Mine disaster early warning method and system

Abstract

The invention relates to the technical field of mining, in particular to a mine disaster prediction method and system. The method comprises the following steps: acquiring geologic structure data corresponding to a mine, wherein the geologic structure data comprises rock stratum distribution, fault occurrence and underground water occurrence state; constructing a coupling model corresponding to the mine based on the rock stratum distribution, the fault occurrence and the underground water occurrence state, and determining a rock mass constitutive relation and coupling parameters in the coupling model; determining a prediction boundary condition of the coupling model based on the rock constitutive relation and the coupling parameters; and acquiring field monitoring data of the mine, correcting the coupling model based on the field monitoring data and the prediction boundary condition so as to determine a potential disaster point based on the corrected coupling model, and generating a risk early warning strategy and a risk recovery strategy corresponding to the potential disaster point. According to the embodiment of the invention, a full-chain prevention and control system from disaster prediction to emergency disposal is constructed, and the safety management level and disaster prevention and control capability of mine engineering are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining, and in particular to a mine disaster early warning method and system. Background Art

[0002] With the further development of science and technology, industrial production has grown rapidly like a blowout. The consumption of large amounts of mineral resources is driving the country's rapid development and the continuous improvement of people's production and living standards. In the future, maintaining economic development will still require sufficient mineral resources to support it. As a result, the demand for mineral resources in many industries in industrial production has shown an increasing trend year by year.

[0003] However, as mining activities progress, the stability of the underground rock mass is disrupted. The original rock stress field changes with excavation, and stress is redistributed, forming a secondary stress field. If the new stress field changes less than the original stress field, and the rock mass's physical and mechanical properties can withstand this change, the underground rock mass will reach a new equilibrium state and continue to remain stable. If the rock mass itself cannot withstand the changes in the stress field, it is very likely to become unstable, and phenomena such as surrounding rock deformation and roof movement, which are manifestations of ground pressure activity, will inevitably occur. If timely monitoring and prediction and early warning are not carried out, it is very likely to cause safety accidents to the normal production operations of mining enterprises, causing property damage at the least and casualties at the worst. Therefore, a technical solution that can predict mining disasters in advance is urgently needed. Summary of the Invention

[0004] To solve the above technical problems, embodiments of the present invention provide a mine disaster early warning method and system.

[0005] According to one aspect of an embodiment of the present invention, a mine disaster early warning method is provided, comprising: obtaining geological structural data corresponding to a mine, the geological structural data including rock layer distribution, fault occurrence, and groundwater occurrence; constructing a coupling model corresponding to the mine based on the rock layer distribution, the fault occurrence, and the groundwater occurrence, and determining the rock mass constitutive relationship and coupling parameters in the coupling model; determining the predicted boundary conditions of the coupling model based on the rock mass constitutive relationship and the coupling parameters; obtaining on-site monitoring data of the mine, and correcting the coupling model based on the on-site monitoring data and the predicted boundary conditions, so as to determine potential disaster points based on the corrected coupling model, and generate risk warning strategies and risk recovery strategies corresponding to the potential disaster points.

[0006] According to one aspect of an embodiment of the present invention, determining the rock mass constitutive relationship and coupling parameters in the coupling model includes: obtaining strength parameters corresponding to the coupling model, the strength parameters including elastic modulus, Poisson's ratio, uniaxial compressive strength, triaxial compressive strength, cohesion and internal friction angle; inverting the principal stress direction and principal stress magnitude of the initial geostress field of the coupling model based on the hydraulic fracturing method; and determining the rock mass constitutive relationship and coupling parameters in the coupling model based on the strength parameters, the principal stress direction and the principal stress magnitude.

[0007] According to one aspect of an embodiment of the present invention, the method also includes: obtaining multi-source data of the mine, and fusing the multi-source data and numerical simulation results based on a Bayesian update algorithm to obtain a fused multi-source data set; performing sensitivity analysis on the fused multi-source data set, and taking multi-source data with sensitivity greater than a preset sensitivity threshold as target data; determining the actual boundary conditions of the mine based on the target data, and correcting the coupling model based on the actual boundary conditions and the predicted boundary conditions.

[0008] According to one aspect of an embodiment of the present invention, the target data includes rock deformation, and the correcting the coupling model based on the actual boundary conditions and the predicted boundary conditions includes: determining the rock deformation of the mine based on the on-site monitoring data; determining the porosity change rate of the mine based on the rock deformation, and determining the actual seepage field boundary conditions of the mine based on the porosity change rate; updating the predicted seepage field boundary conditions in the coupling model based on the actual seepage field boundary conditions, so as to correct the coupling model based on the updated seepage field boundary conditions.

[0009] According to one aspect of an embodiment of the present invention, the target data includes rock microseismic data, and the correction of the coupling model based on the actual boundary conditions and the predicted boundary conditions includes: determining the rock microseismic data of the mine based on the field monitoring data, the microseismic data including spatial coordinate data of microseismic events; determining the actual damage boundary conditions of the mine based on the spatial coordinate data; and updating the predicted damage boundary conditions in the coupling model based on the actual damage boundary conditions, so as to correct the coupling model based on the updated damage boundary conditions.

[0010] According to one aspect of an embodiment of the present invention, the target data includes support structure data, and the correction of the coupling model based on the actual boundary conditions and the predicted boundary conditions includes: determining the support structure data corresponding to the rock mass of the mine based on the field monitoring data, the support structure including anchor stress data; determining an anchor stress threshold, and determining the actual stress boundary conditions of the mine based on the anchor stress threshold and the anchor stress data; updating the predicted stress boundary conditions in the coupling model based on the actual stress boundary conditions, so as to correct the coupling model based on the updated stress boundary conditions.

[0011] According to one aspect of an embodiment of the present invention, the method of determining potential disaster points based on the revised coupling model and generating risk warning strategies and risk recovery strategies corresponding to the potential disaster points includes: determining the plastic zone distribution, plastic zone volume and plastic zone diffusion rate corresponding to the mine based on the revised coupling model; determining rock burst potential disaster points based on the plastic zone distribution, the plastic zone volume and the plastic zone diffusion rate, and generating risk warning strategies and risk recovery strategies corresponding to the rock burst potential disaster points.

