Thick coal seam cross-cut coal uncovering gas control and support control method and system

Abstract

The invention relates to the technical field of gas control, and discloses a thick coal seam cross-cut coal uncovering gas control and support control method and system. The method comprises the following steps: acquiring an outburst risk assessment index and a drilling scheme through coal seam parameter data analysis; recording grouting pipeline information and monitoring a grouting process to form a quality data set; constructing a support load diagram to determine temporary support parameters; performing matching analysis to determine permanent support parameters to generate a stress map; analyzing the gas gradient of the key point to determine extraction parameters; and performing correlation analysis and identification on the top plate sinking characteristics and the gas abnormal mode to form safety decision data. According to the method, time sequence correlation analysis is performed on the gas dynamic response characteristics and the support-surrounding rock interaction state, and the mapping relation between the roof sinking critical characteristics and the gas concentration abnormal mode is identified, so that the safety and controllability of the rock cross-cut coal uncovering operation of the thick coal seam are improved.

Description

Technical Field

[0001] The present application relates to the field of gas control technology, and in particular to a method and system for gas control and support management in thick coal seam stone gate coal uncovering. Background Art

[0002] During coal mining, the operation of uncovering coal in thick coal seams faces the dual safety risks of gas outburst and roof instability. The traditional method of uncovering coal mainly adopts a technical route that combines pre-extraction of gas with graded support, including drilling pre-extraction, grouting reinforcement, temporary support and permanent support. Currently, the widely used technologies include: multi-stage pre-extraction method, which reduces the gas content during coal uncovering by burying extraction pipelines at a long distance; comprehensive grouting technology, which uses high-pressure grouting equipment to inject cement slurry into the coal seam to enhance the strength of the coal body; double-wall support system, which uses a combination of temporary support and permanent support to enhance the stability of the tunnel; and a safety monitoring system based on a sensor network, which monitors the working environment in real time by deploying gas sensors, stress monitors and displacement meters. These technologies have achieved certain results in practical applications and provided technical support for safe production in coal mines.

[0003] However, the existing technologies have obvious deficiencies in practical applications. First, gas control and support management are often regarded as two independent technical links, lacking systematic integration and coordination, which makes it impossible to effectively identify the correlation between gas dynamic changes and surrounding rock deformation, increasing safety hazards. Secondly, the traditional data acquisition method is mainly based on single-point static measurement, lacks dynamic analysis of the temporal and spatial evolution of parameters, and is difficult to accurately predict the precursor characteristics of sudden disasters. Third, the determination of support parameters mostly relies on empirical judgment and lacks quantitative optimization methods, resulting in unstable support effects and the existence of over-support or under-support. Fourth, safety management and control decisions lack data-driven precise models, and cannot form targeted early warning and disposal plans based on comprehensive analysis of multi-source information, reducing management and control efficiency. Finally, under complex geological conditions, existing technologies are difficult to adapt to special situations such as thick coal seams, high gas content, and broken surrounding rocks, and safety risks are greatly increased. Summary of the invention

[0004] The present application provides a method and system for gas control and support management in thick coal seam stone gate coal uncovering, which is used to identify the mapping relationship between the critical characteristics of roof subsidence and the abnormal gas concentration pattern by performing time-series correlation analysis on the dynamic response characteristics of gas and the support-surrounding rock interaction state, and form scientific safety management decision data, thereby improving the safety and controllability of thick coal seam stone gate coal uncovering operations.

[0005] In the first aspect, the present application provides a method for gas control and support management in stone gate coal uncovering in thick coal seams, the method comprising: obtaining coal seam parameter data in the stone gate coal uncovering area through data acquisition equipment, including coal seam thickness value, solidity coefficient and gas content value, performing multivariate regression analysis on the data, and obtaining a coal seam outburst hazard assessment index and an optimal drilling parameter scheme; recording grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitoring the grouting process by using pressure-flow curve fitting, identifying grouting saturation feature points, and forming a grouting quality evaluation data set; based on the grouting quality evaluation data Construct a temporary support load distribution diagram, determine the π-beam position and anchor net parameter configuration through stress dispersion rules, and generate a support effectiveness index table; match and analyze the support effectiveness index table with permanent support design parameters, determine the U-steel spacing and anchor cable preload value, and generate a support structure stress state data map; perform gas concentration gradient analysis on key points in the support structure stress state data map, determine the extraction position and negative pressure value, and draw a gas control efficiency curve; perform time series correlation analysis based on the gas control efficiency curve and support state data, identify the critical characteristics of roof subsidence and abnormal gas concentration patterns, and form safety management and control decision data.

[0006] In a second aspect, the present application provides a thick coal seam stone gate coal uncovering gas control and support control system, the thick coal seam stone gate coal uncovering gas control and support control system comprising:

[0007] The regression module is used to obtain the coal seam parameter data of the Shimen coal uncovering area through data acquisition equipment, including coal seam thickness value, solidity coefficient and gas content value, and perform multivariate regression analysis on the data to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter plan;

[0008] A monitoring module, used to record the grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitor the grouting process by using pressure-flow curve fitting, identify the grouting saturation feature points, and form a grouting quality evaluation data set;

[0009] A configuration module, used to construct a temporary support load distribution diagram based on the grouting quality evaluation data set, determine the π-shaped beam position and anchor net parameter configuration through stress dispersion rules, and generate a support effectiveness index table;

[0010] A matching module is used to match and analyze the support effectiveness index table with the permanent support design parameters, determine the U-shaped steel spacing and anchor cable preload values, and generate a support structure stress state data map;

[0011] An analysis module is used to perform gas concentration gradient analysis on key points in the stress state data map of the support structure, determine the extraction position and negative pressure value, and draw a gas control efficiency curve;

[0012] The identification module is used to perform time-series correlation analysis based on the gas control efficiency curve and support status data, identify critical features of roof subsidence and abnormal gas concentration patterns, and form safety management and control decision data.

[0013] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned thick coal seam stone gate coal gas control and support management method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned thick coal seam stone gate coal gas control and support management method.

[0015] In the technical solution provided by the present application, by establishing a multivariate regression analysis model for coal seam parameter data, an accurate assessment of the danger of coal seam outburst and a scientific determination of the optimal drilling parameter scheme are achieved, which effectively reduces the blindness of drilling arrangement and improves the efficiency of gas pre-extraction. At the same time, the method uses pressure-flow curve fitting technology to dynamically monitor the grouting process, accurately identify the grouting saturation feature points, solve the problem of inaccurate saturation judgment in the traditional grouting process, and greatly improve the controllability and uniformity of grouting quality. On this basis, the present invention realizes the optimal configuration of the π-beam position and anchor net parameters by constructing a temporary support load distribution diagram and applying stress dispersion rules, significantly enhancing the stability and reliability of temporary support, and through the matching analysis of the support effectiveness index table and the permanent support design parameters, a scientific and reasonable U-shaped steel spacing and anchor cable preload value are determined, forming a complete support structure stress state data map, providing data support for the optimal design of the support structure, and greatly improving the overall performance of the support system. It is particularly noteworthy that the present invention innovatively introduces a gas concentration gradient analysis method, studies the gas distribution characteristics of key points in the support structure stress state data map, scientifically determines the extraction position and negative pressure value, and significantly improves the gas control efficiency. The most distinctive feature is that the present invention successfully identifies the intrinsic connection between the critical characteristics of roof subsidence and the abnormal gas concentration pattern through the time series correlation analysis of the gas control efficiency curve and the support state data, forms a systematic safety management and decision-making data, and realizes the dual prevention of gas disasters and roof accidents. In the present invention, the application contribution of artificial intelligence algorithms is particularly prominent, especially in the links of multivariate regression analysis, curve fitting, time series correlation analysis, etc., through the in-depth mining of massive monitoring data by machine learning algorithms, complex laws and correlations that are difficult to detect by traditional methods are discovered, and the early warning accuracy is significantly improved. The four-level early warning classification standard and corresponding disposal measures data table constructed on this basis provide clear and definite decision-making guidance for on-site operators, greatly improving the pertinence and effectiveness of emergency disposal. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of an embodiment of the method for coal gas control and support management in the rock gate of thick coal seams in the embodiment of the present application;

[0018] Figure 2 It is a schematic diagram of an embodiment of the system for controlling gas management and support of rock-gate coal mining in thick and thick coal seams in the embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiment of the present application provides a method and system for gas control and support management of thick coal seam stone gate uncovering. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the present application, an embodiment of the method for controlling gas in the rock-gate coal mining of thick coal seams and supporting and managing the coal mine includes:

[0022] Step S101, obtaining coal seam parameter data of the stone gate coal uncovering area through data acquisition equipment, including coal seam thickness value, solidity coefficient and gas content value, performing multivariate regression analysis on the data, and obtaining a coal seam outburst hazard assessment index and an optimal drilling parameter scheme;

[0023] Step S102, recording the grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitoring the grouting process by using pressure-flow curve fitting, identifying the grouting saturation feature points, and forming a grouting quality evaluation data set;

[0024] Step S103, constructing a temporary support load distribution diagram based on the grouting quality evaluation data set, determining the π-shaped beam position and anchor net parameter configuration through the stress dispersion rule, and generating a support effectiveness index table;

[0025] Step S104, matching and analyzing the support effectiveness index table with the permanent support design parameters, determining the U-shaped steel spacing and anchor cable preload values, and generating a support structure stress state data map;

[0026] Step S105, performing gas concentration gradient analysis on key points in the support structure stress state data map, determining the extraction position and negative pressure value, and drawing a gas control efficiency curve;

[0027] Step S106: Perform time series correlation analysis based on the gas control efficiency curve and support status data to identify critical features of roof subsidence and abnormal gas concentration patterns, and form safety management and control decision data.

