An integrated control method for coal mine ventilation and heat reduction based on intelligent scheduling

Abstract

The present invention discloses an integrated control method for coal mine ventilation and heat reduction based on intelligent scheduling, which relates to the technical field of coal mine ventilation and heat reduction control, and includes the following steps: arranging a variety of monitoring equipment on the ventilation paths and spare ventilation paths of the mine to collect temperature, humidity, gas concentration, and wind speed and volume data; the central control system summarizes the monitoring data in real time, analyzes the ventilation conditions in the air duct, and identifies whether there are abnormal changes. The present invention ensures continuous ventilation through multi-path redundancy and dynamic switching, reduces risks such as gas explosions, and uses airflow simulation models to provide early warnings to ensure the safety of miners. Combined with reinforcement learning algorithms and simulation models, rapid response and adaptive adjustment are achieved, air volume distribution is optimized, shutdowns are avoided, and economic benefits are improved. At the same time, by intelligently planning ventilation paths and dynamically adjusting fan loads, energy consumption and maintenance costs are reduced, equipment life is extended, and the sustainable operation of mines is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine ventilation and heat reduction control, and in particular to an integrated control method for coal mine ventilation and heat reduction based on intelligent scheduling. Background Art

[0002] Integrated ventilation and cooling control for coal mines based on intelligent scheduling is a control solution that integrates and optimizes a coal mine's ventilation and cooling systems. It achieves dynamic scheduling of ventilation and cooling through intelligent algorithms and real-time monitoring equipment. In this system, the intelligent scheduling module automatically adjusts air volume, wind speed, and cooling equipment operating strategies based on the mine's temperature, humidity, air quality, and work requirements to ensure a safe and comfortable mine environment. This integrated control not only improves energy efficiency and reduces energy consumption, but also effectively mitigates hazards such as high temperatures and high humidity within the mine, helping to protect the health and safety of miners.

[0003] The existing technology has the following deficiencies:

[0004] In the integrated control of ventilation and heat reduction in coal mines, if a sudden event (such as a localized collapse, equipment blockage, or waste accumulation) causes a partial or complete blockage of a main air duct within the mine, and the intelligent scheduling system fails to promptly identify and adjust the ventilation strategy for other ducts, air volume in some areas will decrease sharply or even stagnate. This can cause a rapid increase in regional temperature and the accumulation of harmful gases, seriously threatening miners' safety and potentially increasing the risk of gas explosions and fires. Because these issues represent sudden changes in physical channels, they are difficult to predict and respond to immediately using standard algorithms, placing higher demands on the system's real-time responsiveness.

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

[0006] The purpose of the present invention is to provide an integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling. Through multi-path redundant ventilation and dynamic switching mechanisms, it ensures uninterrupted ventilation when the main air duct is blocked, timely discharges harmful gases and controls the temperature, and reduces the risk of gas explosions and poisoning. The system uses an airflow simulation model to predict gas accumulation and temperature rise in advance, and generates an early warning to ensure the safety of miners. Combined with the reinforcement learning algorithm, the system achieves rapid response and adaptive adjustment, and can quickly propose an air volume optimization plan when detecting the risk of blockage, avoid shutdowns and improve economic benefits. At the same time, through precise air volume distribution and path optimization, energy consumption and maintenance costs are effectively reduced, equipment life is extended, and the sustainable operation of mines is promoted to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, comprising the following steps:

[0008] Various monitoring devices are deployed along the mine's ventilation routes and backup ventilation routes to collect data on temperature, humidity, gas concentration, and wind speed and volume. The central control system aggregates the monitoring data in real time, analyzes the ventilation conditions within the air ducts, and identifies any abnormal changes.

[0009] An air duct status perception module is set up to quickly determine whether the air duct has potential blockage by monitoring the data mutation characteristics of the equipment. After detecting the risk of blockage, the system generates an alarm message and locks the abnormal location, prompting the dispatch system to prioritize the status of the abnormal location area.

[0010] In the event of air duct blockage, fuzzy logic algorithms are used to calculate alternative backup air duct paths and dynamically allocate air volume based on air volume demand, current path load, temperature, and gas concentration.

[0011] Utilizing reinforcement learning algorithms, ventilation strategies are continuously optimized based on historical data and real-time feedback, improving the speed of response to emergencies. When a path is repeatedly blocked, the reinforcement learning algorithm adaptively reduces the ventilation weight of that path and prioritizes other paths.

[0012] Combined with the airflow simulation model, it can predict the gas diffusion and temperature rise after the air duct is blocked, and provide early warning and air volume optimization solutions; through real-time simulation of the airflow distribution of different air paths under blockage scenarios, it can predict high-risk areas in advance and automatically adjust the air volume and temperature control strategies to ensure ventilation and heat reduction effects.

[0013] Preferably, the specific steps for the central control system to summarize the monitoring data in real time, analyze the ventilation conditions in the air duct, and identify abnormal changes are as follows:

[0014] The central control system continuously receives data from various ventilation paths in the mine through a multi-point sensor network, including temperature, humidity, gas concentration, wind speed and air volume;

[0015] After data preprocessing, the central control system conducts a multi-dimensional cross-analysis of the data from each monitoring point. If it detects a decrease in wind speed but an increase in gas concentration, it creates a trend model for the data over consecutive time periods and uses regression analysis to predict wind speed changes over the next few minutes. This helps determine whether the current anomaly is a random occurrence or a systemic problem.