[0012] According to one aspect of an embodiment of the present invention, determining potential disaster points based on the revised coupling model and generating risk warning strategies and risk recovery strategies corresponding to the potential disaster points include: determining the displacement vector field corresponding to the mine based on the revised coupling model; determining the gradient tensor and shear displacement corresponding to the displacement vector field; determining the displacement potential disaster points based on the gradient tensor and the shear displacement, and generating risk warning strategies and risk recovery strategies corresponding to the displacement potential disaster points.

[0013] According to one aspect of an embodiment of the present invention, the determining of potential disaster points based on the revised coupling model and the generation of risk warning strategies and risk recovery strategies corresponding to the potential disaster points include: determining a coupled numerical model corresponding to the mine based on the revised coupling model, the coupled numerical model including a coupled model of rock seepage field and stress field; determining the seepage velocity field and pore water pressure distribution of the mine based on the coupled model of rock seepage field and stress field; determining potential disaster points of water inrush and water inrush prediction time based on the seepage velocity field and the pore water pressure distribution, and generating risk warning strategies and risk recovery strategies corresponding to the potential disaster points of water inrush based on the water inrush prediction time.

[0014] According to one aspect of an embodiment of the present invention, a mine disaster prediction system is provided, which includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses a mine disaster prediction method as described.

[0015] In the technical solution provided in the embodiments of the present invention, a coupling model is constructed by integrating multi-source geological structure data such as rock layer distribution, fault occurrence and groundwater storage status, and the prediction boundary conditions are determined based on the rock mass constitutive relationship and coupling parameters, thereby achieving accurate modeling of the complex geological environment of the mine; subsequently, the model is dynamically corrected through on-site monitoring data, which can effectively overcome the influence of geological uncertainty on the prediction accuracy, so that the model can more realistically reflect the multi-field coupling effects of rock mass stress, seepage, deformation, etc.; finally, based on the corrected model, potential disaster points are accurately identified and risk warning and recovery strategies are generated, and a full-chain prevention and control system from disaster prediction to emergency response is constructed, which significantly improves the safety management level and disaster prevention and control capabilities of mining projects, and provides scientific and reliable technical support for disaster prediction in mining areas to ensure personnel safety and reduce economic losses.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the specification, are used to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and it is clear that a person skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings: Figure 1 is a specific flow chart of a mine disaster prediction method shown in an exemplary embodiment of the present invention; Figure 2 A schematic structural diagram of a mine disaster prediction system suitable for implementing an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0019] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0020] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0021] In this application, "plurality" refers to two or more. "And / or" describes the relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0022] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0023] First and foremost, it's important to note that mine disaster prediction is a key measure for preventing and controlling geological hazards in mines. By predicting these hazards, we can develop appropriate prevention and control measures, reduce their impact on the mining environment and ecology, and promote sustainable development. For example, before mining operations begin, potential geological hazards can be predicted based on the geological structure, hydrogeological conditions, and engineering geological conditions of the mining area, allowing for the development of appropriate prevention and control measures.

[0024] In an alternative embodiment, see Figure 1 In order to achieve the problem of predicting mine disasters, the present invention proposes a mine disaster prediction method, wherein the mine disaster prediction method at least includes steps S110 to S140, which are described in detail as follows: Step S110 , obtaining geological structure data corresponding to the mine, the geological structure data including rock layer distribution, fault occurrence and groundwater occurrence status.

[0025] For example, direct and indirect information about underground rock formations and structures can be obtained through drilling and geophysical exploration (such as seismic and electromagnetic exploration); then hydrogeological surveys can be conducted to analyze groundwater parameters, and surface geological features can be extracted using remote sensing and Geographic Information System Technology (GIS). The data can then be integrated through three-dimensional geological modeling software to construct a visual model corresponding to the mine. The coupled model can then be discretized into a finite element grid (such as tetrahedral and hexahedral units), where the grid density needs to be optimized based on the calculation accuracy and efficiency requirements.

[0026] Step S120 : constructing a coupling model corresponding to the mine based on the distribution of rock layers, the occurrence of faults, and the state of groundwater, and determining the rock mass constitutive relationship and coupling parameters in the coupling model.

[0027] For example, a 3D geometric model can be generated in geological modeling software by integrating rock formation distribution data (such as borehole data and seismic exploration results), fault occurrence information (strike, dip, inclination, and throw), and groundwater occurrence (permeability coefficient, pore water pressure distribution). The model is then meshed, ensuring that the mesh near faults is denser to capture deformation characteristics. The constitutive model is selected based on the rock mass type. For example, a linear elastic model is typically used for intact rock masses, while a Mohr-Coulomb plasticity model is used for rock masses with developed joints. Fault slip behavior can be simulated using contact surface elements. Subsequently, by combining the seepage and stress coupling effects, the effective stress principle and Darcy's law are introduced, and boundary conditions for permeability coefficient and pore water pressure are set to simulate the influence of groundwater on the rock mass stress field. Finally, through parameter inversion and history matching methods, model parameters (such as elastic modulus, cohesion, and permeability coefficient) are adjusted to align the numerical results with field monitoring data (displacement, stress, and pore water pressure), thereby verifying the model's reliability.

[0028] Step S130: determining the prediction boundary conditions of the coupling model based on the rock mass constitutive relationship and the coupling parameters.

[0029] For example, the model's predictive boundary conditions can be set based on the rock mass constitutive relationship and coupling parameters: in stress field analysis, mining disturbances (such as working face advancement), initial ground stress conditions, and fixed constraint boundaries are applied; in seepage field analysis, the head boundary of the aquifer (such as a constant head boundary or flow boundary) and the zero flow boundary of the aquiclude are set. Finite element or finite difference methods are then used to discretize the coupled model, solving the spatiotemporal evolution of the seepage velocity field and the stress field, ultimately achieving coordinated prediction of rock mass stability and groundwater dynamics during mining. Three-dimensional numerical simulation of the mine can be achieved through multi-physics coupling software, and actual monitoring data can be continuously fed back to the model to optimize parameters and boundary conditions, ensuring the reliability of the prediction results.

