Rapid hardening and drying method and device for silicate material

Abstract

The invention relates to the technical field of solid drying, and discloses a rapid hardening and drying method and device for a silicate material. The method comprises the following steps: carrying out stress pre-regulation and double-shaft stirring on raw materials through three-dimensional network analysis to obtain slurry; a prefabricated part is prepared through partition pressure control and temperature gradient adjustment; carrying out acoustic emission signal acquisition and time-frequency analysis to evaluate a stress field; airflow drying is optimized by adopting an orthogonal fan array; differential treatment is carried out through a phased array microwave source; and finally, a finished product is obtained through ultrasonic stress elimination and speckle strain monitoring. Through multi-dimensional data acquisition, dynamic feedback adjustment and an intelligent control strategy, accurate stress regulation and control in the drying process are achieved.

Description

Technical Field

[0001] The present application relates to the field of solid drying, and in particular to a method and device for rapid solidification and drying of silicate materials. Background Art

[0002] In the field of rapid setting and drying of silicate materials, the traditional drying process mainly relies on conventional heating and natural drying to remove moisture from the products. At present, a variety of drying technologies have been developed in the industry, including hot air drying, vacuum drying and microwave drying. Among them, hot air drying removes moisture from the material by heating the air, vacuum drying uses pressure difference to achieve water evaporation, and microwave drying uses electromagnetic waves to excite the vibration of water molecules to generate heat. These methods have been widely used in industrial production and have achieved certain results.

[0003] However, existing drying technologies have obvious shortcomings. First, traditional drying methods often lead to large stress gradients inside the material, causing quality problems such as product cracking and deformation. Secondly, a single drying method is difficult to adapt to the drying needs of different regions, resulting in uneven drying and affecting product quality stability. In addition, the existing technology lacks real-time monitoring and precise control of the internal stress state of the material, and cannot effectively prevent and eliminate stress concentration during the drying process. At the same time, the energy utilization efficiency is low, the drying cycle is long, and the production efficiency is limited. Summary of the invention

[0004] The present application provides a method and device for rapid setting and drying of silicate materials. In view of the problems of inaccurate stress control and uneven drying in the existing silicate material drying technology, precise stress control during the drying process is achieved through multi-dimensional data acquisition, dynamic feedback adjustment and intelligent control strategy.

[0005] In the first aspect, the present application provides a rapid coagulation and drying method for silicate materials, which comprises: performing stress pre-control treatment on silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis, and performing variable frequency dual-axis stirring according to shear stress monitoring data to obtain a pre-controlled slurry; based on the pre-controlled slurry, performing gradient injection treatment on the core area, transition area and surface area using a partitioned pressure control system, and obtaining a silicate preform with an initial stress field through coordinated control of the temperature gradient; collecting acoustic emission signals from the silicate preform, and analyzing the stress field distribution through a time-frequency feature analysis algorithm. Dynamic evaluation is performed, and the temperature and humidity parameters are adjusted by a closed-loop feedback system to obtain pre-dried silicate products; based on the stress field distribution data of the pre-dried silicate products, the airflow intensity and direction are dynamically optimized through an orthogonal fan array control algorithm to obtain airflow-dried silicate products; the stress field of the airflow-dried silicate products is scanned, and the high and low stress areas are subjected to differentiated energy input processing through a phased array microwave source to obtain microwave-dried silicate products; the microwave-dried silicate products are placed in an ultrasonic stress elimination system, and the surface strain field is dynamically monitored through a speckle strain detection algorithm to obtain a target silicate product.

[0006] In a second aspect, the present application provides a quick-setting and drying device for silicate materials, the quick-setting and drying device for silicate materials comprising: A stirring module is used to perform stress pre-regulation on silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis, and to perform variable frequency biaxial stirring according to shear stress monitoring data to obtain pre-regulated slurry; The injection module is used to perform gradient injection treatment on the core area, transition area and surface area according to the pre-adjusted slurry using a zoned pressure control system, and obtain a silicate preform with an initial stress field through coordinated control of the temperature gradient; An adjustment module is used to collect acoustic emission signals from the silicate preform, dynamically evaluate the stress field distribution through a time-frequency characteristic analysis algorithm, and adjust the temperature and humidity parameters using a closed-loop feedback system to obtain a pre-dried silicate preform; A drying module, used for dynamically optimizing the airflow intensity and direction through an orthogonal fan array control algorithm according to the stress field distribution data of the pre-dried silicate product to obtain an airflow dried silicate product; A processing module, used for scanning the stress field of the airflow dried silicate product, and performing differentiated energy input processing on high and low stress areas through a phased array microwave source to obtain a microwave dried silicate product; The monitoring module is used to place the microwave-dried silicate product in an ultrasonic stress relief system, and dynamically monitor the surface strain field through a speckle strain detection algorithm to obtain a target silicate product.

[0007] In the technical solution provided in the present application, the design of stress pre-regulation treatment of silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis realizes precise stress control in the raw material mixing process and effectively avoids the problem of uneven stress distribution in traditional mixing methods; the design of gradient injection treatment of the core area, transition area and surface area using a partitioned pressure control system establishes a complete pressure gradient control system to ensure the uniformity of pressure distribution in various areas of the workpiece; the design of dynamic evaluation of stress field distribution using acoustic emission signal acquisition and time-frequency feature analysis algorithm realizes real-time monitoring and precise evaluation of stress field distribution, providing an accurate basis for subsequent process parameter adjustment; the design of dynamic optimization of airflow intensity and direction based on orthogonal fan array control algorithm realizes precise regulation of airflow field and improves drying uniformity; the design of differentiated energy input treatment of high and low stress areas through phased array microwave source realizes precise control of energy input and avoids local overheating or underheating; the design of combining ultrasonic stress elimination technology with speckle strain detection algorithm realizes precise elimination and real-time monitoring of stress field. Especially in the application of artificial intelligence algorithms, the present invention adopts a variety of intelligent algorithms such as time-frequency feature analysis algorithm, orthogonal fan array control algorithm and speckle strain detection algorithm. The organic combination of these algorithms not only improves the automation level of the process, but also significantly enhances the optimization ability and control accuracy of process parameters. Through the intelligent optimization and dynamic adjustment of the algorithm, the coordinated control of the temperature field, pressure field and stress field in the drying process is achieved, ensuring the stability and consistency of product quality. At the same time, the application of these algorithms also greatly improves energy utilization efficiency, shortens the production cycle, and reduces production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of a method for rapid coagulation and drying of silicate materials in an embodiment of the present application; Figure 2 This is a schematic diagram of the process of stress pre-regulation treatment of silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis in the embodiment of the present application; Figure 3 This is a schematic diagram of an embodiment of a rapid solidification and drying device for silicate materials in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method and apparatus for rapid coagulation and drying of silicate materials. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , one embodiment of the rapid coagulation and drying method of silicate materials in the embodiment of the present application includes: Step S101, performing stress pre-regulation treatment on silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis, and performing variable frequency biaxial stirring according to shear stress monitoring data to obtain pre-regulated slurry; Step S102, according to the pre-adjusted slurry, a zoned pressure control system is used to perform gradient injection treatment on the core area, transition area and surface area, and a silicate preform with an initial stress field is obtained through coordinated control of the temperature gradient; Step S103, collecting acoustic emission signals from the silicate preform, dynamically evaluating the stress field distribution through a time-frequency characteristic analysis algorithm, and adjusting the temperature and humidity parameters using a closed-loop feedback system to obtain a pre-dried silicate preform; Step S104, according to the stress field distribution data of the pre-dried silicate product, the airflow intensity and direction are dynamically optimized by using an orthogonal fan array control algorithm to obtain an airflow dried silicate product; Step S105, scanning the stress field of the airflow dried silicate product, and performing differentiated energy input processing on the high and low stress areas through a phased array microwave source to obtain a microwave dried silicate product; Step S106: placing the microwave-dried silicate product in an ultrasonic stress relief system, and dynamically monitoring the surface strain field through a speckle strain detection algorithm to obtain a target silicate product.

