A cooling method and system based on potassium ion fire extinguishing core

Abstract

The application discloses a cooling method and system based on a potassium ion fire extinguishing core, heat distribution at a moment is calculated through thermal simulation, a core heat peak value is accurately positioned, a high-temperature dense area and a low-heat buffer area are divided, and a heat gradient mapping is generated. When the gradient of the high-temperature area exceeds a threshold value, a chemical endothermic layer is activated to absorb radiant heat, a data processing algorithm is combined to separate radiant heat and convective heat, a dominant heat transfer mode is preferentially processed, a heat-conducting fiber network of the low-heat buffer area is dynamically adjusted, convective heat is dispersed, and heat balance is achieved. The application monitors a shell temperature fluctuation curve, integrates the synergistic effect of the chemical endothermic layer and the heat-conducting fiber network, outputs stable temperature regulation parameters, significantly reduces shell temperature fluctuation, improves the safety and stability of a fire extinguishing device, and provides an efficient solution for accurate heat management in a complex heat environment.

Description

Technical Field

[0001] This invention relates to the field of fire extinguishing device control technology, and in particular discloses a cooling method and system based on a potassium ion fire extinguishing core. Background Technology

[0002] Firefighting plays a crucial role in protecting life and property, especially in high-temperature sensitive environments such as electrical equipment and confined spaces, where the balance between efficient fire suppression and safety protection is an indispensable requirement. Potassium ion fire suppression technology, due to its high efficiency and lack of oxygen consumption, is widely used in such scenarios. However, existing fire suppression systems face significant challenges in practical applications, necessitating technological breakthroughs to improve safety and practicality.

[0003] Currently, most fire extinguishing devices are designed with an overemphasis on extinguishing efficiency, neglecting the impact of high temperatures on the device itself and the surrounding environment. Traditional methods often rely on single insulation materials or simple heat dissipation structures, which are insufficient to cope with the instantaneous high temperatures generated during the combustion of potassium ion extinguishing cores. This high temperature can not only cause deformation of the device's outer casing but also pose a risk of burns or secondary fires. For example, when extinguishing fires in confined spaces, excessively high casing temperatures can damage surrounding sensitive equipment, limiting the application scope of the technology.

[0004] High-temperature control is a major technical challenge in this field, particularly in effectively managing the extreme heat generated by the extinguishing core. When a potassium ion extinguishing core burns, the core temperature often exceeds 1000°C, and heat is rapidly transferred to the outer shell via radiation and conduction. If the heat cannot be dispersed or absorbed in time, the outer shell temperature will rise rapidly, leading to a decrease in the safety of the device. Adding to the complexity, cooling measures must achieve efficient heat management without interfering with the chemical reaction of the extinguishing core; otherwise, the extinguishing effect may be weakened. This contradiction between rapid high-temperature transfer and safe cooling constitutes the core technical challenge.

[0005] Furthermore, the complexity of cooling measures also lies in how to achieve the coordinated operation of multiple thermal management mechanisms within a limited space. Single physical heat dissipation or chemical heat absorption methods are insufficient to comprehensively address the challenges of high temperatures. For example, relying solely on a metal casing for heat dissipation may fail due to excessively rapid heat transfer, while using only heat-absorbing materials may affect the structural stability of the device due to the release of gases after material decomposition. In real-world business scenarios, such as electrical cabinet fires, fire extinguishing devices must quickly extinguish flames in confined spaces while ensuring that the casing temperature does not damage other equipment inside the cabinet. This requirement places extremely high demands on the overall performance of the cooling system.

[0006] Therefore, how to effectively control the temperature of the device's outer shell through multiple heat management mechanisms while ensuring the high efficiency of potassium ion fire extinguishing cores has become a key issue that urgently needs to be addressed in the fire protection field. Summary of the Invention

[0007] This invention provides a cooling method and system based on a potassium ion fire extinguishing core, aiming to solve at least one of the defects existing in the prior art.

[0008] One aspect of the present invention relates to a cooling method based on a potassium ion fire extinguishing core, comprising the following steps: S100: Obtain thermal radiation data and conduction path information during the combustion process of the potassium ion fire extinguishing core, calculate the instantaneous high temperature distribution pattern through the preset thermal simulation system, and obtain the core heat peak position in the high temperature distribution pattern. S200. Based on the location of the core heat peak, the high temperature distribution pattern is divided into zones using data processing algorithms to determine the boundary line between the high temperature dense area and the low heat buffer zone, and to obtain the zone heat gradient mapping of the zone division. S300: Extract the applicable level of multiple heat management mechanisms from the partitioned heat gradient mapping. If the gradient in the high-temperature dense area exceeds the threshold, activate the chemical heat absorption layer to absorb radiant heat and determine the remaining heat conduction vector after the initial cooling. S400. For the remaining heat conduction vector, the data processing algorithm is used to distinguish between radiative heat and convective heat components, obtain the classified heat component intensity sequence, and determine the heat transfer mode sequence that needs to be prioritized. S500 uses a heat transfer mode sequence to guide the dynamic adjustment of the physical heat dissipation structure, deploys a thermally conductive fiber network in a low heat buffer to disperse convective heat components, and obtains an adjusted overall thermal balance state description. S600: Monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the shell temperature fluctuation curve shows that the temperature tends to stabilize, integrate the synergistic output of the chemical heat absorption layer and the thermally conductive fiber network to obtain the final temperature control parameters.

[0009] Further, step S100 includes: S110. Thermal radiation data and conduction path information are obtained from the potassium ion fire extinguishing core through thermal radiation sensors and thermal imagers. The thermal radiation data is recorded by a high-frequency data acquisition module to obtain the raw dataset. S120. If the sensor accuracy of the original dataset is higher than the preset threshold, the conduction path information is calibrated through the thermal conductivity coefficient database, and the heat flux distribution is calculated using the finite element analysis tool to obtain the heat flux density distribution map. S130. Based on the heat flux density distribution diagram, a thermal simulation system is used to dynamically simulate the combustion state. Combined with the temperature gradient, an instantaneous high temperature distribution pattern is generated to obtain the temperature field simulation results. S140. Extract the core heat peak from the temperature field simulation results using the peak location algorithm, and analyze the temperature field using grid partitioning technology to determine the location of the core heat peak.

[0010] Further, step S200 includes: S210. The high-temperature distribution pattern is divided into regions using grid partitioning technology. Data processing algorithms are used to analyze the location of the core heat peak and determine the preliminary high-temperature dense area and low-temperature buffer zone. S220. If the heat gradient at the boundary line between the high-temperature dense area and the low-temperature buffer zone is higher than the preset threshold, the boundary line between the high-temperature dense area and the low-temperature buffer zone is optimized by the heat flow distribution analysis tool to obtain the accurate partition boundary line. S230. Based on the precise partition boundary lines, use dynamic simulation technology to calculate the heat gradient distribution within each partition and generate a partition heat gradient mapping.

[0011] Further, step S300 includes: S310. Obtain heat gradient distribution data from the partitioned heat gradient mapping, and use grid analysis tools to divide the heat gradient distribution data into regions to determine the high-temperature dense areas. S320. If the heat gradient in a high-temperature dense area exceeds a preset threshold, the heat absorption reaction is activated by the chemical heat absorption layer control tool to obtain heat distribution data after initial cooling. S330. Based on the heat distribution data after the initial cooling, use heat flow distribution analysis tools to calculate the remaining heat conduction direction and the remaining heat conduction intensity to obtain the remaining heat conduction vector.

[0012] Further, step S400 includes: S410. Obtain the first heat flow distribution data from the remaining heat conduction vector, and decompose the first heat flow distribution data using a data processing algorithm to obtain the initial radiative heat component and the initial convective heat component. S420. If the intensity of the initial radiant heat component exceeds the preset threshold, the initial radiant heat component is prioritized using a thermal component analysis tool to obtain a radiant heat priority sequence. S430. Based on the radiative heat priority sequence, the initial convective heat components are classified a second time using a heat flow allocation algorithm to obtain a convective heat component intensity sequence. S440. By using a sequence integration tool, the radiative heat priority sequence and the convective heat component intensity sequence are fused to determine the heat transfer mode sequence that needs to be prioritized.

