One-map management method suitable for rock burst mine

Abstract

The invention relates to the technical field of mine safety informatization, and discloses a one-map management method suitable for a rock burst mine. The method comprises the following steps of: integrating a plurality of anti-scour element layers by taking a mine mining plane graph as a basic base graph; by automatically collecting real-time operation data of pressure relief drilling construction, micro-seismic monitoring, probe monitoring and the like, layer information is dynamically updated, and it is ensured that one map reflects the real state of a mine in real time. And based on the updated layer, the system performs automatic impact risk analysis and generates an anti-impact measure decision instruction to guide the site to accurately implement prevention and control measures such as pressure relief and support. And acquiring mine environment feedback data after the measures are executed in real time, dynamically comparing the mine environment feedback data with an expected target, and adaptively adjusting a layer integration strategy. According to the method, real-time, dynamic and visual monitoring and intelligent decision making of the rock burst risk are achieved, the hysteresis of traditional management is overcome, and the mine safety guarantee level is improved.

Description

Technical Field

[0001] This invention relates to the field of mine safety information technology, specifically a single-map management method applicable to mines prone to rock bursts. Background Technology

[0002] Rockburst is one of the major hazards faced in deep coal mining, and its prevention and control rely heavily on the comprehensive analysis and judgment of various safety monitoring information in the mine. Current rockburst monitoring and management typically employs multiple decentralized systems operating independently. The data generated by these systems is independent and fragmented. Technicians need to manually collect and analyze data from different sources, and annotate it on paper drawings or simple electronic diagrams to assess risks and develop prevention and control plans.

[0003] This management approach, based on manual integration and static drawings, suffers from significant shortcomings in the timeliness of information updates and the comprehensiveness of analysis. Data updates lag far behind the dynamic changes in downhole production, and drawings fail to reflect the latest operational activities and disaster evolution in real time. Existing risk assessments rely heavily on the personal experience of technical personnel, making it difficult not only to accurately identify stress concentration areas but also to calculate monitoring blind zones using scientific algorithms. Traditional methods often rely on experience to determine the coverage of monitoring points without considering the density of monitoring points to create a uniform grid or calculating the distance from grid units to monitoring points, resulting in ambiguous blind zone identification. Differences in the professional backgrounds and practical experiences of different technical personnel can lead to biases in the interpretation and judgment of the same monitoring data, resulting in highly subjective risk analysis conclusions. Manually analyzing massive amounts of scattered data is time-consuming, and the entire decision-making chain, from discovering data anomalies to formulating targeted anti-rockburst measures, is excessively long. In the rapidly changing downhole environment, this lagging decision-making model cannot meet the timeliness requirements of rockburst prevention and control, potentially missing the optimal prevention and control opportunity and allowing small risks to evolve into major disasters.

[0004] The existing rockburst monitoring and management lacks a closed-loop management model of "measure implementation - environmental feedback - dynamic comparison - layer strategy adjustment". It is impossible to judge the effectiveness of measures by quantitatively comparing feedback data with expected targets, nor can it adaptively adjust the integrated strategy of rockburst prevention element layers. It can only rely on manual experience for adjustment, which further restricts the continuous improvement capability of rockburst prevention and control and cannot meet the dynamic needs of long-term safety management in mines.

[0005] Mine rockfall prevention management requires a technological approach that can break down information silos and achieve automatic data fusion and real-time visualization. The core issue lies in how to deeply integrate multi-source heterogeneous real-time monitoring data with mine geospatial information, and based on the results of this dynamic fusion, realize an automated closed loop from data to decision-making, in order to overcome the shortcomings of information lag and low efficiency of manual decision-making in existing technologies. Summary of the Invention

[0006] The purpose of this invention is to provide a single-map management method suitable for mines prone to rock bursts, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a single-map management method suitable for mines prone to rock bursts, the method comprising: Import the mine excavation plan as the base map, and integrate multiple anti-erosion element layers on the base map; Collect real-time mine operation data, including pressure relief drilling construction data, microseismic monitoring data, and probe monitoring data; The information in the multiple anti-collision element layers is dynamically updated based on the collected real-time operation data; Impact risk analysis is performed based on the updated impact mitigation element layer, and impact mitigation measure decision instructions are generated. Implement on-site anti-impact measures according to the anti-impact measure decision instructions.

[0008] Real-time collection of mine environment feedback data after the implementation of the measures, dynamic comparison of the mine environment feedback data with the expected targets of the anti-erosion measure decision instructions, and adaptive adjustment of the integration strategy of the anti-erosion element layer based on the comparison results.

[0009] Preferably, the import of the mine excavation plan as the base map specifically includes: The latest version of the mining plan is obtained from the mine geographic information system. The coordinates of the mining plan are uniformly calibrated and the scale is adjusted to ensure that the base map is consistent with the actual spatial layout of the mine.

[0010] Preferably, integrating multiple anti-collision element layers on the base map specifically includes: On the base map, draw the anti-impact drilling design area layer, the pressure relief drilling layer, the micro-vibration arrangement layer, and the probe arrangement layer respectively; The anti-impact drilling design area layer is divided into different level areas according to the stress assessment results and marked with different colors; The pressure relief borehole layer is labeled with the borehole number, depth, construction date, and completion status of each borehole; The microseismic array layer is labeled with the sensor number, monitoring range, and real-time operating status of each sensor; The probe layout layer labels the type, installation location, and data transmission status of each probe.

[0011] Preferably, the collection of real-time mine operation data specifically includes: The mine monitoring system automatically collects microseismic event data, mining progress data, and borehole construction log data. The real-time operational data is collected at a predetermined frequency and stored in a central database for dynamically updating the information in the anti-collision element layer.

[0012] Preferably, dynamically updating the information in the multiple anti-collision element layers based on the collected real-time operational data specifically includes: The newly collected real-time operation data is compared with the existing data in the anti-collision element layer to identify the changed parts; For the aforementioned pressure relief borehole layer, update the borehole location and status information based on the borehole construction log data; For the aforementioned microseismic arrangement layer, the sensor monitoring range and status label are adjusted based on the microseismic event data; For the probe arrangement layer, the probe position and transmission status are updated based on the probe monitoring data; The updated information is displayed using a visual differentiation method in the anti-collision element layer.

[0013] Preferably, the impact risk analysis based on the updated impact protection element layer specifically includes: By comprehensively analyzing the information in the anti-impact drilling design area layer, pressure relief drilling layer, micro-seismic layout layer, and probe layout layer, stress concentration areas and monitoring blind spots are identified. Based on historical shock event data and real-time monitoring data, calculate the risk index and determine the risk level; The risk analysis results are compared with preset thresholds to generate risk warning signals.

[0014] Preferably, the comprehensive analysis of information from the anti-blowout borehole design area layer, the pressure relief borehole layer, the microseismic layout layer, and the probe layout layer to identify stress concentration areas and monitoring blind spots includes: The anti-impact drilling design area layer is divided into grids, and the stress level weighting value in each grid cell is calculated. Overlay stress relief borehole layers to analyze the matching relationship between borehole coverage and stress level, and identify areas with insufficient coverage as potential stress concentration areas; By integrating the microseismic layout layer and the probe layout layer, a spatial coverage algorithm is used to calculate the monitoring blind zone, which is defined as the area that is more than a threshold away from the nearest monitoring point. Stress concentration areas and monitoring blind spots are mapped onto the base map, and high-risk locations and blind spot boundaries are marked.

[0015] Preferably, the calculation of the monitoring blind zone using the spatial coverage algorithm includes: Read the geographic coordinates of each sensor in the microseismic layout layer and the geographic coordinates of each probe in the probe layout layer to form a set of monitoring points; The mine area is divided into a uniform grid, and the size of each grid cell is adaptively determined based on the density of monitoring points. For the center point of each grid cell, calculate the Euclidean distance to all points in the monitoring point set, and select the minimum distance as the nearest monitoring distance for that grid cell; The nearest monitoring distance is compared with a dynamic threshold. If the nearest monitoring distance exceeds the dynamic threshold, the grid cell is marked as a monitoring blind zone. Adjacent marked grid cells are aggregated to form a continuous blind zone region, and the coordinates of the blind zone boundary are output.

[0016] Preferably, dividing the mine area into a uniform grid includes: Obtain the geographic boundary coordinates of the mining area, and determine the grid division range based on the boundary coordinates; The average point distance is calculated based on the spatial distribution density of the monitoring point set. A uniform grid array is generated by using half the average point spacing as the side length of the grid cell. Perform boundary trimming on the grid array to remove grid cells outside the mining area.

[0017] Preferably, the step of performing impact risk analysis and generating impact mitigation measure decision instructions based on the updated impact mitigation element layer includes: Multi-source spatial attributes were extracted from the updated anti-shocking element layer, including the density distribution of pressure relief boreholes, the energy release value of microseismic events, and the stress value monitored by the probe. A risk assessment matrix is ​​constructed by normalizing and weighting the multi-source spatial attributes to output a comprehensive risk score. Risk levels are determined based on comprehensive risk scores, and a predefined pool of mitigation measures is matched accordingly. The anti-scouring measures library stores decision instructions corresponding to different risk levels, including drilling parameter adjustment, monitoring point addition and deletion, and mining speed control; When generating anti-collision measure decision instructions, an execution priority and timestamp are attached, and the instructions are distributed to field devices via a message queue.

