Wireless remote monitoring explosion-proof heater

Abstract

The present application relates to the technical field of explosion-proof control and wireless networking of heating equipment, and specifically discloses a wireless remote monitoring explosion-proof heater, which comprises a real measurement collection module, a state deduction module, a deduction early warning module and an early warning control module, an explosion-proof heater control architecture is constructed in a key area of a mine, real measurement data packets are collected, a core state deduction model is constructed in the control architecture, the real measurement data packets are taken as input, a dynamic process state and a field evolution simulation state are calculated, a simulation state snapshot at a current time is obtained by collation, the core state deduction model is continuously used to deduce a state evolution sequence within a preset prediction evaluation time length, a dynamic combustion explosion risk index is calculated to judge an early warning level, and a hard cut inherent cooling time is pre-calculated and stored, and a differentiated collaborative disposal is executed based on the early warning level, so that a passive temperature threshold response is realized to a safe active control and accurate reset based on a physical model prediction.

Description

Technical Field

[0001] This invention relates to explosion-proof control technology for heating equipment and the field of wireless networking, specifically to a wireless remote monitoring explosion-proof heater. Background Technology

[0002] When the temperature of the intake air in a mine is too low during winter, ice and frost easily form on the walls of the shaft, roadways, and ventilation facilities. This can obstruct the operation of hoisting containers, damage shaft equipment, and reduce the ventilation cross-section. Furthermore, underground workers face the risk of working in low temperatures, and precision instruments may malfunction. Therefore, it is necessary to install explosion-proof heaters in key areas of the mine to heat the intake air. Since underground coal mines are hazardous environments for gas explosions, the heating equipment must be explosion-proof, ensuring both heating functionality and explosion safety.

[0003] Existing technologies struggle to predict the migration and accumulation trends of methane concentration in the ventilation flow field. When the intake air velocity fluctuates or local ventilation is impaired, methane may accumulate locally on the downwind side of the heater. Single-point methane concentration monitoring cannot capture this spatial evolution process, leading to the neglect of methane accumulation risks. Furthermore, the thermal inertia characteristics of explosion-proof heaters are not considered. A large amount of heat stored in the heating element and heat transfer medium continues to be conducted to the surface after power failure, causing the shell temperature to remain above the methane ignition temperature for a prolonged period. Due to the lack of quantitative calculation of the residual heat decay process after power failure, the minimum waiting time required for safe power restoration cannot be determined, creating a blind spot in safety control. Existing methods have a single risk assessment dimension, relying solely on independent thresholds for temperature or methane concentration. They fail to dynamically correlate and analyze the methane accumulation rate, hot surface temperature evolution trend, and ventilation effectiveness to achieve predictive assessment and graded early warning of safety status.

[0004] In addition, existing remote monitoring systems for explosion-proof heaters in mines generally use point-to-point radio frequency links or rely on wired extensions of existing industrial ring networks underground for on-site communication, without constructing a standardized self-organizing wireless networking architecture.

[0005] To address the above problems, this invention proposes a wireless remote monitoring explosion-proof heater. Summary of the Invention

[0006] The purpose of this invention is to provide a wireless remote monitoring explosion-proof heater to solve three problems in the control of existing explosion-proof heaters in the scenario where the ventilation heating and ice prevention and gas explosion hazards coexist in winter mines. These problems include: the inability to predict the high-temperature duration window due to the lack of quantitative prediction of the residual heat decay process after power failure; the inability to predict the evolution trend of combustion and explosion risks due to the lack of coupling deduction of gas concentration field and temperature field; and the inaccurate timing of power restoration due to the lack of pre-calculation of inherent cooling time based on physical models and reliance on fixed delays or manual judgment for safe power restoration.

[0007] The objective of this invention can be achieved through the following technical solution: a wireless remote monitoring explosion-proof heater, comprising the following modules: Actual measurement and data acquisition module: Construct an explosion-proof heater control architecture with an environmental sensing network and an equipment thermal status tracking network in key areas of the mine, collect thermal gas environment data and body thermal status data and organize them into actual measurement data packages; State simulation module: The core state simulation model is built in the control architecture. The core state simulation model integrates the heater thermal inertia dynamic sub-model, the mine ventilation network sub-model and the field evolution sub-model. With the measured data packet as input, it calculates the dynamic process state including residual heat decay after power failure, gas accumulation and gas dilution, and the field evolution simulation state including gas temperature field and gas concentration field. The simulation state snapshot at the current moment is obtained by sorting it out. The simulation and early warning module uses the current state snapshot as the starting simulation benchmark and the core state simulation model to continuously simulate the state evolution sequence within the preset prediction and evaluation time. Based on the state evolution sequence, it calculates the dynamic combustion and explosion risk index to determine the early warning level and uses the heater thermal inertia dynamic sub-model to pre-calculate and store the inherent cooling time of hard cut-off. Early warning control module: Performs differentiated collaborative handling based on early warning level. For level 3 early warning, it executes a safety cut-off and forced thermal lockout reset process based on the inherent cooling time of hard cut-off.

[0008] Furthermore, the method for obtaining the test data packets is as follows: Explosion-proof heaters that transfer heat indirectly through a heat-conducting medium are installed in key areas of the mine, and a control architecture including an environmental sensing network and an equipment thermal status tracking network is established. The environmental sensing network collects gas concentration data, wind speed vectors at the air inlet of the explosion-proof heater, and ambient temperature and humidity in key areas of the mine within the heating and ventilation monitoring area, and records them as thermal gas environmental data. The equipment thermal status tracking network collects the surface temperature of the heating element, the internal temperature of the heat transfer medium, and the heat dissipation temperature of the outer shell of the explosion-proof heater, and records them as the thermal status data of the body. The thermal gas environment data and the bulk thermal state data are time-aligned and then merged and packaged into a measured data package. The measured data packets and the control commands from the remote control center interact bidirectionally via an encrypted link through an electrically isolated wireless communication terminal, forming a monitoring and operation link.

[0009] Furthermore, the method for obtaining the simulated state snapshot is as follows: The heating and ventilation monitoring area is spatially discretized to obtain discrete grid nodes; Using the gas concentration data, wind speed vector and ambient temperature in the measured data package, the initial values ​​of gas concentration, wind speed three-component values, boundary conditions and gas temperature values ​​at each discrete grid node are obtained by spatial interpolation and input into the core state deduction model. The core state simulation model integrates a heater thermal inertia dynamic sub-model, a mine ventilation network sub-model, and a field evolution sub-model, which run at a preset discrete time step period and output a snapshot of the simulated state.

[0010] Furthermore, the method for calculating the dynamic process state of residual heat decay after power failure using the heater thermal inertia dynamic sub-model is as follows: The explosion-proof heater is abstracted as a lumped parameter thermal network containing three nodes: heating element, heat conduction medium, and shell. The lumped parameter temperature value is directly extracted from the body thermal state data in the measured data package. When powered on, the real-time power is used as the internal heat source input of the heating element node, and the temperature value of the lumped parameter of the three nodes is updated by combining the convective heat dissipation of the shell node to the environment. When the power supply circuit is cut off, the internal heat source term of the heating element node is automatically set to zero. Relying solely on the heat stored in the node for conduction and dissipation, the residual heat decay curve after power failure is continuously deduced as the temperature value of the lumped parameter of the three nodes decreases monotonically. This curve serves as the dynamic process state output of residual heat decay after power failure.

