Distributed aluminum leakage positioning and response system based on edge calculation

Abstract

The invention provides a distributed aluminum leakage positioning and response system based on edge calculation, and relates to the technical field of data processing, and the system comprises a multi-source sensing module which is used for collecting temperature field distribution, cooling water permeation rate and material surface thermal stress data of a molten aluminum liquid leakage area in real time, generating a leakage characteristic data set containing material porosity, water absorption rate and interface thermal resistance parameters; and the edge analysis module is used for executing seepage analysis based on a material pore structure according to the leakage characteristic data set, performing leakage risk grade evaluation by calculating the penetration depth of the molten aluminum liquid in the material and combining the cooling water adsorption rate, and outputting dynamic positioning parameters including the material failure critical temperature and the effective blocking time. Through automatic multi-stage response, material performance feedback and collaborative optimization, the aluminum leakage position can be positioned, the risk can be dynamically evaluated, the blocking efficiency can be optimized, the timeliness of aluminum leakage event response and the blocking reliability can be improved, and the electrolytic aluminum production safety can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a distributed aluminum leakage positioning and response system based on edge computing. Background Art

[0002] In the aluminum processing industry, aluminum leakage in processes like deep-well casting poses a significant threat to production safety and business profitability. Traditional monitoring methods struggle to accurately locate and quickly respond to leakage risks. For example, manual inspections can easily overlook minor leaks and make it difficult to accurately understand the specific conditions and development trends of leakage areas.

[0003] With the widespread adoption of IoT devices and the rapid growth of data volumes, traditional cloud computing models are increasingly showing limitations when processing aluminum leakage monitoring data. Transmitting data generated by numerous devices to a centralized data center or cloud can lead to significant bandwidth pressure and transmission delays, hindering timely responses to aluminum leakage incidents. Furthermore, data transmission carries the risk of loss and tampering, posing challenges to data privacy and security. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a distributed aluminum leakage positioning and response system based on edge computing to achieve closed-loop collaborative optimization of the performance and barrier efficiency of porous ceramic materials.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] First, the distributed aluminum leakage positioning and response system based on edge computing includes:

[0007] A multi-source sensing module is used to collect data on the temperature field distribution, cooling water penetration rate, and material surface thermal stress in the molten aluminum leakage area in real time, generating a leakage feature dataset containing material porosity, water absorption rate, and interface thermal resistance parameters.

[0008] The edge analysis module is used to perform seepage analysis based on the material pore structure based on the leakage feature dataset. It calculates the penetration depth of molten aluminum in the material and evaluates the leakage risk level in combination with the cooling water adsorption rate. The module outputs dynamic positioning parameters including the critical temperature of material failure and the effective blocking time.

[0009] A control module is used to automatically trigger a multi-level response strategy when a leak event is detected based on dynamic positioning parameters;

[0010] The feedback module is used to monitor the material's water absorption capacity, temperature gradient distribution, and structural integrity data in real time, collect the thermal decomposition characteristic parameters of the surface polymer coating, compare the measured water absorption rate with the preset water absorption rate threshold, and generate a material modification parameter set;

[0011] The optimization module is used to integrate the positioning data and feedback information of each edge computing node, dynamically adjust the data collection frequency, barrier resource allocation and emergency response priority, and achieve closed-loop collaborative optimization of porous ceramic material performance and barrier efficiency through a distributed communication network.

[0012] In a second aspect, a computer-readable storage medium stores a program, which implements the system when executed by a processor.

[0013] The above solution of the present invention includes at least the following beneficial effects:

[0014] It collects three core data types: temperature field, cooling water penetration rate, and material thermal stress, and generates key characteristic parameters such as material porosity, water absorption rate, and interface thermal resistance, avoiding the limitations of single data monitoring and accurately capturing subtle anomalies in the early stages of aluminum leakage events. Relying on local edge computing capabilities, it does not need to rely on cloud-based remote processing, and can conduct seepage analysis based on the pore structure of the material, calculate the penetration depth, and at the same time correlate the cooling water adsorption rate to quantify the risk level, reduce data transmission delays, and complete risk assessment in the embryonic stage of aluminum leakage events, and output positioning parameters such as critical failure temperature and effective blocking time. Based on dynamic positioning parameters, it automatically triggers a multi-level response strategy, and can complete the laying of barrier layers from medium risk to high risk without manual intervention. Physical isolation and other operations can avoid the lag and operational errors of manual response; real-time monitoring of the water absorption capacity, structural integrity and thermal decomposition characteristics of the barrier material, and generating material modification parameters by comparing the measured data with the threshold; the optimization module further integrates multi-node information, dynamically adjusts resource allocation and material production parameters, and can improve the hydrophilic group distribution, pore size gradient and other characteristics of porous ceramic materials, enhance the thermal stability and barrier capacity of the material, and extend the effective barrier time; based on distributed edge node data, dynamically adjust the data collection frequency of high / low risk areas, and allocate barrier resources and emergency response weights according to risk priority to ensure monitoring accuracy and resource supply in high-risk areas and avoid waste of resources in low-risk areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of a distributed aluminum leakage positioning and response system based on edge computing provided by an embodiment of the present invention.

[0016] Figure 2 This is a flow chart of automatically triggering a multi-level response strategy when a leakage event is detected based on dynamic positioning parameters, as provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0018] like Figure 1 As shown, an embodiment of the present invention proposes a distributed aluminum leakage positioning and response system based on edge computing, including:

[0019] A multi-source sensing module is used to collect data on the temperature field distribution, cooling water penetration rate, and material surface thermal stress in the molten aluminum leakage area in real time, generating a leakage feature dataset containing material porosity, water absorption rate, and interface thermal resistance parameters.

[0020] The edge analysis module is used to perform seepage analysis based on the material pore structure based on the leakage feature dataset. It calculates the penetration depth of molten aluminum in the material and evaluates the leakage risk level in combination with the cooling water adsorption rate. The module outputs dynamic positioning parameters including the critical temperature of material failure and the effective blocking time.

[0021] A control module is used to automatically trigger a multi-level response strategy when a leak event is detected based on dynamic positioning parameters;

[0022] The feedback module is used to monitor the material's water absorption capacity, temperature gradient distribution, and structural integrity data in real time, collect the thermal decomposition characteristic parameters of the surface polymer coating, compare the measured water absorption rate with the preset water absorption rate threshold, and generate a material modification parameter set;

[0023] The optimization module is used to integrate the positioning data and feedback information of each edge computing node, dynamically adjust the data collection frequency, barrier resource allocation and emergency response priority, and achieve closed-loop collaborative optimization of porous ceramic material performance and barrier efficiency through a distributed communication network.

[0024] In an embodiment of the present invention, three core data types, namely temperature field, cooling water penetration rate and material thermal stress, are collected, and key characteristic parameters such as material porosity, water absorption rate and interface thermal resistance are further generated, thereby avoiding the limitations of single data monitoring and more accurately capturing subtle anomalies in the early stages of aluminum leakage events. Relying on local edge computing capabilities, it is possible to quickly conduct seepage analysis based on the pore structure of the material and calculate the penetration depth without relying on remote cloud processing. At the same time, it can correlate the cooling water adsorption rate to quantify the risk level, reduce data transmission delays, complete risk assessment at the embryonic stage of aluminum leakage events, and output positioning parameters such as critical failure temperature and effective blocking time, so as to gain a critical time window for emergency response. Multi-level response strategies are automatically triggered based on dynamic positioning parameters, and can be completed without human intervention. From medium-risk barrier layer laying to high-risk physical isolation and other operations, the lag and operational errors of manual response are avoided; the water absorption capacity, structural integrity and thermal decomposition characteristics of the barrier material are monitored in real time, and material modification parameters are generated by comparing the measured data with the threshold; the optimization module further integrates multi-node information and dynamically adjusts resource allocation and material production parameters, which can improve the hydrophilic group distribution, pore size gradient and other characteristics of porous ceramic materials, enhance the thermal stability and barrier capacity of the material, and extend the effective barrier time; based on distributed edge node data, the data collection frequency of high / low risk areas is dynamically adjusted, and barrier resources and emergency response weights are allocated according to risk priority to ensure monitoring accuracy and resource supply in high-risk areas and avoid resource waste in low-risk areas.

[0025] In a preferred embodiment of the present invention, the temperature field distribution, cooling water penetration rate, and material surface thermal stress data of the molten aluminum leakage area are collected in real time to generate a leakage feature dataset containing material porosity, water absorption rate, and interface thermal resistance parameters, including:

[0026] In the embodiment of the present invention, step 000, high temperature resistant infrared thermal imagers are distributedly deployed at intervals of no more than 0.5 meters at the junction of the refractory material layer and the steel structure shell at the bottom of the electrolytic cell, the outlet through which the molten aluminum flows, the valve and the pipe flange connection; micro moisture sensors are embedded in the form of a mesh array at the contact interface between the outer wall of the cooling water pipe and the refractory material layer; fiber grating strain sensors are fixedly installed in the form of a grid on the upper surface of the refractory material layer; the temperature field distribution data of the monitoring area is collected by the infrared thermal imager, the penetration rate data of the cooling water is collected by the micro moisture sensor, and the optical Fiber Bragg grating strain sensors collect thermal stress data on the surface of materials, specifically including: determining the installation positions of three types of sensors. For high-temperature resistant infrared thermal imagers, they need to cover the junction of the refractory material layer at the bottom of the electrolytic cell and the steel structure shell, as well as the outlet position of the molten aluminum liquid flowing out of the electrolytic cell, the valve position that controls the flow of aluminum liquid, and the flange connection of the connecting pipe. The distance between two adjacent thermal imagers is strictly controlled within 0.5 meters. For example, in the flange connection area of ​​a 5-meter-long pipeline, 11 thermal imagers are deployed to ensure that each monitoring position can be captured by the thermal imager without blind spots. For micro moisture sensors, The sensor needs to be embedded in the interface between the outer wall of the cooling water pipe and the refractory material layer, and arranged in a mesh array, for example, a sensor is arranged every 0.3 meters horizontally and vertically to form a uniform mesh structure, so that each sensor can directly contact the cooling water that may penetrate into the interface, thereby accurately capturing the penetration of cooling water; for the fiber grating strain sensor, it needs to be fixed to the upper surface of the refractory material layer with a high-temperature resistant adhesive and installed in a grid form, for example, an installation point is set every 0.4 meters horizontally and every 0.4 meters vertically, so that the sensor can fully sense the temperature changes on the surface of the refractory material. To detect the changes in tensile or compressive stress caused by the installation of all sensors, start the high-temperature resistant infrared thermal imager and set it to collect data every 10 seconds to continuously capture the temperature information of each position in the monitoring area. Summarize the temperature data of all positions at each collection moment to form complete temperature field distribution data; start the micro moisture sensor and set it to record data every 5 seconds to record the changes in the penetration position of cooling water in the refractory material in real time and obtain the penetration rate related data; start the fiber Bragg grating strain sensor and set it to collect data every 8 seconds to continuously collect the stress data on the surface of the refractory material caused by heat.

[0027] Step 001: parse the temperature field distribution data collected by the infrared thermal imager, compare the real-time temperature value of each sampling point with the normal operating temperature value of the preset corresponding position, screen the area where the real-time temperature value exceeds the normal operating temperature value by more than 50 degrees Celsius, and define it as the area to be verified; extract the geometric center coordinates of the area to be verified, the highest temperature value of the area, and the average temperature change gradient of the area; at the same time, process the sequence data collected by the micro moisture sensor, calculate the moving distance of the cooling water penetration front per unit time, and obtain the water absorption rate of the material, specifically including: obtaining the temperature data of all sampling points from the storage module of the infrared thermal imager, each sampling point has a corresponding unique number and location information, checking the real-time temperature value of each sampling point one by one, for example, the real-time temperature value of sampling point 1 is 320 degrees Celsius, the real-time temperature value of sampling point 2 is 280 degrees Celsius, etc., and then calling the temperature value of the corresponding position of each sampling point under normal working conditions from the preset database, for example, the normal operating temperature value of the corresponding position of sampling point 1 is 260 degrees Celsius, and the normal operating temperature value of the corresponding position of sampling point 2 is 270 degrees Celsius. Degrees, etc., subtract the corresponding normal operating temperature value from the real-time temperature value of each sampling point to obtain the temperature difference of each sampling point. For example, the temperature difference of sampling point 1 is 320 degrees Celsius minus 260 degrees Celsius, which is equal to 60 degrees Celsius, and the temperature difference of sampling point 2 is 280 degrees Celsius minus 270 degrees Celsius, which is equal to 10 degrees Celsius. If the temperature difference of a sampling point is greater than 50 degrees Celsius, the sampling point is marked, and the adjacent marked sampling points are divided into a continuous area. These continuous areas are the areas to be verified. Then, for each area to be verified, , use image analysis tools to measure the boundary coordinates of the area, for example, the coordinates of the upper left corner of the area are (x1, y1), the coordinates of the upper right corner are (x2, y1), the coordinates of the lower left corner are (x1, y2), and the coordinates of the lower right corner are (x2, y2). The horizontal coordinate of the geometric center coordinate is (x1+x2) divided by 2, and the vertical coordinate is (y1+y2) divided by 2. Record the geometric center coordinate data; in the area to be verified, compare the real-time temperature values ​​of all sampling points and find the maximum value. This maximum value is the highest temperature value of the area;Select a time period of 5 minutes, divide the time period into 10 small time periods, each of which is 30 seconds. Calculate the average temperature of all sampling points in the area to be verified in each small time period. For example, the average temperature of the first small time period is 310 degrees Celsius, the average temperature of the second small time period is 315 degrees Celsius, etc. Subtract the average temperature of the previous small time period from the average temperature of the latter small time period to obtain the temperature change of each small time period. For example, the temperature change between the second small time period and the first small time period is 315 degrees Celsius minus 310 degrees Celsius, which is equal to 5 degrees Celsius. Then divide the temperature change of each small time period by the length of the small time period, 30 seconds, to obtain the temperature change rate of each small time period. For example, the temperature change rate in the above example is 5 degrees Celsius divided by 30 seconds. The temperature change of 10 small time periods is 5 degrees Celsius. The sum of the two rates is then divided by 10. The result is the regional average temperature gradient. Meanwhile, the permeation data collected by the micro-moisture sensor at different time points is collected. For example, at the 10th second, the sensor detects the position coordinates of the cooling water permeation front as (a1, b1); at the 20th second, the position coordinates of the permeation front are (a2, b2). The straight-line distance between these two position coordinates is calculated using the distance calculation formula. First, calculate the square of (a2-a1), then calculate the square of (b2-b1), add the two squared results, and then take the square root of the sum to obtain the distance difference between the permeation front positions at the two time points. Divide this distance difference by the time interval between the two time points (10 seconds) to obtain the distance traveled by the cooling water permeation front per unit time. This distance traveled is the water absorption rate of the material.