[0028] It is understandable that the execution subject of this application can be a thick coal seam stone gate coal gas control and support management system, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, the coal seams in the Shimen coal-uncovering area were collected for parameter collection, and a portable coal seam parameter detector was used for multi-point sampling and measurement to obtain the coal seam thickness value (15.8m in this case), the firmness coefficient (0.22) and the gas content value. After normalization, these raw data were input into the multivariate regression equation, which used a weighted coefficient method, where the coal seam thickness value weighted coefficient was 0.35, the firmness coefficient weighted coefficient was 0.4, and the gas content value weighted coefficient was 0.25, and the coal seam outburst hazard assessment index was calculated. When the index exceeds 0.6, it indicates a high risk of outburst, and a more intensive drilling arrangement is required. Subsequently, the drilling density coefficient was determined based on the index. Combined with the cross-sectional dimensions of the tunnel (6m wide and 4.5m high), a double-row staggered drilling arrangement was designed. The first row of holes (No. 1-11) were constructed along the tunnel roof toward both sides, with a spacing of 500mm and an inclination of 20°; the second row of holes (No. 12-21) were 500mm away from the first row of holes, with an inclination of 5°, forming the optimal drilling parameter plan.

[0030] Grouting reinforcement was implemented according to the optimal drilling parameter scheme. First, the installation information of each grouting pipe (2-inch iron pipe, each 1m long, with holes staggered every 200mm) was recorded, including the spatial position and installation angle. During the grouting process, the grouting pressure value and flow value were collected every 5 seconds to form a time series data stream. These real-time data were curve fitted by the least squares method to draw the pressure-flow change curve. When the pressure continues to rise and the flow begins to decrease, and the rate of change reaches the preset threshold (usually a 30% decrease in flow and a 50% increase in pressure), it can be identified as a grouting saturation feature point. For example, at a certain grouting point, when the pressure rises from the initial 1.5MPa to 2.3MPa, and the flow drops from 50L / min to 35L / min, the system marks the point as having reached saturation. The saturation feature point information of all grouting points is associated with the spatial position of the pipeline to form a grouting quality evaluation data set.

[0031] A temporary support load distribution map is constructed based on the grouting quality evaluation data set. The rock stress data around the tunnel is obtained through the ground stress monitoring equipment, and the three-dimensional load distribution map is constructed by combining the spatial distribution information in the grouting quality evaluation data set. The optimal installation position of the π-beam (4m long) is determined by the stress dispersion rule (more support points need to be set in the high stress area, and the stress transfer path needs to remain continuous). For tunnels with a coal seam thickness of 15.8m, it is usually necessary to install π-beams in the middle of the roof and 500mm from both sides, and fix them on the U-shaped shed beam through the 420T scraper conveyor chain. At the same time, the anchor net parameter configuration is determined according to the stress distribution, including the anchor net specifications (2000mm×1100mm, Steel bar welding) and anchor arrangement ( The spacing between rows is 1000mm×1000mm). By integrating the position of the π-beam and the parameters of the anchor net, the support resistance value, coverage area ratio and anchor force distribution coefficient are quantified to generate a support effectiveness index table.

[0032] The support effectiveness index table was matched and analyzed with the permanent support design parameters. The support resistance distribution data was extracted from the support effectiveness index table and compared with the permanent support design standard. 29U steel was selected as the support structure. The safety of different U-steel spacings (from 400mm to 700mm) was evaluated and the optimal spacing was determined to be 500mm. According to the U-steel layout and the roadway section, the anchor cable installation position was calculated and set. The 8.2m long steel strand is used as a deep anchoring element, and the anchor preload value (usually 120kN to 150kN) is determined at the same time. The determined U-shaped steel spacing and anchor preload value are input into the support structure analysis program to obtain the internal stress transfer path of the support system and the stress value of the key nodes, and then the contour lines and deformation vector field are drawn to form the support structure stress state data map.

[0033] Gas concentration gradient analysis is performed on key points in the stress state data map of the support structure. Multi-point gas concentration sampling is performed at the stress peak point, stress concentration zone and low stress zone to obtain spatial distribution data. The gas concentration contour map is drawn by interpolation algorithm to identify gas enrichment areas and diffusion channels. In the process of coal mining in the 13# coal seam, it is usually found that there is an abnormally high gas concentration area near the fault zone (up to 2% or more). At this time, it is necessary to determine the best extraction position in combination with geological structural characteristics. For areas with different enrichment levels, gradient negative pressure values ​​are set, using -15kPa for high concentration areas, -12kPa for medium areas, and -8kPa for low concentration areas to form a position-negative pressure correspondence table. By continuously monitoring the extraction volume and residual gas concentration values ​​in different time periods (such as one sampling point every 4 hours), a time series change curve is drawn, and the inflection point and plateau period are marked to form a gas control efficiency curve. The gas control efficiency curve is analyzed in time series with the support state data. The gas concentration change rate and extraction attenuation coefficient are extracted from the gas control efficiency curve to establish a gas dynamic response feature library. At the same time, the roof sinking rate, surrounding rock deformation and support resistance change values ​​in the support status data are time-synchronized and integrated to generate a support-surrounding rock interaction time series table. Data anomalies and change trend inflection points are identified by cross-comparison, and the correlation strength coefficient is calculated. When the roof sinking acceleration suddenly increases (such as from 0.2mm / day to 0.8mm / day) or the cumulative deformation reaches the warning value (usually 25mm), it is determined to be a critical feature of roof sinking. Similarly, when the gas concentration increases sharply (increases by more than 0.3% within 30 minutes), the fluctuation frequency is abnormal (the number of fluctuations exceeds 5 times within 24 hours), or the extraction efficiency decreases significantly (the extraction volume decreases by more than 40%), it is summarized as an abnormal gas concentration mode. Combined with these characteristics, a four-level warning classification standard (attention level, warning level, danger level and emergency level) and corresponding disposal measures are established to form safety management and control decision data.

[0034] In the embodiment of the present application, by establishing a multivariate regression analysis model for coal seam parameter data, an accurate assessment of the danger of coal seam outburst and a scientific determination of the optimal drilling parameter scheme are achieved, effectively reducing the blindness of drilling arrangement and improving the efficiency of gas pre-extraction. At the same time, the method uses pressure-flow curve fitting technology to dynamically monitor the grouting process, accurately identify the grouting saturation feature points, solve the problem of inaccurate saturation judgment in the traditional grouting process, and greatly improve the controllability and uniformity of the grouting quality. On this basis, the present invention realizes the optimal configuration of the π-beam position and the anchor net parameters by constructing a temporary support load distribution diagram and applying stress dispersion rules, significantly enhancing the stability and reliability of temporary support, and through the matching analysis of the support effectiveness index table and the permanent support design parameters, a scientific and reasonable U-shaped steel spacing and anchor cable preload value are determined, forming a complete support structure stress state data map, providing data support for the optimal design of the support structure, and greatly improving the overall performance of the support system. It is particularly noteworthy that the present invention innovatively introduces a gas concentration gradient analysis method, studies the gas distribution characteristics of key points in the support structure stress state data map, scientifically determines the extraction position and negative pressure value, and significantly improves the gas control efficiency. The most distinctive feature is that the present invention successfully identifies the intrinsic connection between the critical characteristics of roof subsidence and the abnormal gas concentration pattern through the time series correlation analysis of the gas control efficiency curve and the support state data, forms a systematic safety management and decision-making data, and realizes the dual prevention of gas disasters and roof accidents. In the present invention, the application contribution of artificial intelligence algorithms is particularly prominent, especially in the links of multivariate regression analysis, curve fitting, time series correlation analysis, etc., through the in-depth mining of massive monitoring data by machine learning algorithms, complex laws and correlations that are difficult to detect by traditional methods are discovered, and the early warning accuracy is significantly improved. The four-level early warning classification standard and corresponding disposal measures data table constructed on this basis provide clear and definite decision-making guidance for on-site operators, greatly improving the pertinence and effectiveness of emergency disposal.

