An artificial intelligence-based coal mine dust diffusion simulation and control method

Abstract

The application discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, relates to the technical field of coal mine dust diffusion simulation and control, and comprises the following steps: collecting environmental data such as dust concentration, ventilation flow rate, temperature, humidity and equipment running state through a monitoring system in the coal mine, and performing cleaning and format processing in combination with historical working condition data. The application utilizes reinforcement learning and virtual extreme scene simulation, improves the identification and emergency response capability of the system to edge working conditions, effectively reduces the dust diffusion and explosion risk, and guarantees the safety of the mine. Through dynamic adjustment of the ventilation and water spraying systems, efficient utilization of resources is realized, energy consumption and operation cost are reduced, and the service life of the equipment is prolonged. The system has self-adaptive learning capability, can optimize the control strategy according to the monitoring data and historical events, can automatically adjust even if the environment changes, improves the robustness and flexibility, and reduces manual intervention.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine dust diffusion simulation and control, and particularly relates to a coal mine dust diffusion simulation and control method based on artificial intelligence. BACKGROUND

[0002] The coal mine dust diffusion simulation and control based on artificial intelligence refers to using artificial intelligence technology to model and analyze the generation, diffusion and governance process of dust in coal mines. Through AI algorithms, the system can predict the propagation path, concentration distribution and possible dangerous areas of dust based on real-time monitoring data and historical data. In addition, the AI model can also optimize dust suppression measures, such as adjusting the ventilation system or automatically controlling the water spraying system, to achieve dynamic management of dust concentration and ensure the health and safety of miners. This technology integrates deep learning, Internet of Things sensors and adaptive control strategies, improving the efficiency and intelligence level of dust control.

[0003] The prior art has the following shortcomings:

[0004] Coal mine dust diffusion simulation relies on historical data and trained models under different working conditions, but in actual application, the model may not cover extreme cases (such as rare ventilation failures, sudden gas bursts, etc.). Since these scenarios rarely occur in training data, the model is difficult to accurately predict their impact. If the system fails to identify these abnormal situations in time and make adjustments, dust may quickly spread to the working area, exposing miners to high-concentration dust environments, increasing the risk of pneumoconiosis, and even causing dust explosions. This technical defect highlights the vulnerability of simulation models in edge scenarios, and once it happens, the consequences are difficult to control.

[0005] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a coal mine dust diffusion simulation and control method based on artificial intelligence, which uses reinforcement learning algorithms and virtual extreme scenario simulation to improve the system's ability to identify and respond to abnormal situations, quickly execute emergency control strategies, reduce dust diffusion and explosion risks, and ensure safe operation of the mine. At the same time, by monitoring and predicting the trend of dust diffusion in real time, the system only activates the ventilation and water spraying equipment when necessary, achieving precise scheduling of resources, reducing energy and water consumption, and reducing costs and extending equipment life. In addition, the system has adaptive learning ability, which can continuously optimize decisions based on running data, automatically adjust strategies even if environmental conditions change, improve robustness and flexibility, and reduce the need for manual intervention to solve the problems in the background technology mentioned above.

[0007] To achieve the above object, the present application provides the following technical solution: a coal mine dust diffusion simulation and control method based on artificial intelligence, comprising the following steps:

[0008] Collect environmental data through the monitoring system in the coal mine, and combine historical working condition data for cleaning and formatting processing; mark and exclude abnormal data, and construct a multidimensional data set for subsequent model training;

[0009] Based on normal working condition data, simulate the normal diffusion path of dust in the coal mine using a physical model, and generate a preliminary dust diffusion prediction model through a machine learning algorithm as the basis for subsequent algorithms;

[0010] Use reinforcement learning algorithm to simulate rare virtual extreme scenarios, automatically label the data of virtual extreme scenarios, and fuse them with normal working condition data for model training, to enhance the model's ability to identify and respond to edge scenarios;

[0011] Based on deep learning algorithm, combine real-time monitoring data to dynamically predict the diffusion trend of future dust, and in the prediction process, the model adaptively allocates weights to give higher warning levels to rare abnormal situations, and performs real-time rolling update on the prediction results;

[0012] Use reinforcement learning algorithm to realize automatic adjustment of dust control strategy, automatically adjust the operation state of ventilation equipment according to the predicted diffusion trend, trigger the water spraying system, and record the effect of the control behavior.

