Preparation method of high-pressure and high-permeability polyurethane grouting material and effect prediction system

Abstract

The application relates to the technical field of high-pressure-resistant high-permeability polyurethane grouting material preparation, and discloses a high-pressure-resistant high-permeability polyurethane grouting material preparation method and an effect prediction system. The method discloses a high-pressure-resistant high-permeability polyurethane grouting material preparation method and an effect prediction system. The preparation method is used for training a proportioning optimization network and a temperature control network, and is used for associating output parameters; rheological parameters, temperature and pressure data are collected in real time, a penetration pressure difference is calculated, an injection rate threshold value is generated, and a synthesis or correction instruction is generated after matching and checking. The effect prediction system comprises modeling, monitoring, analysis and other units, and realizes multi-parameter dynamic analysis and performance prediction. Through data driving regulation and prediction, the material preparation precision and the penetration stability under high pressure are improved, and problems such as large batch difference and insufficient parameter coordination in traditional processes are solved.

Description

Technical Field

[0001] This invention relates to the field of high-pressure resistant and high-permeability polyurethane grouting material preparation technology, specifically to a method for preparing high-pressure resistant and high-permeability polyurethane grouting material and an effect prediction system. Background Technology

[0002] Polyurethane grouting materials are important seepage prevention, plugging, and structural reinforcement materials in the field of civil engineering, and their performance directly affects the quality and safety of projects. The application of traditional polyurethane grouting materials under high-pressure environments faces significant technical bottlenecks, mainly in the following aspects:

[0003] Current polyurethane grouting material preparation methods largely rely on fixed ratios of polyols and isocyanates, lacking a dynamic correlation model between raw material rheological parameters and reaction conditions. For example, differences in hydroxyl values, molecular weight distributions of polyols, or functionality of isocyanates from different batches can lead to fluctuations in rheological parameters such as initial viscosity and gel time. However, traditional processes cannot capture these changes in real time and adjust the ratio accordingly, easily resulting in uneven cross-linking of the reaction system and ultimately unstable compressive strength and permeability of the material. Furthermore, reaction temperature control often employs preset isothermal modes without dynamically optimizing the temperature profile using real-time rheological data, potentially causing localized overheating or incomplete reactions, affecting the uniformity of the material's microstructure.

[0004] In underground engineering and tunneling through water-rich strata, grouting materials must maintain high permeability under high pressure to ensure effective diffusion of the grout into tiny fissures. Traditional methods rely solely on experience to set the material injection rate, without establishing a quantitative correlation model between the pressure field and permeability. For example, when the pressure distribution within the reaction system is uneven, a fixed injection rate may lead to localized grout accumulation or premature blockage of the permeation path, reducing grouting efficiency. Furthermore, the lack of real-time monitoring of dynamic parameters such as molecular diffusion coefficients and viscosity gradients during the reaction process makes it difficult to accurately predict the material's permeation path and diffusion range under high pressure, resulting in uncontrollable grouting effects.

[0005] The high pressure resistance and high permeability of polyurethane grouting materials need to be achieved through the coordinated control of multiple parameters such as proportioning, temperature, and pressure. In traditional preparation processes, each parameter control module operates independently, lacking a correlation mechanism based on reaction time. For example, the proportioning adjustment and temperature control are not dynamically coupled according to the reaction process, which may lead to an excessively fast early reaction rate, forming a rigid gel that hinders later penetration; or an excessively slow reaction rate, resulting in grout loss. In addition, pressure monitoring is only used for safety warnings and does not form a closed-loop feedback with material injection rate and proportion optimization, failing to achieve intelligent control of "monitoring-analysis-control" and making it difficult to meet the high performance and high reliability requirements of complex engineering projects.

[0006] Current performance predictions for polyurethane grouting materials are mostly based on static empirical formulas, failing to integrate real-time rheological data, temperature profiles, and pressure parameters. For example, calculating the relationship between osmotic pressure difference and injection rate using fixed formulas cannot reflect the impact of dynamic changes in the reaction system on permeability, leading to significant discrepancies between predicted results and actual performance. The lack of predictive models capable of real-time correlation of multiple physical field parameters makes it difficult for engineers to accurately assess material performance during the preparation stage, necessitating adjustments through subsequent on-site testing, increasing construction costs and timelines.

[0007] Traditional techniques lack dynamic proportioning optimization, multi-parameter collaborative control, and real-time performance prediction models, resulting in insufficient permeability and stability of polyurethane grouting materials under high-pressure environments, thus limiting their application in complex geological conditions. Therefore, there is an urgent need to develop a data-driven intelligent preparation method and prediction system to achieve precise control and reliable prediction of material properties. Summary of the Invention

[0008] The purpose of this invention is to provide a method for preparing high-pressure resistant and high-permeability polyurethane grouting materials and an effect prediction system to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing a high-pressure resistant, high-permeability polyurethane grouting material, the method comprising:

[0010] Step S1: Obtain the initial rheological parameters of the polyol component and the isocyanate component; train multiple sets of rheological parameters under different ratios using a rheological analysis model to obtain a ratio optimization network; train multiple sets of reaction temperature curves to obtain a temperature control network; and correlate the output parameters of the ratio optimization network and the temperature control network according to the reaction time.

[0011] Step S2: Obtain the real-time rheological parameters of the polyol component and isocyanate component of the current batch, as well as the polymerization reactor temperature data collected synchronously when obtaining the real-time rheological parameters.

[0012] Step S3: The optimal ratio adjustment amount is obtained by identifying the real-time rheological parameters through the ratio optimization network; the temperature control command is obtained by identifying the polymerization reactor temperature data through the temperature control network.

[0013] Step S4: Obtain the pressure sensor monitoring value of the current reaction system;

[0014] Step S5: Calculate the real-time osmotic pressure difference of the reaction system based on the monitoring value of the pressure sensor;

[0015] Step S6: Generate a material injection rate threshold based on the real-time osmotic pressure difference;

[0016] Step S7: If the material injection rate threshold matches the optimal ratio adjustment amount, then obtain the set reaction temperature curve according to the correlation between the ratio optimization network and the temperature control network parameters; determine whether the temperature control command corresponds to the set reaction temperature curve. If yes, generate a material synthesis command; otherwise, generate a parameter correction command.

[0017] Preferably, step S2 includes:

[0018] Step S21: A rheometer probe is installed on the side wall of the polymerization reactor; the rheometer probe is connected to the data input terminal of the reaction controller;

[0019] Step S22: A first mass flow meter is installed on the outlet pipeline of the polyol storage tank; a second mass flow meter is installed on the outlet pipeline of the isocyanate storage tank.

[0020] Step S23: Three temperature sensors are axially and equidistantly distributed inside the polymerization reactor; the temperature sensors are connected to the input terminal of the reaction controller.

[0021] Step S24: When the reaction controller receives the reaction start signal, the output of the reaction controller drives the rheometer probe to collect real-time rheological parameters; at the same time, it drives the temperature sensor to collect the temperature data of the polymerization reactor.

