Coal mining hole fracture seepage control monitoring system

Abstract

The invention discloses a coal mining hole fracture seepage control monitoring system, which relates to the technical field of coal mine safety, and comprises a monitoring system module, a data analysis and linkage effect judgment module, a gas leakage risk assessment and prediction module, a risk mode identification and prevention suggestion generation module and a dynamic adjustment and risk control module, water flow sensors are arranged around a coal mining hole, a multi-sensor linkage monitoring system is constructed, and multi-dimensional data of water flow, gas flow and pressure change of cracks around the coal mining hole are obtained in real time. Through multi-sensor linkage monitoring, combination of fluid dynamics and a gas diffusion model and application of an intelligent algorithm, real-time monitoring and automatic adjustment of mine safety management are realized, and gas accumulation and flood risks are effectively prevented. Meanwhile, mining progress, resource allocation and personnel arrangement are optimized through machine learning, mining efficiency is improved, resource waste is reduced, cost is reduced, and mine safety and economic benefits are improved.

Description

Technical Field

[0001] The invention relates to the technical field of coal mine safety, and in particular to a coal mine mining hole fissure seepage control monitoring system. Background Art

[0002] Coal mining hole fissure seepage control monitoring refers to the real-time monitoring and control of fissure seepage around the mining holes in the mine during the coal mining process to prevent water infiltration from adversely affecting mine production safety and the environment. During the mining process, cracks may be formed due to the rupture and deformation of coal seams and rock strata, and water flows into the mine through these cracks, leading to risks such as floods, landslides or groundwater pollution. To this end, technical means are used to monitor the water flow in these cracks, obtain data such as seepage rate and pressure, and analyze the seepage path to detect abnormal conditions in a timely manner and take effective measures to control them to ensure the safety of the mine and environmental protection. For example, sensors, monitoring wells, geological radars and other equipment are used to monitor changes in fissure water flow, and combined with hydraulic models and geological data, mining operation plans are dynamically adjusted to avoid mine disasters caused by water flow problems.

[0003] The prior art has the following deficiencies:

[0004] In the existing technology of coal mining hole fissure seepage control monitoring, the linkage effect of fissure seepage and gas leakage is a serious technical problem. During coal mining, the formation of fissures may not only lead to water infiltration, but also be accompanied by gas leakage, especially in coal seams with high gas content. If the monitoring system cannot accurately identify the gas flow in the fissures, the gas may quickly diffuse into the deep mine through these fissures, forming a local high-concentration gas accumulation area, which is very likely to cause catastrophic events such as gas explosions or mine fires. This hidden danger is often due to the insufficient monitoring accuracy of the existing monitoring equipment for water flow and gas flow, or the failure to take the interaction between the two into consideration, resulting in the failure to detect gas leakage in time. If preventive measures are not taken in time, it may seriously threaten the life safety of mine workers and cause significant losses to mine facilities.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a coal mining hole fissure seepage control monitoring system. Through a real-time monitoring system based on multi-sensor linkage, a combination of fluid dynamics and gas diffusion models, and the application of intelligent algorithms, the mine safety management significantly improves the real-time and accuracy. The system can monitor water flow, gas concentration and pressure changes in real time, promptly detect potential safety hazards and automatically adjust the mining plan to avoid gas accumulation or flood risks. At the same time, machine learning optimizes mining progress, resource allocation and personnel arrangements, improves mine mining efficiency, reduces resource waste, reduces costs, and ultimately improves mine safety and economic benefits to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a coal mining hole fissure seepage control monitoring system, including a monitoring system module, a data analysis and linkage effect judgment module, a gas leakage risk assessment and prediction module, a risk pattern recognition and prevention suggestion generation module, and a dynamic adjustment and risk control module:

[0008] The monitoring system module sets water flow sensors around the coal mining holes to build a multi-sensor linkage monitoring system to obtain multi-dimensional data on water flow, gas flow and pressure changes in the cracks around the coal mining holes in real time;

[0009] The data analysis and linkage effect judgment module determines the water flow and gas flow characteristics of the fractures around the mining hole based on real-time monitoring data, and determines whether there is a linkage effect between the two;

[0010] Gas leakage risk assessment and prediction module, based on real-time monitoring data and gas leakage risk assessment results, dynamically adjusts mining plans according to prediction results to avoid high-concentration gas accumulation and ensure mine safety;

[0011] The risk pattern identification and prevention suggestion generation module uses machine learning algorithms to analyze water and gas flow data during coal mining, compares historical data with real-time data, identifies potential risk patterns, and generates prevention suggestion recommendations;

[0012] The dynamic adjustment and risk control module combines real-time monitoring data with predictive analysis results during coal mining to comprehensively evaluate the dynamic changes of water flow and gas flow in the mine, and timely adjusts monitoring strategies and mining operation plans to ensure that the risks of water flow and gas flow are effectively controlled and to avoid the accumulation of high-concentration gas.

[0013] Preferably, water flow sensors are arranged around the coal mining hole, a multi-sensor linkage monitoring system is constructed, and the specific steps of obtaining multi-dimensional data of water flow, gas flow and pressure changes in the cracks around the coal mining hole in real time are as follows:

[0014] Real-time monitoring of water flow, gas flow and pressure changes around coal mining holes;

[0015] After installing the selected sensors at different locations around the coal mining hole, a multi-sensor linkage monitoring system was built;

[0016] Once the sensor is installed and started to operate, real-time monitoring and data transmission are carried out;

[0017] After data collection and transmission are completed, data analysis and decision support are carried out.

[0018] Preferably, the specific steps of determining the water flow and gas flow characteristics of the fractures around the mining hole based on the real-time monitoring data and judging whether there is a linkage effect between the two are as follows:

[0019] Preprocess and clean real-time monitoring data;

[0020] After data cleaning is completed, key features are extracted and analysis methods are selected based on the characteristics of the monitoring data;

[0021] Conduct correlation analysis between water flow and gas flow characteristics;

[0022] After completing the data analysis, the next step is to determine whether there is a linkage effect between water flow and gas flow, and conduct corresponding risk assessment.

