Coal mine dust diffusion simulation and control method based on artificial intelligence

Abstract

The invention discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, and relates to the technical field of coal mine dust diffusion simulation and control, and the method comprises the following steps: collecting environmental data such as dust concentration, ventilation flow velocity, temperature, humidity and equipment operation state through a monitoring system in a coal mine; and cleaning and formatting processing are carried out in combination with historical working condition data. According to the method, reinforcement learning and virtual extreme scene simulation are utilized, the recognition and emergency response capability of the system to edge working conditions is improved, dust diffusion and explosion risks are effectively reduced, and mine safety is guaranteed. By dynamically adjusting the ventilation and water spraying system, efficient utilization of resources is achieved, energy consumption and operation cost are reduced, and the service life of equipment is prolonged. The system has an adaptive learning capability, can optimize a control strategy according to monitoring data and historical events, can automatically adjust even if the environment changes, improves robustness and flexibility, and reduces manual intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine dust diffusion simulation and control, and in particular to a coal mine dust diffusion simulation and control method based on artificial intelligence. Background Art

[0002] AI-based coal mine dust diffusion simulation and control refers to the use of AI technology to model and analyze the generation, diffusion and control process of dust in coal mines. Through AI algorithms, the system can predict the dust propagation path, concentration distribution and possible danger areas based on real-time monitoring data and historical data. In addition, the AI ​​model can also optimize dust suppression measures, such as the adjustment of the ventilation system or the automatic control of the water spray system, to achieve dynamic management of dust concentration and ensure the health and safety of miners. This technology combines deep learning, IoT sensors and adaptive control strategies to improve the efficiency and intelligence of dust control.

[0003] The prior art has the following deficiencies:

[0004] Coal mine dust diffusion simulation relies on historical data and training models for different working conditions, but in actual applications, the model may not cover extreme situations (such as rare ventilation failures, sudden gas eruptions, etc.). Since these scenarios appear very rarely in the training data, it is difficult for the model to accurately predict their impact. If the system fails to identify these abnormal conditions and make adjustments in time, dust may quickly spread to the work area, exposing miners to high concentrations of dust, which not only increases the risk of pneumoconiosis, but may also cause dust explosions. This technical flaw highlights the vulnerability of simulation models in edge scenarios, and once it occurs, the consequences are difficult to control.

[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 method for simulating and controlling coal mine dust diffusion based on artificial intelligence. By using reinforcement learning algorithms and virtual extreme scene simulations, the system's ability to identify and respond to abnormal situations is improved, and emergency control strategies can be quickly implemented to reduce the risk of dust diffusion and explosion, thereby ensuring safe operation of the mine. At the same time, by predicting the trend of dust diffusion through real-time monitoring and deep learning, the system only enables ventilation and water spraying equipment when necessary, achieves accurate scheduling of resources, reduces energy and water consumption, reduces costs, and extends equipment life. In addition, the system has adaptive learning capabilities, can continuously optimize decisions based on operating data, and can automatically adjust strategies even when environmental conditions change, thereby improving robustness and flexibility, and reducing the need for manual intervention, in order to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for simulating and controlling coal mine dust diffusion based on artificial intelligence, comprising the following steps:

[0008] Environmental data is collected through the monitoring system in the coal mine, and is cleaned and formatted in combination with historical operating data; abnormal data is marked and removed, and a multidimensional data set is constructed for subsequent model training;

[0009] Based on normal operating data, a physical model is used to simulate the normal diffusion path of dust in coal mines, and a preliminary dust diffusion prediction model is generated through a machine learning algorithm as the basis for subsequent algorithms;

[0010] Use reinforcement learning algorithms to simulate rare virtual extreme scenarios, automatically annotate the data of virtual extreme scenarios, and integrate them with normal working condition data to train the model, thereby enhancing the model's ability to identify and respond to edge scenarios.

[0011] Based on deep learning algorithms and combined with real-time monitoring data, the future dust diffusion trend is dynamically predicted. During the prediction process, the model adaptively assigns weights, gives higher warning levels to rare abnormal situations, and updates the prediction results in real time.

[0012] A reinforcement learning algorithm is used to automatically adjust the dust control strategy, automatically adjust the operating status of the ventilation equipment according to the predicted diffusion trend, trigger the water spray system, and record the effect of the control behavior.

[0013] Preferably, cleaning and formatting in combination with historical operating condition data refers to integrating the monitoring data collected in real time in the coal mine environment with historical data under different time periods and operating conditions in the past to improve the integrity and quality of the data. The cleaning process includes deleting missing values ​​or erroneous data, filling in key data gaps, and eliminating redundant data that does not conform to the actual operating conditions.

[0014] Formatting is the process of unifying data from different sources into a standard format to facilitate model training and analysis.

[0015] Preferably, the specific steps of constructing a coal mine dust diffusion prediction model based on normal operating condition data are as follows:

[0016] A physical model of dust diffusion inside coal mines is established based on the principles of fluid mechanics and aerodynamics, and the properties of dust particles and the influence of gravity sedimentation effect are introduced;

[0017] Based on the physical model, a preliminary dust diffusion simulation is carried out in the computing environment. The input parameters of each area are substituted into the model to simulate the diffusion path and concentration distribution of dust in the mine.

[0018] After the simulation results are verified, key characteristic variables are extracted from the data set of normal working conditions. By analyzing the correlation between the input variables, the law of dust diffusion is learned and a preliminary prediction model is generated;

[0019] After building the preliminary prediction model, verify and optimize it, and use data sets that were not involved in the training or real-time monitoring data to test the prediction model and check the accuracy and stability of its output.

