Distributed aluminum leakage positioning and response system based on edge computing

Abstract

The application provides a distributed aluminum leakage positioning and response system based on edge computing, and relates to the technical field of data processing, and comprises a multi-source sensing module, which is used for collecting temperature field distribution of a molten aluminum liquid leakage area, cooling water penetration rate and material surface thermal stress data in real time, and generating a leakage characteristic data set containing material porosity, water absorption rate and interface thermal resistance parameters; an edge analysis module, which is used for performing seepage analysis based on material pore structure according to the leakage characteristic data set, performing leakage risk level evaluation by calculating the penetration depth of the molten aluminum liquid in the material in combination with the cooling water absorption rate, and outputting dynamic positioning parameters containing material failure critical temperature and effective blocking time. Through automatic multi-level response, material performance feedback and collaborative optimization, the application can position the aluminum leakage position, dynamically evaluate the risk and optimize the blocking effectiveness, improve the timeliness and blocking reliability of the aluminum leakage event response, and ensure the safety of the electrolytic aluminum production.

Description

TECHNICAL FIELD

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

[0002] In the aluminum processing industry, the aluminum leakage problem in deep well casting and other links poses a significant threat to production safety and enterprise efficiency. Traditional monitoring methods are often difficult to achieve accurate positioning and rapid response when dealing with aluminum leakage risks. For example, manual inspection methods are prone to overlook small leakage situations and are difficult to monitor the specific situation and development trend of the leakage area in real time.

[0003] With the widespread application of Internet of Things devices and the rapid growth of data volume, traditional cloud computing models have gradually shown some limitations in processing aluminum leakage monitoring data. During the process of transmitting large amounts of data generated by devices to a unified data center or cloud, there may be significant bandwidth pressure and transmission delay problems, which to some extent affect the timely response capability to aluminum leakage events. At the same time, there are risks of loss and tampering during data transmission, and data privacy and security are challenged. SUMMARY

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

[0005] To solve the above technical problems, the technical solution of the present application is as follows:

[0006] In a first aspect, the distributed aluminum leakage positioning and response system based on edge computing comprises:

[0007] A multi-source sensing module for real-time acquisition of temperature field distribution, cooling water penetration rate and material surface thermal stress data of molten aluminum leakage area, generating a leakage characteristic data set containing material porosity, water absorption rate and interfacial thermal resistance parameters;

[0008] An edge analysis module for performing seepage analysis based on material pore structure according to the leakage characteristic data set, calculating the penetration depth of molten aluminum in the material, and combining the water absorption rate to evaluate the leakage risk level, and outputting dynamic positioning parameters containing material failure critical temperature and effective barrier time;

[0009] A control module for automatically triggering a multi-level response strategy when detecting a leakage event according to the dynamic positioning parameters;

[0010] A feedback module is configured to monitor the water absorption capacity, temperature gradient distribution and structural integrity data of the material 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] An optimization module is configured to integrate the positioning data and feedback information of each edge computing node, dynamically adjust the data acquisition frequency, barrier resource allocation and emergency response priority, and realize closed-loop collaborative optimization of the performance and barrier effectiveness of the porous ceramic material through a distributed communication network.

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

[0013] The above scheme of the present application at least has the following beneficial effects:

[0014] Three types of core data, including 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 generated, avoiding the limitations of single data monitoring and accurately capturing subtle abnormalities in the early stage of the aluminum leakage event; relying on local edge computing capabilities, without relying on remote cloud processing, seepage analysis can be carried out based on the pore structure of the material, the penetration depth is calculated, and the risk level is quantified by associating the water absorption rate, the data transmission delay is reduced, the risk assessment can be completed in the early stage of the aluminum leakage event, and positioning parameters such as failure critical temperature and effective barrier time are output; based on the dynamic positioning parameters, a multi-level response strategy is automatically triggered without manual intervention, and operations such as laying a barrier layer for medium risk to physical isolation for high risk can be completed, avoiding the hysteresis and operation errors of manual response; the water absorption capacity, structural integrity and coating 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, dynamically adjusts resource allocation and material production parameters, and can improve the hydrophilic group distribution, pore size gradient and other characteristics of the porous ceramic material, improve the thermal stability and barrier ability of the material, and prolong the effective barrier time; based on the distributed edge node data, the data acquisition frequency of high / low risk areas is dynamically adjusted, and the barrier resources and emergency response weight are allocated according to the risk priority, ensuring the monitoring accuracy and resource supply of high-risk areas and avoiding resource waste in low-risk areas. BRIEF DESCRIPTION OF DRAWINGS

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

[0016] Figure 2 is a flowchart of a multi-level response strategy automatically triggered when a leakage event is detected according to the dynamic positioning parameters. DETAILED DESCRIPTION

[0017] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms without being limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thoroughly and completely understood, and will fully convey the scope of the present disclosure to those skilled in the art.

[0018] As Figure 1 shown, the embodiments of the present application propose an edge computing-based distributed aluminum leakage positioning and response system, comprising:

[0019] A multi-source sensing module is configured to collect temperature field distribution, cooling water penetration rate, and material surface thermal stress data of a molten aluminum leakage area in real time, and generate a leakage characteristic data set containing material porosity, water absorption rate, and interface thermal resistance parameters.

[0020] An edge analysis module is configured to perform seepage analysis based on material pore structure according to the leakage characteristic data set, evaluate the leakage risk level by calculating the penetration depth of molten aluminum in the material in combination with the water absorption rate, and output dynamic positioning parameters containing material failure critical temperature and effective barrier time.

[0021] A regulation module is configured to automatically trigger a multi-level response strategy when a leakage event is detected according to the dynamic positioning parameters.

[0022] A feedback module is configured to monitor water absorption capacity, temperature gradient distribution, and structural integrity data of the material in real time, and collect 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] An optimization module is configured to integrate positioning data and feedback information of each edge computing node, dynamically adjust data acquisition frequency, barrier resource allocation, and emergency response priority, and realize closed-loop collaborative optimization of the performance and barrier effectiveness of the porous ceramic material through a distributed communication network.

[0024] In the embodiments of the present application, three types of core data, temperature field, cooling water penetration rate and material thermal stress, are collected, and further key characteristic parameters such as material porosity, water absorption rate and interface thermal resistance are generated, avoiding the limitations of single data monitoring, and more accurately capturing the subtle abnormalities in the early stage of the aluminum leakage event; relying on local edge computing capabilities, without relying on remote cloud processing, seepage analysis can be quickly carried out based on the material pore structure, the penetration depth is calculated, and the risk level is quantified by associating the water absorption rate, reducing data transmission delay, and completing risk assessment in the early stage of the aluminum leakage event, outputting positioning parameters such as failure critical temperature and effective blocking time, to gain a key time window for emergency response; based on dynamic positioning parameters, multi-level response strategies are automatically triggered without manual intervention to complete operations such as laying blocking layers for medium-risk and physical isolation for high-risk, avoiding the lag and operation errors of manual response; real-time monitoring of the water absorption capacity, structural integrity and coating thermal decomposition characteristics of the blocking material, generating material modification parameters by comparing the measured data with the threshold value; the optimization module further integrates multi-node information, dynamically adjusts resource allocation and material production parameters, and can improve the hydrophilic group distribution and pore size gradient of the porous ceramic material, improve the thermal stability and blocking ability of the material, and prolong the effective blocking time; based on distributed edge node data, the data acquisition frequency of high / low risk areas is dynamically adjusted, and the blocking resources and emergency response weight are allocated according to the risk priority, to ensure the monitoring accuracy and resource supply of high-risk areas, and avoid resource waste in low-risk areas.

[0025] In a preferred embodiment of the present application, the temperature field distribution of the molten aluminum liquid leakage area, the cooling water penetration rate and the material surface thermal stress data are collected in real time, and the leakage characteristic data set containing the material porosity, water absorption rate and interface thermal resistance parameters is generated, including:

[0026] In the embodiment of the present application, step 000, at the junction of the refractory layer and the steel structure shell at the bottom of the electrolytic cell, the outlet through which the molten aluminum liquid flows, the valve and the flange connection of the pipeline, the high-temperature-resistant infrared thermal imager is distributed and arranged at an interval of not more than 0.5 meters; at the contact interface between the outer wall of the cooling water pipeline and the refractory layer, the micro water content sensor is embedded and arranged in a mesh array form; on the upper surface of the refractory layer, the fiber Bragg grating strain sensor is fixedly installed in a grid form; 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 water content sensor, and the thermal stress data of the material surface is collected by the fiber Bragg grating strain sensor, which specifically comprises: determining the installation position of the three types of sensors, for the high-temperature-resistant infrared thermal imager, the junction position of the refractory layer and the steel structure shell at the bottom of the electrolytic cell, the outlet position of the molten aluminum liquid flowing out of the electrolytic cell, the valve position for controlling the flow of the aluminum liquid, and the flange connection of the connecting pipeline, the distance between two adjacent thermal imagers is strictly controlled to be within 0.5 meters, such as 11 thermal imagers are arranged in the flange connection area of the pipeline with a length of 5 meters, to ensure that each monitoring position can be captured by the thermal imager without dead angle; for the micro water content sensor, it needs to be embedded at the interface between the outer wall of the cooling water pipeline and the refractory layer, arranged in a mesh array, such as one sensor is arranged every 0.3 meters in the horizontal and vertical directions, forming a uniform mesh structure, so that each sensor can directly contact the cooling water that may penetrate the interface, thereby accurately capturing the penetration of the cooling water; for the fiber Bragg grating strain sensor, it needs to be fixed on the upper surface of the refractory layer with high-temperature-resistant adhesive and installed in a grid form, such as one installation point is set every 0.4 meters in the horizontal direction and every 0.4 meters in the vertical direction, so that the sensor can comprehensively sense the tensile or compressive stress change of the refractory material surface caused by temperature change, after the installation of all sensors is completed, the high-temperature-resistant infrared thermal imager is started, and data is collected every 10 seconds, the temperature information of each position in the monitoring area is continuously captured, all position temperature data at each collection time is summarized to form complete temperature field distribution data; the micro water content sensor is started, and data is recorded every 5 seconds, the penetration position change of the cooling water in the refractory material is recorded in real time, and the penetration rate related data is obtained; the fiber Bragg grating strain sensor is started, and data is collected every 8 seconds, and the stress data of the refractory material surface caused by heat is continuously collected.

[0027] 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 preset corresponding position normal working condition temperature value, screen out the area whose real-time temperature value exceeds the normal working condition temperature value by more than 50 degrees Celsius, define it as the area to be verified; extract the geometric center coordinates of the area to be verified, the area maximum temperature value and the area average temperature change gradient; at the same time, process the sequence data collected by the micro water content sensor, calculate the moving distance of the cooling water penetration front in unit time, get the water absorption rate of the material, which includes: obtaining the temperature data of all sampling points from the storage module of the infrared thermal imager, each sampling point has corresponding unique number and position information, checking the real-time temperature value of each sampling point one by one, such as 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., then call out the temperature value of each sampling point corresponding position in normal working state from the preset database, such as the normal working condition temperature value of sampling point 1 corresponding position is 260 degrees Celsius, the normal working condition temperature value of sampling point 2 corresponding position is 270 degrees Celsius, etc., subtract the real-time temperature value of each sampling point from the corresponding normal working condition temperature value to get the temperature difference value of each sampling point, such as the temperature difference value of sampling point 1 is 320 degrees Celsius minus 260 degrees Celsius equal to 60 degrees Celsius, the temperature difference value of sampling point 2 is 280 degrees Celsius minus 270 degrees Celsius equal to 10 degrees Celsius, if the temperature difference value of a certain sampling point is greater than 50 degrees Celsius, mark this sampling point, then divide the adjacent marked sampling points into a continuous area, these continuous areas are the areas to be verified, then for each area to be verified, measure the boundary coordinates of the area by image analysis tool, such as the left upper corner coordinates of the area are (x1, y1), the right upper corner coordinates are (x2, y1), the left lower corner coordinates are (x1, y2), the right lower corner coordinates are (x2, y2), calculate the horizontal coordinate of the geometric center coordinates as (x1+x2) divided by 2, the vertical coordinate as (y1+y2) divided by 2, record this geometric center coordinates data; compare the real-time temperature values of all sampling points in the area to be verified to find the maximum value, which is the area maximum temperature value;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 verification area 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 next small time period to obtain the temperature change of each small time period, for example, the temperature change of the second small time period and the first small time period is 315 degrees Celsius minus 310 degrees Celsius, which is 5 degrees Celsius. Divide the temperature change of each small time period by the time 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. Add the temperature change rates of the 10 small time periods and divide by 10 to obtain the area average temperature change gradient. At the same time, collect the penetration data collected by the micro water content sensor at different time points, for example, at the 10th second, the sensor monitors the position coordinates of the cooling water penetration front as (a1, b1); at the 20th second, the position coordinates of the penetration front are (a2, b2). Calculate the straight line distance between the two position coordinates by the distance calculation formula, that is, first calculate the square of (a2-a1), then calculate the square of (b2-b1), add the two square results, and then take the square root of the added result to obtain the distance difference between the two time points. Divide the distance difference by the time interval between the two time points, 10 seconds, to obtain the moving distance of the cooling water penetration front per unit time, which is the water absorption rate of the material.