[0023] Preferably, based on real-time monitoring data and gas leakage risk assessment results, the mining plan is dynamically adjusted according to the prediction results to avoid high-concentration gas accumulation and ensure the safety of the mine in the following specific steps:

[0024] Before predicting the potential gas accumulation area, first obtain the real-time monitoring data of the cracks around the coal mining hole;

[0025] After acquiring real-time data and performing feature extraction, the next step is to conduct a gas leakage risk assessment;

[0026] The data obtained from the fluid dynamics model and the gas diffusion model are further used to predict risks using intelligent algorithms;

[0027] Based on the risk prediction results, instructions are automatically generated to dynamically adjust the mining plan to avoid the accumulation of high-concentration gas and ensure mine safety.

[0028] Preferably, the specific steps of analyzing the water flow and gas flow data in the coal mining process through machine learning algorithms, comparing historical data with real-time data, identifying potential risk patterns, and generating preventive measures are as follows:

[0029] First, the water flow and gas flow data in the coal mining process are preprocessed and key features are extracted. After the feature extraction is completed, the data will be ready for analysis by the machine learning algorithm. The formula is as follows:

[0030] X t =[Q w (t),C g (t),ΔP(t),…]

[0031] , where X t is the eigenvector at time t, Q w (t) is the water flow through the fracture at time t, C g (t) refers to the gas concentration measured in the cracks around the coal mining hole at time t, and ΔP(t) is the pressure change measured in the cracks inside the mine at time t;

[0032] After the data is prepared, the machine learning algorithm is used to train the data to identify potential risk patterns. By learning the known risk situations in historical data, the model can identify new potential risks. The expression is as follows:

[0033] f(X t )=w T X t +b

[0034] , where f(X t ) is the output of the support vector machine, which represents the current input data X t The predicted value, X t is the eigenvector at time t, wT 是 The transpose of the weight vector w, b is the bias term;

[0035] After predicting the real-time data through the trained machine learning model, a risk score calculation expression is obtained as follows:

[0036]

[0037] , where R t is the mine risk score at time t, α i is the risk score R at time i t The weight coefficient of contribution, i is the time index, n is the total number of times, f(X t-i ) is the model output at time ti;

[0038] The risk score R t , automatically generates preventive measures suggestions to deal with the predicted high-risk areas. The generation formula is as follows:

[0039]

[0040] , where ΔS t is the mining plan that needs to be adjusted at time t, λ1 and λ2 are weight coefficients, λ1 represents the risk score R t The impact on the adjustment of mining plan, λ2 represents the mining efficiency (S t ) on the adjustment of the plan, efficiency (S t ) is the mining plan S t Productivity at the present moment.

[0041] Preferably, during the coal mining process, the real-time monitoring data and the prediction analysis results are combined to comprehensively evaluate the dynamic changes of water flow and gas flow in the mine, and the monitoring strategy and mining operation plan are adjusted in time to ensure that the risks of water flow and gas flow are effectively controlled and the specific steps to avoid high-concentration gas accumulation are as follows:

[0042] In the coal mining process, it is first necessary to obtain real-time data on water flow, gas concentration and pressure through a multi-sensor monitoring system. In order to conduct effective prediction and risk assessment, the data is preprocessed and feature extracted to form a dynamic risk index. The calculation expression is as follows:

[0043] R d =α1Q w +α2C g +α3P

[0044] , where Q w is the water flow rate, C g is the gas concentration, P is the pressure in the mine, α1, α2 and α3 are the weight coefficients of water flow rate, gas concentration and pressure in the mine, respectively, R d is the dynamic risk index;

[0045] Combining the fluid dynamics model and the gas diffusion model, the gas accumulation area in the coal mine fissure is predicted. By analyzing the linkage effect of water flow and gas flow, the distribution of gas concentration in time and space is calculated. The calculation expression is as follows:

[0046]

[0047] , where

[0048] Where: C v is the gas concentration, D is the gas diffusion coefficient, is the Laplace operator of gas concentration, v is the gas flow rate, is the gradient of gas concentration, λ is the gas source term, R v is the gas reaction term;

[0049] The dynamic risk index R obtained d and gas concentration distribution C v , will serve as a basis to help the mine safety management system dynamically adjust the mining plan and ventilation strategy to avoid gas leakage and explosion risks. The optimization target formula is as follows:

[0050]

[0051] , where F(S,V) is the optimization objective function and A1 is the dynamic risk index R d The weight coefficient, C v (x, t) is the distribution of gas at different locations x and time t in the mine, A2 is the gas concentration distribution C v The weight coefficient, S is the mining speed, S max is the maximum mining speed limit, V is the ventilation flow rate, V min is the minimum ventilation flow limit, A3 is the weight coefficient of mining speed S, A4 is the weight coefficient of ventilation flow, and Ω is the spatial area.

[0052] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0053] The present invention significantly improves the real-time and accuracy of coal mine safety management through the real-time monitoring system based on multi-sensor linkage, the combination of fluid dynamics and gas diffusion models, and the application of intelligent algorithms. Traditional mine safety management usually relies on manual inspections, which has a certain time lag and risk of misjudgment, especially in emergency situations, and cannot respond quickly to possible safety hazards. However, this solution collects key data such as water flow, gas concentration, and pressure in real time through sensors, and dynamically simulates the mine environment in combination with fluid dynamics and gas diffusion models to promptly detect potential risks of water flow changes or gas leakage. When an abnormal increase in water flow or an excessive gas concentration is detected, the system can immediately trigger an early warning and automatically adjust the mining plan or ventilation strategy, thereby effectively avoiding safety accidents caused by gas accumulation or floods. Through this real-time early warning and intelligent adjustment, mine safety management has shifted from relying on manual inspections to fully automated intelligent monitoring, greatly improving the accuracy and response speed of accident prevention.

