An intelligent control method for a coal mine ventilation system based on environmental perception

Abstract

The application discloses an intelligent control method for a coal mine ventilation system based on environment sensing, and relates to the technical field of intelligent control of coal mine ventilation systems, and comprises the following steps: collecting environment data of different areas of a mine, including gas concentration, temperature and humidity, air flow rate and oxygen content, and monitoring the running state of ventilation equipment in real time; the data of each sensor is synchronously processed through a data fusion technology to generate a unified environment sensing model. Through the collaborative work of an expert system and deep learning, the application realizes real-time monitoring and emergency plan execution under extreme working conditions, improves response speed and decision accuracy. The ventilation line switching is optimized by using a reinforcement learning algorithm, the adaptive ability and redundant guarantee of the system are enhanced, and the airflow is ensured not to be interrupted at critical moments. Through the abnormal comparison of a knowledge base and a rule base, false positives and false negatives are reduced, the system efficiency is improved, the operation burden is reduced, and the safety and stable operation of mine personnel are maximally guaranteed.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for coal mine ventilation systems, and specifically to an intelligent control method for coal mine ventilation systems based on environmental perception. Background Technology

[0002] Intelligent control of coal mine ventilation systems based on environmental perception refers to the dynamic adjustment of ventilation systems in coal mines through the integration of environmental monitoring sensors and intelligent algorithms. This system can sense key environmental parameters inside the mine in real time, such as methane concentration, temperature and humidity, wind speed, and dust levels, and optimize airflow and direction control through data analysis and predictive models. Intelligent control can not only automatically increase ventilation based on changes in hazardous gases, but also reduce ventilation when unnecessary, thereby reducing energy consumption and operating costs, and ensuring miner safety and environmental controllability. Such a system effectively improves the accuracy and efficiency of coal mine ventilation management, reduces manual intervention, and enhances mine safety and environmental protection levels.

[0003] The existing technology has the following shortcomings:

[0004] When intelligent control is implemented in coal mine ventilation systems, in complex mine environments such as sudden gas eruptions, fires, or localized collapses, the system's intelligent algorithms may experience response delays or misjudgments due to insufficient training data or over-reliance on normal operating condition data. For example, if the system fails to adjust the ventilation volume or switch to backup ventilation lines in a timely manner, harmful gases in hazardous areas may not be able to be quickly expelled or oxygen supply may be insufficient. This lag could lead to major safety accidents, such as gas explosions or worker asphyxiation.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent control method for coal mine ventilation systems based on environmental perception. Through the collaborative work of expert systems and deep learning, it solves the problem of response lag in traditional ventilation systems under extreme conditions, achieving millisecond-level emergency plan generation, ensuring accurate decision-making, and reducing the risk of gas explosions and oxygen shortages. With the help of virtual simulation training using reinforcement learning algorithms, the system can adaptively select the optimal ventilation path when sudden collapses or air duct obstructions occur, and ensure stable airflow through multi-line exhaust, improving system reliability. Anomaly comparison analysis using a knowledge base and rule base effectively reduces sensor false alarms, improves system efficiency, and reduces the burden on operators, maximizing mine safety and the stability of the production environment, thus solving the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control method for coal mine ventilation systems based on environmental perception, which combines multi-source environmental data acquisition with intelligent algorithms to dynamically monitor and control the complex environment of coal mines in real time, in order to address the problems of response delay and judgment errors under extreme working conditions, specifically including the following steps:

[0008] Collect environmental data from different areas of the mine, including gas concentration, temperature and humidity, air velocity and oxygen content, and monitor the operation status of ventilation equipment in real time; use data fusion technology to process the data from various sensors synchronously to generate a unified environmental perception model, ensuring the consistency and accuracy of data from different areas inside and outside the mine;

[0009] The system utilizes a multi-level early warning model to identify potential extreme situations. It uses a threshold detection algorithm built from historical data and a rule base to determine whether extreme operating conditions exist. In the event of extreme operating conditions, it issues a real-time alarm to the management personnel and triggers the system's automatic response module.

[0010] The adaptive neural network algorithm continuously updates the prediction model for changes in airflow and gas concentration in the mine. When a drastic fluctuation in environmental data is detected, the neural network algorithm immediately calculates the optimal ventilation adjustment strategy and sends instructions to the relevant fan equipment to ensure that harmful gases are quickly discharged.

[0011] A reinforcement learning-based algorithm is used to pre-train ventilation route switching schemes under various extreme conditions in a virtual simulation environment; when the main ventilation route is blocked, the optimal backup ventilation route is automatically selected according to the current operating conditions to ensure that the airflow in critical areas is not interrupted.

[0012] By introducing an expert system, which combines a knowledge base with deep learning algorithms, the nonlinear relationships between multiple variables under complex conditions can be analyzed. When the system algorithm encounters uncertainties or data anomalies, the expert system is triggered to compare and analyze historical cases, assist in making key decisions, and ensure that the algorithm has redundant safety guarantees when executing strategies.

