive frequency of the feeding device of the molding machine according to the material filling degree at the feed inlet of the molding machine, so as to regulate the feeding rate of the molding machine; The target slurry pumping rate is calculated based on the feeding rate of the molding machine and the preset matching relationship, wherein the preset matching relationship is used to represent the linear relationship between the feeding rate of the molding machine and the slurry pumping rate. The output power of the slurry pumping equipment is adjusted according to the target slurry pumping rate so that the current slurry pumping rate reaches the target slurry pumping rate.

[0009] In some embodiments, adjusting the current pressing pressure during the molding pressing stage includes: Obtain slab hardness distribution information; Vibration signals generated during the pressing process of the slab are collected by a high-frequency vibration sensor. These vibration signals are used to reflect stress changes and potential microcrack formation inside the slab. Based on the slab hardness distribution information, a pressing pressure variation curve is generated; The target pressing pressure is determined based on the pressing pressure change curve and the vibration signal; Adjust the roller spacing according to the target pressing pressure so that the current pressing pressure reaches the target pressing pressure.

[0010] In some embodiments, adjusting the current heating power during the drying stage includes: Obtain information on the moisture distribution and temperature distribution of the slab. The drying area is divided into multiple drying sub-areas; Based on the slab moisture distribution information and the slab temperature distribution information, predict the moisture evaporation rate corresponding to each drying sub-region; Based on the moisture evaporation rate corresponding to each drying sub-region, an area to be adjusted is identified, and the area to be adjusted is used to represent the area where the moisture evaporation rate exceeds the preset evaporation rate range. The drying rate of the area to be adjusted is evaluated based on the moisture evaporation rate of the area to be adjusted. Based on the drying rate, the current heating power of the area to be adjusted is adjusted so that the moisture evaporation rate is restored to the preset evaporation rate range.

[0011] In some embodiments, adjusting the current heating power of the area to be adjusted according to the drying rate includes: Acquire images of the slab surface and slab surface humidity information; The surface image of the slab is analyzed to determine the degree of crusting on the slab surface. The current heating power of the area to be adjusted is determined based on the degree of skin formation on the slab surface, the slab surface humidity information, and the drying rate.

[0012] In some embodiments, the step of analyzing the degree of crusting on the slab surface image to obtain the degree of crusting on the slab surface includes: Texture feature analysis is performed on the surface image of the slab to obtain the texture feature analysis results; Gloss analysis was performed on the surface image of the slab to obtain the gloss analysis results; Based on the texture feature analysis results and the gloss analysis results, the degree of skin formation on the slab surface is identified.

[0013] In some embodiments, evaluating the drying rate of the area to be adjusted based on the moisture evaporation rate of the area to be adjusted includes: Obtain air humidity; Based on the air humidity, the moisture evaporation attenuation coefficient is determined by consulting the mapping table between air humidity and moisture evaporation attenuation coefficient. The water evaporation rate of the area to be adjusted is corrected based on the water evaporation attenuation coefficient. The drying rate of the area to be adjusted is evaluated based on the corrected moisture evaporation rate.

[0014] On the other hand, embodiments of the present invention provide a control system for the processing of building gypsum board, comprising: The data acquisition module is used to acquire data on the internal structure of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage. The material proportioning stage data includes material weight data, the slurry preparation stage data includes slurry viscosity data and slurry conductivity data, the molding and pressing stage data includes slab structural density data and molding and pressing pressure data, and the drying stage data includes steam pressure data. The process state deviation identification module is used to analyze the internal structural state of the finished gypsum board, the material proportioning stage data, the slurry preparation stage data, the molding and pressing stage data, and the drying stage data to identify the process state deviation set. The process state deviation set is used to reflect the deviation between the sensor data of different production stages and the corresponding benchmark parameter values. The production stages include the material proportioning stage, the slurry preparation stage, the molding and pressing stage, the drying stage, and the finished product inspection stage. The product quality potential impact assessment module is used to assess the potential impact on product quality based on the process state deviation set and the risk association rule set, wherein the risk association rule set is used to record the impact of different production stages on subsequent stages. The processing parameter adjustment module is used to adjust the processing parameters according to the potential impact on product quality. The processing parameters include the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage.

[0015] The embodiments of this application include at least the following beneficial effects: First, the embodiments of this application obtain data on the internal structural state of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage. Then, they analyze the data on the internal structural state of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage to identify the process state deviation set. Then, based on the process state deviation set and the risk association rule set, they assess the potential impact on product quality. Finally, based on the potential impact on product quality, they adjust the processing parameters. Thus, they can combine the process state deviation set and the potential impact on product quality to adjust the processing parameters, thereby achieving processing control and improving control accuracy and product quality.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0018] Figure 1 This is a flowchart of a method for controlling the processing of building gypsum board according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps of adjusting processing parameters based on potential impacts on product quality according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the steps of adjusting the current heating power during the drying stage in an embodiment of the present invention. Figure 4 This is a schematic diagram of a building gypsum board processing control system according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0020] In related technologies, the production of gypsum board, especially when using recycled construction waste as the main raw material, faces the significant challenge of ensuring product quality stability. Traditional production control systems often rely on preset process parameters, assuming that the properties of raw materials are stable and predictable. However, when the source of raw materials is complex and highly volatile, this assumption no longer holds, leading to a series of uncontrollable problems in the production process, ultimately affecting product performance and causing resource waste. Therefore, designing a production method and system that can adapt to raw material fluctuations and achieve precise control throughout the entire process is crucial for improving the efficiency of construction solid waste recycling and increasing product added value.

[0021] For example, in a factory dedicated to the recycling of construction waste, a newly built gypsum board production line is in operation. Its core technology utilizes recycled gypsum board from demolition sites as the main raw material. The production line's automated control system is crucial, integrating control points such as material proportioning, slurry preparation, molding and pressing, drying, and finished product inspection. After the process starts, the recycled gypsum powder, after preliminary crushing and screening, along with a measured amount of additives and water, is fed into a large mixer. The material proportioning module, through precise weighing sensors, strictly controls the input of each component according to the preset formula to ensure the standardization of the mixed slurry's weight composition. The uniformly mixed slurry is then continuously pumped to the molding station, evenly spread between two layers of facing paper to form a slab. Driven by a conveyor belt, the slab enters the roll forming zone, where a pressure feedback device monitors and adjusts the pressure applied by the rollers in real time to ensure the slab's thickness and density meet design requirements. The formed wet board is cut into standard sizes and then sent to a drying kiln tens of meters long. Multiple temperature control actuators are distributed within the kiln, controlling the kiln temperature in stages according to a pre-programmed sequence. Through a precise heating and cooling process, excess moisture in the core material is evaporated, while simultaneously promoting the crystallization transformation of dihydrate gypsum, ultimately forming a finished gypsum board with sufficient strength. The goal of the entire process design is to achieve precise control over the entire production process, thereby transforming construction waste of varying compositions into building materials of stable quality.

[0022] However, in actual production, this control system encountered unexpected challenges, the root of which lay precisely in the complexity of the raw materials. Unlike pure and stable natural gypsum or desulfurized gypsum, gypsum powder recovered from construction waste comes from a wide variety of sources and exhibits highly variable properties. Different batches of recycled material may originate from buildings of different ages and brands, with variations in their original formulas and types of additives. Even more challenging is the fact that the recycled material often contains various impurities that are difficult to completely remove, such as wallpaper fibers that have not been fully separated, paint coating particles, and even trace amounts of cement powder.