[0012] It is understandable that the execution subject of the present application may be a quick-setting drying device for silicate materials, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Specifically, a three-dimensional network is used to construct an analysis system to pre-regulate the stress of raw materials. Specifically, the stress distribution data of silicate raw materials, polypropylene fibers and nano-silicon dioxide are collected through a sensor array, and a network composed of strain gauges and pressure sensors is used to obtain real-time monitoring values ​​of shear stress. Based on these data, the speed and direction of the biaxial stirring device are adjusted by a frequency converter to achieve uniform mixing of the materials. In the data processing process, the collected stress data is converted to the frequency domain through Fourier transform, the main frequency components are extracted, and a stress-frequency relationship model is established to guide the dynamic adjustment of stirring parameters.

[0014] After obtaining the pre-adjusted slurry, the zoned pressure control system performs gradient injection on different areas of the part. The system includes multiple sets of independently controlled pressure pumps and temperature control units, and uses digital pressure sensors to monitor the pressure distribution in the core area, transition area and surface area in real time. The temperature gradient collaborative control uses multi-point temperature measuring elements and heater arrays to dynamically adjust the heating power according to the temperature feedback data of each area to ensure the uniformity of the temperature field. Subsequently, the acoustic emission signal is collected and analyzed, and the piezoelectric sensor array is used to detect the acoustic signals generated by the silicate preform during the drying process. The time-frequency feature analysis algorithm performs wavelet transform on the collected acoustic emission signals to extract time domain and frequency domain feature parameters, including signal energy, main frequency and spectrum distribution. By establishing a mapping relationship between the acoustic emission characteristics and the stress field distribution, the internal stress state of the preform is evaluated. The closed-loop feedback system adjusts the ambient temperature and humidity parameters according to the evaluation results, and uses the PID control algorithm to achieve precise adjustment of the parameters.

[0015] After the pre-drying stage is completed, the orthogonal fan array control algorithm optimizes the airflow parameters. Based on the computational fluid dynamics model, the algorithm divides the fan array into multiple independent control areas, and the fan speed and guide plate angle of each area can be adjusted separately. By establishing a correlation model between the airflow field distribution and the stress field, the operating parameters of the fans in each area are optimized to achieve precise control of the airflow intensity and direction. After the airflow drying is completed, the phased array microwave source performs differentiated energy input processing on the workpiece. A directional microwave field is generated by a microwave emission array. According to the stress field scanning data, a lower power density is used for high stress areas and a higher power density is used for low stress areas to achieve spatial regulation of energy input. The distribution of microwave power is based on the stress field distribution data and the dielectric properties of the material, and directional energy transmission is achieved through phase modulation.

[0016] In the ultrasonic stress relief system, the speckle strain detection algorithm is used to monitor the surface strain field. The displacement field information of the workpiece surface is obtained by laser speckle interferometry technology, and the strain distribution is calculated by combining the digital image correlation algorithm. The algorithm preprocesses the speckle image, extracts the feature points, and calculates the strain field distribution by tracking the displacement of the feature points, realizing real-time evaluation of the stress state.

[0017] For example, in the actual production process, for the processing of a batch of silicate parts, the shear stress data of the raw materials are collected through strain gauges, and the spectrum characteristics are obtained through Fourier transformation to guide the dynamic adjustment of the speed of the biaxial stirring device within the range of 50-200rpm. In the partition injection stage, the pressure in the core area is maintained at 2.5MPa, the transition area is 2.0MPa, and the surface area is 1.5MPa, forming a reasonable pressure gradient. After the signal collected in the acoustic emission detection stage is transformed by wavelet, the characteristic signal with the main frequency component in the range of 20-100kHz is extracted to evaluate the internal stress distribution. In the airflow drying stage, the fan array adjusts the wind speed within the range of 5-15m / s according to the stress distribution data. The power density of the phased array microwave source is dynamically adjusted within the range of 0.5-2.0kW / cm² to achieve differentiated energy input. Finally, the surface strain distribution is obtained by speckle strain detection, and the strain value is controlled within the range of 0.1%-0.5% to ensure product quality. In each process, data collection, processing and feedback constitute a closed-loop control, ensuring precise control of process parameters and stability of product quality.

[0018] In the embodiments of the present application, the design of stress pre-regulation of silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis realizes precise stress control during the raw material mixing process, effectively avoiding the problem of uneven stress distribution in traditional mixing methods; the design of gradient injection treatment of the core area, transition area and surface area using a partitioned pressure control system establishes a complete pressure gradient control system to ensure the uniformity of pressure distribution in each area of ​​the workpiece; the design of dynamic evaluation of stress field distribution using acoustic emission signal acquisition and time-frequency feature analysis algorithm realizes real-time monitoring and precise evaluation of stress field distribution, providing an accurate basis for subsequent process parameter adjustment; the design of dynamic optimization of airflow intensity and direction based on orthogonal fan array control algorithm realizes precise regulation of airflow field and improves drying uniformity; the design of differentiated energy input treatment of high and low stress areas through phased array microwave source realizes precise control of energy input and avoids local overheating or underheating; the design of combining ultrasonic stress elimination technology with speckle strain detection algorithm realizes precise elimination and real-time monitoring of stress field. Especially in the application of artificial intelligence algorithms, the present invention adopts a variety of intelligent algorithms such as time-frequency feature analysis algorithm, orthogonal fan array control algorithm and speckle strain detection algorithm. The organic combination of these algorithms not only improves the automation level of the process, but also significantly enhances the optimization ability and control accuracy of process parameters. Through the intelligent optimization and dynamic adjustment of the algorithm, the coordinated control of the temperature field, pressure field and stress field in the drying process is achieved, ensuring the stability and consistency of product quality. At the same time, the application of these algorithms also greatly improves energy utilization efficiency, shortens the production cycle, and reduces production costs.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) The silicate raw material, polypropylene fiber and nano-silicon dioxide were mixed according to the mass fractions of 98.4%, 1.0% and 0.6% by a high-precision mass measurement system to obtain the standard material ratio data; (2) Substitute the standard material ratio data into the three-dimensional network stress analysis equation, calculate the force between the mixture particles, obtain the three-dimensional stress distribution data through response surface modeling, and obtain the optimal ratio parameters; (3) Based on the optimal ratio parameters, the shear force sensor array is used to perform a 360-degree dynamic scan of the mixture, collect stress data at 15 spatial points, and obtain a stress monitoring data set; (4) Extract time domain features and frequency domain features from the stress monitoring data set, establish a stress-time curve, determine the key stress inflection point through wavelet analysis, and obtain the stress peak feature vector; (5) A speed optimization function is constructed based on the stress peak eigenvector, and the optimal speed reduction curve is calculated in the range of 150-80 rpm to obtain a segmented speed control sequence; (6) The segmented speed control sequence is input into the biaxial stirring system, and the shear stress value is collected every 0.5 second. When the shear stress of 30 consecutive sampling points is stable at 2.8±0.2MPa, the pre-controlled slurry is obtained.

