Management system for real-time monitoring and early warning of coal and gas outburst parameters in mines

Abstract

The present invention discloses a management system for real-time monitoring and early warning of coal and gas outburst parameters in mines, which belongs to the field of safe production. The management system specifically comprises: collecting gas and ground stress data in the mine in real time through gas sensors and microseismic sensors, and using time series analysis, spatial data mining technology and multi-scale coupling strategy to identify the changing laws and potential correlations of ground stress and gas parameters; establishing a comprehensive analysis and prediction model for outburst, evaluating the risk level of coal and gas outburst, and designing a three-level early warning mechanism and emergency plan; the method can be flexibly adjusted according to the actual conditions of the mine, effectively preventing and controlling coal and gas outburst accidents, and improving mine safety.

Description

Technical Field

[0001] The invention belongs to the field of safe production, and specifically is a management system for real-time monitoring and early warning of coal and gas outburst parameters in mines. Background Art

[0002] Under the action of original stress, most of the gas in coal seams is stored in the microscopic void structure of coal seams in an adsorbed state. This adsorbed gas does not have the energy to drive outbursts. Only when the top rock layer under the action of ground stress suddenly breaks, a strong impact force is generated on the coal seam. Under the action of the impact force, the original coal seam is instantly broken into powder, and the permeability of the coal seam suddenly changes, causing the adsorbed gas to desorb, change from an adsorbed state to a free state, and expand in the coal seam, which generates the energy to drive outbursts. Microseismic signals usually refer to seismic wave signals generated when underground rocks break or stress is released. When the number of microseismic signals increases and high-energy microseismic signals appear, it indicates that the risk of coal and gas outbursts is relatively high.

[0003] Coal and gas outburst in mines is a very prominent and complex dynamic phenomenon in the process of coal mining, which seriously threatens the safe production of mines and the personal safety of miners. In recent years, with the increase in mining depth, the pressure in mines has continued to increase, and the possibility of coal and gas outburst has also increased.

[0004] For example, a Chinese patent with authorization announcement number CN110533887B discloses a discrete modal warning method, device and storage medium for coal and gas outburst disasters based on real-time monitoring data, including: using the current time as a reference to move forward several time periods, using the coal mine gas monitoring system to calculate in real time the changing trend and oscillation variance of the relative gas outburst volume of each mining face in each time period, forming a discrete modal warning sequence, through the historical records of coal and gas outburst precursor information, combined with the occurrence mechanism of coal and gas outburst accidents, implicit gas geology and mining process information, using big data analysis methods to determine the warning modal parameters and trend warning classification thresholds, to achieve online real-time dynamic classification warning and alarm resolution of coal and gas outburst disasters.

[0005] Existing technologies show that "outbursts are stimulated by ground stress and driven by gas". As long as the concentration of ground stress exceeds the compressive strength of the main bearing layer of the coal seam, the strong impact force generated by the sudden fracture of the main bearing rock layer will break the coal seam, causing the coal seam permeability to suddenly change. The low permeability caused by the compression of the coal seam before the fracture suddenly turns into high permeability, which causes a large amount of adsorbed gas in the coal seam to desorb and form free gas; the free gas quickly gathers and expands in the coal seam, forming the driving energy that drives the outburst. Therefore, it is not appropriate to use the gas outburst volume alone to warn the danger of outbursts, and it is impossible to find the early signs of the danger of outbursts; in addition, the change of gas outburst volume is closely related to the mining process and mining intensity. The mining process, mining intensity, and ventilation air volume will cause great changes in gas concentration and gas outburst volume; using gas outburst volume alone to warn the danger of outbursts does not touch the essence of the occurrence of outbursts, but only sees the appearance of a large amount of gas outbursts when outbursts occur, and it is difficult to achieve a very accurate warning of the danger of coal and gas outbursts. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a management system for real-time monitoring and early warning of coal and gas outburst parameters in mines, which collects the changes of ground stress and gas outburst in real time in the same time and space as analysis parameters, including: collecting gas and ground stress data in the mine in real time through gas sensors and microseismic monitoring equipment, and using time series analysis, spatial data mining technology and multi-scale coupling strategy to identify the changing patterns and potential correlations of ground stress and gas parameters; establishing a comprehensive analysis and prediction model for outburst, evaluating the risk level of coal and gas outburst, and designing a three-level early warning mechanism and emergency plan; the method can be flexibly adjusted according to the actual conditions of the mine, effectively preventing and controlling coal and gas outburst accidents, and improving mine safety.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A management system for real-time monitoring and early warning of coal and gas outburst parameters in mines, including: data processing module, data analysis module, prediction module and risk assessment module;

[0009] The data processing module includes a data acquisition unit, which is used to collect gas data and ground stress data; the gas data includes gas concentration, gas outflow and ventilation status in the mine; the ground stress data includes the number of small events, the number of large events, the released energy, the maximum amplitude and the duration of the signal of the microseismic event;

[0010] The data analysis module includes a law recognition unit, in which a multi-scale coupling strategy is configured, and the multi-scale coupling strategy is used to identify the change law of ground stress and gas parameters according to the change trends of ground stress and gas parameters;

[0011] The prediction module includes a model building unit and an optimization unit. The model building unit is configured with an integration strategy, which is used to build a multi-parameter comprehensive prediction model based on historical data and mechanism analysis to predict coal and gas outbursts; the optimization unit is configured with a particle swarm optimization algorithm to verify the multi-parameter comprehensive prediction model and optimize it according to actual conditions;

[0012] The risk assessment module includes a risk assessment unit and an emergency plan formulation unit; the risk assessment unit is configured with a risk assessment strategy, which is used to assess and classify the risks of coal and gas outbursts; the emergency plan formulation unit is used to formulate emergency plans and emergency response measures according to the risk level;

