Coal mining strategy analysis method and system based on coal mine gas concentration

Abstract

This application relates to the field of data processing technology and discloses a method and system for analyzing coal mining strategies based on coal mine gas concentration. The method includes: collecting gas concentration data through an intelligent track inspection device, correcting the data by combining it with coal seam parameters to obtain dynamic concentration distribution data, generating a risk heat map based on this data through multi-dimensional analysis, determining differentiated mining parameters based on risk assessment, optimizing ventilation parameters through airflow control, and finally performing correlation analysis on the parameters to obtain a mining strategy scheme. This application uses an intelligent track inspection device to collect gas concentration data, coordinates with cleaning and air-blowing components to keep the track clean, utilizes the reciprocating motion of the detection probe to expand the detection range, and combines multi-dimensional data analysis to formulate differentiated mining process parameters and ventilation control strategies, solving the technical problems of small detection range, insufficient data analysis, and unreasonable mining strategies in existing technologies.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and system for analyzing coal mining strategies based on coal mine gas concentration. Background Technology

[0002] During coal mining, it is necessary to monitor the methane concentration at the working face. Currently, in my country, methane concentration monitoring in coal mines generally uses traditional methane sensors fixed to the intake and return air sides of the working face for real-time monitoring, combined with manual inspection. Some existing technologies use track-based inspection devices to monitor methane concentration, including structures such as slides, baffles, wheels, and conveyor belts, driven by dual-axis motors, and equipped with probes and smoke sensors to detect methane concentration. Other technologies use track-based inspection robots with rainproof structures, including components such as the body, mounting base, and protective cover, achieving protection through sliders and spring mechanisms.

[0003] However, existing gas concentration detection technologies have some shortcomings: First, small stones tend to accumulate on the track during the movement of the detection device, affecting its normal operation; second, the position of the detection probe is relatively fixed, resulting in a small detection range and making it difficult to obtain comprehensive gas concentration distribution data; third, existing technologies lack in-depth analysis and utilization of the detection data, making it impossible to formulate scientific mining strategies based on gas concentration data, thus posing safety hazards. Summary of the Invention

[0004] This application provides a method and system for analyzing coal mining strategies based on coal mine gas concentration. It is used to collect gas concentration data through an intelligent track inspection device, keep the track clean in conjunction with cleaning and air blowing components, expand the detection range by utilizing the reciprocating motion of the detection probe, and formulate differentiated mining process parameters and ventilation control strategies by combining multi-dimensional data analysis. This solves the technical problems of small detection range, insufficient data analysis, and unreasonable mining strategies in the prior art.

[0005] Firstly, this application provides a method for analyzing coal mining strategies based on coal mine gas concentration. The method includes: collecting gas concentration data from the coal mine working face using an intelligent track inspection device; collecting multi-layer gas concentration data using a vertically reciprocating detection probe; generating time-series data based on sampling time, location, and environmental parameters to obtain an original gas concentration dataset; performing spatial data processing on the sampling point data based on the original gas concentration dataset; and performing data correction by combining coal seam thickness, dip angle, and faults to obtain dynamic gas concentration distribution data; and performing multi-dimensional analysis on the gas concentration change rate, gas pressure, and coal seam permeability based on the dynamic gas concentration distribution data. Analysis and weight calculations are performed to generate a regional risk level heat map, obtaining regional gas outburst risk assessment data. Based on this data, the working face is divided into blocks according to risk level. Mining speed parameters are set in conjunction with gas emission volume to determine the working face advancement direction, resulting in differentiated mining process parameters. Based on these parameters, airflow distribution is calculated for the ventilation system, and local ventilation parameters are adjusted using real-time monitoring data to obtain the working face ventilation network operation parameters. Multi-dimensional correlation analysis is then conducted on the working face ventilation network operation parameters, the differentiated mining process parameters, and the regional gas outburst risk assessment data to establish quantitative safety benefit indicators, resulting in an optimized coal mining strategy.

[0006] Secondly, this application provides a coal mining strategy analysis system based on coal mine gas concentration, the coal mining strategy analysis system based on coal mine gas concentration includes:

[0007] The data acquisition module is used to collect gas concentration data of the coal mine working face through the intelligent track inspection device. It uses a vertically reciprocating detection probe to collect multi-layer gas concentration data and generates time series data based on sampling time, location and environmental parameters to obtain the original gas concentration dataset.

[0008] The processing module is used to perform spatial data processing on the sampling point data based on the original gas concentration dataset, and to perform data correction by combining coal seam thickness, dip angle and faults to obtain dynamic gas concentration distribution data;

[0009] The calculation module is used to perform multi-dimensional analysis and weight calculation on the gas concentration change rate, gas pressure, and coal seam permeability based on the gas concentration dynamic distribution data, generate a regional risk level heat map, and obtain regional gas outburst risk assessment data.

[0010] The segmentation module is used to divide the working face into blocks according to the risk level based on the regional gas outburst risk assessment data, set the mining speed parameters in combination with the gas emission rate, determine the working face advance direction, and obtain differentiated mining process parameters.

[0011] The allocation module is used to calculate the air volume allocation of the ventilation system according to the differentiated mining process parameters, and adjust the local ventilation parameters through real-time monitoring data to obtain the working face ventilation network operation parameters;

[0012] The correlation module is used to perform multi-dimensional correlation analysis on the operating parameters of the working face ventilation network, the parameters of the differentiated mining process, and the regional gas outburst risk assessment data, to establish quantitative indicators of safety benefits, and to obtain an optimized coal mining strategy.

[0013] The technical solution provided in this application collects gas concentration data from the coal mine working face using an intelligent track inspection device. A vertically reciprocating detection probe collects multi-layered gas concentration data, generating time-series data based on sampling time, location, and environmental parameters to obtain a comprehensive raw gas concentration dataset, thus expanding the spatial range of gas concentration detection. By performing spatial data processing on the sampling point data and combining it with geological parameters such as coal seam thickness, dip angle, and faults for data correction, accurate dynamic distribution data of gas concentration is obtained. Multi-dimensional analysis and weight calculation are performed on parameters such as gas concentration change rate, gas pressure, and coal seam permeability to generate a regional risk level heat map. Accurate assessment of gas outburst risk was achieved; based on regional gas outburst risk assessment data, the working face was divided into blocks according to risk level, and mining speed parameters were reasonably set in conjunction with gas emission volume to determine the working face advancement direction and formulate scientific differentiated mining process parameters; based on the differentiated mining process parameters, the ventilation system's air volume distribution was calculated, and local ventilation parameters were dynamically adjusted through real-time monitoring data to ensure intelligent control of the ventilation system; multi-dimensional correlation analysis was conducted on the working face ventilation network operation parameters, differentiated mining process parameters, and regional gas outburst risk assessment data to establish a quantitative indicator system for safety benefits and obtain an optimized coal mining strategy. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of an embodiment of the coal mining strategy analysis method based on coal mine gas concentration in this application.