[0023] These subtle differences in composition mean that even if the material proportioning module ensures a completely consistent macroscopic weight ratio for each batch of slurry, its microscopic physicochemical properties can vary significantly. For example, a batch of recycled material mixed with a small amount of cement powder will set much faster because the cement particles provide additional nuclei for gypsum crystallization. Conversely, another batch containing organic residues from old paint may inhibit the normal hydration reaction of gypsum, resulting in a much longer setting time. These inherent variations in the slurry's setting properties are imperceptible to existing proportioning systems that rely solely on weight control.

[0024] This potential difference in properties has a chain reaction of negative impacts on subsequent molding and drying processes. When a sheet of slurry that sets too quickly enters the rolling zone, it may begin to lose its fluidity and harden before being fully compacted to the standard thickness. At this point, although the pressure feedback device shows that the applied pressure value is normal, in reality, this pressure is acting on a hardening material, which can easily cause microscopic cracks or stress concentrations that are difficult to detect with the naked eye inside the core. Such a sheet may have a satisfactory appearance, but its internal structure is defective, its mechanical strength will be greatly reduced, and it will be prone to breakage during handling or installation.

[0025] Conversely, if the material entering the rolling zone is a slurry that sets too slowly, its soft internal structure will exacerbate the problems after entering the drying kiln. The temperature control actuator still executes the heating according to the standard procedure, but this drying curve, designed for "normal" boards, is too harsh for such wet boards with high moisture content and unstable structure. The high temperature inside the kiln causes the surface moisture of the board to evaporate and harden rapidly, forming a dense "shell" that traps a large amount of internal moisture in the core. This trapped moisture vaporizes under continuous heating, generating enormous vapor pressure, which may cause delamination and blistering between the facing paper and the plaster core, and in severe cases, even deformation of the board inside the kiln. Ultimately, the finished boards emerging from the kiln are either substandard products that are still damp inside and lack strength, or defective products with surface flaws.

[0026] The embodiments of this application will be explained in detail below with reference to the accompanying drawings: Figure 1 This is an optional flowchart of a method for controlling the processing of building gypsum board provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0027] Step S101: Obtain the internal structural state of the finished gypsum board, material proportioning stage data, slurry preparation stage data, molding and pressing stage data, and drying stage data. The material proportioning stage data includes material weight data, the slurry preparation stage data includes slurry viscosity data and slurry conductivity data, the molding and pressing stage data includes board structural density data and molding and pressing pressure data, and the drying stage data includes steam pressure data. Step S102: Analyze the internal structural state of the finished gypsum board, the data of the material proportioning stage, the data of the slurry preparation stage, the data of the molding and pressing stage, and the data of the drying stage to identify the process state deviation set. The process state deviation set is used to reflect the deviation between the sensor data of different production stages and the corresponding benchmark parameter values. The production stages include the material proportioning stage, the slurry preparation stage, the molding and pressing stage, the drying stage, and the finished product inspection stage. Step S103: Based on the process state deviation set and the risk association rule set, assess the potential impact on product quality. The risk association rule set is used to record the impact of different production stages on subsequent stages. Step S104: Adjust the processing parameters according to the potential impact on product quality. The processing parameters include the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage.

[0028] Steps S101 to S104 as shown in the embodiments of this application can adjust the processing parameters by combining the process state deviation set and the potential impact on product quality, so as to achieve processing control and improve control accuracy and product quality.

[0029] In some embodiments, steps S101-S104 may first acquire data on the internal structural state of the finished gypsum board, material proportioning stage data, slurry preparation stage data, molding and pressing stage data, and drying stage data. The material proportioning stage data includes material weight data; the slurry preparation stage data includes slurry viscosity and conductivity data; the molding and pressing stage data includes slab structural density and molding and pressing pressure data; and the drying stage data includes steam pressure data. Specifically, the internal structural state of the finished gypsum board reflects the density, crack distribution, and delamination of the board. When collecting material proportioning stage data, the weight values ​​of recycled gypsum powder, additives, and water are read in real time by a weighing sensor installed below the batching silo. The weighing sensor is a strain gauge type sensor with a range of 0-500 kg, installed on the bottom support structure of the batching silo, and communicates with the control system via an RS485 interface. The sampling frequency is set to 10 times per second. During the slurry preparation stage, slurry viscosity and conductivity data were collected using an online viscometer and a conductivity meter, respectively. The online viscometer was a rotary viscometer, installed in the bypass of the mixing tank outlet pipe, with a measurement range of 100-1000 mPa·s and an output of 4-20 mA analog signal. The conductivity meter used a contact electrode, installed in the same bypass pipe, with a measurement range of 0-5000 μS / cm and a temperature compensation range of 0-80℃. During the molding and pressing stage, slab structural density data were obtained using a density scanning device, and molding and pressing pressure data were obtained using a pressure sensor. The density scanning device used a nuclear density meter or microwave density meter, installed at the molding machine outlet. The pressure sensor was a piezoresistive sensor, installed in the hydraulic cylinder circuit of the pressure roller, with a range of 0-10 MPa. When collecting data during the drying stage, pressure transmitters are used to collect steam pressure data in various areas of the drying kiln. The pressure transmitters are diffused silicon sensors, distributed in the preheating section, main drying section and cooling section of the drying kiln. At least two measuring points are installed in each section, with a range of 0-1MPa and an output signal of 4-20mA.

[0030] Then, the internal structural state of the finished gypsum board, data from the material proportioning stage, slurry preparation stage, molding and pressing stage, and drying stage are analyzed to identify the process state deviation set. This set reflects the deviation between the sensor data at different production stages and the corresponding benchmark parameter values. The production stages include material proportioning, slurry preparation, molding and pressing, drying, and finished product inspection. Based on the acquired data, a comprehensive analysis of the aforementioned five types of data is performed to identify the process state deviation set. The analysis process includes comparing the real-time acquired sensor data with the benchmark parameter values ​​for the corresponding stages to calculate the deviation value. For example, comparing the real-time slurry viscosity value with the median of the standard viscosity range yields the viscosity deviation; comparing the molding and pressing pressure with the target pressure value yields the pressure deviation. The deviation is calculated using the formula: Deviation = (Real-time value - Benchmark value) / Benchmark value × 100%. This process state deviation set not only records the deviation of a single parameter but also records the combination of deviations between the sensor data at different production stages and the corresponding benchmark parameter values, forming a deviation set reflecting the overall deviation of the production system from the standard state. The production process includes material proportioning, slurry preparation, molding and pressing, drying, and finished product testing. Each stage has corresponding baseline parameter values, which are determined based on the characteristics of standard raw materials and can be dynamically adjusted according to raw material fluctuations. The process state deviation set refers to the set of deviations between the real-time sensor data collected at each production stage and the preset baseline parameter values ​​for that stage throughout the entire production process. This set includes not only the absolute deviation of a single parameter but also the relative deviation pattern after multiple parameters are correlated. For example, in the slurry preparation stage, when the real-time slurry viscosity is 450 mPa·s and the baseline value is 400 mPa·s, the recorded viscosity deviation is +50 mPa·s; when the real-time molding and pressing pressure is 2.5 MPa and the baseline value is 2.8 MPa, the recorded pressure deviation is -0.3 MPa. These individual deviations are further combined to form a deviation vector. For example, correlating the slurry conductivity deviation with the viscosity deviation forms a composite deviation index reflecting the slurry's coagulation characteristics.

