Gas control and support pipe control method and system for thick coal seam crosscut uncovering coal

Abstract

The application relates to the technical field of gas control, and discloses a thick coal seam crosscut coal uncovering gas control and support pipe control method and system. The method comprises the following steps: obtaining a outburst danger evaluation 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; matching analysis to determine permanent support parameters to generate a stress atlas; analyzing key point gas gradient to determine extraction parameters; and correlation analysis to identify roof subsidence characteristics and gas anomaly patterns to form safety decision data. The application performs time sequence correlation analysis on the gas dynamic response characteristics and the support-surrounding rock interactive state, identifies the mapping relationship between the roof subsidence critical characteristics and the gas concentration anomaly pattern, and thus improves the safety and controllability of thick coal seam crosscut coal uncovering operation.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of gas control, and particularly relates to a thick coal seam cross-cut coal uncovering gas control and support management and control method and system. BACKGROUND

[0002] In the process of coal mining, thick coal seam cross-cut coal uncovering operation faces the dual safety risks of gas outburst and roof instability. The traditional uncovering method mainly adopts the technical route combining pre-extraction of gas and staged support, including measures such as borehole pre-extraction, grouting reinforcement, temporary support and permanent support. The widely used technologies include: multi-stage pre-extraction method, which reduces the gas content during uncovering by burying a long-distance extraction pipeline; 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 roadway; and a safety monitoring system based on a sensor network, which monitors the working environment in real time by laying gas sensors, stress monitors and displacement meters. These technologies have achieved certain results in practical application and provided technical support for coal mine safety production.

[0003] However, the existing technologies have obvious deficiencies in practical application. First, gas control and support management and control are often regarded as two independent technical links, lacking systematic integration and cooperation, which leads to the inability to effectively identify the correlation between gas dynamic changes and surrounding rock deformation, increasing the safety hazards. Second, the traditional data collection method mainly relies on single-point static measurement, lacking dynamic analysis of the spatiotemporal evolution law of parameters, making it difficult to accurately predict the precursor characteristics of sudden disasters. Third, the determination of support parameters mainly depends on experience, lacking quantitative optimization methods, leading to unstable support effect and phenomena of over-supporting or insufficient supporting. Fourth, the safety control decision lacks a data-driven accurate model, which cannot form a targeted early warning and disposal scheme according to the comprehensive analysis of multi-source information, reducing the control efficiency. Finally, under complex geological conditions, the existing technologies are difficult to adapt to special situations such as thick coal seam, high gas content and broken surrounding rock, greatly increasing the safety risks. SUMMARY

[0004] The application provides a thick coal seam cross-cut coal uncovering gas control and support management and control method and system, which is used for identifying the mapping relationship between the roof subsidence critical feature and the gas concentration abnormal pattern by performing time sequence correlation analysis on the gas dynamic response characteristics and the support-surrounding rock interaction state, forming scientific safety control decision data, and thus improving the safety and controllability of thick coal seam cross-cut coal uncovering operation.

[0005] In a first aspect, the application provides a thick coal seam crosscut coal uncovering gas control and support management and control method, the thick coal seam crosscut coal uncovering gas control and support management and control method comprises: obtaining coal seam parameter data of a crosscut coal uncovering area through a data acquisition device, including coal seam thickness value, firmness coefficient and gas content value, performing multiple regression analysis on the data to obtain a coal seam outburst danger evaluation index and an optimal drilling parameter scheme;According to the optimal drilling parameter scheme, record the grouting pipeline installation information, use pressure-flow curve fitting to dynamically monitor the grouting process, identify the grouting saturation characteristic points, and form a grouting quality evaluation data set;Based on the grouting quality evaluation data set, a temporary support load distribution map is constructed, the position of the π-shaped beam and the anchor net parameter configuration are determined through stress dispersion rules, and a support efficiency index table is generated;The support efficiency index table is matched and analyzed with the permanent support design parameters to determine the U-shaped steel spacing and the anchor cable pretightening force value, and a support structure stress state data atlas is generated;The key points in the support structure stress state data atlas are subjected to gas concentration gradient analysis to determine the extraction position and the negative pressure value, and a gas control efficiency curve is drawn;According to the time sequence correlation analysis of the gas control efficiency curve and the support state data, the roof subsidence critical characteristics and the gas concentration abnormal mode are identified, and a safety management and control decision data is formed.

[0006] In a second aspect, the application provides a thick coal seam crosscut coal uncovering gas control and support management and control system, the thick coal seam crosscut coal uncovering gas control and support management and control system comprises:

[0007] A regression module is configured to obtain coal seam parameter data of a crosscut coal uncovering area through a data acquisition device, including coal seam thickness value, firmness coefficient and gas content value, perform multiple regression analysis on the data to obtain a coal seam outburst danger evaluation index and an optimal drilling parameter scheme.

[0008] A monitoring module is configured to record the grouting pipeline installation information according to the optimal drilling parameter scheme, use pressure-flow curve fitting to dynamically monitor the grouting process, identify the grouting saturation characteristic points, and form a grouting quality evaluation data set.

[0009] A configuration module is configured to construct a temporary support load distribution map based on the grouting quality evaluation data set, determine the position of the π-shaped beam and the anchor net parameter configuration through stress dispersion rules, and generate a support efficiency index table.

[0010] A matching module is configured to match and analyze the support efficiency index table with the permanent support design parameters to determine the U-shaped steel spacing and the anchor cable pretightening force value, and generate a support structure stress state data atlas.

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

[0012] A recognition module is used for time sequence correlation analysis of the gas control efficiency curve and support state data, identification of roof subsidence critical features and gas concentration abnormal patterns, and formation of safety management and control decision data.

[0013] The third aspect of the present application provides a computer device, comprising a memory and at least one processor, the memory has instructions stored therein; the at least one processor invokes the instructions in the memory to enable the computer device to perform the thick coal seam crossheading coal uncovering gas control and support management and control method.

[0014] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium has instructions stored therein, when the instructions are run on a computer, the computer is enabled to perform the thick coal seam crossheading coal uncovering gas control and support management and control method.