[0013] Preferably, the cleaning and formatting processing in combination with historical working condition data refers to the fusion of real-time monitoring data collected in the coal mine environment with historical data at different time periods and working conditions to improve the completeness and quality of the data, the cleaning process includes deleting missing or erroneous data, filling in key data gaps, and removing redundant data that does not match the actual working conditions;

[0014] Formatting processing is to unify data from different sources into a standard format for model training and analysis.

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

[0016] Establish a physical model of dust diffusion inside the coal mine based on the principles of fluid mechanics and aerodynamics, and introduce the properties of dust particles and the influence of gravity settling effect;

[0017] Based on the physical model, perform preliminary dust diffusion simulation in the calculation environment, substitute the input parameters of each region into the model, and simulate the diffusion path and concentration distribution of dust in the mine;

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

[0019] After the preliminary prediction model is constructed, it is verified and optimized, and the data set not participating in the training or real-time monitoring data is used to test the prediction model to check the accuracy and stability of its output.

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

[0021] Before starting reinforcement learning training, define the extreme scenarios encountered by the coal mine system, then build a complete state space, i.e. a combination of variables describing different states of the coal mine, and consider the change interval and extreme case of each variable between different variable combinations;

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

[0023] Use reinforcement learning algorithm for multiple rounds of simulation. In each round, the agent explores different operation combinations in the virtual environment to generate a large amount of extreme scenario data. After each simulation run, the system automatically records and labels important state-action-result sequences. The labeled data includes the conditions of extreme events, the control measures taken and the evolution process of the events;

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

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

[0026] In the real-time monitoring system, the collected data is converted into a multi-dimensional matrix for processing. Let the monitoring data matrix at time t be: Where m is the number of monitoring indicators, n is the number of different collection nodes, and all data is normalized to values in the [0, 1] interval to improve the stability and training efficiency of the model. The expression for normalization is:

[0027]

[0028] Where x ij(t) represents the raw data collected from the i-th monitoring index and the j-th collection node at time t, min(x ij ) represents the minimum monitoring value found from all historical data of the i-th monitoring index, max(x ij ) represents the maximum value of the i-th monitoring index at all nodes and all times, is the normalized data value, which scales the original data to the range of [0, 1];

[0029] At the initialization of the model, a deep feedforward neural network is used to construct the prediction architecture, the input layer receives the normalized matrix X(t), the hidden layer uses an activation function to extract high-order features, and 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 l-th layer, W (l) is the weight matrix of the l-th layer, b (l) is the bias matrix of the l-th layer, and σ(·) is a nonlinear activation function.

[0030] At initialization, the weight matrix W (l) uses an adaptive allocation strategy 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 allocate weights in real time, ensuring that rare abnormal situations trigger higher alert levels. The adaptive weight adjustment mechanism is based on an attention matrix A(t) for dynamically adjusting the importance of different input features, and the expression of the attention matrix is: where Q(t) and K(t) are the query and key matrices of the input matrix, d is the dimension of the matrix, and is used for normalization adjustment.

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

[0033] In order to ensure that the model can continuously optimize in a dynamic environment, a rolling prediction strategy is used, and every time a time window Δt passes, the system appends the newly collected data to the input matrix, and the appended input matrix is: X(t+Δt) = [X(t) | X new (t+Δt)], where X new(t+Δt) is a newly collected data matrix, the model updates the parameters in real time based on the new data of the data matrix, and performs rolling prediction, and outputs a new diffusion trend Y(t+Δt), meanwhile, an adaptive optimization mechanism of a loss function is introduced to dynamically adjust the weight matrix of the model according to the prediction error, and the expression of dynamic adjustment is: Wherein, y i (t) is an actual monitoring value, is a predicted value, is an L2 regularization term, used to prevent overfitting, is a loss function, used to measure the error between the prediction result of the model and the true value, and lambda is a regularization coefficient, used to control the complexity of the model to avoid overfitting.

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

[0035] An intelligent control system based on reinforcement learning is designed, the state space and action space are defined, the granularity of action is established, and through clear definition of state and action, it is ensured that the system can make diversified decisions in different scenarios;

[0036] A reward function for dust control is defined, a positive reward is given 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, a negative punishment is given, in addition, a secondary reward is designed according to the resource use efficiency to encourage the system to optimize the control strategy;

[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 according to the current state, observes its effect, and then updates the strategy according to the reward function feedback, through the balance of exploration and utilization, the agent not only explores new strategies, but also utilizes existing successful experience to continuously optimize the control ability for different scenarios;

[0038] After training, the reinforcement learning model is deployed in the actual coal mine environment, real-time receives the data feedback by the monitoring system, and predicts the future dust diffusion trend according to the current state, the system automatically executes the optimized control action, and continuously records the effect of the control behavior, and the actual running data is fed back to the model for further adaptive update.