[0011] Furthermore, the method for calculating the dynamic process state of gas accumulation and gas dilution in the mine ventilation network sub-model is as follows: Starting with the three-component wind speed values ​​at each node of the air inlet boundary surface, the three-dimensional incompressible fluid flow equation is solved to obtain the initial three-component wind speed values ​​at each discrete grid node, including the x-direction velocity component, y-direction velocity component and z-direction velocity component. For the z-direction velocity component, the thermal buoyancy correction term is calculated based on the temperature difference between the temperature value of the lumped parameter of the heating element node and the current ambient temperature, and the three components of wind speed are updated. Using the three components of wind speed at each discrete grid node on the air inlet boundary and the methane concentration at each discrete grid node as initial conditions, the advection transport calculation and turbulent diffusion calculation are performed sequentially within each discrete time step to update the methane concentration value of each discrete grid node. The gas concentration values ​​of each node after the current discrete time step are compared with those of the previous discrete time step node by node. Nodes with increased gas concentration values ​​are marked as gas accumulation nodes and the increase is recorded. Nodes with decreased gas concentration values ​​are marked as gas dilution nodes and the decrease is recorded. The increase, decrease, and updated gas concentration values ​​of each node are combined as the dynamic process status output of gas accumulation and gas dilution.

[0012] Furthermore, the field evolution sub-model calculates the field evolution simulation state in the following way: The system receives the three-component wind speed values ​​of each discrete grid node from the mine ventilation network model, and receives the lumped parameter temperature values ​​of each node from the heater thermal inertia dynamic sub-model as the boundary conditions of the temperature field. Advection transport and turbulent diffusion calculations were performed on the gas temperature values ​​at each node to obtain the simulated gas temperature field. The methane concentration values ​​of each node output by the mine ventilation network model are directly read and processed to obtain the simulated methane concentration field. The simulated gas temperature field and gas concentration field are used as the output of the field evolution simulation state.

[0013] Furthermore, the method for determining the warning level is as follows: Starting with the current simulated state snapshot as the initial simulation benchmark, the core state simulation model is iteratively run step by step, accumulating simulations until the preset prediction and evaluation duration, and the state evolution sequence is obtained by arranging them in chronological order; if a new measured data packet is received during the simulation process, it is immediately interrupted and a new round of simulation is started with the simulated state snapshot generated by the new data packet as the benchmark. The dynamic combustion and explosion risk index is composed of a weighted fusion of the gas accumulation risk component, the hot surface ignition risk component, and the ventilation deterioration risk component. The gas accumulation risk component is obtained by normalizing the average rate of increase of gas concentration values ​​of the node where the explosion-proof heater is located and the neighboring nodes on the downwind side in the state evolution sequence. The hot surface ignition risk component is obtained by segmenting and mapping the difference between the predicted value of the temperature value of the lumped parameter of the heating element node and the gas ignition safety threshold. The ventilation deterioration risk component is calculated based on the ratio of the three-component modulus of the wind speed of the node where the explosion-proof heater is located to the preset minimum safe ventilation volume. The dynamic combustion and explosion risk indicators are compared sequentially with preset multi-level warning thresholds to determine the warning level, including Level 1 warning, Level 2 warning and Level 3 warning.

[0014] Furthermore, the calculation method for the inherent cooling time of hard cutoff is as follows: Using the lumped parameter temperature values ​​of each node in the current simulation state snapshot as the initial state, the heat source term in the heating element node is forcibly set to zero, and the heater thermal inertia dynamic sub-model is iteratively run. When the lumped parameter temperature value of the heating element node drops below the gas ignition safety threshold for the first time, the iteration time is taken as the inherent cooling time for hard cut-off.

[0015] Furthermore, the differentiated collaborative handling method is as follows: When a Level 1 warning is triggered, a Level 1 warning information package is generated, which includes dynamic combustion and explosion risk index values, key parameter prediction curves, field distribution cross-sectional cloud maps, and the proportion of each risk component, and uploaded to the remote control center. Automatic intervention is not performed on heater power and variable frequency fan speed. When a Level 2 warning is triggered, a Level 2 warning information package is generated and uploaded, and a local control strategy is automatically executed. This strategy includes two parallel actions: reducing the real-time power of the heater to the safe lower limit power value and increasing the speed of the variable frequency fan. During the execution, risk indicators are continuously monitored and analyzed. If the risk indicators fall below the Level 1 warning threshold, the heater power is gradually restored and the fan base speed is restored after a delay. If the risk indicators continue to deteriorate and reach the Level 3 warning threshold, the Level 3 warning handling process is immediately initiated.

[0016] Furthermore, the procedure for implementing safety disconnection and forced thermal lockout reset for Level 3 early warning is as follows: A cut-off signal is sent to the safety cut-off link connected in series in the power supply circuit of the explosion-proof heater, instantly disconnecting the power supply circuit and generating a power outage confirmation signal; Upon receiving the power outage confirmation signal, the pre-stored inherent cooling time of the hard cut-off is read and loaded as the countdown initial value of the forced thermal lockout timer. The power outage state is forcibly maintained during the timing period, while all power restoration commands are blocked. During the timing period, the measured temperature decay comparison and verification are performed simultaneously: the measured surface temperature of the heating element is collected to form the measured temperature decay curve. At the same time, the heater thermal inertia dynamic sub-model is run under the condition of no internal heat source, starting from the temperature value of the lumped parameter of each node at the safe cut-off moment, to generate the predicted residual heat decay curve. The measured value is compared with the predicted value. If the deviation exceeds the preset tolerance, a heat decay abnormality mark is generated and uploaded. When all three conditions are met—the forced thermal lockout timer countdown reaches zero, the measured surface temperature of the heating element is confirmed to have dropped below the gas ignition safety threshold, and the risk indicators have been confirmed by on-site personnel to have fallen back to below the safe range—the power restoration lockout is lifted, and the safety cut-off circuit is allowed to be restored to standby status through authorized manual reset operations, and the heater power supply circuit is restored to energized status. All key events throughout the process, along with the operator's identity information and timestamps, are written to the tamper-proof full-process log storage area and uploaded to the remote control center's historical database for long-term storage via an encrypted link.

[0017] The beneficial effects of this invention are as follows: 1. This invention abstracts the explosion-proof heater into a three-node lumped parameter thermal network through a heater thermal inertia dynamic sub-model. After power is cut off, the internal heat source term is automatically removed, and the residual heat decay curve after power failure is continuously deduced. This solves the problem that the existing technology cannot predict the high temperature sustained window of the heating element after power failure. At the same time, it pre-calculates the inherent cooling time of hard cut-off required for the temperature of the first node to drop to the gas ignition safety threshold, providing a physical mechanism-level time reference for forced thermal lockout reset, and improving the judgment of power restoration timing from experience-dependent to model-driven precise quantitative decision-making.

[0018] 2. This invention generates a state evolution sequence through the collaborative deduction of a three-sub-model, and calculates a dynamic combustion and explosion risk index by weighted fusion of the gas accumulation risk component, the hot surface ignition risk component, and the ventilation deterioration risk component. This solves the problem in the prior art that the isolated monitoring of the heater thermal state, ventilation flow field, and gas concentration field makes it impossible to predict the risk evolution trend under the coupling effect of multiple physical fields. Through the dual verification of forced thermal lockout timing and prediction-measured temperature decay deviation comparison, the invention achieves full-chain safety control from lockout to re-energization after disconnection, significantly improving the operational safety of mine explosion-proof heaters. Attached Figure Description