[0035] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0036] (1) A portable coal seam parameter detector was used to sample five points in the stone gate coal uncovering area to obtain the original data of coal seam thickness, solidity coefficient, and gas content;

[0037] (2) Normalize the original data, remove outliers, and form a standardized coal seam parameter set;

[0038] (3) Substitute the standardized coal seam parameter set into the multivariate regression equation of gas outburst tendency, where the weighting coefficient of coal seam thickness is 0.35, the weighting coefficient of solidity coefficient is 0.4, and the weighting coefficient of gas content is 0.25, and the coal seam outburst hazard assessment index is obtained;

[0039] (4) Determine the drilling density coefficient based on the correlation table between the coal seam outburst hazard assessment index and the geological structure complexity;

[0040] (5) Generate a double-row staggered drilling layout diagram by comparing the drilling density coefficient with the tunnel cross-sectional dimensions;

[0041] (6) Perform coverage analysis on the double-row staggered drilling layout, adjust the drilling angle and depth parameters, and obtain a three-dimensional distribution map of the drilling trajectory;

[0042] (7) Based on the three-dimensional distribution map of the drilling trajectory, determine the azimuth, inclination and length values ​​of each drilling hole to form the optimal drilling parameter plan.

[0043] Specifically, a portable coal seam parameter detector was used to conduct five-point sampling measurements in the stone gate coal uncovering area. The detector is a special device that integrates coal seam thickness measurement, firmness test and gas content detection functions, and is characterized by strong portability and high precision. The five-point sampling method refers to taking a sample point every 20 meters along the direction of the tunnel in the proposed coal uncovering area, and measuring the three key parameters of coal seam thickness, firmness coefficient and gas content at each sample point, so as to obtain the basic characteristic data of the coal seam. For example, in the 113 transportation stone gate coal uncovering project of Laoyingshan Coal Mine, the original data obtained by five-point sampling showed that the average thickness of the coal seam was 15.8 meters, the average firmness coefficient was 0.22, and the average gas content was 8.5 cubic meters / ton, indicating that the coal seam was thick and had poor firmness, and had a high gas content. These original data were then normalized and outliers were removed. Normalization is a mathematical transformation method that unifies raw data of different dimensions and magnitudes into the interval [0,1]. This is achieved by subtracting the minimum value of the parameter from each data point and then dividing it by the range of the parameter. In this process, abnormal data points that deviate significantly from the average value are also eliminated, such as data where the gas content at a certain point is suddenly more than 50% higher than the average value, or the thickness of the coal seam differs from other points by more than 30%. Through this process, a standardized coal seam parameter set is formed, which enables comprehensive analysis and comparison between different parameters.

[0044] The standardized coal seam parameter set is substituted into the multivariate regression equation of gas outburst tendency. This equation is a mathematical model for comprehensively evaluating the danger of coal seam outburst, which takes into account the three main influencing factors of coal seam thickness, solidity coefficient and gas content, and determines the influence weight of each factor based on historical data analysis. Specifically, the weighting coefficient of coal seam thickness is 0.35, the weighting coefficient of solidity coefficient is 0.4, and the weighting coefficient of gas content is 0.25. For example, for the standardized data set, if the standardized coal seam thickness value of a point is 0.9, the standardized solidity coefficient is 0.2, and the standardized gas content is 0.7, then the coal seam outburst hazard assessment index of the point is calculated as: 0.9×0.35+0.2×0.4+0.7×0.25=0.555. By calculating the assessment index of all sample points and taking their average value, the coal seam outburst hazard assessment index of the entire coal mining area is obtained.

[0045] The drilling density coefficient is determined based on the calculated coal seam outburst hazard assessment index and the geological structure complexity correlation table. The geological structure complexity correlation table is a data comparison table based on a large amount of engineering experience, which corresponds the coal seam outburst hazard assessment index and the complexity of the geological structure to different drilling density coefficients. For example, when the coal seam outburst hazard assessment index is between 0.5-0.7 and the geological structure is of medium complexity, the corresponding drilling density coefficient is 1.2; when the index is greater than 0.7 or the geological structure is highly complex, the drilling density coefficient will increase to 1.5 or higher. The drilling density coefficient directly determines the spacing and row spacing of the grouting boreholes. The larger the coefficient, the denser the drilling arrangement.

[0046] After determining the drilling density coefficient, a double-row staggered drilling layout diagram is generated by comparing it with the tunnel section size. The specific operation is to multiply the drilling density coefficient by the standard drilling spacing of the tunnel (usually 600mm) to obtain the actual drilling spacing that should be used. For example, when the drilling density coefficient is 1.2, the actual drilling spacing should be 600mm÷1.2=500mm. According to this spacing, the first row of drill holes (No. 1-11) is arranged circumferentially on the tunnel roof, and then the second row of drill holes (No. 12-21) is arranged 500mm away from the first row of drill holes to form a staggered drilling layout diagram to ensure the continuity and integrity of the grouting coverage.

[0047] A coverage analysis is performed on the generated double-row staggered borehole layout to ensure that the grouting can fully penetrate into all areas inside the coal seam. The coverage analysis includes calculating the effective grouting radius of each borehole (usually 0.25m), and then checking whether there are blind areas covered by grouting or areas with excessive overlap. By adjusting the drilling angle (such as adjusting the inclination angle of the first row of boreholes to 20° and the inclination angle of the second row of boreholes to 5°) and depth parameters (such as setting the length of the first row of boreholes to 29m and the length of the second row of boreholes to 42m), the spatial distribution of the boreholes is optimized to obtain a three-dimensional distribution map of the drilling trajectory to ensure the uniformity of the grouting reinforcement effect. Based on the three-dimensional distribution map of the drilling trajectory, the specific parameters of each borehole are determined, including the azimuth (ranging from 124° to 136°), inclination (20° for the first row and 5° for the second row) and length values ​​(29m for the first row and 42m for the second row), to form the optimal drilling parameter plan.

[0048] Through actual case verification, after a certain mining area adopted this method, it successfully uncovered a 15.8-meter-thick coal seam that was originally highly dangerous. The strength of the coal body after grouting reinforcement was significantly improved, the gas extraction efficiency was improved, the coal uncovering period was shortened by 40%, and no roof collapse or gas exceeding the limit accidents occurred during the entire process, which demonstrated the practical value and safety guarantee role of this method in the process of uncovering coal in thick coal seams.

[0049] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0050] (1) Generate a grouting pipeline layout navigation map based on the optimal drilling parameter plan, mark the spatial position coordinates and installation angle of each pipeline, and record the grouting pipeline installation information;

[0051] (2) Input the grouting pipeline installation information into the grouting monitoring system, set the data sampling frequency to 5 seconds / time, and synchronously collect the grouting pressure value and grouting flow value;

[0052] (3) Arrange the collected grouting pressure values ​​and grouting flow values ​​in dual-parameter time series and draw a scatter plot of pressure-flow dynamic changes;

[0053] (4) curve fitting is performed on the pressure-flow dynamic change scatter diagram by the least square method to obtain the characteristic curve of the grouting process;

[0054] (5) Identify the inflection point on the characteristic curve of the grouting process. When the pressure rises and the flow rate decreases to a threshold, it is marked as the grouting saturation characteristic point;

[0055] (6) The grouting saturation feature points are combined with the grouting pipeline installation information to establish a corresponding relationship table between spatial position and saturation time, thus forming a grouting quality evaluation data set.

[0056] Specifically, a grouting pipe layout navigation map is generated based on the optimal drilling parameter scheme, and the three-dimensional coordinates, direction angles, and depth information of each borehole are converted into grouting pipe installation instructions. For example, for the 21 boreholes used to transport the stone gate, each borehole is marked with precise spatial position coordinates, including X, Y, and Z three-dimensional coordinate values ​​and installation angles. For example, the position coordinates of borehole No. 1 may be (0,0,4.5), with an azimuth of 124° and an inclination of 20°; while the position coordinates of borehole No. 12 are (0.5,0,4.5), with an azimuth of 126° and an inclination of 5°. By inputting these parameters into the grouting navigation software, the specific installation path and position of each grouting pipe are generated, forming a grouting pipe installation information record.

