A method for predicting the danger of rock burst in a mine

Abstract

The present application belongs to the field of mine, and particularly relates to a dangerousness prediction method for rock burst mine, comprising the following steps: S1, establishing a unified space-time reference network, S2, mapping coordinates of multi-source data, S3, constructing a real-time data fusion hub, S4, defining and dividing dynamic risk units, S5, calculating unit-specific risk indicators, S6, constructing a unit risk fusion evaluation model, S7, deploying a model parameter adaptive algorithm, S8, generating a mine risk situation cloud map, S9, superimposing the risk cloud map and a three-dimensional geological model, S10, establishing a hierarchical and zoned early warning mechanism, S11, constructing an intelligent matching knowledge base for prevention and control measures, S12, automatically generating targeted prevention and control decision suggestions. The dangerousness prediction method for rock burst mine solves the problem of "data islands" caused by inconsistent space-time references of various monitoring systems by establishing a millisecond-level time synchronization network and a unified coordinate mapping system.

Description

Technical Field

[0001] This invention relates to the field of mining technology, specifically a method for predicting the hazards of mines prone to rock bursts. Background Technology

[0002] Rockbursts are a typical dynamic disaster in deep mining, characterized by their sudden onset and destructive power. To monitor their precursors, mines typically deploy various monitoring systems, such as microseismic, ground stress, and roadway deformation monitoring systems. These systems collect different physical parameters to provide data support for rockburst early warning.

[0003] Currently, these monitoring systems often operate independently, resulting in inconsistent spatiotemporal benchmarks and heterogeneous data formats, leading to "data silos" that hinder effective comprehensive analysis. Existing assessment methods often rely on single indicators, resulting in low accuracy in early warning; early warning information is not presented intuitively, and prevention and control decisions heavily depend on human experience. In view of this, we propose a method for predicting the hazards of mines prone to rock bursts. Summary of the Invention

[0004] The main objective of this invention is to provide a method for predicting the hazards of mines prone to rock bursts, which can solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention proposes a method for predicting the risk of rockburst mines, comprising the following steps: S1. Establish a unified spatiotemporal reference network: Deploy a high-precision time synchronization server in the mine monitoring network to provide a unified timestamp service for all monitoring equipment; S2. Unified mapping of coordinates of multi-source data: Based on coordinate transformation parameters, by executing coordinate transformation algorithms, the independent coordinate systems of each monitoring system are all transformed to the absolute coordinate system of the mine. S3. Construct a real-time data fusion hub: Create a data access and processing module with data cleaning and alignment functions to receive, clean, and align multi-source data after spatiotemporal standardization, and label it with a unified spatiotemporal tag to form a standardized data stream; S4. Define and divide dynamic risk units: Based on the mining engineering plan and geological structure map, the monitoring area is divided into gridded risk units; S5. Calculate unit-specific risk indicators: For each risk unit, extract the data belonging to this unit from the standardized data stream and calculate its exclusive multi-dimensional risk indicators. S6. Construct a unit risk fusion assessment model: Establish a multi-indicator comprehensive evaluation algorithm to fuse and calculate the multi-dimensional indicators of each risk unit and output the comprehensive risk value of the unit. S7. Deployment of model parameter adaptive algorithm: The evaluation model has a built-in feedback adjustment mechanism. When a rockburst event actually occurs, the risk data of each unit before the event is automatically traced back, and the model fusion weight and judgment threshold are corrected based on the traceback results. S8. Generate a cloud map of mine risk situation: Based on the comprehensive risk value of all risk units, use spatial interpolation algorithm to generate a continuous risk distribution surface in the entire monitoring area and render it in the form of a heat map; S9. Overlay of risk cloud map and 3D geological model: The generated risk heat map layer is overlaid on the high-precision 3D geological model of the mine in real time. S10. Establish a hierarchical and zoned early warning mechanism: Set two-level early warning thresholds, namely global and unit. When the global risk exceeds the threshold, a mine-level alarm is triggered. When any independent risk unit exceeds its threshold, the corresponding unit is highlighted and flashed in the three-dimensional model as an alarm. S11. Construct a knowledge base for intelligent matching of prevention and control measures: Pre-store the optimal prevention and control processes, equipment and parameters corresponding to different risk modes in the database; S12. Automatically generate targeted prevention and control decision recommendations: When an early warning is triggered, the system automatically analyzes the spatial distribution and risk patterns of high-risk units, matches the best prevention and control measures from the knowledge base, and generates a recommended treatment plan containing specific locations, methods, and parameters.

