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

Abstract

The invention relates to the technical field of data processing, and discloses a coal mining strategy analysis method and system based on coal mine gas concentration. The method comprises the steps of collecting gas concentration data through an intelligent track inspection device, performing data correction in combination with coal seam parameters to obtain concentration dynamic distribution data, performing multi-dimensional analysis based on the concentration dynamic distribution data to generate a risk thermodynamic diagram, determining differentiated mining parameters according to risk assessment, optimizing ventilation parameters through air volume regulation and control, and obtaining a risk analysis result. And finally, correlation analysis is carried out on each parameter to obtain a mining strategy scheme. The intelligent track inspection device collects gas concentration data and is matched with the sweeping assembly and the blowing assembly to keep the track clean, the detection range is expanded through reciprocating motion of the detection probe, and differential mining process parameters and ventilation regulation and control strategies are formulated in combination with multi-dimensional data analysis; the technical problems of small detection range, insufficient data analysis and unreasonable mining strategy in the prior art are solved.

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 Art

[0002] During coal mining, it is necessary to detect the gas concentration in the coal mining and excavation working face. At present, the detection of coal mine gas concentration in China generally adopts the traditional methane sensor fixed on the intake and return air sides of the excavation working face for real-time monitoring, combined with manual inspection. Some existing technologies use a track inspection device to monitor the gas concentration, including structures such as a slideway, a baffle, a runner, a conveyor belt, etc., which are driven to move by a biaxial motor, and are equipped with a probe and a smoke sensor to detect the gas concentration. Some other technologies use a track inspection robot with a rain protection structure, including components such as a fuselage, a card seat, a protective cover, etc., and achieve the protection function through a slider and a spring mechanism.

[0003] However, the existing gas concentration detection technologies have some deficiencies: First, during the movement of the detection device, small gravels are likely to accumulate on the track, affecting the normal movement of the device; Second, the position of the detection probe is relatively fixed, the detection range is small, and it is difficult to obtain comprehensive gas concentration distribution data; Third, the existing technologies lack in-depth analysis and utilization of the detection data, and it is impossible to formulate a scientific mining strategy based on the gas concentration data, resulting in potential 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, which is used to collect gas concentration data through an intelligent track inspection device, cooperate with a cleaning component and a blowing component to keep the track clean, use the reciprocating movement of the detection probe to expand the detection range, and combine multi-dimensional data analysis to formulate differential mining process parameters and ventilation control strategies, solving the technical problems of small detection range, insufficient data analysis, and unreasonable mining strategies in the existing technologies.

[0005] In a first aspect, the present application provides a method for analyzing coal mining strategies based on coal mine gas concentration. The method for analyzing coal mining strategies based on coal mine gas concentration includes: collecting gas concentration data of a coal mine working face through an intelligent track inspection device, collecting multi-layer gas concentration data using a detection probe that moves vertically back and forth, generating time series data based on sampling time, location, and environmental parameters, and obtaining an original gas concentration data set; according to the original gas concentration data set, performing spatial data processing on the sampling point data, combining coal seam thickness, dip angle, and faults for data correction, and obtaining dynamic gas concentration distribution data; based on the dynamic gas concentration distribution data, performing multi-dimensional analysis and weight calculation on the gas concentration change rate, gas pressure, and coal seam permeability, generating a heat map of regional risk levels, and obtaining sub-region gas outburst risk assessment data; according to the sub-region gas outburst risk assessment data, dividing the working face into blocks according to the risk level, setting mining speed parameters in combination with the gas emission volume, determining the advancing direction of the working face, and obtaining differential mining process parameters; according to the differential mining process parameters, calculating the air volume distribution of the ventilation system, adjusting the local ventilation parameters through real-time monitoring data, and obtaining the operation parameters of the working face ventilation network; performing multi-dimensional correlation analysis on the operation parameters of the working face ventilation network, the differential mining process parameters, and the sub-region gas outburst risk assessment data, establishing a safety benefit quantification index, and obtaining an optimized coal mining strategy plan.

[0006] In a second aspect, the present application provides a system for analyzing coal mining strategies based on coal mine gas concentration. The system for analyzing coal mining strategies based on coal mine gas concentration includes:

[0007] A collection module, configured to collect gas concentration data of a coal mine working face through an intelligent track inspection device, collect multi-layer gas concentration data using a detection probe that moves vertically back and forth, generate time series data based on sampling time, location, and environmental parameters, and obtain an original gas concentration data set;

[0008] A processing module, configured to perform spatial data processing on the sampling point data according to the original gas concentration data set, combine coal seam thickness, dip angle, and faults for data correction, and obtain dynamic gas concentration distribution data;

[0009] A calculation module, configured to perform multi-dimensional analysis and weight calculation on the gas concentration change rate, gas pressure, and coal seam permeability based on the dynamic gas concentration distribution data, generate a heat map of regional risk levels, and obtain sub-region gas outburst risk assessment data;

[0010] A division module, configured to divide the working face into blocks according to the risk level according to the sub-region gas outburst risk assessment data, set mining speed parameters in combination with the gas emission volume, determine the advancing direction of the working face, and obtain differential mining process parameters;

[0011] A distribution module, configured to perform air volume distribution calculation on the ventilation system according to the differential mining process parameters, adjust local ventilation parameters through real-time monitoring data, and obtain the operation parameters of the working face ventilation network;

[0012] An association module, configured to perform multi-dimensional association analysis on the operation parameters of the working face ventilation network, the differential mining process parameters, and the regional gas outburst risk assessment data, establish a safety benefit quantification index, and obtain an optimized coal mining strategy plan.