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

[0040] The application enables the system to identify and respond to complex situations outside the normal working condition through reinforcement learning algorithm and simulation of virtual extreme scenarios. By simulating various extreme situations and labeling and training their data, the model has high sensitivity to edge scenarios. Once an anomaly occurs, the system can quickly identify and execute the corresponding emergency control strategy. 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 explosion, providing a guarantee for the long-term safe operation of the mine.

[0041] The application maximizes the utilization efficiency of energy and water resources by dynamically adjusting the operating state of the ventilation equipment and the water spraying system. Through real-time monitoring and deep learning algorithm to predict the future dust diffusion trend, the ventilation and water spraying equipment is triggered only 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 precise resource scheduling. This intelligent optimization not only reduces operating costs, but also prolongs the service life of equipment and improves the operating efficiency of the mine.

[0042] The application has self-adaptive learning ability through continuous training and feedback of reinforcement learning. As the running time increases, the system will continuously optimize its decision-making strategy and control process based on monitoring data and analysis of 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 self-adaptive learning function makes the system more robust and flexible when facing more complex scenarios in the future, greatly reducing the need for manual intervention. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only represent some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0044] Figure 1 The method flowchart of the coal mine dust diffusion simulation and control method based on artificial intelligence. DETAILED DESCRIPTION

[0045] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the inventive aspects of the example implementations to those skilled in the art.

[0046] The present application provides a method for simulating and controlling coal mine dust diffusion based on artificial intelligence. Figure 1The illustrated coal mine dust diffusion simulation and control method based on artificial intelligence includes the following steps:

[0047] Collect environmental data such as dust concentration, ventilation flow rate, temperature, humidity, and equipment operating status through the monitoring system in the coal mine, and combine historical working condition data for cleaning and formatting processing; mark and remove abnormal data, and construct a multidimensional data set for subsequent model training;

[0048] Combine historical working condition data for cleaning and formatting processing, which means that the real-time monitoring data collected in the coal mine environment is fused with historical data at different time periods and working conditions to improve the completeness and quality of the data. The cleaning process includes deleting missing values or error data (such as abnormal values caused by sensor failure), filling key data gaps (such as using interpolation algorithms or average values to fill), and removing redundant data that does not match the actual working conditions. Formatting processing is to unify data from different sources into a standard format, so that it can be classified according to time series, spatial location, and equipment state dimensions, facilitating model training and analysis. This process ensures that the data set is accurate and complete, reduces noise data interference to the model, and provides a high-quality data foundation for subsequent AI model training.

[0049] Based on normal working condition data, use a physical model to simulate the normal diffusion path of dust in the coal mine, and generate a preliminary dust diffusion prediction model through machine learning algorithms as the basis for subsequent algorithms;

[0050] The specific steps for building a coal mine dust diffusion prediction model based on normal working condition data are as follows:

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

[0052] Based on the above physical model, preliminary dust diffusion simulation is carried out in the computing environment. The system substitutes the input parameters of each region 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 model prediction has a large error compared with the actual monitored dust concentration, the model parameters (such as wind loss coefficient, friction loss) need to be optimized. This step aims to ensure that the model is highly consistent with the actual situation under normal conditions, laying a data foundation for subsequent machine learning algorithms.

[0053] After the simulation results are verified, key feature variables are extracted from the normal working condition data set, such as wind speed changes at different time points, temperature and humidity fluctuations, time series trends of dust concentration, etc. These feature variables are input into the machine learning model for training. Common algorithms include random forest, support vector machine (SVM) or XGBoost model. These models learn the rules of dust diffusion by analyzing the relationship between input variables and generate a preliminary prediction model. 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 constructed, it needs to be verified and optimized. Use the data set that did not participate in the training or real-time monitoring data to test the prediction model and check its accuracy and stability. Compare the prediction results with the actual observed dust diffusion trend. If there is an error, you can improve the performance of the model 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 be used as the basis for subsequent deep learning and dynamic prediction algorithms to handle more complex or extreme working conditions.