[0016] Multiple thresholds and logic rules are set to automatically determine whether there are anomalies. To improve detection accuracy, a dynamic threshold adjustment strategy is adopted, which automatically optimizes the threshold according to different conditions in different time periods or environmental conditions to reduce false positives and missed negatives.

[0017] Once an abnormal situation is identified, an alarm is automatically generated and pushed to the mine dispatch center and relevant persons in charge. The system conducts a comprehensive analysis of the current status to determine whether emergency measures need to be implemented immediately.

[0018] Preferably, the specific steps for quickly determining potential blockage of the air duct by the air duct state sensing module are as follows:

[0019] The air duct status sensing module is connected to various monitoring devices in real time to continuously collect wind speed, wind pressure, temperature, humidity and gas concentration data at different locations in the mine;

[0020] Once a potential mutation is detected, the perception module immediately starts data difference calculation, that is, comparing the current monitoring data with the historical benchmark data;

[0021] To improve the accuracy of judgment, the perception module integrates multi-point data for collaborative analysis. By comparing and analyzing different areas, it narrows down the scope of potential congestion areas. At the same time, it considers the time lag between different sensors to further confirm the location of the congestion. If multiple sensors detect an anomaly at the same time, the area is marked as a high-risk point.

[0022] When the perception module finally confirms that the anomaly meets the blockage characteristics, it will immediately send an early warning signal to the central control system and initiate intelligent emergency decision-making. The system will automatically select an alternative ventilation path based on the urgency of the blockage location and trigger relevant equipment when necessary.

[0023] Preferably, the specific steps of calculating the backup air duct path and dynamically allocating the air volume using the fuzzy logic algorithm are as follows:

[0024] The system is based on the wind speed V i , temperature T i , gas concentration C i and the current load L i Perform fuzzy processing on each air duct i and define the fuzzy set μ to represent the applicability of the air duct, where: Where, is the fuzzy membership function of wind speed, which is used to judge whether the air duct can provide sufficient ventilation. is a fuzzy function of temperature, which evaluates the suitability of the temperature in the duct and avoids high temperature areas. is a fuzzy function of gas concentration, filtering out paths with high concentrations of harmful gases. is the fuzzy function of the current load, which evaluates whether the load of the air duct is too high; the minimum value of the membership function is taken as the final suitability score μ of each air duct i , candidate air duct path set S 候选 will include only those μ iFor air ducts exceeding the threshold α set by the system, the expression of the candidate air duct path set is: S 候选 ={i|μ i ≥α};

[0025] Construct a comprehensive benefit objective function z for each candidate path i , which is used to measure the ventilation superiority under different conditions. The expression of the comprehensive benefit objective function is: w1, w2, w3, w4 are weight parameters to ensure that the importance of different factors in decision making is balanced, and w1+w2+w3+w4=1, V max , T max , C max , L max Respectively represent the maximum values ​​of the system's wind speed, temperature, gas concentration, and load, and are used for normalization processing;

[0026] Objective function Z i After calculation, the air duct with the largest value is selected as the preferred alternative path.

[0027] Preferably, in order to ensure the reasonable distribution of air volume on each path, a multi-constraint optimization method is used to calculate the optimal air volume Q of each path by the following formula: i , the calculation expression is: Q i ≤Q i，max , where Q i is the air volume allocated to the i-th path, Q 总 is the total air volume currently required by the mine, Q i，max The maximum tolerable wind volume for each path to avoid overloading;

[0028] After the air volume distribution is completed, the operation status of each path is continuously monitored, and the future scheduling strategy is optimized through the reinforcement learning model, with real-time feedback data V i 实时 , T i 实时 , C i 实时 , L i 实时 It will be used as a new input to update the fuzzy membership function and weight parameters. The loss function of the reinforcement learning model is defined as: Among them, Γ(θ) is the loss function, which is used to measure the deviation between the expected state and the actual state. T is the training period, and the system updates the model parameter θ every T time steps.

[0029] Preferably, the specific steps of optimizing ventilation strategy using reinforcement learning algorithm are:

[0030] The state of the mine ventilation system is represented as a state space, which includes the real-time parameters of different air ducts and the historical records of whether each path has been blocked;

[0031] By combining historical data with current status through Q-learning or deep reinforcement learning models, we can determine whether a certain path has a tendency to be repeatedly blocked. For high-risk paths, we can reduce their usage weight and reduce future ventilation dependence.

[0032] When real-time feedback indicates that a certain air duct is blocked or the air volume is insufficient, the optimal ventilation strategy is recalculated based on the latest status and executed immediately;

[0033] When the system finds that a certain path is repeatedly blocked through multiple runs, the reinforcement learning algorithm will automatically lower the priority of the path and adjust its ventilation weight to the minimum or mark it as a backup path.

[0034] Preferably, the specific steps of predicting gas diffusion and temperature rise after air duct blockage based on the airflow simulation model are as follows:

[0035] An airflow simulation model was established based on the mine's physical layout and historical data, including the duct geometry, bifurcation nodes, wind speed and pressure distribution, and temperature and humidity characteristics. Actual wind speeds, air volumes, and gas concentrations across different ventilation paths were input into the model as initial conditions. Power consumption data for mine equipment was also integrated to ensure that the simulation results were highly consistent with actual conditions.

[0036] Once the risk of blockage in the air duct is detected, the gas diffusion path and temperature change trend are immediately predicted through simulation models;

[0037] Based on the simulation results, detailed warning information is generated, including the area where gas diffusion occurs, the time window for temperature increase, and the affected air duct nodes. Warning thresholds are set based on the simulated diffusion time to ensure that the dispatch center and on-site miners receive warnings before the safety critical point and can quickly take emergency measures;

[0038] A real-time feedback mechanism is used to dynamically adjust the model to ensure that the airflow simulation model is always synchronized with the actual situation, avoiding outdated predictions that mislead decision-making.

