Gas control and safety assurance method and system for coal mine areas under complex geological conditions

Abstract

The present application relates to the field of data processing technology, and discloses a method and system for gas control and safety assurance in coal mine areas under complex geological conditions. The method includes: conducting geophysical exploration, drilling, and logging exploration in the coal mine area to obtain geological parameters; conducting statistical analysis on the data to construct a three-dimensional gas model; identifying high-gas areas based on the model and arranging a directional drilling network; implementing differentiated extraction and permeability enhancement measures based on the drilling network; fusing extraction and sensor data to predict gas change trends using a neural network; and performing intelligent diagnosis based on an early warning matrix to form a disposal plan. Under complex geological conditions, the present application achieves the scientific arrangement of directional drilling and the optimized design of differentiated extraction parameters by accurately grasping the gas distribution law. At the same time, it establishes a gas early warning decision support system based on artificial intelligence to improve the accuracy of gas control and the timeliness of early warning, thereby ensuring safe production in coal mines.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for gas control and safety assurance in coal mine areas under complex geological conditions. Background Art

[0002] Gas is one of the most significant safety hazards in coal mining, especially in complex geological conditions such as fault development, complex geological structures, and high-gas areas. Gas control is particularly problematic. Traditional gas control methods primarily include ventilation dilution, gas extraction, and regional pre-extraction. Ventilation dilution reduces gas concentration by increasing ventilation volume, but its effectiveness is limited in high-gas mines. Gas extraction extracts gas from coal seams through drilling, effectively reducing gas content, but traditional drilling arrangements often lack scientific rationale. Regional pre-extraction involves pre-extraction of coal seam gas prior to mining, which can reduce gas outbursts during mining. However, its implementation is long and inefficient. Furthermore, existing gas monitoring technologies primarily rely on sampling and testing at fixed measurement points, resulting in relatively scattered monitoring data and difficulty in gaining a comprehensive understanding of gas distribution and migration patterns.

[0003] However, existing gas control methods have numerous shortcomings. First, under complex geological conditions, especially in areas with unique geological structures such as faults and folds, gas distribution is highly non-uniform, making the traditional method of uniformly arranged extraction drilling inefficient. Second, inaccurate gas parameter measurements make it difficult to accurately grasp the gas distribution patterns, resulting in a lack of targeted extraction scheme design. Third, existing gas monitoring systems generally have lags, making it impossible to effectively predict gas concentration trends and providing early warnings. Fourth, there is a lack of differentiated design for gas extraction parameters under different geological conditions, and a single parameter is difficult to adapt to complex and changing geological environments. Fifth, in terms of emergency response, there is a lack of differentiated disposal processes based on risk levels, resulting in low emergency response efficiency. These issues seriously hinder the safe and efficient mining of coal mines under complex geological conditions. Summary of the Invention

[0004] This application provides a method and system for gas control and safety assurance in coal mine areas under complex geological conditions. It is used to accurately grasp the gas distribution law under complex geological conditions, realize the scientific arrangement of directional drilling and the optimal design of differentiated extraction parameters, and at the same time establish a gas early warning decision support system based on artificial intelligence to improve the accuracy of gas control and the timeliness of early warning, thereby ensuring safe production in coal mines.

[0005] In the first aspect, the present application provides a method for gas control and safety assurance in coal mine areas under complex geological conditions, and the method for gas control and safety assurance in coal mine areas under complex geological conditions includes: conducting geophysical exploration, drilling, and well logging exploration in the coal mine area to obtain multidimensional geological parameter data; performing geostatistical analysis on the multidimensional geological parameter data to construct a three-dimensional gas distribution model; identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a borehole space network; implementing differentiated extraction parameters and permeability enhancement measures for different pressure zones according to the borehole space network and gas pressure distribution, and establishing an efficient gas extraction system; performing outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculating the gas concentration change trend through a neural network algorithm, and generating a multi-level early warning judgment matrix; based on the multi-level early warning judgment matrix, performing intelligent diagnosis and analysis on the causes of gas anomalies, and generating risk level assessment results and a differentiated disposal process database.

[0006] In a second aspect, the present application provides a gas control and safety assurance system for coal mine areas under complex geological conditions, the gas control and safety assurance system for coal mine areas under complex geological conditions comprising:

[0007] The exploration module is used to conduct geophysical exploration, drilling, and logging exploration in coal mining areas to obtain multi-dimensional geological parameter data;

[0008] An analysis module, configured to perform geostatistical analysis on the multi-dimensional geological parameter data and construct a three-dimensional gas distribution model;

[0009] an identification module for identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network;

[0010] An extraction module is used to implement differentiated extraction parameters and permeability enhancement measures for different pressure zones based on the borehole spatial network and gas pressure distribution, thereby establishing an efficient gas extraction system;

[0011] A fusion module is used to perform outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix;

[0012] The diagnosis module is used to perform intelligent diagnosis and analysis on the causes of gas anomalies based on the multi-level early warning judgment matrix, and generate risk level assessment results and a differentiated disposal process database.

[0013] On the third aspect, a coal mine area gas control and safety assurance device under complex geological conditions is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the coal mine area gas control and safety assurance device under complex geological conditions executes the above-mentioned coal mine area gas control and safety assurance method under complex geological conditions.

[0014] In a fourth aspect, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer is enabled to execute the above-mentioned method for gas control and safety assurance in coal mine areas under complex geological conditions.

[0015] In the technical solution provided in this application, the multi-dimensional geological parameter data obtained through geophysical prospecting, drilling and logging exploration of the coal mine area is combined with the three-dimensional gas distribution model constructed by geostatistical analysis. It not only realizes the precise grasp of the gas distribution law under complex geological conditions and improves the accuracy of gas distribution prediction, but also provides a scientific basis for the subsequent directional drilling layout; based on the three-dimensional gas distribution model, the high-gas area is identified and the directional drilling network is designed and implemented. Through the fan layout method and the three-layer layout principle, the intersection probability of the borehole and the gas-enriched zone is significantly improved, and the drilling network coverage rate reaches more than 85%, creating basic conditions for efficient extraction; differentiated extraction parameters and permeability enhancement measures are implemented according to the borehole space network and gas pressure distribution, and different negative pressure values ​​and extraction time strategies are adopted for different pressure areas. At the same time, targeted permeability enhancement techniques such as hydraulic fracturing, CO2 phase change blasting and negative pressure pulse are implemented according to the difference in permeability coefficient. The technology has greatly improved the gas extraction efficiency and reduced the gas content in the working face to a safe allowable range; the real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing, and a hybrid prediction model constructed by applying convolutional neural networks and long short-term memory networks is used to achieve accurate prediction of the gas concentration change trend in the next 4 hours, significantly enhancing the timeliness and accuracy of the early warning. The application of neural network algorithms enables the system to automatically identify the nonlinear patterns and time series characteristics of gas concentration changes, taking into account the complex interactions between multiple influencing factors, and achieving more accurate concentration predictions through deep learning of historical data. Intelligent diagnosis and analysis of the causes of gas anomalies are carried out based on a multi-level early warning judgment matrix, and the possible causes of gas anomalies are identified through decision trees and rule inference algorithms. Risk level assessment results and a differentiated disposal process database are generated, realizing the transformation from passive response to active prevention. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 This is a schematic diagram of an embodiment of a method for gas control and safety assurance in a coal mine area under complex geological conditions in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of a coal mine area gas control and safety assurance system under complex geological conditions in an embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of coal mine area gas control and safety assurance equipment under complex geological conditions in an embodiment of the present invention. DETAILED DESCRIPTION

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

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the method for gas control and safety assurance in coal mine areas under complex geological conditions includes:

[0022] Step S101: Conduct geophysical exploration, drilling, and logging exploration in the coal mine area to obtain multi-dimensional geological parameter data;

[0023] Step S102: performing geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model;

[0024] Step S103: Identify high-gas areas based on the three-dimensional gas distribution model, design and implement a directional drilling network layout plan, and generate a drilling space network;

[0025] Step S104: Implement differentiated extraction parameters and permeability enhancement measures for different pressure zones based on the borehole spatial network and gas pressure distribution to establish an efficient gas extraction system;

[0026] Step S105: Perform outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix;

[0027] Step S106: Based on the multi-level early warning judgment matrix, intelligent diagnosis and analysis are performed on the causes of gas anomalies to generate risk level assessment results and a differentiated disposal process database.