[0054] The intelligent decision-making system of the present invention not only improves the safety of the mine, but also effectively optimizes the resource allocation and mining efficiency of the mine. In mine mining, traditional operating methods often rely on a predetermined mining schedule and fixed resource allocation, and lack the ability to flexibly respond to environmental changes. Through the analysis and optimization of real-time data by machine learning algorithms, the system can dynamically adjust the mining schedule, working face location and personnel resource configuration. For example, if the system detects an abnormal increase in gas concentration in a certain area, it can immediately adjust the mining plan, suspend or postpone operations in the area, and strengthen ventilation to reduce the risk of gas accumulation. This data-driven intelligent adjustment not only ensures the reasonable allocation of various resources during the mining process, but also reduces unnecessary waste of resources, optimizes the production process of the mine, and improves mining efficiency. At the same time, the precise optimization of resource allocation by the intelligent system can also effectively reduce the cost of coal mining, increase economic benefits, and ultimately improve the operating efficiency and safety of the entire mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a module schematic diagram of the coal mining hole fissure seepage control monitoring system of the present invention. DETAILED DESCRIPTION

[0057] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0058] The present invention provides Figure 1 The coal mining hole fissure seepage control monitoring system shown includes a monitoring system module, a data analysis and linkage effect judgment module, a gas leakage risk assessment and prediction module, a risk pattern recognition and prevention suggestion generation module, and a dynamic adjustment and risk control module:

[0059] The monitoring system module sets water flow sensors around the coal mining holes to build a multi-sensor linkage monitoring system to obtain multi-dimensional data on water flow, gas flow and pressure changes in the cracks around the coal mining holes in real time;

[0060] The specific steps of setting up water flow sensors around coal mining holes, building a multi-sensor linkage monitoring system, and obtaining multi-dimensional data of water flow, gas flow and pressure changes in the cracks around coal mining holes in real time are as follows:

[0061] Real-time monitoring of water flow, gas flow and pressure changes around coal mining holes;

[0062] First, you need to choose the right type of sensor. In order to realize a multi-dimensional monitoring system, it is usually necessary to select water flow sensors, gas concentration sensors, and pressure sensors. These sensors should have high precision, high stability, and anti-interference ability, and be able to adapt to the complex underground environment of coal mines. Water flow sensors are mainly used to detect the flow rate, flow rate, and direction of water in cracks; gas concentration sensors are used to monitor the concentration of gas (such as methane) and other harmful gases; pressure sensors are used to monitor groundwater level changes and fluctuations in gas pressure in mines. The location of the sensor is crucial and must be reasonably arranged according to the geological structure and mining progress of the mine to ensure that all potential crack areas are covered to obtain comprehensive monitoring data.

[0063] After installing the selected sensors at different locations around the coal mining hole, a multi-sensor linkage monitoring system was built;

[0064] This system not only requires seamless connection between sensors, but also needs to be equipped with corresponding data acquisition devices. These devices should have the ability to synchronize data transmission, real-time processing and storage. The data acquisition device transmits the collected information such as water flow, gas concentration, pressure, etc. to the ground control center in real time. In order to ensure the accuracy and completeness of the data, a dedicated calibration device can be installed on each sensor to calibrate the sensor regularly. In addition, the system also needs to be equipped with remote monitoring and alarm functions to monitor the working status of each sensor in real time to ensure that sensor failures can be discovered and repaired in time.

[0065] Once the sensor is installed and started to operate, real-time monitoring and data transmission are carried out;

[0066] All sensors transmit the collected data to the ground monitoring center in real time through wireless or wired networks to ensure the real-time and accuracy of the data. At this time, the control system of the monitoring center will receive the change data of water flow rate, gas concentration and underground pressure in real time and perform preliminary processing. The data can be processed by time series analysis method to monitor the change trend of water flow and gas in the mine. For example, if the water flow in a certain fissure increases sharply or the gas concentration rises, it means that there may be a risk of water seepage or gas leakage in the fissure. According to the changes in these data, the system automatically generates an alarm signal and prompts the operator to take corresponding preventive measures.

[0067] After data collection and transmission are completed, data analysis and decision support are carried out;

[0068] Through multi-dimensional analysis of real-time data and combined with the geological conditions of the mine, the system can identify potential risk areas and conduct a comprehensive analysis of water flow, gas leakage and pressure changes. For example, the linkage effect of water flow and gas flow may indicate the risk of gas leakage in a certain fracture area, or the pressure fluctuation in the mine caused by the change of groundwater permeability. These analysis results can provide effective decision-making support for mine managers, helping them to predict and respond to potential safety risks in advance. The system can also continuously optimize data processing and prediction models based on machine learning algorithms to improve the efficiency and accuracy of mine safety management.

[0069] The data analysis and linkage effect judgment module determines the water flow and gas flow characteristics of the fractures around the mining hole based on real-time monitoring data, and determines whether there is a linkage effect between the two;

[0070] Based on the real-time monitoring data, the specific steps to determine the water flow and gas flow characteristics in the fractures around the mining hole and determine whether there is a linkage effect between the two are as follows:

[0071] Preprocess and clean real-time monitoring data;

[0072] Since there may be noise, outliers or missing data during the monitoring process, these problems need to be solved through reasonable processing methods. Common preprocessing steps include data interpolation, denoising, smoothing, etc. For missing values, interpolation methods (such as linear interpolation, spline interpolation, etc.) can be used to fill them to ensure data integrity. For outliers, statistical methods such as box plots or standard deviation methods can be used to detect and remove these values ​​to prevent them from interfering with subsequent analysis. In addition, the raw data needs to be normalized to eliminate the differences between the measurement units and scales of different sensors to facilitate data comparison and integration in the subsequent analysis process.

[0073] After data cleaning is completed, key features are extracted and analysis methods are selected based on the characteristics of the monitoring data;

[0074] Water flow data, gas concentration data, and pressure data usually have time series characteristics. Therefore, the first thing to do is to extract features that can reflect the actual situation from these data, such as the rate of change of water flow rate, the fluctuation amplitude of gas concentration, the periodicity of pressure change, etc. These features are the basis for further analysis. Next, depending on the target task, different statistical analysis methods can be selected, such as correlation analysis, regression analysis, or statistical modeling. Correlation analysis can reveal the linear or nonlinear relationship between water flow and gas flow; regression analysis can quantify the relationship between the two and further establish a mathematical model. This process requires selecting the most appropriate method based on the specific problem and data characteristics, and determining the parameters of the analysis method through experiments.

