Intelligent grouting simulation method and system based on adaptive sparse representation

Abstract

The invention belongs to the technical field of grouting simulation, and particularly discloses an intelligent grouting simulation method and system based on adaptive sparse representation, and the method comprises the steps: obtaining real-time data of a grouting construction site, and carrying out the preprocessing; based on the geological conditions of the target area, a multi-scale geological model is constructed, and the multi-scale geological model is obtained by coupling an overall geological model and a local detail model; carrying out grid division on the whole geologic model by using finite element analysis, and carrying out grid division on the local detail model by using an adaptive sparse representation method; the grouting process is preliminarily simulated by utilizing real-time data of a grouting construction site, a simulation result is compared with actual grouting data, and when the deviation between the simulation result and the actual grouting data exceeds a set threshold value, model parameters are automatically adjusted so as to continuously optimize the grouting simulation result. According to the method, the simulation calculation time can be remarkably shortened, and the requirements of real-time monitoring and rapid decision making are met.

Description

Technical Field

[0001] The present invention relates to the technical field of grouting simulation, and in particular to an intelligent grouting simulation method and system based on adaptive sparse representation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Grouting technology is widely used in underground engineering. Grout is injected into foundations, rock cracks or soil pores to improve the bearing capacity of the foundation, control groundwater flow, or reinforce weak strata to meet engineering needs. The traditional grouting process involves the flow, penetration, solidification and diffusion of slurry. However, these behaviors are affected by many complex factors, including the diversity of geological structures, the rheological properties of slurry, and the control accuracy of construction technology. Therefore, accurately simulating and predicting the grouting effect has become a key challenge in engineering.

[0004] In actual engineering, geological structures are usually non-uniform and multi-scale. For example, the fracture network of rock and soil has tiny pores on a small scale, but appears as a large fault zone on a large scale. After injection, the slurry will gradually fill the gaps in these structures with pressure, and its flow path and diffusion rate will change with factors such as geological characteristics, slurry viscosity, and grouting pressure, making construction control more difficult.

[0005] Traditional numerical simulation methods, such as the finite element method (FEM) and the finite difference method (FDM), are widely used in engineering simulation. However, when dealing with complex, multi-scale geological structures, the complex multi-scale geological structures and the nonlinear diffusion behavior of the slurry require a lot of computing resources, and the calculation is time-consuming, which is not conducive to real-time application; the finite element and finite difference methods have limitations in simulating the details of multi-scale geological structures, especially when the structure contains a large number of tiny cracks and pores. These methods are difficult to accurately capture the actual diffusion path of the slurry, resulting in insufficient simulation accuracy; traditional methods are difficult to flexibly adjust in the face of changing construction conditions (such as slurry flow, pressure, etc.), and usually require complex model adjustments, resulting in low simulation efficiency, and it is difficult to provide real-time feedback under dynamic conditions, and lack of adaptability. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes an intelligent grouting simulation method and system based on adaptive sparse representation, which divides the simulation area according to different scales of the fluid diffusion process, thereby realizing multi-level precise processing of porous media; at the same time, adaptive sparse representation technology is introduced, so that the system can dynamically adjust the solution accuracy according to changes in fluid diffusion characteristics.

[0007] In some embodiments, the following technical solutions are adopted: An intelligent grouting simulation method based on adaptive sparse representation, comprising: Obtain real-time data from the grouting construction site and perform preprocessing; Based on the geological conditions of the target area, a multi-scale geological model is constructed, wherein the multi-scale geological model is obtained by coupling the overall geological model and the local detail model; the overall geological model is meshed using finite element analysis, and the local detail model is meshed using an adaptive sparse representation method; The real-time data of the grouting construction site is used to preliminarily simulate the grouting process, and the simulation results are compared with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted to continuously optimize the grouting simulation results.

[0008] As a further solution, on the overall geological model, the overall fault and fracture zone structure is taken as the simulation target; on the local detail model, the cracks and pores are modeled in detail, and the multi-scale feature decomposition is completed to accurately simulate the slurry diffusion path.

[0009] As a further solution, the adaptive sparse representation method is used to mesh the local detail model. The specific process is as follows: Through the dictionary learning algorithm, a sparse dictionary of geological features is generated, and the sparse coefficient vector x is initialized according to the input data 0 ; Using the online gradient descent method, the sparse coefficient vector x is adaptively adjusted according to the actual construction data at each time step. t ; All sparse coefficient vectors constitute a sparse coefficient matrix. The area where non-zero sparse coefficients are dispersed in the sparse coefficient matrix is ​​regarded as a low-sparse area, and the area where non-zero sparse coefficients are concentrated is regarded as a high-sparse area. The grid density of the low-sparse area is set to be greater than the grid density of the high-sparse area.