[0020] Specifically, Figure 2 As shown, it is a schematic diagram of the process of stress pre-regulation of silicate raw materials, polypropylene fibers and nano-silicon dioxide by three-dimensional network construction and analysis in the embodiment of the present application. The silicate raw materials (98.4%), polypropylene fibers (1.0%) and nano-silicon dioxide (0.6%) are accurately proportioned by a high-precision mass measurement system to generate standard proportioning data; the proportioning data is input into the three-dimensional network stress analysis equation, and the optimal proportioning parameters are obtained by particle force calculation and response surface modeling; a shear force sensor array is used to perform 360-degree dynamic scanning at 15 spatial points with a sampling frequency of 1kHz; time domain and frequency domain feature extraction is performed on the collected stress monitoring data, and the key stress inflection point and peak eigenvector are determined by wavelet analysis; a speed optimization function is constructed in the range of 150-80 rpm based on the eigenvector, the optimal speed drop curve is calculated, and a segmented speed control sequence is generated; the control sequence is input into the dual-axis stirring system, and the shear stress value is collected every 0.5 seconds. When the shear stress of 30 consecutive sampling points is stable within the set range, the preparation of the pre-regulated slurry is completed.

[0021] The raw materials are proportioned through a high-precision mass metering system. Specifically, the silicate raw materials, polypropylene fibers and nano-silicon dioxide are accurately weighed through an electronic balance and a microbalance, and the weighing data is input into a digital control unit to generate standard material proportion data.

[0022] The material ratio data is input into the three-dimensional network stress analysis equation to calculate the inter-particle force. The three-dimensional network stress analysis equation is expressed as:

[0023] in, is a three-dimensional stress tensor, which represents the stress state at any point of the material; is the particle type weight coefficient, reflecting the contribution of different types of particles to the total stress; is the particle potential energy function, describing the interaction potential energy between particles; is the Laplace operator, which is used to calculate the spatial second-order derivative of the potential energy; is the density correction factor, which adjusts the effect of density distribution on stress; is the local density distribution function, describing the material density at each point in space; is the force vector, which represents the interaction force between particles; is the time response coefficient, reflecting the dependence of stress on time; is the strain variable, which indicates the degree of deformation of the material. Calculate the combined effect of density and force within the entire volume, where n is the total number of particle types, represents the area of ​​volume integration, and t represents the time variable.

[0024] Based on the optimal ratio parameters, a 360-degree dynamic scan is performed using a shear force sensor array. The sensor array consists of 15 stress sensors, each with a sampling frequency of 1kHz, which collects spatial point stress data in real time. The collected data is extracted for time domain and frequency domain features, and wavelet analysis is used to determine the key stress inflection point:

[0025] in, is the wavelet transform coefficient, which represents the time-frequency domain characteristics; a is the scale parameter, which controls the expansion and contraction of the wavelet; b is the translation parameter, which controls the translation of the wavelet; is the stress time domain signal; is the wavelet basis function, used for signal decomposition; is the feature weight coefficient, which adjusts the importance of different features; is the peak stress amplitude. Realize time-frequency analysis of signals. Weight the characteristic peaks, is the number of characteristic peaks.

[0026] Construct the speed optimization function based on the stress peak eigenvector:

[0027] in, is the function of speed changing with time; is the maximum speed value, set the initial stirring speed; is the speed attenuation coefficient, which controls the speed reduction rate; is the weight coefficient of each stage, which adjusts the speed change in different stages; It is a time window function, defining the time range of each stage; is the shear stress increment, reflecting the effect of stress change on the rotation speed. Describes the overall decay trend of the speed, the summation term Realize segmented fine control, Indicates the total number of stages.

[0028] Taking the processing of a batch of silicate parts as an example, 9.84kg of silicate raw materials, 0.10kg of polypropylene fiber and 0.06kg of nano-silicon dioxide were weighed by a high-precision electronic balance. The material data was substituted into the three-dimensional network stress analysis equation, and the inter-particle force of the mixture was iteratively calculated by the finite element method to obtain the spatial stress distribution data. Then the shear force sensor array was used for scanning. 15 sensors were distributed at different azimuths and elevations in the spherical coordinate system, and each sensor collected stress data every 1ms. The collected data sequence was subjected to wavelet transformation, characteristic peaks were extracted and stress-time curves were established. Based on the curve characteristics, a speed optimization function was constructed to calculate the optimal control sequence for the speed to gradually decrease from 150 rpm to 80 rpm. The sequence was input into a biaxial stirring device, and the shear stress value was collected every 0.5 seconds. When the shear stress of 30 consecutive sampling points was stable within the range of 2.8±0.2MPa, the preparation of the pre-regulated slurry was completed.

[0029] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) The pre-controlled slurry is scanned at 32×32 points by the pressure data acquisition array, the spatial distribution parameters of the pressure field are calculated based on the spatial spectrum density analysis, and the sampling data is reconstructed in three dimensions using the stress field reconstruction algorithm to obtain a three-dimensional pressure distribution map; (2) The pressure distribution stereogram is imported into the intelligent pressure controller, and the pressure control curves with the core area pressure set to 18-20 MPa, the transition area pressure set to 15-17 MPa, and the surface area pressure set to 12-14 MPa are calculated through multiple iterative regression. The pressure gradient characteristics are extracted to obtain the zone pressure optimization plan; (3) According to the partition pressure optimization scheme and combined with the pressure-time response characteristics, a pressure control compensation function is established. 24 pressure sensing nodes are arranged in the three partitions respectively, with a sampling period of 0.01 seconds. A dynamic compensation matrix of pressure data is constructed to obtain real-time feedback data of pressure control. (4) Based on the real-time feedback data of pressure control, the temperature of the core area is adjusted to 35±0.2℃, the temperature of the transition area is adjusted to 38±0.2℃, and the temperature of the surface area is adjusted to 40±0.2℃ through temperature-pressure coupling optimization calculation. The temperature zone boundary characteristic curve is established to obtain multi-region temperature control data; (5) Reconstruct the time series of multi-region temperature control data, extract the evolution characteristics of the temperature field, establish a boundary temperature gradient adaptive optimization model, construct a temperature control optimization curve through temperature fluctuation variance analysis, and obtain an intelligent temperature compensation sequence; (6) The intelligent temperature compensation sequence is input into the temperature gradient controller. When the temperature stability index of the core zone, transition zone and surface zone is greater than 0.98 and lasts for 180 seconds, and the pressure fluctuation coefficient is less than 0.02, a silicate preform with an initial stress field is obtained.

[0030] Specifically, a 32×32 dot matrix scan is performed on the pre-regulated slurry. The pressure data acquisition array consists of 1024 micro pressure sensors, each with a measurement range of 0-50MPa and an accuracy of 0.01MPa. The pressure field distribution parameters are calculated by spatial spectral density analysis, and the sampled data is reconstructed in three dimensions using a stress field reconstruction algorithm. The spatial spectral density analysis extracts the spatial distribution characteristics of the pressure field by calculating the spatial correlation function and power spectral density of the pressure data.

[0031] The stress field reconstruction algorithm uses a layer-by-layer iterative method to reconstruct the three-dimensional structure of the pressure field from the bottom layer to the top layer. In the reconstruction process, the least squares method is used to fit the sampled data, and the pressure values ​​of the unsampled points are filled by the interpolation algorithm, and finally a pressure distribution stereogram with a resolution of 256×256×128 is generated. Based on this map, the pressure control curves of the three regions are determined by multiple iterative regression calculations. The regression calculation uses piecewise polynomial fitting to model the pressure change law of each region, and the pressure gradient characteristics are extracted to form a partition pressure optimization scheme. The pressure control compensation function is based on the pressure-time response characteristics. 24 pressure sensing nodes are arranged in the core area, transition area and surface area, respectively, with a sampling period of 0.01 seconds. The sampled data are fitted in real time by the least squares method, and a 72×72 pressure data dynamic compensation matrix is ​​constructed to realize real-time feedback of pressure control. Each element of the compensation matrix represents the pressure deviation value at the corresponding position, which is used to dynamically adjust the pressure control parameters.