[0028] Step 002, the geometric center coordinates of the area to be verified, the average temperature change gradient of the area, 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 time and space matched and fused to obtain a fused data set; based on the fused data set, according to the fourier heat conduction law, the thermal resistance parameter of the material and the molten aluminum liquid contact interface is obtained by calculating the ratio of temperature gradient and heat flux density; according to darcy's law, the equivalent porosity of the material under the current thermal-mechanical state is calculated by analyzing the relationship between the cooling water penetration rate and the pressure gradient, which specifically includes: arranging the geometric center coordinates of the area to be verified, the average temperature change gradient of the area, the water absorption rate of the material obtained in step 001, 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, the area from coordinates (c1, d1) to (c2, d2), from all the thermal stress distribution data collected by the fiber bragg grating strain sensor, the thermal stress data collected between the 5th minute and the 10th minute and falling within the coordinates (c1, d1) to (c2, d2) region is screened out, the screened thermal stress distribution data is matched with the geometric center coordinates of the area to be verified, the average temperature change gradient of the area, and the water absorption rate of the material, for the geometric center coordinates (e, f) of the area to be verified, the temperature change gradient data and the water absorption rate of the material collected at the 7th minute, the thermal stress data collected by the fiber bragg grating strain sensor at the 7th minute and the coordinate (e, f) position is matched accurately, to ensure that each spatial position has complete average temperature change gradient, water absorption rate of the material and thermal stress data at the same time point. All the matched data are integrated into the same data table according to the order of spatial coordinates (from left to right, from top to bottom) and collection time (from early to late), each row of the table corresponds to a spatial position and a collection time, and each column corresponds to a data type, thereby forming a fused data set; when calculating the thermal resistance parameter of the material and the molten aluminum liquid contact interface based on the fused data set, the fourier heat conduction law formula is followed , wherein q is the heat flux density, unit W / m 2 ; k is the thermal conductivity of the material, unit , such as porous ceramic at room temperature , linear correction at high temperature; is the temperature gradient, unit ℃ / m, first extract the temperature gradient data of each spatial position at each collection time point from the fused data set , at the same time, through the heat flux density monitoring equipment (deployed at the same position with the infrared thermal imager, range 0-5000W / m 2, precision ± 1%) to obtain the corresponding position, corresponding time heat flux density data q, according to the Fourier heat conduction law derived thermal resistance calculation relationship, namely the material interface thermal resistance R = ΔT / q, wherein ΔT is the temperature difference, obtained by the temperature gradient ∇T and material contact thickness Δx product, namely , material contact thickness Δx is obtained by the early material installation record, such as refractory layer thickness is 0.3m, in actual calculation, first through the temperature gradient and calculation , again and heat flux q, get the position at this time point material and molten aluminum liquid contact interface thermal resistance parameters, units , all positions, all time point thermal resistance parameters are arranged in order of space coordinates and time, form a complete thermal resistance parameter set; when calculating the equivalent porosity of the material, according to Darcy law , wherein v is the fluid permeation rate, unit m / min; K is the material permeability, unit m 2 ; μ is the dynamic viscosity of fluid, unit ; is the pressure gradient, unit , first from the fusion data set extraction of each position each time point cooling water permeation rate data v, such as coordinates (i, j) in the 9th minute of permeation rate v is 0.05 m / min; at the same time, the pressure sensor (with the same position of micro water sensor, range 0-2000Pa, precision ± 5Pa) is used to measure the pressure data of the position at the 9th minute, through the pressure difference of adjacent two pressure sensors (spacing 0.5m) , is the sensor spacing 0.5m, namely ; according to Darcy law deduced permeability K calculation formula , negative sign indicates that the permeation direction is opposite to the pressure gradient direction, the actual calculation takes the absolute value, from the material attribute database to call the dynamic viscosity of cooling water under the current working condition, such as 20 DEG C , v = 0.05 m / min (converted to ), K = 0.5 , into the formula, the material permeability K is calculated; combined with the permeability and porosity correlation formula (based on porous medium theory , wherein ε is the equivalent porosity of the material; d is the average pore size of the material, unit m, through the early material detection report, such as refractory average pore size d = 5 × 10 -5 m), the inverse of the formula is obtained, and the calculation formula of the equivalent porosity ε is obtained ε = [180K(1 - ε ) ² / d ² ] (1 / 3) Through iterative calculation (initial assumption ε0=0.3, substitute formula to calculate ε1, and then substitute ε1 to calculate 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, the material porosity, water absorption rate and interface thermal resistance parameters are associated and integrated with the corresponding spatial coordinate information and collection time stamp to generate a standardized leakage characteristic data set, which specifically includes: collecting the material porosity data of each spatial position at each collection time point calculated in step 002, the water absorption rate data of each spatial position at each collection time point obtained in step 001, and the interface thermal resistance parameter data of each spatial position at each collection time point obtained in step 002, while arranging 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) and the like, and the time stamp information when each parameter is collected or calculated, which is accurate to the specific year, month, day, hour, minute and second when the data collection or calculation is completed, such as 2025-08-26 10:30:15; then a unified data table is established, and the columns of the table are set as spatial coordinate information, collection time stamp, material porosity, water absorption rate and interface thermal resistance parameter; each material porosity data, corresponding water absorption rate data, corresponding interface thermal resistance parameter data, and their common spatial coordinate information and collection time stamp information are filled in the corresponding positions in the table one by one, such as the material porosity corresponding to the spatial coordinate (k1, l1) and the collection time stamp 2025-08-26 10:30:15 is 0.2, the water absorption rate is 0.04 meters per minute, and the interface thermal resistance parameter is 0.2 square meters Kelvin per watt, which are filled in the corresponding columns of the table row; the data in the table is uniformly processed in format, the spatial coordinate information is uniformly expressed in the form of (X coordinate value, Y coordinate value), such as (1.2 meters, 3.5 meters); the collection time stamp is uniformly in the format of "year-month-day hour: minute: second", such as "2025-08-26 10:30:15"; the material porosity is uniformly kept to three decimal places, the water absorption rate is uniformly kept to three decimal places and the unit is meters per minute, and the interface thermal resistance parameter is uniformly kept to three decimal places and the unit is square meters Kelvin per watt; after such associated integration and format standardization processing, the table containing complete data of all monitoring positions and all collection time points is the standardized leakage characteristic data set.

[0030] In this embodiment, the sensor deployment is comprehensive and accurate, capable of collecting data from multiple key positions and dimensions, ensuring that no information related to molten aluminum leakage, such as temperature, cooling water penetration, and material thermal stress, is missed; by comparing and screening the data with normal working condition data, key temperature parameters are accurately extracted; data fusion realizes multi-dimensional information integration, and the temperature, water absorption rate, and thermal stress data are associated according to space and time, so that the fused data set can comprehensively reflect the complex situation of the leakage area, and the thermal resistance parameter and equivalent porosity calculated based on this are more in line with the actual situation, improving the accuracy of the parameters; the standardized leakage feature data set generated finally closely combines key parameters with space-time information and has a unified format, improving the data utilization efficiency of the overall system.

[0031] In a preferred embodiment of the present application, based on the leakage feature data set, a percolation analysis based on the material pore structure is performed, the penetration depth of the molten aluminum in the material is calculated, and the water absorption rate is combined to evaluate the leakage risk level, and the dynamic positioning parameters including the material failure critical temperature and the effective blocking time are output, including:

[0032] In the embodiment of the present application, step 100a, according to the three-dimensional pore network topology structure, the balance relationship between capillary pressure and viscous resistance is calculated by combining the water absorption rate and the interfacial thermal resistance parameter, specifically including: constructing a three-dimensional pore network topology structure through the OpenPNM library of Python, obtaining three-dimensional pore network topology structure data, the structure containing information such as the size distribution, connectivity mode and branch node position of the pores, for example, the pore diameter is from 0.1 millimeter to 1 millimeter, part of the pores are in a network connection, there are 2 to 4 pores branches at the branch node, and the spatial coordinates of each branch node are recorded, the water absorption rate and the interfacial thermal resistance parameter are extracted from the leakage feature data set, wherein the water absorption rate reflects the flow speed of the cooling water in the pores, for example, the water absorption rate of a certain area is 0.02 meters per minute, and the interfacial thermal resistance parameter reflects the thermal transfer resistance of the material and the contact surface of the molten aluminum, for example, the interfacial thermal resistance parameter of a certain contact surface is 0.3 square meters Kelvin per watt; when calculating the capillary pressure, the radius of the pore, the surface tension of the molten aluminum and the solid-liquid interface contact angle need to be combined, and the calculation formula is Pc = γ cos θ / r wherein Pc is the capillary pressure, unit is Pascal, γ is the surface tension of molten aluminum liquid, unit is Newton per meter, θ is the solid-liquid interface contact angle, unit is degree, r is the pore radius, unit is meter; γ needs to be determined according to the purity and temperature of the current molten aluminum liquid, if the purity of the molten aluminum liquid is 99.7% and the temperature is 700 degrees Celsius, the surface tension is 0.85 Newton per meter; if the purity decreases to 99.5% and the temperature increases to 720 degrees Celsius, the surface tension is adjusted to 0.83 Newton per meter, the value needs to be obtained through the previously built thermal physical property database of molten aluminum liquid, which covers the common purity range (99.0%-99.9%) and temperature interval (680-750 degrees Celsius) in the electrolytic aluminum production scene, and can directly match the current working condition parameters to output the corresponding surface tension value; the solid-liquid interface contact angle is related to the material surface characteristics 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 of the molten aluminum liquid and the ceramic is 110 degrees at 700 degrees Celsius; if the ceramic surface is coated with an aluminum-philic coating, the contact angle is reduced to 80 degrees, the contact angle value is derived from the preset solid-liquid interface characteristic database, which covers the scenarios of untreated surface of porous ceramic, surface coated with aluminum-philic coating and the common temperature interval of 680-750 degrees Celsius, and can directly match the material type and molten aluminum liquid temperature involved in the analysis to retrieve the corresponding contact angle value.

[0033] When calculating the capillary pressure of a single pore, the obtained surface tension value of the molten aluminum liquid is multiplied by the cosine value of the solid-liquid interface contact angle, and the obtained result is divided by the pore radius, for example, the surface tension of 0.85 Newton per meter is multiplied by the cosine value of 110 degrees, and then divided by the pore radius of 0.0002 meter, i.e. 0.2 millimeter, to obtain the capillary pressure of a single pore; when calculating the viscous resistance, the viscosity of the molten aluminum liquid, the local flow rate and the pore length need to be combined, and the calculation formula is wherein Fv is the viscous resistance, unit is Pascal, η is the viscosity of the molten aluminum liquid, unit is Pascal second, v is the local flow rate, unit is meter per second, L is the pore length, unit is meter; when calculating the total resistance of the porous ceramic, the capillary pressure and the viscous resistance of each pore need to be summed up, and the calculation formula is L is the pore length, unit: meter, υ is the local flow rate, unit: meter per second, r is the pore radius, unit: meter; the viscosity of the molten aluminum liquid is also related to the temperature, the viscosity at 700 degrees Celsius is 0.0012 Pascal seconds, and the viscosity at 720 degrees Celsius is 0.0011 Pascal seconds, the numerical value is from the thermal physical property parameter database of the molten aluminum liquid, which collects the viscosity values at different temperatures, which can be directly matched and called according to the current temperature; the local flow rate is determined by the three-dimensional pore flow simulation calculation in the seepage analysis, the specific process is, based on the pore distribution characteristics of the porous ceramic, including pore diameter, connectivity, distribution density, a three-dimensional pore network space is constructed, the finite element grid is divided, the grid size is not more than 1 / 5 of the minimum pore diameter to ensure the calculation accuracy, the molten aluminum liquid is regarded as an incompressible Newtonian fluid, the physical parameters such as its density, 2375 kg / m , , dynamic viscosity are substituted, the finite element method is used to solve the Navier-Stokes equation 3 , 3 , the standard density value of the molten aluminum liquid under the common working condition of 680-750 degrees Celsius; t is time, unit: second, which is set according to the simulation accuracy requirement, such as the time step of each iteration calculation is 0.01 second; υ is the flow velocity vector, unit: m / s, which represents the flow speed and direction of the molten aluminum liquid at different positions in the pore, including the components of X, Y and Z three spatial directions; p is the pressure of the molten aluminum liquid in the pore, unit: Pa, which refers to the pressure generated by the molten aluminum liquid itself flowing in the pore wall, the pressure values at different positions are different, such as the pressure at the pore inlet needs to be set according to the actual working condition, for example, it is set to 0.5 Pa (the initial pressure of the molten aluminum liquid flowing from the electrolytic cell to the pore), the internal pressure of the pore gradually changes along the flow path; g is the acceleration of gravity, unit: m / s 2 , 2 , the value is 9.8 m / s 2 , the standard gravity acceleration on the earth's surface, the direction is along the negative direction of Z axis, that is, vertically downward; ∇ is the gradient operator, which is a vector differential operator, which can be expressed as in the three-dimensional rectangular coordinate system, when acting on a scalar physical quantity, such as pressure p, , represents the variation rate vector of the physical quantity in space, that is, the pressure gradient, taking the pressure gradient as an example, the calculation process is based on the node pressure data after the finite element grid division, in the X direction, the partial derivative of a node is obtained by 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 divided by the interval of the two nodes in the X direction; similarly, the partial derivatives in the Y direction and the Z direction can be calculated, and finally combined as ; The Laplace operator is a scalar differential operator, represented in three-dimensional Cartesian coordinates as: The Laplace operator is used to describe the second-order rate of change of a physical quantity in space, expressed as the velocity vector υ. For example, its components are calculated as follows, with the X-direction component being... ,in From X to adjacent nodes The difference of first-order partial derivatives divided by the spacing yields the result. (The first-order partial derivative is calculated by selecting two adjacent nodes in the X-direction and using the difference between the two nodes...) (Velocity difference divided by node spacing); the calculation of second-order partial derivatives in the Y and Z directions is similar; the calculation logic for the Y and Z direction components is consistent with that in the X direction, respectively... , .

[0034] The pore inlet is a pressure boundary, meaning the pressure of molten aluminum at all finite element nodes at the pore inlet is set to a fixed value, such as 0.5 Pa, to ensure a stable inflow of molten aluminum into the pore. The outlet is a free-flow boundary, meaning the pressure at the pore outlet is set to equal the external atmospheric pressure (0.1 Pa), allowing molten aluminum to flow freely out of the outlet without additional flow obstruction. The pore wall is a no-slip boundary, meaning the molten aluminum velocity at all finite element nodes at the pore wall is set to 0 m / s, simulating the zero-velocity state caused by friction when molten aluminum contacts the pore wall in actual working conditions. After solving for the flow field distribution, the velocity vector data of all finite element nodes within the pore are obtained. First, the spatial range of a single pore is determined, and then the pore is extracted from the three-dimensional pore network topology. The central axis of the pore is defined as follows: if it extends along the X-axis, the coordinates of the axis are (x, y0, z0), where y0 and z0 are fixed values. Then, all finite element nodes on the central axis are selected, and the velocity vectors of these nodes in the axial direction (e.g., the X-axis) are extracted. These components are summed and divided by the number of nodes to obtain the average velocity along the central axis of the pore, which serves as the local velocity. For example, after simulation calculations, the X-direction velocities of five nodes on the central axis of a pore are summed to obtain a single value, which is then divided by the number of nodes to obtain the average velocity. The pore length is extracted from the three-dimensional pore network topology, i.e., 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, in units of... For example, at 700 degrees Celsius, the value is 0.0012. r is the pore radius in meters, such as 0.0002m. The viscosity, local velocity and pore length are multiplied together and then divided by the square of the pore radius to obtain the viscous resistance of a single pore.

