Grouting digital twin virtual-reality interactive pre-control analysis method and system

Abstract

The present disclosure provides a grouting digital twin virtual-reality interactive pre-control analysis method and system, which relates to the field of geotechnical engineering technology, including constructing a multi-scale geological model; performing numerical simulation of the grouting process based on the multi-scale geological model, obtaining simulated grouting parameters and on-site grouting monitoring data; fusing and aligning the simulated grouting parameters with the on-site monitoring data, and performing principal component analysis and eigenvalue decomposition, selecting principal component eigenvectors to construct a vector matrix; using particle filtering to predict the grouting state of the vector matrix after dimensionality reduction, establishing a state transition model to describe the change of grouting parameters, calculating the likelihood of each particle according to the predicted state of the set particle, and updating the particle weight according to the likelihood; calculating the predicted state of each time step according to the updated particle weight, evaluating the current grouting effect according to the predicted state, and realizing real-time pre-control analysis of the grouting process.

Description

Technical Field

[0001] The present disclosure relates to the field of geotechnical engineering technology, and in particular to a grouting digital twin virtual-reality interactive pre-control analysis method and system. Background Art

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

[0003] Grouting technology is widely used in underground spaces and geotechnical engineering, especially for improving the mechanical properties of underground soil and preventing and controlling sudden water and mud inrush disasters. However, with the increasing complexity of engineering environments, traditional grouting process control methods face many challenges. In particular, under complex geological conditions, slurry flow paths and diffusion effects are difficult to predict, and the evaluation and adjustment of grouting effects are often delayed, leading to risks to project quality and safety. Digital twin technology has made significant progress in multiple fields by constructing virtual models corresponding to physical entities, enabling real-time interaction and dynamic synchronization between the two. Applying digital twin technology to the grouting process enables accurate simulation and dynamic monitoring of the grouting process. Through two-way feedback between virtual models and physical data, slurry flow, diffusion, and its interaction with the geological environment can be tracked in real time, providing a scientific basis for the prediction, adjustment, and optimization of the grouting process.

[0004] Although digital twin technology has achieved success in other industries, its practical application in the grouting field is still in its early stages. In particular, the following problems still exist in the interaction between the grouting entity and the virtual model:

[0005] 1) Current interaction methods mainly rely on static models and traditional sensor data, lacking real-time and intelligence.

[0006] 2) Traditional grouting monitoring and control methods are difficult to adapt to the complex and dynamically changing geological environment, and cannot effectively integrate multi-source data for accurate prediction and optimization.

[0007] 3) It is difficult to break through the limitations of existing methods and establish an interactive control system with real-time feedback, efficient prediction and optimization. Summary of the Invention

[0008] In order to solve the above problems, the present disclosure proposes a virtual-reality interactive pre-control analysis method and system for grouting digital twins. According to the fusion of grouting simulation data and field monitoring data, they are aligned in a unified space, and the particle state is predicted through particle filtering. The difference between the predicted value of each particle and the field monitoring data is calculated, and the particle weight is updated according to the likelihood. The grouting state is predicted in real time, the grouting effect is evaluated, and instant pre-control analysis of the grouting process is realized.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions:

[0010] The grouting digital twin virtual-reality interactive pre-control analysis method includes:

[0011] Determine the target area for grouting and construct a multi-scale geological model;

[0012] Perform numerical simulation of the grouting process based on a multi-scale geological model to obtain simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data;

[0013] The simulated grouting parameters are fused and aligned with the field monitoring data, and the principal component analysis and eigenvalue decomposition are performed on the fused and aligned data. The principal component eigenvectors are selected to construct a vector matrix.

[0014] The vector matrix is ​​reduced in dimension, and the particle filter is used to predict the grouting parameter state. The changes in grouting parameters are described by establishing a state transition model. The predicted state of the set particles is compared with the field monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood.

[0015] Based on the updated particle weights, a new particle set is generated and multiple iterations are performed. The predicted state of each time step is calculated according to the updated particle weights. The current grouting effect is evaluated based on the predicted state to assess whether the state of the current time step needs to be adjusted, thereby achieving real-time pre-control analysis of the grouting process.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions:

[0017] The grouting digital twin virtual-reality interactive pre-control analysis system includes:

[0018] Initialization module, used to determine the target area for grouting and build a multi-scale geological model;

[0019] A data acquisition module is used to perform numerical simulation of the grouting process based on a multi-scale geological model, obtain the simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data;

[0020] The data fusion module is used to fuse and align the simulated grouting parameters with the field monitoring data, perform principal component analysis and eigenvalue decomposition on the fused and aligned data, and select the principal component eigenvectors to construct a vector matrix;

[0021] The state update module is used to reduce the dimension of the vector matrix and use particle filtering to predict the grouting parameter state of the reduced vector matrix. The changes in the grouting parameters are described by establishing a state transition model. The predicted state of the set particles is compared with the on-site monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood.