[0028] It is understandable that the execution subject of this application can be a coal mine area gas control and safety assurance system under complex geological conditions, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, multidimensional geological parameter data is collected through geophysical exploration, drilling, and well logging. Specifically, geophysical exploration uses high-precision seismic exploration technology, with survey line spacing of no more than 50 meters and receiver spacing of no more than 10 meters, to obtain underground geological structure data in the coal mining area. Drilling is arranged according to the fault distribution in the coal mining area, using a grid layout of 30 meters x 30 meters (near faults) and 50 meters x 50 meters (other areas). Data such as coal seam thickness, inclination, and gas pressure are collected through boreholes. Well logging uses acoustic, density, natural potential, and resistivity logging to provide more detailed physical parameter data of the underground rock formations. This data is processed using specialized software to remove outliers and interference signals, generating a standardized geological parameter dataset, which further lays the foundation for subsequent analysis. Geostatistical analysis is performed on the collected multidimensional geological parameter data to construct a three-dimensional gas distribution model. Data processing methods include variogram analysis and kriging interpolation algorithms, which can model the spatial variability of the coal mining area and the correlation between different parameters. The variogram analysis helps determine how various geological parameters change in different locations by calculating the degree of variation of geological parameters in space, revealing the spatial distribution patterns of parameters such as gas content, gas pressure and coal seam permeability in the coal mine area. Next, the Kriging interpolation method will interpolate and estimate areas lacking data based on the calculation results of the variogram to fill in the data gaps in space, thereby ensuring that the generated three-dimensional gas distribution model has sufficient accuracy. Based on the three-dimensional gas distribution model, high-gas areas are identified and a directional drilling network layout plan is designed. This process mainly relies on the identification of different gas area grades through gas content data: special grade (gas content>15m 3 / t), Level 1 (gas content 10-15m 3 / t), Level II (gas content 5-10m 3 / t) and level three (gas content <5m 3 / t). Based on these area divisions, the drilling density is designed. The drilling spacing in the special-grade area is no more than 15 meters, in the first-grade area no more than 25 meters, in the second-grade area no more than 35 meters, and in the third-grade area no more than 50 meters. The rationality and accuracy of the drilling layout are crucial to the effectiveness of subsequent extraction work. Based on different gas pressure zones, they are divided into high-pressure, medium-pressure and low-pressure zones. Each zone corresponds to different extraction parameters: the high-pressure zone adopts a low-negative-pressure, long-term extraction strategy, the medium-pressure zone adopts a medium-negative-pressure extraction strategy, and the low-pressure zone uses a high-negative-pressure, short-term extraction strategy. At the same time, for areas with poor coal seam permeability, permeability-enhancing measures such as hydraulic fracturing, CO2 phase change blasting technology, and negative pressure pulse technology are adopted. These technical means significantly improve the gas extraction efficiency by enhancing the permeability of the coal seam, ensuring the safety and efficiency of extraction.

[0030] Outlier detection is performed by fusing real-time data from the efficient gas extraction system with data from the sensor network. This process primarily utilizes neural network algorithms, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), to predict real-time gas concentration data. By combining data from different regions and sensors, the data fusion process corrects for potential noise and errors, ensuring the accuracy of the gas concentration trend forecast. The prediction results are evaluated using a multi-level early warning judgment matrix to identify gas anomaly risk points, providing a scientific basis for subsequent safety decisions. Based on the multi-level early warning judgment matrix, intelligent diagnostic analysis of the causes of gas anomalies is performed. Decision trees and rule-based inference algorithms are used to analyze possible causes of gas anomalies, such as ventilation system failures, sudden geological changes, or extraction system anomalies. This analysis results in the calculation of the probability, impact range, and severity of different gas accidents. Differentiated emergency response procedures are then developed based on risk levels. Particularly serious risks trigger specific emergency response plans, while major and high-risk risks trigger corresponding emergency response measures, ensuring a rapid response and effective handling of gas accidents in the mine.

[0031] In the embodiment of the present application, a three-dimensional gas distribution model is constructed by combining multi-dimensional geological parameter data obtained through geophysical prospecting, drilling, and logging exploration of the coal mine area with geostatistical analysis. This not only achieves accurate grasp of the gas distribution law under complex geological conditions and improves the accuracy of gas distribution prediction, but also provides a scientific basis for subsequent directional drilling layout. Based on the three-dimensional gas distribution model, the high-gas area is identified and the directional drilling network is designed and implemented. Through the fan-shaped layout method and the three-layer layout principle, the intersection probability of the borehole and the gas-rich zone is significantly improved, and the drilling network coverage rate reaches more than 85%, creating basic conditions for efficient extraction. Different extraction parameters and permeability enhancement measures are implemented according to the borehole space network and gas pressure distribution. Different negative pressure values ​​and extraction time strategies are adopted for different pressure areas. At the same time, targeted permeability enhancement technologies such as hydraulic fracturing, CO2 phase change blasting and negative pressure pulse are implemented according to the difference in permeability coefficient. The gas extraction efficiency has been greatly improved, and the gas content in the working face has been reduced to a safe allowable range. The real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing. By applying a hybrid prediction model constructed by convolutional neural networks and long short-term memory networks, an accurate prediction of the gas concentration change trend in the next 4 hours is achieved, which significantly enhances the timeliness and accuracy of the early warning. The application of neural network algorithms enables the system to automatically identify the nonlinear patterns and time series characteristics of gas concentration changes, taking into account the complex interactions between multiple influencing factors, and achieving more accurate concentration predictions through deep learning of historical data. Based on the multi-level early warning judgment matrix, intelligent diagnosis and analysis of the causes of gas anomalies are carried out, and the possible causes of gas anomalies are identified through decision trees and rule inference algorithms. Risk level assessment results and a differentiated disposal process database are generated, realizing the transformation from passive response to active prevention.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] Obtain underground geological structure data through high-precision seismic exploration with survey line spacing no greater than 50 meters and detection point spacing no greater than 10 meters;

[0034] Drill holes were arranged according to a grid pattern of 30 m x 30 m near the main faults and 50 m x 50 m in other areas to collect data on coal seam thickness, dip, and gas pressure.

[0035] Perform acoustic logging, density logging, natural potential logging and resistivity logging on each borehole to generate logging curve data;

[0036] Eliminate outliers from underground geological structure data, coal seam thickness, inclination, gas pressure data, and well logging curve data to generate a standardized geological parameter data set;

[0037] Apply variogram analysis and Kriging interpolation algorithm to the standardized geological parameter dataset to calculate the spatial variation pattern and parameter correlation;

[0038] According to the spatial variation law and parameter correlation, the coal mining area is divided into ultra-high complexity zone, high complexity zone, medium complexity zone and low complexity zone, and geological complexity zoning data is generated.

[0039] Specifically, high-precision seismic exploration with line spacing no greater than 50 meters and receiver spacing no greater than 10 meters obtains underground geological structural data. This geological structural data primarily includes coal seam depth, dip, and stratigraphic variations, information crucial for the subsequent construction of gas distribution models. Next, the grid layout of boreholes is based on the distribution of faults within the coal mine area. To ensure a comprehensive understanding of coal seam thickness, dip, and gas pressure, a dense 30m x 30m grid of boreholes is typically deployed near major faults, while borehole spacing in other areas is 50m x 50m. These boreholes collect basic coal seam physical parameters, including gas pressure, thickness, and dip. Furthermore, acoustic logging, density logging, spontaneous potential logging, and resistivity logging data collected during drilling are comprehensively analyzed to generate logging curves. These curves reveal the physical properties of the coal seam and its permeability, enabling preliminary assessments of gas storage and release.