[0018] Preferably, the step of dynamically comparing the mine environment feedback data with the expected targets of the anti-erosion measure decision instructions includes: Extract expected target values ​​from the anti-impact measure decision instructions, including pressure stability threshold, vibration control threshold, and displacement allowance threshold; Calculate the differences between pressure change values ​​and pressure stability thresholds, vibration amplitude values ​​and vibration control thresholds, and displacement values ​​and allowable displacement thresholds in the environmental feedback dataset. A deviation index is generated based on the difference, and the deviation index is compared with a preset tolerance in real time. The integration strategy for adaptively adjusting the anti-collision feature layer based on the comparison results includes: When the deviation index exceeds the preset tolerance, the integration strategy adjustment process is initiated. Based on the magnitude and trend of the deviation index, dynamically modify the layer display parameters, data update cycle, or layer overlay order of the anti-collision element layer; Reassess stress concentration areas and monitoring blind spots, and adjust the integration method of the anti-impact borehole design area layer, pressure relief borehole layer, microseismic layout layer, and probe layout layer.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This transforms mine rockfall prevention management from a static, manually drawn map-based model to a real-time, data-driven, visual perception model. Managers can intuitively grasp the spatiotemporal evolution of stress and vibration fields around the mining face, and any abnormal areas caused by mining continuity or geological changes can be instantly identified on the map. It eliminates management blind spots caused by delayed information updates, ensuring that the identification and location of risk sources are based on continuous, objective field data, thus improving the timeliness and accuracy of risk perception.

[0020] Based on a layer of real-time updated data, the system assesses the current mine safety situation. Through a built-in risk assessment model, it fuses and intelligently analyzes multi-source information, automatically classifying impact hazard levels and generating specific prevention and control decision-making instructions. This transforms risk assessment and decision-making from a qualitative process relying on personal experience into a systematic, automated analysis process. The system can quickly identify complex risk coupling patterns that are difficult for the human eye to perceive, outputting clear instructions such as adjusting borehole parameters, adding or deleting monitoring points, and controlling mining speed. This overcomes the physical limitations of human decision-making response speed, achieving a rapid closed loop from risk perception to measure generation, and shortening the time lag between hazard identification and engineering response. The decision-making process overcomes the differences in subjective human factors and the physical limitations of response speed, shortening the time lag between hazard identification and engineering response, making the formulation and implementation of impact prevention measures more forward-looking and scientific.

[0021] By collecting real-time feedback data on the mine environment after the implementation of anti-rockfall measures, and dynamically comparing it with the expected targets of the anti-rockfall measure decision-making instructions, the integrated strategy of the anti-rockfall element layers is adaptively adjusted based on deviation indicators. This includes modifying layer display parameters, data update cycles, or layer overlay order, and reassessing stress concentration areas and monitoring blind spots, forming a complete closed loop of "data acquisition - layer update - risk analysis - instruction execution - feedback comparison - strategy adjustment." This mechanism effectively solves the shortcomings of traditional management, which lacks closed-loop optimization and relies solely on manual experience to adjust prevention and control strategies. It makes the formulation and implementation of anti-rockfall measures more forward-looking and scientific, continuously adapting to the dynamically changing safety status of the mine. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the working principle of the single-map management method for mines prone to rock bursts as described in this invention. Figure 2 A flowchart for impact risk analysis based on the updated impact protection element layer; Figure 3 A flowchart for implementing on-site anti-impact measures. Detailed Implementation

[0023] 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.

[0024] Please see Figure 1 This invention provides a single-map management method suitable for mines prone to rock bursts, the specific implementation of which is as follows: Using the mine excavation plan as the base map, multiple rockburst prevention element layers are integrated. The layer information is dynamically updated by collecting real-time mine operation data. Based on the updated layers, rockburst risk analysis is performed and rockburst prevention measure decision instructions are generated to guide the implementation of rockburst prevention measures on site. At the same time, environmental feedback data after the implementation of measures is collected and dynamically compared with the expected targets, and the layer integration strategy is adaptively adjusted to achieve real-time, dynamic, and visual monitoring and intelligent decision-making of rockburst risk.

[0025] Example 1: A single-map management method applicable to mines prone to rock bursts involves importing a mine excavation plan as the base map. The latest version of the excavation plan is obtained from the mine's geographic information system. The plan undergoes coordinate unification calibration and scale adjustment to ensure consistency between the base map and the actual spatial layout of the mine. Coordinate unification calibration uses the mine's unified coordinate system standard. A geographic registration tool aligns the origin of the excavation plan's coordinates with the actual measurement control points of the mine, eliminating paper distortion and offset errors. The geographic registration tool is implemented using digital map software, such as ArcGIS or a similar platform, to import control point coordinate data for registration. Scale adjustment involves scaling and stretching the base map according to the actual dimensions of the mine's excavation work, matching the paper's scale with the real space. Scaling is achieved by adjusting pixel density or vector scale factor, and stretching uses geometric transformation algorithms to correct distortion, ensuring the accuracy of subsequent layer overlays. In some embodiments, the coordinate unification calibration process also includes verifying coordinate accuracy, comparing total station or GPS measurement data with the base map, re-registering when the deviation exceeds a threshold, adjusting the scale and combining iterative optimization with actual mine roadway length and angle data until the matching error is less than a preset value. It is understandable that the preparation of the base map is fundamental to subsequent layer integration, and the consistency of spatial reference must be ensured.

[0026] When integrating multiple anti-scour element layers onto the base map, separate layers are drawn for the anti-scour borehole design area, pressure relief borehole, microseismic layout, and probe layout. The anti-scour borehole design area layer is divided into different levels of regions based on stress assessment results and marked with different colors. The stress assessment, based on geological exploration data and historical impact event analysis, classifies regions into high-risk, medium-risk, and low-risk levels, filled with red, yellow, and green respectively. Geological exploration data includes rock stress test results and geological structure distribution. Historical impact event analysis calls on event records in the database to calculate frequency and intensity. The classification uses a weighted scoring method, combining stress values ​​and event probabilities to determine thresholds. The pressure relief borehole layer labels each borehole with its number, depth, construction date, and completion status. The number uses a unique identifier such as a serial number or QR code. The depth records the actual meters of the borehole and stores it as a numerical field. The construction date indicates the excavation timestamp. The completion status is indicated by icons, such as a checkmark icon. The microseismic layout layer labels each sensor's number, monitoring range, and real-time operating status. Sensor numbers correspond to physical devices and are linked to a device database. Monitoring ranges are displayed as circular or polygonal areas, with the coverage radius calculated based on the sensor sensitivity model. Real-time operating status is distinguished by color, indicating normal, faulty, or offline status; for example, green indicates normal transmission. The probe layout layer labels each probe's type, installation location, and data transmission status. Probe types include stress probes and displacement probes. Installation locations are accurate to roadway coordinates and encoded using geographic coordinates. Data transmission status indicates whether data is being uploaded normally, based on heartbeat packets or data stream detection. Optionally, layer integration is achieved through geographic information system software, overlaying various feature layers onto a base map to form an integrated visual interface. The overlay process uses layer management functions to control the display order and transparency, facilitating comprehensive management of the anti-scour layout. In some embodiments, the color labeling of the anti-scour borehole design area layer supports dynamic adjustment, automatically re-dividing the area when stress assessment data is updated. The completion status icon of the stress relief borehole layer supports interactive display of detailed information. The monitoring range of the microseismic layout layer supports dynamic redrawing based on sensor data, and the data transmission status of the probe layout layer is refreshed in real time. It is understandable that layer integration provides an intuitive view of spatial data and supports collaborative analysis of multiple elements.

[0027] In practical implementation, the step of importing the mine excavation plan as the base map further includes data preprocessing, checking data integrity when exporting the excavation plan from the mine geographic information system, supplementing missing parts by manual entry or scanning, optimizing control point matching using the least squares method for coordinate unification and calibration, and applying affine transformation to correct nonlinear deformations during scale adjustment. The anti-scour element layer is drawn using vector graphics tools to create polygon and point features. High-risk areas of the anti-scour borehole design area layer are filled with dark red and textured for differentiation, medium-risk areas are displayed with a yellow gradient, and low-risk areas are transparently covered with light green. Stress assessment results are periodically imported from the monitoring system to update the division. The numbering of the stress relief borehole layer uses a rule-based coding system containing roadway information and serial numbers. Depth data is correlated with borehole logs to verify accuracy. Construction dates are automatically synchronized with the production system time, and completion status is marked according to the construction report. For the microseismic deployment layer, sensor numbers are bound to hardware MAC addresses to ensure uniqueness. Monitoring range calculation is based on sensor deployment density and signal attenuation models, and real-time operating status is obtained through the equipment health monitoring API. The probe deployment layer stores specifications and parameters categorized by probe type. Installation locations are entered using coordinate measurements taken with a total station. Data transmission status is monitored for latency and packet loss using network diagnostic tools. Layer attributes such as visibility and filtering conditions are set during layer integration, supporting queries and displays by time range or attribute. Optionally, the coordinate system for the base map and anti-scour element layers can be either WGS84 or the local mining coordinate system, with compatibility ensured through middleware conversion. In some embodiments, the stress assessment result classification levels support custom threshold adjustments, the completion status icon for the stress relief borehole layer supports drag-and-drop updates, the monitoring range visualization of the microseismic deployment layer supports 3D rendering, and the probe deployment layer includes an alarm trigger notification mechanism for data transmission status. It is understood that this detailed implementation ensures the accuracy and real-time performance of single-map management.

