Large-scale rock burst digital twin simulation prediction method and system

Abstract

This invention discloses a large-scale digital twin simulation and prediction method and system for rockburst, relating to the fields of mine safety and digital technology. The prediction method includes the following steps: constructing a three-dimensional geometric model, obtaining the geomechanical parameters of each rock layer, and assigning the geometric model to form an initial digital twin; collecting multi-source monitoring data from the mine, fusing multi-source heterogeneous data and mapping it to the digital twin model, driving the model state update to keep it synchronized with the physical mine; establishing constitutive equations and performing dynamic time-step solutions; inputting different future mining plans or prevention schemes, simulating possible morphologies of unknown areas, analyzing the impact of mining activities on mine stability, and predicting the dangerous areas, evolution paths, and degree of danger of rockburst under these future scenarios; repeating the above steps with the data obtained after on-site adjustments and monitoring, dynamically adjusting and continuously optimizing the digital twin model.

Description

Technical Field

[0001] This invention relates to the field of mine safety and digital technology, and in particular to a large-scale digital twin simulation prediction method and system for rockburst. Background Technology

[0002] Rockbursts, a typical dynamic rock mass disaster faced in deep coal mining, are the final result of the coupled evolution of multiple physical fields, including the stress field, damage field, and fracture field, within the coal-rock mass system under mining-induced stress. With increasing mining depth, the underground mining structure becomes increasingly complex, leading to a wider range of disaster formation and more intricate disaster-causing mechanisms, placing higher demands on the accuracy and foresight of prediction and early warning technologies. Existing rockburst prediction technologies have the following shortcomings: early warning methods based on microseismic and stress monitoring data can reflect anomalies in local areas, but they struggle to reveal the spatiotemporal evolution mechanism of disasters in large-scale mining environments, and early warnings often exhibit a lag. While traditional numerical simulation methods such as finite element method and discrete element method can analyze from the perspective of mechanical mechanism, the models are usually static or quasi-static and cannot be synchronized with the real-time changing physical state of the mine, resulting in the problem of "accurate initial model but distorted simulation process". Existing technology lacks effective simulation of complex coupling effects of multi-physics fields, making it difficult to fully reproduce the complete disaster-generating process of rockburst. At the same time, the model cannot use subsequent monitoring data and evolution results to learn and optimize itself, resulting in poor sustainability of predictive ability.

[0003] A search of existing technologies revealed Chinese Patent Publication No. CN116484186A, entitled "A Smart Early Warning Method and Device for Rockburst Based on Multi-Field Coupling." This patent involves deploying sensors to monitor multi-field characteristic parameters of stress, fracture, and displacement fields in coal mines. It constructs a data training set using energy fields as labels, establishes a multi-layer deep neural network model, and then inputs the monitored data of stress, fracture, and displacement fields into the corresponding models to predict the magnitude of the energy field. The maximum predicted energy field is selected as the predicted value, and impacts are classified and early warnings are issued based on the magnitude of the energy field. Its shortcomings lie in the fact that while this invention proposes using the evolution of energy field signals caused by the evolution of multi-field physical signals as a criterion for early warning analysis of rockburst disasters, it lacks the fusion evolution results of multi-field coupling. It cannot demonstrate the comprehensive evolution results of the overall structure of large mining areas, nor the full-cycle evolution law of disaster formation, evolution, and occurrence under large-scale conditions, and therefore cannot comprehensively guide safe production in mines. Chinese patent application publication number CN119558025A, titled "Digital Twin Model and Method for Coal Mine Disaster Scenarios Based on Multimodal Data," describes a method for constructing a comprehensive digital twin model. This model involves collecting raw data from within the mine, cleaning, normalizing, and denoising the data, and then fusing it using a data fusion algorithm. Based on this model, real-time mapping and simulation analysis are performed. By monitoring the mine's internal environment and equipment status, the model predicts disaster development trends. Based on the model analysis results, coal mine disaster prevention and control measures are optimized, and a real-time monitoring and early warning system is established to detect anomalies and take timely measures. However, this invention has several shortcomings. While it proposes using internal mine data to construct a digital twin model for real-time mapping and simulation prediction of disaster development trends, it lacks consideration of the coupling and constitutive relationships between multiple physical fields and the comparative analysis of evolution results under different scenario conditions. It cannot achieve dynamic real-time mapping of the multi-field coupled mine digital twin model or the simulation and prediction of the dynamic evolution of disasters. Furthermore, it cannot accurately reconstruct the entire lifecycle of disaster evolution under large mining conditions, and therefore cannot efficiently guide safe mine operations and production.

[0004] In recent years, digital twin technology has provided a new paradigm for solving the aforementioned problems due to its ability to achieve bidirectional dynamic mapping and interaction between physical entities and virtual models. However, existing technologies still face technical bottlenecks in constructing high-fidelity digital twins in large-scale mining environments and realizing real-time data-driven multi-field coupled simulation and advanced prediction. Therefore, there is an urgent need for a comprehensive solution that can achieve real-time synchronization of mine conditions, multi-field coupled dynamic simulation, proactive prediction of future scenarios, and model self-optimization. Summary of the Invention

[0005] This solution addresses the problems and needs raised above by proposing a large-scale digital twin simulation prediction method and system for rockbursts. Due to the adoption of the following technical features, it can achieve the above-mentioned technical objectives and bring about several other technical effects.

[0006] One objective of this invention is to propose a large-scale digital twin simulation prediction method for rockbursts, characterized by comprising the following steps: S10: Collect geological exploration data, tunnel layout map and mining engineering plan, import into 3D modeling software to construct a 3D geometric model including faults, folds and working face, experimentally obtain the geomechanical parameters of each rock layer, and assign them as physical properties to the geometric model to form a digital twin model with mechanical response capability. S20: Deploy an underground monitoring system to collect multi-source monitoring data from the mine in real time, use data cleaning and spatiotemporal registration algorithms to fuse the multi-source monitoring data and map it into a digital twin model, drive the digital twin model's state update, and keep it synchronized with the physical mine; S30: Establish constitutive equations considering the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E. Based on the numerical method of finite element and discrete element coupling, perform dynamic time-step solution to simulate and reproduce the entire process of stress evolution, fracture initiation-expansion-connection, energy accumulation and release in large-scale coal and rock mass under the disturbance of mining activities. S40: Based on the digital twin transparent geometric model and multiphysics coupling, different future mining plans or prevention schemes are input to simulate the possible forms of unknown areas, analyze the impact of mining activities on mine stability, and predict the dangerous areas, evolution paths and degree of danger of rockburst under future scenarios. S50: Based on the dynamic simulation and prediction results under different preset scenarios, targeted prevention and control measures are adopted. The data obtained after on-site adjustment and monitoring are repeatedly executed in steps S10-S40. The large-scale mine geology and mining digital twin model is dynamically adjusted and continuously optimized until safe production is achieved, thus completing the dynamic prediction and simulation of the formation, evolution and occurrence of mine rockburst disasters under large-scale conditions.