[0013] The specific steps of the multi-scale coupling strategy include:

[0014] A1: Collect gas data and geostress data and perform preprocessing. At the same time, according to the characteristics of the data and analysis requirements, divide the geostress and gas parameters into different scales, and establish a preliminary multi-scale coupling model for each scale;

[0015] A2: Based on the preliminary multi-scale coupling model, determine the area that needs to be meshed and set the size b and density of the mesh. , and use the adaptive meshing method according to b and Perform non-uniform initial grid division on the preliminary multi-scale coupled model;

[0016] A3: Determine the coupling area based on the initial mesh division;

[0017] A4: Use the improved finite element method on the inner region of the coupling region to obtain the stress distribution in the inner region;

[0018] A5: Use the improved boundary element method on the outer region of the coupling region to obtain the stress distribution in the outer region;

[0019] A6: The solutions of the inner and outer regions are adjusted by an iterative method to perform continuity treatment on the inner and outer regions of the coupled region until the continuity condition is met and the final solution is obtained;

[0020] A7: Construct the final multiscale coupling model based on the final solution obtained, compare the final multiscale coupling model with the actual observation data, verify the accuracy of the final multiscale coupling model, and optimize the final multiscale coupling model based on the verification results;

[0021] A8: Use the optimized final multi-scale coupling model to simulate and predict the changing trends of geostress and gas parameters, and identify the changing patterns of geostress and gas parameters at different scales and their mutual correlations by analyzing the simulation results;

[0022] The specific steps of the integration strategy include:

[0023] B1: Select the basic characteristic index set for predicting coal and gas outburst from the data actually collected in the mining area , and preprocessing is performed, where represents the mth basic characteristic index, and m represents the number of basic characteristic indexes;

[0024] B2: Based on the mechanism of coal and gas outburst, analyze the contribution of each influencing factor to the outburst and conduct mechanism analysis;

[0025] B3: Select C prediction models based on support vector machines as base models and use the preprocessed Input the base model for training and learning to obtain the prediction results of C base models ,in, represents the prediction result of the jth base model, represents the prediction function of the jth base model;

[0026] B4: Adopt the ensemble learning method based on weighted average to fuse the prediction results of C base models to obtain the final comprehensive prediction result ,in, represents the weight of the j-th base model.

[0027] Specifically, the specific steps of A4 include:

[0028] A4.1: Load the determined coupling area. In the coupling area, according to the physical mechanism of coal and gas outburst in the mine, obtain the internal area that needs to be analyzed by finite element analysis, and establish a finite element model based on the geometric shape and material properties of the internal area;

[0029] A4.2: Divide the internal region into interconnected hexahedral finite elements and select N nodes inside each hexahedral finite element to represent the stress state of the finite element;

[0030] A4.3: Calculate the stiffness matrix for each hexahedral finite element , and assemble the stiffness matrices of all hexahedral finite elements into an overall stiffness matrix ,in, represents the strain matrix, express The transpose of represents the elastic matrix, and V represents the volume of the hexahedral finite element.

[0031] Specifically, the specific steps of A4 also include:

[0032] A4.4: Apply force boundary conditions on the boundaries of the finite element model and apply external loads on the internal regions ,in, represents the concentrated force applied to the i-th node, express The mean of

[0033] A4.5: Solve the discretized finite element model to obtain the stress distribution in the internal area. The formula is:

[0034] ;

[0035] in, Represents the stress tensor, which is used to describe the stress distribution inside the coal seam. represents the damage factor of the previous step, represents the damage increment, Indicates reference temperature The elastic matrix under Indicates temperature, represents the phase volume fraction, represents the total strain tensor, represents the plastic strain tensor, which is used to describe the irreversible deformation of the material. represents the plastic strain in the previous step, represents the plastic strain increment, n represents the plastic flow direction, represents the total strain tensor at the i-th node, represents the strain on the ith node, represents the overall stiffness at the i-th node.

[0036] Specifically, the specific steps of A5 include:

[0037] A5.1: Based on the physical mechanism of coal and gas outburst in mines, establish partial differential equations to describe the changes in pressure and gas concentration parameters in the external area of ​​mines ,in, represents the partial derivative, u represents the gas concentration, A represents the diffusion coefficient, k represents the rate at which gas is absorbed or consumed, and B represents the rate at which gas is released from the coal seam;

[0038] A5.2: Convert the partial differential equation into a boundary integral equation and discretize the boundary integral equation, divide the continuous external region into discrete units, and select nodes in each unit to represent the parameter value in the unit;

[0039] A5.3: For each node, establish an algebraic equation about the node parameters based on the boundary integral equation and discretization method, and combine the discretized equations of all nodes into a linear algebraic equation system;

[0040] A5.4: Use sensors to monitor the concentrated stress and gas concentration parameters of the working face in real time, and input the monitoring data into the linear algebraic equation group for real-time solution;

[0041] A5.5: Based on the solution results, obtain the stress distribution in the external area.

[0042] Specifically, the specific steps of the risk assessment strategy include:

[0043] C1: Obtain real-time monitoring data of coal and gas outburst , and Conduct statistical analysis to identify risk factors for coal and gas outbursts, including: represents the real-time monitoring data of the g-th coal and gas outburst, and g represents the number of real-time monitoring data of coal and gas outburst;

[0044] C2: According to the risk factor identification results, the identified risk factors are classified and corresponding weights are assigned to risk factors of different categories;

[0045] C3: Assess the probability of coal and gas outburst accidents caused by risk factors and severity , and combined with the weight distribution results, calculate the quantitative evaluation value of each risk factor. The formula is:

[0046] ;

[0047] in, represents the quantitative evaluation value of the kth risk factor, represents the probability that the kth risk factor leads to coal and gas outburst accidents, represents the severity of coal and gas outburst accidents caused by the kth risk factor, represents the weight of the kth risk factor, r represents the number of risk factors;

[0048] C4: Based on the quantitative assessment value, the risk of coal and gas outburst is assessed to obtain the overall risk level, and the risk of coal and gas outburst is divided into three levels: low risk, medium risk and high risk;

[0049] C5: Develop corresponding emergency plans and emergency response measures according to the risk level;

[0050] C6: Continuously monitor the risks of coal and gas outbursts, and update risk assessment results and emergency plans in real time.