[0015] In the technical scheme provided in the application, by establishing a coal seam parameter data multiple regression analysis model, accurate assessment of coal seam outburst danger and scientific determination of optimal drilling parameter scheme are realized, blind drilling arrangement is effectively reduced, and gas pre-extraction efficiency is improved. Meanwhile, the method uses pressure-flow curve fitting technology to dynamically monitor the grouting process, accurately identifies the saturation feature point of grouting, solves the problem of inaccurate saturation judgment in the traditional grouting process, and greatly improves the controllability and uniformity of grouting quality. On this basis, the application realizes the optimal configuration of the position of the π-shaped beam and the parameters of the anchor net by constructing a temporary support load distribution map and using stress dispersion rules, significantly enhances the stability and reliability of the temporary support, and determines the scientific and reasonable U-shaped steel spacing and anchor pre-tightening force value through matching analysis of the support efficiency index table and the permanent support design parameters, forming a complete support structure stress state data atlas, providing data support for the optimal design of the support structure, and greatly improving the overall performance of the support system. It is particularly worth noting that the application innovatively introduces a gas concentration gradient analysis method to study the gas distribution characteristics of the key points in the support structure stress state data atlas, scientifically determines the extraction position and negative pressure value, and significantly improves the gas control efficiency. Most characteristic is that the application successfully identifies the internal relationship between the roof subsidence critical feature and the abnormal mode of gas concentration through time sequence correlation analysis of the gas control efficiency curve and the support state data, forms a systematic safety management and control decision data, and realizes the dual prevention of gas disasters and roof accidents. In the application, the application of artificial intelligence algorithms is particularly prominent, especially in multiple regression analysis, curve fitting, time sequence correlation analysis and other aspects. Through machine learning algorithm, massive monitoring data is deeply mined to find complex rules and correlations that are difficult to detect by traditional methods, so that the early warning accuracy is significantly improved. On this basis, the four-level early warning grading standards and corresponding disposal measure data table provide clear and explicit decision guidance for on-site operators, greatly improving the pertinence and effectiveness of emergency disposal. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical scheme of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can obtain other drawings based on these drawings without creative labor.

[0017] Figure 1 An embodiment schematic diagram of the thick coal seam cross-cut coal uncovering gas control and support management and control method in the embodiment of the application;

[0018] Figure 2 An embodiment schematic diagram of the thick coal seam cross-cut coal uncovering gas control and support management and control system in the embodiment of the application;

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

[0020] The embodiment of the present application provides a thick coal seam crosscut coal uncovering gas control and support management and control method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units 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. Please refer to Figure 1 One embodiment of the thick coal seam crosscut coal uncovering gas control and support management and control method in the embodiment of the present application includes:

[0022] Step S101, obtaining coal seam parameter data of a crosscut coal uncovering area through a data acquisition device, including a coal seam thickness value, a firmness coefficient and a gas content value, performing multiple regression analysis on the data to obtain a coal seam outburst danger evaluation index and an optimal drilling parameter scheme;

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

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

[0025] Step S104, matching and analyzing the support efficiency index table and permanent support design parameters, determining a U-shaped steel spacing and an anchor cable pretightening force value, and generating a support structure stress state data atlas;

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

[0027] In step S106, the gas control efficiency curve is correlated with the support state data in time sequence to identify the roof subsidence critical characteristics and the gas concentration abnormal pattern, and to form the safety management and control decision data.

[0028] It can be understood that the execution subject of the present application can be a thick coal seam crosscut coal uncovering gas control and support management and control system, and can also be a terminal or a server, and the specific implementation is not limited herein. The server is taken as an example for description of the execution subject of the present application.

[0029] Specifically, the parameters of the coal seam in the crosscut coal uncovering area are collected, and the portable coal seam parameter detector is used for multi-point sampling and measurement to obtain the coal seam thickness value (15.8 m in this case), the firmness coefficient (0.22), and the gas content value. After normalization processing of these raw data, the data are input into a multiple regression equation, and the equation adopts a weighted coefficient method, in which the coal seam thickness value is assigned a weight coefficient of 0.35, the firmness coefficient is assigned a weight coefficient of 0.4, and the gas content value is assigned a weight coefficient of 0.25. The coal seam outburst risk assessment index is calculated. When the index exceeds 0.6, it indicates that there is a high outburst risk, and a more intensive drilling arrangement scheme needs to be adopted. Then, the drilling density coefficient is determined according to the index, and combined with the roadway cross section size (6 m wide and 4.5 m high), a double-row staggered drilling arrangement scheme is designed, in which the first row of drill holes (No. 1-11) is constructed along the roadway roof to the two sides, with a spacing of 500 mm and an inclination angle of 20°; the second row of drill holes (No. 12-21) is 500 mm away from the first row of drill holes, with an inclination angle of 5°, to form an optimal drilling parameter scheme.

[0030] According to the optimal drilling parameter scheme, grouting reinforcement is started. First, the installation information of each grouting pipeline (2-inch iron pipe, 1 m long, staggered punching every 200 mm) is recorded, including the spatial position and the installation angle. During the grouting process, the grouting pressure value and the flow value are collected every 5 seconds to form a time sequence data stream. These real-time data are curve-fitted by the least square method to draw a pressure-flow variation curve. When the pressure continuously rises and the flow starts to decrease, and the change rate reaches a preset threshold value (usually 30% reduction in flow and 50% increase in pressure), the saturated feature point of grouting can be identified. For example, at a certain grouting point, when the pressure rises from the initial 1.5 MPa to 2.3 MPa, and the flow decreases from 50 L / min to 35 L / min, the system marks that the point has reached the saturated state. The saturated feature point information of all grouting points is correlated with the spatial position of the pipeline to form a grouting quality evaluation data set.

[0031] Construct a temporary support load distribution map based on the grouting quality evaluation data set. Obtain the rock stress data around the tunnel through the ground stress monitoring equipment, and construct a three-dimensional load distribution map based on the spatial distribution information in the grouting quality evaluation data set. Determine the optimal installation position of the π-beam (4m long) through the stress dispersion rule (more support points need to be set in high stress areas, 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 beams 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 support effectiveness index table was generated by integrating the π-beam position and anchor mesh parameters to quantify the support resistance value, coverage area ratio, and anchor force distribution coefficient.

[0032] The support effectiveness index table was matched 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. By conducting a safety assessment of different U-steel spacings (from 400mm to 700mm), 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. An 8.2m-long steel strand serves as a deep anchoring element, and the anchor preload is determined (typically 120kN to 150kN). The determined U-steel spacing and anchor preload are input into a support structure analysis program to determine the stress transfer paths within the support system and the stress values ​​at key nodes. Contour lines and deformation vector fields are then drawn to create a data map of the support structure's stress state.

[0033] Gas concentration gradient analysis is carried out on key points in the stress state data graph of the support structure. Multi-point gas concentration sampling is carried out at stress peak points, stress concentration zones and low stress areas to obtain spatial distribution data. Through the interpolation algorithm, the gas concentration contour map is drawn to identify the gas enrichment area and diffusion channel. During the uncovering of 13# coal seam, it is usually found that there is an abnormally high value area of gas concentration near the fault zone (up to 2% or more), at which time the best extraction position should be determined in combination with the geological structure characteristics. For different enrichment areas, set the gradient negative pressure value, -15 kPa for high concentration area, -12 kPa for medium area, -8 kPa for low concentration area, and -negative pressure corresponding table for formation position. Through continuous monitoring of the extraction amount and residual gas concentration value at different time periods (such as every 4 hours a sampling point), a time sequence change curve is drawn to mark the inflection point and platform period, and a gas control efficiency curve is formed. The gas control efficiency curve is correlated with the support state data in time sequence. 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 subsidence rate, surrounding rock deformation and support resistance change value in the support state data are integrated in time synchronization to generate a support-surrounding rock interactive time sequence table. Through cross comparison, data abnormal points and change trend inflection points are identified, and the correlation strength coefficient is calculated. When the roof subsidence acceleration suddenly increases (such as from 0.2 mm / day to 0.8 mm / day) or the cumulative deformation reaches the warning value (usually 25 mm), it is determined as the critical feature of roof subsidence. Similarly, when the gas concentration increases sharply (increases by 0.3% or more within 30 minutes), the fluctuation frequency is abnormal (fluctuates more than 5 times within 24 hours) or the extraction efficiency decreases significantly (the extraction amount decreases by 40% or more), it is summarized as the abnormal mode of gas concentration. Combined with these characteristics, four-level early warning grading standards (attention level, warning level, danger level and emergency level) and corresponding measures are established to form safety control decision data.