[0040] After outliers are removed from geological structure data, coal seam thickness, inclination, gas pressure, and well log data, these data are integrated into a standardized geological parameter dataset. Outlier removal is achieved by identifying and removing data points that do not conform to geological patterns or have excessive detection errors. This dataset, after outliers are removed, more accurately reflects the geological characteristics of the coal mining area, ensuring the accuracy of subsequent analysis. Once the standardized geological parameter dataset is complete, the next step is to apply variogram analysis and kriging interpolation algorithms to the data to calculate spatial variation patterns and parameter correlations. Variogram analysis is a statistical method that measures the spatial variation of geological parameters to help determine whether gas occurrence in certain areas is highly concentrated or heterogeneous. For example, coal seams near faults are more likely to be gas-rich, while other locations may be gas-poor. By calculating the variogram, these spatial variations can be quantified, thereby identifying distinct gas distribution areas. The kriging interpolation algorithm uses the spatial information derived from the variogram to predict gas distribution in areas with missing data, thereby forming a complete gas spatial distribution map.

[0041] Based on the analysis of spatial variation patterns and parameter correlations, coal mining areas are divided into different geological complexity zones, including ultra-high complexity zones, high complexity zones, medium complexity zones, and low complexity zones. Specifically, the division of these zones is determined by multiple factors such as gas content, coal seam permeability, and the distribution of faults. In high-complexity and ultra-high-complexity zones, special gas control measures are taken due to the high gas content and poor coal seam permeability. These areas are usually the focus of extraction. In low-complexity zones, control measures can be relatively relaxed due to the low gas content or good permeability.

[0042] Gas distribution models can accurately reflect the gas reserves, distribution, and changing trends within a coal mine area, supporting the development of subsequent extraction strategies and safety measures. For example, in highly complex areas, gas pressures can reach as high as 1.2 MPa, and coal seams have poor permeability. This information, through kriging interpolation, can predict gas accumulation trends across the entire area, enabling the design of a targeted, dense drilling network and, where necessary, the implementation of permeability enhancement measures such as hydraulic fracturing to improve extraction efficiency and ensure mine safety. Conversely, in low-complexity areas, where gas pressures are lower and permeability is better, the frequency and intensity of extraction can be reduced.

[0043] The core of data processing lies not only in acquiring and analyzing gas distribution data but also in closely integrating this data with actual mine extraction operations. Comparative analysis with real-time sensor monitoring data can further optimize extraction parameters and reduce the occurrence of gas accidents. For example, during the extraction process, by real-time monitoring of gas concentration and extraction flow rate, if the gas concentration in a particular borehole remains below the design standard for a prolonged period, the system will automatically adjust the extraction strategy, increasing the negative pressure or initiating additional drilling to ensure the continuity and safety of extraction operations.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] Based on the standardized geological parameter data set and geological complexity zoning data, a 3D skeleton model of the regional geological structure with an accuracy of 5m x 5m x 1m was established;

[0046] The gas content, gas pressure and coal seam permeability data in the multi-dimensional geological parameter data are mapped to the three-dimensional skeleton model through the trilinear interpolation algorithm to generate the initial gas distribution grid;

[0047] Apply the random forest algorithm to the data-sparse areas in the initial gas distribution grid to predict parameters and generate gas parameter spatial distribution data;

[0048] Based on the spatial distribution data of gas parameters and the geological complexity zoning data, the finite element method is used to calculate the stress distribution state in the region and establish a stress-gas coupling analysis model;

[0049] The fault influence coefficient is set for the fault zone in the stress-gas coupling analysis model. The influence weights of 0.8, 0.6, and 0.4 are assigned according to the size of the fault, and the fault gas enrichment coefficient is calculated.

[0050] The fault gas enrichment coefficient and stress-gas coupling analysis model are input into the multiphase flow numerical simulator to calculate the gas migration law under different mining conditions and generate a three-dimensional gas distribution model.

[0051] Specifically, based on standardized geological parameter data sets and geological complexity zoning data, a three-dimensional skeleton model of the regional geological structure with an accuracy of 5 meters × 5 meters × 1 meter was constructed. The construction of the three-dimensional skeleton model depends on the spatial coordinates and morphological characteristics of the geological data. Through high-precision measurement and data acquisition, combined with known geological structure information, this three-dimensional skeleton model can accurately describe the morphology of coal seams, the distribution of faults and the spatial distribution of other geological features. This model provides a spatial basis for subsequent gas distribution simulation and extraction optimization. Each grid cell in the three-dimensional skeleton model represents a small area in the coal mine area. The accuracy of the model (5 meters × 5 meters × 1 meter) ensures that the spatial distribution of gas can be accurately depicted between different coal seams and geological areas.

[0052] Important parameters such as gas content, gas pressure, and coal seam permeability from the multidimensional geological parameter data are mapped to a three-dimensional skeleton model using a trilinear interpolation algorithm to generate an initial gas distribution grid. The trilinear interpolation algorithm is a commonly used method for interpolating three-dimensional spatial data. It estimates unknown areas based on the surrounding environment of known data points, thereby generating the distribution of parameters such as gas content and pressure in three-dimensional space. Specifically, the trilinear interpolation algorithm uses the known values ​​surrounding each grid point to infer the gas content and gas pressure at each location in three-dimensional space, forming a complete gas distribution grid. After the initial gas distribution grid is generated, sparse data areas need to be supplemented using a random forest algorithm. Random forest is a machine learning algorithm based on an ensemble of decision trees that can predict gas parameters in areas not directly collected by analyzing existing geological parameter data. The random forest algorithm can effectively fill data gaps, optimize the spatial prediction of gas distribution, and make the spatial distribution data of gas parameters more accurate. During the specific implementation process, the random forest algorithm will learn the relationship between features from the existing gas pressure, content and permeability data, and infer the gas parameters of the missing area based on the combination of these features.

[0053] Based on the generated spatial distribution data of gas parameters and geological complexity zoning data, the finite element method (FEM) is used to calculate the stress distribution within the region. The finite element method (FEM) is a numerical analysis technique widely used to calculate the numerical solutions of complex physical phenomena. In this process, the coal mine area is divided into multiple finite regions, each of which is assigned corresponding geological and mechanical properties. The stress distribution within the region is then calculated by solving the stress-strain equation. Stress distribution is a key factor influencing gas release and migration. Accurate stress calculation can reveal the stress state between different coal seams and possible areas of pressure accumulation. For fault zones, a fault influence coefficient is set, with different influence weights assigned based on the size of the fault. For example, the influence coefficient for large faults is 0.8, for medium faults is 0.6, and for small faults is 0.4. Fault zones are often key areas of gas enrichment. Therefore, calculating the fault influence coefficient can clarify the contribution of these areas to gas enrichment and provide key guidance for subsequent extraction operations. By incorporating these fault influence coefficients into the stress-gas coupling analysis model, the distribution and migration trends of gas in the fault zone can be calculated more accurately. The fault gas enrichment coefficient and the stress-gas coupling analysis model are input into a multiphase flow numerical simulator to simulate the gas migration patterns. Multiphase flow numerical simulation technology can simulate the dynamic migration process of gas in coal seams under different mining conditions. By considering the impact of stress redistribution on gas release, the simulator can predict the changes in gas in coal seams under different mining stages and gas extraction methods. By comparing with actual coal mine extraction data, the model parameters can be continuously optimized to ensure that it accurately reflects the dynamic changes in gas and generate a complete three-dimensional gas distribution model.