[0016] Figure 2 This is a schematic diagram of the risk level heat map in the embodiments of this application;

[0017] Figure 3 This is a schematic diagram of an embodiment of the coal mining strategy analysis system based on coal mine gas concentration in this application. Detailed Implementation

[0018] This application provides a method and system for analyzing coal mining strategies based on coal mine gas concentration. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the coal mining strategy analysis method based on coal mine gas concentration in this application includes:

[0020] Step S101: Collect gas concentration data of the coal mine working face through the intelligent track inspection device. Collect multi-layer gas concentration data using the vertical reciprocating motion detection probe. Generate time series data based on sampling time, location and environmental parameters to obtain the original gas concentration dataset.

[0021] Step S102: Based on the original gas concentration dataset, perform spatial data processing on the sampling point data, and perform data correction by combining coal seam thickness, dip angle and fault to obtain dynamic distribution data of gas concentration.

[0022] Step S103: Based on the dynamic distribution data of gas concentration, perform multi-dimensional analysis and weight calculation on the gas concentration change rate, gas pressure, and coal seam permeability to generate a regional risk level heat map and obtain regional gas outburst risk assessment data.

[0023] Step S104: Based on the regional gas outburst risk assessment data, the working face is divided into blocks according to the risk level. The mining speed parameters are set in combination with the gas emission rate, the working face advance direction is determined, and differentiated mining process parameters are obtained.

[0024] Step S105: Calculate the air volume distribution of the ventilation system based on the differentiated mining process parameters, and adjust the local ventilation parameters through real-time monitoring data to obtain the working face ventilation network operation parameters;

[0025] Step S106: Conduct multi-dimensional correlation analysis on the working face ventilation network operation parameters, differentiated mining process parameters, and regional gas outburst risk assessment data to establish quantitative safety benefit indicators and obtain coal mining strategy optimization schemes.

[0026] It is understood that the executing entity of this application can be a coal mining strategy analysis system based on coal mine gas concentration, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0027] Specifically, gas concentration data is collected using an intelligent track inspection device. This device includes a motor drive system, a detection probe movement mechanism, and a data acquisition system. The motor drive system consists of a motor driving a drive gear, which meshes with a driven gear. The driven gear drives a connecting shaft, which in turn drives a rotating shaft via a bevel gear, enabling the vertical reciprocating motion of the detection probe. Specifically, the detection probe makes a vertical displacement under the push of a cam, a protrusion moves within a groove, and a connecting spring provides a reset force, thus ensuring that the detection probe can collect data at different heights.

[0028] During the data collection process, the detection probe remained at each sampling location for 5 seconds, recording the gas concentration value, sampling timestamp, sampling height, and ambient temperature. The sampling data was stored in real-time via a monitor, forming a basic data table. The data table includes a time column (t), spatial coordinate columns (x, y, z), a gas concentration column (c), and an ambient temperature column (T). The collected data underwent preliminary screening to remove outliers, generating a standardized raw gas concentration dataset.

[0029] When performing spatial data processing on the original gas concentration dataset, sampling points are located using three-dimensional coordinates, and the gas concentration gradient between adjacent sampling points is calculated. To address the influence of coal seam thickness, a thickness correction coefficient is introduced to correct the gas concentration. Simultaneously, considering the impact of coal seam dip angle on gas distribution, the gas distribution in inclined coal seams is transformed to a horizontal plane through coordinate transformation. For fault areas, fault locations are marked, and the attenuation effect of the fault on gas distribution is calculated, resulting in dynamic gas concentration distribution data considering geological factors. Based on the dynamic gas concentration distribution data, the rate of change of gas concentration at each sampling point is calculated, and a pressure-concentration correlation is established by combining it with gas pressure monitoring data. Coal seam permeability data is used as an important reference indicator, and various parameters are weighted and calculated. Through multi-dimensional analysis of each parameter, the working face is divided into different risk level zones, generating a risk level heat map and forming regional gas outburst risk assessment data.

[0030] Based on regional gas outburst risk assessment data and real-time gas emission data, the safe mining speed range for each region is determined. By analyzing the relative positions and risk level distribution of each region, the optimal working face advancement direction is planned, and differentiated mining procedure parameters are formulated. These parameters include mining speed limits, mining sequence, and advancement path for each region.

[0031] For differentiated mining process parameters, the required basic ventilation volume for each area is calculated, and the ventilation system is allocated accordingly. The speed of local ventilation fans is adjusted based on real-time monitoring data to ensure sufficient ventilation in each area. The operating status of the ventilation system is continuously monitored, and airflow distribution data is recorded to form the operating parameters of the working face ventilation network.

[0032] A multi-dimensional correlation analysis was conducted on the operating parameters of the working face ventilation network, parameters of differentiated mining procedures, and regional gas outburst risk assessment data. The safety of the mining plan was evaluated by analyzing the matching degree between ventilation effectiveness and mining speed. A quantitative safety benefit index system was established based on the risk level and ventilation conditions of each region, generating optimized coal mining strategies.

[0033] For example, taking a coal mine working face as an example, an intelligent track inspection device monitors gas concentration at the working face. The detection probe collects data every 0.5 meters within a height range of 0-3 meters, collecting 5 sets of data at each location. Data processing reveals three gas-rich areas in the working face, located in the upper, middle, and lower parts respectively. Combining data on coal seam thickness (average 4.5 meters) and dip angle (28 degrees), the gas distribution is corrected. Through multi-dimensional analysis, the working face is divided into three risk levels: high, medium, and low. Differentiated mining parameters are established based on the risk level: mining speed not exceeding 2 meters / shift in high-risk areas, not exceeding 3 meters / shift in medium-risk areas, and not exceeding 4 meters / shift in low-risk areas. By optimizing and adjusting the ventilation system, the ventilation volume in high-risk areas is ensured to be 30% higher than the basic requirement, achieving safe mining.