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

[0040] By employing multi-path redundant ventilation and a dynamic switching mechanism, this invention enables the system to quickly switch to a backup duct when the main air duct is blocked, ensuring uninterrupted ventilation, timely exhaust of harmful gases, and temperature control. This mechanism significantly reduces the likelihood of high-risk accidents such as gas explosions and carbon monoxide poisoning. Furthermore, the system uses airflow simulation models for prediction, anticipating areas of gas accumulation and temperature rise before problems occur, generating timely warning information and avoiding catastrophic accidents caused by delayed response. This ensures the safety of miners and the stability of the mine environment, enabling managers to proactively control safety risks rather than passively respond.

[0041] This invention combines a reinforcement learning algorithm with an airflow simulation model, enabling the system to respond quickly and adjust adaptively. When the system detects a blockage risk, it can complete data analysis and airflow prediction within seconds and propose an optimized air volume adjustment plan. This not only ensures the ventilation needs of the work area but also avoids a complete shutdown due to untimely processing. At the same time, the system records all operational data during the emergency process, which is used to further optimize the reinforcement learning model, thereby continuously improving future response speed and accuracy. This efficient emergency management reduces downtime and equipment loss caused by accidents, thereby improving the economic benefits of the mine.

[0042] Powered by a reinforcement learning algorithm, this invention enables the system to intelligently plan ventilation paths based on different time periods, equipment operating conditions, and environmental changes, preventing fans from operating at high loads for extended periods. By precisely allocating and dynamically adjusting air volume, the system significantly reduces energy consumption while ensuring safety. Furthermore, adaptive optimization of path weights allows the system to gradually avoid air ducts prone to blockage, reducing the need for frequent cleaning and overhauls and lowering maintenance costs. In the long term, this intelligent scheduling mechanism not only extends equipment life but also brings significant energy savings and a sustainable operating model to the mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 This is a method flow chart of a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling according to the present invention. DETAILED DESCRIPTION

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

[0046] The present invention provides Figure 1 The integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling is shown, comprising the following steps:

[0047] Various monitoring devices are deployed along the mine's main and backup ventilation routes to collect data on temperature, humidity, gas concentration, and wind speed and volume. The central control system aggregates this monitoring data in real time, analyzes the ventilation conditions within the ducts, and identifies any abnormal changes.

[0048] The specific steps for the central control system to summarize monitoring data in real time, analyze ventilation conditions in the air duct, and identify abnormal changes are as follows:

[0049] The central control system continuously receives data from various ventilation paths in the mine through a multi-point sensor network, including temperature, humidity, gas concentrations (such as methane and carbon monoxide), wind speed, and air volume. Sensor data is collected at a high frequency (such as once per second), and the system needs to filter out interference signals and noisy data. The preprocessing stage includes data denoising, outlier detection (such as breakpoints or sensor failures), and filling in missing data through interpolation algorithms. The purpose of this step is to ensure that the data entering the analysis module is accurate and complete, providing a high-quality data foundation for subsequent analysis.

[0050] After data preprocessing, the central control system conducts a multi-dimensional cross-analysis of the data from each monitoring point. For example, it correlates and compares the wind speed and air volume in different areas with their gas concentration and temperature and humidity data. If the wind speed is detected to be reduced but the gas concentration is increased, it means that there may be air duct obstruction or localized poor ventilation. At the same time, the system will also perform trend modeling on the data of continuous time periods, and predict the wind speed changes in the next few minutes through regression analysis to help determine whether the current anomaly is an accidental situation or a systemic problem. In addition, the trend model of historical data will provide a baseline for the system to more accurately identify sudden changes.

[0051] The system sets multiple thresholds and logic rules to automatically determine whether there are abnormalities. For example, when the wind speed drops by more than 30% and lasts for more than 30 seconds, the system will determine that the air duct is potentially blocked; when the gas concentration in a certain area exceeds the specified safety threshold and the air volume does not increase synchronously, the system will issue a warning. In order to improve the accuracy of detection, the system adopts a dynamic threshold adjustment strategy, that is, according to different conditions in different time periods (such as day and night shifts) or environmental conditions (such as during equipment maintenance), these thresholds are automatically optimized to reduce false alarms and missed alarms.

[0052] Once the system identifies an abnormal situation (such as an abnormal decrease in air volume or an increase in the concentration of harmful gases), it automatically generates an alarm and pushes it to the mine dispatch center and relevant persons in charge. The system also conducts a comprehensive analysis of the current status to determine whether emergency measures need to be immediately implemented, such as activating alternative ventilation paths, starting emergency cooling equipment, or notifying miners to evacuate. During this process, the central control system will record all events and response times for subsequent analysis and optimization algorithm training. At the same time, the system will enter intensive data collection mode, collecting data at a higher frequency to provide more information support for subsequent abnormal cause analysis.

[0053] An air duct status sensing module is set up to quickly determine whether the air duct is potentially blocked by monitoring data mutation characteristics in the equipment (such as a sudden drop in wind speed to 0 or a sudden change in wind pressure in a certain area). After detecting a blockage risk, the system generates an alarm message and locks the abnormal location, prompting the dispatch system to prioritize the status of the area.