[0028] In practical implementation, coordinate unification calibration involves multi-step verification. After initial calibration, control point residual analysis is used to assess accuracy. If the residual is too large, control points are reselected or the transformation model is adjusted. Scale adjustment includes iterative correction based on field measurement data, such as using tunnel cross-section dimensions as a benchmark to adjust the base map scale. The drawing of the anti-scour element layer is integrated with automated scripts. The stress assessment results of the anti-scour borehole design area layer are optimized and divided using machine learning algorithms. Geological parameters and historical data are input to train the model, and risk level probabilities are output. Color annotations support transparency adjustment to avoid obscuring the base map. The labeling information of the stress relief borehole layer enables batch import, directly loading borehole data from Excel or databases. Depth units are unified in meters and decimal precision is supported. Construction date format is standardized to ISO8601, and completion status icons are designed with animated effects to indicate changes. The sensor number management of the microseismic layout layer includes maintenance log links. Monitoring range calculation considers the impact of obstacles and uses ray tracing. Real-time working status integrates predictive models to provide early warnings of faults. The probe type database of the probe layout layer is expanded to support new device registration. Installation location coordinates use differential GPS to improve accuracy, and data transmission status monitoring includes bandwidth usage statistics. The integrated layer interface provides a toolbar for layer sorting and exporting, and supports simultaneous display of multiple views. Understandably, these implementation details enhance system reliability and user experience.

[0029] In practical implementation, the import process of the base map optimizes network transmission efficiency; large-scale mining plan maps adopt block loading technology to reduce latency; unified coordinate calibration supports batch processing of multiple drawings; and scale adjustment integrates image recognition algorithms to automatically detect marker points. The drawing of the anti-scour element layer enables interactive editing; the division results of the anti-scour borehole design area layer support manual overlay adjustment; the color labeling scheme provides colorblind-friendly options; borehole data verification for the pressure relief borehole layer includes logical checks such as depth rationality; construction date conflict detection prevents duplicate entry; and completion status is synchronized with mobile devices for updates. The sensor deployment optimization of the microseismic layout layer uses coverage analysis tools; the monitoring range is dynamically adjusted based on event density; and real-time working status historical records support trend analysis. Probe calibration data for the probe layout layer is integrated into the annotation; installation position deviation correction uses filtering algorithms; and data transmission status reports generate statistical charts. The layer integration platform supports multi-user collaborative editing, and access control ensures data security. It can be understood that the comprehensive implementation method covers the entire operation process, improving management efficiency.

[0030] Example 2: This example details the process of collecting real-time mine operation data. A mine monitoring system automatically collects microseismic event data, mining progress data, and borehole construction log data. The mine monitoring system consists of sensor units deployed underground, data acquisition stations, and network transmission equipment. Microseismic event data is captured in real-time by a microseismic sensor network. The sensor units use triaxial accelerometers or ground acoustic probes, deployed in the roof and floor of the roadway and in the coal and rock mass. The data acquisition station receives the raw signals and performs preliminary filtering and analog-to-digital conversion. The network transmission equipment sends data packets to a ground server via industrial Ethernet or a fiber optic ring network. Mining progress data is obtained from a mining equipment monitoring system installed on the coal mining machine, tunneling machine, or hydraulic support, integrating displacement sensors, speed sensors, and tilt sensors to record the working face advance distance, mining volume, and timestamps in real time. Borehole construction log data comes from a borehole operation record system, which includes a handheld terminal or a fixed data entry station. Operators input borehole number, location, depth, construction personnel, and completion status information. Real-time operational data is collected at a predetermined frequency and stored in a central database for subsequent processing. Microseismic event data is collected once per second to ensure timely capture of dynamic changes. Mining progress data is collected once per minute based on the production rhythm. Drilling operation log data is automatically uploaded after each shift. The central database uses a relational database structure, such as MySQL or PostgreSQL, and establishes data tables to store various types of real-time data. The data table design includes timestamp fields, equipment identifier fields, and data value fields, and B-tree indexes are used to optimize query efficiency. It can be understood that high-frequency data collection and structured storage provide a reliable data foundation for subsequent layer updates.

[0031] In practice, information in multiple anti-rockfall element layers is dynamically updated based on the collected real-time operational data. The newly collected real-time operational data is compared with existing data in the anti-rockfall element layers to identify changes. The comparison process employs difference detection algorithms, such as recording and comparing timestamps and data values ​​one by one, or using hash values ​​to verify data integrity. Furthermore, real-time mine environmental feedback data after the implementation of on-site anti-rockfall measures must be collected. The collected feedback data types correspond to the expected goals of the anti-rockfall measure decision-making instructions, specifically including coal and rock mass pressure data, microseismic activity data, and roadway displacement-related data in the implementation area. Coal and rock mass pressure data is obtained through stress probes, microseismic activity data is collected using a microseismic sensor network, and roadway displacement-related data is recorded by displacement monitoring equipment. For some construction quality-related feedback information that cannot be automatically collected by equipment, on-site personnel can input it using dedicated recording equipment. All collected mine environmental feedback data must be associated with the corresponding location information of the implementation area and synchronously stored in the central database to provide data support for subsequent dynamic comparison. For the pressure relief borehole layer, the borehole location and status information are updated based on the borehole construction log data. Newly constructed boreholes are marked on the map. The marking operation is achieved through the feature addition tool of GIS software. The coordinate point data of the borehole is input to generate vector point features. The status icon of the completed borehole changes from "in progress" to "completed". The status change triggers the attribute table update. The location information is accurate through coordinate matching. The coordinate matching adopts a spatial query method, which performs buffer analysis between the coordinates in the log data and the point features in the layer, with the tolerance set to within one meter. For the microseismic deployment layer, the sensor monitoring range and status label are adjusted according to the microseismic event data. When the sensor detects abnormal vibration, the monitoring range layer highlights the affected area. The highlighting effect is achieved using color gradient or flashing animation. The affected area is calculated as a polygon range based on the vibration energy attenuation model. The sensor status is automatically updated to a fault indicator based on the data transmission interruption or abnormality. The status judgment is based on the number of data packet reception timeouts or verification errors. For the probe deployment layer, the probe position and transmission status are updated based on probe monitoring data. After a probe is moved or reinstalled, its position coordinates are synchronously corrected. This correction process utilizes the probe's built-in positioning module or manual coordinate measurement. The transmission status is determined based on the data packet loss rate, calculated by comparing the number of received data packets per unit time with the expected number. Updated information is displayed in the anti-surge element layer using visual differentiation methods, such as flashing effects to indicate new changes and color gradients to indicate status transitions. The sensitivity and duration of these visual differentiation parameters can be adjusted in the interface settings. This dynamic update mechanism ensures the synchronization of layer information with the actual downhole conditions.

[0032] In some embodiments, the data comparison process is further optimized. For microseismic event data, streaming processing technology is used to calculate the deviation between event feature values ​​and historical patterns in real time. If the deviation exceeds a threshold, it is marked as a changed part. For mining progress data, time series analysis methods are used to detect abrupt changes in the advance distance, and the areas corresponding to the abrupt changes are highlighted in the layer. The update of the pressure relief borehole layer integrates verification logic. The new borehole location overlaps with existing boreholes to avoid duplicate marking. Changes in status information trigger workflow notifications to relevant responsible persons for review. The monitoring range adjustment of the microseismic layout layer introduces machine learning algorithms. A prediction model is trained based on the distribution of historical events to dynamically optimize the display shape of the sensor coverage area. Sensor status markings add health scores and comprehensively calculate runtime, data quality, and environmental factors. The transmission status monitoring of the probe layout layer enhances network diagnostic functions, draws a data transmission path topology map in real time, locates fault nodes, and supports differential positioning technology to improve accuracy in position coordinate correction. The visual differentiation method is expanded to multi-dimensional prompts, such as using symbol size to indicate the level of data importance and using transparency to indicate data freshness. It can be understood that these optimization measures improve the intelligence and reliability of layer updates.