[0020] Preferably, the specific steps of using the reinforcement learning algorithm to simulate a virtual extreme scenario are as follows:

[0021] Before starting reinforcement learning training, define the extreme scenarios encountered by the coal mine system. Then, construct a complete state space, that is, a combination of variables that describe different states of the coal mine. Between the variable combinations of different states, the change range and extreme cases of each variable must be taken into account.

[0022] The core of the reinforcement learning model is that the intelligent agent continuously learns how to deal with extreme situations in the virtual coal mine environment. The intelligent agent takes different actions according to the changes in the state space. In order to guide the intelligent agent to learn the correct response strategy, a reward mechanism is defined to optimize the response ability to extreme working conditions.

[0023] Using reinforcement learning algorithms to conduct multiple rounds of simulation, each round the agent will explore different combinations of operations in the virtual environment, generating a large amount of data on extreme scenarios. After each simulation run, the system automatically records and annotates important state-action-result sequences. The annotated data includes the conditions for the occurrence of extreme events, the control measures taken, and the evolution of the events.

[0024] The labeled virtual extreme scenario data is integrated with the normal operating condition data for further training of machine learning. By inputting both extreme data and normal data into the model, the model's ability to recognize different scenarios is enhanced.

[0025] Preferably, the steps of predicting the dynamic diffusion of coal mine dust based on the deep learning algorithm are as follows:

[0026] In the real-time monitoring system, the collected data is converted into a multidimensional matrix for processing. Suppose the monitoring data matrix at a certain time t is: Among them, m is the number of monitoring indicators, n is the number of different collection nodes, and all data are normalized and converted into values ​​in the interval [0, 1] to improve the stability and training efficiency of the model. The normalized expression is:

[0027]

[0028] , where x ij(t) represents the original data collected from the i-th monitoring indicator and the j-th collection node at time t, min(x ij ) means finding the minimum monitoring value from all historical data of the i-th monitoring indicator, max(x ij ) represents the maximum value of the i-th monitoring indicator in all nodes and all time. is the normalized data value, scaling the original data to the range between [0, 1];

[0029] When the model is initialized, a deep feedforward neural network is used to build a prediction architecture. The input layer receives the normalized matrix X(t), and the hidden layer uses an activation function to extract high-order features. The input-output relationship of each hidden layer neuron is: H (l) =σ(W (l) ·H (l-1) +b (l) ), where H (l) is the output matrix of the lth layer, W (l) is the weight matrix of the lth layer, b (l) is the bias matrix of the lth layer, σ(·) is the nonlinear activation function;

[0030] At initialization, the weight matrix W (l) An adaptive allocation strategy is adopted to give higher initial weights to input features related to abnormal events.

[0031] Preferably, during the model training process, an adaptive weight adjustment mechanism is used to assign weights in real time to ensure that rare abnormal situations trigger higher warning levels. The adaptive weight adjustment mechanism is based on the attention matrix A(t) to dynamically adjust the importance of different input features. The expression of the attention matrix is: Where Q(t) and K(t) are the query and key matrices of the input matrix, and d is the dimension of the matrix, which is used for normalization adjustment;

[0032] The model evaluates the input data in real time according to the anomaly detection module. If an abnormal fluctuation in the indicator is detected, the weight of the corresponding feature is increased in the attention matrix. The updated weight affects the output of the model as follows: Y(t) = A(t) H (L) , where Y(t) is the predicted output of the model, i.e., the dust diffusion trend in the future time period;

[0033] In order to ensure that the model can be continuously optimized in a dynamic environment, a rolling prediction strategy is adopted. After each time window Δt, the system appends the newly collected data to the input matrix. The input matrix after appending is: X(t+Δt)=[X(t)|X new (t+Δt)], where X new(t+Δt) is the newly collected data matrix. The model updates the parameters in real time based on the new data of the data matrix, performs rolling prediction, and outputs the new diffusion trend Y(t+Δt). At the same time, the adaptive optimization mechanism of the loss function is introduced to dynamically adjust the weight matrix of the model according to the prediction error. The expression of dynamic adjustment is: Among them, y i (t) is the actual monitoring value, is the predicted value, is the L2 regularization term, used to prevent overfitting, is the loss function, which is used to measure the error between the model prediction result and the true value. λ is the regularization coefficient, which is used to control the complexity of the model and avoid overfitting.

[0034] Preferably, the specific steps of using the reinforcement learning algorithm to realize automatic adjustment of the dust control strategy are as follows:

[0035] Design an intelligent control system based on reinforcement learning, clarify the state space and action space, establish the granularity of actions, and ensure that the system can make diverse decisions in different scenarios by clearly defining states and actions;

[0036] Define a reward function for dust control, give positive rewards when the dust concentration is reduced to a safe level in a certain round; if the dust concentration exceeds the safety threshold or the diffusion trend is not controlled, give negative penalties. In addition, design secondary rewards based on resource utilization efficiency to encourage the system to optimize control strategies;

[0037] During training, the system is placed in a virtual coal mine environment for multiple rounds of simulation. In each round of training, the agent selects an action based on the current state, observes its effect, and then updates the strategy based on the reward function feedback. By balancing exploration and utilization, the agent explores new strategies while also utilizing existing successful experiences, continuously optimizing its control capabilities for different scenarios.