[0054] The specific steps for quickly determining potential blockage in the air duct using the air duct status perception module are as follows:

[0055] The air duct status sensing module communicates with various monitoring devices in real time, continuously collecting data on wind speed, wind pressure, temperature, humidity, and gas concentration at various locations within the mine. The module's built-in monitoring logic specifically detects patterns of dramatic changes in the data, such as a sudden drop in wind speed or an abnormal increase in wind pressure over a short period of time. Compared to conventional data analysis, this module requires millisecond-level response times to capture unusual fluctuations at the moment of an emergency. These sudden changes often indicate physical obstructions, such as localized landslides or blocked air ducts, so the module must ensure low latency in data transmission.

[0056] Once a possible mutation is detected, the perception module immediately starts data difference calculation, that is, comparing the current monitoring data with the historical benchmark data. Historical data can include normal wind speed and pressure levels in the same area or typical ranges of change in different time periods (such as day and night). If the wind speed drops suddenly to 0 or the wind pressure increases by more than the set threshold, the perception module will further verify whether the duration of the abnormality meets the trigger condition (such as the wind speed abnormality lasts for more than 5 seconds). In addition, the module will also eliminate false alarms based on the current system status (such as whether there is equipment maintenance or switching operations).

[0057] To improve accuracy, the perception module integrates data from multiple points for collaborative analysis. For example, if the wind speed in one area is zero but the wind pressure in an adjacent area is elevated, there's a higher probability that the air duct is partially blocked. This comparative analysis between these areas allows the perception module to narrow down the potential blockage. Furthermore, the perception module considers the time lag between different sensors to further identify the possible location of the blockage. If multiple sensors detect an anomaly simultaneously, that area is marked as a high-risk point.

[0058] When the perception module finally confirms that the anomaly meets the characteristics of a blockage, it will immediately send an early warning signal to the central control system and initiate intelligent emergency decision-making. The system will automatically select an alternative ventilation path based on the urgency of the blockage location and trigger relevant equipment when necessary (such as starting additional fans or opening dampers). If the blocked area is near the miners' work area, the module will also instruct the system to issue an evacuation warning to the miners. The module also has a learning function and will archive the detection data for subsequent optimization of thresholds and judgment models to improve the speed and accuracy of responses to similar events in the future.

[0059] In the event of air duct blockage, a fuzzy logic algorithm calculates alternative backup air duct paths and dynamically allocates air volume based on air volume demand, current path load, temperature, and gas concentration. The algorithm calculates the feasibility of different air path combinations in real time to ensure smooth air volume switching and avoid affecting other areas due to air volume adjustments.

[0060] The specific steps of the fuzzy logic algorithm to calculate the backup air duct path and dynamically allocate the air volume are as follows:

[0061] The system is based on the wind speed (V i ), temperature (T i ), gas concentration (C i ) and current load (L i ) fuzzifies each air duct i. Define the fuzzy set μ to represent the applicability of the air duct, where: Where, is the fuzzy membership function of wind speed, which is used to judge whether the air duct can provide sufficient ventilation. is a fuzzy function of temperature, which evaluates the suitability of the temperature in the duct and avoids high temperature areas. is a fuzzy function of gas concentration, filtering out paths with higher concentrations of harmful gases. is the fuzzy function of the current load, which evaluates whether the load of the air duct is too high; the minimum value of these membership functions is taken as the final suitability score μ of each air duct i The set of candidate air duct paths S 候选 will include only those μ i For air ducts exceeding the threshold α set by the system, the expression of the candidate air duct path set is: S 候选 ={i|μ i ≥α}.

[0062] Construct a comprehensive benefit objective function z for each candidate path i , which is used to measure the ventilation superiority under different conditions. The expression of the comprehensive benefit objective function is: w1, w2, w3, w4 are weight parameters to ensure that the importance of different factors in decision making is balanced, and w1+w2+w3+w4=1, V max , T max , C max , L max Respectively represent the maximum values ​​of the system's wind speed, temperature, gas concentration, and load, and are used for normalization processing;

[0063] Objective function Z i After calculation, the air duct with the largest value will be selected as the preferred alternative path.

[0064] To ensure the reasonable distribution of air volume on each path, the system adopts a multi-constraint optimization method to calculate the optimal air volume Q of each path through the following formula: i , the calculation expression is: Q i ≤Q i，max , where Q i is the air volume allocated to the i-th path, Q 总 is the total air volume currently required by the mine, Q i，max The maximum tolerable wind volume for each path to avoid overloading;

[0065] In the optimization model, "maximize" is used to describe the optimization goal, that is, to maximize a certain indicator to its maximum value. In the scenario of coal mine ventilation control, this usually means maximizing the comprehensive benefits of one or more paths, such as improving the efficiency of air volume utilization or reducing the accumulation of harmful gases. The objective function to be maximized (such as ∑Q i ·Z i) is the core result pursued by the system during the optimization process.

[0066] "Subject to" (constraints) are used to define the restrictions that the optimization process must meet. These conditions ensure that the model does not exceed the boundaries of reality when pursuing the maximization goal. For example, the air volume distribution must meet the total air volume demand (∑Q i =Q 总 ) and the maximum air volume limit of a single path (Q i ≤Q i，max These constraints ensure the feasibility and safety of the optimization results and avoid system overload or risk accumulation in actual applications.

[0067] In short, “Maximize” refers to the goal to be achieved by the optimization model, while “Subject to” defines the boundary conditions that must be followed to ensure that the optimization is carried out within a reasonable range.