[0034] In the embodiment of the present application, by establishing a coal seam parameter data multiple regression analysis model, accurate assessment of coal seam outburst danger and scientific determination of the optimal drilling parameter scheme are realized, effectively reducing the blindness of drilling arrangement and improving the gas pre-extraction efficiency. Meanwhile, the method uses pressure-flow curve fitting technology to dynamically monitor the grouting process, accurately identifies the saturation feature point of grouting, solves the problem of inaccurate saturation judgment in the traditional grouting process, and greatly improves the controllability and uniformity of grouting quality. On this basis, the present application realizes the optimal configuration of the position of the π-shaped beam and the anchor net parameter by constructing a temporary support load distribution map and using stress dispersion rules, significantly enhances the stability and reliability of the temporary support, and determines the scientific and reasonable U-shaped steel spacing and anchor pre-tightening force value through matching analysis of the support efficiency index table and the permanent support design parameter, forming a complete support structure stress state data atlas, which provides data support for the optimal design of the support structure and greatly improves the overall performance of the support system. It is particularly worth noting that the present application innovatively introduces a gas concentration gradient analysis method to study the gas distribution characteristics of the key points in the support structure stress state data atlas, scientifically determines the extraction position and negative pressure value, and significantly improves the gas control efficiency. The most characteristic is that the present application successfully identifies the internal relationship between the roof subsidence critical feature and the abnormal mode of gas concentration through time sequence correlation analysis of the gas control efficiency curve and the support state data, forms a systematic safety control decision data, and realizes the dual prevention of gas disasters and roof accidents. In the present application, the application of artificial intelligence algorithm is particularly outstanding, especially in multiple regression analysis, curve fitting, time sequence correlation analysis and other links. Through machine learning algorithm, the massive monitoring data is deeply mined, the complex rules and correlations that are difficult to be detected by traditional methods are found, the early warning accuracy is significantly improved, and the four-level early warning grading standards and corresponding disposal measure data table are built, which provides clear and definite decision guidance for the on-site operators, greatly improving the pertinence and effectiveness of emergency disposal.

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

[0036] (1) using a portable coal seam parameter detector to take five samples in the cross-cut coal uncovering area, obtaining coal seam thickness, firmness coefficient, gas content original data;

[0037] (2) normalizing the original data, eliminating outliers, forming a standardized coal seam parameter set;

[0038] (3) substituting the standardized coal seam parameter set into the gas outburst tendency multiple regression equation, wherein the coal seam thickness weighting coefficient is 0.35, the firmness coefficient weighting coefficient is 0.4, and the gas content weighting coefficient is 0.25, to obtain the coal seam outburst danger evaluation index;

[0039] (4) According to the correlation table of coal seam outburst risk assessment index and geological structure complexity, the borehole density coefficient is determined;

[0040] (5) Through the comparison of borehole density coefficient and roadway cross section size, the double-row staggered borehole layout diagram is generated;

[0041] (6) The coverage range analysis is performed on the double-row staggered borehole layout diagram, the borehole angle and depth parameters are adjusted, and the three-dimensional borehole trajectory distribution diagram is obtained;

[0042] (7) Based on the three-dimensional borehole trajectory distribution diagram, the borehole azimuth angle, inclination angle and length values are determined, and the optimal borehole parameter scheme is formed.

[0043] Specifically, a portable coal seam parameter detector is used to measure five points in the stone gate coal uncovering area. The detector is a special device integrating coal seam thickness measurement, solidity test and gas content detection function, with the characteristics of strong portability and high precision. The five-point sampling method is to take a sample point every 20 meters along the roadway trend in the proposed coal uncovering area, and measure the coal seam thickness, solidity coefficient and gas content at each sample point to obtain the basic characteristic data of the coal seam. For example, in the old eagle mountain coal mine 113 transport stone gate coal uncovering engineering, the original data obtained by five-point sampling shows that the average thickness of the coal seam is 15.8 meters, the average value of the solidity coefficient is 0.22, and the average value of the gas content is 8.5 cubic meters / ton, indicating that the coal seam is thick and the solidity is poor, with high gas content. Then the original data is normalized and the abnormal values are removed. Normalization is a mathematical transformation method that unifies different dimensions and different orders of magnitude of original data to the interval [0, 1], which is realized by subtracting the minimum value of the parameter from each data point and then dividing by the range of the parameter. In this process, abnormal data points that deviate significantly from the average value will also be removed, such as a point whose gas content is suddenly higher than 50% of the average value, or the coal seam thickness is more than 30% different from other points. Through this processing, 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 gas outburst tendency multivariate regression equation. The equation is a mathematical model for comprehensively evaluating the outburst danger of the coal seam, taking into account the three main influencing factors of coal seam thickness, firmness coefficient and gas content, and determining the influence weight of each factor according to historical data analysis. Specifically, the coal seam thickness weighting coefficient is 0.35, the firmness coefficient weighting coefficient is 0.4, and the gas content weighting coefficient 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 firmness coefficient is 0.2, and the standardized gas content is 0.7, the coal seam outburst danger evaluation index of the point is calculated as: 0.9×0.35+0.2×0.4+0.7×0.25=0.555. By calculating the evaluation index of all sample points and taking the average value, the coal seam outburst danger evaluation index of the entire coal uncovering area is obtained.

[0045] According to the calculated coal seam outburst danger evaluation index, the drilling density coefficient is determined in combination with 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 danger evaluation index and the geological structure complexity degree to different drilling density coefficients. For example, when the coal seam outburst danger evaluation index is between 0.5-0.7 and the geological structure is moderately complex, the corresponding drilling density coefficient is 1.2; while 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 drill holes, and the larger the coefficient, the denser the drill hole arrangement.

[0046] After determining the drilling density coefficient, the double-row staggered drill hole layout diagram is generated by comparing with the roadway cross-sectional size. The specific operation is to multiply the drilling density coefficient by the standard drill hole spacing of the roadway (usually 600mm) to obtain the actual drill hole spacing to be used. For example, when the drilling density coefficient is 1.2, the actual drill hole spacing should be 600mm ÷ 1.2 = 500mm. According to this spacing, the first row of drill holes (1-11) is arranged along the circumference of the roadway roof, and then the second row of drill holes (12-21) is arranged 500mm away from the first row of drill holes, forming a staggered drill hole layout diagram, ensuring the continuity and integrity of the grouting coverage.