[0013] Preferably, the detailed steps for synchronously processing sensor data and generating a unified environmental perception model using data fusion technology are as follows:

[0014] First, real-time data is collected from various sensors deployed in different areas of the mine;

[0015] Perform time synchronization and spatial alignment processing;

[0016] Data from different types of sensors are fused and processed using a multi-source information fusion algorithm to unify data from multiple sensors into a single environmental perception model.

[0017] After data fusion is completed, the system generates a unified environmental perception model for both inside and outside the mine, and updates it dynamically in real time.

[0018] The preferred method for a multi-level early warning model to identify potential extreme situations and trigger an automatic response module is as follows:

[0019] The system first acquires environmental data from sensors in the mine and compares the acquired environmental data with historical data; the multi-level early warning model identifies potential extreme working conditions by setting multiple risk levels, and each level corresponds to a different numerical range and response strategy;

[0020] Using a threshold detection algorithm based on historical data and a rule base, we can analyze in real time whether the collected environmental data has reached the critical value of extreme working conditions.

[0021] Once the system detects extreme working conditions, it immediately triggers the preset alarm system and notifies the mine management personnel through multiple channels. In accordance with the mine emergency plan, it generates a real-time alarm report and records the entire detection and response process of the event through the log function, which facilitates subsequent analysis and optimization.

[0022] Upon detecting extreme operating conditions, the preset emergency module is automatically triggered.

[0023] Preferably, the detailed steps for updating the prediction model of air flow and gas concentration in the mine and optimizing the ventilation volume based on the adaptive neural network algorithm are as follows:

[0024] First, the neural network model receives real-time environmental data collected by sensors as input, including gas concentration C(t), temperature T(t), humidity H(t), air velocity v(t), and oxygen content O2(t). This input data is organized into a vector X(t) = [C(t), T(t), H(t), v(t), O2(t)] and input into the adaptive neural network model in real time. The neural network employs either a recurrent neural network or a long short-term memory network to capture the complex time-series relationships within the mine, and updates the model's internal state using the following formula: h t =σ(W h ·h t-1 +W x ·X(t)+b h ), where h t It is the hidden layer state, W h and W x The weights b of the state transition matrix and the input matrix are respectively. h σ is the bias vector, and σ is the activation function. By continuously inputting time series data, its internal state is gradually updated, providing a basis for subsequent prediction and optimization of ventilation volume.

[0025] By training an adaptive neural network, the system continuously learns the changing patterns of airflow and gas concentration within the mine and updates the prediction model in real time. The prediction model, based on a multi-step prediction strategy, calculates the gas concentration and airflow velocity at future time t+Δt. The prediction process is expressed by the following formula: in, and , respectively, represent the predicted future gas concentration and air velocity; f and g are nonlinear mapping functions of the neural network; ∈ and η are noise terms used to simulate random disturbance factors in the mine.

[0026] Preferably, when a drastic fluctuation in mine environmental data is detected, the neural network immediately calculates the optimal ventilation adjustment strategy to achieve rapid removal of harmful gases. The optimization strategy is solved based on the following objective function:

[0027]

[0028] Where Q(t) is the ventilation volume of the fan at time t; C safe It is the gas concentration below the safety threshold; v target α is the target air velocity; E(Q) is the energy consumption function of the ventilation system; α, β, and γ are weighting parameters that control the balance between safety, air velocity, and energy consumption.

[0029] Preferably, the detailed steps for virtual simulation training and optimal ventilation route switching based on reinforcement learning algorithms are as follows:

[0030] First, a virtual simulation environment is constructed to simulate the real terrain structure and ventilation system of the mine, including the layout of the ventilation ducts, the distribution of the main and backup fans, and the various key operating areas of the mine.

[0031] Using a reinforcement learning framework, the mine's ventilation system is defined as the "environment" and the algorithm as the "agent". The algorithm learns how to make optimal decisions under different extreme working conditions by continuously interacting with the virtual environment.

[0032] After extensive training, the reinforcement learning algorithm forms a policy network, which is used to select the optimal ventilation path in different situations.

[0033] When the main ventilation route is blocked, the system immediately calls the optimal backup ventilation scheme calculated by the reinforcement learning algorithm and automatically performs the switching operation. The algorithm will quickly select a path that ensures that the airflow in the critical area is not interrupted based on the current sensor data and status.

[0034] Preferably, the specific steps for implementing decision support by combining expert systems and deep learning algorithms are as follows:

[0035] Deep learning models are introduced to handle complex nonlinear relationships. These models are trained on a large amount of mine data and can predict the changing trends and potential risks between environmental variables. The framework combines expert systems and deep learning. When the system receives new data, it triggers corresponding expert rules based on the prediction results of deep learning.

[0036] Continuously monitor the mine's environmental data, and trigger the anomaly detection module when anomalies are detected.

[0037] After receiving an anomaly, the expert system uses an inference engine to select the case that best fits the current situation from the knowledge base and compares it with the prediction results of the deep learning model.

[0038] After completing the joint analysis, the system generates specific emergency decision-making plans and provides decision-making suggestions to mine managers through a visual interface, while also indicating potential risks and implementation steps.