[0010] As a further solution, the online gradient descent method is used to adaptively adjust the sparse coefficient vector x according to the actual grouting data at each time step. t , specifically: Assume that the input data y t The sparse representation at time step t is: t ≈Dx t , where D is a fixed dictionary matrix, x t is the sparse coefficient; Construct the objective function based on the reconstruction error at the current time step t: ; Calculate the gradient of the objective function: ;in is the gradient of the reconstruction error with respect to the sparse coefficients, sign(x t ) is the gradient of the sparsity constraint; Use gradient descent to update the sparse coefficient x online t : ; where λ is the regularization parameter, η t is the learning rate.

[0011] As a further solution, after the construction data of the grouting construction site is updated, the dictionary D is updated using the K-SVD algorithm. Based on the updated dictionary D, the dictionary D and the sparse coefficient matrix X are iteratively updated again using the sparse coding method, and the grid density is re-divided based on the sparse coefficient matrix.

[0012] As a further solution, the real-time data of the grouting construction site was used to conduct a preliminary simulation of the grouting process. The specific process is as follows; Set the initial conditions and boundary conditions for grouting; For the overall geological model, the flow equation is: , solve the flow behavior of the soil grouting liquid, and obtain the pressure, flow velocity and permeability coefficient at different positions in the soil layer; where k(r) is the permeability coefficient of the soil layer, which depends on the properties of the soil layer; p(r) is the pressure field of the grouting liquid; q(r) is the source term of the grouting liquid, which is usually expressed as the injection rate of the grouting liquid; For the local detail model, through the diffusion equation: ; Solve the concentration distribution of slurry in the soil pores; where C(r,t) is the slurry concentration at position r and time t; D(r) is the diffusion coefficient of the soil pores; is the gradient operator; The time step Δt is set, and the flow and diffusion processes within each time step are gradually solved. The calculation results of each step will be used as the initial conditions for the next step until the iterative process is completed. In the calculation of each time step, the pressure, flow velocity and permeability coefficient calculated by the overall geological model are passed as input to the local detail model. At the same time, the calculation results of the local detail model are fed back to the overall geological model to adjust the parameters of the overall geological model.

[0013] As a further solution, the simulation results are compared with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted; the model parameters include but are not limited to: permeability coefficient, grouting pressure, grouting flow rate, soil layer porosity, diffusion coefficient, fluidity and viscosity of the slurry; the adjustment process of the permeability coefficient is specifically as follows: Define the error function E(k) to represent the difference between the actual data and the simulation results; ; Among them, p actual (x i ) is the actual measured pressure value, p sim (x i ,k) is the pressure value obtained by simulation calculation, k is the permeability coefficient; x i is the i-th column vector of the sparse coefficient matrix X.

[0014] By minimizing the error function E(k), the optimal permeability coefficient k is obtained opt , so that the deviation between the simulation results and the actual data is minimized; according to the obtained optimal permeability coefficient, the permeability coefficient in the model is updated.

[0015] In other embodiments, the following technical solutions are adopted: An intelligent grouting simulation system based on adaptive sparse representation, comprising: Data acquisition module, used to acquire real-time data of the grouting construction site and perform preprocessing; An adaptive meshing module is used to construct a multi-scale geological model based on the geological conditions of the target area, wherein the multi-scale geological model is obtained by coupling the overall geological model and the local detail model; the overall geological model is meshed using finite element analysis, and the local detail model is meshed using an adaptive sparse representation method; The grouting simulation module is used to perform a preliminary simulation of the grouting process using real-time data from the grouting construction site, and compare the simulation results with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted to continuously optimize the grouting simulation results.

[0016] In other embodiments, the following technical solutions are adopted: A terminal device comprises a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the above-mentioned intelligent grouting simulation method based on adaptive sparse representation.

[0017] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned intelligent grouting simulation method based on adaptive sparse representation.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) By adopting a multi-scale model, the system can simultaneously consider both macroscopic structure and microscopic features. This multi-level modeling approach can accurately capture subtle changes within the geological body, such as the distribution of cracks and pores, the flow path of the slurry, etc. Compared with the traditional single-scale model, the present invention significantly improves the prediction accuracy in the simulation of slurry diffusion and solidification, thereby effectively reducing the construction risks caused by inaccurate predictions.