[0032] The temperature-pressure coupling optimization calculation is based on thermodynamic theory and establishes a correlation model between temperature and pressure. The coupled equations are solved by the finite element method to obtain the optimal temperature zoning control strategy. When implementing temperature zoning control, the PID algorithm is used to accurately adjust the heating power to achieve stable temperature control. At the same time, the temperature zoning boundary characteristic curve is established to describe the temperature transition law between each area.

[0033] Time series reconstruction technology is used to analyze the dynamic evolution characteristics of the temperature field. The delayed coordinate method is used to reconstruct the phase space of the temperature field, and the stability of the temperature field is evaluated by calculating the correlation dimension and Lyapunov index. The boundary temperature gradient adaptive optimization model is based on fuzzy control theory, dynamically adjusts the control parameters according to the temperature fluctuation variance, and generates an intelligent temperature compensation sequence.

[0034] Taking the preparation of a batch of silicate preforms as an example, the pressure data of the pre-controlled slurry was collected by 32×32 dot scanning. After the sampling data was analyzed by spatial spectral density, the spatial distribution characteristics of the pressure field were obtained, and the three-dimensional pressure distribution map was reconstructed. Through multiple iterative regression, the pressure control curves of 18-20MPa in the core area, 15-17MPa in the transition area, and 12-14MPa in the surface area were calculated. 72 pressure sensing nodes collected pressure data in real time, and a dynamic compensation matrix was constructed for pressure control. The temperature-pressure coupling optimization determined the temperature set values ​​of the three areas, and the temperature was precisely adjusted through PID control. After 180 seconds of stable operation, when the temperature stability index of the core area, transition area and surface area exceeded 0.98 and the pressure fluctuation coefficient was less than 0.02, the preparation of the silicate preform with the initial stress field was completed. The whole process achieved precise control of silicate materials through multi-level data collection, analysis and control. The sampling data of each pressure sensor entered the data processing unit after analog-to-digital conversion. Spatial spectral density analysis calculates the autocorrelation function of pressure data, and then obtains the power spectral density through fast Fourier transform. The stress field reconstruction adopts a hierarchical iterative strategy. The reconstruction of each layer is based on the results of the previous layer. The pressure value of the sampling point is fitted by the least squares method, and the data of the unsampled points are filled by cubic spline interpolation. In the process of constructing the pressure control compensation matrix, the weighted least squares method is used to fit the real-time sampling data, and the weight coefficient is dynamically adjusted according to the reliability of the measurement point. The dynamic evolution characteristics of the temperature field are extracted by calculating the autocorrelation function and cross-correlation function of the temperature series, which are used to evaluate the stability and uniformity of the temperature field. The entire data processing process forms a complete closed-loop control system, which ensures the accuracy and controllability of the silicate material preparation process.

[0035] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) The silicate preform was scanned 360 degrees by an acoustic emission sensor array. 32 signal collection points were set on each scanning surface with a sampling frequency of 500 kHz. The amplitude, frequency, and duration characteristic parameters of the acoustic emission signal were extracted to obtain the original acoustic emission data set. (2) Input the original acoustic emission data set into the feature analysis processor, establish the mapping relationship between the acoustic emission signal and the stress field through the stress wave propagation theory, extract the stress characteristic value of each sampling point, construct the three-dimensional stress distribution matrix, and obtain the time domain characteristic data of the stress field; (3) Perform spectrum conversion on the time domain characteristic data of the stress field, extract the main frequency components in the range of 0-100 kHz, establish the frequency domain mapping function of the stress field, calculate the spatial distribution characteristics of the stress field, and obtain the dynamic evaluation parameters of the stress field; (4) Based on the dynamic evaluation parameters of the stress field, a closed-loop control equation for temperature and humidity was constructed. The temperature of the drying chamber was adjusted in stages within the range of 40-75°C, and the relative humidity was dynamically compensated within the range of 45%-65%. The temperature and humidity response curve was established to obtain the environmental parameter optimization data. (5) Perform multi-dimensional analysis on environmental parameter optimization data, extract the variation law of temperature and humidity field, establish parameter compensation strategy, determine the optimal control range through temperature and humidity gradient calculation, and obtain closed-loop control sequence; (6) The closed-loop control sequence is substituted into the temperature and humidity control system. When the stress field fluctuation index is less than 0.03, the temperature field stability is greater than 0.95, and the relative humidity change rate is less than 0.02 / min within 180 seconds of continuous monitoring, the pre-dried silicate product is obtained.

[0036] Specifically, the acoustic emission sensor array performs a 360-degree stereo scan on the silicate preform. The acoustic emission sensor is piezoelectric, with a frequency response range of 0-1MHz and a sensitivity of -65dB. 32 signal acquisition points are arranged on each scanning surface, with a sampling frequency of 500kHz. The amplitude, frequency and duration characteristic parameters of the acoustic emission signal are extracted to form an acoustic emission original data set. The mapping relationship between the acoustic emission signal and the stress field is established through the stress wave propagation theory. The propagation speed of the stress wave in the material is related to the elastic modulus and density of the material. By measuring the time difference between the sound wave reaching different sensors and combining the sound wave attenuation characteristics, the sound source position and stress distribution are calculated. The stress characteristic value of each sampling point is extracted, a three-dimensional stress distribution matrix is ​​constructed, and the time domain characteristic data of the stress field is obtained.

[0037] The stress field time domain characteristic data is converted into a spectrum and the main frequency component is extracted to establish the stress field frequency domain mapping function:

[0038] in, is the frequency domain distribution function of the stress field, is the frequency response function, is the attenuation coefficient, is the spatial position vector, is the sound source intensity, is the frequency characteristic function, is the material acoustic parameter, is the displacement field. The integral term represents the combined contribution of the sound sources within the volume, represents the effect of displacement field on stress, is the total number of sound sources.

[0039] Based on the dynamic evaluation parameters of the stress field, a closed-loop control equation for temperature and humidity is constructed to adjust the temperature and relative humidity of the drying chamber in sections. The following equations are used for multidimensional analysis of the temperature and humidity field:

[0040] in, is the temperature and humidity field control function, is the temperature diffusion coefficient, is the humidity transfer coefficient, is the temperature field, is the humidity field, is the regulating factor, is the temperature and humidity coupling term, is the energy change. Describe the spatial distribution of the temperature field, represents the time evolution of the humidity field, and the summation term Realize coordinated control of temperature and humidity. Indicates the total number of adjustment factors.

[0041] Taking the pre-drying treatment of a batch of silicate preforms as an example, a 360-degree scan was performed by an acoustic emission sensor array. 32 sensors were distributed in a ring shape on each scanning surface, with an interval angle of 11.25 degrees. The sampling frequency was 500kHz, and the amplitude, frequency and duration of the acoustic emission signal were recorded. Through the analysis of stress wave propagation theory, the corresponding relationship between the acoustic emission signal and the stress field was established, and the stress distribution matrix was calculated. The time domain data was fast Fourier transformed, the main frequency component in the range of 0-100kHz was extracted, and the frequency domain mapping function was applied to evaluate the stress field distribution. Based on the evaluation results, the drying parameters were adjusted by the temperature and humidity closed-loop control equation, the temperature was adjusted in stages within the range of 40-75℃, and the relative humidity was dynamically compensated within the range of 45%-65%. After 180 seconds of stable operation, the stress field fluctuation index, temperature field stability and relative humidity change rate were monitored to complete the preparation of pre-dried silicate parts. When processing the acoustic emission signal, the noise was removed by bandpass filtering, and the amplitude, frequency and duration characteristics of the signal were extracted. The arrival time difference of stress waves is determined by the threshold trigger method, and the sound source position is calculated by combining the propagation speed of sound waves in the material. The spectrum analysis uses fast Fourier transform, the window function uses Hanning window, and the frequency resolution is 1Hz. The temperature and humidity control uses a segmented PID algorithm, dynamically adjusts the control parameters according to different temperature ranges, and realizes precise control of the temperature and humidity field.