[0039] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0040] This invention effectively solves the problem of delayed response in traditional ventilation systems under extreme conditions by introducing the collaborative work of expert systems and deep learning. In emergencies such as fires, gas eruptions, or collapses, the system utilizes deep learning models for real-time monitoring and prediction, rapidly analyzing complex changes within the mine and automatically triggering emergency plans within the expert system. This collaborative mechanism enables the system to generate emergency plans within milliseconds, reducing the time delay caused by reliance on manual judgment. Unlike traditional fixed strategies, this invention uses intelligent reasoning based on historical cases and real-time data to ensure the accuracy and adaptability of decision-making. This efficient and precise response capability significantly reduces the risks of gas explosions and oxygen shortages, providing a strong guarantee for safe mine operations.

[0041] This invention employs virtual simulation training using reinforcement learning algorithms, enabling the system to optimize ventilation path switching strategies under various complex conditions. In actual operation, when a main duct is blocked due to a collapse or equipment failure, the system automatically selects the optimal backup ventilation path based on an adaptive reinforcement learning model, and adjusts fan output and duct status according to real-time operating conditions. Simultaneously, the system incorporates a redundant safety mechanism, allowing for stable airflow through multi-line combined exhaust when one path fails. This dynamic adaptive capability ensures high system reliability in the face of uncertainty and unforeseen events, significantly reducing the risk of accidents caused by duct blockage or insufficient ventilation.

[0042] This invention significantly reduces the risk of misjudgment caused by sensor false alarms and data anomalies through anomaly comparison analysis using a knowledge base and rule base. It effectively avoids unnecessary alarms triggered by a single data error, thus improving system operating efficiency. Simultaneously, the system provides detailed risk assessments and emergency decision support through joint analysis using expert systems and deep learning, reducing the judgment burden on operators in complex situations. This not only improves the operational efficiency of the ventilation system but also maximizes the safety of personnel and the stability of the production environment within the mine. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0044] Figure 1 This is a flowchart of a method for intelligent control of a coal mine ventilation system based on environmental perception, according to the present invention. Detailed Implementation

[0045] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0046] This invention provides, for example Figure 1 The method for intelligent control of coal mine ventilation systems based on environmental perception combines multi-source environmental data acquisition with intelligent algorithms to dynamically monitor and control the complex environment of coal mines in real time, addressing response delays and judgment errors under extreme operating conditions. Specifically, it includes the following steps:

[0047] Collect environmental data from different areas of the mine, including gas concentration, temperature and humidity, air velocity and oxygen content, and monitor the operating status of the main ventilation equipment in real time; use data fusion technology to process the data from various sensors synchronously to generate a unified environmental perception model, ensuring the consistency and accuracy of data from different areas inside and outside the mine;

[0048] The detailed steps for synchronously processing sensor data and generating a unified environmental perception model using data fusion technology are as follows:

[0049] First, real-time data is collected from various sensors deployed in different areas of the mine (such as gas concentration sensors, temperature and humidity sensors, air velocity sensors, and oxygen detection sensors). Since these sensors may be located at different depths, in different areas, or near different equipment, and their acquisition frequencies and data formats may differ, preprocessing is necessary. Preprocessing includes data format standardization (e.g., converting different units), outlier filtering (removing sudden erroneous data), and missing data imputation (using linear interpolation or nearest-neighbor value filling) to ensure the reliability and consistency of the data source.

[0050] The complex environment of mines can lead to discrepancies in data acquisition times from sensors in different areas, necessitating time synchronization and spatial alignment. Time synchronization utilizes the Network Time Protocol (NTP) or internal timestamps to ensure that measurement data from different sensors are based on the same time scale. Spatial alignment is performed based on the sensor's geographical location and installation position, ensuring that the data spatially corresponds to specific areas or ventilation paths within the mine. This process helps avoid misinterpretations due to temporal or spatial data discrepancies, resulting in more accurate subsequent analyses.

[0051] Data from different types of sensors is fused using multi-source information fusion algorithms, such as Kalman filtering, Bayesian estimation, or weighted average fusion, to unify data from multiple sensors into a single environmental perception model. This process requires differentiating the data based on the weights, accuracy, and real-time performance of different sensors. For example, gas concentration sensors may have a higher weight in critical areas, while air velocity data needs frequent updates to reflect changes in wind direction within the mine. The fused data not only provides a more comprehensive view of the environment but also reduces errors caused by the failure of a single sensor.

[0052] After data fusion, the system generates a unified environmental perception model for both inside and outside the mine, and updates it dynamically in real time. This model displays the environmental conditions of various areas of the mine through a visual interface, including methane concentration distribution, temperature and humidity, oxygen content, and wind speed and direction. As new data is continuously input, the system automatically updates the model, ensuring that managers can obtain the latest mine environmental information and monitor the ventilation system in real time. Furthermore, the accumulation of historical data in the model supports the training of intelligent algorithms and the prediction of extreme working conditions, further improving the system's predictive capabilities and emergency response efficiency.

[0053] The system utilizes a multi-level early warning model to identify potential extreme situations (such as a sharp rise in gas concentration or an abnormal increase in temperature); it uses a threshold detection algorithm built from historical data and a rule base to determine whether extreme working conditions exist, and issues a real-time alarm to management personnel when extreme working conditions occur, while simultaneously triggering the system's automatic response module.

