Coal mine ventilation and heat reduction integrated control method based on intelligent scheduling

Abstract

The invention discloses a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, and relates to the technical field of coal mine ventilation and heat reduction control, and the method comprises the following steps: arranging a plurality of monitoring devices on a ventilation path and a standby ventilation path of a mine, and collecting temperature, humidity, gas concentration and wind speed and volume data; the central control system gathers monitoring data in real time, analyzes the ventilation condition in the air duct and identifies whether abnormal changes exist or not. Continuous ventilation is ensured through multi-path redundancy and dynamic switching, risks of gas explosion and the like are reduced, early warning is performed by means of the airflow simulation model, and safety of miners is guaranteed. And in combination with a reinforcement learning algorithm and a simulation model, quick response and self-adaptive adjustment are realized, air distribution is optimized, shutdown is avoided, and economic benefits are improved. Meanwhile, by intelligently planning a ventilation path and dynamically adjusting the fan load, energy consumption and maintenance cost are reduced, the service life of equipment is prolonged, and sustainable operation of a mine is promoted.

Description

Technical Field

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

[0002] The integrated control of ventilation and cooling in coal mines based on intelligent scheduling is a control solution that integrates and optimizes the ventilation and cooling systems of coal mines, and realizes dynamic scheduling of ventilation and cooling through intelligent algorithms and real-time monitoring equipment. In this system, the intelligent scheduling module automatically adjusts the air volume, wind speed and operation strategy of cooling equipment according to the temperature, humidity, air quality and work requirements in the mine to ensure the safety and comfort of the mine environment. This integrated control not only improves energy efficiency and reduces energy consumption, but also effectively reduces the hazards of high temperature and high humidity in the mine, which helps to protect the health and safety of miners.

[0003] The prior art has the following deficiencies:

[0004] In the integrated control of ventilation and heat reduction in coal mines, when the main air duct is partially or completely blocked due to sudden factors (such as local collapse, equipment blockage, waste accumulation, etc.) inside the mine, if the intelligent scheduling system fails to identify and adjust the ventilation strategy of other air ducts in time, the air volume in some areas will be sharply reduced or even stagnant. This will cause the temperature in the area to rise rapidly and harmful gases to accumulate, seriously threatening the safety of miners and may increase the risk of gas explosions and fires. Since such problems are sudden changes in physical channels, they are difficult to predict and respond to immediately through general algorithms, so higher requirements are placed on the real-time response capabilities of the system.

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

[0006] The purpose of the present invention is to provide 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, discharges harmful gases in time and controls the temperature, reducing the risk of gas explosion 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 arranged on the ventilation paths and backup ventilation paths of the mine to collect data on temperature, humidity, gas concentration, wind speed and volume; the central control system summarizes the monitoring data in real time, analyzes the ventilation conditions in the air duct, and identifies whether there are any abnormal changes;

[0009] Set up an air duct status perception module to quickly determine whether the air duct has potential blockage by monitoring the data mutation characteristics in the equipment; after detecting the risk of blockage, the system generates an alarm message and locks the abnormal location, prompting the dispatching system to give priority to the status of the abnormal location area;

[0010] In the case of air duct blockage, the fuzzy logic algorithm is used to calculate the alternative backup air duct path, and the air volume is dynamically allocated according to the air volume demand, current path load, temperature and gas concentration;

[0011] Using reinforcement learning algorithms, ventilation strategies are continuously optimized through historical data and real-time feedback to improve the response speed to emergencies. When a path is repeatedly blocked, the ventilation weight of the path is adaptively reduced through reinforcement learning algorithms, and other paths are planned with priority.

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

[0013] Preferably, the specific steps for the central control system to collect monitoring data in real time, analyze 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 multi-dimensional cross-analysis on the data of each monitoring point. If it detects that the wind speed decreases but the gas concentration increases, it conducts trend modeling on the data of the continuous time period and predicts 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.