[0013] In the technical solution provided by this application, the intelligent track inspection device is used to collect gas concentration data of the coal mine working face. The detection probe that moves vertically reciprocally is used to collect multi-layer gas concentration data. Time series data is generated according to the sampling time, position, and environmental parameters, and a comprehensive original gas concentration data set is obtained, expanding the spatial range of gas concentration detection; through spatial data processing of the sampling point data and data correction in combination with geological parameters such as coal seam thickness, dip angle, and fault, 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 heat map of regional risk levels, realizing the precise assessment of gas outburst risks; according to the regional gas outburst risk assessment data, the working face is divided into blocks according to the risk level, the mining speed parameters are reasonably set in combination with the gas emission volume, the advancing direction of the working face is determined, and scientific differential mining process parameters are formulated; based on the differential mining process parameters, air volume distribution calculation is performed on the ventilation system, and local ventilation parameters are dynamically adjusted through real-time monitoring data, ensuring the intelligent control of the ventilation system; multi-dimensional association analysis is performed on the operation parameters of the working face ventilation network, the differential mining process parameters, and the regional gas outburst risk assessment data, establishing a safety benefit quantification index system, and obtaining an optimized coal mining strategy plan. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

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

[0016] Figure 2 It is a schematic diagram of the heat map of risk levels in an embodiment 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 the embodiments of the present application. Detailed implementation manners

[0018] The embodiments of the present application provide a method and system for analyzing coal mining strategies based on coal mine gas concentration. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific processes of the embodiments of the present application are described below. Please refer to Figure 1 An embodiment of the method for analyzing coal mining strategies based on coal mine gas concentration in the embodiments of the present application includes:

[0020] Step S101: Collect gas concentration data of the coal mining face through an intelligent track inspection device, collect multi-layer gas concentration data using a detection probe that moves vertically back and forth, generate time series data based on the sampling time, location and environmental parameters, and obtain the original gas concentration data set;

[0021] Step S102: According to the original gas concentration data set, perform spatial data processing on the sampling point data, and combine the coal seam thickness, dip angle and fault to perform data correction to obtain the 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, generate a regional risk level heat map, and obtain the sub-regional gas outburst risk assessment data;

[0023] Step S104: According to the sub-regional gas outburst risk assessment data, divide the working face into blocks according to the risk level, combine the gas emission volume to set the mining speed parameter, determine the advancing direction of the working face, and obtain the differential mining process parameters;

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

[0025] Step S106: Perform multi-dimensional correlation analysis on the operation parameters of the working face ventilation network, the differential mining process parameters, and the regional gas outburst risk assessment data, establish a safety benefit quantification index, and obtain an optimized coal mining strategy plan.

[0026] It can be understood that the execution entity of this application can be a coal mining strategy analysis system based on coal mine gas concentration, or a terminal or a server. Specifically, it is not limited here. In this embodiment of the application, the server is taken as the execution entity for illustration.

[0027] Specifically, gas concentration data is collected by 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 drives the active gear by the motor. The active gear meshes with the passive gear, and the passive gear drives the connecting shaft. The connecting shaft drives the rotating shaft through bevel gears to realize the vertical reciprocating movement of the detection probe. Specifically, the detection probe makes a vertical displacement under the push of the cam, the convex block moves in the groove, and the connecting spring provides a reset force, so as to ensure that the detection probe can collect data at different heights.

[0028] During the collection process, the detection probe stays at each sampling position for 5 seconds, records the gas concentration value, and at the same time records the sampling timestamp, sampling height, and ambient temperature. The sampling data is stored in real time through the monitor to form a basic data table. The data table includes a time column (t), a spatial coordinate column (x, y, z), a gas concentration column (c), and an ambient temperature column (T). The collected data is preliminarily screened to remove outliers, and a normalized original gas concentration data set is generated.

[0029] When performing spatial data processing on the original gas concentration data set, the sampling points are located according to three-dimensional coordinates, and the gas concentration gradient between adjacent sampling points is calculated. Considering the influence of coal seam thickness, a thickness correction coefficient is introduced to correct the gas concentration. At the same time, considering the influence of coal seam dip on gas distribution, the gas distribution in the inclined coal seam is transformed to the horizontal plane through coordinate transformation. For the fault area, the fault position is marked, and the attenuation effect of the fault on gas distribution is calculated to obtain the dynamic gas concentration distribution data considering geological factors. Based on the dynamic gas concentration distribution data, the gas concentration change rate of each sampling point is calculated, and combined with the gas pressure monitoring data, a pressure-concentration correlation relationship is established. Taking the coal seam permeability data as an important reference index, weighted calculation is performed on each parameter. Through multi-dimensional analysis of each parameter, the working face is divided into different risk level areas, a risk level heat map is generated, and regional gas outburst risk assessment data is formed.

[0030] Based on the gas outburst risk assessment data for different regions and combined with the real-time monitored gas emission data, determine the safe mining speed range for each region. By analyzing the relative position relationship and risk level distribution of each region, plan the optimal working face advancing direction and formulate differential mining process parameters. The mining process parameters include information such as the mining speed limit, mining sequence, and advancing path for each region.

[0031] For the differential mining process parameters, calculate the basic ventilation volume required for each region and distribute the air volume in the ventilation system. Adjust the speed of the local ventilator through real-time monitoring data to ensure that each region obtains sufficient ventilation volume. Continuously monitor the operating state of the ventilation system, record the air volume distribution data, and form the operating parameters of the working face ventilation network.

[0032] Conduct multi-dimensional correlation analysis on the operating parameters of the working face ventilation network, differential mining process parameters, and gas outburst risk assessment data for different regions. By analyzing the matching degree between the ventilation effect and the mining speed, evaluate the safety of the mining plan. Combining the risk levels and ventilation conditions of each region, establish a quantitative safety benefit index system and generate an optimized coal mining strategy plan.