[0038] After training, the reinforcement learning model is deployed in an actual coal mine environment, receiving data feedback from the monitoring system in real time and predicting future dust diffusion trends based on the current status. The system automatically performs optimized control actions, continuously records the effects of control behaviors, and feeds back actual operating data to the model for further adaptive updates.

[0039] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0040] The present invention uses reinforcement learning algorithms and simulation of virtual extreme scenarios to enable the system to identify and respond to complex situations beyond conventional working conditions. By simulating multiple extreme situations and annotating and training their data, the model is made highly sensitive to edge scenarios. Once an abnormality occurs, the system can quickly identify and execute corresponding emergency control strategies. This efficient emergency response capability not only reduces the health risks caused by dust diffusion, but also reduces the probability of major accidents such as dust explosions, thereby providing a guarantee for the long-term safe operation of mines.

[0041] The present invention maximizes the utilization efficiency of energy and water resources by dynamically adjusting the operating status of ventilation equipment and water spraying systems. It predicts future dust diffusion trends through real-time monitoring and deep learning algorithms, and triggers ventilation and water spraying equipment only when necessary, thereby reducing unnecessary energy and water consumption. In particular, in the control of fan power and water spraying frequency, the reinforcement learning model gradually optimizes various parameters to achieve accurate resource scheduling. This intelligent optimization not only reduces operating costs, but also extends the service life of equipment and improves the operating efficiency of mines.

[0042] The present invention uses continuous training and feedback of reinforcement learning to enable it to have adaptive learning capabilities. As the running time increases, the system will continuously optimize its decision-making strategy and control process based on the analysis of monitoring data and historical events. Even if the environmental conditions in the mine change, the system can automatically adjust the model parameters to ensure the accuracy and effectiveness of the control strategy. The addition of the adaptive learning function makes the system more robust and flexible in the face of more complex scenarios in the future, greatly reducing the need for manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1 The present invention is a method flow chart of a method for simulating and controlling coal mine dust diffusion based on artificial intelligence. DETAILED DESCRIPTION

[0045] 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.

[0046] The present invention provides Figure 1The method for simulating and controlling coal mine dust diffusion based on artificial intelligence includes the following steps:

[0047] Environmental data such as dust concentration, ventilation velocity, temperature, humidity, and equipment operating status are collected through the monitoring system in the coal mine, and cleaned and formatted in combination with historical operating data; abnormal data is marked and removed, and a multidimensional data set is constructed for subsequent model training;

[0048] Cleaning and formatting data in combination with historical operating condition data refers to the fusion of monitoring data collected in real time in the coal mine environment with historical data from different time periods and operating conditions in the past to improve the integrity and quality of the data. The cleaning process includes deleting missing values ​​or erroneous data (such as outliers caused by sensor failures), filling in key data gaps (for example, using interpolation algorithms or average value filling), and eliminating redundant data that does not match the actual operating conditions. Formatting is to unify data from different sources into a standard format so that it can be classified by dimensions such as time series, spatial location, and equipment status, which is convenient for model training and analysis. This process ensures that the data set is accurate and complete, reduces the interference of noise data on the model, and provides a high-quality data foundation for subsequent AI model training.

[0049] Based on normal operating data, a physical model is used to simulate the normal diffusion path of dust in coal mines, and a preliminary dust diffusion prediction model is generated through a machine learning algorithm as the basis for subsequent algorithms;

[0050] The specific steps of constructing a coal mine dust diffusion prediction model based on normal operating conditions data are as follows:

[0051] First, a physical model of dust diffusion inside a coal mine is established based on the principles of fluid mechanics and aerodynamics. The model needs to consider parameters such as the geometric structure of underground tunnels, the layout of the ventilation system, and the direction of wind flow. At the same time, the properties of dust particles (such as particle size and density) and the influence of gravity sedimentation effects should be introduced. Normal operating data, such as wind speed, temperature and humidity, and equipment operating status in various areas of the mine, are also used as input parameters to help the physical model simulate the movement and sedimentation behavior of dust in different spaces. This step ensures that the model has the ability to describe complex environments and provides an accurate physical basis for data analysis and simulation.

[0052] Based on the above physical model, a preliminary dust diffusion simulation is performed in the computing environment. The system substitutes the input parameters of each area into the model to simulate the diffusion path and concentration distribution of dust in the mine. In this process, the accuracy of the simulation results should be verified by comparing with the existing normal working condition monitoring data. For example, if the error between the model prediction and the actual monitored dust concentration is large, the model parameters (such as wind flow loss coefficient, friction loss) need to be adjusted. This step aims to ensure that the model is highly consistent with the actual situation under normal circumstances, laying a data foundation for subsequent machine learning algorithms.

[0053] After the simulation results are verified, key feature variables are extracted from the data set of normal working conditions, such as wind speed changes at different time points, fluctuations in temperature and humidity, and time series trends of dust concentration. These feature variables are input into the machine learning model for training. Commonly used algorithms include random forest, support vector machine (SVM) or XGBoost model. These models analyze the association between input variables, learn the law of dust diffusion, and generate preliminary prediction models. The focus of this step is to combine the results of the physical model with the machine learning algorithm so that the model can make accurate predictions based on historical and real-time data.

[0054] After the preliminary prediction model is built, it needs to be verified and optimized. Use a data set that is not involved in the training or real-time monitoring data to test the prediction model and check the accuracy and stability of its output. Compare the prediction results with the actual observed dust diffusion trend. If there are errors, the performance of the model can be improved by tuning the model hyperparameters (such as tree depth, learning rate) or improving the feature selection process. In addition, cross-validation can be used to avoid model overfitting. The optimized preliminary model will serve as the basis for subsequent deep learning and dynamic prediction algorithms to handle more complex or extreme working conditions.

