Coal mine ventilation and exhaust device and control system thereof

Abstract

The invention relates to the technical field of coal mine safety ventilation, in particular to a coal mine ventilation and exhaust device and a control system thereof. According to the technical scheme, the coal mine ventilation and exhaust device and the control system thereof comprise a ventilation pipeline outer cavity, a ventilation fan, an air volume induction module, a filtering device, a gas detection module, a control module, an air suction machine and a protection sieve plate, the ventilation fan is arranged in the ventilation pipeline outer cavity, and the air volume induction module is arranged on one side of the ventilation pipeline outer cavity; a filtering device is arranged on the other side of the outer cavity of the ventilating pipeline; according to the invention, deep integration of multi-source data and dynamic environment modeling are realized through the intelligent environment prediction module, the temporal-spatial resolution of environment monitoring and the comprehensiveness of risk assessment are remarkably improved, safety early warning is upgraded from passive threshold judgment to active coupling analysis, the false alarm rate is greatly reduced, potential risks are intervened in advance, and the safety of the mine environment is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine safety ventilation, and particularly relates to a coal mine ventilation and exhaust device and a control system thereof. BACKGROUND

[0002] The coal mine ventilation and exhaust device is an intelligent environmental control system specially designed for underground mines. Its core is to build a gas flow channel through the outer cavity of the ventilation pipeline, integrate hardware such as a ventilation fan, a filtering device, and a gas detection module, combine multi-source sensor data fusion and intelligent algorithms, and realize harmful gas concentration monitoring, dust filtering, airflow dynamic regulation, and energy collaborative management in the mine. The device builds a mine environment dynamic model through edge computing nodes, predicts gas diffusion paths and dust change trends, and uses an adaptive adjustment module to adjust fan speed, guide vane angle, and pressure valve opening in real time. In normal, emergency, and energy-saving working conditions, it optimizes the balance between energy consumption and efficiency. Its emergency disposal module can quickly start explosion suppression and automatic sealing plans, while the energy management module integrates photovoltaic, energy storage, and pressure recovery technologies to reduce dependence on city power.

[0003] The existing coal mine ventilation and exhaust device and its control system rely on single-point sensor data and lack multi-source data fusion and dynamic modeling capabilities, resulting in delayed harmful gas diffusion path prediction, inaccurate dust concentration change trend judgment, and risk assessment based only on single threshold judgment, which cannot comprehensively analyze the coupling of explosion triangle elements and thermodynamic factors, and is prone to cause untimely or incorrect safety warnings.

[0004] To solve the problem of the existing coal mine ventilation and exhaust device and its control system relying on single-point sensor data and lacking multi-source data fusion and dynamic modeling capabilities, leading to delayed harmful gas diffusion path prediction, inaccurate dust concentration change trend judgment, and risk assessment based only on single threshold judgment, which cannot comprehensively analyze the coupling of explosion triangle elements and thermodynamic factors, and is prone to cause untimely or incorrect safety warnings, the present application realizes multi-source data deep integration and dynamic environment modeling through an intelligent environment prediction module. A multi-parameter sensor array and a 3D laser scanner cooperatively collect mine environment parameters and spatial structure data. An edge computing node builds a high-precision dynamic model after preprocessing the data, and simulates gas and dust motion trajectories using the finite element method. A load prediction unit predicts future ventilation load and dust concentration changes using time series analysis and neural network. A risk assessment unit verifies the coupling state of the three elements through an explosion triangle calculation engine, and integrates a thermodynamic simulator to analyze the acceleration effect of hot convection on gas diffusion. Finally, a visual warning terminal realizes hierarchical warning and AR path superposition, significantly improving the spatiotemporal resolution of environmental monitoring and the comprehensiveness of risk assessment, upgrading safety warning from passive threshold judgment to active coupling analysis, greatly reducing false positives and intervening in potential dangers in advance, and ensuring mine environmental safety. SUMMARY

[0005] In order to overcome the existing coal mine ventilation and exhaust device and its control system, it is necessary to rely on single-point sensor data, lack of multi-source data fusion and dynamic modeling capability, leading to harmful gas diffusion path prediction lag, dust concentration change trend inaccurate judgment, and risk assessment based on single threshold judgment, unable to comprehensively analyze the coupling of explosion triangle elements and thermodynamic factors, prone to cause safety warning not timely or misjudgment.

[0006] The technical scheme of the present application is: a coal mine ventilation and exhaust device and its control system, comprising a ventilation pipeline outer cavity, a ventilation fan, a wind volume sensing module, a filtering device, a gas detection module, a control module, an air suction machine and a protective sieve plate, the ventilation pipeline outer cavity is internally provided with a ventilation fan, one side of the ventilation pipeline outer cavity is provided with a wind volume sensing module, the other side of the ventilation pipeline outer cavity is provided with a filtering device, the top surface of the filtering device is provided with a control module, one side of the control module is provided with a gas detection module, one side of the filtering device is provided with an air suction machine, and the air suction machine is provided with a protective sieve plate at the air inlet end.