[0042] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Surface stress distribution parameters were extracted from the stress field distribution data of pre-dried silicate parts, stress distribution vectors were constructed by eight-point stress sampling, and the stress values ​​at each sampling point were piecewise linearly interpolated to obtain the complete stress field distribution curve; (2) Based on the complete stress field distribution curve, the orthogonal airflow control equation is established, and the fan speed in the four directions of up, down, left and right is controlled in the range of 0-5m / s. The airflow velocity in each area is coordinated and optimized to obtain the airflow field intensity distribution table; (3) Dynamically adjust the airflow direction of the orthogonal fan array according to the airflow field intensity distribution table, collect airflow direction data every 0.1 second, calculate the airflow vector in each direction through wind speed field reconstruction, and obtain the airflow guidance parameters; (4) The airflow guidance parameters are input into the wind field optimization processing unit, and the optimal airflow distribution scheme is calculated according to the coupling relationship between the wind field and the stress field. The airflow intensity in the high stress area is gradually attenuated to obtain the airflow optimization control sequence; (5) Based on the airflow optimization control sequence, the fan speed compensation value is calculated, the speed and air outlet angle of each fan are adjusted, a local stress release channel is constructed, and the wind field uniformity is evaluated every 1 second to obtain wind field control data; (6) The wind field control data is substituted into the orthogonal fan control system. When it is detected that the airflow velocity fluctuation coefficient in each direction is less than 0.05 for 300 consecutive seconds and the wind field uniformity index is greater than 0.92, the airflow dried silicate product is obtained.

[0043] Specifically, the surface stress distribution parameters are extracted from the stress field distribution data of the pre-dried silicate parts. The eight-point stress sampling method is used to arrange eight stress measurement points at equal intervals along the circumferential direction on the surface of the parts. Each measurement point collects local stress values ​​through a high-precision strain gauge. The stress distribution vector is composed of these eight measurement values. The stress values ​​between adjacent sampling points are interpolated by the cubic spline interpolation algorithm to generate a 360-degree complete stress field distribution curve. The interpolation process uses a piecewise cubic polynomial to ensure the continuity and smoothness of the curve. The orthogonal airflow control equation is established based on the complete stress field distribution curve. Orthogonal airflow control involves fans in four directions: up, down, left, and right. The fan speed range in each direction is 0-5m / s. By establishing a correlation model between airflow velocity and stress distribution, the airflow in the four directions is collaboratively optimized. The optimization process takes into account the mutual interference effect of the airflow, and determines the optimal speed combination of fans in each direction through iterative calculation to form an airflow field intensity distribution table.

[0044] The airflow field intensity distribution table is used to guide the dynamic adjustment of the orthogonal fan array. A high-frequency sampling airflow direction sensor is used to collect airflow direction data every 0.1 second. Through the airflow field reconstruction algorithm, the sampled data is converted into an airflow vector field, and the airflow velocity and direction components in each direction are calculated. The airflow guidance parameters include characteristic quantities such as velocity magnitude, direction angle, and turbulence intensity. The airflow guidance parameters are input into the wind field optimization processing unit, and the optimal airflow distribution is calculated based on the coupling relationship between the wind field and the stress field. For areas with high stress, a progressive attenuation strategy is adopted to promote stress release by adjusting the local airflow intensity. The progressive attenuation processing is implemented through a piecewise function to ensure a smooth transition of airflow intensity. The final generated airflow optimization control sequence contains detailed operating parameters of each fan at different time periods.

[0045] Based on the airflow optimization control sequence, the speed compensation value of each fan is calculated. The compensation calculation takes into account factors such as the fan characteristic curve, airflow resistance and local pressure loss. By adjusting the fan speed and the guide plate angle, a local stress release channel is formed in the high stress area. The wind field uniformity is evaluated every 1 second, and the uniformity of the wind field is quantified by calculating the variance and mean ratio of the velocity field.

[0046] Taking the airflow drying of a batch of silicate preforms as an example, 8 stress measurement points were arranged on the surface of the parts, and local stress values ​​were collected by strain gauges. The cubic spline interpolation algorithm was applied to expand the 8 discrete data points into 360 data points to form a continuous stress distribution curve. According to the stress distribution characteristics, the speed configuration of the fans in four directions was calculated. The dynamic change data of the airflow field was obtained through high-frequency sampling at intervals of 0.1 seconds. The wind field optimization processing implemented progressive airflow control in high-stress areas based on the stress-airflow coupling model. After 300 seconds of stable operation, when it was detected that the airflow velocity fluctuation coefficient in each direction was less than 0.05 and the wind field uniformity index was greater than 0.92, the preparation of airflow-dried silicate parts was completed. The entire process achieved precise control of the airflow drying process through multi-level data acquisition, analysis and control.

[0047] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Perform a 24-point array stress field scan on the airflow-dried silicate product, record the stress distribution of the scan data at a sampling interval of 0.01 seconds, and form a three-dimensional stress distribution map through spatial interpolation processing to obtain stress field spatial data; (2) Based on the stress field spatial data, the boundaries of high and low stress areas are divided, and the stress values ​​are divided into two gradients: 2.5-3.0 MPa in the high stress area and 1.0-2.0 MPa in the low stress area. The stress values ​​of different stress areas are statistically analyzed to obtain stress partition data; (3) Energy distribution of the phased array microwave source is performed based on stress partition data. The phase difference of the 24 microwave sources is set within the range of 0-180 degrees. The energy output of each microwave source is collaboratively optimized to obtain the microwave energy distribution sequence. (4) Based on the microwave energy distribution sequence, a power of 2 kW / m³ and a pulse ratio of 2 s:3 s were used for energy input in the high stress area, and a power of 4 kW / m³ and a pulse ratio of 3 s:2 s were used for energy input in the low stress area, and the energy input control parameters were obtained; (5) Substitute the energy input control parameters into the microwave control system, collect energy absorption data every 0.5 seconds, calculate the spatial distribution characteristics of the energy field, dynamically compensate the energy input intensity, and obtain energy field optimization data; (6) The microwave energy uniformity of the energy field optimization data is evaluated. When it is detected that the energy field fluctuation rate in each region is less than 0.03 for 240 consecutive seconds and the energy uniformity index is greater than 0.95, a microwave-dried silicate product is obtained.

[0048] Specifically, the stress of airflow dried silicate parts was detected by a 24-point array stress field scanner. The stress field scanning uses 24 high-precision strain sensors arranged in a 4×6 matrix to cover the surface of the parts. The sampling interval is set to 0.01 seconds, and each sensor records the local stress value in real time. The discrete sampling point data is reconstructed in three dimensions by the Kriging spatial interpolation algorithm to generate a three-dimensional stress distribution map with a resolution of 100×100×50. For the three-dimensional stress distribution map, the stress threshold is set for regional division. The stress value of 2.5-3.0MPa is defined as a high stress area, and 1.0-2.0MPa is defined as a low stress area. The regional growing algorithm is used to determine the boundary of the stress area, and the boundary curve is fitted by the least squares method. Statistical analysis is performed on different stress areas, and the stress mean, standard deviation and spatial distribution characteristics of each area are calculated to form stress partition data.