[0022] Preferably, step S5 includes: acquiring the viscosity gradient monitoring value of the current reaction system; calculating the real-time molecular diffusion coefficient of the reaction system based on the pressure sensor monitoring value and the viscosity gradient monitoring value; acquiring the material permeation path length based on the real-time molecular diffusion coefficient; and generating the reaction start signal based on the material permeation path length.

[0023] Preferably, step S5 includes: dividing the reaction system into three-dimensional pressure distribution regions based on the pressure sensor monitoring values; obtaining the osmotic pressure difference change rate and the coordinates of the intersection point of the region boundaries based on the three-dimensional pressure distribution regions; and generating the reaction initiation signal based on the osmotic pressure difference change rate and the coordinates of the intersection point of the region boundaries.

[0024] Preferably, step S2 includes:

[0025] Obtain the first real-time flow rate value of the polyol component and the second real-time flow rate value of the isocyanate component;

[0026] Determine whether the first real-time flow rate value and the second real-time flow rate value reach the set ratio range. If so, verify the molar ratio of the first real-time flow rate value and the second real-time flow rate value based on the molecular weight ratio of the polyol and the isocyanate. If the verification passes, obtain the real-time rheological parameters and the polymerization reactor temperature data. If it fails, return to this step to obtain the flow rate value again.

[0027] Determine whether the first real-time traffic value and the second real-time traffic value reach a set ratio range. If not, generate a traffic anomaly alarm signal.

[0028] Preferably, step S7 includes: if the material injection rate threshold does not match the optimal ratio adjustment amount, then obtain the real-time viscosity value through the viscometer at the bottom of the reactor outlet, and return to this step to determine whether the material injection rate threshold matches the optimal ratio adjustment amount.

[0029] Preferably, the present invention also includes a high-pressure resistant and high-permeability polyurethane grouting material effect prediction system, the system comprising:

[0030] The modeling unit is configured to acquire the initial rheological parameters of the polyol component and the isocyanate component, train multiple sets of rheological parameters under different ratios through a rheological analysis model to obtain a ratio optimization network, train multiple sets of reaction temperature curves to obtain a temperature control network, and correlate the output parameters of the ratio optimization network and the temperature control network according to the reaction time.

[0031] The monitoring unit is configured to acquire real-time rheological parameters of the polyol component and isocyanate component of the current batch, as well as the polymerization reactor temperature data collected synchronously when acquiring the real-time rheological parameters.

[0032] The analysis unit is configured to identify the real-time rheological parameters through the ratio optimization network to obtain the optimal ratio adjustment amount; and to identify the polymerization reactor temperature data through the temperature control network to obtain temperature control commands.

[0033] A pressure acquisition unit is configured to acquire the pressure sensor monitoring values ​​of the current reaction system.

[0034] A permeation calculation unit is configured to calculate the real-time permeation pressure difference of the reaction system based on the monitoring values ​​of the pressure sensor.

[0035] A rate generation unit configured to generate a material injection rate threshold based on the real-time osmotic pressure difference;

[0036] The execution unit is configured to, if the material injection rate threshold matches the optimal ratio adjustment amount, obtain the set reaction temperature curve based on the correlation between the ratio optimization network and the temperature control network parameters; determine whether the temperature control command corresponds to the set reaction temperature curve; if yes, generate a material synthesis command; otherwise, generate a parameter correction command.

[0037] Preferably, the monitoring unit is further configured as follows:

[0038] A rheometer probe is installed on the side wall of the polymerization reactor; the rheometer probe is connected to the data input terminal of the reaction controller;

[0039] A first mass flow meter is installed on the outlet pipeline of the polyol storage tank; a second mass flow meter is installed on the outlet pipeline of the isocyanate storage tank.

[0040] Three temperature sensors are axially and equidistantly distributed inside the polymerization reactor; the temperature sensors are connected to the input terminal of the reaction controller.

[0041] When the reaction controller receives the reaction start signal, the output of the reaction controller drives the rheometer probe to collect real-time rheological parameters; at the same time, it drives the temperature sensor to collect the temperature data of the polymerization reactor.

[0042] Preferably, the permeation calculation unit is further configured to: acquire the viscosity gradient monitoring value of the current reaction system; calculate the real-time molecular diffusion coefficient of the reaction system based on the pressure sensor monitoring value and the viscosity gradient monitoring value; acquire the material permeation path length based on the real-time molecular diffusion coefficient; and generate the reaction start signal based on the material permeation path length; or

[0043] The permeation calculation unit is further configured to divide the reaction system into three-dimensional pressure distribution regions based on the pressure sensor monitoring values; obtain the rate of change of osmotic pressure difference in each region and the coordinates of the intersection point of the region boundary based on the three-dimensional pressure distribution regions; and generate the reaction start signal based on the rate of change of osmotic pressure difference and the coordinates of the intersection point of the region boundary.

[0044] Preferably, the monitoring unit is further configured as follows:

[0045] Obtain the first real-time flow rate value of the polyol component and the second real-time flow rate value of the isocyanate component;

[0046] Determine whether the first real-time flow rate value and the second real-time flow rate value reach the set ratio range. If so, verify the molar ratio of the first real-time flow rate value and the second real-time flow rate value based on the molecular weight ratio of the polyol and the isocyanate. If the verification passes, obtain the real-time rheological parameters and the polymerization reactor temperature data. If it fails, return to this step to obtain the flow rate value again.

[0047] Determine whether the first real-time traffic value and the second real-time traffic value reach a set ratio range. If not, generate a traffic anomaly alarm signal.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] By collecting initial rheological parameters (such as viscosity and shear rate) of polyols and isocyanates, and using a ratio optimization network to train rheological characteristics under different ratios, intelligent analysis of real-time rheological data is achieved. When the rheological parameters of the current batch of raw materials deviate from the preset range, the system automatically calculates the optimal ratio adjustment amount to compensate for performance fluctuations caused by batch differences in raw materials. For example, if the viscosity of a certain batch of polyol is too high, the system can increase the proportion of isocyanate in real time to balance the crosslinking density, ensuring that the compressive strength and permeability coefficient of materials in different batches remain consistent, thus solving the stability problem of traditional fixed ratio processes.

[0050] The temperature control network establishes a dynamic correlation model between temperature and reaction time by training multiple sets of reaction temperature curves. During the reaction, the system automatically generates the set reaction temperature curve based on real-time rheological data and ratio adjustments, enabling dynamic switching of temperature parameters. For example, in the initial stage of the reaction, heating accelerates the cross-linking induction period; in the middle stage, isothermal control controls the gelation rate; and in the later stage, cooling stabilizes the microstructure. This avoids the reaction lag or overheating decomposition problems caused by traditional isothermal control, improves the uniformity of molecular chain distribution, and thus enhances its high-pressure resistance.