[0007] Preferably, the ventilation pipeline outer cavity provides a gas flow channel and accommodates other components, optimizes the airflow path to reduce resistance, ensures exhaust efficiency, the ventilation fan rotates the blades to generate negative pressure or positive pressure, drives the air circulation inside and outside the mine, the wind volume sensing module monitors the wind speed / wind volume data in the pipeline in real time, the filtering device efficiently captures coal dust, explosive coal powder particles and aerosol, prevents dust accumulation from causing safety hazards, the gas detection module detects methane, carbon monoxide and oxygen concentration in real time, the control module receives the wind volume / gas data and calculates the optimal control strategy through the PID algorithm, the air suction machine sucks the air inside the mine, and the protective sieve plate prevents foreign matter inside the mine from entering the inside of the device.

[0008] The coal mine ventilation and exhaust device control system comprises the following modules: Intelligent environment prediction module: used for building a mine environment dynamic model based on multi-source data, predicting harmful gas diffusion path and dust concentration change trend; Self-adaptive airflow regulation module: used for dynamically adjusting the working state of the ventilation system according to real-time environmental parameters, realizing the optimal balance of energy consumption and efficiency; Energy management module: used for optimizing system energy consumption structure, realizing multi-energy collaborative power supply and energy recycling; Emergency disposal module: used for quickly starting multi-level emergency plan under abnormal working conditions to ensure personnel and equipment safety; Data fusion analysis module: used for integrating multi-dimensional sensor data to build a mine environment digital twin system; Device self-maintenance module: used for prolonging the service life of the device through predictive maintenance and reducing the risk of system downtime.

[0009] As preferred, the intelligent environment prediction module comprises: A11: an environment modeling unit comprising a multi-parameter sensor array, a 3D laser scanner, and an edge computing node, for constructing a mine environment dynamic model, predicting harmful gas diffusion paths and dust concentration trends; A12: a load prediction unit comprising a time series analysis chip, a historical data storage, and a neural network accelerator, for predicting future ventilation system workloads based on time series analysis and neural networks; A13: a risk assessment unit comprising an explosion triangle calculation engine, a thermodynamic simulator, and a visual warning terminal, for assessing environmental safety risk levels through explosion triangle calculations and thermodynamic simulations.

[0010] As preferred, the intelligent environment prediction module comprises the following steps when working: S11: a multi-parameter sensor array collects real-time methane, carbon monoxide, dust concentration, and temperature and humidity data in the mine, and transmits them to the edge computing node through a wired network; S12: a 3D laser scanner scans the mine roadway space structure at a set period, generates high-precision three-dimensional point cloud data, and aligns with the historical model in space; S13: the edge computing node preprocesses sensor data and point cloud data, including outlier rejection, data interpolation, and coordinate system conversion; S14: based on the processed multi-source data, a mine environment dynamic model is constructed, and a finite element method is used to simulate gas diffusion paths and dust transport trajectories; S15: the load prediction unit retrieves historical ventilation system operation data, establishes an ARIMA prediction model through a time series analysis chip, and generates a future 24-hour ventilation load curve; S16: the neural network accelerator loads a pre-trained LSTM network, combines real-time weather data and mining progress to predict dust concentration trends; S17: the risk assessment unit receives the prediction results, verifies the coupling state of the three elements of methane concentration-oxygen concentration-ignition source through the explosion triangle calculation engine; S18: the thermodynamic simulator runs CFD simulation to evaluate the acceleration effect of high-temperature area thermal convection on gas diffusion, and generates a three-dimensional thermal distribution map; S19: the visual warning terminal triggers graded warnings according to risk levels, and displays dangerous area boundaries and escape routes on AR devices; S110: the dynamic model is compared with actual monitoring data every 10 minutes, and the prediction bias is corrected through Kalman filtering algorithm to update the environment state matrix.

[0011] As preferred, the adaptive air flow regulation module comprises: A21: a dynamic matching unit comprising a variable frequency drive, an adjustable guide vane set, and a pressure compensation valve, for adjusting the guide vanes and the pressure valve according to real-time parameters to achieve precise control of air flow; A22: a mode switching unit comprising a multi-working-condition algorithm library, a state machine controller, and an emergency protocol memory, for switching the multi-working-condition operation modes such as normal / emergency / energy-saving through the state machine controller; A23: an efficiency optimization unit comprising a fluid mechanics simulation chip, a resistance coefficient calculator, and an energy-saving evaluation model, for maximizing the energy efficiency of the ventilation system by using fluid mechanics simulation and energy-saving model.

