Grouting diffusion prediction method and system of complex geology multi-attribute constraint

Abstract

The invention discloses a grouting diffusion prediction method and system for complex geology multi-attribute constraint, and the prediction method comprises the steps: obtaining the multi-source attribute information of a complex geologic body, and the multi-source attribute information comprises fracture characteristics, pore characteristics and water burst characteristics; the multi-source attribute information is input into the trained grouting diffusion dynamic prediction model, a grouting diffusion prediction result of the complex geologic body is output, and the grouting diffusion result comprises the permeation rate, pressure distribution and boundary conditions. In the training process of the grouting diffusion dynamic prediction model, the deep learning model and the real-time monitoring system are combined, prediction is continuously carried out according to monitoring data in the grouting process, the prediction result is adjusted through a feedback mechanism, the prediction model is optimized, and the precision and reliability of the prediction result are improved.

Description

Technical Field

[0001] The invention relates to the technical field of grouting diffusion simulation in underground engineering, and in particular to a grouting diffusion prediction method and system under complex geological multi-attribute constraints. Background Art

[0002] The problem of grouting diffusion in complex geological environments has always been a key technical challenge in engineering investigation and underground construction. Traditional grouting diffusion prediction methods usually focus on a single physical parameter (such as permeability or porosity), and often ignore the complex multi-attribute coupling effects within the geological body. However, the physical parameters such as fractures, pores, and water inflow in actual geological bodies have significant spatial and temporal variations, which makes it impossible for single-attribute models to fully and accurately simulate the diffusion behavior of slurry. With the increasing complexity of geological bodies, the accuracy and reliability of traditional models in practical applications are limited. In recent years, researchers have tried to make up for these shortcomings by means of multi-source data fusion and dynamic coupling models, especially in the coupling analysis of multiple factors such as fractures, pores and water inflow. The multi-attribute coupling model is based on multi-source data such as resistivity and porosity, and can more realistically reflect the diffusion process of slurry in complex geological bodies, but it still faces technical difficulties such as real-time monitoring and dynamic feedback control.

[0003] Most of the existing grouting diffusion simulations are based on simplified physical models. Although they have high computational efficiency, they cannot take into account the requirements of accurate simulation and real-time performance in complex geological bodies. The rise of deep learning technology provides a new solution for the processing and prediction of multi-source data. Through deep learning methods such as neural networks, data from different sensors (such as CT imaging, laser scanning, seismic waves, etc.) can be integrated to achieve dynamic calculation and prediction of physical parameters in complex geological bodies, especially in the accurate prediction of slurry diffusion paths. However, the current grouting diffusion model based on deep learning still faces problems such as difficulty in data acquisition, insufficient training samples, and difficulty in real-time monitoring, and has not yet been widely used in industrial practice. In terms of experimental platforms and monitoring systems, recent studies have constructed a variety of geological body models and monitored the grouting diffusion process through real-time monitoring equipment (such as pressure sensors, resistivity imaging, flow meters, etc.). These experimental devices provide a more intuitive simulation environment for research, but there are still limitations such as insufficient fusion of multi-source data and difficulty in fully simulating complex geological bodies. Therefore, despite a certain amount of technical accumulation, the existing grouting diffusion prediction methods and experimental platforms still need to be further improved, especially in terms of data fusion, model optimization and real-time feedback control. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a grouting diffusion prediction method and system with complex geological multi-attribute constraints. Through deep learning and multi-source data fusion methods, the simulation results can more accurately reflect the slurry diffusion process in complex geological bodies.

[0005] The technical solution of the present invention is as follows:

[0006] In a first aspect of the present invention, a method for predicting grouting diffusion under complex geological multi-attribute constraints is provided, which is characterized by comprising:

[0007] Acquiring multi-source attribute information of a complex geological body, wherein the multi-source attribute information includes fracture characteristics, pore characteristics and water inflow characteristics;

[0008] Inputting multi-source attribute information into a trained grouting diffusion dynamic prediction model, and outputting grouting diffusion prediction results of complex geological bodies, wherein the grouting diffusion results include permeability, pressure distribution and boundary conditions;

[0009] The establishment and training process of the grouting diffusion dynamic prediction model is as follows:

[0010] Build a grouting diffusion simulation experimental system to simulate the grouting diffusion process in complex geology;

[0011] Acquire multi-source attribute information and permeability of the simulated formation of the experimental system, establish a multi-attribute coupled permeability model, and embed the multi-attribute coupled permeability model into the grouting diffusion dynamic prediction model; at the same time, embed the dynamic equation as a physical constraint condition into the grouting diffusion dynamic prediction model;

[0012] Conduct numerical simulation on the experimental system, obtain the permeability rate, pressure distribution and diffusion boundary during the grouting experiment, and train the grouting diffusion dynamic prediction model;

[0013] After the training is completed, the pressure, resistivity and concentration of the grouting experiment at the current moment are obtained in real time, and input into the grouting diffusion dynamic prediction model to predict the grouting diffusion process at the next moment;

[0014] During the prediction process, the grouting diffusion dynamic prediction model continuously adjusts the prediction results through a feedback mechanism according to the grouting results of the grouting experiment to optimize the grouting diffusion dynamic prediction model.

[0015] In a second aspect of the present invention, a grouting diffusion prediction system with complex geological multi-attribute constraints is provided.

