Coal mine rock burst risk assessment system and method

Abstract

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine rock burst risk prevention, and in particular to a coal mine rock burst hazard assessment system and method. Background Art

[0002] Coal mine rock burst (also known as coal and gas outburst or rock burst) is one of the common major dynamic disasters in coal mining. It is characterized by suddenness, great destructiveness, and difficulty in prediction. Existing rock burst monitoring methods mostly rely on single sensor data and lack comprehensive analysis of geological structure, stress evolution, and energy evolution processes, making it difficult to achieve effective early warning.

[0003] Especially in complex tunnel networks, traditional single-sensor data struggles to accurately identify stress transmission pathways in high-risk areas, resulting in limited early warning effectiveness. Furthermore, existing assessment systems, which mostly rely on static indicators and ignore the correlation between microseismic events and historical disaster information, lack the ability to dynamically assess rock burst hazards. Therefore, a rock burst risk assessment system that integrates multi-source data, dynamic evolution analysis, and spatial modeling is urgently needed to improve the accuracy and timeliness of early warnings. Summary of the Invention

[0004] The purpose of this application is to provide a coal mine rock burst hazard assessment system and method to solve the problems raised in the above background technology.

[0005] According to one aspect of the present application, a coal mine rock burst hazard assessment system is provided, comprising: The data acquisition module is used to collect geological parameters and monitoring data from stress sensors, displacement sensors, microseismic monitors, and inclination measuring devices in real time. The geological parameters include tunnel depth, strike length, inclination, elastic modulus of the roof bearing coal pillar, and coal seam compressive strength. The monitoring data includes microseismic energy and stress change rate. Energy accumulation rate calculation module, used to calculate the energy accumulation rate of the roadway based on the superposition of static load and dynamic load , adjust the influence of static load terms and dynamic load terms; The tunnel network modeling module is used to construct a tunnel network diagram based on the node and edge graph. The nodes are tunnel monitoring points. The weight of the edge is calculated by the ratio of the tunnel section length to the average elastic modulus of the adjacent rock layer to obtain the high stress conduction path. The risk assessment module is used to couple the energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and according to the risk index Construct risk-prominent areas in the roadway network diagram; and The early warning and control module highlights the areas according to the generated risks The region is divided into four levels, and corresponding early warning and control measures are implemented according to the different levels.

[0006] Preferably, the method for constructing the above-mentioned high-risk area is: ; Where: For nodes The risk value at For neighboring nodes The risk value, is the distance between nodes, is the distance attenuation factor.

[0007] Preferably, the roadway energy accumulation rate Set to: ; in, is the length, The depth of the tunnel, is the inclination angle, is the elastic modulus of the roof pressure-bearing coal pillar, is the compressive strength of the coal seam, is the microseismic energy, is the stress change rate, and It is the weight coefficient obtained by fitting with historical data, which is used to adjust the influence of static load items and dynamic load items.

[0008] Preferably, in the lane network modeling module, the weight of the lane network graph edge Set to: ; in, is the edge weight, For nodes To Node The length of the roadway section, is the ratio of the average elastic modulus of adjacent rock layers, and Node and The elastic modulus at .

[0009] Preferably, in the risk assessment module, the dynamic risk index Set to: ; in, is the critical path edge set, is the safety distance threshold, is the minimum distance to the historical impact event, is the normalization coefficient, is the roadway energy accumulation rate, is the sum of the weights of all edges on the high stress conduction channel.

[0010] Preferably, the early warning and control module divides the region into: Red Zone , marked as an extremely high-risk area, the corresponding early warning and control measures are to trigger the sound and light alarm and start the emergency recovery speed limit; Orange Zone , marked as a high-risk area, the corresponding early warning and control measures are to control mining parameters and strengthen support; Yellow area , marked as a medium-risk area, the corresponding early warning and control measures are to remind people to pay attention to dynamic changes on site; Green Area , marked as a safe area, and the corresponding early warning and control measures are normal operations.

[0011] Preferably, the critical path is identified by using a Dijkstra algorithm, and the path with the largest weight is selected as the high stress conduction channel.

[0012] Preferably, the energy accumulation rate calculation module also includes a partition correction, which uses a correction coefficient according to the fault zone or stable rock formation where the current tunnel is located. , the correction formula is: , Where, It is a fault zone, It is a stable rock formation.