[0016] Set multiple thresholds and logic rules to automatically determine whether there are anomalies. In order to improve the accuracy of detection, adopt a dynamic threshold adjustment strategy, that is, automatically optimize the threshold according to different conditions in different time periods or environmental states 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 of 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 with 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] In order to improve the accuracy of judgment, the perception module integrates multi-point data for collaborative analysis. Through comparative analysis between regions, the scope of potential congestion areas is narrowed down. At the same time, the time lag between different sensors is referred to to further confirm the location of the congestion. If multiple sensors detect anomalies 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 a 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 related 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 Each air duct i is fuzzified and the fuzzy set μ is defined to represent the applicability of the air duct, where: In the formula, 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 air duct and avoids high temperature paths. 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 that exceed the system-set threshold α, 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 superiority of ventilation 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 wind speed, temperature, gas concentration and load of the system, which are used for normalization processing;

[0026] Objective function Z i After calculation, the air duct with the largest value is selected as the preferred option for the backup 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 ith path, Q 总 is the total air volume currently required by the mine, Q i，max The maximum tolerable air volume for each path to avoid overloading;

[0028] After the air volume distribution is completed, the operation status of each path will continue to be monitored, and the future scheduling strategy will be 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 the ventilation strategy using the 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] Through Q-learning or deep reinforcement learning models, historical data is combined with the current status to determine whether a certain path has a tendency to be repeatedly blocked. For high-risk paths, their usage weight is reduced to 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 the gas diffusion and temperature rise after the air duct is blocked based on the air flow simulation model are as follows:

[0035] An airflow simulation model is established based on the physical layout and historical data of the mine, including the geometric structure of the air duct, bifurcation nodes, wind speed and pressure distribution, and temperature and humidity characteristics. The actual wind speed, air volume, and gas concentration of different ventilation paths are input into the model as initial conditions. At the same time, the power consumption data of the mine equipment is integrated to ensure that the simulation results are highly consistent with the actual situation.

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

[0037] Based on the simulation results, detailed warning information is generated, including the area where gas diffusion exists, the time window of temperature rise, and the affected air duct nodes. The warning threshold is set according to 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 and to avoid outdated predictions that mislead decision-making.

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

[0040] The present invention adopts multi-path redundant ventilation and dynamic switching mechanism, so that the system can quickly switch to the backup air duct when the main air duct is blocked, ensuring uninterrupted ventilation, timely exhaust of harmful gases and temperature control. This mechanism significantly reduces the possibility of high-risk accidents such as gas explosions and carbon monoxide poisoning. Furthermore, the system uses the airflow simulation model to make predictions, predict the areas where gas accumulates and temperature rises before the problem occurs, and generate early warning information in time, avoiding catastrophic accidents caused by delayed response. It ensures the life safety of miners and the stability of the mine environment, allowing managers to actively control safety risks rather than passively respond.

[0041] The present invention combines the reinforcement learning algorithm and the airflow simulation model to enable the system to have the ability of rapid response and adaptive adjustment. When the system detects the risk of blockage, it can complete data analysis and airflow prediction within seconds, and propose an optimized air volume adjustment plan. This not only guarantees the ventilation needs of the working area, but also avoids the complete shutdown caused by untimely processing. At the same time, the system records all operating data during the emergency process for further optimization of the reinforcement learning model, so that the response speed and accuracy will continue to improve in the future. This efficient emergency management reduces the downtime and equipment loss caused by accidents, and improves the economic benefits of the mine.

[0042] With the support of reinforcement learning algorithms, the present invention enables the system to intelligently plan ventilation paths according to different time periods, equipment operation conditions and environmental changes, thereby avoiding long-term high-load operation of the fan. Through precise allocation and dynamic adjustment of air volume, the system significantly reduces energy consumption while ensuring safety. In addition, the adaptive optimization of path weights enables the system to gradually avoid air ducts that are prone to blockage, reducing the need for frequent cleaning and maintenance and reducing maintenance costs. In the long run, this intelligent scheduling mechanism not only extends the life of equipment, but also brings significant energy savings and a sustainable development operation 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, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 The present invention is a method flow chart of a method for integrated control of ventilation and heat reduction in coal mines based on intelligent scheduling. DETAILED DESCRIPTION