[0013] Further, step S500 includes: S510. Obtain the second heat flow distribution data from the heat transfer mode sequence, and use a data processing algorithm to classify the second heat flow distribution data to obtain the initial intensity sequence of convective heat components. S520. If the intensity of at least one convective heat component in the initial intensity sequence exceeds a preset threshold, a thermally conductive fiber network is deployed in the low-heat buffer using a thermally conductive fiber network configuration tool to determine the adjusted fiber network distribution state. S530. Based on the fiber network distribution state, the physical heat dissipation structure is dynamically adjusted using a heat flow distribution algorithm to obtain the adjusted heat dissipation structure parameter sequence. S540. The sequence of heat dissipation structure parameters is fused with the initial intensity sequence using a sequence integration tool to obtain the final overall thermal equilibrium state description.

[0014] Further, step S600 includes: S610. Monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the temperature change amplitude in the shell temperature fluctuation curve is lower than the preset threshold, determine that the temperature is approaching a stable state through the curve analysis tool and identify the stable state. S620. Based on the steady state indicator, the heat absorption efficiency data of the chemical heat absorption layer and the heat conduction data of the thermally conductive fiber network are fused using a parameter integration tool to obtain a coordinated output parameter sequence. S630: The coordinated output parameter sequence is optimized using the parameter generation tool to obtain the final temperature control parameters.

[0015] Another aspect of the present invention relates to a cooling system based on a potassium ion fire extinguishing core, for performing the above-described cooling method based on a potassium ion fire extinguishing core, comprising: The core heat peak location acquisition module is used to acquire heat radiation data and conduction path information during the combustion process of potassium ion fire extinguishing core, calculate the instantaneous high temperature distribution pattern through a preset thermal simulation system, and obtain the core heat peak location in the high temperature distribution pattern. The partitioned heat gradient mapping acquisition module is used to divide the high temperature distribution pattern into partitions based on the core heat peak location and data processing algorithms, determine the boundary line between the high temperature dense area and the low heat buffer zone, and obtain the partitioned heat gradient mapping. The residual heat conduction vector judgment module is used to extract the applicable level of multiple heat management mechanisms from the partition heat gradient mapping. If the gradient in the high-temperature dense area exceeds the threshold, the chemical heat absorption layer is activated to absorb radiant heat and the residual heat conduction vector after the initial cooling is determined. The heat transfer mode sequence determination module is used to distinguish between radiative heat and convective heat components based on the remaining heat conduction vector through data processing algorithms, obtain the classified heat component intensity sequence, and determine the heat transfer mode sequence that needs to be prioritized for processing. The overall thermal equilibrium state description acquisition module is used to guide the dynamic adjustment of the physical heat dissipation structure with the heat transfer mode sequence, deploy a thermally conductive fiber network in the low thermal buffer to disperse the convective heat components, and obtain the adjusted overall thermal equilibrium state description. The temperature control parameter acquisition module is used to monitor the shell temperature fluctuation curve from the overall thermal equilibrium state description. If the shell temperature fluctuation curve shows that the temperature tends to stabilize, the module integrates the synergistic output of the chemical heat absorption layer and the thermally conductive fiber network to obtain the final temperature control parameters.

[0016] The beneficial effects achieved by this invention are as follows: This invention provides a cooling method and system based on a potassium ion fire extinguishing core. Addressing the issue of temperature fluctuations in the outer shell of fire extinguishing devices caused by uneven high-temperature distribution and complex heat conduction paths, the method uses thermal simulation to calculate instantaneous high-temperature distribution, accurately pinpointing the core heat peak, dividing the high-temperature dense zone into a low-temperature buffer zone, and generating a heat gradient mapping. When the gradient in the high-temperature zone exceeds a threshold, a chemical heat-absorbing layer is activated to absorb radiative heat. Combined with data processing algorithms, radiative and convective heat are separated, prioritizing the dominant heat transfer mode. The thermally conductive fiber network in the low-temperature buffer zone is dynamically adjusted to disperse convective heat, achieving thermal balance. By monitoring the outer shell temperature fluctuation curve and integrating the synergistic effect of the chemical heat-absorbing layer and the thermally conductive fiber network, this invention outputs stable temperature control parameters, significantly reducing outer shell temperature fluctuations, improving the safety and stability of the fire extinguishing device, and providing an efficient solution for precise thermal management in complex thermal environments. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of an embodiment of a cooling method based on a potassium ion fire extinguishing core according to the present invention. Figure 2 This is a functional block diagram of an embodiment of a cooling system based on a potassium ion fire extinguishing core according to the present invention.

[0018] Explanation of icon numbers: 10. Core heat peak location acquisition module; 20. Zone heat gradient mapping acquisition module; 30. Remaining heat conduction vector judgment module; 40. Heat transfer mode sequence determination module; 50. Overall thermal balance state description acquisition module; 60. Temperature control parameter acquisition module. Detailed Implementation

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] like Figure 1 As shown, the first embodiment of the present invention proposes a cooling method based on a potassium ion fire extinguishing core, comprising the following steps: Step S100: Obtain thermal radiation data and conduction path information during the combustion process of the potassium ion extinguishing core, calculate the instantaneous high temperature distribution pattern through a preset thermal simulation system, and obtain the core heat peak position in the high temperature distribution pattern.

[0021] High-precision sensing devices are used to synchronously collect thermal radiation intensity data (such as infrared radiation flux) and heat conduction path information (such as the conduction rate along the extinguishing core shell and the agent medium) during the combustion of potassium ion fire extinguishing cores. The multi-source data is input into a preset thermal simulation system (such as a finite element thermal analysis model), and the instantaneous thermal field distribution during combustion is reconstructed through numerical calculations to generate a high-temperature distribution pattern containing spatial coordinates and temperature values. Then, a peak identification algorithm is used to filter out the areas with the highest temperature and the most concentrated heat in the thermal field, and the core heat peak location (usually manifested as a local temperature extreme point and its spatial range) is determined, providing a precise target for subsequent zoned cooling and heat management.

[0022] Step S200: Based on the location of the core heat peak, the high temperature distribution pattern is divided into zones using a data processing algorithm to determine the boundary line between the high temperature dense area and the low heat buffer zone, and to obtain the zone heat gradient mapping of the zone division.

[0023] Using the core heat peak location determined in step S100 as a spatial reference, spatial clustering, gradient analysis, and other data processing algorithms are employed to hierarchically divide the temperature data in the instantaneous high-temperature distribution pattern. By setting a temperature gradient threshold (e.g., each 100℃ is a gradient level), the system distinguishes between high-temperature dense areas (close to the peak location, with temperatures close to the peak) and low-temperature buffer zones (far from the peak location, with significantly lower temperatures) and calculates the temperature abrupt change boundary line between the two areas. Finally, the partitioning results are integrated with the temperature gradient change pattern to generate a partitioned heat gradient mapping that includes spatial partitions, temperature gradient values, and boundary line coordinates, providing spatial and quantitative basis for the subsequent hierarchical activation of multiple heat management mechanisms.

[0024] Step S300: Extract the applicable level of the multiple heat management mechanism from the partitioned heat gradient mapping. If the gradient of the high-temperature dense area exceeds the threshold, activate the chemical heat absorption layer to absorb radiant heat and determine the remaining heat conduction vector after the initial cooling.

[0025] Based on the partitioned heat gradient mapping generated in step S200, the applicable levels of multiple heat management mechanisms (such as chemical heat absorption and physical heat dissipation) corresponding to different temperature gradient intervals are extracted (the higher the gradient, the stronger the management mechanism). When the temperature gradient value of the high-temperature dense area exceeds the preset safety threshold (such as 80℃ / mm), the chemical heat absorption layer (containing potassium salt composite heat absorption material) is triggered and activated, and the radiant heat is absorbed through material phase change and chemical reaction. After the heat absorption layer is activated, the temperature change of the thermal field is calculated in real time based on the thermal simulation system. Combined with the heat conduction, radiation and convection transfer laws, the direction, rate and intensity of the unabsorbed residual heat in space are determined, forming a residual heat conduction vector containing vector direction, numerical value and transfer path, providing a dynamic thermal field basis for subsequent targeted heat dissipation measures.