[0033] In its implementation, the central database design includes a data cleaning module. Before raw data is entered into the database, noise reduction and formatting are performed. Microseismic event data is cleaned of electrical interference and mechanical vibration noise. Mining progress data is used to calibrate sensor zero drift. Drilling operation log data is verified for the integrity of required fields. The data acquisition frequency is dynamically adjusted based on network bandwidth and storage capacity. When the system load is high, the acquisition frequency of non-critical data is automatically reduced; for example, the acquisition frequency of microseismic event data is adjusted from once per second to once every five seconds to ensure the real-time performance of core data. The dynamic update process employs a transaction processing mechanism to ensure the atomicity of data comparison and layer modification, avoiding data inconsistencies during the update process. Spatial indexes are used to accelerate queries for updating the location of the pressure relief borehole layer; for example, R-tree indexes are used to quickly locate nearby boreholes. Status information change records are logged for future reference. The monitoring range calculation for the microseismic deployment layer integrates a 3D geological model, accurately drawing the affected area considering the propagation characteristics of rock strata. Sensor status annotations are associated with the maintenance work order system, and fault identification automatically generates maintenance tasks. The transmission status judgment of the probe deployment layer uses adaptive thresholds, dynamically adjusting the packet loss rate criteria based on network conditions. Location coordinate correction supports batch import and manual verification. The rendering of the visual differentiation method utilizes GPU acceleration to ensure smoothness when displaying a large number of elements simultaneously. Understandably, this detailed implementation guarantees the stable operation of the system in complex mining environments.

[0034] In some embodiments, the acquisition of real-time operational data adds redundant backup channels, automatically switching to a wireless mesh network or carrier communication when the main network is interrupted. Data storage adopts a distributed architecture, with backups across multiple nodes to prevent single points of failure. The dynamic update process supports offline mode, and downhole mobile terminals can cache changed data, which is then synchronized to the central database after network recovery. The update interface for the stress-relief borehole layer is an open API, allowing third-party operational systems to push data. The status labeling of the microseismic layout layer integrates predictive maintenance functions, providing early warnings of potential faults based on sensor lifetime models. The transmission status monitoring of the probe layout layer is extended to end-to-end performance analysis, recording the entire latency distribution from the probe to the server. Visual differentiation methods add accessibility design options, such as using patterns and textures to assist color differentiation. It is understood that the enhanced implementation improves the robustness and accessibility of the system.

[0035] Optionally, the data acquisition frequency can be customized according to the mine's production intensity. During high-intensity mining, the acquisition frequency of mining progress data is increased to once every thirty seconds. A burst mode is added to microseismic event data acquisition, temporarily increasing to ten times per second when a predictor event is detected. A version control mechanism is introduced into the dynamic update process. Layer changes save historical versions for backtracking analysis. Changes to the status of the stress relief borehole layer require secondary confirmation to prevent accidental operations. Adjustments to the monitoring range of the microseismic deployment layer allow manual intervention to correct algorithm results. Data compensation is added to the transmission status monitoring of the probe deployment layer, and interpolation algorithms are used to fill missing values ​​during short-term interruptions. Visual differentiation effects support user customization, such as customizing the flashing frequency and color scheme. These optional features enhance the system's flexibility to adapt to the needs of different mines.

[0036] Example 3: See Figure 2This embodiment details the process of impact risk analysis based on the updated anti-scour element layer. When comprehensively analyzing information from the anti-scour borehole design area layer, the pressure relief borehole layer, the microseismic layout layer, and the probe layout layer to identify stress concentration areas and monitoring blind spots, the anti-scour borehole design area layer is first divided into grids. The grid division accuracy needs to be determined based on the actual spatial range of the mine's mining area. The side length of the resulting grid cells is set according to the anti-scour management requirements to ensure that each grid cell accurately corresponds to the specific underground working area. When calculating the stress level weighted value within each grid cell, the stress assessment basic data stored in the anti-scour borehole design area layer is called, including the rock strata physical parameters at the location of the grid cell, historical stress monitoring records, and the correlation coefficients of surrounding impact events. These data are integrated into a single stress level weighted value using a weighting algorithm. This weighted value directly reflects the degree of stress concentration in the coal and rock mass within the grid cell. When overlaying the pressure relief borehole layer, it is necessary to ensure that the coordinate system of the anti-impact borehole design area layer and the pressure relief borehole layer are completely consistent to avoid distortion of analysis results due to coordinate deviation. After overlaying, the number of pressure relief boreholes, borehole depth, and borehole distribution density in each grid cell are counted using a spatial correlation algorithm. These data are matched and analyzed with the stress level weighting value of the grid cell. If the pressure relief borehole coverage density of a certain grid cell is lower than the minimum coverage standard corresponding to the stress level weighting value, or the borehole depth does not reach the effective depth required for stress relief in that area, then the area where the grid cell is located is marked as a potential stress concentration area.

[0037] When integrating the microseismic deployment layer and the probe deployment layer, it is necessary to extract the geographic coordinate information of all monitoring devices in both layers, establish a unified monitoring point set including microseismic sensors and various probes, and use a spatial coverage algorithm to calculate the coverage range of this monitoring point set. The criterion for determining the monitoring blind zone is that the straight-line distance from any location in the area to the nearest monitoring point exceeds a preset threshold. This threshold needs to be comprehensively set based on the effective monitoring radius of the monitoring equipment, the data transmission attenuation characteristics, and the accuracy requirements of mine safety monitoring. Finally, the identified stress concentration areas and monitoring blind zones are superimposed onto the base map using a spatial mapping algorithm. On the base map, high-risk locations and blind zone boundaries are marked with boundary lines and fill colors of different colors, respectively. The marking color of high-risk locations needs to correspond to the stress level weighting value, while the blind zone boundaries are marked with a conspicuous dashed line style to ensure that managers can intuitively distinguish different types of risk areas.

[0038] When calculating the stress level weighted value within each grid cell, the weighting algorithm must include weights for rock strata physical parameters, historical stress records, and the impact of impact events. Each weight coefficient must be calibrated based on the mine's past experience in preventing rockbursts and actual field monitoring data. The calibration process requires multiple rounds of data verification to ensure the weight allocation conforms to the mine's geological conditions. When analyzing the matching relationship between borehole coverage and stress level by overlaying a stress relief borehole layer, a coverage assessment model needs to be established. This model converts parameters such as the number of boreholes, borehole depth, and borehole spacing into a unified coverage quantification index. This index is then compared with the stress level weighted value. If the coverage quantification index is lower than the critical value corresponding to the stress level weighted value, it is directly identified as a potential stress concentration area. When integrating the microseismic layout layer and the probe layout layer to establish a monitoring point set, the coordinate data of the monitoring equipment needs to be verified a second time. By comparing it with the actual measurement control points underground in the mine, coordinate offsets caused by equipment installation deviations or data entry errors are corrected to ensure the accuracy of the location information of the monitoring point set. When using the spatial coverage algorithm to calculate monitoring blind spots, the mine area needs to be divided into continuous spatial grids. The distance to the nearest monitoring point is calculated by traversing the center point of each spatial grid. If the distance exceeds a preset threshold, it is marked as a blind spot grid. Finally, adjacent blind spot grids are aggregated to form a continuous monitoring blind spot area. The aggregation process requires the use of a spatial topology analysis algorithm to avoid the occurrence of scattered blind spot grids that lead to unclear blind spot boundaries.

[0039] The coverage assessment model is established with the actual needs of mine rockburst prevention and control as its core. Combining the mechanism of stress relief boreholes in releasing stress in coal and rock masses, the core input parameters of the model are first identified as the number of boreholes, borehole depth, and borehole spacing. Then, parameter processing rules are determined based on the degree of influence of each parameter on the stress relief coverage effect. For the number of boreholes, a grid cell is used as the statistical unit to count the total number of boreholes actually completed and reaching the effective stress relief depth within that cell, serving as the basic quantitative data. For borehole depth, the minimum effective depth required for stress relief in that area is used as the benchmark. The actual depth of each borehole is compared with the benchmark depth to obtain the effective depth coefficient of a single borehole. For borehole spacing, based on the correlation between stress transmission and the stress relief influence range, the overlap threshold of the stress relief influence range of adjacent boreholes is used as the standard to calculate the ratio of the actual borehole spacing to the standard spacing, obtaining the spacing rationality coefficient. During the parameter transformation process, the number of boreholes, the effective depth coefficient, and the reasonable spacing coefficient are first standardized to eliminate the dimensional differences between different parameters. Then, based on the weight of each parameter's influence on the coverage effect (determined based on historical mine stress relief engineering data and stress monitoring results), a weighted summation method is used to integrate the three into a unified coverage quantification index. The value range of this index is consistent with the value range of the stress level weighted value to facilitate direct comparison. When the coverage quantification index is lower than the critical value corresponding to the stress level weighted value, the area is determined to be a potential stress concentration area.

[0040] In practical implementation, when mapping stress concentration areas and monitoring blind spots onto the base map, layer overlay technology must be used to ensure that the mapped area is accurately aligned with the geographical features of the base map, such as tunnels and working faces. After mapping, a thematic layer containing stress concentration areas and monitoring blind spots must be generated. This thematic layer can be displayed or hidden independently of other layers, allowing managers to select and view content according to actual needs. When marking high-risk locations and blind spot boundaries, corresponding risk level fields and blind spot identification fields must be added to the attribute database of the base map. The marking information is then associated with the geographical feature attributes of the base map, enabling users to view detailed stress data, monitoring equipment distribution, and historical impact event records for the marked area simply by clicking on it, thus improving the interactivity of the single-map management and data query efficiency.