[0055] The above four steps gradually build a dust diffusion prediction system based on normal working conditions from physical modeling, simulation verification, feature extraction to model optimization. The system not only provides theoretical support through 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 algorithm to simulate rare but possible virtual extreme scenarios (such as partial failure of ventilation system or sudden release of gas), automatically label the data of virtual extreme scenarios, and fuse with normal working condition data for model training, to enhance the model's ability to recognize and respond to edge scenarios;

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

[0058] Before starting the reinforcement learning training, it is necessary to define the extreme scenarios that the coal mine system may encounter in detail, such as partial failure of the ventilation system, sudden release of gas, or abnormal dust deposition caused by sudden changes in humidity. Then, a complete state space is constructed, which describes the combination of variables in different states of the coal mine, such as wind speed, gas concentration, and equipment operating status. Between these states, the change interval and extreme case of each variable (such as wind speed suddenly dropping to 0 or gas concentration suddenly rising to the danger threshold) must be considered. This step ensures that the system can generate complex environmental changes close to reality during simulation, laying the foundation for subsequent simulation.

[0059] The core of the reinforcement learning model is the agent that continuously learns how to respond to extreme situations in a virtual coal mine environment. The agent takes different actions (such as simulating ventilation strategy adjustment or triggering dust suppression measures) based on changes in the state space. To guide the agent to learn the correct response strategy, defining a reasonable reward function is crucial. For example, give positive rewards when the agent successfully controls gas concentration or reduces dust diffusion in the simulation, and give negative penalties when virtual accidents occur (such as dust explosion). The reward mechanism ensures that the model can preferentially learn effective emergency measures and optimize the response capability to extreme working conditions.

[0060] Use reinforcement learning algorithms for multiple rounds of simulation, and in each round the agent will explore different combinations of operations in the virtual environment to generate a large amount of data for extreme scenarios. After each simulation run, the system automatically records and labels important state-action-result sequences (such as "gas eruption - device shutdown - ventilation recovery"). These labeled data include the conditions under which extreme events occur, the control measures taken, and the evolution of the event. The automatic processing of these virtually generated data labels 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 combined with normal working condition data for further training of machine learning or deep learning models. By inputting extreme data and normal data into the model together, the model's ability to recognize different scenarios is enhanced. For example, give more weight to extreme data during model training to improve 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 system's robustness 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 improve the model's prediction and response capabilities using virtual data. Through state space construction, reward mechanism design, virtual data generation and automatic labeling, and fusion training of extreme scenarios and regular data, the model can work efficiently in edge conditions and reduce safety hazards in coal mine production.

[0063] Based on deep learning algorithm, combined with real-time monitoring data, dynamically predict the diffusion trend of future dust, in the prediction process, the model adaptively allocates weights, gives higher warning level to rare abnormal situations, and performs real-time rolling update on the prediction results;

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

[0065] In the real-time monitoring system, the collected dust concentration, ventilation speed, humidity, temperature, gas concentration and equipment running state data are converted into a multi-dimensional matrix for processing. Let the monitoring data matrix at time t be: Wherein, m is the number of monitoring indicators (such as temperature, humidity), n is the number of different collection nodes. All data are normalized to convert them into values within the interval [0, 1] to improve the stability and training efficiency of the model. The expression for normalization is:

[0066]

[0067] Where, x ij (t) represents the original data collected at time t 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 or sensor location), min(x ij ) represents the minimum monitoring value (taking 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 at all nodes and all times, is the normalized data value, which scales the original data to the range of [0, 1];

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

[0069] During model initialization, a deep feedforward neural network (DNN) is used to build the prediction architecture. The input layer receives the normalized matrix X(t), and the hidden layer uses activation functions (such as ReLU or Swish) to extract high-order features. The input-output relationship of each hidden layer neuron is: (l) H (l) = σ(W (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, and σ(·) is a nonlinear activation function.

[0070] During initialization, the weight matrix W (l) is given a higher initial weight for input features related to abnormal events. For example, abnormal gas concentration can be given a larger initial value through empirical priori.

[0071] During model training, an adaptive weight adjustment mechanism is used to assign weights in real time, ensuring that rare abnormal situations trigger higher alert levels. This mechanism is based on the attention matrix A(t), which 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, used for normalization adjustment.

[0072] Softmax is an activation function commonly used in machine learning and deep learning models, 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, and the sum of all elements is 1, used to represent the possibility of a certain input belonging to different categories.