[0055] The above four steps, from physical modeling, simulation verification, feature extraction to model optimization, gradually build a dust diffusion prediction system based on normal working conditions. The system not only provides theoretical support using physical models, but also enhances the capture of complex data relationships through machine learning, laying the foundation for more intelligent prediction and control.

[0056] Use reinforcement learning algorithms to simulate rare but possible virtual extreme scenarios (such as partial failure of the ventilation system or sudden gas release), automatically annotate the data of virtual extreme scenarios, and integrate them with normal operating data to train the model, thereby enhancing the model's ability to identify and respond to edge scenarios;

[0057] The specific steps of using reinforcement learning algorithms to simulate virtual extreme scenarios are as follows:

[0058] Before starting reinforcement learning training, it is necessary to define in detail the extreme scenarios that the coal mine system may encounter, such as partial failure of the ventilation system, sudden gas release, or abnormal dust deposition caused by sudden changes in humidity. Subsequently, a complete state space is constructed, that is, a combination of variables that describe different states of the coal mine, such as wind speed, gas concentration, equipment operating status, etc. Between these states, the range of changes in each variable and extreme situations (such as a sudden drop in wind speed to 0 or a sudden increase in gas concentration to a dangerous threshold) must be taken into account. This step ensures that the system can generate complex environmental changes close to the real situation during the simulation process, laying the foundation for subsequent simulations.

[0059] The core of the reinforcement learning model is that the agent continuously learns how to deal with extreme situations in a virtual coal mine environment. The agent takes different actions according to the changes in the state space, such as adjusting the simulated ventilation strategy or triggering dust suppression measures. In order to guide the agent to learn the correct response strategy, it is key to define a reasonable reward mechanism (RewardFunction). For example, when the agent successfully controls the gas concentration or reduces the spread of dust in the simulation, it is given a positive reward, and when a virtual accident occurs (such as a dust explosion), it is given a negative penalty. The reward mechanism ensures that the model can first learn effective emergency measures and optimize the response to extreme conditions.

[0060] Using reinforcement learning algorithms to conduct multiple rounds of simulation, each round the agent will explore different combinations of operations in the virtual environment, generating a large amount of data for extreme scenarios. After each simulation run, the system automatically records and annotates important state-action-result sequences (such as "gas eruption-equipment shutdown-ventilation restoration"). These annotated data include the conditions under which extreme events occurred, the control measures taken, and the evolution of the event. The automated labeling of these virtually generated data ensures that the system can quickly collect high-quality training data to make up for the scarcity and incompleteness of real data.

[0061] The labeled virtual extreme scenario data will be integrated with the normal operating condition data for further training of machine learning or deep learning models. By inputting extreme data into the model together with regular data, the model's ability to recognize different scenarios is enhanced. For example, higher weights are given to extreme data during model training to increase the model's sensitivity to rare events. After multiple training and cross-validation, the model can handle both normal and edge scenarios, achieving high-precision prediction and control capabilities. This process greatly improves the robustness of the system in complex environments, ensuring that the model performs more reliably in reality.

[0062] The above four steps describe in detail how to simulate extreme scenarios through reinforcement learning and use virtual data to improve the model's prediction and response capabilities. Through state space construction, reward mechanism design, virtual data generation and automatic labeling, and fusion training of extreme scenarios and conventional data, the model can work efficiently under marginal conditions and reduce safety hazards in coal mine production.

[0063] Based on deep learning algorithms and combined with real-time monitoring data, the future dust diffusion trend is dynamically predicted. During the prediction process, the model adaptively assigns weights, gives higher warning levels to rare abnormal situations, and updates the prediction results in real time.

[0064] The steps for predicting the dynamic diffusion of coal mine dust based on deep learning algorithm are as follows:

[0065] In the real-time monitoring system, the collected data such as dust concentration, ventilation speed, humidity, temperature, gas concentration and equipment operation status are converted into a multidimensional matrix for processing. Suppose the monitoring data matrix at a certain time t is: Among them, m is the number of monitoring indicators (such as temperature and humidity), and n is the number of different acquisition nodes. All data are normalized and converted into values ​​in the interval [0, 1] to improve the stability and training efficiency of the model. The normalized expression is:

[0066]

[0067] , where x ij (t) represents the raw data collected from the i-th monitoring indicator (such as temperature, humidity, wind speed, etc.) and the j-th collection node (such as a specific mining area, sensor location) at time t, min(x ij ) means to find the minimum monitoring value (the minimum value among all time points and all nodes) from all historical data of the i-th monitoring indicator, max(x ij ) represents the maximum value of the i-th monitoring indicator in all nodes and all time. is the normalized data value, scaling the original data to the range between [0, 1];

[0068] This step ensures that data can be processed uniformly under different dimensions, avoiding interference of parameters with large numerical ranges on the model.

[0069] When the model is initialized, a deep feedforward neural network (DNN) is used to build a prediction architecture. The input layer receives the above normalized matrix X(t), and the hidden layer uses an activation function (such as ReLU or Swish) to extract high-order features. The input-output relationship of each hidden layer neuron is: (l) =σ(W (l) ·H (l-1)+b (l) ), where H (l) is the output matrix of the lth layer, W (l) is the weight matrix of the lth layer, b (l) is the bias matrix of the lth layer, σ(·) is the nonlinear activation function;

[0070] At initialization, the weight matrix W (l) Adaptive allocation strategy is adopted to give higher initial weights to input features related to abnormal events. For example, abnormal gas concentration can be given a larger initial value through empirical priors.