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] Figure 1 This is a module architecture diagram of a wireless remote monitoring explosion-proof heater according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the specific steps of a wireless remote monitoring explosion-proof heater according to an embodiment of the present invention; Figure 3 This is a time-series extrapolation curve of the dynamic combustion and explosion risk index and its components as described in the embodiments of the present invention; Figure 4 This is a real-time operational status diagram of the remote monitoring center for the explosion-proof heater under the secondary early warning state as described in this embodiment of the invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0022] Example 1 Please see Figure 1 and Figure 2 As shown in the embodiment of the present invention, a wireless remote monitoring explosion-proof heater aims to solve three problems in the control of existing explosion-proof heaters in the scenario where the ventilation heating and anti-icing of mine air coexist with the dangerous environment of gas explosion in winter: the lack of quantitative prediction of the residual heat decay process after power failure, making it impossible to predict the high temperature duration window; the lack of coupling deduction of gas concentration field and temperature field, making it impossible to predict the evolution trend of combustion and explosion risk; and the lack of pre-calculation of inherent cooling time based on physical model, resulting in inaccurate power restoration timing due to reliance on fixed delay or manual judgment. The invention achieves a shift from passive temperature threshold response to safe active control and precise reset based on physical model prediction. Specifically, it includes the following modules: Actual measurement and data acquisition module: Construct an explosion-proof heater control architecture with an environmental sensing network and an equipment thermal status tracking network in key areas of the mine, collect thermal gas environment data and body thermal status data and organize them into actual measurement data packages; It should be noted that in critical areas of the mine, the ambient temperature in winter is lower than the equipment antifreeze requirements or the temperature of the incoming airflow is close to the freezing point, which makes it easy for ice to form on the surface of the shaft, roadway walls and ventilation facilities. This can cause the hoisting container to be obstructed, shaft equipment to be damaged or the ventilation cross section to be reduced. In addition, underground workers face the risk of working in low temperatures. Therefore, it is necessary to install explosion-proof heaters to heat the incoming airflow. It should be noted that when the temperature of the mine intake airflow is too low, causing the precision instruments underground to malfunction or their measurement accuracy to decrease, it is necessary to use explosion-proof heaters to maintain the ambient temperature in the critical areas of the mine within the allowable operating range of the equipment. Since underground coal mines are dangerous environments for gas explosions, any heating equipment must have explosion-proof performance in order to simultaneously meet the dual requirements of preventing the shaft from freezing, ensuring the safety of hoisting and transportation, and providing a suitable working environment temperature for underground workers. Specifically, explosion-proof heaters are installed in key areas of the mine. The explosion-proof heaters transfer heat indirectly through a heat transfer medium. Based on the explosion-proof heaters, an explosion-proof heater control architecture is built, which includes an environmental sensing network and an equipment thermal status tracking network. The environmental sensing network includes a downwind methane concentration gradient monitoring array, a wind speed probe, and an ambient temperature and humidity sensor. The installation location of the explosion-proof heater and the downwind side along the ventilation flow direction are defined as the heating and ventilation monitoring area. The downwind methane concentration gradient monitoring array deploys several methane concentration sensors in the heating and ventilation monitoring area at preset intervals to collect methane concentration data. The wind speed probe is deployed at the air inlet of the explosion-proof heater to obtain the wind speed vector in the incoming airflow. The ambient temperature and humidity sensor is deployed in key areas of the mine to obtain ambient temperature and humidity. The gas concentration data, wind speed vector, ambient temperature and ambient humidity collected by the environmental sensing network are uniformly organized and recorded as thermal gas environmental data. The equipment thermal status tracking network includes armored thermocouples and surface-mount thermocouples. Specifically, a predetermined number of armored thermocouples are evenly distributed along the length of the heating element surface of the explosion-proof heater to directly measure the surface temperature of the heating element; a predetermined number of armored thermocouples are distributed in the middle layer of the heat-conducting medium in the thickness direction inside the heat-conducting medium to measure the internal temperature of the heat-conducting medium; and surface-mount thermocouples are distributed on the outer wall of the explosion-proof heater shell, corresponding to the orthographic projection area of ​​the internal heating element and its edge position, to measure the heat dissipation temperature of the shell. The surface temperature of the heating element, the internal temperature of the heat-conducting medium, and the heat dissipation temperature of the outer shell collected by the device thermal status tracking network are uniformly compiled and recorded as the body thermal status data. Both the environmental sensing network and the equipment thermal status tracking network are connected to a multi-channel temperature monitoring instrument and a local data acquisition unit. The multi-channel temperature monitoring instrument records the channel temperature value of each channel in a continuous scanning manner and adds a high-precision timestamp to each channel temperature value. The local data acquisition unit aggregates the gas concentration data along with the corresponding gas concentration sensor device number and high-precision timestamp to complete time alignment. The time-aligned thermal gas environment data and the body thermal status data are merged and packaged into a measured data package. The measured data packets and the control commands from the remote control center interact bidirectionally through an encrypted link via an electrically isolated wireless communication terminal, forming a monitoring and operation link. It should be noted that the electrically isolated wireless communication terminal has a built-in wireless communication module that supports self-organizing network protocols. It is compatible with Z-Wave and general Sub-1GHz / 2.4GHz frequency band wireless networking standards. It can work with Z-Wave routing nodes and repeaters already deployed in the mine roadway to build a disaster-resistant and self-healing wireless backbone network. When any communication terminal experiences link degradation due to local obstruction or electromagnetic interference, the wireless networking protocol automatically performs link reconstruction and data detour transmission under the mesh topology to ensure the redundancy and reliability of the monitoring and operation link in the complex electromagnetic environment underground and the real-time reachability of remote control commands.