[0034] In this embodiment, an intelligent track inspection device is used to collect gas concentration data from the coal mine working face. A vertically reciprocating detection probe collects multi-layered gas concentration data, generating time-series data based on sampling time, location, and environmental parameters to obtain a comprehensive raw gas concentration dataset, thus expanding the spatial range of gas concentration detection. By performing spatial data processing on the sampling point data and combining it with geological parameters such as coal seam thickness, dip angle, and faults for data correction, accurate dynamic distribution data of gas concentration is obtained. Multi-dimensional analysis and weight calculation are performed on parameters such as gas concentration change rate, gas pressure, and coal seam permeability to generate a regional risk level heat map, achieving… Accurate assessment of gas outburst risk was achieved; based on regional gas outburst risk assessment data, the working face was divided into blocks according to risk level, and mining speed parameters were reasonably set in conjunction with gas emission volume to determine the working face advancement direction and formulate scientific differentiated mining process parameters; based on the differentiated mining process parameters, the ventilation system's air volume distribution was calculated, and local ventilation parameters were dynamically adjusted through real-time monitoring data to ensure intelligent control of the ventilation system; multi-dimensional correlation analysis was conducted on the working face ventilation network operation parameters, differentiated mining process parameters, and regional gas outburst risk assessment data to establish a quantitative indicator system for safety benefits and obtain an optimized coal mining strategy.

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

[0036] (1) Start the motor on the intelligent track inspection device to drive the walking wheels to move along the external track, and at the same time start the cleaning component and the air blowing component of the intelligent track inspection device;

[0037] (2) The motor output drives the drive gear to rotate, and the drive gear drives the driven gear to rotate, thus realizing power transmission;

[0038] (3) The driven gear drives the bevel gear to rotate through the connecting shaft, the bevel gear drives the rotating shaft to rotate within the limit rod, and the rotating shaft drives the lever to clean the external track;

[0039] (4) The long pin is driven to rotate by the pulley at the output end of the motor, and the detection probe is pushed to reciprocate in the vertical direction by the cam at the end of the long pin;

[0040] (5) The detection probe collects multi-layer gas concentration data during the reciprocating motion. The protrusion of the detection probe moves back and forth in the groove and is reset by the connecting spring.

[0041] (6) The detection probe transmits the collected gas concentration data to the monitor in real time, and the monitor records the sampling time, location and environmental parameters;

[0042] (7) Perform time-series processing on the gas concentration data recorded by the monitor and spatially stratify it according to the sampling location;

[0043] (8) Pair the stratified gas concentration data with environmental parameters to establish a spatiotemporal correlation matrix;

[0044] (9) Perform data standardization and serialization on the spatiotemporal correlation matrix to obtain the original gas concentration dataset.

[0045] Specifically, the operation of the intelligent track inspection device involves the coordinated work of a mechanical motion system and a data acquisition system. The motor on the intelligent track inspection device is started, and the motor drives the traveling wheels to move within the external track via a transmission mechanism. Simultaneously with the movement of the traveling wheels, the cleaning and air-blowing components begin to operate. The cleaning component consists of a lever, while the air-blowing component includes a nozzle, an air bladder, an inlet pipe, and an outlet pipe. The air bladder uses a one-way valve to control the direction of air intake and exhaust, ensuring the track surface remains clean.

[0046] The motor's output end is fixedly connected to the driving gear, which meshes with the driven gear. The two driven gears are symmetrically distributed about the center of the driving gear, forming a stable power transmission system. The driven gear drives the connecting shaft to rotate. The connecting shaft and the rotating shaft are connected via bevel gears, which are located on the end faces of both shafts. The rotating shaft rotates within a limit rod, with a limit ball positioned between the inside of the rotating shaft and the inner wall of the limit rod to prevent axial displacement. Multiple levers are evenly distributed on the surface of the rotating shaft, each with an arc-shaped surface, cleaning the external track as the shaft rotates. A pulley is also connected to the motor output end, driving a long pin to rotate via a belt. A cam is welded to the end of the long pin. When the cam rotates, it periodically pushes the detection probe, causing it to reciprocate vertically. Protrusions are located on both sides of the detection probe, engaging with grooves on the inner wall of the outer casing for guidance. One end of the connecting spring is connected to the protrusion, and the other end is fixed to the inner wall of the housing. When the cam pushes the detection probe downward, the connecting spring is compressed and stores elastic potential energy. When the cam separates from the detection probe, the connecting spring releases the potential energy and drives the detection probe to reset upward.

[0047] During the reciprocating motion of the detection probe, gas concentration data is continuously collected at different heights. The monitor records the gas concentration value at each sampling point, along with the corresponding sampling timestamp, spatial coordinates, and ambient temperature. The collected data is arranged chronologically to form an initial time series. The data is then stratified according to sampling height, with each height layer forming an independent data subset. For each height layer's data subset, the gas concentration data is paired and correlated with environmental parameters. During data pairing, for each gas concentration measurement, the ambient temperature value at the closest sampling time is matched. This establishes a spatiotemporal correlation matrix, where rows represent different time points, columns represent different spatial locations, and matrix elements contain gas concentration values ​​and corresponding environmental parameters.

[0048] Data standardization is performed on the spatiotemporal correlation matrix. The standardization process includes outlier removal, data normalization, and serialization. For example, in gas concentration monitoring at a coal mine working face, the detection probe reciprocates within a height range of 0-3 meters, with a cycle of 60 seconds. It pauses for 5 seconds at each sampling location to collect gas concentration data. The monitor records data once per second, including the gas concentration value, sampling time, location coordinates, and ambient temperature. The data is recorded in the format {timestamp, x-coordinate, y-coordinate, z-coordinate, gas concentration, ambient temperature}. The data is divided into 6 layers at 0.5-meter height intervals, each layer containing measurement data from one complete cycle. Outlier handling is performed on each layer, removing data points whose deviation from adjacent measuring points exceeds 3 times the standard deviation. Then, the data in each layer is normalized so that the data is distributed within the [0,1] interval. The processed data is organized into a spatiotemporal correlation matrix, where each element contains the normalized gas concentration value and the corresponding ambient temperature value, forming the original gas concentration dataset.