[0075] Conduct correlation analysis between water flow and gas flow characteristics;

[0076] First, correlation analysis is used to check the linear relationship between water flow rate and gas concentration. The correlation between water flow and gas flow data can be measured by calculating the Pearson correlation coefficient. If the correlation between the two is strong, it means that there may be a certain linkage effect between water flow and gas flow. If further in-depth analysis is required, regression analysis (such as multivariate linear regression, nonlinear regression or time series analysis) can be applied to establish a mathematical model of water flow and gas flow to predict the trend of their mutual influence. In addition, the cross-correlation function can be used to study the lag relationship between two time series (such as water flow and gas concentration) to determine the potential linkage pattern between them.

[0077] After completing the data analysis, the next step is to determine whether there is a linkage effect between water flow and gas flow, and conduct corresponding risk assessment;

[0078] The linkage effect refers to the phenomenon that water flow and gas flow influence each other and work together. For example, water flow in a crack may cause rapid diffusion of gas, or changes in gas flow may in turn affect the direction of water flow. By analyzing the results of regression model and correlation analysis, it is possible to further confirm whether there is a linkage effect. If the regression model or correlation analysis results show a strong interdependence, it can be judged that there is indeed a linkage effect between water flow and gas flow. At this time, a risk assessment should be conducted on the mine to identify potential safety hazards (such as gas leaks or floods) that may be caused by the linkage between water flow and gas flow. Further risk assessment may include prediction of gas accumulation, optimization design of ventilation system, etc. to ensure the safety of the mine.

[0079] Gas leakage risk assessment and prediction module, based on real-time monitoring data and gas leakage risk assessment results, dynamically adjusts mining plans according to prediction results to avoid high-concentration gas accumulation and ensure mine safety;

[0080] Based on real-time monitoring data and gas leakage risk assessment results, the mining plan is dynamically adjusted according to the prediction results to avoid high-concentration gas accumulation and ensure mine safety. The specific steps are as follows:

[0081] Before predicting the potential gas accumulation area, first obtain the real-time monitoring data of the cracks around the coal mining hole;

[0082] These data include multi-dimensional information such as water flow, gas flow, and pressure changes. Combined with the "characteristic analysis of water flow and gas flow" mentioned above, the real-time monitoring system continuously monitors the environmental changes of coal mining holes and surrounding fissures through equipment such as water flow sensors, gas concentration sensors, and pressure sensors. After data collection, feature extraction is required, such as extracting parameters such as water flow rate, rate of change of gas concentration, and pressure fluctuations. These features provide basic data for subsequent fluid dynamics models and gas diffusion models. Feature extraction needs to pay attention not only to the static values ​​of these parameters, but also to their dynamic changes, such as the rate of change of water flow and the fluctuation trend of gas concentration, which is crucial for judging the potential risk of gas accumulation.

[0083] After acquiring real-time data and performing feature extraction, the next step is to conduct a gas leakage risk assessment;

[0084] First, based on the real-time monitoring data and the water and gas flow characteristics analyzed in the previous steps, the water and gas flows in the mine are simulated through fluid dynamics models (such as the Navier-Stokes equation) and gas diffusion models (such as the Advancement-Diffusion model). These models can simulate the diffusion, flow and accumulation of gas in the cracks. In particular, when there is a linkage effect between water flow and gas flow, the model can simulate the impact of water flow on gas flow, and then calculate the diffusion path and speed of gas in the mine. The gas diffusion model helps analyze the accumulation trend of gas and predict the concentration changes of gas in different areas to determine whether there are risk areas for gas accumulation. Through this risk assessment, possible high-concentration gas accumulation areas can be identified and provide a scientific basis for subsequent prevention and control measures.

[0085] The data obtained from the fluid dynamics model and the gas diffusion model are further used to predict risks using intelligent algorithms;

[0086] In this step, combined with intelligent algorithms such as machine learning and deep learning (such as random forests, support vector machines, neural networks, etc.), the system can compare and analyze historical data and real-time data, and learn gas diffusion patterns and their linkage effects with water flow. The machine learning algorithm will identify potential gas accumulation areas and predict the trend of gas concentration changes in these areas in the future based on actual data and simulation results. With the support of intelligent algorithms, the system can issue an early warning when it predicts that the gas concentration has reached a dangerous value and identify high-risk areas in advance. At this point, the algorithm not only relies on the basic characteristics of water flow and gas flow, but also can enhance the accuracy and reliability of the prediction model through training on past mining data.

[0087] Based on the risk prediction results, it automatically generates instructions for dynamically adjusting the mining plan to avoid the accumulation of high-concentration gas and ensure mine safety;

[0088] Adjustments to mining plans may involve multiple aspects, including adjusting the mining schedule, changing the location of the working face, and enhancing the ventilation system of the mine. If the prediction results show that the gas concentration in a certain area is on the rise, the system can adjust the mining plan for that area in real time, postpone or suspend operations, or optimize ventilation flow to accelerate the dilution and discharge of gas. In addition, the intelligent system can dynamically adjust gas leakage protection measures based on real-time monitoring and prediction results, such as adding gas detectors, strengthening water flow control, etc., to ensure that the risk of gas explosion is minimized during the mining process. At the same time, these decision plans will be fed back and optimized in real time at the control center to form a closed-loop control to ensure the long-term safety of the mine.

[0089] The risk pattern identification and prevention suggestion generation module analyzes the water flow and gas flow data in the coal mining process through machine learning algorithms (such as deep learning, support vector machine, random forest, etc.), compares historical data with real-time data, identifies potential risk patterns, and generates prevention measures.

[0090] The specific steps for analyzing water and gas flow data during coal mining by machine learning algorithms (such as deep learning, support vector machines, random forests, etc.), comparing historical data with real-time data, identifying potential risk patterns, and generating preventive measures are as follows:

[0091] First, the water and gas flow data in the coal mining process are preprocessed and key features are extracted. This process involves removing noise from the raw data, filling missing values, and standardizing the data. The preprocessed data needs to extract features that can reflect the risk pattern. After the feature extraction is completed, the data will be ready for analysis by the machine learning algorithm. The formula is as follows:

[0092] X t =[Qw (t),C g (t),ΔP(t),…]

[0093] , where X t is the eigenvector at time t, Q w (t) is the water flow through the fracture at time t, C g (t) refers to the concentration of gas (usually gas or methane) measured in the cracks around the coal mining hole at time t, and ΔP(t) is the pressure change measured in the cracks inside the mine at time t;

[0094] These features will serve as input data for subsequent machine learning model analysis and training.