[0068] After the air volume distribution is completed, the operating status of each path will continue to be monitored, and the future scheduling strategy will be optimized through the reinforcement learning model. i 实时 , T i 实时 , C i 实时 , L i 实时 ) will be used as new input to update the fuzzy membership function and weight parameters. The loss function of the reinforcement learning model is defined as: Where Γ(θ) is the loss function, which is used to measure the deviation between the expected state and the actual state. T is the training period, and the system updates the model parameters θ every T time steps.

[0069] Through this feedback mechanism, the system can gradually improve the accuracy of air volume distribution and the rationality of air duct selection, and realize adaptive optimization control of mine ventilation.

[0070] Using a reinforcement learning algorithm, the system continuously optimizes ventilation strategies based on historical data and real-time feedback, improving response speed to emergencies. When the system detects repeated congestion on a particular path, the algorithm adaptively reduces the ventilation weight of that path and prioritizes other high-reliability paths.

[0071] Specific steps to optimize ventilation strategy using reinforcement learning algorithm:

[0072] Reinforcement learning algorithms require representing the state of a mine ventilation system as a state space. Specifically, the state space encompasses real-time parameters such as wind speed, wind pressure, gas concentration, and temperature of different air ducts, as well as historical records of whether each path has experienced blockage. The system updates the environmental model based on these states at each time step. Furthermore, the core of the algorithm is the establishment of a reward mechanism: when a ventilation path reduces energy consumption while maintaining normal air volume and safe gas concentration, the system will provide positive rewards; when blockage or gas accumulation is detected, the system will impose negative penalties. This mechanism ensures that the algorithm can continuously optimize and prioritize efficient and reliable ventilation paths.

[0073] The algorithm relies not only on real-time monitoring data but also extracts patterns of long-term ventilation behavior from historical data. Specifically, the system analyzes which air ducts have been blocked repeatedly, the time and frequency of occurrence, and the corresponding changes in airflow. Using Q-learning or deep reinforcement learning (DQN) models, the system combines this historical data with the current state to determine whether a particular path has a tendency to be repeatedly blocked. For these high-risk paths, the system will reduce their usage weight, thereby reducing future ventilation dependence and prioritizing the planning of other stable paths.

[0074] When real-time feedback indicates a blockage or insufficient airflow in a particular air duct, the system recalculates the optimal ventilation strategy based on the latest status and immediately executes it. For example, the algorithm adjusts fan power and damper opening in real time to redirect airflow to alternative paths. Furthermore, to adapt to the dynamic changes in the mine environment, a reinforcement learning algorithm regularly updates the strategy model, continuously optimizing weight distribution based on new data. This adaptive optimization process enables the system to respond quickly to emergencies while avoiding unnecessary impacts to other areas caused by frequent adjustments.

[0075] When the system finds that a certain path is repeatedly blocked after multiple runs, the reinforcement learning algorithm will automatically lower the priority of the path and adjust its ventilation weight to the minimum or mark it as a backup path. This process is based on the exploration-utilization balance principle, that is, in the early stages, the system will continue to try different ventilation combinations (exploration), and in the later stages, it will rely more on verified efficient paths (utilization). To improve the long-term performance of the system, the algorithm will also input the characteristics of frequently blocked paths into the model for reference in future decision-making, ensuring more accurate ventilation planning and reducing risks and energy consumption in the long-term operation of the mine.

[0076] The system uses an airflow simulation model to predict gas diffusion and temperature rise after air duct blockage, providing early warning and air volume optimization solutions. The algorithm simulates the airflow distribution of different air ducts in real time under blockage scenarios, predicts high-risk areas in advance, and automatically adjusts air volume and temperature control strategies to ensure effective ventilation and heat reduction.

[0077] The specific steps for predicting gas diffusion and temperature rise after air duct blockage based on the airflow simulation model are as follows:

[0078] The airflow simulation model is built based on the mine's physical layout and historical data, including the geometry of the air ducts, bifurcation nodes, wind speed and pressure distribution, and temperature and humidity characteristics. The system inputs the actual wind speed, air volume, and gas concentration of different ventilation paths into the model as initial conditions. It also integrates power consumption data from mine equipment (such as fan operating parameters) to ensure that the simulation results are highly consistent with actual conditions. During the initialization phase, the system also fine-tunes the model parameters based on seasonal temperature differences and cyclical changes in equipment operation within the mine to adapt it to different operating environments.

[0079] Once the risk of blockage in the air duct is detected, the system will immediately predict the gas diffusion path and temperature change trend through the simulation model. Specifically, the model will assume that part of the air duct is blocked or completely interrupted, recalculate the air volume distribution of each branch path, and simulate the flow direction and concentration changes of the gas. For example, if a certain area accumulates methane due to a sudden decrease in air volume, the system can predict its concentration change and diffusion time within seconds. At the same time, the simulation model will dynamically track temperature changes to determine whether the weakening of airflow will cause a rapid rise in temperature, especially in deep well operation areas, which is crucial to safety.

[0080] Based on the simulation results, the system generates detailed warning information, including the likely areas of gas diffusion, the time window for temperature rise, and the affected air duct nodes. To prevent further risks, the system proposes airflow optimization plans, such as activating backup fans, closing some dampers, or directing more airflow through unobstructed air ducts. Furthermore, the system sets warning thresholds based on the simulated diffusion time, ensuring that the dispatch center and on-site miners receive warnings before safety critical points and can quickly implement emergency measures.