[0030] Step S140 , obtaining on-site monitoring data of the mine, and modifying the coupling model based on the on-site monitoring data and the predicted boundary conditions, so as to determine potential disaster points based on the modified coupling model, and generate risk warning strategies and risk recovery strategies corresponding to the potential disaster points.

[0031] For example, monitoring equipment (such as displacement meters, strain gauges, and pore water pressure gauges) is deployed in key areas of a mine (such as goafs, fault zones, and high-stress areas) to collect real-time data on rock displacement, stress changes, and groundwater dynamics. The monitoring data is then compared with the results of numerical model calculations, and the model input parameters (such as elastic modulus, permeability coefficient, and constitutive model parameters) are adjusted through parameter inversion or model calibration techniques (such as sensitivity analysis and genetic algorithm optimization) to ensure that the model output is consistent with the measured data. Then, based on the modified model, the rock mass response under different mining conditions is simulated to identify potential disaster points such as stress concentration, plastic zone penetration, or displacement mutations. For these identified disaster points, risk warning strategies (such as setting warning thresholds, increasing monitoring frequency, and implementing dynamic support) and risk recovery strategies (such as reserving safe coal pillars, optimizing mining sequences, and strengthening drainage measures) are formulated based on engineering experience. Finally, the warning and recovery strategies are integrated into the mine safety management system to achieve dynamic control and rapid response to disaster risks. In addition, in actual application, the entire process of correcting the mine coupling model through the mine's on-site monitoring data needs to be continuously iterated, and the model needs to be continuously updated according to the new monitoring data to ensure the effectiveness of the mine disaster warning and recovery strategy.

[0032] In some embodiments of the present invention, by comprehensively acquiring mine geological structure data, constructing a coupling model and accurately defining the rock mass constitutive relationship and coupling parameters, a digital simulation of the complex geological environment of the mine is achieved. By setting the predicted boundary conditions and dynamically feedback-correcting the on-site monitoring data, the model accuracy can be calibrated in real time, and the dynamic changes of the geological structure can be effectively captured, thereby accurately identifying potential disaster points and upgrading the traditional static analysis to dynamic prediction, which significantly improves the timeliness and accuracy of mine disaster warning. At the same time, through the coordinated generation of risk warning strategies and recovery plans, a full-chain technical support from warning to disposal is provided for disaster prevention and control, ultimately enhancing the mine's production safety management capabilities and reducing the economic losses and personnel risks caused by geological disasters.

[0033] Further, based on the above embodiment, please refer to Figure 2 In one exemplary embodiment provided by the present invention, the specific implementation process of the above step S120 may further include steps S210 to S230, which are described in detail as follows: Step S210 , obtaining strength parameters corresponding to the coupling model, the strength parameters including elastic modulus, Poisson's ratio, uniaxial compressive strength, triaxial compressive strength, cohesion, and internal friction angle.

[0034] For example, mechanical parameters (such as elastic modulus, porosity ratio, uniaxial compressive strength, triaxial compressive strength, cohesion, and internal friction angle) measured in the laboratory using the coupled model can be assigned to the corresponding rock layers in the numerical model. For example, the rock mass constitutive relationship can adopt a nonlinear model that takes into account damage evolution, whose parameters are jointly determined through indoor rock mechanics tests and field stress inversion. The elastic modulus, Poisson's ratio, and strength parameters are then obtained through uniaxial / triaxial compression tests. In the triaxial compression test, the rock mass sample is initially loaded isotropically until the principal stress equals the predetermined confining pressure. The axial stress is then increased at a certain rate until the sample fails, and the maximum axial stress is recorded. This not only obtains the triaxial compressive strength of the rock, but also the Mohr strength envelope, thereby obtaining the rock's cohesion and internal friction angle.

[0035] Step S220 , inverting the principal stress directions and principal stress magnitudes of the initial geostress field of the coupling model based on the hydraulic fracturing method.

[0036] On the other hand, the principal stress directions and values ​​of the initial geostress field can be inverted through the measured data of hydraulic fracturing. Specifically, the data are processed and analyzed using methods such as geophysical modeling, geomechanical modeling, and numerical simulation. The observed data are compared with the simulation results using algorithms such as inverse problem solving, least squares fitting, and inversion optimization. By continuously adjusting the model parameters and fitting errors, the inversion results of the geostress field are gradually optimized to obtain the principal stress directions and values ​​of the initial geostress field.

[0037] Alternatively, the stress state of a point in the rock mass is usually described by the magnitude and direction of the principal stress. By projecting the rock mass stress in three dimensions, the compressive stress is defined as positive and the tensile stress as negative, and the stress is decomposed into three stress components: the vertical stress component , horizontal ground stress component and ,in, It is mainly caused by the gravity of the overburden layer, and its size is related to the rock burial depth, volume, and density. The calculation formula is: in, is the maximum burial depth, g is the acceleration due to gravity, is the density of the overlying rock, For depth.

[0038] Moreover, under the influence of tectonic movement, the underground crust movement is extremely complex, resulting in differences in horizontal ground stress and a certain directionality. and The magnitudes are not equal and are not equal to 0. The magnitude order of the ground stress components is > > .

[0039] Step S230 : determining the rock mass constitutive relationship and coupling parameters in the coupling model based on the strength parameters, principal stress directions, and principal stress magnitudes.

[0040] In some feasible embodiments, the Mohr-Coulomb elastic-plastic model may be selected as the constitutive relation, and its calculation formula is: in, For cohesion, is the internal friction angle, and are the maximum and minimum principal stresses, respectively. The principal stress directions are determined by field measurements or numerical inversion, and the principal stress magnitudes are used to initialize the model stress field. Then, the stress-strain relationship combined with the elastic stage can be expressed as: in, As the main strain, is the elastic modulus, is Poisson's ratio, and the elastic parameters can be dynamically adjusted according to the principal stress value to reflect the influence of stress state on rock deformation. Among them, the coupling parameters usually include elastic modulus , Poisson's ratio , cohesion , internal friction angle These parameters can be obtained through laboratory tests or field tests and corrected in combination with the principal stress values. and Poisson's ratio Dynamic adjustments are made to reflect the influence of stress state on the deformation characteristics of rock mass.