[0033] For example, taking a coal mine working face as an example, an intelligent track inspection device monitors the gas concentration in the coal mining face. The detection probe collects data every 0.5 meters within the height range of 0 - 3 meters, and 5 groups of data are collected at each position. Through data processing, it is found that there are three gas enrichment regions in this working face, located in the upper, middle, and lower parts of the working face respectively. Combining the coal seam thickness (average 4.5 meters) and dip angle (28 degrees) data, correct the gas distribution. After multi-dimensional analysis, the working face is divided into three risk level regions: high, medium, and low. Formulate differential mining parameters according to the risk levels: the mining speed in the high-risk region does not exceed 2 m / shift, in the medium-risk region does not exceed 3 m / shift, and in the low-risk region does not exceed 4 m / shift. Through the optimization adjustment of the ventilation system, ensure that the ventilation volume in the high-risk region is 30% higher than the basic demand to achieve safe mining.

[0034] In the embodiments of the present application, a smart rail inspection device is used to collect gas concentration data of a coal mining face. A detection probe that moves vertically back and forth is used to collect gas concentration data of multiple layers. Time series data is generated based on the sampling time, location, and environmental parameters to obtain a comprehensive original gas concentration data set, expanding the spatial range of gas concentration detection. By performing spatial data processing on the sampling point data and combining 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 heat map of regional risk levels, achieving precise assessment of gas outburst risks. According to the sub-regional gas outburst risk assessment data, the working face is divided into blocks according to the risk level, and the mining speed parameters are reasonably set in combination with the gas emission volume. The advancing direction of the working face is determined, and scientific differential mining process parameters are formulated. Based on the differential mining process parameters, air volume distribution calculation is performed on the ventilation system, and local ventilation parameters are dynamically adjusted through real-time monitoring data to ensure intelligent control of the ventilation system. The operating parameters of the working face ventilation network, differential mining process parameters, and sub-regional gas outburst risk assessment data are subjected to multi-dimensional correlation analysis to establish a safety benefit quantification index system, and an optimized coal mining strategy plan is obtained.

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

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

[0037] (2) Drive the driving gear to rotate through the output end of the motor, and the driving gear drives the driven gear to rotate to achieve 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 in the limiting rod, and the rotating shaft drives the lever to clean the external track;

[0039] (4) Drive the long pin to rotate through the pulley at the output end of the motor, and push the detection probe to move back and forth in the vertical direction through the cam at the end of the long pin;

[0040] (5) The detection probe collects gas concentration data of multiple layers during the reciprocating movement. The convex block of the detection probe reciprocates in the groove and is reset through 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) Serialize the gas concentration data recorded by the monitor in terms of time and perform spatial stratification according to the sampling locations.

[0043] (8) Pair the stratified gas concentration data with the environmental parameters to establish a spatio-temporal correlation matrix.

[0044] (9) Perform data standardization and serialization processing on the spatio-temporal correlation matrix to obtain the original gas concentration data set.

[0045] Specifically, the working process of the intelligent track inspection device involves the collaborative work of the mechanical motion system and the data acquisition system. Start the motor on the intelligent track inspection device, and the motor drives the walking wheels to move within the external track through the transmission mechanism. While the walking wheels are moving, the cleaning component and the air blowing component start to work. The cleaning component consists of a lever, and the air blowing component includes a nozzle, an airbag, an intake pipe, and an exhaust pipe. The airbag controls the intake and exhaust directions through a one-way valve to ensure the cleanliness of the track surface.

[0046] The output end of the motor is fixedly connected to the driving gear, and the driving gear meshes with the driven gears. 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, and the connecting shaft and the rotating shaft are driven by bevel gears. The bevel gears are arranged on the end faces of the connecting shaft and the rotating shaft. The rotating shaft rotates within the limiting rod, and the limiting balls are arranged between the inside of the rotating shaft and the inner wall of the limiting rod to prevent axial displacement of the rotating shaft. A plurality of levers are evenly distributed on the surface of the rotating shaft, and the surface of the lever is in an arc structure, and the external track is cleaned as the rotating shaft rotates. The output end of the motor is also connected to a pulley, and the pulley drives the long pin to rotate through a belt. The end of the long pin is welded with a cam. When the cam rotates, it periodically pushes the detection probe, causing the detection probe to perform a reciprocating motion in the vertical direction. Convex blocks are provided on both the left and right sides of the detection probe, and the convex blocks cooperate with the grooves on the inner wall of the housing to play a guiding role. One end of the connecting spring is connected to the convex block, 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 movement of the detection probe, gas concentration data at different heights are continuously collected. The monitor records the gas concentration values at each sampling point, and simultaneously records parameters such as the corresponding sampling timestamp, spatial position coordinates, and ambient temperature. The collected data is arranged in chronological order to form an initial time series. Then the data is stratified according to the sampling height, and each height layer forms an independent data subset. For the data subset of each height layer, the gas concentration data is paired and associated with the environmental parameters. During the data pairing process, for each gas concentration measurement value, the ambient temperature value at the sampling moment closest to it is found for matching. In this way, a spatio-temporal association matrix is established. The rows of the matrix represent different time points, the columns represent different spatial positions, and the matrix elements contain the gas concentration values and the corresponding environmental parameters.

[0048] Data normalization processing is performed on the spatio-temporal association matrix. The normalization process includes removing outliers, data normalization, and serialization processing. For example, in the gas concentration monitoring of a coal mine working face, the detection probe reciprocates within the height range of 0 - 3 meters, and the movement period is 60 seconds. It stays at each sampling position for 5 seconds to collect gas concentration data. The monitor records data once per second, including the gas concentration value, sampling time, position coordinates, and ambient temperature. The data is recorded in the form of {timestamp, x coordinate, y coordinate, z coordinate, gas concentration, ambient temperature}. The data is divided into 6 layers at a height interval of 0.5 meters, and each layer contains the measurement data of a complete cycle. Outlier processing is performed on the data of each layer, and data points with a deviation from adjacent measurement points exceeding 3 times the standard deviation are removed. Then the data of each layer is normalized so that the data is distributed within the interval [0, 1]. The processed data is organized into a spatio-temporal association matrix, and each element in the matrix contains the normalized gas concentration value and the corresponding ambient temperature value, forming the original gas concentration data set.