[0047] Coverage analysis is performed on the generated double-row staggered borehole layout diagram, with the purpose of ensuring that grouting can fully penetrate into various areas inside the coal seam. Coverage analysis includes calculating the effective grouting radius of each borehole (usually 0.25 m), and then checking whether there are blind areas covered by grouting or areas with excessive overlap. By adjusting the borehole angle (e.g., 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 (e.g., setting the length of the first row of boreholes to 29 m and the length of the second row of boreholes to 42 m), the spatial distribution of the boreholes is optimized, and a three-dimensional borehole trajectory distribution diagram is obtained to ensure the uniformity of grouting reinforcement effect. Based on the three-dimensional borehole trajectory distribution diagram, the specific parameters of each borehole are determined, including the azimuth angle (from 124° to 136°), the inclination angle (20° for the first row and 5° for the second row), and the length value (29 m for the first row and 42 m for the second row), forming an optimal borehole parameter scheme

[0048] Through actual cases, it is verified that after a certain mining area adopts this method, a 15.8-meter-thick coal seam with high outburst risk is successfully uncovered, the coal body strength after grouting reinforcement is significantly improved, the gas extraction efficiency is improved, the coal uncovering period is shortened by 40%, and no roof collapse or gas overrun accident occurs during the whole process, which reflects the practical value and safety guarantee of this method in the process of thick coal seam cross-cut uncovering.

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

[0050] (1) Generate a grouting pipe layout navigation diagram according to the optimal borehole parameter scheme, mark the spatial position coordinates and installation angle of each pipe, and record the grouting pipe installation information;

[0051] (2) Input the grouting pipe 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) Perform double-parameter time sequence arrangement on the collected grouting pressure value and grouting flow value, and draw a pressure-flow dynamic change scatter plot;

[0053] (4) Perform curve fitting on the pressure-flow dynamic change scatter plot by the least squares method to obtain a grouting process characteristic curve;

[0054] (5) Identify the inflection point position on the grouting process characteristic curve, and mark it as a grouting saturation characteristic point when the pressure rises and the flow decreases to a threshold value;

[0055] (6) Combine the grouting saturation characteristic point with the grouting pipe installation information to establish a correspondence table between the spatial position and the saturation time, and form a grouting quality evaluation data set.

[0056] Specifically, the grouting pipe layout navigation map is generated according to the optimal drilling parameter scheme, and the three-dimensional coordinates, direction angle and depth information of each drilling hole are converted into grouting pipe installation guidance. For example, for the 21 drilling holes used for transporting the cross-cut, the precise spatial position coordinates of each drilling hole are marked, including X, Y, Z three-dimensional coordinate values and installation angle. For example, the position coordinates of No. 1 drilling hole can be (0, 0, 4.5), the azimuth angle is 124°, and the inclination angle is 20°; and the position coordinates of No. 12 drilling hole are (0.5, 0, 4.5), the azimuth angle is 126°, and the inclination angle is 5°. By inputting these parameters into the grouting navigation software, the specific installation path and position of each grouting pipe are generated to form the grouting pipe installation information record.

[0057] After inputting the grouting pipe installation information into the grouting monitoring device, the data sampling frequency is set to 5 seconds / time, which ensures the continuity and real-time of data acquisition, and at the same time, does not produce too much redundant data. During the grouting process, the pressure sensor and flow meter continuously monitor the grouting pressure value and grouting flow value to form two groups 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 a grouting point starts grouting, the pressure values (1.50 MPa, 1.62 MPa, 1.75 MPa...) and flow values (50 L / min, 48 L / min, 47 L / min...) are recorded respectively.

[0058] The double-parameter time sequence arrangement of the collected grouting pressure value and grouting flow value refers to taking time as the horizontal axis and representing the pressure and flow parameters on the same graph, but using different vertical axis scales to form a three-dimensional relationship graph of time-pressure-flow. By removing the time factor, the pressure is directly taken as the horizontal axis and the flow is directly taken as the vertical axis to draw a pressure-flow dynamic change scatter plot. This representation method intuitively shows the change relationship between pressure and flow in the grouting process, which is helpful to identify the key change points in the grouting process.

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

[0060] Q = a · e -β·P + γ · P + δ

[0061] where a represents the initial flow coefficient, β represents the flow decay coefficient, γ represents the pressure influence factor, and δ represents the base flow constant. By applying the least squares method to the scattered data, the optimal values of these four parameters are calculated to minimize the sum of squared errors between the predicted values and the actual observed values. 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 observation values at time point t, respectively. By solving this minimization problem using numerical optimization algorithms such as gradient descent or Newton's method, the optimal estimates of parameters a, β, γ, and δ are obtained, and the grouting process characteristic curve is obtained. For example, in a certain grouting process, a = 65, β = 0.3, γ = -5, and δ = 25 are calculated. This set of parameters describes the characteristics of the grouting process at that point and can be used to predict the flow changes under different pressures.

[0064] After obtaining the grouting process characteristic curve, the inflection point position needs to be identified on the curve, which is the point where the trend of pressure rising and flow decreasing reaches a threshold. This point is marked as the grouting saturation feature point. The mathematical definition of the inflection point is the point where the second derivative of the curve is zero, indicating the position where the curve changes from concave to convex or from convex to concave. In the grouting process, when the pressure continues to rise but the flow begins to decrease significantly, it indicates that the medium (coal body) has been fully filled with grouting material, and the marginal benefit of continued grouting begins to decline. The specific determination criteria are: when the pressure rising rate exceeds a preset value (such as 0.2 MPa per minute) and the flow decreasing rate exceeds another preset value (such as 5 L per minute), it is determined that the grouting saturation point has been reached. The mathematical expression is:

[0065]

[0066] where represents the pressure change rate, represents the flow change rate, θ P and θ Q are the thresholds for pressure rising and flow decreasing, respectively.

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

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

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

[0070] (2) Divide the three-dimensional load distribution numerical field by the isosurface, identify the high stress concentration area and the low stress relaxation area, and mark the temporary support key control points;

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

[0072] (4) Based on the center coordinates of the π-shaped beam, divide the anchor net coverage area of the roadway surrounding rock surface, calculate the anchor net unit size and lap width, and generate the anchor net parameter configuration table;

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

[0074] (6) Through the adjustment effect evaluation of the support structure on the stress field, the support resistance value, the coverage area rate, and the anchoring force distribution coefficient are quantified, and the support efficiency index table is generated.