[0006] Preferably, in step S1, a high-precision network time protocol server is deployed to achieve millisecond-level time synchronization of all monitoring devices; in step S2, a coordinate transformation model including rotation, translation, and scaling parameters is used to achieve batch automatic conversion and unification of spatial coordinates of multi-source data. Through millisecond-level time synchronization, the time series of events monitored by different systems are comparable, laying the foundation for analyzing the temporal patterns of disaster precursors; and the unified coordinate transformation model completely breaks down the spatial barriers between various monitoring systems, enabling all data to be accurately superimposed on the same "map".

[0007] Preferably, in step S3, the data fusion engine is configured to perform data validity verification and outlier removal, and to match and align data from different systems within a preset time window and spatial grid based on a unified spatiotemporal benchmark. This configuration significantly improves the data quality upon which subsequent analysis depends by "purifying" the original data. At the same time, the spatiotemporal alignment operation integrates multi-source heterogeneous data into a structured, standardized data stream, providing a directly usable data source for risk calculation based on a unified grid.

[0008] Preferably, the multi-dimensional risk indicators in step S5 include the energy release rate calculated based on microseismic events, the stress concentration coefficient calculated based on ground stress data, and the convergence rate calculated based on tunnel deformation data. This combination of indicators quantifies the three most critical physical dimensions in the process of rockburst incubation: "energy," "stress," and "deformation," overcoming the limitations of single-indicator assessment and providing comprehensive and three-dimensional information input for subsequent integrated risk assessment.

[0009] Preferably, the multi-index comprehensive evaluation algorithm in steps S6 and S7 adopts a weighted average model, and its weight parameters are dynamically optimized by a machine learning classifier trained with historical disaster events as samples. The machine learning classifier is a random forest, support vector machine, or neural network. By introducing machine learning methods to dynamically optimize the weights, the model can automatically learn and quantify the intrinsic relationship between each index and the probability of rockburst occurrence, avoiding the subjectivity of manually setting weights. This enables the risk assessment model to have self-learning and adaptive capabilities, and it continuously evolves and becomes more accurate as the number of samples increases.

[0010] Preferably, the spatial interpolation algorithm in step S8 adopts the Kriging interpolation method. Its variogram model is fitted according to the spatial distribution characteristics of the comprehensive risk value of the risk unit. The Kriging interpolation method is an optimal unbiased estimator. It considers the spatial correlation of risk values ​​through the variogram function, which can make the generated risk situation surface not only smooth and continuous, but also conform to the spatial variation law of geological body parameters, thereby revealing the potential risk gradient change trend between discrete points.

[0011] Preferably, in step S9, the risk heat map is used as a semi-transparent color layer by a graphics rendering engine and is fused and drawn in real time with the triangular network geometry of the three-dimensional geological model. This overlay rendering method enables the abstract risk data to be seamlessly integrated with real geographical elements such as tunnels, coal seams, and geological structures, providing decision-makers with an immersive risk perception experience and greatly improving the intuitiveness of risk positioning and situational understanding.

[0012] Preferably, the unit-level targeted alarm in step S10 is achieved by modifying the shader parameters of the corresponding risk unit mesh object in the 3D scene to drive it to periodically highlight and flash. Using shaders for highlighting and flashing alarm is an efficient technique with minimal impact on system performance. It can accurately and conspicuously identify local danger areas in complex 3D scenes, achieving a "point-and-shoot" targeted alarm effect and effectively guiding attention.

[0013] Preferably, in step S11, the risk model is defined based on a specific combination of precursory microseismic activity characteristics, stress mutation signals, and deformation acceleration trends. By defining this multi-parameter coupled risk model, rather than relying on a single anomalous signal, the false alarm rate can be significantly reduced, the reliability of early warning can be improved, and a clear classification basis can be provided for subsequent precise matching of differentiated prevention and control measures.

[0014] Preferably, the disposal plan recommendation generated in step S12 clearly includes the risk unit grid number that requires pressure relief blasting, the recommended borehole azimuth angle and depth parameters. This upgrades the disposal plan from a qualitative suggestion to a quantitative and directly executable work instruction, which greatly improves the accuracy and efficiency of prevention and control construction and achieves a seamless connection from "perceiving risk" to "eliminating risk".