[0026] Step S400: For the remaining heat conduction vector, the data processing algorithm is used to distinguish between radiative heat and convective heat components, obtain the classified heat component intensity sequence, and determine the heat transfer mode sequence that needs to be prioritized.

[0027] Using the remaining heat conduction vector (including transfer direction and heat flux density) output in step S300 as input, a heat component decomposition algorithm (such as a separation model based on the difference between radiation and convection heat characteristics) is adopted, combined with the thermal field environment parameters of the potassium ion fire extinguishing core (such as the thermal conductivity of the medium and the air flow velocity), to distinguish the proportion and spatial distribution of radiative heat (such as radial outward heat radiation) and convective heat (such as axial heat flow transfer) in the vector; heat flux density data of the two types of heat components at different times are obtained through time-series sampling to form a classified heat component intensity sequence; then, priority rules are formulated according to the heat component intensity (such as whether the radiative heat flux density exceeds the threshold) and the transfer risk (such as whether the convective heat points to the sensitive area of ​​the shell), to determine the heat transfer mode sequence that needs to be prioritized (such as "radiative heat first, then convective heat" or "only radiative heat is prioritized"), providing a clear target order for subsequent physical heat dissipation structure adjustments.

[0028] Step S500: The heat transfer mode sequence is used to guide the dynamic adjustment of the physical heat dissipation structure. A thermally conductive fiber network is deployed in the low heat buffer to disperse the convective heat components, thereby obtaining a description of the adjusted overall thermal balance state.

[0029] Using the heat transfer mode sequence determined in step S400 (such as "radiative heat first, then convective heat" or "convective heat priority") as a guide, the physical heat dissipation structure (such as the deployment density and spatial layout of the thermally conductive fiber network) is dynamically adjusted for the convective heat components that need to be addressed in the low-heat buffer zone. By laying a fiber network with high thermal conductivity (such as copper-based composite fiber) along the convective heat transfer path (such as the axial range of Z=100-177mm), the rapid thermal conductivity of the fiber is used to disperse convective heat and accelerate the diffusion of heat to the environment. After the structural adjustment is completed, the temperature distribution and heat flux density changes of the thermal field are monitored in real time. Combined with heat balance analysis (heat dissipation power ≥ remaining heat generation power), an overall thermal balance state description including temperature field distribution, heat flux density vector, and heat dissipation efficiency is generated to verify the effectiveness of the heat dissipation structure adjustment for thermal field stability.

[0030] Step S600: Monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the shell temperature fluctuation curve shows that the temperature tends to stabilize, integrate the synergistic output of the chemical heat absorption layer and the thermally conductive fiber network to obtain the final temperature control parameters.

[0031] Based on the overall thermal balance state description generated in step S500, temperature sensors (such as multiple points along the axial and radial directions of the shell) deployed at key locations on the fire extinguishing core shell are used to collect shell temperature data in real time and generate time-temperature fluctuation curves. Stability judgment algorithms (such as variance analysis and fluctuation amplitude threshold) are used to determine whether the curves meet the stable condition of "small temperature fluctuations and no obvious upward or downward trend". If stable, the heat absorption parameters (such as heat absorption rate and material usage) of the previous chemical heat absorption layer (step S300) and the heat dissipation parameters (such as thermal conductivity and deployment density) of the thermally conductive fiber network (step S500) are integrated. Through synergy effect analysis (such as power matching of heat absorption and heat dissipation), the final temperature control parameters, including the temperature control target value, mechanism synergy ratio, and material parameters, are generated, providing a standardized basis for subsequent mass production of fire extinguishing cores and optimization of the cooling system.

[0032] Furthermore, the cooling method based on a potassium ion fire extinguishing core provided in this embodiment includes step S100 as follows: S110. Thermal radiation data and conduction path information are obtained from the potassium ion fire extinguishing core through thermal radiation sensors and thermal imagers. The thermal radiation data is recorded by a high-frequency data acquisition module to obtain the raw dataset.

[0033] The heat transferred by the potassium ion fire extinguishing core through thermal conduction is calculated using the following formula: (1) In formula (1), This represents the heat transferred through thermal conduction. This indicates the thermal conductivity of the potassium ion fire extinguishing core material. This represents the cross-sectional area for heat conduction. This represents the temperature gradient along the conduction path. Indicates a time interval.

[0034] In scenarios where thermal radiation sensors and thermal imagers are used to acquire thermal radiation data of potassium ion fire extinguishing cores, the thermal radiation sensor captures the radiant energy on the surface of the fire extinguishing core through infrared detection, while the thermal imager generates a two-dimensional thermal distribution image. Assuming that in a certain experiment, the thermal radiation sensor records a radiation intensity of 500 W / m² in the core area, and the thermal imager shows a core temperature of approximately 800°C, the sensor accuracy reaches 0.1°C, exceeding the preset threshold of 0.5°C, ensuring data reliability. The acquisition module records data at a frequency of 100 Hz, forming a raw dataset containing temperature, radiation intensity, and timestamps. This high-frequency acquisition captures instantaneous thermal changes, avoids data loss, and improves the accuracy of subsequent analysis.

[0035] When calibrating the conduction path information using a thermal conductivity database, the thermal conductivity of the potassium ion fire extinguishing core material is extracted from the database, such as 0.5 W / (m·K). Combined with the path image generated by the thermal imager, the calibration determines that heat is conducted radially outward from the center of the core, and the path deviation is corrected to ±0.2 mm.

[0036] S120. If the sensor accuracy of the original dataset is higher than the preset threshold, the conduction path information is calibrated through the thermal conductivity coefficient database, and the heat flux distribution is calculated using the finite element analysis tool to obtain the heat flux density distribution map.

[0037] The following formula is used to determine whether the sensor accuracy of the original dataset is higher than a preset threshold: (2) In formula (2), Indicates the sensor's measurement accuracy. This indicates the preset precision threshold. This represents the precision determination coefficient, when the ratio is... Greater than the accuracy determination coefficient Start the calibration procedure at that time.

[0038] The conduction path information is calibrated using the thermal conductivity database using the following formula: (3) In formula (3), This represents the calibrated thermal conductivity coefficient. This represents the reference thermal conductivity coefficient in the database. This represents the temperature correction factor. Indicates temperature deviation. This represents the pressure correction factor. This indicates the pressure value.

[0039] The heat flux distribution is calculated based on Fourier's law using the following formula to generate a heat flux density distribution map: (4) In formula (4), This represents the heat flux density vector at the location coordinates. Indicates the thermal conductivity of a material. Represents the temperature gradient. and These represent the temperatures at... and Partial derivatives in direction, and Represents a unit vector.

[0040] Using finite element analysis tools such as ANSYS software, the calibrated path information was input into the mesh model to calculate the heat flux distribution and generate a heat flux density distribution map. The heat flux density distribution map shows that the heat flux density in the core region reaches 1000 W / m², decreasing to 200 W / m² at the edges. This heat flux density distribution map visually reflects the areas of concentrated heat, which helps to optimize the design of the fire suppression core.

[0041] S130. Based on the heat flux density distribution diagram, a thermal simulation system is used to dynamically simulate the combustion state. Combined with the temperature gradient, an instantaneous high temperature distribution pattern is generated to obtain the temperature field simulation results.

[0042] The following formula is used to generate the instantaneous high-temperature distribution pattern under combustion conditions: (5) In formula (5), Represents the instantaneous temperature field distribution in three-dimensional space. Indicates the ambient reference temperature. Indicates the total number of temperature sources. Indicates the first The amplitude coefficient of a temperature source, Indicates the first The attenuation coefficient of a temperature source, Indicates up to the The distance between the temperature sources The angular frequency representing temperature fluctuations. This indicates the phase angle.