[0007] Furthermore, the large-scale rockburst digital twin simulation prediction method and system according to the present invention may also have the following technical features: In one example of the present invention, in step S10, the experimental acquisition of the geomechanical parameters of each rock layer specifically includes: rock mass density, elastic modulus, compressive strength, cohesion and internal friction angle of each rock layer.

[0008] In one example of the present invention, S20 specifically includes the following steps: S21: Deploy microseismic monitoring system, stress monitoring system, drill cuttings monitoring equipment, and roadway convergence monitoring system within the large-scale mining area to collect physical quantities of stress, strain, microseismic activity, acoustic emission, and electromagnetic radiation within the mining area in real time, and construct and reconstruct the initial conditions of multiple fields such as stress field, damage field, fracture field, seepage field, and energy field within the large-scale mining area. S22: Data cleaning, specifically including noise filtering and outlier detection, where... The expression for noise filtering is as follows: In the formula, For a moment The smoothed value, For a moment The original value, Let be the window radius and be the window size. ; The expression for outlier detection is as follows: or In the formula, The mean of the data samples. The standard deviation of the data sample; This represents the data value at time n. S23: Perform spatiotemporal data registration, specifically including spatial registration and temporal registration; among which... Spatial registration The expression is: in, For rotation, scaling, and shearing matrices, It is a translation vector; Time registration The expression is: In the formula, and Neighboring time points.

[0009] In one example of the present invention, in step S30, a constitutive equation considering the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E is established, specifically including the following: S31: Considering the seepage effect inside the unit body, pore pressure in the seepage field is introduced to establish an improved stress field equation: In the formula, The shear modulus of the rock material. , Displacement at and Components in direction, For the Laplace term of the displacement, The gradient of volumetric strain, For volume forces Components in direction, Poisson's ratio for rock materials For BIOT coefficient, Pore ​​pressure; S32: Establish the damage field equation based on the damage mechanics of materials during plastic strain: In the formula, For damage evolution rate, To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, To accumulate plastic strain, For Heaviside step function, The plastic strain threshold at which damage begins. For plastic strain rate tensor; For cumulative plastic strain rate; S33: Establishing the fracture field relationship: When the damage reaches the damage threshold, it is considered that a fracture has formed. The expansion of the fracture will affect the seepage field and the stress field. The direction of fracture expansion is determined by the direction of the maximum principal stress or the direction of the energy release rate. S34: Establishing the seepage field equations considering the seepage effect of fluids inside the coal and rock mass under the condition of a large mining area: in, For flow rate, For penetration rate, For fluid viscosity, Pore ​​pressure; For gradient operators; S35: Considering elastic deformation energy, energy loss due to work, and heat transfer relationships, the energy field conservation equation is established: in, Energy density, including elastic strain energy and damage dissipation energy. Energy flux It serves as a heat source or mechanical energy source; S36: Based on the improved stress field equation, damage field equation, fracture field equation, seepage field equation, and energy field conservation equation, establish the following coupling equations: stress field σ-damage field D coupling equation, stress field σ-fracture field F coupling equation, stress field σ-seepage field S coupling equation, stress field σ-energy field E coupling equation, damage field D-fracture field F coupling equation, damage field D-seepage field S coupling equation, damage field D-energy field E coupling equation, fracture field F-seepage field S coupling equation, fracture field F-energy field E coupling equation, and seepage field S-energy field E coupling equation.

[0010] In one example of the present invention, the coupling equations of stress field σ-damage field D, stress field σ-fracture field F, stress field σ-seepage field S, stress field σ-energy field E, damage field D-fracture field F, damage field D-seepage field S, damage field D-energy field E, fracture field F-seepage field S, fracture field F-energy field E, and seepage field S-energy field E specifically include the following: (1) Introducing damage variables, the stress field σ-damage field D coupling equation is constructed through the relationship between stress, strain and damage: In the formula, Let B be the damage evolution rate, and let B be the damage variable. To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, For equivalent plastic strain rate, For Cauchy stress, Let be the elastic stiffness tensor. For elastic strain; (2) Construct the stress field σ-crack field F coupling relationship based on the functional relationship between the stress on the material and the crack length: Criterion: When At that time, the material underwent Type I fracture; In the formula, It is a Type I stress intensity factor, which depends on the far-field stress. and crack length , For the fracture toughness of the material; (3) Construct the stress field σ-seepage field S coupling equation by combining the external stress and internal seepage pressure of the unit: In the formula, For the total stress, For effective stress, For Biot coefficient, Pore ​​pressure, For unit tensors, For penetration rate, Initial penetration rate, The coupling coefficient; This is the initial effective stress; (4) Derive the coupled equations of stress field σ and energy field E from the work done by stress and strain: In the formula, External force power It is the elastic strain energy. For plastic dissipation work, As kinetic energy, For stress, In response to the situation; (5) Coupling relationship between damage field D and fracture field F: Formation of macroscopic cracks In the formula, B is the damage variable; (6) Introduce the damage coefficient to construct the coupled equations of the damage field D and the seepage field S: In the formula, k is the permeation tensor. For the initial permeation tensor, For material geometric parameters, The damage coefficient is... The density of cracks associated with damage; (7) Introduce the damage energy release rate to construct the coupling relationship between the damage field D and the energy field E: In the formula, For damage dissipation power, To reduce the rate of energy release from damage, Damage coefficient; (8) Introduce the fracture aperture variable to construct the coupled equations of the fracture field F and the seepage field S: In the formula, For the flow rate of the fracture fluid, For crack opening, For fluid dynamic viscosity, The hydraulic gradient along the fracture; (9) Introduce the relative sliding rate to construct the coupling relationship between the fracture field F and the energy field E: In the formula, For frictional power dissipation, Frictional stress at the crack surface The relative sliding rate of the fracture surface; (10) Consider the work done by the fluid inside the unit to construct the coupling relationship between the seepage field S and the energy field E: In the formula, Power for seepage flow The seepage velocity vector, This represents the pressure gradient.

[0011] In one example of the present invention, in step S40, different future mining plans or prevention schemes include, but are not limited to, the following: Normal progress: The working face is set to continue advancing at the current pace for the next week; Crossing a tectonic zone: The working face is set to advance to the vicinity of a known fault within the next three days; Change the mining parameters: reduce the working face advance speed to 4 meters per day, or add pressure relief-related mining measures; Ultra-long working face: As the working face continues to advance, an ultra-long working face is formed within the mining area, which causes more intense periodic pressure, roadway convergence and floor heave problems. Isolated working face: This setting indicates that the coal seam around the current working face has been mined out, forming an "island" state surrounded by the goaf. Blasting pressure relief: Within the current mining area, directional blasting pressure relief measures are implemented on the coal and rock mass. The instantaneous adjustment of the stress field, the expansion and penetration of fractures, and the energy release process under blasting disturbance are simulated to evaluate the effectiveness of this measure in reducing the risk of rockburst. Coal seam water injection: High-pressure water injection is set up to simulate the softening effect of water seepage on the mechanical properties of coal, the influence of pore pressure changes on effective stress, and evaluate the effectiveness of water injection measures in preventing rockbursts.