[0057] After the grouting pipeline installation information is input into the grouting monitoring equipment, the data sampling frequency is set to 5 seconds / time. This frequency ensures the continuity and real-time nature of data acquisition, and does not generate too much redundant data. During the grouting process, the pressure sensor and flow meter continuously monitor the grouting pressure value and the grouting flow value to form two sets of time series data. These data are transmitted in real time through the data acquisition module to record the pressure and flow parameters at each time point. For example, at 0 seconds, 5 seconds, 10 seconds... and other time points after the grouting starts at a certain grouting point, the pressure value (1.50MPa, 1.62MPa, 1.75MPa...) and flow value (50L / min, 48L / min, 47L / min...) are recorded respectively.

[0058] The collected grouting pressure and grouting flow values ​​are arranged in a dual-parameter time series, which means that the time is used as the horizontal axis, and the two parameters of pressure and flow are represented on the same chart, but different vertical axis scales are used to form a three-dimensional relationship diagram of time-pressure-flow. The time factor is removed, and the pressure is directly used as the horizontal axis and the flow is used as the vertical axis to draw a scatter plot of the dynamic changes of pressure-flow. This representation method intuitively shows the changing relationship between pressure and flow during the grouting process, which helps to identify the key change points in the grouting process.

[0059] The characteristic curve of the grouting process is obtained by fitting the pressure-flow dynamic change scatter plot through the least square method. The least square method is a mathematical optimization technique that finds the best fitting function of the data by minimizing the sum of squares of errors. In the grouting process, the relationship between pressure (P) and flow (Q) can be expressed by the following formula:

[0060] Q=α·e -β·P +γ·P+δ

[0061] Among them, α represents the initial flow coefficient, β represents the flow attenuation coefficient, γ represents the pressure influence factor, and δ represents the basic flow constant. By applying the least squares method to the scattered data, the optimal values ​​of these four parameters are calculated so that the sum of the square errors between the predicted value and the actual observed value is minimized. The specific calculation process is to solve the following optimization problem:

[0062]

[0063] Where T is the total number of sampling points, P t and Q t are the pressure and flow observations at time point t, respectively. The minimization problem is solved by a numerical optimization algorithm (such as gradient descent or Newton's method) to obtain the optimal estimated values ​​of the parameters α, β, γ, and δ, and then the characteristic curve of the grouting process is obtained. For example, in a certain grouting process, it is calculated that α=65, β=0.3, γ=-5, δ=25. This set of parameters describes the characteristics of the grouting process at this point and can be used to predict flow changes under different pressures.

[0064] After obtaining the characteristic curve of the grouting process, it is necessary to identify the inflection point on the curve, that is, the point where the pressure rises and the flow rate decreases to a threshold value. This point is marked as the grouting saturation characteristic point. The mathematical definition of the inflection point is the point where the second-order derivative of the curve is zero, indicating the position where the curve changes from concave to convex or from convex to concave. During the grouting process, when the pressure continues to rise but the flow rate begins to drop significantly, it means that the pores of the medium (coal body) have been fully filled with grouting materials, and the marginal benefits of continuing grouting begin to decline. The specific judgment criteria are: when the pressure rise rate exceeds a preset value (such as 0.2MPa per minute) and at the same time the flow rate decrease rate exceeds another preset value (such as 5L per minute), it can be determined that the grouting saturation point has been reached. The mathematical expression is:

[0065]

[0066] in, represents the rate of change of pressure, represents the flow rate change rate, θ P and θ Q are the thresholds for pressure increase and flow rate decrease, respectively.

[0067] The grouting saturation feature points are combined with the grouting pipe installation information to establish a table of correspondence between spatial position and saturation time, forming a grouting quality evaluation data set. The three-dimensional coordinates of each grouting hole are associated with the time point when the position reaches the saturated state to form a space-time mapping table. This data set not only contains spatial position and saturation time, but also includes multi-dimensional information such as the total amount of grouting, pressure value at saturation, and characteristic parameters of the grouting process. With these data, the reinforcement quality of the entire grouting area can be comprehensively evaluated to identify potential weak areas or over-grouting areas.

[0068] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0069] (1) Obtain rock mass stress data around the tunnel through ground stress and deformation monitoring equipment, and construct a three-dimensional load distribution numerical field by combining the spatial position information in the grouting quality evaluation data set;

[0070] (2) Perform isosurface division on the three-dimensional load distribution numerical field, identify high stress concentration areas and low stress relaxation areas, and mark the key control points of temporary support;

[0071] (3) According to the spatial distribution of the key control points of the temporary support, the installation position of the π-shaped beam is optimized by using the stress dispersion rule to determine the center coordinates and orientation angle of the π-shaped beam;

[0072] (4) Based on the center coordinates of the π-shaped beam, the anchor net coverage area on the tunnel surrounding rock surface is divided, the anchor net unit size and overlap width are calculated, and an anchor net parameter configuration table is generated;

[0073] (5) Integrate the anchor net parameter configuration table with the π-beam position information, map it to the three-dimensional load distribution numerical field, and simulate the adjustment effect of the support structure on the stress field;

[0074] (6) By evaluating the adjustment effect of the support structure on the stress field, the support resistance value, coverage area ratio, and anchoring force distribution coefficient are quantified to generate a support effectiveness index table.

[0075] Specifically, the stress data of the rock mass around the tunnel is obtained through the ground stress deformation monitoring equipment. These equipment include stress sensors, displacement meters and anchor dynamometers, which are distributed at key locations around the tunnel to record the stress state of the surrounding rock in real time. The stress data obtained include the magnitude, direction and displacement change of the principal stress, which are associated with the spatial position information in the grouting quality evaluation data set. The grouting quality evaluation data set contains information such as the three-dimensional coordinates of the grouting points, the grouting saturation time, and the total amount of grouting. Through the spatial interpolation algorithm, the discrete stress measurement point data is fused with the grouting quality data to construct a three-dimensional load distribution numerical field. For example, in the transportation stone gate coal uncovering project, 15 stress monitoring points were installed, covering the roof, two sides and bottom plate of the tunnel. Combined with the quality evaluation data of 21 grouting points, a three-dimensional load distribution numerical field containing 150,000 grid nodes was generated through the Kriging interpolation method.

[0076] The isosurface division of the constructed three-dimensional load distribution numerical field refers to dividing the entire numerical field into different stress areas according to the magnitude of the stress value. The principal stress value or equivalent stress value is usually used as the basis for division, and several stress thresholds are set to divide the numerical field into high stress concentration area, medium stress area and low stress relaxation area. The high stress concentration area refers to the area where the stress value exceeds 70% of the surrounding rock strength. These areas are prone to damage caused by stress concentration; while the low stress relaxation area refers to the area where the stress value is lower than 50% of the initial stress of the surrounding rock. These areas often have loosened or broken. Through computer graphics processing technology, these different stress areas are visualized in three-dimensional space, and the key control points that need to be focused on are marked. These control points are usually located at the junction of high stress and low stress, or in areas with large stress gradients, and are the focus of temporary support system design.

[0077] According to the spatial distribution of the marked temporary support key control points, the stress dispersion rule is used to optimize the installation position of the π-beam. The stress dispersion rule means that the support structure should be able to effectively disperse the concentrated stress and prevent sudden damage caused by local high stress. Specifically, the π-beam should be installed at a position that can cover the most key control points and coordinate with the overall stress field distribution. By calculating the support effectiveness index of each candidate position (taking into account factors such as the number of key points covered, the matching degree of the support structure and the stress direction), the optimal π-beam center coordinates are determined. At the same time, the orientation angle of the π-beam should be perpendicular to the principal stress direction to provide the maximum support resistance. For example, in a certain section of the tunnel, it is calculated that three π-beams need to be installed, located at the center line of the tunnel and 500 mm to the left and right of the center line. Each π-beam is 4 meters long and has a direction angle perpendicular to the tunnel direction.

[0078] Based on the determined π-beam center coordinates, the anchor net coverage area of ​​the tunnel surrounding rock surface is divided. As an important component of temporary support, the anchor net is mainly used to prevent the coal wall from spalling and falling. The division of the anchor net coverage area needs to consider the shape of the tunnel section, the degree of surrounding rock fragmentation and the position of the π-beam. The tunnel surface is usually divided into several rectangular coverage areas, and each area is covered with a standard size anchor net. The anchor net specification used for Laoyingshan Coal Mine is 2000mm×1100mm, which is composed of The anchor net is welded with steel bars. When calculating the size of the anchor net unit, the size of the actual coverage area and the required overlap width between the anchor nets must be considered. The overlap width is usually 10% of the anchor net length, which is about 200mm, to ensure that there is no gap between the anchor nets. Through these parameters, the anchor net parameter configuration table is generated, including the number of each anchor net, the installation location coordinates, the orientation, and the number of fixed points.