[0012] As preferred, the adaptive air flow regulation module comprises the following steps when working: S21: the dynamic matching unit acquires the wind pressure data of each roadway branch in real time through the pressure sensor network, and calculates the current air volume distribution in combination with the anemometer measurement value; S22: the variable frequency drive receives the air volume demand instruction, adjusts the motor speed of the ventilator, and synchronously adjusts the opening angle of the adjustable guide vane set; S23: the pressure compensation valve monitors the pressure fluctuation of the total return air roadway, and automatically adjusts the valve opening degree when the pressure change exceeds the threshold value to maintain the stability of the main ventilation circuit; S24: the state machine controller of the mode switching unit continuously evaluates the environmental parameters, and switches to the emergency disposal mode when detecting the methane concentration overrun signal; S25: the multi-working-condition algorithm library calls the preset emergency ventilation strategy, and quickly increases the air volume supply of the accident area through the PID control algorithm; S26: the fluid mechanics simulation chip of the efficiency optimization unit simulates the current air flow state, and calculates the optimal guide vane angle and fan operation frequency combination; S27: the resistance coefficient calculator analyzes the roadway support structure change data, generates a real-time resistance distribution map, and guides the adjustment strategy of the pressure compensation valve; S28: the energy-saving evaluation model compares the energy consumption data under different adjustment schemes, selects the parameter combination with the highest fan operation efficiency, and issues an execution instruction; S29: when detecting the photovoltaic power fluctuation, the intelligent regulation unit switches to the energy storage system power supply mode to maintain the stable operation of the variable frequency drive; S210: the dynamic matching unit generates a regulation effect report every 5 minutes, evaluates the air flow control precision through the resistance coefficient change rate, and triggers the model self-learning mechanism.

[0013] As preferred, the energy management module comprises: A31: Energy monitoring unit, including power quality analyzer, photovoltaic sensor and thermoelectric generator monitor, for real-time acquisition and analysis of photovoltaic, mains, energy storage system power quality parameters; A32: Intelligent deployment unit, including multi-power management chip, lithium battery pack and super capacitor array, for realizing photovoltaic priority intelligent power supply strategy switching through multi-power management chip; A33: Energy recovery unit, including thermoelectric conversion device, kinetic energy recovery generator and excess pressure utilization turbine, for converting waste energy such as heat energy, kinetic energy and excess pressure into usable electric energy.

[0014] As preferred, the emergency disposal module includes: A41: Rapid response unit, including gas explosion suppressor, automatic sealing valve and emergency power-off device, for automatically starting the suppressor and sealing valve for emergency disposal at the gas explosion critical point; A42: Personnel positioning unit, including UWB positioning base station, vital sign monitoring bracelet and escape guide screen, for realizing real-time positioning and health monitoring of miners through UWB base station and vital sign bracelet; A43: Communication guarantee unit, including explosion-proof intercom, relay antenna and emergency broadcast system, for maintaining stable communication of explosion-proof intercom and relay antenna in extreme environment.

[0015] As preferred, the data fusion analysis module includes: A51: Data acquisition unit, including optical fiber sensing network, wireless Mesh node and data compression chip, for realizing efficient multi-dimensional data acquisition through optical fiber sensing network and wireless Mesh node; A52: Feature extraction unit, including deep learning accelerator, anomaly detection engine and pattern recognition processor, for extracting environmental anomaly features from massive data using deep learning accelerator; A53: Digital twin unit, including three-dimensional modeling engine, virtual simulation platform and AR visualization terminal, for building a three-dimensional digital model of the mine environment for virtual simulation and AR visualization.

[0016] As preferred, the device self-maintenance module includes: A61: State monitoring unit, including vibration sensor, temperature patrol instrument and lubricating oil analyzer, for real-time monitoring of device running health status through vibration and temperature sensors; A62: Fault diagnosis unit, including expert system knowledge base, fuzzy reasoning engine and self-repairing algorithm library, for intelligent diagnosis of device faults using expert systems and fuzzy reasoning engines; A63: Maintenance execution unit, including automatic oil injection device, filter cleaning robot and spare parts replacement mechanical arm, for realizing predictive maintenance operation by automatic oil injection device and cleaning robot.