[0031] Then, based on the process state deviation set and the risk association rule set, the potential impact on product quality is assessed. The risk association rule set records the impact of different manufacturing stages on subsequent stages. For example, the rule set might include rules such as: when the conductivity deviation in the slurry preparation stage exceeds a threshold and is a positive deviation, it indicates that the slurry setting speed will accelerate, which will increase the risk of internal microcracks in the molding and pressing stage, with a risk level of high. The assessment process queries and matches the condition entries in the current deviation set with those in the rule set, quantifies the potential impact of each deviation on the final product quality, and generates a risk report containing the potential impact on product quality. It can be understood that the risk association rule set is a knowledge base that records the influence relationships between different manufacturing stages. It records the potential impact path and degree of specific state deviations in preceding stages on the product quality of subsequent stages, such as the negative impact weight of high viscosity deviation in the slurry preparation stage on density uniformity in the molding and pressing stage. The construction of this rule set is based on the mining and analysis of historical production data: at least 500 batches of production data from the past 12 months are collected, recording the sensor data and final product quality inspection results for each batch at each stage of material proportioning, slurry preparation, molding and pressing, and drying; association rule mining algorithms (such as the Apriori algorithm or FP-Growth algorithm) are used, with a minimum support of 0.15 and a minimum confidence of 0.7, to mine frequent itemsets; and the association rules that meet the conditions are organized into rule entries and stored in a relational database.

[0032] Finally, based on the potential impact on product quality, the processing parameters are adjusted. These parameters include the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage. When a risk report indicating a high risk related to potential product quality impacts is displayed, the system calculates the required parameter adjustments. For example, if the risk association rule set indicates that excessively rapid slurry setting will lead to molding defects, the slurry pumping rate is reduced to extend the slurry's residence time in the pipeline, while the pressing pressure in the molding and pressing stage is reduced to avoid over-compacting the hardening slurry. If the drying stage indicates a risk of localized overheating, the heating power in the corresponding area is reduced. This adjustment is not an isolated single-parameter adjustment but a multi-parameter coordinated adjustment guided by the risk association rule set, ensuring that parameter corrections in preceding stages effectively compensate for quality risks in subsequent stages.

[0033] Through the above technical solution, this embodiment realizes closed-loop control of the entire process from raw material input to finished product output, breaking through the limitations of traditional control that only focuses on a single link or external process parameters, and can actively adapt to the inherent fluctuations of recycled raw materials to ensure product quality uniformity.

[0034] In some embodiments, obtaining the internal structural state of the finished gypsum board in step S101 may include, but is not limited to, the following steps: During the finished product inspection stage, vibration excitation is applied to the finished gypsum board using a micro-vibration excitation device. After applying vibration excitation, the acoustic response signal of the finished gypsum board was collected; Spectral analysis of the acoustic response signal is performed to extract the characteristic parameters of the board material, including the resonant frequency and damping ratio. Based on the characteristic parameters of the board material, the structural state of the finished gypsum board is evaluated to obtain the internal structural state of the finished gypsum board.

[0035] In some embodiments, vibration excitation can be applied to the finished gypsum board during the finished product inspection stage using a micro-vibration excitation device. This device can be a piezoelectric ceramic vibrator, which is in close contact with the board surface and applies a sweeping vibration signal with a frequency range between 20Hz and 2000Hz; or it can be an electromagnetic vibrator, which drives a mass block to generate mechanical vibration through an alternating magnetic field. The amplitude of the excitation force is controlled between 0.5N and 5N to ensure sufficient vibration response is generated without damaging the board. The vibration excitation point is typically chosen at the geometric center of the board, but multi-point excitation can also be used to obtain more comprehensive structural information.

[0036] After applying vibration excitation, the acoustic response signal of the finished gypsum board is acquired. This can be achieved by accelerometers arranged on the surface of the board, with at least three sensors located at the center and two side edges of the board. Piezoelectric accelerometers with a sensitivity of 100 mV / g and a frequency response range covering 0.5 Hz to 10 kHz can be used as the sensor type. The acquired signal is a time-domain vibration signal, recording the displacement, velocity, or acceleration response of the board under excitation.

[0037] Next, spectral analysis is performed on the acoustic response signal to extract the material's characteristic parameters, including resonant frequency and damping ratio. Spectral analysis can employ a Fast Fourier Transform (FFT) algorithm to convert the time-domain signal into a frequency-domain signal, identifying the material's resonant frequency and damping ratio. The specific processing flow is as follows: denoising the acquired time-domain signal by using a bandpass filter to retain the 20Hz-5000Hz frequency band; applying a windowing function (Hanning window) to reduce spectral leakage; performing an FFT to obtain the spectrum; identifying the peak frequency in the spectrum as the resonant frequency; and calculating the damping ratio using the half-power bandwidth method. The resonant frequency reflects the stiffness characteristics of the material; materials with internal cracks or delamination will have significantly lower resonant frequencies than dense, intact materials. The damping ratio reflects the dissipation characteristics of vibrational energy; internal defects cause energy scattering and dissipation at the crack interface, resulting in an increased damping ratio. In addition to these two core parameters, higher-order modal frequencies and mode shape information can also be extracted from the spectrum to construct a more refined structural state fingerprint.

[0038] Finally, based on the characteristic parameters of the board material, the structural state of the finished gypsum board is evaluated to obtain the internal structural state of the finished gypsum board. The evaluation process is based on a pre-established parameter-defect mapping model, which is trained with a large number of samples. The input is characteristic parameters such as resonant frequency and damping ratio, and the output is the type of internal defect (such as microcracks, delamination, and pores) and the severity level. The model training process is as follows: At least 300 sample plates with known internal quality states are collected (verified via CT scans or sectioning), and the resonant frequency (in Hz), damping ratio (dimensionless), and number of mode shapes are recorded for each plate. The samples are divided into a training set (80%) and a validation set (20%). Support Vector Machine (SVM) or Random Forest algorithms are used, with resonant frequency and damping ratio as input features, and defect type (coded as 0 = no defects, 1 = microcracks, 2 = delamination, 3 = holes) and severity level (1-5) as output labels. Hyperparameters (such as the C and gamma values ​​of SVM) are optimized using a grid search method. Training is stopped and model parameters are saved when the validation set accuracy exceeds 85%. The trained model is deployed to the industrial control computer at the finished product inspection station, receiving feature parameters extracted from acoustic response signals in real time and outputting the internal structural state assessment results. For example, when the fundamental resonant frequency is detected to be 15% lower than the standard value and the damping ratio is higher than the standard value by 30%, it is determined that severe internal delamination exists. The results of this internal structure status assessment are not only used for the quality grading of the current batch of products, but also serve as feedback data to participate in the adjustment and optimization of preceding process parameters, forming a closed loop for quality traceability.

[0039] Through the above technical solution, this embodiment achieves accurate and non-destructive testing of internal structural defects in finished products by introducing micro-vibration excitation and acoustic response analysis. This provides a reliable feedback basis for the fine adjustment of preceding process parameters and effectively breaks through the technical bottleneck that traditional testing methods cannot identify internal hidden dangers.