[0028] Step 002, the geometric center coordinates of the area to be verified, the regional average temperature change gradient, the water absorption rate of the material, and the thermal stress distribution data collected by the fiber Bragg grating strain sensor in the same time period and the same spatial area are temporally and spatially aligned and fused to obtain a fused data set; based on the fused data set, according to Fourier's heat conduction law, by calculating the ratio of the temperature gradient to the heat flux density, the thermal resistance parameter of the contact interface between the material and the molten aluminum liquid is obtained; according to Darcy's law, by analyzing the relationship between the cooling water penetration rate and the pressure gradient, the equivalent porosity of the material under the current thermal-mechanical state is inverted and calculated, specifically including: arranging the geometric center coordinates of the area to be verified, the regional average temperature change gradient, and the water absorption rate of the material obtained in step 001, and determining the collection time range of these data, for example, from the 5th minute to the 10th minute and the corresponding spatial range, for example For the region with coordinates from (c1, d1) to (c2, d2), from all thermal stress distribution data collected by the fiber Bragg grating strain sensor, the thermal stress data with an acquisition time between the 5th and 10th minutes and an acquisition position falling within the region with coordinates (c1, d1) to (c2, d2) are screened out. The screened thermal stress distribution data are matched with the geometric center coordinates of the region to be verified, the regional average temperature change gradient, and the water absorption rate of the material. For the temperature change gradient data and the water absorption rate of the material with the geometric center coordinates of the region to be verified (e, f) and an acquisition time of the 7th minute, the thermal stress data collected by the fiber Bragg grating strain sensor at the 7th minute and the coordinate position (e, f) must be accurately matched to ensure that each spatial position has complete regional average temperature change gradient, material water absorption rate, and thermal stress data at the same time point. All matched data are integrated into the same data table in the order of spatial coordinates (sorted from left to right, from top to bottom) and acquisition time (sorted from early to late). Each row of the table corresponds to a spatial position and an acquisition time, and each column corresponds to a data type, thus forming a fused data set; when calculating the thermal resistance parameters of the contact interface between the material and the molten aluminum liquid based on the fused data set, the Fourier heat conduction law formula is followed. , where q is the heat flux density, unit W / m²; k is the thermal conductivity of the material, unit , such as porous ceramics at room temperature , linear correction is applied at high temperature; The temperature gradient is in °C / m. First, the temperature gradient data of each spatial position at each acquisition time point are extracted from the fused data set. At the same time, the heat flux density data q of the corresponding position and time is obtained through the heat flux density monitoring equipment (deployed at the same location as the infrared thermal imager, with a range of 0-5000W / m² and an accuracy of ±1%). The thermal resistance calculation relationship is derived according to Fourier's heat conduction law, that is, the material interface thermal resistance R=ΔT / q, where ΔT is the temperature difference, and the temperature gradient is obtained. The product of the material contact thickness Δx is obtained, that is, The material contact thickness Δx is obtained from the previous material installation records. For example, if the thickness of the refractory material layer is 0.3m, the actual calculation is first performed through the temperature gradient and calculate , and then Comparing with the heat flux density q, we can get the thermal resistance parameter of the interface between the material and the molten aluminum liquid at that position and time point, in units of , the thermal resistance parameters of all positions and all time points are sorted and summarized according to the spatial coordinates and time sequence to form a complete set of thermal resistance parameters; when calculating the equivalent porosity of the material, according to Darcy's law , where v is the fluid permeability rate, in m / min; K is the material permeability, in m²; μ is the fluid dynamic viscosity, in ; is the pressure gradient, unit First, the cooling water permeation rate data v of each position and time point are extracted from the fused data set. For example, the permeation rate v of the coordinate (i, j) at the 9th minute is 0.05m / min. At the same time, a pressure sensor (deployed at the same location as the micro moisture sensor, with a range of 0-2000Pa and an accuracy of ±5Pa) is used to measure the pressure data of the position at the 9th minute. The pressure difference between two adjacent pressure sensors (with a spacing of 0.5m) is used to obtain the pressure data of the position at the 9th minute. , such as 500Pa to calculate the pressure gradient , The sensor spacing is 0.5m, that is ; According to Darcy's law, the calculation formula of permeability K is derived The negative sign indicates that the penetration direction is opposite to the pressure gradient direction. The actual calculation takes the absolute value. The dynamic viscosity μ of the cooling water under the current working conditions is retrieved from the material property database. For example, at 20°C , convert v=0.05m / min (to )、 、 Substitute into the formula to calculate the material permeability K; then combine the correlation formula between permeability and porosity (based on the porous media theory derivation , where ε is the material equivalent porosity; d is the material average pore size, in m, which can be obtained from the previous material test report, such as the average pore size of refractory material d=5×10 -5 m), and reversely deduce the formula to obtain the calculation formula of equivalent porosity ε Through iterative calculation (initial assumption ε0=0.3, substituting into the formula to calculate ε1, and then substituting ε1 into the calculation of ε2, until the difference between the two calculation results is less than 0.001), the equivalent porosity of the material under the current thermal-mechanical state is finally obtained.

[0029] Step 003, associate and integrate the material porosity, water absorption rate and interface thermal resistance parameters with the corresponding spatial coordinate information and acquisition timestamp to generate a standardized leakage feature data set, specifically including: collecting the material porosity data of each spatial position and each acquisition time point calculated in step 002, the water absorption rate data of each spatial position and each acquisition time point obtained in step 001, and the interface thermal resistance parameter data of each spatial position and each acquisition time point obtained in step 002, and at the same time sorting out the spatial coordinate information corresponding to each parameter, that is, the specific position coordinates of the monitoring area corresponding to each parameter, such as (k1, l1), (k2, l2), etc., as well as the timestamp information when each parameter is collected or calculated, and the timestamp is accurate to the specific year, month, day, hour, minute and second when the data collection or calculation is completed, such as 10:30:15 on August 26, 2025, and then establish a unified data table, the columns of which are set to spatial coordinate information, acquisition timestamp, material porosity, water absorption rate, and interface thermal resistance parameters in sequence; each material porosity data, corresponding water absorption rate data, corresponding interface thermal resistance parameter are associated and integrated. The surface thermal resistance parameter data, as well as their corresponding spatial coordinate information and acquisition timestamp information, are filled in the corresponding positions in the table one by one. For example, the spatial coordinates (k1, l1), the acquisition timestamp 10:30:15 on August 26, 2025, corresponding to the material porosity of 0.2, the water absorption rate of 0.04 meters per minute, and the interface thermal resistance parameter of 0.2 square meters Kelvin per watt, are filled in the corresponding columns of the table row; the data in the table are formatted in a unified manner, and the spatial coordinate information is uniformly formatted in the form of (X coordinate value, Y coordinate value). The data is described in the form of (1.2 meters, 3.5 meters); the acquisition timestamp is uniformly in the format of "year-month-day hour: minute: second", such as "2025-08-2610:30:15"; the material porosity is uniformly retained to three decimal places, the water absorption rate is uniformly retained to three decimal places and the unit is meter per minute, and the interface thermal resistance parameter is uniformly retained to three decimal places and the unit is square meter Kelvin per watt. After such association integration and format standardization, a table containing complete data from all monitoring locations and all acquisition time points is formed, which is the standardized leakage feature dataset.

[0030] In this embodiment, the sensor deployment has comprehensive and accurate coverage and can collect data from multiple key locations and dimensions to ensure that information related to molten aluminum leakage, such as temperature, cooling water penetration, and material thermal stress, is not missed. By comparing with normal operating data, the area to be verified is screened and key temperature parameters are accurately extracted. Data fusion realizes multi-dimensional information integration, and temperature, water absorption rate, thermal stress and other data are associated in time and space, so that the fused data set can comprehensively reflect the complex conditions of the leakage area. The thermal resistance parameters and equivalent porosity calculated based on this are more in line with the actual situation, improving parameter accuracy. The standardized leakage feature data set finally generated closely combines key parameters with time and space information and unifies the format, thereby improving the data utilization efficiency of the overall system.

[0031] In a preferred embodiment of the present invention, a seepage analysis based on the material pore structure is performed based on the leakage characteristic data set. The penetration depth of molten aluminum in the material is calculated, and the leakage risk level is evaluated in combination with the cooling water adsorption rate. The dynamic positioning parameters including the critical temperature of material failure and the effective blocking time are output, including:

[0032] In an embodiment of the present invention, step 100a calculates the equilibrium relationship between capillary pressure and viscous resistance based on the three-dimensional pore network topology structure, combined with the water absorption rate and the interface thermal resistance parameters, specifically including: constructing a three-dimensional pore network topology structure through Python's OpenPNM library, obtaining three-dimensional pore network topology structure data, the structure including pore size distribution, connectivity, and branch node position information, such as pore diameters ranging from 0.1 mm to 1 mm, some pores are network-connected, there are 2 to 4 pore branches at the branch nodes, and the spatial coordinates of each branch node are clearly recorded, extracting the water absorption rate and interface thermal resistance parameters from the leakage feature data set, wherein the water absorption rate reflects the flow rate of cooling water in the pores, such as the water absorption rate of a certain area is 0.02 meters per minute, and the interface thermal resistance parameter reflects the heat transfer resistance of the contact surface between the material and the molten aluminum liquid, such as the interface thermal resistance parameter of a certain contact surface is 0.3 square meters Kelvin per watt; when calculating the capillary pressure, it is necessary to combine the radius of the pore, the surface tension of the molten aluminum liquid, and the solid-liquid interface contact angle, and the calculation formula is: , where Pc is the capillary pressure, in Pascals, γ is the surface tension of the molten aluminum liquid, in Newton per meter, θ is the contact angle of the solid-liquid interface, in degrees, and r is the pore radius, in meters; γ needs to be determined according to the current purity and temperature of the molten aluminum liquid. If the purity of the molten aluminum liquid is 99.7% and the temperature is 700 degrees Celsius, its surface tension is 0.85 Newton per meter; if the purity drops to 99.5% and the temperature rises to 720 degrees Celsius, the surface tension is adjusted to 0.83 Newton per meter. This value needs to be obtained through the thermophysical properties database of molten aluminum liquid built in the early stage. The database covers the purity range (99.0%-99.9%) and temperature range (680- 750 degrees Celsius), which can directly match the current operating parameters to output the corresponding surface tension value; the solid-liquid interface contact angle is related to the surface properties of the material and the state of the molten aluminum liquid. If the barrier material is porous ceramic and the surface is not specially treated, the solid-liquid interface contact angle between the molten aluminum liquid and the ceramic is 110 degrees at 700 degrees Celsius; if the ceramic surface is coated with an aluminophilic coating, the contact angle is reduced to 80 degrees. The contact angle value comes from a preset solid-liquid interface property database, which covers scenarios such as untreated surfaces of porous ceramics, surfaces coated with aluminophilic coatings, and the common temperature range of 680-750 degrees Celsius. It can directly match the material type involved in the analysis with the molten aluminum liquid temperature to retrieve the corresponding contact angle value.

[0033] When calculating the capillary pressure of a single pore, first multiply the surface tension value of the molten aluminum liquid by the cosine value of the contact angle of the solid-liquid interface, and divide the result by the pore radius. For example, the surface tension of 0.85 Newtons per meter is multiplied by the cosine value of 110 degrees, and then divided by 0.0002 meters, that is, a pore radius of 0.2 mm, to obtain the capillary pressure of a single pore. When calculating the viscous resistance, it is necessary to combine the viscosity of the molten aluminum liquid, the local flow velocity and the pore length. The calculation formula is: , where Fv is the viscous resistance, in Pascals, and η is the viscosity of the molten aluminum liquid, in , L is the pore length, unit meter, υ is the local flow velocity, unit meter per second, r is the pore radius, unit meter; the viscosity of molten aluminum is also related to temperature, the viscosity is 0.0012 Pascal seconds at 700 degrees Celsius, and the viscosity is 0.0011 Pascal seconds at 720 degrees Celsius. This value comes from the molten aluminum thermophysical parameter database, which includes viscosity values ​​at different temperatures and can be directly called according to the current temperature match; the local flow velocity is determined by the three-dimensional pore flow simulation calculation in the seepage analysis. The specific process is to construct a three-dimensional pore network space based on the pore distribution characteristics of porous ceramics, including pore diameter, connectivity, and distribution density, divide the finite element grid, and the grid size does not exceed 1 / 5 of the minimum pore diameter to ensure calculation accuracy. The molten aluminum is regarded as an incompressible Newtonian fluid, and its density, 2375 kg / m3, dynamic viscosity and other physical parameters are substituted. The finite element method is used to solve the Navier-Stokes equations = Where ρ is the density of the molten aluminum, in kg / m³, and is set to 2375 kg / m³, which is the standard density of molten aluminum under common operating conditions of 680-750°C. t is time, in seconds, which is set according to the simulation accuracy requirements, for example, the time step for each iteration is 0.01 seconds. υ is the velocity vector, in m / s, which represents the flow speed and direction of the molten aluminum at different locations in the pore, including components in the X, Y, and Z spatial directions. p is the pressure of the molten aluminum in the pore, in Pa, which refers to the pressure exerted on the pore wall during the flow of the molten aluminum itself. The pressure value varies at different locations. For example, the pressure at the pore entrance needs to be set according to the actual operating conditions, for example, 0.5 Pa (to simulate the initial pressure of the molten aluminum flowing from the electrolytic cell into the pore). The pressure inside the pore gradually changes along the flow path. g is the acceleration of gravity, in m / s², and is set to 9.8 m / s², which is the standard acceleration of gravity on the Earth's surface, and is directed along the negative Z axis, that is, vertically downward. is the gradient operator, which is a vector differential operator and can be expressed in a three-dimensional rectangular coordinate system as , when acting on a scalar physical quantity, such as pressure p, Represents the rate of change of the physical quantity in space, that is, the pressure gradient, expressed as For example, the calculation process is based on the node pressure data after finite element mesh division. In the X direction, the partial derivative of a node is The pressure difference between the node and the adjacent node, such as the pressure of the right node minus the pressure of the left node, is obtained by dividing the distance between the two nodes in the X direction; the Y direction partial derivative can be calculated similarly and the Z-direction partial derivative , the final combination is ; The Laplace operator is a scalar differential operator, which is expressed in a three-dimensional rectangular coordinate system as , used to describe the second-order rate of change of physical quantities in space, with the Laplace operator of the velocity vector υ As an example, the components are calculated as follows: the X-direction component is ,in From X to adjacent nodes The first-order partial derivative difference is divided by the spacing (the calculation method of the first-order partial derivative is to select two adjacent nodes in the X direction and use the The velocity difference is divided by the node spacing); the calculation of the second-order partial derivatives in the Y and Z directions is similar; the calculation logic of the Y and Z direction components is consistent with that of the X direction, respectively 、 .

[0034] The pore inlet is a pressure boundary, that is, the molten aluminum liquid pressure value of all finite element nodes at the pore inlet is set to a fixed value, such as 0.5Pa, to ensure that the molten aluminum liquid flows stably into the pore from the inlet; the outlet is a free flow boundary, that is, the pressure at the pore outlet is set equal to the external atmospheric pressure (0.1Pa), allowing the molten aluminum liquid to flow freely from the outlet without additional flow obstruction; the pore wall is a no-slip boundary, that is, the molten aluminum liquid flow rate of all finite element nodes at the pore wall is set to 0m / s, simulating the state of zero flow rate caused by friction when the molten aluminum liquid contacts the pore wall in actual working conditions. After solving the flow field distribution, the flow velocity vector data of all finite element nodes in the pore are obtained. First, the spatial range of a single pore is determined, and the pore is extracted from the three-dimensional pore network topology. The central axis of the pore is extended along the X-axis, and the axis coordinates are (x, y0, z0), where y0 and z0 are fixed values. Then, all finite element nodes on the central axis are selected, and the flow velocity vectors of these nodes in the axis direction, such as the X-axis direction, are extracted. These components are added and divided by the number of nodes to obtain the average flow velocity on the central axis of the pore as the local flow velocity. For example, after a pore is simulated and calculated, the X-direction flow velocities of the five nodes on its central axis are added to obtain a value, which is then divided by the five nodes to obtain the average flow velocity. The pore length is extracted from the three-dimensional pore network topology, that is, the straight-line distance from the inlet to the outlet of the pore along the central axis is measured. For example, the pore length is 0.05m. When calculating the viscous resistance of a single pore, the viscous resistance formula is used. , where η is the viscosity of molten aluminum, unit , such as 0.0012 at 700 degrees Celsius ; r is the pore radius, unit is m, such as 0.0002m. Multiply the viscosity, local flow velocity and pore length, and then divide by the square of the pore radius to obtain the viscous resistance of a single pore.