[0019] (2) The present invention adopts sparse representation technology, which enables the system to process high-dimensional geological data with lower computational complexity. By sparsely representing geological features, the system can efficiently store and calculate key information, reducing unnecessary data redundancy and computational burden. In large-scale engineering projects, it can significantly shorten the simulation calculation time and meet the needs of real-time monitoring and rapid decision-making.

[0020] (3) The present invention has a powerful dynamic feedback capability, which can monitor the key parameters in the grouting process in real time and automatically adjust the model parameters according to the actual situation. This real-time optimization mechanism ensures the flexibility and adaptability of the grouting process, and can promptly respond to various uncertainties that arise during the construction process, such as sudden changes in geological conditions or changes in slurry properties, to ensure the reliability of the construction effect.

[0021] (4) The intelligent simulation method of the present invention is applicable to various types of geotechnical engineering projects, including tunnel excavation, foundation reinforcement, groundwater control, etc. Under different geological conditions and engineering requirements, the system can flexibly adjust the model and parameters to achieve personalized simulation services. This wide applicability makes the present invention have a high promotion value in actual engineering.

[0022] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an intelligent grouting simulation method based on adaptive sparse representation in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] Embodiment 1 In one or more embodiments, an intelligent grouting simulation method based on adaptive sparse representation is disclosed, combined with Figure 1 , specifically including the following process: S101: Acquire real-time data of the grouting construction site and perform preprocessing.

[0027] In this embodiment, a variety of sensors are used to collect real-time data of the grouting construction site, including: grouting pressure, grouting flow, grouting velocity, soil permeability change data, etc. The drilling method, seismic method and other methods are used to collect geological data on site, including soil layer structure, porosity, permeability coefficient, crack characteristics (width, direction, connectivity), soil particle size and distribution, geotechnical parameters (such as compression modulus, tensile strength), etc.; at the same time, it is necessary to obtain the viscosity, fluidity, curing time, viscosity and shear stress relationship, and permeability characteristics of the slurry of the grouting material.

[0028] Since field data may be affected by noise, the system first performs data denoising, using algorithms such as wavelet transform and Kalman filtering to eliminate random noise, and then normalizes and standardizes the data to ensure the compatibility and consistency of the model with multi-source data, completing data preprocessing.

[0029] S102: Based on the geological conditions of the target area, a multi-scale geological model is constructed, where the multi-scale geological model is obtained by coupling the overall geological model and the local detail model.

[0030] In this embodiment, on a macro scale, an overall geological model is constructed with overall faults, fault zones and other structures as simulation targets; the overall geological model includes the overall structure of the soil layer, the permeability characteristics of the soil layer, porosity, permeability coefficient and other parameters to simulate the large-scale flow and pressure distribution during the slurry injection process.

[0031] At the microscopic scale, fine cracks, pores, etc. are modeled in detail to construct a local detail model; the porosity and pore distribution of the soil are obtained, and a three-dimensional grid is used to represent the pore structure of the soil. Each grid unit (for example, a cube or octahedron) represents a small space between soil particles, simulating the flow of slurry. Depending on the needs of the grouting simulation, the grid can be refined or coarsened. Important areas (such as cracks and areas with large porosity) can use finer grids, while other unimportant areas use coarser grids.

[0032] S103: Using finite element analysis to mesh the overall geological model, and using an adaptive sparse representation method to mesh the local detail model.

[0033] In order to improve the adaptability and computational efficiency of the model, this embodiment uses finite element analysis to divide the macro structure as a whole, and on this basis, uses sparse representation methods to characterize the micro structure in detail. Finite element analysis is combined with sparse representation, and coarse grid division is used for the overall geological model, and sparse representation of adaptive grid division is applied to the local detail model to achieve accurate modeling of information at different scales, thereby completing multi-scale feature decomposition to accurately simulate the slurry diffusion path.

[0034] As a specific example, the adaptive sparse representation method is used to mesh the local detail model. The specific process is as follows: S1031: Discretize the data of the local detail model into an initial grid point set, represented by the matrix Y∈R m ×n , where m is the dimension of the physical property vector (such as porosity, particle density, etc.) at each grid point, and n is the number of initial grid points.

[0035] S1032: Normalize each column of Y to ensure that different physical parameters have consistent dimensions; use filtering or noise reduction technology to remove noise in the data to reduce interference with subsequent sparse representation.