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

[0046] The present invention provides Figure 1 The invention provides a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, comprising the following steps:

[0047] Various monitoring devices are arranged on the main ventilation paths and backup ventilation paths of the mine to collect data on temperature, humidity, gas concentration, wind speed and volume; the central control system summarizes these monitoring data in real time, analyzes the ventilation conditions in the air duct, and identifies whether there are 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 concentration (such as methane, 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 noise data. The preprocessing stage includes data denoising, abnormal point 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 multi-dimensional cross-analysis of the data at each monitoring point, such as correlating and comparing the wind speed and air volume in different areas with their gas concentration and temperature and humidity data. If it is detected that the wind speed decreases but the gas concentration increases, it means that there may be air duct obstruction or local 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 shift and night shift) 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 will automatically generate an alarm and push it to the mine dispatch center and relevant persons in charge. The system will also conduct a comprehensive analysis of the current status to determine whether emergency measures need to be taken immediately, such as enabling alternate 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 to collect data at a higher frequency to provide more information support for subsequent abnormal cause analysis.

[0053] Set up an air duct status perception module to quickly determine whether the air duct has potential blockage by monitoring the data mutation characteristics in the equipment (such as the wind speed in a certain area suddenly drops to 0 or the wind pressure suddenly changes); after detecting the risk of blockage, the system generates an alarm message and locks the abnormal location, prompting the dispatch system to give priority to the status of the area;

[0054] The specific steps to quickly determine potential blockage of the air duct through the air duct status perception module are as follows:

[0055] The air duct status perception module is connected to various monitoring devices in real time, and continuously collects wind speed, wind pressure, temperature, humidity and gas concentration data at different locations in the mine. The module's built-in monitoring logic specifically detects drastic change patterns in the data, such as a sharp drop in wind speed in a short period of time, an abnormal increase in wind pressure, etc. Compared with conventional data analysis, this module requires millisecond-level response to capture abnormal fluctuations at the moment of an emergency. These mutation characteristics often indicate physical obstructions, such as local landslides or air duct blockages, so the module needs to ensure low latency in data transmission.

[0056] Once a possible mutation is detected, the perception module immediately starts the data difference calculation, that is, comparing the current monitoring data with the historical benchmark data. Historical data can include normal wind speed and wind 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 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] In order to improve the accuracy of judgment, the perception module will integrate multi-point data for collaborative analysis. For example, if the wind speed in a certain area is 0 but the wind pressure in the adjacent area increases, it is more likely that the air duct is partially blocked. Through this comparative analysis between regions, the perception module can narrow the scope of potential blockage areas. At the same time, the perception module will refer to the time lag between different sensors to further confirm the possible location of the blockage. If multiple sensors detect anomalies at the same time, the area is marked as a high-risk point.

[0058] When the perception module finally confirms that the anomaly meets the characteristics of the blockage, it will immediately send a 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 related equipment when necessary (such as starting additional fans or opening dampers). If the blocked area is near the miner's work area, the module will also instruct the system to issue an evacuation warning to the miners. The module also has a learning function, which will archive the detection data for subsequent optimization of thresholds and judgment models to improve the speed and accuracy of response to similar events in the future.

[0059] In the event of air duct blockage, the fuzzy logic algorithm is used to calculate alternative backup air duct paths, and the air volume is dynamically allocated 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 other areas being affected by 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 ) Fuzzy processing is performed on each air duct i. The fuzzy set μ is defined to represent the applicability of the air duct, where: In the formula, 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 air duct and avoids high temperature paths. 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 these membership functions is taken as the final suitability score μ of each air duct i The candidate air duct path set S 候选 will include only those μ i For air ducts that exceed the system-set threshold α, 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 superiority of ventilation 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 option for the backup 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 ith path, Q 总 is the total air volume currently required by the mine, Q i，max The maximum tolerable air 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 possible 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 restrictions that must be met during the optimization process. 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 practical 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 operation status of each path will continue to be monitored, and the future scheduling strategy will be optimized through the reinforcement learning model. Real-time feedback data (V 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: 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.