[0015] This invention provides a method for predicting the hazards of mines prone to rock bursts. It has the following beneficial effects: (1) The method for predicting the risk of rockburst mines by establishing a millisecond-level time synchronization network and a unified coordinate mapping system completely solves the problem of "data silos" caused by inconsistent spatiotemporal references among various monitoring systems. Specifically, by deploying a PTP time server and adopting a seven-parameter Bursa coordinate transformation model, it ensures that all monitoring data have a unified spatiotemporal label, providing a reliable and consistent data foundation for subsequent accurate analysis, and significantly improving the usability and reliability of the data.

[0016] (2) This method for predicting the risk of rockburst mines divides the monitoring area into gridded risk units and integrates multi-dimensional indicators such as energy, stress, and deformation for calculation, thereby achieving refined spatial management of the mine's risk situation. In particular, the introduction of an adaptive optimization mechanism based on machine learning algorithms such as random forests enables the assessment model to dynamically adjust weight parameters according to historical disaster data, overcoming the limitations of traditional methods that rely on single indicators and fixed parameters, and significantly improving the accuracy and foresight of early warning.

[0017] (3) The risk prediction method for rockburst mines forms a complete risk prevention and control closed loop through the deep fusion visualization of risk cloud maps and three-dimensional geological models, a hierarchical and zonal early warning mechanism, and intelligent matching of prevention and control measures. The system can automatically identify the spatial distribution and risk patterns of high-risk areas and generate a recommended treatment plan containing specific locations, methods, and parameters. This transforms the traditional decision-making model that relies on human experience into a precise and efficient model that is intelligently recommended by the system, greatly improving the speed and scientific nature of mine emergency response. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall steps of the present invention; Figure 2 This is a schematic diagram of some steps in the present invention; Figure 3 This is a schematic diagram of some steps in the present invention; Figure 4 This is a schematic diagram of some steps in the present invention; Figure 5 This is a schematic diagram of some steps in the present invention; Figure 6 This is a schematic diagram of some steps in the present invention; Figure 7 This is a schematic diagram of some steps in the present invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 - Figure 7 This invention proposes a method for predicting the hazards of mines prone to rock bursts. This method is implemented through a computer system deployed in the mine's monitoring center. This system includes a data acquisition interface, a data processing unit, a risk assessment engine, and a 3D visualization platform. The specific implementation process is as follows: S1. Establish a unified spatiotemporal reference network: Deploy a high-precision time synchronization server in the mine monitoring network to provide a unified timestamp service for all monitoring equipment.

[0023] Furthermore, in step S1, a high-precision network time protocol server is deployed to achieve millisecond-level time synchronization of all monitoring devices. Specifically, a server supporting the IEEE 1588 Precision Time Protocol (PTP) is selected as the master clock and synchronized with the acquisition stations of various monitoring systems (such as microseismic monitoring systems and online stress monitoring systems) via the mine industrial Ethernet. The network switch must support PTP transparent clock functionality to reduce synchronization errors. With this configuration, the time synchronization accuracy of over 200 monitoring nodes across the entire network can be controlled within ±1 millisecond, ensuring a precise correlation between microseismic event sequences and stress mutation data on the timeline, laying the foundation for analyzing causal relationships.

[0024] S2. Unified mapping of multi-source data coordinates: Based on coordinate transformation parameters, the independent coordinate systems of each monitoring system are transformed to the mine absolute coordinate system by executing the coordinate transformation algorithm.

[0025] Furthermore, in step S2, a coordinate transformation model incorporating rotation, translation, and scaling parameters is used to achieve batch automatic conversion and unification of spatial coordinates from multiple data sources. Specifically, a seven-parameter Bursa model is employed for coordinate transformation. During implementation, coordinates of at least four common control points are first collected in both the mine's absolute coordinate system and the microseismic monitoring system's coordinate system. Then, the optimal transformation parameters are solved automatically and in batches using the least squares method. The transformation accuracy should be controlled within ±0.5 meters to ensure precise spatial matching of stress monitoring points, microseismic event sources, and roadway deformation measurement points, providing an accurate spatial reference for risk calculations based on a unified grid.

[0026] S3. Construct a real-time data fusion hub: Create a data access and processing module with data cleaning and alignment functions to receive, clean, and align multi-source data after spatiotemporal standardization, and label it with a unified spatiotemporal tag to form a standardized data stream.