[0043] Based on heat flux density distribution maps, the thermal simulation system can dynamically simulate combustion states. The system simulates the thermal interaction between the flame and the extinguishing core by inputting heat flux data, and generates an instantaneous high-temperature distribution pattern by combining temperature gradients. Assuming the simulation results show a peak temperature of 850℃ in the core area, with the gradient decreasing outwards by 5℃ per millimeter, the temperature field simulation results clearly show the heat diffusion trend, providing a basis for evaluating fire extinguishing efficiency. Compared to static analysis, dynamic simulation can more realistically reflect the combustion suppression process and improve the targeting of fire extinguishing strategies.

[0044] S140. Extract the core heat peak from the temperature field simulation results using the peak location algorithm, and analyze the temperature field using grid partitioning technology to determine the location of the core heat peak.

[0045] The core thermal peak is calculated using the following formula: (6) In formula (6), This represents the peak value of the core heat in the temperature field. Represents grid points Temperature value at that location, Representing the entire temperature field region, the partial derivative terms Represents grid points The second derivative condition at temperature is used to ensure the grid points The point is a local maximum.

[0046] The discretized temperature field after mesh generation is obtained by the following formula: (7) In formula (7), This represents the discretized temperature field after mesh generation. and These represent the horizontal and vertical coordinates of the grid nodes, respectively. and Indicates the starting coordinates. and Indicates grid spacing. and This indicates the number of nodes in each direction of the grid; Indicates in Direction from the initial point Start with step size The number of moves; Indicates in Direction from the initial point Start with step size The number of times the vehicle moved.

[0047] The location of the core heat peak is determined by the following formula: (8) In formula (8), Indicates the location coordinates of the core heat peak. Represents grid points The calorie value at that location, and Represents grid points Heat values ​​of four adjacent grid points, condition terms Ensure grid points The heat value at a certain point is greater than the heat values ​​of its four adjacent grid points, thus locating the local peak position.

[0048] The peak location algorithm extracts the core heat peak from the temperature field simulation results. By scanning the temperature field, the algorithm identifies the highest point at 850℃ and locates its coordinates as the grid center point (0, 0). Using a grid generation technique, the temperature field is divided into 100×100 grid cells. Analyzing the temperature mean and gradient changes in each cell, the peak location is determined with an accuracy of ±0.1mm. This fine-grained location helps to precisely control the distribution of the fire extinguishing core material and optimize heat release efficiency. Grid generation also reveals the non-uniformity of the temperature field, providing data support for improving the fire extinguishing core structure.

[0049] Furthermore, the cooling method based on a potassium ion fire extinguishing core provided in this embodiment includes step S200 as follows: S210. The high-temperature distribution pattern is divided into regions using grid partitioning technology. Data processing algorithms are used to analyze the location of the core heat peak and determine the preliminary high-temperature dense area and low-temperature buffer zone.

[0050] The initial high-temperature dense zone and low-temperature buffer zone are determined using the following formula: (9) In formula (9), Indicates the region classification identifier. Indicates the local thermal density index, This indicates the threshold for identifying areas with high temperature and high density. This indicates the threshold for determining the low-heat buffer zone. When the value equals 1, it is a high-temperature dense area. When the value is 0, it is a low-heat buffer zone. When the value is negative 1, it is a transition region.

[0051] When using mesh generation technology to segment the high-temperature distribution pattern, the combustion area of ​​the potassium ion fire extinguishing core is divided into multiple mesh cells to analyze the differences in heat distribution. Specifically, a 100×100 mesh is assumed, with each cell representing a 0.1mm×0.1mm area. Temperature data acquired by a thermal imager is used to mark high-temperature dense areas and low-temperature buffer zones. For example, the core area temperature may reach 820℃, while the edge area temperature drops to 300℃. After segmentation, the central mesh shows high-temperature dense characteristics, while the edge mesh exhibits low-temperature buffer characteristics. This segmentation method facilitates subsequent data processing and clarifies the thermal characteristics of different areas.

[0052] The data processing algorithm is used to analyze the location of core heat peaks. It scans the grid to extract the highest temperature point; for example, assuming the cell at coordinates (0, 0) has a temperature of 820°C, the algorithm further calculates the average temperature of adjacent grid cells to identify areas of high-temperature concentration. For instance, grid cells with temperatures above 700°C are classified as high-temperature concentration areas, while those below 400°C are categorized as low-temperature buffer zones. This data processing algorithm can quickly pinpoint areas of concentrated heat, providing data support for subsequent optimization.

[0053] S220. If the heat gradient at the boundary line between the high-temperature dense area and the low-temperature buffer zone is higher than the preset threshold, the boundary line between the high-temperature dense area and the low-temperature buffer zone is optimized by the heat flow distribution analysis tool to obtain the accurate partition boundary line.

[0054] The heat gradient at the boundary line is obtained by the following formula: (10) In formula (10), This represents the heat gradient at the boundary line. This indicates the temperature of a densely populated high-temperature area. This indicates the temperature of the low-temperature buffer zone. Indicates the vertical distance at the boundary line. Indicates the normal direction of the boundary line.

[0055] The following formula is used to achieve iterative optimization of the boundary line through heat flux distribution analysis: (11) In formula (11), This indicates the location of the optimized, precise partition boundary line. Indicates the initial boundary line location. This represents the optimization step size coefficient. Represents the heat flux distribution potential function. Indicates boundary line parameters, This indicates the parameter increment.

[0056] For thermal gradient analysis of the boundary line between high-temperature dense areas and low-temperature buffer zones, a gradient threshold of 10℃ / mm can be set. If a gradient of 15℃ / mm is detected in a boundary region, exceeding the threshold, it indicates a drastic heat transition. Heat flux distribution analysis tools, such as finite element method-based software, can optimize the boundary line. Specifically, these tools recalculate the heat distribution in the boundary region by adjusting the heat flux input of the mesh elements, generating a smoothly transitioning boundary line. After optimization, the boundary line gradient is reduced to 8℃ / mm, and the boundary position deviation is controlled within ±0.15mm. This optimization ensures a more uniform heat distribution.

[0057] S230. Based on the precise partition boundary lines, use dynamic simulation technology to calculate the heat gradient distribution within each partition and generate a partition heat gradient mapping.

[0058] The following formula is used to generate the partitioned thermal gradient map: (12) In formula (12), Indicates the first The heat gradient mapping function for each partition. and Represents the parametric coordinates within the partition. and This represents the highest order of the basis functions. The matrix representing the control coefficients of the heat gradient. and This represents the basis functions used to construct the mapping.

[0059] Based on precise zone boundary lines, dynamic simulation technology calculates the heat gradient distribution within each zone. The simulation system inputs optimized boundary line data to simulate the diffusion of heat from high-temperature dense zones to low-temperature buffer zones. For example, simulation results show that the heat gradient at the center of the high-temperature dense zone is 12℃ / mm, decreasing to 5℃ / mm at the edges. The generated zone heat gradient mapping clearly shows the heat flow direction, with a heat flux density as high as 900 W / m² in the central region and decreasing to 250 W / m² at the edges. This mapping intuitively reflects the heat distribution pattern and helps in analyzing the thermal performance of the fire extinguishing core.

[0060] Dynamic simulation technology can also be combined with a thermal conductivity database to calibrate heat flow parameters in different regions. Assuming the thermal conductivity of the potassium ion fire extinguishing core material is 0.6 W / (m·K), the simulation system can adjust the heat flow calculation based on this parameter to ensure the simulation results closely approximate actual combustion scenarios. The mapping results can also be used to evaluate heat diffusion efficiency; for example, optimized zone boundary lines result in more uniform heat distribution and reduce localized overheating.

[0061] Furthermore, the cooling method based on a potassium ion fire extinguishing core provided in this embodiment includes step S300 as follows: S310. Obtain heat gradient distribution data from the partitioned heat gradient mapping, and use grid analysis tools to divide the heat gradient distribution data into regions to determine the high-temperature dense areas.