[0036] S1033: Generate a sparse dictionary of geological features through a dictionary learning algorithm, and initialize the sparse coefficient vector x according to the input data 0 ; According to the geological characteristics and slurry flow behavior, a suitable sparse dictionary (the dictionary can be constructed based on wavelet transform or Fourier transform) is selected to describe geological data of different scales and characteristics. In the initial stage, a basic dictionary is constructed based on the geological data collected on site and the results of previous geological studies, which includes various geological characteristics that may affect slurry diffusion (such as fracture characteristics, porosity distribution, etc.).

[0037] For each column of data point y i∈Y (representing the feature vector of a grid point), in a fixed dictionary D∈R m×K , K represents the number of sparse bases (usually K>m), solve the following optimization problem: ; Assign a weight ω to each sample i , weight ω i Dynamically adjust according to regional importance (such as crack concentration or porosity); through weighted dictionary learning, the feature extraction ability of key areas can be enhanced.

[0038] Among them, λ is a regularization parameter that controls the balance between sparsity and error. Simply put, λ determines whether the model pays more attention to accuracy (reducing error) or simplicity (sparseness) when fitting actual data. A larger λ tends to make the model more sparse and reduce the number of bases used; while a smaller λ pays more attention to the fitting accuracy of the data and allows more bases to be used.

[0039] In this embodiment, the method for selecting λ is as follows: ① Divide the data set (construction data collected at different time steps during the grouting process) into k subsets (usually 5 or 10 subsets) according to the time step. ② For each k-time training and validation process, select one subset as the validation set and the remaining subsets as the training set. ③ Train the model on the training set and evaluate the performance of the model with the validation set to calculate the error or loss value (the gap between the simulation and the actual data). ④ Experiment with different λ values ​​and select the λ that minimizes the validation error, thereby optimizing the sparse representation and parameter adjustment in the simulation process.

[0040] Use the orthogonal matching pursuit (OMP) or Lasso algorithm to solve the initial sparse coefficient vector x 0 , update to obtain the sparse coefficient matrix X.

[0041] After determining the sparse dictionary D and the sparse coefficient matrix X, the real-time collected geological feature data (such as fracture distribution, permeability, etc.) are input into the sparse representation module, and the input geological feature data are processed by the sparse coding algorithm (such as LASSO or OMP algorithm). First, the correlation between the input data and each basis in the sparse dictionary is calculated, and several bases with the highest correlation are selected. Then, the input data is reconstructed using the selected base linear combination to achieve the purpose of sparse representation. After sparse processing, key geological features are extracted, such as the direction of the dominant fractures, the distribution pattern of the main pores, etc. These features will be used in subsequent grouting simulations.

[0042] In sparse representation, the correlation between the input data and each basis in the sparse dictionary is calculated to determine which basis contributes most to the reconstruction of the input data. This correlation is usually calculated based on the similarity measure between vectors, and the specific method is as follows: Assume the input data vector is y∈R n , the dictionary is D=[d 1 ,d 2 ,…,d k ]∈R n×k , where d i is the i-th basis vector in the dictionary, usually normalized to a unit vector (i.e. ||d i || 2 = 1), the sparse coefficient is x∈R k Input data y and dictionary basis vector d i The correlation r i It is usually expressed as the inner product of the two: ; in, Represents y and d i The dot product reflects the consistency of their directions, and the absolute value This is because both positive and negative correlations can indicate strong correlations. If the dictionary basis vectors are not normalized, they need to be normalized: .

[0043] r i The larger the value, the greater the dictionary basis vector d i The closer to the direction of y, the greater the contribution. i Sort and select several bases with the highest correlation to represent y.

[0044] S1034: Using the online gradient descent method, the sparse coefficient vector x is adaptively adjusted according to the actual grouting process data at each time step. t , to ensure that the simulation results are consistent with the actual situation on site, thereby optimizing the grouting simulation accuracy.

[0045] Specifically, during the grouting construction process, as time goes by, the system will collect and process the on-site grouting process data at regular intervals. These data include grouting pressure, flow, soil layer permeability, grouting liquid diffusion depth, etc. At each time step, the system adjusts the sparse coefficient of the current simulation through these data to improve the simulation accuracy.

[0046] In this embodiment, in order to adapt to the changes in different geological conditions and construction stages, the sparse coefficient vector x is adaptively adjusted according to the actual grouting data of each time step. t ; The specific process is as follows: (1) Define the sparse representation model. Assume that the input data y t The sparse representation at time step t is: t ≈Dx t , where D is a fixed dictionary matrix, xt is a sparse coefficient vector, and D and y t It is known at every moment.