[0040] In some embodiments, such as Figure 2 As shown, in step S104, adjusting the processing parameters based on the potential impact on product quality may include, but is not limited to, the following steps: Step S201: Identify the sources of abnormalities in the processing based on the potential impact on product quality; Step S202: If the abnormality in the processing process is caused by the slurry coagulation rate exceeding the upper limit, then adjust the current slurry pumping rate in the slurry preparation stage and adjust the current pressing pressure in the molding and pressing stage. Step S203: If the abnormality in the processing is caused by the slurry coagulation rate being lower than the lower limit, then adjust the current heating power in the drying stage.

[0041] In some embodiments, the sources of anomalies in the processing can be identified first based on their potential impact on product quality. Specifically, the identification of anomaly sources is based on a combination of key indicative parameters from the process state deviation set. For example, when the slurry conductivity increases significantly and the slurry viscosity increases rapidly, it is identified as the slurry setting rate exceeding the upper limit; when the slurry conductivity is low and the viscosity remains below the standard value, it is identified as the slurry setting rate falling below the lower limit. The identification logic can be implemented using a decision tree, mapping multi-dimensional deviation data to specific anomaly types. The decision tree model is built through training on historical data, using conductivity deviation, viscosity deviation, and temperature deviation as input nodes, and anomaly type (too fast setting, too slow setting, normal) as leaf nodes. It is generated using the CART algorithm, and the model is solidified for real-time identification when the classification accuracy reaches over 90%.

[0042] If the abnormality in the processing is due to the slurry setting rate exceeding the upper limit, then adjust the current slurry pumping rate in the slurry preparation stage and the current pressing pressure in the molding and pressing stage. If the abnormality is identified as the slurry setting rate exceeding the upper limit, it indicates that the slurry is rapidly losing fluidity and hardening prematurely. In this case, it is necessary to adjust the current slurry pumping rate in the slurry preparation stage and the current pressing pressure in the molding and pressing stage. The adjustment logic is as follows: increase the slurry pumping rate to reduce the residence time of the slurry in the pipeline and molding machine, avoiding excessive hardening of the slurry before reaching the molding zone; at the same time, reduce the pressing pressure in the molding and pressing stage, because hardened slurry is prone to internal cracks under high pressure, and reducing the pressure can reduce stress concentration. The specific adjustment range can be determined by looking up a table; for example, for every 10% increase in setting rate beyond the upper limit, increase the pumping rate by 15% and decrease the pressing pressure by 8%.

[0043] If the abnormality in the processing is due to the slurry setting rate being lower than the lower limit, the current heating power in the drying stage should be adjusted. If the abnormality is identified as the slurry setting rate being lower than the lower limit, it indicates slow slurry hydration, a soft structure, and high moisture content. In this case, the current heating power in the drying stage needs to be adjusted. The adjustment logic is as follows: increase the heating power to accelerate moisture evaporation and compensate for insufficient structural strength caused by slow setting; or adopt a segmented heating strategy, first preheating with lower power to allow the slurry to initially solidify, and then increasing the power for main drying. The power adjustment is calculated based on the difference between the slab moisture content and the target drying time. For example, for every hour of delayed setting, the initial drying power is increased by 20% to avoid subsequent deformation and delamination due to a soft structure.

[0044] Through the above technical solution, this embodiment, through this classification and handling mechanism based on anomaly source identification, can adopt precise control strategies for different condensation characteristics, avoiding molding defects caused by excessively rapid condensation and drying defects caused by excessively slow condensation, and significantly improving process adaptability and product qualification rate.

[0045] In some embodiments, adjusting the current slurry pumping rate in the slurry preparation stage in step S202 may include, but is not limited to, the following steps: Obtain the material filling degree at the feed inlet of the molding machine; Adjust the drive frequency of the feeding equipment of the molding machine according to the material filling degree at the inlet of the molding machine, so as to regulate the feeding rate of the molding machine; The target slurry pumping rate is calculated based on the feeding rate of the molding machine and the preset matching relationship. The preset matching relationship is used to represent the linear relationship between the feeding rate of the molding machine and the slurry pumping rate. Adjust the output power of the slurry pumping equipment according to the target slurry pumping rate so that the current slurry pumping rate reaches the target slurry pumping rate.

[0046] In some embodiments, the material filling degree at the feed inlet of the molding machine can be obtained first. Specifically, the material filling degree can be detected in various ways. A laser rangefinder sensor can be installed above the feed inlet to measure the distance from the slurry surface to the sensor in real time, and the filling height can be calculated. Alternatively, an industrial camera can be used to capture images of the feed inlet, and image processing algorithms can be used to identify the slurry's filling state. A capacitive level sensor can also be installed on the side wall of the feed inlet to detect the material level height using the difference in dielectric constant between the slurry and air. The filling degree is usually expressed as a percentage, with 0% indicating an empty chamber and 100% indicating full overflow.

[0047] Then, based on the material filling level at the molding machine's inlet, the drive frequency of the feeding device is adjusted to regulate the molding machine's feeding rate. The feeding device typically consists of a screw conveyor or belt conveyor driven by a variable frequency motor. When the filling level is below 30%, the drive frequency is reduced to slow down the feeding and prevent cavitation; when the filling level is above 80%, the drive frequency is increased to accelerate the feeding and prevent overflow. The frequency adjustment range is typically between 20Hz and 50Hz, corresponding to different conveying speeds.

[0048] Then, based on the feeding rate of the molding machine and the preset matching relationship, the target slurry pumping rate is calculated. The preset matching relationship represents the linear relationship between the feeding rate of the molding machine and the slurry pumping rate. This linear relationship is established based on the principle of material balance, ensuring that the volume of slurry pumped per unit time is equal to the volume of slurry consumed by the molding machine. The process of establishing the preset matching relationship is as follows: During the production line debugging phase, multiple sets of data are collected through calibration experiments, recording different feeding rates. (Unit: L / min) Corresponding stable operating pumping rate (Unit: L / min); Linear fitting was performed using the least squares method to obtain the relationship. ,in This is the matching coefficient (usually 1.1-1.3). A compensation constant (typically 5-10 L / min) is used; the fitted coefficients are stored in the control system database to form a preset matching relationship table. For example, if the molding machine feed rate is 100 liters per minute and the matching coefficient is 1.2, then the target slurry pumping rate is 120 liters per minute, with a 20% safety margin to compensate for pipeline resistance losses. The matching relationship can be dynamically corrected according to the slurry viscosity; the higher the viscosity, the larger the safety margin coefficient.

[0049] Finally, based on the target slurry pumping rate, the output power of the slurry pumping equipment is adjusted to ensure that the current slurry pumping rate reaches the target rate. The slurry pumping equipment is typically a variable frequency drive (VFD) screw pump or centrifugal pump. The adjustment process is achieved through a PID control algorithm, which compares the actual pumping rate (measured by a flow meter) with the target value in real time, dynamically adjusting the VFD's output frequency to change the motor speed and pump displacement. For example, when the actual rate is lower than the target value, the VFD frequency is gradually increased from 40Hz to 45Hz until the flow rate meets the target.

[0050] Through the above technical solution, this embodiment establishes a cascaded adjustment mechanism of feed filling degree, feed rate, and pumping rate, which realizes dynamic matching between slurry supply and molding requirements, effectively avoids the accumulation and blockage of slurry that coagulates too quickly at the inlet of the molding machine, and ensures the continuous and stable molding process.