[0049] Based on the stress partition data, energy is distributed to 24 phased array microwave sources. The phased array microwave source adopts magnetron mode, with an operating frequency of 2.45GHz, and realizes directional energy transmission through phase modulation. The phase difference of each microwave source is adjusted in the range of 0-180 degrees, and the phase difference is dynamically calculated by the sliding average algorithm to achieve precise control of the energy field. The power configuration of each microwave source is optimized by genetic algorithm to obtain the microwave energy distribution sequence. According to the energy distribution sequence, differentiated energy input strategies are implemented for different stress areas. The high stress area adopts a pulse mode of 2kW / m³ power and 2 seconds of emission and 3 seconds of pause, and the low stress area adopts a pulse mode of 4kW / m³ power and 3 seconds of emission and 2 seconds of pause. The pulse control adopts a precision timer with a time error of less than 1 millisecond. The energy absorption efficiency is calculated by real-time monitoring of the changes in the dielectric parameters of the material.

[0050] During the energy input process, energy absorption data is collected every 0.5 seconds. The surface temperature distribution is monitored by an infrared temperature sensor array, and the spatial distribution characteristics of the energy field are calculated in combination with the dielectric parameter measurement results. The energy input intensity is compensated in real time through the proportional integral control algorithm, and the compensation cycle is synchronized with the sampling cycle to ensure the uniformity of the energy field.

[0051] Taking the microwave drying of a batch of silicate products as an example, stress distribution data was obtained by scanning the stress field at 24 points. After spatial interpolation processing, two high stress areas and three low stress areas were identified. The phased array microwave source configured power according to the stress partition. The 8 microwave sources in the high stress area adopted low power pulse mode, and the 16 microwave sources in the low stress area adopted high power pulse mode. Through real-time monitoring at intervals of 0.5 seconds, energy absorption data was obtained and dynamic compensation was performed. After 240 seconds of stable operation, when the energy field fluctuation rate in each area was detected to be less than 0.03 and the energy uniformity index was greater than 0.95, the preparation of microwave dried silicate products was completed. The stress field scanning data was subjected to median filtering to remove noise, and then a continuous stress distribution map was generated by the Kriging interpolation algorithm. The phase calculation of the phased array microwave source adopted the sliding average method, and the power optimization adopted the genetic algorithm. The energy field uniformity was evaluated by the variance analysis method, and the uniformity was quantified by calculating the coefficient of variation of the energy density in each area.

[0052] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Place the microwave-dried silicate product in an ultrasonic stress relief system, perform three-dimensional scanning using an ultrasonic exciter with a power of 0.5-1.0 W / cm², collect surface strain data, and obtain an original strain data set; (2) Extracting spatial speckle feature points from the original strain data set, digitizing the speckle image through 20-40 kHz frequency modulation, constructing the spatial distribution matrix of the surface strain field, and obtaining the speckle feature data; (3) Perform dynamic response analysis on the speckle feature data, record the strain field changes at a sampling interval of 0.02 seconds, establish the strain-time relationship curve, and obtain the surface strain evolution data; (4) Based on the surface strain evolution data, the ultrasonic energy is input into different zones, the strain field is divided into different zones according to a gradient of 0.05%, and energy compensation calculations are performed on different strain regions to obtain the strain field control parameters; (5) The ultrasonic output is precisely controlled by adjusting the strain field parameters, the strain field uniformity is evaluated every 1 second, the surface strain distribution characteristics are calculated, and the strain field optimization data is obtained; (6) The strain field optimization data is input into the ultrasonic control system. When the surface strain value is detected to be less than 0.01% and the strain field uniformity is greater than 0.98 for 360 consecutive seconds, the target silicate product is obtained.

[0053] Specifically, a microwave-dried silicate workpiece is placed in an ultrasonic stress relief system. An ultrasonic vibrator with a power of 0.5-1.0W / cm² is used for three-dimensional scanning. The vibrator is made of piezoelectric ceramic material and has an operating frequency of 20-40kHz. The data acquisition system consists of a high-speed digital image processor and a CCD camera. The surface speckle image is collected through a charge-coupled device, and the strain data is recorded to form an original strain data set. The process of extracting spatial speckle feature points from the original data set adopts a digital image correlation algorithm. The speckle image is grayed, and the median filter is used to eliminate noise, and then the feature points are extracted by the corner detection algorithm. The feature point extraction adopts the Harris corner detection method to calculate the gray gradient of the local area of ​​the image and determine the position of the feature point. The frequency modulation adopts amplitude modulation, and the modulation frequency is scanned in the range of 20-40kHz to achieve high-quality digital processing of the speckle image. According to the extracted feature point coordinates, the surface strain field spatial distribution matrix is ​​constructed, and the matrix dimension is 1024×1024, and each unit records the strain value of the corresponding position.

[0054] When performing dynamic response analysis on the speckle feature data, the sampling interval is set to 0.02 seconds. The time-series variation data of the strain field is obtained through continuous sampling, and the strain-time data is fitted by the least squares method to establish a strain field evolution model. The model calculation takes into account the elastic properties and strain hysteresis effect of the material. The model parameters are optimized through iterative calculation to obtain the surface strain evolution data. The ultrasonic energy is partitioned and input controlled based on the surface strain evolution data. The strain field is partitioned according to the gradient value of 0.05%, and the boundary of each strain area is determined by the regional growing algorithm. For different strain areas, an energy compensation model is established to calculate the required ultrasonic power. The compensation calculation takes into account the acoustic impedance characteristics and energy attenuation law of the material. The energy distribution equation is solved by the numerical integration method to obtain the strain field control parameters.

[0055] The strain field control parameters are used for precise control of ultrasonic output. The control system uses a digital PID algorithm to adjust the ultrasonic power according to the real-time feedback strain data. The uniformity of the strain field is evaluated every 1 second, and the degree of dispersion of the strain distribution is calculated using the variance analysis method. The evaluation results are used to optimize the control parameters. The strain field optimization data includes the power adjustment sequence and frequency modulation parameters, which are used to achieve dynamic control of the strain field.

[0056] Taking the stress relief of a batch of silicate parts as an example, the surface was scanned by an ultrasonic vibrator. After the speckle image collected by the CCD camera was digitized, about 10,000 feature points were extracted. The feature point data was fitted by the least squares method to obtain the initial strain field distribution. Based on the gradient value of 0.05%, the strain field was divided into 5 regions, and the ultrasonic energy required for each region was calculated respectively. Through real-time monitoring at intervals of 1 second, the strain field change data was obtained and dynamically compensated. After 360 seconds of stable operation, when the surface strain value was detected to be less than 0.01% and the strain field uniformity was greater than 0.98, the preparation of the target silicate product was completed. The speckle image was grayed and filtered for preprocessing, and then the feature points were extracted by the Harris corner detection algorithm. The strain field evolution model was fitted by the least squares method, and the energy compensation calculation was calculated by the numerical integration method. The uniformity of the strain field was evaluated by variance analysis, and the uniformity was quantified by calculating the coefficient of variation.

[0057] The above describes the quick-setting and drying method of the silicate material in the embodiment of the present application. The following describes the quick-setting and drying device of the silicate material in the embodiment of the present application. Figure 3 In one embodiment of the quick-setting and drying device for silicate materials in the present application, the device comprises: The stirring module 201 is used to perform stress pre-regulation treatment on the silicate raw material, polypropylene fiber and nano-silicon dioxide through three-dimensional network construction analysis, and perform variable frequency biaxial stirring according to shear stress monitoring data to obtain pre-regulated slurry; The injection module 202 is used to perform gradient injection treatment on the core area, transition area and surface area according to the pre-adjusted slurry by using the zoned pressure control system, and obtain a silicate preform with an initial stress field through coordinated control of the temperature gradient; The adjustment module 203 is used to collect acoustic emission signals from the silicate preform, dynamically evaluate the stress field distribution through a time-frequency characteristic analysis algorithm, and adjust the temperature and humidity parameters using a closed-loop feedback system to obtain a pre-dried silicate preform; A drying module 204 is used to dynamically optimize the airflow intensity and direction according to the stress field distribution data of the pre-dried silicate product by using an orthogonal fan array control algorithm to obtain an airflow dried silicate product; The processing module 205 is used to scan the stress field of the airflow dried silicate product, and perform differentiated energy input processing on the high and low stress areas through a phased array microwave source to obtain a microwave dried silicate product; The monitoring module 206 is used to place the microwave-dried silicate product in an ultrasonic stress relief system, and dynamically monitor the surface strain field through a speckle strain detection algorithm to obtain a target silicate product.