[0075] Specifically, the stress data of the surrounding rock mass around the roadway is obtained by the in-situ stress deformation monitoring equipment, which includes stress sensors, displacement meters, and anchor force meters, etc., distributed at key positions around the roadway, and records the stress state of the surrounding rock in real time. The obtained stress data includes the magnitude and direction of the principal stress and the displacement change, 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 point, the grouting saturation time, and the total grouting volume. Through spatial interpolation algorithm, the discrete stress measurement point data and grouting quality data are fused to construct a three-dimensional load distribution numerical field. For example, in the transportation cross-cut coal uncovering engineering, 15 stress monitoring points are installed, covering the roof, two sides and floor of the roadway, combined with the quality evaluation data of 21 grouting points, a three-dimensional load distribution numerical field containing 150,000 grid nodes is generated by Kriging interpolation method.

[0076] Isosurface partitioning of the constructed three-dimensional load distribution numerical field refers to dividing the entire numerical field into different stress regions based on the magnitude of the stress values. Principal stress values ​​or equivalent stress values ​​are usually used as the basis for this division, and several stress thresholds are set to divide the numerical field into high stress concentration regions, medium stress regions, and low stress relaxation regions. High stress concentration regions are areas where stress values ​​exceed 70% of the surrounding rock strength and are prone to damage caused by stress concentration. Low stress relaxation regions, on the other hand, are areas where stress values ​​are less than 50% of the initial stress of the surrounding rock and are often already loose or broken. Using computer graphics processing technology, these different stress regions are visualized in three-dimensional space, and key control points requiring focused support are marked. These control points are typically located at the junction of high and low stresses, 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 concentrated stress to 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 maximum support resistance. For example, in a certain section of the tunnel, it was calculated that three π-beams needed to be installed, located on the center line of the tunnel and 500 mm to the left and right of the center line. Each π-beam is 4 m long and has an orientation 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 tunnel cross-sectional shape, 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 made of The anchor net is welded together with steel bars. When calculating the unit size of the anchor net, consider the actual coverage area and the required overlap width between the anchor nets. The overlap width is typically 10% of the anchor net length, or approximately 200mm, to ensure a seamless connection between the anchor nets. Using these parameters, an anchor net parameter configuration table is generated, including information such as each anchor net's number, installation location coordinates, orientation, and number of fixing points.

[0079] The generated anchor net parameter configuration table is integrated with the position information of the π-shaped beam and mapped into a 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 changes the stress distribution of the surrounding rock, which is usually manifested as a decrease in the peak stress of the high stress concentration area and an increase in the restraint of the low stress relaxation area. By comparing the stress distribution cloud diagrams before and after the support, the support effect can be directly evaluated, with special attention paid to the stress changes at key control points.

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

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

[0082] (1) Extract the support resistance value distribution data from the support effectiveness index table and compare and analyze it with the historical schemes in the permanent support design standard library to select the U-shaped steel specification and layout mode with the highest adaptation degree;

[0083] (2) Based on the U-shaped steel specification and layout mode, list sort different U-shaped steel spacing values to obtain the best U-shaped steel spacing through safety redundancy coefficient evaluation;

[0084] (3) According to the U-shaped steel spacing and the roadway cross-section parameters, calculate the anchor cable spatial arrangement points and determine the anchor cable length and anchoring depth in combination with the surrounding rock geological parameters;

[0085] (4) Set the anchor cable pretightening force gradient for each position of the anchor cable spatial arrangement points to form a pretightening force numerical distribution mapping table;

[0086] (5) Input the U-shaped steel spacing and anchor cable pretightening force values into the support structure stress analysis program to obtain the stress transmission path and key node stress value of the support system;

[0087] (6) Draw stress contour lines and deformation vector fields through the stress transmission path and key node stress value of the support system to form a support structure stress state data atlas.

[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 in 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 screened out. For example, in this example, 29U-shaped steel is selected as the main support unit.

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

[0090] Based on the determined U-steel spacing and tunnel cross-sectional 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 that requires consideration of maximizing support effectiveness and construction feasibility. The calculation formula for 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, and λ3 are weight coefficients related to the roadway height, support spacing, and rock mass quality, respectively. The anchor cable anchorage depth is determined by the following formula:

[0093]

[0094] where D anchorage is the anchor cable anchoring depth, F tensile is the anchor cable design pre-tightening force, d anchor is the anchor cable diameter, τ bond is the bond strength between the anchor cable and the grouting body, Δu is the expected deformation of the surrounding rock of the roadway, and η is the deformation compensation coefficient. The reasonable arrangement scheme of the anchor cable is calculated, including key parameters such as the anchor cable position, length, and anchoring depth.

[0095] The anchor cable pre-tightening force gradient is set for each position of the anchor cable spatial arrangement point, forming a pre-tightening force numerical distribution mapping table. The anchor cable pre-tightening force refers to the initial tension applied when installing the anchor cable, which is crucial for controlling the deformation of the surrounding rock. According to the type of surrounding rock and stress distribution, different positions of the anchor cable need to be set with different pre-tightening forces. Generally, the anchor cable pre-tightening force in the high stress area is set larger, while the anchor cable pre-tightening force in the low stress area is relatively smaller. The pre-tightening force numerical distribution mapping table records the coordinates of each anchor cable position and the corresponding pre-tightening force value, providing accurate guidance for anchor cable installation. For example, the anchor cable pre-tightening force at the center of the roadway roof may be set to 150 kN, while the anchor cable pre-tightening force at the two sides may be set to 120 kN.

[0096] The determined U-shaped steel spacing and anchor cable pre-tightening force value are input into the support structure stress analysis program to obtain the internal stress transmission path and key node stress value of the support system. The support structure stress analysis adopts the finite element method or the discrete element method, which discretizes the entire support system into a finite number of units, establishes a mechanical model, and solves 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 of the U-shaped steel and the anchor cable, the contact point of the U-shaped steel and the floor, etc.). These data reflect the stress state of the support system under the action of the surrounding rock pressure, which helps to evaluate the safety and stability of the support system.

[0097] Through the internal stress transmission path and key node stress value of the support system, stress contours and deformation vector fields are drawn to form a support structure stress state data atlas. The stress contours are curves connecting 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 of the support structure. The stress state data atlas comprehensively displays the mechanical behavior of the support system in the working state, including stress concentration areas, deformation trends, and potential weak links, etc.

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

[0099] (1) Mark the stress peak points, stress concentration zones, and low stress areas on the support structure stress state data graph, and obtain the initial gas concentration values of key points by multi-point sampling method;

[0100] (2) Spatially interpolate the initial gas concentration values of key points to draw a gas concentration contour distribution map and identify gas enrichment areas and diffusion channels;

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

[0102] (4) Set a gradient negative pressure value of -15 kPa to -8 kPa for different gas enrichment degree areas and establish a position-negative pressure correspondence table;

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

[0104] (6) Draw a time series change curve of the extraction amount and residual gas concentration value at different time periods, mark the inflection points and plateau periods, and form a gas control efficiency curve.