[0041] In some embodiments of the present invention, by obtaining key strength parameters of the rock mass in a coupled model and combining it with hydraulic fracturing to accurately invert the principal stress directions and magnitudes of the initial geostress field, a quantitative characterization of the rock mass's mechanical properties and geostress state is achieved. This multi-parameter fusion analysis method can more realistically reflect the nonlinear deformation and failure mechanisms of the rock mass under complex stress paths, thereby improving the coupled model's accuracy in depicting the rock mass's constitutive relationships (such as the stress-strain relationship) and coupling parameters (such as the seepage-stress coupling coefficient).

[0042] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the specific implementation process of the above mine disaster prediction method may further include steps S310 to S330, which are described in detail as follows: Step S310 , obtaining multi-source data of the mine, and fusing the multi-source data and numerical simulation results based on a Bayesian update algorithm to obtain a fused multi-source data set.

[0043] For example, in the process of multi-source data fusion in mines, multi-source heterogeneous data such as geological exploration data, environmental monitoring data, and production equipment data are first obtained through sensor networks, remote sensing monitoring, historical databases, etc., and the data are cleaned and standardized; then, numerical simulation software is used to construct a mine rock mechanics model or ventilation network model to generate simulation results under different working conditions; then, based on the Bayesian update algorithm, the observed values ​​of multi-source data are used as the likelihood function, and the predicted results of numerical simulation are used as the prior distribution. The posterior probability distribution is updated through iterative calculation, and the model parameters are dynamically adjusted to reduce the deviation between the prediction and the actual measurement; finally, the updated posterior distribution parameters are fed back to the numerical model, and a multi-source data set that integrates spatiotemporal consistency and uncertainty is output to provide support for mine safety warning and optimization decision-making.

[0044] Step S320 , determining target data in the fused multi-source data set based on sensitivity analysis, where the target data includes multi-source data having a sensitivity greater than a preset sensitivity threshold.

[0045] Step S330 : determining dynamic correction model parameters based on the target data, and correcting the coupling model based on the dynamic correction model parameters.

[0046] For example, in mining engineering, coupled models (such as seepage, stress, and damage coupled models) are used to describe the interactions between different physical fields within a mining system. Changes in coupling parameters can significantly impact model output. Therefore, sensitivity analysis can be used to determine the sensitivity of individual data in a fused multi-source dataset to changes in the coupling parameters within the mine's coupled model. This can provide a scientific basis for data collection priorities and mine hazard assessments.

[0047] Alternatively, in some feasible embodiments, it can be assumed that the coupling model output of the mine is Y (for example, key indicators such as displacement, stress, and seepage), and the coupling parameters are , ,… , the data variables in the fused multi-source dataset are , ,… , then the coupling model can be expressed as: in, is a functional relationship of the coupling model.

[0048] Then, for the coupling parameters (1,2,…,m) for data variables The sensitivity of the change of (i=1,2,…,n) can be measured by the first-order partial derivative, thereby defining the sensitivity coefficient , which is calculated as follows: in, Indicates the coupling model output Y pair coupling parameters The first-order partial derivative of , which reflects the rate at which the model output changes with the coupling parameters; is a normalization factor used to eliminate the dimension effect and make the sensitivity coefficients of different coupling parameters comparable; Represents coupling parameters For data variables The rate of change of , which reflects the degree of influence of the data variable on the coupling parameters, and the absolute value is taken to ensure that the sensitivity coefficient is non-negative, which is convenient for comparing the sensitivity of each data variable to different coupling parameters.

[0049] Calculate each data variable For each coupling parameter Sensitivity coefficient Afterwards, the data variables can be sorted according to different requirements. For example, for each coupling parameter, the data variables can be sorted from largest to smallest according to their sensitivity coefficients to identify the data variables that are most sensitive to changes in that coupling parameter. Alternatively, the sensitivity coefficients under all coupling parameters can be comprehensively analyzed to determine the importance of the data variables in the entire coupling model.

[0050] Then, the multi-source data with sensitivity coefficients greater than a preset sensitivity threshold is used as target data, and the dynamic model correction parameters of the mine coupling model are determined according to the target data, so as to correct the coupling model according to the dynamic model correction parameters.

[0051] In some examples of the present invention, a Bayesian update algorithm efficiently integrates multi-source mine data (such as monitoring data and geological exploration data) with numerical simulation results, effectively integrating multidimensional information and reducing data uncertainty, thereby improving the integrity and credibility of the dataset. By using sensitivity analysis to select key data with significant impact on the model as target data, the focus can be placed on core variables, avoiding interference from redundant information and accurately determining the actual boundary conditions of the mine. Dynamically modifying the coupled model by combining actual boundary conditions with initial predicted boundary conditions makes the model more consistent with the actual geological and engineering environment, significantly improving the accuracy of mine disaster prediction and rock mass stability analysis, and enhancing the precision of mine disaster prediction.

[0052] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the above target data includes rock mass deformation, and the specific implementation process of correcting the coupling model based on the actual boundary conditions and the predicted boundary conditions may further include steps S410 to S430, which are described in detail as follows: Step S410, determining the rock deformation of the mine based on the on-site monitoring data; Step S420, determining the porosity change rate of the mine based on the rock mass deformation, and determining the actual seepage field boundary conditions of the mine based on the porosity change rate; Step S430 : updating the predicted seepage field boundary conditions in the coupling model based on the actual seepage field boundary conditions, so as to modify the coupling model based on the updated seepage field boundary conditions.