[0051] The system monitors the reaction system pressure in real time using pressure sensors and calculates the molecular diffusion coefficient and permeation path length based on viscosity gradient data, dynamically generating a material injection rate threshold. When the pressure distribution is uneven, the system automatically divides the system into three-dimensional pressure zones and adjusts the injection strategy according to the rate of change of osmotic pressure difference in each zone to avoid local blockage or slurry loss. For example, the injection rate is reduced in high-pressure zones to prevent premature gel formation, while the rate is increased in low-pressure zones to expand the permeation range, ensuring uniform diffusion of the slurry in complex pressure fields and improving the material's permeability and grouting efficiency.

[0052] A multi-physics collaborative control model is established by correlating the output parameters of the ratio optimization network and the temperature control network along the reaction time axis. For example, when the ratio adjustment causes a change in the reaction exothermic rate, the temperature control network simultaneously adjusts the heating curve to maintain thermal equilibrium, avoiding system burst polymerization or incomplete solidification caused by uneven exothermic reaction. Simultaneously, pressure monitoring and flow calibration modules (such as a mass flow meter to monitor the flow ratio of polyol and isocyanate in real time) form a dual control system, ensuring precise matching of the molar ratio of the reaction system. This fundamentally guarantees the stability of the cross-linked structure and improves the compressive strength and impermeability of the material.

[0053] The performance prediction system integrates modeling, monitoring, and analysis units. By collecting rheological parameters, temperature data, and pressure values ​​in real time, it dynamically predicts the permeability and mechanical properties of materials. Compared to traditional empirical formulas, this system, based on a neural network model built from a large amount of training data, can accurately capture the nonlinear relationships between parameters. For example, it predicts the molecular diffusion coefficient using pressure sensor values ​​and viscosity gradients, thereby assessing the permeation path length. This provides real-time and reliable performance references for engineering applications, reducing on-site testing costs and construction risks.

[0054] From raw material flow monitoring (such as real-time calibration of the molar ratio using the first and second mass flow meters) to automatic control of temperature and pressure parameters, the entire preparation process achieves closed-loop automation. For example, when the flow ratio exceeds the set range, the system automatically triggers an alarm and re-collects data, avoiding imbalances caused by human error. The axially equidistant distribution design of the rheometer probe and temperature sensor ensures spatial uniformity of data acquisition, reduces monitoring errors caused by deviations in the measurement point positions, and improves the intelligence and standardization of the preparation process. Attached Figure Description

[0055] Figure 1 This is a schematic diagram illustrating the working principle of the high-pressure resistant and high-permeability polyurethane grouting material preparation method described in this invention.

[0056] Figure 2 This is a graph showing the osmotic pressure calculation based on the viscosity gradient.

[0057] Figure 3 This is an analysis diagram of the three-dimensional pressure distribution permeability;

[0058] Figure 4 A control chart for dual verification of raw material flow rate;

[0059] Figure 5 Design diagram of the unit architecture for the effect prediction system. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Please see Figures 1-5 The present invention relates to a method for preparing a high-pressure resistant and high-permeability polyurethane grouting material, the specific implementation steps of which are as follows:

[0062] Step S1: Constructing the ratio optimization network and temperature control network. First, the initial rheological parameters of the polyol and isocyanate components are obtained. A rheological analysis model is then used to train the rheological parameters under multiple different ratios to establish a ratio optimization network. This network can output corresponding ratio adjustment strategies based on the differences in rheological parameters. Simultaneously, multiple reaction temperature curves are trained to obtain a temperature control network, which can generate corresponding temperature control commands based on temperature data. Furthermore, based on the reaction time dimension, the correlation between the output parameters of the ratio optimization network and the temperature control network is established to form a dynamic collaborative control model.

[0063] Step S2: Real-time data acquisition. Obtain the real-time rheological parameters of the polyol and isocyanate components of the current batch, and simultaneously acquire the polymerization reactor temperature data at the time of acquiring the real-time rheological parameters, providing real-time basic data for subsequent parameter analysis.

[0064] Step S3: Parameter Optimization and Control. Real-time rheological parameters are input into the proportioning optimization network, and the optimal proportioning adjustment amount is identified through the network algorithm to optimize the raw material component ratio; the polymerization reactor temperature data is input into the temperature control network, and the network calculation identifies and generates temperature control commands to achieve dynamic control of the reaction temperature.

[0065] Step S4: Pressure Data Acquisition. The pressure monitoring values ​​of the reaction system are acquired in real time using pressure sensors within the system, providing crucial data for calculating the osmotic pressure difference.

[0066] Step S5: Osmotic pressure difference calculation. Based on the pressure sensor monitoring values, the real-time osmotic pressure difference of the reaction system is calculated using a specific algorithm. This parameter is used to characterize the osmotic performance status of the reaction system.

[0067] Step S6: Injection Rate Threshold Generation. Based on the real-time osmotic pressure difference, combined with the material reaction characteristics and process requirements, a material injection rate threshold is generated as the basis for controlling the material injection rate.

[0068] Step S7: Instruction Generation and Verification. First, determine whether the material injection rate threshold matches the optimal ratio adjustment amount. If they match, obtain the set reaction temperature curve based on the correlation between the ratio optimization network and the temperature control network parameters. Next, determine whether the temperature control command corresponds to the set reaction temperature curve. If they correspond, generate a material synthesis command and start the material synthesis process. If they do not correspond, generate a parameter correction command to correct the relevant network parameters or control strategies.

[0069] The present invention will be further described below with reference to Examples 1 to 5:

[0070] Example 1: The core of step S2 is to obtain the real-time rheological parameters of the polyol component and isocyanate component of the current batch, and to simultaneously collect the temperature data of the polymerization reactor. The implementation method is achieved through the collaborative design of hardware configuration, signal transmission logic and data acquisition process.

[0071] In terms of the hardware structure design of the polymerization reactor, a rheometer probe is installed on the side wall of the reactor. The installation position of the rheometer probe must allow direct contact with the reaction system to ensure that the collected rheological parameters accurately reflect the internal state of the system. The rheometer probe is connected to the data input terminal of the reaction controller. The connection method can be wired transmission (such as shielded cable) or wireless transmission (such as Bluetooth or Wi-Fi), but the stability and anti-interference ability of the signal transmission must be guaranteed to avoid data loss or distortion due to signal interruption or noise interference. The type of rheometer probe can be selected according to the characteristics of the reaction system. For example, a cone-plate rheometer probe can be used for high-viscosity systems, while a rotating cylindrical rheometer probe can be used for low-viscosity systems. Its function is to monitor the rheological parameters of the reaction system in real time, such as viscosity, shear rate, and shear stress. These parameters are key to judging whether the material ratio is reasonable and whether the reaction process is normal.