[0071] During the model training process, an adaptive weight adjustment mechanism is used to assign weights in real time to ensure that rare abnormal situations trigger higher warning levels. This mechanism is based on the attention matrix A(t) and is used to dynamically adjust the importance of different input features. The expression of the attention matrix is: Where Q(t) and K(t) are the query and key matrices of the input matrix, and d is the dimension of the matrix, which is used for normalization adjustment;

[0072] Softmax is an activation function commonly used in machine learning and deep learning models. It is mainly used in multi-classification tasks to convert the values ​​in a vector into a probability distribution. The output of softmax is a probability vector, where the sum of all elements is 1, which is used to indicate the possibility that a certain input belongs to different categories.

[0073] The model evaluates the input data in real time according to the anomaly detection module. If abnormal fluctuations in indicators such as gas concentration or wind speed are detected, the weight of the corresponding feature in the attention matrix is ​​increased. The updated weight affects the output of the model: Y(t) = A(t)·H (L) , where Y(t) is the predicted output of the model, i.e., the dust diffusion trend in the future time period.

[0074] In order to ensure that the model can be continuously optimized in a dynamic environment, a rolling prediction strategy is adopted. After each time window Δt, the system appends the newly collected data to the input matrix. The input matrix after appending is: X(t+Δt)=[X(t)|X new (t+Δt)], where X new (t+Δt) is the newly collected data matrix. The model updates parameters in real time based on these new data, performs rolling predictions, and outputs the new diffusion trend Y(t+Δt). At the same time, an adaptive optimization mechanism of the loss function (such as AdamW) is introduced to dynamically adjust the model's weight matrix according to the prediction error. The expression for dynamic adjustment is: Among them, y i (t) is the actual monitoring value, is the predicted value, is the L2 regularization term, used to prevent overfitting, is a loss function, which is often used to measure the error between the model prediction result and the true value. In machine learning and deep learning, by minimizing the loss function, the model can gradually improve the accuracy of the prediction. λ is a regularization coefficient, which is used to control the complexity of the model and avoid overfitting. Its function is to constrain the weights in the model, and it is often used with L1 or L2 regularization terms. N is the number of samples.

[0075] The reinforcement learning algorithm is used to automatically adjust the dust control strategy, automatically adjust the operating status of the ventilation equipment according to the predicted diffusion trend, trigger the water spray system, and record the effect of the control behavior;

[0076] In a feedback loop, the system continuously learns the effects of adjustments and optimizes future decisions to minimize dust dispersion and accident risks.

[0077] The specific steps to achieve automatic adjustment of dust control strategy using reinforcement learning algorithm are as follows:

[0078] In this step, an intelligent control system based on reinforcement learning (RL) is designed to clarify the state space and action space. The state space includes variables such as dust concentration, real-time operating status of ventilation equipment, humidity level, equipment temperature, and air flow rate; the action space includes operations such as adjusting the wind speed of ventilation equipment, turning on or off the ventilation system in different areas, and triggering the sprinkler system. This step also requires establishing the granularity of the action, such as the amplitude of the change when adjusting the wind speed. By clearly defining states and actions, ensure that the system can make diverse decisions in different scenarios.

[0079] The core of reinforcement learning is the reward mechanism, so it is necessary to define a reward function for dust control. The system gives positive rewards when it reduces the dust concentration to a safe level in a certain round; if the dust concentration exceeds the safety threshold or the diffusion trend is not controlled, a negative penalty is given. In addition, secondary rewards can be designed based on resource utilization efficiency (such as ventilation power consumption and water spraying volume) to encourage the system to optimize control strategies and avoid waste. Through the feedback of the reward function in the iterative process, the reinforcement learning model will gradually learn to optimize the control strategy under different conditions.

[0080] The training process uses algorithms such as Q-learning or deep Q-network (DQN) to place the system in a virtual coal mine environment for multiple rounds of simulation. In each round of training, the agent selects an action based on the current state (such as increasing the wind speed or triggering the water spray system), observes its effect, and then updates the strategy based on the reward function feedback. Through the balance of exploration vs. exploitation, the agent explores new strategies while also leveraging existing successful experiences to continuously optimize its control capabilities for different scenarios. During the training process, the control behavior and effects of each round will be recorded to evaluate the system's response to different strategies.

[0081] After training, the reinforcement learning model is deployed in an actual coal mine environment, receiving data feedback from the monitoring system in real time and predicting future dust diffusion trends based on the current status. The system automatically performs optimized control actions, such as adjusting the operating status of ventilation equipment in real time, or dynamically triggering the water spray system based on dust concentration. At the same time, the system continuously records the effects of control actions and feeds back actual operating data to the model for further adaptive updates. Even in the face of emergencies or changes in equipment status, the system can quickly adjust its strategy based on new data to ensure that dust concentrations remain at a safe level.

[0082] Summary: Through the above four steps, the reinforcement learning algorithm realizes the automatic adjustment of the dust control strategy in coal mines. From model design, reward function construction to training and online application, each link accurately solves the real-time and complexity problems of dust control. The system can not only dynamically optimize the control strategy in a changing environment, but also has the ability of adaptive learning to ensure continuous and efficient operation under various working conditions.