[0051] Specifically, in the emergency plan formulation unit, the risk levels include threat, danger, and emergency. The specific contents of the emergency plan and emergency response measures formulated according to the risk level include:

[0052] D1: If the risk level is a threat level, strengthen daily monitoring, regularly analyze data, and locally reinforce the excavation or mining face, increase ventilation, and slow down the excavation or mining speed;

[0053] D2: If the risk level is dangerous, the emergency response mechanism will be activated, an on-site risk assessment will be organized, gas extraction will be increased, and remote control of excavation or mining operations will be adopted. At the same time, safety inspections and risk assessments will be carried out, and a decision on whether to resume operations will be made based on the assessment results;

[0054] D3: If the risk level is critical, the emergency rescue plan will be activated, all excavation or mining operations will be stopped immediately, all personnel will be evacuated to a safe area, the excavation face will be closed, the backup ventilation system will be started, and safety inspections and risk assessments will be carried out at the same time to ensure safety before resuming operations.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention proposes a management system for real-time monitoring and early warning of coal and gas outburst parameters in mines, and optimizes and improves the architecture, operating steps and processes. The system has the advantages of simple processes, low investment and operating costs, and low production work costs.

[0057] 2. The present invention proposes a management system for real-time monitoring and early warning of coal and gas outburst parameters in mines. By integrating the data acquisition capabilities of gas sensors and microseismic monitoring equipment, combined with time series analysis, spatial data mining and multi-scale coupling strategies, it realizes accurate analysis of the changing trends of coal seam ground stress and gas outburst volume in the same time and space, emphasizing the role of ground stress in early warning of outbursts. This method improves the accuracy and reliability of early warning of coal and gas outburst disasters.

[0058] 3. The present invention proposes a management system for real-time monitoring and early warning of coal and gas outburst parameters in mines, establishes a comprehensive analysis and prediction model for outbursts based on the changing trends of ground stress and gas parameters, and designs a three-level early warning mechanism and corresponding emergency plans. This not only makes the early warning of coal and gas outburst disasters more timely and effective, but also enables the flexible adjustment of emergency measures according to the actual conditions of the mine, thereby minimizing the risk and loss of disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a management system architecture diagram for real-time monitoring and early warning of coal and gas outburst parameters in mines according to the present invention;

[0060] Figure 2 This is a process flow chart of the multi-scale coupling strategy implementation process of the management system for real-time monitoring and early warning of coal and gas outburst parameters in a mine according to the present invention;

[0061] Figure 3 The present invention is a process flow chart for implementing the risk assessment strategy of the management system for real-time monitoring and early warning of coal and gas outburst parameters in mines. DETAILED DESCRIPTION

[0062] Example 1

[0063] See also Figure 1 The present invention provides an embodiment: a management system for real-time monitoring and early warning of coal and gas outburst parameters in a mine, comprising:

[0064] Data processing module, data analysis module, prediction module, risk assessment module;

[0065] A data processing module is used to collect gas data in real time through gas sensors and pre-process the collected gas data and ground stress data;

[0066] The data analysis module is used to analyze the changing trends of coal seam ground stress and gas emission in the same time and space by using time series analysis and spatial data mining technology, and to identify the changing rules and potential correlations of ground stress and gas parameters;

[0067] It should be noted that the ground stress and gas concentration parameters are the basis for data analysis, and the parameter acquisition methods include:

[0068] (1) Direct measurement data: including gas data collected in real time by gas sensors, such as gas concentration, gas outflow, and ventilation status, and microseismic signals generated by coal rock under the action of ground stress captured by microseismic monitoring equipment. These data provide direct information about gas and ground stress;

[0069] (2) Indirect inference data: In addition to directly measured data, properties and possible evolution processes can also be inferred indirectly by querying historical data and geological history information;

[0070] (3) Multi-source data fusion: By fusing data from different sources, this includes integrating direct measurement data and indirect inference data, as well as cross-validating and comprehensively analyzing data from multiple disciplines such as physics and chemistry.

[0071] The prediction module is used to establish a comprehensive analysis and prediction model for coal and gas outburst based on the changing trends of ground stress and gas parameters and the occurrence mechanism of coal and gas outburst, so as to predict the risk of coal and gas outburst;

[0072] The risk assessment module is used to evaluate the risk level of coal and gas outbursts based on the output results of the comprehensive analysis and prediction model of outbursts, design a three-level early warning mechanism, and formulate corresponding emergency plans and outburst prevention measures according to the risk level. The three-level early warning mechanism includes threat, danger, and critical levels.

[0073] The data processing module includes: a data acquisition unit and a preprocessing unit;

[0074] A data acquisition unit is used to collect gas data and ground stress data in the mine in real time, wherein the gas data includes the gas concentration, gas outflow and ventilation status in the mine, and the ground stress data includes the number of small events, the number of large events, the released energy, the maximum amplitude and the duration of the signal of the microseismic event;

[0075] Among them, the ground stress data is obtained by using microseismic monitoring equipment to capture the microseismic signals generated by coal and rock under the action of ground stress, and then statistically analyzing the microseismic signals to obtain the ground stress data.

[0076] The preprocessing unit is used to perform preprocessing operations such as cleaning and formatting on the collected gas data to ensure the accuracy and consistency of the data. At the same time, it counts, analyzes and preprocesses the microseismic signals to extract ground stress data.