[0022] The grouting prediction module is used to generate a new particle set based on the updated particle weights and perform multiple iterations. It calculates the predicted state of each time step according to the updated particle weights, evaluates the current grouting effect based on the predicted state, and evaluates whether the state of the current time step needs to be adjusted, thereby realizing real-time pre-control analysis of the grouting process.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] A computer program product includes a computer program, which, when executed by a processor, implements the grouting digital twin virtual-reality interactive pre-control analysis method.

[0025] According to some embodiments, the present disclosure adopts the following technical solutions:

[0026] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the grouting digital twin virtual-reality interactive pre-control analysis method is implemented.

[0027] According to some embodiments, the present disclosure adopts the following technical solutions:

[0028] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the grouting digital twin virtual-reality interactive pre-control analysis method.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The disclosed method for virtual-reality interactive pre-control analysis of grouting digital twins extracts key features through principal component analysis (PCA) and eigenvalue decomposition. By constructing a principal component eigenvector matrix, it not only achieves data dimensionality reduction and highlights the most representative variables, but also enhances the interpretability of the model, helping to more clearly identify and understand the key factors in the grouting process.

[0031] This paper presents a method for interactive pre-control analysis of grouting digital twins, combining virtual and real-world scenarios. By combining particle filtering with a state transition model, the system compares predicted states with on-site monitoring data, calculates the likelihood of each particle, and updates its weight. This method not only improves the intelligence and automation of the grouting process but also enables refined assessment of grouting results and real-time adjustment of grouting parameters to ensure construction quality and efficiency. Furthermore, through multiple iterations and particle updates, the system possesses strong adaptability and robustness, effectively supporting optimization and decision-making analysis of the grouting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0033] Figure 1 This is a flow chart of the grouting pre-control analysis method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0036] 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 disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is 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, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0037] Example 1

[0038] In one embodiment of the present disclosure, a grouting digital twin virtual-reality interactive pre-control analysis method is provided, comprising the following steps:

[0039] Step 1: Determine the target area for grouting and construct a multi-scale geological model;

[0040] Step 2: numerically simulate the grouting process based on the multi-scale geological model, obtain the simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data;

[0041] Step 3: Fuse and align the simulated grouting parameters with the field monitoring data, perform principal component analysis and eigenvalue decomposition on the fused and aligned data, and select the principal component eigenvectors to construct a vector matrix;

[0042] Step 4: Reduce the dimension of the vector matrix and use particle filtering to predict the grouting parameter state. A state transition model is established to describe the changes in grouting parameters. The predicted state of the set particles is compared with the field monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood.

[0043] Step 5: Based on the updated particle weights, a new particle set is generated and multiple iterations are performed. The predicted state of each time step is calculated according to the updated particle weights. The current grouting effect is evaluated based on the predicted state to determine whether the state of the current time step needs to be adjusted, thereby achieving real-time pre-control analysis of the grouting process.

[0044] As an embodiment, the specific implementation process of the disclosed grouting digital twin virtual-reality interactive pre-control analysis method is as follows:

[0045] Step 1: Determine the target area for grouting and construct a multi-scale geological model;

[0046] Specifically, special equipment is used for drilling, and a mine resistivity video imaging logging instrument is used for multi-drill drilling to collect geological information, including panoramic imaging image information, GR curve, apparent resistivity curve, natural potential curve, etc. in the hole, and data such as drilling direction, inclination, and drilling depth are obtained to judge the stratum and lithology changes in the survey area, and to obtain geological data of the grouting target area, including rock and soil layer structure, porosity, permeability, crack characteristics (width, direction, connectivity), soil particle size and distribution, rock and soil mechanics parameters (such as compression modulus, tensile strength), etc. A multi-scale geological model is constructed based on the geological data. The multi-scale geological model includes a macro-geological model and a micro-geological model. The macro-geological model simulates the overall structure of the rock and soil layer, the permeability characteristics, porosity and permeability coefficient of the rock and soil layer, and simulates the large-scale flow and pressure distribution during the slurry injection process. The micro-geological model simulates the detailed structure of the rock and soil layer, and performs detailed modeling of small cracks and pores to obtain the porosity and pore distribution of the rock and soil layer. A three-dimensional grid is used to represent the pore structure of the rock and soil layer. Each grid unit (such as a polyhedron) represents a small space between rock and soil particles, which is used to simulate the flow of slurry. The multi-scale geological model is obtained by associating and coupling the macro-geological model and the micro-geological model.