[0051] In some embodiments, adjusting the current pressing pressure in the molding and pressing stage in step S202 may include, but is not limited to, the following steps: Obtain slab hardness distribution information; Vibration signals generated during the pressing process of the slab are collected by a high-frequency vibration sensor. The vibration signals are used to reflect the stress changes and potential microcrack formation inside the slab. Based on the slab hardness distribution information, a pressing pressure variation curve is generated; The target pressing pressure is determined based on the pressing pressure change curve and vibration signal; Adjust the roller spacing according to the target pressing pressure so that the current pressing pressure reaches the target pressing pressure.

[0052] In some embodiments, the hardness distribution information of the slab can be obtained first. Specifically, the hardness distribution can be obtained in various ways. For example, a multi-point hardness gauge array can be arranged before the molding machine inlet, with 5 to 7 indentation-type hardness testing heads evenly distributed along the width of the slab to measure the slurry hardness at different locations in real time. Alternatively, ultrasonic hardness testing technology can be used to calculate the hardness value by measuring the propagation speed of ultrasonic waves in the slurry, achieving non-contact measurement. Furthermore, the current hardness distribution can be predicted based on historical data of slurry temperature and setting time using a setting kinetics model. The slab hardness distribution information is represented in a two-dimensional matrix, reflecting the differences in hardening degree at different transverse positions of the slab.

[0053] Then, a high-frequency vibration sensor is used to collect the vibration signals generated by the slab during the pressing process. These vibration signals reflect stress changes and potential microcrack formation within the slab. The high-frequency vibration sensor can be placed on the roller bearing housing or the frame, using a piezoelectric accelerometer with a sampling frequency set between 10kHz and 20kHz to capture high-frequency acoustic emission signals generated by stress release and microcrack initiation within the slab. After amplification and filtering, characteristic parameters such as root mean square (RMS), peak factor, and spectral energy distribution are extracted from the vibration signal. When microcracks form within the slab, they are accompanied by stress wave release within a specific frequency range (typically 50kHz to 200kHz), manifested as a sudden increase in the high-frequency components of the vibration signal.

[0054] Based on the slab hardness distribution information, a pressing pressure variation curve is generated. This curve describes the required pressure distribution along the longitudinal or transverse direction of the slab. Areas with higher hardness require reduced pressure to avoid over-compaction and cracking, while areas with lower hardness can have their pressure appropriately increased to ensure sufficient density. The curve generation is based on finite element simulation, with the hardness distribution matrix as input and a sequence of target pressure values ​​for each roller or platen as output.

[0055] The target pressing pressure is determined based on the pressing pressure variation curve and vibration signal. The determination process employs a fusion decision-making mechanism: the base pressure value is calculated based on the hardness distribution curve, and corrections are made based on abnormal indications in the vibration signal. If the vibration signal shows a high-frequency abnormal peak in a certain area, indicating that microcracks may be forming in that area, the pressing pressure in that area is immediately reduced; if the vibration signal is stable, the pressure is maintained or slightly increased to ensure density. The determination of the target pressing pressure also considers the real-time thickness feedback of the slab, forming a multi-closed-loop control system of thickness, hardness, and vibration.

[0056] Finally, the roller spacing is adjusted according to the target pressing pressure to achieve the target pressing pressure. Roller spacing adjustment is achieved through a servo hydraulic system or an electric actuator. For the hydraulic system, the pressure of the hydraulic cylinder is adjusted by a proportional valve, thereby changing the gap between the upper and lower pressure rollers; for the electric actuator, the lifting position of the roller bearing housing is precisely controlled by a servo motor. The adjustment accuracy is typically controlled within 0.1mm, with a response time of less than 100 milliseconds, ensuring it can follow rapid changes in the hardness distribution of the slab.

[0057] Through the above technical solution, this embodiment achieves refined pressure management of the slurry forming process with excessively fast coagulation by introducing a composite control strategy of hardness distribution sensing and vibration monitoring, effectively suppressing the generation of internal microcracks and improving the structural integrity and mechanical strength of the plate.

[0058] In some embodiments, such as Figure 3 As shown, in step S203, adjusting the current heating power during the drying stage may include, but is not limited to, the following steps: Step S301: Obtain slab moisture distribution information and slab temperature distribution information; Step S302: Divide the drying area into multiple drying sub-areas; Step S303: Based on the slab moisture distribution information and slab temperature distribution information, predict the moisture evaporation rate corresponding to each drying sub-region; Step S304: Identify the area to be adjusted based on the moisture evaporation rate corresponding to each drying sub-area. The area to be adjusted is used to represent the area where the moisture evaporation rate exceeds the preset evaporation rate range. Step S305: Evaluate the drying rate of the area to be adjusted based on the moisture evaporation rate of the area to be adjusted; Step S306: Adjust the current heating power of the area to be adjusted according to the drying rate so that the moisture evaporation rate is restored to the preset evaporation rate range.

[0059] In some embodiments, based on the molding and pressing stage control mechanism for situations where the slurry setting rate exceeds the upper limit, when the abnormal source identification result of the processing is that the slurry setting rate is below the lower limit, the hydration reaction process of the slurry is slowed down, resulting in a loose internal crystal structure, high porosity, and a free water content significantly exceeding the normal level. If such high-moisture slabs are directly introduced into the standard drying process, they are very prone to forming a dense hardened layer due to the rapid evaporation of surface moisture. This hardened layer seals the internal moisture within the core, and after continuous heating and vaporization, it generates expansion stress, causing delamination of the facing paper, blistering of the core, or even overall deformation, which may affect product quality.

[0060] To this end, information on the slab's moisture and temperature distribution can be obtained first. In practical applications, the slab's moisture distribution reflects the differences in moisture content in the slab's transverse and longitudinal directions, forming the basis for developing differentiated drying strategies. This information is acquired before the slab enters the drying kiln or in the initial stage, using a microwave moisture meter or near-infrared moisture sensor array positioned at the outlet of the forming machine. Specifically, an online microwave moisture detection system can be used, with its transmitting and receiving antennas positioned on the upper and lower sides of the slab, respectively. Utilizing the absorption characteristics of water molecules on microwave energy, it scans and measures along the width of the slab at 10-20 cm intervals, forming a two-dimensional moisture distribution matrix containing the moisture content at various transverse points. The slab's temperature distribution information can be obtained using a non-contact infrared thermometer array, uniformly arranged along the width of the slab, with each thermometer covering a detection width of approximately 15-20 cm. It outputs the surface temperature values ​​of each detection point in real time, serving as an initial thermal state reference.

[0061] The drying area is then divided into multiple drying sub-regions. This division is a crucial step in achieving refined control. The division is based on the physical structure of the drying kiln and the configuration of the heating units. Each drying sub-region refers to a virtual control unit that discretizes the continuous drying space according to control requirements. For a roller kiln approximately 40 meters long with 5-7 independent temperature control zones, each temperature control zone is further divided into 2-4 drying sub-regions along its width, forming 10-28 virtual control units. The division boundaries are aligned with the transverse moisture distribution characteristics of the slab, ensuring that high-moisture areas fall within the control range of independent sub-regions. For example, the kiln body can be divided along its length into a preheating zone, a heating zone, a constant-temperature zone, and a cooling zone. Each zone can then be divided along its width into three sub-regions: left, middle, and right, forming a total of 12 independently controllable drying sub-regions.