[0095] After the data is prepared, a machine learning algorithm (such as random forest, support vector machine or deep learning model) is used to train the data to identify potential risk patterns. At this time, historical data is combined with real-time data to train the model to identify the patterns of water flow and gas flow. By learning the known risk situations in the historical data, the model can identify new potential risks. The expression is as follows:

[0096] f(X t )=w T X t +b

[0097] , where f(X t ) is the output of the support vector machine, which represents the current input data X t The predicted value (risk category), such as high risk or low risk, X t is the characteristic vector at time t. The characteristic vector contains multiple input data, which reflects the changes in water flow, gas flow and pressure during coal mining. T is the transpose of the weight vector w, and b is the bias term, which ensures that the model's prediction results are adapted to the data;

[0098] This step uses the weight vector w and bias term b obtained after training to adjust the input feature vector X t Classify and predict the current risk status of the mine.

[0099] After predicting the real-time data through the trained machine learning model, a risk score calculation expression is obtained as follows:

[0100]

[0101] , where R t is the mine risk score at time t, α i is the risk score R at time i tThe weight coefficient of contribution, i is the time index, n is the total number of times, f(X t-i ) is the model output at time ti;

[0102] In this step, α i The weight coefficient will be adjusted according to the impact of historical data on the model output, so that the model can better capture the time dependency in the data, thereby improving the accuracy of risk prediction.

[0103] The risk score R t , automatically generates preventive measures suggestions to deal with predicted high-risk areas. For example, when the risk score exceeds a certain threshold, the system will recommend strengthening ventilation, adjusting the mining progress, or changing the location of the working surface. In order to optimize these preventive measures, the reinforcement learning algorithm can be combined to dynamically adjust the mining plan so that the mine can carry out the most effective mining under the premise of ensuring safety. The generation formula is as follows:

[0104]

[0105] , where ΔS t is the mining plan that needs to be adjusted at time t, indicating the preventive measures taken (such as adjusting the working surface or increasing ventilation), λ1 and λ2 are weight coefficients, λ1 represents the risk score R t The impact on the adjustment of mining plan, λ2 represents the mining efficiency (S t ) on the impact of program adjustment, ·efficiencty(S t ) is the mining plan S t Productivity at the present moment.

[0106] This step means that the system generates the best preventive measures recommendations by optimizing the trade-off between risk score and mining efficiency to ensure the safety and production efficiency of the coal mine. The system can dynamically respond to changes in risks in the mine by adjusting the working surface, strengthening ventilation or other countermeasures.

[0107] The dynamic adjustment and risk control module combines real-time monitoring data with forecast analysis results during coal mining to comprehensively evaluate the dynamic changes of water and gas flow in the mine, and timely adjusts monitoring strategies and mining operation plans to ensure that the risks of water and gas flow are effectively controlled and to avoid the accumulation of high-concentration gas;

[0108] In the process of coal mining, the real-time monitoring data and forecast analysis results are combined to comprehensively evaluate the dynamic changes of water flow and gas flow in the mine, and timely adjust the monitoring strategy and mining operation plan to ensure that the risks of water flow and gas flow are effectively controlled and avoid the accumulation of high-concentration gas. The specific steps are as follows:

[0109] In the coal mining process, it is first necessary to obtain real-time data on water flow, gas concentration and pressure through a multi-sensor monitoring system. In order to conduct effective prediction and risk assessment, the data is preprocessed and feature extracted to form a dynamic risk index. The calculation expression is as follows:

[0110] R d =α1Q w +α2C g +α3P

[0111] , where Q w is the water flow rate, which indicates the flow speed of water through the cracks, C g is the gas concentration, which indicates the concentration of harmful gases such as methane in the mine, P is the pressure inside the mine, which represents the gas pressure inside the mine, α1, α2 and α3 are the weight coefficients of water flow rate, gas concentration and pressure inside the mine, respectively, R d is the dynamic risk index;

[0112] This step calculates a dynamic risk index R by weighting these parameters. d , so that the safety of the mine can be evaluated based on real-time monitoring results. d If the value exceeds a certain threshold, it indicates that the mine is at high risk and needs to be adjusted in time.

[0113] Combining the fluid dynamics model and the gas diffusion model, the gas accumulation area in the coal mine fissure is predicted. By analyzing the linkage effect of water flow and gas flow, the distribution of gas concentration in time and space is calculated. The calculation expression is as follows:

[0114] , where

[0116] Where: C v is the gas concentration, which indicates the gas concentration at a certain location, and D is the gas diffusion coefficient, which indicates the diffusion capacity of gas in the cracks. is the Laplace operator of gas concentration, which indicates the diffusion trend of gas in space, v is the gas flow rate, which indicates the flow rate of gas in the cracks, is the gradient of gas concentration, indicating the spatial rate of change of gas concentration, λ is the gas source term, indicating the gas generation rate, such as due to coal seam rupture, gas release, etc., R v is the gas reaction term, which indicates the reaction rate between gas and water flow and other factors;

[0117] This step can predict the spatial distribution of gas in the mine and the changes in gas concentration at different locations by solving the diffusion, convection and source terms of gas, so as to identify potential gas accumulation areas. vIf the safety threshold is reached or exceeded, it indicates that there is a risk of gas explosion in the area and immediate measures must be taken.

[0118] The dynamic risk index R obtained d and gas concentration distribution C v , will serve as a basis to help the mine safety management system dynamically adjust the mining plan and ventilation strategy to avoid gas leakage and explosion risks. The optimization target formula is as follows:

[0119] F(S,V)=A1R d +A2∫ Ω C v (x,t) 2 dx+A3(SS max ) 2 +A4(VV min ) 2

[0120] , where F(S,V) is the optimization objective function, which represents the safety of the mine. The goal is to minimize the function, and A1 is the dynamic risk index R d The weight coefficient, C v (x, t) is the distribution of gas at different locations x and time t in the mine, A2 is the gas concentration distribution C v The weight coefficient, S is the mining speed, that is, the mining progress, S max is the maximum mining speed limit, V is the ventilation flow rate, V min is the minimum ventilation flow limit, A3 is the weight coefficient of mining speed S, A4 is the weight coefficient of ventilation flow, and Ω is the spatial area.