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

[0050] (1) Mark the sampling points in the original gas concentration dataset according to the three-dimensional spatial coordinates to construct a spatial sampling point distribution sequence;

[0051] (2) Calculate the gas concentration difference between adjacent sampling points in the spatial sampling point distribution sequence to generate gas concentration gradient data;

[0052] (3) Correlate the coal seam thickness data with the gas concentration gradient data to calculate the thickness influence factor;

[0053] (4) Perform coordinate transformation on the gas concentration gradient data according to the coal seam dip angle data to obtain dip angle correction data;

[0054] (5) Spatial matching of fault location information and dip correction data to mark the fault-affected area;

[0055] (6) Perform fault attenuation processing on the gas concentration data in the fault-affected area to generate regional gas distribution data;

[0056] (7) Sort the regional gas distribution data according to the time dimension and calculate the changing trend of gas concentration at each sampling point;

[0057] (8) Dynamically update the regional gas distribution data based on the changing trend to obtain dynamic gas concentration distribution data.

[0058] Specifically, each sampling point is located using a three-dimensional coordinate system (x, y, z), where x and y represent the position on the horizontal plane of the working face, and z represents the sampling height. A spatial sampling point distribution sequence is formed through data labeling, with each sampling point containing its location coordinates and corresponding gas concentration value. When calculating the gas concentration difference between adjacent sampling points, the spatially closest pair of sampling points is selected, and the rate of change of gas concentration between them is calculated. Gas concentration gradients are calculated for both the horizontal and vertical directions, forming gas concentration gradient data. The gas concentration gradient reflects the spatial distribution and variation characteristics of gas.

[0059] When considering the influence of coal seam thickness, a formula for calculating the thickness influence factor is introduced:

[0060]

[0061] Among them, F h H is the thickness influencing factor. i H represents the local coal seam thickness. m The average coal seam thickness. For the gas concentration gradient, ω i λ is the weighting coefficient. h d is the thickness attenuation coefficient. i denoted as , where is the distance from the centerline of the coal seam, and n is the number of sampling points.

[0062] The influence of coal seam dip angle is addressed using the coordinate transformation formula:

[0063]

[0064] Among them, (x' p ,y' p ,z' p (x) represents the corrected coordinates. p ,y p ,z p (η) represents the original coordinates, θ represents the coal seam dip angle, and (η) represents the original coordinates. x ,η y ,η z) is the displacement correction vector.

[0065] When dealing with the effects of faults, a fault attenuation calculation formula is introduced:

[0066]

[0067] Among them, C f C0 represents the gas concentration after the fault's influence, C0 represents the original gas concentration, and β represents the gas concentration after the fault's influence. k Let μ be the fault influence coefficient. f R is the fault attenuation coefficient. k γ is the distance to the fault. k Stress influencing factor denoted as the stress direction angle, and m as the number of faults.

[0068] Fault locations are marked based on geological exploration data, and the fault location information is spatially matched with dip-corrected data. For each sampling point, the distance to the nearest fault is calculated, and the fault-affected area is marked. Fault attenuation processing is used to obtain gas distribution data considering the fault's influence. The processed regional gas distribution data is arranged chronologically, and the characteristics of gas concentration variation over time are analyzed. By calculating the rate of change of gas concentration at different time points, the dynamic trend of gas concentration is obtained. Finally, the gas distribution data is dynamically updated based on the trend, forming dynamic gas concentration distribution data.

[0069] For example, a coal mining face is 100 meters long, with an average coal seam thickness of 4.5 meters and a dip angle of 28 degrees, and contains a fault striking at 45 degrees. A detection device is deployed at 20 sampling points on the face, with each point collecting data at a different height. The sampling points are marked using three-dimensional coordinates, and the gas concentration gradient between adjacent points is calculated. Combined with the coal seam thickness data, it is found that the gas concentration gradient is higher in areas with greater thickness. The data is projected onto a horizontal plane through dip coordinate transformation to correct the gas distribution characteristics. Based on the fault location information, the distance from each sampling point to the fault is calculated, and the gas concentration data within a 10-meter radius on both sides of the fault is attenuated. The processed data is arranged in chronological order of collection time, and the temporal trend of gas concentration is calculated, thus generating dynamic gas concentration distribution data.

[0070] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0071] (1) Sample the dynamic distribution data of gas concentration at time intervals and calculate the rate of change of gas concentration at each sampling point;

[0072] (2) Correlate the gas pressure data and gas concentration change rate at each sampling point to form pressure-concentration correlation data;

[0073] (3) Normalize the pressure-concentration correlation data to generate standardized gas parameter data;

[0074] (4) Weighted superposition of coal seam permeability data and standardized gas parameter data to generate a comprehensive risk index;

[0075] (5) Divide the working area into risk levels according to the comprehensive risk index and mark the areas with different risk levels;

[0076] (6) Generate a regional risk level heat map according to the spatial distribution of areas with different risk levels;

[0077] (7) Dynamically update the regional risk level heat map and record the trend of risk level changes;

[0078] (8) Spatial mapping of risk level change trends yields regional gas outburst risk assessment data.

[0079] Specifically, in the coal mining strategy analysis method for coal mine gas concentration, the calculation of the gas concentration change rate uses the following formula:

[0080]

[0081] Among them, V rate G represents the rate of change in gas concentration. j δ represents the gas concentration value at the sampling point. j ψ is the time weighting coefficient. j t is the time decay factor. s q represents the sampling interval, and q represents the number of sampling points. Dynamic distribution data of gas concentration is sampled periodically, and the gas concentration difference between adjacent time points is calculated to form gas concentration change trend data. Correlation analysis is performed between gas pressure data and the rate of change of gas concentration to establish a pressure-concentration correspondence. When correlating data, the spatial relationship between pressure monitoring points and concentration sampling points is considered, and the nearest pressure measurement point data is selected for matching. The matched pressure-concentration correlation data is normalized, mapping the data value range uniformly to the [0,1] interval to generate standardized gas parameter data.

[0082] The following weighted summation formula is used when calculating the comprehensive risk index:

[0083]

[0084] Among them, R index W is a comprehensive risk index. k To standardize the gas parameter value, P k T is a parameter for coal seam permeability. k D is the temperature influence factor. k As a deep impact factor, αk and ξ k These are the weighting coefficients, ρ k Here, n is the number of parameters and the depth correction coefficient is used. The calculated comprehensive risk index is divided into different risk level regions according to a set threshold, forming an initial regional risk level distribution map. Through spatial interpolation, a continuous risk level heatmap is generated, visually displaying the risk status of each region of the working face. The heatmap is updated regularly to record changes in the risk level of each region. Figure 2 The diagram shown is a schematic representation of the risk level heatmap in this embodiment of the application. The diagram uses a 5×5 matrix to display the gas risk distribution in different areas of the working face, with colors transitioning from red to green to indicate decreasing risk levels. Specifically, red areas (risk index > 0.9) represent high-risk areas requiring close monitoring and special handling; yellow areas (0.5 < risk index < 0.7) represent medium-risk areas requiring enhanced monitoring; and green areas (risk index < 0.4) represent low-risk areas where normal mining is possible. The value in each cell represents the risk index for that area. The distribution of the risk index provides a clear visual representation of the spatial distribution characteristics of the working face's risk levels, aiding in the development of differentiated mining strategies and ventilation control plans. The overall distribution of the heatmap shows that the upper left area of ​​the working face has a higher risk level, gradually decreasing towards the lower right.