[0062] Based on the slab moisture and temperature distribution information, the moisture evaporation rate for each drying sub-region is predicted. The prediction of the moisture evaporation rate is based on a mass and heat transfer model, which describes the rate at which moisture migrates from the slab into the air under specific thermal conditions. The model comprehensively considers parameters such as the initial moisture content of the slab, current temperature, sub-region hot air temperature, wind speed, and vapor partial pressure, and calculates the theoretical evaporation rate using the following formula: In the formula, Evaporation rate The heat transfer coefficient is... For hot air temperature, For slab temperature, The mass transfer coefficient is . It is the saturated vapor pressure. This is the actual vapor pressure. This is a moisture diffusion correction factor. The heat transfer coefficient and mass transfer coefficient were determined by calibration using historical operating data of this sub-region. The calibration method was as follows: the actual evaporation rate of this sub-region under different combinations of hot air temperature and wind speed was recorded, and the coefficient values ​​were obtained by fitting multiple linear regression.

[0063] Based on the moisture evaporation rate corresponding to each drying sub-zone, areas requiring adjustment are identified. These areas represent those where the moisture evaporation rate exceeds a preset evaporation rate range. Identification of these areas is achieved by comparing the predicted evaporation rate with the preset evaporation rate range. The preset evaporation rate range is a safe dehydration rate interval set according to the slab specifications and quality requirements. This range ensures that moisture evaporates at a suitable rate, neither too fast leading to surface crusting nor too slow resulting in low production efficiency. For example, for standard 12mm thick paper-faced gypsum board, the suitable evaporation rate range is 0.8-1.2 kg / (m²). 2 (min). When the predicted evaporation rate of a certain sub-region is lower than 0.8, it is identified as a dehydration lag zone; when it is higher than 1.2, it is identified as a dehydration too fast risk zone.

[0064] Finally, the drying rate of the area to be adjusted is evaluated based on the moisture evaporation rate. The evaluation of the drying rate considers not only the instantaneous evaporation rate but also incorporates the integral calculation of the cumulative dehydration amount to comprehensively reflect the drying process of the slab. This evaluation result reflects the overall drying process and health status of the slab in that sub-region. Based on the drying rate, the current heating power of the area to be adjusted is then adjusted to restore the moisture evaporation rate to the preset range. For example, for sub-regions with excessively low evaporation rates, the opening of the steam radiator valves or the power of the electric heaters in that region is increased to raise the hot air temperature by 3-5°C; for sub-regions with excessively high evaporation rates, the heating power is reduced or the exhaust air volume is increased to suppress excessively rapid surface hardening. Adjustment commands for each sub-region are issued independently through a PLC controller, achieving true zoned and precise temperature control.

[0065] Through the above technical solution, this embodiment effectively avoids the problem of local over-drying or under-drying caused by the traditional whole-kiln uniform temperature control by establishing a dynamic zoning control mechanism based on the spatial distribution of moisture. It is particularly suitable for slow-setting slurries with high water content and soft structure, and can significantly reduce the stratification and foaming defect rate caused by surface crusting and internal moisture retention.

[0066] In some embodiments, step S306, adjusting the current heating power of the area to be adjusted according to the drying rate, may include, but is not limited to, the following steps: Step S401: Obtain the slab surface image and slab surface humidity information; Step S402: Analyze the degree of crusting on the slab surface image to obtain the degree of crusting on the slab surface; Step S403: Adjust the current heating power of the area to be adjusted based on the degree of skin formation on the slab surface, the slab surface humidity information, and the drying rate.

[0067] In some embodiments, when a dense hardened layer forms prematurely on the slab surface, without direct perception of the surface condition, it is difficult to accurately grasp the timing and extent of heat reduction even when adjusting the heating power of the sub-region. Traditional contact-based humidity detection interferes with the slab surface structure, while single temperature monitoring cannot distinguish between surface hardening and normal dehydration.

[0068] To achieve this, surface images and moisture information of the slab can be acquired first. The slab surface images are obtained by placing industrial cameras in observation windows or dedicated inspection channels in key sections of the drying kiln (typically located at the end of the preheating section and the beginning of the main drying section). The cameras are dustproof and fogproof, with lenses equipped with air curtain protection to prevent steam and dust adhesion. They are also equipped with LED ring lights to eliminate the influence of the dim kiln environment, and the frame rate is set to 5-10 frames per second to ensure the capture of changes in the surface microstructure. Slab surface moisture information is obtained using microwave humidity sensors or resistive humidity sensor arrays. The sensor probes are positioned non-contactly or with a light touch 5-10 cm above the slab to monitor the surface moisture content in real time.

[0069] Then, the surface image of the slab is analyzed to determine the degree of skin formation. This skin formation analysis is based on an image feature extraction algorithm. When skin formation begins on the slab surface, its microstructure changes from loose and porous to dense and smooth, and the surface reflectivity changes accordingly. The roughness and uniformity of the surface texture are quantified by extracting gray-level co-occurrence matrix features and local binary pattern features from the image; simultaneously, the surface gloss index is calculated by analyzing the image brightness distribution. The degree of skin formation is classified into four levels: no skin formation, light skin formation, moderate skin formation, and severe skin formation, corresponding to different surface hardening states.

[0070] Then, based on the degree of skin formation on the slab surface, the surface humidity information, and the drying rate, the current heating power of the area to be adjusted is determined. The adjustment decision for heating power can employ a multi-factor fusion strategy. When moderate to severe skin formation is detected and the surface humidity is still above the threshold (indicating that internal moisture has not yet fully migrated), the heating power of that sub-area is immediately reduced by 20%-30%, and the exhaust air volume is appropriately increased. When the skin formation is slight and the humidity is decreasing normally, the current power is maintained. When there is no skin formation but the drying rate is low, a moderate increase in power is permissible. This dynamic adjustment based on the actual surface condition effectively avoids over-drying or under-drying caused by relying solely on time-temperature curve control.

[0071] Through the above technical solution, this embodiment achieves precise control of the drying critical state by introducing visual recognition and quantitative evaluation of the surface skinning degree. It can suppress premature skinning in a timely manner, ensure smooth discharge of internal moisture, and significantly reduce the risk of internal vapor pressure accumulation caused by the sealing of the surface hardening layer.

[0072] In some embodiments, step S402 involves analyzing the degree of crusting on the slab surface image to obtain the degree of crusting on the slab surface, which may include, but is not limited to, the following steps: Texture feature analysis was performed on the slab surface image to obtain the texture feature analysis results; Gloss analysis was performed on the surface image of the slab to obtain the gloss analysis results; Based on the results of texture feature analysis and gloss analysis, the degree of skin formation on the slab surface is identified.

[0073] In some embodiments, texture feature analysis can be performed on the slab surface image to obtain the texture feature analysis results. Texture feature analysis can be implemented using the Gray-Level Co-occurrence Matrix (GLCM) method. This method describes the texture roughness and contrast of an image by statistically analyzing the frequency of grayscale values ​​of pixel pairs at specific distances and directions. The color image is converted to a grayscale image, and pixel pairs with a distance of 1 pixel and directions of 0°, 45°, 90°, and 135° are selected. Four feature quantities—contrast, correlation, energy, and homogeneity—are calculated. The texture of the crusted surface is characterized by low contrast, high energy, and high homogeneity, i.e., uniform grayscale distribution and reduced detail. By setting weights for each feature quantity (e.g., contrast weight 0.4, energy weight 0.3, and homogeneity weight 0.3), a comprehensive texture index is calculated as the texture feature analysis result. A higher comprehensive texture index indicates a more severe crusting.