[0079] The generated anchor net parameter configuration table is integrated with the π-beam position information and mapped to the three-dimensional load distribution numerical field to simulate and analyze the adjustment effect of the support structure on the stress field. This process uses finite element numerical simulation technology to add the support structure as a boundary condition to the original stress field model and recalculate the stress distribution. The addition of the support structure will change the stress distribution of the surrounding rock, which is usually manifested as a reduction in the peak stress in the high stress concentration area and an increase in the constraint force in the low stress relaxation area. By comparing the stress distribution cloud map before and after support, the support effect can be intuitively evaluated, with special attention to the stress changes at the key control points.

[0080] Through the quantitative evaluation of the stress field adjustment effect of the support structure, a series of index parameters reflecting the support effectiveness are calculated. The support resistance value refers to the maximum resistance that the support structure can provide, usually in MPa; the coverage area ratio refers to the proportion of the tunnel surface area effectively covered by the support structure to the total surface area; the anchor force distribution coefficient reflects the uniformity of the anchor force in space. Under ideal conditions, the coefficient should be close to 1, indicating that the anchor force is evenly distributed. These indicators comprehensively reflect the effectiveness and reliability of the temporary support system, forming a support effectiveness index table, which provides a reference for the subsequent permanent support system design.

[0081] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0082] (1) Extract the support resistance value distribution data from the support effectiveness index table, compare and analyze the historical schemes in the permanent support design standard library, and select the U-shaped steel specifications and layout methods with the highest adaptability;

[0083] (2) Based on the U-steel specifications and layout methods, different U-steel spacing values ​​are sorted in a list, and the optimal U-steel spacing is obtained by evaluating the safety redundancy coefficient;

[0084] (3) calculating the spatial arrangement points of the anchor cables according to the U-shaped steel spacing and the tunnel section parameters, and determining the anchor cable length and anchoring depth in combination with the surrounding rock geological parameters;

[0085] (4) setting the anchor cable preload force gradient at each position of the anchor cable spatial arrangement point to form a preload force value distribution mapping table;

[0086] (5) Inputting the U-shaped steel spacing and anchor cable preload values ​​into a support structure stress analysis program to obtain the internal stress transfer path of the support system and the stress values ​​of key nodes;

[0087] (6) Through the internal stress transfer path of the support system and the stress values ​​of key nodes, stress contour lines and deformation vector fields are drawn to form a data map of the stress state of the support structure.

[0088] Specifically, the support resistance value distribution data is extracted from the support effectiveness index table. The support resistance value refers to the maximum resistance that the support structure can provide, usually expressed in MPa. The support resistance value distribution data contains the support strength information required at different locations of the tunnel. These data are compared and analyzed with the historical schemes in the permanent support design standard library. The permanent support design standard library is a data set that contains support schemes that have been successfully applied under different geological conditions, and records information such as U-shaped steel specifications, spacing, and supporting anchor cable parameters. The fitness score is obtained by calculating the difference between the current support resistance value distribution and each scheme in the standard library. The fitness score is calculated using methods such as Euclidean distance or cosine similarity. The higher the score, the better the match. After comparison, the U-shaped steel specifications and layout methods with the highest fitness are selected. For example, in this example, 29U-shaped steel is selected as the main support unit.

[0089] Based on the determined 29U steel specifications and layout methods, different U-steel spacing values ​​are sorted and evaluated. U-steel spacing refers to the distance between two adjacent U-steel supports, usually ranging from 400mm to 700mm. List sorting is to arrange all possible spacing values ​​in order from small to large, and then calculate the corresponding safety redundancy coefficient for each spacing value. The safety redundancy coefficient refers to the ratio of the actual support strength to the theoretical required support strength. The larger the coefficient, the higher the safety margin of the support system. The calculation of the safety redundancy coefficient takes into account multiple factors such as the bending strength of the U-steel, the cross-sectional dimensions of the tunnel, and the surrounding rock pressure. By comparing the safety redundancy coefficients at different spacings, the optimal U-steel spacing is selected. In practical applications, the 113 transportation stone gate coal uncovering project of Laoyingshan Coal Mine selected a U-steel spacing of 500mm, which not only meets safety requirements but also takes into account economy.

[0090] According to the determined U-shaped steel spacing and tunnel section parameters, the anchor cable spatial layout points are calculated, and the anchor cable length and anchoring depth are determined in combination with the surrounding rock geological parameters. Anchor cable layout is a three-dimensional spatial layout problem, which requires consideration of the maximum support effect and construction feasibility. The calculation formula for the anchor cable spatial layout points is as follows:

[0091]

[0092] Among them, L anchor Represents the length of the anchor cable, H tunnel is the roadway height, S spacing is the U-shaped steel spacing, K rock is the surrounding rock strength coefficient, RQD is the rock quality index (Rock Quality Designation), λ1, λ2, λ3 are weight coefficients related to tunnel height, support spacing and rock quality respectively. The anchor cable anchoring depth is determined by the following formula:

[0093]

[0094] Among them, D anchorage is the anchorage depth of the anchor cable, F tensile is the anchor cable design preload, d anchor is the cable diameter, τ bond is the bonding strength between the anchor cable and the grouting body, Δu is the expected deformation of the tunnel surrounding rock, and η is the deformation compensation coefficient. The reasonable layout of the anchor cable is calculated, including key parameters such as the anchor cable position, length and anchoring depth.

[0095] The anchor preload gradient is set for each position of the anchor cable spatial arrangement point to form a preload value distribution mapping table. Anchor cable preload refers to the initial tension applied when installing the anchor cable, which is crucial to controlling the deformation of the surrounding rock. Depending on the surrounding rock type and stress distribution, different preloads need to be set for anchor cables at different positions. Generally, the anchor preload in high-stress areas is set larger, while the anchor preload in low-stress areas is relatively small. The preload value distribution mapping table records the coordinates of each anchor cable position and the corresponding preload value, providing precise guidance for anchor cable installation. For example, the anchor preload in the center of the tunnel roof may be set to 150kN, while the anchor preload on both sides may be set to 120kN.

[0096] The determined U-steel spacing and anchor preload values ​​are input into the support structure stress analysis program to obtain the stress transfer path and key node stress values ​​within the support system. The support structure stress analysis uses the finite element method or discrete element method to discretize the entire support system into a finite number of units, establish a mechanical model, and solve the node displacement and unit stress. The analysis process takes into account the interaction between the support structure and the surrounding rock, and calculates the stress state and deformation of each key node in the support system (such as the connection point between the U-steel and the anchor cable, the contact point between the U-steel and the bottom plate, etc.). These data reflect the stress state of the support system under the pressure of the surrounding rock, which helps to evaluate the safety and stability of the support system.

[0097] Through the stress transfer path inside the support system and the force values ​​of key nodes, stress contour lines and deformation vector fields are drawn to form a data map of the stress state of the support structure. Stress contour lines refer to curves that connect points with the same stress value in space, which intuitively show the stress distribution. The deformation vector field describes the deformation direction and size of each point in the support structure. The stress state data map comprehensively displays the mechanical behavior of the support system under working conditions, including key information such as stress concentration areas, deformation trends, and potential weak links.

[0098] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0099] (1) Mark the stress peak point, stress concentration zone and low stress area on the stress state data map of the support structure, and obtain the initial value of gas concentration at the key points through multi-point sampling method;

[0100] (2) Perform spatial interpolation on the initial values ​​of gas concentration at key points, draw gas concentration contour distribution maps, and identify gas enrichment areas and diffusion channels;

[0101] (3) Determine the extraction location and drilling direction based on the gas concentration contour distribution map and the location of the coal seam geological fault zone;

[0102] (4) For areas with different gas enrichment levels, set a gradient negative pressure value from -15 kPa to -8 kPa and establish a position-negative pressure correspondence table;

[0103] (5) Arrange the gas extraction system according to the extraction position and negative pressure value, and record the extraction volume and residual gas concentration value in different time periods;

[0104] (6) The extraction volume and the residual gas concentration value in the different time periods are plotted into a time series change curve, and the inflection point and the plateau period are marked to form a gas control efficiency curve.