[0085] When conducting regional gas outburst risk assessments, a spatial mapping formula is used:

[0086]

[0087] Among them, E risk Q represents the regional risk assessment value. m S is the regional risk index. m Z represents the area of ​​the region. m τ is the regional depth factor. m φ is the regional weighting coefficient. m κ is the spatial attenuation coefficient. m denoted as the depth of influence coefficient, and p represents the number of regions.

[0088] For example, a coal mining face collects gas concentration data at 5-minute intervals. Time-series analysis is performed on the collected dynamic distribution data of gas concentration to calculate the rate of change of gas concentration at each sampling point. Then, real-time data from gas pressure sensors is spatially matched and correlated with the concentration change rate data. The correlated data is normalized to eliminate dimensional differences between different parameters. Combined with coal seam permeability test data, a comprehensive risk index for each area of ​​the working face is calculated through weighted overlay. Based on the risk index, the working face is divided into high, medium, and low risk level areas, generating a risk level heat map. Through continuous monitoring and data updates, the dynamic changes in risk levels are recorded, forming regional gas outburst risk assessment data. Throughout the entire process, all data collection, processing, and analysis follow a unified spatiotemporal standard, ensuring the accuracy and reliability of the risk assessment results.

[0089] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0090] (1) Classify and label the regional gas outburst risk assessment data according to the risk level to generate working face risk zoning data;

[0091] (2) Monitor the gas emission volume of each area in the risk zoning data of the working face and record the time-series emission volume data;

[0092] (3) Group the time series outflow data according to the regional risk level and calculate the outflow threshold for each region;

[0093] (4) Perform an inverse mapping on the outflow threshold to generate a benchmark value for regional mining speed;

[0094] (5) The regional mining speed benchmark value and the risk level are weighted and combined to obtain the mining speed parameter;

[0095] (6) Determine the relative positional relationship between each area based on the risk zoning data of the working face, and generate the spatial layout data of the working face;

[0096] (7) Plan the advancement path based on the working face spatial layout data and mark the working face advancement direction;

[0097] (8) Correlate the working face advance direction with the mining speed parameters to obtain differentiated mining process parameters.

[0098] Specifically, the regional gas outburst risk assessment data is categorized into three risk levels: high, medium, and low. Different risk identifier values ​​are set according to each risk level to generate risk zoning data for the working face. This risk zoning data includes the spatial extent of each zone, its risk level value, and a zone identifier code. Gas emission sensors are installed in each zone marked in the working face risk zoning data for real-time monitoring. Emission data is collected at fixed time intervals, recording the temporal changes in gas emission in each zone. The temporal emission data includes the collection timestamp, zone number, and emission value.

[0099] The following formula is used to calculate the outflow threshold:

[0100]

[0101] Among them, Y th U is the outflow threshold. i For regional gas emission, b i As the risk level weight, v i t is the time decay coefficient. i For monitoring time, σ i M is a geological influence factor. i Let n be the area of ​​the region, and n be the number of monitoring points.

[0102] The formula for converting the outflow threshold into a mining rate benchmark value:

[0103]

[0104] Among them, V base K is the benchmark value for mining speed. j A is the speed conversion coefficient. j For the region parameter, L j B is the length of the region. j χ is the geological condition coefficient. j ζ is the distance decay factor. j is the regional correction factor, and m is the number of regions.

[0105] The regional mining speed benchmark value is weighted and combined with the risk level, with higher-risk areas receiving smaller weights and lower-risk areas receiving larger weights, to obtain the mining speed parameters. The working face risk zoning data contains spatial location information for each area. By analyzing the risk level distribution of adjacent areas, the relative positional relationships between areas are determined, generating working face spatial layout data. Based on the working face spatial layout data, the overall mining advance direction is determined. The determination of the advance direction needs to consider the distribution characteristics of regional risk levels, prioritizing advancement from low-risk areas to high-risk areas. The working face advance path is marked, forming an advance direction plan. The planned advance direction is correlated with the mining speed parameters of each area to establish a spatiotemporal correspondence of mining procedures, forming differentiated mining procedure parameters.

[0106] For example, a coal mining face is divided into three zones based on risk assessment data. Two gas emission sensors are installed in each zone, collecting data every 10 minutes. The collected emission data are grouped by zone, and the average emission and its trend for each zone are calculated. The emission threshold for high-risk zones is set according to the strictest standard, the standard for medium-risk zones is appropriately relaxed, and the basic standard is used for low-risk zones. A benchmark value for mining speed is calculated in reverse based on the emission threshold, and mining speed parameters are determined by combining this with the regional risk level. By analyzing the spatial layout of the working face, an advancement path starting from the low-risk zone is formulated, resulting in a differentiated mining procedure plan. Throughout the data processing, a strict correspondence is maintained between all parameters to ensure the scientific validity and rationality of the mining procedure parameters.

[0107] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0108] (1) The regional mining speed in the differentiated mining process parameters is statistically divided into segments, and the required basic air volume for each segment is calculated.

[0109] (2) Monitor the air volume of the main fan and local fans in the ventilation system and record the air volume distribution data;

[0110] (3) Compare and analyze the air volume distribution data with the basic air volume to determine the air volume adjustment requirements;

[0111] (4) Prioritize the air volume adjustment needs according to the regional risk level and generate an air volume allocation sequence;

[0112] (5) Dynamically adjust the speed of the local ventilation fan based on the air volume distribution sequence to generate local ventilation parameters;

[0113] (6) Correlation analysis was performed between local ventilation parameters and real-time monitored gas concentration data to mark areas with abnormal ventilation;

[0114] (7) Real-time correction of local ventilation parameters in areas with abnormal ventilation and updating of air volume distribution data;

[0115] (8) Record and analyze the air volume distribution data in time sequence to obtain the operating parameters of the working face ventilation network.