[0013] Preferably, the system further comprises a parameter calibration tool, which is used to invert the historical impact event data. 、 and The optimal value of the safety distance threshold is provided, and a visual interface is provided to adjust the safety distance threshold. and risk level classification standards.

[0014] Preferably, the risk assessment module uses the Floyd-Warshall algorithm to calculate the shortest path between nodes.

[0015] According to another aspect of the present application, a method for assessing the risk of rock burst in a mine is provided, comprising the following steps: S1. Real-time collection of geological parameters and monitoring data from stress sensors, displacement sensors, microseismic monitors, and inclination measuring devices. The geological parameters include tunnel depth, strike length, inclination, elastic modulus of roof pressure-bearing coal pillars, and coal seam compressive strength. The monitoring data includes microseismic energy and stress change rate. S2. Calculate the energy accumulation rate of the roadway based on the superposition of static and dynamic loads , adjust the influence of static load and dynamic load

[0016] S3. Construct a tunnel network diagram based on the node and edge graph, where the nodes are tunnel monitoring points, and calculate the edge weights by the ratio of the tunnel section length to the average elastic modulus of the adjacent rock layer to obtain the high stress conduction path; S4, used for coupling energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and according to the risk index Construct risk-prominent areas in the lane network diagram

[0017] S5, used for coupling energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and based on the risk index Construct risk-prominent areas in the lane network map.

[0018] The present invention collects monitoring data such as tunnel stress, displacement, and microseismic data in real time, combines it with geological information such as tunnel burial depth and lithologic parameters, calculates the energy accumulation rate of the tunnel, then constructs a tunnel network diagram, identifies high-stress conduction paths, and calculates a dynamic risk index based on historical impact events, energy indicators, and other factors. The risk area is delineated and, based on the risk level, the system automatically triggers corresponding early warning and control measures to achieve precise early warning and dynamic control, assisting mine operation decision-making and accident prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the structure of a method for assessing the risk of rock burst in coal mines according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the process of determining risk-prominent areas in a coal mine rock burst hazard assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1-Figure 2The embodiment of the present invention provides a coal mine rock burst hazard assessment system, including: a data acquisition module, an energy accumulation rate calculation module, a tunnel network modeling module, a risk assessment module and an early warning and control module.

[0022] Among them, the data acquisition module is used to collect geological parameters and monitoring data from stress sensors, displacement sensors, microseismic monitors and inclination measuring devices in real time. The geological parameters include tunnel burial depth, strike length, inclination, elastic modulus of the roof bearing coal pillar and compressive strength of the coal seam. The monitoring data includes microseismic energy and stress change rate.

[0023] The energy accumulation rate calculation module is used to calculate the energy accumulation rate of the roadway based on the superposition of static load and dynamic load. , adjust the influence of static load and dynamic load, tunnel energy accumulation rate The calculation method is: (Formula 1); Where: is the length, The depth of the tunnel, is the inclination angle, is the elastic modulus of the roof pressure-bearing coal pillar, is the compressive strength of the coal seam, is the microseismic energy, is the stress change rate, and It is the weight coefficient obtained by fitting with historical data, which is used to adjust the influence of static load items and dynamic load items.

[0024] Furthermore, the energy accumulation rate calculation module also includes partition correction, which uses correction coefficients according to the fault zone or stable rock layer where the current tunnel is located. , the correction formula is: (Formula 2), Where, It is a fault zone, It is a stable rock formation.

[0025] The tunnel network modeling module is used to construct a tunnel network diagram based on the node and edge diagram. The nodes are tunnel monitoring points, and the weight of the edge is calculated by the ratio of the tunnel section length to the average elastic modulus of the adjacent rock layer to obtain the high stress conduction path. In the tunnel network modeling module, the weight of the edge of the tunnel network diagram is The calculation method is: (Formula 3); Where: is the edge weight, For nodes To Node The length of the roadway section, is the ratio of the average elastic modulus of adjacent rock layers, and Node and The elastic modulus at .

[0026] The risk assessment module is used to couple the energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and based on the risk index Construct risk-prominent areas in the roadway network diagram.

[0027] Among them, in the risk assessment module, the dynamic risk index The calculation method is: (Formula 4); Where, is the critical path edge set, is the safety distance threshold, is the minimum distance to the historical impact event, is the normalization coefficient, is the roadway energy accumulation rate, The critical path is the sum of the weights of all edges on the high stress conduction channel. The critical path is identified by the Dijkstra algorithm, and the path with the largest weight is selected as the high stress conduction channel. In addition, the risk assessment module calculates the shortest path between nodes by using the Floyd-Warshall algorithm.