[0077] The data analysis module includes: time series analysis unit, spatial data mining unit, and pattern recognition unit;

[0078] Time series analysis unit, used to analyze the time variation trend of ground stress and gas parameters, and provide time dimension information for multi-parameter comprehensive prediction model;

[0079] Spatial data mining unit, used to mine the spatial distribution of ground stress and gas parameters, and provide spatial dimension information for multi-parameter comprehensive prediction model;

[0080] The law identification unit is used to identify the changing laws of ground stress and gas parameters according to their changing trends by adopting a multi-scale coupling strategy.

[0081] The prediction module includes: model building unit and optimization unit;

[0082] Model building unit, used to build a multi-parameter comprehensive prediction model based on historical data and mechanism analysis using an integrated strategy to predict the risk of coal and gas outburst;

[0083] The optimization unit is used to verify the multi-parameter comprehensive prediction model using the particle swarm optimization algorithm and optimize it according to actual conditions to improve the prediction accuracy of the multi-parameter comprehensive prediction model.

[0084] The risk assessment module includes: risk assessment unit, early warning mechanism design unit, and emergency plan formulation unit;

[0085] Risk assessment unit, used to assess the risk level of coal and gas outbursts, providing a basis for the formulation of early warning mechanisms and emergency plans;

[0086] The early warning mechanism design unit is used to design a three-level early warning mechanism to ensure timely early warning when coal and gas outburst risks occur;

[0087] The emergency plan formulation unit is used to formulate emergency plans and emergency response measures according to the risk level to ensure rapid response and effective response when coal and gas outburst risks occur.

[0088] It should be noted that the specific implementation steps of the overall process of the present invention include:

[0089] S1: Gas data are collected in real time through gas sensors, including gas concentration, gas outflow and ventilation status in the mine. At the same time, microseismic sensors are used to capture microseismic signals generated by coal and rock under the action of ground stress, and these signals are counted and analyzed to obtain ground stress data, including the number of small events, the number of large events, the released energy, the maximum amplitude and the duration of the signal of microseismic events, and the gas data and ground stress data are preprocessed;

[0090] S2: Use time series analysis and spatial data mining technology to analyze the changing trends of coal seam ground stress and gas emission in the same time and space, and identify the changing laws and potential correlations of ground stress and gas parameters through data mining algorithms. At the same time, use multi-scale coupling strategies to identify the changing laws of ground stress and gas parameters;

[0091] S3: Based on the changing trends of ground stress and gas parameters and combined with the occurrence mechanism of coal and gas outburst, a comprehensive analysis and prediction model for outburst is established;

[0092] S4: Based on the output results of the comprehensive analysis and prediction model for coal and gas outbursts, the risk level of coal and gas outbursts is assessed, a three-level early warning mechanism is designed, and corresponding emergency plans and outburst prevention measures are formulated according to the risk level. The three-level early warning mechanism includes threat, danger, and critical levels;

[0093] S5: Establish corresponding emergency mechanisms based on the actual conditions of each mine.

[0094] It should be noted that in the present invention, the core of management lies in comprehensive and real-time monitoring of the risks of coal and gas outburst in mines, and formulating and implementing corresponding preventive measures accordingly. Specifically, effective management of the risks of coal and gas outburst in mines is achieved through real-time monitoring, data analysis, predictive modeling, risk assessment and early warning mechanisms, and the establishment and implementation of emergency mechanisms.

[0095] Example 2

[0096] See also Figure 2-Figure 3 , the specific steps of the multi-scale coupling strategy in this embodiment include:

[0097] A1: Collect gas data and geostress data and perform preprocessing. At the same time, according to the characteristics of the data and analysis requirements, divide the geostress and gas parameters into different scales, and establish a preliminary multi-scale coupling model for each scale;

[0098] A2: Based on the preliminary multi-scale coupling model, determine the area that needs to be meshed and set the size b and density of the mesh. , and use the adaptive meshing method according to b and Perform non-uniform initial grid division on the preliminary multi-scale coupled model;

[0099] It should be noted that the significance of step A2 is to provide an accurate computational grid for subsequent numerical simulation and coupling analysis. Although the preliminary multi-scale coupling model can reflect the overall morphology and structure of the geological body, in order to carry out accurate numerical simulation and coupling analysis, the model needs to be gridded. The quality of grid division directly affects the accuracy and efficiency of numerical simulation. Through regional grid division, grids of different sizes can be set according to the geological characteristics and analysis requirements of different regions, thereby improving the accuracy and efficiency of numerical simulation.

[0100] Among them, the size and density of the grid are set mainly according to the analysis requirements and computing resources. For example, in areas with complex geological features or high analysis requirements, a smaller grid size and a higher grid density can be set to improve the accuracy of numerical simulation; in areas with simple geological features or low analysis requirements, a larger grid size and a lower grid density can be set to save computing resources.

[0101] Furthermore, the specific steps of A2 include:

[0102] (1) Based on the preliminary multiscale coupling model, use the meshing tool to generate an initial mesh. The initial mesh can be uniform or non-uniform, depending on the complexity of the multiscale coupling model and the analysis requirements.

[0103] (2) Perform finite element analysis on the initial mesh to obtain initial solutions, which will serve as the basis for subsequent adaptive mesh adjustment;

[0104] (3) Evaluate the accuracy of the initial solution, usually by comparing the differences between the node strain, stress and other physical quantities and the expected solution, and determine which areas of the mesh need to be refined or coarsened based on the error evaluation results;

[0105] (4) Automatically adjust the size and density of the grid based on the error evaluation results. In areas with larger errors, the grid is refined by adding more nodes and elements; in areas with smaller errors, the grid is coarsened by reducing the number of nodes and elements.