[0023] State simulation module: The core state simulation model is built in the control architecture. The core state simulation model integrates the heater thermal inertia dynamic sub-model, the mine ventilation network sub-model and the field evolution sub-model. With the measured data packet as input, it calculates the dynamic process state including residual heat decay after power failure, gas accumulation and gas dilution, and the field evolution simulation state including gas temperature field and gas concentration field. The simulation state snapshot at the current moment is obtained by sorting it out. Specifically, within the heating and ventilation monitoring area, the space of the heating and ventilation monitoring area is discretized according to the preset three-dimensional grid resolution to obtain multiple discrete grid nodes, each of which has unique three-dimensional spatial coordinates. It should be noted that the grid resolution is preset based on the geometric dimensions and ventilation flow characteristics of key areas in the mine; The system receives the measured data packet, extracts the gas concentration data collected by each gas concentration sensor in the heating and ventilation monitoring area and the corresponding gas concentration sensor device number from the measured data packet, and uses the inverse distance weighted interpolation algorithm based on the three-dimensional spatial coordinates of each gas concentration sensor in the heating and ventilation monitoring area. It uses each discrete grid node as the center and a preset search radius to filter the gas concentration sensors in the neighborhood of that discrete grid node. It uses the reciprocal of the spatial distance from each gas concentration sensor to the discrete grid node as the weight to perform a weighted average of the gas concentration data of each gas concentration sensor, and calculates the gas concentration value on each discrete grid node. The wind speed vector collected by the wind speed probe is extracted from the measured data packet. The wind speed vector is used as the initial wind speed condition at the inlet of the upwind boundary of the heating and ventilation monitoring area. The initial wind speed condition is assigned to each discrete grid node on the air inlet boundary surface of the heating and ventilation monitoring area to obtain the three components of wind speed of the discrete grid nodes. The inlet of the upwind boundary of the heating and ventilation monitoring area represents the upstream side boundary section of the heating and ventilation monitoring area along the ventilation flow direction where the air inlet of the explosion-proof heater is located. The air inlet enters the heating and ventilation monitoring area through this upstream side boundary section. Among them, the three wind speed components represent the x-direction velocity component, y-direction velocity component, and z-direction velocity component obtained by decomposing the wind speed vector collected by the wind speed probe at the air inlet of the explosion-proof heater along the three-dimensional orthogonal coordinate axis. The three velocity components together describe the speed and direction of the incoming airflow in space. The ambient temperature collected by the ambient temperature and humidity sensor is extracted from the measured data packet, and the ambient temperature is used as the initial value of the gas temperature value of all discrete grid nodes in the heating and ventilation monitoring area. The core state simulation model integrates three collaborative sub-models: the heater thermal inertia dynamic sub-model, the mine ventilation network sub-model, and the field evolution sub-model, which operate at a preset discrete time step period. It should be noted that the discrete time step is smaller than the acquisition step of the measured data packet; Among them, the heater thermal inertia dynamic sub-model abstracts the explosion-proof heater into a three-node lumped parameter thermal network, which includes the lumped parameter temperature values ​​of the three nodes. The first node corresponds to the heating element, the second node corresponds to the heat conduction medium, and the third node corresponds to the shell. The lumped parameter temperature values ​​are directly extracted from the body thermal state data in the measured data package, and correspond to the surface temperature of the heating element, the internal temperature of the heat conduction medium, and the heat dissipation temperature of the shell, respectively. For each node, define the node heat capacity parameter and the inter-node thermal resistance parameter. The node heat capacity parameter is determined by obtaining and calculating the product of the mass and specific heat capacity of the material corresponding to the node. The inter-node thermal resistance parameter is determined by obtaining the heat transfer path length, material thermal conductivity and heat transfer area between two adjacent nodes. Inter-node thermal resistance parameter = heat transfer path length / (material thermal conductivity × heat transfer area). In each discrete time step, the heater thermal inertia dynamic sub-model reads the lumped parameter temperature values ​​of the three nodes from the previous moment and calculates the temperature difference-driven heat flow between adjacent nodes. The temperature difference-driven heat flow is the ratio of the difference in lumped parameter temperature values ​​between adjacent nodes to the thermal resistance parameter between nodes. When the explosion-proof heater is powered on, the heater thermal inertia dynamic sub-model obtains the real-time power of the explosion-proof heater and uses the real-time power of the explosion-proof heater as the internal heat source input of the first node; for the third node, the heater thermal inertia dynamic sub-model calculates the convective heat dissipation from the shell to the environment based on the difference between the corresponding lumped parameter temperature value and the current ambient temperature, as well as the convective heat transfer thermal resistance parameter between the shell and the surrounding air; combining the internal heat source input and the convective heat dissipation, the balance equation of heat inflow and outflow for each node is constructed, and the lumped parameter temperature values ​​of the three nodes are solved and updated. When the power supply circuit of the explosion-proof heater is cut off and the real-time power of the explosion-proof heater suddenly drops to zero, the heater thermal inertia dynamic sub-model automatically sets the internal heat source term of the heating element node to zero. It relies solely on the heat stored in the three nodes to be conducted between the nodes and dissipated to the environment by the outer shell nodes. It continuously deduces and constructs the residual heat decay curve after power failure, in which the lumped parameter temperature value of the three nodes decreases monotonically with time. This curve is then summarized as the dynamic process state of residual heat decay after power failure. It should be noted that the role of the heater thermal inertia dynamic sub-model is to abstract the explosion-proof heater into a lumped parameter thermal network of three nodes: heating element, heat conduction medium, and shell. The heat conduction, storage, and dissipation process between each layer of the structure is described by the node heat capacity parameters and the inter-node thermal resistance parameters. When powered on, the model receives the real-time power of the explosion-proof heater as the internal heat source input. After power failure, the internal heat source term is automatically removed. The model relies solely on the heat stored in each node to conduct between nodes and dissipate to the environment, continuously deduce the residual heat decay curve after power failure. Compared with the shortcomings of the existing technology, which only relies on a single temperature threshold to determine power failure and cannot predict the duration of residual heat after power failure, this sub-model achieves accurate quantification of the sustained high temperature time window caused by the release of its own heat capacity in each layer of the explosion-proof heater after power failure. This provides a physical mechanism-level calculation basis for the subsequent pre-calculation of the inherent cooling time. The mine ventilation network sub-model uses the three components of wind speed at each discrete grid node on the air intake boundary of the heating ventilation monitoring area and the methane concentration at each discrete grid node in the heating ventilation monitoring area as initial conditions. Advection transport calculation and turbulent diffusion calculation are performed sequentially within each discrete time step. Specifically, starting with the three-component wind velocity values ​​of each discrete grid node on the air inlet boundary of the heating and ventilation monitoring area, the mine ventilation network sub-model uses a steady-state solution algorithm for three-dimensional incompressible fluid flow to iteratively solve the momentum equation and continuity equation on all discrete grid nodes within the heating and ventilation monitoring area. The momentum equation includes convection terms, pressure gradient terms, and viscous diffusion terms, while the continuity equation constrains the velocity components in each direction of each discrete grid node to satisfy mass conservation. During the iterative solution process, the mine ventilation network sub-model uses the SIMPLE algorithm. Assuming a pressure field, the momentum equation is solved to obtain the temporary velocity field of each discrete grid node. The pressure correction equation is constructed through the continuity equation to solve for the pressure correction amount. The pressure field and temporary velocity field are updated based on the pressure correction amount. The iteration is repeated until the residuals of the velocity components in each direction of each discrete grid node are lower than the preset convergence threshold. After the solution converges, each discrete grid node in the heating and ventilation monitoring area obtains a set of three wind speed components consisting of the x-direction velocity component, the y-direction velocity component, and the z-direction velocity component. For the z-direction velocity component, the lumped parameter temperature value of the first node output by the heater thermal inertia dynamic sub-model at the current discrete time step is obtained, and the difference between the lumped parameter temperature value and the current ambient temperature is calculated and denoted as the element temperature difference. When the element temperature difference is greater than the preset temperature difference threshold, the z-direction velocity component is added to the current basis with a thermal buoyancy correction term consisting of the product of the element temperature difference and the thermal buoyancy proportional coefficient. The thermal buoyancy proportional coefficient is pre-calibrated according to the spatial scale and air thermal expansion coefficient of the key area of ​​the mine. All z-direction velocity components are traversed to complete the update of the three wind speed components. In the advection transport calculation, for each discrete grid node, the mine ventilation network sub-model obtains the gas concentration value and the x-direction velocity component, y-direction velocity component and z-direction velocity component of the three wind speed components of the discrete grid node at the current moment. Within each discrete time step, the mine ventilation network sub-model determines the downstream adjacent discrete grid node of gas advection in the x-direction based on the sign of the velocity component in the x-direction. The gas concentration value of the current discrete grid node is multiplied by the velocity component in the x-direction, the discrete time step, and the cross-sectional area of ​​the discrete grid node in the x-direction to obtain the amount of gas transported from the current discrete grid node to the downstream adjacent discrete grid node in the x-direction within that discrete time step, which is denoted as the gas advection transport amount in the x-direction. The downstream adjacent discrete grid nodes in the y-direction are determined based on the sign of the velocity component in the y-direction. The gas concentration value of the current discrete grid node is multiplied by the velocity component in the y-direction, the discrete time step, and the cross-sectional area of ​​the discrete grid node in the y-direction to obtain the gas advection rate in the y-direction. The downstream adjacent discrete grid nodes in the z-direction are determined based on the sign of the velocity component in the z-direction. The gas concentration value of the current discrete grid node is multiplied by the velocity component in the z-direction, the discrete time step, and the cross-sectional area of ​​the discrete grid node in the z-direction to obtain the gas advection rate in the z-direction. The gas advection transport volume of the current discrete grid node in the x, y and z directions is accumulated and added to the gas concentration value of the corresponding downstream adjacent discrete grid node. At the same time, the corresponding gas advection transport volume is subtracted from the gas concentration value of the current discrete grid node. This process is repeated for all discrete grid nodes, and the gas concentration value of the corresponding discrete grid node is updated to complete the advection transport calculation within one discrete time step. In the turbulent diffusion calculation, for any discrete grid node, the mine ventilation network model obtains the gas concentration value of each discrete grid node after the advection transport calculation update. The gas concentration value of the current discrete grid node is compared with the gas concentration value of each of its adjacent discrete grid nodes one by one, and the concentration difference between the two is calculated. The grid center distance between the current discrete grid node and the corresponding adjacent discrete grid node is also calculated. The concentration difference is ratioed to the grid center distance to obtain the concentration gradient of the current discrete grid node in the corresponding adjacent direction. The concentration gradient is multiplied by the pre-calibrated turbulent diffusion coefficient and the interface area between the current discrete grid node and the adjacent discrete grid node to obtain the turbulent diffusion flux of the discrete grid node in the corresponding adjacent direction. The turbulent diffusion flux in each adjacent direction is accumulated into the gas concentration value of the corresponding adjacent discrete grid node. At the same time, the sum of the turbulent diffusion flux in each adjacent direction is subtracted from the gas concentration value of the current discrete grid node. All discrete grid nodes are traversed, and the gas concentration value of the corresponding discrete grid node is updated to complete the turbulent diffusion calculation within one discrete time step. By superimposing advection transport calculations and turbulent diffusion calculations, the dynamic process state of gas accumulation and gas dilution is output. Specifically, the gas concentration values ​​of each discrete grid node after the final update of this discrete time step are compared with the gas concentration values ​​of the previous discrete time step node by node. For discrete grid nodes whose gas concentration values ​​increase compared to the previous discrete time step, they are marked as gas accumulation nodes and the increase in gas concentration is recorded. For discrete grid nodes whose gas concentration values ​​decrease compared to the previous discrete time step, they are marked as gas dilution nodes and the decrease in gas concentration is recorded. The gas concentration increase of all gas accumulation nodes and the gas concentration decrease of all gas dilution nodes within this discrete time step are compiled together with the updated gas concentration values ​​of each discrete grid node and used as the dynamic process state output of gas accumulation and gas dilution within this discrete time step. It should be noted that the role of the mine ventilation network model is to use the wind speed vector collected by the wind speed probe as the upwind boundary condition, and to use a three-dimensional incompressible fluid flow solution algorithm to calculate the initial three-component wind speed values ​​of each discrete grid node. On this basis, a thermal buoyancy correction term is added to obtain the effective three-component wind speed values. Advection transport calculation and turbulent diffusion calculation are performed in sequence to output the dynamic process state of gas accumulation and gas dilution. Compared with the shortcomings of the existing technology, which only monitors the gas concentration at a single point threshold and cannot predict the migration and accumulation trend of gas in the ventilation flow field, this sub-model realizes a complete simulation of the dynamic changes in the spatiotemporal distribution of gas concentration under the combined action of the main ventilation transport, turbulent mixing and dispersion, and local disturbance of the thermal plume. It provides evolution data of flow field and concentration field for the calculation of the gas accumulation risk component and ventilation deterioration risk component in the dynamic combustion and explosion risk index. The field evolution sub-model calculates the field evolution simulation state of the gas temperature field and the gas concentration field; It should be noted that the gas temperature field represents the spatial distribution of gas temperature values ​​at each discrete grid node within the heating and ventilation monitoring area, while the gas concentration field represents the spatial distribution of gas concentration values ​​at each discrete grid node within the heating and ventilation monitoring area. Specifically, the field evolution sub-model receives the three-component wind speed values ​​at each discrete grid node from the mine ventilation network model, and receives the lumped parameter temperature values ​​at each node from the heater thermal inertia dynamic sub-model as the boundary conditions for the temperature field. Starting from the gas temperature values ​​at each discrete grid node at the current moment, it adopts the same upwind mode as the mine ventilation network model, and sequentially performs the advection transport calculation and turbulent diffusion calculation of the gas temperature field within each discrete time step. The calculation object is the gas temperature value of each discrete grid node, and the gas temperature value of each discrete grid node is obtained. The simulated evolved gas temperature field is then obtained. For the gas concentration field, the gas concentration values ​​of each discrete grid node output by the mine ventilation network model are directly read and the simulated evolved gas concentration field is obtained. The field evolution sub-model organizes the simulated gas temperature field and gas concentration field as the output of the field evolution simulation state for this discrete time step; The dynamic process states, including residual heat decay, gas accumulation, and gas dilution after power failure, are processed together with the field evolution simulation states, including the gas temperature field and the gas concentration field, to obtain a snapshot of the simulation state at the current moment.