[0027] Furthermore, in step S3, the data fusion engine is configured to perform data validity verification and outlier removal, and based on a unified spatiotemporal reference, match and align data from different systems within a preset time window and spatial grid. Specific implementation includes: data validity verification using a range check method to remove physical outliers that significantly exceed the sensor's measurement range (e.g., stress values ​​less than 0 or greater than 50 MPa); outlier removal using a statistical box plot method, calculating the upper quartile (Q3) and lower quartile (Q1) for each monitoring parameter, and treating data exceeding the range [Q1-1.5IQR, Q3+1.5IQR] as statistical outliers and removing them, where IQR is the interquartile range. Data alignment uses a fixed 5-minute time window, with a 20m×20m×10m spatial grid defined in S4 as the basic unit, and assigning a unified spatiotemporal label (timestamp, grid ID) to each data point.

[0028] S4. Define and divide dynamic risk units: Based on the mining engineering plan and geological structure map, the monitoring area is divided into gridded risk units.

[0029] Furthermore, in step S4, based on the mine's geological conditions and monitoring accuracy requirements, the current mining area and its impact zone are divided into 20m × 20m × 10m (length × width × height) cubic grids. Each grid serves as an independent risk unit and is uniquely coded according to the rule of "mining area number - strike sequence number - dip sequence number - stratigraphic sequence number". The grid size can be dynamically adjusted according to actual needs; for example, in structurally complex areas, it can be densified to 10m × 10m × 5m to meet the requirements of refined assessment.

[0030] S5. Calculate unit-specific risk indicators: For each risk unit, extract the data belonging to this unit from the standardized data stream and calculate its own multi-dimensional risk indicators.

[0031] Furthermore, in step S5, multi-dimensional risk indicators include the energy release rate calculated based on microseismic events, the stress concentration factor calculated based on ground stress data, and the convergence rate calculated based on roadway deformation data. The specific calculation methods are as follows: For a given risk unit, the energy release rate (E) is obtained by summing the energy (in J) of all microseismic events that fell into the unit within the past hour and dividing by the time (1 hour), i.e., E = ΣEi / 1h; the stress concentration factor (K) is calculated by the ratio of the real-time maximum value (σ_max) of all stress measurement points within the unit to the background original rock stress value (σ0) of the mine area, i.e., K = σ_max / σ0; the convergence rate (V) is obtained by acquiring the roof and floor approach increment (ΔS) over the past 24 hours from monitoring stations installed in roadways near the unit and dividing by the time, i.e., V = ΔS / 24h. These quantified indicators collectively constitute a digital profile of the risk status of the unit.

[0032] S6. Construct a unit risk fusion assessment model: Establish a multi-indicator comprehensive evaluation algorithm to fuse and calculate the multi-dimensional indicators of each risk unit and output the comprehensive risk value of the unit.

[0033] S7. Deploy model parameter adaptive algorithm: The evaluation model has a built-in feedback adjustment mechanism. When a rockburst event actually occurs, the risk data of each unit before the event is automatically backtracked, and the model fusion weights and judgment thresholds are corrected based on the backtracking results.

[0034] Furthermore, in steps S6 and S7, the multi-index comprehensive evaluation algorithm adopts a weighted average model, and its weight parameters are dynamically optimized using a machine learning classifier trained on historical disaster events as samples. The machine learning classifier can be a random forest, support vector machine, or neural network. In this embodiment, the random forest algorithm is preferred. The specific implementation process is as follows: collect five rockburst cases that occurred in this mine in the past three years, and extract the three risk index data of each risk unit in the 24 hours before the event as positive samples, while randomly selecting data under safe conditions within the same period as negative samples. Use the Scikit-learn library to build a random forest model containing 100 decision trees for training. After training, the feature importance (feature_importances_) output by the model is the latest weight (w1, w2, w3) of the three indicators: energy release rate, stress concentration coefficient, and convergence rate. The overall risk value R is calculated using the formula R = w1 * E_norm + w2 * K_norm + w3 * V_norm, where E_norm, K_norm, and V_norm are normalized index values. When a new shock event occurs, the system automatically adds the data before and after the event to the training set, triggering model retraining and thus completing online adaptive optimization of the weight parameters.

[0035] S8. Generate a mine risk situation cloud map: Based on the comprehensive risk value of all risk units, use a spatial interpolation algorithm to generate a continuous risk distribution surface in the entire monitoring area and render it in the form of a heat map.