[0074] Then, gloss analysis was performed on the slab surface image to obtain the gloss analysis results. Gloss analysis is based on the statistical characteristics of image brightness distribution. Due to densification, the crust surface exhibits specular reflection-like properties, producing obvious highlight areas under directional light sources. By extracting the V channel (luminance) from the image's HSV color space, the proportion and distribution concentration of bright pixels (V>200) were calculated. Simultaneously, edge intensity was detected using image gradient operators (such as the Sobel operator), as the edge intensity of the crust surface is typically low. Combining the proportion of bright areas and edge smoothness, the gloss index was calculated as the gloss analysis result.

[0075] Based on the results of texture feature analysis and gloss analysis, the degree of skin formation on the slab surface is identified. The final identification of the skin formation degree is determined by a weighted fusion of the texture index T and the gloss index G. A two-dimensional feature space is established, with preset feature center points for four levels: no skin formation, light, moderate, and heavy skin formation. The K-nearest neighbor algorithm or support vector machine classifier is used to map the (T,G) feature vector of the current image to the corresponding skin formation level. For example, when T>0.7 and G>0.6, it is judged as heavy skin formation; when T<0.3 and G<0.4, it is judged as no skin formation. The classification model is obtained through training on historical samples. The specific training process is as follows: at least 500 slab surface image samples with different drying stages and different skin formation degrees are collected. Experienced process engineers label the skin formation level, extract the T and G feature values ​​of each image as input, and use the labeled level as the output label. The classifier is trained using cross-validation. When the accuracy of the validation set exceeds 90%, the model parameters are fixed, and the model is updated periodically with newly collected samples to adapt to changes in raw materials.

[0076] Through the above technical solution, this embodiment significantly improves the accuracy and robustness of skin formation identification by fusing texture and gloss dual image features, effectively eliminating interference from raw material color differences and light fluctuations, and providing a reliable visual feedback basis for fine adjustment of drying power.

[0077] In some embodiments, in step S305, evaluating the drying rate of the area to be adjusted based on the moisture evaporation rate of the area to be adjusted may include, but is not limited to, the following steps: Obtain air humidity; Based on the air humidity, the moisture evaporation attenuation coefficient is determined by consulting the mapping table between air humidity and moisture evaporation attenuation coefficient; The water evaporation rate of the area to be adjusted is corrected based on the water evaporation attenuation coefficient. The drying rate of the area to be adjusted is evaluated based on the corrected moisture evaporation rate.

[0078] In some embodiments, during the assessment of drying rate based on moisture evaporation rate, ambient humidity, as a key external disturbance factor, significantly affects the actual dehydration efficiency, but is often overlooked. When the ambient air humidity is high, even if the slab itself has a normal moisture content and the heating power is sufficient, the driving force for moisture diffusion into the bulk air decreases, resulting in an actual evaporation rate lower than the theoretically predicted value. Conversely, a dry environment accelerates surface moisture evaporation, potentially inducing premature skin formation. Without compensating for ambient humidity, the drying assessment based on the theoretical evaporation rate will produce a systematic bias, leading to errors in control decisions.

[0079] Therefore, the air humidity can be obtained first. The air humidity can be obtained by temperature and humidity sensors placed in the return air channels or environment of each sub-area of ​​the drying kiln. The sensors adopt the principle of capacitance or resistance, with a measurement range of 0-100%RH and an accuracy of ±2%RH. Data is collected every 30 seconds and a moving average is taken to eliminate instantaneous fluctuations.

[0080] Then, based on the air humidity, the moisture evaporation attenuation coefficient is determined by consulting a mapping table between air humidity and the moisture evaporation attenuation coefficient. This mapping table, established based on experimental data, reflects the quantitative impact of environmental humidity on moisture evaporation efficiency. The establishment process is as follows: Under laboratory conditions, a standard slab sample (300mm × 300mm × 12mm, initial moisture content approximately 50%) is placed in a temperature- and humidity-controlled environmental chamber. The temperature is kept constant (e.g., 60℃), and the relative humidity is varied (from 20% to 90% in 10% increments). The ratio of the actual evaporation rate under different humidity conditions to the evaporation rate under standard humidity (50% RH) is measured; this ratio is the evaporation attenuation coefficient. The following mapping table is established by averaging at least three parallel experiments. In practical applications, the above mapping table is consulted based on the real-time detected air humidity value, and the precise evaporation attenuation coefficient is determined using linear interpolation. For example, when the detected humidity is 65%RH, the table shows that 60-70%RH corresponds to 0.83, which is then calculated through interpolation. ≈0.80.

[0081] Then, based on the water evaporation attenuation coefficient, the water evaporation rate of the area to be adjusted is corrected. The corrected water evaporation rate can be obtained by multiplying the water evaporation rate of the area to be adjusted by the water evaporation attenuation coefficient. When the environment is dry ( When >1), the corrected rate increases, and the system tends to reduce heating power to prevent crust formation; when the environment is humid ( When the rate is less than 1), the corrected rate decreases, and the system increases the heating power or extends the drying time to compensate for the efficiency loss. Finally, the drying rate of the area to be adjusted is evaluated based on the corrected moisture evaporation rate. The drying rate evaluation value is recalculated based on the corrected evaporation rate, making the control decision closer to the actual physical mass transfer process.

[0082] Through the above technical solution, this embodiment effectively eliminates the interference of environmental humidity fluctuations caused by seasonal changes, weather conditions, or poor ventilation in the kiln on drying quality, and significantly improves the accuracy of drying rate assessment and the environmental robustness of the control system.

[0083] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiments of this application obtain data on the internal structural state of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage. Then, the data on the internal structural state of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage are analyzed to identify the process state deviation set. Then, based on the process state deviation set and the risk association rule set, the potential impact on product quality is assessed. Finally, based on the potential impact on product quality, the processing parameters are adjusted. Thus, the processing parameters can be adjusted by combining the process state deviation set and the potential impact on product quality to achieve processing control, thereby improving control accuracy and product quality.

[0084] like Figure 4 As shown, this embodiment of the invention also provides a control system for the processing of building gypsum board, including: The data acquisition module 501 is used to acquire data on the internal structure of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage. The material proportioning stage data includes material weight data, the slurry preparation stage data includes slurry viscosity data and slurry conductivity data, the molding and pressing stage data includes board structural density data and molding and pressing pressure data, and the drying stage data includes steam pressure data. The process state deviation identification module 502 is used to analyze the internal structural state of the finished gypsum board, the material proportioning stage data, the slurry preparation stage data, the molding and pressing stage data, and the drying stage data, and to identify the process state deviation set. The process state deviation set is used to reflect the deviation between the sensor data of different production stages and the corresponding benchmark parameter values. The production stages include the material proportioning stage, the slurry preparation stage, the molding and pressing stage, the drying stage, and the finished product inspection stage. Product quality potential impact assessment module 503 is used to assess the potential impact on product quality based on the process state deviation set and the risk association rule set. The risk association rule set is used to record the impact of different production stages on subsequent stages. The processing parameter adjustment module 504 is used to adjust the processing parameters according to the potential impact on product quality. The processing parameters include the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage.