[0017] Advantages of the present application: 1、The existing coal mine ventilation and exhaust device and its control system rely on single-point sensor data, lack multi-source data fusion and dynamic modeling capability, leading to prediction lag of harmful gas diffusion path, inaccurate judgment of dust concentration change trend, and risk assessment based on single threshold judgment only, unable to comprehensively analyze coupling of three elements of explosion triangle and thermodynamic factors, prone to cause safety warning not timely or misjudgment, the present scheme realizes deep integration of multi-source data and dynamic environment modeling through intelligent environment prediction module, multi-parameter sensor array and 3D laser scanner cooperatively collect mine environment parameters and spatial structure data, edge computing nodes construct high-precision dynamic model after preprocessing data, and simulate gas and dust motion trajectory by combining finite element method; the load prediction unit predicts future ventilation load and dust concentration change by using time series analysis and neural network; the risk assessment unit verifies three-element coupling state through explosion triangle calculation engine, and analyzes the acceleration effect of high-temperature area thermal convection on gas diffusion by integrating thermodynamic simulator, finally realizes hierarchical warning and AR path superposition through visual warning terminal, significantly improves the spatiotemporal resolution of environment monitoring and the comprehensiveness of risk assessment, upgrades safety warning from passive threshold judgment to active coupling analysis, greatly reduces the false alarm rate and intervenes in potential danger in advance, and ensures mine environment safety; 2、The existing coal mine ventilation and exhaust device and its control system mostly run in fixed working condition mode, cannot dynamically adjust guide vane, pressure valve and fan speed according to real-time environment parameters, leading to low airflow control precision, and lack multi-working condition switching mechanism, making it difficult to realize optimal balance of energy consumption and efficiency in conventional, emergency, energy-saving and other scenes, and often appearing over-ventilation or insufficient ventilation, the present scheme realizes dynamic matching and multi-working condition optimization through self-adaptive airflow regulation module, the dynamic matching unit calculates air volume distribution in real time by using pressure sensor network and anemometer, the frequency drive synchronously adjusts fan speed and guide vane angle, and the pressure compensation valve automatically stabilizes the main ventilation loop pressure; the mode switching unit assesses environment parameters in real time through state machine controller, automatically switches to emergency mode when detecting abnormal conditions such as methane concentration exceeding limit, calls preset ventilation strategy and quickly increases air volume of accident area through PID algorithm; the efficiency optimization unit calculates optimal parameter combination based on fluid mechanics simulation and energy-saving model, generates real-time resistance distribution atlas combined with resistance coefficient analysis, and guides adjustment strategy, this improvement upgrades airflow control from fixed mode to dynamic and accurate adjustment, reduces invalid energy consumption in conventional working condition, quickly responds to demand in emergency working condition, realizes optimal balance of energy consumption and efficiency in all scenes, and improves system adaptability and economy; 3、The existing coal mine ventilation and exhaust device and its control system, energy management depends on single power supply, lacks photovoltaic, energy storage and other multi-energy collaborative mechanism, and does not effectively recover the waste energy such as heat energy, kinetic energy and excess pressure generated in the ventilation process, resulting in high system operation cost, low energy utilization rate and difficulty in meeting the low carbon demand. The energy management module realizes multi-energy collaboration and waste energy recovery, the energy monitoring unit collects real-time photovoltaic, power, energy storage system power quality parameters to provide data basis for intelligent allocation; The intelligent allocation unit gives priority to photovoltaic, dynamically switches the power supply strategy through multi-power management chip, and combines the energy storage characteristics of lithium battery and super capacitor to smooth photovoltaic fluctuation; The energy recovery unit integrates a thermoelectric conversion device, a kinetic energy recovery generator and a pressure utilization turbine to convert the heat energy, fan kinetic energy and pipeline excess pressure generated in the ventilation process into usable electric energy, and build a full-link energy management system of "power generation-energy storage-recovery". It not only reduces the dependence on power through multi-energy collaboration, but also improves the overall energy utilization rate through waste energy recovery, significantly reduces the system operation cost and promotes green and low-carbon transformation, which meets the intelligent and sustainable development trend of coal mine. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The first three-dimensional structure of the coal mine ventilation and exhaust device of the present application is shown. Figure 2 The second three-dimensional structure of the coal mine ventilation and exhaust device of the present application is shown. Figure 3 The first internal three-dimensional structure of the coal mine ventilation and exhaust device of the present application is shown. Figure 4 The second internal three-dimensional structure of the coal mine ventilation and exhaust device of the present application is shown. Figure 5 The control system framework flowchart of the coal mine ventilation and exhaust device of the present application is shown. Figure 6 The working flowchart of the self-adaptive airflow regulation module of the control system of the coal mine ventilation and exhaust device of the present application is shown. Explanation of reference numerals: 1, ventilation duct cavity; 2, ventilation fan; 3, air volume sensing module; 4, filter device; 5, gas detection module; 6, control module; 7, air suction machine; 8, protective sieve plate. DETAILED DESCRIPTION

[0019] The present application will be further described below in conjunction with the drawings and examples.

[0020] Please refer to Figures 1-4The application provides an embodiment: a coal mine ventilation and exhaust device, comprising a ventilation pipe outer cavity 1, a ventilation fan 2, an air volume sensing module 3, a filtering device 4, a gas detection module 5, a control module 6, an air suction machine 7 and a protective sieve plate 8, the ventilation pipe outer cavity 1 is internally provided with the ventilation fan 2, one side of the ventilation pipe outer cavity 1 is provided with the air volume sensing module 3, the other side of the ventilation pipe outer cavity 1 is provided with the filtering device 4, the top surface of the filtering device 4 is provided with the control module 6, one side of the control module 6 is provided with the gas detection module 5, one side of the filtering device 4 is provided with the air suction machine 7, and the air inlet end of the air suction machine 7 is provided with the protective sieve plate 8.

[0021] The ventilation pipe outer cavity 1 provides a gas flow channel and accommodates other components, optimizes the air flow path to reduce resistance, ensures exhaust efficiency, the ventilation fan 2 rotates the blades to generate negative pressure or positive pressure, drives the air circulation inside and outside the mine, the air volume sensing module 3 monitors the wind speed / air volume data in the pipe in real time, the filtering device 4 efficiently captures coal dust, explosive coal powder particles and aerosols, prevents dust accumulation from causing safety hazards, the gas detection module 5 detects the concentration of methane, carbon monoxide and oxygen in real time, the control module 6 receives the air volume / gas data and calculates the optimal control strategy through the PID algorithm, the air suction machine 7 sucks the air inside the mine, and the protective sieve plate 8 prevents foreign matter inside the mine from entering the inside of the device.