[0062] The high-temperature, densely populated area is derived using the following formula: (13) In formula (13), This represents the set of identified high-temperature, densely populated areas. Represents grid cells The heat gradient value, This represents the mean of the heat gradient across all grid cells. The standard deviation of the heat gradient This indicates that the threshold coefficient is used to control the sensitivity of identification in high-temperature areas.

[0063] When acquiring heat gradient distribution data from zonal heat gradient mapping, a grid analysis tool is used to refine the data for region division. The grid analysis tool identifies heat management levels—high-temperature dense areas and low-temperature buffer areas—by mapping heat gradient data onto a pre-defined grid system. Specifically, assuming a 100×100 grid system, with each cell representing a 0.1 mm area, and heat gradient data acquired using a thermal imager, the core region might have a gradient of 15℃ / mm, while the edge region drops to 3℃ / mm. By comparing the gradient values ​​of each cell, the grid analysis tool marks areas with gradients higher than 10℃ / mm as high-temperature dense areas and areas lower than 5℃ / mm as low-temperature buffer areas. This division method facilitates the formulation of subsequent heat management strategies.

[0064] S320. If the heat gradient in a high-temperature dense area exceeds a preset threshold, the heat absorption reaction is activated through a chemical heat absorption layer control tool to obtain heat distribution data after initial cooling.

[0065] The heat distribution data after initial cooling is obtained using the following formula: (14) In formula (14), This indicates the temperature distribution after the initial cooling process. This represents the initial temperature distribution before cooling. This indicates the heat removed by the heat-absorbing layer. Indicates the quality of the affected area. This indicates specific heat capacity.

[0066] If the heat gradient in a high-temperature, densely populated region exceeds a preset threshold, such as 10°C / mm, an endothermic reaction is activated using a chemical endothermic layer control tool. The chemical endothermic layer is typically composed of materials with high heat capacity, such as sodium bicarbonate-based composites, which decompose and absorb heat at high temperatures. Assuming the central grid temperature in the high-temperature, densely populated region is 800°C with a gradient of 12°C / mm, exceeding the threshold, the chemical endothermic layer control tool rapidly reduces the region's temperature by spraying endothermic material or triggering a built-in reaction device. After the reaction, the central grid temperature may drop to 600°C, and the gradient may decrease to 8°C / mm. This method effectively absorbs heat through a chemical reaction, providing a more stable data foundation for subsequent heat flow analysis.

[0067] S330. Based on the heat distribution data after the initial cooling, use heat flow distribution analysis tools to calculate the remaining heat conduction direction and the remaining heat conduction intensity to obtain the remaining heat conduction vector.

[0068] The remaining heat conduction vector is obtained by the following formula: (15) In formula (15), Represents the remaining heat conduction vector. Indicates the effective thermal conductivity. This indicates the remaining temperature distribution after the initial cooling process. This represents the gradient of the remaining temperature field. , , These represent the unit vectors along the three coordinate axes, respectively. Indicates temperature along Rate of change in the axial direction, Indicates temperature along Rate of change in the axial direction, Indicates temperature along Rate of change in the axial direction.

[0069] Based on the heat distribution data after initial cooling, a heat flux analysis tool is used to calculate the direction and intensity of remaining heat conduction. This tool determines the main direction of heat flow by analyzing the temperature difference between grid cells and the thermal conductivity of the materials. Assuming the thermal conductivity of the potassium ion extinguishing core is 0.5 W / (m·K), the tool identifies the diffusion trend of heat from the central high-temperature region to the peripheral low-temperature region. Specifically, the heat flux density in the central grid may be 850 W / m², decreasing to 200 W / m² in the peripheral grid. The tool generates a residual heat conduction vector map through vector calculation, showing that the heat flow mainly diffuses radially, with the intensity gradually weakening in the boundary region. This vector map visually reflects the heat flow pattern, facilitating the optimization of heat management strategies.

[0070] Furthermore, the cooling method based on a potassium ion fire extinguishing core provided in this embodiment includes step S400 as follows: Step S410: Obtain the first heat flow distribution data from the remaining heat conduction vector, and decompose the first heat flow distribution data using a data processing algorithm to obtain the initial radiative heat component and the initial convective heat component.

[0071] The basic decomposition relationship of heat flux distribution data is described by the following formula: (16) In formula (16), This represents the total heat flow of the first heat flow distribution data. Indicates the initial radiant heat composition. Indicates the initial convective heat composition. and Represents spatial coordinates, Indicates the time parameter.

[0072] When obtaining the initial heat flux distribution data from the remaining heat conduction vector, a heat flux analysis tool is used to initially extract the direction and intensity of heat flow. Specifically, assuming the heat conduction vector map shows a heat flux density of 750 W / m² in the core region and 150 W / m² in the edge region, the heat flux analysis tool uses vector decomposition technology to organize the heat flux distribution data into a processable structured dataset. This heat flux distribution dataset contains information on heat flux direction, intensity, and regional distribution, providing a foundation for subsequent analysis. Heat flux analysis tools are typically based on infrared thermal imaging or sensor array data to ensure data accuracy.

[0073] The data processing algorithm decomposes the initial heat flux distribution data, separating the initial radiative heat component and the initial convective heat component. For example, in the core region heat flux, radiative heat may account for 60%, approximately 450 W / m², while convective heat accounts for 40%, approximately 300 W / m². The decomposition process can be implemented using Fourier transform or spectral analysis algorithms to separate the mixed heat flux signal into independent components of different heat transfer modes. This separation method facilitates subsequent targeted processing.

[0074] S420. If the intensity of the initial radiative heat component exceeds the preset threshold, the initial radiative heat component is prioritized using a thermal component analysis tool to obtain a radiative heat priority sequence.

[0075] The following formula is used to determine the ranking position of each thermal component in the priority sequence through normalization and weight allocation: (17) In formula (17), Indicates the first Priority weights of each radiant heat component Indicates the first The intensity value of the initial radiant heat component, This represents the total quantity of all radiant heat components. Indicates the first The importance coefficient of each thermal component.

[0076] If the initial intensity of radiant heat components exceeds a preset threshold, such as 400 W / m², a thermal composition analysis tool is used to prioritize them. This tool generates a priority sequence based on the intensity, distribution range, and impact on the system. For example, assuming the core region has a radiant heat of 450 W / m² and the peripheral region has 100 W / m², the tool will mark the core region as high priority and the peripheral region as low priority. This prioritization helps to concentrate resources on addressing the accumulation of radiant heat in high-temperature areas.

[0077] Step S430: Based on the radiative heat priority sequence, the initial convective heat components are classified a second time using a heat flow allocation algorithm to obtain a convective heat component intensity sequence.

[0078] The redistribution of the initial convective heat composition is now calculated using the following formula: (18) In formula (18), Indicates the first The new values ​​of the convective heat components after processing by the heat flux distribution algorithm. Indicates the first The initial convective heat composition, Indicates assignment to the first The heat flux area of ​​each component Represents the total heat flux area. Indicates the first The heat flow distribution efficiency of each component.

[0079] The final convective heat composition intensity sequence is generated using the following formula: (19) In formula (19), Indicates the convective heat composition intensity sequence of the th One intensity value, Indicates the first The convective heat composition after classification This represents the total number of convective heat components after classification. Indicates the first The component is related to the first The contribution factor of each intensity location, Indicates the first Intensity correction factor at each location.

[0080] Based on a radiative heat priority sequence, the heat flux allocation algorithm performs a secondary classification of the initial convective heat components, generating a convective heat component intensity sequence. For example, 300 W / m² of convective heat in the core region can be further subdivided into high-speed convection of 150 W / m² and low-speed convection of 150 W / m². The heat flux allocation algorithm categorizes convective heat into different intensity levels by analyzing parameters such as convection velocity and medium properties. This classification method facilitates precise control of convective heat management strategies.