[0035] By comparing the size of 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, increasing the pore radius can reduce the capillary pressure or local flow rate, that is, increasing the local flow rate can increase the viscous resistance; if the viscous resistance is greater than the capillary pressure, then adjust the parameters in the opposite direction until the numerical values of the capillary pressure and the viscous resistance are equal, at this time, the balance relationship between the capillary pressure and the viscous resistance is obtained, for example, in the above example, the capillary pressure 1453.5 Pascal is much greater than the viscous resistance 7.5 Pascal, the pore radius can be increased to 0.005 meters, and the capillary pressure is calculated again to be 0.85 Newton per meter multiplied by 0.3420 divided by 0.005 meters, which is equal to 58.14 Pascal. At the same time, the local flow rate is increased to 0.04 meters per second, and the viscous resistance is calculated again 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 Pascal. It is still not equal, so continue to fine-tune until the numerical values of the two are consistent.

[0036] Step 100b, based on the balance relationship between the capillary pressure and the viscous resistance, dynamically simulating the flow path selection probability and penetration direction of the molten aluminum liquid at the pore branch node, specifically including: determining the specific structure of the pore branch node, such as a branch node with three branch pores, respectively denoted as branch 1, branch 2 and branch 3. From the calculation results of step 100a, the balance values of the capillary pressure and the viscous resistance of each branch pore are obtained. Assuming that the balance value of branch 1 is 200 Pascal, the balance value of branch 2 is 300 Pascal, and the balance value of branch 3 is 100 Pascal. First, calculate the sum of all branch balance values, that is, 200 Pascal plus 300 Pascal plus 100 Pascal, which is equal to 600 Pascal. Then, calculate the flow path selection probability of each branch. The probability of branch 1 is 200 Pascal divided by 600 Pascal, which 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. Adding these three probabilities together confirms that the total is 1, ensuring accurate calculation. The determination of the penetration direction needs to be combined with the spatial trend of the branch pore. Use an angle instrument 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 inclined upward, then the penetration direction is upward at 30 degrees. If the angle between branch 2 and the horizontal plane is 15 degrees and inclined downward, then the penetration direction is downward at 15 degrees. If the angle between branch 3 and the horizontal plane is 0 degrees and extends horizontally, then the penetration direction is horizontal. At the same time, the angle information of each branch is recorded in detail in a data table.

[0037] At step 100c, the dynamic contact angle change data of the molten aluminum liquid at the solid-liquid interface is calculated according to the flow path selection probability and the penetration direction in combination with the interfacial thermal resistance parameter, specifically including: first, the specific division of the monitoring area is determined, the area where the molten aluminum liquid may leak is divided 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 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 the key areas such as the refractory material layer at the bottom of the electrolytic cell, the cooling water pipeline contact interface, and the outlet and valve through which the molten aluminum liquid flows are covered, the interfacial thermal resistance parameter corresponding to each monitoring point is extracted from the leakage feature data set, the interfacial thermal resistance parameters of different materials are different, for example, the interfacial thermal resistance parameter of the porous ceramic material is between 0.2 and 0.5 square meters kelvin per watt, if the materials of the monitoring points (0.2 meters, 0.2 meters), (0.4 meters, 0.4 meters), etc. in the current monitoring area are all porous ceramics, the interfacial thermal resistance parameters of these monitoring points are all 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, the interfacial thermal resistance parameter thereof 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 of the solid-liquid interface, for example, the monitoring point with an interfacial thermal resistance parameter of 0.4 square meters kelvin per watt has a smaller interface temperature rise in the same time compared with the monitoring point with an interfacial thermal resistance parameter of 0.3 square meters kelvin per watt; for each monitoring point, the flow path selection probability thereof is checked, for example, the selection probability of the monitoring point (0.2 meters, 0.2 meters) is 0.5, which belongs to a high-probability area, meaning that the molten aluminum liquid flows more concentratedly in the pore channel where the monitoring point is located, the amount of aluminum liquid passing through the monitoring point per unit time is larger, and 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 a low-probability area, the molten aluminum liquid flows dispersedly in the pore channel where the monitoring point is located, the amount of aluminum liquid passing through per unit time is small, the heat accumulates slowly at the interface, and the temperature rises slowly.

[0038] When calculating the dynamic contact angle, the initial contact angle corresponding to each monitoring point is first determined. The initial contact angle needs to be obtained by measuring in advance under the same conditions, that is, prepare a sample with the same material as the monitoring point, such as a porous ceramic sample consistent with the (0.2 meters, 0.2 meters) monitoring point, a composite refractory material sample consistent with the (0.6 meters, 0.6 meters) monitoring point; place the sample in the same temperature environment as the actual working condition, such as a constant temperature environment of 700 degrees Celsius, and drop molten aluminum liquid on the surface of the sample, and use a high-precision optical imaging device to capture the contact interface image formed by the molten aluminum liquid and the sample surface; measure the included angle between the contact interface and the sample surface in the image by image analysis tool, which is the initial contact angle. For example, at 700 degrees Celsius, the contact angle measurement result of molten aluminum liquid and porous ceramic sample is 110 degrees, and the contact angle measurement result of molten aluminum liquid and composite refractory material sample is 105 degrees. These measurement values are respectively taken as the initial contact angle of the corresponding monitoring point, and then the contact angle is adjusted according to the interface temperature change of each monitoring point. First, collect the interface temperature of each monitoring point at different time periods by high-temperature resistant temperature sensor, such as the temperature of monitoring point (0.2 meters, 0.2 meters) at the initial moment is 700 degrees Celsius, and the temperature rises to 720 degrees Celsius after 10 seconds. The interface temperature change in this period is 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, which is 2; then calculate the value of the decrease in contact angle, that is, 2 times 5 degrees, which is 10 degrees; finally, subtract the decreased value from the initial contact angle of this monitoring point, that is, 110 degrees minus 10 degrees, which is the contact angle of this monitoring point at the end of this period, which is 100 degrees. According to the above method, for each monitoring point, the interface temperature is collected at different time, such as every 5 seconds, the temperature change in each period is calculated, and then the contact angle value at each time is obtained. For example, the initial contact angle of monitoring point (0.6 meters, 0.6 meters) is 105 degrees, the temperature rises to 715 degrees Celsius at the 5th second, the temperature change is 15 degrees Celsius, 15 degrees Celsius divided by 10 degrees Celsius is 1.5, the contact angle decreases by 1.5 times 5 degrees, which is 7.5 degrees, and the contact angle is 105 degrees minus 7.5 degrees, which is 97.5 degrees. At the 10th second, the temperature rises to 730 degrees Celsius, the temperature change is 30 degrees Celsius compared with the initial temperature, the contact angle decreases by 3 times 5 degrees, which is 15 degrees, and the contact angle is 105 degrees minus 15 degrees, which is 90 degrees. The contact angle values of all monitoring points at different times are arranged in a table according to time sequence and spatial coordinates, which contains three columns of monitoring point coordinates, time, and contact angle value, forming dynamic contact angle change data.

[0039] Step 100d, integrating the flow path selection probability, the penetration direction and the dynamic contact angle change data to generate the penetration path distribution, the local flow rate and the interface contact angle parameter, specifically including: using the visual analysis software, marking the flow path selection probability on the two-dimensional plan view of the monitoring area according to the spatial position, setting the color gradient rule, selecting the area with the probability between 0.4 and 1 to be marked with dark color, such as dark red, the area with the probability between 0.2 and 0.4 to be marked with medium color, such as orange, and the area with the probability between 0 and 0.2 to be marked with light color, such as yellow, after the marking, an intuitive penetration path distribution image is formed, from which it can be clearly seen that the molten aluminum liquid is more likely to flow along the dark area, when calculating the local flow rate, first determine the reference flow rate, which is set according to the actual running statistical data of the same aluminum processing in the industry, such as the conventional flow velocity of the molten aluminum liquid in the similar barrier material pores in the similar production links such as deep well casting and electrolytic cell aluminum liquid conveying, which is set to 0.01 meters per second, the pore diameter of each position is extracted from the three-dimensional pore network topology structure, such as the pore diameter of a certain position is 0.2 millimeters, i.e. 0.0002 meters, and the corresponding flow path selection probability is 0.5, according to the calculation method of local flow rate = selection probability x reference flow rate ÷ pore diameter square, when arranging the dynamic contact angle change data, a table containing time, spatial coordinates (X axis, Y axis) and contact angle value is established, the time recording interval is set to every 10 seconds, at each time point, the contact angle value corresponding to the spatial coordinates (such as X axis 0.2 meters, Y axis 0.3 meters, X axis 0.4 meters, Y axis 0.5 meters, etc.) is extracted from the dynamic contact angle change data, and these information is filled in the table one by one, for example, at the 10th second, the contact angle value of the coordinates (0.2 meters, 0.3 meters) is 100 degrees, and the contact angle value of the coordinates (0.4 meters, 0.5 meters) is 95 degrees; at the 20th second, the contact angle value of the coordinates (0.2 meters, 0.3 meters) is 95 degrees, and the contact angle value of the coordinates (0.4 meters, 0.5 meters) is 90 degrees; and so on, to complete the data filling of all time points and all spatial coordinates, finally forming an interface contact angle parameter table containing spatial position and time information.

[0040] At step 101a, based on the pore network topology in the permeation path distribution, the local flow rate and the interfacial contact angle parameters, the heat conduction and convection diffusion coupling calculation is performed, and the heat conduction rate of the molten aluminum liquid, the flow diffusion rate and the constraint condition of the thermal stress on the pore deformation are defined, which specifically includes: when calculating the heat conduction rate, first obtain the thermal conductivity of the current barrier material, for example, the thermal conductivity of porous ceramic is 0.8 watts per meter kelvin, and the temperature gradient at this position is extracted from the temperature distribution data, the temperature distribution data is obtained from the molten aluminum liquid-barrier material coupling temperature monitoring system established in the early stage, the system arranges K-type thermocouple sensors at key positions in the pore network with an interval of 0.1 meters, the measurement accuracy is ±1 degree Celsius, the range is 0-1000 degrees Celsius, real-time acquisition of temperature data at different times is performed, and temperature distribution data is formed, the temperature gradient at a certain position is 500 degrees Celsius per meter (℃ / m), the specific extraction process is as follows: select two adjacent monitoring points in the temperature distribution data, point A coordinates (x1, y1, z1) and point B coordinates (x2, y2, z2), the distance between the two points is 0.1 meters calculated by three-dimensional coordinates, the actual measured temperature of point A is 700 degrees Celsius, and the actual measured temperature 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 when calculating the heat conduction rate to represent the rate, for example, the temperature gradient at a certain position is 500 degrees Celsius per meter (the temperature decreases 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 × absolute value of temperature gradient (℃ / m), the calculation result unit is W / m 2 , 0.8 × 500 (℃ / m) is multiplied, and the heat conduction rate is 400 W / m 2 , 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 degree Celsius, and the local flow rate is extracted from step 100d, such as 0.005 meters per second, the temperature difference between the molten aluminum liquid and the pore wall at this position is calculated, such as 700 degrees Celsius minus 650 degrees Celsius, and 50 degrees Celsius is obtained, according to the flow diffusion rate = local flow rate × heat capacity × temperature difference, 0.005 meters per second is multiplied by 1000 joules per kilogram degree Celsius and then multiplied by 50 degrees Celsius, and the flow diffusion rate is 250 joules per kilogram second, when defining the constraint condition of the thermal stress on the pore deformation, first determine the thermal stress bearing limit of the material, for example, the thermal stress bearing limit of the porous ceramic is 100 megapascals, if the monitored thermal stress at a certain position is 110 megapascals, which exceeds the limit value by 10 megapascals, the calculation of the exceeding ratio is 10 megapascals divided by 100 megapascals, which is 10%, and the pore diameter increases by 2%; if the thermal stress is 90 megapascals, which is lower than the limit value, the pore diameter remains unchanged; if the thermal stress is 120 megapascals, which exceeds the limit value by 20%, the pore diameter increases by 4%, and the corresponding relationship is established in this way.

[0041] Step 101b: Based on the constraints, perform a thermal conduction analysis of the molten aluminum in the pore network. Combine local velocity data to calculate the convection diffusion path and generate temperature distribution and heat flux density variation data within each pore channel. Specifically, according to the pore network topology, starting from the inlet position where the molten aluminum enters the pore, such as coordinates (0, 0), set the initial temperature to the actual temperature of the molten aluminum, such as 700 degrees Celsius, and set the time interval to 10 seconds. Calculate the temperature of each adjacent pore sequentially. Taking the pore A adjacent to the inlet as an example, obtain the heat conduction rate at this location from step 101a as 400 watts per square meter, and simultaneously obtain the cross-sectional area of ​​pore A, such as... The calculation of temperature requires consideration of factors such as square meters, material heat capacity (e.g., 880 joules per kilogram per degree Celsius), and mass per unit length (e.g., 0.002 kilograms per meter). This temperature calculation necessitates derivation using the law of conservation of energy: energy transferred by heat conduction = heat conduction rate × cross-sectional area × time, which is equivalent to 400 watts per square meter × [missing value]. Square meters × 10 seconds; Temperature increment = Energy transferred by heat conduction ÷ (Material heat capacity × Mass per unit length), i.e., Energy transferred by heat conduction ÷ (880 J / kg Celsius × 0.002 kg / m), therefore, the temperature of pore A = initial inlet temperature 700 degrees Celsius + temperature increment; Next, calculate the temperature of pore B adjacent to pore A, using the same calculation logic, i.e., first calculate the transferred energy using the heat conduction rate, the cross-sectional area of ​​pore B, and time, then combine the material heat capacity and 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 with local flow velocity data to calculate the temperature of pore A. When calculating the flow diffusion path, taking a local flow velocity of 0.005 m / s at a certain location as an example, with a time interval of 10 seconds, the flow distance = 0.005 m / s × 10 seconds = 0.05 m. The temperature distribution of the pores along this flow distance is updated using the temperature calculation method described above. When calculating the heat flux density change data, the heat flux density should be obtained according to Fourier's law of heat conduction, by multiplying the material's thermal conductivity by the temperature gradient (temperature difference between adjacent pores divided by the pore spacing), in watts per square meter. This is then multiplied by the cross-sectional area of ​​the corresponding pore (in square meters) to obtain the heat flow rate, in watts. For example, if the calculated heat flux density of a certain pore is 400 watts per square meter, multiplying it by its cross-sectional area... The heat flow rate is calculated per square meter, and recorded every 10 seconds to form curves showing the change of heat flux density and heat flow rate over time.