[0024] The simulation and early warning module uses the current state snapshot as the starting simulation benchmark and the core state simulation model to continuously simulate the state evolution sequence within the preset prediction and evaluation time. Based on the state evolution sequence, it calculates the dynamic combustion and explosion risk index to determine the early warning level and uses the heater thermal inertia dynamic sub-model to pre-calculate and store the inherent cooling time of hard cut-off. Specifically, the current snapshot of the simulated state is used as the starting reference for the core state inference model. Starting from the starting reference, the core state inference model iterates forward step by step with a preset inference time step. It should be noted that the extrapolation time step is the same as the discrete time step. Each iteration sequentially updates the lumped parameter temperature values ​​of the heater thermal inertia dynamic sub-model, calculates the advection and turbulent diffusion of the gas concentration field and outputs the dynamic process state of gas accumulation and dilution in the mine ventilation network model, and calculates the advection and turbulent diffusion of the gas temperature field and outputs the state of the field evolution simulation in the field evolution sub-model. The output of each iteration is organized and recorded as a snapshot of the deduction state. The core state deduction model continues to iterate until the sum of the accumulated deduction time steps reaches the preset prediction and evaluation time. The multiple deduction state snapshots arranged in chronological order are organized into a state evolution sequence. It should be noted that during the simulation process, once a new time-aligned and encapsulated experimental data packet is received, the current simulation process is immediately interrupted, and the updated simulated state snapshot generated by the new experimental data packet is used as the starting simulation benchmark to start a new round of simulation, thereby realizing the asynchronous iterative update of the state evolution sequence. Calculate dynamic combustion and explosion risk indicators based on state evolution sequences; Specifically, the dynamic combustion and explosion risk index is composed of a weighted fusion of the gas accumulation risk component, the hot surface ignition risk component, and the ventilation deterioration risk component; The calculation method for the gas accumulation risk component is as follows: extract the gas concentration values ​​of the discrete grid node where the explosion-proof heater is located and its downwind neighboring discrete grid nodes from the state evolution sequence in each simulation state snapshot, calculate the average rate of increase of the gas concentration value within the most recent consecutive preset simulation time steps, the average rate of increase = the increment of the gas concentration value within the most recent consecutive preset simulation time steps / (simulation time step × number of simulation time steps); compare the average rate of increase with the preset gas accumulation rate benchmark value after normalization, the gas accumulation risk component is the ratio of the average rate of increase to the preset gas accumulation rate benchmark value, the value is 1 when the ratio is greater than 1, and the value is 0 when the ratio is less than 0; The calculation method for the hot surface ignition risk component is as follows: extract the predicted value of the temperature value of the first node lumped parameter in each simulated state snapshot from the state evolution sequence, calculate the difference between the predicted value of the temperature value of the first node lumped parameter and the gas ignition safety threshold, when the difference is less than or equal to the preset first safety margin, the hot surface ignition risk component takes the maximum value of 1; when the difference is greater than the preset second safety margin, the hot surface ignition risk component takes the value of 0; when the difference is between the preset first safety margin and the second safety margin, the hot surface ignition risk component is obtained by performing inverse index normalization on the difference based on the first safety margin and the second safety margin using the maximum-minimum value normalization method. The ventilation degradation risk component is calculated as follows: extract the three wind speed components from the discrete grid node where the explosion-proof heater is located in each simulated state snapshot from the state evolution sequence, calculate the modulus of the three wind speed components, calculate the ratio between the modulus of the three wind speed components and the preset minimum safe ventilation volume, and the ventilation degradation risk component is the difference between 1 and the obtained ratio. When the difference is greater than 1, the value is 1, and when the difference is less than 0, the value is 0. The dynamic combustion and explosion risk index is obtained by weighting and summing the risk components of gas accumulation, hot surface ignition, and ventilation deterioration according to preset weighting coefficients. The preset weighting coefficients are gas accumulation weighting coefficient, hot surface ignition weighting coefficient, and ventilation deterioration weighting coefficient, respectively. It should be noted that the preset weight coefficient sum is 1, and it is determined using the analytic hierarchy process (AHP). The specific steps are as follows: First, a third-order judgment matrix is ​​constructed. The rows and columns of the judgment matrix correspond to the gas accumulation risk component, the hot surface ignition risk component, and the ventilation deterioration risk component, respectively. The element in the i-th row and j-th column of the judgment matrix represents the importance scale value of the i-th risk component relative to the j-th risk component. The importance scale value is comprehensively assessed based on the proportion of accidents associated with each risk component in the mine's historical accident statistics and the experience of industry safety experts, and the value ranges from 1 to 9 and its reciprocal. The maximum eigenvalue of the judgment matrix and its reciprocal are then calculated. The corresponding eigenvector is normalized by dividing each element of the eigenvector by the sum of its elements. The first element of the normalized eigenvector is the gas accumulation weight coefficient, the second element is the hot surface ignition weight coefficient, and the third element is the ventilation deterioration weight coefficient. A consistency test is performed on the judgment matrix, and the consistency ratio is calculated as: consistency ratio = consistency index ÷ average random consistency index. When the consistency ratio is less than 0.1, the judgment matrix passes the consistency test and the weight coefficients are usable; otherwise, the importance scale values ​​in the judgment matrix are adjusted and recalculated until the consistency test is passed. The dynamic combustion and explosion risk indicators are compared sequentially with the preset first-level warning threshold, second-level warning threshold, and third-level warning threshold to determine the warning level. When the dynamic combustion and explosion risk index exceeds the Level 1 warning threshold but has not yet reached the Level 2 warning threshold, a Level 1 warning is triggered; when the dynamic combustion and explosion risk index exceeds the Level 2 warning threshold but has not yet reached the Level 3 warning threshold, a Level 2 warning is triggered; when the dynamic combustion and explosion risk index reaches the Level 3 warning threshold, a Level 3 warning is triggered. For example, such as Figure 3 As shown, Figure 3 This is a time-series projection curve of the dynamic combustion and explosion risk index and its components. The calculated dynamic combustion and explosion risk index (solid dark line) and its three components—gas accumulation, hot surface ignition, and ventilation deterioration—are plotted against a preset three-level warning threshold (purple dashed line in the figure, threshold 0.85) over time. The graph clearly shows that during the projection process, although the hot surface ignition risk component continuously increases, the initial ventilation deterioration risk significantly decreases, causing the overall risk index to peak at approximately 72 seconds and then decline. This time-series curve provides the remote control center with an intuitive basis for judging the macro-risk situation, assisting operators in grasping the dynamic evolution of risks in real time, and is an important visual auxiliary data for determining the warning level. In parallel, the inherent cooling time for hard cut-off is pre-calculated using a heater thermal inertia dynamic sub-model; It should be noted that the inherent cooling time of hard cut-off refers to the total time required for the temperature value of the first node lumped parameter, derived from the heater thermal inertia dynamic sub-model under the condition of no internal heat source, to monotonically decay from the initial value at the time of cut-off to the gas ignition safety threshold after the safety cut-off is performed at the current moment. Specifically, the method for pre-calculating the inherent cooling time of hard cutoff is as follows: using the lumped parameter temperature values ​​of each node in the current simulation state snapshot as the initial state, the internal heat source term of the first node in the heater thermal inertia dynamic sub-model is forcibly set to zero to simulate the working condition of the explosion-proof heater power supply circuit being cut off; the heater thermal inertia dynamic sub-model is iteratively run step by step with the extrapolation time step, and the process of the lumped parameter temperature value of the first node monotonically decreasing as the number of iteration steps increases is monitored; when the lumped parameter temperature value of the first node drops below the gas ignition safety threshold for the first time, the total number of iteration steps from the start of the simulation cutoff to that moment is recorded, and the total number of iteration steps is multiplied by the extrapolation time step to obtain the inherent cooling time of hard cutoff in seconds; The inherent cooling time of the hard cutoff is written into a non-volatile memory area for storage, serving as a mandatory latching parameter for the subsequent safety reset process; It should be noted that the heater thermal inertia dynamic sub-model abstracts the explosion-proof heater into a lumped parameter thermal network consisting of three nodes: the heating element, the heat-conducting medium, and the outer shell. The heat conduction, storage, and dissipation processes between the structural layers are described by the node heat capacity parameters and the inter-node thermal resistance parameters. After the power supply circuit is cut off, the internal heat source term is automatically set to zero, and the residual heat decay curve after power failure, showing a monotonically decreasing lumped parameter temperature value over time, is continuously simulated. This solves the technical problem in existing technologies where the high-temperature sustained window of the heating element after a power failure cannot be predicted, making the previously invisible residual heat decay process quantifiable. While calculating the dynamic process state, the lumped parameter temperature value of each node in the current simulation state snapshot is used as the initial state to forcibly set the internal heat source term to zero. The total time required for the lumped parameter temperature value of the first node to monotonically decay from the initial value at the cutoff time to the gas ignition safety threshold is pre-calculated as the inherent cooling time of hard cutoff. This solves the technical problem in the existing technology that the timing of power restoration is judged based on fixed delay or human experience without the support of a physical model. It provides a physical mechanism-level time reference for the forced thermal lockout reset process, and transforms the determination of the safe power restoration timing from experience-dependent to model-driven precise quantitative decision-making.