[0054] For example, in a certain mining area, preliminary exploration data showed that the gas pressure in the area varied significantly at different depths, especially near the fault zones of the coal seams, where gas enrichment was more pronounced. Based on this, the stress distribution results calculated using a three-dimensional gas distribution model and the finite element method showed that the stress state of the coal seam was highly correlated with the gas-enriched areas. The random forest algorithm predicted gas pressure data for some unexplored areas, and combined with the influence coefficient of the fault, a complete gas distribution map was ultimately generated. This map clearly identified areas where gas extraction was more difficult and areas with higher gas pressure, optimized the gas extraction plan, and used a multiphase flow simulator to predict the dynamic distribution of gas at different mining stages.

[0055] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0056] Based on the gas content data in the three-dimensional gas distribution model, the coal mine area is divided into special-grade areas, first-grade areas, second-grade areas, and third-grade areas, and gas area classification data is generated;

[0057] Based on the gas area classification data, the drilling density standard is formulated, with the spacing between special-grade areas not exceeding 15 meters, first-grade areas not exceeding 25 meters, second-grade areas not exceeding 35 meters, and third-grade areas not exceeding 50 meters;

[0058] Input the drilling density standard and 3D gas distribution model into the computer-aided design system, design a three-layer layout plan for areas with coal seam thickness greater than 3 meters, and design a drilling plan perpendicular to the coal seam trend for areas with inclination greater than 15 degrees, and generate the initial drilling trajectory design;

[0059] The fan-shaped layout method is applied to the area near the fault in the initial drilling trajectory design. With the fault as the center, drill holes are arranged at 15° angles around the fault to generate a drilling layout design diagram.

[0060] Drilling is carried out according to the drilling layout design drawing. The magnetic direction finding system and gyroscope direction finding system are used to monitor the drilling trajectory in real time. When the deviation from the designed trajectory exceeds 0.5 meters, the trajectory is corrected to complete the drilling construction.

[0061] Perform borehole imaging inspection and pressure testing on completed boreholes, calculate the borehole network coverage, and add additional boreholes when the coverage is lower than 85%, and generate a borehole space network database containing the borehole spatial coordinates, length, direction and diameter.

[0062] Specifically, coal mine areas are classified into gas zones based on gas content data from a three-dimensional gas distribution model. This process first analyzes gas content data within different areas of the coal mine and categorizes the entire area into special-grade, primary-grade, secondary-grade, and tertiary-grade zones. Special-grade zones typically represent areas with extremely high gas content, often exceeding established warning levels; primary-grade zones are those with relatively high gas content; secondary zones are those with moderate gas content; and tertiary zones are those with relatively low gas content. This classification clearly identifies the gas risks of different areas.

[0063] After completing the gas area classification, drilling density standards are designed based on the risk level of each area. For special-grade areas with extremely high gas content, the spacing between boreholes is set to no more than 15 meters to ensure that intensive extraction can effectively reduce the gas content in the area. For first-grade areas with higher gas content, the drilling spacing is set to no more than 25 meters; for second-grade areas with moderate gas content, the drilling spacing is no more than 35 meters; and for third-grade areas with lower gas content, the drilling spacing is 50 meters. The establishment of drilling density standards ensures that extraction work in each area can be differentiated according to the actual gas content, thereby improving extraction efficiency and reducing resource waste during the extraction process.

[0064] Once the drill hole density standards are set, this data is fed into a computer-aided design system. The system then optimizes the drill hole layout based on coal seam thickness, inclination, and gas distribution characteristics. For areas with coal seam thickness exceeding 3 meters, a three-layer layout is designed, with drill holes placed in the upper, middle, and lower layers to ensure effective gas extraction from each seam. For areas with coal seam inclinations greater than 15°, drill holes are arranged perpendicular to the strike of the coal seam to maximize intersection with gas-rich zones, improving extraction efficiency. Based on these requirements, a preliminary drill hole trajectory design is generated to ensure the rationality and accuracy of the design.

[0065] For specific areas within the drilling trajectory design, such as those near fault zones, a fan-shaped layout was used. Near the fault, holes were placed at 15° angles outward from the fault. This ensured comprehensive coverage of the gas-rich areas surrounding the fault zone, reducing the risk of missed gas and improving gas extraction efficiency. Finally, a drilling layout design was generated based on this plan, and drilling was carried out according to the design.

[0066] During the drilling process, a magnetic direction-finding system and a gyroscope direction-finding system monitor the borehole trajectory in real time. If the actual borehole trajectory deviates by more than 0.5 meters from the planned trajectory, the system automatically issues an alarm, requesting a trajectory correction to ensure the borehole is accurately positioned as designed. This approach prevents reduced extraction efficiency and safety hazards caused by borehole trajectory deviation, thereby ensuring efficient and safe gas control. After drilling is completed, borehole imaging and pressure testing are performed. Imaging provides a clear view of the borehole's actual shape, ensuring there are no blockages or other issues. Pressure testing verifies the borehole's tightness and gas pressure distribution. These tests ensure that each borehole meets the designed standards. During testing, the coverage of the borehole network is calculated. If the coverage in a particular area falls below 85%, additional boreholes are added and the borehole layout adjusted until the coverage standard is achieved. The design of these additional boreholes should be adjusted based on factors such as coal seam permeability and gas pressure to ensure the expected gas control results in each area.

[0067] After drilling and acceptance testing, a detailed borehole spatial network database is generated. This database contains information such as the spatial coordinates, length, direction, and diameter of each borehole, providing crucial data support for subsequent gas extraction. This database not only provides precise technical support for subsequent extraction, but also enables dynamic evaluation of extraction effectiveness based on real-time monitoring data, providing a basis for subsequent improvements and optimization.

[0068] For example, in a certain area, a three-dimensional gas distribution model analysis revealed that the gas content in this area was high and located in the first-level area. Based on the drilling density standard, the drilling spacing was set to no more than 25 meters, and a three-layer layout was adopted. After drilling construction, borehole imaging inspection and pressure testing revealed that the boreholes in some areas deviated from the designed trajectory by 0.6 meters, resulting in the gas extraction efficiency in this area not meeting expectations. Based on this situation, the drilling trajectory was adjusted and additional drilling was added to ensure that the coverage rate of this area reached more than 85%, thereby improving the efficiency of gas extraction and ensuring that the gas content in this area can be effectively controlled within a safe range.

[0069] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] Based on the gas pressure data from the borehole spatial network database and the three-dimensional gas distribution model, the coal mine area is divided into high-pressure, medium-pressure and low-pressure areas, and a gas pressure zoning map is generated;

[0071] Based on the gas pressure zoning map, a low-negative-pressure, long-term drainage strategy with a negative pressure of no more than 10 kPa and a duration of at least 30 days is implemented in high-pressure areas; a medium-negative-pressure drainage strategy with a negative pressure of 15-25 kPa and a duration of 15-25 days is implemented in medium-pressure areas; and a high-negative-pressure drainage strategy with a negative pressure of 25-35 kPa and a duration of 7-15 days is implemented in low-pressure areas. Differentiated drainage parameter plans are generated.

[0072] According to the coal seam permeability data in the three-dimensional gas distribution model, the permeability coefficient is less than 0.1m 2 / (MPa 2 ·D) The hydraulic fracturing technology is implemented in the area with a permeability coefficient of 0.1-0.5m 2 / (MPa 2 ·d) The area between the two places is subjected to CO2 phase change blasting technology, and the permeability coefficient is greater than 0.5m 2 / (MPa 2 · Implement negative pressure pulse technology in the area d) to generate an antireflection technology solution;

[0073] Carry out permeability enhancement operations on the boreholes according to the permeability enhancement technology plan, verify the permeability enhancement effect through pressure monitoring and flow monitoring, and improve the permeability of the coal seam;

[0074] Design the main and branch pipe network structure based on the borehole spatial network database. The main pipe diameter should be no less than 219mm, the branch pipe diameter should be no less than 159mm, and the single borehole extraction pipe diameter should be no less than 89mm. Generate the extraction pipe system design plan.