[0054] The detailed steps of the multi-level early warning model in identifying potential extreme situations and triggering the automatic response module are as follows:

[0055] The system first acquires environmental data such as gas concentration, temperature, humidity, oxygen content, and air velocity from sensors in the mine, and compares this data with historical data. A multi-level early warning model identifies potential extreme working conditions by setting multiple risk levels, such as normal, warning, alarm, and emergency. Each level corresponds to a different numerical range and response strategy. For example, when the gas concentration is slightly higher than normal, the system enters a warning state; if it continues to rise, a higher-level alarm is triggered. This hierarchical mechanism ensures that the system can identify anomalies early, reducing the probability of sudden accidents.

[0056] The system utilizes a threshold detection algorithm built upon historical data and a rule base to analyze in real time whether collected environmental data reaches the critical values ​​for extreme working conditions. The rule base is established based on past mine accident cases and safety regulations, including multi-dimensional combinations of conditions, such as a sustained methane concentration exceeding a certain level, a sudden drop in airflow velocity, or a significant fluctuation in temperature within a short period. The algorithm not only supports monitoring single variables but also detects complex hazards through multi-parameter correlation analysis. For example, high methane concentration accompanied by a decrease in airflow velocity is identified as a higher risk. If a preset threshold is detected, the system immediately escalates to a higher-level alarm.

[0057] Once the system detects extreme conditions, it immediately triggers pre-set alarm systems and notifies mine management personnel through multiple channels, such as SMS, voice alarms, and emergency notifications on the control room display screen. The alarm not only indicates the specific risk type (e.g., excessive gas or fire) but also specifies the affected area and recommended emergency measures. Management personnel can view detailed data trends and system-recommended response strategies through a visual interface, ensuring rapid decision-making. Furthermore, the system generates a real-time alarm report based on the mine's emergency plan and records the entire detection and response process through a log function, facilitating subsequent analysis and optimization.

[0058] Upon detecting extreme operating conditions, the system not only relies on manual response but also automatically triggers pre-set emergency modules. For example, when the gas concentration exceeds the safety threshold and continues to rise within a short period, the system automatically adjusts the main fan speed or activates backup ventilation lines to expel harmful gases and increase oxygen supply. Simultaneously, if a fire or landslide risk is detected, the system activates corresponding isolation devices or closes some air ducts to prevent the spread of danger. The entire process continuously monitors the execution effect through a feedback loop and dynamically adjusts control strategies to ensure efficient execution of emergency measures and minimize the occurrence of safety accidents.

[0059] The adaptive neural network algorithm continuously updates the prediction model for changes in airflow and gas concentration in the mine. When a drastic fluctuation in environmental data is detected, the neural network algorithm immediately calculates the optimal ventilation adjustment strategy and sends instructions to the relevant fan equipment to ensure that harmful gases are quickly discharged.

[0060] The detailed steps for updating the prediction model of air flow and gas concentration in mines and optimizing ventilation volume based on the adaptive neural network algorithm are as follows:

[0061] First, the neural network model receives real-time environmental data collected by sensors as input, including gas concentration C(t), temperature T(t), humidity H(t), air velocity v(t), and oxygen content O2(t). This input data forms a vector X(t) = [C(t), T(t), H(t), v(t), O2(t)], which is fed into the adaptive neural network model in real time. This neural network employs a recurrent neural network (RNN) or a long short-term memory network (LSTM) to capture the complex time-series relationships within the mine and updates the model's internal state using the following formula: h t =σ(W h ·h t-1 +W x ·X(t)+b h ), where h t It is the hidden layer state, W h and W x The weights b of the state transition matrix and the input matrix are respectively. h σ is the bias vector, and σ is the activation function (such as ReLU or Tanh). By continuously inputting time series data, the model gradually updates its internal state, providing a foundation for subsequent prediction and optimization of ventilation volume.

[0062] Through training with an adaptive neural network, the system continuously learns the changing patterns of airflow and gas concentration within the mine and updates the prediction model in real time. The prediction model, based on a multi-step prediction strategy, calculates the gas concentration and airflow velocity at the future time t+Δt. This prediction process is expressed by the following formula: in, and denoted by , respectively, the predicted future gas concentration and air velocity; f and g are nonlinear mapping functions of the neural network; ∈ and η are noise terms used to simulate random disturbance factors in the mine. After each new data input, the model adaptively optimizes the prediction accuracy by adjusting the weights using the backpropagation algorithm (BPTT) and gradient descent method.

[0063] When a drastic fluctuation in mine environmental data is detected (such as a rapid increase in C(t) or a sudden decrease in v(t), the neural network immediately calculates the optimal ventilation adjustment strategy to achieve rapid removal of harmful gases. The optimization strategy is solved based on the following objective function: Where Q(t) is the ventilation volume of the fan at time t; C safe It is the gas concentration below the safety threshold; v target The target air velocity is E(Q); the energy consumption function of the ventilation system is E(Q); α, β, and γ are weighting parameters that control the balance between safety, air velocity, and energy consumption. After solving the optimization problem using gradient descent or the Lagrange multiplier method, the system immediately sends instructions to the ventilation equipment to adjust the ventilation volume, ensuring that harmful gases are quickly discharged and the mine environment returns to a safe state.