[0049] In a 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 data set according to the three-dimensional space coordinates to construct a spatial sampling point distribution sequence;

[0051] (2) Calculate the difference in gas concentration 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 data to obtain dip correction data;

[0054] (5) Perform spatial matching on the fault location information and dip correction data, and mark the fault influence area;

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

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

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

[0058] Specifically, each sampling point is located according to the three-dimensional coordinate system (x, y, z), where x and y represent the positions on the working face horizontal plane, and z represents the sampling height. A spatial sampling point distribution sequence is formed through data marking, and each sampling point contains the position coordinates and the corresponding gas concentration value. When calculating the gas concentration difference between adjacent sampling points, the sampling point pair with the closest spatial distance is selected, and the gas concentration change rate between them is calculated. The gas concentration gradient is calculated separately for the horizontal and vertical directions to form gas concentration gradient data. The gas concentration gradient reflects the distribution change characteristics of gas in space.

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

[0060]

[0061] where F h is the thickness influence factor, H i is the local coal seam thickness, H m is the average coal seam thickness, is the gas concentration gradient, ω i is the weight coefficient, λ h is the thickness attenuation coefficient, d i is the distance from the coal seam center line, and n is the number of sampling points.

[0062] For the influence of coal seam dip, the coordinate transformation formula is adopted:

[0063]

[0064] where (x' p , y' p , z' p ) are the corrected coordinates, (x p , y p , z p ) are the original coordinates, θ is the coal seam dip, (η x , η y , η z) is the displacement correction vector.

[0065] When dealing with the influence of faults, introduce the fault attenuation calculation formula:

[0066]

[0067] Among them, C f is the gas concentration after the influence of the fault, C 0 is the original gas concentration, β k is the fault influence coefficient, μ f is the fault attenuation coefficient, R k is the distance to the fault, γ k is the stress influence factor, is the stress direction angle, and m is the number of faults.

[0068] Mark the fault positions according to the geological exploration data, and perform spatial matching on the fault position information and the data after dip angle correction. For each sampling point, calculate its distance to the nearest fault and mark the fault influence area. Obtain the gas distribution data considering the fault influence through fault attenuation processing. Arrange the processed regional gas distribution data in chronological order and analyze the variation characteristics of the gas concentration over time. By calculating the change rate of the gas concentration at different time points, obtain the dynamic change trend of the gas concentration. Finally, dynamically update the gas distribution data according to the change trend to form the dynamic distribution data of the gas concentration.

[0069] For example, a coal mining face is 100 meters long, the average coal seam thickness is 4.5 meters, the dip angle is 28 degrees, and there is a fault with a strike of 45 degrees. The detection device arranges 20 sampling points on the working face, and data is collected at different heights for each sampling point. Mark the sampling points according to the three-dimensional coordinates, and calculate the gas concentration gradient between adjacent points. Combining with the coal seam thickness data, it is found that the gas concentration gradient value is higher in the areas with larger thickness. Project the data onto the horizontal plane through dip angle coordinate transformation to correct the gas distribution characteristics. According to the fault position information, calculate the distance from each sampling point to the fault, and perform attenuation processing on the gas concentration data within 10 meters on both sides of the fault. Arrange the processed data in the order of collection time, calculate the time variation trend of the gas concentration, and complete the generation of the dynamic distribution data of the gas concentration.

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

[0071] (1) Perform time interval sampling on the dynamic distribution data of the gas concentration, and calculate the change rate of the gas concentration at each sampling point;

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

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

[0074] (4) Weightedly superimpose the coal seam gas permeability data and the standardized gas parameter data to generate a comprehensive risk index;

[0075] (5) Classify the risk levels of the working face area according to the comprehensive risk index, and mark different risk level areas;

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

[0077] (7) Dynamically update the thermal map of the regional risk levels and record the changing trend of the risk levels;

[0078] (8) Perform spatial mapping on the changing trend of the risk levels to obtain the gas outburst risk assessment data for different regions.

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

[0080]

[0081] where V rate is the gas concentration change rate, G j is the gas concentration value at the sampling point, δ j is the time weight coefficient, ψ j is the time decay factor, t s is the sampling interval time, and q is the number of sampling points. Regular sampling is performed on the dynamic distribution data of gas concentration, the difference in gas concentration between adjacent time points is calculated to form the gas concentration change trend data. The gas pressure data and the gas concentration change rate are correlated and analyzed to establish the pressure-concentration correspondence relationship. When correlating the data, the spatial position relationship between the pressure monitoring point and the concentration sampling point is considered, and the data of the nearest pressure measurement point is selected for matching. The matched pressure-concentration correlation data is normalized, and the data value range is uniformly mapped to the interval [0,1] to generate standardized gas parameter data.

[0082] When calculating the comprehensive risk index, the following weighted superposition formula is adopted:

[0083]

[0084] where R index is the comprehensive risk index, W k is the standardized gas parameter value, P k is the coal seam gas permeability parameter, T k is the temperature influence factor, D kis the depth influence factor, α k and ξ k are the weight coefficients respectively, ρ k is the depth correction coefficient, and n is the number of parameters. The calculated comprehensive risk index is divided into different risk level regions according to the set threshold to form an initial regional risk level distribution map. Through spatial interpolation processing, a continuous risk level heat map is generated to visually display the risk status of each area of the working face. The heat map is updated regularly to record the changes in the risk levels of each area. As Figure 2 shown, it is a schematic diagram of the risk level heat map in the embodiment of this application. In the figure, a 5×5 matrix form is used to display the gas risk distribution in different areas of the working face, and the color gradient from red to green indicates the risk degree from high to low. Among them, the red area (risk index > 0.9) represents a high-risk area that requires key monitoring and special treatment; the yellow area (0.5 < risk index < 0.7) represents a medium-risk area that requires enhanced monitoring; the green area (risk index < 0.4) represents a low-risk area where normal mining can be carried out. The value in each grid represents the risk index of that area. Through the distribution of the risk index, the spatial distribution characteristics of the risk levels of the working face can be intuitively seen, which helps to formulate differential mining strategies and ventilation control plans. From the overall distribution of the heat map, it can be seen that the risk in the upper left area of the working face is relatively high, and the risk gradually decreases towards the lower right direction.