[0072] In the metering stage of the raw material delivery pipeline, a first mass flow meter is installed on the outlet pipeline of the polyol storage tank, and a second mass flow meter is installed on the outlet pipeline of the isocyanate storage tank. The installation of the mass flow meters must comply with pipeline design specifications to ensure stable fluid flow and avoid affecting metering accuracy due to fluid disturbances caused by pipeline bends, valves, and other components. The two mass flow meters independently measure the real-time flow rates of the polyol and isocyanate components, respectively, with metering accuracy meeting process requirements (e.g., ±0.1%FS) to achieve precise monitoring of the feed rates of both components. The output signals of the mass flow meters (e.g., 4-20mA current signal, RS485 digital signal) are connected to the input terminal of the reaction controller, enabling the reaction controller to acquire real-time flow data of the two components, providing basic data support for subsequent flow ratio verification and molar ratio calculation.

[0073] For temperature monitoring in the polymerization reactor, three temperature sensors are axially and equidistantly distributed inside the reactor. This axial equidistant distribution aims to cover areas at different heights within the reactor, avoiding the limitations of single-point temperature monitoring and ensuring the acquisition of the temperature distribution of the reaction system in the vertical direction. The temperature sensors can be thermocouples or resistance temperature detectors (RTDs), with a measurement range covering a preset reaction temperature range (e.g., 0-200℃) and an accuracy meeting process control requirements (e.g., ±0.5℃). Each of the three temperature sensors is connected to the input of the reaction controller via independent signal lines. The reaction controller can acquire temperature data from each sensor in real time and process the multiple temperature data points using algorithms (e.g., averaging, determining temperature gradients) to more accurately reflect the overall temperature state and distribution characteristics within the polymerization reactor.

[0074] Regarding the control logic and data acquisition process, when the reaction controller receives a reaction start signal, it triggers a real-time data acquisition program. The reaction start signal can be manually issued by the operator through a human-machine interface, or automatically generated by the upper-level control system according to a preset process sequence. Upon receiving the start signal, the output of the reaction controller synchronously drives the rheometer probe and temperature sensor to begin operation: on one hand, it sends an acquisition command to the rheometer probe, causing it to acquire real-time rheological parameters at a set frequency (e.g., once per second), and feeds the acquired data back to the reaction controller via a signal transmission link; on the other hand, it sends an activation signal to the temperature sensor, enabling it to enter real-time monitoring mode, continuously acquiring polymerization reactor temperature data and transmitting it to the reaction controller. This synchronous driving mechanism ensures that the acquisition time points of the rheological parameters and temperature data are consistent, avoiding data mismatch problems caused by time differences, thereby guaranteeing the accuracy of parameter correlation in subsequent analysis.

[0075] To ensure the reliability of data acquisition, hardware equipment requires regular calibration and maintenance. For example, rheometer probes need regular zero-point calibration and sensitivity verification to ensure the accuracy of rheological parameter measurements; mass flow meters need regular flow calibration, which can be performed using standard volume containers or high-precision calibration devices to ensure that flow measurement errors are within acceptable limits; temperature sensors need regular temperature calibration, which can be performed by comparison using a constant temperature bath or a standard temperature source to ensure the authenticity of temperature measurements. Furthermore, the reaction controller's software system needs to have data filtering and outlier detection functions to preprocess the acquired raw data, eliminating abnormal data points caused by equipment vibration, electromagnetic interference, and other factors, ensuring the quality of data input to the proportioning optimization network and temperature control network.

[0076] During actual operation, operators can view rheological parameters, temperature data, and flow information in real time through the human-machine interface (HMI) of the reaction controller. The HMI can be designed as a graphical interface, dynamically displaying the changing trends of various parameters in the form of curves, tables, etc., allowing operators to intuitively judge the state of the reaction system. For example, a sudden change in the rheological parameter curve may indicate an abnormal material ratio or a sudden change in the reaction rate; abnormal fluctuations in temperature data may indicate a malfunction in the heating or cooling system; and deviations in flow information may indicate blockage in metering equipment or pipeline leaks. Operators can take timely adjustment measures based on the interface prompts, such as manually adjusting the ratio and checking the equipment operating status, achieving manual intervention and monitoring of the preparation process.

[0077] Example 2: This example expands upon the calculation logic of step S5. The core lies in introducing collaborative analysis of viscosity gradient monitoring values ​​and pressure sensor monitoring values ​​to calculate the real-time molecular diffusion coefficient and material permeation path length of the reaction system, thereby generating a reaction initiation signal. Details are as follows:

[0078] In the data acquisition phase, it is necessary to obtain the viscosity gradient monitoring values ​​and pressure sensor monitoring values ​​of the current reaction system. Viscosity gradient monitoring values ​​are acquired in real time through a viscosity monitoring device installed inside the polymerization reactor. This device can consist of multiple viscosity sensors distributed radially or axially along the polymerization reactor, for example, sensors installed at different radii or heights on the inner wall of the reactor to monitor the viscosity differences of the reaction system in spatial dimensions. Each viscosity sensor can be based on a rotational, vibrational, or ultrasonic method; the appropriate type must be selected according to the physical characteristics of the reaction system (such as viscosity range and flowability) to ensure accurate capture of subtle changes in the viscosity gradient. Pressure sensor monitoring values ​​are obtained through pressure sensors installed inside the polymerization reactor or in pipelines. These sensors must possess high sensitivity and stability, be able to respond in real time to dynamic fluctuations in pressure within the reaction system, and have a measurement range covering the pressure range that may occur during the preparation process.

[0079] In the parameter calculation stage, the real-time molecular diffusion coefficient of the reaction system is first calculated using pressure sensor readings and viscosity gradient readings. The molecular diffusion coefficient is a physical quantity characterizing the ability of molecules to diffuse in a medium, and its calculation requires combining fluid dynamics theory with the characteristic parameters of the reaction system. Specifically, the pressure sensor readings reflect the overall pressure state of the reaction system, while the viscosity gradient readings reflect the differences in viscous resistance in different regions of the system. By establishing a coupled model of pressure and viscosity gradient, the motion law of molecules overcoming viscous resistance under pressure is analyzed, and thus the real-time molecular diffusion coefficient is derived. The construction of this model needs to comprehensively consider factors such as the molecular structure of the polyol and isocyanate, and the chemical state of the reaction stage (e.g., the degree of prepolymer formation), to ensure that the calculation results can accurately reflect the actual ability of molecular diffusion.

[0080] After obtaining the real-time molecular diffusion coefficient, the material penetration path length is further calculated. The material penetration path length refers to the spatial distance over which a material can effectively diffuse and undergo a chemical reaction within the reaction system. Its calculation requires consideration of the real-time molecular diffusion coefficient, reaction time, material characteristic parameters (such as molecular weight and diffusion activation energy), and the geometric parameters of the polymerization reactor (such as inner diameter and height). For example, by integrating the molecular diffusion coefficient over time and combining it with the spatial coordinates of the reaction system, the diffusion distance of material molecules in different directions can be simulated, thereby determining the specific length of the material penetration path. This process requires the use of numerical calculation methods (such as the finite difference method and the finite element method) to discretize the diffusion process, enabling accurate calculation of the penetration path in complex three-dimensional space.