[0064] The algorithm employs a reinforcement learning-based approach to pre-train ventilation route switching schemes under various extreme conditions in a virtual simulation environment. When fires, landslides, or other events cause some main ventilation routes to be blocked, the algorithm automatically selects the optimal backup ventilation route based on the current operating conditions to ensure uninterrupted airflow in critical areas.

[0065] The detailed steps of virtual simulation training and optimal ventilation route switching based on reinforcement learning algorithm are as follows:

[0066] First, a virtual simulation environment is constructed to simulate the actual terrain structure and ventilation system of a mine, including the layout of air ducts, the distribution of main and backup fans, and various key operational areas of the mine. The virtual environment also needs to accurately simulate the impact of various extreme conditions (such as fire, gas eruption, and collapse) on the ventilation path. Virtual sensors configured in the simulation environment can simulate real-time changes in gas concentration, wind speed, and temperature, providing dynamic virtual data input for the algorithm. Through this high-precision simulation system, the reinforcement learning algorithm can be repeatedly trained under various extreme scenarios to explore and optimize ventilation path switching schemes.

[0067] Employing a reinforcement learning framework (such as Deep Q-Network (DQN) or Proximal Policy Optimization (PPO)), the mine's ventilation system is defined as the "environment," and the algorithm as the "agent." The algorithm learns to make optimal decisions under various extreme conditions by continuously interacting with the virtual environment. During each simulation, the algorithm takes corresponding ventilation route switching actions based on the current operating state (such as blocked air ducts or abnormal gas concentrations), and receives rewards or penalties based on the results. For example, if the switched route successfully expels harmful gases and maintains oxygen supply, the algorithm is rewarded; conversely, if the decision results in airflow obstruction, a penalty is imposed. Through repeated training, the algorithm gradually optimizes its selection strategy, achieving rapid response capabilities to different complex situations.

[0068] After extensive training, the reinforcement learning algorithm forms a policy network used to select the optimal ventilation path under different scenarios. However, since the mine environment may change over time (such as the opening of new ventilation ducts or the blockage of existing ones), the system must support dynamic policy updates. In actual operation, the algorithm continuously collects real-time data and compares it with the results in virtual simulation. If the current operating conditions deviate from the training data, the system triggers new virtual training to update the policy network. In this way, the algorithm not only adapts to static historical data but also adaptively optimizes the ventilation path according to changes in the mine.

[0069] When a fire, collapse, or other event occurs in the mine that obstructs some main ventilation lines, the system immediately invokes the optimal backup ventilation scheme calculated by reinforcement learning algorithms and automatically executes the switching operation. Based on current sensor data and status, the algorithm quickly selects a path that ensures uninterrupted airflow in critical areas. For example, if a main ventilation duct is blocked by a collapse, the system will automatically activate the backup duct and fans, and adjust the airflow distribution to maintain air circulation in all areas. The entire switching process is monitored by a feedback loop to ensure the switched lines operate normally. If any abnormality occurs, the system will automatically recalculate the alternative scheme, further improving the efficiency and reliability of emergency response.

[0070] The system incorporates an expert system, which combines a knowledge base with deep learning algorithms to analyze the nonlinear relationships between multiple variables in complex situations. When the system algorithm encounters uncertainties or data anomalies, it triggers the expert system to compare and analyze historical cases, assist in making critical decisions, and ensure that the algorithm has redundant safety guarantees when executing strategies.

[0071] The specific steps for implementing decision support by combining expert systems with deep learning algorithms are as follows:

[0072] The core of the expert system is a knowledge base, containing operating rules, safety regulations, and historical case data for the mine ventilation system. The rules within the knowledge base are structured through logical reasoning chains, including specific solutions and emergency plans for situations such as excessive gas levels, fires, and landslides. Simultaneously, the system incorporates deep learning models (such as LSTM or Transformer networks) to handle complex nonlinear relationships. These models, trained on extensive mine data, are capable of predicting trends and potential risks among environmental variables. The framework combines the expert system and deep learning; when the system receives new data, it triggers corresponding expert rules based on the predictions from deep learning, improving the intelligence level of emergency response.

[0073] The system continuously monitors environmental data in the mine, such as methane concentration, temperature, humidity, and wind speed. When anomalies are detected (e.g., data fluctuations exceeding expectations or discrepancies with model predictions), the anomaly detection module is triggered. For example, if the methane concentration rises sharply in a short period while the ventilation fan operates normally, it indicates a possible unforeseen pipe blockage or sensor error. In this case, the system compares the historical case library from the expert system with the latest predictions from the deep learning model to analyze whether similar situations exist and determine whether the current operating condition requires manual intervention or should be processed automatically.

[0074] Upon receiving an anomaly report, the expert system uses its inference engine to select the case closest to the current situation from the knowledge base and compares it with the predictions of the deep learning model. For example, if an abnormal gas concentration is detected and data indicates that the main ventilation line is blocked, the system will infer that a collapse may have occurred and suggest switching to an alternate ventilation duct. The inference results of the expert system and the analysis of deep learning complement each other, avoiding misjudgments by a single algorithm. Joint analysis not only improves the accuracy of judgments but also provides redundancy for the system's automatic response, ensuring that even if some modules fail, the system can still take appropriate measures.