[0012] Another objective of this invention is to propose a large-scale digital twin simulation and prediction system for rockbursts, comprising: The digital twin module is configured to collect geological exploration data, tunnel layout maps and mining engineering plan maps, import them into 3D modeling software to construct a 3D geometric model containing faults, folds and working faces, experimentally obtain the geomechanical parameters of each rock layer, and assign them as physical properties to the geometric model to form a digital twin model with mechanical response capabilities. The model update module is configured to deploy an underground monitoring system, collect multi-source monitoring data from the mine in real time, use data cleaning and spatiotemporal registration algorithms to fuse the multi-source monitoring data and map it into the digital twin model, drive the digital twin model state update, and keep it synchronized with the physical mine. The constitutive equation module is configured to establish constitutive equations that consider the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E. Based on the numerical method of finite element and discrete element coupling, it performs dynamic time-step solution to simulate and reproduce the entire process of stress evolution, fracture initiation-expansion-connection, energy accumulation and release in large-scale coal and rock mass under the disturbance of mining activities. The simulation and prediction module is configured to be based on a digital twin transparent geometric model and multiphysics coupling. Different future mining plans or prevention schemes are input to simulate the possible morphology of unknown areas, analyze the impact of mining activities on mine stability, and predict the dangerous areas, evolution paths and degree of danger of rockburst under future scenarios. The adjustment and optimization module is configured to take targeted prevention and control measures based on the dynamic simulation and prediction results under different preset scenarios. The data obtained after on-site adjustment and monitoring will repeatedly trigger the digital twin module, model update module, constitutive equation module and simulation and prediction module to run in sequence. The large-scale mine geology and mining digital twin model will be dynamically adjusted and continuously optimized until safe production is achieved, and the dynamic prediction and simulation of the formation, evolution and occurrence of mine rockburst disaster under large-scale conditions will be completed.

[0013] In one example of the present invention, the geomechanical parameters of each rock layer obtained experimentally include: rock mass density, elastic modulus, compressive strength, cohesion and internal friction angle of each rock layer.

[0014] In one example of the present invention, the model update module includes: The initial condition unit is configured to deploy microseismic monitoring systems, stress monitoring systems, drill cuttings monitoring equipment, and roadway convergence monitoring systems within a large-scale mining area. It collects physical quantities such as stress, strain, microseismic activity, acoustic emission, and electromagnetic radiation within the mining area in real time, and constructs and reconstructs multiple initial conditions for stress field, damage field, fracture field, seepage field, and energy field within the large-scale mining area. The data processing unit is configured for data cleaning, specifically including noise filtering and outlier detection. The expression for noise filtering is as follows: In the formula, For a moment The smoothed value, For a moment The original value, Let be the window radius and be the window size. ; The expression for outlier detection is as follows: or In the formula, The mean of the data samples. The standard deviation of the data sample; This represents the data value at time n. The data registration unit is configured for registering spatiotemporal data, specifically including spatial registration and temporal registration; among which... Spatial registration The expression is: in, For rotation, scaling, and shearing matrices, It is a translation vector; Time registration The expression is: In the formula, and Neighboring time points.

[0015] In one example of the present invention, the constitutive equation module includes: The stress field equation element is configured to consider seepage within the element body. By introducing pore pressure from the seepage field, an improved stress field equation is established: In the formula, The shear modulus of the rock material. , Displacement at and Components in direction, For the Laplace term of the displacement, The gradient of volumetric strain, For volume forces Components in direction, Poisson's ratio for rock materials For BIOT coefficient, Pore ​​pressure; The damage field equation element is configured to establish the damage field equation based on the damage mechanics of the material during plastic strain: In the formula, For damage evolution rate, To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, To accumulate plastic strain, For Heaviside step function, The plastic strain threshold at which damage begins. For plastic strain rate tensor; For cumulative plastic strain rate; The fracture field relationship unit is configured to establish fracture field relationships: when the damage reaches the damage threshold, a fracture is considered to form, and the expansion of the fracture will affect the seepage field and stress field; the direction of fracture expansion is determined by the direction of the maximum principal stress or the direction of the energy release rate. The seepage field equation element is configured to establish the seepage field equation considering the fluid seepage effect inside the coal and rock mass under the condition of a large mining area: in, For flow rate, For penetration rate, For fluid viscosity, Pore ​​pressure; For gradient operators; The energy field conservation equation unit is configured to comprehensively consider elastic deformation energy, work loss energy, and heat transfer relationships to establish the energy field conservation equation: in, It is energy density, including elastic strain energy and damage dissipation energy. It is energy flux. It is a heat source or mechanical energy source; The coupling equation unit is configured to establish the following coupling equations based on the improved stress field equation, damage field equation, fracture field equation, seepage field equation, and energy field conservation equation: stress field σ-damage field D coupling equation, stress field σ-fracture field F coupling equation, stress field σ-seepage field S coupling equation, stress field σ-energy field E coupling equation, damage field D-fracture field F coupling equation, damage field D-seepage field S coupling equation, damage field D-energy field E coupling equation, fracture field F-seepage field S coupling equation, fracture field F-energy field E coupling equation, and seepage field S-energy field E coupling equation.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention uses real-time data to drive the digital twin model to change synchronously with the physical mine, overcoming the shortcomings of traditional numerical simulation models that are static and lagging, and realizing the synchronization and dynamic evolution of the model between the virtual and real worlds.

[0017] This invention reveals the formation mechanism and entire process of rockburst more comprehensively and realistically through multi-field coupled calculations of stress, damage, cracks, seepage, and energy.

[0018] This invention can perform "sand table simulations" of different future mining schemes and prevention measures, enabling proactive and advanced prediction of rockbursts and providing forward-looking basis for disaster prevention decisions.

[0019] This invention dynamically presents complex mine geological conditions and mechanical states in a visual manner, enabling transparent insight and intelligent management of the underground environment, thereby improving the level of mine transparency and intelligence.