[0022] Please refer to Figures 5-6 In this embodiment, the coal mine ventilation and exhaust device control system comprises the following modules: Intelligent environment prediction module: used for constructing a mine environment dynamic model based on multi-source data, predicting a harmful gas diffusion path and a dust concentration change trend; Self-adaptive air flow regulation module: used for dynamically adjusting the working state of the ventilation system according to real-time environmental parameters, realizing optimal balance of energy consumption and efficiency; Energy management module: used for optimizing the energy consumption structure of the system, realizing multi-energy collaborative power supply and energy recycling; Emergency disposal module: used for quickly starting a multi-stage emergency plan under abnormal working conditions to ensure the safety of personnel and equipment; Data fusion analysis module: used for integrating multi-dimensional sensor data to construct a mine environment digital twin system; Device self-maintenance module: used for prolonging the service life of the device and reducing the risk of system downtime through predictive maintenance.

[0023] As preferred, the intelligent environment prediction module comprises: A11: an environment modeling unit, comprising a multi-parameter sensor array, a 3D laser scanner and an edge computing node, used for constructing a mine environment dynamic model, predicting a harmful gas diffusion path and a dust concentration change trend; A12: Load prediction unit, including time series analysis chip, historical data storage and neural network accelerator, for predicting future working load of ventilation system based on time series analysis and neural network; A13: Risk assessment unit, including explosion triangle calculation engine, thermodynamic simulator and visual warning terminal, for assessing environmental safety risk level through explosion triangle calculation and thermodynamic simulation.

[0024] As preferred, the intelligent environment prediction module includes the following steps when working: S11: A multi-parameter sensor array collects real-time data of methane, carbon monoxide, dust concentration and temperature and humidity in the mine, and transmits them to the edge computing node through a wired network; S12: A 3D laser scanner scans the mine roadway space structure at a set period, generates high-precision three-dimensional point cloud data and aligns with the historical model in space; S13: The edge computing node preprocesses the sensor data and point cloud data, including outlier rejection, data interpolation and coordinate system conversion; S14: Based on the processed multi-source data, a mine environment dynamic model is constructed, and the finite element method is used to simulate the gas diffusion path and dust transport trajectory; S15: The load prediction unit calls historical ventilation system operation data, establishes an ARIMA prediction model through a time series analysis chip, and generates a future 24-hour ventilation load curve; S16: The neural network accelerator loads a pre-trained LSTM network, combines real-time weather data and mining progress to predict the dust concentration trend; S17: The risk assessment unit receives the prediction results, verifies the coupling state of the three elements of methane concentration-oxygen concentration-ignition source through the explosion triangle calculation engine; S18: The thermodynamic simulator runs CFD simulation to evaluate the acceleration effect of thermal convection in high-temperature areas on gas diffusion, and generates a three-dimensional thermal distribution map; S19: The visual warning terminal triggers a graded warning according to the risk level, and displays the dangerous area boundary and escape path on the AR device; S110: The dynamic model is compared with the actual monitoring data every 10 minutes, and the prediction deviation is corrected through Kalman filtering algorithm to update the environmental state matrix.

[0025] As preferred, the adaptive airflow regulation module includes: A21: Dynamic matching unit, including variable frequency drive, adjustable guide vane group and pressure compensation valve, for adjusting guide vane and pressure valve according to real-time parameters to realize precise control of airflow; A22: mode switching unit, including multi-condition algorithm library, state machine controller and emergency protocol memory, for switching multi-condition operation modes such as normal / emergency / energy-saving through the state machine controller; A23: efficiency optimization unit, including fluid mechanics simulation chip, resistance coefficient calculator and energy-saving evaluation model, for maximizing the energy efficiency of the ventilation system by using fluid mechanics simulation and energy-saving model.

[0026] As preferred, the adaptive air flow regulation module includes the following steps when working: S21: The dynamic matching unit obtains the wind pressure data of each roadway branch in real time through the pressure sensor network, and calculates the current air volume distribution in combination with the anemometer measurement value; S22: The variable frequency driver receives the air volume demand instruction, adjusts the motor speed of the ventilator, and synchronously adjusts the opening angle of the adjustable guide vane group; S23: The pressure compensation valve monitors the pressure fluctuation of the total return air roadway, and automatically adjusts the valve opening degree when the pressure change exceeds the threshold value, to maintain the stability of the main ventilation circuit; S24: The state machine controller of the mode switching unit continuously evaluates the environmental parameters, and switches to the emergency disposal mode when detecting the methane concentration exceeding signal; S25: The multi-condition algorithm library calls the preset emergency ventilation strategy, and quickly increases the air volume supply of the accident area through the PID control algorithm; S26: The fluid mechanics simulation chip of the efficiency optimization unit simulates the current air flow state, calculates the optimal guide vane angle and fan operating frequency combination; S27: The resistance coefficient calculator analyzes the roadway support structure change data, generates a real-time resistance distribution map, and guides the adjustment strategy of the pressure compensation valve; S28: The energy-saving evaluation model compares the energy consumption data under different adjustment schemes, selects the parameter combination with the highest fan operating efficiency, and issues an execution instruction; S29: When detecting photovoltaic power fluctuation, the intelligent allocation unit switches to the energy storage system power supply mode to maintain the stable operation of the variable frequency driver; S210: The dynamic matching unit generates an adjustment effect report every 5 minutes, evaluates the air flow control precision through the resistance coefficient change rate, and triggers the model self-learning mechanism.