[0053] Continuing with the above-mentioned embodiment, by analyzing the sensitivity of each data point within the mine's multi-source data, target data with the greatest impact on the output parameters of the mine coupling model is identified. This target data includes the mine's rock mass deformation. Specifically, on-site monitoring equipment (such as displacement sensors and total stations) at the mine acquires real-time rock mass displacement data, including surface subsidence, tunnel convergence, and stratum movement. The cumulative deformation and deformation rate of the mine's rock mass are then determined based on the acquisition time of this data. Rock mass deformation can lead to compression or expansion of pores and fractures, thereby changing the pore volume. The global distribution of the porosity change rate can also be calculated based on the spatial distribution of rock mass deformation. For example, in a numerical model, the porosity change rate of each cell is calculated using node displacements.

[0054] Furthermore, the permeability of the rock mass can be ) and porosity ( ) usually have a positive correlation, which can be described by an empirical formula or theoretical model. The specific calculation formula can be as follows: in, is the initial permeability of the mine rock mass, is the initial porosity, and n is an empirical index, generally ranging from 2 to 3.

[0055] Then, based on the full-field distribution of the porosity change rate of the mining rock mass, the permeability field of the rock mass can be updated, and new seepage field boundary conditions can be set. For example, the head distribution can be determined by correcting the head distribution based on the pore water pressure monitoring data; the flow boundary can be determined by adjusting the flow input or output conditions based on the permeability change; and the pore water pressure boundary can be determined by correcting the pore water pressure distribution based on the porosity change rate. In the solid-liquid coupling model, the seepage field and stress field are usually coupled through the following calculation formula: Where h is the water head, is stress, is the water storage rate, is the rock mass density, is the acceleration due to gravity.

[0056] The rock mass deformation and porosity change rate, determined based on field monitoring data, are then incorporated into the aforementioned calculation formula to obtain the mine's actual seepage field boundary conditions. These actual seepage field boundary conditions are then used to update the predicted seepage field boundary conditions in the coupled model, thereby updating the hydraulic head boundary, flow boundary, and pore water pressure boundary in the coupled model.

[0057] In some embodiments of the present invention, field monitoring data is used to quantify rock mass deformation in mines, thereby inferring the porosity change rate and determining the actual seepage field boundary conditions, thereby dynamically capturing the seepage-stress coupling effect in mines. Furthermore, by feeding the actual seepage field boundary conditions back into the coupled model and updating the predicted values, errors caused by geological complexity or initial assumption bias can be effectively corrected, allowing the coupled model to more realistically reflect the impact of rock mass pore structure changes on groundwater seepage. This iterative process significantly improves the coupled model's prediction accuracy under complex hydrogeological conditions, enhances the reliability of the simulation of the coupled seepage and stress fields in mining projects, and provides a reliable scientific basis for mine disaster prediction.

[0058] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the target data includes rock mass microseismic data, and the specific implementation process of correcting the coupling model based on the actual boundary conditions and the predicted boundary conditions may further include steps S510 to S530, which are described in detail as follows: Step S510, determining rock mass microseismic data of the mine based on the on-site monitoring data, where the microseismic data includes spatial coordinate data of microseismic events; Step S520, determining the actual damage boundary conditions of the mine based on the spatial coordinate data; Step S530 : updating the predicted damage boundary conditions in the coupling model based on the actual damage boundary conditions, so as to modify the coupling model based on the updated damage boundary conditions.

[0059] For example, a microseismic monitoring system (such as a seismograph or accelerometer) at a mine site can collect real-time microseismic event data within the rock mass. This microseismic data typically includes the spatial coordinates of the microseismic event, representing the three-dimensional location of the event; the time of occurrence, magnitude (energy), and waveform characteristics of the microseismic event. The spatial coordinates of the microseismic event are projected onto a three-dimensional mine model to generate a spatial distribution map of the microseismic events. The magnitude of the microseismic event can also be calculated based on the spatial coordinates of the microseismic time and the waveform amplitude or energy release. Based on the distribution density and magnitude of the microseismic events, the boundaries of the damaged area are delineated, and the accumulated microseismic energy per unit volume is calculated. Finally, the initial fractures or weak planes in the corresponding damaged area of ​​the mine are determined.

[0060] Furthermore, in the solid-liquid damage coupling model, the loss evolution of the mine can be expressed as follows: in, is the damage variable, is stress, For strain, It is the micro-seismic energy. Strain is a physical quantity that describes the change in the relative position of various points inside an object when it is subjected to force or deformation, and reflects the degree of deformation of the rock mass.

[0061] The spatial coordinates and damage degree of the actually monitored microseismic events can then be imported into the coupling model. Then, based on the damage area delineated by the microseismic data, the damage field distribution of the model can be initialized, thereby revealing the correction of the damage boundary conditions of the mine. Furthermore, by solving the mine coupling model, the updated stress field, seepage field and damage field distribution can be obtained.

[0062] In some embodiments of the present invention, the spatial coordinate information of microseismic events in the mining rock mass is extracted through field monitoring data, and the location of rock fracture and damage evolution is accurately located, thereby determining the actual damage boundary conditions. By dynamically comparing and updating the actual damage boundary conditions with the predicted values ​​in the coupling model, the errors caused by geological uncertainties or initial assumption deviations in the model can be corrected in real time, so that the model can more realistically reflect the damage accumulation and spatial expansion process of the rock mass under mining disturbances. This dynamic feedback mechanism based on microseismic monitoring significantly improves the simulation accuracy of the coupling model for the progressive destruction process of the rock mass, enhances the mine disaster warning and prevention and control capabilities, and provides reliable technical support for optimizing mining plans and ensuring operational safety.

[0063] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the above target data also includes support structure data, and the specific implementation process of correcting the coupling model based on the actual boundary conditions and the predicted boundary conditions may further include steps S610 to S630, which are described in detail as follows: Step S610, determining support structure data corresponding to the rock mass of the mine based on the on-site monitoring data, the support structure including anchor stress data; Step S620, determining an anchor stress threshold, and determining an actual stress boundary condition of the mine based on the anchor stress threshold and the anchor stress data; Step S630 : updating the predicted stress boundary conditions in the coupling model based on the actual stress boundary conditions, so as to modify the coupling model based on the updated stress boundary conditions.