[0020] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0022] Figure 1 This is a flowchart of a large-scale digital twin simulation and prediction method for rockburst according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the sensor network deployment of a multi-source heterogeneous monitoring system according to an embodiment of the present invention; Figure 3 The coupling formula and relationship transformation diagram for multi-field coupling according to an embodiment of the present invention; Figure 4 The diagram shows a comparative analysis of dynamic simulation prediction results under different preset scenarios according to embodiments of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0025] According to a first aspect of the present invention, a large-scale digital twin simulation prediction method for rockburst is provided, such as... Figure 1 As shown, it includes the following steps: S10: Collect geological exploration data, tunnel layout map and mining engineering plan, import into 3D modeling software to construct a 3D geometric model including faults, folds and working face, experimentally obtain the geomechanical parameters of each rock layer, and assign them as physical properties to the geometric model to form an initial digital twin with mechanical response capability. S20: Deploy an underground monitoring system to collect multi-source monitoring data from the mine in real time, use data cleaning and spatiotemporal registration algorithms to fuse the multi-source monitoring data and map it into a digital twin model, drive the digital twin model's state update, and keep it synchronized with the physical mine; S30: Establish constitutive equations considering the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E. Based on a numerical method that couples finite element (e.g., FLAC3D) and discrete element (e.g., PFC3D) methods, perform dynamic time-stepping solutions to simulate and reproduce the entire process of stress evolution, fracture initiation-propagation-connection, energy accumulation and release in large-scale coal and rock masses under the disturbance of mining activities. S40: Based on the digital twin transparent geometric model and multiphysics coupling, different future mining plans or prevention schemes are input to simulate the possible forms of unknown areas, analyze the impact of mining activities on mine stability, predict the dangerous areas, evolution paths and degree of rockburst under future scenarios, and provide a basis for decision-making. S50: Based on the dynamic simulation and prediction results under different preset scenarios, targeted prevention and control measures are adopted. The data obtained after on-site adjustment and monitoring are repeatedly executed in steps S10-S40 above. The large-scale mine geology and mining digital twin model is dynamically adjusted and continuously optimized until safe production is achieved. This completes the dynamic prediction and simulation of the formation, evolution and occurrence of mine rockburst disasters under large-scale conditions, realizes the digitalization, transparency and intelligence of underground coal mines, provides a reference for the prevention and control of rockburst disasters throughout the entire mining cycle, and guides safe construction operations in underground coal mines.

[0026] This prediction method, driven by real-time data, enables the digital twin model to change synchronously with the physical mine, overcoming the shortcomings of traditional numerical simulation models that are static and lagging, and achieving virtual-real synchronization and dynamic evolution of the model. This prediction method, through multi-field coupled calculations of stress, damage, cracks, seepage, and energy, reveals the disaster-causing mechanism and the entire process of rockburst more comprehensively and realistically.

[0027] This prediction method can perform "sand table simulations" of different future mining plans and prevention measures, enabling proactive and advanced prediction of rockbursts and providing forward-looking basis for disaster prevention decisions; This prediction method dynamically presents complex mine geological conditions and mechanical states in a visual manner, enabling transparent insight and intelligent management of the underground environment, and improving the level of mine transparency and intelligence.

[0028] In one example of the present invention, in step S10, the geomechanical parameters of each rock layer obtained experimentally include physical parameters such as rock mass density, elastic modulus, compressive strength, cohesion and internal friction angle of each rock layer, and the three-dimensional modeling software used includes 3DMine, GOCAD, etc.

[0029] In one example of the present invention, such as Figure 2 As shown, step S20 specifically includes the following steps: S21: Deploy microseismic monitoring system, stress monitoring system, drill cuttings monitoring equipment, and roadway convergence monitoring system within the large-scale mining area to collect physical quantities of stress, strain, microseismic activity, acoustic emission, and electromagnetic radiation within the mining area in real time, and construct and reconstruct the initial conditions of multiple fields such as stress field, damage field, fracture field, seepage field, and energy field within the large-scale mining area. Specifically, microseismic, fiber optic vibration, and acoustic emission sensors are evenly distributed along the mining face and main roadways, with a spacing of 50-100 meters; electromagnetic radiation, fiber optic strain, and stress sensors are densely arranged in high-risk areas (such as geological fault zones and goaf edges), with a spacing of 20-50 meters. Real-time acquisition of physical parameters of coal and rock strata related to rockburst. Sensor types and parameters: (1) Microseismic sensor: monitors low-frequency vibration signals generated by coal and rock fracturing, with a frequency range of 10 Hz - 1 kHz, a sampling rate of 1 kHz, and a sensitivity of 0.1 mV / g. (2) Fiber optic vibration sensor: monitors medium- and high-frequency micro-fracture ground sound signals generated during the coal and rock fracturing process, with a frequency monitoring range of 500 Hz - 3 kHz, a sampling rate of 1 MHz, and a sensitivity of 0.1 V / g. (3) Acoustic emission sensor: captures high-frequency acoustic wave signals of coal and rock micro-crack propagation, with a frequency range of 20 kHz - 1 MHz, a sampling rate of 2 MHz, and a sensitivity of 10 V / MPa. (4) Electromagnetic radiation sensor: detects electromagnetic signals induced by stress changes, with a frequency range of 1 kHz - 10 MHz, a sampling rate of 10 MHz, and a measurement range of 0-100 μV. (5) Fiber optic strain sensor: monitors changes in the strain field of coal and rock mass within the mining area, with a grating center wavelength of 1527~1568 nm and a strain measurement range of 15000 nm. The demodulation rate is 1Hz and the dynamic range is 35dB. (6) Stress sensor: measures the stress state of coal and rock strata, with a range of 0-50 MPa, a sampling rate of 1 Hz, and an accuracy of ±0.1 MPa.

[0030] S22: Data cleaning specifically includes noise filtering and outlier detection, among which, The expression for noise filtering is as follows: In the formula, For a moment The smoothed value, For a moment The original value, Let be the window radius and be the window size. ; The expression for outlier detection is as follows: or In the formula, The mean of the data samples. The standard deviation of the data sample; This represents the data value at time n. S23: Perform spatiotemporal data registration, specifically including spatial registration and temporal registration; among which... Spatial registration The expression is: in For rotation, scaling, and shearing matrices, It is a translation vector.

[0031] Time registration The expression is: In the formula, and Neighboring time points.

[0032] Assuming at adjacent time points and Between these ranges, the data values ​​change linearly. This is suitable for data with stable changes.