[0106] (5) Use the modified mesh to re-perform finite element analysis and obtain a new solution;

[0107] (6) Compare the difference between the new and old solutions. If the difference is less than the preset convergence standard, the solution is considered to have converged and the adaptive meshing process is completed. Otherwise, return to step (3) for the next round of error evaluation and mesh modification.

[0108] A3: Based on the initial mesh, determine the coupling area. The coupling area is mainly determined based on the analysis requirements and the characteristics of the physical problem. The coupling area is usually the area where two or more different physical fields interact with each other. The physical fields include structural fields, fluid fields, and temperature fields.

[0109] A4: Use the improved finite element method on the inner region of the coupling region to obtain the stress distribution in the inner region;

[0110] A5: Use the improved boundary element method on the outer region of the coupling region to obtain the stress distribution in the outer region;

[0111] A6: The solutions of the inner and outer regions are adjusted by an iterative method to perform continuity treatment on the inner and outer regions of the coupled region until the continuity condition is met and the final solution is obtained;

[0112] Furthermore, the specific steps of A6 include:

[0113] (1) Obtain the solution results of the internal area and the external area;

[0114] (2) Check whether the physical quantities on the boundary of the coupling region meet the continuity conditions, such as temperature continuity and pressure continuity;

[0115] If the continuity condition is not met, continue to iterate;

[0116] If the continuity condition is met, the iteration is judged whether it converges according to the set convergence criterion;

[0117] If converged, the final solution is output;

[0118] If it does not converge, adjust the iteration parameters or the initial solution and iterate again.

[0119] A7: Construct the final multiscale coupling model based on the final solution obtained, compare the final multiscale coupling model with the actual observation data, verify the accuracy of the final multiscale coupling model, and optimize the final multiscale coupling model based on the verification results;

[0120] A8: The optimized final multi-scale coupling model is used to simulate and predict the changing trends of geostress and gas parameters. By analyzing the simulation results, the changing patterns of geostress and gas parameters at different scales and their mutual correlations are identified.

[0121] The specific steps of A4 include:

[0122] A4.1: Load the determined coupling region. Within the coupling region, obtain the internal region that needs to be analyzed by finite element analysis according to the analysis objectives and physical properties, and establish a finite element model based on the geometry and material properties of the internal region.

[0123] A4.2: Divide the internal region into interconnected hexahedral finite elements and select N nodes inside each hexahedral finite element to represent the stress state of the finite element;

[0124] Furthermore, the specific process of dividing the internal area into interconnected hexahedral finite elements in A4.2 includes:

[0125] (1) Determine the geometry and boundaries of the internal area: Determine the geometry and boundary conditions of the internal area through the CAD model;

[0126] (2) Selecting a meshing strategy: The meshing strategy determines how to divide the internal area into hexahedral finite elements. The strategy is selected mainly based on the complexity of the internal area, the required computational accuracy, and the availability of computing resources.

[0127] (3) Generate hexahedral mesh:

[0128] 1) Node generation: Select a series of nodes on the boundary and inside of the internal area, which will serve as the vertices of the hexahedral elements;

[0129] 2) Element generation: Generate hexahedral elements by connecting adjacent nodes. These elements should be connected to each other to form a complete mesh;

[0130] 3) Mesh optimization: In order to improve calculation accuracy and efficiency, it may be necessary to optimize the generated mesh, including adjusting the location of nodes, the size and shape of cells, etc.

[0131] A4.3: Calculate the stiffness matrix for each hexahedral finite element , and assemble the stiffness matrices of all hexahedral finite elements into an overall stiffness matrix ,in, represents the strain matrix, express The transpose of represents the elastic matrix, V represents the volume of the hexahedral finite element;

[0132] A4.4: Apply force boundary conditions on the boundaries of the finite element model and apply external loads on the internal regions ,in, represents the concentrated force applied to the i-th node, express The mean of

[0133] A4.5: Solve the discretized finite element model to obtain the stress distribution in the internal area. The formula is:

[0134] ;

[0135] in, Represents the stress tensor, which is used to describe the stress distribution inside the coal seam. represents the damage factor of the previous step, represents the damage increment, Indicates reference temperature The elastic matrix under Indicates temperature, represents the phase volume fraction, represents the total strain tensor, represents the plastic strain tensor, which is used to describe the irreversible deformation of the material. represents the plastic strain in the previous step, represents the plastic strain increment, n represents the plastic flow direction, represents the total strain tensor at the i-th node, represents the strain on the ith node, represents the overall stiffness at the i-th node.

[0136] The specific steps of A5 include:

[0137] A5.1: Based on the physical mechanism of coal and gas outburst in mines, establish partial differential equations to describe the changes in pressure and gas concentration parameters in the external area of ​​mines ,in, represents the partial derivative, u represents the gas concentration, A represents the diffusion coefficient, k represents the rate at which gas is absorbed or consumed, and B represents the rate at which gas is released from the coal seam;

[0138] A5.2: Convert the partial differential equation into a boundary integral equation and discretize the boundary integral equation, divide the continuous external region into discrete units, and select nodes in each unit to represent the parameter value in the unit;

[0139] A5.3: For each node, establish an algebraic equation about the node parameters based on the boundary integral equation and discretization method, and combine the discretized equations of all nodes into a linear algebraic equation system;

[0140] A5.4: The concentrated stress and gas concentration parameters of the working face are monitored in real time by sensors, and the monitoring data are input into a linear algebraic equation group for real-time solution. The linear algebraic equation group solution process is the prior art content in this field, and is not the inventive solution of the present application, and will not be elaborated here;

[0141] A5.5: Based on the solution results, the stress distribution in the external area is calculated by the extrapolation method, wherein the specific calculation process and calculation formula of the extrapolation method are the prior art content in this field, which is not the inventive solution of this application and will not be elaborated here.