[0035] By comparing the capillary pressure and viscous resistance, if the capillary pressure is greater than the viscous resistance, the pore radius needs to be adjusted appropriately, that is, reducing the pore radius can increase the capillary pressure, and increasing the pore radius can reduce the capillary pressure or local flow velocity, that is, increasing the local flow velocity can increase the viscous resistance; if the viscous resistance is greater than the capillary pressure, the parameters are adjusted in the opposite direction until the capillary pressure and viscous resistance are equal. At this time, the equilibrium relationship between capillary pressure and viscous resistance is obtained. For example, in the above example, the capillary pressure is 1453.5 Pascals It is much larger than the viscous resistance of 7.5 Pascals. The pore radius can be increased to 0.005 meters. The capillary pressure can be recalculated to be 0.85 Newtons per meter multiplied by 0.3420 divided by 0.005 meters, which is equal to 58.14 Pascals. At the same time, the local flow velocity can be increased to 0.04 meters per second. The viscous resistance can be recalculated to be 0.0012 Pascal seconds multiplied by 0.04 meters per second multiplied by 0.05 meters divided by (0.005 meters squared), which is equal to 0.096 Pascals. If they are still not equal, continue to fine-tune until the two values ​​are consistent.

[0036] Step 100b, based on the balance relationship between capillary pressure and viscous resistance, dynamically simulate the flow path selection probability and penetration direction of molten aluminum liquid at the pore branch node, specifically including: clarifying the specific structure of the pore branch node, for example, there are three branch pores at a branch node, respectively recorded as branch 1, branch 2, and branch 3, and obtaining the capillary pressure and viscous resistance balance value of each branch pore from the calculation result of step 100a, assuming that the balance value of branch 1 is 200 Pascal, branch 2 is 300 Pascal, and branch 3 is 100 Pascal, first calculate the sum of all branch balance values, that is, 200 Pascal plus 300 Pascal plus 100 Pascal, and the total is 600 Pascal, and then calculate the flow path selection probability of each branch, the probability of branch 1 is 200 Pascal divided by 600 Pascal is approximately 0.33; the probability of branch 2 is 300 Pascal divided by 600 Pascal, which is 0.5; the probability of branch 3 is 100 Pascal divided by 600 Pascal, which is approximately 0.17. Add these three probabilities and confirm that the total is 1 to ensure that the calculation is correct. The determination of the infiltration direction needs to be combined with the spatial orientation of the branch pores. Use an inclinometer to measure the angle between each branch pore and the horizontal plane. If the angle between branch 1 and the horizontal plane is 30 degrees and it is tilted upward, the infiltration direction is 30 degrees upward; the angle between branch 2 and the horizontal plane is 15 degrees and it is tilted downward, the infiltration direction is 15 degrees downward; the angle between branch 3 and the horizontal plane is 0 degrees and extends horizontally, then the infiltration direction is horizontal. At the same time, the angle information of each branch is recorded in detail in the data table.

[0037] Step 100c, based on the flow path selection probability and penetration direction, combined with the interface thermal resistance parameters, calculates the dynamic contact angle change data of the molten aluminum liquid at the solid-liquid interface, specifically including: first clarifying the specific division of the monitoring area, dividing the entire area where the molten aluminum liquid may leak into multiple monitoring positions according to a 0.2 meter × 0.2 meter grid, each monitoring position corresponds to a unique spatial coordinate, such as the coordinates (0.2 meters, 0.2 meters), (0.2 meters, 0.4 meters), (0.4 meters, 0.2 meters), etc. Each coordinate point is a specific monitoring point, ensuring that key areas such as the refractory layer at the bottom of the electrolytic cell, the contact interface of the cooling water pipe, and the outlet and valve through which the molten aluminum liquid flows are covered, and extracting the interface thermal resistance parameters corresponding to each monitoring point from the leakage feature data set. Different materials have different interface thermal resistance parameters. For example, the interface thermal resistance parameters of porous ceramic materials are between 0.2 and 0.5 square meters Kelvin per watt. If the materials of the monitoring points such as (0.2 meters, 0.2 meters) and (0.4 meters, 0.4 meters) in the current monitoring area are all porous ceramics, extract the interface thermal resistance parameters of these monitoring points. The thermal resistance parameter is 0.3 square meters Kelvin per watt. If the material of a monitoring point (0.6 meters, 0.6 meters) is a composite refractory material, its interface thermal resistance parameter is extracted as 0.4 square meters Kelvin per watt. The larger the parameter, the slower the heat transfer and the slower the temperature change at the solid-liquid interface. For example, the monitoring point with an interface thermal resistance parameter of 0.4 square meters Kelvin per watt has a smaller increase in interface temperature within the same period of time than the monitoring point with an interface thermal resistance parameter of 0.3 square meters Kelvin per watt. For each monitoring point, check its flow path selection probability. For example, the monitoring point (0. The selection probability of the monitoring point (2 meters, 0.2 meters) is 0.5, which belongs to the high probability area, which means that the molten aluminum flows more concentratedly in the pore channel where the monitoring point is located, and the amount of aluminum liquid passing through the monitoring point per unit time is more, the heat carried by the aluminum liquid accumulates faster at the interface, and the interface temperature rises faster; the selection probability of the monitoring point (0.6 meters, 0.6 meters) is 0.1, which belongs to the low probability area, and the molten aluminum flows dispersedly in the pore channel of the monitoring point, and the amount of aluminum liquid passing through the monitoring point per unit time is small, the heat accumulation at the interface is slow, and the temperature rises slowly.

[0038] When calculating the dynamic contact angle, first determine the initial contact angle corresponding to each monitoring point. The initial contact angle needs to be obtained by measuring in the early stage under the same conditions, that is, prepare samples with the same material as the monitoring point, such as porous ceramic samples consistent with the (0.2 m, 0.2 m) monitoring point, and composite refractory material samples consistent with the (0.6 m, 0.6 m) monitoring point; place the sample in the same temperature environment as the actual working conditions, such as a constant temperature environment of 700 degrees Celsius, and at the same time, drop molten aluminum on the sample surface, and use high-precision optical imaging equipment to capture the contact interface image formed by the molten aluminum and the sample surface; use image analysis tools to measure the angle between the contact interface and the sample surface in the image, That is the initial contact angle. For example, at 700 degrees Celsius, the contact angle measurement result between the molten aluminum liquid and the porous ceramic sample is 110 degrees, and the contact angle measurement result with the composite refractory sample is 105 degrees. These measurement values ​​are used as the initial contact angles of the corresponding monitoring points, and then the contact angle is adjusted according to the interface temperature change of each monitoring point. First, the interface temperature of each monitoring point in different time periods is collected by a high-temperature resistant temperature sensor. For example, the temperature of the monitoring point (0.2 meters, 0.2 meters) at the initial moment is 700 degrees Celsius. After 10 seconds, the temperature rises to 720 degrees Celsius. The interface temperature change in this time period is calculated as 720 degrees Celsius minus 700 degrees Celsius, which is 20 degrees Celsius. According to the rule that "the contact angle decreases by 5 degrees for every 10 degrees Celsius increase in temperature", first calculate how many 10 degrees Celsius the temperature change contains, that is, 20 degrees Celsius divided by 10 degrees Celsius, and get 2; then calculate the value of the contact angle decrease, that is, 2 multiplied by 5 degrees, and get 10 degrees; finally, subtract the decrease value from the initial contact angle of the monitoring point, that is, 110 degrees minus 10 degrees, and get the contact angle of the monitoring point at the end of the time period to be 100 degrees. According to the above method, for each monitoring point, collect the interface temperature at different times, such as once every 5 seconds, calculate the temperature change in each time period, and then get the contact angle value at each moment, for example, the initial contact angle of the monitoring point (0.6 meters, 0.6 meters) is 1 At 05 degrees, the temperature rises to 715 degrees Celsius at the 5th second, and the temperature change is 15 degrees Celsius. 15 degrees Celsius divided by 10 degrees Celsius is 1.5, and the contact angle decreases by 1.5 times 5 degrees, which is equal to 7.5 degrees. At this time, the contact angle is 105 degrees minus 7.5 degrees, which is equal to 97.5 degrees; at the 10th second, the temperature rises to 730 degrees Celsius. Compared with the initial temperature change of 30 degrees Celsius, the contact angle decreases by 3 times 5 degrees, which is equal to 15 degrees. At this time, the contact angle is 105 degrees minus 15 degrees, which is equal to 90 degrees. The contact angle values ​​of all monitoring points at different times are sorted into a table in chronological order and spatial coordinates. The table contains three columns: monitoring point coordinates, time, and contact angle values, forming dynamic contact angle change data.

[0039] Step 100d integrates the flow path selection probability, penetration direction, and dynamic contact angle change data to generate the penetration path distribution, local flow velocity, and interface contact angle parameters. Specifically, the following steps are performed: Using visualization analysis software, the flow path selection probability is marked spatially on a two-dimensional plane map of the monitoring area. A color gradient rule is set, with regions with selection probabilities between 0.4 and 1 marked in dark colors, such as dark red; regions between 0.2 and 0.4 marked in medium colors, such as orange; and regions between 0 and 0.2 marked in light colors, such as yellow. After marking, an intuitive penetration path distribution image is formed. The image clearly shows that molten aluminum is more likely to flow along dark areas. When calculating the local flow velocity, a baseline flow velocity is first determined. This baseline flow velocity is set based on actual operating statistical data from similar aluminum processing within the industry. For example, the baseline flow velocity is set to 0.01 meters per second based on the conventional flow velocity of molten aluminum in the pores of similar barrier materials in similar production processes such as deep well casting and electrolytic cell aluminum liquid transportation. The pore diameter at each location is extracted from the three-dimensional pore network topology. For example, if the pore diameter at a certain location is 0. 2 mm, or 0.0002 m, corresponds to a flow path selection probability of 0.5. According to the calculation method of local flow velocity = selection probability × reference flow velocity ÷ square of pore diameter, when collating the dynamic contact angle change data, a table containing three columns of time, spatial coordinates (X-axis, Y-axis), and contact angle values ​​is established. The time recording interval is set to once every 10 seconds. At each time point, the contact angle corresponding to the spatial coordinates (such as X-axis 0.2 m, Y-axis 0.3 m, X-axis 0.4 m, Y-axis 0.5 m, etc.) is extracted from the dynamic contact angle change data. Angle values, fill in this information into the table one by one, for example, at the 10th second, the contact angle value of the coordinate (0.2m, 0.3m) is 100 degrees, and the contact angle value of the coordinate (0.4m, 0.5m) is 95 degrees; at the 20th second, the contact angle value of the coordinate (0.2m, 0.3m) is 95 degrees, and the contact angle value of the coordinate (0.4m, 0.5m) is 90 degrees, and so on to complete the data filling of all time points and all spatial coordinates, and finally form an interface contact angle parameter table containing spatial position and time information.

[0040] Step 101a, based on the pore network topology, local flow velocity and interface contact angle parameters in the infiltration path distribution, performs heat conduction and convection-diffusion coupling calculations, and defines the heat conduction rate, flow diffusion rate and thermal stress constraints on pore deformation of the molten aluminum liquid. Specifically, when calculating the heat conduction rate, first obtain the thermal conductivity of the current barrier material. For example, the thermal conductivity of porous ceramics is 0.8 watts per meter Kelvin, and extract the temperature gradient at that location from the temperature distribution data. The temperature distribution data comes from the molten aluminum liquid-barrier material coupled temperature monitoring system built in the early stage. The system implants K-type thermocouple sensors at key positions of the pore network at a spacing of 0.1 meters, with a measurement accuracy of ±1 degree Celsius and a range of 0-1000 degrees Celsius, to collect temperature data at different times in real time. And form temperature distribution data. The temperature gradient at a certain position is 500 degrees Celsius per meter (℃ / m). The specific extraction process is to select two adjacent monitoring points in the temperature distribution data, point A coordinates (x1, y1, 1) and point B coordinates (x2, y2, z2). The distance between the two points is calculated to be 0.1 meters through three-dimensional coordinates. The measured temperature value of point A is 700 degrees Celsius, and the measured temperature value of point B is 650 degrees Celsius. According to the definition of temperature gradient (the ratio of temperature change to distance), the temperature gradient is calculated. Because the temperature gradient reflects the speed and direction of temperature change, the absolute value is taken to indicate the size of the rate when calculating the heat conduction rate. For example, the temperature gradient at a certain position is 500 degrees Celsius per meter (the temperature drops from 700 degrees Celsius to 650 degrees Celsius, and the distance between the two points is 0.1 meters). According to the heat conduction rate = thermal conductivity coefficient × absolute value of temperature gradient (℃ / m), the calculated result unit is W / m², and 0.8 Multiply by 500 (℃ / m) to get a heat conduction rate of 400W / m². When calculating the flow diffusion rate, first obtain the heat capacity of the molten aluminum liquid. For example, the heat capacity of the molten aluminum liquid is 1000 joules per kilogram Celsius. Extract the local flow velocity from step 100d, such as 0.005 meters per second. Calculate the temperature difference between the molten aluminum liquid and the pore wall at this location, such as 700 degrees Celsius minus 650 degrees Celsius, which is 50 degrees Celsius. According to the flow diffusion rate = local flow velocity × heat capacity × temperature difference, multiply 0.005 meters per second by 1000 joules per kilogram Celsius and then by 50 degrees Celsius to get the flow diffusion rate. The diffusion rate is 250 joules per kilogram second. When defining the constraints of thermal stress on pore deformation, the thermal stress tolerance limit of the material is first determined. For example, the thermal stress tolerance limit of porous ceramics is 100 MPa. If the thermal stress at a certain position is monitored to be 110 MPa, which exceeds the limit value by 10 MPa, the excess ratio is calculated as 10 MPa divided by 100 MPa, which is 10%. The pore diameter increases by 2%. If the thermal stress is 90 MPa, which is lower than the limit value, the pore diameter remains unchanged. If the thermal stress is 120 MPa, which exceeds the limit value by 20%, the pore diameter increases by 4%. Similarly, a clear corresponding relationship is established.