[0073] The model evaluates the input data in real time according to the anomaly detection module. If abnormal fluctuations are detected in gas concentration or wind speed, etc., the weight of the corresponding feature in the attention matrix is increased. 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.

[0074] To ensure that the model can continuously optimize in a dynamic environment, a rolling prediction strategy is adopted. Every time a time window Δt passes, the system appends the newly collected data to the input matrix, and the appended input matrix 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 these new data and performs rolling prediction, outputting a new diffusion trend Y(t+Δt). At the same time, an adaptive optimization mechanism of the loss function (such as AdamW) is introduced, which dynamically adjusts the weight matrix of the model according to the prediction error. The expression of dynamic adjustment is: where 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, commonly used to measure the error between the model's predicted results and the true values. In machine learning and deep learning, by minimizing the loss function, the model can gradually improve the accuracy of prediction, λ is the regularization coefficient, used to control the complexity of the model to avoid overfitting. Its role is to constrain the weights in the model, often used with L1 or L2 regularization terms, N is the number of samples.

[0075] Adopting reinforcement learning algorithm to realize automatic adjustment of dust control strategy, automatically adjusting the running state of ventilation equipment, triggering water spraying system according to the predicted diffusion trend, and recording the effect of control behavior;

[0076] In the feedback loop, the system continuously learns the effect of the adjusted and optimizes future decisions to minimize dust diffusion and accident risk to the greatest extent.

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

[0078] In this step, an intelligent control system based on reinforcement learning (RL) is designed, and the state space and action space are defined. The state space includes dust concentration, real-time running state of ventilation equipment, humidity level, equipment temperature, and air flow rate, etc. The action space includes adjusting the wind speed of ventilation equipment, turning on or off the ventilation system in different areas, triggering the water spraying system, etc. The granularity of action, such as the change amplitude when adjusting the wind speed, is also established. By clearly defining the state and action, the system can make diversified decisions in different scenarios.

[0079] The core of reinforcement learning is the reward mechanism, so a reward function for dust control needs to be defined. 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, it gives negative punishment. In addition, secondary rewards can also be designed according to resource use efficiency (such as ventilation power consumption, water spraying amount) to encourage the system to optimize the control strategy 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 employs algorithms such as Q-learning or Deep Q-Network (DQN), placing 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 wind speed or triggering a water sprinkler system), observes its effect, and then updates the policy based on the reward function feedback. Through a balance between exploration and exploitation, the agent continuously optimizes its control capabilities for different scenarios while exploring new strategies and utilizing existing successful experiences. During training, the control behavior and effects of each round are recorded to evaluate the system's response to different strategies.

[0081] After training, the reinforcement learning model is deployed in a real coal mine environment, receiving data from the monitoring system in real time and predicting future dust dispersion trends based on the current state. The system automatically executes 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. Simultaneously, the system continuously records the effectiveness of the control actions and feeds the actual operating data back 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 concentration remains at a safe level.

[0082] In summary, through the four steps described above, the reinforcement learning algorithm enables the automatic adjustment of dust control strategies in coal mines. From model design and reward function construction to training and online application, each step precisely addresses the real-time and complexity issues of dust control. The system not only dynamically optimizes control strategies in changing environments but also possesses adaptive learning capabilities, ensuring 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 utilizes a reinforcement learning model combined with real-time monitoring data to achieve intelligent dynamic adjustment of ventilation equipment. The system connects to a monitoring network to acquire parameters such as dust concentration, wind speed, temperature, and humidity in different areas of the mine in real time. When the dust concentration gradually increases and approaches a set safety threshold, the model predicts the dust diffusion trend over a future period and formulates adjustment strategies in advance. For example, when dust accumulates in a certain area, the system automatically increases the power of the fans in that area to accelerate airflow, diluting the dust and guiding it to ventilation outlets away from the work area. To prevent local equipment from overloading, the system can also rationally adjust the operating status of other fans according to the overall air duct layout to achieve a balanced distribution of airflow.

[0085] During dynamic adjustments, the system fully considers the complex structure and ventilation resistance of coal mines. If the airflow in a ventilation roadway is insufficient or some equipment malfunctions, the system will immediately activate backup fans and optimize the airflow path to ensure that the airflow can cover key areas. Furthermore, the system continuously tracks dynamic changes in airflow during monitoring to prevent uncontrolled dust diffusion due to sudden airflow interruptions. For example, if the airflow suddenly reverses or a fan in a certain area stops operating, the system will quickly change the operating strategy of other fans to restore a reasonable ventilation direction.