[0116] Specifically, the regional mining speed in the differentiated mining process parameters is statistically analyzed in segments, and the mining area is divided into multiple independent segments based on the spatial layout of the working face. The mining speed of each segment is directly related to the basic air volume required for that segment, and the basic air volume requirement needs to be calculated based on parameters such as mining speed, working face cross-sectional area, and gas emission rate. Air volume monitoring sensors are installed on the main and local fans of the ventilation system to collect air volume data in real time. The air volume monitoring points for the main fans are set in the main intake and return air roadways, while the monitoring points for the local fans are set at the intake and return air outlets of each segment of the working face. The monitoring data includes air volume values, air pressure values, and collection timestamps, forming an air volume distribution dataset.

[0117] The actual airflow distribution data is compared and analyzed with the calculated basic airflow demand to calculate the airflow difference for each section. The airflow difference reflects the deviation between the actual ventilation volume and the demand, providing a basis for subsequent ventilation adjustments. Airflow adjustment needs are prioritized according to the risk level of each area of ​​the working face. High-risk areas have the highest priority, followed by medium-risk areas, and finally low-risk areas. An airflow allocation sequence is determined based on the priority, and local ventilation fans are adjusted in sequence. Airflow distribution is changed by adjusting the speed of the local ventilation fans; the speed adjustment range is determined based on the fan's performance parameters. During the adjustment process, airflow changes are monitored in real time, and the operating parameters of each local ventilation fan are recorded. Simultaneously, changes in methane concentration are monitored, and the relationship between local ventilation parameters and methane concentration is analyzed.

[0118] When an abnormally high or fluctuating methane concentration is detected in a certain area, that area is marked as a ventilation anomaly area. The ventilation anomaly area is then analyzed in detail to identify the causes of unreasonable airflow distribution. Based on the analysis results, local ventilation parameters are corrected in real time, including adjusting fan speeds and changing airflow distribution ratios. The corrected airflow distribution data needs to be updated in the airflow distribution plan promptly. All airflow distribution data is recorded chronologically, including airflow values ​​for each section, fan operating parameters, and methane concentration. By analyzing this time-series data, the operating patterns of the ventilation system are summarized, forming a set of operating parameters for the working face ventilation network. This set of operating parameters contains the operating characteristics of the ventilation system under different working conditions, providing data support for the optimization and adjustment of the ventilation system.

[0119] For example, a coal mining face is 150 meters long and divided into 5 sections of 30 meters each. By analyzing differentiated mining process parameters, the mining speed for each section is determined. Based on the mining speed and gas emission parameters, the required basic air volume for each section is calculated. Twelve air volume sensors are installed on the working face: two at the main fan inlet and two at the local ventilation fan inlet for each section. The air volume sensors collect data once per minute, recording the air volume value and the collection time. Comparative analysis reveals that the actual air volume in a certain section is lower than the basic air volume requirement, and this section has a higher risk level; therefore, it is prioritized for adjustment. The rotation speed of the local ventilation fan in this section is adjusted, while monitoring changes in gas concentration. If the gas concentration in a local area remains high, the air volume distribution scheme is further optimized to ensure the rational operation of the ventilation system.

[0120] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0121] (1) Perform time-series analysis on the operating parameters of the working face ventilation network and record the operating status data of the ventilation system;

[0122] (2) Match the mining speed in the differentiated mining process parameters with the ventilation system operation status data to generate speed-ventilation correlation data;

[0123] (3) Classify the regional gas outburst risk assessment data into regional safety status data;

[0124] (4) Cross-analyze the speed-ventilation correlation data with the regional safety status data to calculate the quantitative indicators of safety benefits;

[0125] (5) Based on the quantitative indicators of safety benefits, the mining priority of each area of ​​the working face is sorted to generate mining sequence data;

[0126] (6) Perform spatiotemporal planning processing on the mining sequence data and coordinate the mining progress with ventilation capacity;

[0127] (7) The collaborative configuration results are comprehensively verified with the regional gas outburst risk assessment data, and the mining plan is adjusted accordingly;

[0128] (8) The adjusted mining plan is subjected to safety verification and economic analysis to obtain the optimized coal mining strategy.

[0129] Specifically, a time-series analysis was performed on the operating parameters of the working face ventilation network. The operating data of the main and local fans were arranged chronologically, recording parameters such as air volume, air pressure, and fan speed at each time point. Simultaneously, the operating status of the ventilation system was categorized into three states: normal operation, transitional adjustment, and abnormal operation, generating a ventilation system operating status dataset. Spatiotemporal matching was performed between the mining speed data from the differentiated mining process parameters and the ventilation system operating status data to establish the correlation between the mining speed of each section and the ventilation parameters at the corresponding time. By analyzing the impact of changes in mining speed on the ventilation system operating status, speed-ventilation correlation data was generated. This correlation data includes the correspondence between parameters such as mining speed, ventilation volume, and ventilation pressure.

[0130] The regional gas outburst risk assessment data was further subdivided into safety levels, refining the original three risk levels of high, medium, and low. High-risk areas were further subdivided into extremely high and relatively high risk, medium-risk areas into slightly high and slightly low risk, and low-risk areas into relatively low and extremely low risk, forming a six-level safety level system. Based on these subdivided safety levels, each area of ​​the working face was marked, generating regional safety status data. Cross-analysis was performed between velocity-ventilation correlation data and regional safety status data to calculate the safety factor for each area under different mining speeds and ventilation conditions. The calculation of the safety factor comprehensively considers three factors: gas emission rate, ventilation capacity, and mining speed, generating a quantitative indicator of safety benefits. This quantitative indicator reflects the degree to which economic benefits are achieved while ensuring safety.

[0131] Based on quantitative safety benefit indicators, mining priorities are ranked for each area of ​​the working face. The principle for determining priorities is to prioritize areas with higher quantitative safety benefit indicators, provided safety requirements are met. Mining sequence data is generated through priority ranking, including the order and timing of mining for each area. Spatiotemporal planning is then performed on the mining sequence data to match the mining schedule with the ventilation system's capacity. Spatiotemporal planning needs to consider the connection between adjacent areas, the ventilation system's adjustment capacity, and gas control requirements. The specific mining time and ventilation parameter configuration scheme for each area are determined through planning.

[0132] The planned configuration scheme is comprehensively verified against regional gas outburst risk assessment data to check whether the scheme meets safety production requirements. Verification includes the rationality of the mining sequence, the adaptability of ventilation capacity, and the effectiveness of gas control. Based on the verification results, the mining scheme is adjusted and optimized to form a mining scheme that meets safety requirements. The adjusted mining scheme undergoes safety verification and economic analysis. Safety verification mainly checks the safety margin of the scheme under various operating conditions, while economic analysis calculates the input-output ratio of the scheme. Through verification and analysis, the optimal coal mining strategy is determined.