[0081] The system not only relies on the results of a single simulation, but also uses a real-time feedback mechanism to dynamically adjust the model. For example, when the state of the air duct changes (such as the recovery of the blocked part or the adjustment of the fan speed), the system will re-input the latest data and immediately update the simulation results. This adaptive adjustment ensures that the airflow simulation model is always synchronized with the actual situation, avoiding outdated predictions that mislead decision-making. At the same time, the system will record all simulation processes and actual response effects to optimize future model parameters to make them more accurate in similar scenarios. Ultimately, this closed-loop feedback mechanism greatly improves the system's adaptability and prediction accuracy.

[0082] Specific implementation method 1: multi-path redundant ventilation and dynamic switching;

[0083] In order to ensure the stability and safety of the mine ventilation system, this embodiment prevents the risks caused by blockage of a single path by setting up a multi-path redundant ventilation system. The main air duct in the mine is responsible for the main daily ventilation tasks, while the backup air duct maintains low power consumption or intermittent operation under normal circumstances, and intervenes immediately when the main air duct is detected to be blocked. In order to ensure the redundancy of the system, each air duct must have sufficient air volume transmission potential, which requires detailed calculation of the air flow requirements of each path in the planning stage, and ensuring that the pressure difference between different air ducts does not have a negative impact on the overall stability of the system. The position of the fan and damper has also been optimized to ensure that the air volume can be quickly redirected in the event of blockage.

[0084] When the system detects a sudden change in the wind speed or pressure of a main air duct, triggering a possible blockage alarm, the intelligent scheduling module will immediately switch the ventilation task to the backup air duct. At this time, the fan of the backup air duct automatically accelerates, and the damper opens according to the scheduling instruction to ensure that the airflow can be smoothly diverted to the new path, avoiding a sudden drop in air volume in certain areas. During the dynamic switching process, the role of the reinforcement learning algorithm is to ensure that the entire ventilation network will not produce a negative chain reaction due to sudden path switching. For example, when a backup path is activated, the system will evaluate its impact on the air volume of other branches and adjust the opening and closing status of the relevant dampers to ensure the balance of ventilation inside the mine.

[0085] During this process, a reinforcement learning algorithm records the historical status and switching results of all air ducts, identifying paths with frequent blockages or unstable operation and automatically reducing the weight of these paths in future scheduling. This process not only optimizes scheduling strategies but also improves the stability of mine operations. Compared with traditional fixed ventilation solutions, this dynamic switching and adaptive optimization model offers greater flexibility and significantly reduces the risk of single points of failure. With this multi-path redundant structure, the system ensures continuous ventilation and cooling through intelligent scheduling even in the event of unpredictable landslides or blockages within the mine.

[0086] Specific implementation method 2: early warning system and emergency air volume adjustment strategy;

[0087] To ensure that the mine ventilation system can respond to emergencies, this implementation utilizes an airflow simulation model combined with a real-time early warning system to achieve rapid response and early warning of blockage risks. When a blockage risk is detected in a particular air duct, the system not only issues an alarm but also uses airflow simulation to predict potentially affected areas and temperature trends. The simulation model analyzes the airflow distribution after the air duct is blocked in real time based on the specific layout of the mine, gas diffusion characteristics, and current wind speed and volume data. Within seconds, the system can simulate the impact of different airway switching schemes on temperature, humidity, and gas concentration, and generate optimized air volume adjustment instructions based on the results.

[0088] The core of the early warning system lies in its advanced prediction and multi-level warning mechanism. The system not only sends detailed risk reports to the management center but also takes appropriate measures based on different risk levels. For example, if it detects a slow temperature rise and no significant change in harmless gas concentrations, the system will issue a "Level 1 Warning," prompting management to strengthen monitoring. However, if airflow simulation results indicate that a certain area will experience gas accumulation or a temperature surge due to airflow interruption within a short period of time, the system will immediately issue a "Level 3 Emergency Warning," simultaneously activating backup fans and cooling equipment, and notifying on-site miners to evacuate quickly. Furthermore, the system can dynamically adjust the operating modes of dampers and fans to ensure that the maximum amount of air is directed to the affected area.

[0089] When executing the emergency air volume adjustment strategy, the system will prioritize the optimal path generated by the reinforcement learning algorithm and evaluate the adjustment effect in real time based on the simulation results. If a certain air duct fails to return to normal or a new blockage risk arises, the system will continue to optimize the air volume distribution to ensure that the impact of the accident is minimized. This early warning and emergency adjustment strategy not only improves the system's emergency response capabilities, but also effectively avoids secondary risks caused by incorrect operations or misjudgments. For example, if a branch experiences a sudden drop in wind speed due to excessive airflow, the system will immediately redistribute the air volume to ensure smooth airflow. This closed-loop adjustment process ensures that the safe environment in the mine can be guaranteed even in emergencies.

[0090] Specific implementation method three: adaptive optimization intelligent scheduling system;

[0091] During the long-term optimization of ventilation scheduling, this implementation leverages the adaptive capabilities of a reinforcement learning algorithm to continuously optimize the operational efficiency and stability of the mine ventilation network. The reinforcement learning algorithm leverages historical data and real-time feedback to establish a state-behavior mapping model, continuously optimizing ventilation path planning and air volume distribution. The system tracks the actual operation of each air duct, such as which paths are prone to congestion during peak hours and which areas are most sensitive to changes in gas concentrations, and automatically adjusts ventilation strategies based on this data.

[0092] If a path experiences frequent congestion or unstable operation, the system automatically reduces its ventilation weight and prioritizes other highly reliable paths. During this process, the algorithm employs an exploration-exploitation balance strategy: in the initial stages, the system frequently tries different ventilation solutions to discover new optimal paths; in later stages, it relies more on proven efficient paths to reduce unnecessary adjustments. After each adjustment, the system verifies the effectiveness of the new solution using an airflow simulation model. If the air volume distribution is found to be unsatisfactory, the algorithm further adjusts the weights and path planning.