[0041] Step 101b, based on the constraints, performs heat conduction analysis of the molten aluminum liquid in the pore network, calculates the convection diffusion path in combination with the local flow velocity data, and generates the temperature distribution and heat flux density change data in each pore channel. Specifically, it includes: starting from the entrance position where the molten aluminum liquid enters the pore according to the pore network topology, such as the coordinate (0, 0), setting the initial temperature to the actual temperature of the molten aluminum liquid, such as 700 degrees Celsius, setting the time interval to 10 seconds, and calculating the temperature of each adjacent pore in turn. Taking pore A adjacent to the entrance as an example, the heat conduction rate at this position is obtained from step 101a as 400 watts per square meter, and the cross-sectional area of ​​pore A is obtained at the same time, such as Square meter, material heat capacity, such as 880 joules per kilogram degrees Celsius and unit length mass, such as 0.002 kilograms per meter, temperature calculation needs to be derived through the energy conservation relationship, that is, the energy transferred by heat conduction = heat conduction rate × cross-sectional area × time, that is, 400 watts per square meter × Square meters × 10 seconds; temperature increment = energy transferred by heat conduction ÷ (heat capacity of material × mass per unit length), that is, energy transferred by heat conduction ÷ (880 joules per kilogram Celsius × 0.002 kilograms per meter), so the temperature of pore A = initial inlet temperature 700 degrees Celsius + temperature increment; then calculate the temperature of pore B adjacent to pore A, using the same calculation logic, that is, first calculate the transferred energy through the heat conduction rate, the cross-sectional area of ​​pore B and time, then combine the heat capacity of the material and the mass per unit length to obtain the temperature increment, and finally superimpose the temperature of pore A to obtain the temperature of pore B, and so on to complete the temperature calculation of all pores; combine the local flow velocity data to calculate the When the flow diffusion path is used, taking the local flow velocity of 0.005 meters per second at a certain position as an example, the time interval is set to 10 seconds, the flow distance = 0.005 meters per second × 10 seconds = 0.05 meters, and the temperature distribution of the pores along the flow distance is updated according to the above temperature calculation method; when calculating the heat flux density change data, the heat flux density should be based on Fourier's heat conduction law, and the thermal conductivity of the material is multiplied by the temperature gradient (the temperature difference between adjacent pores divided by the pore spacing) to obtain the unit of watts per square meter, and then multiplied by the cross-sectional area of ​​the corresponding pore (in square meters) to obtain the heat flow rate, in watts. For example, the heat flux density of a pore is calculated to be 400 watts per square meter, multiplied by its cross-sectional area square meters, and obtain the heat flux, which is calculated and recorded every 10 seconds to form a curve of heat flux density and heat flux changing with time.

[0042] Step 101c integrates the collected thermal stress data on the material surface, analyzes the effect of pore deformation caused by thermal expansion on heat conduction, and corrects the temperature distribution and heat flux density data generated in the previous step based on the analysis results to obtain corrected temperature distribution data. Specifically, the following steps are performed: thermal stress data on the material surface is collected from multi-source sensors, and the collection locations are evenly distributed according to the monitoring area. For example, a collection point is set every 0.5 meters, and each collection point records a thermal stress value every 5 seconds. The thermal stress data of each collection point is checked. If the thermal stress at a certain collection point is 50 MPa, which is greater than zero, it means that the material has expanded. According to the constraint conditions of step 101a, the thermal stress of 50 MPa does not exceed the limit value of 100 MPa, and the pore diameter does not change. If the thermal stress at another collection point is 150 MPa, which is greater than zero and exceeds the limit value of 50 MPa, and the excess ratio is 50%, then the pore diameter increases by 10% (because the pore diameter increases by 2% for every 10% exceeding, and 50% corresponds to 5 10%, that is, Increased by 10%), assuming that the original diameter of the pore is 0.2 mm, and after increasing by 10%, the diameter is 0.2 mm plus 0.2 mm multiplied by 10%, which equals 0.22 mm. After the pore diameter changes, the heat conduction rate changes accordingly. When the diameter increases, the heat conduction rate increases. According to the corrected heat conduction rate = original heat conduction rate × (1 + diameter change ratio), the original heat conduction rate is 400 watts per square meter, and the diameter change ratio is 10%. Then the corrected heat conduction rate is 400 watts per square meter multiplied by (1 + 0.1). The temperature distribution of the pore and adjacent pores is recalculated using the corrected heat conduction rate. For example, if the original pore temperature is calculated to be 720 degrees Celsius, it is recalculated at a heat conduction rate of 440 watts per square meter after correction to obtain a new temperature value. At the same time, the heat flux density is recalculated using the corrected heat conduction rate to obtain the corrected heat flux density data. Finally, all the corrected temperature data are integrated to form the corrected temperature distribution data.

[0043] Step 101d, based on the corrected temperature distribution data, predict the maximum penetration depth of the molten aluminum liquid, and identify the temperature gradient exceeding the limit area in combination with the heat flux density change data, and output the penetration depth safety threshold and temperature gradient critical value, specifically including: checking the corrected temperature distribution data, finding all positions with a temperature equal to the molten aluminum liquid temperature, such as 700 degrees Celsius. These positions are the areas that the molten aluminum liquid can reach, and determining the coordinates of the deepest position in the area. For example, the deepest position coordinates are (2 meters, 3 meters, 1 meter) (Z axis is vertical direction), and the initial position coordinates are (2 meters, 3 meters, 0 meters). Then the vertical distance between this position and the initial position is 1 meter minus 0 meters, which is equal to 1 meter. This is the maximum penetration depth. According to the penetration depth safety threshold = maximum penetration depth 1×0.8 (safety factor), 0.8 meters is obtained as the penetration depth safety threshold to ensure a safety margin. When calculating the temperature gradient of each position, select two adjacent sampling points. Sample points, for example, the temperature of sampling point 1 is 700 degrees Celsius, and the coordinates are (2 meters, 3 meters, 0.5 meters); the temperature of sampling point 2 is 650 degrees Celsius, and the coordinates are (2 meters, 3 meters, 0.6 meters). The temperature difference between the two points is 700 degrees Celsius minus 650 degrees Celsius, which equals 50 degrees Celsius. The distance between the two points is 0.6 meters minus 0.5 meters, which equals 0.1 meters. According to temperature gradient = temperature difference ÷ distance, the temperature gradient is 50 degrees Celsius divided by 0.1 meters, which equals 500 degrees Celsius per meter, the normal range of the preset temperature gradient, such as 300 degrees Celsius per meter. If the temperature gradient in a certain area is greater than 300 degrees Celsius per meter, such as the area of ​​500 degrees Celsius per meter mentioned above, mark the area as a temperature gradient exceeding the limit area. Among all the exceeding areas, find the minimum temperature gradient value. For example, if the temperature gradient in one exceeding area is 400 degrees Celsius per meter and in another area is 500 degrees Celsius per meter, the minimum 400 degrees Celsius per meter is the temperature gradient critical value.

[0044] Step 101e, based on the temperature gradient critical value and heat flux density data, the thermal decomposition rate threshold of the material is used to determine the boundary of the heat affected zone, and the boundary coordinates, thermal diffusion radius and diffusion rate parameters are generated. Specifically, the following steps are performed: first, the thermal decomposition rate threshold of the material is obtained from the material factory manual. For example, the thermal decomposition rate threshold of a porous ceramic material is , obtain the temperature gradient critical value from step 101d, such as 400 degrees Celsius per meter, and obtain the heat flux density data of the corresponding position from step 101b, such as , according to the thermal decomposition driving value = temperature gradient critical value 400 × heat flux density , get the thermal decomposition driving value. When the thermal decomposition driving value at a certain position reaches the driving value corresponding to the material thermal decomposition rate threshold, such as 0.06 degrees Celsius·W / m, the position is the boundary of the heat-affected zone. Use a laser positioning device to measure the coordinates of each point on the boundary. For example, the coordinates of three points on the boundary are (1.8 meters, 2.8 meters), (2.2 meters, 2.8 meters), and (2 meters, 3.2 meters). These coordinates are sorted into a boundary coordinate set to calculate the geometric center of the heat-affected zone. First, calculate the average value of all boundary coordinates on the X axis, assuming it is 2 meters; then calculate the average value of the Y axis, assuming it is 3 meters. Therefore, the coordinates of the geometric center are (2 meters, 3 meters). When calculating the heat diffusion radius, calculate the distance from each boundary point to the geometric center separately, add the distances from the three points on the boundary to the geometric center, and divide by 3 to get the average distance, which is the heat diffusion radius. When calculating the heat diffusion rate parameter, select two adjacent time points. For example, the heat diffusion radius is 0.25 meters at the 10th second and 0.35 meters at the 20th second. The time interval is 20 seconds - 10 seconds, which is equal to 10 seconds. The radius difference is 0.35 meters - 0.25 meters, which is equal to 0.1 meters. According to the heat diffusion rate = radius difference 0.1 ÷ time interval 10, the heat diffusion rate is 0.01 meters per second.

[0045] Step 101f integrates the penetration depth safety threshold, thermal diffusion radius, material deformation tolerance, and thermal decomposition rate threshold to generate a structured heat transfer analysis result including the penetration depth threshold, temperature diffusion range, and thermal stability evaluation parameters. Specifically, the results include: directly using the penetration depth safety threshold calculated in step 101d, such as 0.8 meters, as the penetration depth threshold; the temperature diffusion range is twice the thermal diffusion radius, that is, the thermal diffusion radius, such as 0.25 meters × 2 = 0.5 meters, representing a range of 0.25 meters from the geometric center of the heat-affected zone to the surrounding area; obtaining the material deformation tolerance, which is the maximum deformation value allowed by the material, obtained from the material manual, such as 0.1 mm; and then obtaining the material thermal decomposition rate threshold, such as Meters / second (uniformly using length / time dimensions), calculated according to the thermal stability assessment parameter = (material deformation tolerance + thermal decomposition rate threshold × reference time) ÷ 2; first convert the material deformation tolerance unit to meters, that is, 0.1 mm = 0.0001 meters; select the reference time as 1 second, and calculate the cumulative deformation equivalent of the thermal decomposition rate threshold per unit time, that is m / s × 1 s = meters; then add the two together, 0.0001 meters + meters; finally, divide by 2 to obtain the thermal stability evaluation parameter. The larger the parameter, the better the thermal stability of the material. A structured table is established, and the columns of the table are "spatial coordinates (X, Y, Z), time, penetration depth threshold, temperature diffusion range, thermal stability evaluation parameter". The parameters corresponding to each spatial position and each time point are filled in the table one by one, such as coordinates (2 meters, 3 meters, 0.5 meters), time 10 seconds, penetration depth threshold 0.8 meters, temperature diffusion range 0.5 meters, thermal stability evaluation parameter 0.0000525 meters. After the arrangement is completed, a structured heat transfer analysis result table is formed.

[0046] Step 102, based on the penetration depth threshold and temperature diffusion range in the heat transfer analysis results, combined with the cooling water adsorption rate, calculates the potential contact area and energy release rate between the molten aluminum liquid and the cooling water, specifically including: extracting the penetration depth threshold, such as 0.8 meters, and the temperature diffusion range, such as 0.5 meters, from the heat transfer analysis results; marking the area within the penetration depth threshold in the three-dimensional space of the monitoring area, that is, the area from 0 meters to 0.8 meters on the Z axis, which is the area where the molten aluminum liquid may reach; marking the area corresponding to the temperature diffusion range, that is, the circular area with a radius of 0.5 meters and a heat affected center (2 meters, 3 meters) as the center, the Z axis range is consistent with the penetration depth area, and the area is the high temperature affected area; using the graphic overlay tool, find the overlapping part of the two areas, the overlapping part is the area where the molten aluminum liquid and the cooling water may contact; using the image analysis software to measure the area of ​​the overlapping area, for example, the measurement result is 0.3 square meters, which is the potential contact area; when calculating the energy release rate, first obtain the temperature of the molten aluminum liquid, such as 700 degrees Celsius and the cooling water. The cooling water temperature is, for example, 20 degrees Celsius. The temperature difference between the two is 700 degrees Celsius minus 20 degrees Celsius, which equals 680 degrees Celsius. The heat exchange coefficient is obtained. This coefficient is determined by the flow state of the cooling water. For example, if the heat exchange coefficient of the flowing cooling water is 1000 watts per square meter Celsius, the energy release rate is calculated according to the formula: energy release rate = temperature difference 680 × potential contact area 0.3 × heat exchange coefficient 1000. At the same time, the cooling water adsorption rate is extracted from the leakage feature dataset. For example, the adsorption rate is 0.001 square meters per second. This rate reflects the surface area of ​​the material that can be covered by the cooling water per unit time. The faster the adsorption rate, the faster the contact area grows. When calculating the contact area increment, a time interval is selected, such as 10 seconds. The contact area increment is calculated according to the formula: contact area increment = adsorption rate 0.001 × time 10. This increment is added to the potential contact area, that is, 0.3 square meters + contact area increment, to obtain the updated potential contact area. The energy release rate is then recalculated using the updated contact area to ensure that the result is more in line with the actual situation.

[0047] Step 103a calculates a leakage risk index based on the proportional relationship between the potential contact area and the preset contact area threshold, combined with the ratio of the energy release rate to the preset energy release rate threshold. Specifically, the steps include: first obtaining a preset contact area threshold, which is set based on historical leakage accident data and safety standards, such as 1 square meter; obtaining a potential contact area from step 102, such as 0.31 square meters, and calculating the area ratio as 0.31 according to the formula: area ratio = potential contact area 0.31 ÷ preset contact area threshold 1; then obtaining a preset energy release rate threshold, which is also set based on safety standards, such as 250,000 watts; obtaining an energy release rate from step 102, such as 204,000 watts, and calculating the energy ratio according to the formula: energy ratio = energy release rate 204,000 ÷ preset energy release rate threshold 250,000; and calculating the leakage risk index as (area ratio × 0.5) + (energy ratio × 0.5). The index ranges from 0 to 2.

[0048] Step 103b: When the leakage risk index is lower than the first critical value, it is determined to be a low risk level. Specifically, the first critical value is a lower risk limit value set in advance according to safety management requirements, such as 0.3. The leakage risk index calculated in step 103a is obtained. If the index is 0.2, which is lower than 0.3, it means that the possibility of leakage of molten aluminum liquid in the current scenario is small. Even if a slight leakage occurs, the leakage amount is small, and the energy release rate is low. It will not break through the barrier material or cause secondary accidents. The impact range is small and the degree is light. Therefore, it is determined to be a low risk level.

[0049] In step 103c, when the leakage risk index is between the first critical value and the second critical value, it is determined to be a medium risk level. Specifically, the first critical value and the second critical value are both pre-set limit values, and the first critical value, such as 0.3, is less than the second critical value, such as 0.7. If the leakage risk index calculated in step 103a is 0.563 (greater than or equal to 0.3 and less than 0.7), it indicates that there is a certain leakage risk of the current molten aluminum liquid. A small amount of aluminum liquid may penetrate into the barrier material, and the energy release rate is at a medium level. If timely intervention is not taken, the leakage range may expand or the barrier material may fail locally, causing damage to equipment within a certain range or production interruption. Therefore, it is determined to be a medium risk level.

[0050] In step 103d, when the leakage risk index is higher than the second critical value or the energy release rate exceeds the safety margin, a high risk level is determined. Specifically, the second critical value is a pre-set higher risk limit, such as 0.7, and the safety margin is the maximum allowable value of the energy release rate, such as 300,000 watts. If the leakage risk index calculated in step 103a is 0.8 (greater than or equal to 0.7) or the energy release rate is 320,000 watts (exceeding the safety margin of 300,000 watts), it indicates that the current molten aluminum leakage risk is high, and significant leakage may have occurred. The aluminum liquid penetration depth is close to or exceeds the safety threshold, and the energy release rate is high. This is very likely to cause serious safety accidents such as large-scale failure of barrier materials, violent vaporization of cooling water, or even explosion, resulting in significant equipment losses, casualties, or long-term production interruptions. Therefore, the high risk level is determined.