[0081] In the logic judgment and signal generation stage, a reaction start signal is generated based on the material penetration path length. The triggering conditions for the reaction start signal need to be preset. For example, when the material penetration path length reaches a certain proportion (e.g., 80%) of the polymerization reactor diameter or a preset minimum effective reaction distance, it is considered that the material has sufficiently diffused and meets the conditions for uniform reaction, at which point the reaction start signal is generated. This signal can be transmitted to the actuators of the polymerization reactor, such as the stirring system and heating / cooling system, through the output port of the reaction controller, triggering the formal start of the reaction program. If the material penetration path length does not reach the preset threshold, the real-time molecular diffusion coefficient and the material penetration path length continue to be monitored until the conditions are met.

[0082] To ensure the accuracy of the calculation process, relevant parameters need to be calibrated and verified. For example, viscosity monitoring devices require regular multi-point calibration by testing in standard fluids of known viscosity to correct sensor measurement errors; pressure sensors need to be calibrated using standard pressure sources to ensure the accuracy of pressure measurement. Furthermore, molecular diffusion models and permeation path calculation methods need theoretical verification and experimental calibration. Model parameters and calculation coefficients can be adjusted by comparing simulation results with actual material diffusion experimental data (such as observing the material diffusion range using staining tracers) to improve model reliability.

[0083] In practical applications, the viscosity gradient and pressure state of the reaction system may be affected by various factors, such as fluctuations in the raw material ratio, temperature changes, and stirring rate. Therefore, the implementation method of this embodiment needs to be dynamically adaptable, capable of responding to changes in these factors in real time and adjusting the calculation results. For example, when a sudden increase in the polymerization reactor temperature is detected, the reaction controller can automatically call the temperature correction coefficient to adjust the calculation model of the molecular diffusion coefficient to reflect the impact of temperature changes on molecular mobility; when an abnormal increase in the viscosity gradient is detected, the system can automatically trigger an early warning mechanism to prompt the operator to check the raw material ratio or the operating status of the stirring device to avoid deviations in calculation results due to abnormal parameters.

[0084] Operators can view the dynamic changes of parameters such as viscosity gradient, pressure value, real-time molecular diffusion coefficient, and material permeation path length in real time through the system's monitoring interface. The monitoring interface can be designed as a three-dimensional visualization model, intuitively displaying the distribution of viscosity and pressure within the reaction system, as well as the expansion process of the material permeation path. For example, different colors can be used to map the intensity of the viscosity gradient, contour lines can represent pressure distribution, and dynamic curves can track the extension trajectory of the material permeation path, helping operators quickly grasp the internal state of the reaction system, promptly identify potential problems, and take intervention measures.

[0085] Example 3: This example describes another implementation of step S5. A three-dimensional pressure distribution model of the reaction system is constructed using pressure sensor monitoring values, thereby generating a reaction initiation signal. The specific implementation includes pressure data acquisition, three-dimensional region division, parameter calculation, signal generation, and system interaction. Each step is implemented collaboratively through hardware configuration and algorithmic logic.

[0086] Pressure data acquisition relies on pressure sensors installed inside the polymerization reactor or on critical pipelines. These sensors must be multi-point positioned, evenly distributed at different heights on the top, bottom, and side walls of the reactor to obtain pressure values ​​of the reaction system in space. Piezoresistive or capacitive pressure sensors can be used, with measurement accuracy meeting process requirements (e.g., ±0.5% FS) and a measurement range covering 0 to the design maximum reaction pressure (e.g., 150 MPa). Each pressure sensor is connected to the reaction controller via an independent signal transmission line (e.g., shielded cable) to ensure real-time pressure data is transmitted to the system with low latency and high fidelity.

[0087] In the three-dimensional pressure distribution region segmentation stage, the reaction controller processes the pressure sensor monitoring values ​​using a three-dimensional spatial modeling algorithm. This algorithm, based on the finite element analysis principle, discretizes the internal space of the polymerization reactor into multiple tiny cubic units (mesh), and the pressure value of each unit is calculated through interpolation of data from neighboring sensors. The specific formula is as follows:

[0088]

[0089] Where P(x,y,z,t) represents the pressure value at spatial coordinates (x,y,z) at time t, n is the number of sensors involved in the interpolation, and P i (t) represents the pressure value measured by the i-th sensor at time t, w i (t) represents the weighting coefficient of the i-th sensor at time t. This coefficient is dynamically adjusted based on the spatial distance between the sensor and the target coordinates and the signal reliability (the closer the distance and the higher the reliability, the greater the weight). Using this formula, the system can construct a three-dimensional pressure distribution cloud map of the reaction system at any time, intuitively presenting the spatial distribution characteristics of pressure (such as the location and range of high-pressure and low-pressure areas).

[0090] After the regions are divided, the system needs to calculate the rate of change of osmotic pressure difference in each three-dimensional pressure distribution region. The rate of change of osmotic pressure difference reflects the rate of pressure change over time within a specific region, and the calculation formula is:

[0091]

[0092] Where, ΔP rateP(t) represents the rate of change of osmotic pressure difference at time t, P(t) represents the average pressure value in the region at the current time, and P(t-Δt) represents the average pressure value in the region at the previous time Δt (time interval, such as 1 second). This parameter is used to measure the stability of the pressure distribution: if the absolute value of the rate of change is large, it indicates that the pressure fluctuation in the region is violent, and there may be problems such as uneven reaction or fluid disturbance; if the rate of change approaches zero, it indicates that the pressure state tends to be stable.

[0093] Boundary intersection points refer to the locations where two or more pressure distribution regions meet. Their coordinates are obtained by solving for the intersection lines of pressure isosurfaces in different regions. For example, for a high-pressure region and a low-pressure region, their boundary is formed by isosurfaces corresponding to a pressure threshold (e.g., set to 80 MPa). The intersection line between these isosurfaces is the regional boundary, and the intersection point of the boundaries is the junction point. These coordinate points are key locations for pressure abrupt changes, and their positional changes can reflect the movement of the fluid interface within the reaction system or the direction of the chemical reaction.

[0094] In the reaction start-up signal generation logic, the system first presets a threshold for the rate of change of osmotic pressure difference (e.g., ±5 MPa / s) and trigger conditions for the coordinates of the boundary intersection point (e.g., the distance the intersection point moves exceeds 10% of the diameter of the polymerization reactor). When the rate of change of osmotic pressure difference in any region exceeds the threshold, or the change in the coordinates of the boundary intersection point meets the trigger conditions, the system determines that the reaction system has reached a pressure state suitable for starting the reaction and generates a reaction start-up signal. This signal is transmitted to the actuators such as the stirrer and heating device in the polymerization reactor through a digital output module to start the material mixing and chemical reaction process. If the conditions are not met, the system continuously monitors changes in pressure distribution until the parameters meet the preset standards.