[0033] In one example of the present invention, such as Figure 3 As shown, in step S30, a constitutive equation considering the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E is established, specifically including the following: S31: Considering the seepage effect inside the unit body, pore pressure in the seepage field is introduced to establish an improved stress field equation: In the formula, The shear modulus of the rock material. , Displacement at and Components in direction, For the Laplace term of the displacement, The gradient of volumetric strain, For volume forces Components in direction, Poisson's ratio for rock materials For BIOT coefficient, Pore ​​pressure; S32: Establish the damage field equation based on the damage mechanics of materials during plastic strain: In the formula, For damage evolution rate, To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, To accumulate plastic strain, For Heaviside step function, The plastic strain threshold at which damage begins. For plastic strain rate tensor; For cumulative plastic strain rate; S33: Establishing the fracture field relationship: When the damage reaches the damage threshold, it is considered that a fracture has formed. The expansion of the fracture will affect the seepage field and the stress field. The direction of fracture expansion is determined by the direction of the maximum principal stress or the direction of the energy release rate. S34: Establishing the seepage field equations considering the seepage effect of fluids inside the coal and rock mass under the condition of a large mining area: in, For flow rate, For penetration rate, For fluid viscosity, Pore ​​pressure; For gradient operators; S35: Considering elastic deformation energy, energy loss due to work, and heat transfer relationships, the energy field conservation equation is established: in, It is energy density, including elastic strain energy and damage dissipation energy. It is energy flux. It is a heat source or mechanical energy source; S36: Based on the improved stress field equation, damage field equation, fracture field equation, seepage field equation, and energy field conservation equation, establish the following coupling equations: stress field σ-damage field D coupling equation, stress field σ-fracture field F coupling equation, stress field σ-seepage field S coupling equation, stress field σ-energy field E coupling equation, damage field D-fracture field F coupling equation, damage field D-seepage field S coupling equation, damage field D-energy field E coupling equation, fracture field F-seepage field S coupling equation, fracture field F-energy field E coupling equation, and seepage field S-energy field E coupling equation.

[0034] In one example of the present invention, such as Figure 3 As shown, the coupling equations for stress field σ-damage field D, stress field σ-fracture field F, stress field σ-seepage field S, stress field σ-energy field E, damage field D-fracture field F, damage field D-seepage field S, damage field D-energy field E, fracture field F-seepage field S, fracture field F-energy field E, and seepage field S-energy field E are specifically included as follows: (1) Introducing damage variables, the stress field σ-damage field D coupling equation is constructed through the relationship between stress, strain and damage: In the formula, Let B be the damage evolution rate, and let B be the damage variable. To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, For equivalent plastic strain rate, For Cauchy stress, Let be the elastic stiffness tensor. For elastic strain; (2) Construct the stress field σ-crack field F coupling relationship based on the functional relationship between the stress on the material and the crack length: Criterion: When At that time, the material underwent Type I fracture; In the formula, It is a Type I stress intensity factor, which depends on the far-field stress. and crack length , For the fracture toughness of the material; (3) Construct the stress field σ-seepage field S coupling equation by combining the external stress and internal seepage pressure of the unit: In the formula, For the total stress, For effective stress, For Biot coefficient, Pore ​​pressure, For unit tensors, For penetration rate, Initial penetration rate, The coupling coefficient; This is the initial effective stress; (4) Derive the coupled equations of stress field σ and energy field E from the work done by stress and strain: In the formula, External force power It is the elastic strain energy. For plastic dissipation work, As kinetic energy, For stress, In response to the situation; (5) Coupling relationship between damage field D and fracture field F: Formation of macroscopic cracks In the formula, B is the damage variable; (6) Introduce the damage coefficient to construct the coupled equations of the damage field D and the seepage field S: In the formula, For the permeation tensor, For the initial permeation tensor, For material geometric parameters, The damage coefficient is... The density of cracks associated with damage; (7) Introduce the damage energy release rate to construct the coupling relationship between the damage field D and the energy field E: In the formula, For damage dissipation power, To reduce the rate of energy release from damage, Damage coefficient; (8) Introduce the fracture aperture variable to construct the coupled equations of the fracture field F and the seepage field S: In the formula, For the flow rate of the fracture fluid, For crack opening, For fluid dynamic viscosity, The hydraulic gradient along the fracture; (9) Introduce the relative sliding rate to construct the coupling relationship between the fracture field F and the energy field E: In the formula, For frictional power dissipation, Frictional stress at the crack surface The relative sliding rate of the fracture surface; (10) Consider the work done by the fluid inside the unit to construct the coupling relationship between the seepage field S and the energy field E: In the formula, Power for seepage flow The seepage velocity vector, This represents the pressure gradient.

[0035] Based on the governing equations and multi-field coupling relationships described above, a dynamic time-step solution is performed using a numerical method that couples finite element method FLAC3D and discrete element method PFC3D to simulate the entire process of stress redistribution, fracture initiation-propagation-connection, energy accumulation and release within a large-scale coal and rock mass under the disturbance of mining activities.

[0036] In one example of the present invention, such as Figure 4 As shown, in step S40, different future mining plans or prevention schemes include, but are not limited to, the following: Normal progress (Scenario A): Set the working face to continue advancing at the current speed (e.g., 8 meters per day) for the next week; Crossing a tectonic zone (Scenario B): Assume that the working face will advance to the vicinity of a known fault within the next three days; Change the mining parameters (Scenario C): Set the working face advance speed to 4 meters per day, or add pressure relief-related mining measures; Ultra-long working face (Scenario D): The working face is set to advance continuously, forming an ultra-long working face within the mining area, which will cause more intense periodic pressure, roadway convergence and floor heave problems. Isolated working face (Scenario E): Set the coal seam in the area (one or more sides) surrounding the current working face to be mined out, forming an "isolated" state surrounded by the goaf. Blasting pressure relief (Scenario F): Within the current mining area, directional blasting pressure relief measures are implemented on the coal and rock mass. The instantaneous adjustment of the stress field, the expansion and penetration of fractures, and the energy release process under blasting disturbance are simulated, and the effectiveness of this measure in reducing the risk of rockburst is evaluated. Coal seam water injection (Scenario G): Set up high-pressure water injection into the coal face to simulate the softening effect of water seepage on the mechanical properties of the coal, the impact of pore pressure changes on effective stress, and evaluate the effectiveness of water injection measures in preventing rockbursts.

[0037] By pre-setting different scenarios and future mining plans, the dynamic evolution process of the stress field, damage field, fracture field, seepage field and energy field of the three-dimensional digital twin model is simulated in real time. Based on the multi-field coupling equation proposed in step S30, the mutual influence and coupling effect between multiple fields are simulated. The changes of relevant physical parameters of the multi-physics field are recorded in real time. According to the technical parameter indicators specified in the "Coal Mine Safety Regulations", the dynamic mining situation under large mining conditions and the coal seam impact risk under different pre-set scenarios are evaluated, and the safety production of coal mines is comprehensively guided.