[0042] Step 101c, integrate the collected material surface thermal stress data, analyze the influence of the 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 the corrected temperature distribution data, which specifically includes: collecting thermal stress data of the material surface from multiple sensors, and the collection positions are uniformly distributed according to the monitoring area, such as setting a collection point every 0.5 meters, and recording the thermal stress value every 5 seconds at each collection point, checking the thermal stress data of each collection point, if the thermal stress of a collection point is 50 megapascals and greater than zero, it indicates that the material expands, according to the constraint condition of step 101a, the thermal stress of 50 megapascals does not exceed the limit value of 100 megapascals, and the pore diameter does not change; if the thermal stress of another collection point is 150 megapascals and greater than zero, it exceeds the limit value of 50 megapascals by 50%, then the pore diameter increases by 10% (because it increases by 2% for every 10%, 50% corresponds to 5 10%, that is, an increase of 10%), assuming that the original diameter of the pore is 0.2 millimeters, after increasing by 10%, the diameter is 0.2 millimeters plus 0.2 millimeters multiplied by 10%, which is equal to 0.22 millimeters, after the change of the pore diameter, 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), using the corrected heat conduction rate to recalculate the temperature distribution of the pore and the adjacent pores, for example, the original pore temperature is calculated to be 720 degrees Celsius, and after correction, the heat conduction rate is recalculated to be 440 watts per square meter, and a new temperature value is obtained; at the same time, the corrected heat conduction rate is used to recalculate the heat flux density to obtain the corrected heat flux density data, and finally all the corrected temperature data is integrated to form the corrected temperature distribution data.

[0043] Step 101d, based on the corrected temperature distribution data, the maximum penetration depth of molten aluminum liquid is predicted, and the temperature gradient over-limit area is identified in combination with the heat flux density change data, and the penetration depth safety threshold and the temperature gradient critical value are output, specifically including: looking up the corrected temperature distribution data, finding all positions where the temperature is equal to the temperature of molten aluminum liquid, such as 700 degrees Celsius, these positions are the areas that can be reached by molten aluminum liquid, and the coordinates of the deepest position in the area are determined, such as the deepest position coordinates are (2 meters, 3 meters, 1 meter) (Z axis is the vertical direction), the initial position coordinates are (2 meters, 3 meters, 0 meters), and the vertical distance between the position and the initial position is 1 meter minus 0 meter, which is equal to 1 meter, which 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, which ensures that there is a safety margin, when calculating the temperature gradient of each position, two adjacent sampling points are selected, such as sampling point 1 temperature is 700 degrees Celsius, coordinates (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 is equal to 50 degrees Celsius, the distance between the two points is 0.6 meters minus 0.5 meters, which is equal to 0.1 meters, according to the temperature gradient = temperature difference ÷ distance, the temperature gradient is 50 degrees Celsius divided by 0.1 meters, which is equal to 500 degrees Celsius per meter, the normal range of temperature gradient is preset, such as 300 degrees Celsius per meter, if the temperature gradient of a certain area is greater than 300 degrees Celsius per meter, such as the area with a temperature gradient of 500 degrees Celsius per meter, mark the area as a temperature gradient over-limit area, find the minimum temperature gradient value in all over-limit areas, such as a certain over-limit area temperature gradient is 400 degrees Celsius per meter, and another area is 500 degrees Celsius per meter, the minimum 400 degrees Celsius per meter is the temperature gradient critical value.

[0044] Step 101e, according to the temperature gradient critical value and the heat flux density data, the heat affected zone boundary is determined by the material thermal decomposition rate threshold, and the boundary coordinates, heat diffusion radius and diffusion rate parameters are generated, specifically including: first, the thermal decomposition rate threshold of the material is obtained, which is obtained from the factory manual of the material, such as the thermal decomposition rate threshold of a certain porous ceramic material is , the temperature gradient critical value is obtained from step 101d, such as 400 degrees Celsius per meter, the heat flux density data of the corresponding position is obtained from step 101b, such as , according to the thermal decomposition driving value = temperature gradient critical value 400 × heat flux density , the heat decomposition driving value is obtained, when the heat decomposition driving value of a certain position reaches the driving value corresponding to the material heat decomposition rate threshold, such as 0.06 degrees Celsius·watts per meter, the position is the heat affected zone boundary, the coordinates of each point on the boundary are measured using a laser positioning instrument, such as 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), the coordinates are sorted to form a boundary coordinate set, the geometric center of the heat affected zone is calculated, the average value of the X-axis of all boundary coordinates is calculated, assuming it is 2 meters; the average value of the Y-axis is calculated, assuming it is 3 meters, so the geometric center coordinates are (2 meters, 3 meters), when calculating the heat diffusion radius, the distance from each boundary point to the geometric center is calculated, the distances from the three points on the boundary to the geometric center are added and then divided by 3 to obtain the average distance, which is the heat diffusion radius, when calculating the heat diffusion rate parameter, two adjacent time points are selected, such as 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, equal to 10 seconds, the radius difference is 0.35 meters-0.25 meters, 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, integrate the penetration depth safety threshold, the heat diffusion radius, the material deformation tolerance and the heat decomposition rate threshold to generate a structured heat transfer analysis result containing the penetration depth threshold, the temperature diffusion range and the heat stability evaluation parameter, specifically including: directly taking 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 heat diffusion radius, i.e. the heat diffusion radius, such as 0.25 meters x 2=0.5 meters, representing a range of 0.25 meters from the geometric center of the heat affected zone to the four directions; obtain the material deformation tolerance, which is the maximum deformation value allowed by the material, obtained from the material manual, such as 0.1 millimeters, and obtain the material heat decomposition rate threshold, such as meters / second (uniformly in length / time dimension), calculate according to the heat stability evaluation parameter=(material deformation tolerance+heat decomposition rate threshold x reference time)÷2; first convert the material deformation tolerance unit to meters, i.e. 0.1 millimeters=0.0001 meters; select a reference time of 1 second, calculate the cumulative deformation equivalent of the heat decomposition rate threshold in unit time, i.e. meters / second x 1 second= meters; add them together, 0.0001 meters+ meters; and finally divided by 2 to obtain a thermal stability evaluation parameter, the larger the parameter, the better the thermal stability of the material, and a structured table is established, the columns of the table are in turn "spatial coordinates (X, Y, Z), time, penetration depth threshold, temperature diffusion range, thermal stability evaluation parameter", and the corresponding parameters of each spatial position and each time point are filled in the table one by one, for example, the coordinate (2 meters, 3 meters, 0.5 meters), the time is the 10th second, the penetration depth threshold is 0.8 meters, the temperature diffusion range is 0.5 meters, and the thermal stability evaluation parameter is 0.0000525 meters, and after the arrangement is completed, a structured heat transfer analysis result table is formed.

[0046] Step 102, according to the penetration depth threshold and the temperature diffusion range in the heat transfer analysis result, the potential contact area and the energy release rate of the molten aluminum liquid and the cooling water are calculated combined with the water absorption rate, 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 result, marking the area within the penetration depth threshold range in the three-dimensional space of the monitoring area, that is, the area of the Z axis from 0 meters to 0.8 meters, which is the area that the molten aluminum liquid may reach; mark the area corresponding to the temperature diffusion range, that is, the circular area with the heat affected center (2 meters, 3 meters) as the center and the radius of 0.5 meters, the Z axis range is consistent with the penetration depth area, which is the high temperature affected area, find out the overlapping part of the two areas through the graphic overlay tool, which is the area that the molten aluminum liquid and the cooling water may contact, measure the area of the overlapping area using image analysis software, 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 temperature of the cooling water, such as 20 degrees Celsius, the temperature difference between the two is 700 degrees Celsius minus 20 degrees Celsius, equal to 680 degrees Celsius; obtain the heat exchange coefficient, which is determined according to the flow state of the cooling water, for example, the heat exchange coefficient of the flowing cooling water is 1000 watts per square meter per degree Celsius, according to the energy release rate = temperature difference 680 x potential contact area 0.3 x heat exchange coefficient 1000, the energy release rate is calculated, and the water absorption rate is extracted from the leakage characteristic data set, for example, the water absorption rate is 0.001 square meters per second, which reflects the material surface area covered by the cooling water per unit time, the faster the water absorption rate, the faster the contact area grows, when calculating the increment of the contact area, select a time interval, such as 10 seconds, according to the contact area increment = water absorption rate 0.001 x time 10, the contact area increment is calculated, and the increment is added to the potential contact area, that is, 0.3 square meters + contact area increment, to obtain the updated potential contact area, and the updated contact area is used to recalculate the energy release rate to ensure that the result is more realistic.

[0047] Step 103a, according to the proportional relationship between the potential contact area and the preset contact area threshold, combined with the ratio of the energy release rate and the preset energy release rate threshold, the leakage risk index is calculated, specifically including: first get the preset contact area threshold, the threshold is set according to the historical leakage accident data and safety standard, such as 1 square meter; From step 102, the potential contact area is obtained, such as 0.31 square meters, according to the area ratio = potential contact area 0.31 ÷ preset contact area threshold 1, the area ratio is 0.31, and the preset energy release rate threshold is obtained, which is also set according to the safety standard, such as 250000 watts; From step 102, the energy release rate is obtained, such as 204000 watts, according to the energy ratio = energy release rate 204000 ÷ preset energy release rate threshold 250000, the energy ratio is obtained, according to the leakage risk index = (area ratio x 0.5) + (energy ratio x 0.5), the index ranges between 0 and 2.

[0048] Step 103b, when the leakage risk index is lower than the first critical value, it is determined as low risk level, specifically including: the first critical value is the lower risk limit value set in advance according to the safety management requirement, 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 molten aluminum leakage in the current scene is small, even if there is slight leakage, the leakage amount is small, and the energy release rate is low, which will not break through the barrier material or cause secondary accidents, and the impact range is small and the degree is light, so it is determined as low risk level.

[0049] Step 103c, when the leakage risk index is between the first critical value and the second critical value, it is determined as medium risk level, specifically including: the first critical value and the second critical value are both preset 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 means that there is a certain leakage risk of molten aluminum at present, a small amount of aluminum liquid may penetrate into the barrier material, and the energy release rate is at a medium level, if not intervened in time, it may expand the leakage range or cause local barrier material failure, causing a certain range of equipment damage or production interruption, therefore, it is determined as medium risk level.

[0050] 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 as a high risk level, specifically including: the second critical value is a pre-set higher risk limit value, such as 0.7, and the safety tolerance is the maximum allowed 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 tolerance of 300,000 watts), it means that the current molten aluminum leakage risk is high, and obvious leakage may have occurred. The penetration depth of the aluminum liquid is close to or exceeds the safety threshold, the energy release rate is high, and it is easy to cause large-area failure of the barrier material, severe vaporization of cooling water or even explosion, etc. Serious safety accidents, causing major equipment loss, personnel casualties or long-term production interruption, therefore, it is determined as a high risk level.

[0051] Step 104, according to the risk level, mapping the pre-set failure critical temperature threshold and barrier time threshold, generating dynamic positioning parameters including target area coordinates, failure temperature warning value and response time window, specifically including: pre-establishing a corresponding relationship table of risk level and failure critical temperature threshold, barrier time threshold, low risk level corresponds to higher failure critical temperature threshold, such as 600 degrees Celsius, and longer barrier time threshold, such as 30 minutes, meaning that the material can remain stable below 600 degrees Celsius, and has 30 minutes to take response measures; the medium risk level corresponds to the medium threshold, such as 550 degrees Celsius and 20 minutes, the material is stable below 550 degrees Celsius, and needs to be responded within 20 minutes; the high risk level corresponds to the lower failure critical temperature threshold, such as 500 degrees Celsius, and the shorter barrier time threshold, such as 10 minutes, the material is stable below 500 degrees Celsius, and needs to be intervened urgently within 10 minutes. Obtain the geometric center coordinates of the heat affected zone from step 101e, such as (2 meters, 3 meters), take the coordinates as the target area coordinates, which represent the position where the leakage risk is most concentrated, according to the current determined risk level, such as medium risk, extract the failure critical temperature threshold (550 degrees Celsius) from the corresponding relationship table as the failure temperature warning value, which reminds that the material will face the risk of failure when the temperature reaches 550 degrees Celsius; extract the barrier time threshold (20 minutes) as the response time window, that is, emergency response measures must be started within 20 minutes. Integrate the target area coordinates, failure temperature warning value, response time window and current risk level to form dynamic positioning parameters.

[0052] The embodiment can accurately grasp the penetration law and temperature change characteristics of the molten aluminum liquid by seepage analysis and porous medium heat transfer calculation of the pore structure of the material, and provide a reliable basis for risk assessment; dynamic simulation of the flow path and the change of the contact angle makes the prediction of parameters such as the penetration depth more in line with the actual situation, and improves the accuracy of the analysis; the contact area and the energy release rate are calculated in combination with the water absorption rate, the division of the risk level is more comprehensive, and different degrees of leakage risk can be effectively distinguished; according to the risk level mapping failure critical temperature and barrier time, the output dynamic positioning parameters are targeted, and the whole process is integrated and dynamically corrected through multiple parameters to ensure the rigor of the analysis results.