[0051] Step 104: Map the preset critical failure temperature threshold and the isolation time threshold according to the risk level, and generate dynamic positioning parameters including the target area coordinates, the failure temperature warning value and the response time window. Specifically, the following steps are performed: a correspondence table between the risk level, the critical failure temperature threshold and the isolation time threshold is pre-established. A low risk level corresponds to a higher critical failure temperature threshold, such as 600 degrees Celsius and a longer isolation time threshold, such as 30 minutes, which means that the material can remain stable below 600 degrees Celsius and there is 30 minutes to take response measures; a medium risk level corresponds to a medium threshold, such as 550 degrees Celsius and 20 minutes, which means that the material is stable below 550 degrees Celsius and a response is required within 20 minutes; a high risk level corresponds to a medium threshold, such as 550 degrees Celsius and 20 minutes, which means that the material is stable below 550 degrees Celsius and a response is required within 20 minutes. For a lower critical failure temperature threshold, such as 500°C, and a shorter holdoff time threshold, such as 10 minutes, the material is stable below 500°C and requires emergency intervention within 10 minutes. The geometric center coordinates of the heat-affected zone (e.g., (2 meters, 3 meters)) are obtained from step 101e and used as the target area coordinates, representing the location with the highest leakage risk. Based on the current risk level, such as medium, the critical failure temperature threshold (550°C) is extracted from the correspondence table as the failure temperature warning value, alerting the user that the material is at risk of failure when the temperature reaches 550°C. The holdoff time threshold (20 minutes) is extracted as the response time window, indicating that emergency response measures must be initiated within 20 minutes. The target area coordinates, failure temperature warning value, response time window, and current risk level are integrated to form dynamic positioning parameters.

[0052] This embodiment, through seepage analysis of the material pore structure and heat transfer calculation of porous media, can accurately grasp the penetration law and temperature change characteristics of molten aluminum liquid, providing a reliable basis for risk assessment; dynamically simulate the flow path and contact angle changes, so that the prediction of parameters such as penetration depth is more in line with the actual situation, and the accuracy of the analysis is improved; combine the cooling water adsorption rate to calculate the contact area and energy release rate, so that the risk level division is more comprehensive and can effectively distinguish different degrees of leakage risks; map the critical failure temperature and blocking time according to the risk level, so that the output dynamic positioning parameters are targeted. The entire process ensures the rigor of the analysis results through multi-parameter integration and dynamic correction.

[0053] like Figure 2 As shown, in another preferred embodiment of the present invention, when a leakage event is detected according to dynamic positioning parameters, a multi-level response strategy is automatically triggered, which may include:

[0054] In an embodiment of the present invention, step 200, when the leakage risk level in the dynamic positioning parameters is ≥ medium risk, the corrosion-resistant ejection device is activated, and a porous ceramic barrier layer is directionally laid between the leakage point and the cooling water pipe according to the target area coordinates. Specifically, the step 200 includes: extracting the leakage risk level from the dynamic positioning parameters, and if the level is medium risk or high risk, immediately triggering the start instruction of the corrosion-resistant ejection device, first obtaining the target area coordinates in the dynamic positioning parameters, for example, the target area coordinates are (3 meters, 4 meters, 0.5 meters), and simultaneously determining the actual position coordinates of the leakage point, such as (3.2 meters, 4.1 meters, 0.6 meters) and The outer wall position coordinates of the cooling water pipe, such as (2.8 meters, 3.9 meters, 0.4 meters), calculate the shortest path distance between the leak point and the cooling water pipe, that is, first calculate the distance between the two on the horizontal plane (XY axis), subtract the X-axis coordinate of the cooling water pipe from the X-axis coordinate of the leak point to obtain the X-axis difference; subtract the Y-axis coordinate of the cooling water pipe from the Y-axis coordinate of the leak point to obtain the Y-axis difference; add the square of the X-axis difference to the square of the Y-axis difference to obtain the square sum; then calculate the square root of the square sum to obtain the horizontal distance; then calculate the distance between the two in the vertical direction (Z axis), subtract the Z-axis coordinate of the cooling water pipe from the Z-axis coordinate of the leak point The Z-axis coordinate of the path is calculated to obtain the vertical distance. Finally, the square of the horizontal distance is added to the square of the vertical distance to obtain the total square sum. The square root of the total square sum is then calculated to obtain the shortest path distance between the leak point and the cooling water pipe. With this shortest path as the center, the laying range of the porous ceramic barrier layer is determined. That is, the laying width needs to cover 0.3 meters on both sides of the path. Therefore, the total width is the coverage width on one side of the path plus the coverage width on the other side, that is, 0.3 meters plus 0.3 meters equals 0.6 meters. The laying length extends from the edge of the leak point to the outer wall of the cooling water pipe. First, calculate the distance from the edge of the leak point to the starting point of the path and take 0. 1 meter, ensuring coverage around the leak point, plus the shortest path distance to obtain the total length. Assuming that the final paving range is determined to be a rectangular area with a length of 0.59 meters and a width of 0.6 meters, it is ensured that the channel for the molten aluminum liquid to flow to the cooling water pipe can be completely blocked; the corrosion-resistant ejection device pre-stores the cut porous ceramic barrier layer. The size of each barrier layer is preset according to common leakage situations, such as 1.5 meters in length, 0.8 meters in width, and 0.05 meters in thickness. There are multiple ejection channels in the device, each corresponding to a different direction angle, according to the coordinates of the target area and the installation position coordinates of the device itself, such as (2.5 meters, 3.5 meters, 0 meters), calculate the ejection angle, that is, first calculate the X-axis difference and Y-axis difference between the device and the center of the target area in the horizontal plane, and construct a horizontal rectangular coordinate system with the installation position of the device as the origin. The X-axis difference is the adjacent side and the Y-axis difference is the opposite side. The horizontal angle is calculated by the tangent function (the tangent value is equal to the opposite side divided by the adjacent side) to ensure that the ejection direction accurately points to the horizontal center of the target paving area; at the same time, calculate the vertical height difference between the center of the target area and the device, as well as the horizontal distance from the device to the center of the target area, with the horizontal distance as the adjacent side and the vertical height difference as the opposite side, and calculate the vertical angle by the tangent function (the tangent value is equal to the opposite side divided by the adjacent side) to avoid the barrier layer from being too high or too low and deviating from the target area when laid; after the ejection device is started, the high-pressure gas propulsion mechanism in the device ejects the porous ceramic barrier layer at a speed of 2 meters per second according to the calculated horizontal and vertical angles, ensuring that the barrier layer falls smoothly on a 0.59 meter long and 0.6 meter wide area. The target paving area; after paving is completed, the device's built-in position detection component measures the deviation between the edge of the barrier layer and the edge of the target area. If the deviation is less than 0.1 meter, the paving is deemed qualified. If the deviation is greater than 0.1 meter, the fine-tuning function is activated, and a small push mechanism pushes the barrier layer 0.02 meters at a time. After pushing, the deviation is measured again until the deviation is less than 0.1 meter, completing the directional paving. After the ejection device is activated, the high-pressure gas propulsion mechanism within the device ejects the porous ceramic barrier layer at a calculated angle. The ejection speed is controlled at 2 meters per second to ensure that the barrier layer lands smoothly in the target paving area. After paving is completed, the device's built-in position detection component confirms that the barrier layer completely covers the preset paving range. If there are any uncovered areas, such as an edge deviation exceeding 0.1 meter, the device's fine-tuning function is activated, and the small push mechanism adjusts the barrier layer to the correct position, finally completing the directional paving between the leak point and the cooling water pipe.

[0055] Step 201: After laying the porous ceramic barrier layer, monitor the temperature gradient distribution in real time. If the local temperature reaches the critical failure temperature threshold, dynamically adjust the hydrophilic group distribution density and pore size gradient of the barrier layer to optimize the water absorption rate and thermal stability. Specifically, after the porous ceramic barrier layer is laid, immediately start the micro temperature sensors distributed on the surface of the barrier layer. The sensors are arranged at a spacing of 0.2 meters to form a grid-like monitoring array. The temperature data of each sensor position is collected every 5 seconds. Based on the collected temperature data, the temperature difference between each adjacent sensor is calculated and then divided by the spacing between adjacent sensors, that is, 0.2 meters, to obtain the temperature gradient distribution data of each position; extract the critical failure temperature threshold from the dynamic positioning parameters. For example, the critical failure temperature threshold is 600 degrees Celsius. The real-time temperature of each monitoring position is continuously compared with the threshold. If the temperature of a monitoring position reaches 600 degrees Celsius, it means that the local area is close to the failure state of the barrier layer, and the dynamic adjustment mechanism needs to be activated. First, the water absorption rate of the local area is analyzed. Through the moisture sensor arranged inside the barrier layer, the amount of cooling water absorbed by the area per unit time is collected. For example, 0.02 kilograms of cooling water is absorbed in 10 seconds, and the current water absorption rate is obtained by dividing it by the time and the volume of the area, such as 0.001 cubic meter. If the water absorption rate is lower than the preset optimal water absorption rate, such as 0.03 kilograms per second per cubic meter, the distribution density of the hydrophilic groups of the barrier layer is adjusted, that is, the barrier layer is closed. The chemical control component preset within the layer is an array of embedded micro sustained-release capsules. Each capsule has a diameter of 0.002 meters and is evenly distributed within the porous ceramic barrier layer at a spacing of 0.05 meters by 0.05 meters. The capsules are sealed to store an organosilane regulator containing a hydroxyl hydrophilic group. Each capsule is equipped with an electrically controlled micro valve on top. The valve is connected to the temperature-humidity linkage control circuit within the barrier layer, releasing the regulator containing the hydrophilic group to the local area. The amount of regulator released is calculated based on the difference in water absorption rate. Specifically, the current water absorption rate of the local area measured by the moisture sensor is first obtained, such as 0.02 kilograms per second per cubic meter, and then the difference between the current water absorption rate and the optimal water absorption rate, i.e., 0. 0.03 kilograms per second per cubic meter minus 0.02 kilograms per second per cubic meter equals 0.01 kilograms per second per cubic meter. According to the corresponding relationship calibrated in the early stage, for every 0.01 kilograms per second per cubic meter increase in water absorption rate, 0.5 milliliters of regulator must be released into the barrier layer area per unit volume (1 cubic meter). The regulator release amount of the local area is determined. If the volume of the local area is 0.0008 cubic meters (0.4 meters long × 0.4 meters wide × 0.005 meters thick), the product of the unit volume release and the area volume is calculated first, that is, 0.5 milliliters per cubic meter multiplied by 0.0008 cubic meters. Then, the control circuit activates the corresponding number of micro sustained-release capsule valves in the area, and each capsule can release 0.To calculate the amount of regulator needed to be 0.001 ml, four capsules must be activated. Once the valve is opened, the organosilane regulator in the capsules is slowly released to the ceramic pore surface through osmosis, chemically bonding with the oxides on the pore walls. This increases the density of hydrophilic groups and thus elevates the water absorption rate in that area to the optimal range. A micromechanical adjustment structure within the barrier layer changes the pore diameter. For areas where the temperature reaches the critical failure threshold, the pore diameter is adjusted from the original 0.2 mm to 0.15 mm. This reduction in pore diameter increases the flow resistance of the molten aluminum and improves the adsorption capacity for cooling water. During adjustment, the mechanical structure gradually squeezes the pore walls, reducing the pore diameter by 0.01 mm each time. After each adjustment, wait three seconds and monitor the temperature and water absorption rate again until the temperature drops below the critical failure threshold and the water absorption rate reaches the optimal range, completing dynamic adjustment.

[0056] Step 202: When the leakage risk level is high or the heat diffusion radius exceeds the limit, the cooling water pressure control unit is linked to generate a high-pressure nitrogen isolation air curtain to form a dynamic physical barrier to isolate the molten aluminum liquid from the cooling water. Specifically, the process includes: continuously monitoring the leakage risk level and heat diffusion radius data in the dynamic positioning parameters. If the risk level becomes high or the heat diffusion radius exceeds the preset safety radius, such as 0.8 meters, a linkage instruction is immediately sent to the cooling water pressure control unit. The coordinates of the target area, such as (3 meters, 4 meters, 0.5 meters) and the position of the cooling water pipeline are first obtained. For example, if the pipeline is laid along the Y-axis, the coordinates of the center of the outer wall are (2.5 meters, 4 meters, 0.5 meters). The generation range of the high-pressure nitrogen isolation air curtain is determined. The air curtain needs to cover the entire heat diffusion area. And a continuous barrier is formed on the path where the molten aluminum liquid may flow. The coverage width of the air curtain is set to 1.5 times the heat diffusion radius. For example, when the heat diffusion radius is 0.9 meters, the air curtain width is 0.9 meters multiplied by 1.5, which is equal to 1.35 meters, to ensure that the molten aluminum liquid can be completely blocked from spreading. The cooling water pressure control unit is equipped with a high-pressure nitrogen storage tank and multiple air curtain nozzles. The nozzles are evenly distributed around the outer wall of the cooling water pipe. The spacing between each nozzle is 0.2 meters, that is, the distance between two adjacent nozzles in the circumferential direction or axial direction of the outer wall of the pipe is 0.2 meters. According to the coordinates of the target area, the opening number and spray angle of each nozzle are calculated. Specifically, the projection range of the air curtain area to be covered on the horizontal plane is first determined. With the coordinates of the target area as the center, the air curtain covers The width is 1.35 meters, so the X-axis starting coordinate of the projection range is the X-axis coordinate of the target area minus half of the air curtain coverage width, that is, 3 meters minus (1.35 meters divided by 2) equals 3 meters minus 0.675 meters equals 2.325 meters; the X-axis ending coordinate is the X-axis coordinate of the target area plus half of the air curtain coverage width, that is, 3 meters plus 0.675 meters equals 3.675 meters; the Y-axis range is consistent with the Y-axis coordinate of the target area, which is 4 meters; then corresponding to the position of each nozzle, assuming that the nozzles are arranged along the X-axis direction of the outer wall of the cooling water pipe, the X-axis coordinate of the first nozzle is 2.1 meters, and the X-axis coordinate of each subsequent nozzle increases by 0.2 meters (because the spacing is 0.2 meters), that is, the second nozzle is 2.1 meters plus 0.2 meters equals 2.3 meters, The third one is 2.3 meters plus 0.2 meters, which equals 2.5 meters. The fourth one is 2.5 meters plus 0.2 meters, which equals 2.7 meters. The fifth one is 2.7 meters plus 0.2 meters, which equals 2.9 meters. The sixth one is 2.9 meters plus 0.2 meters, which equals 3.1 meters. The seventh one is 3.1 meters plus 0.2 meters, which equals 3.3 meters. The eighth one is 3.3 meters plus 0.2 meters, which equals 3.5 meters. The ninth one is 3.5 meters plus 0.2 meters, which equals 3.7 meters. The tenth one is 3.7 meters plus 0.2 meters, which equals 3.9 meters. The nozzles with X-axis coordinates within the projection range (2.325 meters to 3.675 meters) are selected, namely the fifth one (2.9 meters) to the eighth one (3.5 meters). However, according to actual coverage requirements, the projection range needs to be expanded to 1.8 meters to 3.675 meters on the X axis.8 meters, the nozzles that meet the conditions are the fifth (2.9 meters) to the twelfth (2.9 meters plus (12 minus 5) multiplied by 0.2 meters equals 4.3 meters, adjust the projection range to 2.8 meters to 4.3 meters), a total of 8 nozzles, to ensure that the projection area is completely covered by the nozzles; so that the nitrogen ejected from the nozzles can form a continuous air curtain in the target area, first determine the relative position of each nozzle and the center of the target area, for the nozzle located on the left side of the cooling water pipe, that is, the nozzle X-axis coordinate is less than the target area X-axis coordinate by 3 meters, such as the fifth to eighth nozzles, calculate the horizontal and vertical offsets between the nozzle and the center of the target area, the horizontal offset is the target area X-axis coordinate minus the nozzle X-axis coordinate, such as the horizontal offset of the fifth nozzle is 3 Meters minus 2.9 meters equals 0.1 meters. The vertical offset is the target area's Z-axis coordinate minus the nozzle's Z-axis coordinate. Assuming the nozzle's Z-axis coordinate is 0.5 meters and the target area's Z-axis coordinate is also 0.5 meters, the vertical offset is 0.5 meters minus 0.5 meters, which equals 0. Next, the spray angle is determined using trigonometric functions based on the horizontal and vertical offsets. First, the spray angle is defined. A right triangle is formed with the nozzle's location as the origin, the horizontal direction (X-axis direction) as the adjacent side, the nozzle's spray direction toward the target area as the hypotenuse, and the vertical direction (Z-axis direction) as the opposite side. The spray angle is the angle between the hypotenuse and the horizontal adjacent side. For the nozzle located on the left side of the cooling water pipe, such as the fifth nozzle, the X-axis coordinate is 2. 9 meters, first calculate the horizontal offset, that is, the X-axis coordinate of the target area is 3 meters minus the X-axis coordinate of the nozzle is 2.9 meters, and the horizontal offset is 0.1 meters; the vertical offset has been calculated to be 0 meters. In a right triangle, the tangent function is defined as the length of the opposite side divided by the length of the adjacent side, that is, tan (injection angle) = vertical offset ÷ horizontal offset. Substitute the vertical offset of 0 meters and the horizontal offset of 0.1 meters to obtain tan (injection angle) = 0 meters ÷ 0.1 meters = 0. It can be seen from the trigonometric function table that when the tangent value is 0, the corresponding angle is 0 degrees. However, combined with actual needs, the nitrogen needs to cover the target area upward to avoid insufficient air curtain height caused by only spraying in the horizontal direction. The vertical upward angle needs to be superimposed on the horizontal angle. Therefore, in addition to the horizontal angle of 0 degrees corresponding to the horizontal offset, an additional vertical angle of 30 degrees is set, resulting in a spray angle of 30 degrees upward to the right. This means that the nozzle's spray direction and the horizontal direction (positive X-axis) are at an angle of 30 degrees. The horizontal component ensures that the nitrogen gas reaches the right X-axis position of the target area, while the vertical component ensures that the nitrogen gas remains at a sufficient height to intersect with the nitrogen gas from other nozzles in the target area, pointing upward toward the target area. For nozzles located on the right side of the cooling water pipe, that is, nozzles with X-axis coordinates greater than 3 meters, such as the ninth through twelfth nozzles, the horizontal offset is the nozzle's X-axis coordinate minus the target area's X-axis coordinate. For example, the horizontal offset for the ninth nozzle is 3.7 meters minus 3 meters, which equals 0.7 meters, vertical offset is 0, similarly adjust the spray angle of the nozzle on the right to 30 degrees to the upper left, ensuring that the nitrogen sprayed from all nozzles intersects in the target area to form a complete air curtain.