[0047] Step 2: numerically simulate the grouting process based on the multi-scale geological model, obtain the simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data;

[0048] Specifically, based on the constructed multi-scale geological model and the slurry flow behavior simulated in the multi-scale geological model, initial conditions, namely the initial grouting pressure distribution, slurry concentration, etc., are set to predict and simulate the grouting parameters during the grouting process, including:

[0049] Set grouting boundary conditions, such as grouting boundary pressure, slurry injection rate, porosity, flow restrictions of rock and soil layers, etc. Set the time step and gradually solve the slurry flow and diffusion process within each time step. The calculation results of each step will serve as the initial conditions for the next step.

[0050] The solution process is converted into a system of linear equations in the form of: 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.

[0051] The calculation results of the macro-geological model and the micro-geological model are correlated and iteratively integrated to complete the final grouting behavior simulation. In each round of calculation, the pressure field, flow field, and permeability coefficient calculated by the macro-geological model will be passed as input to the micro-geological model. The calculation results of the micro-geological model will affect the parameters of the multi-scale geological model. That is, if the permeability of certain areas in the micro-geological model changes, the permeability coefficient of the corresponding area in the macro-geological model also needs to be adjusted. The multi-scale coupling process is regarded as an iterative process. Each iteration will transmit effective information between the macro-geological model and the micro-geological model, and timely feedback the model parameters for adjustment. Specifically, the specific iterative process of simulation coupling is as follows:

[0052] The existing finite element method is used in the macro geological model to simulate the flow behavior of the grouting fluid in the rock and soil layer. The macro geological model is solved by the existing finite element method to obtain information such as pressure distribution and flow velocity at different positions in the rock and soil layer. In the micro geological model, the diffusion equation is used to solve:

[0053]

[0054] where C(r,t) is the slurry concentration at position r and time t; D(r) is the diffusion coefficient of the soil pores; and ∇ is the gradient operator.

[0055] The micro-geological model calculates the concentration distribution of slurry in the pores of rock and soil layers by solving the diffusion equation.

[0056] Based on the adjustments to the macro-geological model, the macro-geological model is solved again, and the new macro-results are used as input to the diffusion equation of the micro-geological model, continuing the computational solution. The results of the micro-geological model are then fed back into the macro-geological model, and the process continues iterating. During each iteration, the parameter changes between the models are checked to determine convergence. At the end of each iteration, the changes in parameters of the macro- and micro-geological models, such as the pressure field, concentration distribution, and permeability coefficient, are calculated. If the change is less than a preset threshold, convergence is considered achieved. An error threshold is set, and iterations cease when the error in the model results falls below this threshold.

[0057] In this embodiment, the convergence judgment standard can be set as: |X 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 have converged and the iteration can be stopped.

[0058] This embodiment uses the coupled iterative solution of the macro geological model and the micro geological model to simulate and solve the grouting process more accurately, and finally iteratively obtains the simulated grouting parameters, including simulation results of data such as grouting speed, grouting pressure, phase fraction and viscosity parameters.

[0059] Furthermore, the simulated grouting parameters are used to carry out engineering grouting in the actual target area, and sensors are used to obtain real-time on-site monitored grouting pressure, grouting rate, slurry diffusion and other information under the actual slurry diffusion conditions.

[0060] Step 3: Fuse and align the simulated grouting parameters with the field monitoring data, perform principal component analysis and eigenvalue decomposition on the fused and aligned data, and select the principal component eigenvectors to construct a vector matrix;

[0061] Specifically, 1) First, the simulated grouting parameters and field monitoring data are preprocessed and aligned. The preprocessing includes cleaning and interpolation. The alignment operation is denoising alignment, which means unifying the format of the preprocessed field monitoring data and the numerical simulation results, aligning them in time and space coordinate systems, and mapping them into the same spatial domain.