[0075] After completing the joint analysis, the system generates specific emergency decision-making plans and provides decision-making suggestions to mine managers through a visual interface, while also indicating potential risks and execution steps. For example, the system will recommend specific routes for switching to backup ventilation lines and inform them of the possible impact areas and time windows. If managers confirm the execution, the system will automatically implement the decision and continuously monitor the actual effects during the execution process. If the actual situation does not match expectations, the system will quickly trigger the redundancy safety mechanism of the expert system, re-invoking the historical case library and deep learning models for analysis to ensure that the system has continuous adjustment capabilities and maximizes mine safety.

[0076] Specific implementation method 1: Automatic judgment and emergency handling of abnormal situations based on knowledge base;

[0077] In the daily operation of a mine, key parameters such as gas concentration, temperature, humidity, and wind speed may become abnormal due to fluctuations in sensor data or sudden environmental changes. This system, leveraging the knowledge base of an expert system, enables automatic judgment and emergency handling of these anomalies. When the system detects a rapid increase in gas concentration within a short period, but the main ventilation equipment is operating normally, it triggers the knowledge base comparison module of the expert system. The knowledge base pre-stores a large number of historical cases and operating rules, covering scenarios such as sensor false alarms, duct blockages, and ventilation equipment malfunctions. The system quickly compares these historical cases to determine whether the current anomaly is caused by equipment failure, a false alarm, or a change in actual operating conditions.

[0078] If the comparison reveals a high degree of similarity between the current situation and a previously recorded false alarm event—for example, if a sensor previously sent erroneous data due to a malfunction—the system will promptly prompt management to check the sensor status and switch to a backup sensor as the data source. Simultaneously, the system will analyze correlated data from other sensors, such as oxygen content and airflow rate, to further verify whether the abnormal gas concentration is due to sensor error. If the system determines it to be a false alarm, it will automatically mark the data as abnormal and prevent it from entering further decision-making processes, thus avoiding triggering unnecessary emergency responses.

[0079] In more complex scenarios, such as when methane concentration rises accompanied by a decrease in airflow velocity in the main ventilation duct, the system will determine that there may be a blockage in the duct. In this case, the expert system will generate emergency recommendations based on historical blockage cases in its knowledge base, prompting management personnel to conduct on-site inspections. Simultaneously, the system will simulate whether to increase ventilation or switch to a backup duct. If a high-risk event is identified, such as a continued rise in methane concentration without a decrease in duct resistance, the system will escalate the alarm and recommend immediate isolation and evacuation operations. This implementation method, through rapid comparison and emergency handling by the expert system, reduces the time required for manual judgment, improves the response speed of mine environmental management, and ensures operational safety and reliability.

[0080] Specific Implementation Method Two: Deep Learning Prediction Model-Assisted Ventilation Line Switching Optimization;

[0081] When a localized collapse occurs in the mine, obstructing some main ventilation ducts, the ventilation system must quickly switch to backup lines to maintain unimpeded airflow. To this end, the system incorporates a deep learning prediction model. By analyzing real-time environmental data and historical records, it predicts the changing trends of variables such as gas concentration, wind speed, temperature, and humidity, thereby selecting the optimal ventilation path. The system has been pre-trained to simulate different mine scenarios and has mastered the performance of each ventilation line under various conditions through the deep learning model. The model can calculate the airflow capacity of the backup lines in real time under various conditions and predict the gas emission effect and oxygen supply level in advance.

[0082] For example, when a main ventilation duct in a mine becomes blocked due to a collapse, the deep learning model takes over the analysis of the current data and performs simulations for each backup line. If the wind speed in a backup line is insufficient to remove gas in a short time, the system continues to evaluate other lines and considers whether to activate a multi-line combined ventilation strategy. In this case, the system may activate two or more backup ventilation ducts simultaneously to avoid insufficient flow problems that may occur when switching between single lines. In addition, the model also predicts the impact of airflow switching on the surrounding area, such as how increased wind speed may lead to increased gas concentration or temperature fluctuations in other areas. Through multi-line combined ventilation, the system achieves flexible scheduling in complex scenarios, ensuring that air circulation in the mine can still be maintained even in extreme situations such as collapses or ventilation duct blockages.

[0083] Throughout the process, the system monitors the performance after the switch in real time, comparing the actual data with the model predictions. If the performance after the duct switch does not meet expectations (e.g., the gas concentration does not decrease as predicted), the system will immediately recalculate the backup plan and make secondary adjustments. This approach ensures the adaptability of the ventilation system in dynamic environments and, through the optimization capabilities of deep learning algorithms, minimizes the risks of airflow obstruction and oxygen deficiency.