[0105] Specifically, key positions are marked on the data map of the stress state of the support structure, including the peak stress point, stress concentration zone and low stress area. The peak stress point refers to the position with the largest stress value in the support structure, which usually appears in the center of the tunnel roof or the connection between the two sides and the roof; the stress concentration zone refers to the continuous area where the stress value is significantly higher than the surrounding area. These areas are often high-risk areas for deformation and damage of the tunnel; the low stress area is an area with lower stress values. These areas may have undergone plastic deformation or loosening and breaking. The gas concentration is measured at these key positions by the multi-point sampling method to obtain the initial value of the gas concentration. The multi-point sampling method refers to the use of a portable gas detector to perform fixed-point sampling at pre-marked key points. Usually, 3-5 data are collected at each point and the average value is taken to ensure the accuracy and representativeness of the data. For example, in a thick coal seam stone gate coal uncovering project, 15 key points were marked, including 5 peak stress points, 6 points on stress concentration zones and 4 low stress area points. The gas concentration values ​​measured at these points ranged from 0.3% to 1.8%.

[0106] The spatial interpolation of the initial values ​​of gas concentration at key points is to infer the gas distribution of the entire area from limited discrete measurement point data. Spatial interpolation methods include inverse distance weighted method, Kriging method and spline function method, among which Kriging method is the most commonly used because it not only considers the distance factor, but also the spatial autocorrelation, and can generate more accurate interpolation results. Through interpolation calculation, the gas concentration distribution data in the entire tunnel space is obtained, and then the gas concentration contour distribution map is drawn. Contour line refers to the curve formed by connecting points with equal gas concentration, and different concentration levels are represented by different colors or line types. By analyzing the distribution characteristics of contour lines, gas enrichment areas and diffusion channels are identified. Gas enrichment areas refer to areas with dense contour lines and high concentration values, which usually appear in fault fracture zones, stress relaxation areas or coal seam thickness mutations; diffusion channels refer to the dominant path for gas migration from enrichment areas to low concentration areas, which are often related to tunnel structures, geological cracks or poor support areas.

[0107] According to the gas concentration contour distribution map and the location of the geological fault zone of the coal seam, the extraction location and drilling direction are determined. The geological fault zone refers to the broken zone caused by faults, folds or other geological structures in the coal seam. These areas are often the main places for gas enrichment and are also channels for easy migration of gas. The principle for determining the extraction location is: give priority to locations with high gas concentration and near the geological fault zone, while considering construction feasibility and extraction efficiency. The drilling direction should be perpendicular to the direction of the fault zone to maximize penetration of the gas-enriched area. Generally speaking, at least 1-2 extraction boreholes are arranged in each gas-enriched area, and the borehole spacing is usually 15-20 meters. The borehole length is determined according to the thickness of the coal seam and the gas distribution range, usually 20-50 meters.

[0108] For areas with different gas enrichment levels, set gradient negative pressure values ​​and establish a position-negative pressure correspondence table. The negative pressure value refers to the pressure difference of the extraction system relative to the atmospheric pressure, and the unit is kPa. Negative values ​​indicate that it is lower than the atmospheric pressure. The principle of gradient negative pressure design is: the higher the gas concentration, the greater the negative pressure is set, forming a directional flow of gas from the high-concentration area to the low-concentration area. According to practical experience, high-concentration areas (gas concentration>1.0%) are usually set to a negative pressure of -15kPa, medium-concentration areas (gas concentration 0.5%-1.0%) are set to a negative pressure of -12kPa, and low-concentration areas (gas concentration<0.5%) are set to a negative pressure of -8kPa. By establishing a position-negative pressure correspondence table, appropriate negative pressure values ​​are assigned to each extraction borehole to ensure the scientificity and effectiveness of the extraction system.

[0109] By determining the extraction position and negative pressure value, the gas extraction system is arranged, and the extraction effect data is recorded. The gas extraction system includes components such as drilling holes, sealing devices, gas collection pipelines and extraction pumps. Two types of key data need to be recorded regularly during the extraction process: one is the extraction volume, that is, the volume of gas extracted from the borehole per unit time, usually expressed in cubic meters per minute; the other is the residual gas concentration value, that is, the gas concentration remaining in the working face or tunnel after extraction, usually expressed as a percentage. These data are usually recorded every 4 hours or per shift to form a time series data set. For example, during the 72-hour continuous extraction process of a certain extraction borehole, the extraction volume and residual gas concentration are recorded every 4 hours, generating a total of 18 sets of time series data.

[0110] Plotting the recorded extraction volume and residual gas concentration values ​​in different time periods into a time series change curve is an important means of data visualization and analysis. The time series change curve uses time as the horizontal axis and the extraction volume and gas concentration as the vertical axis (can be represented by a double vertical axis), which intuitively shows the changing trends of these two key indicators during the extraction process. Mark the inflection point and plateau period on the curve. The inflection point refers to the point where the slope of the curve changes significantly, usually indicating a turning point in the extraction efficiency; the plateau period refers to the stage when the curve tends to be horizontal, indicating that the extraction has entered a stable state. By analyzing the time and location of the inflection point, as well as the duration and numerical level of the plateau period, a gas control efficiency curve is formed, which comprehensively reflects the working effect of the extraction system and the overall efficiency of gas control.

[0111] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0112] (1) Extract the gas concentration change rate and gas extraction attenuation coefficient from the gas control efficiency curve and establish a gas dynamic response feature library;

[0113] (2) The roof subsidence rate, surrounding rock deformation and support resistance change values ​​in the support status data are integrated synchronously to generate a support-surrounding rock interaction time series table;

[0114] (3) cross-comparing the gas dynamic response feature library with the support-surrounding rock interaction time series table, identifying data anomalies and change trend inflection points, and marking the correlation strength coefficient;

[0115] (4) Based on the correlation strength coefficient, the roof subsidence acceleration mutation point and the cumulative deformation threshold point are extracted from the support-surrounding rock interaction time series table to determine the roof subsidence critical characteristics;

[0116] (5) performing trend analysis on the data in the gas dynamic response feature library, identifying intervals of abrupt gas concentration increases, intervals of abnormal fluctuation frequency, and intervals of decreased extraction efficiency, and summarizing them as abnormal gas concentration patterns;

[0117] (6) Based on the critical characteristics of roof subsidence and the abnormal gas concentration pattern, a four-level warning classification standard and a corresponding disposal measure data table are established to form safety management and control decision-making data.

[0118] Specifically, key indicators are extracted from the gas control efficiency curve. The gas concentration change rate refers to the change in gas concentration per unit time, which is calculated by differentially calculating the gas concentration values ​​at adjacent time points on the gas control efficiency curve. Taking the coal-excavating working face as an example, gas concentration data is collected every 30 minutes. Assuming that the gas concentration measured at time t1 is 0.8%, and the concentration measured at time t2 (30 minutes later) is 0.65%, the gas concentration change rate for this time period is -0.5% / hour. The gas extraction attenuation coefficient indicates the degree of attenuation of the extraction effect over time, and is obtained by calculating the ratio of the gas extraction volume in a continuous time period. When continuous monitoring found that the gas extraction volume of a borehole on the first day was 15m 3 / hour, and dropped to 12m the next day 3 / hour, and dropped to 9.6m on the third day 3 / hour, and the daily attenuation coefficient of the borehole is about 0.8 through data analysis. These change rates and attenuation coefficient data are classified and sorted according to time and space positions to form a gas dynamic response feature library, which contains the time series change rules of gas parameters in each area.

[0119] Next, the data collected by the support status monitoring system are integrated and processed. The roof sinking rate is calculated by dividing the roof displacement measured by the displacement sensor by the corresponding time interval. The surrounding rock deformation is directly measured by a multi-point displacement meter. The change in support resistance is obtained by monitoring the change in the stress state of the support structure through a pressure sensor. During the Shimen coal mining process, the roof of a certain measuring point sank 2.8mm within 24 hours, and the roof sinking rate was 0.117mm / hour; at the same time, the surrounding rock deformation monitoring showed that the side wall was squeezed inward by 1.5mm, and the pressure sensor reading on the U-shaped steel support increased from the initial 16.8MPa to 17.5MPa. These data are synchronously integrated according to a unified time base to generate a support-surrounding rock interaction time series table containing timestamps, position coordinates, and various parameter values.

[0120] The cross-comparison between the gas dynamic response feature library and the support-surrounding rock interaction time series table is achieved through a data association analysis algorithm, which first aligns the two data sets in time and then calculates the correlation coefficient between the parameters. When it is found that the roof subsidence rate in a certain area suddenly increases, the gas concentration also fluctuates abnormally. The system will mark this data point and calculate the correlation strength coefficient. The correlation strength coefficient is calculated by the Pearson correlation coefficient method, with a value range of -1 to 1. The closer the absolute value is to 1, the stronger the correlation. For example, in a coal-uncovering working face, it was found that the correlation coefficient between the roof subsidence rate and the gas concentration change rate reached 0.85, indicating that the two have a strong positive correlation, and the point is marked as a high-correlation anomaly point.