[0058] Through the coordinated cooperation of the above-mentioned components, the design of stress pre-control treatment of silicate raw materials, polypropylene fibers and nano-silicon dioxide is carried out through three-dimensional network construction analysis, which realizes precise stress control in the raw material mixing process and effectively avoids the problem of uneven stress distribution in traditional mixing methods; the design of gradient injection treatment of the core area, transition area and surface area using a partitioned pressure control system establishes a complete pressure gradient control system to ensure the uniformity of pressure distribution in each area of ​​the workpiece; the design of dynamic evaluation of stress field distribution using acoustic emission signal acquisition and time-frequency feature analysis algorithm realizes real-time monitoring and precise evaluation of stress field distribution, providing an accurate basis for subsequent process parameter adjustment; the design of dynamic optimization of airflow intensity and direction based on orthogonal fan array control algorithm realizes precise control of airflow field and improves drying uniformity; the design of differentiated energy input treatment of high and low stress areas through phased array microwave source realizes precise control of energy input and avoids local overheating or underheating; the design of combining ultrasonic stress elimination technology with speckle strain detection algorithm realizes precise elimination and real-time monitoring of stress field. Especially in the application of artificial intelligence algorithms, the present invention adopts a variety of intelligent algorithms such as time-frequency feature analysis algorithm, orthogonal fan array control algorithm and speckle strain detection algorithm. The organic combination of these algorithms not only improves the automation level of the process, but also significantly enhances the optimization ability and control accuracy of process parameters. Through the intelligent optimization and dynamic adjustment of the algorithm, the coordinated control of the temperature field, pressure field and stress field in the drying process is achieved, ensuring the stability and consistency of product quality. At the same time, the application of these algorithms also greatly improves energy utilization efficiency, shortens the production cycle, and reduces production costs.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for rapid coagulation and drying of silicate materials, characterized in that: The rapid coagulation and drying method of the silicate material comprises: The silicate raw materials, polypropylene fibers and nano-silicon dioxide are subjected to stress pre-regulation through three-dimensional network construction analysis, and variable frequency biaxial stirring is performed according to shear stress monitoring data to obtain pre-regulated slurry; According to the pre-adjusted slurry, the core area, transition area and surface area are subjected to gradient injection treatment by using a zoned pressure control system, and a silicate preform with an initial stress field is obtained by coordinated control of the temperature gradient; Acoustic emission signals are collected from the silicate preform, stress field distribution is dynamically evaluated by a time-frequency characteristic analysis algorithm, and temperature and humidity parameters are adjusted by a closed-loop feedback system to obtain a pre-dried silicate preform; According to the stress field distribution data of the pre-dried silicate product, the airflow intensity and direction are dynamically optimized by an orthogonal fan array control algorithm to obtain an airflow dried silicate product; Scanning the stress field of the airflow dried silicate product, and performing differentiated energy input processing on high and low stress areas through a phased array microwave source to obtain a microwave dried silicate product; The microwave dried silicate product is placed in an ultrasonic stress elimination system, and the surface strain field is dynamically monitored by a speckle strain detection algorithm to obtain a target silicate product.

2. The rapid coagulation and drying method of silicate materials according to claim 1, characterized in that: The method of performing stress pre-regulation on silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis, and performing variable frequency biaxial stirring according to shear stress monitoring data to obtain pre-regulated slurry includes: The silicate raw material, polypropylene fiber and nano-silicon dioxide were proportioned according to the mass fractions of 98.4%, 1.0% and 0.6% by a high-precision mass measurement system to obtain the standard proportion data of the materials; Substituting the standard material ratio data into the three-dimensional network stress analysis equation, calculating the force between the mixture particles, obtaining the three-dimensional stress distribution data through response surface modeling, and obtaining the optimal ratio parameters; Based on the optimal ratio parameters, the mixture is dynamically scanned 360 degrees in all directions using a shear force sensor array to collect stress data of 15 spatial points to obtain a stress monitoring data set; Extracting time domain features and frequency domain features from the stress monitoring data set, establishing a stress-time curve, determining key stress inflection points through wavelet analysis, and obtaining a stress peak feature vector; A speed optimization function is constructed according to the stress peak characteristic vector, an optimal speed reduction curve is calculated in the range of 150-80 rpm, and a segmented speed control sequence is obtained; The segmented speed control sequence is input into the biaxial stirring system, and the shear stress value is collected every 0.5 seconds. When the shear stress of 30 consecutive sampling points is stabilized at 2.8±0.2 MPa, the pre-regulated slurry is obtained.

3. The rapid coagulation and drying method of silicate materials according to claim 1, characterized in that: The method comprises: performing gradient injection treatment on the core area, transition area and surface area by using a zoned pressure control system according to the pre-adjusted slurry, and obtaining a silicate preform with an initial stress field by coordinated control of the temperature gradient, including: The pre-controlled slurry is scanned with a 32×32 dot matrix through a pressure data acquisition array, the spatial distribution parameters of the pressure field are calculated based on the spatial spectrum density analysis, and the sampling data is reconstructed in three dimensions using a stress field reconstruction algorithm to obtain a three-dimensional pressure distribution map; The pressure distribution stereogram is imported into an intelligent pressure controller, and a pressure control curve is calculated by multiple iterative regression, with the pressure in the core area set at 18-20MPa, the pressure in the transition area set at 15-17MPa, and the pressure in the surface area set at 12-14MPa, and the pressure gradient characteristics are extracted to obtain a zone pressure optimization scheme; According to the partition pressure optimization scheme, combined with the pressure-time response characteristics, a pressure control compensation function is established, 24 pressure sensing nodes are arranged in the three partitions respectively, the sampling period is 0.01 seconds, and a pressure data dynamic compensation matrix is ​​constructed to obtain real-time feedback data of pressure control; Based on the real-time feedback data of the pressure control, the temperature of the core zone is adjusted to 35±0.2°C, the temperature of the transition zone is adjusted to 38±0.2°C, and the temperature of the surface zone is adjusted to 40±0.2°C through temperature-pressure coupling optimization calculation, and a temperature zone boundary characteristic curve is established to obtain multi-zone temperature control data; Reconstructing the time series of the multi-region temperature control data, extracting the temperature field evolution characteristics, establishing a boundary temperature gradient adaptive optimization model, constructing a temperature control optimization curve through temperature fluctuation variance analysis, and obtaining an intelligent temperature compensation sequence; The intelligent temperature compensation sequence is input into the temperature gradient controller, and when the temperature stability index of the core zone, transition zone and surface zone is greater than 0.98 and lasts for 180 seconds, and the pressure fluctuation coefficient is less than 0.02, the silicate preform with the initial stress field is obtained.