[0085] When conducting the gas outburst risk assessment for different regions, the spatial mapping formula is used:

[0086]

[0087] where, E risk is the regional risk assessment value, Q m is the regional risk index, S m is the regional area, Z m is the regional depth factor, τ m is the regional weight coefficient, φ m is the spatial attenuation coefficient, κ m is the depth influence coefficient, and p is the number of regions.

[0088] For example, a certain coal mining face collects gas concentration data at 5-minute intervals. Perform time series analysis on the collected dynamic distribution data of gas concentration, and calculate the gas concentration change rate at each sampling point. Then, perform spatial matching and correlation analysis on the real-time data of the gas pressure sensor and the concentration change rate data. Normalize the correlated data to eliminate the dimensional differences between different parameters. Combine the coal seam permeability test data, and calculate the comprehensive risk index of each area of the working face through weighted superposition. Divide the working face into three risk level areas of high, medium, and low according to the risk index, and generate a risk level heat map. Through continuous monitoring and data update, record the dynamic change process of the risk level, and form the gas outburst risk assessment data for each area. During the whole process, the collection, processing, and analysis of all data follow unified spatio-temporal standards, ensuring the accuracy and reliability of the risk assessment results.

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

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

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

[0092] (3) Group and statistically analyze the time series emission volume data according to the regional risk level, and calculate the emission volume threshold for each area;

[0093] (4) Perform inverse mapping on the emission volume threshold to generate the regional mining speed reference value;

[0094] (5) Perform weighted combination of the regional mining speed reference value and the risk level to obtain the mining speed parameter;

[0095] (6) Determine the relative position relationship between each area according to the working face risk zoning data to generate the working face spatial layout data;

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

[0097] (8) Perform data association on the advancing direction of the working face and the mining speed parameter to obtain the differential mining process parameter.

[0098] Specifically, the risk assessment data of regional gas outburst are marked with risk level classification, divided into three risk levels: high, medium, and low. Different risk identification values are set according to the risk level to generate the risk zoning data of the working face. The risk zoning data includes the spatial range, risk level value, and area identification code of each area. Gas emission sensors are installed in each area marked in the risk zoning data of the working face for real-time monitoring. The emission data is collected at fixed time intervals, and the time series change of gas emission in each area is recorded. The time series emission data includes information such as collection timestamp, area number, and emission value.

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

[0100]

[0101] Where, Y th is the emission threshold, U i is the regional gas emission, b i is the risk level weight, v i is the time decay coefficient, t i is the monitoring time, σ i is the geological influence factor, M i is the area of the region, and n is the number of monitoring points.

[0102] The formula for converting the emission threshold to the benchmark value of mining speed is:

[0103]

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

[0105] The benchmark value of the regional mining speed is weighted and combined with the risk level. The weight value of the high-risk area is small, and the weight value of the low-risk area is large to obtain the mining speed parameter. The risk zoning data of the working face contains the spatial location information of each area. By analyzing the risk level distribution of adjacent areas, the relative position relationship between areas is determined, and the spatial layout data of the working face is generated. According to the spatial layout data of the working face, the overall mining advancement direction is determined. The determination of the advancement direction needs to consider the distribution characteristics of the regional risk level, and it is preferred to advance from the low-risk area to the high-risk area. Mark the advancement path of the working face to form an advancement direction plan. Associate the planned advancement direction with the mining speed parameters of each area to establish the spatio-temporal correspondence relationship of the mining process and form the differential mining process parameters.

[0106] For example, a coal mining face is divided into three areas according to the risk assessment data. Two gas emission sensors are installed in each area, and data is collected every 10 minutes. The collected emission data is grouped by area, and the average emission and change trend of each area are calculated. The emission threshold of the high-risk area is set according to the strictest standard, the standard of the medium-risk area is appropriately relaxed, and the basic standard is adopted for the low-risk area. The benchmark value of the mining speed is inversely calculated according to the emission threshold, and the mining speed parameter is determined in combination with the regional risk level. By analyzing the spatial layout of the working face, an advancement path starting from the low-risk area is formulated to form a differential mining process plan. During the entire data processing process, strict correspondence relationships are maintained among the parameters to ensure the scientificity and rationality of the mining process parameters.

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

[0108] (1) Segment and statistically analyze the regional mining speed in the differential mining process parameters, and calculate the required basic air volume for each section;

[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 requirement;

[0111] (4) Sort the air volume adjustment requirements according to the regional risk level to generate an air volume distribution sequence;

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

[0113] (6) Perform correlation analysis on the local ventilation parameters and the real-time monitored gas concentration data to mark the ventilation abnormal areas;

[0114] (7)Perform real-time correction on the local ventilation parameters in the area with abnormal ventilation and update the air volume distribution data;

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

[0116] Specifically, conduct segmented statistics on the regional mining speed among the differential mining process parameters, and divide the mining area into multiple independent sections according to the spatial layout of the working face. There is a direct relationship between the mining speed of each section and the basic air volume required for that section. It is necessary to calculate the basic air volume demand based on parameters such as the mining speed, cross-sectional area of the working face, and gas emission volume. Install air volume monitoring sensors on the main fan and local fans of the ventilation system to collect air volume data in real time. The air volume monitoring points of the main fan are set in the main intake airway and return airway, and the monitoring points of the local fans are set at the air inlets and outlets of each section of the working face. The monitoring data includes air volume values, air pressure values, and acquisition timestamps, forming an air volume distribution data set.