[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 reinforcement learning algorithms, the system continuously optimizes ventilation strategies through historical data and real-time feedback to improve the response speed to emergencies. When the system finds that a certain path is repeatedly blocked, the algorithm can adaptively reduce the ventilation weight of the path and prioritize other high-reliability paths.

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

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

[0073] The algorithm not only relies 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 multiple times, when and how often, and the corresponding airflow changes. Through Q-learning or deep reinforcement learning (DQN) models, the system combines these historical data with the current state to determine whether a certain path has a tendency to be repeatedly blocked. For these high-risk paths, the system will reduce their usage weights to reduce future ventilation dependence and prioritize planning other stable paths.

[0074] When real-time feedback indicates that a certain air duct is blocked or the air volume is insufficient, the system will recalculate the optimal ventilation strategy based on the latest status and execute it immediately. For example, the algorithm will direct the air volume to other alternative paths by adjusting the fan power and the opening degree of the damper in real time. At the same time, in order to adapt to the dynamic changes in the mine environment, the reinforcement learning algorithm will regularly update the strategy model and continuously optimize the weight distribution based on new data. This adaptive optimization process enables the system to respond quickly to emergencies while avoiding unnecessary impacts on 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 initial stage, the system will continue to try different ventilation combinations (exploration), and in the later stage, it will rely more on verified efficient paths (utilization). In order 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, to ensure more accurate ventilation planning and reduce risks and energy consumption in the long-term operation of the mine.

[0076] The system combines the airflow simulation model to predict the gas diffusion and temperature rise after the air duct is blocked, and provides early warning and air volume optimization solutions; the algorithm simulates the airflow distribution of different air ducts in blockage scenarios in real time, predicts high-risk areas in advance, and automatically adjusts the air volume and temperature control strategy to ensure ventilation and heat reduction effects;

[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 physical layout and historical data of the mine, including the geometry of the air duct, 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, and integrates the power consumption data of the mine equipment (such as fan operating parameters) to ensure that the simulation results are highly consistent with the actual situation. During the initialization stage, the system will also fine-tune the model parameters based on seasonal temperature differences and periodic changes in equipment operation in the mine to make it suitable for 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 possible areas of gas diffusion, time windows for temperature rise, and affected air duct nodes. To prevent further risks, the system proposes air volume optimization solutions, such as starting backup fans, closing some dampers, or directing more airflow through unobstructed air ducts. In addition, the system sets warning thresholds 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.

[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-enter 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 to avoid outdated predictions that mislead decisions. 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 resilience 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 implementation method 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 ensure that the pressure difference between different air ducts does not have a negative impact on the overall stability of the system. The positions of the fans and dampers are also 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 to avoid 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 negative chain reactions 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, the reinforcement learning algorithm records the historical status and switching effects of all air ducts, finds out the paths with frequent blockages or unstable operation, and automatically reduces the weight of these paths in future scheduling. This process not only optimizes the scheduling strategy, but also improves the stability of mine operations. Compared with traditional fixed ventilation solutions, this dynamic switching and adaptive optimization mode has higher flexibility and can greatly reduce the risks caused by single point failures. Under this multi-path redundant structure, even if there are unpredictable landslides or blockages in the mine, the system can ensure the continuity of ventilation and cooling through intelligent scheduling.

[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 adopts an airflow simulation model combined with a real-time early warning system to achieve rapid response and early warning of blockage risks. When a certain air duct is found to be at risk of blockage, the system will not only issue an alarm, but also predict the areas that may be affected and the temperature change trend through airflow simulation. The simulation model will analyze the airflow distribution after the air duct is blocked in real time based on the specific layout of the mine, the gas diffusion characteristics, and the current wind speed and volume data. The system can simulate the impact of different air duct switching schemes on temperature, humidity, and gas concentration within seconds, and generate optimized air volume adjustment instructions based on the results.