[0038] According to a second aspect of the present invention, a large-scale rockburst digital twin simulation and prediction system comprises: The digital twin module is configured to collect geological exploration data, tunnel layout maps and mining engineering plan maps, import them into 3D modeling software to construct a 3D geometric model containing faults, folds and working faces, experimentally obtain the geomechanical parameters of each rock layer, and assign them as physical properties to the geometric model to form a digital twin model with mechanical response capabilities. The model update module is configured to deploy an underground monitoring system, collect multi-source monitoring data from the mine in real time, use data cleaning and spatiotemporal registration algorithms to fuse the multi-source monitoring data and map it into the digital twin model, drive the digital twin model state update, and keep it synchronized with the physical mine. The constitutive equation module is configured to establish constitutive equations that consider the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E. It uses a numerical method based on the coupling of finite element (e.g., FLAC3D) and discrete element (e.g., PFC3D) methods to perform dynamic time-stepping solutions, simulating and reproducing the entire process of stress evolution, fracture initiation-propagation-connection, energy accumulation and release in large-scale coal and rock masses under the disturbance of mining activities. The simulation and prediction module is configured to be based on a digital twin transparent geometric model and multiphysics coupling. Different future mining plans or prevention schemes are input to simulate the possible morphology of unknown areas, analyze the impact of mining activities on mine stability, and predict the dangerous areas, evolution paths and degree of danger of rockburst under future scenarios. The adjustment and optimization module is configured to dynamically simulate and predict results under different preset scenarios, and then adopt targeted prevention and control measures. The data obtained after on-site adjustments and monitoring repeatedly triggers the sequential execution of the digital twin module, model update module, constitutive equation module, and simulation and prediction module. This dynamically adjusts and continuously optimizes the large-scale mine geological and mining digital twin model until safe production is achieved. This completes the dynamic prediction and simulation of the formation, evolution, and occurrence of mine rockburst disasters under large-scale conditions. It realizes the digitalization, transparency, and intelligence of underground coal mines, providing a reference for the prevention and control of rockburst disasters throughout the entire mining cycle and guiding safe underground construction operations.

[0039] This prediction system, driven by real-time data, enables the digital twin model to change synchronously with the physical mine, overcoming the shortcomings of traditional numerical simulation models that are static and lagging, and achieving virtual-real synchronization and dynamic evolution of the model. This prediction system, through multi-field coupled calculations of stress, damage, cracks, seepage, and energy, reveals the disaster-causing mechanism and entire process of rockbursts more comprehensively and realistically.

[0040] This prediction system can perform "sand table simulations" of different future mining plans and prevention measures, enabling proactive and advanced prediction of rockbursts and providing forward-looking basis for disaster prevention decisions; This prediction system dynamically presents complex mine geological conditions and mechanical states in a visual manner, enabling transparent insight and intelligent management of the underground environment, and improving the level of mine transparency and intelligence.

[0041] In one example of the present invention, the geomechanical parameters of each rock layer obtained experimentally include: rock mass density, elastic modulus, compressive strength, cohesion and internal friction angle of each rock layer.

[0042] In one example of the present invention, the model update module includes: The initial condition unit is configured to deploy microseismic monitoring systems, stress monitoring systems, drill cuttings monitoring equipment, and roadway convergence monitoring systems within a large-scale mining area. It collects physical quantities such as stress, strain, microseismic activity, acoustic emission, and electromagnetic radiation within the mining area in real time, and constructs and reconstructs multiple initial conditions for stress field, damage field, fracture field, seepage field, and energy field within the large-scale mining area. The data processing unit is configured for data cleaning, specifically including noise filtering and outlier detection. The expression for noise filtering is as follows: In the formula, For a moment The smoothed value, For a moment The original value, Let be the window radius and be the window size. ; The expression for outlier detection is as follows: or In the formula, The mean of the data samples. The standard deviation of the data sample; This represents the data value at time n. The data registration unit is configured for registering spatiotemporal data, specifically including spatial registration and temporal registration; among which... Spatial registration The expression is: in For rotation, scaling, and shearing matrices, It is a translation vector.

[0043] Time registration The expression is: In the formula, and Neighboring time points.

[0044] In one example of the present invention, the constitutive equation module includes: The stress field equation element is configured to consider seepage within the element body. By introducing pore pressure from the seepage field, an improved stress field equation is established: In the formula, The shear modulus of the rock material. , Displacement at and Components in direction, Let j be the Laplace term of the displacement, where jj represents the displacement with respect to coordinate x. j Taking the two partial derivatives and summing them, we get the second spatial derivative, which reflects the shear deformation characteristics within the rock micro-element. Let be the gradient of the volumetric strain, where Let be the divergence of displacement, representing the volumetric expansion or compressive strain of the rock. Taking the derivative with respect to i gives the volumetric strain gradient. For volume forces Components in direction, Poisson's ratio for rock materials For BIOT coefficient, Pore ​​pressure; The damage field equation element is configured to establish the damage field equation based on the damage mechanics of the material during plastic strain: In the formula, For damage evolution rate, To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, To accumulate plastic strain, For Heaviside step function, The plastic strain threshold at which damage begins. For plastic strain rate tensor; For cumulative plastic strain rate; The fracture field relationship unit is configured to establish fracture field relationships: when the damage reaches the damage threshold, a fracture is considered to form, and the expansion of the fracture will affect the seepage field and stress field; the direction of fracture expansion is determined by the direction of the maximum principal stress or the direction of the energy release rate. The seepage field equation element is configured to establish the seepage field equation considering the fluid seepage effect inside the coal and rock mass under the condition of a large mining area: in, For flow rate, For penetration rate, For fluid viscosity, Pore ​​pressure; For gradient operators; The energy field conservation equation unit is configured to comprehensively consider elastic deformation energy, work loss energy, and heat transfer relationships to establish the energy field conservation equation: in, It is energy density, including elastic strain energy and damage dissipation energy. It is energy flux. It is a heat source or mechanical energy source; The coupling equation unit is configured to establish the following coupling equations based on the improved stress field equation, damage field equation, fracture field equation, seepage field equation, and energy field conservation equation: stress field σ-damage field D coupling equation, stress field σ-fracture field F coupling equation, stress field σ-seepage field S coupling equation, stress field σ-energy field E coupling equation, damage field D-fracture field F coupling equation, damage field D-seepage field S coupling equation, damage field D-energy field E coupling equation, fracture field F-seepage field S coupling equation, fracture field F-energy field E coupling equation, and seepage field S-energy field E coupling equation.

[0045] It should be noted that the large-scale rockburst digital twin simulation prediction system of the present invention can also perform any of the processing described in the previously described large-scale rockburst digital twin simulation prediction method, the specific details of which will not be repeated here.