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

[0054] In the embodiment of the present application, in step 200, when the leakage risk level in the dynamic positioning parameter is greater than or equal to the medium risk, the corrosion-resistant ejection device is activated, and the porous ceramic barrier layer is laid between the leakage point and the cooling water pipeline according to the target area coordinates, which specifically includes: extracting the leakage risk level from the dynamic positioning parameter, if the level is medium risk or high risk, immediately triggering the start command of the corrosion-resistant ejection device, first obtaining the target area coordinates in the dynamic positioning parameter, such as target area coordinates (3 meters, 4 meters, 0.5 meters), at the same time 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 pipeline, such as (2.8 meters, 3.9 meters, 0.4 meters), calculating the shortest path distance between the leakage point and the cooling water pipeline, that is, first calculating the distance between the two in the horizontal plane (X-Y axis), subtracting the X-axis coordinate of the cooling water pipeline from the X-axis coordinate of the leakage point to obtain the X-axis difference; subtract the Y-axis coordinate of the cooling water pipeline from the Y-axis coordinate of the leakage 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 sum of squares; then calculate the square root of the sum of squares 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 pipeline from the Z-axis coordinate of the leakage point to obtain the vertical distance, finally add the square of the horizontal distance to the square of the vertical distance to obtain the total sum of squares, and then calculate the square root of the total sum of squares to obtain the shortest path distance between the leakage point and the cooling water pipeline. Take the shortest path as the center to determine the laying range of the porous ceramic barrier layer, that is, the laying width needs to cover each side of the path by 0.3 meters, so the total width is the sum of the covering width on one side of the path and the covering 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 leakage point to the outer wall of the cooling water pipeline, first calculate the distance from the edge of the leakage point to the starting point of the path, take 0.1 meters to ensure covering the leakage point periphery, and then add the shortest path distance to obtain the total length, assuming that the finally determined laying range is a rectangular area with a length of 0.59 meters and a width of 0.6 meters, which can completely block the channel for the molten aluminum liquid to flow to the cooling water pipeline; the corrosion-resistant ejection device pre-stores the cut porous ceramic barrier layer, the size of each barrier layer is pre-set according to common leakage conditions, such as 1.5 meters long, 0.8 meters wide, and 0.05 meters thick, and the device is provided with a plurality of ejection channels, each channel corresponds to a different direction angle, according to the target area coordinates and the installation position coordinates of the device itself, such as (2.5 meters, 3.5 meters, 0 meters), the ejection angle is calculated, that is, the X-axis difference and the Y-axis difference of the device and the center of the target area on the horizontal plane are calculated, a horizontal rectangular coordinate system is constructed with the device installation position as the origin, the X-axis difference is the adjacent side, and the Y-axis difference is the opposite side, the angle in the horizontal direction is calculated by the tangent function (the tangent value is equal to the opposite side divided by the adjacent side), and it is ensured that the ejection direction is accurately pointed to the horizontal center of the target laying area; at the same time, the vertical height difference between the target area center and the device, and the horizontal distance from the device to the target area center are calculated, and the horizontal distance is taken as the adjacent side and the vertical height difference is taken as the opposite side, the angle in the vertical direction is calculated by the tangent function (the tangent value is equal to the opposite side divided by the adjacent side), so as to avoid deviation of the blocking layer from the target area when laying; after starting the ejection device, the high-pressure gas pushing mechanism in the device pushes the porous ceramic blocking layer at a speed of 2 meters per second according to the calculated horizontal angle and vertical angle, so as to ensure that the blocking layer is smoothly landed on the target laying area with a length of 0.59 meters and a width of 0.6 meters; after laying is completed, the position detection assembly of the device is used to measure the deviation of the edge of the blocking layer from the edge of the target area, if the deviation is less than 0.1 meters, it is determined that the laying is qualified; if the deviation is greater than 0.1 meters, the fine adjustment function is started, the small pushing mechanism is used to push the blocking layer, each time by 0.02 meters, the deviation is measured again after pushing, until the deviation is less than 0.1 meters, the directional laying is completed, after starting the ejection device, the high-pressure gas pushing mechanism in the device ejects the porous ceramic blocking layer along the calculated angle, the ejection speed is controlled at 2 meters per second, so as to ensure that the blocking layer can be smoothly landed on the target laying area, after laying is completed, the position detection assembly of the device is used to confirm whether the blocking layer completely covers the preset laying range, if there is a local uncovered area, such as an edge deviation of more than 0.1 meters, the fine adjustment function of the device is started, the small pushing mechanism is used to adjust the blocking layer to the correct position, and finally the directional laying between the leakage point and the cooling water pipeline is completed.

[0055] Step 201, after laying the porous ceramic barrier layer, the temperature gradient distribution is monitored in real time, if the local temperature reaches the failure critical temperature threshold, the distribution density of hydrophilic groups and the pore size gradient of the barrier layer are dynamically adjusted to optimize the water absorption rate and thermal stability, specifically including: after the laying of the porous ceramic barrier layer is completed, immediately start the micro temperature sensor distributed on the surface of the barrier layer, the arrangement interval of the sensor is 0.2 meters, forming a grid-shaped monitoring array, collecting temperature data of each sensor position every 5 seconds, according to the collected temperature data, the temperature difference between each adjacent sensor is calculated, and then divided by the interval of adjacent sensors, that is, 0.2 meters, to obtain the temperature gradient distribution data of each position; the failure critical temperature threshold is extracted from the dynamic positioning parameters, for example, the failure critical 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 certain monitoring position reaches 600 degrees Celsius, it means that the local area is close to the failure state of the barrier layer, the dynamic adjustment mechanism needs to be started, first analyze the water absorption rate of the local area, through the water sensor arranged inside the barrier layer, collect the amount of cooling water absorbed by the area in unit time, for example, 0.02 kg of cooling water is absorbed in 10 seconds, divided by the time and the volume of the area, such as 0.001 cubic meters, to obtain the current water absorption rate; if the water absorption rate is lower than the preset optimal water absorption rate, such as 0.03 kg per second per cubic meter, the distribution density of hydrophilic groups of the barrier layer is adjusted, that is, through the pre-set chemical regulation component inside the barrier layer, the component is an embedded micro slow-release capsule array, each capsule has a diameter of 0.002 meters, and is uniformly distributed in the porous ceramic barrier layer with an interval of 0.05 meters x 0.05 meters, the capsule stores organosilane regulator containing hydroxyl hydrophilic group inside, and a micro valve is arranged at the top of each capsule, which can be electrically controlled to open, the valve is connected with the temperature-humidity linkage control circuit inside the barrier layer, and the regulator containing hydrophilic groups is released to the local area, the release amount of the regulator is calculated according to the difference of the water absorption rate, that is, the current water absorption rate of the local area measured by the water sensor is obtained, such as 0.02 kg per second per cubic meter, and the difference between the current water absorption rate and the optimal water absorption rate is calculated, that is, 0.03 kg per second per cubic meter minus 0.02 kg per second per cubic meter equals 0.01 kg per second per cubic meter, according to the corresponding relationship calibrated in advance, that is, for every 0.01 kg per second per cubic meter of water absorption rate improvement, 0.5 milliliter of regulator needs to be released to the barrier layer area with a unit volume (1 cubic meter), to determine the release amount of the regulator in the local area; if the volume of the local area is 0.0008 cubic meters (length 0.4 meters x width 0.4 meters x thickness 0.005 meters), first calculate the product of the unit volume release amount and the area volume, that is, 0.5 milliliter per cubic meter multiplied by 0.0008 cubic meters, then activate the corresponding number of micro slow-release capsule valves in the area through the control circuit, and each capsule can release 0.0001The 4 capsules are activated for 1 milliliter of conditioner calculation. After the valve is opened, the organosilane conditioner in the capsule is slowly released to the surface of the ceramic pore through osmosis and chemically bonds with the oxide on the pore wall surface, increasing the distribution density of hydrophilic groups and thus improving the water absorption rate of the region to the optimal range. Through the micro mechanical adjustment structure in the barrier layer, the diameter of the pore is changed. For the region whose temperature reaches the failure threshold, the pore diameter is adjusted from the original 0.2 millimeters to 0.15 millimeters. The reduction in pore size increases the flow resistance of the molten aluminum liquid and improves the adsorption capacity of the cooling water. The pore wall is gradually extruded during adjustment, with a 0.01 millimeter reduction in pore size each time. After each adjustment, wait for 3 seconds, and then monitor the temperature and water absorption rate again. The dynamic adjustment is completed when the temperature is below the failure threshold and the water absorption rate reaches the optimal range.

[0056] Step 202, when the leakage risk level is high risk or the thermal diffusion radius exceeds the limit, the linkage cooling water pressure control unit generates a high-pressure nitrogen gas isolation air curtain to form a dynamic physical barrier to isolate the molten aluminum liquid from the cooling water, specifically including: continuously monitoring the leakage risk level and thermal diffusion radius data in the dynamic positioning parameters, if the risk level becomes high risk or the thermal diffusion radius exceeds the preset safety radius, such as 0.8 meters, immediately send a linkage instruction to the cooling water pressure control unit, first get the target area coordinates, such as (3 meters, 4 meters, 0.5 meters), and the position of the cooling water pipeline, such as the pipeline is laid along the Y axis direction, and the outer wall center coordinates are (2.5 meters, 4 meters, 0.5 meters), determine the generation range of the high-pressure nitrogen gas isolation air curtain, the air curtain needs to cover the entire thermal diffusion area, and form a continuous barrier on the path where the molten aluminum liquid may flow, the coverage width of the air curtain is set to 1.5 times the thermal diffusion radius, for example, when the thermal diffusion radius is 0.9 meters, the air curtain width is 0.9 meters multiplied by 1.5 equals 1.35 meters, to ensure that the molten aluminum liquid diffusion can be completely blocked; the cooling water pressure control unit is provided with a high-pressure nitrogen gas storage tank and a plurality of air curtain nozzles, the nozzles are uniformly distributed around the outer wall of the cooling water pipeline, the distance between every two adjacent nozzles in the circumferential direction or axial direction of the pipeline outer wall is 0.2 meters, according to the target area coordinates, the opening number and jet angle of each nozzle are calculated, specifically, first determine the projection range of the air curtain area on the horizontal plane, taking the target area coordinates as the center, the air curtain coverage width is 1.35 meters, so the X axis starting coordinate of the projection range is the target area X axis coordinate 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 terminal coordinate is the target area X axis coordinate 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 target area Y axis coordinate, which is 4 meters; then correspond 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 pipeline, the X axis coordinate of the first nozzle is 2.1 meters, the X axis coordinate of each subsequent nozzle increases by 0.2 meters (because the distance is 0.2 meters), that is, the second nozzle is 2.1 meters plus 0.2 meters equals 2.3 meters, the third nozzle is 2.3 meters plus 0.2 meters equals 2.5 meters, the fourth nozzle is 2.5 meters plus 0.2 meters equals 2.7 meters, the fifth nozzle is 2.7 meters plus 0.2 meters equals 2.9 meters, the sixth nozzle is 2.9 meters plus 0.2 meters equals 3.1 meters, the seventh nozzle is 3.1 meters plus 0.2 meters equals 3.3 meters, the eighth nozzle is 3.3 meters plus 0.2 meters equals 3.5 meters, the ninth nozzle is 3.5 meters plus 0.2 meters equals 3.7 meters, the tenth nozzle is 3.7 meters plus 0.2 meters equals 3.9 meters, select the nozzles whose X axis coordinates are within the projection range (2.325 meters to 3.675 meters), that is, the fifth nozzle (2.9 meters) to the eighth nozzle (3.5 meters), but according to the actual coverage demand, the projection range needs to be expanded to 1.8 meters to 3.8 meters, at this time the nozzles meeting the conditions are the fifth (2.9 meters) to the twelfth (2.9 meters plus (12 minus 5) times 0.2 meters, which is 4.3 meters, adjust the projection range to 2.8 meters to 4.3 meters), a total of 8 nozzles, ensuring that the projection area is completely covered by the nozzles; nitrogen gas sprayed by the nozzles can form a continuous gas curtain in the target area, first determine the relative position of each nozzle to the center of the target area, for the nozzles located on the left side of the cooling water pipeline, that is, the X-axis coordinate of the nozzle is less than the X-axis coordinate of the target area by 3 meters, such as the fifth to the eighth nozzles, calculate the horizontal offset and vertical offset of the nozzle from the center of the target area, the horizontal offset is the X-axis coordinate of the target area minus the X-axis coordinate of the nozzle, such as the horizontal offset of the fifth nozzle is 3 meters minus 2.9 meters, which is 0.1 meter; the vertical offset is the Z-axis coordinate of the target area minus the Z-axis coordinate of the nozzle, assuming the Z-axis coordinate of the nozzle is 0.5 meters, and the Z-axis coordinate of the target area is also 0.5 meters, then the vertical offset is 0.5 meters minus 0.5 meters, which is 0; next, according to the horizontal offset and the vertical offset, the jet angle is determined by the trigonometric function, first define the jet angle, take the position of the nozzle as the origin, the horizontal direction (X-axis direction) as the adjacent side, the jet direction of the nozzle pointing to the target area as the hypotenuse, and the vertical direction (Z-axis direction) as the opposite side, forming a right triangle, the jet angle is the included angle between the hypotenuse and the horizontal adjacent side; for the nozzles located on the left side of the cooling water pipeline, 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, which is 2.9 meters, and the horizontal offset is 0.1 meter; the vertical offset has been calculated as 0 meters, in the right triangle, the definition of the tangent function is the length of the opposite side divided by the length of the adjacent side, that is, tan (jet angle) = vertical offset ÷ horizontal offset, substituting the vertical offset 0 meters and the horizontal offset 0.1 meters, we get tan (jet angle) = 0 meters ÷ 0.1 meters = 0, through the trigonometric function table, it is known that when the tangent value is 0, the corresponding angle is 0 degrees, but combined with the actual demand, it is necessary to let the nitrogen gas cover the target area upwards to avoid insufficient gas curtain height caused by horizontal jet only, so it is necessary to superimpose a vertical upward angle on the basis of the horizontal angle, therefore, on the basis of the 0-degree horizontal angle corresponding to the horizontal offset, an additional 30-degree vertical upward angle is set, finally forming a 30-degree upward jet angle to the right, that is, the included angle between the nozzle jet direction and the horizontal direction (X-axis positive direction) is 30 degrees, among which the horizontal component ensures that the nitrogen gas can cover the target area X-axis position to the right, and the vertical component ensures that the nitrogen gas can maintain sufficient height and intersect with the nitrogen gas sprayed by other nozzles in the target area, pointing upwards to the target area; for the nozzles located on the right side of the cooling water pipeline, that is, the X-axis coordinate of the nozzle is greater than 3 meters, such as the ninth to the twelfth nozzles, the horizontal offset is the X-axis coordinate of the nozzle minus the X-axis coordinate of the target area, such as the horizontal offset of the ninth nozzle is 3.7 meters minus 3 meters, which is 0.7 meters, the vertical offset is 0, and the jet angle of the right nozzle is adjusted to 30 degrees to the left and up, so that the nitrogen gas sprayed by all the nozzles converges in the target area to form a complete gas curtain.