[0142] The specific steps of the integration strategy include:

[0143] B1: Select the basic characteristic index set for predicting coal and gas outburst from the data actually collected in the mining area , and preprocessing is performed, where represents the mth basic characteristic index, and m represents the number of basic characteristic indexes;

[0144] B2: Based on the mechanism of coal and gas outburst, the contribution of each influencing factor to the outburst is analyzed, and the mechanism analysis is performed. The mechanism analysis is the prior art content in this field, and is not the inventive solution of this application, and will not be elaborated here;

[0145] B3: Select C prediction models based on support vector machines as base models and use the preprocessed Input the base model for training and learning to obtain the prediction results of C base models ,in, represents the prediction result of the j-th base model, represents the prediction function of the j-th base model. Similarly, the specific process of training and learning the base model is the prior art content in this field, which is not the inventive solution of the present application and will not be described in detail here;

[0146] B4: Adopt the ensemble learning method based on weighted average to fuse the prediction results of C base models to obtain the final comprehensive prediction result ,in, Represents the weight of the j-th base model, wherein the ensemble learning method based on weighted average is the prior art content in this field, which is not the creative solution of the present application and will not be elaborated here.

[0147] Specific steps in a risk assessment strategy include:

[0148] C1: Obtain real-time monitoring data of coal and gas outburst , and Conduct statistical analysis to identify risk factors for coal and gas outbursts, including: represents the real-time monitoring data of the g-th coal and gas outburst, and g represents the number of real-time monitoring data of coal and gas outburst;

[0149] Furthermore, the specific process of identifying the risk factors of coal and gas outburst includes:

[0150] (1) Conduct ground stress monitoring and gas concentration monitoring to obtain the monitored ;

[0151] (2) Based on the collected data and on-site investigation results, the risk of coal and gas outburst is assessed using risk assessment methods. The comprehensive impact of factors such as coal seam thickness, burial depth of rich coal, mechanical properties of coal, gas content, and geological structure should be fully considered during the assessment process;

[0152] (3) Based on the risk assessment results, a coal and gas outburst prediction map is drawn. The prediction map should comprehensively reflect the known outburst laws, classify the degree of outburst danger, circle the areas with different degrees of outburst danger, and propose the locations and intensities where outbursts are likely to occur;

[0153] (4) Based on steps (1) to (3), further identify potential risk factors for coal and gas outburst, where the risk factors include:

[0154] Coal seam thickness and mining depth: The thicker the coal seam and the deeper the mining depth, the greater the probability of coal and gas outbursts. Generally, the deeper the coal seam is, the greater the gas pressure is, and the relative gas pressure will also increase, causing the risk of coal and gas outburst accidents to increase. At the same time, the deeper the coal seam is, the greater the stress on the coal seam will gradually increase, which will also increase the risk of coal and gas outbursts. The greater the mining intensity, the greater the concentrated stress formed. Therefore, mining depth and mining intensity are considered to be important factors affecting coal and gas outburst accidents.

[0155] Mechanical properties of coal: Weak and brittle coal seams have low compressive strength and are prone to fracture and collapse, increasing the possibility of coal and gas outbursts;

[0156] Gas content: When the gas content in the coal body is high, the risk of outburst accidents increases;

[0157] Geological structure: The risk of coal and gas outburst is higher in areas with complex geological structure and developed faults;

[0158] Mining conditions: unreasonable mining sequence, excessive mining speed and mining intensity may also lead to coal and gas outbursts.

[0159] C2: According to the risk factor identification results, the identified risk factors are classified and corresponding weights are assigned to risk factors of different categories;

[0160] Furthermore, the specific steps of C2 include:

[0161] (1) Risk factors are divided into different categories according to their nature and source, such as geological structure factors, coal seam factors, mining condition factors, and ventilation factors. Each category may contain multiple specific risk factors. For example, geological structure factors may include faults and folds.

[0162] (2) Formulate a weight allocation principle based on the importance, frequency, and impact of each risk factor in coal and gas outburst accidents. The weight allocation should be based on expert experience, historical data, and statistical analysis results;

[0163] (3) According to the weight allocation principle, corresponding weights are assigned to risk factors of different categories. The weight value is usually expressed as a number between 0 and 1, and the sum of the weights of all risk factors should be equal to 1.

[0164] C3: Assess the probability of coal and gas outburst accidents caused by risk factors and severity , and combined with the weight distribution results, calculate the quantitative evaluation value of each risk factor. The formula is:

[0165] ;

[0166] in, represents the quantitative evaluation value of the kth risk factor, represents the probability that the kth risk factor leads to coal and gas outburst accidents, represents the severity of coal and gas outburst accidents caused by the kth risk factor, represents the weight of the kth risk factor, r represents the number of risk factors;

[0167] C4: Based on the quantitative assessment value, the risk of coal and gas outburst is assessed to obtain the overall risk level, and the risk of coal and gas outburst is divided into three levels: low risk, medium risk and high risk;

[0168] Furthermore, the specific steps of C4 include:

[0169] (1) Load the pre-built risk model, input the quantitative assessment value, and calculate the risk value G;

[0170] (2) Set the risk threshold as , ;

[0171] like , it means that the current situation is relatively safe and the risk of coal and gas outburst is low;

[0172] like , it means that the current situation requires attention and preventive measures, and the risk of coal and gas outburst is medium risk;

[0173] like , it means that the current situation has a high risk of outburst and emergency measures need to be taken immediately. The risk of coal and gas outburst is high;

[0174] (3) When When an emergency occurs, the early warning system is automatically triggered, an alarm is sounded, and detailed early warning information is generated, including risk level, affected area, and recommended measures. At the same time, mine managers and staff are notified in a timely manner through text messages, phone calls, and broadcasts.