[0028] Furthermore, the method for constructing the high-risk area is as follows: (Formula 5); Where: For nodes The risk value at For neighboring nodes The risk value, is the distance between nodes, is the distance attenuation factor.

[0029] Reference Figure 2 The early warning and control module divides the region into four levels according to the generated risk-prominent areas, and takes corresponding early warning and control measures according to the different levels of areas. The early warning and control module divides the region into four levels according to the generated risk-prominent areas: Red Zone , marked as an extremely high risk area, the corresponding early warning and control measures are to trigger the sound and light alarm and start the emergency recovery speed limit, and when it is detected When the machine is in operation, it will automatically generate a stop operation instruction and upload it to the monitoring platform of the mine management. At the same time, it will send a stop instruction to the coal mining machine or pressure relief equipment to lock the drive system of the coal mining machine or pressure relief equipment. Orange Zone , marked as a high-risk area, the corresponding early warning and control measures are to control mining parameters and strengthen support; Yellow area , marked as a medium-risk area, the corresponding early warning and control measures are to remind people to pay attention to dynamic changes on site; Green Area , marked as a safe area, and the corresponding early warning and control measures are normal operations.

[0030] Taking a coal mine as an example, the specific implementation steps are as follows: the data acquisition module collects the geological parameters of the coal mine tunnel including the depth , strike length , inclination , Elastic modulus of roof rock , Coal seam compressive strength .

[0031] A set of stress sensors is arranged every 50m along the roadway, and a microseismic monitor is installed every 100m. Displacement sensors are added at key nodes such as intersections and fault zones. After obtaining the monitoring data, the noise is eliminated through Kalman filtering and uploaded to the edge computing node in real time. The sample data fragment is as follows: .

[0032] Based on the regression analysis of historical accident data, we can determine 、 , the current area is a fault zone, the correction coefficient .

[0033] (Refer to Formula 1) The static load contribution calculation process is: ; The calculation process of dynamic load contribution is: .

[0034] (Refer to Formula 2) The total energy accumulation rate calculation process is: .

[0035] A lane network graph is constructed based on the node and edge graph. The nodes are lane monitoring points. In this embodiment, a simplified lane network containing 5 nodes is constructed. For details, refer to Table 1: Table 1 is a simplified lane network table with 5 nodes node Coordinates (x, y, z) elastic modulus 1 (0, 0, 0) 2.5 2 (200, 0, 0) 2.3 3 (200, 100, 0) 2.8 4 (400, 100, 0) 2.6 5 (400, 200, 0) 2.4 The process of calculating edge weights based on the node coordinates and elastic modulus in Table 1 is as follows: (Refer to Formula 3) Side 1-2: m, .

[0036] (Refer to Formula 3) Side 2-3: m, The calculation of the remaining edge weights is similar.

[0037] The critical path identification uses Dijkstra algorithm to calculate the maximum weight path as 1-2-3-4-5, and the weight and .

[0038] Set a safe distance , historical impact distance , normalization coefficient , therefore, (referring to Formula 4) the risk index calculation process is: .

[0039] The process of constructing the high-risk areas is as follows: Node 2 , neighboring nodes 1 and 3 、 .

[0040] Node spacing , , attenuation coefficient .

[0041] (Refer to Formula 5) The calculation process of the thermal value of node 2 is: .

[0042] Thermal value via node 2 The calculation results show that in the risk highlight area generated by the construction, node 2 is marked as a green area ( ), the early warning and control module prompts the area to allow normal operations according to the area level.

[0043] In summary, the present invention collects monitoring data such as tunnel stress, displacement, and microseismic data in real time, combines it with geological information such as tunnel burial depth and lithologic parameters, calculates the energy accumulation rate of the tunnel, and then constructs a tunnel network diagram to identify high-stress conduction paths. It also calculates a dynamic risk index based on historical impact events, energy indicators, and other factors, delineates risk areas, and automatically triggers corresponding early warning and control measures based on the risk level, achieving accurate early warning and dynamic control, and assisting mine operation decision-making and accident prevention.