[0027] As preferred, the energy management module includes: A31: Energy monitoring unit, including power quality analyzer, photovoltaic sensor and thermoelectric generator monitor, for real-time acquisition and analysis of power quality parameters of photovoltaic, mains and energy storage system; A32: Intelligent allocation unit, including multi-power management chip, lithium battery pack and super capacitor array, for switching the intelligent power supply strategy of photovoltaic priority through the multi-power management chip; A33: Energy recovery unit, including thermoelectric conversion device, kinetic energy recovery generator and surplus pressure utilization turbine, for converting waste energy such as heat energy, kinetic energy and surplus pressure into usable electric energy.

[0028] As preferred, the emergency disposal module includes: A41: Quick response unit, including gas explosion inhibitor, automatic sealing valve and emergency power-off device, for automatically starting the inhibitor and sealing valve for emergency disposal at the critical point of gas explosion; A42: Personnel positioning unit, including UWB positioning base station, vital sign monitoring bracelet and escape guide screen, for realizing real-time positioning and health monitoring of miners through UWB base station and vital sign bracelet; A43: Communication guarantee unit, including explosion-proof intercom, relay antenna and emergency broadcast system, for maintaining stable communication of explosion-proof intercom and relay antenna in extreme environment.

[0029] As preferred, the data fusion analysis module includes: A51: Data acquisition unit, including optical fiber sensing network, wireless Mesh node and data compression chip, for realizing efficient acquisition of multi-dimensional data through optical fiber sensing network and wireless Mesh node; A52: Feature extraction unit, including deep learning accelerator, anomaly detection engine and pattern recognition processor, for extracting environmental abnormal features from massive data using deep learning accelerator; A53: Digital twin unit, including three-dimensional modeling engine, virtual simulation platform and AR visualization terminal, for constructing three-dimensional digital model of mine environment for virtual simulation and AR visualization.

[0030] As preferred, the device self-maintenance module includes: A61: State monitoring unit, including vibration sensor, temperature patrol instrument and lubricating oil analyzer, for monitoring the health status of device operation in real time through vibration and temperature sensors; A62: Fault diagnosis unit, including expert system knowledge base, fuzzy reasoning engine and self-repairing algorithm library, for using expert system and fuzzy reasoning engine for intelligent diagnosis of device fault; A63: Maintenance execution unit, including automatic oil injection device, filter cleaning robot and spare parts replacement mechanical arm, for realizing predictive maintenance operation through automatic oil injection device and cleaning robot.

[0031] Example 1 Implementation background: A large coal mine due to the depth of the mine more than 800 meters, roadway structure is complex, the traditional ventilation system has the following problems: (1) environmental monitoring relies on single-point sensor, can not predict the diffusion path of harmful gas, once because of methane accumulation caused 2 gas explosion accidents; (2) the long-term operation of the fixed speed of the fan, energy consumption accounts for 35% of the total power consumption of the mine, and the emergency response time is more than 15 minutes; (3) the dust filtration efficiency is only 78%, the equipment failure rate is as high as 12 times / year, resulting in an average of 40 hours / year of unplanned downtime, to solve the above problems, the coal mine introduces the ventilation and exhaust device and control system in this scheme, through multi-module cooperation to realize dynamic environmental monitoring, accurate airflow regulation and energy efficient management.

[0032] Implementation steps: S31: install multi-parameter sensor array and 3D laser scanner at key positions of main roadway, return airway and other key positions of the mine, with a spacing of not more than 50 meters; S32: connect the ventilation fan 2, the filter device 4, the air suction machine 7 in sequence according to the airflow path outside the cavity 1 of the ventilation duct, and install the protective screen plate 8 at the air inlet end of the air suction machine 7; S33: deploy the control module 6 on the top surface of the filter device 4, integrate intelligent environmental prediction module, adaptive airflow regulation module and other algorithm library; S34: arrange pressure sensor network and anemometer in each branch roadway of the mine, and connect the variable frequency drive and pressure compensation valve of the dynamic matching unit; S35: the environmental modeling unit generates a three-dimensional point cloud model of the mine through the 3D laser scanner, and constructs a dynamic environmental model by fusing multi-parameter sensor data; S36: the risk assessment unit configures explosion triangle threshold, and integrates CFD simulation parameters of thermodynamic simulator; S37: the adaptive airflow regulation module sets multi-working condition algorithm library, including normal mode, emergency mode and energy saving mode; S38: the energy monitoring unit accesses the mine photovoltaic power generation system, mains and energy storage lithium battery pack, and sets photovoltaic priority power supply strategy; S39: the energy recovery unit connects the ventilation fan residual pressure turbine and the roadway temperature difference power generation device, and configures the thermoelectric conversion efficiency ≥8%, kinetic energy recovery efficiency ≥15%; S310: the intelligent allocation unit sets multi-power switching rules: when the photovoltaic power supply is insufficient, the energy storage system is automatically started, and when the energy storage power is less than 20%, the mains is switched; S311: the rapid response unit configures gas explosion suppressor and automatic sealing valve; S312: the personnel positioning unit deploys UWB base station and vital signs bracelet, and installs escape guide screen at the intersection of the roadway; S313: The device self-maintenance module sets the vibration sensor, temperature patrol instrument, and filter core cleaning robot to automatically execute filter cleaning every day. S314: Simulate the scenario of methane concentration exceeding the limit, and verify whether the emergency disposal module starts the inhibitor, sealing valve, and emergency power-off within 30 seconds. S315: Test whether the adaptive air flow adjustment module reaches the target value within 10 seconds when switching from normal mode to emergency mode, and whether the air volume fluctuation is less than 5%. S316: Monitor whether the energy management module seamlessly switches the energy storage system when the photovoltaic power generation fluctuates, and whether the total energy consumption of the system is reduced by more than 20%.