[0105] Specifically, mark the key positions on the support structure stress state data graph, including stress peak points, stress concentration zones, and low stress areas. Stress peak points refer to the positions with the maximum stress value in the support structure, usually appearing at the center of the roadway roof or the connection between the two sides and the roof. Stress concentration zones refer to continuous areas with significantly higher stress values than the surrounding areas, which are often high-risk areas of roadway deformation and damage. Low stress areas are regions with lower stress values, which may have undergone plastic deformation or loose and broken. Measure the gas concentration at these key positions by multi-point sampling method to obtain the initial gas concentration values. Multi-point sampling method refers to using a portable gas detector to perform fixed-point sampling at the pre-marked key points, usually collecting 3-5 data at each point and taking the average value to ensure the accuracy and representativeness of the data. For example, in a certain thick coal seam cross-cut uncovering coal engineering, 15 key points were marked, including 5 stress peak points, 6 points on stress concentration zones, and 4 low stress area points, and the measured gas concentration values at these points ranged from 0.3% to 1.8%.

[0106] The initial value of the obtained key point gas concentration is spatially interpolated to infer the gas distribution in the entire region from the discrete measurement data. The spatial interpolation methods include inverse distance weighting, Kriging, and spline function, among which Kriging is the most commonly used because it not only considers the distance factor but also the spatial autocorrelation, and can generate relatively accurate interpolation results. Through interpolation calculation, the gas concentration distribution data in the entire roadway space is obtained, and then a gas concentration contour distribution map is drawn. The contour line is a curve connecting points with the same gas concentration, and different concentrations are represented by different colors or line types. By analyzing the contour distribution characteristics, the gas enrichment area and diffusion channel are identified. The gas enrichment area is an area with dense contour lines and high concentration values, usually occurring in fault fracture zones, stress relaxation areas, or coal seam thickness mutation places; the diffusion channel is the dominant path for gas migration from the enrichment area to the low concentration area, often related to the roadway structure, geological fractures, or poorly supported areas.

[0107] According to the gas concentration contour distribution map, combined with the position of the coal seam geological fracture zone, the extraction position and drilling direction are determined. The geological fracture 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 of gas enrichment and the channels for gas migration. The principle of determining the extraction position is to preferentially select the position with high gas concentration and near the geological fracture zone, while considering the construction feasibility and extraction efficiency. The drilling direction should be perpendicular to the strike of the fracture zone to maximize the penetration of the gas enrichment area. Generally, at least one or two extraction drillings are arranged in each gas enrichment area, with a drilling spacing of 15-20 meters, and the drilling length is determined according to the coal seam thickness and gas distribution range, usually 20-50 meters.

[0108] For different gas enrichment areas, set the gradient negative pressure value and establish the position-negative pressure correspondence table. The negative pressure value refers to the pressure difference of the extraction system relative to atmospheric pressure, with a unit of kPa, and a negative value indicates lower than atmospheric pressure. The principle of gradient negative pressure design is to set a larger negative pressure in areas with higher gas concentration to form directional gas flow from high concentration areas to low concentration areas. According to practical experience, a negative pressure of -15 kPa is usually set for high concentration areas (gas concentration > 1.0%), a negative pressure of -12 kPa is set for medium concentration areas (gas concentration 0.5%-1.0%), and a negative pressure of -8 kPa is set for low concentration areas (gas concentration < 0.5%). By establishing the position-negative pressure correspondence table, appropriate negative pressure values are allocated to each extraction drilling to ensure the scientificity and effectiveness of the extraction system.

[0109] By determining the extraction position and negative pressure value, a gas extraction system is arranged, and the extraction effect data is recorded. The gas extraction system includes drilling, hole sealing device, gas collecting pipeline and extraction pump and other components. Two types of key data need to be recorded regularly during the extraction process: one is the extraction amount, that is, the volume of gas extracted from the drilling per unit time, usually expressed in cubic meters per minute; the second is the residual gas concentration value, that is, the residual gas concentration in the working face or roadway after extraction, usually expressed in percentage. These data are usually recorded once every 4 hours or every shift, forming a time series data set. For example, a certain extraction drilling records the extraction amount and residual gas concentration every 4 hours during the continuous 72 hours of extraction, resulting in 18 groups of time series data.

[0110] Plotting the recorded extraction amount and residual gas concentration values at different time periods into a time series change curve is an important means of data visualization and analysis. The time series change curve takes time as the horizontal axis and extraction amount and gas concentration as the vertical axis (double vertical axis can be used), which intuitively shows the change trend of the two key indicators during the extraction process. The inflection point and the platform period are marked on the curve, the inflection point is the point where the slope of the curve changes obviously, usually indicating the turning point of the extraction efficiency; the platform period is the stage where the curve tends to be horizontal, indicating that the extraction enters a stable state. By analyzing the time and position of the inflection point, as well as the duration and numerical level of the platform 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 performing step S106 can specifically include the following steps:

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

[0113] (2) Time-synchronously integrating the roof subsidence rate, surrounding rock deformation and support resistance change value in the support state data to generate a support-surrounding rock interaction time series table;

[0114] (3) Cross-comparing the gas dynamic response feature library and the support-surrounding rock interaction time series table to identify data abnormal points and change trend inflection points, and marking the correlation intensity coefficient;

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

[0116] (5) Trend analysis is performed on the data in the gas dynamic response feature library to identify the gas concentration steep increase interval, the fluctuation frequency abnormal interval and the extraction efficiency decline interval, and to summarize the gas concentration abnormal pattern;

[0117] (6) Combined with the roof subsidence critical feature and gas concentration anomaly pattern, four-level early warning grading standards and corresponding disposal measures data table are established to form safety control decision data.

[0118] Specifically, key indicators are extracted from the gas control efficiency curve. The gas concentration change rate refers to the change amount of gas concentration per unit time, which is obtained by difference calculation on the gas concentration values of adjacent time points on the gas control efficiency curve. Taking the coal uncovering face as an example, the gas concentration data is collected every 30 minutes. Assuming that the gas concentration measured at t1 time is 0.8%, and the concentration measured at t2 time (30 minutes later) is 0.65%, then the gas concentration change rate of this time period is -0.5% / hour. The gas extraction decay coefficient represents the degree of decay of extraction effect with time, which is obtained by calculating the ratio of gas extraction amount in consecutive time periods. When continuous monitoring found that the first day gas extraction amount of a drill hole was 15m 3 / hour, the second day decreased to 12m 3 / hour, and the third day decreased to 9.6m 3 / hour, through data analysis, it is concluded that the daily decay coefficient of the drill hole is about 0.8. These change rate and decay coefficient data are classified and arranged according to time and spatial position to form a gas dynamic response feature library, which contains the time sequence variation law of gas parameters in each region.

[0119] Next, the data collected by the support state monitoring system is integrated and processed. The roof subsidence 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 the multi-point displacement meter. The support resistance change value is obtained by monitoring the change of the stress state of the support structure through the pressure sensor. During the process of uncovering coal in the crosscut, the roof of a certain measuring point subsided 2.8mm in 24 hours, so the roof subsidence rate is 0.117mm / hour. At the same time, the surrounding rock deformation monitoring shows that the side wall is extruded inward by 1.5mm, and the pressure sensor reading on the U-shaped steel support increases from the initial 16.8MPa to 17.5MPa. These data are integrated synchronously according to the unified time reference to generate a support-surrounding rock interaction time sequence table containing time stamp, position coordinates and parameter values.