[0175] C5: Develop corresponding emergency plans and emergency response measures according to the risk level;

[0176] Among them, the emergency plan should include emergency measures such as emergency evacuation, rescue procedures, equipment shutdown, etc. to ensure personnel safety and reduce property losses; anti-burst response measures include adjusting mining methods, strengthening ventilation management, installing monitoring equipment, etc. to reduce the risk of coal and gas outbursts.

[0177] C6: Continuously monitor the risks of coal and gas outbursts, and update risk assessment results and emergency plans in real time.

[0178] In the emergency plan formulation unit, the risk levels include threat, danger, and critical. The specific contents of the emergency plan and emergency response measures formulated according to the risk level include:

[0179] D1: If it is a threat level;

[0180] Emergency plan: Strengthen daily monitoring, regularly analyze data, and ensure the normal operation of the early warning system;

[0181] Measures to prevent sudden outbursts: adjust the speed of excavation or mining to avoid excessive concentrated stress on the working face caused by excessive excavation; strengthen ventilation to reduce gas concentration; and locally reinforce the excavation face;

[0182] Construction adjustment: appropriately slow down the excavation or mining speed to ensure safe excavation;

[0183] D2: If it is a dangerous level;

[0184] Emergency plan: Initiate emergency response mechanism, organize professionals to assess risks on site, and prepare emergency supplies and equipment;

[0185] Measures to prevent coal seam collapse: Strengthen gas extraction to reduce coal seam gas pressure; use remote control for excavation or mining operations to reduce personnel exposure risks; strengthen support to prevent coal seam collapse;

[0186] Among them, the setting of remote control should at least ensure that the operator is within the safety range and avoids direct exposure to potential dangers. This safety range usually needs to be determined according to the specific conditions of the work site, the performance of the equipment and the safety standards. In the present invention, in order to ensure the safety of the operators, a relatively large control distance is set, such as more than 80 meters, or even hundreds of meters, but this distance is not absolute, but needs to be dynamically adjusted according to the specific conditions of the work site.

[0187] Construction adjustment: Suspend excavation or mining operations to relieve stress tension and wait for the concentrated stress to transfer to the deep part. At this time, the microseismic signal will be greatly weakened. Conduct safety inspections and risk assessments, and decide whether to resume operations based on the assessment results;

[0188] D3: If it is a critical level:

[0189] Emergency plan: immediately stop all excavation and mining operations, evacuate all personnel to a safe area; activate the emergency rescue plan and contact external rescue forces;

[0190] Measures to prevent coal outbursts: close the excavation or mining face to prevent gas leakage; start the backup ventilation system to ensure air circulation underground; strengthen monitoring and pay close attention to coal seam dynamics;

[0191] Construction adjustments: Completely stop excavation or mining operations, monitor the frequency and intensity of microseismic signals, conduct thorough safety inspections and risk assessments, and develop detailed rectification plans to ensure that concentrated stress is significantly relieved and excavation can only be resumed when it is safe.

[0192] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. A management system for real-time monitoring and early warning of coal and gas outburst parameters in mines, characterized in that: include: Data processing module, data analysis module, prediction module and risk assessment module; The data processing module includes a data acquisition unit, which is used to collect gas data and ground stress data; the gas data includes gas concentration, gas outflow and ventilation status in the mine; the ground stress data includes the number of small events, the number of large events, the released energy, the maximum amplitude and the duration of the signal of the microseismic event; The data analysis module includes a law recognition unit, in which a multi-scale coupling strategy is configured, and the multi-scale coupling strategy is used to identify the change law of ground stress and gas parameters according to the change trends of ground stress and gas parameters; The prediction module includes a model building unit and an optimization unit. The model building unit is configured with an integration strategy, which is used to build a multi-parameter comprehensive prediction model based on historical data and mechanism analysis to predict coal and gas outbursts; the optimization unit is configured with a particle swarm optimization algorithm to verify the multi-parameter comprehensive prediction model and optimize it according to actual conditions; The risk assessment module includes a risk assessment unit and an emergency plan formulation unit; the risk assessment unit is configured with a risk assessment strategy, and the risk assessment strategy is used to assess and classify the risk of coal and gas outburst; The emergency plan formulation unit is used to formulate emergency plans and emergency response measures according to risk levels; The specific steps of the multi-scale coupling strategy include: A1: Collect gas data and geostress data and perform preprocessing. At the same time, according to the characteristics of the data and analysis requirements, divide the geostress and gas parameters into different scales, and establish a preliminary multi-scale coupling model for each scale; A2: Based on the preliminary multi-scale coupling model, determine the area that needs to be meshed and set the size b and density of the mesh. , and use the adaptive meshing method according to b and Perform non-uniform initial grid division on the preliminary multi-scale coupled model; A3: Determine the coupling area based on the initial mesh division; A4: Use the improved finite element method on the inner region of the coupling region to obtain the stress distribution in the inner region; A5: Use the improved boundary element method on the outer region of the coupling region to obtain the stress distribution in the outer region; A6: The solutions of the inner and outer regions are adjusted by an iterative method to perform continuity treatment on the inner and outer regions of the coupled region until the continuity condition is met and the final solution is obtained; A7: Construct the final multiscale coupling model based on the final solution obtained, compare the final multiscale coupling model with the actual observation data, verify the accuracy of the final multiscale coupling model, and optimize the final multiscale coupling model based on the verification results; A8: Use the optimized final multi-scale coupling model to simulate and predict the changing trends of geostress and gas parameters, and identify the changing patterns of geostress and gas parameters at different scales and their mutual correlations by analyzing the simulation results; The specific steps of the integration strategy include: B1: Select the basic characteristic index set for predicting coal and gas outburst from the data actually collected in the mining area , and preprocessing is performed, where represents the mth basic characteristic index, and m represents the number of basic characteristic indexes; B2: Based on the mechanism of coal and gas outburst, analyze the contribution of each influencing factor to the outburst and conduct mechanism analysis; B3: Select C prediction models based on support vector machines as base models and use the preprocessed Input the base model for training and learning to obtain the prediction results of C base models ,in, represents the prediction result of the j-th base model, represents the prediction function of the jth base model; B4: Adopt the ensemble learning method based on weighted average to fuse the prediction results of C base models to obtain the final comprehensive prediction result ,in, represents the weight of the j-th base model.