[0033] Data comparison table:

[0034] The embodiments of the application are described in detail above with reference to the drawings, but the application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the application.

Claims

1. A coal mine ventilation and exhaust system; characterized in that: It includes a ventilation duct outer cavity (1), a ventilation fan (2), an air volume sensing module (3), a filter device (4), a gas detection module (5), a control module (6), an air intake machine (7), and a protective screen plate (8). The ventilation fan (2) is installed inside the ventilation duct outer cavity (1). The air volume sensing module (3) is installed on one side of the ventilation duct outer cavity (1). The filter device (4) is installed on the other side of the ventilation duct outer cavity (1). The control module (6) is installed on the top surface of the filter device (4). The gas detection module (5) is installed on one side of the control module (6). The air intake machine (7) is installed on one side of the filter device (4). The air intake end of the air intake machine (7) is equipped with a protective screen plate (8).

2. A control system for ventilation and exhaust systems in coal mines, characterized in that: It consists of the following modules: Intelligent environment prediction module: used to build dynamic models of the mine environment based on multi-source data, and predict the diffusion paths of harmful gases and the trend of dust concentration changes; Adaptive airflow adjustment module; used to dynamically adjust the working status of the ventilation system according to real-time environmental parameters to achieve the optimal balance between energy consumption and efficiency; Energy management module: used to optimize the system's energy consumption structure and realize multi-energy coordinated power supply and energy recovery and utilization; Emergency Response Module: Used to quickly activate multi-level emergency plans under abnormal operating conditions to ensure the safety of personnel and equipment; Data fusion and analysis module: used to integrate multi-dimensional sensor data to build a digital twin system for the mine environment; Equipment self-maintenance module: Used to extend equipment life and reduce system downtime risk through predictive maintenance.

3. The coal mine ventilation and exhaust control system according to claim 2, characterized in that: The intelligent environment prediction module includes: A11: Environmental Modeling Unit, including a multi-parameter sensor array, a 3D laser scanner, and edge computing nodes, is used to build dynamic models of the mine environment and predict the diffusion paths of harmful gases and the changing trends of dust concentration; A12: Load forecasting unit, including a time series analysis chip, historical data storage, and neural network accelerator, used to predict the future workload of the ventilation system based on time series analysis and neural networks; A13: Risk assessment unit, including an explosion triangle calculation engine, a thermodynamic simulator, and a visualization and early warning terminal, is used to assess the level of environmental safety risks through explosion triangle calculation and thermodynamic simulation.

4. The coal mine ventilation and exhaust control system according to claim 3, characterized in that: The intelligent environment prediction module operates by including the following steps: S11: A multi-parameter sensor array collects real-time data on methane, carbon monoxide, dust concentration, temperature, and humidity within the mine, and transmits this data to edge computing nodes via a wired network; S12: The 3D laser scanner scans the spatial structure of mine roadways at a set cycle, generating high-precision 3D point cloud data and aligning it spatially with historical models; S13: Edge computing nodes preprocess sensor data and point cloud data, including outlier removal, data interpolation, and coordinate system transformation; S14: Based on the processed multi-source data, a dynamic model of the mine environment is constructed, and the finite element method is used to simulate the gas diffusion path and dust transport trajectory; S15: The load prediction unit retrieves historical ventilation system operation data and establishes an ARIMA prediction model through a time series analysis chip to generate the ventilation load curve for the next 24 hours; S16: A neural network accelerator is loaded with a pre-trained LSTM network to predict dust concentration trends by combining real-time meteorological data and mining progress. S17: The risk assessment unit receives the prediction results and verifies the coupling status of the three elements of methane concentration, oxygen concentration, and ignition source through the explosion triangle calculation engine; S18: The thermodynamic simulator runs CFD simulations to evaluate the accelerating effect of thermal convection on gas diffusion in high-temperature regions and generates a three-dimensional thermodynamic distribution map; S19: The visual early warning terminal triggers tiered early warnings based on risk levels, and overlays the boundaries of dangerous areas and escape routes onto AR devices; S110: The dynamic model is compared with the actual monitoring data every 10 minutes, and the prediction deviation is corrected by the Kalman filter algorithm to update the environmental state matrix.