[0057] The control unit adjusts the output pressure of the nitrogen gas, and the initial pressure is set to 3 MPa. The nozzle sprays nitrogen gas to form a high-pressure nitrogen gas isolation curtain. At the same time, through the gas curtain pressure monitoring assembly, the actual pressure of the gas curtain is measured in real time. If the actual pressure is lower than 2.8 MPa, it means that the gas curtain density is insufficient, and there may be a leak. At this time, the control unit increases the output of nitrogen gas to increase the pressure to 3.2 MPa. If the actual pressure is higher than 3.2 MPa, to avoid excessive diffusion of the gas curtain and waste of nitrogen gas, the pressure is reduced to 2.9 MPa. The pressure of the gas curtain is always maintained between 2.8 and 3.2 MPa to form a stable dynamic physical barrier to block the contact between the molten aluminum liquid and the cooling water.

[0058] In this embodiment, when the leakage risk reaches the medium risk and above, a porous ceramic barrier layer is laid in a targeted manner to quickly establish a physical barrier between the leakage point and the cooling water pipeline, effectively delaying or even blocking the flow of molten aluminum liquid to the cooling water pipeline, and avoiding contact between the two to cause danger. Real-time monitoring of the temperature of the barrier layer and dynamic adjustment of the hydrophilic group and pore size gradient can optimize the water absorption capacity and thermal stability of the barrier layer according to the actual temperature changes, ensuring that the barrier layer still maintains effective performance when approaching the failure critical temperature, thereby prolonging the barrier time. For high-risk leakage or thermal diffusion exceeding the limit, a high-pressure nitrogen gas isolation curtain can form a dynamic and continuous physical barrier. Compared with a fixed barrier layer, the coverage of the gas curtain is more flexible and can be adjusted in real time according to the thermal diffusion range, more comprehensively isolating the molten aluminum liquid from the cooling water and reducing the risk of violent reaction. The entire multi-level response strategy does not require manual intervention and is automatically executed from activating the device to completing the adjustment, enabling quick and targeted measures to be taken at different stages of the development of a leakage event, improving the timeliness and accuracy of emergency response, and minimizing the safety hazards and losses caused by leakage events.

[0059] In a preferred embodiment of the present application, 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 high polymer coating are collected. The measured water absorption rate is compared with the preset water absorption rate threshold to generate a set of material modification parameters, which can include:

[0060] In the embodiment of the present application, step 300, calculate the measured water absorption rate according to the real-time monitored water absorption capacity data, compare the measured water absorption rate with the preset water absorption rate threshold to generate the water absorption rate deviation coefficient; identify the local overheating area based on the temperature gradient distribution data, and calculate the thermal stress deformation coefficient in combination with the micro-crack propagation rate in the structural integrity data, specifically including: extracting the water absorption capacity of the material from the real-time monitoring data, such as the amount of cooling water absorbed by the material in 10 minutes is 0.5 kg, and the total mass of the material participating in water absorption in 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 equals 0.25, that is, the measured water absorption rate is 25%; obtain the preset water absorption rate threshold, such as the preset water absorption rate threshold is 30%, when calculating the water absorption rate deviation coefficient, the measured water absorption rate is subtracted from the preset water absorption rate threshold, and the difference is 25% minus 30% equals -5%, then the difference is divided by the preset water absorption rate threshold, that is, -5% divided by 30% is approximately -0.17, this result is the water absorption rate deviation coefficient, and the coefficient is negative, indicating that the actual water absorption capacity is lower than the preset standard; identify the local overheating area based on the temperature gradient distribution data, first collect the temperature data of each position of the material, such as dividing the surface of the material into multiple 10 cm x 10 cm monitoring units, the temperature of each unit is 50°C, 48°C, 65°C, etc., calculate the temperature difference between adjacent units, such as the temperature difference between the unit of 65°C and the adjacent unit of 50°C is 15°C, and then divided by the distance between the two units 0.1 m, to get the temperature gradient 150°C per meter, set the temperature gradient threshold to 100°C per meter, mark the area where the unit with a temperature gradient exceeding the threshold as a local overheating area, such as the area where the unit with a temperature gradient of 150°C per meter is located is the local overheating area.

[0061] Extract the micro-crack propagation rate of the local overheating area from the structural integrity data, such as monitoring that the micro-crack has expanded from 0.1 mm to 0.3 mm in 1 hour, the expansion length is 0.3 mm minus 0.1 mm equals 0.2 mm, and the expansion length is divided by the time 1 hour to get the micro-crack propagation rate 0.2 mm per hour, when calculating the thermal stress deformation coefficient, first subtract the room temperature 25°C of the material from the highest temperature 65°C of the local overheating area to get the temperature difference 40°C, then multiply the temperature difference by the micro-crack propagation rate 0.2 mm per hour to get 8 mm / °C per hour, and finally divide this result by the thermal expansion coefficient of the material, such as taking , that is, 8 divided by , the obtained value is the thermal stress deformation coefficient.

[0062] Step 301, obtain the thermal weight loss rate through the thermal decomposition characteristic parameters of the high polymer coating, and correlate the water absorption rate deviation coefficient and 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 high polymer coating, such as 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 in the 10-degree Celsius temperature change interval, that is, 9.9 grams minus 9.7 grams equals 0.2 grams. Then divide the mass loss by the temperature change interval of 10 degrees Celsius to get the weight loss per degree Celsius, which is 0.02 grams per degree Celsius. Then multiply this value by the temperature change rate of the material's environment, such as 5 degrees Celsius per hour, which is 0.02 grams per degree Celsius multiplied by 5 degrees Celsius per hour, which is 0.1 grams per hour. This is the thermal weight loss rate. When correlating the water absorption rate deviation coefficient and the thermal stress deformation coefficient, first convert the water absorption rate deviation coefficient, such as -0.17, to its absolute value to get 0.17. Then multiply this absolute value by the thermal stress deformation coefficient, assuming it is 1000, to get the first value 170. Then multiply the thermal weight loss rate 0.1 grams per hour by the reciprocal of the thermal expansion coefficient of the material, assuming the reciprocal of the thermal expansion coefficient is 1 ÷ ( / degree Celsius), which has a physical meaning of the temperature change required for the material to produce unit linear expansion. Get the second value, which represents the relationship between the thermal weight loss rate and the thermal expansion characteristics of the material. Finally, add the two values to get a total, which establishes the correlation between the three parameters and reflects the degree of mutual influence between the material's water absorption capacity deviation, thermal stress deformation, and coating thermal decomposition.

[0063] Step 302, based on the difference between the thermal weight loss rate and the preset decomposition threshold, superimpose the water absorption rate deviation coefficient and the thermal stress deformation coefficient to generate a material modification parameter set containing the hydrophilic group density adjustment amount, the pore size gradient optimization parameter, and the coating reinforcement thickness, specifically including: obtaining the preset thermal decomposition threshold, such as the preset thermal weight loss rate threshold is 0.05 grams per hour. Calculate the difference between the thermal weight loss rate and the threshold, which is 0.1 grams per hour minus 0.05 grams per hour, equal to 0.05 grams per hour. When calculating the hydrophilic group density adjustment amount, first multiply the above difference 0.05 grams per hour by 1000 to get 50, then multiply by the absolute value of the water absorption rate deviation coefficient 0.17, that is, 50 × 0.17 = 8.5. This value is the hydrophilic group density adjustment amount, indicating that 8.5 units of hydrophilic group density need to be increased to improve the water absorption capacity and offset the negative impact of thermal decomposition. When calculating the pore size 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 / degree Celsius), to get 1000 ÷ ( ), to get a result, then divide the result by The intermediate value is assumed to be 0.8, and the value obtained by adding the thermal weight loss rate difference 0.05 to the intermediate value is the pore size gradient optimization parameter, which indicates that the pore size gradient of the material needs to be adjusted by the proportion to reduce the change range of the pore diameter to enhance the structural stability; when calculating the coating reinforcement thickness, the superimposed effects of the thermal weight loss rate and the thermal stress deformation are comprehensively considered, 0.2 millimeters is obtained by multiplying the thermal weight loss rate 0.1 grams per hour by the conversion coefficient 2; then add the intermediate value 0.8 obtained in the pore size gradient optimization parameter calculation process, that is, 0.2+0.8=1.0, this value is the coating reinforcement thickness, which indicates that 1.0 millimeters of thickness needs to be added to the original coating to resist the damage caused by thermal decomposition and stress deformation; the hydrophilic group density adjustment amount 8.5 units, the pore size gradient optimization parameter, and the coating reinforcement thickness 1.0 millimeters are arranged together to form the material modification parameter set.

[0064] In this embodiment, by monitoring the performance parameters of the material in real time and performing detailed calculations, the temperature distribution of the water absorption capacity and the structural integrity state of the material can be accurately mastered, data support is provided for material modification, and the modification measures are targeted; the water absorption rate deviation coefficient and the thermal stress deformation coefficient generated can clearly reflect the difference between the actual performance of the material and the preset standard and the degree of temperature influence, helping to determine the modification direction; the correlation of the thermal weight loss rate and other parameters can comprehensively evaluate the thermal stability of the high polymer coating, avoid the one-sidedness caused by single parameter evaluation, and make the material modification more comprehensive; the finally generated material modification parameter set contains the specific adjustment values of the hydrophilic group density, the pore size gradient and the coating thickness, which can directly guide the material modification operation and improve the modification efficiency and effect.

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

[0066] In the embodiment of the present application, step 400, the dynamic positioning parameters and material modification parameter sets of each edge node are aggregated, and the regional thermal hazard situation data is generated based on the leakage risk level distribution, specifically including: collecting all the dynamic positioning parameters transmitted by the central control unit from all the edge computing nodes, the edge nodes being high-temperature-resistant monitoring terminals deployed around the electrolytic cell, a total of 8, corresponding to 1-4 firebrick regions at the bottom of the electrolytic cell, A / B segment regions of the cooling water pipes on both sides, the feeding port region, and the aluminum outlet region, each edge node transmitting data to the central control unit through a wired transmission to avoid high-temperature interference with wireless signals, the data including target region coordinates of the sub-region, accurate to centimeters, such as the target coordinates of the 1st firebrick region being (2.1 meters, 3.3 meters, 0.6 meters), the failure temperature warning value, such as the 1st region being 500 degrees Celsius, the response time window, such as the 1st region being 10 minutes, and the leakage risk level being determined in real time by the node's built-in algorithm, which first collects the real-time temperature of the sub-region through the temperature sensor carried by the node, collects the impact pressure of the molten aluminum liquid on the barrier layer through the pressure sensor, and collects the actual penetration depth of the aluminum liquid in the barrier layer through the penetration sensor, and then compares the three data with the node's pre-set safety thresholds respectively; wherein 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 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, all three data are within the safety range, and it is determined to be low risk; if only one of the three data exceeds the corresponding safety threshold, it is determined to be medium risk; if two or more of the three data exceed the corresponding safety threshold, it is determined to be high risk, for example, the node 1 (1st firebrick region) collects the real-time temperature of 480 degrees Celsius, the impact pressure of 0.35 MPa, and the penetration depth of 0.6 meters, two of the three data exceed the safety threshold, and the risk level is determined to be high risk; the node 2 (cooling water pipe A segment region) collects the real-time temperature of 430 degrees Celsius, the impact pressure of 0.32 MPa, and the penetration depth of 0.45 meters, only one of the three data exceeds the safety threshold, and it is determined to be medium risk; the nodes 3 to 8 (the remaining 6 sub-regions) collect the real-time temperature below 420 degrees Celsius, the impact pressure below 0.28 MPa, and the penetration depth below 0.4 meters, all three data do not exceed the safety threshold, and it is determined to be low risk; at the same time, the central control unit receives the material modification parameter set of each sub-region, which is generated by the material monitoring module of each node and includes the hydrophilic group density adjustment amount, the pore diameter gradient optimization parameter representing the pore diameter change ratio, and the coating reinforcement thickness, such as the node 1 sub-region having a serious thermal decomposition, the hydrophilic group density adjustment amount being 8.5 per square micrometer, 6 per square micrometer for node 2 sub-area, 3 per square micrometer for node 3 to 8 sub-areas due to low risk, the central control unit arranges the dynamic positioning parameters and material modification parameters into an Excel format regional parameter summary table in the format of "sub-area code, coordinate range, parameter type, value", ensuring 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, first divide the entire monitoring area (electrolytic cell and surrounding 3 meters) into 24 grid units (6 columns horizontally, 4 rows vertically, column numbers A-F, row numbers 1-4) by the central control unit according to 1 meter x 1 meter, each grid unit is marked with the corresponding sub-area attribution, such as grid A1 corresponds to the 1st firebrick area of node 1, grid B2 corresponds to the cooling water pipeline A area of node 2, then according to the risk level labeling rule, each grid unit is marked with a color label, high-risk grid (A1) is marked red, medium-risk grid (B2) is marked orange, and low-risk grid (the remaining 22) is marked blue; count the number of each risk level grid, 1 high-risk, 1 medium-risk, and 22 low-risk, a total of 24 grid units (1 plus 1 plus 22 equals 24, verified without omission), calculate the proportion of each risk level, that is, the high-risk proportion is divided by the total grid unit number, the medium-risk proportion is divided by the total grid unit number, and the low-risk proportion is divided by the total grid unit number, finally integrate "grid coordinates, color labels, risk proportions, and corresponding sub-area material modification parameters" to generate regional thermal hazard situation data, the situation data needs to be marked 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 acquisition frequency of high-risk area and low-risk area acquisition frequency, specific including: the central control unit first analyzes the risk level gradient distribution in the regional thermal hazard situation data, and divides the eight sub-regions into three gradient zones according to the risk level, that is, the high-risk gradient zone (only the first firebrick region corresponding to node 1), the medium-risk gradient zone (only the cooling water pipeline A section region corresponding to node 2), and the low-risk gradient zone (the remaining six sub-regions corresponding to nodes 3 to 8); Set the initial data acquisition frequency reference value to 10 seconds / time (suitable for conventional risk areas), and dynamically adjust according to the risk level gradient coefficient, that is, the acquisition frequency coefficient of the high-risk gradient zone is 0.5 (i.e. the reference value x 0.5), the adjusted acquisition frequency = 10 seconds x 0.5 = 5 seconds / time, to ensure high-density monitoring of high-risk areas; The medium-risk gradient zone maintains the reference frequency of 10 seconds / time, balancing monitoring accuracy and system load; The acquisition frequency coefficient of the low-risk gradient zone is 2 (i.e. the reference value x 2), and the adjusted acquisition frequency = 10 seconds x 2 = 20 seconds / time, to reduce the data transmission pressure of low-risk areas; The central control unit sends frequency adjustment instructions to each edge node through the industrial bus, and the instructions include node number, new acquisition period (accurate to milliseconds), and effective time (effective immediately); After receiving the instructions, each edge node updates the sampling timer parameters in the local embedded system, for example, node 1 modifies the original 10-second timer to a 5-second timer, node 2 keeps the 10-second timer unchanged, and nodes 3 to 8 modify 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 sending the instructions: the sampling interval of high-risk node 1 should be within the range of 4.8-5.2 seconds (allowing ±0.2 seconds error), the sampling interval of medium-risk node 2 should be within the range of 9.8-10.2 seconds, and the sampling interval of low-risk nodes 3 to 8 should be within the range of 19.8-20.2 seconds; If the sampling interval of a node exceeds the allowed range for three consecutive times (for example, node 1 has a 6-second interval), the central control unit will send a secondary calibration instruction to forcibly synchronize the node clock with the system master clock (accuracy of 1 millisecond level); At the same time, dynamically adjust the data caching strategy: high-risk area nodes enable double caching mechanism (main cache + standby cache) to prevent data loss; Medium-risk area nodes remain single caching; Low-risk area nodes can enable cache compression (compression rate 50%), reducing storage space occupation.