[0086] This intelligent ventilation adjustment not only ensures the safety of miners' working environment but also reduces energy consumption by optimizing the operation mode of the fans. Traditional ventilation methods typically rely on manual experience to adjust fan speeds, but this method cannot respond to emergencies in real time, easily leading to insufficient airflow or equipment overload. This implementation utilizes the adaptive optimization capabilities of reinforcement learning, enabling rapid adjustments based on different working conditions, ensuring safety while reducing energy waste. Furthermore, the system can record the effects of each fan adjustment and use this data to optimize future adjustment strategies, gradually improving the model's response accuracy and decision-making efficiency.

[0087] Implementation Method 2: Precise triggering and dynamic control of intelligent sprinkler systems;

[0088] In this implementation, the system utilizes reinforcement learning algorithms and a dust diffusion prediction model to achieve intelligent control of the water spraying system inside the mine. The system first predicts the future diffusion path and trend of dust based on real-time collected dust concentration, humidity, and air velocity. When an increase in dust concentration and a risk of diffusion are detected in a certain area, the system will trigger the water spraying equipment in advance to suppress dust diffusion by increasing air humidity. The area and timing of water spraying are dynamically determined by the system, for example, prioritizing precise water spraying to high-risk areas (such as the end of airflow or areas where workers congregate).

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

[0090] A significant advantage of intelligent sprinkler systems is their improved water resource utilization efficiency. In traditional methods, mines typically use fixed time intervals and water volumes for sprinkler systems, which can easily lead to water waste or insufficient water supply. This implementation utilizes reinforcement learning algorithms, enabling the system to flexibly determine whether sprinklering is necessary and when to stop, thus avoiding resource waste. Furthermore, the system possesses self-learning capabilities, allowing it to gradually adjust its sprinkler strategy by analyzing historical data and actual control effects to better suit the mine's specific conditions. In addition, the system can play a crucial role in extreme situations, such as rapidly spraying water to dilute harmful gases during sudden gas releases, ensuring the safety of workers.

[0091] Implementation Method 3: Emergency Event Identification and Control Strategies;

[0092] This implementation focuses on emergency response and control strategies for sudden events in coal mines, particularly handling extreme situations such as gas outbursts or ventilation system failures. During mine operations, sudden events can cause rapid dust dispersion and significant safety risks; therefore, the system must possess efficient identification and rapid response capabilities. The reinforcement learning model continuously monitors the mine's environmental conditions. Once an anomaly is detected (such as a sudden drop in ventilation volume or a sudden increase in gas concentration in a certain area), the system immediately executes preset emergency control strategies. Specifically, the system will block ventilation openings in high-risk areas to prevent dust and gas from spreading to other areas and quickly activate backup ventilation equipment to restore airflow.

[0093] Following an emergency, the system will trigger a multi-layered emergency response mechanism. First, miners will immediately receive an alarm signal, and the system will automatically guide workers to evacuate to a safe area. Furthermore, if certain areas have reached dangerous dust or gas concentrations exceeding safe levels, the system will automatically trigger safety devices, such as activating automatic fire suppression systems or shutting down electrical 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 various equipment and dynamically adjust operations based on real-time data. For example, if backup ventilation equipment malfunctions, it will promptly switch to other backup solutions.

[0094] Another key feature of this implementation is the system's post-event analysis and optimization capabilities. After each emergency, the system meticulously records all emergency operations and control effects, including alarm trigger times, control measures, and miner evacuation routes. After the event, the system reviews and analyzes this data to identify shortcomings in control and updates the model's emergency strategy. In this way, the system gradually improves its response to extreme events and can handle similar events more quickly and accurately in the future. Furthermore, the system's intelligent optimization function enables it to achieve optimal control strategies even with limited resources, minimizing losses and risks caused by emergencies.

[0095] This invention utilizes reinforcement learning algorithms and simulations of virtual extreme scenarios to enable the system to identify and respond to complex situations beyond routine operating conditions, such as sudden gas releases and ventilation equipment malfunctions. Traditional models often perform poorly in responding to emergencies due to a lack of extreme data, while this system, by simulating various extreme situations and labeling and training the data, makes the model highly sensitive to edge scenarios. Once an anomaly occurs, the system can quickly identify and execute corresponding emergency control strategies (such as adjusting airflow direction, triggering alarms, and activating the water spray system). 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 the mine.