[0085] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0086] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for controlling the processing of building gypsum board, characterized in that, Includes the following steps: The system acquires data on the internal structure of the finished gypsum board, material proportioning stage data, slurry preparation stage data, molding and pressing stage data, and drying stage data. The material proportioning stage data includes material weight data, the slurry preparation stage data includes slurry viscosity data and slurry conductivity data, the molding and pressing stage data includes slab structural density data and molding and pressing pressure data, and the drying stage data includes steam pressure data. The internal structural state of the finished gypsum board, the material proportioning stage data, the slurry preparation stage data, the molding and pressing stage data, and the drying stage data are analyzed to identify the process state deviation set. The process state deviation set is used to reflect the deviation between the sensor data of different production stages and the corresponding benchmark parameter values. The production stages include the material proportioning stage, the slurry preparation stage, the molding and pressing stage, the drying stage, and the finished product inspection stage. Based on the process state deviation set and the risk association rule set, the potential impact on product quality is assessed. The risk association rule set is used to record the impact of different production stages on subsequent stages. Based on the potential impact on product quality, the processing parameters are adjusted, including the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage.

2. The method for controlling the processing of building gypsum board according to claim 1, characterized in that, The process of obtaining the internal structural state of the finished gypsum board includes: During the finished product inspection stage, vibration excitation is applied to the finished gypsum board using a micro-vibration excitation device. After applying vibration excitation, the acoustic response signal of the finished gypsum board is collected; The acoustic response signal is subjected to spectral analysis to extract the characteristic parameters of the plate material, including the resonant frequency and damping ratio. Based on the characteristic parameters of the board material, the structural state of the finished gypsum board is evaluated to obtain the internal structural state of the finished gypsum board.

3. The method for controlling the processing of building gypsum board according to claim 1, characterized in that, The adjustment of processing parameters based on the potential impact on product quality includes: Identify sources of abnormalities in the processing procedure based on the potential impact on product quality. If the abnormality in the processing is caused by the slurry coagulation rate exceeding the upper limit, then the current slurry pumping rate in the slurry preparation stage is adjusted, and the current pressing pressure in the molding and pressing stage is adjusted. If the abnormality in the processing is caused by the slurry coagulation rate being lower than the lower limit, then the current heating power in the drying stage is adjusted.

4. The method for controlling the processing of building gypsum board according to claim 3, characterized in that, Adjusting the current slurry pumping rate during the slurry preparation stage includes: Obtain the material filling degree at the feed inlet of the molding machine; Adjust the drive frequency of the feeding device of the molding machine according to the material filling degree at the feed inlet of the molding machine, so as to regulate the feeding rate of the molding machine; The target slurry pumping rate is calculated based on the feeding rate of the molding machine and the preset matching relationship, wherein the preset matching relationship is used to represent the linear relationship between the feeding rate of the molding machine and the slurry pumping rate. The output power of the slurry pumping equipment is adjusted according to the target slurry pumping rate so that the current slurry pumping rate reaches the target slurry pumping rate.

5. The method for controlling the processing of building gypsum board according to claim 3 is characterized in that, Adjusting the current pressing pressure during the molding and pressing stage includes: Obtain slab hardness distribution information; Vibration signals generated during the pressing process of the slab are collected by a high-frequency vibration sensor. These vibration signals are used to reflect stress changes and potential microcrack formation inside the slab. Based on the slab hardness distribution information, a pressing pressure variation curve is generated; The target pressing pressure is determined based on the pressing pressure change curve and the vibration signal; Adjust the roller spacing according to the target pressing pressure so that the current pressing pressure reaches the target pressing pressure.

6. The method for controlling the processing of building gypsum board according to claim 3, characterized in that, Adjusting the current heating power during the drying stage includes: Obtain information on the moisture distribution and temperature distribution of the slab. The drying area is divided into multiple drying sub-areas; Based on the slab moisture distribution information and the slab temperature distribution information, predict the moisture evaporation rate corresponding to each drying sub-region; Based on the moisture evaporation rate corresponding to each drying sub-region, an area to be adjusted is identified, and the area to be adjusted is used to represent the area where the moisture evaporation rate exceeds the preset evaporation rate range. The drying rate of the area to be adjusted is evaluated based on the moisture evaporation rate of the area to be adjusted. Based on the drying rate, the current heating power of the area to be adjusted is adjusted so that the moisture evaporation rate is restored to the preset evaporation rate range.

7. The method for controlling the processing of building gypsum board according to claim 6, characterized in that, The step of adjusting the current heating power of the area to be adjusted according to the drying rate includes: Acquire images of the slab surface and slab surface humidity information; The surface image of the slab is analyzed to determine the degree of crusting on the slab surface. The current heating power of the area to be adjusted is determined based on the degree of skin formation on the slab surface, the slab surface humidity information, and the drying rate.

8. The method for controlling the processing of building gypsum board according to claim 7, characterized in that, The step of analyzing the degree of crusting on the surface image of the slab to obtain the degree of crusting on the slab surface includes: Texture feature analysis is performed on the surface image of the slab to obtain the texture feature analysis results; Gloss analysis was performed on the surface image of the slab to obtain the gloss analysis results; Based on the texture feature analysis results and the gloss analysis results, the degree of skin formation on the slab surface is identified.

9. The method for controlling the processing of building gypsum board according to claim 6, characterized in that, The step of evaluating the drying rate of the area to be adjusted based on the moisture evaporation rate of the area to be adjusted includes: Obtain air humidity; Based on the air humidity, the moisture evaporation attenuation coefficient is determined by consulting the mapping table between air humidity and moisture evaporation attenuation coefficient. The water evaporation rate of the area to be adjusted is corrected based on the water evaporation attenuation coefficient. The drying rate of the area to be adjusted is evaluated based on the corrected moisture evaporation rate.

10. A control system for the processing of building gypsum board, characterized in that, include: The data acquisition module is used to acquire data on the internal structure of the finished gypsum board, the material proportioning stage, the slurry preparation stage, the molding and pressing stage, and the drying stage. The material proportioning stage data includes material weight data, the slurry preparation stage data includes slurry viscosity data and slurry conductivity data, the molding and pressing stage data includes slab structural density data and molding and pressing pressure data, and the drying stage data includes steam pressure data. The process state deviation identification module is used to analyze the internal structural state of the finished gypsum board, the material proportioning stage data, the slurry preparation stage data, the molding and pressing stage data, and the drying stage data to identify the process state deviation set. The process state deviation set is used to reflect the deviation between the sensor data of different production stages and the corresponding benchmark parameter values. The production stages include the material proportioning stage, the slurry preparation stage, the molding and pressing stage, the drying stage, and the finished product inspection stage. The product quality potential impact assessment module is used to assess the potential impact on product quality based on the process state deviation set and the risk association rule set. The risk association rule set is used to record the impact of different production stages on subsequent stages. The processing process parameter adjustment module is used to adjust the processing process parameters based on the potential impact on product quality. The processing process parameters include the current slurry pumping rate in the slurry preparation stage, the current pressing pressure in the molding and pressing stage, and the current heating power in the drying stage.

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