[0046] The foregoing description, with reference to preferred embodiments, details an exemplary implementation of the digital twin simulation prediction method and system for large-scale rockbursts proposed in this invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. A method for large-scale digital twin simulation and prediction of rockburst, characterized in that, Includes the following steps: S10: Collect geological exploration data, tunnel layout map and mining engineering plan, import into 3D modeling software to construct a 3D geometric model including faults, folds and working face, experimentally obtain the geomechanical parameters of each rock layer, and assign them as physical properties to the geometric model to form a digital twin model with mechanical response capability. S20: Deploy an underground monitoring system to collect multi-source monitoring data from the mine in real time, use data cleaning and spatiotemporal registration algorithms to fuse the multi-source monitoring data and map it into a digital twin model, drive the digital twin model's state update, and keep it synchronized with the physical mine; S30: Establish constitutive equations considering the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E. Based on the numerical method of finite element and discrete element coupling, perform dynamic time-step solution to simulate and reproduce the entire process of stress evolution, fracture initiation-expansion-connection, energy accumulation and release in large-scale coal and rock mass under the disturbance of mining activities. S40: Based on the digital twin transparent geometric model and multiphysics coupling, different future mining plans or prevention schemes are input to simulate the possible forms of unknown areas, analyze the impact of mining activities on mine stability, and predict the dangerous areas, evolution paths and degree of danger of rockburst under future scenarios. S50: Based on the dynamic simulation and prediction results under different preset scenarios, targeted prevention and control measures are adopted. The data obtained after on-site adjustment and monitoring are repeatedly executed in steps S10-S40. The large-scale mine geology and mining digital twin model is dynamically adjusted and continuously optimized until safe production is achieved, thus completing the dynamic prediction and simulation of the formation, evolution and occurrence of mine rockburst disasters under large-scale conditions.

2. The large-scale rockburst digital twin simulation and prediction method according to claim 1, characterized in that, In step S10, the specific geomechanical parameters of each rock layer obtained by the experiment include: rock mass density, elastic modulus, compressive strength, cohesion and internal friction angle of each rock layer.

3. The large-scale rockburst digital twin simulation and prediction method according to claim 1, characterized in that, S20 specifically includes the following steps: S21: Deploy microseismic monitoring system, stress monitoring system, drill cuttings monitoring equipment, and roadway convergence monitoring system within the large-scale mining area to collect physical quantities of stress, strain, microseismic activity, acoustic emission, and electromagnetic radiation within the mining area in real time, and construct and reconstruct the initial conditions of multiple fields such as stress field, damage field, fracture field, seepage field, and energy field within the large-scale mining area. S22: Data cleaning, specifically including noise filtering and outlier detection, where... The expression for noise filtering is as follows: In the formula, For a moment The smoothed value, For a moment The original value, Let be the window radius and be the window size. ; The expression for outlier detection is as follows: or In the formula, The mean of the data samples. The standard deviation of the data sample; This represents the data value at time n. S23: Perform spatiotemporal data registration, specifically including spatial registration and temporal registration; among which... Spatial registration The expression is: in, For rotation, scaling, and shearing matrices, It is a translation vector; Time registration The expression is: In the formula, and Neighboring time points.

4. The large-scale rockburst digital twin simulation and prediction method according to claim 1, characterized in that, In step S30, constitutive equations considering the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E are established, specifically including the following: S31: Considering the seepage effect inside the unit body, pore pressure in the seepage field is introduced to establish an improved stress field equation: In the formula, The shear modulus of the rock material. , Displacement at and Components in direction, For the Laplace term of the displacement, The gradient of volumetric strain, For volume forces Components in direction, Poisson's ratio for rock materials For BIOT coefficient, Pore ​​pressure; S32: Establish the damage field equation based on the damage mechanics of materials during plastic strain: In the formula, For damage evolution rate, To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, To accumulate plastic strain, For Heaviside step function, The plastic strain threshold at which damage begins. For plastic strain rate tensor; For cumulative plastic strain rate; S33: Establishing the fracture field relationship: When the damage reaches the damage threshold, it is considered that a fracture has formed. The expansion of the fracture will affect the seepage field and the stress field. The direction of fracture expansion is determined by the direction of the maximum principal stress or the direction of the energy release rate. S34: Establishing the seepage field equations considering the seepage effect of fluids inside the coal and rock mass under the condition of a large mining area: in, For flow rate, For penetration rate, For fluid viscosity, Pore ​​pressure; For gradient operators; S35: Considering elastic deformation energy, energy loss due to work, and heat transfer relationships, the energy field conservation equation is established: in, Energy density, including elastic strain energy and damage dissipation energy. Energy flux It serves as a heat source or mechanical energy source; S36: Based on the improved stress field equation, damage field equation, fracture field equation, seepage field equation, and energy field conservation equation, establish the following coupling equations: stress field σ-damage field D coupling equation, stress field σ-fracture field F coupling equation, stress field σ-seepage field S coupling equation, stress field σ-energy field E coupling equation, damage field D-fracture field F coupling equation, damage field D-seepage field S coupling equation, damage field D-energy field E coupling equation, fracture field F-seepage field S coupling equation, fracture field F-energy field E coupling equation, and seepage field S-energy field E coupling equation.

5. The large-scale rockburst digital twin simulation and prediction method according to claim 4, characterized in that, The coupling equations for stress field σ-damage field D, stress field σ-fracture field F, stress field σ-seepage field S, stress field σ-energy field E, damage field D-fracture field F, damage field D-seepage field S, damage field D-energy field E, fracture field F-seepage field S, fracture field F-energy field E, and seepage field S-energy field E are specifically included as follows: (1) Introducing damage variables, the stress field σ-damage field D coupling equation is constructed through the relationship between stress, strain and damage: In the formula, Let B be the damage evolution rate, and let B be the damage variable. To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, For equivalent plastic strain rate, For Cauchy stress, Let be the elastic stiffness tensor. For elastic strain; (2) Construct the stress field σ-crack field F coupling relationship based on the functional relationship between the stress on the material and the crack length: Criterion: When At that time, the material underwent Type I fracture; In the formula, It is a Type I stress intensity factor, which depends on the far-field stress. and crack length , For the fracture toughness of the material; (3) Construct the stress field σ-seepage field S coupling equation by combining the external stress and internal seepage pressure of the unit: In the formula, For the total stress, For effective stress, For Biot coefficient, Pore ​​pressure, For unit tensors, For penetration rate, Initial penetration rate, The coupling coefficient; This is the initial effective stress; (4) Derive the coupled equations of stress field σ and energy field E from the work done by stress and strain: In the formula, External force power It is the elastic strain energy. For plastic dissipation work, As kinetic energy, For stress, In response to the situation; (5) Coupling relationship between damage field D and fracture field F: Formation of macroscopic cracks In the formula, B is the damage variable; (6) Introduce the damage coefficient to construct the coupled equations of the damage field D and the seepage field S: In the formula, k is the permeation tensor. For the initial permeation tensor, For material geometric parameters, The damage coefficient is... The density of cracks associated with damage; (7) Introduce the damage energy release rate to construct the coupling relationship between the damage field D and the energy field E: In the formula, For damage dissipation power, To reduce the rate of energy release from damage, Damage coefficient; (8) Introduce the fracture aperture variable to construct the coupled equations of the fracture field F and the seepage field S: In the formula, For the flow rate of the fracture fluid, For crack opening, For fluid dynamic viscosity, The hydraulic gradient along the fracture; (9) Introduce the relative sliding rate to construct the coupling relationship between the fracture field F and the energy field E: In the formula, For frictional power dissipation, Frictional stress at the crack surface The relative sliding rate of the fracture surface; (10) Consider the work done by the fluid inside the unit to construct the coupling relationship between the seepage field S and the energy field E: In the formula, Power for seepage flow The seepage velocity vector, This represents the pressure gradient.