[0036] Furthermore, in step S8, the spatial interpolation algorithm employs Kriging interpolation, with its variogram model fitted based on the spatial distribution characteristics of the comprehensive risk value of each risk unit. Specifically, the experimental variogram of the comprehensive risk value of all risk units is first calculated, and then a spherical model is used for fitting. The three key parameters of the model—nugget, sill, and range—are determined using maximum likelihood estimation. Based on the fitted variogram model, ordinary Kriging is used to perform optimal unbiased estimation of the risk value in the unsampled area, generating a smooth and continuous risk distribution surface covering the entire monitoring area, which is then rendered using a continuous color spectrum from green (low risk) to red (high risk).

[0037] S9. Risk cloud map overlay with 3D geological model: The generated risk heat map layer is overlaid on the high-precision 3D geological model of the mine in real time.

[0038] Furthermore, in step S9, the risk heatmap is rendered as a semi-transparent color layer using a graphics rendering engine, and then integrated with the triangular mesh geometry of the 3D geological model in real time. Specifically, this is achieved using the Three.js graphics engine with WebGL technology. The risk distribution surface generated by Kriging interpolation is converted into isosurface data in GeoJSON format, and rendered as a semi-transparent color layer with an opacity of 0.6 in the Three.js scene. Through coordinate transformation, it is precisely overlaid with the triangular mesh of the 3D geological model containing elements such as coal seams, rock strata, and tunnels, achieving integrated visualization of risk information and the real geological environment.

[0039] S10. Establish a hierarchical and zoned early warning mechanism: Set two-level early warning thresholds, namely global and unit. When the global risk exceeds the threshold, a mine-level alarm is triggered. When any independent risk unit exceeds its threshold, the corresponding unit is highlighted and flashed in the three-dimensional model as an alarm.

[0040] Furthermore, in step S10, the unit-level targeted alarm is achieved by modifying the shader parameters of the corresponding risk unit mesh object in the 3D scene, driving it to periodically highlight and flash. Technically, in the Three.js engine, the `emissive` property of the corresponding risk unit mesh material (MeshBasicMaterial) is modified to periodically switch its color between [0x000000, 0xff0000] at a frequency of 1Hz, while simultaneously changing the `emissiveIntensity` value periodically between [0, 1], thus producing a striking red and black flashing effect that precisely attracts attention in complex 3D scenes.

[0041] S11. Construct a knowledge base for intelligent matching of prevention and control measures: Pre-store the optimal prevention and control processes, equipment and parameters corresponding to different risk modes in the database.

[0042] Furthermore, in step S11, risk modes are defined based on a specific combination of precursory microseismic activity characteristics, stress abrupt change signals, and deformation acceleration trends. Several typical risk modes are specifically defined; for example, "Mode A" is defined as an energy release rate > 1000 J / h and a stress concentration factor > 2.0, corresponding to the "large-diameter pressure relief borehole" control process; "Mode B" is defined as a convergence rate > 5 mm / d lasting more than 6 hours, corresponding to the "high-pressure grouting reinforcement" control process. The knowledge base is built using a MySQL relational database, storing information including the control process name, applicable equipment models, construction parameters (such as borehole diameter, depth, spacing, grouting pressure, etc.), and safety precautions.

[0043] S12. Automatically generate targeted prevention and control decision recommendations: When an early warning is triggered, the system automatically analyzes the spatial distribution and risk patterns of high-risk units, matches the best prevention and control measures from the knowledge base, and generates a recommended treatment plan containing specific locations, methods, and parameters.

[0044] Furthermore, the disposal plan recommendation generated in step S12 explicitly includes the risk unit grid number requiring pressure relief blasting, and the recommended borehole azimuth and depth parameters. In practice, when the system identifies a risk unit numbered "A-05-12-01" triggering a "Mode A" warning, it automatically matches the "large-diameter pressure relief borehole" scheme from the knowledge base and generates a PDF recommendation containing the following: a schematic diagram of the risk unit location, the recommended number of boreholes (8), the borehole azimuth (along the direction of maximum principal stress, such as N45°E), the borehole depth (15m), the borehole diameter (150mm), the charge parameters (5kg emulsion explosive per borehole), and the expected construction time and safety monitoring requirements, forming a standardized operating document that can directly guide on-site construction.

[0045] It should be noted that the servers, sensing devices, network devices, and other hardware involved in this invention are all conventional devices in the field, and their selection, installation, and debugging can be determined by those skilled in the art according to actual needs. The data processing algorithms, machine learning models, and graphics rendering methods described are all well-known technologies in the computer field, and those skilled in the art can determine the specific implementation parameters through technical manuals or routine experiments.