[0057] The control unit adjusts the output pressure of nitrogen, sets the initial pressure to 3 MPa, starts the nozzle to spray nitrogen, and forms a high-pressure nitrogen isolation air curtain; at the same time, the actual pressure of the air curtain is measured in real time through the air curtain pressure monitoring component. If the actual pressure is lower than 2.8 MPa, it means that the air curtain density is insufficient and there may be a loophole. At this time, the control unit increases the nitrogen output and raises the pressure to 3.2 MPa; if the actual pressure is higher than 3.2 MPa, in order to avoid excessive diffusion of the air curtain and waste of nitrogen, the pressure is reduced to 2.9 MPa, and the air curtain pressure is always maintained between 2.8 and 3.2 MPa, forming a stable dynamic physical barrier to prevent the molten aluminum liquid from contacting the cooling water.

[0058] In this embodiment, when the leakage risk reaches medium or above, a porous ceramic barrier layer is laid in a directional manner to quickly establish a physical barrier between the leak point and the cooling water pipe, effectively delaying or even blocking the flow of molten aluminum into the cooling water pipe, preventing dangerous contact between the two. Real-time monitoring of the barrier layer temperature and dynamic adjustment of the hydrophilic groups and pore size gradient optimizes the barrier layer's water absorption capacity and thermal stability based on actual temperature changes, ensuring that the barrier layer maintains effective performance even when approaching the critical failure temperature, thereby extending the barrier effect. For high-risk leaks or excessive thermal diffusion, a high-pressure nitrogen isolation air curtain forms a dynamic and continuous physical barrier. Compared to a fixed barrier layer, the air curtain has a more flexible coverage area and can be adjusted in real time based on the thermal diffusion range, more comprehensively isolating the molten aluminum from the cooling water and reducing the risk of violent reactions. The entire multi-level response strategy requires no human intervention and is executed automatically from device activation to completion. This allows for rapid and targeted measures at different stages of a leak event, improving the timeliness and accuracy of emergency response and minimizing safety hazards and losses caused by the leak.

[0059] In a preferred embodiment of the present invention, the water absorption capacity, temperature gradient distribution, and structural integrity data of the material are monitored in real time, and the thermal decomposition characteristic parameters of the surface polymer coating are collected. The measured water absorption rate is compared with a preset water absorption rate threshold to generate a material modification parameter set, which may include:

[0060] In an embodiment of the present invention, step 300 calculates the measured water absorption rate based on the real-time monitored water absorption capacity data, compares the measured water absorption rate with a preset water absorption rate threshold, and generates a water absorption rate deviation coefficient; identifies the local overheating area based on the temperature gradient distribution data, and calculates the thermal stress deformation coefficient in combination with the microcrack growth rate in the structural integrity data, specifically including: extracting the water absorption capacity of the material from the real-time monitoring data, for example, the amount of cooling water absorbed by the material within 10 minutes is 0.5 kg, and the total mass of the material involved in water absorption during this time period is recorded as 2 kg. When calculating the measured water absorption rate, the amount of cooling water absorbed is divided by the total mass of the material, that is, 0.5 kg divided by 2 kg is equal to 0.25, that is, the measured water absorption rate is 25%; obtains the preset water absorption rate threshold, for example, the preset water absorption rate threshold is 30%, and when calculating the water absorption rate deviation coefficient, the preset water absorption rate threshold is subtracted from the measured water absorption rate, and the difference is 25% minus 30% equals -5%. , and then divide this difference by the preset water absorption threshold, that is, -5% divided by 30% is approximately equal to -0.17. This result is the water absorption deviation coefficient. A negative coefficient indicates that the actual water absorption capacity is lower than the preset standard; identify local overheating areas based on temperature gradient distribution data, first collect temperature data at various locations of the material, such as dividing the surface of the material into multiple 10 cm by 10 cm monitoring units, and the temperature of each unit is 50 degrees Celsius, 48 ​​degrees Celsius, 65 degrees Celsius, etc., calculate the temperature difference between adjacent units, such as the temperature difference between a 65-degree Celsius unit and the adjacent 50-degree Celsius unit is 15 degrees Celsius, and then divide it by the distance between the two units, 0.1 meter, to obtain a temperature gradient of 150 degrees Celsius per meter. Set the temperature gradient threshold to 100 degrees Celsius per meter, and mark the area where the unit with a temperature gradient exceeding the threshold is located as a local overheating area. For example, the area where the unit with the above-mentioned temperature gradient of 150 degrees Celsius per meter is located is a local overheating area.

[0061] Extract the microcrack growth rate of the local overheating area from the structural integrity data. For example, if the microcrack is monitored to grow from 0.1 mm to 0.3 mm in 1 hour, the growth length is 0.3 mm minus 0.1 mm, which equals 0.2 mm. Divide the growth length by the time of 1 hour to get the microcrack growth rate of 0.2 mm per hour. When calculating the thermal stress deformation coefficient, first subtract the material's normal temperature of 25 degrees Celsius from the highest temperature of the local overheating area of ​​65 degrees Celsius to get a temperature difference of 40 degrees Celsius. Then multiply this temperature difference by the microcrack growth rate of 0.2 mm per hour to get 8 mm degrees Celsius per hour. Finally, divide this result by the thermal expansion coefficient of the material. For example, , which is 8 divided by , the value obtained is the thermal stress deformation coefficient.

[0062] Step 301, obtain the thermal weight loss rate through the thermal decomposition characteristic parameters of the polymer coating, and associate the water absorption rate deviation coefficient with the thermal stress deformation coefficient, specifically including: extracting the mass change data of the coating at different temperatures from the collected thermal decomposition characteristic parameters of the polymer coating, for example, the initial mass of the coating is 10 grams, the mass at 50 degrees Celsius is 9.9 grams, and the mass at 60 degrees Celsius is 9.7 grams. When calculating the thermal weight loss rate, first calculate the mass loss within the temperature change range of 10 degrees Celsius, that is, 9.9 grams minus 9.7 grams equals 0.2 grams, and then divide the mass loss by the temperature change range of 10 degrees Celsius to obtain the weight loss per unit temperature of 0. 0.02 grams per degree Celsius, and then multiply this value by the temperature change rate of the material environment, for example, if the temperature rises by 5 degrees Celsius per hour, that is, 0.02 grams per degree Celsius multiplied by 5 degrees Celsius per hour equals 0.1 grams per hour, which is the thermal weight loss rate; when correlating the water absorption rate deviation coefficient with the thermal stress deformation coefficient, first convert the water absorption rate deviation coefficient, such as -0.17, to an absolute value of 0.17, and then multiply this absolute value by the thermal stress deformation coefficient. Assuming that the thermal stress deformation coefficient is 1000, the first value is 170, and then multiply the thermal weight loss rate of 0.1 grams per hour by the inverse of the thermal expansion coefficient of the material. Assuming that the inverse of the thermal expansion coefficient is 1÷( / degrees Celsius), the physical meaning is the temperature change required for the material to produce unit linear expansion, and the second value is obtained, which represents the linkage between the thermal weight loss rate and the thermal expansion characteristics of the material. Finally, the two values ​​are added together, that is, the first value plus the second value to obtain a sum. The correlation relationship between the three parameters is established through this sum, reflecting the degree of mutual influence between the material water absorption capacity deviation, thermal stress deformation and thermal decomposition of the coating.

[0063] Step 302 generates a material modification parameter set including a hydrophilic group density adjustment amount, an aperture gradient optimization parameter, and a coating reinforcement thickness based on the difference between the thermal weight loss rate and a preset decomposition threshold, superimposed with a water absorption rate deviation coefficient and a thermal stress deformation coefficient. Specifically, the method includes: obtaining a preset thermal decomposition threshold, for example, the preset thermal weight loss rate threshold is 0.05 g / h, and calculating the difference between the thermal weight loss rate and the threshold, i.e., 0.1 g / h minus 0.05 g / h equals 0.05 g / h; when calculating the hydrophilic group density adjustment amount, first multiply the above difference 0.05 g / h by 1000 to obtain 50, and then multiply it by the absolute value of the water absorption rate deviation coefficient 0.17, i.e., 50×0.17=8.5, which is the hydrophilic group density adjustment amount, indicating that the hydrophilic group density needs to be increased by 8.5 units to improve the water absorption capacity and offset the negative impact of thermal decomposition; when calculating the aperture gradient optimization parameter, first divide the thermal stress deformation coefficient, assuming it is 1000, by the thermal expansion coefficient of the material (assuming it is 0.05). per degree Celsius), we get 1000÷( ), get a result, and then divide the result by (Order of magnitude adjustment coefficient) The intermediate value is assumed to be 0.8. The value obtained by adding the intermediate value to the thermal weight loss rate difference of 0.05 is the pore gradient optimization parameter, which means that the pore gradient of the material needs to be adjusted according to this ratio to reduce the amplitude of the pore diameter change to enhance the structural stability; when calculating the coating reinforcement thickness, the superimposed effect of the thermal weight loss rate and the thermal stress deformation is comprehensively considered. First, the thermal weight loss rate of 0.1 grams per hour is multiplied by the conversion coefficient 2 to obtain 0.2 mm; then it is added to the intermediate value of 0.8 obtained in the calculation process of the pore gradient optimization parameter, that is, 0.2+0.8=1.0. This value is the coating reinforcement thickness, which means that the thickness of the original coating needs to be increased by 1.0 mm to resist the damage caused by thermal decomposition and stress deformation; the hydrophilic group density adjustment amount of 8.5 units, the pore gradient optimization parameter, and the coating reinforcement thickness of 1.0 mm are sorted together to form a material modification parameter set.

[0064] This embodiment, by real-time monitoring of various performance parameters of the material and performing detailed calculations, can accurately grasp the material's water absorption capacity, temperature distribution, and structural integrity status, provide data support for material modification, and ensure that the modification measures are targeted; the generated water absorption rate deviation coefficient and thermal stress deformation coefficient can clearly reflect the difference between the actual performance of the material and the preset standard and the degree of temperature influence, helping to clarify the modification direction; by correlating the thermal weight loss rate with other parameters, the thermal stability of the polymer coating can be comprehensively evaluated, avoiding the one-sidedness caused by a single parameter evaluation, and making the material modification consideration more comprehensive; the final generated material modification parameter set, including specific adjustment values ​​for the hydrophilic group density pore size gradient and coating thickness, can directly guide the material modification operation and improve the modification efficiency and effect.

[0065] In a preferred embodiment of the present invention, the positioning data and feedback information of each edge computing node are integrated, the data collection frequency, barrier resource allocation and emergency response priority are dynamically adjusted, and closed-loop collaborative optimization of porous ceramic material performance and barrier efficiency is achieved through a distributed communication network, which may include:

[0066] In an embodiment of the present invention, step 400 aggregates the dynamic positioning parameters and material modification parameter sets of each edge node, and generates regional thermal hazard situation data based on the leakage risk level distribution, specifically including: the central control unit collects the dynamic positioning parameters transmitted by all edge computing nodes, and the edge nodes are high-temperature resistant monitoring terminals deployed around the electrolytic cell, a total of 8, corresponding to the 8 monitoring sub-areas of the No. 1 to No. 4 refractory brick areas at the bottom of the electrolytic cell, the A / B section areas of the cooling water pipes on both sides, the feed port area, and the aluminum outlet area. Each edge node transmits data to the central control unit through wired transmission to avoid high temperature interference with the wireless signal. The data contains the target area coordinates of the sub-area, accurate to centimeters, such as No. 1 The target coordinates of the refractory brick area are (2.1 meters, 3.3 meters, 0.6 meters), the failure temperature warning value, such as 500 degrees Celsius for area 1, the response time window, such as 10 minutes for area 1, and the leakage risk level are determined in real time by the node's built-in algorithm. When determining, the real-time temperature of the sub-area is first collected through the temperature sensor carried by the node, the impact pressure of the molten aluminum liquid on the barrier layer is collected through the pressure sensor, and the actual penetration depth of the aluminum liquid in the barrier layer is collected through the penetration sensor. These three data are then compared with the safety thresholds preset in the node; the temperature safety threshold is set to 450 degrees Celsius, the pressure safety threshold is set to 0.3 MPa, and the penetration depth safety threshold is set to 0.5 meters; if the real-time temperature is If the temperature does not exceed 450 degrees Celsius, the impact pressure does not exceed 0.3 MPa, and the penetration depth does not exceed 0.5 meters, and the three data are all within the safety range, it is judged to be low risk; if only one of the three data exceeds the corresponding safety threshold, it is judged to be medium risk; if two or more of the three data exceed the corresponding safety threshold, it is judged to be high risk. For example, the real-time temperature collected by node 1 (No. 1 refractory brick area) is 480 degrees Celsius, the impact pressure is 0.35 MPa, and the penetration depth is 0.6 meters. Two of the three data exceed the safety threshold, and the risk level is judged to be high risk; the real-time temperature collected by node 2 (section A of the cooling water pipe) is 430 degrees Celsius, the impact pressure is 0.32 MPa, and the penetration depth is At 0.45 meters, only one of the three data points exceeded the safety threshold, resulting in a medium risk assessment. The real-time temperatures collected at nodes 3 to 8 (the remaining six sub-areas) were all below 420 degrees Celsius, the impact pressures below 0.28 MPa, and the penetration depths below 0.4 meters. None of these three data points exceeded the safety threshold, resulting in a low risk assessment. Simultaneously, the central control unit receives the material modification parameter set for each sub-area, generated by the material monitoring module at each node. This parameter set includes the hydrophilic group density adjustment (units per square micron), the pore size gradient optimization parameter (representing the pore diameter change ratio), and the coating reinforcement thickness (units in millimeters). For example, due to severe thermal decomposition in sub-area 1, the hydrophilic group density adjustment is 8.5 / square micron, the node 2 sub-area is 6 / square micron, and the node 3 to 8 sub-areas are all 3 / square micron due to low risk. The central control unit organizes the dynamic positioning parameters and material modification parameters into a regional parameter summary table in Excel format according to the format of "sub-area number, coordinate range, parameter type, and value" to ensure that each data can be traced back to the corresponding edge node and sub-area; when generating regional thermal hazard situation data based on the leakage risk level distribution, the central control unit first divides the entire monitoring area (electrolytic cell and the surrounding 3-meter range) into 24 grid units of 1 meter × 1 meter (6 columns horizontally and 4 rows vertically, column number AF, row number 1-4), and each grid unit is marked with the corresponding sub-area affiliation, such as grid A1 corresponds to the No. 1 refractory brick area of ​​node 1, grid B2 corresponds to the cooling water pipe section A area of ​​node 2, and then according to risk, etc. According to the level labeling rule, each grid cell is color-coded: high-risk grids (A1) are colored red, medium-risk grids (B2) are colored orange, and low-risk grids (the remaining 22) are colored blue. The number of grids at each risk level is counted: 1 high-risk, 1 medium-risk, and 22 low-risk, for a total of 24 grid cells (1 plus 1 plus 22 equals 24, verifying that no grid cells are missing). The proportion of each risk level is calculated: the high-risk proportion is calculated by dividing the number of high-risk grids by the total number of grid cells, the medium-risk proportion is calculated by dividing the number of medium-risk grids by the total number of grid cells, and the low-risk proportion is calculated by dividing the number of low-risk grids by the total number of grid cells. Finally, the "grid coordinates, color labels, risk proportions, and corresponding sub-region material modification parameters" are integrated to generate regional thermal hazard situation data. This situation data must be labeled with the specific risk level of each grid, the target area coordinates, and the required material adjustment amount.

[0067] Step 401, according to the risk level gradient change in the regional thermal hazard situation data, dynamically adjust the data collection frequency of the high-risk area and the low-risk area, specifically including: the central control unit first analyzes the risk level gradient distribution in the regional thermal hazard situation data, and divides the 8 sub-areas into three gradient areas according to the risk level, namely, the high-risk gradient area (only the No. 1 refractory brick area corresponding to node 1), the medium-risk gradient area (only the cooling water pipe section A area corresponding to node 2), and the low-risk gradient area (the remaining 6 sub-areas corresponding to nodes 3 to 8); set the initial data collection frequency reference value to 10 seconds / time (Applicable to conventional risk areas), dynamically adjust according to the risk level gradient coefficient, that is, the acquisition frequency coefficient of the high-risk gradient area is 0.5 (i.e., the reference value × 0.5), and the adjusted acquisition frequency = 10 seconds × 0.5 = 5 seconds / time, ensuring high-density monitoring of high-risk areas; the medium-risk gradient area maintains the reference frequency of 10 seconds / time, balancing monitoring accuracy and system load; the acquisition frequency coefficient of the low-risk gradient area is 2 (i.e., the reference value × 2), and the adjusted acquisition frequency = 10 seconds × 2 = 20 seconds / time, reducing the data transmission pressure in the low-risk area; the central control unit sends data to each edge node through the industrial bus The frequency adjustment instruction is sent, which includes the node number, the new collection period (accurate to milliseconds), and the effective time (immediate effect). After receiving the instruction, each edge node updates the sampling timer parameters in the local embedded system. For example, node 1 changes the original 10-second timer to a 5-second timer, node 2 keeps the 10-second timer unchanged, and nodes 3 to 8 change the timer to 20 seconds. To verify the adjustment effect, the central control unit counts the actual sampling interval of each node within 1 minute after the instruction is sent: the sampling interval of high-risk node 1 should be within the range of 4.8-5.2 seconds (with an error of ±0.2 seconds allowed), and the sampling interval of medium-risk node 1 should be within the range of 5.2 seconds (with an error of ±0.2 seconds allowed). Node 2 is within the range of 9.8-10.2 seconds, and low-risk nodes 3 to 8 are within the range of 19.8-20.2 seconds; if the sampling interval of a node exceeds the allowable range for three consecutive times (such as a 6-second interval in node 1), the central control unit will send a secondary calibration instruction to force synchronization of the node clock and the system master clock (accuracy of 1 millisecond); at the same time, the data caching strategy is dynamically adjusted: nodes in high-risk areas enable a double cache mechanism (primary cache + backup cache) to prevent data loss; nodes in medium-risk areas maintain a single cache; nodes in low-risk areas can enable cache compression (compression rate 50%) to reduce storage space usage.

[0068] Step 402, after adjusting the acquisition frequency, obtain the penetration depth safety threshold and thermal diffusion radius data in real time, and allocate the porous ceramic barrier layer reserve and the nitrogen isolation air curtain coverage range according to the risk level priority in the situation data, specifically including: each edge node transmits data to the central control unit at the adjusted frequency, that is, the high-risk gradient area node sends the penetration depth safety threshold once every 5 seconds, which is measured by the node's laser ranging unit, that is, the maximum allowable penetration depth and thermal diffusion radius of the molten aluminum liquid, which is measured by the infrared thermal imager, that is, the circular diffusion radius of the high-temperature area, for example, node 1 transmits data for the penetration depth safety threshold of 0.8 meters and the thermal diffusion radius of 0.9 meters; the medium-risk gradient area node sends once every 10 seconds, such as node 2 transmits the penetration depth safety threshold once every 10 seconds. The safety threshold of penetration depth is 1 meter, and the heat diffusion radius is 0.6 meters. The nodes in the low-risk gradient area send data every 20 seconds. For example, node 4 transmits a penetration depth safety threshold of 1.2 meters and a heat diffusion radius of 0.3 meters. The central control unit allocates the reserve of porous ceramic barrier layer according to the risk level priority. The total reserve is 100 square meters (stored in warehouse No. 3 of the electrolysis workshop, stored in 5 rolls, each roll is 20 square meters). The priority is from high to low: high-risk gradient area > medium-risk gradient area > low-risk gradient area. The allocation of each area is calculated. The heat diffusion area of ​​the high-risk gradient area is calculated first (according to the circular area formula). The heat diffusion radius is 0.9 meters, the diameter = 0.9 meters × 2 = 1.8 meters, and the heat diffusion area = 3.14 × (1.8 meters ÷ 2)² = 3.14×0.81 square meters = 2.5434 square meters. Since high-risk areas require double coverage (to prevent damage to the single-layer barrier layer), the allocation amount = heat diffusion area 2.5434×2, rounded to 5.1 square meters (cut from volume 1); the heat diffusion radius of the medium-risk gradient area is 0.6 meters, diameter = 0.6 meters × 2 = 1.2 meters, heat diffusion area = 3.14×(1.2 meters ÷ 2)² = 3.14×0.36 square meters = 1.1304 square meters, covered at 1.5 times (taking into account both safety and economy), the allocation amount = 1.1304 square meters × 1.5, rounded to 1.7 square meters (cut from the remaining part of volume 1); the heat diffusion radius of the low-risk gradient area is 0.3 meters, diameter = 0.3 meters × 2 = 0.6 m, heat diffusion area = 3.14 × (0.6 m ÷ 2)² = 3.14 × 0.09 m2 = 0.2826 m2, coverage is 1 times, allocation amount = 0.2826 m2 × 1 ≈ 0.28 m2, rounded to 0.3 m2 (cut from roll 2); the remaining 100 m2 - 5.1 m2 - 1.7 m2 - 0.3 m2 = 92.9 m2, as an emergency reserve, stored in the warehouse spare area; when allocating the nitrogen isolation air curtain coverage range, the central control unit sends instructions to the cooling water pressure control unit. The air curtain coverage radius of the high-risk gradient area = the heat diffusion radius 0.9 × 2 = 1.8 m, and the coverage center and target area coordinates (2.1 m, 3.3 m, 0.6 meters), activate the eight air curtain nozzles (numbered 1-8) in the corresponding area; in the medium-risk gradient area, the air curtain coverage radius = the heat diffusion radius of 0.6 meters x 1.5 = 0.9 meters, with the coverage centers at (3.2 meters, 4.1 meters, and 0.5 meters), activate four air curtain nozzles (numbered 9-12); in the low-risk gradient area, the air curtain coverage radius = the heat diffusion radius of 0.3 meters x 1 = 0.3 meters, with the coverage centers at (1.5 meters, 2.2 meters, and 0.4 meters), activate two air curtain nozzles (numbered 13-14), ensuring that the air curtain fully covers the heat diffusion range of each area.

[0069] Step 403: After the barrier resources are allocated, the actual temperature gradient distribution data of the barrier layer is deployed and the heat flux density anomaly values ​​of the local overheating area are extracted. Specifically, the following steps are performed: On-site construction personnel deploy porous ceramic barrier layers in each risk area according to the resource allocation instructions. In the high-risk gradient area, grid A1 lays a 5.1 square meter barrier layer with a thickness of 0.05 meter. In the medium-risk gradient area, grid B2 lays a 1.7 square meter barrier layer. In the low-risk gradient area, grid C3 lays a 0.3 square meter barrier layer. After laying, the edges are fixed with a high-temperature resistant adhesive to prevent displacement. After deployment is completed, the central control unit controls the temperature sensor array in each area to collect data: 20 platinum resistance temperature sensors are arranged on the surface of the barrier layer in the high-risk area at a spacing of 5 cm × 5 cm. The sensor (accuracy ±0.1 degrees Celsius) collects temperature every 5 seconds; 10 sensors are arranged in the medium-risk area, collecting temperature every 10 seconds; 5 sensors are arranged in the low-risk area, collecting temperature every 20 seconds. For example, the 20 temperature data collected in the high-risk area for the first time are 65 degrees Celsius, 64.8 degrees Celsius, 66.2 degrees Celsius, ..., 68 degrees Celsius (a total of 20 values). When calculating the temperature gradient, the data analysis module of the central control unit processes it according to the adjacent sensor pairing rules, and numbers the 20 sensors 1-20 according to the position, and calculates the temperature difference between sensors 1 and 2, 2 and 3, ..., 19 and 20. For example, if the temperature of sensor 1 is 65 degrees Celsius and the coordinates are (2.1 meters, 3.3 meters, 0.6 meters) and the temperature of sensor 2 is 66 .2 degrees Celsius, coordinates (2.15 meters, 3.3 meters, 0.6 meters) as an example, temperature difference = 66.2 degrees Celsius - 65 degrees Celsius = 1.2 degrees Celsius, the distance between the two sensors = 2.15 meters - 2.1 meters = 0.05 meters, temperature gradient = 1.2 degrees Celsius ÷ 0.05 meters = 24 degrees Celsius / meter, according to this method to calculate the temperature gradient of all adjacent sensors, organize them into a table of sensor number, temperature gradient, and corresponding position, and form the actual temperature gradient distribution data of the barrier layer; set the normal range of temperature gradient to 0-30 degrees Celsius / meter, based on the manual calibration of porous ceramic materials, automatically screen out areas with a temperature exceeding 30 degrees Celsius / meter, that is, local overheating areas, such as sensor 15, temperature 72 degrees Celsius and 16, temperature 78 degrees Celsius Temperature difference = 6 degrees Celsius, spacing 0.05 meters, temperature gradient = 6 degrees Celsius ÷ 0.05 meters = 120 degrees Celsius / meter. This area (coordinates (2.3 meters, 3.5 meters, 0.6 meters)) is determined to be a localized overheating area. To calculate the heat flux density for this localized overheating area, first extract the heat conduction rate of this area from step 101a, 400 watts / square meter. Measure the cross-sectional area of ​​the barrier layer, i.e., the overheating area is circular with a diameter of 0.2 meters. The area = 3.14 × (0.2 meters ÷ 2)² = 0.0314 square meters. Heat flux density = heat conduction rate × cross-sectional area = 400 watts / square meter × 0.0314 square meters = 12.56 watts. The normal heat flux density range is set to 0-3 watts, based on the material's thermal load capacity. 12.56 watts exceeds the normal range, indicating an abnormal heat flux value. The data analysis module extracts this abnormal value and the corresponding overheating area coordinates (2.3 meters, 3.5 meters, 0.6 meters) and stores them in the abnormality database.

[0070] Step 404 calculates the thermal stress deformation coefficient based on the heat flux density anomaly value, and superimposes the thermal decomposition characteristic parameters in the material modification parameter set to generate real-time optimization instructions for the hydrophilic group distribution density and pore size gradient. Specifically, the central control unit extracts the maximum heat flux density anomaly value of each local overheating area from the anomaly database. For example, if there are two overheating areas in the high-risk area, the maximum anomaly values ​​are 12.56 watts and 10.8 watts respectively, and 12.56 watts is selected as the basis for calculation; when calculating the thermal stress deformation coefficient, it is performed in three steps, wherein the temperature difference is calculated by the temperature sensor collecting the highest temperature of the overheating area, 78 degrees Celsius, and the material normal temperature, 25 degrees Celsius, and the workshop ambient temperature, the temperature difference = 78 degrees Celsius - 25 degrees Celsius = 53 degrees Celsius; the maximum heat flux density anomaly value is multiplied by the temperature difference, that is, 12.56 watts × 53 degrees Celsius = 665.68 watts・degrees Celsius; the material is porous ceramic, and the thermal expansion coefficient is / degrees Celsius (obtained from the material manual), thermal stress deformation coefficient = 665.68W・degrees Celsius ÷ ( / degrees Celsius); the thermal decomposition characteristic parameter of the region, i.e., the thermal weight loss rate, is extracted from the material modification parameter set and measured by a thermogravimetric analyzer, and the result is 0.12 g / h (data before optimization); when superimposing the thermal decomposition characteristic parameters, the thermal stress deformation coefficient is multiplied by the thermal weight loss rate to obtain the superposition result; the preset threshold of the superposition result is set to Watt・degrees Celsius² g / h, based on historical fault data settings, the current superposition results are as follows If the threshold is exceeded, real-time optimization instructions need to be generated, including adjustment of the distribution density of hydrophilic groups, that is, the calculation adjustment increment = superposition result ÷ ( ) (increment coefficient), assuming the result is rounded to 7 units, the original hydrophilic group density adjustment is 8.5 units, and the adjusted value is 8.5 units + 7 units = 15.5 units (units / square micron); the pore gradient optimization parameter is adjusted, that is, the original parameter is 0.85, according to "the superposition result exceeds the threshold value by 1×10 6 , the parameter plus 0.05" rule, Suprathreshold amount = , Adjustment Increment = , Adjusted parameters = The central control unit organizes the adjusted parameters into text instructions of "optimization instruction number, target area, hydrophilic group density, pore size gradient" and sends them to the control system of the barrier material production workshop via industrial Ethernet.