[0062] 2) Perform principal component analysis and eigenvalue decomposition on the preprocessed and aligned data to map these high-dimensional data into a low-dimensional space, retaining only the most important information. The specific steps are as follows:

[0063] First, the pre-processed and aligned simulation grouting parameters are fused with the field monitoring data to form a data matrix. Each column in the data matrix represents a different grouting parameter (such as pressure, flow, temperature, etc.), and each row represents a sample or observation time. For each column of grouting parameter features (each variable), the mean and standard deviation of the feature are calculated, and then standardized:

[0064]

[0065] in, is the original data; is the mean of the feature; is the standard deviation.

[0066] Furthermore, the covariance matrix is ​​calculated and the eigenvalue decomposition is performed on the covariance matrix to obtain a set of eigenvalues ​​and corresponding eigenvectors. According to the size of the eigenvalues, the eigenvectors corresponding to the first few eigenvalues ​​are selected as principal components, and the principal component eigenvectors are combined into a vector matrix.

[0067] Among them, the covariance matrix is ​​calculated:

[0068]

[0069] in, is the data matrix; is the number of samples; is the characteristic number; is the transpose of the data matrix.

[0070] Furthermore, the covariance matrix C is decomposed into eigenvalues:

[0071]

[0072] in, represents the eigenvector; Represents the eigenvalue.

[0073] After eigendecomposition, we get a set of eigenvalues The corresponding eigenvector , according to the size of the eigenvalue, select the eigenvectors corresponding to the first few eigenvalues ​​as the principal component eigenvectors required for dimensionality reduction, and form the principal component eigenvectors into a vector matrix . Vector Matrix The dimension is ,in The dimension after dimensionality reduction is the above standardized data matrix With vector matrix Multiply the original Dimensional feature data is mapped to In the new space of dimensionality, the dimensionality reduction of data is realized:

[0074]

[0075] Among them, if There are n monitoring data and simulation data, which can be expressed as:

[0076]

[0077] Step 4: Use particle filtering to predict the state of grouting parameters on the vector matrix after dimensionality reduction. By establishing a state transition model to describe the changes in grouting parameters, the predicted state of the set particles is compared with the field monitoring data, the likelihood of each particle is calculated, and the weight of the particle is updated according to the likelihood; the particles represent the state of the grouting parameters in the data, such as pressure, flow rate, and slurry viscosity, and the state of each particle is .

[0078] Specifically, each particle in the vector matrix after dimensionality reduction is given the same weight:

[0079]

[0080] in, The weights set for the particles, N represents the total number of particles, i For one of the particles,

[0081] Furthermore, a state transition model is established , the objects being described are grouting pressure, flow rate and slurry viscosity, then the state can be expressed as , the evolution form of state update prediction for grouting pressure, flow rate and slurry viscosity is:

[0082]

[0083] Since there is a positive correlation between pressure and flow rate, the grouting pressure update is expressed as:

[0084] ;

[0085] in, Indicates the pressure of the current moment; Indicates the pressure at the next moment; Indicates the influence coefficient of pressure on flow rate; Indicates the flow rate at the current moment.

[0086] The grouting flow rate decays exponentially with the increase of pressure, and the updated flow rate is expressed as:

[0087] ;

[0088] in, Indicates the flow rate at the current moment; Indicates the flow rate at the next moment; Indicates the attenuation coefficient of pressure to flow rate changes.

[0089] The viscosity of the slurry increases with time, and the viscosity update is expressed as:

[0090] ;

[0091] in, Indicates the slurry viscosity at the current moment; Indicates the slurry viscosity at the next moment; It represents the influence coefficient of flow rate on slurry viscosity; It represents the attenuation coefficient of pressure on the change of slurry viscosity; Indicates the time-dependent viscosity adjustment factor. 、 、 Denoted as process noise, it represents the random disturbance of the system or modeling error.

[0092] Furthermore, the predicted state of the described particle is obtained by performing an evolutionary form of state update prediction on the grouting pressure, flow rate, and slurry viscosity. The predicted state of the described particle is compared with the field monitoring data, the likelihood of each particle (i.e., the difference between the particle prediction value and the monitoring data) is calculated, and the particle weight is updated according to the likelihood.

[0093] Among them, the likelihood function of the particle is expressed as:

[0094]

[0095]

[0096] in, Represented as field monitoring data; represents the predicted state of the particle; represents the noise variance of the monitoring data; represents simulation data; represents the noise variance of the simulated data.

[0097] Furthermore, the particle weight update formula is:

[0098]

[0099] in, Indicates the particle prediction value and on-site monitoring data Likelihood between Represents particle prediction values ​​and simulation data Likelihood between Indicates the weight of the particle at the previous moment.