[0121] Based on the correlation strength coefficient, the roof subsidence acceleration mutation point and cumulative deformation threshold point are extracted from the support-surrounding rock interaction time series table. The roof subsidence acceleration mutation point refers to the time point when the rate of change of the roof subsidence rate changes significantly, which is obtained by performing second-order difference calculation on the roof subsidence rate data. When the second-order difference value exceeds the preset threshold (such as 0.05mm / hour2), it is marked as an acceleration mutation point. The cumulative deformation threshold point is determined based on the cumulative value of the surrounding rock deformation. When the cumulative deformation of a certain measuring point reaches the set critical value (such as the cumulative subsidence of the roof is 10mm), it is marked as a threshold point. By analyzing the distribution law and occurrence conditions of these characteristic points, the critical characteristics of roof subsidence are summarized, which are important indicators for predicting possible instability of the roof.

[0122] Perform trend analysis on the data in the gas dynamic response feature library to identify intervals of sharp increase in gas concentration, abnormal fluctuation frequency, and decreased extraction efficiency. The interval of sharp increase in gas concentration refers to the period when the gas concentration increases by more than 0.2% in a short period of time (such as within 1 hour); the interval of abnormal fluctuation frequency refers to the period when the gas concentration fluctuation frequency is significantly higher than the normal level, which is identified by analyzing the spectral characteristics of the gas concentration time series data through fast Fourier transform; the interval of decreased extraction efficiency is the period when the extraction volume continues to be lower than the expected value and shows a downward trend. The characteristics of these abnormal intervals are summarized as gas concentration abnormality patterns to provide a basis for safety management and control decisions.

[0123] The critical characteristics of roof subsidence are combined with the abnormal gas concentration pattern to establish a four-level warning classification standard and a corresponding disposal measure data table. The four-level warning corresponds to different risk levels: Level 4 (blue) indicates slight risk, Level 3 (yellow) indicates medium risk, Level 2 (orange) indicates high risk, and Level 1 (red) indicates severe risk. Each risk level has corresponding trigger conditions and disposal measures. For example, when the roof subsidence rate is detected to exceed 0.2 mm / hour and the gas concentration fluctuation frequency is abnormal, a level 3 warning is triggered, and the corresponding disposal measures include increasing the monitoring frequency, adjusting support parameters, and strengthening local extraction. These warning standards and disposal measures constitute safety management and control decision data, providing clear risk management guidance for on-site operators.

[0124] Taking a thick coal seam stone gate coal mining face as an example, the above method is used to analyze the safety management decision data. The gas monitoring data of the working face for 7 consecutive days shows that the average gas extraction attenuation coefficient of the extraction borehole is 0.85, and the daily change rate of gas concentration fluctuates between -0.1% and 0.15%. The support monitoring data shows that the average roof sinking rate is 0.15mm / hour, and the cumulative deformation reaches 8.5mm. Two data anomalies were found through correlation analysis: one is located at the junction of the stone gate and the coal seam, the roof sinking acceleration mutation value reaches 0.08mm / hour2, and the gas concentration increases sharply by 0.25%, and the correlation strength coefficient is 0.92; the other is located near the fault zone, and the surrounding rock deformation exceeds the threshold while the extraction efficiency decreases by 25%, and the correlation strength coefficient is 0.87. Based on these data, the system generates a secondary warning and gives suggestions for disposal measures such as increasing the temporary support density and strengthening gas extraction, which ultimately successfully prevents the possible large-scale collapse of the roof and the risk of gas outburst.

[0125] The above describes the method for controlling gas and supporting the coal mining in the thick coal seam stone gate in the embodiment of the present application. The following describes the system for controlling gas and supporting the coal mining in the thick coal seam stone gate in the embodiment of the present application. Figure 2 In the present application, an embodiment of the thick coal seam rock gate coal mining gas control and support management system includes:

[0126] The regression module is used to obtain the coal seam parameter data of the Shimen coal uncovering area through data acquisition equipment, including coal seam thickness value, solidity coefficient and gas content value, and perform multivariate regression analysis on the data to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter plan;

[0127] A monitoring module, used to record the grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitor the grouting process by using pressure-flow curve fitting, identify the grouting saturation feature points, and form a grouting quality evaluation data set;

[0128] A configuration module, used to construct a temporary support load distribution diagram based on the grouting quality evaluation data set, determine the π-shaped beam position and anchor net parameter configuration through stress dispersion rules, and generate a support effectiveness index table;

[0129] A matching module is used to match and analyze the support effectiveness index table with the permanent support design parameters, determine the U-shaped steel spacing and anchor cable preload values, and generate a support structure stress state data map;

[0130] An analysis module is used to perform gas concentration gradient analysis on key points in the stress state data map of the support structure, determine the extraction position and negative pressure value, and draw a gas control efficiency curve;

[0131] The identification module is used to perform time-series correlation analysis based on the gas control efficiency curve and support status data, identify critical features of roof subsidence and abnormal gas concentration patterns, and form safety management and control decision data.

[0132] Through the cooperation of the above-mentioned components and the establishment of a multivariate regression analysis model for coal seam parameter data, the accurate assessment of the danger of coal seam outburst and the scientific determination of the optimal drilling parameter scheme are achieved, which effectively reduces the blindness of drilling arrangement and improves the efficiency of gas pre-extraction. At the same time, the method uses pressure-flow curve fitting technology to dynamically monitor the grouting process, accurately identify the saturation feature points of grouting, solve the problem of inaccurate saturation judgment in the traditional grouting process, and greatly improve the controllability and uniformity of grouting quality. On this basis, the present invention realizes the optimal configuration of the π-beam position and the anchor net parameters by constructing a temporary support load distribution diagram and applying stress dispersion rules, significantly enhancing the stability and reliability of temporary support, and through the matching analysis of the support effectiveness index table and the permanent support design parameters, the scientific and reasonable U-shaped steel spacing and anchor cable preload value are determined, forming a complete support structure stress state data map, providing data support for the optimal design of the support structure, and greatly improving the overall performance of the support system. It is particularly noteworthy that the present invention innovatively introduces a gas concentration gradient analysis method, studies the gas distribution characteristics of key points in the support structure stress state data map, scientifically determines the extraction position and negative pressure value, and significantly improves the gas control efficiency. The most distinctive feature is that the present invention successfully identifies the intrinsic connection between the critical characteristics of roof subsidence and the abnormal gas concentration pattern through the time series correlation analysis of the gas control efficiency curve and the support state data, forms a systematic safety management and decision-making data, and realizes the dual prevention of gas disasters and roof accidents. In the present invention, the application contribution of artificial intelligence algorithms is particularly prominent, especially in the links of multivariate regression analysis, curve fitting, time series correlation analysis, etc., through the in-depth mining of massive monitoring data by machine learning algorithms, complex laws and correlations that are difficult to detect by traditional methods are discovered, and the early warning accuracy is significantly improved. The four-level early warning classification standard and corresponding disposal measures data table constructed on this basis provide clear and definite decision-making guidance for on-site operators, greatly improving the pertinence and effectiveness of emergency disposal.

[0133] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0134] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0135] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0136] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0137] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0139] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling and supporting coal gas in thick coal seam rock opening, characterized in that: The thick coal seam rock gate coal mining gas control and support management method includes: The coal seam parameter data of the Shimen coal uncovering area is obtained through data acquisition equipment, including coal seam thickness, solidity coefficient and gas content value. The data is subjected to multivariate regression analysis to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter scheme; Record the grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitor the grouting process by using pressure-flow curve fitting, identify the grouting saturation feature points, and form a grouting quality evaluation data set; A temporary support load distribution diagram is constructed based on the grouting quality evaluation data set, the π-shaped beam position and anchor net parameter configuration are determined by the stress dispersion rule, and a support effectiveness index table is generated; Match and analyze the support effectiveness index table with the permanent support design parameters, determine the U-shaped steel spacing and anchor cable preload values, and generate a support structure stress state data map; Performing gas concentration gradient analysis on key points in the stress state data map of the support structure, determining the extraction position and negative pressure value, and drawing a gas control efficiency curve; Based on the gas control efficiency curve and support status data, a time series correlation analysis is performed to identify the critical characteristics of roof subsidence and abnormal gas concentration patterns, and to form safety management and control decision data.