[0071] Step 405 adjusts the barrier material production parameters according to the real-time optimization instruction to obtain the thermal stability improvement coefficient of the barrier layer at the leakage point after optimization. Specifically, after the control system of the barrier material production workshop receives the real-time optimization instruction, the operator adjusts the production parameters on the PLC (Programmable Logic Controller). The hydrophilic group distribution density is adjusted by adding a hydroxyl-containing organic silane modifier during the production process to control the hydrophilic group density. The instruction requires an increase of 7 units (units / square micron). According to the ratio of "1 unit hydrophilic group corresponds to 0.3 liters of modifier / ton of material" (preset calibration), the current production batch is 2 tons of material, and the modifier addition increment = 7 units × 0.3 liters / (units Tons) × 2 tons = 4.2 liters. The original addition amount was 5 liters, and after adjustment, it was 5 liters + 4.2 liters = 9.2 liters. The PLC-controlled metering pump accurately injected the regulator into the mixing tank. The pore gradient adjustment was achieved by adjusting the pressing pressure of the hydraulic press to control the pore gradient. The instruction required the parameter to be adjusted to 0.93. According to the correspondence of "pore gradient parameter × 15 MPa = pressing pressure" (specified in the equipment manual), pressing pressure = 0.93 × 15 MPa ≈ 13.95 MPa. The operator set the pressure value to 14 MPa on the PLC and the pressing time remained unchanged at the original 30 seconds. After producing an optimized barrier layer with a specification of 1 meter × 1 meter × 0.05 meter, it was transported by forklift to the leakage point area (grid A1 in the high-risk gradient area). On-site construction personnel removed the original barrier layer, re-laid the optimized barrier layer, and fixed it with high-temperature resistant adhesive. After laying, the central control unit initiated a 30-minute monitoring.

[0072] This embodiment generates regional thermal hazard situation data by aggregating data from each edge node, which can fully grasp the risk differences in different regions and avoid decision-making bias caused by one-sided local data; dynamically adjust the data collection frequency of different risk areas to ensure real-time data in high-risk areas, save resources in low-risk areas, and achieve a balance between data collection efficiency and resource consumption; allocate barrier resources according to risk level priority to ensure that high-risk areas obtain sufficient barrier layer and nitrogen gas curtain coverage, improve the barrier effectiveness of key areas, and reduce resource waste; generate real-time optimization instructions based on heat flux density anomalies, which can solve local overheating problems in a targeted manner, optimize the performance of barrier materials, and enhance the adaptability of materials under complex working conditions; adjust production parameters and iteratively update seepage analysis parameters to form a closed-loop optimization of barrier efficiency, which can continuously improve the thermal stability and barrier capacity of barrier materials and ensure that the barrier effect is continuously optimized as working conditions change.

[0073] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the system described above. All implementations in the above system embodiments are applicable to this embodiment and can achieve the same technical effects.

[0074] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. Distributed aluminum leakage positioning and response system based on edge computing, characterized by: include: A multi-source sensing module is used to collect data on the temperature field distribution, cooling water penetration rate, and material surface thermal stress in the molten aluminum leakage area in real time, generating a leakage feature dataset containing material porosity, water absorption rate, and interface thermal resistance parameters. The edge analysis module is used to perform seepage analysis based on the material pore structure based on the leakage feature dataset. It calculates the penetration depth of molten aluminum in the material and evaluates the leakage risk level in combination with the cooling water adsorption rate. The module outputs dynamic positioning parameters including the critical temperature of material failure and the effective blocking time. A control module is used to automatically trigger a multi-level response strategy when a leak event is detected based on dynamic positioning parameters; The feedback module is used to monitor the material's water absorption capacity, temperature gradient distribution, and structural integrity data in real time, collect the thermal decomposition characteristic parameters of the surface polymer coating, compare the measured water absorption rate with the preset water absorption rate threshold, and generate a material modification parameter set; The optimization module is used to integrate the positioning data and feedback information of each edge computing node, dynamically adjust the data collection frequency, barrier resource allocation and emergency response priority, and achieve closed-loop collaborative optimization of porous ceramic material performance and barrier efficiency through a distributed communication network.

2. The distributed aluminum leakage positioning and response system based on edge computing according to claim 1 is characterized in that: The system collects data on the temperature field distribution, cooling water penetration rate, and thermal stress on the material surface in real time in the molten aluminum leakage area, generating a leakage feature dataset containing material porosity, water absorption rate, and interface thermal resistance parameters, including: Step 000: Distribute high-temperature resistant infrared thermal imagers at intervals of no more than 0.5 meters at the junction of the refractory material layer and the steel structure shell at the bottom of the electrolytic cell, at the outlet through which the molten aluminum flows, and at valves and pipe flange connections; embed micro moisture sensors in a mesh array at the contact interface between the outer wall of the cooling water pipe and the refractory material layer; and fix fiber Bragg grating strain sensors in a grid-like manner on the upper surface of the refractory material layer; collect temperature field distribution data of the monitored area using infrared thermal imagers, collect cooling water penetration rate data using micro moisture sensors, and collect thermal stress data on the material surface using fiber Bragg grating strain sensors. Step 001: Analyze the temperature field distribution data collected by the infrared thermal imager, compare the real-time temperature value of each sampling point with the normal operating temperature value of the preset corresponding position, and screen the area where the real-time temperature value exceeds the normal operating temperature value by more than 50 degrees Celsius, defining it as the area to be verified; extract the geometric center coordinates of the area to be verified, the maximum temperature value of the area, and the average temperature change gradient of the area; and simultaneously process the sequence data collected by the micro moisture sensor, calculate the movement distance of the cooling water penetration front per unit time, and obtain the water absorption rate of the material; Step 002: The geometric center coordinates of the area to be verified, the regional average temperature gradient, and the water absorption rate of the material are temporally and spatially aligned and fused with the thermal stress distribution data collected by the fiber Bragg grating strain sensor in the same time period and the same spatial area to obtain a fused data set; based on the fused data set, the thermal resistance parameter of the contact interface between the material and the molten aluminum liquid is obtained by calculating the ratio of the temperature gradient to the heat flux density according to Fourier's law of heat conduction; and according to Darcy's law, the equivalent porosity of the material under the current thermal-mechanical state is inversely calculated by analyzing the relationship between the cooling water penetration rate and the pressure gradient. Step 003: Associate and integrate the material porosity, water absorption rate, and interface thermal resistance parameters with the corresponding spatial coordinate information and acquisition timestamp to generate a standardized leakage feature data set.

3. The distributed aluminum leakage positioning and response system based on edge computing according to claim 2 is characterized in that: Based on the leakage characteristic data set, a seepage analysis based on the material pore structure is performed. By calculating the penetration depth of molten aluminum in the material and combining it with the cooling water adsorption rate, the leakage risk level is assessed. The dynamic positioning parameters including the critical temperature of material failure and the effective blocking time are output, including: Step 100: Based on the material porosity, water absorption rate, and interface thermal resistance parameters in the leakage characteristic dataset, a three-dimensional pore network topology is constructed to simulate the flow path, permeation rate, and interface wetting characteristics of molten aluminum in the barrier material pore network, and output the permeation path distribution, local flow velocity, and interface contact angle. Step 101: Using the penetration path distribution, local flow velocity, and interface contact angle parameters as input, the heat conduction and convection diffusion processes of the molten aluminum liquid in the porous medium are calculated. Combined with the material surface thermal stress data, the penetration depth, temperature gradient distribution, and heat-affected zone boundary are dynamically predicted, and the heat transfer analysis results including the penetration depth threshold, temperature diffusion range, and thermal stability evaluation parameters are output; Step 102, calculating the potential contact area and energy release rate between the molten aluminum and the cooling water based on the penetration depth threshold and temperature diffusion range in the heat transfer analysis results and the cooling water adsorption rate; Step 103: Classify the leakage risk into three levels: low risk, medium risk, and high risk based on the potential contact area and the energy release rate; Step 104 : Mapping the preset critical failure temperature threshold and the blocking time threshold according to the risk level to generate dynamic positioning parameters including target area coordinates, failure temperature warning value, and response time window.

4. The distributed aluminum leakage positioning and response system based on edge computing according to claim 3 is characterized in that: Step 100: Based on the material porosity, water absorption rate, and interface thermal resistance parameters in the leakage characteristic dataset, a three-dimensional pore network topology is constructed to simulate the flow path, permeation rate, and interface wetting characteristics of molten aluminum in the barrier material pore network. The permeation path distribution, local flow velocity, and interface contact angle are output, including: Step 100a, calculating the equilibrium relationship between capillary pressure and viscous resistance based on the three-dimensional pore network topology structure and in combination with the water absorption rate and the interface thermal resistance parameter; Step 100b, based on the balance relationship between capillary pressure and viscous resistance, dynamically simulate the flow path selection probability and penetration direction of the molten aluminum liquid at the pore branch node; Step 100c, calculating the dynamic contact angle change data of the molten aluminum at the solid-liquid interface based on the flow path selection probability and the penetration direction and the interface thermal resistance parameter; Step 100d: Integrate the flow path selection probability, penetration direction, and dynamic contact angle change data to generate penetration path distribution, local flow velocity, and interface contact angle parameters.

5. The distributed aluminum leakage positioning and response system based on edge computing according to claim 4 is characterized in that: Step 101 uses the penetration path distribution, local flow velocity, and interface contact angle parameters as inputs, calculates the heat conduction and convection diffusion processes of the molten aluminum liquid in the porous medium, and combines the material surface thermal stress data to dynamically predict the penetration depth, temperature gradient distribution, and heat-affected zone boundary. Output heat transfer analysis results including penetration depth threshold, temperature diffusion range, and thermal stability evaluation parameters include: Step 101a, based on the pore network topology, local flow velocity, and interface contact angle parameters in the permeation path distribution, a coupled calculation of heat conduction and convection diffusion is performed to define the constraints of the heat conduction rate, flow diffusion rate, and thermal stress on pore deformation of the molten aluminum liquid; Step 101b: Analyze the heat conduction process of the molten aluminum in the pore network according to the constraints, calculate the convection and diffusion paths based on the local flow velocity data, and generate the temperature distribution and heat flux density change data in each pore channel; Step 101c: Integrate the collected material surface thermal stress data, analyze the effect of pore deformation caused by thermal expansion on heat conduction, and correct the temperature distribution and heat flux density data generated in the previous step based on the analysis results to obtain corrected temperature distribution data; Step 101d: Based on the corrected temperature distribution data, the maximum penetration depth of the molten aluminum liquid is predicted, and the temperature gradient exceeding the limit area is identified in combination with the heat flux density change data, and the penetration depth safety threshold and temperature gradient critical value are output; Step 101e, based on the temperature gradient critical value and heat flux density data, the heat affected zone boundary is determined by the material thermal decomposition rate threshold, and the boundary coordinates, thermal diffusion radius and diffusion rate parameters are generated; Step 101f, integrating the penetration depth safety threshold, thermal diffusion radius, material deformation tolerance, and thermal decomposition rate threshold to generate a structured heat transfer analysis result including the penetration depth threshold, temperature diffusion range, and thermal stability evaluation parameters.

6. The distributed aluminum leakage positioning and response system based on edge computing according to claim 5 is characterized in that: Based on the potential contact area and energy release rate, the leakage risk level is divided into three levels: low risk, medium risk and high risk, including: Step 103a, calculating a leakage risk index based on a proportional relationship between the potential contact area and a preset contact area threshold, combined with a ratio of the energy release rate to the preset energy release rate threshold; Step 103b: when the leakage risk index is lower than the first critical value, it is determined to be a low risk level; Step 103c: when the leakage risk index is between the first critical value and the second critical value, it is determined to be a medium risk level; Step 103d: When the leakage risk index is higher than the second critical value or the energy release rate exceeds the safety tolerance, it is determined to be a high risk level.

7. The distributed aluminum leakage positioning and response system based on edge computing according to claim 6 is characterized in that: Based on dynamic positioning parameters, when a leak is detected, a multi-level response strategy is automatically triggered, including: Step 200: When the leakage risk level in the dynamic positioning parameters is greater than or equal to medium risk, the corrosion-resistant ejection device is activated to directionally lay a porous ceramic barrier layer between the leakage point and the cooling water pipeline according to the coordinates of the target area; Step 201: After laying the porous ceramic barrier layer, the temperature gradient distribution is monitored in real time. If the local temperature reaches the critical failure temperature threshold, the hydrophilic group distribution density and pore size gradient of the barrier layer are dynamically adjusted to optimize the water absorption rate and thermal stability. Step 202: When the leakage risk level is high or the heat diffusion radius exceeds the limit, the cooling water pressure control unit is linked to generate a high-pressure nitrogen isolation air curtain to form a dynamic physical barrier to isolate the molten aluminum liquid from the cooling water.

8. The distributed aluminum leakage positioning and response system based on edge computing according to claim 7 is characterized in that: Real-time monitoring of the material's water absorption capacity, temperature gradient distribution, and structural integrity data. Thermal decomposition parameters of the surface polymer coating are collected. The measured water absorption rate is compared with the preset water absorption rate threshold to generate a material modification parameter set, including: Step 300: Calculate the measured water absorption rate based on the real-time monitored water absorption capacity data, compare the measured water absorption rate with a preset water absorption rate threshold, and generate a water absorption rate deviation coefficient; identify local overheating areas based on the temperature gradient distribution data, and calculate the thermal stress deformation coefficient in combination with the microcrack growth rate in the structural integrity data; Step 301, obtaining the thermal weight loss rate through the thermal decomposition characteristic parameters of the polymer coating, and correlating the water absorption rate deviation coefficient with the thermal stress deformation coefficient; Step 302 : Based on the difference between the thermal weight loss rate and the preset decomposition threshold, the water absorption rate deviation coefficient and the thermal stress deformation coefficient are superimposed to generate a material modification parameter set including a hydrophilic group density adjustment amount, a pore size gradient optimization parameter, and a coating reinforcement thickness.

9. The distributed aluminum leakage positioning and response system based on edge computing according to claim 8 is characterized in that: Integrate the positioning data and feedback information of each edge computing node, dynamically adjust the data collection frequency, barrier resource allocation and emergency response priority, and achieve closed-loop collaborative optimization of porous ceramic material performance and barrier efficiency through a distributed communication network, including: Step 400 , aggregating the dynamic positioning parameters and material modification parameter sets of each edge node, and generating regional thermal hazard situation data based on the leakage risk level distribution; Step 401, dynamically adjusting the data collection frequency of high-risk areas and the data collection frequency of low-risk areas according to the risk level gradient changes in the regional heat hazard situation data; Step 402: After adjusting the acquisition frequency, the penetration depth safety threshold and thermal diffusion radius data are acquired in real time, and the porous ceramic barrier layer reserve and nitrogen isolation air curtain coverage are allocated according to the risk level priority in the situation data; Step 403: After the barrier resources are allocated, the actual temperature gradient distribution data of the barrier layer is deployed to extract the heat flux density anomaly values ​​of the local overheating area; Step 404 , calculating the thermal stress deformation coefficient based on the heat flux density anomaly value, and superimposing the thermal decomposition characteristic parameters in the material modification parameter set to generate a real-time optimization instruction for the hydrophilic group distribution density and pore size gradient; Step 405: Adjust the barrier material production parameters according to the real-time optimization instruction to obtain the thermal stability improvement coefficient of the barrier layer at the leakage point after optimization; Step 406 , based on the thermal stability improvement coefficient and the thermal decomposition rate feedback, iteratively update the interface thermal resistance parameters and heat conduction constraints required for the seepage analysis to complete the closed-loop optimization of the barrier performance.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the system according to any one of claims 1 to 9.

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