[0041] When using the spatial coverage algorithm to calculate the monitoring blind zone, the geographic coordinates of each microseismic sensor in the microseismic layout layer are first read through the data interface. These geographic coordinates must include latitude, longitude, and relative underground elevation information. Simultaneously, the geographic coordinates of each probe in the probe layout layer are read to ensure that the coordinate data formats of both types of equipment are consistent. This coordinate data is then imported into a spatial database to form a monitoring point set. When dividing the mine area into a uniform grid, the size of the grid cell needs to be determined based on the spatial distribution density of the monitoring point set. If the monitoring points are densely distributed in a certain area, the side length of the grid cell can be appropriately reduced; if the monitoring points are sparsely distributed, the side length of the grid cell can be appropriately increased. This ensures that the grid division meets the accuracy requirements for calculating the monitoring blind zone while avoiding a decrease in computational efficiency due to an excessive number of grid cells. For the center point of each grid cell, the straight-line distance from the center point to all monitoring points in the monitoring point set is calculated using the Euclidean distance formula. The formula is as follows:

[0042] in: This represents the straight-line distance from the center point of the grid cell to the monitoring point. , , These represent the three-dimensional coordinates of the center point of each grid cell. , , These represent the three-dimensional coordinates of the monitoring point.

[0043] After calculation, the minimum distance is selected from all distance values ​​and taken as the nearest monitoring distance for that grid cell. When comparing the nearest monitoring distance with a dynamic threshold, the dynamic threshold needs to be set differently depending on the type of monitoring equipment. The dynamic threshold for microseismic sensors should refer to their monitoring frequency and signal capture sensitivity, while the dynamic threshold for probes should refer to their measurement range and data accuracy. If the nearest monitoring distance of a grid cell exceeds the dynamic threshold for the corresponding type of equipment, the grid cell is marked as a monitoring blind zone. When aggregating adjacent marked grid cells to form a continuous blind zone area, a region growing algorithm is used. Using any marked grid cell as a seed point, adjacent marked grid cells are gradually merged. During the merging process, the spatial connectivity of adjacent grid cells needs to be determined to ensure that the boundary of the formed blind zone area is continuous and without breaks. Finally, the boundary coordinates of the blind zone area are output using a coordinate extraction algorithm. The boundary coordinates need to be stored in the form of closed polygons for easy subsequent annotation and display on the base map.

[0044] When dividing the mine area into a uniform grid, an adaptive grid partitioning algorithm can be introduced. This algorithm can automatically adjust the grid cell side length according to the spatial distribution density of the monitoring point set. Smaller side-length grid cells are generated in densely populated areas to improve calculation accuracy, while larger side-length grid cells are generated in sparsely populated areas to improve calculation efficiency. The implementation of the adaptive grid partitioning algorithm needs to be based on a statistical model of monitoring point density. This model determines the density level by calculating the number of monitoring points per unit area, and then assigns different grid cell side lengths according to the density level. When calculating the Euclidean distance from the center point of the grid cell to the monitoring point, parallel computing technology can be used. Multiple threads can be used to calculate the distance values ​​of multiple grid cells simultaneously, shortening the total calculation time. During parallel computing, the computational tasks need to be reasonably allocated to avoid errors in the calculation results due to data competition between threads. After marking a grid cell as a monitoring blind zone, a blind zone type identifier can be added to the spatial database to distinguish whether it is a microseismic monitoring blind zone, a probe monitoring blind zone, or a blind zone combining both, facilitating the subsequent development of targeted blind zone re-monitoring plans. When aggregating adjacent marked grid cells, an eight-neighbor search method can be used. Taking each marked grid cell as the center, search for adjacent grid cells in eight directions around it. If adjacent grid cells are also marked grid cells, they are merged. This method can quickly identify and aggregate all continuous blind zone grid cells, ensuring the integrity of the blind zone area.

[0045] The statistical model for monitoring point density is established based on the spatial extent of the mine monitoring area. First, the mine's mining area is divided into several continuous statistical sub-regions according to geographical boundaries. This ensures that the spatial scale of each sub-region is appropriate, facilitating accurate counting of monitoring points while reflecting the true density of monitoring point distribution within the region. In the density calculation stage, the total number of monitoring points (including microseismic sensors and various probes) within each sub-region is counted. Then, combined with the actual area of ​​the sub-region, the number of monitoring points per unit area is calculated, serving as the core data for density statistics. The density levels are divided based on the accuracy requirements of mine safety monitoring and the effectiveness analysis results of historical monitoring data. The number of monitoring points per unit area is divided into several intervals, each interval corresponding to a density level. The density levels, from low to high, correspond to a sparse to dense distribution of monitoring points. The correspondence between grid cell side length and density level is determined based on a balance between the accuracy requirements of monitoring data acquisition and computational efficiency. The higher the density level (the denser the monitoring points), the smaller the grid cell side length, to ensure accurate capture of spatial differences in monitoring data; the lower the density level (the sparser the monitoring points), the larger the grid cell side length, to avoid wasting computational resources due to overly fine grid division. Furthermore, the grid cell side length corresponding to all density levels is adapted to the effective monitoring range of the monitoring points, ensuring that the grid cells can completely cover the area of ​​effect of the monitoring points.

[0046] In some embodiments, when outputting the blind zone boundary coordinates, statistical information such as the area and perimeter of the blind zone can be generated simultaneously. This statistical information needs to be stored together with the boundary coordinates in the blind zone information data table to facilitate managers' understanding of the size of each blind zone. Simultaneously, the blind zone boundary coordinates can be exported as a CAD format file, supporting further analysis and planning of the blind zone in CAD software. For example, when planning the installation location of new monitoring equipment, the blind zone boundary coordinates can be directly overlaid in the CAD file to intuitively judge the coverage effect of the new equipment on the blind zone. Furthermore, a blind zone query function can be added to the one-map management system. Managers can quickly locate and view the blind zone distribution in a specific area by entering query conditions such as area name and coordinate range. The query results should be presented on the base map in a highlighted manner, while also displaying detailed statistical information and boundary coordinates of the blind zone.

[0047] Optionally, after aggregating adjacent marked grid cells to form a continuous blind zone, the blind zone can be smoothed at its boundaries. A curve fitting algorithm can be used to correct the polygonal outline of the blind zone boundary, making the boundary line more closely match the actual distribution of the blind zone and avoiding the jagged edges caused by grid division. During the boundary smoothing process, the smoothing degree must be controlled to ensure that the deviation between the smoothed boundary coordinates and the original marked grid cells is within an allowable range, without affecting the accuracy of the actual extent of the blind zone. Simultaneously, feature point markers can be added to the boundary line of the blind zone, each corresponding to a specific coordinate value. This facilitates the location of key positions on the blind zone boundary during subsequent on-site surveys, providing accurate coordinate references for the installation of blind zone supplementary surveying equipment.

[0048] Understandably, the above steps enable the systematic and accurate identification of stress concentration areas and monitoring blind spots in mines, providing a clear spatial basis for the subsequent development of targeted rockburst prevention measures. The various algorithms and data processing methods employed in the identification process effectively improve the accuracy and reliability of the identification results, adapting to the geological conditions and monitoring needs of different mines. Furthermore, visually annotating the identification results on the base map allows managers to more intuitively grasp the distribution of rockburst risks in the mine, improving the efficiency and targeting of risk management.

[0049] It is understandable that in the process of using spatial coverage algorithms to calculate monitoring blind zones, the data at each stage is strictly verified and processed to avoid blind zone identification errors caused by data errors or algorithm defects. The dynamic threshold setting can fully consider the performance differences of different types of monitoring equipment, ensuring the rationality of blind zone judgment standards. Aggregating adjacent marked grid units to form continuous blind zone areas can avoid the interference of scattered blind zone grids on subsequent risk analysis and measure formulation. The output blind zone boundary coordinates provide accurate spatial parameters for the installation of on-site supplementary monitoring equipment and the adjustment of monitoring range. The whole process forms a complete monitoring blind zone identification process, which can effectively support the refined monitoring and management of mine rockburst.