[0084] Specific implementation method three: Collaborative emergency response plan execution using expert systems and deep learning;

[0085] In extreme situations such as mine fires and gas eruptions, the system leverages the collaboration of an expert system and deep learning to ensure precise execution of emergency plans. The expert system's knowledge base stores detailed safety procedures and emergency plans, including instructions on switching ventilation lines, isolating hazardous areas, and personnel evacuation procedures. When extreme situations occur, the deep learning model first rapidly analyzes current environmental variables, such as a sharp rise in temperature and an abnormal increase in gas concentration, and predicts the impact of these changes on various areas. Based on these predictions, the system requests the expert system to invoke the most appropriate emergency plan.

[0086] For example, when a deep learning model predicts that the high temperatures caused by a fire may damage the main ventilation duct, the expert system will refer to fire emergency plans in the knowledge base, suggesting the closure of the affected area and switching to a backup ventilation duct. Simultaneously, the system will automatically increase the output power of both the main and backup fans to ensure efficient emergency ventilation. Throughout this process, the deep learning model will continuously provide real-time data support to ensure that the expert system's plan is implemented in accordance with the current situation. If the switched ventilation duct fails to achieve the expected results, the system will trigger a new calculation cycle to dynamically adjust the emergency plan.

[0087] The advantage of this collaborative approach lies in improving the system's emergency response speed under complex operating conditions. The deep learning model is responsible for analyzing complex nonlinear relationships and making real-time predictions, while the expert system provides the execution steps of the emergency plan through a knowledge base. The combination of the two not only improves the accuracy of decision-making but also effectively avoids misjudgments or delays caused by a single system. Through this collaborative mechanism, the system possesses high adaptability and flexibility, ensuring the safe operation of the mine and the timely evacuation of personnel even in complex and extreme environments.

[0088] This invention effectively solves the problem of delayed response in traditional ventilation systems under extreme conditions by introducing the collaborative work of expert systems and deep learning. In emergencies such as fires, gas eruptions, or collapses, the system utilizes deep learning models for real-time monitoring and prediction, rapidly analyzing complex changes within the mine and automatically triggering emergency plans within the expert system. This collaborative mechanism enables the system to generate emergency plans within milliseconds, reducing the time delay caused by reliance on manual judgment. Unlike traditional fixed strategies, this invention uses intelligent reasoning based on historical cases and real-time data to ensure the accuracy and adaptability of decision-making. This efficient and precise response capability significantly reduces the risks of gas explosions and oxygen shortages, providing a strong guarantee for safe mine operations.

[0089] This invention employs virtual simulation training using reinforcement learning algorithms, enabling the system to optimize ventilation path switching strategies under various complex conditions. In actual operation, when a main duct is blocked due to a collapse or equipment failure, the system automatically selects the optimal backup ventilation path based on an adaptive reinforcement learning model, and adjusts fan output and duct status according to real-time operating conditions. Simultaneously, the system incorporates a redundant safety mechanism, allowing for stable airflow through multi-line combined exhaust when one path fails. This dynamic adaptive capability ensures high system reliability in the face of uncertainty and unforeseen events, significantly reducing the risk of accidents caused by duct blockage or insufficient ventilation.

[0090] This invention significantly reduces the risk of misjudgments caused by sensor false alarms and data anomalies through anomaly comparison analysis using a knowledge base and rule base. For example, when a sensor falsely reports excessively high methane concentration, the system compares and analyzes historical cases and data from other sensors to determine whether further emergency measures are needed. This mechanism avoids unnecessary alarms triggered by a single data error, improving system efficiency. Simultaneously, the system provides detailed risk assessments and emergency decision support through joint analysis using expert systems and deep learning, reducing the judgment burden on operators in complex situations. This intelligent processing method not only improves the operational efficiency of the ventilation system but also maximizes the safety of personnel and the stability of the production environment within the mine.