[0100] Furthermore, according to the weight of the current particle update, a new particle set is generated, and multiple iterations are performed to achieve the fusion prediction of numerical simulation data and field monitoring data. The fusion prediction state of each time step is calculated according to the updated particle weight through the weighted average method:

[0101]

[0102] in, represents the optimal prediction state of the current grouting process, represents the weight of the particle at the current moment, Indicates the predicted state of the particle at the current moment.

[0103] As an embodiment, the current grouting effect is evaluated based on the fusion prediction results to determine whether the state of the current time step needs to be adjusted:

[0104] For grouting pressure, according to the prediction results of the next time step, the grouting pressure of the next stage is adjusted by comprehensively considering the equipment capacity, the bearing capacity of the formation, and the design requirements of the grouting effect:

[0105] Equipment Capability Assessment:

[0106]

[0107] in, Indicates current pressure; Maximum pressure bearing capacity of equipment

[0108] Assessment of stratum bearing capacity:

[0109]

[0110] in, Indicates the current grouting pressure; Indicates the compressive strength of the rock formation; Indicates the permeability coefficient of the formation; Indicates the permeability of the rock formation.

[0111] Evaluation of grouting effect design requirements:

[0112]

[0113] in, Indicates the actual diffusion range; Indicates the diffusion range of design requirements.

[0114] The comprehensive evaluation formula is obtained:

[0115]

[0116] If the result Indicates that the current grouting pressure may be too high and needs to be reduced. Indicates that the current grouting pressure may be too low and needs to be increased.

[0117] The grouting pressure is adjusted according to the evaluation results, the initial parameters of the numerical simulation are reset according to the adjusted grouting parameters, and a new state prediction and fusion analysis with the monitoring data are performed to obtain a new fusion prediction result. The grouting effect is then evaluated and adjusted again, and the above process is repeated to achieve real-time pre-control analysis of the grouting process.

[0118] Example 2

[0119] In one embodiment of the present disclosure, a grouting digital twin virtual-reality interactive pre-control analysis system is provided, comprising:

[0120] Initialization module, used to determine the target area for grouting and build a multi-scale geological model;

[0121] A data acquisition module is used to perform numerical simulation of the grouting process based on a multi-scale geological model, obtain the simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data;

[0122] The data fusion module is used to fuse and align the simulated grouting parameters with the field monitoring data, perform principal component analysis and eigenvalue decomposition on the fused and aligned data, and select the principal component eigenvectors to construct a vector matrix;

[0123] The state update module is used to reduce the dimension of the vector matrix and use particle filtering to predict the grouting parameter state of the reduced vector matrix. The changes in the grouting parameters are described by establishing a state transition model. The predicted state of the set particles is compared with the on-site monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood.

[0124] The grouting prediction module is used to generate a new particle set based on the updated particle weights and perform multiple iterations. It calculates the predicted state of each time step according to the updated particle weights, evaluates the current grouting effect based on the predicted state, and evaluates whether the state of the current time step needs to be adjusted, thereby realizing real-time pre-control analysis of the grouting process.

[0125] Example 3

[0126] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the grouting digital twin virtual-reality interactive pre-control analysis method.

[0127] Example 4

[0128] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the grouting digital twin virtual-reality interactive pre-control analysis method is implemented.

[0129] Example 5

[0130] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the grouting digital twin virtual-reality interactive pre-control analysis method.

[0131] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. Grouting digital twin virtual-real interactive pre-control analysis method, characterized by: include: Determine the target area for grouting and construct a multi-scale geological model; Perform numerical simulation of the grouting process based on a multi-scale geological model to obtain simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data; The simulated grouting parameters are fused and aligned with the field monitoring data, and the principal component analysis and eigenvalue decomposition are performed on the fused and aligned data. The principal component eigenvectors are selected to construct a vector matrix. The vector matrix is ​​reduced in dimension, and the particle filter is used to predict the grouting parameter state. The changes in grouting parameters are described by establishing a state transition model. The predicted state of the set particles is compared with the field monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood. Based on the updated particle weights, a new particle set is generated and multiple iterations are performed. The predicted state of each time step is calculated according to the updated particle weights. The current grouting effect is evaluated based on the predicted state to assess whether the state of the current time step needs to be adjusted, thereby achieving real-time pre-control analysis of the grouting process.