[0047] (2) Construct the objective function based on the reconstruction error of the current time step t: ; (3) For x t , calculate the gradient of the objective function: ;in is the gradient of the reconstruction error with respect to the sparse coefficients, sign(x t ) is the gradient of the sparsity constraint, and λ is the regularization parameter; (4) Use the gradient descent method to update the sparse coefficient x online t : ; where η t is the learning rate, which determines the step size of each update; x t (k) is the sparse coefficient updated for the kth time, x t (k+1) is the sparse coefficient updated for the k+1th time.

[0048] At this time, the sparse coefficient will be dynamically updated at each time step according to the new geological conditions and construction environment. The model's representation of these physical properties can be dynamically adjusted to make the simulation more in line with the actual situation. At each time step, the sparse coefficient is adjusted according to real-time construction data to more accurately reflect the various parameters in the grouting process (such as grouting pressure, flow rate, etc.). The sparse coefficient is a state parameter at each moment in the model. It is adjusted in real time at each time step through the online gradient descent method to better match the construction data and ensure the real-time and accuracy of the simulation.

[0049] A key goal of sparse representation is to retain as few non-zero coefficients as possible through the sparse coefficient x. Therefore, adjusting the sparse coefficient usually results in a reduction in the number of features, keeping the minimum features that contribute significantly to the target output (such as grouting simulation results). With the dynamic adjustment of the sparse coefficient (through online learning, i.e. the online gradient descent method mentioned above), the model will focus more on the important features in the geological model and remove those redundant features that have less impact on the simulation results. For example, under certain geological conditions, some features (such as porosity, fracture density, etc.) may become more important, while other features (such as permeability) may become less important, so these unimportant features will be automatically removed through sparse representation.

[0050] If the adjustment of the sparsity factor causes the distribution of features or important areas in the model to change, the meshing will be optimized according to these changes in the subsequent simulation process. For example, after the sparsity factor is adjusted, the model may focus more on high-density areas (such as areas with dense fractures or high porosity), which may lead to the use of finer meshes in these areas to capture more subtle physical changes.

[0051] Therefore, in this embodiment, all sparse coefficient vectors are used to form a sparse coefficient matrix, and the area in the sparse coefficient matrix where non-zero sparse coefficients are dispersed (indicating that the feature changes greatly and the structure is complex) is used as a low-sparseness area, and the area where non-zero sparse coefficients are concentrated (indicating that the feature is relatively simple and the structure is uniform) is used as a high-sparseness area; the grid density of the low-sparseness area is set to be greater than the grid density of the high-sparseness area; thereby realizing the grid division of the local detail model using the adaptive sparse representation method.

[0052] S104: Perform a preliminary simulation of the grouting process using real-time data from the grouting construction site.

[0053] In this embodiment, after completing the grid division and discretization, the initial conditions, i.e., the initial pressure distribution and slurry concentration, are set according to the geological data and the initial state of the grouting fluid; the boundary conditions are set according to the actual construction environment, such as the boundary pressure, slurry injection rate, porosity, and flow restriction outside the soil layer. The time step Δt is set, and the flow and diffusion process within each time step is gradually solved. The calculation results of each step will be used as the initial conditions for the next step.

[0054] In the discretization process, the solution system obtained is usually a set of linear equations in the following form: AX=B, where A is the coefficient matrix, which contains information such as the flow equation and the diffusion equation; X is the vector of unknown variables (such as pressure and concentration); and B is the constant term, which is usually given by initial conditions, boundary conditions, etc.

[0055] The calculation results of the overall geological model and the local detailed model are coupled to complete the final grouting behavior simulation. In each round of calculation, the pressure field, flow field, and permeability coefficient calculated by the overall geological model will be passed as input to the local detailed model. The calculation results of the local detailed model will affect the parameters of the overall geological model. For example, if the local detailed model finds that the permeability of certain areas has changed (such as the grouting liquid diffuses to certain areas, causing the porosity of the soil to increase or decrease), it is necessary to adjust the permeability or pressure field of the corresponding area in the overall geological model. In multi-scale coupling, the coupling of the overall geological model and the local detailed model is usually an iterative process. Each iteration transfers information between the overall geological model and the local detailed model, and adjusts the model parameters based on the feedback.