[0050] Example 4: See Figure 3This embodiment details the process of performing impact risk analysis and generating impact mitigation measure decision instructions based on an updated impact mitigation element layer. Multi-source spatial attributes are extracted from the updated impact mitigation element layer. For the density distribution of pressure relief boreholes, the ratio of the number of pressure relief boreholes to the area of ​​each grid cell needs to be calculated. For the energy release value of microseismic events, the sum of the energy of all microseismic events within the grid cell in the most recent 24 hours needs to be extracted. For the stress value monitored by the probe, the average of the most recent stress data monitored by all probes within the grid cell needs to be taken. When constructing the risk assessment matrix, the density distribution of pressure relief boreholes, the energy release value of microseismic events, and the stress value monitored by the probe are used as row vectors of the matrix, each row vector corresponding to a different risk influencing factor. The column vectors of the matrix represent different grid cell regions, and the elements in the matrix are the quantified values ​​of each risk influencing factor within the corresponding grid cell. After normalizing the multi-source spatial attributes, a weighted fusion is performed. The normalization process uses the min-max standardization method to convert the values ​​of each attribute to the [0,1] interval. During weighted fusion, a corresponding weight coefficient needs to be assigned to each attribute. The weight coefficient is determined by the analytic hierarchy process (AHP) to ensure that the contribution of each attribute to the comprehensive risk score meets the actual needs of mine rockburst prevention and control. After outputting the comprehensive risk score, the risk level is divided according to the preset score interval. The comprehensive risk score in the [0,30] interval is low risk level, in the [31,70] interval is medium risk level, and in the [71,100] interval is high risk level. When matching the predefined rockburst prevention measures library, the corresponding rockburst prevention measures need to be called according to the risk level of the grid cell. The rockburst prevention measures library stores decision instructions corresponding to different risk levels. The decision instructions corresponding to the low risk level include increasing the monitoring frequency and strengthening daily inspections. The decision instructions corresponding to the medium risk level include adjusting the drilling parameters and supplementing the monitoring points. The decision instructions corresponding to the high risk level include suspending mining operations, increasing the density of pressure relief drilling, and adjusting the mining speed. When generating anti-collision measure decision instructions, an execution priority and timestamp are attached. The execution priority is set according to the risk level: high-risk decision instructions have a priority of 1, medium-risk instructions have a priority of 2, and low-risk instructions have a priority of 3. The timestamp is accurate to the second, recording the generation time of the decision instruction. When distributing to field devices via a message queue, the message queue must use a reliable transmission protocol to ensure that the decision instructions can be accurately and timely sent to the corresponding industrial control computers or mobile terminals in the field. After receiving the instructions, the field devices must return confirmation information to ensure closed-loop management of instruction transmission.

[0051] When dividing the mine area into a uniform grid, the geographic boundary coordinates of the mine area are first obtained through the mine's geographic information system. These geographic boundary coordinates must include both the mine's surface projection boundary and the underground mining area boundary; these two types of boundary coordinates together constitute the complete range of the grid division. The average point distance is calculated based on the spatial distribution density of the monitoring point set. This calculation process involves traversing the coordinates of all monitoring points in the set, calculating the straight-line distance between every two adjacent monitoring points, and then taking the arithmetic mean of all distances to obtain the average point distance. Using half the average point distance as the side length of the grid unit, a uniform grid array is generated within the preset mine area using a grid generation algorithm. The generation of the grid array must ensure that the corner coordinates of each grid unit accurately correspond to the mine's geographic boundary coordinates, avoiding situations where grid units exceed the mine area's boundaries. When trimming the grid array boundaries, it is necessary to determine whether the center point of each grid unit is within the mine area. If the center point of a grid unit exceeds the mine area's boundaries, that grid unit is removed from the array. The final remaining grid units form a uniform grid covering the mine area. The number and distribution of the grid units must meet the accuracy requirements for subsequent monitoring blind zone calculations.

[0052] In practical implementation, when obtaining the geographical boundary coordinates of the mining area, the coordinate data needs to be verified for accuracy. By comparing it with the coordinates of the mine's measurement control points, boundary offsets caused by measurement errors are corrected to ensure that the grid division range is consistent with the actual mining area. When calculating the average point distance of the monitoring point set, if there are isolated monitoring points in the set, i.e., the distance between these monitoring points and other monitoring points is much greater than three times the average point distance, these isolated monitoring points need to be marked separately. In subsequent grid division, the side lengths of the surrounding grid cells can be appropriately adjusted to ensure that the grid accuracy of this area meets the monitoring requirements. When generating a uniform grid array, a grid generation algorithm from computer graphics, such as the Delaunay triangulation algorithm, can be used. This algorithm can automatically generate high-quality uniform grids, avoiding the appearance of narrow or distorted grid cells, ensuring the accuracy of subsequent risk analysis. After trimming the boundaries of the grid array, a grid cell list needs to be generated. The list contains information such as the number, corner coordinates, and center point coordinates of each grid cell, facilitating subsequent data management and querying.

[0053] When constructing a risk assessment matrix, fuzzy mathematics theory can be introduced to fuzzify the elements in the matrix. Considering the uncertainty of risk influencing factors, this allows the matrix to more accurately reflect the impact of each factor on shock risk. After normalizing multi-source spatial attributes, a weighted fusion is performed. If the geological conditions of the mine change, such as the emergence of new geological structures or changes in coal seam thickness, the weighting coefficients need to be readjusted to ensure that the weighted fusion result adapts to changes in geological conditions. The score intervals for classifying risk levels can be dynamically adjusted based on past shock event data from the mine. If shock events occur frequently within a certain risk level interval, the score range of that interval needs to be narrowed to improve the sensitivity of risk level classification. The decision-making instructions in the shock prevention measure library need to be updated regularly. Based on feedback from the implementation of on-site measures, the specific parameters of the instructions should be optimized, such as adjusting the diameter and depth of pressure relief boreholes and optimizing the layout of monitoring points, to ensure the practicality and effectiveness of the decision-making instructions.

[0054] In practical implementation, when distributing decision instructions via message queues, a backup mechanism must be set up in the message queue. If the primary message queue fails, it can automatically switch to the backup message queue to avoid interruption of instruction transmission. After receiving the decision instructions, the field equipment needs to parse the instruction content, extract information such as execution steps, parameter requirements, and time limits, and generate a visualized execution plan to facilitate understanding and execution by field operators. Simultaneously, the field equipment must upload the instruction execution progress in real time, such as the completion rate of drilling operations and the installation progress of monitoring points, to ensure that management personnel can monitor the execution status of the instructions in real time.

[0055] In some embodiments, if the field equipment finds that the content of the instruction does not match the actual working conditions on site after receiving the instruction, such as the drilling depth required by the instruction exceeding the coal seam thickness, it can send an objection message to the management terminal through the message queue. After receiving the objection, the management terminal needs to reassess the risk level and decision instructions to ensure the feasibility of the instruction.

[0056] Optionally, a three-dimensional grid division method can be used when dividing the uniform grid. This method considers not only horizontal coordinates but also vertical elevation information, dividing the mine area into three-dimensional grid units. This more accurately reflects the distribution of impact risks at different mining levels and is suitable for multi-level mining mines. The side length of the three-dimensional grid unit is still determined horizontally by half the average distance between monitoring points, while the side length in the vertical direction is determined based on the coal seam thickness and mining level height, generally 2-5 meters.

[0057] Optionally, when generating impact mitigation decision instructions, associated reference data can be attached, such as historical impact event records for the grid cell and data on the effectiveness of measures implemented at similar risk levels in the past. This provides more reference information for on-site personnel to execute the instructions, helping them to better understand and implement the decisions. The reference data is distributed as an attachment along with the decision instructions, and on-site equipment can view the attachment content by clicking on the instructions.

[0058] It is understandable that the aforementioned uniform grid division steps provide a unified spatial unit for subsequent impact risk analysis, ensuring that risk assessments are conducted at the same spatial scale and improving the comparability and accuracy of risk analysis results. Furthermore, the risk analysis and decision command generation process based on multi-source spatial attributes can transform dispersed monitoring data into specific prevention and control measures, achieving automated conversion from data to decision, reducing manual intervention, and improving the efficiency of rockburst prevention and control.

[0059] Understandably, the establishment and dynamic updating of the anti-rockburst measures database ensures that decision-making instructions are always adapted to the actual conditions of the mine, guaranteeing the pertinence and effectiveness of the measures. Reliable message queue transmission ensures that decision-making instructions are delivered to the site in a timely manner, forming a complete closed loop from risk analysis to measure implementation, providing technical support for the precise prevention and control of mine rockbursts.

[0060] Referring to Table 1, based on the correspondence between risk level and decision instructions, the corresponding anti-impact measures are invoked. For example, low risk level corresponds to the decision instruction of "increasing monitoring frequency and strengthening daily inspections", medium risk level corresponds to the decision instruction of "adjusting drilling parameters and supplementing monitoring points" and high risk level corresponds to the decision instruction of "suspending mining operations, increasing the density of pressure relief drilling, and controlling mining speed".

[0061] Table 1: Relationship Table of Decision-Making Instructions for Anti-Impact Measures

[0062] The formula for calculating the overall risk score is as follows:

[0063] in: Represents the overall risk score. The number of risk-influencing factors in this embodiment These represent the density distribution of the pressure relief borehole, the energy release value of the microseismic event, and the stress value monitored by the probe, respectively. Representing the The weighting coefficients of each risk factor. The weighting coefficients for the density distribution of pressure-relief boreholes. The weighting coefficients for the energy release value of microseismic events. The weighting coefficient for the stress value monitored by the probe is given, and ; Representing the The normalized values ​​of each risk factor.