6. The large-scale rockburst digital twin simulation and prediction method according to claim 1, characterized in that, In step S40, different future mining plans or prevention schemes include, but are not limited to, the following: Normal progress: The working face is set to continue advancing at the current pace for the next week; Crossing a tectonic zone: The working face is set to advance to the vicinity of a known fault within the next three days; Change the mining parameters: reduce the working face advance speed to 4 meters per day, or add pressure relief-related mining measures; Ultra-long working face: As the working face continues to advance, an ultra-long working face is formed within the mining area, which causes more intense periodic pressure, roadway convergence and floor heave problems. Isolated working face: This setting indicates that the coal seam around the current working face has been mined out, forming an "island" state surrounded by the goaf. Blasting pressure relief: Within the current mining area, directional blasting pressure relief measures are implemented on the coal and rock mass. The instantaneous adjustment of the stress field, the expansion and penetration of fractures, and the energy release process under blasting disturbance are simulated to evaluate the effectiveness of this measure in reducing the risk of rockburst. Coal seam water injection: High-pressure water injection is set up to simulate the softening effect of water seepage on the mechanical properties of coal, the influence of pore pressure changes on effective stress, and evaluate the effectiveness of water injection measures in preventing rockbursts.

7. A large-scale digital twin simulation and prediction system for rockburst, characterized in that, include: The digital twin module is configured to collect geological exploration data, tunnel layout maps and mining engineering plan maps, import them into 3D modeling software to construct a 3D geometric model containing faults, folds and working faces, experimentally obtain the geomechanical parameters of each rock layer, and assign them as physical properties to the geometric model to form a digital twin model with mechanical response capabilities. The model update module is configured to deploy an underground monitoring system, collect multi-source monitoring data from the mine in real time, use data cleaning and spatiotemporal registration algorithms to fuse the multi-source monitoring data and map it into the digital twin model, drive the digital twin model state update, and keep it synchronized with the physical mine. The constitutive equation module is configured to establish constitutive equations that consider the coupling effects of stress field σ, damage field D, fracture field F, seepage field S, and energy field E. Based on the numerical method of finite element and discrete element coupling, it performs dynamic time-step solution to simulate and reproduce the entire process of stress evolution, fracture initiation-expansion-connection, energy accumulation and release in large-scale coal and rock mass under the disturbance of mining activities. The simulation and prediction module is configured to be based on a digital twin transparent geometric model and multiphysics coupling. Different future mining plans or prevention schemes are input to simulate the possible morphology of unknown areas, analyze the impact of mining activities on mine stability, and predict the dangerous areas, evolution paths and degree of danger of rockburst under future scenarios. The adjustment and optimization module is configured to take targeted prevention and control measures based on the dynamic simulation and prediction results under different preset scenarios. The data obtained after on-site adjustment and monitoring will repeatedly trigger the digital twin module, model update module, constitutive equation module and simulation and prediction module to run in sequence. The large-scale mine geology and mining digital twin model will be dynamically adjusted and continuously optimized until safe production is achieved, and the dynamic prediction and simulation of the formation, evolution and occurrence of mine rockburst disaster under large-scale conditions will be completed.

8. The large-scale rockburst digital twin simulation and prediction system according to claim 7, characterized in that, The specific geomechanical parameters obtained from the experiment for each rock layer include: rock mass density, elastic modulus, compressive strength, cohesion, and internal friction angle.

9. The large-scale rockburst digital twin simulation and prediction system according to claim 7, characterized in that, The model update module includes: The initial condition unit is configured to deploy microseismic monitoring systems, stress monitoring systems, drill cuttings monitoring equipment, and roadway convergence monitoring systems within a large-scale mining area. It collects physical quantities such as stress, strain, microseismic activity, acoustic emission, and electromagnetic radiation within the mining area in real time, and constructs and reconstructs multiple initial conditions for stress field, damage field, fracture field, seepage field, and energy field within the large-scale mining area. The data processing unit is configured for data cleaning, specifically including noise filtering and outlier detection. The expression for noise filtering is as follows: In the formula, For a moment The smoothed value, For a moment The original value, Let be the window radius and be the window size. ; The expression for outlier detection is as follows: or In the formula, The mean of the data samples. The standard deviation of the data sample; This represents the data value at time n. The data registration unit is configured for registering spatiotemporal data, specifically including spatial registration and temporal registration; among which... Spatial registration The expression is: in, For rotation, scaling, and shearing matrices, It is a translation vector; Time registration The expression is: In the formula, and Neighboring time points.

10. The large-scale rockburst digital twin simulation and prediction system according to claim 7, characterized in that, The constitutive equation module includes: The stress field equation element is configured to consider seepage within the element body. By introducing pore pressure from the seepage field, an improved stress field equation is established: In the formula, The shear modulus of the rock material. , Displacement at and Components in direction, For the Laplace term of the displacement, The gradient of volumetric strain, For volume forces Components in direction, Poisson's ratio for rock materials For BIOT coefficient, Pore ​​pressure; The damage field equation element is configured to establish the damage field equation based on the damage mechanics of the material during plastic strain: In the formula, For damage evolution rate, To reduce the rate of energy release from damage, For material damage parameters, For exponential parameters, To accumulate plastic strain, For Heaviside step function, The plastic strain threshold at which damage begins. For plastic strain rate tensor; For cumulative plastic strain rate; The fracture field relationship unit is configured to establish fracture field relationships: when the damage reaches the damage threshold, a fracture is considered to form, and the expansion of the fracture will affect the seepage field and stress field; the direction of fracture expansion is determined by the direction of the maximum principal stress or the direction of the energy release rate. The seepage field equation element is configured to establish the seepage field equation considering the fluid seepage effect inside the coal and rock mass under the condition of a large mining area: in, For flow rate, For penetration rate, For fluid viscosity, Pore ​​pressure; For gradient operators; The energy field conservation equation unit is configured to comprehensively consider elastic deformation energy, work loss energy, and heat transfer relationships to establish the energy field conservation equation: in, It is energy density, including elastic strain energy and damage dissipation energy. It is energy flux. It is a heat source or mechanical energy source; The coupling equation unit is configured to establish the following coupling equations based on the improved stress field equation, damage field equation, fracture field equation, seepage field equation, and energy field conservation equation: stress field σ-damage field D coupling equation, stress field σ-fracture field F coupling equation, stress field σ-seepage field S coupling equation, stress field σ-energy field E coupling equation, damage field D-fracture field F coupling equation, damage field D-seepage field S coupling equation, damage field D-energy field E coupling equation, fracture field F-seepage field S coupling equation, fracture field F-energy field E coupling equation, and seepage field S-energy field E coupling equation.

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