[0091] 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 environment perception-based intelligent regulation method for a coal mine ventilation system, characterized in that, By combining multi-source environmental data collection with intelligent algorithms, the complex environment of coal mines is dynamically monitored and real-time controlled to address the problems of response delay and judgment failure in extreme working conditions. The specific steps include: Collecting environmental data in different areas of the mine, including gas concentration, temperature and humidity, air flow rate, and oxygen content, and monitoring the operating status of ventilation equipment in real time; using data fusion technology to synchronize the data from various sensors and generate a unified environmental perception model to ensure data consistency and accuracy in different areas of the mine; Using a multi-level early warning model to identify potential extreme situations, a threshold detection algorithm based on historical data and a rule base is used to determine whether an extreme working condition exists and to issue real-time alerts to management personnel in the event of an extreme working condition, while triggering the system's automatic response module; Using an adaptive neural network algorithm, the prediction model for air flow and gas concentration changes in the mine is continuously updated; when a sharp fluctuation in environmental data is detected, the neural network algorithm immediately calculates the optimal ventilation adjustment strategy and sends instructions to the relevant fan equipment to ensure that harmful gases are quickly removed; Using an algorithm based on reinforcement learning, the ventilation line switching scheme in various extreme situations is trained in a virtual simulation environment in advance; when the main ventilation line is blocked, the optimal backup ventilation line is automatically selected based on the current working conditions to ensure uninterrupted airflow in critical areas; Introducing an expert system that combines a knowledge base with deep learning algorithms to analyze the non-linear relationships between multiple variables in complex situations; when the system algorithm encounters uncertain situations or data anomalies, the expert system compares and analyzes historical cases to assist in making key decisions and ensure that the algorithm has redundant safety guarantees when executing strategies; The detailed steps for updating the air flow and gas concentration prediction model in the mine and optimizing the ventilation volume based on the adaptive neural network algorithm are as follows: First, the neural network model receives real-time environmental data collected by sensors as input, including gas concentration. ,temperature ,humidity air velocity and oxygen content The input data is arranged into a vector. The data is fed into an adaptive neural network model in real time. The model employs either a recurrent neural network or a long short-term memory network to capture the complex time-series relationships within the mine, and updates the model's internal state using the following formula: ,in, It is a hidden layer state. and These are the weights of the state transition matrix and the input matrix, respectively. For bias vectors, As an activation function, its internal state is gradually updated by continuously inputting time series data, providing a basis for subsequent prediction and optimization of ventilation volume; Through the training of the adaptive neural network, the variation law of the air flow and the gas concentration in the mine is continuously learned, and the prediction model is updated in real time. The prediction model is based on a multi-step prediction strategy, and the gas concentration and air flow rate at future time points are calculated The prediction process is represented by the following formula: , wherein, and are the predicted future gas concentration and air flow rate, respectively; and are the nonlinear mapping functions of the neural network; and are noise terms used to simulate random interference factors in the mine. When detecting that the mine environment data fluctuates sharply, the neural network immediately calculates the optimal ventilation adjustment strategy to realize the rapid discharge of harmful gases. The optimization strategy is solved based on the following objective function: wherein, is the ventilation volume of the fan at time t; is the gas concentration under the safety threshold; is the target air flow rate; is the energy consumption function of the ventilation system; , , is a weight parameter that controls the balance between safety, flow rate, and energy consumption.

2. The intelligent regulation and control method for the coal mine ventilation system based on environmental perception according to claim 1, characterized in that, The detailed steps for synchronously processing sensor data and generating a unified environmental perception model using data fusion technology are as follows: First, collect real-time data from multiple sensors placed in different areas of the mine; Perform time synchronization and spatial alignment processing; Fuse data from different types of sensors using a multi-source information fusion algorithm to unify the data from multiple sensors into a single environmental perception model; After data fusion, the system generates a unified environmental perception model for the mine and updates it in real time.

3. The intelligent regulation and control method for the coal mine ventilation system based on environmental perception according to claim 1, characterized in that, The detailed steps for the multi-level early warning model to identify potential extreme situations and trigger the automatic response module are as follows: The system first obtains environmental data from sensors in the mine and compares the obtained data with historical data; the multi-level early warning model identifies potential extreme working conditions by setting multiple risk levels, with each level corresponding to a different numerical range and response strategy; Using a threshold detection algorithm based on historical data and a rule base, real-time analysis is performed on the collected environmental data to determine whether it has reached the critical value of an extreme working condition; Once the system detects extreme working conditions, it triggers the preset alarm system immediately and notifies the mine management personnel through multiple channels. According to the mine emergency plan, it generates a real-time alarm report and records the detection and response process of the entire event through the log function, facilitating subsequent analysis and optimization. After detecting extreme working conditions, the preset emergency module is automatically triggered.

4. The intelligent regulation and control method for the coal mine ventilation system based on environmental perception according to claim 1, characterized in that, The detailed steps of virtual simulation training based on reinforcement learning algorithm and optimal ventilation line switching are as follows: First, build a virtual simulation environment to simulate the real terrain structure and ventilation system of the mine, including the layout of the air duct, the distribution of the main fan and standby fan, and each key operation area of the mine. Use the reinforcement learning framework to define the mine's ventilation system as the "environment" and the algorithm as the "agent". The algorithm learns how to make optimal decisions under different extreme working conditions by constantly interacting with the virtual environment. After a large number of training, the reinforcement learning algorithm forms a strategy network for selecting the optimal ventilation path under different situations. When the main ventilation line is blocked, the system immediately calls the optimal standby ventilation scheme calculated by the reinforcement learning algorithm and automatically performs the switching operation. Based on the current sensor data and state, the algorithm quickly selects a path that ensures uninterrupted airflow in the key area.

5. The intelligent regulation and control method for the coal mine ventilation system based on environmental perception according to claim 1, characterized in that, The specific steps of decision support implementation by combining expert systems with deep learning algorithms are as follows: Introduce a deep learning model to handle complex nonlinear relationships. The deep learning model is trained on a large amount of mine data and can predict the trends and potential risks between environmental variables. The framework combines expert systems and deep learning. When the system receives new data, it triggers the corresponding expert rules based on the prediction results of deep learning. Continuously monitor the environmental data of the mine. When an abnormality is detected, the anomaly detection module is triggered. After receiving the abnormal situation, the expert system selects the most suitable case from the knowledge base through the reasoning engine and compares it with the prediction results of the deep learning model. After completing the joint analysis, the system generates a specific emergency decision-making scheme and provides decision-making suggestions to mine management personnel through a visual interface, while marking potential risks and execution steps.

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