[0093] In addition, the system's adaptive optimization capabilities are not limited to path planning, but also dynamically adjust the operating parameters of fans and dampers. For example, when the algorithm finds that a certain path cannot meet the air volume demand, the system will automatically increase the speed of the fan in that area, or close the dampers of some non-critical air ducts to direct more air volume to key areas. During the optimization process, the system will also automatically adjust the air volume distribution plan according to the operating conditions of the mine (such as the temperature difference between day and night or the start and stop of operating equipment). This adaptive optimization ensures that the ventilation system is always in an efficient and energy-saving operating state, and can respond quickly even in the event of an emergency. Ultimately, through continuous learning and adaptive optimization, the intelligent scheduling system has achieved full-process automation of mine ventilation and heat reduction control, improving the safety and stability of overall operations.

[0094] By employing multi-path redundant ventilation and a dynamic switching mechanism, this invention enables the system to quickly switch to a backup duct when the main air duct is blocked, ensuring uninterrupted ventilation, timely exhaust of harmful gases, and temperature control. This mechanism significantly reduces the likelihood of high-risk accidents such as gas explosions and carbon monoxide poisoning. Furthermore, the system uses airflow simulation models for prediction, anticipating areas of gas accumulation and temperature rise before problems occur, generating timely warning information and avoiding catastrophic accidents caused by delayed response. This ensures the safety of miners and the stability of the mine environment, enabling managers to proactively control safety risks rather than passively respond.

[0095] This invention combines a reinforcement learning algorithm with an airflow simulation model, enabling the system to respond quickly and adjust adaptively. When the system detects a blockage risk, it can complete data analysis and airflow prediction within seconds and propose an optimized air volume adjustment plan. This not only ensures the ventilation needs of the work area but also avoids a complete shutdown due to untimely processing. At the same time, the system records all operational data during the emergency process, which is used to further optimize the reinforcement learning model, thereby continuously improving future response speed and accuracy. This efficient emergency management reduces downtime and equipment loss caused by accidents, thereby improving the economic benefits of the mine.

[0096] Powered by a reinforcement learning algorithm, this invention enables the system to intelligently plan ventilation paths based on different time periods, equipment operating conditions, and environmental changes, preventing fans from operating at high loads for extended periods. By precisely allocating and dynamically adjusting air volume, the system significantly reduces energy consumption while ensuring safety. Furthermore, adaptive optimization of path weights allows the system to gradually avoid air ducts prone to blockage, reducing the need for frequent cleaning and overhauls and lowering maintenance costs. In the long term, this intelligent scheduling mechanism not only extends equipment life but also brings significant energy savings and a sustainable operating model to the mine.

[0097] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, characterized in that: The following steps are involved: Various monitoring devices are deployed along the mine's ventilation routes and backup ventilation routes to collect data on temperature, humidity, gas concentration, and wind speed and volume. The central control system aggregates the monitoring data in real time, analyzes the ventilation conditions within the air ducts, and identifies any abnormal changes. An air duct status perception module is set up to quickly determine whether the air duct has potential blockage by monitoring the data mutation characteristics of the equipment. After detecting the risk of blockage, the system generates an alarm message and locks the abnormal location, prompting the dispatch system to prioritize the status of the abnormal location area. In the event of air duct blockage, fuzzy logic algorithms are used to calculate alternative backup air duct paths and dynamically allocate air volume based on air volume demand, current path load, temperature, and gas concentration. Utilizing reinforcement learning algorithms, ventilation strategies are continuously optimized based on historical data and real-time feedback, improving the speed of response to emergencies. When a path is repeatedly blocked, the reinforcement learning algorithm adaptively reduces the ventilation weight of that path and prioritizes other paths. Combined with the airflow simulation model, it can predict the gas diffusion and temperature rise after the air duct is blocked, and provide early warning and air volume optimization solutions; through real-time simulation of the airflow distribution of different air paths under blockage scenarios, it can predict high-risk areas in advance and automatically adjust the air volume and temperature control strategies to ensure ventilation and heat reduction effects.

2. The method for integrated ventilation and heat reduction control of coal mines based on intelligent scheduling according to claim 1, characterized in that: The specific steps for the central control system to summarize monitoring data in real time, analyze ventilation conditions in the air duct, and identify abnormal changes are as follows: The central control system continuously receives data from various ventilation paths in the mine through a multi-point sensor network, including temperature, humidity, gas concentration, wind speed and air volume; After data preprocessing, the central control system conducts a multi-dimensional cross-analysis of the data from each monitoring point. If it detects a decrease in wind speed but an increase in gas concentration, it creates a trend model for the data over consecutive time periods and uses regression analysis to predict wind speed changes over the next few minutes. This helps determine whether the current anomaly is a random occurrence or a systemic problem. Multiple thresholds and logic rules are set to automatically determine whether there are anomalies. To improve detection accuracy, a dynamic threshold adjustment strategy is adopted, which automatically optimizes the threshold according to different conditions in different time periods or environmental conditions to reduce false positives and missed negatives. Once an abnormal situation is identified, an alarm is automatically generated and pushed to the mine dispatch center and relevant persons in charge. The system conducts a comprehensive analysis of the current status to determine whether emergency measures need to be implemented immediately.