[0117] Compare and analyze the actual air volume distribution data with the calculated basic air volume demand, and calculate the air volume difference of each section. The air volume difference reflects the deviation between the actual ventilation volume and the demand, providing a basis for subsequent ventilation adjustment. According to the risk levels of each area of the working face, prioritize the air volume adjustment requirements. The air volume adjustment requirements in high-risk areas have the highest priority, followed by medium-risk areas, and finally low-risk areas. Determine the air volume distribution sequence according to the priority ranking, and adjust the local ventilators in the order of the sequence. Change the air volume distribution by adjusting the rotational speed of the local ventilator, and the rotational speed adjustment range is determined according to the performance parameters of the ventilator. Monitor the change of air volume in real time during the adjustment process and record the operation parameters of each local ventilator. At the same time, monitor the change of gas concentration and analyze the relationship between the local ventilation parameters and the gas concentration.

[0118] When it is found that the gas concentration in a certain area rises abnormally or fluctuates greatly, mark this area as an area with abnormal ventilation. Conduct key analysis on the area with abnormal ventilation to find out the reasons for unreasonable air volume distribution. Perform real-time correction on the local ventilation parameters according to the analysis results, including measures such as adjusting the rotational speed of the ventilator and changing the air flow distribution ratio. The corrected air volume distribution data needs to be updated to the air volume distribution plan in time. All air volume distribution data are recorded in chronological order, including air volume values of each section, ventilator operation parameters, gas concentration, etc. By analyzing these time series data, summarize the operation rules of the ventilation system to form a set of operation parameters for the working face ventilation network. The set of operation parameters includes the operation characteristics of the ventilation system under different working conditions, providing data support for the optimization and adjustment of the ventilation system.

[0119] For example, the length of a coal mining face is 150 meters and it is divided into 5 sections according to 30 meters each. By analyzing the differential mining process parameters, the mining speed of each section is determined. Based on the mining speed and gas emission volume parameters, the basic air volume required for each section is calculated. 12 air volume sensors are installed on the working face, with 2 at the inlet and outlet of the main fan respectively, and 2 at the inlet and outlet of the local ventilator for each section. The air volume sensors collect data once a minute, recording the air volume value and the collection time. Through comparative analysis, it is found that the actual air volume in a certain section is lower than the basic air volume requirement, and the risk level of this section is relatively high. Therefore, it is listed as the priority adjustment object. The rotation speed of the local ventilator in this section is adjusted, and at the same time, the change of gas concentration is monitored. When it is found that the gas concentration in the local area is still relatively high, the air volume distribution plan is further optimized to ensure the reasonable operation of the ventilation system.

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

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

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

[0123] (3) Conduct safety level classification on the regional gas outburst risk assessment data to form regional safety status data;

[0124] (4) Conduct cross-analysis on the speed-ventilation correlation data and the regional safety status data to calculate the safety benefit quantification index;

[0125] (5) Based on the safety benefit quantification index, rank the mining priorities of each area of the working face to generate mining sequence data;

[0126] (6) Conduct space-time planning processing on the mining sequence data and coordinate the mining progress with the ventilation capacity;

[0127] (7) Conduct comprehensive verification on the coordinated configuration result and the regional gas outburst risk assessment data and adjust the mining plan;

[0128] (8) Conduct safety verification and economic analysis on the adjusted mining plan to obtain an optimized coal mining strategy plan.

[0129] Specifically, perform a time series analysis on the operating parameters of the working face ventilation network. Arrange the operating data of the main fan and local fans in chronological order, and record parameters such as air volume, air pressure, and fan speed at each time point. At the same time, divide the operating state of the ventilation system into three states: normal operation, adjustment transition, and abnormal operation, and generate an operating state dataset of the ventilation system. Match the mining speed data in the differential mining process parameters with the operating state data of the ventilation system to establish the correlation between the mining speed of each section and the ventilation parameters at the corresponding moment. By analyzing the impact of the change in mining speed on the operating state of the ventilation system, generate speed-ventilation correlation data. The correlation data includes the corresponding relationships of parameters such as mining speed, air volume, and ventilation pressure.

[0130] Subdivide the safety levels of the risk assessment data for regional gas outburst. Further refine the original three risk levels of high, medium, and low. The high-risk area is subdivided into extremely high risk and relatively high risk, the medium-risk area is subdivided into relatively high risk and relatively low risk, and the low-risk area is subdivided into relatively low risk and extremely low risk, forming a six-level safety level system. Mark each area of the working face according to the subdivided safety levels to generate regional safety state data. Conduct a cross-analysis of the speed-ventilation correlation data and the regional safety state data, and calculate the safety factor of each area under different mining speeds and ventilation conditions. The calculation of the safety factor comprehensively considers three factors: gas emission volume, ventilation capacity, and mining speed, and generates a quantitative index of safety benefits. The quantitative index reflects the degree of achieving economic benefits while ensuring safety.

[0131] Based on the quantitative index of safety benefits, sort the mining priorities of each area of the working face. The principle for determining the priority is to give priority to mining areas with higher quantitative indexes of safety benefits on the premise of meeting safety requirements. Generate mining sequence data through the priority sorting. The mining sequence data includes the mining sequence and mining time arrangement of each area. Perform space-time planning on the mining sequence data to match the mining progress plan with the ventilation capacity of the ventilation system. The space-time planning needs to consider the mining connection between adjacent areas, the adjustment ability of the ventilation system, and the requirements for gas control. Determine the specific mining time and ventilation parameter configuration plan for each area through the planning process.

[0132] Comprehensively check the planned configuration plan with the risk assessment data for regional gas outburst to check whether the plan meets the requirements of safe production. The checking content includes the rationality of the mining sequence, the adaptability of the ventilation capacity, and the effectiveness of gas control. Adjust and optimize the mining plan according to the checking results to form a mining plan that meets safety requirements. Conduct safety verification and economic analysis on the adjusted mining plan. The safety verification mainly checks the safety margin of the plan under various working conditions, and the economic analysis calculates the input-output ratio of the plan. Through the verification and analysis, determine the optimal coal mining strategy plan.