[0081] Step S440: Use a sequence integration tool to fuse the radiative heat priority sequence with the convective heat component intensity sequence to determine the heat transfer mode sequence that needs to be prioritized.

[0082] The priority heat transfer mode sequence is derived using the following formula: (20) In formula (20), This represents the output value of a defined heat transfer mode sequence. Indicates the first One radiative heat priority sequence component Indicates the first One convective heat component intensity component, and These represent the corresponding adjustment parameters. Indicates the radiant heat threshold. This represents the reference value for convective heat.

[0083] The sequence integration tool merges the radiative heat priority sequence with the convective heat component intensity sequence to determine the sequence of heat transfer modes that need to be prioritized. Assuming the integration results show that radiative heat and high-velocity convective heat are the dominant heat transfer modes in the core region, the sequence integration tool generates a sequence indicating that radiative heat should be prioritized, followed by optimization of high-velocity convective heat. This fusion is achieved through weighted analysis or a decision tree model to ensure the targeted nature of the thermal management strategy. The fused sequence provides guidance for subsequent cooling device deployment or material adjustments.

[0084] Preferably, the cooling method based on a potassium ion fire extinguishing core provided in this embodiment includes step S500: Step S510: Obtain the second heat flow distribution data from the heat transfer mode sequence, and use a data processing algorithm to classify the second heat flow distribution data to obtain the initial intensity sequence of convective heat components.

[0085] The initial intensity sequence of convective heat components is obtained by the following formula: (twenty one) In formula (21), This represents the combined value of the initial intensity sequence of convective heat components. This represents the total number of time steps in the heat transfer mode sequence. Indicates the first The linear weighting parameters at time t, Indicates the first Heat flow distribution data values ​​at any given time. Indicates the first The second-order weight parameters at time 1, This represents the second-order gradient operator.

[0086] When obtaining the second heat flux distribution data from the heat transfer pattern sequence, a heat flux distribution analysis tool is used to extract the heat flux characteristics. Assuming the heat transfer pattern sequence shows significant convective heat transfer in the core region, the second heat flux distribution data indicates a convective heat flux density of 500 W / m² in the core region and 120 W / m² in the edge region. The heat flux distribution analysis tool utilizes sensor arrays or thermal imaging technology to generate a structured dataset containing the direction, intensity, and regional distribution of heat flux, providing a foundation for subsequent classification. This analysis method ensures the spatial resolution and accuracy of the data, facilitating the accurate capture of the distribution characteristics of convective heat.

[0087] The data processing algorithm classifies the second heat flux distribution data and extracts the initial intensity sequence of convective heat components. For example, using spectral analysis, the convective heat flux in the core region is decomposed into high-velocity convective heat (300 W / m²) and low-velocity convective heat (200 W / m²). The edge region is decomposed into low-velocity convective heat (100 W / m²) and trace convective heat (20 W / m²). The classification process generates the initial intensity sequence by analyzing convection velocity and medium properties. This sequence clearly reflects the convective heat intensity distribution in different regions, which is helpful for subsequent targeted processing.

[0088] Step S520: If the intensity of at least one convective heat component in the initial intensity sequence exceeds a preset threshold, then deploy a thermally conductive fiber network in the low-heat buffer using a thermally conductive fiber network configuration tool to determine the adjusted fiber network distribution state.

[0089] The following formula is used as the trigger condition for deploying thermally conductive fiber networks: (twenty two) In formula (22), Represents the first intensity in the initial intensity sequence. The intensity value of each convective heat component, This indicates a preset intensity threshold, which triggers the deployment conditions of the thermally conductive fiber network when the intensity of the convective heat component exceeds the threshold.

[0090] The adjusted fiber network distribution is obtained using the following formula: (twenty three) In formula (23), This indicates the adjusted fiber network distribution. This represents the initial fiber network distribution state. This represents the amount of state change that occurs after deployment in a low-temperature buffer zone using the thermally conductive fiber network configuration tool.

[0091] If the initial intensity sequence shows that the high-speed convective heat in the core region (300 W / m²) exceeds the preset threshold of 250 W / m², a thermally conductive fiber network can be deployed in a low-temperature buffer zone using a thermally conductive fiber network configuration tool. The low-temperature buffer zone is typically a low-temperature area in the system, such as a heat dissipation layer near the outer shell. The thermally conductive fiber network configuration tool determines the density and distribution of the fiber network based on the convective heat intensity. For example, a high-density thermally conductive fiber network with a fiber spacing of 2 mm, made of high-thermal-conductivity carbon fiber, can be deployed below the core region to accelerate heat transfer to the low-temperature buffer zone. This deployment method effectively disperses the heat load in high-temperature areas.

[0092] Step S530: Based on the fiber network distribution state, the physical heat dissipation structure is dynamically adjusted using a heat flow distribution algorithm to obtain the adjusted heat dissipation structure parameter sequence.

[0093] The dynamically adjusted heat dissipation structure parameters are obtained using the following formula: (twenty four) In formula (24), This indicates the dynamically adjusted heat dissipation structure parameters. Indicates the original heat dissipation structure parameters. Indicates the structural adjustment coefficient. Indicates the heat flow field in coordinates The gradient vector at that point, This represents the state function of the fiber network distribution.

[0094] Based on the fiber network distribution, the heat flux allocation algorithm dynamically adjusts the physical heat dissipation structure, generating a sequence of adjusted heat dissipation structure parameters. For example, the algorithm adjusts the spacing and angle of the heat dissipation fins according to the thermal conductivity of the fiber network. In the core area, the fin spacing is reduced to 1.5mm, and the angle is optimized to 45 degrees to enhance the efficiency of convective heat dissipation. In the edge areas, a larger spacing of 3mm is maintained to reduce unnecessary material usage. This dynamic adjustment ensures the adaptability of the heat dissipation structure.

[0095] Step S540: The heat dissipation structure parameter sequence is fused with the initial intensity sequence using a sequence integration tool to obtain the final overall thermal equilibrium state description.

[0096] The final overall thermal equilibrium state is described by the following formula: (25) In formula (25), This describes the final overall thermal equilibrium state. The weight function represents the sequence integration tool. Represents the sequence of heat dissipation structure parameters. Represents the initial intensity sequence. Indicates the time range of fusion. This represents the fusion operation operator.

[0097] The sequence integration tool fuses the sequence of heat dissipation structure parameters with the initial intensity sequence to generate a final overall thermal equilibrium description. Through weighted analysis, the tool comprehensively evaluates the matching degree between convective heat intensity and heat dissipation structure parameters. The high-speed convective heat of 300 W / m² in the core area, combined with the optimized heat dissipation fin parameters, forms a thermal equilibrium description, indicating that the heat load in the core area has been effectively distributed and the overall system temperature rise is controlled within a safe range. This fusion method ensures the coordination and targeting of thermal management strategies, providing guidance for subsequent heat dissipation optimization.

[0098] Furthermore, in the cooling method based on a potassium ion fire extinguishing core provided in this embodiment, step S600 includes: Step S610: Monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the temperature change amplitude in the shell temperature fluctuation curve is lower than the preset threshold, the temperature is determined to be in a stable state by using the curve analysis tool, and the stable state indicator is determined.

[0099] The following formula is used to calculate the magnitude of change in the casing temperature over a specific time period: (26) In formula (26), Indicates the range of temperature fluctuation in the outer casing. Indicates time The outer casing temperature value at any given time. and These represent the maximum and minimum values ​​within the monitoring period, respectively.

[0100] The steady-state indicator is derived using the following formula: (27) In formula (27), Indicates a stable state indicator. Indicates the range of temperature change. This represents the preset temperature fluctuation threshold. When the temperature change is below the threshold, it is marked as 1 to indicate a stable state; otherwise, it is marked as 0 to indicate an unstable state.