2. The grouting digital twin virtual-real interactive pre-control analysis method according to claim 1 is characterized in that: The geological data of the grouting target area is obtained, and a multi-scale geological model is constructed based on the geological data. The multi-scale geological model includes a macro-geological model and a micro-geological model. The macro-geological model simulates the overall structure of the rock and soil layer, the permeability characteristics, porosity and permeability coefficient of the rock and soil layer, and simulates the large-scale flow and pressure distribution during the slurry injection process. The micro-geological model simulates the detailed structure of the rock and soil layer, performs detailed modeling of small cracks and pores, obtains the porosity and pore distribution of the rock and soil layer, and uses a three-dimensional grid to represent the pore structure of the rock and soil layer.

3. The grouting digital twin virtual-real interactive pre-control analysis method according to claim 1 is characterized in that: Based on the multi-scale geological model and the flow behavior of the slurry simulated in the multi-scale geological model, the grouting parameters during the grouting process are predicted and simulated to obtain the simulated grouting parameters, including grouting pressure, grouting rate, phase fraction and viscosity parameters. The simulated grouting parameters are used to carry out grouting in the actual target area engineering, and the grouting pressure, grouting rate and slurry diffusion information monitored on site under the actual slurry diffusion conditions are obtained.

4. The grouting digital twin virtual-real interactive pre-control analysis method according to claim 1, characterized in that: The simulated grouting parameters are fused and aligned with the field monitoring data, and the fused and aligned data are subjected to principal component analysis and eigenvalue decomposition, including: the simulated grouting parameters and the field monitoring data are formed into a data matrix, each column in the data matrix represents a different grouting parameter, and each row represents a sample or observation time; for the grouting parameter characteristics of each column, the mean and standard deviation of the grouting parameter characteristics of each column are calculated, and the covariance matrix is ​​calculated, and the eigenvalue decomposition of the covariance matrix is ​​performed to obtain a set of eigenvalues ​​and corresponding eigenvectors. According to the size of the eigenvalues, the eigenvectors corresponding to the first few eigenvalues ​​are selected as principal components, and the principal component eigenvectors are formed into a vector matrix.

5. The grouting digital twin virtual-reality interactive pre-control analysis method according to claim 1, characterized in that: Reduce the dimension of the vector matrix and set the same weight for each particle in the reduced vector matrix: in, The weights set for the particles, N represents the total number of particles, i For one of the particles, a state transition model is established to describe the changes in grouting parameters. The described particle objects are grouting pressure, flow rate and slurry viscosity. The state update prediction of grouting pressure, flow rate and slurry viscosity is performed to obtain the predicted state of the described particle. The predicted state of the described particle is compared with the field monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood.

6. The grouting digital twin virtual-real interactive pre-control analysis method according to claim 1, characterized in that: According to the weight of the current particle update, a new particle set is generated and multiple iterations are performed to achieve the fusion of numerical simulation data and field monitoring data. The predicted state of each time step is calculated based on the updated particle weight through the weighted average method: in, represents the optimal prediction state of the current grouting process, represents the weight of the particle at the current moment, Indicates the predicted state of the particle at the current moment.

7. Grouting digital twin virtual-real interactive pre-control analysis system, characterized by: include: Initialization module, used to determine the target area for grouting and build a multi-scale geological model; A data acquisition module is used to perform numerical simulation of the grouting process based on a multi-scale geological model, obtain the simulated grouting parameters, perform on-site grouting based on the simulated grouting parameters, and obtain on-site monitoring data; The data fusion module is used to fuse and align the simulated grouting parameters with the field monitoring data, perform principal component analysis and eigenvalue decomposition on the fused and aligned data, and select the principal component eigenvectors to construct a vector matrix; The state update module is used to reduce the dimension of the vector matrix and use particle filtering to predict the grouting parameter state of the reduced vector matrix. The changes in the grouting parameters are described by establishing a state transition model. The predicted state of the set particles is compared with the on-site monitoring data, the likelihood of each particle is calculated, and the particle weight is updated according to the likelihood. The grouting prediction module is used to generate a new particle set based on the updated particle weights and perform multiple iterations. It calculates the predicted state of each time step according to the updated particle weights, evaluates the current grouting effect based on the predicted state, and evaluates whether the state of the current time step needs to be adjusted, thereby realizing real-time pre-control analysis of the grouting process.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the grouting digital twin virtual-reality interactive pre-control analysis method described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the grouting digital twin virtual-reality interactive pre-control analysis method as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the grouting digital twin virtual-reality interactive pre-control analysis method as described in any one of claims 1 to 6.

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