[0056] The following is a detailed calculation and iterative convergence judgment process: In the overall geological model, the finite element method (FEM) is used to solve the flow behavior of the grouting fluid in the soil. The equation is: , where k(r) is the permeability coefficient of the soil layer, which depends on the properties of the soil layer; p(r) is the pressure field of the grouting fluid; q(r) is the source term of the grouting fluid, usually expressed as the injection rate of the grouting fluid. The overall geological model obtains information such as pressure distribution and flow rate at different locations in the soil layer by solving the above equations.

[0057] In the local detail model, the following diffusion equation is solved: ; Where C(r,t) is the slurry concentration at position r and time t; D(r) is the diffusion coefficient of the soil pores; is the gradient operator.

[0058] The local detail model calculates the concentration distribution of slurry in the soil pores by solving the diffusion equation.

[0059] According to the adjustment results of the overall geological model, the overall geological model is solved again, and the new macro results are used as the input of the local detail model to continue calculating the local detail model. Then the results of the local detail model are fed back to the overall geological model again, and this process is repeated.

[0060] In each iteration, the parameter changes between the macro and local detail models need to be checked to determine whether convergence has occurred. Common convergence criteria include: change check (at the end of each iteration, the change in the overall geological model and local detail model parameters, such as pressure field, concentration distribution, and permeability coefficient, is calculated. If the change is less than the preset threshold, it is considered to have converged) and error threshold (set an error threshold, and stop iteration when the error of the model result is lower than the threshold).

[0061] In this embodiment, the convergence judgment standard can be set as: k+1 -X k | / |X k |<ε, where X k represents the result of the kth iteration, and ε is the preset convergence error threshold. When the difference between the results of two iterations is less than the preset error threshold, the iteration process is considered to be converged and the iteration can be stopped.

[0062] This embodiment can simulate and solve the grouting process more accurately through the coupled iterative solution of the overall geological model and the local detail model.

[0063] As an optional example, when the construction data of the grouting construction site is updated, the dictionary D is updated using the K-SVD algorithm. Based on the updated dictionary D, the dictionary D and the sparse coefficient matrix X are iteratively updated again using the sparse coding method, and the grid density is re-divided based on the sparse coefficient matrix.

[0064] Each basis in the dictionary represents a possible mode, which can be different physical properties of the soil layer (such as pore structure, permeability) or different flow modes during the grouting process. When the real-time construction data is updated, the dictionary needs to be optimized to ensure that it can accurately represent the various changes in the grouting process. Dictionary update is a global operation that affects the structure of the entire simulation. This embodiment optimizes the dictionary according to the real-time data through the K-SVD algorithm to update the representation capability of each basis. The process of updating the dictionary is to make the dictionary D more reflective of the on-site soil structure and grouting behavior through iterative optimization. The updated dictionary D will redirect the sparse coding process and update the sparse coefficient matrix X to obtain more accurate simulation results. When the dictionary and sparse coefficient matrix are updated, the system will re-divide the grid based on these updated parameters. The change in grid density is based on the influence of the new model parameters on the soil microstructure and the grouting process, optimizing the fineness of the grid to make the simulation results more accurate. By updating the grid density, the details of the grouting process can be better captured, especially in areas where local grouting pressure changes or diffusion paths change significantly.

[0065] S105: Compare the simulation results with the actual grouting data. When the deviation between the two exceeds a set threshold, automatically adjust the model parameters to continuously optimize the grouting simulation results.

[0066] In this embodiment, during the simulation process, the deviation between the actual data of the grouting process (such as the actual flow pressure, flow rate, porosity change of the soil layer, etc.) and the simulation results is monitored in real time. If the deviation exceeds the set threshold, the system will dynamically adjust the model parameters through an adaptive algorithm to optimize the grouting speed and diffusion path to ensure that the grouting process meets the design requirements and construction standards. For example, in the case of uneven slurry diffusion, the system will optimize the diffusion effect by increasing the pressure or adjusting the grouting flow rate.

[0067] Here are the adjustment processes of several main parameters: (1) Adjustment of permeability coefficient. By real-time monitoring of the diffusion range and pressure field of the grouting fluid and comparing the actual permeability rate with the simulation results, the system can automatically adjust the permeability coefficient through optimization algorithms (such as the least squares method) so that the model can better reflect the actual permeability of the soil layer. First, define the error function E(k) to represent the difference between the actual data and the simulation results: ; Among them, p actual (x i ) is the actual measured pressure value, p sim (x i , k) is the pressure value obtained by simulation calculation, and k is the permeability coefficient. By minimizing the error function E(k), the optimal permeability coefficient k is obtained. opt , so that the deviation between the simulation results and the actual data is minimized: .