[0064] For example, based on on-site monitoring data, the support structure data of the mine is first collected in real time through equipment such as anchor stress meters, with a focus on obtaining anchor stress data and its spatial distribution characteristics; then, combined with support design specifications and engineering experience, the safety threshold of anchor stress is determined, and the areas exceeding the threshold in the monitoring data are judged as high stress boundaries, thereby clarifying the actual stress boundary conditions of the mine; finally, the actual stress boundary conditions are introduced into the solid-liquid damage coupling model, and the original predicted stress boundaries in the model are updated (such as adjusting the support parameters or stress assignments in the high stress areas), and the coupling relationship between the stress field, seepage field and damage field is corrected by resolving the model, forming a dynamic feedback mechanism of "monitoring, identification, and correction" to ensure that the model prediction results are consistent with the actual stress state of the mine, providing a reliable basis for support structure optimization and disaster prevention and control.

[0065] Optionally, in the solid-liquid damage coupling model, the stress boundary condition is described by the following expression: in, is the stress tensor, is the rock mass density, is the acceleration due to gravity.

[0066] Then, anchor stress data can be imported into the above expression to identify high-stress areas in the mine (areas where anchor stress exceeds a threshold are identified as high-stress boundaries). This can also determine the active stress boundaries where anchor stress is actively applied to the rock mass, and the passive stress boundaries where anchor stress is passively triggered by rock mass deformation. This can then be used to modify the stress boundaries in the coupled model.

[0067] In some embodiments of the present invention, stress information of mine rock support structures (such as anchor rods) is obtained through on-site monitoring data, and the actual stress boundary conditions are quantified in combination with a preset anchor rod stress threshold, thereby realizing real-time perception of the stress state of the support structure and the stress distribution of the rock mass. On the other hand, by dynamically comparing and updating the actual stress boundary conditions with the predicted values ​​in the coupling model, the stress field prediction deviation of the model caused by the complexity of geological conditions or construction disturbances can be effectively corrected, so that the model can more accurately reflect the synergistic mechanism of the rock mass and the support structure, significantly improving the simulation accuracy of the coupling model for the rock mass stress evolution and support effectiveness, enhancing the reliability of the stress and support coupling effect evaluation in mining engineering, and providing a scientific basis for preventing support failure and ensuring operation safety.

[0068] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the specific implementation process of determining potential disaster points based on the modified coupling model and generating risk warning strategies and risk recovery strategies corresponding to the potential disaster points may further include steps S710 and S720, which are described in detail as follows: Step S710, determining the plastic zone distribution, plastic zone volume, and plastic zone diffusion rate corresponding to the mine based on the revised coupling model; Step S720: determining potential rockburst disaster points based on the plastic zone distribution, plastic zone volume, and plastic zone diffusion rate, and generating risk warning strategies and risk recovery strategies corresponding to the potential rockburst disaster points.

[0069] For example, based on the revised coupled model, numerical simulations are first used to calculate the distribution of the mine's plastic zone—the region where the rock mass undergoes irreversible deformation after being stressed beyond its yield strength. The volume of the plastic zone is then calculated to quantify its spatial extent, and its diffusion rate over time is analyzed (e.g., characterized by volume growth rate or boundary displacement rate). Further, the distribution characteristics of the plastic zone (e.g., areas of high stress concentration or fault intersections), its volume (e.g., rockburst risk increases significantly when exceeding a critical value), and its diffusion rate (e.g., rapid expansion may indicate a sudden rockburst) are combined to comprehensively identify potential rockburst hazard locations. For high-risk areas, a risk warning strategy is generated, including real-time monitoring of anchor stress and microseismic activity, setting graded warning thresholds, and triggering risk recovery strategies, such as immediately halting operations in the high-risk area, initiating active support measures (e.g., grouting or prestressed anchor cable reinforcement), and evacuating personnel to a safe area. A dynamic feedback mechanism is also established to continuously optimize warning and recovery measures based on the model's predictions, forming a comprehensive risk prevention and control system encompassing "monitoring, prediction, warning, and recovery" to ensure safe mine production.

[0070] In some embodiments of the present invention, a modified coupling model accurately quantifies the distribution, volume, and diffusion rate of the plastic zone in a mine rock mass, enabling dynamic tracking of stress concentration and damage evolution in the rock mass. Identifying potential rockburst hazards based on plastic zone parameters effectively captures precursor information for rock mass instability, overcoming the limitations of traditional single-indicator early warning systems. By generating targeted risk warning and recovery strategies, a comprehensive prevention and control system, from disaster identification to emergency response, is established. This significantly improves the accuracy and response efficiency of mine rockburst disaster predictions, providing reliable technical support for ensuring operational safety and reducing economic losses.

[0071] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the specific implementation process of determining potential disaster points based on the modified coupling model and generating risk warning strategies and risk recovery strategies corresponding to the potential disaster points may further include steps S810 to S830, which are described in detail as follows: Step S810, determining the displacement vector field corresponding to the mine based on the modified coupling model; Step S820, determining the gradient tensor and shear displacement corresponding to the displacement vector field; Step S830 : determining a potential disaster point based on the gradient tensor and the shear displacement, and generating a risk warning strategy and a risk recovery strategy corresponding to the potential disaster point.

[0072] For example, the displacement vector reflects the spatial deformation characteristics of the rock mass under the action of force, and the displacement field of the rock mass can be solved by the modified coupling model: in, Along The displacement component in the direction. Then the displacement field vector can be differentiated to obtain the displacement gradient tensor : Among them, the gradient tensor describes the spatial rate of change of displacement, and its eigenvalue and eigenvector can characterize the direction and magnitude of the principal strain force of the rock mass.

[0073] The shear displacement reflects the shear deformation strength of the rock mass, and the calculation formula of the shear displacement of the displacement vector field can be: Optionally, the shear displacement can be set The critical value of the mine is determined, and when the real-time shear displacement of the mine is greater than the preset critical value, it is judged as a high-risk area; on the other hand, if the maximum eigenvalue of the gradient tensor of the mine is A significant increase (increase efficiency greater than the average efficiency) indicates the possibility of sudden failure of the rock mass. This can be combined with geological structures (such as faults and joints) and mining activities to comprehensively identify potential displacement hazard points. This can then generate corresponding risk warning strategies, such as immediately halting operations in high-risk areas, evacuating personnel to safe areas, and initiating active support measures (such as grouting and prestressed anchor reinforcement) to limit displacement progression.