[0121] Implementation method 1: During coal mining, water flow, gas flow and pressure changes in the mine are often accompanied by complex changes in geological conditions. Potential dangerous factors need to be detected and managed in real time through an effective monitoring and early warning system. In order to comprehensively monitor the cracks around the coal mining holes, the deployment of a multi-sensor linkage monitoring system is crucial. The system can collect data from different sensors in the coal mining process in real time to ensure that all safety monitoring needs of the mine are met.

[0122] First of all, the core of the system is the deployment of multiple sensors, including water flow sensors, gas concentration sensors and pressure sensors. Water flow sensors are mainly used to monitor parameters such as water flow rate, flow rate, and flow direction in the cracks to help determine whether there is water infiltration or flood risk in the mine; gas concentration sensors are used to monitor the concentration of gas (such as methane) and other harmful gases in the coal seam in real time to detect the danger of gas leakage early; pressure sensors are mainly used to monitor changes in gas pressure and groundwater level in the mine to help understand gas flow and pressure fluctuations in the mine. In addition, sensors should be reasonably arranged at different depths and locations of coal mining holes to ensure comprehensive monitoring of each crack area. To ensure the stable operation of sensors, all equipment must have anti-interference, explosion-proof, high temperature resistance, corrosion resistance and other characteristics to adapt to complex underground environments.

[0123] Secondly, real-time data collection and transmission are important functions of the multi-sensor linkage system. The data collected by the sensor will be transmitted to the ground monitoring center by wireless or wired means, and all data will be processed and analyzed in real time in the data processing system of the control center. Data processing is not just a simple numerical record, but more importantly, it is a fusion analysis of different types of data to ensure that the data from different sensors can work together. The system uses advanced data fusion technology to combine information such as water flow rate, gas concentration, and pressure changes to generate a multi-dimensional safety monitoring report. For example, when the data of a certain sensor fluctuates abnormally, the system will automatically trigger an alarm to promptly remind staff to intervene to prevent dangerous events such as gas leaks and floods in the mine.

[0124] In order to improve the intelligence of the monitoring system, the real-time monitoring system needs to be combined with the intelligent early warning system. The system uses data analysis models to make intelligent predictions and judgments based on preset thresholds and real-time data. For example, by analyzing the correlation between water flow and gas concentration, if the water flow suddenly increases and the gas concentration rises, the system will determine that there may be a potential risk of gas leakage or flooding in the area. On this basis, the system will generate automatic early warning information to inform managers to perform specific operations, such as timely adjusting mining plans, enhancing ventilation, and strengthening safety inspections. If the system detects that the safety risk reaches the warning value, it will automatically trigger the alarm program and start the emergency plan to ensure the safety of the mine.

[0125] Finally, the system's remote monitoring function and data visualization are also an indispensable part of the system. Through the Internet or a dedicated network, the mine's monitoring data can be transmitted to the remote monitoring platform in real time, allowing the control center to view and process the data at any time. This can not only improve the efficiency of mine safety management, but also realize data sharing and coordination among multiple mines. Using data visualization technology, mine safety managers can quickly understand the current safety status of the mine through charts, heat maps, etc., and detect abnormalities in time and respond. Mine safety managers can also remotely adjust system parameters and optimize monitoring plans.

[0126] In short, the real-time monitoring and intelligent early warning system based on multi-sensor linkage can provide comprehensive technical support for coal mining, ensuring that the mine is under control at all times during the mining process. Through accurate real-time data collection and analysis, combined with the intelligent early warning system, mine safety risks can be identified in a timely manner and preventive measures can be taken, providing strong guarantees for the safe operation of the mine.

[0127] Implementation 2: Gas leakage is a serious safety hazard in coal mining. Gas leakage not only causes excessive gas concentration in the mine, but may also cause catastrophic events such as gas explosions and fires. Therefore, it is crucial to accurately predict the occurrence of gas leakage and take effective preventive measures. To this end, by combining the fluid dynamics model with the gas diffusion model, the gas flow and accumulation in the mine can be scientifically predicted, potential gas leakage risk areas can be identified in advance, and mine safety can be ensured.

[0128] The role of the fluid dynamics model is to simulate the dynamic changes of water flow and gas inside the mine. During coal mining, changes in water flow in the fissures will affect the flow path of gas, and then affect the diffusion of gas. In the fluid dynamics model, the Navier-Stokes equation can be used to describe the flow of water and gas. This model not only considers the speed and direction of water flow, but also combines the permeability of groundwater and the geological characteristics of the fissures to simulate the coupling relationship between water flow and gas flow. This helps to understand the impact of water flow in the fissures on gas diffusion, especially in coal seams with high gas content, where changes in water flow may directly lead to changes in gas leakage rate. Through these fluid dynamics models, the flow path, accumulation location and concentration changes of gas in different areas can be predicted more accurately, providing basic data for subsequent gas diffusion predictions.

[0129] The gas diffusion model further analyzes the diffusion of gas in the mine. The gas diffusion model usually uses the Advancement-Diffusion model to describe the diffusion process of gas. The model takes into account the diffusion speed and flow path of gas in the underground environment, as well as factors affecting diffusion, such as mine ventilation conditions and gas concentration. Combined with the mine's ventilation system, mining progress, and gas leakage sources, the gas diffusion model can simulate the diffusion path, speed, and concentration change trend of gas. Through this model, the concentration change of gas in the mine can be predicted in real time, and the possible gas accumulation area can be determined.