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

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

[0122] Trend analysis is performed on the data in the gas dynamic response feature library to identify the gas concentration steep increase interval, the abnormal fluctuation frequency interval, and the extraction efficiency decline interval. The gas concentration steep increase interval refers to the period when the gas concentration increases by more than 0.2% in a short time (such as 1 hour); the abnormal fluctuation frequency interval refers to the period when the gas concentration fluctuation frequency is significantly higher than the normal level, which is identified by analyzing the frequency spectrum characteristics of the gas concentration time series data through fast Fourier transform; the extraction efficiency decline interval is the period when the extraction amount is continuously lower than the expected value and shows a downward trend. The features of these abnormal intervals are summarized as gas concentration abnormal patterns, providing a basis for safety control decisions.

[0123] The critical characteristics of roof subsidence are combined with the abnormal pattern of gas concentration to establish a four-level early warning grading standard and a corresponding disposal measure data table. The four levels of early warning correspond to different risk levels: four levels (blue) represent a slight risk, three levels (yellow) represent a moderate risk, two levels (orange) represent a higher risk, and one level (red) represents a serious risk. Each risk level has corresponding triggering conditions and disposal measures. For example, when the roof subsidence rate exceeds 0.2 mm / hour and the gas concentration fluctuation frequency is abnormal, a three-level early warning is triggered, and the corresponding disposal measures include increasing the monitoring frequency, adjusting the support parameters, and strengthening local extraction, etc. These early warning standards and disposal measures constitute safety control decision data, providing clear risk control guidance for on-site operators.

[0124] Taking a thick coal seam cross-cut coal uncovering face as an example, the safety control decision data analysis is carried out by the above method. The seven-day continuous gas monitoring data of the working face shows that the gas extraction attenuation coefficient of the extraction drill hole is 0.85 on average, and the daily change rate of gas concentration fluctuates between -0.1% and 0.15%. The support monitoring data shows that the average roof subsidence rate is 0.15 mm / hour, and the cumulative deformation reaches 8.5 mm. Through correlation analysis, two data abnormal points are found: one is located at the intersection of the cross-cut and the coal seam, with a roof subsidence acceleration mutation value of 0.08 mm / hour2, and a sharp increase of 0.25% in gas concentration, with a correlation intensity coefficient of 0.92; the other is located near the fault zone, with a 25% decrease in extraction efficiency while the surrounding rock deformation breaks through the threshold, with a correlation intensity coefficient of 0.87. Based on these data, the system generates a two-level early warning and gives disposal measures such as increasing the temporary support density and strengthening gas extraction, etc., which successfully prevents the possible risks of large-scale roof collapse and gas outburst.

[0125] The above describes the thick coal seam cross-cut coal uncovering gas control and support control method in the embodiments of the present application, and the following describes the thick coal seam cross-cut coal uncovering gas control and support control system in the embodiments of the present application. Please refer to Figure 2 One embodiment of the thick coal seam cross-cut coal uncovering gas control and support control system in the embodiments of the present application includes:

[0126] A regression module is configured to acquire coal seam parameter data of a cross-cut coal uncovering area by a data acquisition device, including a coal seam thickness value, a firmness coefficient, and a gas content value, and perform multiple regression analysis on the data to obtain a coal seam outburst danger evaluation index and an optimal drill hole parameter scheme.

[0127] A monitoring module is configured to record grouting pipe installation information according to the optimal drill hole parameter scheme, perform dynamic monitoring on the grouting process by pressure-flow curve fitting, identify grouting saturation feature points, and form a grouting quality evaluation data set.

[0128] A configuration module is configured to construct a temporary support load distribution map based on the grouting quality evaluation dataset, determine the position of the π-shaped beam and the anchor net parameter configuration through stress dispersion rules, and generate a support efficiency index table;

[0129] A matching module is configured to perform matching analysis on the support efficiency index table and the permanent support design parameters, determine the U-shaped steel spacing and the anchor cable pretightening force value, and generate a support structure stress state data atlas;

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

[0131] An identification module is configured to perform time sequence correlation analysis on the gas control efficiency curve and the support state data, identify the roof subsidence critical feature and the gas concentration abnormal pattern, and form a safety management and control decision data.

[0132] Through the synergistic cooperation of the above various components, through the establishment of a coal seam parameter data multiple regression analysis model, the accurate assessment of the coal seam outburst danger and the scientific determination of the optimal drilling parameter scheme are realized, the blindness of drilling arrangement is effectively reduced, the gas pre-extraction efficiency is improved, and the method adopts the pressure-flow curve fitting technology to dynamically monitor the grouting process, accurately identifies the saturation characteristic point of grouting, solves the problem of inaccurate saturation judgment in the traditional grouting process, and greatly improves the controllability and uniformity of the grouting quality. On this basis, the present application realizes the optimal configuration of the π-shaped beam position and the anchor net parameter by constructing a temporary support load distribution diagram and using the stress dispersion rule, significantly enhances the stability and reliability of the temporary support, and through the matching analysis of the support efficiency index table and the permanent support design parameter, determines the scientific and reasonable U-shaped steel spacing and anchor pre-tightening force value, forms a complete support structure stress state data atlas, provides data support for the optimization design of the support structure, and greatly improves the overall performance of the support system. Especially noteworthy is that the present application innovatively introduces a gas concentration gradient analysis method to study the gas distribution characteristics of the key points in the support structure stress state data atlas, scientifically determines the extraction position and negative pressure value, and significantly improves the gas control efficiency. The most characteristic is that the present application successfully identifies the internal relationship between the roof subsidence critical characteristics and the gas concentration abnormal mode through the time sequence correlation analysis of the gas control efficiency curve and the support state data, forms a systematic safety control decision data, realizes the double prevention of gas disaster and roof accident. In the present application, the application of artificial intelligence algorithm is particularly prominent, especially in the aspects of multiple regression analysis, curve fitting and time sequence correlation analysis, through the deep mining of massive monitoring data by the machine learning algorithm, the complex rules and correlations that are difficult to be perceived by traditional methods are found, the early warning accuracy is significantly improved, and on this basis, the four-level early warning grading standard and the corresponding disposal measure data table are constructed, which provides clear and definite decision guidance for the on-site operators, greatly improves the pertinence and effectiveness of the emergency disposal.

[0133] With reference Figure 3 In the embodiment of the present application, a computer device is also provided, which can be a server, and the internal structure thereof can be as shown in Figure 3The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer device is configured 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 operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store corresponding data in the embodiment. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above method.

[0134] Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied.

[0135] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by the processor to implement the above method. It can be understood that the computer readable storage medium in the 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 method can be completed by a computer program instructing related hardware. 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 above-mentioned embodiment method. Any reference to the memory, storage, database or other medium provided by the present application and used in the embodiment can include non-volatile and / or volatile memory. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double 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, etc.