[0074] In some embodiments of the present invention, a modified coupling model is used to obtain a mine displacement vector field, and its gradient tensor and shear displacement are further analyzed to achieve a refined characterization of rock mass displacement and deformation characteristics. The gradient tensor reveals the spatial rate of change of the displacement field and local stress concentration areas, while the shear displacement reflects the shear failure trend of the rock mass. The combination of the two can accurately identify potential displacement disaster points. The risk warning and recovery strategies generated based on this form a proactive prevention and control mechanism, which can effectively improve the prediction accuracy and response capabilities of displacement disasters in mining projects, providing scientific support for ensuring production safety and reducing geological disaster risks.

[0075] Furthermore, based on the above embodiment, in one exemplary embodiment provided by the present invention, the specific implementation process of determining potential disaster points based on the modified coupling model and generating risk warning strategies and risk recovery strategies corresponding to the potential disaster points may further include steps S910 to S930, which are described in detail as follows: Step S910: determining a coupled numerical model corresponding to the mine based on the revised coupled model, where the coupled numerical model includes a coupled model of rock mass seepage field and stress field; Step S920, determining the seepage velocity field and pore water pressure distribution of the mine based on the coupled model of the rock mass seepage field and stress field; Step S930: determining a potential water inrush disaster point and a water inrush prediction time based on the seepage velocity field and the pore water pressure distribution, and generating a risk warning strategy and a risk recovery strategy corresponding to the potential water inrush disaster point based on the water inrush prediction time.

[0076] For example, based on the modified coupled numerical model, the interaction between groundwater flow and mechanical deformation in the rock mass is first characterized by the dynamic coupling equation of the seepage field and the stress field, where the seepage field is described by Darcy's law: in, is the seepage velocity field, is the permeability coefficient, is the pore water pressure, is the fluid density, g is the acceleration due to gravity, Represents the coordinate component perpendicular to the main seepage direction (such as the x-axis).

[0077] As can be seen from the above embodiment, the stress field of the mine is controlled by the equilibrium equation and expressed as: ,in, is the total stress tensor, is the rock mass density, reflecting the deformation of the rock mass under the action of effective stress; the coupling model can model the nonlinear relationship between the permeability coefficient k and the stress field evolution. The specific calculation formula is as follows: in, is the stress sensitivity coefficient, and the above formula is used to realize the seepage-stress coupling, and then solve the seepage velocity field of the mine and pore water pressure distribution On this basis, by analyzing the spatial heterogeneity of the seepage velocity field (such as identifying fault zones or fracture zones where the seepage velocity suddenly increases) and high-value clusters of pore water pressure (such as areas close to the tensile strength of the rock mass), the potential disaster points of water inrush are comprehensively determined; at the same time, the time of water inrush is predicted by combining the time-varying rate of pore water pressure and the critical water inrush pressure threshold value. For high-risk areas, a graded risk warning strategy and risk recovery strategy are generated according to the critical water inrush pressure value and the predicted water inrush time.

[0078] In some embodiments of the present invention, the dynamic interaction between the rock mass seepage field and the stress field is integrated through a modified coupled numerical model to accurately simulate the seepage-stress coupling mechanism under the complex hydrogeological conditions of the mine. Based on the quantitative analysis of the seepage velocity field and the pore water pressure distribution, the key causes of water inrush disasters can be effectively identified and the time of disaster occurrence can be predicted, breaking through the limitations of traditional single-factor early warning. By generating phased risk warning strategies and risk recovery strategies, a full-process prevention and control system from disaster warning to emergency response is constructed, which significantly improves the active prevention and control capabilities of mine water inrush disasters and provides a scientific decision-making basis for ensuring underground operation safety and reducing economic losses caused by water disasters.

[0079] See also Figure 2 , Figure 2 This is a schematic diagram illustrating the structure of a mine disaster prediction system according to an exemplary embodiment of the present invention. The system includes an input device, a processor, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions. The system utilizes a mine disaster prediction method.

[0080] The memory uses a high-speed solid-state hard drive, which has the characteristics of fast reading and writing speed, large capacity and high reliability. It is mainly used to store data input by the input device and the result data processed by the processor, and can meet the needs of large-scale data storage.

[0081] In this embodiment, the input device includes a data interface module and a monitoring module. The data interface module is used to input the geological structure data corresponding to the mine, which includes rock layer distribution, fault occurrence and groundwater storage status. The monitoring module is used to obtain on-site monitoring data of the mine and provide data input for subsequent mine coupling model correction. The input device supports data format standardization to ensure that the collected and received data is compatible with the processor.

[0082] The processor includes a coupling module, a boundary module and a correction module. The coupling module is used to construct a coupling model corresponding to the mine based on the rock layer distribution, fault occurrence and groundwater storage status of the mine, and further determine the rock mass constitutive relationship and corresponding coupling parameters in the coupling model. The boundary module is used to determine the prediction boundary conditions corresponding to the coupling model based on the rock mass constitutive relationship and coupling parameters of the mine coupling model; the correction module is used to correct the coupling model of the mine based on the on-site monitoring data of the mine input by the input device and the prediction boundary conditions determined by the boundary module, and the correction module is used to determine the potential disaster points of the mine based on the corrected coupling model, and generate risk warning strategies and risk recovery strategies corresponding to the potential disaster points.

[0083] The output device includes a display terminal, a strategy generation module and an early warning prompt module. The display terminal is used to intuitively display the three-dimensional visual coupling model of the mine; the measurement generation module is used to generate risk warning strategies and risk recovery strategies corresponding to potential disaster points; and the early warning prompt module is used to provide risk prompts for potential disaster points in the mine.