[0016] The multi-attribute information acquisition module is configured to: acquire multi-source attribute information of a complex geological body, wherein the multi-source attribute information includes fracture characteristics, pore characteristics and water inflow characteristics;

[0017] The grouting diffusion prediction module is configured to: input multi-source attribute information into a trained grouting diffusion dynamic prediction model, and output grouting diffusion prediction results of complex geological bodies, wherein the grouting diffusion results include permeability, pressure distribution, and boundary conditions;

[0018] The establishment and training process of the grouting diffusion dynamic prediction model is as follows:

[0019] Build a grouting diffusion simulation experimental system to simulate the grouting diffusion process in complex geology;

[0020] Acquire multi-source attribute information and permeability of the simulated formation of the experimental system, establish a multi-attribute coupled permeability model, and embed the multi-attribute coupled permeability model into the grouting diffusion dynamic prediction model; at the same time, embed the dynamic equation as a physical constraint condition into the grouting diffusion dynamic prediction model;

[0021] Conduct numerical simulation on the experimental system, obtain the permeability rate, pressure distribution and diffusion boundary during the grouting experiment, and train the grouting diffusion dynamic prediction model;

[0022] After the training is completed, the pressure, resistivity and concentration of the grouting experiment at the current moment are obtained in real time, and input into the grouting diffusion dynamic prediction model to predict the grouting diffusion process at the next moment;

[0023] During the prediction process, the grouting diffusion dynamic prediction model continuously adjusts the prediction results through a feedback mechanism according to the grouting results of the grouting experiment to optimize the grouting diffusion dynamic prediction model.

[0024] One or more technical solutions of the present invention have the following beneficial effects:

[0025] (1) The present invention conducts coupling analysis based on multi-source information (fracture characteristics, pore characteristics and water gushing characteristics) to dynamically simulate and predict the changes in permeability and diffusion state during the grouting diffusion process. In the adopted grouting dynamic prediction model, a multi-attribute coupling permeability model and a dynamic equation are embedded as physical constraints to ensure that the output of the prediction model conforms to the laws of fluid mechanics. In addition, the dynamic changes in the permeability of the constructed experimental system over time and space will be monitored in real time, and updated to the input layer of the prediction model through a feedback mechanism to further optimize the spatiotemporal coupling characteristics and accurately simulate the diffusion path and speed of the slurry. In this way, the multi-attribute coupling permeability model provides physical consistency constraints and real-time update support for dynamic prediction, thereby improving the accuracy and reliability of the prediction results. The prediction model can accurately predict permeability changes and slurry diffusion behavior, and can be widely used in geological multi-attribute simulation and grouting process optimization in the fields of geological exploration, tunnel construction, scientific research experiments, etc., with good scalability, adaptability and stability.

[0026] (2) The multi-attribute coupled permeability model proposed in the present invention constructs a dynamically coupled permeability prediction model by introducing multiple physical parameters such as fracture characteristics, pore characteristics and water inflow characteristics, which are integrated and used as key constraints and dynamic input characteristics in the prediction model. The model dynamically couples fracture characteristics, pore characteristics and water inflow characteristics to establish a quantitative relationship between multi-source attributes and permeability, and reveals their influence mechanism on permeability changes. In the prediction model, the output of the coupled permeability model - permeability data is used as a physical constraint condition and embedded in the physical constraint integration layer of the grouting diffusion dynamic prediction model to ensure that the model output conforms to the laws of fluid mechanics.

[0027] (3) The dynamic grouting diffusion simulation model proposed in the present invention integrates multiple physical properties, and through deep learning and multi-source data fusion methods, the simulation results can more accurately reflect the slurry diffusion process in complex geological bodies. At the same time, this model can be dynamically updated and optimized with the input of real-time data, overcoming the static and simplified nature of traditional models. The dynamic grouting diffusion simulation model can provide accurate slurry diffusion predictions, can adapt to changes in different geological conditions and different physical parameters, and can adjust and optimize the diffusion process in real time. In the case of missing data or insufficient real-time monitoring data, the model can still fill in the missing information through small sample learning and prediction to ensure the continuity and stability of the simulation process.

[0028] (4) The dynamic grouting diffusion simulation model proposed in the present invention compares the actual monitoring data (such as pressure, flow, concentration) with the prediction results through a real-time feedback mechanism, reversely adjusts the input parameters or weights of the permeability model, and optimizes the accuracy of the permeability prediction. This two-way coupling enables the permeability model and the diffusion prediction model to support each other, and jointly improves the dynamic simulation and prediction accuracy of the slurry diffusion behavior in complex geological bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of the establishment, training and optimization of the grouting diffusion dynamic prediction model in Example 1 of the present invention;

[0030] Figure 2 Schematic diagram of a grouting diffusion simulation experimental system in Example 2 of the present invention;

[0031] In the figure: 1. Complex geological simulation unit; 2. Water injection and grouting unit. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] Example 1

[0034] In a typical embodiment of the present invention, a grouting diffusion prediction method with complex geological multi-attribute constraints is proposed, comprising:

[0035] Step 1: Acquire multi-source attribute information of a complex geological body, wherein the multi-source attribute information includes fracture characteristics, pore characteristics, and water inflow characteristics.

[0036] When obtaining crack characteristics, use 3D laser scanners and cave scanners to measure the crack characteristics of the rock mass, generate 3D point cloud data of the cracks, and use handheld scanners to perform additional scanning of local details;

[0037] When obtaining pore characteristics, a microscopic CT imager is used to obtain porosity data and establish a microscopic model of pore distribution; combined with a pore water pressure gauge, the pressure changes of the fluid inside the pores are monitored in real time to obtain the correlation data between pores and fractures;

[0038] When obtaining the characteristics of water inrush, resistivity equipment and ground penetrating radar are used to measure the distribution information of stratum moisture and water inrush area, the permeability of different layers is measured through the groundwater stratification test sampling system, and the overall strength and crushing structure characteristics of the rock mass are obtained in combination with the elasticity instrument. Flow meters and pressure sensors are arranged at the face and inside the model tunnel to obtain dynamic permeability data.