[0130] In addition, the addition of intelligent algorithms provides further accuracy and reliability to the model. Using machine learning algorithms, such as neural networks and support vector machines, historical mine data can be trained and the prediction results can be continuously optimized based on real-time monitoring data. For example, based on historical gas leakage data, machine learning algorithms can identify potential characteristic patterns of gas leakage and intelligently identify and predict real-time data. Combined with the calculation results of fluid dynamics and gas diffusion models, intelligent algorithms can generate prediction reports for high-risk gas accumulation areas, and adjust mining plans and optimize ventilation systems based on these results to prevent excessive gas concentrations.

[0131] Through this model combination method, the system can make a comprehensive risk prediction of gas leakage during mining, timely detect and control high-risk areas of gas accumulation, and ensure the safety of mine operations. The fluid dynamics and gas diffusion model not only helps identify potential risks, but also provides a scientific basis for subsequent safety management and decision-making.

[0132] Implementation method 3: In the process of coal mining, in addition to real-time monitoring of gas leakage and water flow changes, optimizing mining plans, adjusting operation progress and ventilation systems, etc. are also key links to ensure mine safety. Through the mining plan optimization and intelligent decision-making system based on machine learning, the mining plan can be adjusted in real time with the support of multi-dimensional data to avoid potential risks in advance, thereby improving the safety and operation efficiency of the mine.

[0133] Data collection and feature extraction are the foundation of machine learning systems. First, the system obtains real-time data from multiple sensors, including water flow, gas concentration, mine pressure, etc. By extracting features from this data, the system can identify key safety feature parameters, such as sudden changes in water flow, fluctuations in gas concentration, and sharp increases in pressure. Using these features as input, the system can build models through machine learning algorithms (such as deep learning, support vector machines, etc.) to learn potential risk patterns. For example, the system can identify precursor patterns of gas leaks and build a predictive model based on historical data to accurately determine when and where safety accidents such as gas leaks or floods may occur.

[0134] Intelligent optimization algorithms are used to dynamically adjust mining plans. Machine learning models can receive real-time mine operation data and intelligently adjust mining progress, work surface location, and ventilation based on external conditions (such as weather changes and geological changes).

[0135] For example, when abnormal gas concentration appears in a certain working face, the system will automatically adjust the mining plan, postpone the work in that area or increase the ventilation volume to ensure that the gas concentration is within a safe range. In addition, the system can also optimize resource allocation, predict the risks of different areas based on real-time data, and reasonably deploy mine personnel and equipment to ensure the maximum utilization of resources.

[0136] The decision support system is the core of this solution. Based on the analysis results of machine learning, the system can not only give suggestions for automatic adjustments, but also provide decision support. For example, when the system detects an increase in gas concentration in a certain area, the system will generate a detailed risk report and recommend immediate cessation of operations or increased ventilation. The system will also evaluate the risks and benefits of different decision-making options in real time to help mine managers make more scientific decisions. Through continuous learning and optimization, the machine learning system can continuously improve prediction accuracy and decision-making level, ensure mine safety and improve mining efficiency.

[0137] Through this machine learning-based intelligent decision-making system, the mine can achieve real-time data analysis, risk prediction and intelligent decision-making to ensure the safety, stability and efficiency of the mining process.

[0138] The present invention significantly improves the real-time and accuracy of coal mine safety management through the real-time monitoring system based on multi-sensor linkage, the combination of fluid dynamics and gas diffusion models, and the application of intelligent algorithms. Traditional mine safety management usually relies on manual inspections, which has a certain time lag and risk of misjudgment, especially in emergency situations, and cannot respond quickly to possible safety hazards. However, this solution collects key data such as water flow, gas concentration, and pressure in real time through sensors, and dynamically simulates the mine environment in combination with fluid dynamics and gas diffusion models to promptly detect potential risks of water flow changes or gas leakage. When an abnormal increase in water flow or an excessive gas concentration is detected, the system can immediately trigger an early warning and automatically adjust the mining plan or ventilation strategy, thereby effectively avoiding safety accidents caused by gas accumulation or floods. Through this real-time early warning and intelligent adjustment, mine safety management has shifted from relying on manual inspections to fully automated intelligent monitoring, greatly improving the accuracy and response speed of accident prevention.

[0139] The intelligent decision-making system of the present invention not only improves the safety of the mine, but also effectively optimizes the resource allocation and mining efficiency of the mine. In mine mining, traditional operating methods often rely on a predetermined mining schedule and fixed resource allocation, and lack the ability to flexibly respond to environmental changes. Through the analysis and optimization of real-time data by machine learning algorithms, the system can dynamically adjust the mining schedule, working face location and personnel resource configuration. For example, if the system detects an abnormal increase in gas concentration in a certain area, it can immediately adjust the mining plan, suspend or postpone operations in the area, and strengthen ventilation to reduce the risk of gas accumulation. This data-driven intelligent adjustment not only ensures the reasonable allocation of various resources during the mining process, but also reduces unnecessary waste of resources, optimizes the production process of the mine, and improves mining efficiency. At the same time, the precise optimization of resource allocation by the intelligent system can also effectively reduce the cost of coal mining, increase economic benefits, and ultimately improve the operating efficiency and safety of the entire mine.

[0140] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0141] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0142] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0143] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0144] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0148] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0149] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. Coal mining hole fissure seepage control monitoring system, characterized in that: It includes monitoring system module, data analysis and linkage effect judgment module, gas leakage risk assessment and prediction module, risk pattern identification and prevention suggestion generation module, and dynamic adjustment and risk control module: The monitoring system module sets water flow sensors around the coal mining holes to build a multi-sensor linkage monitoring system to obtain multi-dimensional data on water flow, gas flow and pressure changes in the cracks around the coal mining holes in real time; The data analysis and linkage effect judgment module determines the water flow and gas flow characteristics of the fractures around the mining hole based on real-time monitoring data, and determines whether there is a linkage effect between the two; Gas leakage risk assessment and prediction module, based on real-time monitoring data and gas leakage risk assessment results, dynamically adjusts mining plans according to prediction results to avoid high-concentration gas accumulation and ensure mine safety; The risk pattern identification and prevention suggestion generation module uses machine learning algorithms to analyze water and gas flow data during coal mining, compares historical data with real-time data, identifies potential risk patterns, and generates prevention suggestion recommendations; The dynamic adjustment and risk control module combines real-time monitoring data with predictive analysis results during coal mining to comprehensively evaluate the dynamic changes of water flow and gas flow in the mine, and timely adjusts monitoring strategies and mining operation plans to ensure that the risks of water flow and gas flow are effectively controlled and to avoid the accumulation of high-concentration gas.