[0096] This invention maximizes energy and water resource utilization efficiency by dynamically adjusting the operating status of ventilation equipment and water spraying systems. Traditional mine dust control methods rely heavily on manual experience, leading to resource waste or delayed response. In this solution, the system uses real-time monitoring and deep learning algorithms to predict future dust diffusion trends, triggering ventilation and water spraying equipment only when necessary, thereby reducing unnecessary energy and water consumption. Particularly in controlling fan power and water spraying frequency, reinforcement learning models progressively optimize various parameters, achieving precise resource scheduling. This intelligent optimization not only reduces operating costs but also extends equipment lifespan and improves mine operational efficiency.

[0097] This invention enables the system to adapt and learn adaptively through continuous training and feedback using reinforcement learning. As runtime increases, the system continuously optimizes its decision-making strategies and control processes based on monitoring data and historical event analysis. For example, in responding to multiple extreme events, the system can summarize more efficient emergency response plans and integrate these experiences into subsequent control logic. Even if environmental conditions within the mine change (such as the addition of new equipment or adjustments to ventilation structures), the system can automatically adjust model parameters to ensure the accuracy and effectiveness of the control strategy. The addition of adaptive learning functionality makes the system more robust and flexible in the face of more complex future scenarios, significantly reducing the need for manual intervention.

[0098] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, 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 foregoing 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. An artificial intelligence-based coal mine dust diffusion simulation and control method, characterized in that, The method comprises the following steps: Collecting environmental data through a monitoring system in the coal mine, and combining historical working condition data for cleaning and formatting processing; Marking and removing abnormal data, and constructing a multidimensional data set for subsequent model training; Based on normal working condition data, a preliminary dust diffusion prediction model is generated using a physical model to simulate the normal diffusion path of dust in the coal mine and a machine learning algorithm, serving as the basis for subsequent algorithms; Using reinforcement learning algorithm to simulate rare virtual extreme scenarios, automatically labeling the data of virtual extreme scenarios, and fusing with normal working condition data for training model, enhancing the model's ability to identify and respond to edge scenarios; Based on deep learning algorithm, combining real-time monitoring data, dynamically predicting the diffusion trend of future dust, in the prediction process, the model adaptively allocates weights to give higher warning level to rare abnormal situations, and updates the prediction results in real time; Using reinforcement learning algorithm to realize automatic adjustment of dust control strategy, automatically adjusting the running state of ventilation equipment, triggering water spraying system according to the predicted diffusion trend, and recording the effect of control behavior; The specific steps of using reinforcement learning algorithm to simulate virtual extreme scenarios are as follows: Before starting reinforcement learning training, define the extreme scenarios encountered by the coal mine system, then build a complete state space, which describes the variable combination of different states of the coal mine, and consider the change interval and extreme situation of each variable between different variable combinations; The core of the reinforcement learning model is that the agent continuously learns how to respond to extreme situations in the virtual coal mine environment, the agent takes different actions according to the changes in the state space, in order to guide the agent to learn the correct response strategy, define the reward mechanism to optimize the response ability to extreme working conditions; Use reinforcement learning algorithm for multiple rounds of simulation, in each round the agent will explore different operation combinations in the virtual environment, generating a large amount of extreme scenario data, after each simulation run, the system automatically records and labels important state-action-result sequences, the labeled data includes the conditions of extreme events, the control measures taken and the evolution process of the event; Fuse the labeled virtual extreme scenario data with normal working condition data for further machine learning, by inputting extreme data and normal data into the model together, enhance the model's ability to identify different scenarios.

2. The coal mine dust diffusion simulation and control method based on artificial intelligence according to claim 1, characterized in that, Cleaning and formatting processing of historical working condition data refers to the fusion of real-time monitoring data collected in the coal mine environment with historical data of different time periods and working conditions to improve the completeness and quality of the data, the cleaning process includes deleting missing or incorrect data, filling in key data gaps, and removing redundant data that does not match the actual working conditions; Formatting processing is to unify data from different sources into a standard format for model training and analysis.