[0025] Early warning control module: Performs differentiated collaborative handling based on the early warning level. For level 3 early warning, it executes a safety cut-off and forced thermal lockout reset process based on the inherent cooling time of hard cut-off. Specifically, if a Level 1 warning is triggered, a Level 1 warning information packet is generated; The Level 1 early warning information package includes the current dynamic combustion and explosion risk index value, the predicted curve of the gas concentration value in the state evolution sequence changing with the simulation time step and the predicted curve of the temperature value of the first node lumped parameter changing with the simulation time step, the cross-sectional cloud map of the gas concentration field and gas temperature field in the current simulated state snapshot, and the proportion of the gas accumulation risk component, the hot surface ignition risk component and the ventilation deterioration risk component in the dynamic combustion and explosion risk index. The Level 1 warning information package is uploaded to the remote control center via the encrypted link of the monitoring operation link and the electrically isolated wireless communication terminal. During the Level 1 warning stage, there is no automatic intervention on the real-time power of the explosion-proof heater and the speed of the variable frequency fan used in conjunction with the explosion-proof heater. If a Level II warning is triggered, a Level II warning information package containing updated dynamic combustion and explosion risk indicators and trend visualizations is generated and uploaded, and local control strategies are automatically executed through the monitoring operation link. Specifically, the trend visualization chart is a risk change curve plotted by connecting the dynamic combustion and explosion risk indicators corresponding to each snapshot of the state evolution sequence in chronological order, with the simulation time step as the horizontal axis and the dynamic combustion and explosion risk index value as the vertical axis. The local control strategy includes two parallel actions: fine-tuning the power of the explosion-proof heater and adjusting the ventilation dilution rate in conjunction with the power of the ventilation system. The power fine-tuning action of the explosion-proof heater includes: sending a power reduction command to the power supply circuit of the explosion-proof heater through the monitoring operation link, reducing the real-time power of the explosion-proof heater to the preset safety lower limit power value according to the preset power reduction step size and preset power reduction rate, so as to reduce the temperature value of the first node lumped parameter and thus reduce the risk component of hot surface ignition. The ventilation dilution adjustment action includes: sending a speed increase command to the variable frequency drive of the variable frequency fan through the monitoring operation link, increasing the speed of the variable frequency fan according to the preset speed increase ratio and preset acceleration slope, so as to increase the effective ventilation volume flowing through the heating ventilation monitoring area and accelerate the dilution process of gas accumulation; During the execution of the two parallel actions, the dynamic combustion and explosion risk indicators are continuously monitored in real time by using the core state simulation model with updated measured data packets to simulate the situation. If the dynamic combustion and explosion risk indicators fall below the first-level warning threshold, the real-time power of the explosion-proof heater is gradually restored to the original set value, and the speed of the variable frequency fan is maintained for a preset wind stabilization delay before returning to the base speed. If the dynamic combustion and explosion risk indicators continue to deteriorate and reach the third-level warning threshold, the third-level warning handling process is immediately initiated. For example, such as Figure 4 As shown, Figure 4 This is a real-time operational status diagram of the remote monitoring center for explosion-proof heaters under Level 2 early warning conditions. When the system determines that a Level 2 early warning has been triggered, the remote control center will simultaneously receive and display the following: Figure 2 The monitoring overview interface shown consists of an instrument panel (dynamic combustion and explosion risk index, currently 0.61), core equipment operating parameters (heating element temperature 312℃, heater real-time power 18.5kW), and local control execution status (power fine-tuning and wind speed linkage are in progress). The right and bottom of the interface simultaneously display the state evolution prediction curve and the gas concentration field cross-sectional cloud map. The cross-sectional cloud map, in the form of a heat map, intuitively shows the gas concentration distribution and ventilation direction in the heating and ventilation monitoring area at the current moment. This provides remote decision-makers with comprehensive real-time operational situation awareness and anomaly diagnosis basis, ensuring the transparency and controllability of the automatic control process in the secondary warning stage. If a Level 3 warning is triggered, execute the safety cut-off and forced thermal lockout reset process based on the inherent cooling time of the hard cut-off. Specifically, the safety disconnection is executed as follows: a disconnection signal is sent to the safety disconnection link connected in series in the power supply circuit of the explosion-proof heater, the safety disconnection link is disconnected instantaneously, the power supply circuit of the explosion-proof heater is disconnected, and a power outage confirmation signal is generated after the safety disconnection is completed; If a power outage confirmation signal is received, the forced thermal lockout reset process is immediately initiated. The pre-calculated and stored inherent cooling time of the hard cut-off is read, and the inherent cooling time of the hard cut-off is loaded as the countdown initial value of the forced thermal lockout timer. The forced thermal lockout timer is then started to decrement. During the entire timing cycle of the forced thermal lockout timer, the power supply circuit of the explosion-proof heater is forcibly kept in a power-off state. At the same time, all power restoration commands from the remote control center and any manual power restoration requests from the local operation panel are blocked in the monitoring operation link, so as to achieve absolute prohibition of power restoration operations. During the operation of the forced thermal lockout timer, a simultaneous verification of the measured temperature decay is performed. Specifically, the equipment thermal status tracking network continuously collects measured values ​​of the surface temperature of the heating element, the internal temperature of the heat-conducting medium, and the heat dissipation temperature of the outer casing at a preset inspection frequency. The collected measured values ​​of the surface temperature of the heating element are arranged in chronological order of collection time to form a measured temperature decay curve. Simultaneously, starting from the lumped parameter temperature values ​​of the first, second, and third nodes at the moment of safe cut-off execution, a dynamic sub-model of the heater's thermal inertia is run under conditions without an internal heat source to deduce and generate a predicted residual heat decay curve. In each comparison cycle, the absolute value of the deviation between the measured value of the surface temperature of the heating element at each collection time on the measured temperature decay curve and the corresponding value of the lumped parameter temperature of the first node on the predicted residual heat decay curve is calculated. If the absolute value of the deviation exceeds the preset temperature deviation tolerance, a thermal decay anomaly flag is generated and immediately uploaded to the remote control center through the monitoring operation link. If the three conditions are met—the forced thermal lockout timer countdown reaches zero, the current measured value of the surface temperature of the heating element on the measured temperature decay curve has been confirmed to have dropped below the gas ignition safety threshold, and the dynamic combustion and explosion risk index has been confirmed by on-site safety personnel to have fallen back to below the safe range—the power restoration lockout of the explosion-proof heater power supply circuit is lifted, and the safety disconnection link is restored to the standby state through a manually reset operation with authorized authentication, and the explosion-proof heater power supply circuit is restored to the energized state. All early warning trigger records, safety cut-off event records, start and end timestamps and lockout durations of forced thermal lockout timers, actual temperature decay comparison results, and operator identity information and operation timestamps for manual reset operations are written into the tamper-proof full-process log storage area and uploaded to the historical database of the remote control center via an encrypted link through the monitoring operation link for long-term storage.