[0039] Step 2: input the multi-source attribute information into the trained grouting diffusion dynamic prediction model, and output the grouting diffusion prediction results of the complex geological body, wherein the grouting diffusion results include permeability, pressure distribution and boundary conditions;

[0040] The establishment and training process of the grouting diffusion dynamic prediction model is as follows:

[0041] Build a grouting diffusion simulation experimental system to simulate the grouting diffusion process in complex geology;

[0042] Acquire multi-source attribute information and permeability of the simulated formation of the experimental system, establish a multi-attribute coupled permeability model, and embed the multi-attribute coupled permeability model into the grouting diffusion dynamic prediction model; at the same time, embed the dynamic equation as a physical constraint condition into the grouting diffusion dynamic prediction model;

[0043] Conduct numerical simulation on the experimental system, obtain the permeability rate, pressure distribution and diffusion boundary during the grouting experiment, and train the grouting diffusion dynamic prediction model;

[0044] After the training is completed, the pressure, resistivity and concentration of the grouting experiment at the current moment are obtained in real time, and input into the grouting diffusion dynamic prediction model to predict the grouting diffusion process at the next moment;

[0045] During the prediction process, the grouting diffusion dynamic prediction model continuously adjusts the prediction results through a feedback mechanism according to the grouting results of the grouting experiment to optimize the grouting diffusion dynamic prediction model.

[0046] Specifically, when establishing a multi-attribute coupled permeability model, statistical and machine learning methods are used to model the coupling relationship between multiple attribute information to reveal the influence mechanism of each physical attribute on permeability.

[0047] The multi-attribute coupled permeability model uses key parameters such as fracture characteristics, pore characteristics and water inflow characteristics to establish the constraint relationship between fractures, pores and permeability through the dynamic coupling of multi-attribute information. The model integrates various geological information (such as fracture characteristics, pore characteristics and water inflow characteristics) to reflect their composite impact on formation permeability.

[0048] The process of establishing the multi-attribute coupled permeability model:

[0049] Data collection and attribute selection: Collect multiple attribute data of geological bodies (such as fracture characteristics, pore characteristics and water inflow characteristics, etc.).

[0050] Coupling analysis: Use statistical and machine learning methods (such as regression analysis, support vector machine, etc.) to model the coupling relationship between multiple attributes and reveal the impact mechanism of each physical attribute on permeability.

[0051] Dynamic evolution model: Build a permeability prediction model based on time changes (multi-attribute coupled permeability model), use real-time data monitoring to make dynamic adjustments, and solve the problem of permeability changes of geological bodies under different working conditions.

[0052] The final model:

[0053] The permeability prediction model can provide permeability estimates under different conditions and can adjust the permeability prediction value in real time, making the diffusion path and speed of slurry in complex geological bodies more accurate. The permeability prediction model can not only be used for static permeability analysis, but also can be dynamically optimized and adjusted with the input of real-time data.

[0054] The multi-attribute coupling permeability model is integrated and used as a key constraint and dynamic input feature in the grouting diffusion dynamic prediction model. The multi-attribute coupling permeability model establishes a quantitative relationship between multi-source attributes and permeability by dynamically coupling fracture characteristics, pore characteristics and water inflow characteristics, and reveals the mechanism of their influence on permeability changes. In the grouting diffusion dynamic prediction model, the output of the multi-attribute coupling permeability model - permeability data is embedded in the physical constraint integration layer of the grouting diffusion dynamic prediction model as a physical constraint condition to ensure that the model output conforms to the laws of fluid mechanics (such as Darcy's law and diffusion equation).

[0055] In this embodiment, the structure of the grouting diffusion dynamic prediction model includes two parts: a physical model and a deep learning model.

[0056] Physical model part: Establish physical equations to describe the grouting diffusion process, consider multiple physical properties (such as permeability, pressure distribution, porosity, fracture structure, etc.), and provide constraints for the deep learning model.

[0057] Deep learning model part: Based on the neural network architecture (such as convolutional neural network CNN and long short-term memory network LSTM, etc.), it processes multi-source data from different sensors (such as pressure, temperature, resistivity, etc.) and combines physical constraints for learning and prediction.

[0058] In the physical model, the permeability rate, pressure distribution and diffusion boundary are the key physical properties, which directly affect the diffusion process of the slurry. The dynamic constraint equation is to limit the range of the output results of the neural network through the physical model and ensure that it conforms to the principles of fluid mechanics and geology. The specific constraint equations include:

[0059] Darcy's law: The relationship between the velocity and pressure gradient limiting the flow of a slurry.

[0060] Fluid dynamics equations: coupling of limiting pressure distribution and permeability.

[0061] Diffusion equation: describes the relationship between the diffusion rate of slurry and the boundary.

[0062] Multiphysics coupling: Combine multiple properties such as permeability, pressure distribution, and boundary conditions to dynamically update physical constraints.

[0063] These physical properties are coupled with dynamic constraint equations, and physical constraints such as Darcy's law, fluid dynamics equations, and diffusion equations are coupled with deep learning models: the physical equations are used as constraints for the neural network and embedded into the training process through the physical constraint loss function to ensure that the prediction results conform to the laws of physics; the physical variables (such as permeation rate, pressure distribution, and boundary conditions) are passed as input features to the neural network and participate in learning and prediction together with other data.

[0064] Specifically, the permeability rate is coupled with Darcy's law, the pressure distribution is coupled with the fluid mechanics equation, and the diffusion boundary is coupled with the diffusion equation, including:

[0065] 1. Coupling of penetration rate and Darcy’s law

[0066] The expression of Darcy's law is:

[0067]

[0068] Where: q: permeation rate (flow); k: permeability; Pressure gradient; μ: fluid viscosity.