[0068] x i It is the i-th column vector of the sparse coefficient matrix X, which represents the sparse representation of the i-th data point (such as the measurement data at a certain time or position) in the dictionary D basis.

[0069] Based on the calculated optimal permeability coefficient, the permeability coefficient in the model is updated, and the macro and local detail models are rerun.

[0070] (2) Adjustment of grouting pressure and flow rate. By measuring the flow rate, pressure and velocity field of the grouting fluid in real time and comparing them with the simulation results, the difference between the actual flow rate and pressure and the simulated values ​​can be calculated. If the actual flow rate is low, you can consider increasing the flow rate of the grouting fluid or increasing the grouting pressure.

[0071] (3) Adjustment of soil porosity. The system can automatically update the porosity parameters according to the settlement or porosity change of the soil layer during the actual grouting process. By comparing the deviation between the actual porosity and the model prediction value, the porosity parameters in the overall geological model are adjusted.

[0072] (4) Adjustment of diffusion coefficient: According to the real-time monitored slurry diffusion rate (through pressure changes in the soil, slurry concentration distribution, etc.), adjust the diffusion coefficient in the local detail model.

[0073] The real-time optimization mechanism of this embodiment ensures the flexibility and adaptability of the grouting process, and can promptly respond to various uncertainties that arise during the construction process, such as sudden changes in geological conditions or changes in slurry properties, thereby ensuring the reliability of the construction effect.

[0074] Finally, the simulation results of grouting diffusion range, pressure distribution, flow rate change, etc. are graphically displayed in the form of 3D view or dynamic graph to help users understand the grouting status in real time. If the grouting diffusion trend or pressure distribution exceeds the expected range, the system will trigger a risk warning prompt and recommend adjustment of parameters (such as grouting flow rate and pressure) to reduce risks. At the same time, it provides real-time decision support to help construction personnel timely judge the grouting effect and adjust the grouting strategy to achieve the best grouting effect.

[0075] Embodiment 2 In one or more embodiments, an intelligent grouting simulation system based on adaptive sparse representation is disclosed, comprising: Data acquisition module, used to acquire real-time data of the grouting construction site and perform preprocessing; An adaptive meshing module is used to construct a multi-scale geological model based on the geological conditions of the target area, wherein the multi-scale geological model is obtained by coupling the overall geological model and the local detail model; the overall geological model is meshed using finite element analysis, and the local detail model is meshed using an adaptive sparse representation method; The grouting simulation module is used to perform a preliminary simulation of the grouting process using real-time data from the grouting construction site, and compare the simulation results with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted to continuously optimize the grouting simulation results.

[0076] It should be noted that the specific implementation method of the above modules is the same as that in Example 1 and will not be described in detail.

[0077] Embodiment 3 In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the intelligent grouting simulation method based on adaptive sparse representation described in Example 1.

[0078] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0080] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0081] Embodiment 4 In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the intelligent grouting simulation method based on adaptive sparse representation described in Example 1.

[0082] 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. An intelligent grouting simulation method based on adaptive sparse representation, characterized in that: include: Obtain real-time data from the grouting construction site and perform preprocessing; Based on the geological conditions of the target area, a multi-scale geological model is constructed, wherein the multi-scale geological model is obtained by coupling the overall geological model and the local detail model; the overall geological model is meshed using finite element analysis, and the local detail model is meshed using an adaptive sparse representation method; The real-time data of the grouting construction site is used to preliminarily simulate the grouting process, and the simulation results are compared with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted to continuously optimize the grouting simulation results.

2. The intelligent grouting simulation method based on adaptive sparse representation according to claim 1, characterized in that: In the overall geological model, the overall fault and fault zone structure are simulated; in the local detail model, the cracks and pores are modeled in detail, and the multi-scale feature decomposition is completed to accurately simulate the slurry diffusion path.

3. The intelligent grouting simulation method based on adaptive sparse representation according to claim 1, characterized in that: The adaptive sparse representation method is used to mesh the local detail model. The specific process is as follows: Generate a sparse dictionary of geological features through a dictionary learning algorithm, and initialize the sparse coefficient vector x0 based on the input data; Using the online gradient descent method, the sparse coefficient vector x is adaptively adjusted according to the actual construction data at each time step. t ; All sparse coefficient vectors constitute a sparse coefficient matrix, and the area where the non-zero sparse coefficients are dispersed in the sparse coefficient matrix is ​​regarded as a low sparsity area, and the area where the non-zero sparse coefficients are concentrated is regarded as a high sparsity area; Set the mesh density in areas of low sparsity to be greater than that in areas of high sparsity.