[0026] The technical solution of this invention is as follows: An explosion-proof heater control architecture with an environmental sensing network and an equipment thermal state tracking network is constructed in a key area of ​​the mine. Thermal gas environment data and the thermal state data of the heater itself are collected and processed into measured data packets. A core state deduction model is constructed within the control architecture. This core state deduction model integrates a heater thermal inertia dynamic sub-model, a mine ventilation network sub-model, and a field evolution sub-model. Using the measured data packets as input, the dynamic process states, including residual heat decay after power failure, gas accumulation, and gas dilution, are calculated, along with the field evolution simulation states, including the gas temperature field and the gas concentration field. A snapshot of the simulation state at the current moment is obtained. Using this snapshot as the starting deduction benchmark, the core state deduction model continuously deduces the state evolution sequence within a preset prediction and evaluation period. Based on the state evolution sequence, dynamic combustion and explosion risk indicators are calculated to determine the warning level. The inherent cooling time for hard cutoff is pre-calculated and stored using the heater thermal inertia dynamic sub-model. Differentiated collaborative handling is executed based on the warning level. For a level three warning, a safety cutoff and forced thermal lockout reset process based on the inherent cooling time of hard cutoff is executed.

[0027] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A wireless remote monitoring explosion-proof heater, characterized in that: Includes the following modules: Actual measurement and data acquisition module: Construct an explosion-proof heater control architecture with an environmental sensing network and an equipment thermal status tracking network in key areas of the mine, collect thermal gas environment data and body thermal status data and organize them into actual measurement data packages; State simulation module: The core state simulation model is built in the control architecture. The core state simulation model integrates the heater thermal inertia dynamic sub-model, the mine ventilation network sub-model and the field evolution sub-model. With the measured data packet as input, it calculates the dynamic process state including residual heat decay after power failure, gas accumulation and gas dilution, and the field evolution simulation state including gas temperature field and gas concentration field. The simulation state snapshot at the current moment is obtained by sorting it out. The simulation and early warning module uses the current state snapshot as the starting simulation benchmark and the core state simulation model to continuously simulate the state evolution sequence within the preset prediction and evaluation time. Based on the state evolution sequence, it calculates the dynamic combustion and explosion risk index to determine the early warning level and uses the heater thermal inertia dynamic sub-model to pre-calculate and store the inherent cooling time of hard cut-off. Early warning control module: Performs differentiated collaborative handling based on early warning level. For level 3 early warning, it executes a safety cut-off and forced thermal lockout reset process based on the inherent cooling time of hard cut-off.

2. The wireless remote monitoring explosion-proof heater according to claim 1, characterized in that: The actual method for obtaining the data packet is as follows: Explosion-proof heaters that transfer heat indirectly through a heat-conducting medium are installed in key areas of the mine, and a control architecture including an environmental sensing network and an equipment thermal status tracking network is established. The environmental sensing network collects gas concentration data, wind speed vectors at the air inlet of the explosion-proof heater, and ambient temperature and humidity in key areas of the mine within the heating and ventilation monitoring area, and records them as thermal gas environmental data. The equipment thermal status tracking network collects the surface temperature of the heating element, the internal temperature of the heat transfer medium, and the heat dissipation temperature of the outer shell of the explosion-proof heater, and records them as the thermal status data of the body. The thermal gas environment data and the bulk thermal state data are time-aligned and then merged and packaged into a measured data package. The measured data packets and the control commands from the remote control center interact bidirectionally via an encrypted link through an electrically isolated wireless communication terminal, forming a monitoring and operation link.