[0069] Specific coupling methods include:

[0070] (1) Physical Constraint Layer

[0071] In the deep learning model, a "physical constraint layer" is introduced to embed Darcy's law into the network's training loss function. The penetration rate q predicted by the neural network pred and the permeation rate q calculated according to Darcy's law phys The error between them gives the first physical loss function:

[0072]

[0073] This first loss function affects the network weight update, ensuring that the permeation rate of the network output complies with Darcy's law.

[0074] (2) Input feature enhancement:

[0075] The permeability k and pressure gradient Directly used as input features, added to the input layer of the deep learning model.

[0076] The network is trained using these input features along with other geological attributes (fractures, porosity, temperature, etc.) to achieve more accurate predictions.

[0077] 2. Coupling of pressure distribution and fluid dynamics

[0078] The fluid dynamics equation (pressure field equation) is as follows:

[0079]

[0080] Specific coupling method:

[0081] (1) Physical consistency loss:

[0082] The pressure field P output by the network pred The above equation needs to be satisfied and is implemented through the second physical constraint loss function:

[0083]

[0084] Where P pred represents the pressure field predicted by the neural network;

[0085] During the network training process, the second physical constraint loss function enables the model to learn the pressure distribution that conforms to the laws of fluid dynamics.

[0086] (2) Input feature integration:

[0087] The permeability k and other parameters in the fluid dynamics equation (such as boundary conditions and initial pressure distribution) are used as input features and learned in combination with historical spatiotemporal data.

[0088] (3) Numerical simulation correction:

[0089] CFD (computational fluid dynamics) and other methods are used to solve fluid dynamics equations and generate high-quality training samples to guide the supervised learning of the network.

[0090] 3. Coupling of diffusion boundary and spatial physical constraints

[0091] The diffusion equation (Fick's law) is:

[0092]

[0093] Where: C: concentration field; u: velocity field; D: diffusion coefficient.

[0094] Specific coupling method:

[0095] (1) Concentration conservation constraint:

[0096] During network training, a third physical constraint loss is introduced Ensure that the concentration predictions satisfy the diffusion equation:

[0097]

[0098] In the formula, C pred represents the concentration field predicted by the neural network.

[0099] (2) Boundary conditions and initial conditions constraints:

[0100] The velocity field u and diffusion coefficient D are used as network input features to dynamically simulate the relationship between the diffusion process and spatial constraints. During training, boundary conditions (such as concentration boundary value, velocity boundary) and initial conditions (initial concentration distribution) are used as constraints to ensure that the network output results are reasonable.

[0101] By defining the above physical loss functions (such as Darcy's law, fluid dynamics equations, and diffusion equations), physical laws are embedded in the network training process to ensure that the model output meets physical constraints. The key parameters in the physical equations (such as permeability, pressure gradient, and concentration field) are directly used as network inputs and learned together with other geological attribute data. Numerical simulations (CFD, FEM, etc.) are used to solve physical equations and generate training samples to provide supervised learning data for deep learning.

[0102] In the grouting diffusion dynamic prediction model, the dynamic coupling between the permeability rate, pressure distribution and diffusion boundary is realized through the following mechanisms:

[0103] Physical constraints are embedded in neural networks: The input of the deep learning model includes multiple physical properties, such as permeation rate, pressure distribution, and diffusion boundary, which are adjusted through the physical constraint layer in the network. For example, at each prediction, the network not only relies on historical data, but also makes corrections based on the permeation rate, pressure distribution, and diffusion boundary calculated by the physical model to ensure that the prediction results are consistent with the physical laws.

[0104] Dynamic adjustment of time and space: Based on network structures such as LSTM, the model can process time and space series data, dynamically adjust the pressure distribution and permeation rate at different time and space nodes, and predict the path and speed of slurry diffusion in real time.

[0105] Feedback mechanism: After real-time data (such as sensor data) is input, the model will adjust the predicted values ​​of permeability rate, pressure field and diffusion boundary according to the new data feedback, and automatically adjust the grouting parameters through the optimization algorithm to ensure the accuracy of the simulation process.

[0106] Through the coupling of permeability, pressure distribution and diffusion boundary, the model can accurately simulate and predict the slurry diffusion process in complex geological bodies. The dynamic coupling equation not only ensures physical consistency, but also realizes spatiotemporal dependence and real-time optimization based on multi-source data through deep learning algorithms, improving the accuracy and computational efficiency of the model. This model can provide strong technical support for underground construction, grouting optimization, etc.

[0107] In order to meet the above-mentioned needs for prediction and feedback of grouting diffusion process in complex geological environments, the grouting diffusion dynamic prediction model provided in this embodiment has a new deep learning model structure-multimodal spatiotemporal coupled adaptive neural network (MTSCNN). This model combines the multimodal data fusion, spatiotemporal dynamic adjustment and feedback mechanism of deep learning, and can efficiently handle grouting diffusion simulation problems in complex geological environments.

[0108] MTSCNN (Multimodal Temporal-Spatial Coupled Neural Network) is a multimodal spatiotemporal coupled adaptive neural network architecture. It combines multi-source sensor data (such as pressure sensors, temperature sensors, resistivity imaging, fluid mechanics simulation data, etc.) with the spatial characteristics of geological bodies (such as cracks, porosity, permeability, etc.), and realizes real-time prediction and dynamic feedback of complex underground grouting processes through time series modeling and feedback mechanisms. Specifically, by combining physical properties such as permeability rate, pressure distribution, diffusion boundary with dynamic coupling equations, combined with deep learning methods for prediction and optimization, the multiple influencing factors of slurry diffusion in complex geological bodies are solved. The core idea of ​​the model is to combine physical constraints with the predictive ability of deep learning to perform more accurate simulations and dynamically optimize key parameters in the grouting process.