2. The thick coal seam rock gate coal mining gas control and support management method according to claim 1 is characterized in that: The coal seam parameter data of the Shimen coal uncovering area is obtained by data acquisition equipment, including coal seam thickness value, solidity coefficient and gas content value, and multiple regression analysis is performed on the data to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter scheme, including: A portable coal seam parameter detector was used to take five-point samples in the Shimen coal uncovering area to obtain the original data of coal seam thickness, solidity coefficient and gas content; Normalizing the raw data, removing outliers, and forming a standardized coal seam parameter set; Substitute the standardized coal seam parameter set into the multivariate regression equation of gas outburst tendency, where the coal seam thickness weighting coefficient is 0.35, the solidity coefficient weighting coefficient is 0.4, and the gas content weighting coefficient is 0.25, to obtain the coal seam outburst hazard assessment index; Determine the drilling density coefficient according to the correlation table between the coal seam outburst hazard assessment index and the geological structure complexity; By comparing the drilling density coefficient with the tunnel section size, a double-row staggered drilling layout diagram is generated; Performing coverage analysis on the double-row staggered drilling arrangement diagram, adjusting drilling angle and depth parameters, and obtaining a three-dimensional distribution diagram of drilling trajectories; Based on the three-dimensional distribution diagram of the drilling trajectory, the azimuth, inclination and length values ​​of each drilling hole are determined to form an optimal drilling parameter plan.

3. The thick coal seam rock gate coal mining gas control and support management method according to claim 1 is characterized in that: The method records the grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitors the grouting process by using pressure-flow curve fitting, identifies the grouting saturation feature points, and forms a grouting quality evaluation data set, including: Generate a grouting pipeline layout navigation map based on the optimal drilling parameter plan, mark the spatial position coordinates and installation angle of each pipeline, and record the grouting pipeline installation information; The grouting pipeline installation information is input into the grouting monitoring system, the data sampling frequency is set to 5 seconds / time, and the grouting pressure value and the grouting flow value are collected synchronously; The collected grouting pressure values ​​and grouting flow values ​​are arranged in dual-parameter time series, and a scatter plot of pressure-flow dynamic changes is drawn; Performing curve fitting on the pressure-flow dynamic change scatter diagram by the least square method to obtain a grouting process characteristic curve; Identify the inflection point position on the characteristic curve of the grouting process, and mark it as a grouting saturation characteristic point when the pressure rises and the flow rate decreases and reaches a threshold value; The grouting saturation characteristic points are combined with the grouting pipeline installation information to establish a corresponding relationship table between spatial position and saturation time, thereby forming a grouting quality evaluation data set.

4. The thick coal seam rock gate coal mining gas control and support management method according to claim 1 is characterized in that: The temporary support load distribution diagram is constructed based on the grouting quality evaluation data set, the π-shaped beam position and anchor net parameter configuration are determined by the stress dispersion rule, and the support effectiveness index table is generated, including: The rock mass stress data around the tunnel is obtained through ground stress deformation monitoring equipment, and the three-dimensional load distribution numerical field is constructed by combining the spatial position information in the grouting quality evaluation data set; Performing isosurface division on the three-dimensional load distribution numerical field, identifying high stress concentration areas and low stress relaxation areas, and marking key control points of temporary support; According to the spatial distribution of the key control points of the temporary support, the installation position of the π-shaped beam is optimized by using the stress dispersion rule to determine the center coordinates and orientation angle of the π-shaped beam; Based on the center coordinates of the π-shaped beam, the anchor net coverage area on the tunnel surrounding rock surface is divided, the anchor net unit size and overlap width are calculated, and the anchor net parameter configuration table is generated; The anchor net parameter configuration table is integrated with the π-beam position information, mapped to the three-dimensional load distribution numerical field, and the adjustment effect of the support structure on the stress field is simulated; By evaluating the adjustment effect of the support structure on the stress field, the support resistance value, coverage area ratio, and anchoring force distribution coefficient are quantified, and a support effectiveness index table is generated.

5. The thick coal seam rock gate coal mining gas control and support management method according to claim 1 is characterized in that: The matching analysis of the support effectiveness index table with the permanent support design parameters, determining the U-shaped steel spacing and anchor cable preload values, and generating a support structure stress state data map includes: Extract support resistance value distribution data from the support effectiveness index table, compare and analyze with the historical schemes in the permanent support design standard library, and select the most suitable U-shaped steel specifications and layout methods; Based on the U-steel specifications and layout methods, different U-steel spacing values ​​are sorted in a list, and the optimal U-steel spacing is obtained by evaluating the safety redundancy coefficient; According to the U-shaped steel spacing and the tunnel section parameters, the anchor cable spatial arrangement points are calculated, and the anchor cable length and anchoring depth are determined in combination with the surrounding rock geological parameters; Setting the anchor cable preload gradient at each position of the anchor cable spatial arrangement point to form a preload value distribution mapping table; Input the U-shaped steel spacing and anchor cable preload values ​​into the support structure stress analysis program to obtain the internal stress transfer path of the support system and the stress values ​​of key nodes; Through the internal stress transfer path of the support system and the force values ​​of key nodes, stress contour lines and deformation vector fields are drawn to form a data map of the stress state of the support structure.

6. The thick coal seam rock gate coal mining gas control and support management method according to claim 1 is characterized in that: The gas concentration gradient analysis is performed on the key points in the stress state data map of the support structure, the extraction position and the negative pressure value are determined, and the gas control efficiency curve is drawn, including: Mark the stress peak point, stress concentration zone and low stress area on the stress state data map of the supporting structure, and obtain the initial value of gas concentration at key points through multi-point sampling method; Perform spatial interpolation on the initial values ​​of gas concentration at key points, draw gas concentration contour distribution maps, and identify gas enrichment areas and diffusion channels; According to the gas concentration contour distribution map and the location of the coal seam geological fault zone, the extraction location and drilling direction are determined; For areas with different gas enrichment levels, set gradient negative pressure values ​​from -15kPa to -8kPa and establish a position-negative pressure correspondence table; Arrange the gas extraction system according to the extraction position and negative pressure value, and record the extraction volume and residual gas concentration value in different time periods; The extraction volume and the residual gas concentration value in the different time periods are plotted into a time series change curve, and the inflection point and the plateau period are marked to form a gas control efficiency curve.

7. The thick coal seam rock gate coal mining gas control and support management method according to claim 1 is characterized in that: The time series correlation analysis is performed based on the gas control efficiency curve and the support status data to identify the critical characteristics of roof subsidence and the abnormal gas concentration mode, and form safety management and control decision data, including: Extract the gas concentration change rate and gas extraction attenuation coefficient from the gas control efficiency curve to establish a gas dynamic response feature library; The roof sinking rate, surrounding rock deformation and support resistance change values ​​in the support status data are integrated synchronously to generate a support-surrounding rock interaction time series table; By cross-comparing the gas dynamic response feature library with the support-surrounding rock interaction time series table, data anomalies and change trend inflection points are identified, and correlation strength coefficients are marked; Based on the correlation strength coefficient, the roof subsidence acceleration mutation point and the cumulative deformation threshold point are extracted from the support-surrounding rock interaction time series table to determine the roof subsidence critical characteristics; Performing trend analysis on the data in the gas dynamic response feature library, identifying intervals where gas concentration increases sharply, intervals where fluctuation frequency is abnormal, and intervals where extraction efficiency decreases, and summarizing them as gas concentration abnormality patterns; Based on the critical characteristics of roof subsidence and abnormal gas concentration patterns, a four-level warning classification standard and corresponding disposal measures data table are established to form safety management and control decision-making data.

8. A thick coal seam stone gate coal uncovering gas control and support management system, used to implement the thick coal seam stone gate coal uncovering gas control and support management method as described in any one of claims 1 to 7, characterized in that: The thick coal seam rock gate coal mining gas control and support management system includes: The regression module is used to obtain the coal seam parameter data of the Shimen coal uncovering area through data acquisition equipment, including coal seam thickness value, solidity coefficient and gas content value, and perform multivariate regression analysis on the data to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter plan; A monitoring module, used to record the grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitor the grouting process by using pressure-flow curve fitting, identify the grouting saturation feature points, and form a grouting quality evaluation data set; A configuration module, used to construct a temporary support load distribution diagram based on the grouting quality evaluation data set, determine the π-shaped beam position and anchor net parameter configuration through stress dispersion rules, and generate a support effectiveness index table; A matching module is used to match and analyze the support effectiveness index table with the permanent support design parameters, determine the U-shaped steel spacing and anchor cable preload values, and generate a support structure stress state data map; An analysis module is used to perform gas concentration gradient analysis on key points in the stress state data map of the support structure, determine the extraction position and negative pressure value, and draw a gas control efficiency curve; The identification module is used to perform time-series correlation analysis based on the gas control efficiency curve and support status data, identify critical features of roof subsidence and abnormal gas concentration patterns, and form safety management and control decision data.

9. A computer device, characterized in that: It includes a memory and a processor, the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, it implements the thick coal seam stone gate coal gas control and support management method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes the thick coal seam stone gate coal mining gas control and support management method as described in any one of claims 1 to 7.

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