3. The method for integrated ventilation and heat reduction control in coal mines based on intelligent scheduling according to claim 1, characterized in that: The specific steps for quickly determining potential blockage in the air duct using the air duct status perception module are as follows: The air duct status sensing module is connected to various monitoring devices in real time to continuously collect wind speed, wind pressure, temperature, humidity and gas concentration data at different locations in the mine; Once a potential mutation is detected, the perception module immediately starts data difference calculation, that is, comparing the current monitoring data with the historical benchmark data; To improve the accuracy of judgment, the perception module integrates multi-point data for collaborative analysis. By comparing and analyzing different areas, it narrows down the scope of potential congestion areas. At the same time, it considers the time lag between different sensors to further confirm the location of the congestion. If multiple sensors detect an anomaly at the same time, the area is marked as a high-risk point. When the perception module finally confirms that the anomaly meets the blockage characteristics, it will immediately send an early warning signal to the central control system and initiate intelligent emergency decision-making. The system will automatically select an alternative ventilation path based on the urgency of the blockage location and trigger relevant equipment when necessary.

4. The method for integrated ventilation and heat reduction control in coal mines based on intelligent scheduling according to claim 1, characterized in that: The specific steps of the fuzzy logic algorithm to calculate the backup air duct path and dynamically allocate the air volume are as follows: The system is based on the wind speed V i , temperature T i , gas concentration C i and the current load L i Perform fuzzy processing on each air duct i and define the fuzzy set μ to represent the applicability of the air duct, where: Where, is the fuzzy membership function of wind speed, which is used to judge whether the air duct can provide sufficient ventilation. is a fuzzy function of temperature, which evaluates the suitability of the temperature in the duct and avoids high temperature areas. is a fuzzy function of gas concentration, filtering out paths with high concentrations of harmful gases. is the fuzzy function of the current load, which evaluates whether the load of the air duct is too high; the minimum value of the membership function is taken as the final suitability score μ of each air duct i , candidate air duct path set S 候选 will include only those μ i For air ducts exceeding the threshold α set by the system, the expression of the candidate air duct path set is: S 候选 ={i|μ i ≥α}; Construct a comprehensive benefit objective function Z for each candidate path i , which is used to measure the ventilation superiority under different conditions. The expression of the comprehensive benefit objective function is: w1, w2, w3, w4 are weight parameters to ensure that the importance of different factors in decision making is balanced, and w1+w2+w3+w4=1, V max , T max , C max , L max Respectively represent the maximum values ​​of the system's wind speed, temperature, gas concentration, and load, and are used for normalization processing; Objective function Z i After calculation, the air duct with the largest value is selected as the preferred alternative path.

5. The method for integrated ventilation and heat reduction control in coal mines based on intelligent scheduling according to claim 4, characterized in that: In order to ensure the reasonable distribution of air volume on each path, a multi-constraint optimization method is used to calculate the optimal air volume Q of each path using the following formula: i , the calculation expression is: Q i ≤Q i，max , where Q i is the air volume allocated to the i-th path, Q 总 is the total air volume currently required by the mine, Q i，max The maximum tolerable wind volume for each path to avoid overloading; After the air volume distribution is completed, the operation status of each path is continuously monitored, and the future scheduling strategy is optimized through the reinforcement learning model, with real-time feedback data V i 实时 , T i 实时 , C i 实时 , L i 实时 It will be used as a new input to update the fuzzy membership function and weight parameters. The loss function of the reinforcement learning model is defined as: Among them, Γ(θ) is the loss function, which is used to measure the deviation between the expected state and the actual state. T is the training period, and the system updates the model parameter θ every T time steps.

6. The method for integrated ventilation and heat reduction control in coal mines based on intelligent scheduling according to claim 1, characterized in that: Specific steps to optimize ventilation strategy using reinforcement learning algorithm: The state of the mine ventilation system is represented as a state space, which includes the real-time parameters of different air ducts and the historical records of whether each path has been blocked; Through a deep reinforcement learning model, historical data is combined with current status to determine whether a certain path has a trend of repeated congestion. For high-risk paths, their usage weight is reduced to reduce future ventilation dependence. When real-time feedback indicates that a certain air duct is blocked or the air volume is insufficient, the optimal ventilation strategy is recalculated based on the latest status and executed immediately; When the system finds that a certain path is repeatedly blocked through multiple runs, the reinforcement learning algorithm will automatically lower the priority of the path and adjust its ventilation weight to the minimum or mark it as a backup path.

7. The method for integrated ventilation and heat reduction control in coal mines based on intelligent scheduling according to claim 1, characterized in that: The specific steps for predicting gas diffusion and temperature rise after air duct blockage based on the airflow simulation model are as follows: An airflow simulation model was established based on the mine's physical layout and historical data, including the duct geometry, bifurcation nodes, wind speed and pressure distribution, and temperature and humidity characteristics. Actual wind speeds, air volumes, and gas concentrations across different ventilation paths were input into the model as initial conditions. Power consumption data for mine equipment was also integrated to ensure that the simulation results were highly consistent with actual conditions. Once the risk of blockage in the air duct is detected, the gas diffusion path and temperature change trend are immediately predicted through simulation models; Based on the simulation results, detailed warning information is generated, including the area where gas diffusion occurs, the time window for temperature increase, and the affected air duct nodes. Warning thresholds are set based on the simulated diffusion time to ensure that the dispatch center and on-site miners receive warnings before the safety critical point and can quickly take emergency measures; A real-time feedback mechanism is used to dynamically adjust the model to ensure that the airflow simulation model is always synchronized with the actual situation, avoiding outdated predictions that mislead decision-making.

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