[0083] Implementation method 1: Dynamic adjustment of intelligent ventilation system based on prediction;

[0084] This implementation method realizes intelligent dynamic adjustment of ventilation equipment through the combination of reinforcement learning model and real-time monitoring data. The system is connected to the monitoring network to obtain parameters such as dust concentration, wind speed, temperature and humidity in different areas of the mine in real time. When the dust concentration gradually rises and approaches the set safety threshold, the model predicts the dust diffusion trend in the future and formulates adjustment strategies in advance. For example, when dust accumulates in a certain area, the system will automatically increase the fan power in that area and accelerate the air flow to dilute the dust and guide it to the ventilation outlet away from the working area. In order to prevent local equipment from overloading, the system can also reasonably adjust the operating status of other fans according to the overall air duct layout to achieve a balanced distribution of air volume.

[0085] During the dynamic adjustment process, the system fully considers the complex structure and ventilation resistance of the coal mine. If the wind power in a ventilation tunnel is insufficient or some equipment fails, the system will immediately activate the backup fan and optimize the wind flow path to ensure that the wind can cover the key areas. In addition, during monitoring, the system will continue to track the dynamic changes of the wind flow to avoid uncontrolled dust diffusion due to sudden wind flow interruptions. For example, if the wind flow suddenly reverses or the fan in a certain area stops operating, the system will quickly change the operation strategy of other fans to restore the reasonable ventilation direction.

[0086] This intelligent ventilation adjustment not only ensures the safety of the miners' working environment, but also reduces energy consumption by optimizing the operation mode of the fan. Traditional ventilation modes usually rely on manual experience to adjust the fan speed, but this method cannot respond to emergencies in real time and can easily lead to insufficient air volume or equipment overload. This implementation method uses the adaptive optimization capability of reinforcement learning to quickly adjust according to different working conditions, which not only ensures safety but also reduces energy waste. In addition, the system can also record the effect of each fan adjustment, and use this as a basis to optimize future adjustment strategies, gradually improving the response accuracy and decision-making efficiency of the model.

[0087] Implementation method 2: Precise triggering and dynamic control of intelligent sprinkler system;

[0088] In this implementation, the system uses reinforcement learning algorithms and dust diffusion prediction models to achieve intelligent control of the water spraying system inside the mine. The system first predicts the future dust diffusion path and change trend based on the real-time collected dust concentration, humidity and air flow rate. When it is detected that the dust concentration in a certain area has risen and there is a risk of diffusion, the system will trigger the water spraying equipment in advance to suppress the spread of dust by increasing the air humidity. The area and time of water spraying are dynamically determined by the system, for example, water is sprayed precisely in high-risk areas (such as the end of the wind flow or where workers gather).

[0089] The control strategy after water spraying will also be optimized in real time based on feedback. The system not only records the downward trend of dust concentration after water spraying, but also tracks changes in ambient humidity and whether excessive water spraying causes problems such as slippery working areas or reduced visibility. For example, under normal working conditions, the system may only start water spraying equipment at a low frequency and in a small range to maintain ambient humidity; when large-scale dust diffusion is predicted, multiple water spraying points will be concentrated to spray at high intensity. In addition, if the system detects that the dust concentration is still not up to standard after water spraying, it will further extend the water spraying time or expand the water spraying area to ensure that the dust suppression effect is achieved.

[0090] A significant advantage of the intelligent water spraying system is that it improves the efficiency of water resource use. In the traditional mode, mines usually use fixed time intervals and water volumes for water spraying, but this method is prone to waste of water resources or insufficient water spraying. This implementation method uses a reinforcement learning algorithm to enable the system to flexibly determine whether water spraying is needed and when to stop spraying, thereby avoiding waste of resources. At the same time, the system also has self-learning capabilities, and can gradually adjust the water spraying strategy to make it more in line with the actual situation of the mine by analyzing historical data and actual control effects. In addition, the system can also play a key role in extreme situations, such as quickly spraying water to dilute harmful gases when gas is suddenly released to ensure the safety of operators.

[0091] Implementation method 3: Identification and emergency control strategies of emergencies;

[0092] This implementation method focuses on emergency response and control strategies for emergencies in coal mines, especially dealing with extreme situations such as gas eruptions or ventilation system failures. During mine operations, emergencies can cause dust to spread rapidly and are accompanied by major safety risks, so the system needs to have efficient identification and rapid response capabilities. The reinforcement learning model continuously monitors the environmental status of the mine. Once an abnormal situation is detected (such as a sudden drop in ventilation volume in a certain area or a sudden increase in gas concentration), the system will immediately execute the preset emergency control strategy. Specifically, the system will block the vents in high-risk areas to prevent dust and gas from spreading to other areas, and quickly start backup ventilation equipment to restore air flow.

[0093] After an emergency occurs, the system will trigger a multi-level emergency response mechanism. First, miners will immediately receive an alarm signal, and the system will automatically guide workers to evacuate to a safe area. In addition, if certain areas have reached a dangerous dust concentration or the gas concentration exceeds the standard, the system will automatically trigger safety devices, such as starting the automatic fire extinguishing system or shutting down power equipment to prevent secondary disasters caused by dust explosions or gas combustion. During the execution of the control strategy, the system will continuously monitor the operating status of each device and dynamically adjust operations based on real-time data. For example, when there is a problem with the backup ventilation equipment, it will switch to other backup plans in a timely manner.