[0109] The structure of the grouting diffusion dynamic prediction model includes:

[0110] a. Input layer: multimodal data fusion

[0111] Multi-source data input: The input of the model includes multiple modal data sources, such as: Geological data: including physical properties such as fractures, porosity, and permeability, as static input. Sensor data: including real-time pressure field, temperature field, concentration field, flow field, etc., as dynamic input. Numerical simulation data: fluid mechanics related data (such as velocity field, pressure field) obtained through numerical simulation such as CFD model, as auxiliary information input.

[0112] Each type of data goes through a separate preprocessing layer, such as normalization, standardization, convolutional feature extraction of image data, etc. Data of different modalities are fused through a feature fusion layer to convert different types of data into a unified feature representation.

[0113] b. Spatiotemporal modeling layer:

[0114] Time modeling: LSTM (Long Short-Term Memory Network) is used to process the time series information in the input data. This part of the model is responsible for capturing the dynamic evolution of the grouting process, such as the diffusion process of the slurry, pressure changes, temperature changes, etc.

[0115] Spatial modeling: Use 3D convolutional neural network (3D CNN) to extract spatial features. 3D CNN can effectively capture the spatial information in the geological body, analyze the physical property changes at different locations such as cracks and pores, and their impact on slurry diffusion.

[0116] Spatiotemporal coupling layer: LSTM and 3DCNN are combined to embed a spatiotemporal coupling layer in the model. This layer links the temporal and spatial features to better understand how the diffusion process of the slurry changes with time and space. Specifically, the spatiotemporal coupling layer combines the dynamic evolution of the time series with the distribution information of the spatial features for joint modeling.

[0117] c. Physical Constraint Integration Layer:

[0118] Physical model embedding: To ensure that the deep learning model complies with the laws of fluid mechanics, in this layer, the model will embed the physical laws into the neural network through the "physical constraint loss function" (such as Darcy's law, continuity equation, etc.). The physical constraint loss function will strengthen the model's adaptability to real physical processes so that its prediction results can follow the actual physical laws.

[0119] d. Feedback mechanism and adaptive adjustment:

[0120] Feedback layer: After each prediction, the feedback layer compares the real-time monitoring data (such as pressure, temperature, resistivity and other sensor data) with the prediction results, calculates the error, and corrects the error through the back propagation algorithm. The purpose of the feedback mechanism is to use the actual field data for the adaptive adjustment of the model parameters to optimize the subsequent prediction process.

[0121] Adaptive learning rate adjustment: Based on real-time feedback data, the model's learning rate will be dynamically adjusted to improve the response speed to new data and prediction accuracy. Adaptive learning rate can help the model adjust and converge quickly, avoid overfitting, and ensure the real-time nature of the prediction.

[0122] e. Output layer:

[0123] Prediction results: The output layer will generate the prediction results of grouting diffusion based on the predicted spatiotemporal characteristics, including:

[0124] Diffusion path and concentration distribution: predict the diffusion path and concentration distribution of slurry in geological bodies over time.

[0125] Pressure and velocity fields: predict changes in pressure and velocity during grouting.

[0126] Diffusion Boundary: Predict the boundary of the slurry diffusion and identify the farthest location the slurry may reach.

[0127] The training and optimization process of the grouting diffusion dynamic prediction model is as follows:

[0128] a. Training data:

[0129] The training data sources include

[0130] Field experimental data: experimental data from different geological conditions, including real-time monitoring data from multiple sensors.

[0131] Numerical simulation data: Generate training samples that match actual scenarios based on existing fluid mechanics simulation results.

[0132] Historical grouting data: including monitoring data and prediction results of all previous grouting processes, which are used for model training and error correction.

[0133] b. Loss function design:

[0134] The loss function combines physical constraint loss (such as Darcy's law, fluid dynamics, etc.), prediction error loss (such as MSE or MAE) and feedback adjustment loss (such as error correction) for weighting. By comprehensively optimizing these loss functions, it is ensured that the model not only meets the requirements in terms of accuracy, but also follows the laws of physics and can be adjusted adaptively.

[0135] Technical implementation:

[0136] Deep learning framework: Use mainstream deep learning frameworks, such as TensorFlow or PyTorch, to build and train models.

[0137] Multimodal data interface: Through a unified data processing interface, data from different sensors are integrated and real-time transmission and processing of data is ensured.

[0138] Physical model embedding: Use physics-based loss functions (such as fluid mechanics constraints, boundary conditions, etc.) to optimize the neural network training process and ensure that the model output is consistent with physical laws.

[0139] c. Optimization method:

[0140] The model is optimized using the Adam optimizer, adaptive learning rate, and dynamic update strategy. Based on real-time feedback, the optimizer dynamically adjusts the learning rate to ensure the stability and efficiency of the model during real-time prediction.

[0141] In the process of training and optimization, the realization of prediction and feedback mechanism is the key to ensure that the model reflects the actual geological behavior efficiently and accurately. To achieve this goal, multi-source data fusion, deep learning model, spatiotemporal dynamic adjustment and feedback mechanism will work together to ensure dynamic correction and optimization in the simulation process.