[0101] When monitoring the temperature fluctuation curve of the outer casing, a high-precision infrared thermometer is used to acquire real-time temperature data and generate a continuous temperature fluctuation curve. Specifically, the infrared thermometer captures temperature changes in various areas of the outer casing surface at a sampling frequency of 0.1 seconds, achieving a data resolution of 0.01 degrees Celsius. Assuming the monitoring results show that the temperature fluctuation curve gradually decreases from an initial 5 degrees Celsius to 0.5 degrees Celsius within 10 minutes, falling below a preset threshold of 1 degree Celsius, the curve analysis tool uses Fourier transform technology to analyze the frequency and amplitude of the temperature fluctuations, determining that the temperature is approaching a stable state and generating a stable state indicator. This stable state indicator is a binary flag, with 1 indicating stability and 0 indicating instability; in the current scenario, it is 1. This method ensures accurate capture of dynamic temperature changes, providing a reliable basis for subsequent parameter integration.

[0102] Step S620: Based on the steady state indicator, use a parameter integration tool to fuse the heat absorption efficiency data of the chemical heat absorption layer with the heat conduction data of the thermally conductive fiber network to obtain a coordinated output parameter sequence; The sequence of collaborative output parameters is obtained through the following formula: (28) In formula (28), Indicates the first One collaborative output parameter, and Indicates the fusion weighting coefficient. Indicates the first One heat absorption performance parameter, Indicates the first One thermal conductivity parameter, This represents the stability correction coefficient. Indicates the first A stable state identifier factor.

[0103] Based on steady-state identification, the parameter integration tool fuses the heat absorption efficiency data of the chemical heat absorber layer with the thermal conductivity data of the thermally conductive fiber network. The chemical heat absorber layer, made of phase change material, has a heat absorption efficiency of 2000 J / kg, covers the inner side of the outer shell, and is 2 mm thick. The thermally conductive fiber network, made of graphene fibers with a thermal conductivity of 1500 W / m·K, is deployed between the core heating area and the outer shell. The parameter integration tool uses a weighted algorithm to fuse the heat absorption efficiency data and the thermal conductivity data, generating a co-output parameter sequence. This co-output parameter sequence includes the heat absorber layer coverage area ratio of 0.6 and the thermally conductive fiber distribution density of 0.8 fibers / cm². This fusion method ensures the synergistic effect of both data, optimizing thermal management efficiency.

[0104] Step S630: Optimize the collaborative output parameter sequence using a parameter generation tool to obtain the final temperature control parameters.

[0105] The final temperature control parameters are obtained using the following formula: (29) In formula (29), This indicates the final temperature control parameter. Indicates the length of the co-output parameter sequence. Indicates the first One collaborative output parameter, Indicates the first The weighting coefficients of each parameter. Indicates the intensity of regulation. Indicates the number of control constraints. Indicates the first The influence coefficient of each constraint Indicates the first The control logic of formula (29) generates the final temperature control parameters through "weighted integration of basic parameters + constraint condition adjustment".

[0106] The parameter generation tool optimizes the collaborative output parameter sequence to generate the final temperature control parameters. Based on the sequence data, the tool adjusts the thickness of the chemical heat absorber layer to 2.5 mm and increases the density of thermally conductive fibers in the core region to 1 fiber / cm². During optimization, the tool analyzes the shell temperature distribution, prioritizing the enhancement of heat dissipation capacity in the core region while maintaining the thickness of the heat absorber layer in the edge regions. This adjustment method balances the system's heat load distribution through precise control.

[0107] Please see Figure 2This invention provides a cooling system based on a potassium ion fire extinguishing core, used to execute the aforementioned cooling method based on a potassium ion fire extinguishing core. It includes a core heat peak position acquisition module 10, a zoned heat gradient mapping acquisition module 20, a residual heat conduction vector judgment module 30, a heat transfer mode sequence determination module 40, an overall thermal balance state description acquisition module 50, and a temperature control parameter acquisition module 60. The core heat peak position acquisition module 10 acquires thermal radiation data and conduction path information during the combustion process of the potassium ion fire extinguishing core, calculates the instantaneous high-temperature distribution pattern using a preset thermal simulation system, and obtains the core heat peak position within the high-temperature distribution pattern. The zoned heat gradient mapping acquisition module 20, based on the core heat peak position, uses a data processing algorithm to divide the high-temperature distribution pattern into zones, determines the boundary line between the high-temperature dense area and the low-temperature buffer zone, and obtains the zoned heat gradient mapping. The residual heat conduction vector judgment module 30... 0 is used to extract the applicable level of multiple heat management mechanisms from the partitioned heat gradient mapping. If the gradient in the high-temperature dense area exceeds the threshold, the chemical heat-absorbing layer is activated to absorb radiant heat, and the remaining heat conduction vector after the initial cooling is determined. The heat transfer mode sequence determination module 40 is used to distinguish radiant heat and convective heat components for the remaining heat conduction vector through data processing algorithms, obtain the classified heat component intensity sequence, and determine the heat transfer mode sequence that needs to be prioritized. The overall thermal balance state description acquisition module 50 is used to guide the dynamic adjustment of the physical heat dissipation structure with the heat transfer mode sequence, deploy a thermally conductive fiber network in the low heat buffer to disperse the convective heat components, and obtain the adjusted overall thermal balance state description. The temperature control parameter acquisition module 60 is used to monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the shell temperature fluctuation curve shows that the temperature tends to stabilize, the collaborative output of the chemical heat-absorbing layer and the thermally conductive fiber network is integrated to obtain the final temperature control parameters.

[0108] The cooling method and system based on a potassium ion fire extinguishing core provided in this embodiment, compared with existing technologies, accurately locates the core heat peak by calculating the instantaneous high temperature distribution through thermal simulation, divides the high-temperature dense zone into a low-temperature buffer zone, and generates a heat gradient mapping. When the gradient in the high-temperature zone exceeds a threshold, the chemical heat-absorbing layer is activated to absorb radiant heat. Combined with data processing algorithms, radiant heat and convective heat are separated, prioritizing the dominant heat transfer mode, and dynamically adjusting the thermally conductive fiber network in the low-temperature buffer zone to disperse convective heat and achieve thermal balance. This embodiment monitors the shell temperature fluctuation curve, integrates the synergistic effect of the chemical heat-absorbing layer and the thermally conductive fiber network, outputs stable temperature control parameters, significantly reduces shell temperature fluctuations, improves the safety and stability of the fire extinguishing device, and provides an efficient solution for precise thermal management in complex thermal environments.

[0109] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A cooling method based on a potassium ion fire extinguishing core, used to control the outer shell temperature of a fire extinguishing device, characterized in that, Includes the following steps: S100: Obtain thermal radiation data and conduction path information during the combustion process of the potassium ion fire extinguishing core, calculate the instantaneous high temperature distribution pattern through the preset thermal simulation system, and obtain the core heat peak position in the high temperature distribution pattern. S200. Based on the location of the core heat peak, a data processing algorithm is used to divide the high temperature distribution pattern into zones, determine the boundary line between the high temperature dense area and the low heat buffer zone, and obtain the zone heat gradient mapping of the partition. S300. Extract the applicable level of the multiple heat management mechanism from the partitioned heat gradient mapping. If the gradient of the high-temperature dense area exceeds the threshold, activate the chemical heat absorption layer to absorb radiant heat and determine the remaining heat conduction vector after the initial cooling. S400. For the remaining heat conduction vector, the data processing algorithm is used to distinguish between radiative heat and convective heat components, obtain the classified heat component intensity sequence, and determine the heat transfer mode sequence that needs to be prioritized. S500: The heat transfer mode sequence is used to guide the dynamic adjustment of the physical heat dissipation structure. A thermally conductive fiber network is deployed in the low heat buffer to disperse the convective heat components, thereby obtaining a description of the adjusted overall thermal balance state. S600. Monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the shell temperature fluctuation curve shows that the temperature tends to stabilize, integrate the synergistic output of the chemical heat absorption layer and the thermally conductive fiber network to obtain the final temperature control parameters.