[0094] Another key feature of this implementation is that the system has post-event analysis and optimization capabilities. After each emergency occurs, the system will record in detail all emergency operations and control effects, including alarm trigger time, control measures, and miners' evacuation routes. After the event is over, the system will conduct a retrospective analysis based on this data to identify deficiencies in the control and update the model's emergency strategy. In this way, the system gradually improves its response capabilities to extreme events and will be able to respond to similar events faster and more accurately in the future. In addition, the system's intelligent optimization function enables it to achieve the optimal control strategy with limited resources, minimizing the losses and risks caused by emergencies.

[0095] The present invention uses reinforcement learning algorithms and simulations of virtual extreme scenarios to enable the system to identify and respond to complex situations outside of conventional working conditions, such as sudden gas release, ventilation equipment failure and other extreme events. Traditional models often perform poorly in response to emergencies due to the lack of extreme data, but this system simulates a variety of extreme situations and annotates and trains their data to make the model highly sensitive to edge scenarios. Once an abnormality occurs, the system can quickly identify and execute corresponding emergency control strategies (such as adjusting the direction of wind flow, triggering alarms and water spray systems). This efficient emergency response capability not only reduces the health risks caused by dust diffusion, but also reduces the probability of major accidents such as dust explosions, providing a guarantee for the long-term safe operation of mines.

[0096] The present invention maximizes the utilization efficiency of energy and water resources by dynamically adjusting the operating status of ventilation equipment and water spraying systems. Traditional mine dust control methods rely more on manual experience, and there are problems of resource waste or delayed response. In this solution, the system predicts future dust diffusion trends through real-time monitoring and deep learning algorithms, and only triggers ventilation and water spraying equipment when necessary, thereby reducing unnecessary energy and water consumption. Especially in the control of fan power and water spraying frequency, the reinforcement learning model gradually optimizes various parameters to achieve accurate resource scheduling. This intelligent optimization not only reduces operating costs, but also extends the service life of equipment and improves the operating efficiency of mines.

[0097] The present invention enables adaptive learning capabilities through continuous training and feedback of reinforcement learning. As the running time increases, the system will continuously optimize its decision-making strategies and control processes based on the analysis of monitoring data and historical events. For example, in response to multiple extreme events, the system can summarize more efficient emergency plans and incorporate these experiences into subsequent control logic. Even if the environmental conditions in the mine change (such as adding new equipment or adjusting the ventilation structure), the system can automatically adjust the model parameters to ensure the accuracy and effectiveness of the control strategy. The addition of the adaptive learning function makes the system more robust and flexible in the face of more complex scenarios in the future, greatly reducing the need for manual intervention.

[0098] 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. A method for simulating and controlling coal mine dust diffusion based on artificial intelligence, characterized in that: The following steps are involved: Environmental data is collected through the monitoring system in the coal mine, and cleaned and formatted in combination with historical operating data; Mark and remove abnormal data and build a multidimensional data set for subsequent model training; Based on normal operating data, a physical model is used to simulate the normal diffusion path of dust in coal mines, and a preliminary dust diffusion prediction model is generated through a machine learning algorithm as the basis for subsequent algorithms; Use reinforcement learning algorithms to simulate rare virtual extreme scenarios, automatically annotate the data of virtual extreme scenarios, and integrate them with normal working condition data to train the model, thereby enhancing the model's ability to identify and respond to edge scenarios. Based on deep learning algorithms and combined with real-time monitoring data, the future dust diffusion trend is dynamically predicted. During the prediction process, the model adaptively assigns weights, gives higher warning levels to rare abnormal situations, and updates the prediction results in real time. A reinforcement learning algorithm is used to automatically adjust the dust control strategy, automatically adjust the operating status of the ventilation equipment according to the predicted diffusion trend, trigger the water spray system, and record the effect of the control behavior.

2. The method for simulating and controlling coal mine dust diffusion based on artificial intelligence according to claim 1 is characterized in that: Cleaning and formatting in combination with historical operating data refers to integrating the real-time monitoring data collected in the coal mine environment with historical data from different time periods and operating conditions in the past to improve the integrity and quality of the data. The cleaning process includes deleting missing values ​​or erroneous data, filling in key data gaps, and eliminating redundant data that does not match the actual operating conditions. Formatting is the process of unifying data from different sources into a standard format to facilitate model training and analysis.

3. The method for simulating and controlling coal mine dust diffusion based on artificial intelligence according to claim 1 is characterized in that: The specific steps of constructing a coal mine dust diffusion prediction model based on normal operating conditions data are as follows: A physical model of dust diffusion inside a coal mine is established based on the principles of fluid mechanics and aerodynamics, and the properties of dust particles and the influence of gravity sedimentation effects are introduced; Based on the physical model, a preliminary dust diffusion simulation is carried out in the computing environment. The input parameters of each area are substituted into the model to simulate the diffusion path and concentration distribution of dust in the mine. After the simulation results are verified, key characteristic variables are extracted from the data set of normal working conditions. By analyzing the correlation between the input variables, the law of dust diffusion is learned and a preliminary prediction model is generated; After building the preliminary prediction model, verify and optimize it, and use data sets that were not involved in the training or real-time monitoring data to test the prediction model and check the accuracy and stability of its output.