[0088] The core of the early warning system lies in advance prediction and multi-level early warning mechanism. The system not only pushes detailed risk reports to the management center, but also takes corresponding measures according to different risk levels. For example, when it detects that the temperature is rising slowly and the concentration of harmless gas does not change significantly, the system will issue a "level one warning" to prompt management personnel to strengthen monitoring; but if the airflow simulation results show that a certain area will produce gas accumulation or temperature surge due to airflow interruption in a short period of time, the system will immediately issue a "level three emergency warning", while starting the backup fan and cooling equipment, and notifying the on-site miners to evacuate quickly. On this basis, the system can also dynamically adjust the operation mode of the damper and fan 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 give priority to 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 occurs, 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 emergency response capability of the system, but also effectively avoids secondary risks caused by incorrect operations or misjudgments. For example, if a branch has 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] In the long-term optimization process of ventilation scheduling, this implementation method continuously optimizes the operating efficiency and stability of the mine ventilation network through the adaptive ability of the reinforcement learning algorithm. The reinforcement learning algorithm can use historical data and real-time feedback to establish a state-behavior mapping model, thereby continuously optimizing the planning of ventilation paths and the distribution of air volume. The system tracks the actual operation of each air duct, such as which paths are prone to blockage during peak hours and which areas are more sensitive to changes in gas concentration, and automatically adjusts the ventilation strategy based on this data.

[0092] When a path is frequently blocked or unstable, the system will automatically reduce its ventilation weight and give priority to other high-reliability paths. In this process, the algorithm adopts an exploration-utilization balance strategy: in the initial stage, the system will frequently try different ventilation solutions to find new optimal paths; in the later stages, it will rely more on verified efficient paths to reduce unnecessary adjustments. After each adjustment, the system will also verify the effect of the new solution through an airflow simulation model. If the air volume distribution is found to be unsatisfactory, the algorithm will further adjust the weight 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. In the end, the intelligent scheduling system achieved full-process automation of mine ventilation and heat reduction control through continuous learning and adaptive optimization, improving the safety and stability of overall operations.

[0094] The present invention adopts multi-path redundant ventilation and dynamic switching mechanism, so that the system can quickly switch to the backup air duct when the main air duct is blocked, ensuring uninterrupted ventilation, timely exhaust of harmful gases and temperature control. This mechanism significantly reduces the possibility of high-risk accidents such as gas explosions and carbon monoxide poisoning. Furthermore, the system uses the airflow simulation model to make predictions, predict the areas where gas accumulates and temperature rises before the problem occurs, and generate early warning information in time, avoiding catastrophic accidents caused by delayed response. It ensures the life safety of miners and the stability of the mine environment, allowing managers to actively control safety risks rather than passively respond.

[0095] The present invention combines the reinforcement learning algorithm and the airflow simulation model to enable the system to have the ability of rapid response and adaptive adjustment. When the system detects the risk of blockage, it can complete data analysis and airflow prediction within seconds, and propose an optimized air volume adjustment plan. This not only guarantees the ventilation needs of the working area, but also avoids the complete shutdown caused by untimely processing. At the same time, the system records all operating data during the emergency process for further optimization of the reinforcement learning model, so that the response speed and accuracy will continue to improve in the future. This efficient emergency management reduces the downtime and equipment loss caused by accidents, and improves the economic benefits of the mine.

[0096] With the support of reinforcement learning algorithms, the present invention enables the system to intelligently plan ventilation paths according to different time periods, equipment operation conditions and environmental changes, thereby avoiding long-term high-load operation of the fan. Through precise allocation and dynamic adjustment of air volume, the system significantly reduces energy consumption while ensuring safety. In addition, the adaptive optimization of path weights enables the system to gradually avoid air ducts that are prone to blockage, reducing the need for frequent cleaning and maintenance and reducing maintenance costs. In the long run, this intelligent scheduling mechanism not only extends the life of equipment, but also brings significant energy savings and a sustainable development operation model to the mine.