[0075] According to the differentiated extraction parameter plan and the extraction pipeline system design plan, the extraction pump station equipment is configured, and gas extraction operations are implemented. Monitoring points are set up every 50 meters to monitor the extraction effect in real time. When the extraction concentration and extraction volume do not meet the standards, the extraction parameters are automatically adjusted to establish an efficient gas extraction system that includes data collection, parameter adjustment and gas utilization classification.

[0076] Specifically, the gas pressure zones in the coal mine area are divided into high-pressure zones, medium-pressure zones, and low-pressure zones. The core of this process is to use the gas pressure data in the three-dimensional gas distribution model, combined with the geological information in the borehole space network database, to carry out detailed classification and processing of the gas pressure in different areas. The distribution of gas pressure is a key factor in determining the gas extraction strategy, so it is necessary to accurately divide the mining area according to different levels of gas pressure. Specifically, the gas pressure in the high-pressure zone is usually more than 1.0MPa, the medium-pressure zone is between 0.25MPa and 0.74MPa, and the low-pressure zone is less than 0.25MPa. By distinguishing the gas pressure in different areas, a detailed basis can be provided for the formulation of subsequent extraction strategies.

[0077] Based on the gas pressure zoning map, differentiated extraction strategies are then developed. High-pressure areas are typically areas of high gas concentration. Therefore, a low-negative-pressure, long-term extraction strategy is employed in these areas, with a negative pressure not exceeding 10 kPa and an extraction duration of at least 30 days. This strategy helps prevent structural damage to the coal seam caused by overextraction and maintains coal seam stability. For medium-pressure areas, the extraction negative pressure is set between 15 and 25 kPa, with an extraction duration of 15 to 25 days. Appropriately increasing the negative pressure can effectively improve gas release efficiency while minimizing the impact of overextraction on the coal seam. For low-pressure areas, the extraction strategy utilizes a higher negative pressure of 25 to 35 kPa and a shorter extraction duration of 7 to 15 days. This strategy aims to extract gas quickly and efficiently, ensuring that gas concentrations meet safety standards. These differentiated extraction strategies maximize gas extraction efficiency while avoiding coal seam damage or gas waste caused by overextraction. When implementing extraction strategies, the selection of permeability enhancement technology should be based on coal seam permeability data. The permeability coefficient is an important parameter that affects the gas extraction effect. Through the analysis of permeability data, the permeability enhancement requirements of different coal seams can be determined. 2 / (MPa 2 In the area of ​​d), the coal seam has poor permeability and gas release is difficult, so hydraulic fracturing technology is needed. Hydraulic fracturing technology injects high-pressure liquid into the coal seam to crack the coal seam and increase its permeability, thereby improving the gas desorption rate and extraction efficiency. 2 / (MPa 2 d) to 0.5m2 / (MPa 2 ·d) The area between the two places uses CO2 phase change blasting technology. This technology injects CO2 gas into the coal seam and uses the phase change characteristics of CO2 (from gas to liquid) to generate expansion force, thereby generating micro cracks and enhancing the permeability of the coal seam. It is suitable for areas with medium permeability. For areas with a permeability coefficient greater than 0.5m 2 / (MPa 2 In areas d), the coal seams have good permeability and do not require extensive permeability enhancement measures. However, negative pressure pulse technology can be used to further stimulate microcracks in the coal seam and enhance gas release efficiency. The selection and implementation of each permeability enhancement technology requires verification through pressure and flow monitoring to ensure the effectiveness and safety of the permeability enhancement operation.

[0078] In the design of the gas extraction pipeline, a main and branch pipe network structure was designed based on the borehole spatial network database to ensure the smooth flow and stability of the pipeline during the gas extraction process. The diameter of the main pipeline is set to no less than 219mm, the diameter of the branch pipeline is no less than 159mm, and the diameter of the extraction pipe in a single borehole is no less than 89mm. These designs ensure the pipeline's flow rate requirements and pressure resistance during the extraction process. During the extraction operation, monitoring points are set up every 50 meters to monitor the extraction effect in real time. When the gas concentration and extraction volume at the monitoring point do not meet the predetermined standards, the extraction parameters are automatically adjusted to ensure the smooth completion of the extraction task. This automatic adjustment mechanism helps to promptly respond to emergencies that may arise during the extraction process, such as abnormal fluctuations in gas concentration or insufficient extraction volume.

[0079] These differentiated extraction strategies and enhanced permeability technologies effectively improve gas extraction efficiency and safety, ensuring safe coal mine production. Furthermore, the extraction pipeline system's design and real-time monitoring mechanism ensure efficient gas extraction operations and minimize the occurrence of gas accidents. During implementation, as extraction data is continuously updated, the system dynamically adjusts extraction strategies and enhanced permeability technologies, providing continuous and stable technical support for gas control in coal mine areas.

[0080] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0081] Fixed gas sensors are deployed at intervals of no more than 50 meters in key areas of the mining face, return air lane, and electromechanical chamber. Multi-parameter sensors are installed near the edges of goafs and fault zones to create a multi-layered sensor network.

[0082] Transmit the real-time extraction data of the efficient gas extraction system and the monitoring data collected by the multi-level sensor network to the data processing center for outlier detection, missing value repair and data standardization to generate standardized monitoring data;

[0083] Fuse the standardized monitoring data with the three-dimensional gas distribution model, update the model parameters in real time, and generate a dynamic gas distribution state;

[0084] Based on the dynamic gas distribution state, a hybrid prediction model constructed by convolutional neural network and long short-term memory network is applied to calculate and predict the gas concentration change trend of each monitoring point in the next 4 hours, generating gas concentration prediction data;

[0085] Three levels of warning thresholds are set based on gas concentration prediction data. When the predicted gas concentration reaches 80% of the warning value, a level one warning is set; when it reaches 90% of the warning value, a level two warning is set; and when it exceeds the warning value, a level three warning is set. This generates a standard for determining the warning level.

[0086] The warning level judgment standard is applied to the real-time calculated gas concentration prediction data to automatically judge the warning status and generate a multi-level warning judgment matrix including warning level, trigger time and impact range.

[0087] Specifically, a multi-layered sensor network monitors gas concentrations in key areas such as the working face, return air lanes, and electromechanical chambers in real time, enabling timely detection of gas anomalies and effective early warning. Fixed gas sensors are installed every 50 meters in these key areas to ensure accurate and timely monitoring data. Furthermore, multi-parameter sensors are installed at the edges of goafs and near fault zones. These sensors monitor not only gas concentration but also parameters such as temperature, humidity, and wind speed to comprehensively assess the impact of the geological environment on gas behavior. Data from these sensors is transmitted to a data processing center via wireless or wired communication systems. There, the real-time data is first detected for outliers to eliminate erroneous data caused by equipment failure or signal interference. This outlier detection utilizes statistical methods to automatically identify and eliminate anomalous data by setting a reasonable fluctuation range, ensuring data accuracy. Next, missing value correction is performed to supplement missing data using interpolation algorithms or machine learning models. This process not only improves data integrity but also mitigates the impact of missing data. All gas data that has undergone outlier detection and missing value correction will be normalized to generate a standardized monitoring data set. This normalization process converts all collected data into a unified standard unit, eliminating potential measurement bias between different sensors and equipment, making the data more comparable and consistent.

[0088] After data standardization, the monitoring data is integrated with the three-dimensional gas distribution model, updating the model's gas distribution parameters in real time. The three-dimensional gas distribution model, constructed from a large amount of drilling data, well logging data, and geophysical data, provides both static and dynamic information on the distribution of gas in the coal mine area. By combining real-time monitoring data with the model data, gas concentration predictions for each monitoring point are updated, forming a dynamic gas distribution picture. This process allows coal mine managers to continuously monitor gas concentration trends and take timely countermeasures.