4. The rapid coagulation and drying method of silicate materials according to claim 1, characterized in that: The method collects acoustic emission signals from the silicate preform, dynamically evaluates the stress field distribution by using a time-frequency characteristic analysis algorithm, and adjusts the temperature and humidity parameters by using a closed-loop feedback system to obtain a pre-dried silicate preform, including: The silicate preform was scanned 360 degrees by an acoustic emission sensor array. 32 signal collection points were set on each scanning surface with a sampling frequency of 500 kHz. The amplitude, frequency, and duration characteristic parameters of the acoustic emission signal were extracted to obtain the original acoustic emission data set. The acoustic emission raw data set is input into a feature analysis processor, a mapping relationship between the acoustic emission signal and the stress field is established through stress wave propagation theory, the stress characteristic value of each sampling point is extracted, a three-dimensional stress distribution matrix is ​​constructed, and time-domain characteristic data of the stress field is obtained; Performing spectrum conversion on the stress field time domain characteristic data, extracting the main frequency components in the range of 0-100kHz, establishing the stress field frequency domain mapping function, calculating the stress field spatial distribution characteristics, and obtaining the stress field dynamic evaluation parameters; Based on the stress field dynamic evaluation parameters, a temperature and humidity closed-loop control equation is constructed, the drying chamber temperature is segmentedly adjusted within the range of 40-75°C, the relative humidity is dynamically compensated within the range of 45%-65%, a temperature and humidity response curve is established, and environmental parameter optimization data is obtained; Perform multi-dimensional analysis on the environmental parameter optimization data, extract the variation law of temperature and humidity field, establish parameter compensation strategy, determine the optimal control range through temperature and humidity gradient calculation, and obtain a closed-loop control sequence; Substitute the closed-loop control sequence into the temperature and humidity adjustment system, and when the stress field fluctuation index is less than 0.03, the temperature field stability is greater than 0.95, and the relative humidity change rate is less than 0.02 / min within 180 seconds of continuous monitoring, the pre-dried silicate product is obtained.

5. The rapid coagulation and drying method of silicate materials according to claim 1, characterized in that: The method of dynamically optimizing the airflow intensity and direction according to the stress field distribution data of the pre-dried silicate product by using an orthogonal fan array control algorithm to obtain an airflow dried silicate product includes: Surface stress distribution parameters are extracted from the stress field distribution data of pre-dried silicate parts, stress distribution vectors are constructed through eight-point stress sampling, and the stress values ​​at each sampling point are piecewise linearly interpolated to obtain a complete stress field distribution curve. An orthogonal airflow control equation is established based on the complete stress field distribution curve, the fan speeds in the four directions of up, down, left and right are regulated in a zone within the range of 0-5 m / s, and the airflow speed in each zone is collaboratively optimized to obtain an airflow field intensity distribution table; The airflow direction of the orthogonal fan array is dynamically adjusted according to the airflow field intensity distribution table, the airflow direction data is collected every 0.1 seconds, and the airflow vectors in each direction are calculated by wind speed field reconstruction to obtain airflow guidance parameters; The airflow guiding parameters are input into the wind field optimization processing unit, the optimal airflow distribution scheme is calculated according to the coupling relationship between the wind field and the stress field, the airflow intensity in the high stress area is progressively attenuated, and an airflow optimization control sequence is obtained; Based on the airflow optimization control sequence, the fan speed compensation value is calculated, the speed and air outlet angle of each fan are adjusted, a local stress release channel is constructed, and the wind field uniformity is evaluated every 1 second to obtain wind field control data; The wind field control data is substituted into an orthogonal fan control system, and when it is detected that the airflow velocity fluctuation coefficient in each direction is less than 0.05 and the wind field uniformity index is greater than 0.92 for 300 consecutive seconds, the airflow dried silicate product is obtained.

6. The rapid coagulation and drying method of silicate materials according to claim 1, characterized in that: The stress field of the airflow dried silicate product is scanned, and the high and low stress regions are subjected to differentiated energy input processing by a phased array microwave source to obtain a microwave dried silicate product, including: The airflow dried silicate parts were scanned with 24-point array stress field, and the stress distribution of the scanned data was recorded at a sampling interval of 0.01 seconds. A three-dimensional stress distribution map was formed through spatial interpolation processing to obtain stress field spatial data. Based on the stress field spatial data, the boundaries of high and low stress areas are divided, and the stress values ​​are divided into two gradients: 2.5-3.0 MPa in the high stress area and 1.0-2.0 MPa in the low stress area. The stress values ​​of different stress areas are statistically analyzed to obtain stress partition data; The energy of the phased array microwave source is distributed by using the stress partition data, the phase difference of the 24 microwave sources is set within the range of 0-180 degrees, and the energy output of each microwave source is collaboratively optimized to obtain a microwave energy distribution sequence; Based on the microwave energy distribution sequence, a power of 2 kW / m³ and a pulse ratio of 2s:3s are used for energy input to the high stress area, and a power of 4 kW / m³ and a pulse ratio of 3s:2s are used for energy input to the low stress area, and energy input control parameters are obtained; Substituting the energy input control parameters into the microwave control system, collecting energy absorption data every 0.5 seconds, calculating the spatial distribution characteristics of the energy field, dynamically compensating the energy input intensity, and obtaining energy field optimization data; The microwave energy uniformity evaluation is performed on the energy field optimization data. When it is detected that the energy field fluctuation rate in each region is less than 0.03 and the energy uniformity index is greater than 0.95 for 240 consecutive seconds, the microwave dried silicate product is obtained.

7. The rapid coagulation and drying method of silicate materials according to claim 1, characterized in that: The microwave dried silicate product is placed in an ultrasonic stress relief system, and the surface strain field is dynamically monitored by a speckle strain detection algorithm to obtain a target silicate product, including: The microwave-dried silicate product is placed in an ultrasonic stress relief system, and three-dimensional scanning is performed using an ultrasonic exciter with a power of 0.5-1.0W / cm² to collect surface strain data and obtain an original strain data set; Extracting spatial speckle feature points from the original strain data set, digitally processing the speckle image through 20-40 kHz frequency modulation, constructing a surface strain field spatial distribution matrix, and obtaining speckle feature data; Performing dynamic response analysis on the speckle feature data, recording the strain field changes at a sampling interval of 0.02 seconds, establishing a strain-time relationship curve, and obtaining surface strain evolution data; Based on the surface strain evolution data, ultrasonic energy is input into partitions, the strain field is partitioned according to a gradient of 0.05%, energy compensation calculations are performed on different strain areas, and strain field control parameters are obtained; The ultrasonic output is precisely controlled by the strain field control parameters, the strain field uniformity is evaluated every 1 second, the surface strain distribution characteristics are calculated, and the strain field optimization data is obtained; The strain field optimization data is input into an ultrasonic control system, and when it is detected that the surface strain value is less than 0.01% and the strain field uniformity is greater than 0.98 for 360 consecutive seconds, the target silicate product is obtained.

8. A rapid coagulation and drying device for silicate materials, used to implement the rapid coagulation and drying method for silicate materials as claimed in any one of claims 1 to 7, characterized in that: The quick-setting and drying device for silicate materials comprises: A stirring module is used to perform stress pre-regulation on silicate raw materials, polypropylene fibers and nano-silicon dioxide through three-dimensional network construction analysis, and to perform variable frequency biaxial stirring according to shear stress monitoring data to obtain pre-regulated slurry; The injection module is used to perform gradient injection treatment on the core area, transition area and surface area according to the pre-adjusted slurry using a zoned pressure control system, and obtain a silicate preform with an initial stress field through coordinated control of the temperature gradient; An adjustment module is used to collect acoustic emission signals from the silicate preform, dynamically evaluate the stress field distribution through a time-frequency characteristic analysis algorithm, and adjust the temperature and humidity parameters using a closed-loop feedback system to obtain a pre-dried silicate preform; A drying module, used for dynamically optimizing the airflow intensity and direction through an orthogonal fan array control algorithm according to the stress field distribution data of the pre-dried silicate product to obtain an airflow dried silicate product; A processing module, used for scanning the stress field of the airflow dried silicate product, and performing differentiated energy input processing on high and low stress areas through a phased array microwave source to obtain a microwave dried silicate product; The monitoring module is used to place the microwave-dried silicate product in an ultrasonic stress relief system, and dynamically monitor the surface strain field through a speckle strain detection algorithm to obtain a target silicate product.

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