[0097] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A 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 arranged on the ventilation paths and backup ventilation paths of the mine to collect data on temperature, humidity, gas concentration, wind speed and volume; the central control system summarizes the monitoring data in real time, analyzes the ventilation conditions in the air duct, and identifies whether there are any abnormal changes; Set up an air duct status perception module to quickly determine whether the air duct has potential blockage by monitoring the data mutation characteristics in the equipment; after detecting the risk of blockage, the system generates an alarm message and locks the abnormal location, prompting the dispatching system to give priority to the status of the abnormal location area; In the case of air duct blockage, the fuzzy logic algorithm is used to calculate the alternative backup air duct path, and the air volume is dynamically allocated according to the air volume demand, current path load, temperature and gas concentration; Using reinforcement learning algorithms, ventilation strategies are continuously optimized through historical data and real-time feedback to improve the response speed to emergencies. When a path is repeatedly blocked, the ventilation weight of the path is adaptively reduced through reinforcement learning algorithms, and other paths are planned with priority. Combined with the airflow simulation model, the gas diffusion and temperature rise after the air duct is blocked can be predicted, and early warning and air volume optimization solutions can be given. By real-time simulation of the airflow distribution of different air paths under blockage scenarios, high-risk areas can be predicted in advance, and the air volume and temperature control strategies can be automatically adjusted to ensure ventilation and heat reduction effects.

2. The integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling according to claim 1 is 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 multi-dimensional cross-analysis on the data of each monitoring point. If it detects that the wind speed decreases but the gas concentration increases, it conducts trend modeling on the data of the continuous time period and predicts 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. Set multiple thresholds and logic rules to automatically determine whether there are anomalies. In order to improve the accuracy of detection, adopt a dynamic threshold adjustment strategy, that is, automatically optimize the threshold according to different conditions in different time periods or environmental states 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 integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling according to claim 1 is characterized in that: The specific steps to quickly determine potential blockage of the air duct through the air duct status perception module are as follows: The air duct status sensing module is connected with 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; In order to improve the accuracy of judgment, the perception module integrates multi-point data for collaborative analysis. Through comparative analysis between regions, the scope of potential congestion areas is narrowed down. At the same time, the time lag between different sensors is referred to to further confirm the location of the congestion. If multiple sensors detect anomalies 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 a 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 related equipment when necessary.

4. The integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling according to claim 1 is 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 Each air duct i is fuzzified and the fuzzy set μ is defined to represent the applicability of the air duct, where: In the formula, 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 air duct and avoids high temperature paths. 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 that exceed the system-set threshold α, 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 superiority of ventilation 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 wind speed, temperature, gas concentration and load of the system, which are used for normalization processing; Objective function Z i After calculation, the air duct with the largest value is selected as the preferred option for the backup path.

5. The method for integrated control of ventilation and heat reduction in coal mines based on intelligent scheduling according to claim 4 is 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 by the following formula: i , the calculation expression is: Q i ≤Q i，max , where Q i is the air volume allocated to the ith path, Q 总 is the total air volume currently required by the mine, Q i，max The maximum tolerable air volume for each path to avoid overloading; After the air volume distribution is completed, the operation status of each path will continue to be monitored, and the future scheduling strategy will be 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 integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling according to claim 1 is 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 the deep reinforcement learning model, historical data is combined with the 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 integrated control method for ventilation and heat reduction in coal mines based on intelligent scheduling according to claim 1 is 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 is established based on the physical layout and historical data of the mine, including the geometric structure of the air duct, bifurcation nodes, wind speed and pressure distribution, and temperature and humidity characteristics. The actual wind speed, air volume, and gas concentration of different ventilation paths are input into the model as initial conditions. At the same time, the power consumption data of the mine equipment is integrated to ensure that the simulation results are highly consistent with the actual situation. Once the risk of blockage in the air duct is detected, the gas diffusion path and temperature change trend are immediately predicted through the simulation model; Based on the simulation results, detailed warning information is generated, including the area where gas diffusion exists, the time window of temperature rise, and the affected air duct nodes. The warning threshold is set according to 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 and to avoid outdated predictions that mislead decision-making.

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