[0142] The feedback process ensures that the model can be continuously optimized in actual applications, dynamically adjust grouting parameters, and improve simulation accuracy. The feedback mechanism continuously adjusts the model prediction results based on real-time sensor data and changes in the grouting process to achieve more accurate simulation and control, including:

[0143] 1. Real-time data collection and input:

[0144] Sensor feedback: Various sensors installed on site (such as pressure sensors, resistivity sensors, temperature sensors, etc.) will provide real-time feedback on the data of underground slurry diffusion process, including slurry flow rate, diffusion range, pressure changes, etc.

[0145] Real-time input of experimental data and field monitoring data: These data are continuously input into the deep learning model as new input features to ensure that the model is constantly updated and optimized.

[0146] 2. Dynamic adjustment and optimization:

[0147] Physical constraints and model correction:

[0148] By monitoring data in real time, the model can dynamically adjust physical parameters (such as permeability, pressure distribution, etc.), and these physical parameters will be used as constraints to participate in the next prediction calculation.

[0149] Feedback correction of deep learning models: After receiving real-time data, the model will provide feedback corrections to its prediction results. For example, if the actual slurry diffusion path deviates from the predicted path, the model will adjust the prediction at the next moment through the newly input monitoring data to correct the slurry diffusion path.

[0150] Feedback mechanism implementation:

[0151] Back propagation and weight update: When there is a deviation between the model prediction results and the actual data, the model adjusts the network weights through the back propagation algorithm (such as gradient descent) to gradually approach the real diffusion process. Through continuous iterative training, the model can learn more accurate physical laws and update in real time.

[0152] Real-time optimization: The model is optimized in real time based on feedback data, adjusting the prediction results to match the real-time geological state. For example, the permeability is adjusted according to the feedback pressure distribution, or the speed and path of the slurry flow are adjusted according to the feedback diffusion boundary.

[0153] 3. Feedback and optimization loop:

[0154] Feedback and prediction alternating process: In the actual grouting process, real-time monitoring data is periodically input into the model, and each input causes the model to generate new prediction results. The model adjusts and feedbacks the prediction results to form a closed-loop optimization system. Whenever a variable in the grouting process changes (such as abnormal pressure or slurry diffusion does not meet expectations), the model will automatically make corrections and generate new prediction paths and grouting strategies after optimization.

[0155] Technical implementation of feedback optimization:

[0156] Adaptive learning rate: In order to better perform real-time optimization, the feedback mechanism can be combined with an adaptive learning rate so that the model can dynamically adjust the learning speed according to changes in real-time data during training, thereby improving response efficiency.

[0157] Deep reinforcement learning (DRL): In more complex scenarios, reinforcement learning methods can be used to optimize the grouting strategy. DRL can dynamically adjust the grouting parameters according to the reward function based on the environmental state (such as pressure field, slurry diffusion state, etc.) to achieve the long-term optimal strategy.

[0158] The MTSCNN model structure can provide efficient and accurate grouting diffusion prediction and dynamic optimization solutions by integrating multi-source data (such as sensor data, geological data, and numerical simulation data), spatiotemporal modeling (using LSTM and 3D CNN), physical constraint embedding (fluid mechanics equations, etc.), and feedback mechanisms (error feedback and adaptive adjustment). Through real-time feedback and adaptive learning, the model can continuously adjust and optimize the prediction results, solve the problems of insufficient simulation accuracy and real-time performance in traditional methods, and provide intelligent support for underground projects in complex geological environments.

[0159] Example 2

[0160] In a typical embodiment of the present invention, a grouting diffusion prediction system with complex geological multi-attribute constraints is proposed, comprising:

[0161] The attribute information acquisition module is configured to: acquire multi-source attribute information of a complex geological body, wherein the multi-source attribute information includes fracture characteristics, pore characteristics and water inflow characteristics;

[0162] The grouting diffusion prediction module is configured to: input multi-source attribute information into a trained grouting diffusion dynamic prediction model, and output grouting diffusion prediction results of complex geological bodies, wherein the grouting diffusion results include permeability, pressure distribution, and boundary conditions;

[0163] The establishment and training process of the grouting diffusion dynamic prediction model is as follows:

[0164] Build a grouting diffusion simulation experimental system to simulate the grouting diffusion process in complex geology;

[0165] Acquire multi-source attribute information and permeability of the simulated formation of the experimental system, establish a multi-attribute coupled permeability model, and embed the multi-attribute coupled permeability model into the grouting diffusion dynamic prediction model; at the same time, embed the dynamic equation as a physical constraint condition into the grouting diffusion dynamic prediction model;

[0166] Conduct numerical simulation on the experimental system, obtain the permeability rate, pressure distribution and diffusion boundary during the grouting experiment, and train the grouting diffusion dynamic prediction model;

[0167] After the training is completed, the pressure, resistivity and concentration of the grouting experiment at the current moment are obtained in real time, and input into the grouting diffusion dynamic prediction model to predict the grouting diffusion process at the next moment;

[0168] During the prediction process, the grouting diffusion dynamic prediction model continuously adjusts the prediction results through a feedback mechanism according to the grouting results of the grouting experiment to optimize the grouting diffusion dynamic prediction model.

[0169] Furthermore, the grouting diffusion simulation experimental system is as follows: Figure 2 As shown, including:

[0170] The complex geological simulation unit 1 is used to simulate complex geology, including a model box filled with various rock layers, sand layers and soil layers, and embedded with different geological features such as transverse and longitudinal cracks, sand layers, gravel layers, fault fracture zones and karst caves to simulate complex geological structures, such as:

[0171] Rock layer structure: simulate various rock layers, sand layers and soil layers, including stratum cover, crack settings, cave presets, etc. The model material uses new lightweight cement and water-resistant resin, and the surface is sprayed with water-resistant pigment to simulate the texture of different rock layers.

[0172] Tunnel and structure penetration design: The transparent acrylic tunnel penetration model shows the rock strata, water seepage conditions, crack distribution, and water gushing paths in the tunnel penetration area.