4. The method for simulating and controlling coal mine dust diffusion based on artificial intelligence according to claim 1 is characterized in that: The specific steps of using reinforcement learning algorithms to simulate virtual extreme scenarios are as follows: Before starting reinforcement learning training, define the extreme scenarios encountered by the coal mine system. Then, construct a complete state space, that is, a combination of variables that describe different states of the coal mine. Between the variable combinations of different states, the change range and extreme cases of each variable must be taken into account. The core of the reinforcement learning model is that the intelligent agent continuously learns how to deal with extreme situations in the virtual coal mine environment. The intelligent agent takes different actions according to the changes in the state space. In order to guide the intelligent agent to learn the correct response strategy, a reward mechanism is defined to optimize the response ability to extreme working conditions. Using reinforcement learning algorithms to conduct multiple rounds of simulation, each round the agent will explore different combinations of operations in the virtual environment, generating a large amount of data on extreme scenarios. After each simulation run, the system automatically records and annotates important state-action-result sequences. The annotated data includes the conditions for the occurrence of extreme events, the control measures taken, and the evolution of the events. The labeled virtual extreme scenario data is integrated with the normal operating condition data for further training of machine learning. By inputting both extreme data and normal data into the model, the model's ability to recognize different scenarios is enhanced.

5. The method for simulating and controlling coal mine dust diffusion based on artificial intelligence according to claim 1 is characterized in that: The steps for predicting the dynamic diffusion of coal mine dust based on deep learning algorithm are as follows: In the real-time monitoring system, the collected data is converted into a multidimensional matrix for processing. Suppose the monitoring data matrix at a certain time t is: Among them, m is the number of monitoring indicators, n is the number of different collection nodes, and all data are normalized and converted into values ​​in the interval [0, 1] to improve the stability and training efficiency of the model. The normalized expression is: ， Among them, x ij (t) represents the original data collected from the i-th monitoring indicator and the j-th collection node at time t, min(x ij ) means finding the minimum monitoring value from all historical data of the i-th monitoring indicator, max(x ij ) represents the maximum value of the i-th monitoring indicator in all nodes and all time. is the normalized data value, scaling the original data to the range between [0, 1]; When the model is initialized, a deep feedforward neural network is used to build a prediction architecture. The input layer receives the normalized matrix X(t), and the hidden layer uses an activation function to extract high-order features. The input-output relationship of each hidden layer neuron is: H (l) =σ(W (l) ·H (l-1) +b (l) ), where H (l) is the output matrix of the lth layer, W (l) is the weight matrix of the lth layer, b (l) is the bias matrix of the lth layer, σ(·) is the nonlinear activation function; At initialization, the weight matrix W (l) An adaptive allocation strategy is adopted to give higher initial weights to input features related to abnormal events.

6. The method for simulating and controlling coal mine dust diffusion based on artificial intelligence according to claim 1 is characterized in that: During the model training process, an adaptive weight adjustment mechanism is used to assign weights in real time to ensure that rare abnormal situations trigger higher warning levels. The adaptive weight adjustment mechanism is based on the attention matrix A(t) and is used to dynamically adjust the importance of different input features. The expression of the attention matrix is: Where Q(t) and K(t) are the query and key matrices of the input matrix, and d is the dimension of the matrix, which is used for normalization adjustment; The model evaluates the input data in real time according to the anomaly detection module. If an abnormal fluctuation in the indicator is detected, the weight of the corresponding feature is increased in the attention matrix. The updated weight affects the output of the model as follows: Y(t) = A(t) H (L) , where Y(t) is the predicted output of the model, i.e., the dust diffusion trend in the future time period; In order to ensure that the model can be continuously optimized in a dynamic environment, a rolling prediction strategy is adopted. After each time window Δt, the system appends the newly collected data to the input matrix. The input matrix after appending is: X(t+Δt)=[X(t)|X new (t+Δt)], where X new (t+Δt) is the newly collected data matrix. The model updates the parameters in real time based on the new data of the data matrix, performs rolling prediction, and outputs the new diffusion trend Y(t+Δt). At the same time, the adaptive optimization mechanism of the loss function is introduced to dynamically adjust the weight matrix of the model according to the prediction error. The expression of dynamic adjustment is: Among them, y i (t) is the actual monitoring value, is the predicted value, is the L2 regularization term, used to prevent overfitting, is the loss function, which is used to measure the error between the model prediction result and the true value. λ is the regularization coefficient, which is used to control the complexity of the model and avoid overfitting.

7. The method for simulating and controlling coal mine dust diffusion based on artificial intelligence according to claim 1 is characterized in that: The specific steps to achieve automatic adjustment of dust control strategy using reinforcement learning algorithm are as follows: Design an intelligent control system based on reinforcement learning, clarify the state space and action space, establish the granularity of actions, and ensure that the system can make diverse decisions in different scenarios by clearly defining states and actions; Define a reward function for dust control, give positive rewards when the dust concentration is reduced to a safe level in a certain round; if the dust concentration exceeds the safety threshold or the diffusion trend is not controlled, give negative penalties. In addition, design secondary rewards based on resource utilization efficiency to encourage the system to optimize control strategies; During training, the system is placed in a virtual coal mine environment for multiple rounds of simulation. In each round of training, the agent selects an action based on the current state, observes its effect, and then updates the strategy based on the reward function feedback. By balancing exploration and utilization, the agent explores new strategies while also utilizing existing successful experiences, continuously optimizing its control capabilities for different scenarios. After training, the reinforcement learning model is deployed in an actual coal mine environment, receiving data feedback from the monitoring system in real time and predicting future dust diffusion trends based on the current status. The system automatically performs optimized control actions, continuously records the effects of control behaviors, and feeds back actual operating data to the model for further adaptive updates.

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