[0064] Example 5: In specific implementation, when dynamically comparing the mine environment feedback data with the expected targets of the anti-rockfall measure decision-making instructions, the expected target value is extracted from the anti-rockfall measure decision-making instructions. The pressure stability threshold needs to be determined based on the physical and mechanical parameters of the coal seam in the mine area. For example, for a coal seam with a hardness coefficient f=3-5, the pressure stability threshold is set to 25. The vibration control threshold should refer to the vibration limit requirements for rockburst mines in the National Coal Mine Safety Regulations, and be set at 5 × 10⁻⁶. 4 The allowable displacement threshold needs to be set to 3 based on the roadway support design parameters. / twenty four When calculating the difference between the pressure change value and the pressure stability threshold in the environmental feedback dataset, the pressure change value should be the average of the pressure monitoring values ​​in the mine environmental feedback data within 24 consecutive hours after the implementation of the measures. If this average value is higher than the pressure stability threshold, the difference is positive; if it is lower than the pressure stability threshold, the difference is negative. When calculating the difference between the vibration amplitude value and the vibration control threshold, the vibration amplitude value should be the maximum energy value of the micro-seismic event monitored after the implementation of the measures, and the difference should be obtained by subtracting it directly from the vibration control threshold. When calculating the difference between the displacement value and the allowable displacement threshold, the displacement value should be the cumulative displacement of the roof and floor or the two sides of the roadway within 24 hours after the implementation of the measures, and the difference should be calculated with respect to the allowable displacement threshold. When generating deviation indicators based on the above differences, the deviation indicators should be calculated by combining the absolute values ​​of the three differences using a weighted summation method. Based on the core needs of mine rockburst prevention and control and the statistical analysis results of historical data, the weight values ​​corresponding to the pressure difference, vibration difference, and displacement difference are obtained. The weighting for pressure difference is set to 0.4, vibration difference to 0.3, and displacement difference to 0.3, ensuring that the deviation indicators comprehensively reflect the degree of deviation between environmental feedback data and expected targets. When comparing the deviation indicators with preset tolerances in real time, the preset tolerances need to be set according to the safety redundancy requirements for mine rockburst prevention, for example, set to 2 MPa (corresponding to pressure deviation) and 1 × 10⁻⁶ MPa. 4 The comprehensive tolerance threshold consists of 0.5 mm (corresponding to vibration deviation) and 0.5 mm (corresponding to displacement deviation). If the deviation index exceeds the comprehensive tolerance threshold, it is determined that the implementation effect of the measures has not met expectations.

[0065] In the evolution of mine rockburst disasters, pressure changes in the coal and rock mass are the most direct indicator reflecting the degree of stress accumulation, and have the most critical impact on the prediction of rockburst risk and the evaluation of the effectiveness of prevention and control measures. Therefore, the pressure difference has the highest weight. Vibration difference reflects the energy release state of the coal and rock mass and is an important early warning signal before rockburst occurs. Its impact on risk assessment is second only to pressure change, so its weight is set second. Displacement difference reflects the deformation of the roadway and coal and rock mass, and is a direct manifestation after rockburst occurs. It is also an important reference for verifying the effectiveness of prevention and control measures. Its impact is relatively lower than the former two, so its weight is set the lowest. The weight allocation was based on a retrospective analysis of historical mine rockburst event data, combined with the actual effects of different parameters in previous prevention and control work, to ensure that the weight values ​​can truly reflect the contribution of each parameter to the deviation index, so that the deviation index can comprehensively and accurately reflect the deviation between environmental feedback data and expected targets.

[0066] When adaptively adjusting the integration strategy of the anti-shocking element layer based on the comparison results, if the deviation index exceeds the preset tolerance, the integration strategy adjustment process is initiated. The process first triggers a re-verification of the anti-shocking element layer data, checking for faults in the data acquisition link, such as whether the sensor is offline or whether there is packet loss in data transmission. Based on the magnitude and trend of the deviation index, the layer display parameters of the anti-shocking element layer are dynamically modified. If the pressure deviation is large, the color display intensity of the corresponding area in the anti-shocking borehole design area layer is enhanced, and the transparency is adjusted from the original 50% to 30% to highlight the stress anomaly area. When dynamically modifying the data update cycle, if the vibration deviation continues to increase, the data update cycle of the microseismic arrangement layer is shortened from the original 10 minutes / time to 5 minutes / time to increase the data update frequency. When dynamically modifying the layer overlay order, if the displacement deviation exceeds the tolerance, the overlay order of the probe arrangement layer is adjusted to the top of all layers to ensure that the displacement monitoring data can be visualized first. When reassessing stress concentration areas and monitoring blind spots, it is necessary to use the latest mine environmental feedback data and the same spatial analysis algorithm as the initial assessment to recalculate the stress level weighting value of each grid cell, re-identify stress concentration areas, and simultaneously rerun the spatial coverage algorithm to update the range and boundary coordinates of the monitoring blind spots. When adjusting the integration method of the anti-scour borehole design area layer, the stress relief borehole layer, the microseismic layout layer, and the probe layout layer, if new stress concentration areas are found after reassessment, high-risk area markers should be added to the anti-scour borehole design area layer, and the locations and parameters of new boreholes should be planned in the stress relief borehole layer. If the monitoring blind spot is found to have expanded, the design coordinates of monitoring points should be added to the microseismic layout layer and the probe layout layer to ensure that the blind spot can be effectively covered.

[0067] In practical implementation, when calculating the difference between environmental feedback data and expected targets, the mine environmental feedback data needs to be preprocessed to remove outliers caused by sensor malfunctions. For example, the 3σ criterion can be used to identify and delete values ​​that exceed three times the standard deviation of the normal data range to ensure the accuracy of the difference calculation. When generating deviation indicators, standardization can be used to convert the three differences into standardized values ​​within the range of [0,1], and then perform weighted summation to fix the numerical range of the deviation indicators between [0,1], facilitating a direct comparison with the preset tolerance. When comparing the deviation indicators with the preset tolerance, a comparison rule library needs to be established to clarify the judgment logic for different types of deviations. For example, when the pressure difference is positive and exceeds the pressure tolerance, or the vibration difference is positive and exceeds the vibration tolerance, or the displacement difference is positive and exceeds the displacement tolerance, it is determined that the deviation indicator exceeds the preset tolerance.

[0068] In practical implementation, after initiating the integration strategy adjustment process, an adjustment log must be generated. This log records the triggering reason for the adjustment (e.g., pressure deviation exceeding the standard), the adjustment time, the adjustment content (e.g., modifying the data update cycle), and the person responsible for the adjustment, facilitating subsequent traceability and auditing. When dynamically modifying the layer display parameters of the anti-scour element layers, this must be done through the layer management interface. This interface supports remote calls, allowing managers to directly send parameter modification commands from the ground monitoring center without needing to operate the layer configuration on-site. When reassessing stress concentration areas and monitoring blind spots, distributed computing resources must be utilized. If the mine area is large and the number of grid cells is numerous, the assessment task can be decomposed into multiple sub-tasks and distributed to different computing nodes for parallel processing, shortening the reassessment time. When adjusting the integration method of the anti-scour element layers, an adjustment plan report must be generated. The report includes a comparison of the layers before and after the adjustment, a table of coordinates for newly designed boreholes and monitoring points, and statistics on the adjusted risk levels, providing a complete basis for managers to approve the adjustment plan.

[0069] In practice, the adjusted anti-collision element layers need to undergo consistency verification. Verification includes checking the uniformity of layer coordinates, the completeness of layer attributes, and the absence of offset in layer overlay. Only after passing verification can the layers be officially put into use. If problems are found during verification, the process must be returned to the integration strategy adjustment workflow for revision until verification is successful. Simultaneously, the adjusted layer data must be synchronized to all associated terminal devices, including the display terminals at the ground monitoring center, the mobile terminals of underground workers, and the industrial control computers of the mining equipment, ensuring that all users can access the latest layer information.

[0070] In some embodiments, time series analysis can be introduced when generating deviation indicators to calculate the trend of deviation indicators over a period of time. For example, a sliding window algorithm can be used to calculate the average value and rate of change of deviation indicators over the past hour. If the average value continues to rise or the rate of change is positive, the integration strategy adjustment process is triggered in advance to achieve preventive adjustment and prevent the deviation indicators from expanding further. After adjusting the integration method of the anti-collision element layer, the effect needs to be verified. The verification method is to continuously collect the adjusted mine environment feedback data and recalculate the deviation indicators. If the deviation indicators fall back to the preset tolerance range, the adjustment is deemed effective; if the deviation indicators still exceed the tolerance, the integration strategy adjustment process needs to be restarted until the deviation indicators meet the standard. The results of the effect verification need to be recorded in the adjustment log as a reference for subsequent optimization of the integration strategy.

[0071] Optionally, a dynamic weight adjustment mechanism can be introduced when calculating the deviation index. The weight coefficients can be adjusted based on the mine's current primary risk type. For example, if the mine experiences frequent micro-seismic events recently, the weight of vibration difference can be increased from 0.3 to 0.5, while the weights of pressure difference and displacement difference can be decreased to 0.3 and 0.2 respectively. This allows the deviation index to focus more on the deviation of the current primary risk. When adjusting the integration method of the anti-seismic element layers, a modular integration scheme can be adopted. Each anti-seismic element layer is designed as an independent module. Adjustments only require modifying the correlation parameters between modules, without needing to reconstruct the entire layer system, thus improving the flexibility and efficiency of the adjustment.