[0133] For example, a working face is divided into 5 sections at an interval of 30 meters, and a air volume sensor and a gas concentration sensor are installed in each section. Collect the operation data of the ventilation system for one month, including the air volume and air pressure data recorded every minute. At the same time, record the mining speed data of each section, and establish the corresponding relationship between the mining speed and ventilation parameters. According to the gas outburst risk assessment results, mark the 5 sections according to the six-level safety rating. By analyzing the safety rating, mining speed and ventilation conditions of each section, calculate the safety benefit index. Determine the mining sequence according to the safety benefit index, and formulate a detailed mining progress plan. After the mining plan is checked for safety and economically analyzed, an optimized plan is formed.

[0134] The above describes the analysis method of coal mining strategy based on coal mine gas concentration in the embodiments of the present application. Next, the analysis system of coal mining strategy based on coal mine gas concentration in the embodiments of the present application will be described. Please refer to Figure 3 One embodiment of the analysis system of coal mining strategy based on coal mine gas concentration in the embodiments of the present application includes:

[0135] The acquisition module 201 is used to collect gas concentration data of the coal mine working face through an intelligent track inspection device, collect multi-layer gas concentration data by using a detection probe that moves vertically back and forth, generate time series data according to the sampling time, position and environmental parameters, and obtain the original gas concentration data set;

[0136] The processing module 202 is used to perform spatial data processing on the sampling point data according to the original gas concentration data set, and perform data correction in combination with the coal seam thickness, dip angle and fault to obtain the dynamic distribution data of the gas concentration;

[0137] The calculation module 203 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 dynamic distribution data of the gas concentration, generate a regional risk level heat map, and obtain the sub-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 sub-regional gas outburst risk assessment data, set the mining speed parameters in combination with the gas emission volume, determine the working face advancing direction, and obtain the differential mining process parameters;

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

[0140] An association module 206 is used to perform multi-dimensional association analysis on the operation parameters of the working face ventilation network, the differential mining process parameters, and the regional gas outburst risk assessment data, establish a safety benefit quantification index, and obtain an optimized coal mining strategy plan.

[0141] Through the collaborative cooperation of the above-mentioned various components, the intelligent track inspection device is used to collect gas concentration data of the coal mine working face, and the detection probe moving vertically reciprocally is used to collect multi-layer gas concentration data. Time series data is generated according to the sampling time, location, and environmental parameters, and a comprehensive original gas concentration data set is obtained, expanding the spatial range of gas concentration detection; through spatial data processing of the sampling point data and data correction in combination with geological parameters such as coal seam thickness, dip angle, and fault, 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, realizing the accurate assessment of gas outburst risk; according to the regional gas outburst risk assessment data, the working face is divided into blocks according to the risk level, the mining speed parameters are reasonably set in combination with the gas emission volume, the advancing direction of the working face is determined, and scientific differential mining process parameters are formulated; based on the differential mining process parameters, the air volume distribution of the ventilation system is calculated, and the local ventilation parameters are dynamically adjusted through real-time monitoring data, ensuring the intelligent control of the ventilation system; the operation parameters of the working face ventilation network, the differential mining process parameters, and the regional gas outburst risk assessment data are subjected to multi-dimensional association analysis, and a safety benefit quantification index system is established, obtaining an optimized coal mining strategy plan.

[0142] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A coal mining strategy analysis method based on coal mine gas concentration, characterized in that: The coal mining strategy analysis method based on coal mine gas concentration includes: The intelligent track inspection device is used to collect gas concentration data on the coal mine working face. The vertical reciprocating detection probe is used to collect gas concentration data on multiple levels. Time series data is generated according to the sampling time, location and environmental parameters to obtain the original gas concentration data set. According to the original gas concentration data set, spatial data processing is performed on the sampling point data, and data correction is performed in combination with the coal seam thickness, inclination and fault to obtain gas concentration dynamic 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 the risk level, the mining speed parameters are set in combination with the gas outburst volume, the working face advancement direction is determined, and the differentiated mining process parameters are obtained; According to the differentiated mining process parameters, the air volume distribution of the ventilation system is calculated, and the local ventilation parameters are adjusted through real-time monitoring data to obtain the working face ventilation network operation parameters; A multi-dimensional correlation analysis is performed on the working face ventilation network operating parameters, the differentiated mining process parameters and the regional gas outburst risk assessment data to establish quantitative safety benefit indicators and obtain a coal mining strategy optimization plan.

2. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1 is characterized in that: The gas concentration data of the coal mine working face is collected by the intelligent track inspection device, and the gas concentration data of multiple layers is collected by the vertical reciprocating detection probe. Time series data is generated according to the sampling time, location and environmental parameters to obtain the original gas concentration data set, including: Starting the motor on the intelligent track inspection device to drive the running wheel to move along the outer track, and at the same time starting the cleaning component and the blowing component of the intelligent track inspection device; The output end of the motor drives the driving gear to rotate, and the driving gear drives the passive gear to rotate, thereby realizing power transmission; The passive gear drives the bevel gear to rotate through the connecting shaft, the bevel gear drives the rotating shaft to rotate in the limiting rod, and the rotating shaft drives the lever to clean the external track; The pulley at the output end of the motor drives the long pin to rotate, and the cam at the end of the long pin pushes the detection probe to reciprocate in the vertical direction; The detection probe collects gas concentration data at multiple levels during the reciprocating motion, and the protrusion of the detection probe reciprocates in the groove and is reset by connecting the 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 in time series and spatially stratified according to sampling positions; Pair the stratified gas concentration data with environmental parameters to establish a spatiotemporal correlation matrix; The time-space correlation matrix is ​​subjected to data standardization and serialization processing to obtain the original gas concentration data set.

3. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1 is characterized in that: According to the original gas concentration data set, spatial data processing is performed on the sampling point data, and data correction is performed in combination with the coal seam thickness, inclination and fault to obtain gas concentration dynamic distribution data, including: Marking the sampling points in the original gas concentration data set according to three-dimensional spatial coordinates to construct a spatial sampling point distribution sequence; Calculating the gas concentration difference between adjacent sampling points in the spatial sampling point distribution sequence to generate gas concentration gradient data; Correlating the coal seam thickness data with the gas concentration gradient data to calculate the thickness influencing factor; Performing coordinate transformation on the gas concentration gradient data according to the coal seam inclination data to obtain inclination correction data; spatially matching the fault position information with the dip correction data to mark the fault-affected area; Performing fault attenuation processing on the gas concentration data of the fault-affected area to generate regional gas distribution data; Sorting the regional gas distribution data according to the time dimension, and calculating the change trend of the gas concentration at each sampling point; The regional gas distribution data is dynamically updated based on the change trend to obtain the gas concentration dynamic distribution data.

4. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1 is characterized in that: Based on the gas concentration dynamic distribution data, the gas concentration change rate, gas pressure, and coal seam permeability are analyzed in multiple dimensions and weighted to generate a regional risk level heat map, and obtain regional gas outburst risk assessment data, including: Sampling the gas concentration dynamic distribution data at time intervals, and calculating the gas concentration change rate of each sampling point; Correlating the gas pressure data of each sampling point with the gas concentration change rate to form pressure-concentration correlation data; Normalizing the pressure-concentration correlation data to generate standardized gas parameter data; Weighted superposition of coal seam permeability data and the standardized gas parameter data is performed to generate a comprehensive risk index; Divide the working face area into risk levels according to the comprehensive risk index and mark areas of different risk levels; Generating a regional risk level heat map according to the spatial distribution of the different risk level areas; Dynamically update the risk level heat map of the area and record the risk level change trend; The risk level change trend is spatially mapped to obtain the regional gas outburst risk assessment data.

5. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1, characterized in that: According to 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 outburst volume, the working face advancement direction is determined, and the differentiated mining process parameters are obtained, including: The gas outburst risk assessment data of each region is graded and marked according to the risk level to generate working face risk zoning data; Monitor the gas emission volume in each area of ​​the risk zoning data of the working face and record the time series emission volume data; The time series outflow data are grouped and counted according to the regional risk level, and the outflow threshold of each region is calculated; Performing inverse mapping on the outflow threshold to generate a regional mining speed reference value; Performing a weighted combination of the regional mining speed benchmark value and the risk level to obtain a mining speed parameter; Determine the relative position relationship between the areas according to the working face risk zoning data, and generate working face spatial layout data; Planning the advancement path based on the spatial layout data of the working face and marking the advancement direction of the working face; The working face advancement direction is data-associated with the mining speed parameters to obtain the differentiated mining process parameters.

6. The method for analyzing coal mining strategies based on coal mine gas concentration according to claim 1, characterized in that: According to the differentiated mining process parameters, the air volume distribution calculation of the ventilation system is performed, and the local ventilation parameters are adjusted through real-time monitoring data to obtain the working face ventilation network operation parameters, including: Performing segmented statistics on the regional mining speed in the differentiated mining process parameters to calculate the basic air volume required for each segment; Monitor the air volume of the main fans and local fans in the ventilation system and record the air volume distribution data; Comparing and analyzing the air volume distribution data with the basic air volume to determine the air volume adjustment requirement; Prioritizing the air volume adjustment requirements according to the regional risk levels and generating an air volume allocation sequence; Dynamically adjusting the rotation speed of the local ventilator based on the air volume distribution sequence to generate local ventilation parameters; Correlation analysis is performed on the local ventilation parameters and the real-time monitored gas concentration data to mark the ventilation abnormality area; Correcting the local ventilation parameters of the abnormal ventilation area in real time and updating the air volume distribution data; The air volume distribution data is recorded and analyzed in time series to obtain the operating parameters of the working face ventilation network.

7. The coal mining strategy analysis method based on coal mine gas concentration according to claim 1, characterized in that: The 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 is performed to establish safety benefit quantitative indicators and obtain a coal mining strategy optimization plan, including: Performing time series analysis on the operating parameters of the ventilation network of the working face and recording the operating status data of the ventilation system; 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 safety level of the gas outburst risk assessment data of the sub-regions to form regional safety status data; Cross-analyze the speed-ventilation correlation data with the regional safety status data to calculate a safety benefit quantitative index; Based on the safety benefit quantitative index, the mining priority of each area of ​​the working face is sorted to generate mining sequence data; Performing spatiotemporal planning processing on the mining sequence data to coordinate the mining progress and ventilation capacity; Comprehensively check the collaborative configuration results with the gas outburst risk assessment data of the sub-region and adjust the mining plan; The adjusted mining plan is subjected to safety verification and economic analysis to obtain the coal mining strategy optimization plan.

8. 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 to 7, characterized in that: The coal mining strategy analysis system based on coal mine gas concentration includes: The acquisition module is used to collect gas concentration data on the coal mine working face through the intelligent track inspection device, collect multi-layer gas concentration data using the vertical reciprocating detection probe, generate time series data according to the sampling time, location and environmental parameters, and obtain the original gas concentration data set; A processing module is used to perform spatial data processing on the sampling point data according to the original gas concentration data set, and to perform data correction in combination with the coal seam thickness, inclination and fault to obtain gas concentration dynamic distribution data; A 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; A division 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 outburst volume, determine the working face advancement direction, and obtain differentiated mining process parameters; A distribution module is used to calculate the air volume distribution of the ventilation system according to the differentiated mining process parameters, adjust the local ventilation parameters through real-time monitoring data, and obtain the working face ventilation network operation parameters; The correlation module is used to perform multi-dimensional correlation analysis on the working face ventilation network operating parameters, the differentiated mining process parameters and the regional gas outburst risk assessment data, establish safety benefit quantitative indicators, and obtain a coal mining strategy optimization plan.

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