4. The intelligent grouting simulation method based on adaptive sparse representation according to claim 3, characterized in that: The sparse coefficient vector x is adaptively adjusted according to the actual grouting data at each time step using the online gradient descent method. t , specifically: Assume that the input data y t The sparse representation at time step t is: t ≈Dx t , where D is a fixed dictionary matrix, x t is the sparse coefficient; Construct the objective function based on the reconstruction error at the current time step t: ； Calculate the gradient of the objective function: ;in is the gradient of the reconstruction error with respect to the sparse coefficients, sign(x t ) is the gradient of the sparsity constraint; Use gradient descent to update the sparse coefficient x online t : ; where λ is the regularization parameter, η t is the learning rate.

5. The intelligent grouting simulation method based on adaptive sparse representation according to claim 3, characterized in that: After the construction data of the grouting construction site is updated, the dictionary D is updated using the K-SVD algorithm. Based on the updated dictionary D, the dictionary D and the sparse coefficient matrix X are iteratively updated again using the sparse coding method, and the grid density is re-divided based on the sparse coefficient matrix.

6. The intelligent grouting simulation method based on adaptive sparse representation according to claim 1, characterized in that: The grouting process is preliminarily simulated using real-time data from the grouting construction site. The specific process is as follows: Set the initial conditions and boundary conditions for grouting; For the overall geological model, the flow equation is: , solve the flow behavior of the soil grouting liquid, and obtain the pressure, flow velocity and permeability coefficient at different positions in the soil layer; where k(r) is the permeability coefficient of the soil layer, which depends on the properties of the soil layer; p(r) is the pressure field of the grouting liquid; q(r) is the source term of the grouting liquid, which is usually expressed as the injection rate of the grouting liquid; For the local detail model, through the diffusion equation: ； Solve the concentration distribution of slurry in the soil pores; where C(r,t) is the slurry concentration at position r and time t; D(r) is the diffusion coefficient of the soil pores; is the gradient operator; The time step Δt is set, and the flow and diffusion processes within each time step are gradually solved. The calculation results of each step will be used as the initial conditions for the next step until the iterative process is completed. In the calculation of each time step, the pressure, flow velocity and permeability coefficient calculated by the overall geological model are passed as input to the local detail model. At the same time, the calculation results of the local detail model are fed back to the overall geological model to adjust the parameters of the overall geological model.

7. The intelligent grouting simulation method based on adaptive sparse representation according to claim 1, characterized in that: The simulation results are compared with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted; the model parameters include but are not limited to: permeability coefficient, grouting pressure, grouting flow rate, soil layer porosity, diffusion coefficient, fluidity and viscosity of the slurry; The adjustment process of the permeability coefficient is as follows: Define the error function E(k) to represent the difference between the actual data and the simulation results; ； Among them, p actual (x i ) is the actual measured pressure value, p sim (x i ,k) is the pressure value obtained by simulation calculation, k is the permeability coefficient; x i is the i-th column vector of the sparse coefficient matrix X; By minimizing the error function E(k), the optimal permeability coefficient k is obtained opt , so that the deviation between the simulation results and the actual data is minimized; according to the obtained optimal permeability coefficient, the permeability coefficient in the model is updated.

8. An intelligent grouting simulation system based on adaptive sparse representation, characterized in that: include: Data acquisition module, used to acquire real-time data of the grouting construction site and perform preprocessing; An adaptive meshing module is used to construct a multi-scale geological model based on the geological conditions of the target area, wherein the multi-scale geological model is obtained by coupling the overall geological model and the local detail model; the overall geological model is meshed using finite element analysis, and the local detail model is meshed using an adaptive sparse representation method; The grouting simulation module is used to perform a preliminary simulation of the grouting process using real-time data from the grouting construction site, and compare the simulation results with the actual grouting data. When the deviation between the two exceeds the set threshold, the model parameters are automatically adjusted to continuously optimize the grouting simulation results.

9. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the intelligent grouting simulation method based on adaptive sparse representation as described in any one of claims 1-7.

10. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the intelligent grouting simulation method based on adaptive sparse representation as described in any one of claims 1-7.

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