[0173] Geological layer simulation: Different geological features such as horizontal and vertical cracks, sand layers, gravel layers, fault fracture zones, and caves are embedded in the model to simulate various complex structures in the natural geological environment.

[0174] The water injection and grouting unit 2 includes a dynamic water circulation module and a grouting control module. The dynamic water circulation module is used to simulate the groundwater seepage process, provide constant pressure water flow, and simulate the natural groundwater flow path in the model; the grouting control module realizes the precise control of the grouting rate, pressure and flow rate, and monitors the pressure and flow rate data in the grouting process through multi-source sensors;

[0175] Data acquisition and monitoring units, including pressure sensors, flow sensors, pore water pressure gauges, resistivity imaging systems, and ultrasonic sensors;

[0176] The data processing unit is used to acquire and process data. It is equipped with a desktop computer and professional control software to support real-time data acquisition, processing and analysis. The software system has data visualization function, which can display data such as flow meter, pressure sensor, resistivity imaging in real time to assist in real-time monitoring and control of the grouting process. It supports remote control and monitoring. Through the software interface, the grouting pressure, flow rate and other parameters can be remotely controlled, and data storage, historical data query and experimental data export can be performed at the same time.

[0177] The steps are as follows:

[0178] 1. Device preparation and system initialization

[0179] Check the integrity of each component of the geological model to ensure that the structural presets of fractures, pores, caves, and gravel formations are correct. Initialize the water injection and grouting system to ensure that the water circulation and grouting equipment are working properly. Start the data acquisition system, including 3D laser scanners, cave scanners, CT imagers, resistivity equipment, etc., check the connection status, and ensure smooth signal acquisition. Start the computer and control software, and configure the test parameters, including the initial attributes of the geological model such as fracture distribution, pore structure, and water inflow area.

[0180] 2. Multi-source attribute measurement

[0181] (1) Crack characteristic measurement:

[0182] The crack characteristics (width, length, roughness) of the rock mass are measured using a 3D laser scanner and a cave scanner to generate 3D point cloud data of the cracks. A handheld scanner is used to perform additional scanning of local details to improve the resolution of the crack model.

[0183] (2) Pore structure measurement:

[0184] Use micro-CT imagers to obtain porosity data and establish a microscopic model of pore distribution. Combined with pore water pressure gauges, real-time monitoring of the pressure changes of the fluid inside the pores can be used to obtain correlation data between pores and fractures.

[0185] (3) Measurement of water inflow and permeability characteristics:

[0186] Resistivity equipment and ground penetrating radar are used to measure the distribution information of stratum moisture and water inflow area. The permeability of different layers is measured through the groundwater stratification test sampling system, and the overall strength and crushing structure characteristics of the rock mass are obtained by combining with the elasticity instrument. Flow meters and pressure sensors are arranged at the face and inside the model tunnel to obtain dynamic permeability data.

[0187] 3. Grouting diffusion process experiment

[0188] (1) Grouting system initialization:

[0189] The water injection and grouting test bench is set at a specific location in the formation model, and the initial flow rate and pressure of the grouting are adjusted according to the experimental requirements. The grouting liquid (such as colored water or slurry) is configured to facilitate monitoring of its diffusion process.

[0190] (2) Real-time monitoring of the diffusion process:

[0191] Start the grouting system, gradually increase the grouting pressure, and monitor the speed and direction of slurry diffusion.

[0192] Record flow, pressure, diffusion radius and other parameters in real time, and use control software to visualize the slurry diffusion path. Monitor the permeability changes in the formation through equipment such as resistivity imaging and pore water pressure gauges to identify the changing trends of pores and cracks during the diffusion process.

[0193] 3. Data processing and coupling analysis

[0194] (1) Multi-source data collection and fusion:

[0195] Import data from various sensors and measuring devices, including fracture 3D models, porosity distribution, permeability and pressure changes, etc. Preprocess the collected fracture, porosity and permeability data, unify coordinates and data formats, and ensure consistency of data fusion.

[0196] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A grouting diffusion prediction method with complex geological multi-attribute constraints, characterized in that: include: Acquiring multi-source attribute information of a complex geological body, wherein the multi-source attribute information includes fracture characteristics, pore characteristics and water inflow characteristics; Inputting multi-source attribute information into a trained grouting diffusion dynamic prediction model, and outputting grouting diffusion prediction results of complex geological bodies, wherein the grouting diffusion results include permeability, pressure distribution and boundary conditions; The establishment and training process of the grouting diffusion dynamic prediction model is as follows: Build a grouting diffusion simulation experimental system to simulate the grouting diffusion process in complex geology; Acquire multi-source attribute information and permeability of the simulated formation of the experimental system, establish a multi-attribute coupled permeability model, and embed the multi-attribute coupled permeability model into the grouting diffusion dynamic prediction model; at the same time, embed the dynamic equation as a physical constraint condition into the grouting diffusion dynamic prediction model; Conduct numerical simulation on the experimental system to obtain the permeability rate, pressure distribution and diffusion boundary during the grouting experiment, and train the grouting diffusion dynamic prediction model; After the training is completed, the pressure, resistivity and concentration of the grouting experiment at the current moment are obtained in real time, and input into the grouting diffusion dynamic prediction model to predict the grouting diffusion process at the next moment; During the prediction process, the grouting diffusion dynamic prediction model continuously adjusts the prediction results through a feedback mechanism according to the grouting results of the grouting experiment to optimize the grouting diffusion dynamic prediction model.