2. The cooling method based on a potassium ion fire extinguishing core according to claim 1, characterized in that, Step S100 includes: S110. Obtain thermal radiation data and conduction path information from the potassium ion fire extinguishing core through a thermal radiation sensor and a thermal imager, and record the thermal radiation data using a high-frequency data acquisition module to obtain the raw dataset. S120. If the sensor accuracy of the original dataset is higher than the preset threshold, the conduction path information is calibrated through the thermal conductivity coefficient database, and the heat flux distribution is calculated using finite element analysis tools to obtain a heat flux density distribution map. S130. Based on the heat flux density distribution diagram, a thermal simulation system is used to dynamically simulate the combustion state, and an instantaneous high temperature distribution pattern is generated by combining the temperature gradient to obtain the temperature field simulation results. S140. Extract the core heat peak from the temperature field simulation results using the peak location algorithm, and analyze the temperature field using grid partitioning technology to determine the location of the core heat peak.

3. The cooling method based on a potassium ion fire extinguishing core according to claim 1, characterized in that, Step S200 includes: S210. The high-temperature distribution pattern is divided into regions using grid partitioning technology, and the core heat peak position is analyzed using data processing algorithms to determine the preliminary high-temperature dense area and low-temperature buffer zone. S220. If the heat gradient at the boundary line between the high-temperature dense area and the low-temperature buffer zone is higher than the preset threshold, the boundary line between the high-temperature dense area and the low-temperature buffer zone is optimized by the heat flow distribution analysis tool to obtain the accurate partition boundary line. S230. Based on the precise partition boundary line, use dynamic simulation technology to calculate the heat gradient distribution in each partition and generate a partition heat gradient mapping.

4. The cooling method based on a potassium ion fire extinguishing core according to claim 1, characterized in that, Step S300 includes: S310. Obtain heat gradient distribution data from the partitioned heat gradient mapping, and use a grid analysis tool to divide the heat gradient distribution data into regions to determine the high-temperature dense region. S320. If the heat gradient in the high-temperature dense area exceeds a preset threshold, the heat absorption reaction is activated by the chemical heat absorption layer control tool to obtain heat distribution data after initial cooling. S330. Based on the heat distribution data after the initial cooling, use heat flow distribution analysis tools to calculate the remaining heat conduction direction and the remaining heat conduction intensity to obtain the remaining heat conduction vector.

5. The cooling method based on a potassium ion fire extinguishing core according to claim 1, characterized in that, Step S400 includes: S410. Obtain the first heat flow distribution data from the remaining heat conduction vector, and decompose the first heat flow distribution data using a data processing algorithm to obtain the initial radiative heat component and the initial convective heat component. S420. If the intensity of the initial radiant heat component exceeds a preset threshold, the initial radiant heat component is prioritized using a thermal component analysis tool to obtain a radiant heat priority sequence. S430. Based on the radiative heat priority sequence, the initial convective heat components are classified a second time using a heat flow allocation algorithm to obtain a convective heat component intensity sequence. S440. The radiative heat priority sequence and the convective heat component intensity sequence are fused using a sequence integration tool to determine the heat transfer mode sequence that needs to be prioritized.

6. The cooling method based on a potassium ion fire extinguishing core according to claim 1, characterized in that, Step S500 includes: S510. Obtain second heat flow distribution data from the heat transfer mode sequence, and classify the second heat flow distribution data using a data processing algorithm to obtain an initial intensity sequence of convective heat components. The initial intensity sequence of convective heat components is obtained by the following formula: ； in, This represents the combined value of the initial intensity sequence of convective heat components. This represents the total number of time steps in the heat transfer mode sequence. Indicates the first The linear weighting parameters at time t, Indicates the first Heat flow distribution data values ​​at any given time. Indicates the first The second-order weight parameters at time 1, This represents the second-order gradient operator; S520. If the intensity of at least one convective heat component in the initial intensity sequence exceeds a preset threshold, a thermally conductive fiber network is deployed in the low-heat buffer using a thermally conductive fiber network configuration tool to determine the adjusted fiber network distribution state. The following formula is used as the trigger condition for deploying thermally conductive fiber networks: ； in, Represents the first intensity in the initial intensity sequence. The intensity value of each convective heat component, This indicates a preset intensity threshold, which triggers the deployment conditions for the thermally conductive fiber network when the intensity of the convective heat component exceeds the threshold. The adjusted fiber network distribution is obtained using the following formula: ； in, This indicates the adjusted fiber network distribution. This represents the initial fiber network distribution state. This represents the amount of state change that occurs after deployment in a low-heat buffer zone using the thermally conductive fiber network configuration tool. S530. Based on the fiber network distribution state, the physical heat dissipation structure is dynamically adjusted using a heat flow distribution algorithm to obtain the adjusted heat dissipation structure parameter sequence. S540. The sequence of heat dissipation structure parameters is fused with the initial intensity sequence using a sequence integration tool to obtain the final overall thermal equilibrium state description.

7. The cooling method based on a potassium ion fire extinguishing core according to claim 6, characterized in that, In step S530, the dynamically adjusted heat dissipation structure parameters are obtained using the following formula: ； in, This indicates the dynamically adjusted heat dissipation structure parameters. Indicates the original heat dissipation structure parameters. Indicates the structural adjustment coefficient. Indicates the heat flow field in coordinates The gradient vector at that point, This represents the state function of the fiber network distribution.

8. The cooling method based on a potassium ion fire extinguishing core according to claim 7, characterized in that, In step S540, the final overall thermal equilibrium state is described by the following formula: ； in, This describes the final overall thermal equilibrium state. The weight function represents the sequence integration tool. Represents the sequence of heat dissipation structure parameters. Represents the initial intensity sequence. Indicates the time range of fusion. This represents the fusion operation operator.

9. The cooling method based on a potassium ion fire extinguishing core according to claim 1, characterized in that, Step S600 includes: S610. Monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the temperature change amplitude in the shell temperature fluctuation curve is lower than the preset threshold, determine that the temperature is approaching a stable state through the curve analysis tool and identify the stable state. S620. Based on the stable state identifier, the heat absorption efficiency data of the chemical heat absorption layer and the heat conduction data of the thermally conductive fiber network are fused using a parameter integration tool to obtain a collaborative output parameter sequence. S630. The coordinated output parameter sequence is optimized using a parameter generation tool to obtain the final temperature control parameters.

10. A cooling system based on a potassium ion fire extinguishing core, used to perform the cooling method based on a potassium ion fire extinguishing core as described in any one of claims 1 to 9, characterized in that, include: The core heat peak location acquisition module (10) is used to acquire heat radiation data and conduction path information during the combustion process of potassium ion fire extinguishing core, calculate the instantaneous high temperature distribution pattern through the preset thermal simulation system, and obtain the core heat peak location in the high temperature distribution pattern. The partitioned heat gradient mapping acquisition module (20) is used to divide the high temperature distribution pattern into partitions according to the core heat peak position, and to determine the boundary line between the high temperature dense area and the low heat buffer zone, and to obtain the partitioned heat gradient mapping of the partition. The residual heat conduction vector judgment module (30) is used to extract the applicable level of the multiple heat management mechanism from the partition heat gradient mapping. If the gradient of the high temperature dense area exceeds the threshold, the chemical heat absorption layer is activated to absorb radiant heat and the residual heat conduction vector after the initial cooling is determined. The heat transfer mode sequence determination module (40) is used to distinguish between radiative heat and convective heat components using a data processing algorithm for the remaining heat conduction vector, obtain the classified heat component intensity sequence, and determine the heat transfer mode sequence that needs to be processed first. The overall thermal balance state description acquisition module (50) is used to guide the dynamic adjustment of the physical heat dissipation structure by adopting the heat transfer mode sequence, deploying a thermally conductive fiber network in the low heat buffer to disperse the convective heat components, and obtaining the adjusted overall thermal balance state description. The temperature control parameter acquisition module (60) is used to monitor the shell temperature fluctuation curve from the overall thermal balance state description. If the shell temperature fluctuation curve shows that the temperature tends to stabilize, the module integrates the synergistic output of the chemical heat absorption layer and the thermally conductive fiber network to obtain the final temperature control parameters.

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