[0046] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for predicting the risk of rockburst mines, characterized in that: Includes the following steps: S1. Establish a unified spatiotemporal reference network: Deploy a high-precision time synchronization server in the mine monitoring network to provide a unified timestamp service for all monitoring equipment; S2. Unified mapping of coordinates of multi-source data: Based on coordinate transformation parameters, by executing coordinate transformation algorithms, the independent coordinate systems of each monitoring system are all transformed to the absolute coordinate system of the mine. S3. Construct a real-time data fusion hub: Create a data access and processing module with data cleaning and alignment functions to receive, clean, and align multi-source data after spatiotemporal standardization, and label it with a unified spatiotemporal tag to form a standardized data stream; S4. Define and divide dynamic risk units: Based on the mining engineering plan and geological structure map, the monitoring area is divided into gridded risk units; S5. Calculate unit-specific risk indicators: For each risk unit, extract the data belonging to this unit from the standardized data stream and calculate its exclusive multi-dimensional risk indicators. S6. Construct a unit risk fusion assessment model: Establish a multi-indicator comprehensive evaluation algorithm to fuse and calculate the multi-dimensional indicators of each risk unit and output the comprehensive risk value of the unit. S7. Deployment of model parameter adaptive algorithm: The evaluation model has a built-in feedback adjustment mechanism. When a rockburst event actually occurs, the risk data of each unit before the event is automatically traced back, and the model fusion weight and judgment threshold are corrected based on the traceback results. S8. Generate a cloud map of mine risk situation: Based on the comprehensive risk value of all risk units, use spatial interpolation algorithm to generate a continuous risk distribution surface in the entire monitoring area and render it in the form of a heat map; S9. Overlay of risk cloud map and 3D geological model: The generated risk heat map layer is overlaid on the high-precision 3D geological model of the mine in real time. S10. Establish a hierarchical and zoned early warning mechanism: Set two-level early warning thresholds, namely global and unit. When the global risk exceeds the threshold, a mine-level alarm is triggered. When any independent risk unit exceeds its threshold, the corresponding unit is highlighted and flashed in the three-dimensional model as an alarm. S11. Construct a knowledge base for intelligent matching of prevention and control measures: Pre-store the optimal prevention and control processes, equipment and parameters corresponding to different risk modes in the database; S12. Automatically generate targeted prevention and control decision recommendations: When an early warning is triggered, the system automatically analyzes the spatial distribution and risk patterns of high-risk units, matches the best prevention and control measures from the knowledge base, and generates a recommended treatment plan containing specific locations, methods, and parameters.

2. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: In step S1, a high-precision network time protocol server is deployed to achieve millisecond-level time synchronization of all monitoring devices; in step S2, a coordinate transformation model including rotation, translation and scaling parameters is used to achieve batch automatic conversion and unification of spatial coordinates of multi-source data.

3. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: In step S3, the data fusion engine is configured to perform data validity verification and outlier removal, and to match and align data from different systems within a preset time window and spatial grid based on a unified spatiotemporal reference.

4. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: The multi-dimensional risk indicators mentioned in step S5 include the energy release rate calculated based on microseismic events, the stress concentration factor calculated based on ground stress data, and the convergence rate calculated based on tunnel deformation data.

5. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: In steps S6 and S7, the multi-index comprehensive evaluation algorithm adopts a weighted average model, and its weight parameters are dynamically optimized by a machine learning classifier trained with historical disaster events as samples. The machine learning classifier is a random forest, support vector machine, or neural network.

6. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: The spatial interpolation algorithm described in step S8 uses the Kriging interpolation method, and its variogram model is fitted according to the spatial distribution characteristics of the comprehensive risk value of the risk unit.

7. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: In step S9, the risk heat map is used as a semi-transparent color layer by a graphics rendering engine and is fused and drawn in real time with the triangular mesh geometry of the three-dimensional geological model.

8. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: The unit-level targeted alarm mentioned in step S10 is achieved by modifying the shader parameters of the corresponding risk unit mesh object in the 3D scene to drive it to periodically highlight and flash.

9. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: In step S11, the risk model is defined based on a specific combination of precursory microseismic activity characteristics, stress abrupt change signals, and deformation acceleration trends.

10. The method for predicting the risk of rockburst mines according to claim 1, characterized in that: The proposed treatment plan generated in step S12 clearly includes the risk cell grid number for which pressure relief blasting needs to be performed, as well as the recommended borehole azimuth and depth parameters.

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