[0044] Parts not described in the present invention are the same as those in the prior art or can be implemented using the prior art. Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A coal mine rock burst hazard assessment system, characterized in that: The system comprises: A data acquisition module is used to collect geological parameters and monitoring data from stress sensors, displacement sensors, microseismic monitors, and inclination measuring devices in real time. The geological parameters include tunnel depth, strike length, inclination, elastic modulus of the roof bearing coal pillar, and coal seam compressive strength. The monitoring data includes microseismic energy and stress change rate. Energy accumulation rate calculation module, used to calculate the energy accumulation rate of the roadway based on the superposition of static load and dynamic load , adjust the influence of static load terms and dynamic load terms; The tunnel network modeling module is used to construct a tunnel network diagram based on the node and edge graph. The nodes are tunnel monitoring points. The weight of the edge is calculated by the ratio of the tunnel section length to the average elastic modulus of the adjacent rock layer to obtain the high stress conduction path. Risk assessment module for coupling energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and according to the risk index Constructing risk-prominent areas in the roadway network diagram, and The early warning and control module is used to highlight areas based on the generated risks The region is divided into four levels, and corresponding early warning and control measures are implemented according to the different levels.

2. The system according to claim 1, wherein: The method for constructing the risk-prominent area is as follows: ； in, For nodes The risk value at For neighboring nodes The risk value, is the distance between nodes, is the distance attenuation factor.

3. The system according to claim 1, wherein: The roadway energy accumulation rate Set to: ； in, is the length, The depth of the tunnel, is the inclination angle, is the elastic modulus of the roof pressure-bearing coal pillar, is the compressive strength of the coal seam, is the microseismic energy, is the stress change rate, and It is the weight coefficient obtained by fitting with historical data, which is used to adjust the influence of static load items and dynamic load items.

4. The system according to claim 1, wherein: In the lane network modeling module, the weight of the lane network graph edge Set to: ； in, is the edge weight, For nodes To Node The length of the roadway section, is the ratio of the average elastic modulus of adjacent rock layers, and Node and The elastic modulus at .

5. The system according to claim 1, wherein: In the risk assessment module, the dynamic risk index Set to: ； in, is the critical path edge set, is the safety distance threshold, is the minimum distance to the historical impact event, is the normalization coefficient, is the roadway energy accumulation rate, is the sum of the weights of all edges on the high stress conduction channel.

6. The system according to claim 1, wherein: The early warning and control module divides the region into the following types according to the generated risk-prominent areas: Red Zone , marked as an extremely high-risk area, the corresponding early warning and control measures are to trigger the sound and light alarm and start the emergency recovery speed limit; Orange Zone , marked as a high-risk area, the corresponding early warning and control measures are to control mining parameters and strengthen support; Yellow area , marked as a medium-risk area, the corresponding early warning and control measures are to remind people to pay attention to dynamic changes on site; Green Area , marked as a safe area, and the corresponding early warning and control measures are normal operations.

7. The system according to claim 3, characterized in that: The energy accumulation rate calculation module also includes a partition correction, which uses a correction coefficient based on the fault zone or stable rock layer where the current tunnel is located. , the correction formula is: ， Where, It is a fault zone, It is a stable rock formation.

8. The system according to claim 1, wherein: The system also includes a parameter calibration tool, which is used to invert the historical impact event data. 、 and The optimal value of the safety distance threshold is provided, and a visual interface is provided to adjust the safety distance threshold. and risk level classification standards.

9. The system according to claim 5, characterized in that: The risk assessment module uses the Floyd-Warshall algorithm to calculate the shortest path between nodes.

10. A method for assessing the risk of rock burst in coal mines, characterized in that: The following steps are involved: S1. Real-time collection of geological parameters and monitoring data from stress sensors, displacement sensors, microseismic monitors, and inclination measuring devices. The geological parameters include tunnel depth, strike length, inclination, elastic modulus of roof pressure-bearing coal pillars, and coal seam compressive strength. The monitoring data includes microseismic energy and stress change rate. S2. Calculate the energy accumulation rate of the roadway based on the superposition of static and dynamic loads , adjust the influence of static load terms and dynamic load terms; S3. Construct a tunnel network diagram based on the node and edge graph, where the nodes are tunnel monitoring points, and calculate the edge weights by the ratio of the tunnel section length to the average elastic modulus of the adjacent rock layer to obtain the high stress conduction path; S4, used for coupling energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and according to the risk index Constructing risk-prominent areas in the lane network diagram; S5, used for coupling energy accumulation rate , historical shock events and high stress transmission paths, and comprehensively calculate the dynamic risk index , and based on the risk index Construct risk-prominent areas in the lane network map.

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