[0095] To ensure the accuracy of 3D modeling and parameter calculation, the system requires regular sensor calibration and algorithm verification. Sensor calibration is performed using a standard pressure source to ensure the absolute accuracy of pressure values ​​at each point. Algorithm verification involves comparing actual pressure test data (such as obtaining multi-point measured values ​​by placing a pressure test ball inside a polymerization reactor) with the model calculation results, adjusting the interpolation weighting coefficients and region division parameters to reduce the deviation between theoretical calculations and actual conditions.

[0096] Operators can view real-time 3D pressure distribution cloud maps, osmotic pressure difference change rate curves for each region, and dynamic coordinates of boundary intersection points through a human-machine interface. The interface provides interactive functions, such as zooming in / out of the 3D model, selecting a specific region to view detailed parameters, and exporting historical data. For example, operators can click on a high-pressure region to view the pressure change trend of that region and the pressure difference data with adjacent regions; by observing the movement trajectory of the boundary intersection points, operators can determine the uniformity of material mixing and the advancing speed of the reaction front, thereby assisting in adjusting process parameters (such as increasing the stirring intensity to promote pressure homogenization).

[0097] In terms of hardware compatibility, the 3D pressure distribution analysis module can be integrated into the industrial-grade computer of the reaction controller or distributed through edge computing nodes to reduce the computational load on the main controller. The data storage module synchronously records pressure data, zone division results, and signal generation logs, facilitating subsequent process traceability and optimization. The network interface supports integration with plant-level monitoring systems, enabling remote real-time monitoring and process parameter adjustment.

[0098] Example 4: This example focuses on the flow control logic in step S2. Through real-time flow monitoring, ratio verification, and anomaly handling mechanisms, it ensures that the polyol component and isocyanate component participate in the reaction in a precise ratio. The specific implementation method uses the preparation process of a batch of polyurethane grouting material as an example, detailing the hardware configuration, control flow, and interaction logic as follows:

[0099] In the raw material transportation stage, the polyol storage tank and the isocyanate storage tank are connected to the polymerization reactor via independent pipelines. A first mass flow meter is installed on the outlet pipeline of the polyol storage tank, and a second mass flow meter is installed on the outlet pipeline of the isocyanate storage tank. Taking polyether polyol as the polyol component and MDI (diphenylmethane diisocyanate) as the isocyanate component as an example, the flow characteristics of the two materials are significantly different (polyether polyol has higher viscosity, while MDI has lower viscosity). Therefore, the mass flow meter needs to be selected according to the material characteristics: a Coriolis mass flow meter is selected for the polyether polyol pipeline to ensure the metering accuracy of high-viscosity fluids; a thermal mass flow meter is selected for the MDI pipeline to meet the measurement needs of low-viscosity, volatile fluids. The range of both flow meters is set according to the flow range of the process design (e.g., 0-50 kg / h), and the accuracy of both reaches ±0.1%. Their output signals are connected to the reaction controller through a 4-20mA analog circuit.

[0100] When the operator triggers the "Feed Preparation" command on the human-machine interface, the reaction controller first initializes the flow monitoring program. The first and second mass flow meters begin to collect the first real-time flow value (denoted as Q1) of the polyol component and the second real-time flow value (denoted as Q2) of the isocyanate component, and transmit the data to the controller's memory. Taking a preset material mass ratio of 3:1 (polyether polyol:MDI) as an example, the controller first determines whether the real-time ratio of Q1 to Q2 falls within the set ratio range (e.g., 3:1 ± 5%). If the ratio is between 2.85:1 and 3.15:1, it is considered to have reached the set ratio range, and the next step of the molar ratio verification process is initiated; if the ratio exceeds this range, the controller immediately generates a flow abnormality alarm signal, which is displayed by flashing a red warning light on the interface and sounding an alarm to alert the operator.

[0101] The molar ratio verification process requires calculation based on the molecular weight of the materials. The average molecular weight of polyether polyol is set at 2000 g / mol, and the molecular weight of MDI is 250 g / mol. According to the chemical reaction equation, the ideal molar ratio for the two reactions is 1:2. The controller calculates the real-time molar flow rate using the formula "molar flow rate = mass flow rate / molecular weight": polyol molar flow rate n1 = Q1 / 2000, MDI molar flow rate n2 = Q2 / 250, and the ideal molar ratio threshold is n1:n2 = 1:2 (i.e., n2 = 8n1). The system presets a molar ratio tolerance of ±2%, meaning that when 7.84n1 ≤ n2 ≤ 8.16n1, the verification passes. If this range is exceeded, the controller determines that there is a stoichiometric deviation in the flow ratio, triggers the "reacquire flow value" command, closes the tank outlet valve, clears the current flow data, reopens the valve to collect flow data, and continues until the verification passes.

[0102] Taking a specific data collection as an example: If Q1 = 30 kg / h and Q2 = 10 kg / h, the mass ratio is 3:1, which meets the set ratio range. Calculate the molar flow rate: n1 = 30000 g / h ÷ 2000 g / mol = 15 mol / h, n2 = 10000 g / h ÷ 250 g / mol = 40 mol / h. At this point, n2 = 2.67n1, significantly deviating from the ideal molar ratio of 8:1, indicating insufficient MDI flow. The controller determines the verification failed, immediately closes the valves of both storage tanks, clears the data, and restarts the feed until Q1 = 25 kg / h and Q2 = 10 kg / h are collected. At this point, the mass ratio is 2.5:1 (exceeding the lower limit of the set ratio range of 2.85:1). The controller generates a flow abnormality alarm signal, prompting the operator to check for blockages in the MDI storage tank outlet pipeline or zero-point drift of the flow meter.

[0103] If both the flow rate ratio and molar ratio pass the verification, the controller confirms that the current feed ratio meets the process requirements and triggers the subsequent data acquisition process: driving the rheometer probe to collect real-time rheological parameters (such as viscosity and shear stress), and simultaneously collecting temperature data through the temperature sensor inside the polymerization reactor. This data, along with the flow rate information, is stored to form the initial parameter set for this batch of reaction, used for real-time calculations by the ratio optimization network and the temperature control network.

[0104] In anomaly handling scenarios, if multiple attempts to re-acquire flow rates still fail the proportional or molar ratio verification, the system will automatically lock the feeding process and generate a detailed fault log, recording the time, value, verification result, and anomaly type (such as proportional deviation or molar ratio imbalance) for each flow rate acquisition. Operators can use the log query function to trace the problem. For example, if the MDI flow meter shows low flow rates in multiple measurements, it can be determined that the flow meter is faulty or the pipeline valve opening is insufficient, requiring equipment shutdown and maintenance.

[0105] To enhance operational convenience, the human-machine interface (HMI) includes a "Flow Calibration" shortcut button, allowing operators to perform zero-point calibration and range calibration of the mass flow meter while the equipment is stopped. During calibration, the system automatically disables the feed control logic to prevent safety risks caused by misoperation. Furthermore, the interface provides a historical flow data curve query function, displaying flow fluctuation trends by hour, shift, date, and other dimensions, helping process engineers analyze feed stability and optimize tank pressure control or pipeline layout.