[0068] Step 402, after adjusting the acquisition frequency, real-time acquisition of the penetration depth safety threshold and the thermal diffusion radius data, and according to the risk level priority in the situation data, the porous ceramic barrier layer reserve and the nitrogen isolation gas curtain coverage range are allocated, which specifically includes: each edge node transmits data to the central control unit according to the adjusted frequency, that is, the high-risk gradient area node sends the penetration depth safety threshold every 5 seconds, which is measured by the laser ranging unit of the node, that is, the maximum allowed penetration depth and the 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 of penetration depth safety threshold 0.8 meters and thermal diffusion radius 0.9 meters; the medium-risk gradient area node sends once every 10 seconds, such as node 2 transmits penetration depth safety threshold 1 meter and thermal diffusion radius 0.6 meters; the low-risk gradient area node sends once every 20 seconds, such as node 4 transmits penetration depth safety threshold 1.2 meters and thermal diffusion radius 0.3 meters; the central control unit allocates the porous ceramic barrier layer reserve according to the risk level priority, the total reserve is 100 square meters (stored in No. 3 warehouse of the electrolysis workshop, stored in 5 volumes, each volume is 20 square meters), and the priority from high to low is high-risk gradient area > medium-risk gradient area > low-risk gradient area, and the allocation amount of each area is calculated, wherein the high-risk gradient area first calculates the thermal diffusion area (according to the circular area formula), the thermal diffusion radius is 0.9 meters, the diameter = 0.9 meters x 2 = 1.8 meters, the thermal diffusion area = 3.14 x (1.8 meters ÷ 2) 2 = 3.14 x 0.81 square meters = 2.5434 square meters, because the high-risk area needs double coverage (to prevent single barrier layer damage), the allocation amount = thermal diffusion area 2.5434 x 2, the result is rounded to 5.1 square meters (cut from No. 1 volume); the thermal diffusion radius of the medium-risk gradient area is 0.6 meters, the diameter = 0.6 meters x 2 = 1.2 meters, the thermal diffusion area = 3.14 x (1.2 meters ÷ 2) 2 = 3.14 x 0.36 square meters = 1.1304 square meters, according to 1.5 times coverage (taking into account safety and saving), the allocation amount = 1.1304 square meters x 1.5, the result is rounded to 1.7 square meters (cut from the remaining part of No. 1 volume); the thermal diffusion radius of the low-risk gradient area is 0.3 meters, the diameter = 0.3 meters x 2 = 0.6 meters, the thermal diffusion area = 3.14 x (0.6 meters ÷ 2) 2= 3.14 x 0.09 square meters = 0.2826 square meters, according to 1 times coverage, the distribution amount = 0.2826 square meters x 1 = 0.28 square meters, rounded to 0.3 square meters (cut from No. 2 roll); the remaining 100 square meters - 5.1 square meters - 1.7 square meters - 0.3 square meters = 92.9 square meters, as an emergency reserve, stored in the warehouse reserve area; when distributing 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 = 0.9 x 2 = 1.8 meters of thermal diffusion radius, the coverage center is consistent with the target area coordinates (2.1 meters, 3.3 meters, 0.6 meters), 8 air curtain nozzles (numbers 1-8) in the corresponding area are opened; the air curtain coverage radius of the medium-risk gradient area = 0.6 meters x 1.5 = 0.9 meters of thermal diffusion radius, the coverage center is (3.2 meters, 4.1 meters, 0.5 meters), 4 air curtain nozzles (numbers 9-12) are opened; the air curtain coverage radius of the low-risk gradient area = 0.3 meters x 1 = 0.3 meters of thermal diffusion radius, the coverage center is (1.5 meters, 2.2 meters, 0.4 meters), 2 air curtain nozzles (numbers 13-14) are opened, to ensure that the air curtain completely covers the thermal diffusion range of each area.

[0069] Step 403, after the allocation of the barrier resource, the actual temperature gradient distribution data of the barrier layer is deployed, and the abnormal value of the heat flux density of the local overheating area is extracted, which specifically includes: according to the resource allocation instruction, the on-site construction personnel deploys the porous ceramic barrier layer in each risk area, in the high-risk gradient area, grid A1 lays 5.1 square meters of barrier layer with a thickness of 0.05 meters, in the medium-risk gradient area, grid B2 lays 1.7 square meters, and in the low-risk gradient area, grid C3 lays 0.3 square meters. After laying, use high-temperature-resistant adhesive to fix the edge to prevent displacement. After the deployment is completed, the central control unit controls the temperature sensor array in each area to collect data: 20 platinum resistance temperature sensors (precision ±0.1 degrees Celsius) are arranged on the surface of the barrier layer in the high-risk area at an interval of 5 cm x 5 cm, and the temperature is collected every 5 seconds; 10 sensors are arranged in the medium-risk area, and the temperature is collected every 10 seconds; 5 sensors are arranged in the low-risk area, and the temperature is collected every 20 seconds. For example, the first collection of 20 temperature data in the high-risk area is 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 according to the adjacent sensor pairing rule. The 20 sensors are numbered 1-20 according to the position, and the temperature difference between sensors 1 and 2, 2 and 3, …, 19 and 20 is calculated. Taking sensor 1 temperature 65 degrees Celsius, coordinate (2.1 meters, 3.3 meters, 0.6 meters) and sensor 2 temperature 66.2 degrees Celsius, coordinate (2.15 meters, 3.3 meters, 0.6 meters) as an example, the 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, the temperature gradient = 1.2 degrees Celsius ÷ 0.05 meters = 24 degrees Celsius / meter. Calculate the temperature gradient of all adjacent sensors in this way, and organize a table of sensor number, temperature gradient, and corresponding position to form the actual temperature gradient distribution data of the barrier layer. Set the normal range of temperature gradient to 0-30 degrees Celsius / meter, and automatically filter out areas exceeding 30 degrees Celsius / meter based on the porous ceramic material manual calibration, which are local overheating areas. For example, sensor 15 has a temperature difference of 6 degrees Celsius between sensor 15 and sensor 16, and the temperature gradient = 6 degrees Celsius ÷ 0.05 meters = 120 degrees Celsius / meter. The area (coordinate (2.3 meters, 3.5 meters, 0.6 meters)) is determined to be a local overheating area. Calculate the heat flux density of the local overheating area. First, extract the heat conduction rate of the area from step 101a, which is 400 watts / square meter. Measure the cross-sectional area of the barrier layer, which is a circle with a diameter of 0.2 meters, and the area = 3.14 x (0.2 meters ÷ 2) 2=0.0314 square meters, heat flux density = heat conduction rate × cross-sectional area = 400 W / m² × 0.0314 m² = 12.56 W. The normal range of heat flux density is set to 0-3 W. Based on the material's thermal load limit, 12.56 W exceeds the normal range, which is an abnormal value of heat flux density. The data analysis module extracts the abnormal value and the corresponding coordinates of the overheated area (2.3 m, 3.5 m, 0.6 m) and stores them in the abnormal database.

[0070] Step 404: Calculate the thermal stress deformation coefficient based on the heat flux density anomaly value, and superimpose the thermal decomposition characteristic parameters from the material modification parameter set to generate real-time optimization instructions for the hydrophilic group distribution density and pore size gradient. Specifically, this includes: the central control unit extracting the maximum heat flux density anomaly value for each local overheated area from the anomaly database. For example, in a high-risk area with two overheated areas, the maximum anomaly values ​​are 12.56 W and 10.8 W respectively; 12.56 W is selected as the calculation basis. The calculation of the thermal stress deformation coefficient is performed in three steps. The temperature difference is calculated by collecting the highest temperature of the overheated area (78°C) and the material's ambient temperature (25°C) from the temperature sensor, with the temperature difference being 78°C - 25°C = 53°C. The maximum heat flux density anomaly value is multiplied by the temperature difference, i.e., 12.56 W × 53°C = 665.68 W·°C. The material is porous ceramic with a thermal expansion coefficient of... / degrees Celsius (obtained from the materials handbook), thermal stress deformation coefficient = 665.68 W·°C ÷ ( / degrees Celsius); thermal decomposition characteristic parameters, i.e., thermogravimetric rate, of this region were extracted from the material modification parameter set and measured by a thermogravimetric analyzer, with a result of 0.12 g / h (data before optimization); when superimposing the thermal decomposition characteristic parameters, the thermal stress deformation coefficient was multiplied by the thermogravimetric rate to obtain the superposition result; a preset threshold for the superposition result was set to watts Celsius 2 • g / hour, based on historical fault data settings, the current superimposed result is as follows If the threshold is exceeded, a real-time optimization instruction needs to be generated, including adjustments to the distribution density of hydrophilic groups, i.e., the adjustment increment is calculated as: summation result ÷ ( (Incremental coefficient), assuming the result is rounded to 7 units, the original hydrophilic group density adjustment is 8.5 units, and the adjusted result is 8.5 units + 7 units = 15.5 units (units / square micrometer); pore size gradient optimization parameter adjustment, i.e., the original parameter is 0.85, according to "for every 1×10 of the superimposed result exceeding the threshold..." 6 The rule is to add 0.05 to the parameter, and the amount exceeding the threshold is equal to... Adjust increment = Adjusted parameters = The central control unit organizes the adjusted parameters into text instructions such as "optimization instruction number, target area, hydrophilic group density, and pore size gradient," and sends them to the control system in the barrier material production workshop via industrial Ethernet.

[0071] Step 405: Adjust the production parameters of the barrier material according to the real-time optimization instructions to obtain the thermal stability improvement coefficient of the optimized barrier layer at the leakage point. Specifically, after receiving the real-time optimization instructions, the control system of the barrier material production workshop allows operators to adjust the production parameters on the PLC (Programmable Logic Controller). The adjustment of the hydrophilic group distribution density is achieved by adding a hydroxyl-containing organosilane regulator during the production process to control the hydrophilic group density. The instruction requires an increase of 7 units (units / square micrometer). According to the ratio of "1 unit of hydrophilic group corresponds to 0.3 liters of regulator / ton of material" (preset calibration), the current production batch is 2 tons of material, and the increase in regulator addition = 7 units × 0.3 liters / (unit) (tons) × 2 tons = 4.2 liters. The original addition amount was 5 liters, and after adjustment, it is 5 liters + 4.2 liters = 9.2 liters. The metering pump controlled by PLC accurately injects the regulator into the mixing tank. The pore size gradient adjustment is controlled by adjusting the pressing pressure of the hydraulic press. The instruction requires the parameter to be adjusted to 0.93. According to the correspondence of "pore size gradient parameter × 15 MPa = pressing pressure" (specified in the equipment manual), the pressing pressure = 0.93 × 15 MPa ≈ 13.95 MPa. The operator sets the pressure value to 14 MPa on the PLC, and the pressing time remains unchanged at 30 seconds. After producing the optimized barrier layer with specifications of 1 meter × 1 meter × 0.05 meters, it is transported by forklift to the leakage point area (high-risk gradient area grid A1). On-site construction personnel remove the original barrier layer, re-lay the optimized barrier layer, and fix it with high-temperature resistant adhesive. After the laying is completed, the central control unit starts a 30-minute monitoring period.