2. The management system for real-time monitoring and early warning of coal and gas outburst parameters in mines according to claim 1, characterized in that: The specific steps of A4 include: A4.1: Load the determined coupling area. In the coupling area, according to the physical mechanism of coal and gas outburst in the mine, obtain the internal area that needs to be analyzed by finite element analysis, and establish a finite element model based on the geometric shape and material properties of the internal area; A4.2: Divide the internal region into interconnected hexahedral finite elements and select N nodes inside each hexahedral finite element to represent the stress state of the finite element; A4.3: Calculate the stiffness matrix for each hexahedral finite element , and assemble the stiffness matrices of all hexahedral finite elements into an overall stiffness matrix ,in, represents the strain matrix, express The transpose of represents the elastic matrix, and V represents the volume of the hexahedral finite element.

3. The management system for real-time monitoring and early warning of coal and gas outburst parameters in a mine as claimed in claim 2, characterized in that: The specific steps of A4 also include: A4.4: Apply force boundary conditions on the boundaries of the finite element model and apply external loads on the internal regions ,in, represents the concentrated force applied to the i-th node, express The mean of A4.5: Solve the discretized finite element model to obtain the stress distribution in the internal area. The formula is: ； in, Represents the stress tensor, which is used to describe the stress distribution inside the coal seam. represents the damage factor of the previous step, represents the damage increment, Indicates reference temperature The elastic matrix under Indicates temperature, represents the phase volume fraction, represents the total strain tensor, represents the plastic strain tensor, which is used to describe the irreversible deformation of the material. represents the plastic strain in the previous step, represents the plastic strain increment, n represents the plastic flow direction, represents the total strain tensor at the i-th node, represents the strain on the ith node, represents the overall stiffness at the i-th node.

4. The management system for real-time monitoring and early warning of coal and gas outburst parameters in a mine as claimed in claim 3, characterized in that: The specific steps of A5 include: A5.1: Based on the physical mechanism of coal and gas outburst in mines, establish partial differential equations to describe the changes in pressure and gas concentration parameters in the external area of ​​mines ,in, represents the partial derivative, u represents the gas concentration, A represents the diffusion coefficient, k represents the rate at which gas is absorbed or consumed, and B represents the rate at which gas is released from the coal seam; A5.2: Convert the partial differential equation into a boundary integral equation and discretize the boundary integral equation, divide the continuous external region into discrete units, and select nodes in each unit to represent the parameter value in the unit; A5.3: For each node, establish an algebraic equation about the node parameters based on the boundary integral equation and discretization method, and combine the discretized equations of all nodes into a system of linear algebraic equations; A5.4: Use sensors to monitor the concentrated stress and gas concentration parameters of the working face in real time, and input the monitoring data into the linear algebraic equation group for real-time solution; A5.5: Based on the solution results, obtain the stress distribution in the external area.

5. The management system for real-time monitoring and early warning of coal and gas outburst parameters in a mine as claimed in claim 4, characterized in that: The specific steps of the risk assessment strategy include: C1: Obtain real-time monitoring data of coal and gas outburst , and Conduct statistical analysis to identify risk factors for coal and gas outbursts, including: represents the real-time monitoring data of the g-th coal and gas outburst, and g represents the number of real-time monitoring data of coal and gas outburst; C2: According to the risk factor identification results, the identified risk factors are classified and corresponding weights are assigned to risk factors of different categories; C3: Assess the probability of coal and gas outburst accidents caused by risk factors and severity , and combined with the weight distribution results, calculate the quantitative evaluation value of each risk factor. The formula is: ； in, represents the quantitative evaluation value of the kth risk factor, represents the probability that the kth risk factor leads to coal and gas outburst accidents, represents the severity of coal and gas outburst accidents caused by the kth risk factor, represents the weight of the kth risk factor, r represents the number of risk factors; C4: Based on the quantitative assessment value, the risk of coal and gas outburst is assessed to obtain the overall risk level, and the risk of coal and gas outburst is divided into three levels: low risk, medium risk and high risk; C5: Develop corresponding emergency plans and emergency response measures according to the risk level; C6: Continuously monitor the risks of coal and gas outbursts, and update risk assessment results and emergency plans in real time.

6. The management system for real-time monitoring and early warning of coal and gas outburst parameters in a mine as claimed in claim 5, characterized in that: In the emergency plan formulation unit, the risk levels include threat, danger, and emergency. The specific contents of the emergency plan and emergency response measures formulated according to the risk level include: D1: If the risk level is a threat level, strengthen daily monitoring, regularly analyze data, and locally reinforce the excavation or mining face, increase ventilation, and slow down the excavation or mining speed; D2: If the risk level is dangerous, the emergency response mechanism will be activated, an on-site risk assessment will be organized, gas extraction will be increased, and remote control of excavation or mining operations will be adopted. At the same time, safety inspections and risk assessments will be carried out, and a decision on whether to resume operations will be made based on the assessment results; D3: If the risk level is critical, the emergency rescue plan will be activated, all excavation or mining operations will be stopped immediately, all personnel will be evacuated to a safe area, the excavation face will be closed, the backup ventilation system will be started, and safety inspections and risk assessments will be carried out at the same time to ensure safety before resuming operations.

Images