[0084] In summary, the present invention constructs a coupling model by integrating multi-source geological data such as mine rock strata distribution, fault occurrence and groundwater storage status, and determines the prediction boundary conditions based on the rock mass constitutive relationship and coupling parameters, thereby realizing accurate simulation of the complex geological conditions of the mine; further combining the dynamic correction model with field monitoring data, effectively improving the model's prediction accuracy for the multi-physical field coupling process such as rock mass stress-seepage-deformation, thereby accurately identifying potential disaster points (such as sudden water inrush, rock burst, etc.); finally, by generating targeted risk warning strategies (such as graded warning, emergency response) and recovery strategies (such as grouting reinforcement, drainage and decompression), a full-chain prevention and control system from disaster prediction to emergency response is constructed, which significantly enhances the safety management capabilities and disaster prevention and control effectiveness of mining projects, and provides scientific guarantees for ensuring personnel safety and reducing economic losses.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A mine disaster early warning method, characterized in that: The method comprises: Obtaining geological structure data corresponding to the mine, including rock layer distribution, fault occurrence, and groundwater occurrence; Constructing a coupling model corresponding to the mine based on the rock strata distribution, the fault occurrence, and the groundwater occurrence state, and determining the rock mass constitutive relationship and coupling parameters in the coupling model; Determining the prediction boundary conditions of the coupling model based on the rock mass constitutive relationship and the coupling parameters; Obtaining on-site monitoring data of the mine, and correcting the coupling model based on the on-site monitoring data and the predicted boundary conditions, so as to determine potential disaster points based on the corrected coupling model, and generate risk warning strategies and risk recovery strategies corresponding to the potential disaster points.

2. The method according to claim 1, wherein Determining the rock mass constitutive relationship and coupling parameters in the coupling model includes: Acquiring strength parameters corresponding to the coupling model, wherein the strength parameters include elastic modulus, Poisson's ratio, uniaxial compressive strength, triaxial compressive strength, cohesion, and internal friction angle; Inverting the principal stress direction and principal stress magnitude of the initial geostress field of the coupling model based on the hydraulic fracturing method; The rock mass constitutive relationship and coupling parameters in the coupling model are determined based on the strength parameters, the principal stress directions, and the principal stress magnitudes.

3. The method according to claim 1, wherein The method further comprises: Acquire multi-source data of the mine, and fuse the multi-source data and numerical simulation results based on a Bayesian update algorithm to obtain a fused multi-source data set; Performing sensitivity analysis on the fused multi-source data set, and taking multi-source data with a sensitivity greater than a preset sensitivity threshold as target data; The actual boundary conditions of the mine are determined based on the target data, and the coupling model is modified based on the actual boundary conditions and the predicted boundary conditions.

4. The method according to claim 3, wherein The target data includes rock mass deformation, and the step of correcting the coupling model based on the actual boundary conditions and the predicted boundary conditions includes: determining the rock deformation of the mine based on the on-site monitoring data; Determining a porosity change rate of the mine based on the rock mass deformation, and determining an actual seepage field boundary condition of the mine based on the porosity change rate; The predicted seepage field boundary conditions in the coupling model are updated based on the actual seepage field boundary conditions, so as to correct the coupling model based on the updated seepage field boundary conditions.

5. The method according to claim 3, wherein The target data includes rock mass microseismic data, and the modifying the coupling model based on the actual boundary conditions and the predicted boundary conditions includes: Determining rock mass microseismic data of the mine based on the on-site monitoring data, wherein the microseismic data includes spatial coordinate data of microseismic events; determining actual damage boundary conditions of the mine based on the spatial coordinate data; The predicted damage boundary condition in the coupling model is updated based on the actual damage boundary condition, so as to correct the coupling model based on the updated damage boundary condition.

6. The method according to claim 3, wherein The target data includes support structure data, and the modifying the coupling model based on the actual boundary conditions and the predicted boundary conditions includes: Determining support structure data corresponding to the rock mass of the mine based on the on-site monitoring data, wherein the support structure includes anchor stress data; Determining an anchor stress threshold, and determining an actual stress boundary condition of the mine based on the anchor stress threshold and the anchor stress data; The predicted stress boundary conditions in the coupling model are updated based on the actual stress boundary conditions, so as to correct the coupling model based on the updated stress boundary conditions.

7. The method according to claim 1, wherein The determining of potential disaster points based on the modified coupling model and the generation of risk warning strategies and risk recovery strategies corresponding to the potential disaster points include: Determining the plastic zone distribution, plastic zone volume, and plastic zone diffusion rate corresponding to the mine based on the revised coupling model; Based on the distribution of the plastic zone, the volume of the plastic zone and the diffusion rate of the plastic zone, a potential rockburst disaster point is determined, and a risk warning strategy and a risk recovery strategy corresponding to the potential rockburst disaster point are generated.

8. The method according to claim 1, wherein The determining of potential disaster points based on the modified coupling model and the generation of risk warning strategies and risk recovery strategies corresponding to the potential disaster points include: determining a displacement vector field corresponding to the mine based on the modified coupling model; Determining a gradient tensor and a shear displacement corresponding to the displacement vector field; A potential disaster point of displacement is determined based on the gradient tensor and the shear displacement, and a risk warning strategy and a risk recovery strategy corresponding to the potential disaster point of displacement are generated.

9. The method according to claim 1, wherein The determining of potential disaster points based on the modified coupling model and the generation of risk warning strategies and risk recovery strategies corresponding to the potential disaster points include: Determining a coupled numerical model corresponding to the mine based on the revised coupled model, wherein the coupled numerical model includes a coupled model of rock mass seepage field and stress field; Determining the seepage velocity field and pore water pressure distribution of the mine based on the coupled model of the rock mass seepage field and stress field; A potential water inrush disaster point and a water inrush prediction time are determined based on the seepage velocity field and the pore water pressure distribution, and a risk warning strategy and a risk recovery strategy corresponding to the potential water inrush disaster point are generated based on the water inrush prediction time.

10. A mine disaster prediction system, characterized in that: The system uses the mine disaster prediction method described in any one of claims 1 to 9, characterized in that the system includes an input device, a processor, an output device and a memory, and the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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