[0072] Optionally, when reassessing stress concentration areas and monitoring blind spots, real-time data fusion technology can be introduced to integrate mine environmental feedback data with historical monitoring data and geological exploration data. Data fusion algorithms can be used to improve the reliability of the assessment results; for example, a Kalman filter algorithm can be used to fuse pressure monitoring data from multiple sensors to obtain more accurate stress distribution results. When dynamically modifying layer display parameters, user-defined configurations can be supported. Administrators can set different display parameter schemes according to their personal operating habits and priorities. For example, a "pressure-priority display" scheme can be saved for key pressure monitoring areas, and a "displacement-priority display" scheme can be saved for key displacement monitoring areas, facilitating quick switching between viewing options.

[0073] It is understandable that by dynamically comparing mine environmental feedback data with the expected targets of rockburst prevention measures, deviations in the effectiveness of measures can be identified in a timely manner, providing a clear basis for adjusting integrated strategies and preventing risk accumulation due to measure failure. Furthermore, the adaptive adjustment strategy for rockburst prevention element layers ensures that layer information remains synchronized with the actual safety status of the mine, guaranteeing that a single map continuously and accurately reflects the distribution of rockburst risks, providing reliable support for subsequent risk analysis and decision-making.

[0074] It is understandable that the entire dynamic comparison and strategy adjustment process forms a closed-loop management system. From feedback on the effects of the measures after implementation, to deviation identification, to layer integration and strategy adjustment, and finally to risk assessment and updates, each link is interconnected, enabling continuous optimization of the rockburst prevention and control plan, adapting to the dynamically changing production and safety environment of the mine, and improving the overall prevention and control efficiency and accuracy. The formula for calculating the deviation index is:

[0075] in: Representative deviation index; The pressure change value (unit: MPa) represents the feedback data of the mine environment. The pressure stability threshold (unit: MPa) represents the decision-making instruction for anti-impact measures. Vibration amplitude values ​​in mine environmental feedback data (unit: ×10) 4 J); Vibration control threshold (unit: ×10) in shock protection measure decision-making instructions 4 J); The displacement value (unit: mm) represents the mine environment feedback data. The displacement allowable threshold (unit: mm) represents the displacement threshold in the anti-impact measure decision-making instruction; 0.4, 0.3, and 0.3 are the weighting coefficients corresponding to the pressure difference, vibration difference, and displacement difference, respectively.

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A single-map management method applicable to mines prone to rock bursts, characterized in that, The method is implemented through the following steps: Import the mine excavation plan as the base map, and integrate multiple anti-erosion element layers on the base map; Collect real-time mine operation data, including pressure relief drilling construction data, microseismic monitoring data, and probe monitoring data; The information in the multiple anti-collision element layers is dynamically updated based on the collected real-time operation data; Impact risk analysis is performed based on the updated impact mitigation element layer, and impact mitigation measure decision instructions are generated. Implement on-site anti-impact measures according to the aforementioned anti-impact measure decision instructions; Real-time collection of mine environment feedback data after the implementation of the measures, dynamic comparison of the mine environment feedback data with the expected targets of the anti-erosion measure decision instructions, and adaptive adjustment of the integration strategy of the anti-erosion element layer based on the comparison results.

2. The single-map management method for mines prone to rock bursts according to claim 1, characterized in that, The integration of multiple anti-collision feature layers on the base map specifically includes: On the base map, draw the anti-impact drilling design area layer, the pressure relief drilling layer, the micro-vibration arrangement layer, and the probe arrangement layer respectively; The anti-impact drilling design area layer is divided into different level areas according to the stress assessment results and marked with different colors; The pressure relief borehole layer is labeled with the borehole number, depth, construction date, and completion status of each borehole; The microseismic array layer is labeled with the sensor number, monitoring range, and real-time operating status of each sensor; The probe layout layer labels the type, installation location, and data transmission status of each probe.

3. The single-map management method for mines prone to rock bursts according to claim 1, characterized in that, The collected real-time mine operation data specifically includes: The mine monitoring system automatically collects microseismic event data, mining progress data, and borehole construction log data. The real-time operational data is collected at a predetermined frequency and stored in a central database for dynamically updating the information in the anti-collision element layer.

4. A single-map management method for mines prone to rock bursts, as described in claim 3, is characterized in that... The specific steps of dynamically updating the information in the multiple anti-collision element layers based on the collected real-time operation data include: The newly collected real-time operation data is compared with the existing data in the anti-collision element layer to identify the changed parts; For the aforementioned pressure relief borehole layer, update the borehole location and status information based on the borehole construction log data; For the aforementioned microseismic arrangement layer, the sensor monitoring range and status label are adjusted based on the microseismic event data; For the probe arrangement layer, the probe position and transmission status are updated based on the probe monitoring data; The updated information is displayed using a visual differentiation method in the anti-collision element layer.

5. A single-map management method for mines prone to rock bursts, as described in claim 4, is characterized in that... The impact risk analysis based on the updated impact protection feature layer specifically includes: By comprehensively analyzing the information in the anti-impact drilling design area layer, pressure relief drilling layer, micro-seismic layout layer, and probe layout layer, stress concentration areas and monitoring blind spots are identified. Based on historical shock event data and real-time monitoring data, calculate the risk index and determine the risk level; The risk analysis results are compared with preset thresholds to generate risk warning signals.

6. A single-map management method for mines prone to rock bursts as described in claim 5, characterized in that, The comprehensive analysis of information from the anti-blowout borehole design area layer, the pressure relief borehole layer, the microseismic layout layer, and the probe layout layer to identify stress concentration areas and monitoring blind spots includes: The anti-impact drilling design area layer is divided into grids, and the stress level weighting value in each grid cell is calculated. Overlay stress relief borehole layers to analyze the matching relationship between borehole coverage and stress level, and identify areas with insufficient coverage as potential stress concentration areas; By integrating the microseismic layout layer and the probe layout layer, a spatial coverage algorithm is used to calculate the monitoring blind zone, which is defined as the area that is more than a threshold away from the nearest monitoring point. Stress concentration areas and monitoring blind spots are mapped onto the base map, and high-risk locations and blind spot boundaries are marked.

7. A single-map management method for mines prone to rock bursts as described in claim 6, characterized in that, The calculation of monitoring blind spots using the spatial coverage algorithm includes: Read the geographic coordinates of each sensor in the microseismic layout layer and the geographic coordinates of each probe in the probe layout layer to form a set of monitoring points; The mine area is divided into a uniform grid, and the size of each grid cell is adaptively determined based on the density of monitoring points. For the center point of each grid cell, calculate the Euclidean distance to all points in the monitoring point set, and select the minimum distance as the nearest monitoring distance for that grid cell; The nearest monitoring distance is compared with a dynamic threshold. If the nearest monitoring distance exceeds the dynamic threshold, the grid cell is marked as a monitoring blind zone. Adjacent marked grid cells are aggregated to form a continuous blind zone region, and the coordinates of the blind zone boundary are output.

8. A single-map management method for mines prone to rock bursts as described in claim 7, characterized in that, The process of dividing the mine area into a uniform grid includes: Obtain the geographic boundary coordinates of the mining area, and determine the grid division range based on the boundary coordinates; The average point distance is calculated based on the spatial distribution density of the monitoring point set. A uniform grid array is generated by using half the average point spacing as the side length of the grid cell. Perform boundary trimming on the grid array to remove grid cells outside the mining area.

9. A single-map management method for rockburst mines as described in claim 8, characterized in that, The step of performing impact risk analysis based on the updated impact mitigation feature layer and generating impact mitigation measure decision instructions includes: Multi-source spatial attributes were extracted from the updated anti-shocking element layer, including the density distribution of pressure relief boreholes, the energy release value of microseismic events, and the stress value monitored by the probe. A risk assessment matrix is ​​constructed by normalizing and weighting the multi-source spatial attributes to output a comprehensive risk score. Risk levels are determined based on comprehensive risk scores, and a predefined pool of mitigation measures is matched accordingly. The anti-scouring measures library stores decision instructions corresponding to different risk levels, including drilling parameter adjustment, monitoring point addition and deletion, and mining speed control; When generating anti-collision measure decision instructions, an execution priority and timestamp are attached, and the instructions are distributed to field devices via a message queue.

10. A single-map management method for mines prone to rock bursts as described in claim 9, characterized in that, The step of dynamically comparing the mine environment feedback data with the expected targets of the anti-erosion measure decision instructions includes: Extract expected target values ​​from the anti-impact measure decision instructions, including pressure stability threshold, vibration control threshold, and displacement allowance threshold; Calculate the differences between pressure change values ​​and pressure stability thresholds, vibration amplitude values ​​and vibration control thresholds, and displacement values ​​and allowable displacement thresholds in the environmental feedback dataset. A deviation index is generated based on the difference, and the deviation index is compared with a preset tolerance in real time. The integration strategy for adaptively adjusting the anti-collision feature layer based on the comparison results includes: When the deviation index exceeds the preset tolerance, the integration strategy adjustment process is initiated. Based on the magnitude and trend of the deviation index, dynamically modify the layer display parameters, data update cycle, or layer overlay order of the anti-collision element layer; Reassess stress concentration areas and monitoring blind spots, and adjust the integration method of the anti-impact borehole design area layer, pressure relief borehole layer, microseismic layout layer, and probe layout layer.

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