[0133] For example, a working face is divided into five sections at 30-meter intervals, with each section equipped with an airflow sensor and a gas concentration sensor. One month's worth of ventilation system operation data is collected, including minute-by-minute airflow and air pressure data. Simultaneously, mining speed data for each section is recorded, establishing a correlation between mining speed and ventilation parameters. Based on the gas outburst risk assessment results, the five sections are marked according to a six-level safety classification. By analyzing the safety level, mining speed, and ventilation conditions of each section, safety benefit indicators are calculated. Based on these safety benefit indicators, the mining sequence is determined, and a detailed mining schedule is developed. After safety verification and economic analysis, an optimized mining plan is formed.

[0134] The above describes the coal mining strategy analysis method based on coal mine gas concentration in the embodiments of this application. The following describes the coal mining strategy analysis system based on coal mine gas concentration in the embodiments of this application. Please refer to [link / reference]. Figure 3 One embodiment of the coal mining strategy analysis system based on coal mine gas concentration in this application includes:

[0135] The data acquisition module 201 is used to collect gas concentration data of the coal mine working face through the intelligent track inspection device. It uses a vertically reciprocating detection probe to collect multi-layer gas concentration data and generates time series data based on sampling time, location and environmental parameters to obtain the original gas concentration dataset.

[0136] Processing module 202 is used to perform spatial data processing on the sampling point data based on the original gas concentration dataset, and to perform data correction by combining coal seam thickness, dip angle and fault, so as to obtain dynamic distribution data of gas concentration.

[0137] Calculation module 203 is used to perform multi-dimensional analysis and weight calculation on gas concentration change rate, gas pressure and coal seam permeability based on the gas concentration dynamic distribution data, generate regional risk level heat map, and obtain regional gas outburst risk assessment data.

[0138] The division module 204 is used to divide the working face into blocks according to the risk level based on the regional gas outburst risk assessment data, set the mining speed parameters in combination with the gas emission rate, determine the working face advance direction, and obtain differentiated mining process parameters.

[0139] The allocation module 205 is used to calculate the air volume allocation of the ventilation system according to the differentiated mining process parameters, and adjust the local ventilation parameters through real-time monitoring data to obtain the working face ventilation network operation parameters;

[0140] The correlation module 206 is used to perform multi-dimensional correlation analysis on the working face ventilation network operation parameters, the differentiated mining process parameters, and the regional gas outburst risk assessment data to establish a quantitative index of safety benefits and obtain an optimized coal mining strategy.

[0141] Through the collaborative efforts of the aforementioned components, intelligent track inspection devices collect gas concentration data from the coal mine face. Vertically reciprocating detection probes collect multi-layered gas concentration data, generating time-series data based on sampling time, location, and environmental parameters to obtain a comprehensive raw gas concentration dataset, thus expanding the spatial range of gas concentration detection. Spatial data processing of the sampling point data, combined with geological parameters such as coal seam thickness, dip angle, and faults, yields accurate dynamic distribution data of gas concentration. Multi-dimensional analysis and weight calculations are performed on parameters such as gas concentration change rate, gas pressure, and coal seam permeability to generate regional risk level thermal data. The diagram shows how to accurately assess the risk of gas outbursts. Based on the regional gas outburst risk assessment data, the working face is divided into blocks according to risk level. Mining speed parameters are rationally set based on gas emission volume to determine the working face advancement direction and formulate scientific differentiated mining process parameters. Based on these differentiated mining process parameters, the ventilation system's airflow distribution is calculated, and local ventilation parameters are dynamically adjusted through real-time monitoring data, ensuring intelligent control of the ventilation system. A multi-dimensional correlation analysis is conducted on the working face ventilation network operation parameters, differentiated mining process parameters, and regional gas outburst risk assessment data to establish a quantitative safety benefit index system, resulting in an optimized coal mining strategy.

[0142] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for analyzing coal mining strategies based on coal mine gas concentration, characterized in that, The coal mining strategy analysis method based on coal mine gas concentration includes: The gas concentration data of the coal mine working face is collected by the intelligent track inspection device. The gas concentration data of multiple layers is collected by the vertical reciprocating motion detection probe. Time series data is generated according to the sampling time, location and environmental parameters to obtain the original gas concentration dataset. Based on the original gas concentration dataset, spatial data processing is performed on the sampling point data, and data correction is performed by combining coal seam thickness, dip angle and fault to obtain dynamic gas concentration distribution data. Based on the dynamic distribution data of gas concentration, multi-dimensional analysis and weight calculation are performed on the gas concentration change rate, gas pressure and coal seam permeability to generate a regional risk level heat map and obtain regional gas outburst risk assessment data. Based on the regional gas outburst risk assessment data, the working face is divided into blocks according to risk level. Mining speed parameters are set based on gas emission volume, and the working face advance direction is determined to obtain differentiated mining process parameters. This includes: classifying and labeling the regional gas outburst risk assessment data according to risk level to generate working face risk zoning data; monitoring gas emission volume in each region of the working face risk zoning data and recording time-series emission volume data; grouping and statistically analyzing the time-series emission volume data according to regional risk level to calculate the emission volume threshold for each region; performing an inverse mapping on the emission volume threshold to generate a regional mining speed benchmark value; weighting and combining the regional mining speed benchmark value with the risk level to obtain mining speed parameters; determining the relative positional relationship between each region based on the working face risk zoning data to generate working face spatial layout data; planning the advancement path based on the working face spatial layout data and marking the working face advance direction; and data-linking the working face advance direction with the mining speed parameters to obtain the differentiated mining process parameters. Based on the differentiated mining process parameters, the ventilation system's airflow allocation is calculated, and local ventilation parameters are adjusted through real-time monitoring data to obtain the working face ventilation network operation parameters. This includes: segmenting the regional mining speeds in the differentiated mining process parameters and calculating the required basic airflow for each segment; monitoring the airflow of the main and local fans in the ventilation system and recording the airflow distribution data; comparing the airflow distribution data with the basic airflow to determine airflow adjustment needs; prioritizing the airflow adjustment needs according to regional risk levels to generate an airflow allocation sequence; dynamically adjusting the rotation speed of local fans based on the airflow allocation sequence to generate local ventilation parameters; performing correlation analysis between the local ventilation parameters and real-time monitored gas concentration data to mark areas with abnormal ventilation; real-time correction of the local ventilation parameters in areas with abnormal ventilation to update the airflow allocation data; and recording and analyzing the airflow allocation data over time to obtain the working face ventilation network operation parameters. This paper presents a multi-dimensional correlation analysis of the working face ventilation network operation parameters, the differentiated mining process parameters, and the regional gas outburst risk assessment data to establish a quantitative safety benefit index and obtain an optimized coal mining strategy. The analysis includes: performing time-series analysis on the working face ventilation network operation parameters and recording ventilation system operation status data; matching the mining speed in the differentiated mining process parameters with the ventilation system operation status data to generate speed-ventilation correlation data; classifying the regional gas outburst risk assessment data into safety levels to form regional safety status data; performing cross-analysis on the speed-ventilation correlation data and the regional safety status data to calculate a quantitative safety benefit index; prioritizing the mining of each region of the working face based on the quantitative safety benefit index to generate mining sequence data; performing spatiotemporal planning processing on the mining sequence data to coordinate the mining progress and ventilation capacity; comprehensively verifying the coordinated configuration results with the regional gas outburst risk assessment data and adjusting the mining plan; and conducting safety verification and economic analysis on the adjusted mining plan to obtain the optimized coal mining strategy.

2. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1, characterized in that, The method involves collecting methane concentration data from the coal mine working face using an intelligent track inspection device. This data is obtained by collecting multi-layered methane concentration data using a vertically reciprocating detection probe, and generating time-series data based on sampling time, location, and environmental parameters. The resulting raw methane concentration dataset includes: Start the motor on the intelligent track inspection device to drive the walking wheels to move along the external track, and at the same time start the cleaning component and air blowing component of the intelligent track inspection device; The motor drives the drive gear to rotate through its output end, and the drive gear drives the driven gear to rotate, thus achieving power transmission. The passive gear drives the bevel gear to rotate via the connecting shaft, the bevel gear drives the rotating shaft to rotate within the limiting rod, and the rotating shaft drives the lever to clean the external track; The long pin is rotated by the pulley at the output end of the motor, and the detection probe is pushed to reciprocate in the vertical direction by the cam at the end of the long pin. The detection probe collects multi-layer gas concentration data during reciprocating motion, and the protrusion of the detection probe moves back and forth in the groove, and is reset by a connecting spring; The detection probe transmits the collected gas concentration data to the monitor in real time, and the monitor records the sampling time, location and environmental parameters; The gas concentration data recorded by the monitor is processed into a time series and spatially layered according to the sampling location; The stratified gas concentration data was paired with environmental parameters to establish a spatiotemporal correlation matrix; The spatiotemporal correlation matrix is ​​standardized and serialized to obtain the original gas concentration dataset.

3. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1, characterized in that, The process involves spatial data processing of the sampling point data based on the original gas concentration dataset, combined with data correction based on coal seam thickness, dip angle, and faults, to obtain dynamic gas concentration distribution data, including: The sampling points in the original gas concentration dataset are marked according to three-dimensional spatial coordinates to construct a spatial sampling point distribution sequence; The difference in gas concentration between adjacent sampling points in the spatial sampling point distribution sequence is calculated to generate gas concentration gradient data; The coal seam thickness data is correlated with the gas concentration gradient data to calculate the thickness influence factor; The gas concentration gradient data is transformed using the coal seam dip angle data to obtain dip angle correction data. Spatially match the fault location information with the dip correction data to mark the fault-affected area; The gas concentration data in the fault-affected area are subjected to fault attenuation processing to generate regional gas distribution data; The gas distribution data of the region is sorted according to the time dimension, and the changing trend of gas concentration at each sampling point is calculated. The gas distribution data of the region is dynamically updated based on the changing trend to obtain the dynamic distribution data of gas concentration.

4. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1, characterized in that, Based on the dynamic distribution data of gas concentration, a multi-dimensional analysis and weight calculation are performed on the gas concentration change rate, gas pressure, and coal seam permeability to generate a regional risk level heat map, thus obtaining regional gas outburst risk assessment data, including: The dynamic distribution data of gas concentration is sampled at time intervals, and the rate of change of gas concentration at each sampling point is calculated. The gas pressure data at each sampling point is correlated with the gas concentration change rate to form pressure-concentration correlation data; The pressure-concentration correlation data is normalized to generate standardized gas parameter data; The coal seam permeability data and the standardized gas parameter data are weighted and superimposed to generate a comprehensive risk index; The working area is divided into risk levels according to the comprehensive risk index, and areas with different risk levels are marked. Generate a regional risk level heat map according to the spatial distribution of the regions with different risk levels; The heat map of risk levels in the area is dynamically updated to record the trend of risk level changes. The risk level change trend is spatially mapped to obtain the regional gas outburst risk assessment data.

5. A coal mining strategy analysis system based on coal mine gas concentration, used to implement the coal mining strategy analysis method based on coal mine gas concentration as described in any one of claims 1-4, characterized in that, The coal mining strategy analysis system based on coal mine gas concentration includes: The data acquisition module is used to collect gas concentration data of the coal mine working face through the intelligent track inspection device. It uses a vertically reciprocating detection probe to collect multi-layer gas concentration data and generates time series data based on sampling time, location and environmental parameters to obtain the original gas concentration dataset. The processing module is used to perform spatial data processing on the sampling point data based on the original gas concentration dataset, and to perform data correction by combining coal seam thickness, dip angle and faults to obtain dynamic gas concentration distribution data; The calculation module is used to perform multi-dimensional analysis and weight calculation on the gas concentration change rate, gas pressure, and coal seam permeability based on the gas concentration dynamic distribution data, generate a regional risk level heat map, and obtain regional gas outburst risk assessment data. The segmentation module is used to divide the working face into blocks according to the risk level based on the regional gas outburst risk assessment data, set the mining speed parameters in combination with the gas emission rate, determine the working face advance direction, and obtain differentiated mining process parameters. The allocation module is used to calculate the air volume allocation of the ventilation system according to the differentiated mining process parameters, and adjust the local ventilation parameters through real-time monitoring data to obtain the working face ventilation network operation parameters; The correlation module is used to perform multi-dimensional correlation analysis on the operating parameters of the working face ventilation network, the parameters of the differentiated mining process, and the regional gas outburst risk assessment data, to establish quantitative indicators of safety benefits, and to obtain an optimized coal mining strategy.

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