[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 system, system and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0138] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0139] The above-described and above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling and supporting gas in thick coal seam rock opening, characterized in that: The thick coal seam rock gate coal uncovering gas control and support management method includes: The coal seam parameter data of the Shimen coal uncovering area was obtained through data acquisition equipment, including coal seam thickness, solidity coefficient and gas content. Multiple regression analysis was performed on the data to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter plan, including: A portable coal seam parameter detector was used to take five-point samples in the stone gate coal uncovering area to obtain the original data of coal seam thickness, solidity coefficient and gas content; the original data were normalized and outliers were eliminated to form a standardized coal seam parameter set; the standardized coal seam parameter set was substituted into the multivariate regression equation of gas outburst tendency, where the weighting coefficient of coal seam thickness was 0.35, the weighting coefficient of solidity coefficient was 0.4, and the weighting coefficient of gas content was 0.25, to obtain the coal seam outburst hazard assessment index; the drilling density coefficient was determined according to the correlation table between the coal seam outburst hazard assessment index and the geological structure complexity; the drilling density coefficient was compared with the cross-sectional dimensions of the tunnel to generate a double-row staggered drilling layout diagram; the coverage analysis of the double-row staggered drilling layout diagram was performed, and the drilling angle and depth parameters were adjusted to obtain a three-dimensional distribution diagram of the drilling trajectory; based on the three-dimensional distribution diagram of the drilling trajectory, the azimuth, inclination and length values ​​of each drilling hole were determined to form an optimal drilling parameter scheme; Recording grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitoring the grouting process using pressure-flow curve fitting, identifying grouting saturation feature points, and forming a grouting quality evaluation data set; constructing a temporary support load distribution diagram based on the grouting quality evaluation data set, determining the π-beam position and anchor net parameter configuration through stress dispersion rules, and generating a support effectiveness index table; Matching and analyzing the support effectiveness index table with the permanent support design parameters, determining the U-steel spacing and anchor cable preload values, and generating 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 extraction locations and negative pressure values, 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 uncovering 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 using pressure-flow curve fitting, identifies 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; 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; The collected grouting pressure and grouting flow values ​​are arranged in a dual-parameter time series, and a scatter plot of the dynamic change of pressure and flow 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; Identifying an inflection point on the grouting process characteristic curve, and marking 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.

3. The thick coal seam rock gate uncovering 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 roadway is obtained through ground stress and deformation monitoring equipment. Combined with the spatial position information in the grouting quality evaluation data set, a three-dimensional load distribution numerical field is constructed. 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-beam is optimized using the stress dispersion rule to determine the center coordinates and orientation angle of the π-beam; Based on the center coordinates of the π-shaped beam, the anchor net coverage area on the roadway surrounding rock surface is divided, the anchor net unit size and overlap width are calculated, and an anchor net parameter configuration table is generated; 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; 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.

4. The thick coal seam rock gate uncovering gas control and support management method according to claim 1 is characterized in that: The support effectiveness index table is matched and analyzed with the permanent support design parameters to determine the U-shaped steel spacing and anchor cable preload values, and generate a support structure stress state data map, including: Extract support resistance value distribution data from the support effectiveness index table, compare and analyze with 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-shaped steel specifications and layout methods, different U-shaped steel spacing values ​​are sorted in a list, and the optimal U-shaped steel spacing is obtained by evaluating the safety redundancy coefficient; Calculate the anchor cable spatial arrangement points based on the U-shaped steel spacing and tunnel cross-sectional parameters, and determine the anchor cable length and anchoring depth based on the surrounding rock geological parameters; Setting the anchor cable preload gradient at each 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 and key node stress values ​​of the support system; 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 force state of the support structure.

5. The thick coal seam rock gate uncovering gas control and support management method according to claim 1 is characterized in that: The gas concentration gradient analysis is performed on 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 points, stress concentration zones and low stress areas on the stress state data map of the support structure, and obtain the initial gas concentration values ​​at key points through multi-point sampling method; Perform spatial interpolation on the initial gas concentration values ​​at key points, draw gas concentration contour maps, and identify gas enrichment areas and diffusion channels; 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; 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 values ​​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.

6. The thick coal seam rock-gate coal uncovering 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 pattern, 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 subsidence rate, surrounding rock deformation and support resistance change values ​​in the support status data are integrated in time synchronization 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 to identify intervals of sharp increase in gas concentration, intervals of abnormal fluctuation frequency, and intervals of decreased extraction efficiency, and summarizing them as abnormal gas concentration patterns; Combining the critical characteristics of roof subsidence and the abnormal gas concentration pattern, a four-level early warning classification standard and a corresponding disposal measure data table are established to form safety management and control decision-making data.

7. A thick coal seam rock gate uncovering gas control and support management system, used to implement the thick coal seam rock gate uncovering gas control and support management method according to any one of claims 1 to 6, characterized in that: The thick coal seam rock gate coal uncovering gas control and support management system includes: The regression module is used to obtain coal seam parameter data in the Shimen coal uncovering area through data acquisition equipment, including coal seam thickness, solidity coefficient, and gas content. The data is then subjected to multivariate regression analysis to obtain the coal seam outburst hazard assessment index and the optimal drilling parameter solution, including: A portable coal seam parameter detector was used to take five-point samples in the stone gate coal uncovering area to obtain the original data of coal seam thickness, solidity coefficient and gas content; the original data were normalized and outliers were eliminated to form a standardized coal seam parameter set; the standardized coal seam parameter set was substituted into the multivariate regression equation of gas outburst tendency, where the weighting coefficient of coal seam thickness was 0.35, the weighting coefficient of solidity coefficient was 0.4, and the weighting coefficient of gas content was 0.25, to obtain the coal seam outburst hazard assessment index; the drilling density coefficient was determined according to the correlation table between the coal seam outburst hazard assessment index and the geological structure complexity; the drilling density coefficient was compared with the cross-sectional dimensions of the tunnel to generate a double-row staggered drilling layout diagram; the coverage analysis of the double-row staggered drilling layout diagram was performed, and the drilling angle and depth parameters were adjusted to obtain a three-dimensional distribution diagram of the drilling trajectory; based on the three-dimensional distribution diagram of the drilling trajectory, the azimuth, inclination and length values ​​of each drilling hole were determined to form an optimal drilling parameter scheme; A monitoring module is used to record grouting pipeline installation information according to the optimal drilling parameter scheme, dynamically monitor the grouting process using pressure-flow curve fitting, identify grouting saturation feature points, and form a grouting quality evaluation data set; a configuration module for constructing a temporary support load distribution diagram based on the grouting quality evaluation data set, determining the π-beam position and anchor net parameter configuration through stress dispersion rules, and generating 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.

8. 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 mining gas control and support management method described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is enabled to execute the thick coal seam rock gate uncovering gas control and support management method as described in any one of claims 1 to 6.

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