[0106] Example 5: This example focuses on the anomaly handling mechanism in step S7. Taking the scenario where the material injection rate threshold and the optimal ratio adjustment amount do not match during the preparation of a batch of polyurethane grouting material as an example, the hardware configuration, data feedback process, and dynamic control logic are detailed below:

[0107] When the reaction system enters the parameter matching and verification stage, the system first calculates the real-time osmotic pressure difference based on the pressure sensor monitoring values ​​and generates a material injection rate threshold (e.g., set to 5-8 L / min). Simultaneously, the proportioning optimization network outputs the optimal proportioning adjustment based on real-time rheological parameters (e.g., suggesting an increase of 0.5% by mass in the isocyanate component). If a mismatch is found between the two (e.g., the proportioning adjustment corresponding to the maximum injection volume allowed by the rate threshold needs to be increased by 1.2%), the system determines that the current reaction system's permeability performance and feedstock proportions have not been optimized synergistically, triggering an anomaly handling mechanism.

[0108] At this point, the viscometer installed at the bottom outlet of the reactor begins operation. This viscometer uses a vibration-based measurement principle to monitor the viscosity of the reaction system at the outlet in real time (measurement range 0-5000 mPa·s, accuracy ±1%). Its sensor probe is directly immersed in the material flow, ensuring that the measured value accurately reflects the overall viscosity of the system. Taking a reaction system using polyether polyol and MDI as raw materials as an example, the viscosity is approximately 800 mPa·s in the initial stage of a normal reaction. If the reaction rate is delayed due to a ratio imbalance, the viscosity may remain below 500 mPa·s. The viscometer transmits the real-time viscosity value (denoted as μ) to the reaction controller via an RS485 bus, forming a closed-loop feedback with the ratio optimization network and the permeation calculation unit.

[0109] Upon receiving the viscosity value, the reaction controller first retrieves viscosity-permeability correlation data for similar raw material ratios from the historical database. For example, when μ = 600 mPa·s is detected, the system finds that the optimal material injection rate threshold corresponding to this viscosity value should be 3-5 L / min, while the currently set threshold is 5-8 L / min, indicating that the actual viscosity is too low, and the allowable injection rate should be lowered to avoid material scouring leading to instability in the permeation path. Simultaneously, the ratio optimization network recalculates the optimal ratio adjustment based on the viscosity feedback value: since low viscosity may be due to insufficient isocyanate component, the network outputs an adjustment of increasing the isocyanate mass ratio by 1.0%, and simultaneously corrects the viscosity influence factor in the osmotic pressure difference calculation model.

[0110] The system then compares the newly generated material injection rate threshold (3-5 L / min) with the optimal ratio adjustment (+1.0% isocyanate). If they match (e.g., the adjusted ratio raises the system viscosity to 800 mPa·s, corresponding to a threshold of 5-8 L / min), the system proceeds to the temperature profile verification process. If they still do not match (e.g., the viscosity rises to 700 mPa·s but the threshold remains 3-5 L / min), the system continues to collect real-time viscosity data once per second using a viscometer, repeating the above calculation process. During this process, the operator can observe the viscosity curve fluctuation trend through the human-machine interface to determine whether manual intervention is needed (e.g., checking if the stirrer speed is normal or if there is a fault in the temperature control system).

[0111] Taking another scenario as an example: If the viscosity of the reaction system abnormally increases (μ = 1200 mPa·s) due to temperature fluctuations, exceeding the normal range (800-1000 mPa·s), the material injection rate threshold is automatically lowered to 2-4 L / min. However, the ratio optimization network suggests reducing the polyol component by 0.8% to reduce viscosity. After system comparison, it is found that the minimum ratio adjustment corresponding to the lower limit of the threshold of 2 L / min needs to be reduced by 1.2%, which differs from the suggested 0.8%. Therefore, the viscosity-ratio linkage adjustment is triggered: first, a ratio adjustment of 0.8% is performed, wait 5 minutes for the materials to mix evenly, and then collect a new viscosity value (e.g., reduced to 1050 mPa·s). The threshold adjustment is recalculated to 3-6 L / min, matching the remaining ratio adjustment of 0.4%, and finally the parameter calibration is completed.

[0112] On the hardware side, the outlet viscometer adopts a quick-release design, facilitating rapid disassembly and cleaning during shutdown to prevent material residue from affecting measurement accuracy. The reaction controller incorporates an adaptive filtering algorithm to denoise real-time viscosity data, eliminating instantaneous spikes caused by stirring and turbulence, ensuring that the viscosity value input to the model is a stable and valid value. Simultaneously, the system sets viscosity over-limit alarm thresholds (e.g., below 300 mPa·s or above 1500 mPa·s). If three consecutive measured values ​​exceed the threshold, an emergency shutdown procedure is automatically triggered to prevent uncontrolled pressure loss in the polymerization reactor due to abnormal viscosity.

[0113] In terms of process traceability, each viscosity data acquisition, proportioning adjustment record, and threshold generation result is stored in the database with a timestamp, and can be quickly retrieved by order number or batch number. For example, if three parameter mismatches occur during the preparation of a batch of materials, operators can find by checking the logs that the first mismatch was caused by insufficient valve opening in the isocyanate storage tank, the second by viscosity fluctuations due to a temperature sensor malfunction, and the third by the algorithm model failing to update the molecular weight data of the new batch of raw materials in a timely manner. Based on these records, the process department can optimize equipment maintenance plans, improve sensor calibration procedures, and update the training dataset of the proportioning optimization network.

[0114] The human-machine interface provides a "viscosity-ratio linkage adjustment" visualization module, which displays the dynamic relationship between real-time viscosity (left axis) and ratio adjustment amount (right axis) in the form of a dual-axis curve. Operators can intuitively observe the coupling relationship between the two. The module also provides a manual intervention option, allowing the program to be paused during automatic adjustment, and empirical parameters to be entered for correction. The corrected parameters can be included in the historical database after system verification and used to optimize the control model for subsequent batches.

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0116] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for preparing a high-pressure resistant, high-permeability polyurethane grouting material, characterized in that, The methods include: Step S1: Obtain the initial rheological parameters of the polyol component and the isocyanate component; train multiple sets of rheological parameters under different ratios using a rheological analysis model to obtain a ratio optimization network; train multiple sets of reaction temperature curves to obtain a temperature control network; and correlate the output parameters of the ratio optimization network and the temperature control network according to the reaction time. Step S2: Obtain the real-time rheological parameters of the polyol component and isocyanate component of the current batch, as well as the polymerization reactor temperature data collected synchronously when obtaining the real-time rheological parameters. Step S3: The optimal ratio adjustment amount is obtained by identifying the real-time rheological parameters through the ratio optimization network; the temperature control command is obtained by identifying the polymerization reactor temperature data through the temperature control network. Step S4: Obtain the pressure sensor monitoring value of the current reaction system; Step S5: Calculate the real-time osmotic pressure difference of the reaction system based on the monitoring value of the pressure sensor; Step S6: Generate a material injection rate threshold based on the real-time osmotic pressure difference; Step S7: If the material injection rate threshold matches the optimal ratio adjustment amount, then obtain the set reaction temperature curve according to the correlation between the ratio optimization network and the temperature control network parameters; determine whether the temperature control command corresponds to the set reaction temperature curve. If yes, generate a material synthesis command; otherwise, generate a parameter correction command.