[0072] This embodiment generates regional thermal hazard situation data by aggregating data from various edge nodes, enabling a comprehensive understanding of risk differences across different regions and avoiding decision-making biases caused by incomplete local data. Dynamically adjusting the data collection frequency for different risk areas ensures real-time data in high-risk areas while conserving resources in low-risk areas, achieving a balance between data collection efficiency and resource consumption. Prioritizing barrier resources according to risk level ensures sufficient barrier layers and nitrogen curtain coverage in high-risk areas, improving barrier effectiveness in critical areas and reducing resource waste. Generating real-time optimization instructions based on heat flux density anomalies addresses localized overheating issues, optimizes barrier material performance, and enhances material adaptability under complex operating conditions. Adjusting production parameters and iteratively updating seepage analysis parameters forms a closed-loop optimization of barrier effectiveness, continuously improving the thermal stability and barrier capacity of the material and ensuring that the barrier effect is continuously optimized with changing operating conditions.

[0073] The embodiment of the present application also provides a computer readable storage medium, which stores instructions, and when the instructions are run on a computer, the computer executes the system as described above. All implementation manners in the above system embodiment are suitable for this embodiment and can achieve the same technical effects.

[0074] The above is the preferred embodiment of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, several improvements and refinements can be made, which should also be considered as the protection scope of the present application.

Claims

1. A distributed aluminum leak positioning and response system based on edge computing, characterized in that, The application relates to a multi-source sensing module for collecting temperature field distribution, cooling water penetration rate and material surface thermal stress data of a molten aluminum leakage area in real time, generating a leakage characteristic data set containing material porosity, water absorption rate and interface thermal resistance parameters, an edge analysis module for performing seepage analysis based on material pore structure according to the leakage characteristic data set, carrying out leakage risk level evaluation by calculating the penetration depth of molten aluminum in the material in combination with the water absorption rate, and outputting dynamic positioning parameters containing material failure critical temperature and effective blocking time, a regulation module for automatically triggering a multi-stage response strategy when a leakage event is detected according to the dynamic positioning parameters, a feedback module for monitoring water absorption capacity, temperature gradient distribution and structural integrity data of the material in real time, collecting thermal decomposition characteristic parameters of a surface high polymer coating, comparing the measured water absorption rate with a preset water absorption rate threshold, and generating material modification parameter sets, and an optimization module for integrating positioning data and feedback information of all edge computing nodes, dynamically adjusting data collection frequency, blocking resource allocation and emergency response priority, and realizing closed-loop collaborative optimization of the performance of the porous ceramic material and the blocking efficiency through a distributed communication network. The application relates to a multi-source sensing module for collecting temperature field distribution, cooling water penetration rate and material surface thermal stress data of a molten aluminum leakage area in real time, generating a leakage characteristic data set containing material porosity, water absorption rate and interface thermal resistance parameters, an edge analysis module for performing seepage analysis based on material pore structure according to the leakage characteristic data set, carrying out leakage risk level evaluation by calculating the penetration depth of molten aluminum in the material in combination with the water absorption rate, and outputting dynamic positioning parameters containing material failure critical temperature and effective blocking time, a regulation module for automatically triggering a multi-stage response strategy when a leakage event is detected according to the dynamic positioning parameters, a feedback module for monitoring water absorption capacity, temperature gradient distribution and structural integrity data of the material in real time, collecting thermal decomposition characteristic parameters of a surface high polymer coating, comparing the measured water absorption rate with a preset water absorption rate threshold, and generating material modification parameter sets, and an optimization module for integrating positioning data and feedback information of all edge computing nodes, dynamically adjusting data collection frequency, blocking resource allocation and emergency response priority, and realizing closed-loop collaborative optimization of the performance of the porous ceramic material and the blocking efficiency through a distributed communication network. Step 000, high-temperature infrared thermal imagers are distributedly arranged at an interval of not more than 0.5 meters at the joint of a refractory material layer and a steel structure shell at the bottom of an electrolytic cell, an outlet through which molten aluminum flows, a valve and a flange connection of a pipeline; micro water content sensors are embeddedly arranged in a net array form at the contact interface between the outer wall of a cooling water pipeline and the refractory material layer; fiber bragg grating strain sensors are fixedly installed in a grid form on the upper surface of the refractory material layer; temperature field distribution data of a monitored area are collected and monitored by the infrared thermal imagers, the penetration rate data of the cooling water are collected by the micro water content sensors, and the thermal stress data of the material surface are collected by the fiber bragg grating strain sensors; Step 001, the temperature field distribution data collected by the infrared thermal imagers are analyzed, the real-time temperature value of each sampling point is compared with a preset corresponding position normal working condition temperature value, a region with a real-time temperature value exceeding the normal working condition temperature value by more than 50 degrees Celsius is screened and defined as a region to be verified; the geometric center coordinates, the region highest temperature value and the region average temperature change gradient of the region to be verified are extracted; meanwhile, the sequence data collected by the micro water content sensors are processed, the moving distance of the cooling water penetration front in a unit time is calculated, and the water absorption rate of the material is obtained. ​ ​ 2. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 1, characterized in that, ​ ​ ​ Step 002, the geometric center coordinates of the area to be verified, the average temperature change gradient of the area, 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 spatiotemporally registered and fused to obtain a fused data set; based on the fused data set, according to the Fourier heat conduction law, the thermal resistance parameter of the material and the molten aluminum liquid contact interface is obtained by calculating the ratio of temperature gradient and heat flux density; 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 permeation rate and the pressure gradient; Step 003, the material porosity, water absorption rate and interface thermal resistance parameter are associated and integrated with the corresponding spatial coordinate information and collection time stamp to generate a standardized leakage characteristic data set.

3. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 2, characterized in that, According to the leakage characteristic data set, perform seepage analysis based on the material pore structure, calculate the penetration depth of the molten aluminum liquid in the material, and combine the water absorption rate to evaluate the leakage risk level, output the dynamic positioning parameters including the material failure critical temperature and effective blocking time, including: Step 100, based on the material porosity, water absorption rate and interface thermal resistance parameter in the leakage characteristic data set, construct a three-dimensional pore network topology structure to simulate the flow path, penetration rate and interface wetting characteristics of the molten aluminum liquid in the pore network of the blocking material, output the penetration path distribution, local flow rate and interface contact angle; Step 101, taking the penetration path distribution, local flow rate and interface contact angle parameters as input, through the heat conduction and convective diffusion process calculation of the molten aluminum liquid in the porous medium, combining the material surface thermal stress data, dynamically predicting the penetration depth, temperature gradient distribution and thermal influence area boundary, outputting the heat transfer analysis results including the penetration depth threshold, temperature diffusion range and thermal stability evaluation parameters; Step 102, according to the penetration depth threshold and temperature diffusion range in the heat transfer analysis result, combining the water absorption rate to calculate the potential contact area and energy release rate of the molten aluminum liquid and the cooling water; Step 103, based on the potential contact area and energy release rate, divide the leakage risk level into three levels of low risk, medium risk and high risk; Step 104, according to the risk level, map the preset failure critical temperature threshold and blocking time threshold to generate dynamic positioning parameters including target area coordinates, failure temperature warning value and response time window.

4. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 3, characterized in that, Step 100, based on the material porosity, water absorption rate and interface thermal resistance parameter in the leakage characteristic data set, construct a three-dimensional pore network topology structure to simulate the flow path, penetration rate and interface wetting characteristics of the molten aluminum liquid in the pore network of the blocking material, output the penetration path distribution, local flow rate and interface contact angle, including: Step 100a, according to the three-dimensional pore network topology structure, combining the water absorption rate and interface thermal resistance parameter to calculate the balance relationship between capillary pressure and viscous resistance; Step 100b, based on the balance relationship between capillary pressure and viscous resistance, dynamically simulating the flow path selection probability and penetration direction of the molten aluminum liquid at the pore branch node; Step 100c, according to the flow path selection probability and the penetration direction, the interface thermal resistance parameter is combined to calculate the dynamic contact angle change data of the molten aluminum liquid at the solid-liquid interface; Step 100d, integrate the flow path selection probability, the penetration direction and the dynamic contact angle change data to generate the penetration path distribution, the local flow rate and the interface contact angle parameter.

5. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 4, characterized in that, Step 101, taking the penetration path distribution, the local flow rate and the interface contact angle parameter as input, through the heat conduction and convective diffusion process calculation of the molten aluminum liquid in the porous medium, combined with the material surface thermal stress data, the penetration depth, the temperature gradient distribution and the thermal influence area boundary are dynamically predicted, and the heat transfer analysis results containing the penetration depth threshold, the temperature diffusion range and the thermal stability evaluation parameter are output, including: Step 101a, based on the pore network topology structure in the penetration path distribution, the local flow rate and the interface contact angle parameter, the heat conduction and convective diffusion coupling calculation is carried out, and the heat conduction rate, flow diffusion rate and thermal stress constraint condition of the pore deformation of the molten aluminum liquid are defined; Step 101b, according to the constraint condition, the heat conduction process analysis of the molten aluminum liquid in the pore network is carried out, the convective diffusion path is calculated combined with the local flow rate data, and the temperature distribution and heat flux density change data in each pore channel are generated; Step 101c, integrate the collected material surface thermal stress data, analyze the influence of pore deformation caused by thermal expansion on heat conduction, and modify the temperature distribution and heat flux density data generated in the previous step based on the analysis results to obtain the modified temperature distribution data; Step 101d, based on the modified temperature distribution data, the maximum penetration depth of the molten aluminum liquid is predicted, and the temperature gradient overrun area is identified combined with the heat flux density change data, and the penetration depth safety threshold and the temperature gradient critical value are output; Step 101e, according to the temperature gradient critical value and the heat flux density data, the thermal decomposition rate threshold of the material is used to determine the boundary of the thermal influence area, and the boundary coordinates, thermal diffusion radius and diffusion rate parameters are generated; Step 101f, integrate the penetration depth safety threshold, the thermal diffusion radius, the material deformation tolerance and the thermal decomposition rate threshold to generate the structured heat transfer analysis results containing the penetration depth threshold, the temperature diffusion range and the thermal stability evaluation parameter.

6. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 5, characterized in that, Based on the potential contact area and the energy release rate, three levels of leakage risk grades, including low risk, medium risk and high risk, are divided, including: Step 103a, according to 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, the leakage risk index is calculated; Step 103b, when the leakage risk index is lower than the first critical value, it is determined as low risk level; Step 103c, when the leakage risk index is between the first critical value and the second critical value, it is determined as 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 as high risk level.

7. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 6, characterized in that, According to the dynamic positioning parameter, when the leakage event is detected, the multi-level response strategy is automatically triggered, including: Step 200, when the leakage risk level in the dynamic positioning parameters is ≥ medium risk, activate the corrosion-resistant ejection device, and directionally lay the porous ceramic barrier layer between the leakage point and the cooling water pipeline according to the target area coordinates; Step 201, after laying the porous ceramic barrier layer, real-time monitor the temperature gradient distribution, if the local temperature reaches the failure critical 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; Step 202, when the leakage risk level is high risk or the thermal diffusion radius is out of limit, the cooling water pressure control unit generates a high-pressure nitrogen gas isolation air curtain to form a dynamic physical barrier to isolate the molten aluminum liquid from the cooling water.

8. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 7, characterized in that, Real-time monitor the water absorption capacity, temperature gradient distribution and structural integrity data of the material, and collect the thermal decomposition characteristic parameters of the surface high polymer coating, compare the measured water absorption rate with the preset water absorption rate threshold, and generate a material modification parameter set, including: Step 300, calculate the measured water absorption rate according to the real-time monitored water absorption capacity data, compare the measured water absorption rate with the preset water absorption rate threshold, and generate a water absorption rate deviation coefficient; based on the temperature gradient distribution data, identify the local overheating area, and calculate the thermal stress deformation coefficient combined with the micro-crack propagation rate in the structural integrity data; Step 301, obtain the thermal weight loss rate through the thermal decomposition characteristic parameters of the high polymer coating, and correlate the water absorption rate deviation coefficient and the thermal stress deformation coefficient; Step 302, according to the difference between the thermal weight loss rate and the preset decomposition threshold, superimpose the water absorption rate deviation coefficient and the thermal stress deformation coefficient to generate a material modification parameter set including the hydrophilic group density adjustment amount, the pore size gradient optimization parameter and the coating reinforcement thickness.

9. The edge-computing-based distributed aluminum-leak positioning and response system according to claim 8, characterized in that, Integrate the positioning data and feedback information of each edge computing node, dynamically adjust the data acquisition frequency, barrier resource allocation and emergency response priority, and realize the closed-loop collaborative optimization of the performance and barrier effectiveness of the porous ceramic material through the distributed communication network, including: Step 400, aggregate the dynamic positioning parameters and material modification parameter set of each edge node, and generate regional thermal hazard situation data based on the leakage risk level distribution; Step 401, according to the risk level gradient change in the regional thermal hazard situation data, dynamically adjust the data acquisition frequency of the high-risk area and the acquisition frequency of the low-risk area; Step 402, after adjusting the acquisition frequency, real-time obtain the penetration depth safety threshold and thermal diffusion radius data, and allocate the porous ceramic barrier layer reserve and nitrogen isolation air curtain coverage range according to the risk level priority in the situation data; Step 403, after barrier resource allocation, deploy the actual temperature gradient distribution data of the barrier layer, and extract the thermal flux density abnormal value of the local overheating area; Step 404, calculate the thermal stress deformation coefficient based on the thermal flux density abnormal value, and superimpose 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; Step 405, adjust the barrier material production parameters according to the real-time optimization instructions, and obtain the thermal stability improvement coefficient of the optimized barrier layer at the leakage point; In step 406, based on the thermal stability enhancement coefficient and the thermal decomposition rate feedback, the interface thermal resistance parameter required for seepage analysis and the thermal conduction constraint condition are iteratively updated, and the closed-loop optimization of the barrier performance is completed.

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

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