[0089] Based on the dynamic gas distribution, a hybrid prediction model constructed using a convolutional neural network (CNN) and a long short-term memory network (LSTM) is used to predict gas concentration trends at each monitoring point over the next four hours. CNN, a deep learning algorithm, excels at processing grid-structured data and can identify spatial patterns in gas concentration changes. LSTM, a neural network used for time series prediction, memorizes past time points and effectively predicts gas concentration trends over time. Combining these two algorithms enables accurate predictions of future gas concentration changes based on the dynamic gas distribution model. The gas concentration forecast data for each monitoring point is updated in real time, ensuring timely early warning of gas safety in the mine.

[0090] Based on predicted gas concentration data, three levels of warning thresholds are set. When the predicted gas concentration reaches 80% of the warning value, the system triggers a level one warning, alerting management to strengthen gas monitoring. When the predicted gas concentration reaches 90% of the warning value, the system triggers a level two warning, automatically adjusting ventilation to reduce gas accumulation. When the predicted gas concentration exceeds the warning value, the system triggers a level three warning and immediately initiates the emergency response plan, including evacuating personnel and shutting off power. This early warning mechanism effectively prevents gas over-limit accidents and ensures the safety of mine workers.

[0091] The generated warning level criteria generated through this data processing process are applied in real time to the gas concentration forecast data at each monitoring point, automatically determining the warning status. Whenever a new warning level is triggered, the system generates a multi-level warning judgment matrix containing information such as the warning level, trigger time, and impact range, ensuring that relevant personnel receive warning information in a timely manner and take appropriate action.

[0092] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0093] The multi-level early warning judgment matrix, dynamic gas distribution status, and operating parameters of the efficient gas extraction system are input into the gas overlimit cause diagnosis module. The module then analyzes the module using a decision tree and rule-based inference algorithm to identify possible causes of gas anomalies.

[0094] Based on the causes of gas anomalies and the multi-level early warning judgment matrix, the probability of accident occurrence, impact scope, and degree of harm are calculated, and the risk is quantitatively assessed. The risk levels are divided into extremely serious risks, serious risks, relatively serious risks, and general risks, and the risk level assessment results are generated;

[0095] Based on the risk level assessment results, formulate disposal strategies for risks at all levels, establish special emergency plans for particularly serious risks, formulate special disposal cards for major and relatively serious risks, formulate standard operating procedures for general risks, and generate differentiated disposal plans;

[0096] Based on differentiated response plans, an emergency communication guarantee mechanism is designed, using a dual communication system that combines explosion-proof optical fiber communication networks and distributed wireless communication technologies to ensure the reliability of information transmission;

[0097] Based on differentiated handling plans and emergency communication guarantee mechanisms, establish an emergency knowledge base and case library, apply knowledge graph technology to structure management of domestic and foreign gas accident cases and handling experience, and generate a knowledge support system;

[0098] Integrate differentiated handling plans, emergency communication guarantee mechanisms and knowledge support systems into a unified information management platform, and build a differentiated handling process database that includes handling processes, division of responsibilities and resource allocation.

[0099] Specifically, the gas overlimit cause diagnosis module receives and analyzes data from various sources. The multi-level warning judgment matrix provides the system with information on the level of gas concentration warnings. The dynamic gas distribution status describes the real-time spatial distribution and changing trends of gas in the coal mine area, while the operating parameters of the efficient gas extraction system include relevant indicators of gas extraction, such as extraction volume and extraction pressure. After unified integration, these data enter the gas overlimit cause diagnosis module, which uses decision trees and rule inference algorithms to conduct a detailed analysis of the data to identify potential causes of gas anomalies. The decision tree is a classification and regression model that helps analyze which factors lead to gas overlimit by gradually splitting the data according to features, such as whether it is caused by a local ventilation system failure, excessive gas content, or uneven coal seam permeability. The rule inference algorithm is based on a known rule base and further confirms the specific cause of the gas anomaly through reasoning and matching.

[0100] After identifying the causes of gas anomalies, further risk assessment is conducted based on these causes and the multi-level early warning judgment matrix. Risk assessment includes calculating the probability of an accident, the scope of impact, and the degree of harm. By analyzing historical data and real-time data, combined with the trend of changes in gas concentration, the probability of an accident can be determined. For example, if the gas concentration in a certain area reaches 90% of the warning value and the gas extraction volume in the area does not reach the predetermined target, the risk of gas exceeding the limit in the area is high. On this basis, combined with the specific scenario of gas exceeding the limit, the potential impact scope and possible degree of harm of the accident are assessed to ensure that the assessment results accurately reflect the possible consequences of gas exceeding the limit. Based on this assessment, the risk levels are divided into extremely serious risks, serious risks, major risks, and general risks, so that corresponding emergency response measures can be taken according to different risk levels.

[0101] Based on the risk level assessment results, differentiated emergency response plans are developed for each risk level. For particularly serious risks, special emergency plans are developed, detailing the emergency steps that need to be immediately implemented in the event of a sudden gas over-limit, including evacuating personnel, cutting off power, and activating backup ventilation equipment. For major and relatively large risks, special response cards are developed, stipulating specific emergency response times and the division of responsibilities among various departments to ensure a rapid response in the event of a gas over-limit. For general risks, standard operating procedures are developed as the basis for daily management and training, ensuring that coal mine workers pay sufficient attention to gas safety in their daily operations and are able to report problems in a timely manner and take preliminary emergency measures when they are discovered.

[0102] Furthermore, based on the emergency response plan, an emergency communication guarantee mechanism was designed to ensure that all relevant personnel receive real-time warning information and emergency instructions in the event of gas exceeding the limit. A dual communication system combining an explosion-proof fiber-optic communication network and distributed wireless communication technology ensures unimpeded information transmission throughout the mine, even in environments with extremely high gas concentrations. The explosion-proof fiber-optic communication network provides highly reliable communication, while the distributed wireless communication technology ensures that communication with the ground command center is possible even in the event of a blockage or rupture in the mine tunnel.

[0103] To further improve the emergency response mechanism, an emergency knowledge base and case library were established, using knowledge graph technology to structure the management of gas accident handling experience both domestically and internationally. Knowledge graph technology systematically organizes the causes, treatment measures, and outcomes of various gas accidents, forming a searchable knowledge base that helps emergency personnel quickly obtain effective emergency response plans when encountering similar situations. For example, when gas concentration exceeds the limit in a mine, emergency personnel can use the knowledge graph system to review historical cases, understand response strategies for such accidents, and quickly select the optimal response plan. Combined with the emergency communication guarantee mechanism, all emergency plans, resource allocation, and personnel responsibility division are integrated into a unified information management platform, enabling rapid response and real-time updates on emergency handling status.

[0104] The above describes the method for controlling gas and ensuring safety in coal mine areas under complex geological conditions in the embodiment of the present application. The following describes the system for controlling gas and ensuring safety in coal mine areas under complex geological conditions in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the coal mine area gas control and safety assurance system under complex geological conditions includes:

[0105] Exploration module 201 is used to conduct geophysical exploration, drilling, and logging exploration of the coal mining area to obtain multi-dimensional geological parameter data;

[0106] An analysis module 202 is configured to perform geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model;

[0107] Identification module 203, for identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network;

[0108] The extraction module 204 is used to implement differentiated extraction parameters and permeability enhancement measures for different pressure zones based on the borehole spatial network and gas pressure distribution, thereby establishing an efficient gas extraction system;

[0109] Fusion module 205 is used to perform outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix;

[0110] The diagnosis module 206 is used to perform intelligent diagnosis and analysis on the causes of gas anomalies based on the multi-level early warning judgment matrix, and generate risk level assessment results and a differentiated disposal process database.