2. The method for preparing high-pressure resistant and high-permeability polyurethane grouting material according to claim 1, characterized in that, Step S2 includes: Step S21: A rheometer probe is installed on the side wall of the polymerization reactor; the rheometer probe is connected to the data input terminal of the reaction controller; Step S22: A first mass flow meter is installed on the outlet pipeline of the polyol storage tank; a second mass flow meter is installed on the outlet pipeline of the isocyanate storage tank. Step S23: Three temperature sensors are axially and equidistantly distributed inside the polymerization reactor; the temperature sensors are connected to the input terminal of the reaction controller. Step S24: When the reaction controller receives the reaction start signal, the output of the reaction controller drives the rheometer probe to collect real-time rheological parameters; at the same time, it drives the temperature sensor to collect the temperature data of the polymerization reactor. Step S5 includes: acquiring the viscosity gradient monitoring value of the current reaction system; calculating the real-time molecular diffusion coefficient of the reaction system based on the pressure sensor monitoring value and the viscosity gradient monitoring value; acquiring the material permeation path length based on the real-time molecular diffusion coefficient; and generating the reaction start signal based on the material permeation path length. Step S5 further includes: dividing the reaction system into three-dimensional pressure distribution regions based on the pressure sensor monitoring values; obtaining the osmotic pressure difference change rate and the coordinates of the intersection points of the region boundaries based on the three-dimensional pressure distribution regions; and generating the reaction initiation signal based on the osmotic pressure difference change rate and the coordinates of the intersection points of the region boundaries. Step S5 is performed before step S2.

3. The method for preparing high-pressure resistant and high-permeability polyurethane grouting material according to claim 2, characterized in that, Step S22 includes: Obtain the first real-time flow rate value of the polyol component and the second real-time flow rate value of the isocyanate component; Determine whether the first real-time flow rate value and the second real-time flow rate value reach the set ratio range. If so, verify the molar ratio of the first real-time flow rate value and the second real-time flow rate value based on the molecular weight ratio of the polyol and the isocyanate. If the verification passes, obtain the real-time rheological parameters and the polymerization reactor temperature data. If it fails, return to this step to obtain the flow rate value again. Determine whether the first real-time traffic value and the second real-time traffic value reach a set ratio range. If not, generate a traffic anomaly alarm signal.

4. The method for preparing high-pressure resistant and high-permeability polyurethane grouting material according to claim 1, characterized in that, Step S7 includes: if the material injection rate threshold does not match the optimal ratio adjustment amount, then obtain the real-time viscosity value through the viscometer at the bottom of the reactor outlet, and return to this step to determine whether the material injection rate threshold matches the optimal ratio adjustment amount.

5. A system for predicting the effect of high-pressure resistant and high-permeability polyurethane grouting materials, characterized in that, Its system includes: The modeling unit is configured to acquire the initial rheological parameters of the polyol component and the isocyanate component, train multiple sets of rheological parameters under different ratios through a rheological analysis model to obtain a ratio optimization network, train multiple sets of reaction temperature curves to obtain a temperature control network, and correlate the output parameters of the ratio optimization network and the temperature control network according to the reaction time. The monitoring unit is configured to acquire real-time rheological parameters of the polyol component and isocyanate component of the current batch, as well as the polymerization reactor temperature data collected synchronously when acquiring the real-time rheological parameters. The analysis unit is configured to identify the real-time rheological parameters through the ratio optimization network to obtain the optimal ratio adjustment amount; and to identify the polymerization reactor temperature data through the temperature control network to obtain temperature control commands. A pressure acquisition unit is configured to acquire the pressure sensor monitoring values ​​of the current reaction system. A permeation calculation unit is configured to calculate the real-time permeation pressure difference of the reaction system based on the monitoring values ​​of the pressure sensor. A rate generation unit configured to generate a material injection rate threshold based on the real-time osmotic pressure difference; The execution unit is configured to, if the material injection rate threshold matches the optimal ratio adjustment amount, obtain the set reaction temperature curve based on the correlation between the ratio optimization network and the temperature control network parameters; determine whether the temperature control command corresponds to the set reaction temperature curve; if yes, generate a material synthesis command; otherwise, generate a parameter correction command.

6. The high-pressure resistant and high-permeability polyurethane grouting material effect prediction system according to claim 5, characterized in that, The monitoring unit is also configured to: A rheometer probe is installed on the side wall of the polymerization reactor; the rheometer probe is connected to the data input terminal of the reaction controller; A first mass flow meter is installed on the outlet pipeline of the polyol storage tank; a second mass flow meter is installed on the outlet pipeline of the isocyanate storage tank. Three temperature sensors are axially and equidistantly distributed inside the polymerization reactor; the temperature sensors are connected to the input terminal of the reaction controller. When the reaction controller receives the reaction start signal, the output of the reaction controller drives the rheometer probe to collect real-time rheological parameters; at the same time, it drives the temperature sensor to collect the temperature data of the polymerization reactor. The permeation calculation unit is further configured to: acquire the viscosity gradient monitoring value of the current reaction system; calculate the real-time molecular diffusion coefficient of the reaction system based on the pressure sensor monitoring value and the viscosity gradient monitoring value; acquire the material permeation path length based on the real-time molecular diffusion coefficient; and generate the reaction start signal based on the material permeation path length; or The permeation calculation unit is further configured to divide the reaction system into three-dimensional pressure distribution regions based on the pressure sensor monitoring values; obtain the rate of change of osmotic pressure difference in each region and the coordinates of the intersection point of the region boundary based on the three-dimensional pressure distribution regions; and generate the reaction start signal based on the rate of change of osmotic pressure difference and the coordinates of the intersection point of the region boundary.

7. The high-pressure resistant and high-permeability polyurethane grouting material effect prediction system according to claim 5, characterized in that, The monitoring unit is also configured to: Obtain the first real-time flow rate value of the polyol component and the second real-time flow rate value of the isocyanate component; Determine whether the first real-time flow rate value and the second real-time flow rate value reach the set ratio range. If so, verify the molar ratio of the first real-time flow rate value and the second real-time flow rate value based on the molecular weight ratio of the polyol and the isocyanate. If the verification passes, obtain the real-time rheological parameters and the polymerization reactor temperature data. If it fails, return to this step to obtain the flow rate value again. Determine whether the first real-time traffic value and the second real-time traffic value reach a set ratio range. If not, generate a traffic anomaly alarm signal.

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