3. The wireless remote monitoring explosion-proof heater according to claim 1, characterized in that: The method for obtaining a snapshot of the simulated state is as follows: The heating and ventilation monitoring area is spatially discretized to obtain discrete grid nodes; Using the gas concentration data, wind speed vector and ambient temperature in the measured data package, the initial values ​​of gas concentration, wind speed three-component values, boundary conditions and gas temperature values ​​at each discrete grid node are obtained by spatial interpolation and input into the core state deduction model. The core state simulation model integrates a heater thermal inertia dynamic sub-model, a mine ventilation network sub-model, and a field evolution sub-model, which run at a preset discrete time step period and output a snapshot of the simulated state.

4. The wireless remote monitoring explosion-proof heater according to claim 3, characterized in that: The method for calculating the dynamic process state of residual heat decay after power failure using the heater thermal inertia dynamic sub-model is as follows: The explosion-proof heater is abstracted as a lumped parameter thermal network containing three nodes: heating element, heat conduction medium, and shell. The lumped parameter temperature value is directly extracted from the body thermal state data in the measured data package. When powered on, the real-time power is used as the internal heat source input of the heating element node, and the temperature value of the lumped parameter of the three nodes is updated by combining the convective heat dissipation of the shell node to the environment. When the power supply circuit is cut off, the internal heat source term of the heating element node is automatically set to zero. Relying solely on the heat stored in the node for conduction and dissipation, the residual heat decay curve after power failure is continuously deduced as the temperature value of the lumped parameter of the three nodes decreases monotonically. This curve serves as the dynamic process state output of residual heat decay after power failure.

5. The wireless remote monitoring explosion-proof heater according to claim 3, characterized in that: The method for calculating the dynamic process of gas accumulation and dilution in the mine ventilation network sub-model is as follows: Starting with the three-component wind speed values ​​at each node of the air inlet boundary surface, the three-dimensional incompressible fluid flow equation is solved to obtain the initial three-component wind speed values ​​at each discrete grid node, including the x-direction velocity component, y-direction velocity component and z-direction velocity component. For the z-direction velocity component, the thermal buoyancy correction term is calculated based on the temperature difference between the temperature value of the lumped parameter of the heating element node and the current ambient temperature, and the three components of wind speed are updated. Using the three components of wind speed at each discrete grid node on the air inlet boundary and the methane concentration at each discrete grid node as initial conditions, the advection transport calculation and turbulent diffusion calculation are performed sequentially within each discrete time step to update the methane concentration value of each discrete grid node. The gas concentration values ​​of each node after the current discrete time step are compared with those of the previous discrete time step node by node. Nodes with increased gas concentration values ​​are marked as gas accumulation nodes and the increase is recorded. Nodes with decreased gas concentration values ​​are marked as gas dilution nodes and the decrease is recorded. The increase, decrease, and updated gas concentration values ​​of each node are combined as the dynamic process status output of gas accumulation and gas dilution.

6. The wireless remote monitoring explosion-proof heater according to claim 3, characterized in that: The field evolution sub-model calculates the field evolution simulation state in the following way: The system receives the three-component wind speed values ​​of each discrete grid node from the mine ventilation network model, and receives the lumped parameter temperature values ​​of each node from the heater thermal inertia dynamic sub-model as the boundary conditions of the temperature field. Advection transport and turbulent diffusion calculations were performed on the gas temperature values ​​at each node to obtain the simulated gas temperature field. The methane concentration values ​​of each node output by the mine ventilation network model are directly read and processed to obtain the simulated methane concentration field. The simulated gas temperature field and gas concentration field are used as the output of the field evolution simulation state.

7. The wireless remote monitoring explosion-proof heater according to claim 1, characterized in that: The method for determining the warning level is as follows: Starting with the current simulated state snapshot as the initial simulation benchmark, the core state simulation model is iteratively run step by step, accumulating simulations until the preset prediction and evaluation duration, and the state evolution sequence is obtained by arranging them in chronological order; if a new measured data packet is received during the simulation process, it is immediately interrupted and a new round of simulation is started with the simulated state snapshot generated by the new data packet as the benchmark. The dynamic combustion and explosion risk index is composed of a weighted fusion of the gas accumulation risk component, the hot surface ignition risk component, and the ventilation deterioration risk component. The gas accumulation risk component is obtained by normalizing the average rate of increase of gas concentration values ​​of the node where the explosion-proof heater is located and the neighboring nodes on the downwind side in the state evolution sequence. The hot surface ignition risk component is obtained by segmenting and mapping the difference between the predicted value of the temperature value of the lumped parameter of the heating element node and the gas ignition safety threshold. The ventilation deterioration risk component is calculated based on the ratio of the three-component modulus of the wind speed of the node where the explosion-proof heater is located to the preset minimum safe ventilation volume. The dynamic combustion and explosion risk indicators are compared sequentially with preset multi-level warning thresholds to determine the warning level, including Level 1 warning, Level 2 warning and Level 3 warning.

8. The wireless remote monitoring explosion-proof heater according to claim 1, characterized in that: The inherent cooling time for hard cutoff is calculated as follows: Using the lumped parameter temperature values ​​of each node in the current simulation state snapshot as the initial state, the heat source term in the heating element node is forcibly set to zero, and the heater thermal inertia dynamic sub-model is iteratively run. When the lumped parameter temperature value of the heating element node drops below the gas ignition safety threshold for the first time, the iteration time is taken as the inherent cooling time for hard cut-off.

9. A wireless remote monitoring explosion-proof heater according to claim 1, characterized in that: The differentiated collaborative handling method is as follows: When a Level 1 warning is triggered, a Level 1 warning information package is generated, which includes dynamic combustion and explosion risk index values, key parameter prediction curves, field distribution cross-sectional cloud maps, and the proportion of each risk component, and uploaded to the remote control center. Automatic intervention is not performed on heater power and variable frequency fan speed. When a Level 2 warning is triggered, a Level 2 warning information package is generated and uploaded, and a local control strategy is automatically executed. This strategy includes two parallel actions: reducing the real-time power of the heater to the safe lower limit power value and increasing the speed of the variable frequency fan. During the execution, risk indicators are continuously monitored and analyzed. If the risk indicators fall below the Level 1 warning threshold, the heater power is gradually restored and the fan base speed is restored after a delay. If the risk indicators continue to deteriorate and reach the Level 3 warning threshold, the Level 3 warning handling process is immediately initiated.

10. A wireless remote monitoring explosion-proof heater according to claim 9, characterized in that: The procedure for executing safety disconnection and forced thermal lockout reset for a Level 3 early warning is as follows: A cut-off signal is sent to the safety cut-off link connected in series in the power supply circuit of the explosion-proof heater, instantly disconnecting the power supply circuit and generating a power outage confirmation signal; Upon receiving the power outage confirmation signal, the pre-stored inherent cooling time of the hard cut-off is read and loaded as the countdown initial value of the forced thermal lockout timer. The power outage state is forcibly maintained during the timing period, while all power restoration commands are blocked. During the timing period, the measured temperature decay comparison and verification are performed simultaneously: the measured surface temperature of the heating element is collected to form the measured temperature decay curve. At the same time, the heater thermal inertia dynamic sub-model is run under the condition of no internal heat source, starting from the temperature value of the lumped parameter of each node at the safe cut-off moment, to generate the predicted residual heat decay curve. The measured value is compared with the predicted value. If the deviation exceeds the preset tolerance, a heat decay abnormality mark is generated and uploaded. When all three conditions are met—the forced thermal lockout timer countdown reaches zero, the measured surface temperature of the heating element is confirmed to have dropped below the gas ignition safety threshold, and the risk indicators have been confirmed by on-site personnel to have fallen back to below the safe range—the power restoration lockout is lifted, and the safety cut-off circuit is allowed to be restored to standby status through authorized manual reset operations, and the heater power supply circuit is restored to energized status. All key events throughout the process, along with the operator's identity information and timestamps, are written to the tamper-proof full-process log storage area and uploaded to the remote control center's historical database for long-term storage via an encrypted link.

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