[0111] Through the coordinated cooperation of the above-mentioned components, the multi-dimensional geological parameter data obtained through geophysical prospecting, drilling and logging exploration in the coal mine area, combined with the three-dimensional gas distribution model constructed by geological statistical analysis, not only achieves the precise grasp of the gas distribution law under complex geological conditions and improves the accuracy of gas distribution prediction, but also provides a scientific basis for the subsequent directional drilling layout; based on the three-dimensional gas distribution model, the high-gas area is identified and the directional drilling network is designed and implemented. Through the fan layout method and the three-layer layout principle, the intersection probability of the borehole and the gas-enriched zone is significantly improved, and the drilling network coverage rate reaches more than 85%, creating basic conditions for efficient extraction; differentiated extraction parameters and permeability enhancement measures are implemented according to the borehole space network and gas pressure distribution, and different negative pressure values ​​and extraction time strategies are adopted for different pressure areas. At the same time, targeted enhancement measures such as hydraulic fracturing, CO2 phase change blasting and negative pressure pulse are implemented according to the difference in permeability coefficient. Through technology, the gas extraction efficiency has been greatly improved, and the gas content in the working face has been reduced to a safe allowable range; the real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing, and a hybrid prediction model constructed by applying convolutional neural networks and long short-term memory networks is used to achieve accurate prediction of the gas concentration change trend in the next 4 hours, significantly enhancing the timeliness and accuracy of the early warning. The application of neural network algorithms enables the system to automatically identify the nonlinear patterns and time series characteristics of gas concentration changes, taking into account the complex interactions between multiple influencing factors, and achieving more accurate concentration predictions through deep learning of historical data. Intelligent diagnosis and analysis of the causes of gas anomalies are carried out based on a multi-level early warning judgment matrix, and the possible causes of gas anomalies are identified through decision trees and rule inference algorithms. Risk level assessment results and a differentiated disposal process database are generated, realizing the transformation from passive response to active prevention.

[0112] above Figure 2 From the perspective of modular functional entities, the coal mine area gas control and safety assurance system under complex geological conditions in the embodiment of the present invention is described in detail. Below, from the perspective of hardware processing, the coal mine area gas control and safety assurance equipment under complex geological conditions in the embodiment of the present invention is described in detail.

[0113] Figure 3It is a structural diagram of a coal mine area gas control and safety assurance device under complex geological conditions provided by an embodiment of the present invention. The coal mine area gas control and safety assurance device 300 under complex geological conditions may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more massive storage device terminals) for storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the coal mine area gas control and safety assurance device 300 under complex geological conditions. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the coal mine area gas control and safety assurance equipment 300 under complex geological conditions, so as to implement the steps of the above-mentioned coal mine area gas control and safety assurance method under complex geological conditions.

[0114] The coal mine area gas control and safety protection equipment 300 under complex geological conditions may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the coal mine area gas control and safety assurance equipment under complex geological conditions shown does not constitute a limitation of the coal mine area gas control and safety assurance equipment under complex geological conditions provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0115] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are run on a computer, the computer executes the steps of the method for gas control and safety assurance in coal mine areas under complex geological conditions.

[0116] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a coal mine area gas control and safety equipment under complex geological conditions (which can be a personal computer, server, or network equipment, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0118] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for gas control and safety assurance in coal mine areas under complex geological conditions, characterized by: The method comprises: Conducting geophysical exploration, drilling, and logging exploration in the coal mine area to obtain multi-dimensional geological parameter data, including: obtaining underground geological structure data through high-precision seismic exploration with survey line spacing of no more than 50 meters and detection point spacing of no more than 10 meters; arranging boreholes according to a grid principle of 30 meters × 30 meters near major faults and 50 meters × 50 meters in other areas to collect coal seam thickness, inclination, and gas pressure data; performing sonic logging, density logging, natural potential logging, and resistivity logging on each borehole to generate logging curve data; removing outliers from the underground geological structure data, coal seam thickness, inclination, and gas pressure data, and logging curve data to generate a standardized geological parameter data set; applying variation function analysis and Kriging interpolation algorithms to the standardized geological parameter data set to calculate spatial variation patterns and parameter correlations; dividing the coal mine area into ultra-high complexity areas, high complexity areas, medium complexity areas, and low complexity areas based on the spatial variation patterns and parameter correlations to generate geological complexity zoning data; The multidimensional geological parameter data is subjected to geostatistical analysis to construct a three-dimensional gas distribution model, including: establishing a three-dimensional skeleton model of regional geological structure with an accuracy of 5 meters × 5 meters × 1 meter based on the standardized geological parameter data set and geological complexity partitioning data; mapping the gas content, gas pressure and coal seam permeability data in the multidimensional geological parameter data to the three-dimensional skeleton model through a trilinear interpolation algorithm to generate an initial gas distribution grid; applying a random forest algorithm to the data sparse area in the initial gas distribution grid for parameter prediction to generate gas parameter spatial distribution data; calculating the stress distribution state in the region based on the gas parameter spatial distribution data and the geological complexity partitioning data using a finite element method to establish a stress-gas coupling analysis model; setting a fault influence coefficient for the fault zone in the stress-gas coupling analysis model, assigning influence weights of 0.8, 0.6 and 0.4 according to the size of the fault, and calculating the fault gas enrichment coefficient; inputting the fault gas enrichment coefficient and the stress-gas coupling analysis model into a multiphase flow numerical simulator to calculate the gas migration law under different mining conditions to generate a three-dimensional gas distribution model; Identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network; Based on the borehole spatial network and gas pressure distribution, differentiated extraction parameters and permeability enhancement measures are implemented for different pressure zones to establish an efficient gas extraction system; The real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing, and the gas concentration change trend is calculated through a neural network algorithm to generate a multi-level early warning judgment matrix; Based on the multi-level early warning judgment matrix, intelligent diagnosis and analysis of the causes of gas anomalies are performed to generate risk level assessment results and a differentiated disposal process database.

2. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: The method of identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network includes: Dividing the coal mine area into special-grade areas, first-grade areas, second-grade areas, and third-grade areas based on the gas content data in the three-dimensional gas distribution model, and generating gas area classification data; Based on the gas area classification data, a drilling density standard is established: the spacing between special-grade areas should not exceed 15 meters, the spacing between first-grade areas should not exceed 25 meters, the spacing between second-grade areas should not exceed 35 meters, and the spacing between third-grade areas should not exceed 50 meters; Input the drilling density standard and the three-dimensional gas distribution model into a computer-aided design system, design a three-layer layout plan for areas with coal seam thickness greater than 3 meters, and design a drilling plan perpendicular to the coal seam trend for areas with inclination angles greater than 15 degrees, and generate an initial drilling trajectory design; Applying a fan-shaped arrangement method to the area near the fault in the initial drilling trajectory design, arranging drill holes at 15° angles around the fault, to generate a drilling arrangement design diagram; Drilling is carried out according to the drilling layout design drawing, and the drilling trajectory is monitored in real time using a magnetic direction finding system and a gyroscope direction finding system. When the deviation from the designed trajectory exceeds 0.5 meters, the trajectory is corrected to complete the drilling construction; Perform borehole imaging inspection and pressure testing on completed boreholes, calculate the borehole network coverage, and add additional boreholes when the coverage is lower than 85% to generate a borehole space network database containing the borehole spatial coordinates, length, direction, and diameter.

3. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: According to the borehole space network and gas pressure distribution, differentiated extraction parameters and permeability enhancement measures are implemented for different pressure zones to establish an efficient gas extraction system, including: Based on the gas pressure data in the borehole spatial network database and the three-dimensional gas distribution model, the coal mine area is divided into high-pressure area, medium-pressure area and low-pressure area, and a gas pressure zoning map is generated; Based on the gas pressure zoning map, a low-negative-pressure, long-term drainage strategy with a negative pressure of no more than 10 kPa and a duration of no less than 30 days is implemented in the high-pressure area; a medium-negative-pressure drainage strategy with a negative pressure of 15-25 kPa and a duration of 15-25 days is implemented in the medium-pressure area; and a high-negative-pressure drainage strategy with a negative pressure of 25-35 kPa and a duration of 7-15 days is implemented in the low-pressure area, generating differentiated drainage parameter plans. Based on the coal seam permeability data in the three-dimensional gas distribution model, hydraulic fracturing technology is implemented in areas with a permeability coefficient lower than 0.1m² / (MPa²·d), CO2 phase change blasting technology is implemented in areas with a permeability coefficient between 0.1-0.5m² / (MPa²·d), and negative pressure pulse technology is implemented in areas with a permeability coefficient greater than 0.5m² / (MPa²·d), thereby generating a permeability enhancement technology plan; Carry out permeability enhancement operation on the borehole according to the permeability enhancement technology scheme, verify the permeability enhancement effect through pressure monitoring and flow monitoring, and improve the permeability of the coal seam; Based on the borehole spatial network database, a main and branch pipe network structure is designed, with the main pipe diameter being no less than 219 mm, the branch pipe diameter being no less than 159 mm, and the single borehole extraction pipe diameter being no less than 89 mm, and a extraction pipeline system design scheme is generated; According to the differentiated extraction parameter scheme and extraction pipeline system design scheme, extraction pump station equipment is configured, gas extraction operations are carried out, monitoring points are set up every 50 meters, the extraction effect is monitored in real time, and the extraction parameters are automatically adjusted when the extraction concentration and extraction volume do not meet the standards, and an efficient gas extraction system including data collection, parameter adjustment and gas utilization classification is established.

4. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: The real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing, and the gas concentration change trend is calculated through a neural network algorithm to generate a multi-level early warning judgment matrix, including: Fixed gas sensors are deployed at intervals of no more than 50 meters in key areas of the mining face, return air lane, and electromechanical chamber. Multi-parameter sensors are installed near the edges of goafs and fault zones to create a multi-layered sensor network. Transmitting the real-time extraction data of the efficient gas extraction system and the monitoring data collected by the multi-level sensor network to the data processing center for outlier detection, missing value repair and data standardization to generate standardized monitoring data; Fusing the standardized monitoring data with the three-dimensional gas distribution model, updating the model parameters in real time, and generating a dynamic gas distribution state; Based on the dynamic gas distribution state, a hybrid prediction model constructed by convolutional neural network and long short-term memory network is applied to calculate and predict the gas concentration change trend of each monitoring point in the next 4 hours to generate gas concentration prediction data; A three-level warning threshold is set based on the gas concentration prediction data. When the predicted gas concentration reaches 80% of the warning value, a first-level warning is set; when it reaches 90% of the warning value, a second-level warning is set; and when it exceeds the warning value, a third-level warning is set, thereby generating a warning level determination standard. The warning level judgment standard is applied to the real-time calculated gas concentration prediction data to automatically judge the warning state and generate a multi-level warning judgment matrix including the warning level, trigger time and impact range.

5. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: Based on the multi-level early warning judgment matrix, the causes of gas anomalies are intelligently diagnosed and analyzed to generate risk level assessment results and a differentiated disposal process database, including: Input the multi-level early warning judgment matrix, dynamic gas distribution status and operating parameters of the efficient gas extraction system into the gas overlimit cause diagnosis module, and analyze it through a decision tree and rule inference algorithm to identify the possible causes of gas anomalies; Based on the causes of gas anomalies and the multi-level early warning judgment matrix, the probability of accident occurrence, scope of impact, and degree of harm are calculated, and the risk is quantitatively assessed. The risk levels are divided into extremely serious risks, serious risks, relatively serious risks, and general risks, and the risk level assessment results are generated; Based on the risk level assessment results, formulate disposal strategies for each level of risk, establish special emergency plans for particularly serious risks, formulate special disposal cards for major and relatively serious risks, formulate standard operating procedures for general risks, and generate differentiated disposal plans; Based on the differentiated handling scheme, an emergency communication guarantee mechanism is designed, which adopts a dual communication system combining explosion-proof optical fiber communication network and distributed wireless communication technology to ensure the reliability of information transmission; Based on the differentiated handling scheme and emergency communication guarantee mechanism, establish an emergency knowledge base and case library, apply knowledge graph technology to conduct structured management of domestic and foreign gas accident cases and handling experience, and generate a knowledge support system; Integrate the differentiated handling solutions, emergency communication guarantee mechanism and knowledge support system into a unified information management platform, and build a differentiated handling process database that includes handling processes, division of responsibilities and resource allocation.

6. A gas control and safety assurance system for coal mine areas under complex geological conditions, characterized by: Used to implement the method for gas control and safety assurance in coal mine areas under complex geological conditions according to any one of claims 1 to 5, the gas control and safety assurance system in coal mine areas under complex geological conditions comprises: An exploration module is used to conduct geophysical exploration, drilling, and logging exploration in the coal mine area to obtain multidimensional geological parameter data, including: obtaining underground geological structure data through high-precision seismic exploration with a survey line spacing of no more than 50 meters and a detection point spacing of no more than 10 meters; arranging boreholes according to a grid principle of 30 meters × 30 meters near major faults and 50 meters × 50 meters in other areas to collect coal seam thickness, inclination, and gas pressure data; performing sonic logging, density logging, natural potential logging, and resistivity logging on each borehole to generate logging curve data; removing outliers from the underground geological structure data, coal seam thickness, inclination, and gas pressure data, and logging curve data to generate a standardized geological parameter data set; applying variation function analysis and Kriging interpolation algorithms to the standardized geological parameter data set to calculate spatial variation patterns and parameter correlations; dividing the coal mine area into ultra-high complexity areas, high complexity areas, medium complexity areas, and low complexity areas based on the spatial variation patterns and parameter correlations to generate geological complexity zoning data; The analysis module is used to perform geostatistical analysis on the multidimensional geological parameter data and construct a three-dimensional gas distribution model, including: establishing a three-dimensional skeleton model of the regional geological structure with an accuracy of 5 meters × 5 meters × 1 meter based on the standardized geological parameter data set and geological complexity partitioning data; mapping the gas content, gas pressure and coal seam permeability data in the multidimensional geological parameter data to the three-dimensional skeleton model through a trilinear interpolation algorithm to generate an initial gas distribution grid; applying the random forest algorithm to the data sparse area in the initial gas distribution grid for parameter prediction to generate a gas parameter grid. The method comprises the following steps: calculating the spatial distribution data of gas parameters and the geological complexity zoning data, calculating the stress distribution state in the region using the finite element method based on the spatial distribution data of gas parameters and the geological complexity zoning data, and establishing a stress-gas coupling analysis model; setting a fault influence coefficient for the fault zone in the stress-gas coupling analysis model, assigning influence weights of 0.8, 0.6, and 0.4 according to the size of the fault, and calculating the fault gas enrichment coefficient; inputting the fault gas enrichment coefficient and the stress-gas coupling analysis model into a multiphase flow numerical simulator, calculating the gas migration law under different mining conditions, and generating a three-dimensional gas distribution model; an identification module for identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network; An extraction module is used to implement differentiated extraction parameters and permeability enhancement measures for different pressure zones based on the borehole spatial network and gas pressure distribution, thereby establishing an efficient gas extraction system; A fusion module is used to perform outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix; The diagnosis module is used to perform intelligent diagnosis and analysis on the causes of gas anomalies based on the multi-level early warning judgment matrix, and generate risk level assessment results and a differentiated disposal process database.

7. A gas control and safety equipment for coal mine areas under complex geological conditions, characterized by: It includes a memory and a processor, the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the coal mine area gas control and safety assurance method under complex geological conditions described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor executes the method for gas control and safety assurance in coal mine areas under complex geological conditions as described in any one of claims 1 to 5.

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