2. The coal mining hole fissure seepage control monitoring system according to claim 1 is characterized in that: The specific steps of setting up water flow sensors around coal mining holes, building a multi-sensor linkage monitoring system, and obtaining multi-dimensional data of water flow, gas flow and pressure changes in the cracks around coal mining holes in real time are as follows: Real-time monitoring of water flow, gas flow and pressure changes around coal mining holes; After installing the selected sensors at different locations around the coal mining hole, a multi-sensor linkage monitoring system was built; Once the sensor is installed and started to operate, real-time monitoring and data transmission are carried out; After data collection and transmission are completed, data analysis and decision support are carried out.

3. The coal mining hole fissure seepage control monitoring system according to claim 1 is characterized in that: Based on the real-time monitoring data, the specific steps to determine the water flow and gas flow characteristics in the fractures around the mining hole and determine whether there is a linkage effect between the two are as follows: Preprocess and clean real-time monitoring data; After data cleaning is completed, key features are extracted and analysis methods are selected based on the characteristics of the monitoring data; Conduct correlation analysis between water flow and gas flow characteristics; After completing the data analysis, the next step is to determine whether there is a linkage effect between water flow and gas flow, and conduct corresponding risk assessment.

4. The coal mining hole fissure seepage control monitoring system according to claim 1, characterized in that: Based on real-time monitoring data and gas leakage risk assessment results, the mining plan is dynamically adjusted according to the prediction results to avoid high-concentration gas accumulation and ensure mine safety. The specific steps are as follows: Before predicting the potential gas accumulation area, first obtain the real-time monitoring data of the cracks around the coal mining hole; After acquiring real-time data and performing feature extraction, the next step is to conduct a gas leakage risk assessment; The data obtained from the fluid dynamics model and the gas diffusion model are further used to predict risks using intelligent algorithms; Based on the risk prediction results, instructions are automatically generated to dynamically adjust the mining plan to avoid the accumulation of high-concentration gas and ensure mine safety.

5. The coal mining hole fissure seepage control monitoring system according to claim 1, characterized in that: The specific steps to analyze water and gas flow data during coal mining through machine learning algorithms, compare historical data with real-time data, identify potential risk patterns, and generate preventive measures are as follows: First, the water flow and gas flow data in the coal mining process are preprocessed and key features are extracted. After the feature extraction is completed, the data will be ready for analysis by the machine learning algorithm. The formula is as follows: X t =[Q w (t),C g (t),ΔP(t),…], where X t is the eigenvector at time t, Q w (t) is the water flow through the fracture at time t, C g (t) refers to the gas concentration measured in the cracks around the coal mining hole at time t, and ΔP(t) is the pressure change measured in the cracks inside the mine at time t; After the data is prepared, the machine learning algorithm is used to train the data to identify potential risk patterns. By learning the known risk situations in historical data, the model can identify new potential risks. The expression is as follows: f(X t )=w T X t +b, where f(X t ) is the output of the support vector machine, which represents the current input data X t The predicted value, X t is the eigenvector at time t, w T is the transpose of the weight vector w, and b is the bias term; After predicting the real-time data through the trained machine learning model, a risk score calculation expression is obtained as follows: , where R t is the mine risk score at time t, α i is the risk score R at time i t The weight coefficient of contribution, i is the time index, n is the total number of times, f(X t-i ) is the model output at time ti; The risk score R t , automatically generates preventive measures suggestions to deal with the predicted high-risk areas. The generation formula is as follows: , where ΔS t is the mining plan that needs to be adjusted at time t, λ1 and λ2 are weight coefficients, λ1 represents the risk score R t The impact on the adjustment of mining plan, λ2 represents the mining efficiency (S t ) on the adjustment of the program, efficiency (S t ) is the mining plan S t Productivity at the present moment.

6. The coal mining hole fissure seepage control monitoring system according to claim 1, characterized in that: In the process of coal mining, the real-time monitoring data and forecast analysis results are combined to comprehensively evaluate the dynamic changes of water flow and gas flow in the mine, and timely adjust the monitoring strategy and mining operation plan to ensure that the risks of water flow and gas flow are effectively controlled and avoid the accumulation of high-concentration gas. The specific steps are as follows: In the coal mining process, it is first necessary to obtain real-time data on water flow, gas concentration and pressure through a multi-sensor monitoring system. In order to conduct effective prediction and risk assessment, the data is preprocessed and feature extracted to form a dynamic risk index. The calculation expression is as follows: R d =α1Q w +α2C g +α3P, where Q w is the water flow rate, C g is the gas concentration, P is the pressure in the mine, α1, α2 and α3 are the weight coefficients of water flow rate, gas concentration and pressure in the mine, respectively, R d is the dynamic risk index; Combining the fluid dynamics model and the gas diffusion model, the gas accumulation area in the coal mine fissure is predicted. By analyzing the linkage effect of water flow and gas flow, the distribution of gas concentration in time and space is calculated. The calculation expression is as follows: , where Where: C v is the gas concentration, D is the gas diffusion coefficient, is the Laplace operator of gas concentration, v is the gas flow rate, is the gradient of gas concentration, λ is the gas source term, R v is the gas reaction term; The dynamic risk index R obtained d and gas concentration distribution C v , will serve as a basis to help the mine safety management system dynamically adjust the mining plan and ventilation strategy to avoid gas leakage and explosion risks. The optimization target formula is as follows: , where F(S,V) is the optimization objective function and A1 is the dynamic risk index R d The weight coefficient, C v (x, t) is the distribution of gas at different locations x and time t in the mine, A2 is the gas concentration distribution C v The weight coefficient, S is the mining speed, S max is the maximum mining speed limit, V is the ventilation flow rate, V min is the minimum ventilation flow limit, A3 is the weight coefficient of mining speed S, A4 is the weight coefficient of ventilation flow, and Ω is the spatial area.

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