5. The coal mine ventilation and exhaust control system according to claim 2, characterized in that: The adaptive airflow adjustment module includes: A21: Dynamic matching unit, including frequency converter, adjustable guide vane assembly and pressure compensation valve, used to adjust the guide vanes and pressure valve according to real-time parameters to achieve precise airflow control; A22: Mode switching unit, including a multi-condition algorithm library, a state machine controller, and an emergency protocol memory, is used to switch between normal / emergency / energy-saving and other multi-condition operating modes through the state machine controller; A23: Efficiency optimization unit, including a fluid dynamics simulation chip, a drag coefficient calculator, and an energy-saving assessment model, is used to maximize the energy efficiency of the ventilation system using fluid dynamics simulation and energy-saving models.

6. The coal mine ventilation and exhaust control system according to claim 5, characterized in that: The adaptive airflow regulation module operates by including the following steps: S21: The dynamic matching unit acquires the air pressure data of each roadway branch in real time through the pressure sensor network, and calculates the current air volume distribution by combining the anemometer measurement value; S22: The frequency converter driver receives the air volume demand command, adjusts the fan motor speed, and synchronously adjusts the opening and closing angle of the adjustable guide vane assembly; S23: The pressure compensation valve monitors pressure fluctuations in the main return airway. When the pressure change exceeds the threshold, it automatically adjusts the valve opening to maintain the stability of the main ventilation circuit. S24: The state machine controller of the mode switching unit continuously evaluates environmental parameters and switches to emergency response mode when a methane concentration exceeding the limit is detected; S25: The multi-condition algorithm library retrieves preset emergency ventilation strategies and uses a PID control algorithm to quickly increase the air volume supply to the accident area; S26: The fluid dynamics simulation chip of the efficiency optimization unit simulates the current airflow state and calculates the optimal combination of guide vane angle and fan operating frequency; S27: The resistance coefficient calculator analyzes the changes in roadway support structure data, generates real-time resistance distribution maps, and guides the adjustment strategy of pressure compensation valves; S28: The energy-saving assessment model compares energy consumption data under different adjustment schemes, selects the parameter combination with the highest fan operating efficiency, and issues execution instructions. S29: When fluctuations in photovoltaic power generation are detected, the intelligent dispatching unit switches to the energy storage system power supply mode to maintain the stable operation of the frequency converter drive; S210: The dynamic matching unit generates a regulation effect report every 5 minutes, evaluates the airflow control accuracy by the rate of change of drag coefficient, and triggers the model self-learning mechanism.

7. The coal mine ventilation and exhaust control system according to claim 2, characterized in that: The energy management module includes: A31: Energy monitoring unit, including power quality analyzer, photovoltaic sensor and thermoelectric generator, used to collect and analyze power quality parameters of photovoltaic, mains power and energy storage systems in real time; A32: Intelligent dispatching unit, including a multi-power management chip, lithium battery pack and supercapacitor array, used to achieve intelligent power supply strategy switching with photovoltaic priority through the multi-power management chip; A33: Energy recovery unit, including thermoelectric conversion device, kinetic energy recovery generator and waste pressure utilization turbine, is used to convert waste energy such as heat energy, kinetic energy and waste pressure into usable electrical energy.

8. The coal mine ventilation and exhaust control system according to claim 2, characterized in that: The emergency response module includes: A41: Rapid Response Unit, including a gas explosion suppressor, an automatic sealing valve, and an emergency power-off device, is used to automatically activate the suppressor and sealing valve for emergency response at the critical point of a gas explosion; A42: Personnel positioning unit, including UWB positioning base station, vital sign monitoring wristband and escape guidance screen, used to realize real-time positioning and health monitoring of miners through UWB base station and vital sign wristband; A43: Communication support unit, including explosion-proof walkie-talkies, repeater antennas and emergency broadcast systems, used to maintain stable communication of the explosion-proof walkie-talkies and repeater antennas in extreme environments.

9. The coal mine ventilation and exhaust control system according to claim 2, characterized in that: The data fusion and analysis module includes: A51: Data acquisition unit, including fiber optic sensor network, wireless mesh node and data compression chip, used to achieve efficient multi-dimensional data acquisition through fiber optic sensor network and wireless mesh node; A52: Feature extraction unit, including a deep learning accelerator, an anomaly detection engine, and a pattern recognition processor, used to extract environmental anomaly features from massive amounts of data using the deep learning accelerator; A53: Digital Twin Unit, including a 3D modeling engine, virtual simulation platform and AR visualization terminal, is used to build a 3D digital model of the mine environment for virtual simulation and AR visualization.

10. The coal mine ventilation and exhaust control system according to claim 2, characterized in that: The equipment self-maintenance module includes: A61: Condition monitoring unit, including vibration sensors, temperature monitoring instruments, and lubricating oil analyzers, used to monitor the equipment's operational health status in real time through vibration and temperature sensors; A62: Fault diagnosis unit, including an expert system knowledge base, a fuzzy inference engine, and a self-repairing algorithm library, used for intelligent fault diagnosis of equipment using expert systems and fuzzy inference engines; A63: Maintenance execution unit, including an automatic lubrication device, a filter cleaning robot, and a spare parts replacement robotic arm, for performing predictive maintenance operations via the automatic lubrication device and the cleaning robot.

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