2. The grouting diffusion prediction method with complex geological multi-attribute constraints as claimed in claim 1 is characterized in that: The dynamic equations include Darcy's law, fluid mechanics equations and diffusion equations. During the training of the grouting diffusion dynamic prediction model, the permeability rate is coupled with Darcy's law, the pressure distribution is coupled with the fluid mechanics equations, and the diffusion boundary is coupled with the diffusion equation.

3. The grouting diffusion prediction method with complex geological multi-attribute constraints as claimed in claim 2 is characterized in that: The first physical loss function is obtained by coupling the penetration rate with Darcy's law In the formula, q pred represents the penetration rate predicted by the neural network; q phys represents the permeation rate obtained by numerical simulation; The pressure distribution is coupled with the fluid mechanics equation to obtain the second physical loss function Where P pred represents the pressure field predicted by the neural network; The diffusion boundary is coupled with the diffusion equation to obtain the third physical loss function In the formula, C pred represents the concentration field predicted by the neural network.

4. The grouting diffusion prediction method with complex geological multi-attribute constraints as claimed in claim 1 is characterized in that: The optimization of the grouting diffusion dynamic prediction model includes: Back propagation and weight update: When there is a deviation between the model prediction results and the actual experimental data, the model adjusts the network weights through the back propagation algorithm to gradually approach the real diffusion process; through continuous iterative training, the model can learn more accurate physical laws and update in real time; Real-time optimization: The model is optimized in real time based on feedback data, adjusting the prediction results to match the real-time geological state.

5. The grouting diffusion prediction method with complex geological multi-attribute constraints as claimed in claim 1 is characterized in that: The grouting diffusion dynamic prediction model includes an input layer, a spatiotemporal modeling layer, a physical constraint integration layer, a feedback mechanism and adaptive adjustment layer, and an output layer.

6. The grouting diffusion prediction method with complex geological multi-attribute constraints as claimed in claim 1 is characterized in that: When obtaining crack characteristics, use 3D laser scanners and cave scanners to measure the crack characteristics of the rock mass, generate 3D point cloud data of the cracks, and use handheld scanners to perform additional scanning of local details; When obtaining pore characteristics, a microscopic CT imager is used to obtain porosity data and establish a microscopic model of pore distribution; combined with a pore water pressure gauge, the pressure changes of the fluid inside the pores are monitored in real time to obtain the correlation data between pores and fractures; When obtaining the characteristics of water inrush, resistivity equipment and ground penetrating radar are used to measure the distribution information of stratum moisture and water inrush area, the permeability of different layers is measured through the groundwater stratification test sampling system, and the overall strength and crushing structure characteristics of the rock mass are obtained in combination with the elasticity instrument. Flow meters and pressure sensors are arranged at the face and inside the model tunnel to obtain dynamic permeability data.

7. The grouting diffusion prediction method with complex geological multi-attribute constraints as claimed in claim 1 is characterized in that: When establishing a multi-attribute coupled permeability model, statistical and machine learning methods are used to model the coupling relationship between multiple attribute information to reveal the influence mechanism of each physical attribute on permeability.

8. A grouting diffusion prediction system with complex geological multi-attribute constraints, characterized by: include: The multi-attribute information acquisition module is configured to: acquire multi-source attribute information of a complex geological body, wherein the multi-source attribute information includes fracture characteristics, pore characteristics and water inflow characteristics; The grouting diffusion prediction module is configured to: input multi-source attribute information into a trained grouting diffusion dynamic prediction model, and output grouting diffusion prediction results of complex geological bodies, wherein the grouting diffusion results include permeability, pressure distribution, and boundary conditions; The establishment and training process of the grouting diffusion dynamic prediction model is as follows: Build a grouting diffusion simulation experimental system to simulate the grouting diffusion process in complex geology; Acquire multi-source attribute information and permeability of the simulated formation of the experimental system, establish a multi-attribute coupled permeability model, and embed the multi-attribute coupled permeability model into the grouting diffusion dynamic prediction model; at the same time, embed the dynamic equation as a physical constraint condition into the grouting diffusion dynamic prediction model; Conduct numerical simulation on the experimental system to obtain the permeability rate, pressure distribution and diffusion boundary during the grouting experiment, and train the grouting diffusion dynamic prediction model; After the training is completed, the pressure, resistivity and concentration of the grouting experiment at the current moment are obtained in real time, and input into the grouting diffusion dynamic prediction model to predict the grouting diffusion process at the next moment; During the prediction process, the grouting diffusion dynamic prediction model continuously adjusts the prediction results through a feedback mechanism according to the grouting results of the grouting experiment to optimize the grouting diffusion dynamic prediction model.

9. The grouting diffusion prediction system with complex geological multi-attribute constraints as claimed in claim 8, characterized in that: The grouting diffusion simulation experimental system comprises: Complex geological simulation unit, used to simulate complex geology; The water injection and grouting unit includes a dynamic water circulation module and a grouting control module. The dynamic water circulation module is used to simulate the groundwater seepage process, provide constant pressure water flow, and simulate the natural groundwater flow path in the model; the grouting control module realizes the precise control of the grouting rate, pressure and flow rate, and monitors the pressure and flow rate data during the grouting process through multi-source sensors; Data acquisition and monitoring units, including pressure sensors, flow sensors, pore water pressure gauges, resistivity imaging systems, and ultrasonic sensors; The data processing unit is used to acquire data and perform data processing.

10. The grouting diffusion prediction system with complex geological multi-attribute constraints as claimed in claim 8, characterized in that: The complex geological simulation unit includes a model box, in which various rock layers, sand layers and soil layers are filled, and different geological features such as transverse and longitudinal cracks, sand layers, gravel layers, fault fracture zones and karst caves are embedded to simulate complex geological structures.

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