3. The coal mine dust diffusion simulation and control method based on artificial intelligence according to claim 1, characterized in that, The specific steps of constructing a coal mine dust diffusion prediction model based on normal working condition data are as follows: Based on the principles of fluid mechanics and aerodynamics, a physical model of dust diffusion in the coal mine is established, and the properties of dust particles and the influence of gravity settling effect are introduced; Based on the physical model, preliminary dust diffusion simulation is carried out in the computing environment. The input parameters of each region 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 feature variables are extracted from the normal working condition data set. By analyzing the correlation between input variables, the law of dust diffusion is learned, and a preliminary prediction model is generated. After the preliminary prediction model is constructed, it is verified and optimized. The data set not involved in the training or real-time monitoring data is used to test the prediction model, and the accuracy and stability of the output are checked.

4. The coal mine dust diffusion simulation and control method based on artificial intelligence according to claim 1, characterized in that, The steps of coal mine dust dynamic diffusion prediction based on deep learning algorithm are as follows: In real-time monitoring system, the collected data is converted into a multi-dimensional matrix for processing, and the monitoring data matrix at a certain time t is: wherein, m is the number of monitoring indicators, n is the number of different collection nodes, all data is normalized to convert to values in the interval [0, 1] to improve the stability and training efficiency of the model, and the expression of the normalization processing is: wherein, represents the original data collected from the i th monitoring indicator and the j th collection node at time t, represents the smallest monitoring value found from all historical data of the i th monitoring indicator, represents the maximum value of the i th monitoring indicator at all nodes and all times, is the normalized data value, which scales the original data to the range between 0 and 1. At the initialization of the model, a deep feedforward neural network is used to construct the prediction architecture, the input layer receives the normalized matrix , the hidden layer uses an activation function to extract high-order features, and the input-output relationship of each hidden layer neuron is: , wherein is the output matrix of the l th layer, is the weight matrix of the l th layer, is the bias matrix of the l th layer, is a nonlinear activation function; At initialization, the weight matrix An adaptive allocation strategy is used to give higher initial weights to input features that are relevant to the abnormal event.

5. The coal mine dust diffusion simulation and control method based on artificial intelligence according to claim 4, characterized in that, In the model training process, an adaptive weight adjustment mechanism is used to assign weights in real time, ensuring that rare abnormal situations trigger higher alert levels, and the adaptive weight adjustment mechanism is based on an attention matrix for dynamically adjusting the importance of different input features, and the expression of the attention matrix is: , wherein, and are the query and key matrices of the input matrix, d is the dimension of the matrix, used for normalization adjustment; The model evaluates the input data in real time according to the anomaly detection module, and if an abnormal fluctuation of the index is detected, the weight of the corresponding feature in the attention matrix is increased, and the updated weight affects the output of the model as follows: Here is the predicted output of the model, i.e., the dust diffusion trend in the future time period. To ensure that the model can continuously optimize in a dynamic environment, a rolling prediction strategy is adopted, and after each time window , the system appends the newly collected data to the input matrix, and the appended input matrix is: wherein, is the newly collected data matrix, the model updates the parameters based on the new data of the data matrix in real time, and performs rolling prediction to output a new diffusion trend At the same time, an adaptive optimization mechanism of the loss function is introduced to dynamically adjust the weight matrix of the model according to the prediction error, and the expression of the dynamic adjustment is: wherein, is the actual monitoring value, is the predicted value, is an L2 regularization term, which is used to prevent overfitting, is a loss function, which is used to measure the error between the prediction result of the model and the true value, is a regularization coefficient, which is used to control the complexity of the model and avoid overfitting.

6. The coal mine dust diffusion simulation and control method based on artificial intelligence according to claim 1, characterized in that, The specific steps of realizing automatic adjustment of dust control strategy by using reinforcement learning algorithm are as follows: An intelligent control system based on reinforcement learning is designed. The state space and action space are defined, and the granularity of action is established. By clearly defining the state and action, it is ensured that the system can make diversified decisions in different scenarios. A reward function for dust control is defined. A positive reward is given 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, a negative punishment is given. In addition, according to the resource use efficiency, a secondary reward is designed to encourage the system to optimize the control strategy. 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 according to the reward function feedback. Through the balance of exploration and utilization, the agent not only explores new strategies but also utilizes existing successful experience to continuously optimize the control ability for different scenarios. After training, the reinforcement learning model is deployed in the actual coal mine environment. It receives real-time data feedback from the monitoring system and predicts the future dust diffusion trend based on the current state. The system automatically executes the optimized control action and continuously records the effect of the control behavior. The actual running data is fed back to the model for further adaptive update.

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